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Rulla Alsaedi , Kimberly McKeirnan; Literature Review of Type 2 Diabetes Management and Health Literacy. Diabetes Spectr 1 November 2021; 34 (4): 399–406. https://doi.org/10.2337/ds21-0014

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The purpose of this literature review was to identify educational approaches addressing low health literacy for people with type 2 diabetes. Low health literacy can lead to poor management of diabetes, low engagement with health care providers, increased hospitalization rates, and higher health care costs. These challenges can be even more profound among minority populations and non-English speakers in the United States.

A literature search and standard data extraction were performed using PubMed, Medline, and EMBASE databases. A total of 1,914 articles were identified, of which 1,858 were excluded based on the inclusion criteria, and 46 were excluded because of a lack of relevance to both diabetes management and health literacy. The remaining 10 articles were reviewed in detail.

Patients, including ethnic minorities and non-English speakers, who are engaged in diabetes education and health literacy improvement initiatives and ongoing follow-up showed significant improvement in A1C, medication adherence, medication knowledge, and treatment satisfaction. Clinicians considering implementing new interventions to address diabetes care for patients with low health literacy can use culturally tailored approaches, consider ways to create materials for different learning styles and in different languages, engage community health workers and pharmacists to help with patient education, use patient-centered medication labels, and engage instructors who share cultural and linguistic similarities with patients to provide educational sessions.

This literature review identified a variety of interventions that had a positive impact on provider-patient communication, medication adherence, and glycemic control by promoting diabetes self-management through educational efforts to address low health literacy.

Diabetes is the seventh leading cause of death in the United States, and 30.3 million Americans, or 9.4% of the U.S. population, are living with diabetes ( 1 , 2 ). For successful management of a complicated condition such as diabetes, health literacy may play an important role. Low health literacy is a well-documented barrier to diabetes management and can lead to poor management of medical conditions, low engagement with health care providers (HCPs), increased hospitalizations, and, consequently, higher health care costs ( 3 – 5 ).

The Healthy People 2010 report ( 6 ) defined health literacy as the “degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions.” Diabetes health literacy also encompasses a wide range of skills, including basic knowledge of the disease state, self-efficacy, glycemic control, and self-care behaviors, which are all important components of diabetes management ( 3 – 5 , 7 ). According to the Institute of Medicine’s Committee on Health Literacy, patients with poor health literacy are twice as likely to have poor glycemic control and were found to be twice as likely to be hospitalized as those with adequate health literacy ( 8 ). Associations between health literacy and health outcomes have been reported in many studies, the first of which was conducted in 1995 in two public hospitals and found that many patients had inadequate health literacy and could not perform the basic reading tasks necessary to understand their treatments and diagnoses ( 9 ).

Evaluation of health literacy is vital to the management and understanding of diabetes. Several tools for assessing health literacy have been evaluated, and the choice of which to use depends on the length of the patient encounter and the desired depth of the assessment. One widely used literacy assessment tool, the Test of Functional Health Literacy in Adults (TOFHLA), consists of 36 comprehension questions and four numeric calculations ( 10 ). Additional tools that assess patients’ reading ability include the Rapid Estimate of Adult Literacy in Medicine (REALM) and the Literacy Assessment for Diabetes. Tests that assess diabetes numeracy skills include the Diabetes Numeracy Test, the Newest Vital Sign (NVS), and the Single-Item Literacy Screener (SILS) ( 11 ).

Rates of both diabetes and low health literacy are higher in populations from low socioeconomic backgrounds ( 5 , 7 , 12 ). People living in disadvantaged communities face many barriers when seeking health care, including inconsistent housing, lack of transportation, financial difficulties, differing cultural beliefs about health care, and mistrust of the medical professions ( 13 , 14 ). People with high rates of medical mistrust tend to be less engaged in their care and to have poor communication with HCPs, which is another factor HCPs need to address when working with their patients with diabetes ( 15 ).

The cost of medical care for people with diabetes was $327 billion in 2017, a 26% increase since 2012 ( 1 , 16 ). Many of these medical expenditures are related to hospitalization and inpatient care, which accounts for 30% of total medical costs for people with diabetes ( 16 ).

People with diabetes also may neglect self-management tasks for various reasons, including low health literacy, lack of diabetes knowledge, and mistrust between patients and HCPs ( 7 , 15 ).

These challenges can be even more pronounced in vulnerable populations because of language barriers and patient-provider mistrust ( 17 – 19 ). Rates of diabetes are higher among racial and ethnic minority groups; 15.1% of American Indians and Alaskan Natives, 12.7% of Non-Hispanic Blacks, 12.1% of Hispanics, and 8% of Asian Americans have diagnosed diabetes, compared with 7.4% of non-Hispanic Whites ( 1 ). Additionally, patient-provider relationship deficits can be attributed to challenges with communication, including HCPs’ lack of attention to speaking slowly and clearly and checking for patients’ understanding when providing education or gathering information from people who speak English as a second language ( 15 ). White et al. ( 15 ) demonstrated that patients with higher provider mistrust felt that their provider’s communication style was less interpersonal and did not feel welcome as part of the decision-making process.

To the authors’ knowledge, there is no current literature review evaluating interventions focused on health literacy and diabetes management. There is a pressing need for such a comprehensive review to provide a framework for future intervention design. The objective of this literature review was to gather and summarize studies of health literacy–based diabetes management interventions and their effects on overall diabetes management. Medication adherence and glycemic control were considered secondary outcomes.

Search Strategy

A literature review was conducted using the PubMed, Medline, and EMBASE databases. Search criteria included articles published between 2015 and 2020 to identify the most recent studies on this topic. The search included the phrases “diabetes” and “health literacy” to specifically focus on health literacy and diabetes management interventions and was limited to original research conducted in humans and published in English within the defined 5-year period. Search results were exported to Microsoft Excel for evaluation.

Study Selection

Initial screening of the articles’ abstracts was conducted using the selection criteria to determine which articles to include or exclude ( Figure 1 ). The initial search results were reviewed for the following inclusion criteria: original research (clinical trials, cohort studies, and cross-sectional studies) conducted in human subjects with type 2 diabetes in the United States, and published in English between 2015 and 2020. Articles were considered to be relevant if diabetes was included as a medical condition in the study and an intervention was made to assess or improve health literacy. Studies involving type 1 diabetes or gestational diabetes and articles that were viewpoints, population surveys, commentaries, case reports, reviews, or reports of interventions conducted outside of the United States were excluded from further review. The criteria requiring articles to be from the past 5 years and from the United States were used because of the unique and quickly evolving nature of the U.S. health care system. Articles published more than 5 years ago or from other health care systems may have contributed information that was not applicable to or no longer relevant for HCPs in the United States. Articles were screened and reviewed independently by both authors. Disagreements were resolved through discussion to create the final list of articles for inclusion.

FIGURE 1. PRISMA diagram of the article selection process.

PRISMA diagram of the article selection process.

Data Extraction

A standard data extraction was performed for each included article to obtain information including author names, year of publication, journal, study design, type of intervention, primary outcome, tools used to assess health literacy or type 2 diabetes knowledge, and effects of intervention on overall diabetes management, glycemic control, and medication adherence.

A total of 1,914 articles were collected from a search of the PubMed, MEDLINE, and EMBASE databases, of which 1,858 were excluded based on the inclusion and exclusion criteria. Of the 56 articles that met criteria for abstract review, 46 were excluded because of a lack of relevance to both diabetes management and health literacy. The remaining 10 studies identified various diabetes management interventions, including diabetes education tools such as electronic medication instructions and text message–based interventions, technology-based education videos, enhanced prescription labels, learner-based education materials, and culturally tailored interventions ( 15 , 20 – 28 ). Figure 1 shows the PRISMA diagram of the article selection process, and Table 1 summarizes the findings of the article reviews ( 15 , 20 – 28 ).

Findings of the Article Reviews (15,20–28)

SAHLSA, Short Assessment of Health Literacy for Spanish Adults.

Medical mistrust and poor communication are challenging variables in diabetes education. White et al. ( 15 ) examined the association between communication quality and medical mistrust in patients with type 2 diabetes. HCPs at five health department clinics received training in effective health communication and use of the PRIDE (Partnership to Improve Diabetes Education) toolkit in both English and Spanish, whereas control sites were only exposed to National Diabetes Education Program materials without training in effective communication. The study evaluated participant communication using several tools, including the Communication Assessment Tool (CAT), Interpersonal Processes of Care (IPC-18), and the Short Test of Functional Health Literacy in Adults (s-TOFHLA). The authors found that higher levels of mistrust were associated with lower CAT and IPC-18 scores.

Patients with type 2 diabetes are also likely to benefit from personalized education delivery tools such as patient-centered labeling (PCL) of prescription drugs, learning style–based education materials, and tailored text messages ( 24 , 25 , 27 ). Wolf et al. ( 27 ) investigated the use of PCL in patients with type 2 diabetes and found that patients with low health literacy who take medication two or more times per day have higher rates of proper medication use when using PCL (85.9 vs. 77.4%, P = 0.03). The objective of the PCL intervention was to make medication instructions and other information on the labels easier to read to improve medication use and adherence rates. The labels incorporated best-practice strategies introduced by the Institute of Medicine for the Universal Medication Schedule. These strategies prioritize medication information, use of larger font sizes, and increased white space. Of note, the benefits of PCL were largely seen with English speakers. Spanish speakers did not have substantial improvement in medication use or adherence, which could be attributed to language barriers ( 27 ).

Nelson et al. ( 25 ) analyzed patients’ engagement with an automated text message approach to supporting diabetes self-care activities in a 12-month randomized controlled trial (RCT) called REACH (Rapid Education/Encouragement and Communications for Health) ( 25 ). Messages were tailored based on patients’ medication adherence, the Information-Motivation-Behavioral Skills model of health behavior change, and self-care behaviors such as diet, exercise, and self-monitoring of blood glucose. Patients in this trial were native English speakers, so further research to evaluate the impact of the text message intervention in patients with limited English language skills is still needed. However, participants in the intervention group reported higher engagement with the text messages over the 12-month period ( 25 ).

Patients who receive educational materials based on their learning style also show significant improvement in their diabetes knowledge and health literacy. Koonce et al. ( 24 ) developed and evaluated educational materials based on patients’ learning style to improve health literacy in both English and Spanish languages. The materials were made available in multiple formats to target four different learning styles, including materials for visual learners, read/write learners, auditory learners, and kinesthetic learners. Spanish-language versions were also available. Researchers were primarily interested in measuring patients’ health literacy and knowledge of diabetes. The intervention group received materials in their preferred learning style and language, whereas the control group received standard of care education materials. The intervention group showed significant improvement in diabetes knowledge and health literacy, as indicated by Diabetes Knowledge Test (DKT) scores. More participants in the intervention group reported looking up information about their condition during week 2 of the intervention and showed an overall improvement in understanding symptoms of nerve damage and types of food used to treat hypoglycemic events. However, the study had limited enrollment of Spanish speakers, making the applicability of the results to Spanish-speaking patients highly variable.

Additionally, findings by Hofer et al. ( 22 ) suggest that patients with high A1C levels may benefit from interventions led by community health workers (CHWs) to bridge gaps in health literacy and equip patients with the tools to make health decisions. In this study, Hispanic and African American patients with low health literacy and diabetes not controlled by oral therapy benefited from education sessions led by CHWs. The CHWs led culturally tailored support groups to compare the effects of educational materials provided in an electronic format (via iDecide) and printed format on medication adherence and self-efficacy. The study found increased adherence with both formats, and women, specifically, had a significant increase in medication adherence and self-efficacy. One of the important aspects of this study was that the CHWs shared cultural and linguistic characteristics with the patients and HCPs, leading to increased trust and satisfaction with the information presented ( 22 ).

Kim et al. ( 23 ) found that Korean-American participants benefited greatly from group education sessions that provided integrated counseling led by a team of nurses and CHW educators. The intervention also had a health literacy component that focused on enhancing skills such as reading food package labels, understanding medical terminology, and accessing health care services. This intervention led to a significant reduction of 1–1.3% in A1C levels in the intervention group. The intervention established the value of collaboration between CHW educators and nurses to improve health information delivery and disease management.

A collaboration between CHW educators and pharmacists was also shown to reinforce diabetes knowledge and improve health literacy. Sharp et al. ( 26 ) conducted a cross-over study in four primary care ambulatory clinics that provided care for low-income patients. The study found that patients with low health literacy had more visits with pharmacists and CHWs than those with high health literacy. The CHWs provided individualized support to reinforce diabetes self-management education and referrals to resources such as food, shelter, and translation services. The translation services in this study were especially important for building trust with non-English speakers and helping patients understand their therapy. Similar to other studies, the CHWs shared cultural and linguistic characteristics with their populations, which helped to overcome communication-related and cultural barriers ( 23 , 26 ).

The use of electronic tools or educational videos yielded inconclusive results with regard to medication adherence. Graumlich et al. ( 20 ) implemented a new medication planning tool called Medtable within an electronic medical record system in several outpatient clinics serving patients with type 2 diabetes. The tool was designed to organize medication review and patient education. Providers can use this tool to search for medication instructions and actionable language that are appropriate for each patient’s health literacy level. The authors found no changes in medication knowledge or adherence, but the intervention group reported higher satisfaction. On the other hand, Yeung et al. ( 28 ) showed that pharmacist-led online education videos accessed using QR codes affixed to the patients’ medication bottles and health literacy flashcards increased patients’ medication adherence in an academic medical hospital.

Goessl et al. ( 21 ) found that patients with low health literacy had significantly higher retention of information when receiving evidence-based diabetes education through a DVD recording than through an in-person group class. This 18-month RCT randomized participants to either the DVD or in-person group education and assessed their information retention through a teach-back strategy. The curriculum consisted of diabetes prevention topics such as physical exercise, food portions, and food choices. Participants in the DVD group had significantly higher retention of information than those in the control (in-person) group. The authors suggested this may have been because participants in the DVD group have multiple opportunities to review the education material.

Management of type 2 diabetes remains a challenge for HCPs and patients, in part because of the challenges discussed in this review, including communication barriers between patients and HCPs and knowledge deficits about medications and disease states ( 29 ). HCPs can have a positive impact on the health outcomes of their patients with diabetes by improving patients’ disease state and medication knowledge.

One of the common themes identified in this literature review was the prevalence of culturally tailored diabetes education interventions. This is an important strategy that could improve diabetes outcomes and provide an alternative approach to diabetes self-management education when working with patients from culturally diverse backgrounds. HCPs might benefit from using culturally tailored educational approaches to improve communication with patients and overcome the medical mistrust many patients feel. Although such mistrust was not directly correlated with diabetes management, it was noted that patients who feel mistrustful tend to have poor communication with HCPs ( 20 ). Additionally, Latino/Hispanic patients who have language barriers tend to have poor glycemic control ( 19 ). Having CHWs work with HCPs might mitigate some patient-provider communication barriers. As noted earlier, CHWs who share cultural and linguistic characteristics with their patient populations have ongoing interactions and more frequent one-on-one encounters ( 12 ).

Medication adherence and glycemic control are important components of diabetes self-management, and we noted that the integration of CHWs into the diabetes health care team and the use of simplified medication label interventions were both successful in improving medication adherence ( 23 , 24 ). The use of culturally tailored education sessions and the integration of pharmacists and CHWs into the management of diabetes appear to be successful in reducing A1C levels ( 12 , 26 ). Electronic education tools and educational videos alone did not have an impact on medication knowledge or information retention in patients with low health literacy, but a combination of education tools and individualized sessions has the potential to improve diabetes medication knowledge and overall self-management ( 20 , 22 , 30 ).

There were several limitations to our literature review. We restricted our search criteria to articles published in English and studies conducted within the United States to ensure that the results would be relevant to U.S. HCPs. However, these limitations may have excluded important work on this topic. Additional research expanding this search beyond the United States and including articles published in other languages may demonstrate different outcomes. Additionally, this literature review did not focus on A1C as the primary outcome, although A1C is an important indicator of diabetes self-management. A1C was chosen as the method of evaluating the impact of health literacy interventions in patients with diabetes, but other considerations such as medication adherence, impact on comorbid conditions, and quality of life are also important factors.

The results of this work show that implementing health literacy interventions to help patients manage type 2 diabetes can have beneficial results. However, such interventions can have significant time and monetary costs. The potential financial and time costs of diabetes education interventions were not evaluated in this review and should be taken into account when designing interventions. The American Diabetes Association estimated the cost of medical care for people with diabetes to be $327 billion in 2017, with the majority of the expenditure related to hospitalizations and nursing home facilities ( 16 ). Another substantial cost of diabetes that can be difficult to measure is treatment for comorbid conditions and complications such as cardiovascular and renal diseases.

Interventions designed to address low health literacy and provide education about type 2 diabetes could be a valuable asset in preventing complications and reducing medical expenditures. Results of this work show that clinicians who are considering implementing new interventions may benefit from the following strategies: using culturally tailored approaches, creating materials for different learning styles and in patients’ languages, engaging CHWs and pharmacists to help with patient education, using PCLs for medications, and engaging education session instructors who share patients’ cultural and linguistic characteristics.

Diabetes self-management is crucial to improving health outcomes and reducing medical costs. This literature review identified interventions that had a positive impact on provider-patient communication, medication adherence, and glycemic control by promoting diabetes self-management through educational efforts to address low health literacy. Clinicians seeking to implement diabetes care and education interventions for patients with low health literacy may want to consider drawing on the strategies described in this article. Providing culturally sensitive education that is tailored to patients’ individual learning styles, spoken language, and individual needs can improve patient outcomes and build patients’ trust.

Duality of Interest

No potential conflicts of interest relevant to this article were reported.

Author Contributions

Both authors conceptualized the literature review, developed the methodology, analyzed the data, and wrote, reviewed, and edited the manuscript. R.A. collected the data. K.M. supervised the review. K.M. is the guarantor of this work and, as such, has full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation

Portions of this research were presented at the Washington State University College of Pharmacy and Pharmaceutical Sciences Honors Research Day in April 2019.

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  • Review Article
  • Published: 06 June 2022

The burden and risks of emerging complications of diabetes mellitus

  • Dunya Tomic   ORCID: orcid.org/0000-0003-2471-2523 1 , 2 ,
  • Jonathan E. Shaw   ORCID: orcid.org/0000-0002-6187-2203 1 , 2   na1 &
  • Dianna J. Magliano   ORCID: orcid.org/0000-0002-9507-6096 1 , 2   na1  

Nature Reviews Endocrinology volume  18 ,  pages 525–539 ( 2022 ) Cite this article

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  • Diabetes complications
  • Type 1 diabetes
  • Type 2 diabetes

The traditional complications of diabetes mellitus are well known and continue to pose a considerable burden on millions of people living with diabetes mellitus. However, advances in the management of diabetes mellitus and, consequently, longer life expectancies, have resulted in the emergence of evidence of the existence of a different set of lesser-acknowledged diabetes mellitus complications. With declining mortality from vascular disease, which once accounted for more than 50% of deaths amongst people with diabetes mellitus, cancer and dementia now comprise the leading causes of death in people with diabetes mellitus in some countries or regions. Additionally, studies have demonstrated notable links between diabetes mellitus and a broad range of comorbidities, including cognitive decline, functional disability, affective disorders, obstructive sleep apnoea and liver disease, and have refined our understanding of the association between diabetes mellitus and infection. However, no published review currently synthesizes this evidence to provide an in-depth discussion of the burden and risks of these emerging complications. This Review summarizes information from systematic reviews and major cohort studies regarding emerging complications of type 1 and type 2 diabetes mellitus to identify and quantify associations, highlight gaps and discrepancies in the evidence, and consider implications for the future management of diabetes mellitus.

With advances in the management of diabetes mellitus, evidence is emerging of an increased risk and burden of a different set of lesser-known complications of diabetes mellitus.

As mortality from vascular diseases has declined, cancer and dementia have become leading causes of death amongst people with diabetes mellitus.

Diabetes mellitus is associated with an increased risk of various cancers, especially gastrointestinal cancers and female-specific cancers.

Hospitalization and mortality from various infections, including COVID-19, pneumonia, foot and kidney infections, are increased in people with diabetes mellitus.

Cognitive and functional disability, nonalcoholic fatty liver disease, obstructive sleep apnoea and depression are also common in people with diabetes mellitus.

As new complications of diabetes mellitus continue to emerge, the management of this disorder should be viewed holistically, and screening guidelines should consider conditions such as cancer, liver disease and depression.

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Introduction

Diabetes mellitus is a common, albeit potentially devastating, medical condition that has increased in prevalence over the past few decades to constitute a major public health challenge of the twenty-first century 1 . Complications that have traditionally been associated with diabetes mellitus include macrovascular conditions, such as coronary heart disease, stroke and peripheral arterial disease, and microvascular conditions, including diabetic kidney disease, retinopathy and peripheral neuropathy 2 (Fig.  1 ). Heart failure is also a common initial manifestation of cardiovascular disease in patients with type 2 diabetes mellitus (T2DM) 3 and confers a high risk of mortality in those with T1DM or T2DM 4 . Although a great burden of disease associated with these traditional complications of diabetes mellitus still exists, rates of these conditions are declining with improvements in the management of diabetes mellitus 5 . Instead, as people with diabetes mellitus are living longer, they are becoming susceptible to a different set of complications 6 . Population-based studies 7 , 8 , 9 show that vascular disease no longer accounts for most deaths among people with diabetes mellitus, as was previously the case 10 . Cancer is now the leading cause of death in people with diabetes mellitus in some countries or regions (hereafter ‘countries/regions’) 9 , and the proportion of deaths due to dementia has risen since the turn of the century 11 . In England, traditional complications accounted for more than 50% of hospitalizations in people with diabetes mellitus in 2003, but for only 30% in 2018, highlighting the shift in the nature of complications of this disorder over this corresponding period 12 .

figure 1

The traditional complications of diabetes mellitus include stroke, coronary heart disease and heart failure, peripheral neuropathy, retinopathy, diabetic kidney disease and peripheral vascular disease, as represented on the left-hand side of the diagram. With advances in the management of diabetes mellitus, associations between diabetes mellitus and cancer, infections, functional and cognitive disability, liver disease and affective disorders are instead emerging, as depicted in the right-hand side of the diagram. This is not an exhaustive list of complications associated with diabetes mellitus.

Cohort studies have reported associations of diabetes mellitus with various cancers, functional and cognitive disability, liver disease, affective disorders and sleep disturbance, and have provided new insights into infection-related complications of diabetes mellitus 13 , 14 , 15 , 16 , 17 . Although emerging complications have been briefly acknowledged in reviews of diabetes mellitus morbidity and mortality 11 , 17 , no comprehensive review currently specifically provides an analysis of the evidence for the association of these complications with diabetes mellitus. In this Review, we synthesize information published since the year 2000 on the risks and burden of emerging complications associated with T1DM and T2DM.

Diabetes mellitus and cancer

The burden of cancer mortality.

With the rates of cardiovascular mortality declining amongst people with diabetes mellitus, cancer deaths now constitute a larger proportion of deaths among this population in some countries/regions 8 , 9 . Although the proportion of deaths due to cancer appears to be stable, at around 16–20%, in the population with diabetes mellitus in the USA 7 , in England it increased from 22% to 28% between 2001 and 2018 (ref. 9 ), with a similar increase reported in Australia 8 . Notably, in England, cancer has overtaken vascular disease as the leading cause of death in people with diabetes mellitus and it is the leading contributor to excess mortality in those with diabetes mellitus compared with those without 9 . These findings are likely to be due to a substantial decline in the proportion of deaths from vascular diseases, from 44% to 24% between 2001 and 2018, which is thought to reflect the targeting of prevention measures in people with diabetes mellitus 18 . Over the same time period, cancer mortality rates fell by much less in the population with diabetes mellitus than in that without diabetes 9 , suggesting that clinical approaches for diabetes mellitus might focus too narrowly on vascular complications and might require revision 19 . In addition, several studies have reported that female patients with diabetes mellitus receive less-aggressive treatment for breast cancer compared with patients without diabetes mellitus, particularly with regard to chemotherapy 20 , 21 , 22 , suggesting that this treatment approach might result in increased cancer mortality rates in women with diabetes mellitus compared with those without diabetes mellitus. Although substantial investigation of cancer mortality in people with diabetes mellitus has been undertaken in high-income countries/regions, there is a paucity of evidence from low-income and middle-income countries/regions. It is important to understand the potential effect of diabetes mellitus on cancer mortality in these countries/regions owing to the reduced capacity of health-care systems in these countries/regions to cope with the combination of a rising prevalence of diabetes mellitus and rising cancer mortality rates in those with diabetes mellitus. One study in Mauritius showed a significantly increased risk of all-cause cancer mortality in patients with T2DM 23 , but this study has yet to be replicated in other low-income and middle-income countries/regions.

Gastrointestinal cancers

Of the reported associations between diabetes mellitus and cancer (Table  1 ), some of the strongest have been demonstrated for gastrointestinal cancers.

Hepatocellular carcinoma

In the case of hepatocellular carcinoma, the most rigorous systematic review on the topic — comprising 18 cohort studies with a combined total of more than 3.5 million individuals — reported a summary relative risk (SRR) of 2.01 (95% confidence interval (CI) 1.61–2.51) for an association with diabetes mellitus 24 . This increased risk of hepatocellular carcinoma with diabetes mellitus is supported by the results of another systematic review that included case–control studies 25 . Another review also found that diabetes mellitus independently increased the risk of hepatocellular carcinoma in the setting of hepatitis C virus infection 26 .

Pancreatic cancer

The risk of pancreatic cancer appears to be approximately doubled in patients with T2DM compared with patients without T2DM. A meta-analysis of 36 studies found an adjusted odds ratio (OR) of 1.82 (95% CI 1.66–1.89) for pancreatic cancer among people with T2DM compared with patients without T2DM 27 (Table  1 ). However, it is possible that these findings are influenced by reverse causality — in this scenario, diabetes mellitus is triggered by undiagnosed pancreatic cancer 28 , with pancreatic cancer subsequently being clinically diagnosed only after the diagnosis of diabetes mellitus. Nevertheless, although the greatest risk (OR 2.05, 95% CI 1.87–2.25) of pancreatic cancer was seen in people diagnosed with T2DM 1–4 years previously compared with people without T2DM, those with a diagnosis of T2DM of more than 10 years remained at increased risk of pancreatic cancer (OR 1.51, 95% CI 1.16–1.96) 27 , suggesting that reverse causality can explain only part of the association between T2DM and pancreatic cancer. Although T2DM accounts for ~90% of all cases of diabetes mellitus 29 , a study incorporating data from five nationwide diabetes registries also reported an increased risk of pancreatic cancer amongst both male patients (HR 1.53, 95% CI 1.30–1.79) and female patients (HR 1.25, 95% CI 1.02–1.53) with T1DM 30 .

Colorectal cancer

For colorectal cancer, three systematic reviews have shown a consistent 20–30% increased risk associated with diabetes mellitus 31 , 32 , 33 . One systematic review, which included more than eight million people across 30 cohort studies, reported an incidence SRR of 1.27 (95% CI 1.21–1.34) of colorectal cancer 31 , independent of sex and family history (Table  1 ). Similar increases in colorectal cancer incidence in patients with diabetes mellitus were reported in a meta-analysis of randomized controlled trials (RCTs) and cohort studies 32 and in a systematic review that included cross-sectional studies 33 .

Female-specific cancers

Endometrial, breast and ovarian cancers all occur more frequently in women with diabetes mellitus than in women without diabetes mellitus.

Endometrial cancer

For endometrial cancer, one systematic review of 29 cohort studies and a combined total of 5,302,259 women reported a SRR of 1.89 (95% CI 1.46–2.45) and summary incidence rate ratio (IRR) of 1.61 (95% CI 1.51–1.71) 34 (Table  1 ). Similar increased risks were found in two systematic reviews incorporating cross-sectional studies 35 , 36 , one of which found a particularly strong association of T1DM (relative risk (RR) 3.15, 95% CI 1.07–9.29) with endometrial cancer.

Breast cancer

The best evidence for a link between diabetes mellitus and breast cancer comes from a systematic review of six prospective cohort studies and more than 150,000 women, in which the hazard ratio (HR) for the incidence of breast cancer in women with diabetes mellitus compared with women without diabetes mellitus was 1.23 (95% CI 1.12–1.34) 32 (Table  1 ). Two further systematic reviews have also shown this increased association 37 , 38 .

The association of diabetes mellitus with breast cancer appears to vary according to menopausal status. In a meta-analysis of studies of premenopausal women with diabetes mellitus, no significant association with breast cancer was found 39 , whereas in 11 studies that included only postmenopausal women, the SRR was 1.15 (95% CI 1.07–1.24). The difference in breast cancer risk between premenopausal and postmenopausal women with diabetes mellitus was statistically significant. The increased risk of breast cancer after menopause in women with diabetes mellitus compared with women without diabetes mellitus might result from the elevated concentrations and increased bioavailability of oestrogen that are associated with adiposity 40 , which is a common comorbidity in those with T2DM; oestrogen synthesis occurs in adipose tissue in postmenopausal women, while it is primarily gonadal in premenopausal women 41 . Notably, however, there is evidence that hormone-receptor-negative breast cancers, which typically carry a poor prognosis, occur more frequently in women with breast cancer and diabetes mellitus than in women with breast cancer and no diabetes mellitus 42 , indicating that non-hormonal mechanisms also occur.

Ovarian cancer

Diabetes mellitus also appears to increase the risk of ovarian cancer, with consistent results from across four systematic reviews. A pooled RR of 1.32 (95% CI 1.14–1.52) was reported across 15 cohort studies and a total of more than 2.3 million women 43 (Table  1 ). A SRR of 1.19 (95% CI 1.06–1.34) was found across 14 cohort studies and 3,708,313 women 44 . Similar risks were reported in meta-analyses that included cross-sectional studies 45 , 46 .

Male-specific cancers: prostate cancer

An inverse association between diabetes mellitus and prostate cancer has been observed in a systematic review (RR 0.91, 95% CI 0.86–0.96) 47 , and is probably due to reduced testosterone levels that occur secondary to the low levels of sex hormone-binding globulin that are commonly seen in men with T2DM and obesity 48 . Notably, however, the systematic review that showed the inverse association involved mostly white men (Table  1 ), whereas a systematic review of more than 1.7 million men from Taiwan, Japan, South Korea and India found that diabetes mellitus increased prostate cancer risk 49 , suggesting that ethnicity might be an effect modifier of the diabetes mellitus–prostate cancer relationship. The mechanisms behind this increased risk in men in regions of Asia such as Taiwan and Japan, where most study participants came from, remain unclear. Perhaps, as Asian men develop diabetes mellitus at lower levels of total adiposity than do white men 50 , the adiposity associated with diabetes mellitus in Asian men might have a lesser impact on sex hormone-binding globulin and testosterone than it does in white men. Despite the reported inverse association between diabetes mellitus and prostate cancer in white men, however, evidence suggests that prostate cancers that do develop in men with T2DM are typically more aggressive, conferring higher rates of disease-specific mortality than prostate cancers in men without diabetes mellitus 51 .

An assessment of cancer associations

As outlined above, a wealth of data has shown that diabetes mellitus is associated with an increased risk of various cancers. It has been argued, however, that some of these associations could be due to detection bias resulting from increased surveillance of people with diabetes mellitus in the immediate period after diagnosis 52 , or reverse causality, particularly in the case of pancreatic cancer 53 . However, neither phenomenon can account for the excess risks seen in the longer term. An Australian study exploring detection bias and reverse causality found that standardized mortality ratios (SMRs) for several cancer types in people with diabetes mellitus compared with the general population fell over time, but remained elevated beyond 2 years for pancreatic and liver cancers 54 , suggesting that diabetes mellitus is a genuine risk factor for these cancer types.

A limitation of the evidence that surrounds diabetes mellitus and cancer risk is high clinical and methodological heterogeneity across several of the large systematic reviews, which makes it difficult to be certain of the effect size in different demographic groups. Additionally, many of the studies exploring a potential association between diabetes mellitus and cancer were unable to adjust for BMI, which is a major confounder. However, a modelling study that accounted for BMI found that although 2.1% of cancers worldwide in 2012 were attributable to diabetes mellitus as an independent risk factor, twice as many cancers were attributable to high BMI 55 , so it is likely that effect sizes for cancer risk associated with diabetes mellitus would be attenuated after adjustment for BMI. Notably, however, low-income and middle-income countries/regions had the largest increase in the numbers of cases of cancer attributable to diabetes mellitus both alone and in combination with BMI 55 , highlighting the need for public health intervention, given that these countries/regions are less equipped than high-income countries/regions to manage a growing burden of cancer.

As well as the cancer types outlined above, diabetes mellitus has also been linked to various other types of cancer, including kidney cancer 56 , bladder cancer 57 and haematological malignancies; however, the evidence for these associations is not as strong as for the cancers discussed above 58 . Diabetes mellitus might also be associated with other cancer types such as small intestine cancer, but the rarity of some of these types makes it difficult to obtain sufficient statistical power in analyses of any potential association.

Potential aetiological mechanisms

Several aetiological mechanisms that might be involved in linking diabetes mellitus to cancer have been proposed, including hyperinsulinaemia, hyperglycaemia, inflammation and cellular signalling mechanisms.

Hyperinsulinaemia

Most cancer cells express insulin receptors, through which hyperinsulinaemia is thought to stimulate cancer cell proliferation and metastasis 59 . Hyperinsulinaemia might also promote carcinogenesis through increased local levels of insulin-like growth factor 1 (IGF1), which has potent mitogenic and anti-apoptotic activities 60 , owing to decreased levels of insulin-like growth factor binding proteins. As outlined above, people with diabetes mellitus show a strong risk of pancreatic and liver cancers; this increased risk might occur because insulin is produced by pancreatic β-cells and transported to the liver via the portal vein 61 , thereby exposing the liver and pancreas to high levels of endogenous insulin 59 .

Hyperglycaemia and inflammation

Hyperglycaemia can induce DNA damage 62 , increase the generation of reactive oxygen species 63 and downregulate antioxidant expression 64 , all of which are associated with cancer development. Inflammatory markers, including cytokines such as IL-6, appear to have an important role in the association between diabetes and cancer 65 .

Cellular signalling mechanisms

Several cellular signalling components are common to the pathogenesis of T2DM and cancer. These include the mechanistic target of rapamycin (mTOR), a central controller of cell growth and proliferation; AMP-activated protein kinase, a cellular energy sensor and signal transducer 66 ; and the phosphatidylinositol 3-kinase (PI3K)–AKT pathway, which transduces growth factor signals during organismal growth, glucose homeostasis and cell proliferation 67 . Dysregulation of any of these cellular signalling components or pathways could contribute to the development of cancer and metabolic disorders, including T2DM, and glucose-lowering drugs such as metformin have been associated with a reduction in cancer cell proliferation through effective inhibition of some of these components 68 .

Diabetes mellitus and infections

Infection-related complications.

Although infection has long been recognized as a complication of diabetes mellitus, an association between diabetes mellitus and infection has not been well documented in epidemiological studies 69 . Only in the past decade have major studies quantified the burden of infection-related complications in people with diabetes mellitus and explored the specific infections accounting for this burden. In a US cohort of 12,379 participants, diabetes mellitus conferred a significant risk of infection-related hospitalization, with an adjusted HR of 1.67 (95% CI 1.52–1.83) compared with people without diabetes mellitus 70 (Table  2 ). The association was most pronounced for foot infections (HR 5.99, 95% CI 4.38–8.19), with significant associations also observed for respiratory infection, urinary tract infection, sepsis and post-operative infection, but not for gastrointestinal infection, a category that included appendicitis and gastrointestinal abscesses but not viral or bacterial gastroenteritis. Interestingly, a report from Taiwan demonstrated an association between the use of metformin and a lower risk of appendicitis 71 .

In an analysis of the entire Hong Kong population over the period 2001–2016, rates of hospitalization for all types of infection remained consistently higher in people with diabetes mellitus than in those without diabetes mellitus 72 . The strongest association was seen for hospitalization due to kidney infections, for which the adjusted RR was 4.9 (95% CI 3.9–6.2) in men and 3.2 (95% CI 2.8–3.7) in women with diabetes mellitus compared with those without diabetes mellitus in 2016 (Table  2 ). Diabetes mellitus roughly doubled the risk of hospitalization from tuberculosis or sepsis. The most common cause of infection-related hospitalization was pneumonia, which accounted for 39% of infections across the study period, while no other single cause accounted for more than 25% of infections across the same period. Pneumonia-related hospitalization rates increased substantially from 2001 to 2005, probably as a result of the 2003 severe acute respiratory syndrome (SARS) epidemic and the decreased threshold for pneumonia hospitalization in the immediate post-epidemic period. Rates for hospitalization for influenza increased from 2002 to 2016, possibly because of changes in the virus and increased testing for influenza. Declining rates of hospitalization for tuberculosis, urinary tract infections, foot infections and sepsis could be due to improvements in the management of diabetes mellitus.

Infection-related mortality rates were found to be significantly elevated among 1,108,982 Australians with diabetes mellitus studied over the period 2000–2010 compared with rates in people without diabetes mellitus 73 . For overall infection-related mortality, SMRs were 4.42 (95% CI 3.68–5.34) for T1DM and 1.47 (95% CI 1.42–1.53) for people with T2DM compared with those without diabetes mellitus (Table  2 ). Substantially higher infection-related mortality rates were seen in people with T1DM compared with those with T2DM for all infection types, even after accounting for age. Hyperglycaemia is thought to be a driver of infection amongst people with diabetes mellitus (see below) 73 , which might explain the higher SMRs amongst people with T1DM, in whom hyperglycaemia is typically more severe, than in those with T2DM. The highest SMRs were seen for osteomyelitis, and SMRs for septicaemia and pneumonia were also greater than 1.0 for both types of diabetes mellitus compared with those without diabetes mellitus.

Post-operative infection

Post-operative infection is also an important complication of diabetes mellitus. In a meta-analysis, diabetes mellitus was found to be associated with an OR of 1.77 (95% CI 1.13–2.78) for surgical site infection across studies that adjusted for confounding factors 74 (Table  2 ). The effect size appears to be greatest after cardiac procedures, and one US study of patients undergoing coronary artery bypass grafting found diabetes mellitus to be an independent predictor of surgical site infection, with an OR of 4.71 (95% CI 2.39–9.28) compared with those without diabetes mellitus 75 . Risks of infection of more than threefold were reported in some studies of gynaecological 76 and spinal surgery 77 in people with diabetes mellitus compared with those without diabetes mellitus. Increased risks of infection among people with diabetes mellitus were also observed in studies of colorectal and breast surgery and arthroplasty, suggesting that the association between diabetes mellitus and post-operative infection is present across a wide range of types of surgery 74 .

Respiratory infections

The incidence of hospitalizations due to respiratory infections among people with diabetes mellitus was increasing substantially even before the onset of the coronavirus disease 2019 (COVID-19) pandemic, probably owing to increased life expectancy in these patients as well as an increased likelihood of them being hospitalized for conditions such as respiratory infections, which occur mostly in older age 12 . This rising burden of respiratory infection, in combination with the rising prevalence of diabetes mellitus, highlights the importance of addressing the emerging complications of diabetes mellitus to minimize impacts on health-care systems in current and future global epidemics.

Although diabetes mellitus does not appear to increase the risk of becoming infected with COVID-19 (ref. 78 ), various population-based studies have reported increased risks of COVID-19 complications among people with diabetes mellitus. In a study of the total Scottish population, people with diabetes mellitus were found to have an increased risk of fatal or critical care unit-treated COVID-19, with an adjusted OR of 1.40 (95% CI 1.30–1.50) compared with those without diabetes mellitus 79 (Table  2 ). The risk was particularly high for those with T1DM (OR 2.40, 95% CI 1.82–3.16) 79 . Both T1DM and T2DM have been linked to a more than twofold increased risk of hospitalization with COVID-19 in a large Swedish cohort study 80 . In South Korean studies, T2DM was linked to intensive care unit admission among patients with COVID-19 infection 81 , and diabetes mellitus (either T1DM or T2DM) was linked to a requirement for ventilation and oxygen therapy 82 in patients with COVID-19. Diabetes mellitus appears to be the primary predisposing factor for opportunistic infection with mucormycosis in individuals with COVID-19 (ref. 83 ). The evidence for diabetes mellitus as a risk factor for post-COVID-19 syndrome is inconclusive 84 , 85 . Interestingly, an increase in the incidence of T1DM during the COVID-19 pandemic has been reported in several countries/regions 86 , and some data suggest an increased risk of T1DM after COVID-19 infection 87 , but the evidence regarding a causal effect is inconclusive.

Pneumonia, MERS, SARS and H1N1 influenza

The data regarding diabetes mellitus and COVID-19 are consistent with the published literature regarding other respiratory infections, such as pneumonia, for which diabetes mellitus has been shown to increase the risk of hospitalization 88 and mortality 88 , with similar effect sizes to those seen for COVID-19, compared with no diabetes mellitus. Diabetes mellitus has also been also linked to adverse outcomes in people with Middle East respiratory syndrome (MERS), SARS and H1N1 influenza 89 , 90 , 91 , 92 , suggesting that mechanisms specific to COVID-19 are unlikely to be responsible for the relationship between diabetes mellitus and COVID-19. Unlike the case for COVID-19, there is evidence that people with diabetes mellitus are at increased risk of developing certain other respiratory infections, namely pneumonia 93 and possibly also MERS 94 .

The mechanisms that might link diabetes mellitus and infection include a reduced T cell response, reduced neutrophil function and disorders of humoral immunity.

Mononuclear cells and monocytes of individuals with diabetes mellitus secrete less IL-1 and IL-6 than the same cells from people without diabetes mellitus 95 . The release of IL-1 and IL-6 by T cells and other cell types in response to infection has been implicated in the response to several viral infections 96 . Thus, the reduced secretion of these cytokines in patients with diabetes mellitus might be associated with the poorer responses to infection observed among these patients compared with people without diabetes mellitus.

In the context of neutrophil function, hyperglycaemic states might give rise to reductions in the mobilization of polymorphonuclear leukocytes, phagocytic activity and chemotaxis 97 , resulting in a decreased immune response to infection. Additionally, increased levels of glucose in monocytes isolated from patients with obesity and/or diabetes mellitus have been found to promote viral replication in these cells, as well as to enhance the expression of several cytokines, including pro-inflammatory cytokines that are associated with the COVID-19 ‘cytokine storm’; furthermore, glycolysis was found to sustain the SARS coronavirus 2 (SARS-CoV-2)-induced monocyte response and viral replication 98 .

Elevated glucose levels in people with diabetes mellitus are also associated with an increase in glycation, which, by promoting a change in the structure and/or function of several proteins and lipids, is responsible for many of the complications of diabetes mellitus 99 . In people with diabetes mellitus, antibodies can become glycated, a process that is thought to impair their biological function 100 . Although the clinical relevance of this impairment is not clear, it could potentially explain the results of an Israeli study that reported reduced COVID-19 vaccine effectiveness among people with T2DM compared with those without T2DM 101 .

Diabetes mellitus and liver disease

Nonalcoholic fatty liver disease.

The consequences of nonalcoholic fatty liver disease (NAFLD) make it important to recognize the burden of this disease among people with diabetes mellitus. NAFLD and nonalcoholic steatohepatitis (NASH; an advanced form of NAFLD) are major causes of liver transplantation in the general population. In the USA, NASH accounted for 19% of liver transplantations in 2016 — second only to alcoholic liver disease, which was the cause of 24% of transplantations 102 . In Australia and New Zealand, NAFLD was the primary diagnosis in 9% of liver transplant recipients in 2019, only slightly below the figure for alcoholic cirrhosis of 13% 103 . In Europe, NASH increased as the reason for transplantations from 1% in 2002 to more than 8% in 2016, in parallel with the rising prevalence of diabetes mellitus 104 .

NAFLD is highly prevalent among people with T2DM. In a systematic review of 80 studies across 20 countries/regions, the prevalence of NAFLD among 49,419 people with T2DM was 56% 105 , while the global prevalence of NAFLD in the general population is estimated to be 25% 106 . In a Chinese cohort study of 512,891 adults, diabetes mellitus was associated with an adjusted HR of 1.76 (95% CI 1.47–2.16) for NAFLD compared with no diabetes mellitus 107 (Table  3 ). Another smaller longitudinal Chinese study also reported an increased risk of developing NAFLD among those with T2DM compared with those without T2DM 108 . However, most evidence regarding the association between NAFLD and diabetes mellitus is from cross-sectional studies 109 , 110 , 111 .

NASH and fibrosis

Diabetes mellitus appears to enhance the risk of NAFLD complications, including NASH and fibrosis. An analysis of 892 people with NAFLD and T2DM across 10 studies showed that the prevalence of NASH was 37% (ref. 105 ); figures for the prevalence of NASH in the general population with NAFLD vary greatly across different study populations, ranging from 16% to 68% 112 . Amongst 439 people with T2DM and NAFLD in seven studies, 17% had advanced fibrosis 105 . An analysis of 1,069 people with NAFLD in a US study found that diabetes mellitus was an independent predictor for NASH (OR 1.93, 95% CI 1.37–2.73) and fibrosis (3.31, 95% CI 2.26–4.85) 113 .

Bidirectional relationship between diabetes mellitus and liver disease

The relationship between diabetes mellitus and NAFLD is bidirectional, as NAFLD is associated with an increased risk of developing T2DM 114 . There is also a notable bidirectional relationship between diabetes mellitus and liver cirrhosis. The prevalence of diabetes mellitus in people with liver cirrhosis has been reported as 20–63%, depending on the severity of liver damage, aetiology and diagnostic criteria 115 . In an Italian study of 401 participants with cirrhosis, 63% of those with decompensated liver disease had diabetes mellitus compared with 10% of those with well-compensated liver disease 116 , suggesting that diabetes mellitus is more common in severe cases of liver damage. The association between diabetes mellitus and cirrhosis also varies according to the cause of liver disease. In a US study of 204 people with cirrhosis, the prevalence of diabetes mellitus was 25% among those with cirrhosis caused by hepatitis C virus, 19% among those with cirrhosis from alcoholic liver disease and only 1% among those with cirrhosis due to cholestatic liver disease 117 . Among the causes of cirrhosis, haemochromatosis has the strongest association with diabetes mellitus, with diabetes mellitus mainly resulting from the iron deposition that is characteristic of haemochromatosis 118 .

Several factors have been implicated in the aetiology of liver disease in people with diabetes mellitus, with insulin resistance being the most notable 119 .

Insulin resistance

Insulin resistance causes lipolysis, thereby increasing the circulating levels of free fatty acids, which are then taken up by the liver as an energy source 120 . These fatty acids overload the mitochondrial β-oxidation system in the liver, resulting in the accumulation of fatty acids and, consequently, NAFLD 121 . Of those individuals with NAFLD, 2–3% develop hepatic inflammation, necrosis and fibrosis, which are the hallmarks of NASH 122 . The exact mechanisms leading to steatohepatitis are unclear, although dysregulated peripheral lipid metabolism appears to be important 14 .

Ectopic adipose deposition

Excessive or ectopic deposition of adipose tissue around the viscera and in the liver might be an important mechanism underlying both T2DM and liver disease, particularly NAFLD 123 . Dysfunction of long-term adipose storage in white adipose tissue is known to lead to ectopic adipose deposition in the liver. In this state, increased levels of fatty acyl-coenzyme As, the activated form of fatty acids, might lead to organ dysfunction, including NAFLD 124 . Ectopic adipose deposition leading to organ-specific insulin resistance has emerged as a major hypothesis for the pathophysiological basis of T2DM, and ectopic adipose in the pancreas could contribute to β-cell dysfunction and, thus, the development of T2DM 125 .

Diabetes mellitus and affective disorders

The prevalence of depression appears to be high among people with diabetes mellitus. The strongest evidence for an association comes from a systematic review of 147 studies among people with T2DM, which revealed a mean prevalence of depression of 28% 126 , while the global prevalence of depression in the general population is estimated at around 13% 127 . For T1DM, a systematic review reported a pooled prevalence of depression of 12% compared with only 3% in those without T1DM 128 . The risk of depression among people with diabetes mellitus appears to be roughly 25% greater than the risk in the general population, with consistent findings across several meta-analyses (Table  4 ). A 2013 study found an adjusted RR of 1.25 (95% CI 1.10–1.44) for incident depression among people with diabetes mellitus compared with those without diabetes mellitus 129 . Another systematic review of people with T2DM reported a near identical effect size 130 .

Anxiety and eating disorders

Evidence exists for an association of diabetes mellitus with anxiety, and of T1DM with eating disorders. In a systematic review involving 2,584 individuals with diabetes mellitus, a prevalence of 14% was found for generalized anxiety disorder and 40% for anxiety symptoms, whereas the prevalence of generalized anxiety disorder in the general population is estimated as only 3–4% 131 . People with diabetes mellitus had an increased risk of anxiety disorders (OR 1.20, 95% CI 1.10–1.31) and anxiety symptoms (OR 1.48, 95% CI 1.02–1.93) compared with those without diabetes mellitus in a meta-analysis 132 (Table  4 ), although these findings were based on cross-sectional data. Across 13 studies, 7% of adolescents with T1DM were found to have eating disorders, compared with 3% of peers without diabetes mellitus 133 .

Broader psychological impacts

There is a substantial literature on a broad range of psychological impacts of diabetes mellitus. Social stigma 134 can have profound impacts on the quality of life of not only people with diabetes mellitus, but their families and carers, too 135 . In a systematic review, diabetes mellitus distress was found to affect around one-third of adolescents with T1DM, which was consistent with the results of studies of adults with diabetes mellitus 136 . Diabetes mellitus burnout appears to be a distinct concept, and is characterized by exhaustion and detachment, accompanied by the experience of a loss of control over diabetes mellitus 137 .

Diabetes mellitus and depression appear to have common biological origins. Activation of the innate immune system and acute-phase inflammation contribute to the pathogenesis of T2DM — increased levels of inflammatory cytokines predict the onset of T2DM 138 — and there is growing evidence implicating cytokine-mediated inflammation in people with depression in the absence of diabetes mellitus 139 . Dysregulation of the hypothalamic–pituitary–adrenal axis is another potential biological mechanism linking depression and diabetes mellitus 140 . There have been numerous reports of hippocampal atrophy, which might contribute to chronic activation of the hypothalamic–pituitary–adrenal axis, in individuals with T2DM as well as those with depression 141 , 142 . A meta-analysis found that, although hypertension modified global cerebral atrophy in those with T2DM, it had no effect on hippocampal atrophy 143 . This suggests that, although global cerebral atrophy in individuals with T2DM might be driven by atherosclerotic disease, hippocampal atrophy is an independent effect that provides a common neuropathological aetiology for the comorbidity of T2DM with depression. There is a lack of relevant information regarding the potential aetiological mechanisms that link diabetes to other affective disorders.

Diabetes mellitus and sleep disturbance

Obstructive sleep apnoea.

Obstructive sleep apnoea (OSA) is highly prevalent among people with diabetes mellitus. In a systematic review of 41 studies of adults with diabetes mellitus, the prevalence of OSA was found to be 60% 144 , whereas reports for OSA prevalence in the general population range from 9% to 38% 145 . In a UK study of 1,656,739 participants, T2DM was associated with an IRR for OSA of 1.48 (95% CI 1.42–1.55) compared with no T2DM 146 . A population-based US study reported a HR of 1.53 (95% CI 1.32–1.77) for OSA in people with T2DM compared with those without diabetes mellitus 147 . However, the association in this latter report was attenuated after adjustment for BMI and waist circumference (1.08, 95% CI 1.00–1.16), suggesting that the excess risk of OSA among people with diabetes mellitus might be mainly explained by the comorbidity of obesity. Although most studies on OSA have focused on T2DM, a meta-analysis of people with T1DM revealed a similar prevalence of 52% 148 ; however, this meta-analysis was limited to small studies. The association between T2DM and OSA is bidirectional: the severity of OSA was shown to be positively associated with the incidence of T2DM, independent of adiposity, in a large US cohort study 149 .

The mechanism by which T2DM might increase the risk of developing OSA is thought to involve dysregulation of the autonomic nervous system leading to sleep-disordered breathing 150 . Conversely, the specific mechanism behind OSA as a causative factor for T2DM remains poorly understood. It has been suggested that OSA is able to induce insulin resistance 151 , 152 and is a risk factor for the development of glucose intolerance 152 . However, once T2DM has developed, there is no clear evidence that OSA worsens glycaemic control, as an RCT of people with T2DM found that treating OSA had no effect on glycaemic control 153 .

Diabetes mellitus and cognitive disability

Dementia and cognitive impairment.

Dementia is emerging as a major cause of mortality in both individuals with diabetes mellitus and the general population, and is now the leading cause of death in some countries/regions 9 . However, compared with the general population, diabetes mellitus increases the risk of dementia, particularly vascular dementia. The association is supported by several systematic reviews, including one of eight population-based studies with more than 23,000 people, which found SRRs of 2.38 (95% CI 1.79–3.18) for vascular dementia and 1.39 (95% CI 1.16–1.66) for Alzheimer disease comparing people with diabetes mellitus with those without diabetes mellitus 154 (Table  4 ). Similar results, as well as a RR of 1.21 (95% CI 1.02–1.45) for mild cognitive impairment (MCI), were reported across 19 population-based studies of 44,714 people, 6,184 of whom had diabetes mellitus 155 . Two meta-analyses of prospective cohort studies have shown increased risks of all-cause dementia in people with diabetes mellitus compared with those without diabetes mellitus 156 , 157 , and T2DM has been shown to increase progression to dementia in people with MCI 158 .

The boundaries between Alzheimer disease and vascular dementia remain controversial, and these conditions are often difficult to differentiate clinically 159 . Consequently, vascular dementia might have been misdiagnosed as Alzheimer disease in some studies investigating diabetes mellitus and dementia, resulting in an overestimation of the effect size of the association between diabetes mellitus and Alzheimer disease. Although a cohort study found a significant association between diabetes mellitus and Alzheimer disease using imaging 160 , autopsy studies have failed to uncover an association between diabetes mellitus and Alzheimer disease pathology 161 , 162 , suggesting that vascular mechanisms are the key driver of cognitive decline in people with diabetes mellitus.

Another important finding is a 45% prevalence of MCI among people with T2DM in a meta-analysis, compared with a prevalence of 3–22% reported for the general population 163 . Notably, however, the prevalence of MCI in individuals with T2DM was similar in people younger than 60 years (46%) and those older than 60 years (44%), which is at odds with previous research suggesting that MCI is most common in older people, particularly those aged more than 65 years 164 However, another meta-analysis found cognitive decline in people with T2DM who are younger than 65 years 165 , suggesting that a burden of cognitive disease exists among younger people with diabetes mellitus.

Although there is solid evidence that links diabetes mellitus to cognitive disability, our understanding of the underlying mechanisms is incomplete. Mouse models suggest a strong association between hyperglycaemia, the advanced glycation end products glyoxal and methylglyoxal, enhanced blood–brain barrier (BBB) permeability and cognitive dysfunction in both T1DM and T2DM 166 . The BBB reduces the access of neurotoxic compounds and pathogens to the brain and sustains brain homeostasis, so disruption to the BBB can result in cognitive dysfunction through dysregulation of transport of molecules between the peripheral circulation and the brain 167 . There appears to be a continuous relationship between glycaemia and cognition, with associations found between even high-normal blood levels of glucose and cognitive decline 168 . Another hypothetical mechanism involves a key role for impaired insulin signalling in the pathogenesis of Alzheimer disease. Brain tissue obtained post mortem from individuals with Alzheimer disease showed extensive abnormalities in insulin and insulin-like growth factor signalling mechanisms compared with control brain tissue 169 . Although the synthesis of insulin-like growth factors occurred normally in people with Alzheimer disease, their expression levels were markedly reduced, which led to the subsequent proposal of the term ‘type 3 diabetes’ to characterize Alzheimer disease.

Diabetes mellitus and disability

Functional disability.

Disability (defined as a difficulty in functioning in one or more life domains as experienced by an individual with a health condition in interaction with contextual factors) 170 is highly prevalent in people with diabetes mellitus. In a systematic review, lower-body functional limitation was found to be the most prevalent disability (47–84%) among people with diabetes mellitus 171 The prevalence of difficulties with activities of daily living among people with diabetes mellitus ranged from 12% to 55%, although most studies were conducted exclusively in individuals aged 60 years and above, so the results are not generalizable to younger age groups. A systematic review showed a significant association between diabetes mellitus and falls in adults aged 60 years and above 172 . A 2013 meta-analysis 173 showed an increased risk of mobility disability, activities of daily living disability and independent activities of daily living disability among people with diabetes mellitus compared with those without diabetes mellitus (Table  4 ). Although this analysis included cross-sectional data, results were consistent across longitudinal and cross-sectional studies, suggesting little effect of reverse causality. However, people with functional disabilities that limit mobility (for example, people with osteoarthritis or who have had a stroke) might be more prone to developing diabetes mellitus owing to physical inactivity 174 .

Workplace productivity

Decreased productivity while at work, increased time off work and early dropout from the workforce 175 are all associated with diabetes mellitus, probably partly due to functional disability, and possibly also to comorbidities such as obesity and physical inactivity 176 . Given that young-onset diabetes is becoming more common, and most people with diabetes mellitus in middle-income countries/regions are less than 65 years old 177 , a pandemic of diabetes mellitus-related work disability among a middle-aged population does not bode well for the economies of these regions.

The mechanisms by which diabetes mellitus leads to functional disability remain unclear. One suggestion is that hyperglycaemia leads to systemic inflammation, which is one component of a multifactorial process that results in disability 154 . The rapid loss of skeletal muscle strength and quality seen among people with diabetes mellitus might be another cause of functional disability 178 (Box  1 ). In addition, complications of diabetes mellitus, including stroke, peripheral neuropathy and cardiac dysfunction, can obviously directly cause disability 179 .

Box 1 Diabetes mellitus and skeletal muscle atrophy

Individuals with diabetes mellitus exhibit skeletal muscle atrophy that is typically mild in middle age and becomes more substantial with increasing age.

This muscle loss leads to reduced strength and functional capacity and, ultimately, increased mortality.

Skeletal muscle atrophy results from a negative balance between the rate of synthesis and degradation of contractile proteins, which occurs in response to disuse, ageing and chronic diseases such as diabetes mellitus.

Degradation of muscle proteins is more rapid in diabetes mellitus, and muscle protein synthesis has also been reported to be decreased.

Proposed mechanisms underlying skeletal muscle atrophy include systemic inflammation (affecting both protein synthesis and degradation), dysregulation of muscle protein anabolism and lipotoxicity.

Mouse models have also revealed a key role for the WWP1/KLF15 pathway, mediated by hyperglycaemia, in the pathogenesis of muscle atrophy.

See refs 195 , 196 , 197 , 198 .

Diabetes management and control

Although a detailed discussion of the impacts of anti-diabetes mellitus medications and glucose control on emerging complications is beyond the scope of this Review, their potential effect on these complications must be acknowledged.

Medications

Anti-diabetes mellitus medications and cancer.

In the case of cancer as an emerging complication, the use of medications for diabetes mellitus was not controlled for in most studies of diabetes mellitus and cancer and might therefore be a confounding factor. People taking metformin have a lower cancer risk than those not taking metformin 180 . However, this association is mainly accounted for by other factors. For example, metformin is less likely to be administered to people with diabetes mellitus who have kidney disease 181 , who typically have longer duration diabetes mellitus, which increases cancer risk. A review of observational studies into the association between metformin and cancer found that many studies reporting significant reductions in cancer incidence or mortality associated with metformin were affected by immortal time bias and other time-related biases, casting doubt on the ability of metformin to reduce cancer mortality 182 . Notably, the use of insulin was associated with an increased risk of several cancers in a meta-analysis 183 . However, in an RCT of more than 12,000 people with dysglycaemia, randomization to insulin glargine (compared with standard care) did not increase cancer incidence 184 . Furthermore, cancer rates in people with T1DM and T2DM do not appear to vary greatly, despite substantial differences in insulin use between people with these types of diabetes mellitus.

Anti-diabetes mellitus medications and other emerging complications

Anti-diabetes medications appear to affect the onset and development of some other emerging complications of diabetes mellitus. Results from RCTs suggest that metformin might confer therapeutic effects against depression 185 , and its use was associated with reduced dementia incidence in a systematic review 186 . In an RCT investigating a potential association between metformin and NAFLD, no improvement in NAFLD histology was found among people using metformin compared with those given placebo 187 . An RCT reported benefits of treatment with the glucagon-like peptide 1 receptor agonist dulaglutide on cognitive function in a post hoc analysis 188 , suggesting that trials designed specifically to test the effects of dulaglutide on cognitive function should be undertaken.

Glucose control

Another important consideration is glycaemic control, which appears to have variable effects on emerging complications. A meta-analysis found no association of glycaemic control with cancer risk among those with diabetes mellitus 189 , and an RCT found no effect of intensive glucose lowering on cognitive function in people with T2DM 190 . However, glycaemic control has been associated with improved physical function 191 , decreased COVID-19 mortality 192 and a decreased risk of NAFLD 193 in observational studies of patients with diabetes mellitus; notably, no RCTs have yet confirmed these associations.

Conclusions

With advances in the management of diabetes mellitus and associated increased life expectancy, the face of diabetes mellitus complications is changing. As the management of glycaemia and traditional complications of diabetes mellitus is optimized, we are beginning instead to see deleterious effects of diabetes mellitus on the liver, brain and other organs. Given the substantial burden and risk of these emerging complications, future clinical and public health strategies should be updated accordingly. There is a need to increase the awareness of emerging complications among primary care physicians at the frontline of diabetes mellitus care, and a place for screening for conditions such as depression, liver disease and cancers in diabetes mellitus guidelines should be considered. Clinical care for older people with diabetes mellitus should target physical activity, particularly strength-based activity, to reduce the risk of functional disability in ageing populations. Ongoing high-quality surveillance of diabetes mellitus outcomes is imperative to ensure we know where the main burdens lie. Given the growing burden of these emerging complications, the traditional management of diabetes mellitus might need to broaden its horizons.

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Acknowledgements

D.T. is supported by an Australian Government Research Training Program (RTP) Scholarship and Monash Graduate Excellence Scholarship. J.E.S. is supported by a National Health and Medical Research Council Investigator Grant. D.J.M. is supported by a National Health and Medical Research Council Senior Research Fellowship.

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Dunya Tomic, Jonathan E. Shaw & Dianna J. Magliano

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D.T. researched data for the article and wrote the article. J.E.S and D.J.M. contributed substantially to discussion of the content. D.T., J.E.S. and D.J.M reviewed and/or edited the manuscript before submission.

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Tomic, D., Shaw, J.E. & Magliano, D.J. The burden and risks of emerging complications of diabetes mellitus. Nat Rev Endocrinol 18 , 525–539 (2022). https://doi.org/10.1038/s41574-022-00690-7

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review of literature related to diabetes mellitus

  • Open access
  • Published: 21 February 2024

Factors associated with quality of life among elderly patients with type 2 diabetes mellitus: the role of family caregivers

  • Haijing Zan 1 ,
  • Zhixing Meng 1 ,
  • Jing Li 1 ,
  • Xinjian Zhang 1 &
  • Tao Liu 1  

BMC Public Health volume  24 , Article number:  539 ( 2024 ) Cite this article

Metrics details

As a long-term chronic disease, Type 2 diabetes mellitus (T2DM) patients’ quality of life is affected by both themselves and his/ her close relatives, requiring comprehensive support from family members to ensure that patients are able to manage disease. The objective of this study is to investigate the relationship between caregivers’ sense of coherence, caregiver competence, and T2DM patients’ quality of life, as well as to explore the factors affecting patients with T2DM patients.

This investigation was a cross-sectional study. Between October 2022 and July 2023, 392 participant-caregiver dyads from two hospitals in Jinzhou City, Liaoning Province, were researched. Participants were investigated by General Characteristics Questionnaire, Modified Barthel Index (MBI), Diabetes Specific Quality of Life Scale (DSQLS), Sense of Coherence scale-13 (SOC-13), and Family Caregiver Task Inventory (FCTI). Data were statistically analyzed using SPSS 25. Univariate and multivariate linear regression analyses were used to identify the independent factors associated with the quality of life of elderly patients with T2DM.

The average score of T2DM quality of life was 61.14 (SD = 7.37), quality of life was negatively correlated with sense of coherence ( r =-0.344, P <0.01) and positively correlated with caregiver competence ( r  = 0.522, P <0.01). Furthermore, we found that age, disease duration, activities of daily living scores, sense of coherence, and caregiver competence scores were the main predictors of quality of life (R 2  = 0.375, P  < 0.001).

Conclusions

This study found that high levels of sense of coherence and caregiver competence in family caregivers were associated with better quality of life for patients. Furthermore, we also found that good quality of life was also related to younger age, shorter disease duration, and less dependence. This study offers a feasible example for policymakers to improve the quality of life from the perspective of T2DM patients’ family caregivers.

Peer Review reports

Diabetes is now a major public health concern on a global scale. The International Diabetes Federation (IDF) estimates that 536.6 million people are living with diabetes in 2021, and this number is projected to increase by 46%, reaching 783.2 million by 2045. It was reported that China had 141 million diabetics in 2021, making it the country with the biggest diabetic population worldwide [ 1 ]. Type 2 diabetes mellitus (T2DM), one of the most common forms of diabetes, accounts for about 90% of the total, especially in the middle-aged and elderly population [ 2 ]. In China, the prevalence of T2DM in the elderly is 30% [ 3 ]. With age and complications, patients are often accompanied by a variety of functional impairments and physical limitations ultimately leading to a reduced quality of life [ 4 ].

Quality of life (QoL) has received significant attention as a crucial medical result in recent years and it is the measure most frequently used to assess the effects of chronic illness or its treatment. According to the World Health Organization (WHO), QoL is an individual perception of their position in life in the context of the culture and value systems in which they live and about their goals, expectations, standards, and concerns [ 5 ]. According to a study by Qi et al., elderly Chinese individuals with type 2 diabetes have a poor quality of life [ 6 ]. At present, most researches have been more concerned with how patients can enhance their quality of life on their own and less concerned with the effects of outside variables on patients [ 7 , 8 , 9 ]. However, the role of the family caregivers is not to be overlooked either. They are an important extension of the health care system. As Bowen Family System Theory states that the family should be viewed as a system and family members are an important part of the family system, which implies that group members are interconnected and interdependent [ 10 ]. The patient is also affected, either more or less, by the feelings and actions of family members. According to traditional Chinese culture and medical resources, the major of patients with chronic illnesses and senior citizens typically receive care at home and in the neighborhood. Because of the complexity of diabetes management, some patients will be more reliant on their caregivers than others [ 11 ]. For those with T2DM, this means that the job of the family caregiver is becoming more and more crucial.

Over time, family caregivers may gradually feel stressed and burdened [ 12 ]. Protective psychological factors can reduce the negative impact of caregiving. Sense of coherence (SOC) is a positive psychological resource that reflects an individual’s ability to cope positively with stress in the face of life’s pressures and stimuli [ 13 ]. The concept emerged from Antonovsky Salutogenic Theory, which outlines the unique way in which each person views the world and their circumstances as comprehensible, manageable, and meaningful [ 14 ]. A longitudinal study indicated that SOC can mediate caregiver stress and psychological discomfort [ 15 ]. Another study identified that active psychoeducation of family caregivers can be effective in enhancing caregiving [ 16 ].

Caregiver competence, which has a direct impact on the standard of at-home care, is the ability of main carers to provide proper daily care, look for disease-related knowledge and skills, and provide mental and emotional support to care receivers [ 17 ]. The finding of a study in Northern Thailand demonstrated the influence of diabetes knowledge and behaviors among family caregivers on patients’ QoL [ 18 ]. The prognosis and QoL of patients are significantly impacted by the caregiving competence. Enhancing one’s ability to provide care lessens the burden of providing it, which is crucial for lowering hospital admission rates and conserving healthcare resources.

To our knowledge, there are few reports on the relationship between family caregivers’ SOC, caregiver competence and QoL of patients. In addition, current research has focused on caregivers of patients with cancer, stroke, and dementia and less on T2DM caregivers [ 19 , 20 ]. Therefore, this study considered the inclusion of family caregivers in the investigation of factors affecting the QoL of elderly patients with T2DM and explored the three variables, to provide a reference for improving the QoL of elderly patients with T2DM from the perspective of family caregivers.

Design and procedure

This cross-sectional study was performed from October 2022 to July 2023. According to Kendall’s sample calculation method [ 21 ], sample were measured to be 5–10 times the number of variables. The General Information Questionnaire consists of 18 entries, 4 dimensions of the Quality of Life Scale, 3 dimensions of the Sense of Coherence Scale, and 5 dimensions of the Caregiving Competence Scale, for a total of 30 variables, taking into account a 20% sample loss. As a result, the calculated sample size was at least 180–360. A total of 400 questionnaires with missing data were distributed in this study, and 392 valid questionnaires were recovered, with an effective rate of 98%, meeting the sample size requirements. Therefore, 392 participant-caregiver dyads were investigated from two hospitals in Jinzhou City. Patients must meet the following criteria: at least 6 months with physician-diagnosed T2DM and having related diagnostic test results, a Modified Barthel Index score less than 100 for those who need care and 60 years old or older, and providing informed consent to participate. The exclusion criteria were as follows: people with cognitive or psychiatric disorders and other serious physical illnesses, as well as those taking part in other studies that could affect the findings of this one. At the same time, the inclusion criteria for the family caregivers were as follows: being the participant’s primary family caregiver and older than 18 years old, giving family members care without payment, providing the participant with care for 6 months or longer, and providing informed consent to participate. Before testing, the respondent is fully told of the survey’s relevance and aim, and their informed consent is gained by having them sign an informed consent form. The Jinzhou Medical University Ethics Committee had agreed to the study’s plan (Ethics Approval Number: JZMULL2022079).

Data collection

The research team consisted of three graduate students, all of whom had passed uniform training. The researchers distributed questionnaires on site. The patients and family caregivers who met the inclusion criteria signed informed consent forms after explaining the purpose of the survey, requirements and filling methods of the questionnaire. The questionnaires were filled in anonymously, returned on the spot after completion, and answers were given to the respondents who did not understand the questionnaire. Patients and their family caregivers were timely informed of any omissions.

Demographic characteristic

This section consists of two parts. The first asked demographic questions about the T2DM patients included gender, age, education, marital status, comorbidities, complications, etc. The second asked demographic questions about family caregivers, such as relationship to person with T2DM, duration of care, etc.

Modified barthel index

The Modified Barthel Index (MBI) is a modification of the Barthel Index (BI) with a higher sensitivity than the BI, and it is a significant assessment of activities of daily living (ADL) [ 22 ]. Therefore, this study used the MBI to assess diabetes patients and determine whether they require caregivers. As with the BI, the highest score of the MBI is 100, with higher scores indicating increased ADL. Severe dependence (≤ 40), moderate dependence (41–60), light dependence (61–99), and no dependence (100) are the classifications based on the overall score [ 23 ].

  • Quality of life

This study was evaluated the quality of life of T2DM patients with the Diabetes Specific Quality of Life Scale (DSQLS) [ 24 ], which contains four dimensions, physiological function, psychological, social and therapeutic dimension, with a total of 27 items. The higher the score, the lower the quality of life and the extent to which the disease had afflicted the patients. Among them, the total score of this scale is divided into three grades: general (total score ≥ 80), medium (total score 40–80), and good (total score ≤ 40). In the present study, Cronbach’s α of internal consistency of the scale was 0.737.

  • Caregiver competence

The Family Caregiver Task Inventory (FCTI) was used to measure caregiver competence among the participants in the present study. Compiled by Clark and Rakowski [ 25 ], later translated and revised by Lee [ 26 ], it consists of 25 items divided into five subscales. The scores of these 25 items range from 0 to 2 and the sum of each item represents the caregiver’s caring ability, with higher total scores representing greater difficulty level of caregiver tasks and lower care ability. In this study, the overall Cronbach’s α of the scale was 0.776.

  • Sense of coherence

The sense of coherence was measured with the SOC-13 questionnaire adapted from a longer 29-item scale [ 27 ]. It includes three dimensions: meaningfulness, comprehensibility, and manageability. The answer to the items is based on a seven-point Likert-type scale ranging from 1 to 7. Five variables are negatively worded and are therefore reverse-coded when summing the items to reach an item total. The overall score ranges from 13 to 91, with higher scores indicating that the individual possesses sense of coherence to a better degree. The overall Cronbach’s α of this scale was 0.750.

Statistical analysis

All analyses were conducted using SPSS 25. The study population was described using mean ± standard deviation (Mean ± SD) and n (%). The relationships between sociodemographic factors and QoL scores of T2DM patients were expressed by T-test or one-way ANOVA analyses. Multivariate linear regression analysis was used to identify the independent factors associated with the QoL of patients. The Enter method is used in multiple linear regression. Pearson Correlation was used to measure the relationship between SOC and caregiver competence with QoL. The alpha level for significance in all analyses was determined to be P <0.05.

Baseline characteristics of the participants

Table  1 describes the demographics of T2DM. Among the 392 T2DM, 64.8% of them were in the age groups of 60–70 years. Over half of the respondents was the man (58.4%), and almost all (79.8%) of the patients reported being married. In addition, 83.2% of the patients lived in urban areas and the educational level of the participants was junior or senior high school (63.5%). There were 183 patients (46.7%) with a disease course of 10–20 years and 265 patients (67.6%) with other health problems. In this study, the majority of patients (58.7%) have 1–2 complications and 38.3% of the patients were treated with both oral medications and insulin, followed by insulin therapy (36.2%), and only lifestyle modification, such as dietary control (2.6%). Moreover, most of them had an ADL score was 61–99 points and mild dependence was required in everyday life.

The personal characteristics of family caregivers are presented in Table  2 . The results showed that 61.2% of family caregivers were women and most of them were mostly aged 61–70 years. In this study, 98% of caregivers were married and 74.7% of the caregivers were spouses. Most of them had at least a junior or senior high school level of education (66.8%) and a monthly family income of around 3,000 RMB. Regarding the care information, 42.3% of caregivers with a length of care of 3–5 years, and more than half of caregivers spend an average of 4–8 h a day.

Factors associated with quality of life

Table  1 shows the association between the QoL of T2DM patients and the general demographic characteristics of study participants, including gender (t = -2.051, P  = 0.041), age (t = 28.966, P <0.001), marital status (t=-5.495, P <0.001), living area (t = 3.704, P <0.001), level of education (t = 37.458, P <0.001), duration of T2DM (t = 15.263, P <0.001), complications (t = 24.893, P <0.001) and activities in daily living (t = 14.024, P <0.001). In addition, Table  2 also shows that the gender of the caregiver (t = 3.530, P <0.001), their relationship to the patient (t = 20.475, P <0.001), time spent caring (t = 5.326, P  = 0.005) and average time spent caring (t = 45.867, P <0.001) are factors that are related to the QoL of the patient.

Correlation between SOC, FCTI, and QoL in T2DM patients

The average score of SOC, FCTI, and QoL of T2DM patients and their correlation are shown in Table  3 . Correlation analysis showed that there was a negative correlation between SOC and FCTI ( r =-0.572, P  < 0.01), SOC was negatively correlated with QoL ( r =-0.344, P  < 0.01), FCTI was positively correlated with QoL ( r  = 0.522, P  < 0.01).

Predictors of QoL of T2DM patients

To investigate the crucial factors affecting the level of QoL of T2DM patients. The total score of QoL in patients was set as a dependent variable, the variables were subjected to a multifactorial analysis after excluding those that were highly correlated, based on the clinical context, and the results of the analysis showed that the age, ADL, duration of T2DM, SOC and caregiver competence had a significant effect on the QoL in T2DM patients, as shown in Table  4 .

This cross-sectional study examines the level of QoL of patients with T2DM and its relationship with general demographic characteristics of both the patient and the caregiver, the caregiver’s SOC, and caregiver competence. The data depicted that the total score of QoL among elderly patients with T2DM was 61.14 ± 7.37, which was similar to another study conducted in China that used the same measurement among patients with T2DM [ 28 ], and indicates that the patients with T2DM had a medium QoL in both studies, but higher than reported in an Iran and Ethiopia study [ 8 , 29 ]. This difference might be explained by the improved financial status and the availability of primary medical insurance [ 30 ].

Variable results have been found in the literature addressing the QoL of patients with T2DM and its relationship to sociodemographic parameters. It is common knowledge that age has become a definitive factor in the QoL of patients [ 31 , 32 ]. Research from other studies has confirmed our finding that younger patients had higher QoL scores [ 33 ]. Previous reports found that lower educational level, belonging to the female sex, long duration of illness, and complications were associated with poor QoL in patients with T2DM [ 34 , 35 , 36 ]. Similar to our finding, it may be that higher literacy levels in patients may possess better life skills, more capacity for managing their diseases, and superior informatics proficiency. However, with the prolongation of the course of the disease and complications arise, the patient’s insulin resistance is gradually weakened and the risk factors are increased, resulting in poor blood sugar control and overall decline in patients’ QoL. Our investigation, in contrast to other studies [ 37 , 38 ], did not reach the conclusion that patients with comorbidities had lower QoL. This may be because the scale used in this study was more focused and less sensitive to the impact of other conditions on QoL [ 39 ] as well as individual reporting differences. Healthcare professionals must exercise extra caution when managing the comorbidities of T2DM, despite the fact that we did not find an effect, as other research has demonstrated that QoL deteriorates and lifespan dramatically declines as the number of comorbidities grows [ 40 ].

The factors that influence the QoL of people with T2DM are widely known, but this study argues that there is still a need to identify which of the variables attributable to carers may improve the QoL of patients. Therefore, our study included caregiver-related factors in the analysis as well. The study’s findings revealed that the patient’s QoL was impacted by the caregiver’s gender. As part of the custom, males typically hold positions of authority and control the family economy, while women are mostly in charge of managing the household’s daily operations, such as caring for children and elderly parents. Over time, they might develop their caregiving abilities and gain expertise [ 41 ]. Women are more inclined to adopt the position of caregiver and take on caring responsibilities when a family member is in danger of becoming unwell. In this study, 61.2% of the caregivers were female, and compared to male caregivers, they did a better job of enhancing patients’ quality of life. The results of this study suggest that patients show better QoL when the caregiver is their spouse. Our findings were in accordance with Potier [ 42 ]. A possible explanation is that some individuals can view providing care as a natural part of life and a responsibility to their families. These kinds of perceptions may be more prevalent in spouses than in sons or daughters [ 43 ]. In China, the current predicament facing the children of this generation is that of having both the old and children. They not only have to consider the health of the elderly and the raising of children, but also have to face their work, which will lead to a lack of energy under the multiple pressures, and the traditional Chinese filial piety will aggravate their anxiety, which will in turn increase the burden of caregiving and affect the quality of caregiving. In addition, patients typically have a lower QoL when their caregivers have been providing care for more than five years, on average for eight hours or more per day. The caregiver’s assumption of the caregiving task will have an impact on the original life, and with the prolongation of the caregiving time, the patient’s dependence on him or her increases, and the prolonged time spent facing the patient leads to stress and anxiety, which in turn affects the quality of care and leads to a lowering of the patient’s QoL [ 44 ].

This study confirmed that the SOC of the caregiver was negatively associated with patients’ QoL. But it can predict them positively. This is consistent with research from Taiwan that showed older patients with hip fractures were more likely to have a poorer QoL following surgery if their caregivers had lower psychological functioning [ 45 ]. This implies that when caregivers are in better physical and mental health, their actions give patients a sense of security and support, which influences their mood and boosts their self-assurance in beating the illness. All of this enhances the patients’ quality of life, particularly the psychological part of it. Furthermore, we also found that caregiver competence was positively correlated with patients’ QoL. In line with our findings, Huang et al. [ 46 ] studied hemodialysis patients and showed that patients’ QoL was associated with caregivers’ caregiver competence. This was also a consistent discovery in a study of elderly adults with disabilities [ 47 ]. High levels of caregiver competence among family caregivers indicate a certain amount of knowledge about illness susceptibility factors, fundamental skills, and preventing complications, all of which help to somewhat slow down the patient’s disease progression. In this study, family caregivers’ SOC was negatively correlated with, but positively predictive of, caregiver competence. A Dutch study reached a similar conclusion [ 48 ]: caregivers who experience low levels of SOC are more likely to believe that the care conditions they face are complex and challenging to comprehend and manage, and they may not be motivated to strive for higher levels of care.

Regression analysis showed that age, disease duration, ADL scores, SOC and FCTI were the major predictors of QoL in patients with T2DM. This finding supports research on another chronic disease [ 49 ], demonstrating that SOC has a positive predictive effect on the QoL of T2DM patients. SOC is expected to assist caregivers in making better use of resources. Those who care for others with an optimistic outlook on caring may be better able to handle a difficult situation. Antonovsky’s theory encourages people to reflect on stressful situations so that they can understand stress, think about how to make coping with them and identify the best ways to deal with them that make sense [ 14 ]. Therefore, one plausible explanation is that caregivers with a strong sense of coherence are better able to manage difficulties and chronic stress in the caregiving process, making the best use of the social resources available to them and enhancing caregiving capacity and quality of care [ 50 ]. A survey in Japan concluded that the sense of care burden was lower when the mental health status was high [ 51 ]. This means that high levels of psychological consistency in caregiving tend to be accompanied by less caregiving burden and higher levels of caregiving capacity. In this study, the QoL in T2DM was significantly predicted by caregiver competence as well. Nobahar et al. identified that the therapeutic effect and QoL of hemodialysis patients may be directly impacted by the caregivers’ ability to provide care [ 52 ]. Additionally, higher skill levels imply that carers can combine their own lives with caring responsibilities. This means that in the future, in addition to focusing on the mental health of family caregivers, we should also work to enhance the caregivers’ capacity to provide for their patients by coordinating resources for healthcare and promoting health in order to enhance the patients’ QoL.

T2DM is an incurable disease, so improving the QoL of patients is particularly crucial [ 53 ]. Elderly patients have an increased dependence on their caregivers and studies have confirmed that caregivers often have a direct or indirect impact on the QoL of patients [ 51 , 54 ]. The above findings suggest that clinical interventions should give some attention to carers to improve patients’ quality of life in many ways.

The study’s limitations are convenience sampling and the cross-sectional nature of the study, both of which could not identify the causal link. Another potential drawback associated with the questionnaires’ self-reporting nature is recall bias. Finally, this study’s findings were derived from T2DM patients and their caregivers using healthcare resources from two hospitals in Jinzhou. The findings may, therefore, be transferable to different similar settings or conditions but are not generalizable to the population in a positivist sense.

This is the first study that assessed the relationship between T2DM patients’ QoL and SOC and caregiver competence of caregiver. The results demonstrate a statistically significant relationship between T2DM patients’ enhanced QoL and their caregivers’ high SOC and caregiver competence. High SOC of caregivers typically has less difficulty providing care, are more competent to provide care, and have better patient outcomes. These new relationships could inform the development of clinical interventions to target the group of family caregivers to improve the QoL of patients.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

  • Type 2 diabetes mellitus

Family caregiver task inventory

Activities of daily living

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Zan, H., Meng, Z., Li, J. et al. Factors associated with quality of life among elderly patients with type 2 diabetes mellitus: the role of family caregivers. BMC Public Health 24 , 539 (2024). https://doi.org/10.1186/s12889-024-17917-z

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Gestational Diabetes Mellitus—Recent Literature Review

Robert modzelewski.

1 Endocrinology, Diabetology and Internal Medicine Clinic, Department of Internal Medicine, University of Warmia and Mazury in Olsztyn, 10-719 Olsztyn, Poland

Magdalena Maria Stefanowicz-Rutkowska

2 Department of Endocrinology, Diabetes and Isotope Therapy, Wroclaw Medical University, 50-367 Wroclaw, Poland

Wojciech Matuszewski

Elżbieta maria bandurska-stankiewicz.

Gestational diabetes mellitus (GDM), which is defined as a state of hyperglycemia that is first recognized during pregnancy, is currently the most common medical complication in pregnancy. GDM affects approximately 15% of pregnancies worldwide, accounting for approximately 18 million births annually. Mothers with GDM are at risk of developing gestational hypertension, pre-eclampsia and termination of pregnancy via Caesarean section. In addition, GDM increases the risk of complications, including cardiovascular disease, obesity and impaired carbohydrate metabolism, leading to the development of type 2 diabetes (T2DM) in both the mother and infant. The increase in the incidence of GDM also leads to a significant economic burden and deserves greater attention and awareness. A deeper understanding of the risk factors and pathogenesis becomes a necessity, with particular emphasis on the influence of SARS-CoV-2 and diagnostics, as well as an effective treatment, which may reduce perinatal and metabolic complications. The primary treatments for GDM are diet and increased exercise. Insulin, glibenclamide and metformin can be used to intensify the treatment. This paper provides an overview of the latest reports on the epidemiology, pathogenesis, diagnosis and treatment of GDM based on the literature.

1. Introduction

Gestational diabetes mellitus (GDM) is a state of hyperglycemia (fasting plasma glucose ≥ 5.1 mmol/L, 1 h ≥ 10 mmol/L, 2 h ≥ 8.5 mmol/L during a 75 g oral glucose tolerance test according to IADPSG/WHO criteria) that is first diagnosed during pregnancy [ 1 ]. GDM is one of the most common medical complications of pregnancy, and its inadequate treatment can lead to serious adverse health effects for the mother and child [ 1 , 2 ]. According to the latest estimates of the International Diabetes Federation (IDF), GDM affects approximately 14.0% (95% confidence interval: 13.97–14.04%) of pregnancies worldwide, representing approximately 20 million births annually [ 3 ]. Mothers with GDM are at risk of developing gestational hypertension, pre-eclampsia and termination of pregnancy via Caesarean section [ 4 ]. In addition, GDM increases the risk of complications, including cardiovascular disease, obesity, and impaired carbohydrate metabolism, leading to the development of type 2 diabetes (T2DM) in both mother and infant [ 5 , 6 , 7 ]. The increase in the incidence of GDM also leads to a significant economic burden and deserves greater attention and awareness [ 8 ].

Despite numerous studies, the pathogenesis of GDM remains unclear, and the results obtained so far indicate a complex mechanism of interaction of many genetic, metabolic and environmental factors [ 9 ]. The basic methods of treating GDM include an appropriate diet and increased physical activity, and when these are inadequate, pharmacotherapy, usually insulin therapy, is used. In developing countries, such as Brazil, oral hypoglycemic agents are also used, mainly metformin and glibenclamide (glyburide) [ 10 ]. The prevention and appropriate treatment of GDM are needed to reduce the morbidity, complications and economic effects of GDM that affect society, households and individuals. Though it is well established that the diagnosis of even mild GDM and treatment with lifestyle recommendations and insulin improves pregnancy outcomes, it is controversial as to which type and regimen of insulin are optimal, and whether oral agents can be used safely and effectively to control glucose levels.

2. Aim of the Study

A review of current literature reports on epidemiology, pathogenesis, diagnosis and treatment of GDM.

3. Material and Methods

The study presents an analysis of data that are currently available in the literature that concern the epidemiology, pathogenesis, diagnosis and treatment of GDM. The study was based on reviews, original articles and meta-analyses published in English in the last 10 years.

A literature search was conducted from 1 January 2021 to 31 March 2022 using Web of Science, PubMed, EMBASE, Cochrane, Open Grey and Grey Literature Report. MeSH terms, including “gestational diabetes”, “pregnancy induced diabetes”, “hyperglycemia”, “glucose intolerance”, “insulin resistance”, ”prevalence”, “incidence”, “GDM treatment” and “behavioral treatment”, were used alone or in combination.

4. Results and Discussion

4.1. epidemiology.

The growing problem of overweight and obesity around the world significantly contributes to the steady increase in the incidence of diabetes, including GDM in the population of women of reproductive age [ 11 ]. According to the 2019 report by the International Diabetes Federation (IDF), more than approximately 20.4 million women (14.0% of pregnancies) presented with disorders of carbohydrate metabolism, of which approximately 80% was GDM, i.e., about one in six births was affected by gestational diabetes [ 3 ]. Table 1 presents the analysis of the geographical distribution of GDM [ 3 , 12 ].

The geographical distribution of GDM [ 3 , 12 ].

4.2. GDM Risk Factors

The incidence of hyperglycemia in pregnancy increases with age. According to Mosses et al., GDM was diagnosed in 6.7% of pregnancies in general, but in 8.5% of women over 30 years of age [ 13 ]. Lao et al. showed the highest risk of developing GDM at the ages of 35–39 compared with younger pregnant women (OR 95% CI: 10.85 (7.72–15.25) vs. 2.59 (1.84–3.67)) [ 14 ]. These observations were confirmed by IDF data showing the highest percentage of pregnancies with GDM reaching 37% at the ages of 45–49, which was also conditioned by a lower number of pregnancies with an accompanying general higher percentage of diabetes in this population [ 3 ]. The delivery of a macrosomic child is another important factor that may increase the risk of both GDM and DM2 by up to 20% [ 15 ]. Even after taking into account the age of the woman, pluriparity remains in a linear relationship to the incidence of GDM [ 16 ]. GDM in a previous pregnancy increases the risk of reoccurrence by more than six times [ 17 ]. In women with a BMI of at least 30 kg/m 2 , the GDM frequency is 12.3%, and in women with first-line relatives that have a history of GDM, it is 11.6%. The combination of these two factors increases the risk of GDM up to 61% of cases [ 4 , 18 , 19 ]. More than twice the percentage of pregnancies with GDM was observed in women that were previously treated for polycystic ovary syndrome (PCOS) [ 20 ]. Recent studies indicated that the prevalence of GDM is related to the season and that GDM prevalence increases during the summer compared with winter [ 21 , 22 , 23 ]. Moreover, a 50% increase in the incidence of GDM in pregnancies resulting from in vitro fertilization was described [ 24 ].

4.3. Diagnosing GDM

The decades-long polemic about the diagnosis of GDM has covered two issues: whether to include all pregnant women or only those with risk factors, and whether to use one- or two-stage diagnostic procedures. A GDM diagnosis is only possible if a previous diagnosis of diabetes (i.e., type 1 or type 2 diabetes) had been excluded early in the pregnancy. Screening of only risk groups may result in GDM not being diagnosed in as many as 35–47% of pregnant women, which is certain to affect obstetric results [ 25 ]. The results of the Hyperglycemia Adverse Pregnancy Outcome (HAPO) study of 23,316 women gave a clear outcome that elevated glycemia (but below the threshold for overt diabetes mellitus) showed a linear relationship with the occurrence of maternal and neonatal complications expressed as large for gestational age (LGA) endpoints, the frequency of Caesarean sections, neonatal hypoglycemia and the concentration of the umbilical C-peptide [ 26 ]. The current criteria for the diagnosis of GDM introduced by The International Association of Diabetes and Pregnancy Study Groups (IADPSG), which were based on the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) results, found a threefold increase in GDM diagnoses, which suggests an earlier underestimation. The HAPO group sought to identify new screening values that would better identify pregnancies at risk for perinatal complications. The HAPO study demonstrated a positive linear relationship between screening glucose values and adverse perinatal outcomes. Moreover, the study authors found that perinatal risks began to increase in women with glucose values that were previously considered “normal” [ 27 , 28 ]. Therefore, nowadays, the basis of GDM diagnostics is the administration of 75 g of glucose between 24 and 28 weeks of pregnancy in all pregnant women without previously diagnosed diabetes. The treatment of even mild forms of glucose intolerance in GDM offers an added benefit, as demonstrated by the Australian Carbohydrate Intolerance Study in Pregnant Women (ACHOIS) and Maternal-Fetal Medicine Units Network (MFMU). It was shown that the frequency of obstetric complications is reduced depending on hyperglycemia and pregnancy weight gain. In the ACHOIS study, the composite endpoint (neonatal death, perinatal injury, hyperbilirubinemia, neonatal hypoglycemia and hyperinsulinemia) was significantly reduced with antihyperglycemic intervention, and there was also a lower weight gain (by 1.7 kg on average) and a lower incidence of LGA. In the MFMU study, no changes were noted in the composite endpoint, but the incidence of LGA and shoulder dystocia decreased significantly [ 2 , 29 , 30 ]. The results of these studies showed that most scientific societies implement the recommendations of the IADPSG from 2010 and WHO from 2013 into their daily practice. The introduction of the IADPSG criteria for the screening of GDM increased the prevalence by threefold, albeit with no substantial improvements in GDM-related events for women without risk factors except for reduced risks for LGA, neonatal hypoglycemia and preterm birth [ 31 ]. This led to further research on a group of patients with GDM. In a large randomized trial (among 23,792 pregnant women), Hillier et al. showed that one-step screening, as compared with two-step screening, doubled the incidence of the diagnosis of GDM, but did not affect the risks of LGA, adverse perinatal outcomes, primary Caesarean section, or gestational hypertension or pre-eclampsia [ 32 ]. The GEMS Trial assessed two diagnostic thresholds for GDM—namely, the currently used, higher diagnostic criteria and the IADPSG, lower diagnostic criteria—for their effects on fetal growth, perinatal morbidity, maternal physical and psychological morbidity, and health service utilization. The recently published results of the GEMS Trial showed that lower glycemic criteria (fasting plasma glucose level of at least 92 mg/dL, a 1 h level of at least 180 mg/dL or a 2 h level of at least 153 mg/dL) for the diagnosis of GDM did not result in a lower risk of a large-for-gestational-age infant than the use of higher glycemic criteria (fasting plasma glucose level of at least 99 mg/dL or a 2 h level of at least 162 mg/dL) [ 33 ]. This latest study is another important point in the discussion of the best diagnosis method for GDM. Table 2 presents the criteria for the diagnosis of GDM according to different scientific societies.

The criteria for the diagnosis of GDM according to different scientific societies.

Notes: ADA—American Diabetes Association, ACOG—American College of Obstetricians and Gynecologists, DCCPG—Diabetes Canada Clinical Practice Guidelines, DIPSI—Diabetes in Pregnancy Society Group India, EASD—European Association for the Study of Diabetes, FIGO—International Federation of Gynecology and Obstetrics, ADIPS—Australasian Diabetes in Pregnancy Society, WHO—World Health Organization, IADPSG—International Association of the Diabetes and Pregnancy Study Groups, NICE—National Institute for Health and Care Excellence. 1 There are no established criteria for the diagnosis of diabetes mellitus in pregnancy based on a 1 h post-load value. 2 Refers to the whole blood glucose level. 3 Recommends either the IADPSG one-step or two-step approach; initial screening by measuring plasma or serum glucose concentration 1 h after a 50 g oral glucose load (GCT). Those exceeding the cut-off perform either a 100 g OGTT or 75 g OGTT, requiring two or more venous plasma concentrations to be met or exceed the threshold. 4 Listed in the preferred approach, the alternate approach is the IADPSG, which uses a non-fasting 75 g OGTT. 5 Uses a non-fasting 75 g OGTT.

Many potential markers of GDM occurrence are being described more and more frequently. The greatest hopes are connected with afamine, adiponectin and 1,5-anhydroglucitol [ 34 , 35 ]. Due to the fact that in many countries, prenatal care is provided by gynecologists who can consult other specialists, it seems important to develop predictive models that allow for the identification of women at the highest risk for gestational diabetes in early pregnancy. The Benhalim-2 2020 model, which takes into account interview and biochemical data (propensity score model: history of GDM, FPG, height, triglycerides, age, ethnic origin, first trimester weight, family history of diabetes, HbA1c), showed the highest sensitivity [ 36 ].

4.4. Pathogenesis of Carbohydrate Metabolism Disorders in Pregnancy

Several factors may be responsible for the occurrence of GDM, the most important of which are insulin resistance and beta cell dysfunction, as well as genetic, environmental and dietary factors.

4.4.1. Insulin Resistance

In the pathogenesis of GDM, as in type 2 diabetes, a key role is played by insulin resistance and decreased insulin secretion relative to the patient’s needs. We observe GDM in both obese and lean women [ 37 ]. Insulin resistance induced by pregnancy overlaps with the pre-pregnancy insulin resistance that is already present in obese women, while in lean women, an impaired first phase of insulin secretion is also dominant [ 38 ]. Insulin resistance in pregnancy is predisposed by the diabetogenic effect of placental hormones (human placental lactogen (hPL), human placental growth hormone (hPGH), growth hormone (GH), adrenocorticotropic hormone (ACTH), prolactine (PRL), estrogens and gestagens), increased secretion of pro-inflammatory cytokines (tumor necrosis factor alpha (TNF-α), IL-6, resistin and C-reactive protein (CRP)), adiponectin deficiency, hyperleptinemia and central leptin resistance, impaired glucose transport in skeletal muscles, impaired insulin receptor signaling, and decreased expression and abnormal translocation of GLUT-4 to the cell membrane of adipocytes [ 39 , 40 , 41 ]. An increased secretion of insulin-antagonistic hormones (placental hormones, cortisol) during pregnancy results in an increased insulin resistance, which, at the end of the third trimester, reaches a value similar to full-blown type 2 diabetes [ 9 , 42 ]. Subclinical inflammation in pregnant women as a result of the synthesis of pro-inflammatory cytokines in the placenta and adipose tissue also leads to insulin resistance [ 43 , 44 ]. So far, the effects on the development of insulin resistance due to TNF-α, IL-6 and C-reactive protein have been best studied. Kirwan et al. stated that an increase in insulin resistance, which is characteristic of pregnancy, most strongly correlates with the increase in TNF-α concentration, considering that TNF-α as a marker of insulin resistance during pregnancy [ 45 ]. Furthermore, hyperleptinemia in the first weeks of pregnancy is a predictor of the development of gestational diabetes. According to Qui, the determination of the leptin concentration ≥ 31.0 ng/mL in the 13th week of pregnancy causes a 4.7-fold increase in the risk of GDM compared with the risk at the level of leptinemia of ≤14.3 ng/mL. For every 10 ng/mL increase in leptin concentration, the risk of GDM increases by 21% [ 46 ]. At the same time, GDM is characterized by elevated concentrations of leptin, which leads to hyperleptinemia [ 47 ]. However, pre-pregnancy BMI is a stronger predictor of leptinemia than GDM perse [ 48 ]. In women with gestational diabetes, the concentration of adiponectin is lower than in pregnant women without disturbances of carbohydrate metabolism, regardless of their pre-pregnancy BMI [ 49 ]. It was shown that a low adiponectin concentration in the first and second trimesters of pregnancy is a predictor of diabetes development in pregnancy [ 50 ]. In the Barbour study, a 1.5–2-fold increase in the level of the p85α PI-3-kinase regulatory subunit was found in both the muscle and adipose tissue of obese pregnant and pregnant GDM women compared to obese non-pregnant women. In women with GDM, a 62% increase in the phosphorylation activity of IRS-1 serine residues was found in striated muscle cells compared with the control group of pregnant women without GDM, which points to insulin resistance post-receptor mechanisms [ 43 ].

4.4.2. β-Cell Dysfunction

The analysis of insulin secretion disorders in GDM gives inconclusive results. The mechanisms of β-cell hypertrophy and proliferation, resulting in a 300% increase in insulin secretion in the first two trimesters of physiological pregnancy, is insufficient to explain GDM [ 9 , 39 ]. In the pathogenesis of GDM, we also observed the influence of autoimmune and genetic factors, such as the presence of anti-insulin and/or anti-insulin antibodies, which are at risk of developing DM1 and latent autoimmune diabetes in adults (LADA) [ 51 ]. In cross-sectional studies, the prevalence of mutations in the gene variants GCK, HNF1A, HNF4A, HNF1B and INS in maturity-onset diabetes of the young (MODY) was 0–5% [ 52 ]. Great hopes in the search for the genetic causes of GDM are associated with research on the single nucleotide polymorphism (SNP) related to the cyclin-dependent kinase 5 (CDK5) regulatory subunit associated protein1-like1 gene (CKDAL1). Their presence is associated with an impaired first phase of insulin secretion in DM2 and GDM and leads to a decrease in the mass of beta cells and impairment of their function, leading to GDM [ 53 , 54 ].

4.4.3. Other Factors

A study conducted in Spain showed that carriers of the gene rs7903146 T-allele who followed the Mediterranean diet in early pregnancy had a lower risk of developing GDM [ 55 ]. A growing body of research provides evidence of the importance of DNA methylation in the regulation of gene expression associated with metabolic disturbances in pregnant women and in the metabolic programming of the fetus in the setting of GDM-induced hyperglycemia [ 56 , 57 , 58 ]. In subcutaneous and visceral adipose tissue samples, the insulin receptor mRNA/protein expressions were significantly reduced in women with GDM ( p < 0.05) [ 56 ]. Mothers with GDM displayed a significantly increased global placental DNA methylation (3.22 ± 0.63 vs. 3.00 ± 0.46% (±SD), p = 0.013) [ 57 ]. Additional light was shed on the pathogenesis of GDM by studies on disorders of the placental proteome, where the placental proteome was altered in pregnant women affected by GDM with large-for-gestational-age (LGA), with at least 37 proteins being differentially expressed to a higher degree ( p < 0.05) as compared with those with GDM but without LGA [ 59 ]. In addition, Khosrowbeygi et al. showed that women with GDM had higher values of TNF-α (225.08 ± 27.35 vs. 115.68 ± 12.64 pg/mL, p < 0.001) and lower values of adiponectin (4.50 ± 0.38 vs. 6.37 ± 0.59 µg/mL, p = 0.003) and the adiponectin/TNF-α ratio (4.31 ± 0.05 vs. 4.80 ± 0.07, p < 0.001) than normal pregnant women. The ratio of adiponectin/TNF-α, which decrease significantly in GDM compared with normal pregnancy, might be an informative biomarker for the assessment of pregnant women at high risk of insulin resistance and dyslipidemia and for the diagnosis and therapeutic monitoring aims regarding GDM [ 60 ].

4.5. COVID-19 Pandemic and GDM

The second severe acute respiratory distress syndrome (SEA) coronavirus (SARS-CoV-2) causes an acute respiratory disease called coronavirus disease 2019 (COVID-19). There are limited data on the impact of SARS-CoV-2 infection on the onset and course of GDM. A living systematic review and meta-analysis of 435 studies reported the incidence of COVID-19 in pregnant women of approximately 10% (7–14%) [ 61 ]. The COVID-19 pandemic has caused organizational difficulties related to the correct diagnosis of GDM. In Anglo-Saxon countries, in order to minimize the risk of infection with SARS-CoV-2, replacement of the three-point OGTT was proposed and the assessment of fasting blood glucose and Hba1c were introduced. Postpartum screening postponement and the use of telemedicine were also offered [ 62 ]. However, simplifying the diagnosis of GDM in order to avoid the risk of COVID-19 infection was unfortunately associated with the risk of not diagnosing GDM by as much as 20–30%, which may affect obstetric outcomes [ 63 , 64 , 65 ]. This was confirmed by another study that showed that in the “COVID era”, diagnostics toward GDM cannot be abandoned and the procedures for its detection cannot be simplified [ 66 ]. The COVID-19 pandemic increased the incidence of GDM in 2020 compared with 2019 (13.5% vs. 9%, p = 0.01), especially in women in the first trimester of pregnancy. Experiencing lockdown during the first trimester of gestation increased the risk of GDM in these women by a factor of 2.29 ( p = 0.002) compared with women whose pregnancies occurred before and after lockdown [ 67 ]. This is undoubtedly influenced by the sedentary lifestyle of women during the pandemic and reduced physical activity, most often caused by the fear of leaving their homes due to COVID-19 [ 68 ]. The “lockdown effect” caused a marked deterioration in glycemic control, an increase in the percentage of HBA1c, and weight/BMI gain in patients with DM2 and GDM [ 69 , 70 ].

4.6. Treatment of Gestational Diabetes

Regarding women with GDM, due to the lack of randomized clinical trials, it is extremely difficult to propose an unambiguous and uniform model of management in order to achieve obstetric results similar to the population of healthy women. The treatment of GDM is based on consensus and expert opinion. Analyses of Cochrane Database Reviews showed the lack of unambiguous data on the correlation between the intensity of glycemic control and obstetric outcomes [ 71 ]. Based on a meta-analysis from 2014–2019, Mitanchez et al. indicated that the greatest impact on reducing the number of obstetric complications is achieved by combining dietary treatment with exercise [ 72 ].

4.6.1. Nutritional Treatment

Nutritional recommendations help women to achieve normoglycemia, optimal weight gain and proper development of the fetus, and the introduction of a pharmacological treatment does not release the mother from the obligation to follow the diet [ 73 ]. In GDM, it is necessary to develop an individual nutritional plan based on glycemic self-control, optimal weight gain based on pre-pregnancy BMI, and a calculation of energy requirements and macronutrient proportions, as well as taking into account the mother’s nutritional preferences, together with work, rest and exercise [ 73 ]. Chao et al. indicated better results when using individualized recommendations for a specific woman with GDM in contrast to general recommendations [ 74 ]. It is recommended to eat three main meals and 2–3 snacks a day, often with a snack around 9:30 pm to protect against nocturnal hypoglycemia and morning ketosis [ 6 ]. In a prospective observational study using the 24 h online diet and glycemic tool (“Myfood24 GDM”), better glycemic control was demonstrated with more frequent meals [ 75 ]. In women with GDM, carbohydrates are the most important macronutrient, and their high consumption can cause hyperglycemia. However, glucose is the main energy substrate of the placenta and fetus, and thus, is necessary for their proper growth and metabolism [ 76 ]. According to the ATA, the content of carbohydrates in the diet should constitute 40–50% of the energy requirement, not less than 180 g/day, and consist mainly of starchy foods with a low glycemic index (GI) [ 6 , 73 ]. The recommended dietary fiber intake is 25–28 g per day, which means a portion of about 600 g of fruit and vegetables per day with a minimum of 300 g of vegetables, whole grain bread, pasta and rice [ 73 , 77 , 78 ]. Protein should constitute about 30% of the caloric value, that is about 1.3 g/kg of b.w./d, with the minimum recommended daily intake of 71 g of protein [ 73 ]. Increased intakes of plant protein, lean meat and fish, and reduced intakes of red and processed meats are beneficial in the treatment of GDM and may improve insulin sensitivity [ 79 , 80 ]. A diet with a high fat content is contraindicated (20–30% of the caloric value is recommended, including < 10% saturated fat), as it leads to placental dysfunction and infant obesity, increased inflammation and oxidative stress, and impaired maternal muscle glucose uptake [ 80 , 81 , 82 ]. The consumption of saturated fat should be limited in favor of the consumption of the polyunsaturated fatty acids (PUFA) n-3 (linolenic acid) and n-6 (linoleic acid), which are the most important fatty acids for fetal growth and development. A total intake of n-3 in the amount of 2.7 g/day is considered safe during pregnancy [ 77 ], while additional fish oil supplementation gives inconclusive results [ 83 ]. The recommended weight gain in pregnancy amounts to on average 8–12 kg, depending on the initial body weight ( Table 3 ) [ 78 ].

Weight gain in relation to baseline body weight (BMI).

A weight gain of over 18 kg is associated with a twice higher risk of macrosomia [ 84 , 85 ]. Many studies show an increase in the need for vitamins and minerals in pregnancy, mainly folic acid, vitamin D and iron. All pregnant women are recommended to supplement daily with 400 µg of folic acid and 5.0 µg of vitamin D; additionally, depending on the dietary intake, 500–900 mg of calcium and 27–40 mg of iron are recommended [ 77 ]. The influence of gut microbiota on the development of GDM is interesting [ 86 ]. So far, it was shown that in women in the third trimester of pregnancy, GDM was associated with altered intestinal microflora [ 87 ]. However, in the conducted studies on the beneficial effects of probiotics in the prevention or treatment of GDM, the results are still inconclusive [ 88 , 89 , 90 , 91 ].

The main quality-oriented recommendations include the need to limit or eliminate processed products with a high content of salt, sugar and fats; avoiding unpasteurized milk, raw meat, alcohol and caffeine; and ensuring proper hydration of at least 2 L of water per day. In addition, the effect of the Dietary Approach to Stop Hypertension (DASH) diet on glycemic control was confirmed, and Sarathi et al. indicated that eating a high-protein diet based on soy products reduces insulin requirements in GDM patients [ 92 , 93 ]. Myoinositol (vitamin B8) supplementation or a diet rich in the MYO-INS isomer may improve glycemic control in GDM [ 94 , 95 ].

4.6.2. Exercise in GDM

In women with GDM, the quantitative and qualitative recommendations for exercise are ambiguous in terms of improving glycemic control [ 96 ]. Obstetric indications and contraindications should be followed. If there are no contraindications, the available observational studies indicate the safety of physical activity during pregnancy [ 97 ]. Activities that can be safely started and continued are walking, cycling, swimming, selected pilates and low-intensity fitness exercises. It is safe to continue with (but not initiate) the following after consulting with one’s obstetrician: yoga, running, tennis, badminton and strength exercises. Pregnant people should avoid contact sports, horse riding, surfing, skiing and diving. The analysis of Aune et al. showed a reduction in the risk of GDM by 38% (RR 0.62, 95% CI 0.41–0.94) in physically active women [ 98 ]. An intervention study in overweight patients by Nasiri-Amiri et al. showed a 24% reduction in the risk of GDM in women exercising no more than three times a week [ 99 ]. In women with normal body weight, increased physical activity, according to an analysis by Ming et al., resulted in a lower weight gain in pregnancy without affecting the child’s weight or the frequency of Caesarean sections and a 42% reduction in the risk of GDM (RR 0.58, 95% CI 0.37−0.90, p = 0.01) [ 100 ]. A meta-analysis by Harrison et al. of eight randomized trials showed a significant reduction in fasting and postprandial glucose levels in women with 20–30 min of activity 3–4 times a week [ 101 ].

4.6.3. Pharmacological Treatment

Patients who cannot achieve glycemic targets with a properly balanced diet and elimination of dietary errors should be treated pharmacologically [ 29 ]. Most studies indicate insulin therapy as the safest form of treatment, and OAD (orally administrated drugs) treatment should be introduced only in the case of the patient’s lack of consent to insulin therapy or its unavailability [ 102 ]. Insulin therapy is carried out in the model of functional intensive insulin therapy (FIIT) with the use of subcutaneous injections. The safety of human insulin use in pregnancy was demonstrated [ 103 ]. The safety of the use of aspart and detemir analogs was confirmed in randomized trials [ 104 , 105 , 106 ] and the safety of lispro and glargine analogs was shown in observational studies [ 107 ]; none of the studies showed the passage of insulin analogs across the placenta [ 108 , 109 ]. Currently, metformin and glibenclamide are used as oral medications. Metformin and glibenclamide (glyburide) cross the placenta but are unlikely to be teratogenic [ 110 , 111 ]. The metformin in gestational (MiG) diabetes trial was a landmark study; it was one of the largest randomized controlled trials, in which 751 women with GDM prospectively assessed a composite of neonatal complications as the primary outcome and secondary outcome of neonatal anthropometry at birth. It was concluded that metformin alone, or with supplemental insulin, was not associated with increased perinatal complications. This trial was the basis of many subsequent studies to assess the safety and efficacy of metformin use in GDM [ 112 ]. Some studies showed that the use of metformin during pregnancy is associated with higher body weight, more visceral and subcutaneous tissue, and higher blood glucose levels when the offspring is 9 years old [ 113 ]. The use of glibenclamide, despite its high effectiveness, may result in a higher percentage of intrauterine deaths and neonatal complications, such as hypoglycemia, macrosomia and FGR (fetal growth restriction) [ 114 ]. Although there is an increasing amount of evidence that supports the use of glyburide or metformin for GDM, the American Diabetes Association (ADA) and American College of Obstetricians and Gynecologists (ACOG) still recommend insulin as the primary medical treatment if the glycaemic treatment goals are not achieved with lifestyle intervention due to the lack of evidence regarding the long-term safety of the alternatives [ 115 ]. Sodium-glucose cotransporter-2 (SGLT2) inhibitors block the transporter located in the proximal tubule of kidneys that promotes renal tubular reabsorption of glucose, which causes a decrease in blood glucose levels due to an increase in renal glucose excretion. Among women with diabetes, UTI during pregnancy can be associated with pyelonephritis and sepsis and potential long-term effects on the neonate [ 116 ]. There were some adverse events noted in animal reproductive studies, including adverse effects on renal development when SGLT2 inhibitors were used in the second and third trimesters, although there are no human data available. The use of SGLT2 inhibitors during pregnancy is not recommended [ 110 ]. Recently, some studies reported the use of GLP-1 agents in GDM. GLP-1 agents, including dipeptidyl peptidase-4 (DPP-4) inhibitor and glucagon-like peptide-1 receptor agonist (GLP-1 Ra), enhance insulin secretion in pancreatic b-cell and showed many benefits in treating diabetes mellitus type 2 but are not a common choice for GDM [ 117 , 118 ]. In a systematic review that included 516 patients and investigated the use of GLP-1 agents in GDM (at different time points, including the second trimester of pregnancy and after delivery), Chen et al. showed that the use of GLP-1 agents to normalize blood glucose and can improve insulin resistance, as well as reduce the rate of developing postpartum diabetes compared with a placebo. This systematic review suggested that a dipeptidyl peptidase-4 inhibitor and glucagon-like peptide-1 receptor agonist may be beneficial to GDM patients but need rigorously designed clinical trials to demonstrate this. In particular, whether it can be used during pregnancy to improve pregnancy outcomes or better used to prevent developing diabetes after delivery should be investigated [ 119 ]. The data of a randomized controlled trial, namely, The Treatment of Booking Gestational Diabetes Mellitus (TOBOGM), compared pregnancy outcomes among women with booking GDM receiving immediate or deferred treatment can provide new insights into the diagnosis and treatment of GDM [ 120 ].

5. Conclusions

GDM is one of the most common complications of pregnancy and confers lifelong risks to both women and their children. Observational data demonstrated a linear association between maternal glycemic parameters and risks for adverse pregnancy and offspring outcomes. SARS-CoV-2 infection will undoubtedly affect the risk of GDM. Many doubts regarding the diagnostic criteria and treatment of GDM are still under discussion. Treatment with insulin is effective, but costs and patient experiences limit its use in clinical practice. The use of metformin as a first-line agent for GDM remains controversial due to its transplacental passage and limited long-term follow-up data. Further clinical trials are necessary to use other oral hypoglycemic agents to treat GDM. It is very important for patients with GDM to receive behavioral therapy and to closely cooperate with the doctor. Future work in the field should include studies of both clinical and implementation outcomes, examining strategies to improve the quality of care delivered to women with GDM. The screening and treatment for GDM early in pregnancy are very controversial due to the lack of data from large randomized controlled trials. There is an urgent need for well-designed research that can inform decisions on the best practice regarding gestational diabetes mellitus screening and diagnosis.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, R.M., M.M.S.-R. and E.M.B.-S.; methodology, R.M. and W.M.; formal analysis, E.M.B.-S.; investigation, R.M. and M.M.S.-R.; resources, R.M., M.M.S.-R. and W.M.; data curation, E.M.B.-S.; writing—original draft preparation, R.M. and M.M.S.-R.; writing—review and editing, E.M.B.-S.; visualization, R.M. and M.M.S.-R.; supervision, E.M.B.-S.; project administration, R.M. and W.M. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Conflicts of interest.

The authors declare no conflict of interest.

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  • Open access
  • Published: 19 February 2024

Advances in free fatty acid profiles in gestational diabetes mellitus

  • Haoyi Du 1   na1 ,
  • Danyang Li 1   na1 ,
  • Laura Monjowa Molive 1 &
  • Na Wu   ORCID: orcid.org/0000-0003-1156-3231 1 , 2  

Journal of Translational Medicine volume  22 , Article number:  180 ( 2024 ) Cite this article

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The morbidity of gestational diabetes mellitus (GDM) is increasing and is associated with adverse perinatal outcomes and long-term maternal and infant health. The exact mechanism underlying changes in plasma free fatty acid (FFA) profiles in patients with GDM is unknown. However, it is believed that changes in diet and lipid metabolism may play a role. Fatty acids contain many specific FFAs, and the type of FFA has different impacts on physiological processes; hence, determining changes in FFAs in individual plasma is essential. Alterations in FFA concentration or profile may facilitate insulin resistance. Additionally, some FFAs show potential to predict GDM in early pregnancy and are strongly associated with the growth and development of the fetus and occurrence of macrosomia. Here, we aimed to review changes in FFAs in women with GDM and discuss the relationship of FFAs with GDM incidence and adverse outcomes.

Introduction

The prevalence of obesity has increased globally over the past two decades, with obesity rates exceeding 35% among women in many countries worldwide [ 1 ]. Pre-pregnancy overweight or obesity is a known risk factor for many pregnancy complications to a greater extent than gestational weight gain (GWG). The higher the pre-pregnancy maternal body mass index, the higher the risk of pregnancy complications such as pregnancy-induced hypertension (PIH) and gestational diabetes mellitus (GDM) [ 2 , 3 ]. The prevalence of GDM has increased by over 35% in the last few decades and is still growing [ 4 ]. GDM pathogenesis is unclear; however, its characteristics are similar to those of type 2 diabetes mellitus, with increased insulin resistance (IR) incidence and relative insulin deficiency due to decreased β-cell function and numbers [ 5 , 6 ]. Potential risk factors for GDM include pre-pregnancy overweight and obesity, age and unhealthy dietary habits [ 7 ]. Regarding dietary fat intake, higher intake of total fat saturated fatty acids (SFAs) and cholesterol in pregnant women was previously thought to be associated with an increased risk of GDM [ 8 , 9 ], whereas intake of polyunsaturated fatty acids (PUFAs) and alpha-linolenic acid (ALA) was associated with a decreased risk [ 10 ]. The International Association of Diabetes and Pregnancy Study Groups (IADPSG) and World Health Organization (WHO) recommend a 75 g, 2 h oral glucose tolerance test (OGTT) for pregnant women between 24 and 28 weeks of gestation [ 11 , 12 ]. The American Diabetes Association (ADA) diagnostic criteria are a fasting blood glucose level ≥ 5.1 mmol/l at the 60th minute of the OGTT or a blood glucose concentration ≥ 10 mmol/l at the 120th minute of the OGTT or a fasting blood glucose level ≥ 8.5 mmol/l at the 120th minute of the OGTT [ 13 ]. GDM is one of the most common pregnancy complications [ 14 ]. GDM is associated with poor pregnancy outcomes, abnormal glucose tolerance, and obesity in offspring [ 15 , 16 , 17 ]. Although preventing GDM is not possible, early diagnosis is necessary [ 16 ].

Free fatty acid (FFA) is a physiologically important energy substrate released from adipose tissue through lipolysis in response to the body's energy needs. FFA is an efficient energy store in adipocytes in the form of triacylglycerides (TAGs) and phospholipids in cell membrane components [ 18 , 19 ]. Fat metabolism and carbohydrates are closely related, and alterations in FFAs may be involved in pathophysiologic processes associated with IR in GDM [ 20 ]. This review aimed to summarize the correlation between altered FFA levels and GDM to provide help in early GDM prevention and find a relationship with poor outcomes.

Pathophysiologic process and risk factors of GDM

There is a progressive increase in insulin resistance during normal pregnancy due to an increase in circulating placental hormones, including adrenocorticotropic hormone, adrenocorticotropic hormone-releasing hormone, human placental lactogen, prolactin, estrogen, and progesterone [ 21 , 22 , 23 , 24 , 25 , 26 ]. Maternal obesity also promotes IR in early pregnancy, leading to increased lipolysis in the second trimester [ 27 , 28 ], with the result that elevated FFA levels can further exacerbate IR by inhibiting maternal glucose uptake and stimulating hepatic gluconeogenesis [ 28 , 29 ]. Increased maternal IR leads to elevated maternal postprandial glucose levels and growth-promoting FFAs [ 23 , 27 , 30 ]. In addition, insulin secretion increases during normal pregnancy to maintain maternal blood glucose levels [ 22 ]. In contrast, in pregnant women with GDM, insulin secretion fails to compensate for the progressive increase in insulin resistance during pregnancy, leading to the development of maternal hyperglycemia [ 31 ]. A high-fat and high-sugar diet leads to IR and β-cell dysfunction [ 32 ], and lipotoxicity caused by hypertriglyceridemia during pregnancy also contributes to pancreatic β-cell damage and further reduction in insulin secretion [ 33 , 34 ]. In addition, excessive inflammatory activation may be associated with GDM, and preeclampsia possesses a similar pathophysiological process to GDM [ 35 , 36 ], with pro-inflammatory mediators of arachidonic acid (AA) and linoleic acid (LA) derivatives playing an important role in the development of both [ 37 ]. The pathogenesis of GDM is similar to that of type 2 diabetes mellitus, which is characterized by increased insulin resistance and a decrease in β-cell function and number caused by relative insulin deficiency [ 5 , 6 ]. There are many risk factors that contribute to the development of GDM, such as a history of GDM pregnancies [ 38 ], increasing maternal age [ 39 ], pre-pregnancy overweight or obesity [ 40 ], weight gain during pregnancy [ 41 ], and high dietary fat and low-carbohydrate intake [ 42 ], among other factors.

FFA sources and metabolic processes

Fatty acids (FAs) are usually found in organisms in three main esters: TAGs, phospholipids, and cholesterol esters. When FAs are not present in the plasma as esters, they are referred to as non-esterified FAs or FFAs. FAs are mainly taken up as phospholipids and TAGs. During digestion, TAGs are broken down into monoglyceride, diacylglycerol, and FFAs. Short- and medium-chain FAs (≤ 12 carbons) are directly absorbed by intestinal cells. Long-chain FAs (> 12 carbons) are absorbed and re-converted into TAGs and transported via lipoprotein particles such as high-density, low-density, very low-density lipoproteins and chymotrypsin particles [ 43 ]. Lipoprotein lipases present on the cell surface cleave TAGs to form FFAs that are taken up by the cells [ 44 ]. In the blood, FFAs are mainly carried by serum albumin. In adipose tissues, FFAs are re-esterified to form TAGs, which are stored in adipose droplets of adipocytes and later mobilized through lipolysis, i.e., the hydrolysis of TAGs [ 18 ]. In mitochondria, most FFAs undergo β-oxidation to produce acetyl-coenzyme A, and subsequently, acetyl-CoA enters the Krebs cycle to produce energy as adenosine triphosphate (ATP) [ 18 , 45 ]. FFAs are also important signaling molecules [ 46 ]. In addition to dietary intake, FFAs can be synthesized endogenously, which is referred to as de novo adipogenesis, with the main source being (excess) carbohydrates [ 18 , 44 ].

FFAs are hydrocarbon chains comprising methyl and carboxyl groups at each end. According to the presence or absence and number of carbon–carbon double bonds, FFAs can be categorized as saturated FAs(SFAs, no double bonds), monounsaturated FAs (MUFAs, one double bond), or polyunsaturated FAs (PUFAs, multiple double bonds), and a detailed categorization of the FFA profiles is presented in Fig.  1 A. Figure  1 B indicates the common sources of dietary intake. Depending on the position of the first double bond at the methyl end of the FFA molecule, PUFAs can be subdivided into n-3, n-6, and n-9 FAs. By measuring and quantifying FFAs, based on the length of the aliphatic tails, short-, medium-, and long-chain FAs have < 6, 6–12, and > 12 carbon atoms, respectively [ 47 ]. Commonly used test methods for FFA spectroscopy include gas chromatography- and liquid chromatography-mass spectrometries [ 48 , 49 ]. FFAs play an important energetic role and are involved in regulating intracellular signaling, cell structure, and lipid mediator production [ 50 ]. FFAs can also be converted to sphingolipids and phospholipids, which are involved in cell membrane formation. Among them, ceramide, a type of sphingolipid, is synthesized de novo from SFA (e.g., palmitate) in a multistep enzymatic cascade reaction and is the main scaffold for most complex sphingolipids, which can be broken down into variable FFAs and sphingomyelins. It can affect glucose metabolism by blocking the activation of the anabolic enzyme Akt or protein kinase B (Akt/PKB), inhibiting insulin signaling, and inducing selective insulin resistance, which hinders glucose uptake and storage and promotes hepatic gluconeogenesis [ 51 , 52 , 53 ]. Mustaniemi Sanna et al. found that although ceramides, along with clinical risk factors and triglycerides, did not appear to significantly improve the predictive performance of GDM, unfavorable changes in ceramides could indicate the degree of metabolic disturbances in GDM [ 54 ].

figure 1

A Classification of FFAs; B Common sources of dietary intake. A present the FFAs contained in SFA, PUFA (n-3/n-6), and MUFA, B reflects the common dietary categories of the above fatty acids. FFAs(free fatty acids); SFAs(saturated fatty acids); UFAs(unsaturated fatty acids); MUFAs (monounsaturated fatty acids); PUFAs (polyunsaturated fatty acids)

Association between dietary FA intake and GDM

GDM development is related to many factors, including the type and amount of FAs consumed in diets. The balance between SFA and PUFA intake is important, and women with GDM reportedly consume significantly less fatty fish representing PUFA but consume a higher proportion of saturated fat [ 55 ]. Moreover, SFA intake is positively associated with the development of GDM [ 56 ]. Most current studies tend to investigate dietary intake using questionnaires, which makes knowing the amount of each FFA consumed difficult. Humans consume nutrients as part of complex foods that contain varying amounts of nutrients, which may impact the FFA intake from specific sources.

Dietary sources of FFAs play an important role in regulating inflammation. Increased intake of MUFA may be associated with anti-inflammatory effects. One of the most abundant omega-7 MUFA is palmitoleic acid (POA; C16:1 n-7), derived from fatty fish, fish oil, macadamia nuts, sea buckthorn, and Wagyu beef. Oleic acid (OA; C18:1n9) is one of the most abundant MUFA in the human diet [ 57 , 58 ]. POA activates basal lipolysis and re-esterification, increases lipoprotein lipase (Lpl) activity, regulates n3-PUFA metabolism in membrane phospholipids, and also reduces proinflammatory cytokines to alleviate chronic inflammation. OA may ameliorate inflammation by regu AA metabolism [ 58 ]. Studies have shown that dietary supplementation with palmitoleate is beneficial for improving lipid and glucose metabolism and has also been associated with favorable changes in genes that regulate inflammation [ 58 , 59 ].Yang's previous research has also shown that repeated administration of palmitoleate downregulated the expression of proinflammatory genes, acted as an anti-inflammatory, and blocked glucose or SFA-induced apoptosis of β-cells and improved insulin resistance [ 60 ]. Long-chain PUFAs (LC-PUFAs) include two categories: n-3 and n-6 PUFAs. The human body needs to consume some n-3 and n-6 PUFAs from the diet. For example, ALA and LA as essential fatty acids need to be obtained from diets [ 61 ]. Typical dietary n-3 sources are oily seeds such as chia or flaxseed, fish, green vegetables, and grass-fed animal products, whereas n-6 is usually derived from vegetable oils such as corn or sunflower seeds, and non-grass-fed animal products (Fig.  1 B). Generally, n-6 PUFAs are regarded as pro-inflammatory molecules, whereas n-3 PUFAs (especially eicosapentaenoic acid and docosahexaenoic acid (DHA) are regarded as anti-inflammatory molecules [ 62 ]. Maintaining a balance between n-3 and n-6 PUFAs is important for regulating inflammation and immunomodulation. Diets high in n-3 intake can cause reduced cytokine production and lower cardiovascular disease risk factors [ 63 ]. However, western diets tend to be low in fiber and rich in total fat, rich in n-6 PUFAs as well as saturated fats, including hydrogenated vegetable fat (HVF, rich in trans fat) and interesterified fat (IF), making the n-6/n-3 PUFA ratio high [ 64 , 65 ]. Modern diets tend to increase n-6 intake and decrease n-3 intake owing to the increase in processed food consumption, widespread use of hydrogenated vegetable oils, and impact of industrialized agriculture. This has skewed the n-3 to n-6 ratio in favor of n-6, promoting chronic inflammatory diseases [ 66 ]. Women with GDM have increased and decreased n-6 and n-3 PUFA blood levels, respectively [ 67 ]. In pregnant women with GDM, n-3 PUFA intake is beneficial in alleviating IR and inflammation and decreasing the risk of adverse pregnancy outcomes [ 68 , 69 ]. Combined supplementation with vitamin D and n-3 PUFAs for 6 weeks improves fasting glucose, very low-density lipoprotein cholesterol, serum triglycerides, and insulin levels in patients with GDM [ 70 ]. A higher dietary n-6:n-3 PUFA ratio is associated with higher GDM incidence [ 71 ]. Dietary fat-rich fish and oral n-3 supplements are more effective in increasing n-3 PUFA levels [ 55 ]. One study found no reduction in the GDM incidence in pregnant women who were supplemented with 800 mg of DHA-enriched fish oil per day [ 72 ], and the same results were found in a DHA supplementation study [ 73 ]. Thus, DHA appears to be ineffective in preventing GDM but may have a therapeutic role in women with GDM. Pregnant women with GDM may benefit from DHA supplementation, which may be considered in their treatment. MUFA consumption has benefits, and the inclusion of OA (C18:1, n9)-rich olive oil and pistachios in the Mediterranean diet [ 74 , 75 ] reduces GDM incidence [ 76 ]. Olive oil intake reduces inflammatory markers in the placenta and cord blood of pregnant women with GDM [ 77 ]. In conclusion, further studies are needed to elucidate the relationship between dietary intake of different FA types and GDM. Figure  2 shows the association between dietary FFA intake and GDM occurrence and development.

figure 2

Relationship between dietary intake of FFAs and GDM. Pregnant women with GDM tended to consume more SFA and n-6 PUFAs than n-3 PUFAs, and the results were similar in blood. DHA supplementation is beneficial for pregnant women with GDM. GDM (gestational diabetes mellitus); FFAs(free fatty acids); SFAs(saturated fatty acids); PUFAs (polyunsaturated fatty acids); DHA(docosahexaenoic acid); IR(insulin resistance)

Relationship between FFAs and GDM

Ffas and ir.

IR is the main pathogenesis of GDM, which is mainly characterized by a decrease in sensitivity to insulin [ 6 ]. Circulating FFA is one of the most important factors promoting IR and altering insulin secretion [ 20 , 78 ]. In skeletal muscle, the mechanism by which FFAs cause IR involves protein kinase C activation via diacylglycerol accumulation, leading to decreased tyrosine insulin receptor substrate-1 phosphorylation and phosphatidylinositol-3 kinase activation inhibition [ 79 ]. Acute and chronic elevation of FFA induces IR, and Chronic elevation of FFA levels may impede β-cell compensatory responses [ 80 ]. A study showed that calculating the FFA index during pregnancy can be used to measure insulin sensitivity [ 81 ]. Higher homeostatic model assessment of IR (HOMA-IR) and c-peptide levels can reflect GDM risk. Palmitic acid, stearic acid, AA, dihomo-γ-linolenic acid (DGLA), and DHA positively correlated with HOMA-IR and c-peptide [ 82 ], indicating the effect of SFAs, AAs, DGLAs, and DHAs on GDM development. There may be a relationship between inflammation and the development of IR. The saturated fatty acid palmitate promotes the production of pro-inflammatory cytokines associated with insulin resistance in adipose tissue and/or adipocytes [ 83 ]. In addition, Maternal obesity may exacerbate the development of IR. POA is a fatty acid with anti-inflammatory and insulin-sensitizing properties, and maternal obesity leads to decreased synthesis of POA by syncytiotrophoblast cells, which can lead to IR and persistent mild inflammation in the mother, placenta, or fetus [ 84 ]. Levels of 20-hydroxyeicosatetraenoic acids (20-HETEs), a metabolite of AA, are positively correlated with BMI [ 85 ]. Increased levels of cytochrome P450 (CYP) and 20-HETE, which act on AA to exert enzymatic activity, are associated with the development of IR in obese women with GDM [ 37 ]. Therefore, maternal obesity should also be of concern when considering the effect of changes in FFAs levels on IR. women with GDM who are predominantly characterized by defective insulin sensitivity have significantly higher FFA [ 86 ], also confirming the correlation between FFA and IR.

FFA levels in pregnant women with GDM

In addition to the measurement of dietary FFA intake, several studies have analyzed the association between blood FFAs and GDM. In healthy pregnancies, serum TG and NEFA levels in mid- to late-gestation, increasing with the number of gestational weeks and peaking just before delivery [ 87 ]. The period of greatest increase in FFA and maximum placental FA transport occurs in the last trimester of pregnancy [ 88 ]. A trend towards higher plasma FFA levels in patients with GDM was observed [ 89 , 90 ]. In addition, in a study on the FFA profile of patients with GDM, some differences in FFA levels were found between different gestational periods [ 55 , 91 , 92 , 93 , 94 ]. In Table  1 , we present the characteristics of the population of studies and surveys on the FFA spectrum, while the corresponding results are presented in Table  2 .

FFAs are altered differently during different gestational periods. Total plasma SFA, MUFA, PUFA n-6, and PUFA n-3 levels increased in pregnant women with GDM in early pregnancy [ 55 ]. At mid-pregnancy, with the elevated total FFA, elevated SFA is more pronounced [ 93 ]. In Hou et al.’s [ 91 ] study, various FFAs were elevated, including OA, ALA, and DGLA, similar to the observations in another study [ 94 ]. In early pregnancy, Ma et al. [ 92 ] and Zhang et al. [ 94 ] reported that myristic and palmitic acids were elevated in pregnant women with GDM, and Ma et al. found that palmitic acid was positively associated with GDM risk. In Zhang et al.'s study, dietary supplementation of ALA and DHA was associated with a high risk of GDM with elevated fasting glucose alone, and the levels of most FFAs, such as DGLA, myristic, palmitoleic, linolenic (γ-LA, GLA), and docosahexaenoic acids, were progressively lower from early to late pregnancy; however, the OA levels were consistently high [ 94 ]. In addition, LA levels were reversed [ 92 , 94 ]. Differences in these data can be interpreted by looking at populations of different races and the controls chosen, whether they match the BMI, and whether fasting is required before blood collection. And the type of sample tested has little effect [ 95 ]. Altogether, changes in FFA levels associated with GDM during different gestational periods are complex.

Alterations in the FFA profile of patients with GDM can also affect maternal physiology by altering the levels of FFA transporters, such as increased levels of plasma FA binding protein-4 (FABP4) and other proteins in the serum [ 96 ]. Zhang et al. [ 97 ] found higher concentrations of FABP4 in GDM women in mid- to late-gestation. FABP4 is positively associated with an increased GDM risk in early and mid-gestation [ 98 ].

Treatment of pregnant women with GDM with FFAs

Currently, the treatment of GDM mainly involves lifestyle interventions and drug therapy. Among the medications are insulin injections or oral glibenclamide and metformin, among others [ 99 ]. The traditional dietary treatment for GDM is carbohydrate restriction [ 100 ]. However, this approach usually leads to higher fat intake, as protein intake is constant [ 101 ]. High-fat diets typically increase serum FFAs and promote insulin resistance [ 102 ]. Whereas an appropriate increase in the proportion of carbohydrates and a decrease in the proportion of fat in the diet can still keep blood glucose below current treatment targets and reduce postprandial FFAs [ 103 ].

In terms of pharmacological treatment, it has been suggested that increased insulin sensitivity by metformin can lead to a decrease in FFA levels [ 104 ]. There is no significant difference in FFA levels between the two modalities of glycemic control in pregnant women with GDM, namely insulin therapy or diet [ 105 ].

FFA and GDM fetal growth and development and other pregnancy outcomes

Relationship between ffa and gdm fetal growth and development.

In addition to glucose, fetal growth is also closely related to lipids [ 106 ]. Fasting and 1-h postprandial triglycerides at 16 weeks of gestation are strongly associated with neonatal obesity, more so than maternal glucose [ 107 ]. TGs at delivery were independently associated with large for gestational age (LGA) [ 108 ]. Placental triglyceride levels and FFA levels in umbilical cord plasma are significantly higher in GDM pregnant women, which may be related to altered placental lipogenesis and fatty acid oxidation protein expression and increased placental transport [ 109 , 110 ]. It further led to fetal overgrowth. In addition, GDM pregnant women, independently of BMI, altered the lipid profile of the placenta, with elevated levels of both palmitic and stearic acids [ 93 ]. Higher maternal FFA levels in GDM reportedly lead to increased fetal birth weights [ 86 ], and maternal FFA levels were positively associated with the prevalence of LGA newborns [ 108 , 111 ]. Emilio Herrera et al. showed a positive correlation between maternal FFA and neonatal weight and adiposity in pregnant women with GDM who had good glycemic control. Maternal dyslipidemia in GDM may promote the transfer of maternal FAs to the fetus, leading to an increase in fetal adipose tissue mass, thereby increasing macrosomia risk [ 112 ]. In the study by Fan et al., FFA levels and fetal growth restriction (FGR) were higher in women with GDM than in controls in late pregnancy, and the area under curve value for GDM diagnosis and FGR was 0.84. In addition, a positive correlation was observed between serum FFA and umbilical artery systolic/diastolic ratio (S/D), pulsatility index (PI), and resistance index (RI), reflecting the resistance to blood flow in pregnant women; therefore, fetal growth in a poor intrauterine environment may contribute to FGR development. Moreover, FFA in the serum positively correlated with umbilical artery S/D, PI, and RI values, reflecting blood flow resistance in pregnant women [ 113 ]. Studies on the relationship between the FFA profile levels and fetal growth are further needed to utilize FFA concentration or profile levels to help the fetus grow within the appropriate range.

DHA and AA are essential for fetal CNS development and immune system regulation [ 114 ]. Reduced maternal-to-fetal DHA transfer in GDM may be associated with impaired placental DHA uptake [ 115 , 116 ]. Reduced cord blood DHA levels in patients with GDM may lead to reduced plasma DHA levels in neonates with GDM [ 117 , 118 ]. This outcome affects fetal neurodevelopment in the first 6 months of life [ 119 ]. Cord blood DHA levels positively correlate with maternal DHA levels when patients with GDM have good glycemic control [ 120 ]. Léveillé et al. also observed no reduction in DHA levels in the umbilical cord blood of pregnant women with GDM treated with diet or medication [ 121 ]. It can be seen that effective control of blood glucose levels in GDM patients is beneficial in maintaining fetal DHA levels. To some extent, it favors fetal neurodevelopment. DHA supplementation during pregnancy at 600 mg/d does not improve fetal DHA status [ 122 ].

Association between FFA and other pregnancy outcomes

N-3 and n-6 PUFAs are essential for the structural and functional growth and development of the placenta [ 123 ]. In addition to exhibiting anti-inflammatory properties, n-3 FAs have antioxidant potential and play a crucial part in stimulating placenta formation, angiogenesis, and remodeling in early pregnancy [ 124 , 125 ]. Inadequate maternal LC-PUFA supply and impaired maternal–fetal transfer are associated with adverse pregnancy outcomes, such as preeclampsia, intrauterine growth retardation, and GDM [ 126 , 127 ]. Neonatal hyperbilirubinemia incidence and neonatal hospitalization in pregnant women with GDM can be reduced by supplementation with n-3 PUFAs [ 69 ].

In conclusion, there have been more studies on the impact of altered FFA concentrations on pregnancy outcomes in patients with GDM, but few studies have evaluated the relevance of alterations in different FFA subclasses in patients with GDM to other adverse maternal or neonatal outcome events, and future research could be directed in this direction.

FFAs are derived from various sources, have a high correlation with IR, and are likely to be associated with GDM development. Regarding diet, having a balanced FFA intake is important; however, analyzing a specific FFA is difficult owing to its complexity and various dietary components. Excessive SFA intake can have harmful effects, leading to an increase in inflammation, IR [ 128 ], and cardiovascular disease [ 129 ]. Before GDM diagnosis, SFA intake was higher than that of PUFAs. When the balance of n-6 PUFAs versus n-3 PUFAs favored the former, a positive correlation was observed with GDM incidence, and preventing GDM by supplementing with n-3 PUFAs has not yet been conclusively demonstrated. Available evidence suggests that n-3 PUFA supplementationis not beneficial in reducing the incidence of maternal pregnancy outcomes such as gestational diabetes and hypertension, but is beneficial for neonatal health, such as reducing the incidence of preterm labor and low birth weight, and increasing birth weight and birth length [ 130 ]. Notably, n-3 FA supplementation in pregnant women with GDM is beneficial to reducing IR and inflammation and decreasing adverse pregnancy outcomes. In addition to dietary intake, plasma FFAs reflect FFA levels from various sources, and FFA levels in pregnant women with GDM vary considerably across gestational periods, with the majority being elevated from early to mid-pregnancy and progressively decreasing in late pregnancy. Differences were observed in the results of different studies over the same period, which may be related to factors such as the study populations and whether the relevant indicators were matched. FFAs, palmitic acid, ALA, and DHA are highly correlated with GDM development. In addition, lipocalin is a potent anti-inflammatory/anti-diabetic adipokine [ 131 ]. Saturated fatty acids and omega-3 fatty acids are associated with circulating concentrations of lipocalin in healthy humans [ 132 ]. Lipocalin is associated with the development of GDM during pregnancy [ 133 ]. More studies could be done in the future to evaluate the effects of altered lipocalin and FFAs in patients with GDM.

Lipids are associated with fetal growth and development; FFA levels are closely related to fetal weight, and mechanisms of fetal underweight or overweight require further exploration. Fetal FFA acquisition is regulated by maternal FFA levels and placental mechanisms. DHA is closely related to fetal neurological development, and the reduced DHA transfer to the fetus in patients with GDM may be related to impaired placental uptake, whereas good control of blood glucose promotes the transfer of placental FFAs to an extent, providing sufficient DHA to the fetus. Other adverse pregnancy events are less well-studied. High FFA levels in the postpartum period in women with a previous history of GDM likely contribute to type 2 diabetes mellitus [ 134 , 135 ]. This may be related to reduced sensitivity to insulin via lipolytic inhibition [ 136 ]. Positive recognition of changes in FFAs facilitates a smooth pregnancy course; however, more research and standardization between clinical trials and patient sampling are needed. Finally, appropriate FA intake should be considered before and during pregnancy to optimize maternal and infant outcomes and bring new preventive and therapeutic strategies for GDM.

Availability of data and materials

Not applicable.

Abbreviations

  • Gestational diabetes mellitus

Free fatty acid

Gestational weight gain

International Association of Diabetes and Pregnancy Study Groups

World Health Organization

American Diabetes Association

Body mass index

Pregnancy-induced hypertension

Insulin resistance

Saturated fatty acids

Polyunsaturated fatty acids

Alpha-linolenic acid

Oral glucose tolerance test

Triacylglycerides

Fatty acids

Adenosine triphosphate

Monounsaturated fatty acids

Unsaturated fatty acids

Lipoprotein lipase

Palmitoleic acid

Arachidonic acid

Long-chain polyunsaturated fatty acids

Linoleic acid

Docosahexaenoic acid

Hydrogenated vegetable fat

Interesterified fat

20-Hydroxyeicosatetraenoic acids

Cytochrome P450

Eicosapentaenoic acid

γ-Linolenic acid

Dihomo-γ-linolenic acid

FA binding protein-4

Large for gestational age

Fetal growth restriction

Systolic/diastolic

Pulsatility index

Resistance index

Homeostatic model assessment of IR

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Acknowledgements

In the process of writing this review, we gratefully acknowledge Na Wu for providing intellectual support and technical assistance. She provided a lot of help with the structure and writing standards of the article.

This research was funded by the National Natural Science Foundation of China, grant number 81700706; the Virtual Simulation Experiment Teaching Project of China Medical University, grant number 2020–47; the 345 Talent Project of Shengjing Hospital; the Natural Science Foundation of Liaoning Province, grant number 2021-MS-182; the Science Foundation of Liaoning Education Department, grant number LK201603; and the Clinical Research Project of Liaoning Diabetes Medical Nutrition Prevention Society, grant number LNSTNBYXYYFZXH-RS01B.

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Department of Endocrinology, Shengjing Hospital of China Medical University, Shenyang, 110004, People’s Republic of China

Haoyi Du, Danyang Li, Laura Monjowa Molive & Na Wu

Medical Department, Shengjing Hospital of China Medical University, Liaoning Province, Shenyang, People’s Republic of China

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The study was conceptualized by HD, DL, NW and MLM; literature investigation and manuscript draft were performed by HD and DL; English polishing was done by MLM; and NW carefully revised the manuscript.

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Du, H., Li, D., Molive, L.M. et al. Advances in free fatty acid profiles in gestational diabetes mellitus. J Transl Med 22 , 180 (2024). https://doi.org/10.1186/s12967-024-04922-4

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review of literature related to diabetes mellitus

Diabetes mellitus: a review of the literature and dental implications

Affiliation.

  • 1 Department of Periodontics, Creighton University, School of Dentistry, Omaha, Nebraska, USA.
  • PMID: 11692399

Diabetes mellitus consists of a group of disorders, which are characterized by a lack of insulin production or insulin resistance. There can be various oral manifestations of diabetes, such as xerostomia and an increased incidence of dental caries. Recently, it has been suggested that periodontitis be added as the sixth complication of diabetes mellitus. It has been shown that uncontrolled or poorly controlled diabetics have a greater incidence of severe periodontal disease compared with those patients who are well controlled or have no diabetes mellitus. This has been found for both type 1 and type 2 diabetics. In addition, the diabetic patient may be predisposed to periodontal disease based on the production of advanced glycation end products, which bind to receptors on specific cells such as the monocyte. The success of periodontal treatment appears to be dependent on the control exhibited by the diabetic patient. The well-controlled diabetic will respond well to periodontal treatment, while the uncontrolled or poorly controlled will often not respond well or be stable in the long-term. Because of the large number of diabetics in the US population, dental therapists should be aware of the interactions of the patient's diabetic status, the proposed treatment, and the possible treatment outcomes as well as complications.

Publication types

  • Comparative Study
  • Dental Caries / etiology
  • Diabetes Complications
  • Diabetes Mellitus / classification
  • Diabetes Mellitus / physiopathology*
  • Diabetes Mellitus / prevention & control
  • Disease Susceptibility
  • Glycation End Products, Advanced / metabolism
  • Glycation End Products, Advanced / physiology
  • Insulin / deficiency
  • Insulin Resistance
  • Monocytes / metabolism
  • Periodontal Diseases / etiology*
  • Periodontal Diseases / therapy
  • Periodontitis / etiology
  • Receptors, Cell Surface / metabolism
  • Treatment Outcome
  • Xerostomia / etiology
  • Glycation End Products, Advanced
  • Receptors, Cell Surface

Rosemary ( Rosmarinus officinalis L.) improves biochemical outcomes in diabetes mellitus: a systematic review and meta-analysis of animal studies

  • Published: 20 February 2024

Cite this article

  • Virginia Moura Oliveira 1 ,
  • Letícia Rafaela Silveira 1 ,
  • Kitete Tunda Bunnel 1 ,
  • Caroline Pereira Domingueti 1 ,
  • André Oliveira Baldoni 1 ,
  • Nayara Ragi Baldoni 2 &
  • Renê Oliveira do Couto   ORCID: orcid.org/0000-0002-3748-3427 1  

We report on the systematic review and meta-analysis concerning the efficacy of R. officinalis in treating diabetes mellitus (DM) in animals. This study followed the PRISMA guideline and the protocol was registered in PROSPERO (CRD42021250556). The research was duplicated in the PubMed, Scopus, ScienceDirect, Web of Science, and Virtual Health Library (VHL) databases until December 31st, 2022. No restrictions have been set for language publication. Twenty-three (23) experimental studies of type-1 diabetes mellitus (T1DM) met the eligibility criteria and were included in the qualitative analysis, whereas eighteen (18) underwent a meta-analysis. The R. officinalis derivatives significantly decreased fasting plasma glucose (MD: −120.84 [95% CI; −157.09, −84.59]); increased insulin release (MD; +3.73 [95% CI; +3.17, +4.29]); dwindled blood urea nitrogen (MD: −24.84 [95% CI; −34.78, −14.90]) and creatinine (MD: −0.40 [95% CI; −0.74, −0.06]) levels; and ameliorated liver function or repaired liver damage by decreasing ALT (MD: −36.42; [95% CI; −55.69, −17.14]) and AST (MD: −24.05 [95% CI; −37.84, −10.27]) enzyme levels compared to vehicle control group. Moreover, R. officinalis derivatives improved the lipid profile of diabetic animals by reducing LDL-c levels (MD: −11.74 [95% CI; −21.27, −2.21]). R. officinalis is a nutraceutical that may help in the management of T1DM and its complications. However, some gaps need to be taken into account for this evidence. Greater attention is needed for an analytical standardization of Rosemary extracts besides the demand for high-quality clinical studies dealing with the efficacy of this phytomedicine.

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review of literature related to diabetes mellitus

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review of literature related to diabetes mellitus

Abbreviations

Alkaline phosphatase

Alanine aminotransferase

Aspartate aminotransferase

Blood urea nitrogen

Collaborative Approach to Meta Analysis and Review of Animal Experimental Studies

Confidence interval

Digital Object Identifier System

Health Sciences Descriptors

Diabetes mellitus

Fasting plasma glucose

Serum glycated hemoglobin

High density lipoprotein cholesterol

Low density lipoprotein cholesterol

Mean difference

Medical Subject Headings

Oxidative stress

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Review Manager software

Reactive oxygen species

Standard deviation

Serum insulin level

Systematic Review Center for Laboratory animal Experimentation

Type-1 diabetes mellitus

Type-2 diabetes mellitus

Total cholesterol

Total triglycerides

Virtual Health Library

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Acknowledgements

We appreciate Dr. Zbys Fedorowicz (Veritas Health Sciences Consultancy) and Gesner Francisco Xavier Junior (librarian of the Medical School – Federal University of Minas Gerais) for their valuable support with the review protocol.

Virgínia Moura Oliveira received a scientific initiation grant (PIBIC) from the Universidade Federal de São João del- Rei and Fundação de Amparo à Pesquisa do Estado de Minas Gerais (UFSJ/FAPEMIG; grant number 21092). Kitete Tunda Bunnel received a Master’s grant from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES; grant number 88887.824838/2023-0). This study was also funded in part by the CAPES - Financial Code 001.

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Virginia Moura Oliveira, Letícia Rafaela Silveira, Kitete Tunda Bunnel, Caroline Pereira Domingueti, André Oliveira Baldoni & Renê Oliveira do Couto

Universidade de Itaúna (UIT), Rodovia MG 431 Km 45, s/n, Zip Code: 35680-142, Itaúna, Minas Gerais, Brazil

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ROC: Conceptualization, Funding acquisition, Project administration, Supervision, Writing—review & editing; NRB and CPD: Methodology, Formal analysis, Software, Writing—review & editing; VMO: Data curation, Formal analysis, Investigation, Writing—original draft; LRS: Investigation, Validation; KTB and AOB: Writing—review & editing.

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Virginia Moura Oliveira has no conflict of interest. Letícia Rafaela Silveira has no conflict of interest. Kitete Tunda Bunnel has no conflict of interest. Caroline Pereira Domingueti has no conflict of interest. André Oliveira Baldoni has no conflict of interest. Nayara Ragi Baldoni has no conflict of interest. Renê Oliveira do Couto has no conflict of interest.

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Oliveira, V.M., Silveira, L.R., Bunnel, K.T. et al. Rosemary ( Rosmarinus officinalis L.) improves biochemical outcomes in diabetes mellitus: a systematic review and meta-analysis of animal studies. ADV TRADIT MED (ADTM) (2024). https://doi.org/10.1007/s13596-024-00742-5

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Received : 17 October 2023

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DOI : https://doi.org/10.1007/s13596-024-00742-5

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review of literature related to diabetes mellitus

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Acute choreo-dystonia in a newly diagnosed patient with diabetes mellitus: a case report and review of literature, t.o. akande, o.v. olalusi, d.i. olulana.

Introduction : Diabetes mellitus is a disease with diverse macrovascular and microvascular consequences. One of the unusual effects of  hyperglycemia is involuntary movement, termed hyperglycemia-induced involuntary movement. This could range from hemibalismus,  chorea, choreo-atethosis, tremors to dystonia. Chorea associated with dystonia is a less commonly reported manifestation. When it is  focal, it can be misdiagnosed as stroke or seizure disorder. To the best our knowledge, there is hitherto no case report in sub-Saharan  Africa describing the occurrence of focal choreo-dystonia in type 2 Diabetes Mellitus.

Case presentation : Here, we present a case of a  middle-aged Nigerian woman with focal choreo-dystonia of the right upper limb accompanying the diagnosis of type 2 diabetes.  Achieving euglycemia with insulin resulted in complete resolution of the choreo-dystonia.

Conclusion : Doctors in resource-constrained  settings should be aware of this presentation to avoid misdiagnosis and to provide prompt and goal-oriented management with a view to  reducing morbidity and attendant health-care costs. 

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