Association of depression and parasympathetic activation with glycemic control in type 2 diabetes mellitus

Systems, Social, and Individual Factors Influencing Glycemic Control Among American Indian/Alaska Native Adults with Type 2 Diabetes: A Systematic Review

OBJECTIVES : American Indian/Alaska Native (AI/AN) peoples experience type 2 diabetes (T2DM) at double the rate of White Americans and have 2.3 times greater mortality. Glycemic control is a central goal of diabetes management and is associated with superior physical outcomes and quality of life. The purpose of this systematic review was to identify and synthesize all factors that influence glycemic control among AI/AN people with T2DM.

DESIGN

This is a systematic review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. PubMed, CINAHL, PsychInfo, Embase and Web of Science were searched using terms related to "type 2 diabetes" and "American Indian/Alaska Native" from 2008-2023.

RESULTS

Thirty-three studies were identified. Factors related to glycemic control were 1) healthcare interventions, 2) social determinants of health, 3) self-care behaviors, 4) mental health and psychological factors, and 5) genetic factors. Few factors were consistently associated with improved glycemic control. Multidisciplinary care models that integrate community members providing education/healthcare referrals, and medication adherence had the strongest signals with improved glycemic control. While some studies found relationships between glycemic control and diet, exercise and depression, others did not.

CONCLUSION

While more work is needed to understand influencers of glycemic control in this population, community health representatives, medication adherence and healthcare utilization should be leveraged to improve glycemic control. Future work among AI/AN people with T2DM should focus on how structural and societal factors, like health policy, built environment, and social environment, impact the performance of self-care behaviors and influence glycemic control.

The role of depression and diabetes distress in glycemic control: A meta-analysis

AIMS

This study evaluated the associations between depression, diabetes distress, glycemic control (HbA1c), and self-care behaviours in individuals with diabetes. Findings on these relationships have been inconsistent, highlighting the need for systematic evaluation.

METHODS

Data from 61 studies involving 19,537 participants conducted between 2001 and 2024 were analysed using random-effects models. Subgroup analyses examined variations by diabetes type, geographic location, and measurement tools. Heterogeneity was assessed using I2 statistics.

RESULTS

Depression and diabetes distress were significantly associated with poorer glycemic control (r = 0.23, 95 % CI [0.15 to 0.31], p < 0.001) and reduced self-care behaviours (r = -0.19, 95 % CI [-0.28 to -0.10], p < 0.001). Stronger correlations were observed in mixed diabetes populations (r = 0.35, 95 % CI [0.30 to 0.40], I2 = 0 %) and in studies conducted in Europe (r = 0.28) and North America (r = 0.34). High heterogeneity (I2 = 97.24 %) was identified.

CONCLUSIONS

Depression and diabetes distress are associated with poorer glycemic control and reduced self-care behaviours. Findings highlight the need for standardized measures and longitudinal studies to explore mechanisms underlying these associations.

Effect of Heart Rate Variability Biofeedback on Cardiac Autonomic Activation and Diabetes Self-Care in Patients with Type II Diabetes Mellitus

In type 2 diabetes mellitus (T2DM), decreased autonomic activation and heightened negative emotions may worsen glycemic control. This study investigated the effects of heart rate variability biofeedback (HRVB) on autonomic activation, negative emotions, diabetes self-care, and glycemic control in patients with T2DM. A total of 61 participants with T2DM were assigned to either the HRVB group (n = 30; 62.67 ± 7.28 years; 14 females) or the control group (n = 31; 63.39 ± 6.96 years; 14 females). Both groups received the treatment as usual, and the HRVB group received 60 min of HRVB sessions weekly for 6 weeks. Participants completed psychological questionnaires, a resting electrocardiogram (ECG), and breathing rate assessments at pre- and post-tests. Heart rate variability (HRV) indices were derived from ECG data, and glycated hemoglobin (HbA1c) levels were collected from the electronic medical records. The analysis revealed significant Group × Time interaction effects on HRV indices, breathing rate, depression symptoms, and diabetes self-care behavior. The HRVB group demonstrated higher HRV indices, lower breathing rate, and improved diabetes self-care behavior compared to the control group. Moreover, the HRVB group showed enhanced HRV indices and diabetes self-care behavior, as well as reduced breathing rate and depression in the post-test compared to the pre-test. However, there was no significant interaction effect on HbA1c levels. Six sessions of HRVB proved effective as a complementary therapy for T2DM, enhancing HRV indices, alleviating depressive symptoms, and promoting better diabetes self-care behaviors.

Psychological resilience mediates the relationship between diabetes distress and depression among persons with diabetes in a multi-group analysis

The aim to examine the link between diabetes distress and depression in individuals with diabetes, assess the mediating role of psychological resilience in this relationship, and analyses if these relationships differ between Type 1 and Type 2 diabetes. The study utilized a cross-sectional design. A total of 181 (age 33-72 years, mean = 54.76 years, and SD = 9.05 years) individuals diagnosed with diabetes who were receiving treatment from State Specialist Hospitals in Okitipupa were selected for the study using the convenient sampling technique. The data were analysed using Pearson Multiple correlation and multi-group mediation analysis. The analyses were carried out with Smartpls and IBM/SPSS Version 28.0. The results revealed a significant positive correlation between diabetes distress and depression (r = .80, p < .05), suggesting that higher levels of diabetes distress were associated with increased depression scores. Additionally, psychological resilience partially mediated the relationship between diabetes distress and depression (b = - 0.10, p < .05), signifying that resilience played a crucial role in mitigating the impact of diabetes distress on depression. Furthermore, a multi-group analysis was conducted to explore potential differences between Type 1 and Type 2 diabetes subgroups. The relationship between diabetes distress and depression was found to be more pronounced in the Type 1 subgroup (difference = 0.345, p < .05), while the relationship between psychological resilience and depression was negatively stronger in the Type 2 subgroup (difference = - 0.404, p < .05) compared to the Type 1 subgroup. There is an intricate linkage between diabetes distress, resilience, and depression, emphasizing the differential roles of resilience in Type 1 and Type 2 diabetes. The insights gleaned from this study underscore the importance of considering the type of diabetes when designing interventions and support mechanisms for individuals with diabetes who are also suffering from depression. By advancing our understanding of these dynamics, we can strive for more effective and personalized approaches to improve the overall well-being of those living with diabetes.

Effect of self-help interventions on psychological, glycemic, and behavioral outcomes in patients with diabetes: A meta-analysis of randomized controlled trials

BACKGROUND

Self-help interventions are beneficial for patients with diabetes; however, related studies have reported conflicting results. To date, no review has examined the effect of self-help interventions on diabetes outcomes.

OBJECTIVES

To systematically evaluate the effects of self-help interventions on psychological, glycemic, and behavioral outcomes in patients with diabetes.

DESIGN

A systematic review and meta-analysis of randomized controlled trials.

METHODS

Five databases-PubMed, CINAHL, Embase, PsycINFO, and ClinicalTrials.gov-were searched from 1996, 1937, 1947, 1887, and 2000, respectively, to 2 June 2023. Studies that employed a randomized controlled trial design, enrolled adults with diabetes, implemented a self-help intervention as the main or an additional intervention, and reported the outcomes of interest were included. Studies providing self-help interventions to patients with gestational diabetes or pregnant women were excluded. The primary outcomes were diabetes distress, depression, and anxiety, and the secondary outcomes were glycemic and behavioral outcomes (self-management behavior, self-efficacy, and quality of life). Hedges' g and the associated 95 % confidence interval (CI) were calculated using a random-effects model to obtain the pooled estimates of short-, mid-, and long-term effects of self-help interventions. Heterogeneity was explored using I2 and Q statistics, and moderator analysis was performed to identify the sources of heterogeneity.

RESULTS

Of 17 eligible studies, 16 provided data for meta-analysis. We included 3083 patients with diabetes; the majority were women (61.95 %), and their average age was 55.13 years. Self-help interventions exerted significant short-term effects on diabetes distress (g = -0.363; 95 % CI = -0.554, -0.173), depression (g = -0.465; 95 % CI = -0.773, -0.156), anxiety (g = -0.295; 95 % CI = -0.523, -0.068), glycosylated hemoglobin level (g = -0.497; 95 % CI = -0.791, -0.167), self-efficacy (g = 0.629; 95 % CI = 0.060, 1.197), and quality of life (g = 0.413; 95 % CI = 0.104, 0.721; g = 0.182; 95 % CI = 0.031, 0.333; and g = 0.469; 95 % CI = 0.156, 0.783 for overall, physical, and mental domains, respectively). We also noted significant mid-term effects of self-help interventions on diabetes distress (g = -0.195; 95 % CI = -0.374, -0.016), self-management behavior (g = 0.305; 95 % CI = 0.155, 0.454), and overall quality of life (g = 0.562; 95 % CI = 0.315, 0.810). The certainty of evidence ranged from high to very low certainty for the measured outcomes.

CONCLUSIONS

Self-help interventions may have some positive effects on diabetes distress, anxiety, self-management behavior, and quality of life.

REGISTRATION

This review was registered in PROSPERO (CRD42022329905).

TWEETABLE ABSTRACT

This meta-analysis demonstrated that self-help interventions might improve psychological and behavioral outcomes in patients with diabetes.

Real-time heart rate variability according to ambulatory glucose profile in patients with diabetes mellitus

Background

Autonomic neuropathy commonly occurs as a long-term complication of diabetes mellitus (DM) and can be diagnosed based on heart rate variability (HRV), calculated from electrocardiogram (ECG) recordings. There are limited data on HRV using real-time ECG and ambulatory glucose monitoring in patients with DM. The aim of this study was to investigate real-time HRV according to ambulatory glucose levels in patients with DM.

Methods

A total of 43 patients (66.3 ± 7.5 years) with DM underwent continuous real-time ECG monitoring (225.7 ± 107.3 h) for HRV and ambulatory glucose monitoring using a remote monitoring system. We compared the HRV according to the ambulatory glucose profile. Data were analyzed according to the target in glucose range (TIR).

Results

There were no significant differences in the baseline characteristics of the patients according to the TIR. During monitoring, we checked ECG and ambulatory glucose levels (a total of 15,090 times) simultaneously for all patients. Both time- and frequency-domain HRVs were lower when the patients had poorly controlled glucose levels (TIR < 70%) compared with well controlled glucose levels (TIR > 70%). In addition, heart and respiratory rates increased with real-time glucose levels (P < 0.001).

Conclusions

Poorly controlled glucose levels were independently associated with lower HRV in patients with DM. This was further substantiated by the independent continuous association between real-time measurements of hyperglycemia and lower HRV. These data strongly suggest that cardiac autonomic dysfunction is caused by elevated blood sugar levels.

Using Machine Learning for the Risk Factors Classification of Glycemic Control in Type 2 Diabetes Mellitus

Several risk factors are related to glycemic control in patients with type 2 diabetes mellitus (T2DM), including demographics, medical conditions, negative emotions, lipid profiles, and heart rate variability (HRV; to present cardiac autonomic activity). The interactions between these risk factors remain unclear. This study aimed to use machine learning methods of artificial intelligence to explore the relationships between various risk factors and glycemic control in T2DM patients. The study utilized a database from Lin et al. (2022) that included 647 T2DM patients. Regression tree analysis was conducted to identify the interactions among risk factors that contribute to glycated hemoglobin (HbA1c) values, and various machine learning methods were compared for their accuracy in classifying T2DM patients. The results of the regression tree analysis revealed that high depression scores may be a risk factor in one subgroup but not in others. When comparing different machine learning classification methods, the random forest algorithm emerged as the best-performing method with a small set of features. Specifically, the random forest algorithm achieved 84% accuracy, 95% area under the curve (AUC), 77% sensitivity, and 91% specificity. Using machine learning methods can provide significant value in accurately classifying patients with T2DM when considering depression as a risk factor.

Reduced heart rate variability in people with type 1 diabetes and elevated diabetes distress: Results from the longitudinal observational DIA-LINK1 study

AIMS

People with type 1 diabetes have a higher risk for cardiovascular disease (CVD). Reduced heart rate variability (HRV) is a clinical marker for CVD. In this observational study using continuous HRV measurement across 26 days, we investigated whether psychological stressors (diabetes distress, depressive symptoms) and glycaemic parameters (hypo- and hyperglycaemic exposure, glycaemic variability and HbA_1c ) are associated with lower HRV in people with type 1 diabetes.

METHODS

Data from the non-interventional prospective DIA-LINK1 study were analysed. At baseline, depressive symptoms and diabetes distress were assessed. Glucose values and HRV were recorded daily for 26 days using continuous glucose monitoring (CGM) and a wrist-worn health tracker respectively. Multilevel modelling with participant as nesting factor was used to analyse associations between day-to-day HRV and diabetes distress, depressive symptoms and CGM-derived parameters.

RESULTS

Data from 149 participants were analysed (age: 38.3 ± 13.1 years, HbA_1c : 8.6 ± 1.9%). Participants with elevated diabetes distress had a significantly lower HRV across the 26 days compared to participants without elevated distress (β = -0.28; p = 0.004). Elevated depressive symptoms were not significantly associated with HRV (β = -0.18; p = 0.074). Higher daily exposure to hyperglycaemia (β = -0.44; p = 0.044), higher average exposure to hypoglycaemia (β = -0.18; p = 0.042) and higher HbA_1c (β = -0.20; p = 0.018) were associated with reduced HRV across the 26 days. Sensitivity analysis with HRV averaged across all days corroborated these results.

CONCLUSIONS

Diabetes distress is a clinically meaningful psychosocial stressor that could play a role in the cardiovascular health of people with type 1 diabetes. These findings highlight the need for integrated psychosocial care in diabetes management.

Renin-angiotensin system: The underlying mechanisms and promising therapeutical target for depression and anxiety

Emotional disorders, including depression and anxiety, contribute considerably to morbidity across the world. Depression is a serious condition and is projected to be the top contributor to the global burden of disease by 2030. The role of the renin-angiotensin system (RAS) in hypertension and emotional disorders is well established. Evidence points to an association between elevated RAS activity and depression and anxiety, partly through the induction of neuroinflammation, stress, and oxidative stress. Therefore, blocking the RAS provides a theoretical basis for future treatment of anxiety and depression. The evidence for the positive effects of RAS blockers on depression and anxiety is reviewed, aiming to provide a promising target for novel anxiolytic and antidepressant medications and/or for improving the efficacy of currently available medications used for the treatment of anxiety and depression, which independent of blood pressure management.