Depression-related weight change and incident diabetes in a community sample

Depression and type 2 diabetes risk: a Mendelian randomization study

Background

Extensive observational evidence has suggested an association between depression and type 2 diabetes (T2D). However, the causal relationships between these two diseases require further investigation. This study aimed to evaluate the bidirectional causal effect between two types of depression and T2D using two-sample Mendelian randomization (MR).

Methods

We applied two-step MR techniques, using single-nucleotide polymorphisms (SNPs) as the genetic instruments for analysis. We utilized summary data from genome-wide association studies (GWASs) for major depression (MD), depressive status (frequency of depressed mood in the last two weeks), T2D, and other known T2D risk factors such as obesity, sedentary behavior (time spent watching television), and blood pressure. The analysis utilized inverse variance weighted (IVW), MR-Egger regression, weighted median, weighted mode, MR pleiotropy residual sum, and outlier methods to determine potential causal relationships.

Results

The study found that MD was positively associated with T2D, with an odds ratio (OR) of 1.26 (95% CI: 1.10-1.43, p = 5.6×10-4) using the IVW method and an OR of 1.21 (95% CI: 1.04-1.41, p = 0.01) using the weighted median method. Depressive status was also positively associated with T2D, with an OR of 2.26 (95% CI: 1.03-4.94, p = 0.04) and an OR of 3.62 (95% CI: 1.33-9.90, p = 0.01) using the IVW and weighted median methods, respectively. No causal effects of MD and depressive status on T2D risk factors were observed, and T2D did not influence these factors.

Conclusion

Our study demonstrates a causal relationship between depression and an increased risk of developing T2D, with both major depression and depressive status being positively associated with T2D.

Depressive disorder subtypes, depressive symptom clusters, and risk of obesity and diabetes: A systematic review

BACKGROUND

Overlapping but divided literatures suggest certain depression facets may pose greater obesity and diabetes risk than others. Our objectives were to integrate the major depressive disorder (MDD) subtype and depressive symptom cluster literatures and to clarify which facets are associated with the greatest cardiometabolic disease risk.

METHODS

We conducted a systematic review of published studies examining associations of ≥2 MDD subtypes or symptom clusters with obesity or diabetes risk outcomes. We report which facets the literature is "in favor" of (i.e., having the strongest or most consistent results).

RESULTS

Forty-five articles were included. Of the MDD subtype-obesity risk studies, 14 were in favor of atypical MDD, and 8 showed similar or null associations across subtypes. Of the symptom cluster-obesity risk studies, 5 were in favor of the somatic cluster, 1 was in favor of other clusters, and 5 were similar or null. Of the MDD subtype-diabetes risk studies, 7 were in favor of atypical MDD, 3 were in favor of other subtypes, and 5 were similar or null. Of the symptom cluster-diabetes risk studies, 7 were in favor of the somatic cluster, and 5 were similar or null.

LIMITATIONS

Limitations in study design, sample selection, variable measurement, and analytic approach in these literatures apply to this review.

CONCLUSIONS

Atypical MDD and the somatic cluster are most consistently associated with obesity and diabetes risk. Future research is needed to establish directionality and causality. Identifying the depression facets conferring the greatest risk could improve cardiometabolic disease risk stratification and prevention programs.

The relationship of within-individual and between-individual variation in mental health with bodyweight: An exploratory longitudinal study

BACKGROUND

Poor mental health is associated with obesity, but existing studies are either cross-sectional or have long time periods between measurements of mental health and weight. It is, therefore, unclear how small fluctuations in mental wellbeing within individuals predict bodyweight over short time periods, e.g. within the next month. Studying this could identify modifiable determinants of weight changes and highlight opportunities for early intervention.

METHODS

2,133 UK adults from a population-based cohort completed monthly mental health and weight measurements using a mobile app over a period of 6-9 months. We used random intercept regression models to examine longitudinal associations of depressive symptoms, anxiety symptoms and stress with subsequent weight. In sub-group analyses, we included interaction terms of mental health variables with baseline characteristics. Mental health variables were split into "between-individual" measurements (= the participant's median score across all timepoints) and "within-individual" measurements (at each timepoint, the difference between the participant's current score and their median).

RESULTS

Within-individual variation in depressive symptoms predicted subsequent weight (0.045kg per unit of depressive symptom severity, 95% CI 0.021-0.069). We found evidence of a moderation effect of baseline BMI on the association between within-individual fluctuation in depressive symptoms and subsequent weight: The association was only apparent in those with overweight/obesity, and it was stronger in those with obesity than those with overweight (BMI<25kg/m2: 0.011kg per unit of depressive symptom severity [95% CI -0.017 to 0.039]; BMI 25-29.9kg/m2: 0.052kg per unit of depressive symptom severity [95%CI 0.010-0.094kg]; BMI≥30kg/m2: 0.071kg per unit of depressive symptom severity [95%CI 0.013-0.129kg]). We found no evidence for other interactions, associations of stress and anxiety with weight, or for a reverse direction of association.

CONCLUSION

In this exploratory study, individuals with overweight or obesity were more vulnerable to weight gain following higher-than-usual (for that individual) depressive symptoms than individuals with a BMI<25kg/m2.

Social Support and Depression among Stroke Patients: A Topical Review

Research has shown a protective association between social support and depression, depression among stroke patients, and health impacts of depression. Despite this, not much is known about the effect of social support on depression among stroke patients. This review aims to summarize the current research examining the association between social support and depression among stroke patients. A literature search was performed in PubMed to find original peer-reviewed journal articles from 2016 to 12 March 2023 that examined the association between social support and depression among stroke patients. The search terms were depression and "social support" and stroke, which lead to 172 articles. After abstract review, seven observational studies that studied the target association among stroke patients were selected. One additional study was found using PsycINFO as a complementary source with the same search strategy and criteria. Overall, a negative association was found between social support and depression among stroke patients in eight studies, with more social support leading to lower rates of depression post-stroke. The other study did not find a statistically significant association. Overall, the results of recent studies suggest that social support is negatively associated with depression among stroke patients. In most studies, this association was statistically significant. The findings suggest the importance of improving social support perceived by stroke patients in the prevention of depression after the occurrence of stroke.

Novel targets for ameliorating energy metabolism disorders in depression through stable isotope-resolved metabolomics

The severe harm of depression to human health and life has attracted global attention, but the exact mechanism is not yet known due to the complicated pathogenesis. The existing antidepressants are far from ideal, indicating it is urgently needed to seek safe and effective drugs from a unique perspective. Based on the hypothesis of "mitochondrial dysfunction" proposed recently, we attempt to focus on the substrates supply of energy metabolism. We applied stable isotope-resolved metabolomics, and revealed that significantly decreased TCA cycle and abnormally increased gluconeogenesis pathway in CUMS rats. Pyruvate dehydrogenase (PDH) and pyruvate carboxylase (PC) maybe the key metabolic enzymes. This metabolic reprogramming was confirmed through ELISA assays and Western blot analysis. To explore the causes of substrates supply disorder in depression, we conducted the mitochondrial structure-function evaluation. Interestingly, the levels of the mitochondrial pyruvate carrier (MPC) decreased significantly, which is essential for the entry of pyruvic acid into the TCA cycle. Together, MPC, PDH and PC are expected to become potential novel therapeutic targets for treating depressive disorders. This research provides a unique insight for re-cognizing the pathological mechanisms of depression, the novel targets for development of ideal antidepressants, as well as a paradigm for deciphering abnormal metabolic pathways in other metabolic diseases.