Obesity as a causal risk factor for depression: Systematic review and meta-analysis of Mendelian Randomization studies and implications for population mental health

Associations between obesity, a composite risk score for probable long COVID, and sleep problems in SARS-CoV-2 vaccinated individuals

BACKGROUND

Preliminary data suggests that obesity might hasten the decline in mRNA vaccine-induced immunity against SARS-CoV-2. However, whether this renders individuals with obesity more susceptible to long COVID symptoms post-vaccination remains uncertain. Given sleep's critical role in immunity, exploring the associations between obesity, probable long COVID symptoms, and sleep disturbances is essential.

METHODS

We analyzed data from a survey of 5919 adults aged 18 to 89, all of whom received two SARS-CoV-2 mRNA vaccinations. Participants were categorized into normal weight, overweight, and obesity groups based on ethnicity-specific BMI cutoffs. The probability of long COVID was evaluated using the Post-Acute Sequelae of SARS-CoV-2 (PASC) score, as our survey did not permit confirmation of acute SARS-CoV-2 infection through methods such as antibody testing. Additionally, sleep patterns were assessed through questionnaires.

RESULTS

Participants with obesity exhibited a significantly higher adjusted odds ratio (OR) of having a PASC score of 12 or higher, indicative of probable long COVID in our study, compared to those with normal weight (OR: 1.55, 95% CI: 1.05, 2.28). No significant difference was observed for overweight individuals (OR: 0.92 [95% CI: 0.63, 1.33]). Both obesity and probable long COVID were associated with increased odds of experiencing a heightened sleep burden, such as the presence of obstructive sleep apnea or insomnia (P < 0.001). However, no significant interaction between BMI and probable long COVID status was found.

CONCLUSIONS

Even post-vaccination, individuals with obesity may encounter a heightened risk of experiencing prolonged COVID-19 symptoms. However, confirming our observations necessitates comprehensive studies incorporating rigorous COVID infection testing, such as antibody assays - unavailable in our anonymous survey. Additionally, it is noteworthy that the correlation between probable long COVID and sleep disturbances appears to be independent of BMI.

Association between depression and diabetes mellitus and the impact of their comorbidity on mortality: Evidence from a nationally representative study

BACKGROUND

Depression and diabetes mellitus (DM) are major chronic noncommunicable diseases that impair one's mental and physical well-being and impose substantial burdens on the health system. Depressed individuals have an increased risk of impaired blood glucose, weight gain and dyslipidemia which could induce poorer long-term survival.

METHODS

37,040 individuals from the National Health and Nutrition Examination Survey (NHANES) were included. Depressive symptoms were assessed by the Patient Health Questionnaire (PHQ-9) and classified by the total scores as no (0-4), mild (5-9), moderate (10-14), and severe (15-27). DM was determined based on self-reported medical history, clinical test results, and medication use. Logistic and Cox regression were the main statistical models. All analyses were based on weighted data from complex sampling.

RESULTS

The prevalence of DM was higher in depressed than non-depressed individuals (21.26 % vs. 13.75 %). The adjusted odds ratio (OR) (95 % CI) of comorbid DM increased with depression severity, from 1.00 (reference) for no depression, to 1.22 (1.09,1.36) for mild, 1.62 (1.37,1.92) for moderate, and 1.52(1.28,1.82) for severe depression. Comorbidity of DM and depression significantly associated with a higher risk of all-cause mortality, with a hazard ratio (HR) (95 % CI) = 2.09 (1.64,2.66).

LIMITATIONS

Dynamic demographic and metabolic data were not available.

CONCLUSION

Depression is associated with a higher risk of DM, which may be related to biological, socioeconomic, and medication-related factors. Comorbidity of the two worsens long-term survival. Therefore, blood glucose management and prevention of DM should be emphasized in depressed patients.

Familial confounding of internalising symptoms and obesity in adolescents and young adults; a co-twin analysis

BACKGROUND

Obesity and internalising disorders, including depression and anxiety, often co-occur. There is evidence that familial confounding contributes to the co-occurrence of internalising disorders and obesity in adults. However, its impact on this association among young people is unclear. Our study investigated the extent to which familial factors confound the association between internalising disorders and obesity in adolescents and young adults.

SUBJECTS/METHODS

We used a matched co-twin design to investigate the impact of confounding by familial factors on associations between internalising symptoms and obesity in a sample of 4018 twins aged 16 to 27 years.

RESULTS

High levels of internalising symptoms compared to low levels increased the odds of obesity for the whole cohort (adjusted odds ratio [AOR] = 3.1, 95% confidence interval [CI]: 1.5, 6.8), and in females (AOR = 4.1, 95% CI 1.5, 11.1), but not in males (AOR = 2.8 95% CI 0.8, 10.0). We found evidence that internalising symptoms were associated with an increased between-pair odds of obesity (AOR 6.2, 95% CI 1.7, 22.8), using the paired analysis but not using a within-pair association, which controls for familial confounding. Sex-stratified analyses indicated high internalising symptoms were associated with increased between-pair odds of obesity for females (AOR 12.9, 95% CI 2.2, 76.8), but this attenuated to the null using within-pair analysis. We found no evidence of between or within-pair associations for males and weak evidence that sex modified the association between internalising symptoms and obesity (likelihood ratio test p = 0.051).

CONCLUSIONS

Some familial factors shared by twins confound the association between internalising symptoms and obesity in adolescent and young adult females. Internalising symptoms and obesity were not associated for adolescent and young adult males. Therefore, prevention and treatment efforts should especially address familial shared determinants of obesity, particularly targeted at female adolescents and young adults with internalising symptoms and those with a family history of these disorders.

Identification of factors associated with sarcopenic obesity development: Literature review and expert panel voting

Sarcopenic obesity (SO) is defined as the combination of excess fat mass (obesity) and low skeletal muscle mass and function (sarcopenia). The identification and classification of factors related to SO would favor better prevention and diagnosis. The present article aimed to (i) define a list of factors related with SO based on literature analysis, (ii) identify clinical conditions linked with SO development from literature search and (iii) evaluate their relevance and the potential research gaps by consulting an expert panel. From 4746 articles screened, 240 articles were selected for extraction of the factors associated with SO. Factors were classified according to their frequency in the literature. Clinical conditions were also recorded. Then, they were evaluated by a panel of expert for evaluation of their relevance in SO development. Experts also suggested additional factors. Thirty-nine unique factors were extracted from the papers and additional eleven factors suggested by a panel of experts in the SO field. The frequency in the literature showed insulin resistance, dyslipidemia, lack of exercise training, inflammation and hypertension as the most frequent factors associated with SO whereas experts ranked low spontaneous physical activity, protein and energy intakes, low exercise training and aging as the most important. Although literature and expert panel presented some differences, this first list of associated factors could help to identify patients at risk of SO. Further work is needed to confirm the contribution of factors associated with SO among the population overtime or in randomized controlled trials to demonstrate causality.

Application of a transparent artificial intelligence algorithm for US adults in the obese category of weight

OBJECTIVE AND AIMS

Identification of associations between the obese category of weight in the general US population will continue to advance our understanding of the condition and allow clinicians, providers, communities, families, and individuals make more informed decisions. This study aims to improve the prediction of the obese category of weight and investigate its relationships with factors, ultimately contributing to healthier lifestyle choices and timely management of obesity.

METHODS

Questionnaires that included demographic, dietary, exercise and health information from the US National Health and Nutrition Examination Survey (NHANES 2017-2020) were utilized with BMI 30 or higher defined as obesity. A machine learning model, XGBoost predicted the obese category of weight and Shapely Additive Explanations (SHAP) visualized the various covariates and their feature importance. Model statistics including Area under the receiver operator curve (AUROC), sensitivity, specificity, positive predictive value, negative predictive value and feature properties such as gain, cover, and frequency were measured. SHAP explanations were created for transparent and interpretable analysis.

RESULTS

There were 6,146 adults (age > 18) that were included in the study with average age 58.39 (SD = 12.94) and 3122 (51%) females. The machine learning model had an Area under the receiver operator curve of 0.8295. The top four covariates include waist circumference (gain = 0.185), GGT (gain = 0.101), platelet count (gain = 0.059), AST (gain = 0.057), weight (gain = 0.049), HDL cholesterol (gain = 0.032), and ferritin (gain = 0.034).

CONCLUSION

In conclusion, the utilization of machine learning models proves to be highly effective in accurately predicting the obese category of weight. By considering various factors such as demographic information, laboratory results, physical examination findings, and lifestyle factors, these models successfully identify crucial risk factors associated with the obese category of weight.

Association between body mass index and mental health among nurses: a cross-sectional study in China

PURPOSE

To examine the correlation between body mass index (BMI) and mental well-being in Chinese nurses during the COVID-19 epidemic.

METHOD

This study was conducted in a tertiary hospital using a cross-sectional design. A total of 2,811 nurses were enlisted at Shengjing Hospital in China during the period from March to April, 2022. Information was gathered through a questionnaire that individuals completed themselves. The mental health of the participants was assessed using the Patient Health Questionnaire-9 and the Generalized Anxiety Disorder Assessment-7. Binary logistic regression was used to calculate adjusted odds ratios (ORs) and their corresponding 95% confidence intervals.

RESULTS

The prevalence of nurses experiencing depression and anxiety was 7.8% (219) and 6.7% (189), respectively. Regarding depression after adjustment, the odds ratios (ORs) for each quartile, compared to the lowest quartile, were as follows: 0.91 (95% confidence interval [CI]: 0.53, 1.56), 2.28 (95% CI: 0.98, 3.77), and 2.32 (95% CI: 1.41, 3.83). The p-value for trend was found to be 0.001. The odds ratios (ORs) for anxiety after adjustment were 2.39 (0.83, 4.36), 4.46 (0.51, 7.93), and 2.81 (1.56, 5.08) when comparing the highest quartiles to the lowest quartile. The p-value for trend was 0.009.

CONCLUSION

This study found a positive association between BMI and poor mental health among nurses during the COVID-19 pandemic, particularly in those who were overweight or obesity. The findings could assist in developing interventions and help policy-makers establish appropriate strategies to support the mental health of frontline nurses, especially those who are overweight or obesity.

Interplay between actual and perceived weight on mental health among Canadian Indigenous post-secondary students

OBJECTIVES

Research increasingly focuses on the mental health implications of both actual and perceived weight, particularly among post-secondary students. Considering their unique socio-cultural context and the frequent oversight in research, this study examines these implications specifically among Canadian Indigenous post-secondary students. Recent evidence indicates that students with normal weight may also experience increased mental health risks due to negative weight perceptions. Therefore, this study explores the independent and combined effects of actual and perceived weight on the mental health of this group.

PARTICIPANTS AND METHODS

This study utilized data from the 2017 Aboriginal Peoples Survey, a nationally representative sample of First Nations peoples living off-reserve, Métis, and Inuit. The focus was on Canadian Indigenous post-secondary students aged 19-34 years (n = 1,518). Logistic regression models, stratified by sex, were employed to analyze the data.

RESULTS

Perceptions of being overweight were linked to a higher risk of mood and anxiety disorders, poor self-rated mental health, and suicidal ideation among female students. This pattern was less evident among male students. Notably, female students who were overweight and perceived themselves as such were more likely to report poor mental health across all four indicators examined. In contrast, male students exhibited a less clear pattern. Diverging from recent studies, the findings indicated less robust mental health disparities among students with normal weight who perceived themselves as overweight, potentially due to the insufficient cell size of this category among Indigenous post-secondary students.

CONCLUSIONS

The study highlights the complex interplay between actual and perceived weight and its impact on mental health, particularly among female Indigenous post-secondary students.

Association of cardiovascular health using Life's Essential 8 with depression: Findings from NHANES 2007-2018

OBJECTIVE

Few studies have explored the correlation between cardiovascular health (CVH) and depression. We aimed to investigate the relationship between CVH using Life's Essential 8 (LE8) and depression among US adults.

METHODS

16,362 individuals from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2018 were included. The patient Health Questionnaire (PHQ-9) was utilized to recognized depression (PHQ-9 ≥ 10). LE8 was scored by four health behaviors (sleep, tobacco/nicotine exposure, physical activity and diet) and four health factors (body mass index, non-high-density lipoprotein cholesterol, blood glucose and blood pressure) and classified into low, moderate and high CVH groups. Weighted logistic regressions, restricted cubic splines and sensitivity analyses were utilized to investigate the correlation between LE8 and depression.

RESULTS

1306 subjects had depression (7.98% of the participants), of which 860 (7.42%), 305 (17.24%) and 141 (3.01%) had low, moderate and high CVH, separately. In the fully adjusted model, LE8 was negatively correlated with depression (OR: 5.50, 95% CI 3.92-7.71, P < 0.001). Furthermore, there were inversely dose-response relationships between LE8 and depression (overall P < 0.001).

CONCLUSIONS

Adhering to a high CVH, estimated by the LE8 score, was correlated with lower odds of depression.

Metabolic underpinnings of cancer-related fatigue

Cancer-related fatigue (CRF) is one of the most prevalent and detrimental complications of cancer. Emerging evidence suggests that obesity and insulin resistance are associated with CRF occurrence and severity in cancer patients and survivors. In this narrative review, we analyzed recent studies including both preclinical and clinical research on the relationship between obesity and/or insulin resistance and CRF. We also describe potential mechanisms for these relationships, though with the caveat that because the mechanisms underlying CRF are incompletely understood, the mechanisms mediating the association between obesity/insulin resistance and CRF are similarly incompletely delineated. The data suggest that, in addition to their effects to worsen CRF by directly promoting tumor growth and metastasis, obesity and insulin resistance may also contribute to CRF by inducing chronic inflammation, neuroendocrinological disturbance, and metabolic alterations. Furthermore, studies suggest that patients with obesity and insulin resistance experience more cancer-induced pain and are at more risk of emotional and behavioral disruptions correlated with CRF. However, other studies implied a potentially paradoxical impact of obesity and insulin resistance to reduce CRF symptoms. Despite the need for further investigation utilizing interventions to directly elucidate the mechanisms of cancer-related fatigue, current evidence demonstrates a correlation between obesity and/or insulin resistance and CRF, and suggests potential therapeutics for CRF by targeting obesity and/or obesity-related mediators.

Psychological aspects of rehabilitation nutrition: A position paper by the Japanese Association of Rehabilitation Nutrition (secondary publication)

Psychological aspects of rehabilitation nutrition affect physical, cognitive, and social rehabilitation nutrition. When depression is recognized, not only pharmacotherapy and psychotherapy, but also non-pharmacological therapies such as exercise, nutrition, psychosocial, and other interventions can be expected to improve depression. Therefore, accurate diagnosis and intervention without overlooking depression is important. Psychological aspects of preventive rehabilitation nutrition is also important because depression can be partially prevented by appropriate exercise and nutritional management. Even in the absence of psychological negatives, increasing more psychological positives from a positive psychology perspective can be useful for both patients and healthcare professionals. Positive rehabilitation nutrition interventions can increase more psychological positives, such as well-being, through cognitive-behavioral therapy and mindfulness on their own, as well as through interventions on environmental factors. Consequently, physical, cognitive, and social positives are also expected to be enhanced.

Investigating the causal effects of smoking, sleep, and BMI on major depressive disorder and bipolar disorder: a univariable and multivariable two-sample Mendelian randomization study

Background

Mental disorders, characterized as products of biopsychosocial interactions, have emerged as a leading contributor to the worldwide rise in overall morbidity and disability rates. Life's essentials can affect nearly every aspect of our lives, from physical to mental health. In this study, we try to identify the associations between life's essentials and mental disorders.

Method

Three assumptions of Mendelian randomization (MR) were applied to obtain the genetic instruments associated with smoking, sleep, and body mass index (BMI) in genome-wide association studies. Then, we conducted univariable MR (UVMR) and multivariable MR (MVMR) two-sample analyses to estimate the causal effects of these life's essentials on two mental disorders namely, major depressive disorder (MDD) and bipolar disorder (BD). Additionally, multiple sensitivity analyses were performed to evaluate the reliability and stability of the study results.

Results

In the MR analysis of the association of smoking, sleep, and BMI with MDD, we obtained 78, 39, and 302 genetic instruments, respectively. Smoking [odds ratio (OR), 1.03; 95% confidence interval (CI), 1.01-1.06; p = 0.004], sleep (OR, 1.04; 95% CI, 1.02-1.06; p < 0.001), and BMI (OR, 1.01; 95% CI, 1.01-1.02; p < 0.001) were all considered as risk factors for MDD and were independent of each other (smoking: OR, 1.03, 95% CI, 1.01-1.06, p = 0.008; sleep: OR, 1.03, 95% CI, 1.01-1.05, p = 0.001; and BMI: OR, 1.01, 95% CI, 1.01-1.02, p < 0.001). Additionally, 78, 38, and 297 genetic instruments were obtained in the MR analysis of smoking, sleep, and BMI with BD, respectively. Causal associations were observed between smoking (OR, 2.46; 95% CI, 1.17-5.15; p = 0.017), sleep (OR, 2.73; 95% CI, 1.52-4.92; p < 0.001), and BD, and smoking (OR, 2.43; 95% CI, 1.69-3.16; p = 0.018) might be a mediator in the causal effects of sleep on BD. Finally, there was no inconsistency between sensitivity and causality analysis, proving that our results are convincing.

Conclusion

The study results provide strong evidence that smoking, sleep, and BMI are causally related to MDD and BD, which need further research to clarify the underlying mechanism.