Association between meatless diet and depressive episodes: A cross-sectional analysis of baseline data from the longitudinal study of adult health (ELSA-Brasil)

Investigating the relationship between plant-based dietary protein indices and depression score in the elderly of Shiraz City: A cross-sectional study

Depression is an important and common disorder in the elderly. Plant-based dietary patterns are often considered "healthy" and associated with various health benefits. However, the association between plant-based dietary indices and depression is largely ambiguous. This cross-sectional study aimed to investigate the relationship between plant protein indices and depression in the elderly population. In this cross-sectional study, conducted on 80 elderly people living in Shiraz City, food intake information was collected using a 147-item food frequency questionnaire. Plant-based diet index (PDI), healthy PDI (hPDI), and unhealthy PDI (uPDI) were used to assess dietary patterns. Also, depression was evaluated using the Beck Depression questionnaire. A linear regression method in crude and 2 adjusted models was used to investigate the relationship between dietary indicators and depression. P < .05 was considered significant. Higher PDI and uPDI scores were related to lower intakes of vitamin B12 (P = .04, .03). Also, higher hPDI and uPDI scores were associated with lower saturated fatty acids intakes (P = .04, .01). A significant positive relationship between depression and hPDI was observed in both crude (P = .01), and adjusted (P = .01) models. While, for PDI and uPDI, no significant relationship was observed in any of the models. Plant-based dietary patterns could be possibly related to depression in the older population. However, the evidence is inconsistent and more investigations with larger sample sizes and appropriate designs are needed to clarify this relationship.

Human gut microbiome, diet, and mental disorders

Diet is one of the most important external factor shaping the composition and metabolic activities of the gut microbiome. The gut microbiome plays a crucial role in host health, including immune system development, nutrients metabolism, and the synthesis of bioactive molecules. In addition, the gut microbiome has been described as critical for the development of several mental disorders. Nutritional psychiatry is an emerging field of research that may provide a link between diet, microbial function, and brain health. In this study, we have reviewed the influence of different diet types, such as Western, Mediterranean, vegetarian, and ketogenic, on the gut microbiota composition and function, and their implication in various neuropsychiatric and psychological disorders.

Dietary habits and self-reported health outcomes in a cross-sectional survey of health-conscious adults eating a plant-based diet

BACKGROUND

Given the growing popularity of plant-based diets, this study investigated the dietary habits and self-reported health outcomes of health-conscious adults consuming plant-based diets.

METHODS

A cross-sectional online survey (n = 315) was distributed to members of Plant-Based Health Professionals UK, a community interest company. Dietary intake was assessed through a food frequency questionnaire. Data were summarised descriptively. Dietary habits among respondents following a whole food plant-based (WFPB) and vegan diet were compared using hypothesis tests.

RESULTS

Respondents reported following a WFPB (61%), vegan (28%) and semi plant-based (11%) diet. Median time on current dietary pattern was 5 years. Daily or more frequent consumption was reported for the following foods: fruits 77%, berries 51%, green vegetables 48%, cruciferous vegetables 45%, other vegetables 64%, beans/legumes 41%, whole grains 62%, nuts and all seeds 63%. Consumption of ultra-processed foods and plant-based meat alternatives was low. About 93% of those on a WFPB or vegan diet supplemented with vitamin B12 and 61% with vitamin D. The median body mass index was 22.4 kg/m2. Fifty per cent of participants reported weight loss after adopting a plant-based diet, with a median loss of 6.4 kg. Thirty-five per cent reported reversing or improving an underlying health condition, and 15% were able to stop or reduce prescribed medication use as a result of dietary changes.

CONCLUSIONS

This study suggests that a well-planned plant-based diet is achievable and sustainable in a community setting and can be associated with health benefits. How to best encourage such sustainable diets among the broad population requires further research.

Depression, Anxiety, Emotional Eating, and Body Mass Index among Self-Reported Vegetarians and Non-Vegetarians: A Cross-Sectional Study in Peruvian Adults

Background: Vegetarianism is commonly associated with various health benefits. However, the association between this dietary regimen and aspects of mental health remains ambiguous. This study compared the symptoms of depression and anxiety, emotional eating (EmE), and body mass index (BMI) in Peruvian vegetarian and non-vegetarian adults. Methods: A cross-sectional study was conducted on 768 Peruvian adults, of whom 284 (37%) were vegetarians and 484 (63%) were non-vegetarians. The Depression Patient Health Questionnaire-2 (PHQ-2), Generalized Anxiety Disorder Scale-2 (GAD-2), and an EmE questionnaire were applied; additionally, the BMI was calculated. Simple and multiple linear regression and Poisson regression models with robust variance were used to evaluate the association between depression, anxiety, EmE, and BMI with dietary patterns. Results: The vegetarians (Adjusted Prevalence Ratio [PR] = 0.24, 95% CI 0.16-0.31; p < 0.001) reported more depressive symptoms than the non-vegetarians. This trend persisted for anxiety, with an adjusted PR of 0.17 (95% CI: 0.01-0.29; p = 0.012). However, the vegetarians (adjusted PR = -0.38, 95% CI: -0.61--0.14; p < 0.001) reported lower EmE scores compared to the non-vegetarians. Likewise, the vegetarians had a lower mean BMI than the non-vegetarians (B = -0.16, 95% CI: -0.21--0.08; p < 0.001). Conclusions: Vegetarian diets are associated with increased symptoms of depression and anxiety, as well as lower EmE and BMI scores. Further longitudinal studies are needed to elucidate these associations and determine causality and the underlying mechanisms involved.

The Association between Dietary Protein Intake and Sources and the Rate of Longitudinal Changes in Brain Structure

Few studies have examined dietary protein intake and sources, in combination with longitudinal changes in brain structure markers. Our study aimed to examine the association between dietary protein intake and different sources of dietary protein, with the longitudinal rate of change in brain structural markers. A total of 2723 and 2679 participants from the UK Biobank were separately included in the analysis. The relative and absolute amounts of dietary protein intake were calculated using a 24 h dietary recall questionnaire. The longitudinal change rates of brain structural biomarkers were computed using two waves of brain imaging data. The average interval between the assessments was three years. We utilized multiple linear regression to examine the association between dietary protein and different sources and the longitudinal changes in brain structural biomarkers. Restrictive cubic splines were used to explore nonlinear relationships, and stratified and sensitivity analyses were conducted. Increasing the proportion of animal protein in dietary protein intake was associated with a slower reduction in the total hippocampus volume (THV, β: 0.02524, p < 0.05), left hippocampus volume (LHV, β: 0.02435, p < 0.01) and right hippocampus volume (RHV, β: 0.02544, p < 0.05). A higher intake of animal protein relative to plant protein was linked to a lower atrophy rate in the THV (β: 0.01249, p < 0.05) and LHV (β: 0.01173, p < 0.05) and RHV (β: 0.01193, p < 0.05). Individuals with a higher intake of seafood exhibited a higher longitudinal rate of change in the HV compared to those that did not consume seafood (THV, β: 0.004514; p < 0.05; RHV, β: 0.005527, p < 0.05). In the subgroup and sensitivity analyses, there were no significant alterations. A moderate increase in an individual's intake and the proportion of animal protein in their diet, especially from seafood, is associated with a lower atrophy rate in the hippocampus volume.

Is a plant-based diet effective to maintain a good psycho-affective status in old age? Results of a survey of a long-lived population from Sardinia

BACKGROUND

Depression is common among the elderly, resulting in poor quality of life and elevated healthcare expenditure. Among other factors, dietary habits could also affect this condition, although the specific food patterns involved remain to be established. The present study aimed to assess the role of plant- versus animal-dominant foods consumption on the affective state of nonagenarians from a Sardinian population, Italy, well known for its longevity (Blue Zone).

METHODS

Data, including demographic, education, anthropometric parameters, monthly income, and comorbidity were recorded and analyzed. Symptomatic depression was assessed using the Geriatric Depression Scale (GDS) during a comprehensive home geriatric assessment; nutritional status was evaluated by a validated food frequency questionnaire.

RESULTS

A total of 200 elderly subjects living in the Sardinian Blue Zone (mean age 93.9 ± 3.9 years) participated in the study; symptomatic depression was present in 51% of the whole cohort and was more common among women. Multivariable logistic regression showed a significantly greater risk of depression in people consuming plantbased foods (OR = 1.42, 95% CI 1.04-1.93), whereas moderate animal-derived foods consumption was associated with a better affective state (OR = 0.79, 95% CI 0.62-0.98).

CONCLUSIONS

These findings indicate that a more balanced diet, including animal-derived foods, instead of an exclusive plant-dominant diet, may be more appropriate in the elderly, and abstention from animal-based food intake should not be recommended in advanced age to prevent depression.

Predicting suicidal behavior in individuals with depression over 50 years of age: Evidence from the UK biobank

Objective

To construct applicable models suitable for predicting the risk of suicidal behavior among individuals with depression, particularly on the progression from no history of suicidal behavior to suicide attempts, as well as from suicidal ideation to suicide attempts.

Methods

Based on a prospective cohort from the UK Biobank, a total of 55,139 individuals aged 50 and above with depression were enrolled in the study, among whom 29,528 exhibited suicidal behavior. Specifically, they were divided into control (25,611), suicidal ideation (24,361), and suicide attempt (5167) groups. Least absolute shrinkage and selection operator (LASSO) regression was used to identify a subset of important features for distinguishing suicidal ideation and suicide attempts. We used the Gradient Boosting Decision Tree (GBDT) algorithm with stratified 10-fold cross-validation and grid-search to construct the prediction models for suicidal ideation or suicide attempts. To address the dataset imbalance in classifying suicide attempts, we used random under-sampling. The SHapley Additive exPlanations (SHAP) were used to estimate the important variables in the GBDT model.

Results

Significant differences in sociodemographic, economic, lifestyle, and psychological factors were observed across the three groups. Each classifier optimally utilized 8-11 features. Overall, the algorithms predicting suicide attempts demonstrated slightly higher performance than those predicting suicidal ideation. The GBDT classifier achieved the highest accuracy, with AUROC scores of 0.914 for suicide attempts and 0.803 for suicidal ideation. Distinctive predictive factors were identified for each group: while depression's inherent characteristics crucially distinguished the suicidal ideation group from controls, some key predictors, including the age of depression onset and childhood trauma events, were identified for suicide attempts.

Conclusions

We established applicable machine learning-based models for predicting suicidal behavior, particularly suicide attempts, in individuals with depression, and clarified the differences in predictors between suicidal ideation and suicide attempts.