Association of Plant-Based Diet Indices and Abdominal Obesity with Mental Disorders among Older Chinese Adults

An explainable predictive model for anxiety symptoms risk among Chinese older adults with abdominal obesity using a machine learning and SHapley Additive exPlanations approach

Background

Early detection of anxiety symptoms can support early intervention and may help reduce the burden of disease in later life in the elderly with abdominal obesity, thereby increasing the chances of healthy aging. The objective of this research is to formulate and validate a predictive model that forecasts the probability of developing anxiety symptoms in elderly Chinese individuals with abdominal obesity.

Method

This research's model development and internal validation encompassed 2,427 participants from the 2017-2018 Study of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Forty-six variables were defined based on the Health Ecology Model (HEM) theoretical framework. Key variables were screened using LASSO regression, and the XGBoost (Extreme Gradient Boosting) model was further introduced to forecast the risk of developing anxiety symptoms in the elderly with abdominal obesity. SHapley Additive exPlanations (SHAP) was adopted to further interpret and show how the eigenvalues contributed to the model predictions.

Results

A total of 240 participants (9.89%) with anxiety symptoms out of 2,427 participants were included. LASSO regression identified nine key variables: looking on the bright side, self-reported economic status, self-reported quality of life, self-reported health status, watching TV or listening to the radio, feeling energetic, feeling ashamed/regretful/guilty, feeling angry, and fresh fruits. All the evaluation indicators of the XGBoost model showed good predictive efficacy. Based on the significance of the features identified by SHAP (Model Interpretation Methodology), the feature 'looking on the bright side' was the most important, and the feature 'self-reported quality of life' was the least important. The SHAP beeswarm plot illustrated the impacts of features affected by XGBoost.

Conclusion

Utilizing machine learning techniques, our predictive model can precisely evaluate the risk of anxiety symptoms among elderly individuals with abdominal obesity, facilitating the timely adoption of targeted intervention measures. The integration of XGBoost and SHAP offers transparent interpretations for customized risk forecasts.

The Relationship between Plant-Based Diet Indices and Sleep Health in Older Adults: The Mediating Role of Depressive Symptoms and Anxiety

BACKGROUND

The goal of our research was to determine the effects of plant-based dietary patterns on sleep health among older adults and to examine the parallel mediated effects of anxiety and depression.

METHODS

This investigation utilized data obtained from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS) and contained 6853 participants. Logistic regression and the restricted cubic splines (RCSs) model were employed to examine how plant-based dietary patterns affect sleep health. Additionally, Amos 26.0 was used to construct a structural equation model to examine the parallel mediated effects of anxiety and depression.

RESULTS

A higher plant-based diet index (PDI) was connected to higher odds of better sleep quality (OR = 1.209, 95% CI: 1.039-1.407) and sleep duration (OR = 1.241, 95% CI: 1.072-1.437). Conversely, an elevated unhealthy plant-based diet index (uPDI) was correlated with a lower likelihood of sleep quality (OR = 0.678, 95% CI: 0.574-0.800) and sleep duration (OR = 0.762, 95% CI: 0.647-0.896). The RCSs regression further identified a significant dose-response relationship. Mediation analysis confirmed that anxiety and depression partially mediate the relationship between plant-based diets and sleep health.

CONCLUSIONS

Our study exhibited significant correlations between plant-based diets and sleep health in the elderly. Depression and anxiety were determined as parallel mediators between plant-based diets and sleep health. Controlling early dietary patterns and affective disorder could help improve sleep quality in older adults.

Adherence to the EAT-Lancet diet and incident depression and anxiety

High-quality diets have been increasingly acknowledged as a promising candidate to counter the growing prevalence of mental health disorders. This study aims to investigate the prospective associations of adhering to the EAT-Lancet reference diet with incident depression, anxiety and their co-occurrence in 180,446 UK Biobank participants. Degrees of adherence to the EAT-Lancet diet were translated into three different diet scores. Over 11.62 years of follow-up, participants in the highest adherence group of the Knuppel EAT-Lancet index showed lower risks of depression (hazard ratio: 0.806, 95% CI: 0.730-0.890), anxiety (0.818, 0.751-0.892) and their co-occurrence (0.756, 0.624-0.914), compared to the lowest adherence group. The corresponding hazard ratios (95% CIs) were 0.711 (0.627-0.806), 0.765 (0.687-0.852) and 0.659 (0.516-0.841) for the Stubbendorff EAT-Lancet index, and 0.844 (0.768-0.928), 0.825 (0.759-0.896) and 0.818 (0.682-0.981) for the Kesse-Guyot EAT-Lancet diet index. Our findings suggest that higher adherence to the EAT-Lancet diet is associated with lower risks of incident depression, anxiety and their co-occurrence.

Association between Oral Health and Depressive Symptoms in Chinese Older Adults: The Mediating Role of Dietary Diversity

Diet is a modifiable factor in healthy population aging. Additionally, oral health and diet are important factors affecting depressive symptoms. To assess the mediating role of dietary diversity (DD) in oral health and depressive symptoms in older adults, we selected 8442 participants aged ≥ 65 years from the 2018 Chinese Longitudinal Health Longevity Survey (CLHLS) for a cross-sectional study. Depressive symptoms were determined based on scores on the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10). Dietary diversity scores (DDS) were established based on the frequency of intake of food groups. Oral health was measured by denture use and toothbrushing frequency. Stepwise multiple linear regression and PROCESS macros were used for mediated effects analysis and testing. The sample had a positive detection rate of 44.1% for depressive symptoms, 40.8% for denture use, and 41.9% for once-a-day toothbrushing. Denture use (ρ = -0.077, p < 0.01) and toothbrushing frequency (ρ = -0.115, p < 0.01) were negative predictors of depressive symptoms in older adults. DD significantly mediated the association between denture use (indirect effect -0.047; 95%CI: -0.068-0.028; p < 0.001), toothbrushing frequency (indirect effect -0.041; 95%CI: -0.054-0.030; p < 0.001), and depressive symptoms. Denture use and toothbrushing frequency not only directly reduce the risk of depressive symptoms in older adults, but also indirectly affect depressive symptoms through DD.

Development and validation of a cognitive dysfunction risk prediction model for the abdominal obesity population

Objectives

This study was aimed to develop a nomogram that can accurately predict the likelihood of cognitive dysfunction in individuals with abdominal obesity by utilizing various predictor factors.

Methods

A total of 1490 cases of abdominal obesity were randomly selected from the National Health and Nutrition Examination Survey (NHANES) database for the years 2011-2014. The diagnostic criteria for abdominal obesity were as follows: waist size ≥ 102 cm for men and waist size ≥ 88 cm for women, and cognitive function was assessed by Consortium to Establish a Registry for Alzheimer's Disease (CERAD), Word Learning subtest, Delayed Word Recall Test, Animal Fluency Test (AFT), and Digit Symbol Substitution Test (DSST). The cases were divided into two sets: a training set consisting of 1043 cases (70%) and a validation set consisting of 447 cases (30%). To create the model nomogram, multifactor logistic regression models were constructed based on the selected predictors identified through LASSO regression analysis. The model's performance was assessed using several metrics, including the consistency index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA) to assess the clinical benefit of the model.

Results

The multivariate logistic regression analysis revealed that age, sex, education level, 24-hour total fat intake, red blood cell folate concentration, depression, and moderate work activity were significant predictors of cognitive dysfunction in individuals with abdominal obesity (p < 0.05). These predictors were incorporated into the nomogram. The C-indices for the training and validation sets were 0.814 (95% CI: 0.875-0.842) and 0.805 (95% CI: 0.758-0.851), respectively. The corresponding AUC values were 0.814 (95% CI: 0.875-0.842) and 0.795 (95% CI: 0.753-0.847). The calibration curves demonstrated a satisfactory level of agreement between the nomogram model and the observed data. The DCA indicated that early intervention for at-risk populations would provide a net benefit, as indicated by the line graph.

Conclusion

Age, sex, education level, 24-hour total fat intake, red blood cell folate concentration, depression, and moderate work activity were identified as predictive factors for cognitive dysfunction in individuals with abdominal obesity. In conclusion, the nomogram model developed in this study can effectively predict the clinical risk of cognitive dysfunction in individuals with abdominal obesity.

Plant-Based Diet Indices and Their Association with Frailty in Older Adults: A CLHLS-Based Cohort Study

Within the realm of aging, the nexus between diet and health has garnered considerable attention. However, only select studies have amalgamated insights into the correlation between plant and animal food consumption and frailty. Our aim was to appraise the connections between the overall plant-based diet index (PDI), healthful plant-based diet index (hPDI), and unhealthful plant-based diet index (uPDI) and frailty in the elderly, utilizing data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). This cohort study drew upon CLHLS data spanning from 2008 to 2018. The PDI, hPDI, and uPDI were gauged using a simplified food frequency questionnaire (FFQ). A frailty index, encompassing 35 variables across major health domains, was formulated. Cox proportional hazard models were employed to scrutinize the associations between the three plant-based dietary indices and frailty in older adults, including an exploration of gender disparities in these associations. A cohort of 2883 study participants was encompassed, with 1987 (68.9%) observed to be either frail or in the pre-frail stage. The Cox model with penalized spline exhibited linear associations of PDI, hPDI, and uPDI with the frailty index. Following covariate adjustments, it was discerned that older adults situated in the highest quartiles of PDI (HR = 0.86, 95% CI: 0.77-0.95) and hPDI (HR = 0.83, 95% CI: 0.74-0.93) experienced a 14% and 17% diminished risk of frailty compared to those in the lowest quartiles of PDI and hPDI, respectively. Conversely, when contrasted with those in the lowest quartile of uPDI, older adults adhering to the highest tertile of uPDI exhibited a 21% elevated risk of frailty (HR = 1.21, 95% CI: 1.08-1.36), with both associations achieving statistical significance (p < 0.01). Moreover, additional subgroup analyses revealed that the protective effects of PDI and hPDI against frailty and the deleterious effects of uPDI were more conspicuous in men compared to women. To forestall or decelerate the progression of frailty in the elderly, tailored dietary interventions are imperative, particularly targeting male seniors.