Combined Depression and Malnutrition As an Effective Predictor of First Fall Onset in a Chinese Community-Dwelling Population: A 2-Year Prospective Cohort Study

The association between visceral fat obesity and prefrailty in Chinese older adults: a cross-sectional study

BACKGROUND

The prevalence of obesity is escalating. Previous research has concentrated on the link between frailty and obesity; however, the association between prefrailty and obesity has been less studied. Prefrailty screening and intervention may prevent or postpone frailty in older persons.

OBJECTIVE

The study was to investigate into the relationship between prefrailty and several obesity indicators in Chinese community-dwelling older individuals.

METHODS

This research employed the Frailty Screening Index to investigate the frailty phenotype of people living in Shanghai. Bioelectrical impedance analysis was used for evaluating body composition.

RESULTS

There were 510 participants (39.0%) with high visceral adipose areas. Participants with a high visceral adipose area showed a higher risk of prefrailty (adjusted OR, 1.53; 95% CI, 1.19-1.96), according to multivariate models. When body mass index (BMI) and visceral fat area (VFA) were combined, it was discovered that having an overweight BMI with normal VFA was a protective factor for prefrailty (corrected OR, 0.62; 95% CI, 0.43-0.90), but having a normal weight but excess VFA increased the risk of prefrailty (corrected OR, 1.87; 95% CI, 1.15-3.03).

CONCLUSION

Visceral fat obesity is an independent risk factor for prefrailty in Chinese older adults. Implementing targeted interventions, such as dietary modifications, increased physical activity, and other lifestyle changes, could play a crucial role in reducing the risk of prefrailty and improving overall health outcomes in this population.

Study on plasma metabolomics profiling of depression in Chinese community-dwelling older adults based on untargeted LC/GC‒MS

Depression is a serious psychiatric illness that causes great inconvenience to the lives of elderly individuals. However, the diagnosis of depression is somewhat subjective. Nontargeted gas chromatography (GC)/liquid chromatography (LC)-mass spectrometry (MS) was used to study the plasma metabolic profile and identify objective markers for depression and metabolic pathway variation. We recruited 379 Chinese community-dwelling individuals aged ≥ 65. Plasma samples were collected and detected by GC/LC‒MS. Orthogonal partial least squares discriminant analysis and a heatmap were utilized to distinguish the metabolites. Receiver operating characteristic curves were constructed to evaluate the diagnostic value of these differential metabolites. Additionally, metabolic pathway enrichment was performed to reveal metabolic pathway variation. According to our standard, 49 people were included in the depression cohort (DC), and 49 people age- and sex-matched individuals were included in the non-depression cohort (NDC). 64 metabolites identified via GC‒MS and 73 metabolites identified via LC‒MS had significant contributions to the differentiation between the DC and NDC, with VIP values > 1 and p values < 0.05. Three substances were detected by both methods: hypoxanthine, phytosphingosine, and xanthine. Furthermore, 1-(sn-glycero-3-phospho)-1D-myo-inositol had the largest area under the curve (AUC) value (AUC = 0.842). The purine metabolic pathway is the most important change in metabolic pathways. These findings show that there were differences in plasma metabolites between the depression cohort and the non-depression cohort. These identified differential metabolites may be markers of depression and can be used to study the changes in depression metabolic pathways.

The role of comprehensive geriatric assessment in the identification of different nutritional status in geriatric patients: a real-world, cross-sectional study

Background

Malnutrition is an often unrecognized problem, but it is common in older patients and leads to adverse outcomes.

Aims

The purpose of this study is to analyze the prevalence of the risk of undernutrition in elderly patients and the correlation between CGA and nutritional status, and to determine the nutritional status of elderly patients.

Methods

This is a real-world cross-sectional study of continuously enrolled elderly patients aged 65 years or older with a complete CGA database. CGA inventory was prepared by compiling and screening general information, body composition and blood biochemical results. MNA was also conducted for each elderly patient to screen for malnutrition. A multivariable logistic regression analysis was used to determine the association between the CGA and nutritional assessment.

Result

The average age of the 211 selected elderly patients (160 men and 51 women) was 79.60 ± 9.24 years, and their ages ranged from 65 to 96 years. After controlling for confounders, patients with a history of PUD (OR = 2.353, p = 0.044), increased ADLs & IADLs scores (OR = 1.051, p = 0.042) or GDS scores (OR = 6.078, p < 0.001) may increase the incidence of the risk of undernutrition respectively, while an increase in BMI (OR = 0.858, p = 0.032) may lower the incidence of malnutrition risk. In addition, increased ADLs & IADLs scores (OR = 1.096, p = 0.002) or GDS scores (OR = 11.228, p < 0.001) may increase the incidence of undernutrition. However, increased MMSE (OR = 0.705, p < 0.001), BMI (OR = 0.762, p = 0.034), UAC (OR = 0.765, p = 0.048) and CC (OR = 0.721, p = 0.003) may decrease the incidence of undernutrition, respectively.

Conclusion

The study found that the prevalence of risk of undernutrition in elderly patients was the highest. Risk of undernutrition was independently associated with peptic ulcer disease, ADLs & IADLs, GDS and BMI. However, we found that when the nutritional status reached the level of undernutrition, it was related to more factors, including ADLs & IADLs, MMSE, GDS, BMI, UAC and CC. Determining the level of malnutrition through CGA may help to prevent and intervene malnutrition as early as possible.

Interpretable Machine Learning for Fall Prediction Among Older Adults in China

INTRODUCTION

Falls in older adults are potentially devastating, whereas an accurate fall risk prediction model for community-dwelling older Chinese is still lacking. The objective of this study was to build prediction models for falls and fall-related injuries among community-dwelling older adults in China.

METHODS

This study used data (Waves 2015 and 2018) from 5,818 participants from the China Health and Retirement Longitudinal Study. A total of 107 input variables at the baseline level were regarded as candidate features. Five machine learning algorithms were used to build the 3-year fall and fall-related injury risk prediction models. SHapley Additive exPlanations was used for the prediction model explanation. Analyses were conducted in 2022.

RESULTS

The logistic regression model achieved the best performance among fall and fall-related injury prediction models with an area under the receiver operating characteristic curve of 0.739 and 0.757, respectively. Experience of falling was the most important feature in both models. Other important features included basic activity of daily living, instrumental activity of daily living, depressive symptoms, house tidiness, grip strength, and sleep duration. The important features unique to the fall model were house temperature, sex, and flush toilets, whereas lung function, smoking, and Internet access were exclusively related to the fall-related injury model.

CONCLUSIONS

This study suggests that the optimal models hold promise for screening out older adults at high risk for falls in facilitated targeted interventions. Fall prevention strategies should specifically focus on fall history, physical functions, psychological factors, and home environment.

Malnutrition, Depression, Poor Sleep Quality, and Difficulty Falling Asleep at Night Are Associated with a Higher Risk of Cognitive Frailty in Older Adults during the COVID-19 Restrictions

The COVID-19 restrictions, such as social isolation and disruption of daily routines, can have detrimental effects, including increased stress, anxiety, sleep disturbance, and physical and cognitive decline among older adults. This study aimed to examine the association between nutritional status, depression, sleep quality, falling asleep at night, and cognitive frailty (CF) among older Thai adults during the COVID-19 pandemic. This cross-sectional study included 408 older adults with an average age of 70.54 (5.49) years. CF was determined using Fried's frailty phenotype and the Montreal Cognitive Assessment Basic. The Mini Nutritional Assessment-Short Form, Pittsburgh Sleep Quality Index, and geriatric depression assessment were used for assessment. Multiple logistic regression analysis demonstrated that participants who were malnourished (OR 3.786; 95%CI 1.719-8.335), depressed (OR 5.003; 95%CI 2.399-10.434), had poor sleep quality (OR 1.613; 95%CI 1.041-2.500), and engaged in difficulty falling asleep (OR 1.809; 95%CI 1.022-3.203) had a higher risk of CF compared to those who did not exhibit these factors. Therefore, malnutrition, depression, poor sleep quality, and difficulty falling asleep were identified as risk factors for CF among older adults in Thailand linked to the impact of the COVID-19 pandemic. It is crucial to develop interventions to prevent CF resulting from the mentioned variables.

Latent profile analysis of depression among older adults living alone in China

BACKGROUND

The number of older adults living alone has increased significantly. Depression is one of the significant mental health problems they face; classifying depressive conditions into homogeneous subgroups can help discover hidden information.

METHODS

The data comes from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Latent profile analysis (LPA) was used to identify depression subgroups among elderly living alone, Chi-square tests and Kruskal-Wallis tests were used to univariate analysis, multinomial logistic regression was used to analyze the related factors.

RESULTS

1831 older adults living alone were identified and classified as low-level (30.4 %), moderate-level (55.3 %) and high-level (14.4 %). All variables, except age, were significant in the univariate analysis. Multinomial logistic regression showed that not participating in exercise, sometimes interacting with friends, anxiety symptoms, and impaired IADL were associated with the moderate- and high-level of depression in older adults living alone; good or fair self-rated health and life satisfaction were associated with the low-level of depression in older adults living alone. Anxiety symptoms were associated with high-level of depression in older adults living alone compared to moderate-level; good or fair self-rated health and life satisfaction were associated with moderate-level of depression in older adults living alone.

LIMITATIONS

The CES-D-10 cannot fully determine the presence of depression in elderly people living alone at high-level.

CONCLUSIONS

In future primary health care, it would be more meaningful to provide targeted interventions for different subgroups of depression in older adults living alone.

Long-term trajectories of depressive symptoms and machine learning techniques for fall prediction in older adults:Evidence from the China Health and Retirement Longitudinal Study (CHARLS)

BACKGROUND

Falls are the most common adverse outcome of depression in older adults, yet a accurate risk prediction model for falls stratified by distinct long-term trajectories of depressive symptoms is still lacking.

METHODS

We collected the data of 1617 participants from the China Health and Retirement Longitudinal Study register, spanning between 2011 and 2018. The 36 input variables included in the baseline survey were regarded as candidate features. The trajectories of depressive symptoms were classified by the latent class growth model and growth mixture model. Three data balancing technologies and four machine learning algorithms were utilized to develop predictive models for fall classification of depressive prognosis.

RESULTS

Depressive symptom trajectories were divided into four categories, i.e., non-symptoms, new-onset increasing symptoms, slowly decreasing symptoms, and persistent high symptoms. The random forest-TomekLinks model achieved the best performance among the case and incident models with an AUC-ROC of 0.844 and 0.731, respectively. In the chronic model, the gradient boosting decision tree-synthetic minority oversampling technique obtained an AUC-ROC of 0.783. In the three models, the depressive symptom score was the most crucial component. The lung function was a common and significant feature in both the case and the chronic models.

CONCLUSIONS

This study suggests that the ideal model has a good chance of identifying older persons with a high risk of falling stratified by long-term trajectories of depressive symptoms. Baseline depressive symptom score, lung function, income, and injury experience are influential factors associated with falls of depression evolution.

Characterizing fall risk factors in Belgian older adults through machine learning: a data-driven approach

BACKGROUND

Falls are a major problem associated with ageing. Yet, fall-risk classification models identifying older adults at risk are lacking. Current screening tools show limited predictive validity to differentiate between a low- and high-risk of falling.

OBJECTIVE

This study aims at identifying risk factors associated with higher risk of falling by means of a quality-of-life questionnaire incorporating biological, behavioural, environmental and socio-economic factors. These insights can aid the development of a fall-risk classification algorithm identifying community-dwelling older adults at risk of falling.

METHODS

The questionnaire was developed by the Belgian Ageing Studies research group of the Vrije Universiteit Brussel and administered to 82,580 older adults for a detailed analysis of risk factors linked to the fall incidence data. Based on previously known risk factors, 139 questions were selected from the questionnaire to include in this study. Included questions were encoded, missing values were dropped, and multicollinearity was assessed. A random forest classifier that learns to predict falls was trained to investigate the importance of each individual feature.

RESULTS

Twenty-four questions were included in the classification-model. Based on the output of the model all factors were associated with the risk of falling of which two were biological risk factors, eight behavioural, 11 socioeconomic and three environmental risk factors. Each of these variables contributed between 4.5 and 6.5% to explaining the risk of falling.

CONCLUSION

The present study identified 24 fall risk factors using machine learning techniques to identify older adults at high risk of falling. Maintaining a mental, physical and socially active lifestyle, reducing vulnerability and feeling satisfied with the living situation contributes to reducing the risk of falling. Further research is warranted to establish an easy-to-use screening tool to be applied in daily practice.

Relationship between obesity-related anthropometric indicators and cognitive function in Chinese suburb-dwelling older adults

Background

Studies relating obesity to cognition in older people show conflicting results, which may be explained by the choice of obesity indicators.

Objectives

This study aimed to investigate the relationship between obesity-related indicators and cognitive impairment, especially between different age or gender subgroups, and explore whether obesity-related indicators were related to specific cognitive domains.

Methods

This was a cross-sectional study on 1753 participants aged ≥ 60 years (41.0% men; aged 71.36 ± 5.96 years). Obesity-related indicators included body mass index (BMI), waist circumference (WC), calf circumference (CC), waist to hip ratio (WHR), waist to calf circumstance ratio (WCR), fat to fat-free mass ratio (FM/FFM). The Mini-Mental State Examination scale (MMSE) was used to assess cognitive function. Cognitive impairment was defined as a score ≤ 17 for illiterates, ≤ 20 for participants with primary school education, and ≤ 24 for those with junior high school degrees or above. Multiple logistic regression analysis was used to estimate multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Restricted cubic splines were used to analyze and visualize the linear relationships.

Results

The prevalence of cognitive impairment was 18.77%. In the fully adjusted model, CC was negatively associated with cognitive impairment (OR = 0.94, 95% CI: 0.90−0.98). Further analysis showed that CC correlated positively with recall and place orientation. A higher FM/FFM was found to be associated with a higher prevalence of cognitive impairment (OR: 1.44, 95%CI: 0.88–2.35, P for trend = 0.029); this association was notable in women (P for trend = 0.002) and the oldest (P for trend = 0.009), and so did the potential effect of BMI on cognitive impairment (70–80 years: P for trend = 0.011; ≥ 80 years: P for trend = 0.013). No statistically significant association was found between cognitive impairment and WC, WHR, or WCR.

Conclusion

CC and FM/FFM were associated with cognitive impairment in older people. Future research needs to distinguish the effects of fat and muscle mass on cognitive function, with special attention to different ages and genders.

Malnutrition and Depressive Symptoms in Elderly Palliative Care Patients

Objective: The aim of this study is to determine the prevalence of and relationship between malnutrition and depressive symptoms in older palliative care inpatients. Methods: One hundred and thirty-six older adults were included in the study. The Geriatric Depression Scale, Mini Nutritional Assessment Short Form, Mini Mental State Examination, Barthel index, and Lawton & Brody index were used to evaluate the mood, nutrition status, mental status, and activities of daily living of the patients. Results: The mean age of the patients was 74.88 ± 8.82 years, and 63.2% of the patients were female. According to the nutritional assessment, 39.7% of the patients suffered from malnutrition, while 47.1% were at risk for it, and depressive symptoms were detected in 79.4% of the patients. A strong negative correlation was found between the scores for nutritional and depressive symptoms (r = -.750, 95% CI = -.834 to -.668). Conclusion: Malnutrition, risk of malnutrition, and depressive symptoms were found to occur often among the patients, and the relationship between them was statistically significant. Our results highlight that the quality of life of palliative care patients could be improved by providing adequate nutrition and psychological support.

Association Between Physical Performance and Cognitive Function in Chinese Community-Dwelling Older Adults: Serial Mediation of Malnutrition and Depression

Purpose

We aimed to propose a serial mediational model to further analyze the relationship between poor physical performance, malnutrition, depression and cognitive impairment in Chinese community-dwelling older adults.

Patients and Methods

This study consisted of 1386 community-dwelling Chinese older adults aged 65 years and older in Shanghai, China. Mild cognitive impairment (MCI) was assessed by the Mini-Mental State Examination (MMSE) and Instrumental Activities Of Daily Living (IADL). Physical performance was assessed by short physical performance battery (SPPB). Malnutrition was defined with the Mini Nutritional Assessment (MNA). Depressive symptoms were evaluated by the 30-item Geriatric Depression Scale (GDS). Serial multiple mediator models were used.

Results

The mean age of the final analysis sample was 73.62±6.14, and 57.6% (n=809) were females. The prevalence of MCI was 14.35% (n=199). Physical performance (p<0.001), nutritional status (p=0.025), and depressive symptoms (p=0.002) were correlated with MCI. The serial mediational model revealed that MNA and GDS scores significantly mediated association of SPPB and MMSE scores (c'=0.4728, p<0.001). Furthermore, depressive symptoms significantly mediated the association of physical performance and cognition (p=0.0311), while malnutrition had no independent mediating effect between these two factors (p=0.794).

Conclusion

Our study examined the serial multiple mediation roles of nutritional status and depressive symptoms on the relationship between physical performance and cognitive function in community-dwelling Chinese older adults. Older adults who were in poor physical condition tend to have worse nutritional status, more severe depression, and poorer cognitive function.

Associations between depression, nutrition, and outcomes among individuals with coronary artery disease

OBJECTIVES

Depression and malnutrition are prevalent among people with coronary artery disease (CAD) and can affect their prognosis, but the relationship between depression and malnutrition and its co-effect on prognosis is not clear. We sought to explore that relationship and its effects.

METHODS

In this study, the nutritional and emotional statuses of 547 people with CAD were assessed using the Controlling Nutritional Status (CONUT) score and the Patient Health Questionnaire-9. The average follow-up period was 2.1 y, after which we explored the relationship between depression and nutrition and prognosis.

RESULTS

A total of 41% of the participants were found to have mild malnutrition, 10% moderate to severe malnutrition, 26% had mild depression, and 11% had moderate to severe depression. A total of 20.6% of the participants were found to have comorbidities of depression and malnutrition; and both moderate to severe depression (adjusted hazard ratio [HR], 1.674; 95% confidence interval [CI], 1.098-2.551) and moderate to severe malnutrition (adjusted HR, 1.686; 95% CI, 1.073-2.648) were observed to be risk factors for the composite end point. Participants with comorbidities of depression and malnutrition were found to have increased risk of cardiovascular death (HR, 5.390; 95% CI, 1.483-19.589) and composite end point (adjusted HR, 1.791; 95% CI, 1.133-2.833) compared to those without both depression and malnutrition.

CONCLUSIONS

Depression and malnutrition were found to be risk factors for adverse outcomes in people with CAD. People with CAD should pay attention to their emotional and nutritional statuses, and interventions must be timely and efficient.

Associations between sleep duration, midday napping, depression, and falls among postmenopausal women in China: a population-based nationwide study

OBJECTIVE

To explore the independent and joint associations of sleep duration, midday napping, and depression with fall accidents in Chinese postmenopausal women.

METHODS

A total of 2,378 postmenopausal women aged ≥ 45 years from the baseline survey of the China Health and Retirement Longitudinal Study were enrolled in the study. Each participant provided data on falls, sleep duration, midday napping by a self-reporting approach. We employed the Chinese version of the Center for Epidemiologic Studies Depression Scale to assess depression. Adjusted odds ratios (OR) and 95% confidence intervals (CI) were calculated to estimate the associations of predictor variables with falls using multivariate logistic regression.

RESULTS

Of the 2,378 postmenopausal women, 478 (20.10%) reported falls in the preceding 2 years. Compared with sleep duration of 7 to 8 h/night, sleep duration of 5 to 6 h/night (OR, 1.49; 95% CI, 1.03-2.15) and of ≤ 5 h/night (OR, 1.63; 95% CI, 1.18-2.25) were associated with a higher fall prevalence. However, no significant correlation was found between sleep duration of > 8 h/night and falls. Furthermore, participants with depression were more likely to report falls (OR, 1.78; 95% CI, 1.41-2.25) than their depression-free counterparts. The duration of midday napping was not independently associated with falls, but significant joint associations of sleeping ≤ 6 h/night and no napping (OR, 1.72; 95% CI, 1.07-2.76) or napping > 60 minutes (OR, 2.14; 95% CI, 1.18-3.89) with more falls were found. Similarly, a combined status of sleeping ≤ 6 h/night and depression was related to more falls (OR, 2.97; 95% CI, 1.86-4.74).

CONCLUSION

The present study demonstrates that short sleep duration and depression are independently associated with more falls among postmenopausal women in China. Moreover, short sleep duration combined with no or long napping, short sleep duration combined with depression are jointly correlated with more falls. Future longitudinal studies are warranted to confirm these findings.