Body mass index and trajectories of the cognition among Chinese middle and old-aged adults

Intergenerational Educational Mobility and Cognitive Trajectories Among Middle-Aged and Older Chinese People: An Application of Growth Mixture and Mobility Contrast Models in Longitudinal Analysis

OBJECTIVES

Due to statistical challenges in disentangling the mobility effect (i.e., intergenerational educational mobility) from the position effect (i.e., parental and person's own education), the impact of intergenerational educational mobility on cognitive function remains unclear. We employed a novel approach to identify the mobility effect and investigate the net impact of intergenerational educational mobility on heterogeneous patterns of cognition among middle-aged and older adults in China.

METHODS

Participants aged 45 and older were recruited from the China Health and Retirement Longitudinal Study, a population-based prospective cohort study between 2011 and 2018. We identified cognitive trajectories using the growth mixture model (GMM) and subsequently employed the mobility contrast model (MCM) to examine the effects of intergenerational educational mobility on cognitive patterns stratified by gender.

RESULTS

Almost two thirds of respondents experienced intergenerational educational mobility, and 55% experienced upward mobility. Men had a higher rate of upward mobility than women. Three population-based cognitive patterns were identified: the low cognitive function with decline group (28%), the moderate cognitive function group (47%), and the high cognitive function group (26%). MCM analysis revealed that both upward and downward intergenerational educational mobility negatively affected cognitive trajectory patterns, extending beyond the influence of individuals' current and parental education.

DISCUSSION

In future research, the impact of mobility can be studied in longitudinal data sets by combining the GMM and MCM approaches. The net negative effect of intergenerational educational mobility on cognitive trajectory patterns indicates that it should be recognized as an independent predictor of cognitive decline.

Effect of Repetitive Transcranial Magnetic Stimulation on Post-Stroke Comorbid Cognitive Impairment and Depression: A Randomized Controlled Trial

Background

There are currently no uniform treatments for post-stroke comorbid cognitive impairment and depression (PSCCID).

Objective

To verify whether repetitive transcranial magnetic stimulation (rTMS) can improve PSCCID symptoms and explore the underlying roles of resting-state functional magnetic resonance imaging (rs-fMRI).

Methods

Thirty PSCCID patients were randomized in a 1 : 1 ratio to receive 4 weeks of rTMS (intervention group) or sham rTMS (control group) over the left dorsolateral prefrontal cortex (DLPFC). rs-fMRI was acquired to analyze the functional plasticity of brain regions at baseline and immediately after the last intervention.

Results

Cognition, depression status, and neural electrophysiology were improved in both intervention and control groups after treatment (p = 0.015-0.042), and the intervention group had more significant improvement than the control group. Analysis of functional connectivities (FCs) within the default mood network (DMN) showed that the connection strength of the left temporal pole/left parahippocampal cortex and right lateral temporal cortex/right retrosplenial cortex in the intervention group were enhanced compared with its pre-intervention and that in the control group after treatment (p < 0.05), and the both FC values were positively correlated with MMSE scores (p < 0.001). The intervention group had stronger FCs within the DMN compared with the control group after treatment, and some of the enhanced FCs were correlated with the P300 latency and amplitude.

Conclusions

rTMS over the left DLPFC is an effective treatment for improving both cognitive impairment and depression among patients with PSCCID. The enhanced FCs within the DMN may serve as a compensatory functional recombination to promote clinical recovery.

Association between sarcopenia and frailty in middle-aged and elder population: Findings from the China health and retirement longitudinal study

Background

The relationship between sarcopenia and frailty among middle-aged and elder adults remains unclear. This study conducted a cross-sectional and longitudinal analysis to investigate the association of sarcopenia and frailty in the middle-aged and elder Chinese population.

Methods

Our data were drawn from the China Health and Retirement Longitudinal Study. Sarcopenia status was assessed according to the Asian Working Group for Sarcopenia 2019 criteria and categorised into: no sarcopenia, possible sarcopenia, sarcopenia, and severe sarcopenia. A 38-item deficit-accumulation frailty index was constructed to assess frailty trajectories at each visit. Generalised linear regression models were performed to analyse the cross-sectional associations between sarcopenia and frailty index. The Group-based trajectory modelling was adopted to identify potential frailty trajectories, and we then examined the associations of sarcopenia and frailty trajectories using logistic regression analysis.

Results

A total of 13 218 participants were enrolled in the cross-sectional analysis and 4200 individuals were included in the longitudinal study. The cross-sectional study found that possible sarcopenia (regression coefficient (β) = 0.76; 95% confidence interval (CI) = 0.64-0.87, P < 0.001), sarcopenia (β = 0.56; 95% CI = 0.37-0.75, P < 0.001) and severe sarcopenia (β = 1.35; 95% CI = 0.97-1.73, P < 0.001) were significantly associated with higher frailty index. The longitudinal study indicated that participants with possible sarcopenia (odds ratio (OR) = 2.46; 95% CI = 1.77-3.42, P < 0.001), sarcopenia (OR = 1.87; 95% CI = 1.27-2.74, P < 0.001) and severe sarcopenia (OR = 6.57; 95% CI = 3.14-13.77, P < 0.001) had a higher risk of accelerated progression of frailty compared to those with no sarcopenia.

Conclusions

Possible sarcopenia, sarcopenia, and severe sarcopenia were associated with higher levels of frailty and accelerated progression of frailty. Therefore, clinical medical professionals should pay more attention to frailty status in individuals who have possible sarcopenia and sarcopenia.

Predicting Cognitive Decline in Amyloid-Positive Patients With Mild Cognitive Impairment or Mild Dementia

BACKGROUND AND OBJECTIVES

Cognitive decline rates in Alzheimer disease (AD) vary greatly. Disease-modifying treatments may alter cognitive decline trajectories, rendering their prediction increasingly relevant. We aimed to construct clinically applicable prediction models of cognitive decline in amyloid-positive patients with mild cognitive impairment (MCI) or mild dementia.

METHODS

From the Amsterdam Dementia Cohort, we selected amyloid-positive participants with MCI or mild dementia and at least 2 longitudinal Mini-Mental State Examination (MMSE) measurements. Amyloid positivity was based on CSF AD biomarker concentrations or amyloid PET. We used linear mixed modeling to predict MMSE over time, describing trajectories using a cubic time curve and interactions between linear time and the baseline predictors age, sex, baseline MMSE, APOE ε4 dose, CSF β-amyloid (Aβ) 1-42 and pTau, and MRI total brain and hippocampal volume. Backward selection was used to reduce model complexity. These models can predict MMSE over follow-up or the time to an MMSE value. MCI and mild dementia were modeled separately. Internal 5-fold cross-validation was performed to calculate the explained variance (R2).

RESULTS

In total, 961 participants were included (age 65 ± 7 years, 49% female), 310 had MCI (MMSE 26 ± 2) and 651 had mild dementia (MMSE 22 ± 4), with 4 ± 2 measurements over 2 (interquartile range 1-4) years. Cognitive decline rates increased over time for both MCI and mild dementia (model comparisons linear vs squared vs cubic time fit; p < 0.05 favoring a cubic fit). For MCI, backward selection retained age, sex, and CSF Aβ1-42 and pTau concentrations as time-varying effects altering the MMSE trajectory. For mild dementia, retained time-varying effects were Aβ1-42, age, APOE ε4, and baseline MMSE. R2 was 0.15 for the MCI model and 0.26 for mild dementia in internal cross-validation. A hypothetical patient with MCI, baseline MMSE 28, and CSF Aβ1-42 of 925 pg/mL was predicted to reach an MMSE of 20 after 6.0 years (95% CI 5.4-6.7) and after 8.6 years with a hypothetical treatment reducing decline by 30%.

DISCUSSION

We constructed models for MCI and mild dementia that predict MMSE over time. These models could inform patients about their potential cognitive trajectory and the remaining uncertainty and aid in conversations about individualized potential treatment effects.

Longitudinal association of adverse childhood experiences with cognitive function trajectories among middle-aged and older adults: group-based trajectory modeling

Introduction

Adverse childhood experiences (ACEs) impact cognitive function, but the relationship remains unclear. We aim to identify cognitive function trajectories and scrutinize the correlation between ACEs and cognitive function.

Methods

To identify cognitive trajectories, we employed a group-based trajectory model, and influential factors were determined using multinomial unordered logistic regression analysis.

Results

Three cognitive decline subgroups emerged: low-start decline, high-start stability, and mid-start decline. There is no dose-response relationship between cumulative adverse childhood experiences and cognitive function. The high-start stability group had specific residence and education traits, while sibling death affected them. The mid-start decline group was vulnerable to parental death, physical abuse, and domestic violence. The low-start decline group should consider age structure and childhood friendships.

Conclusions

No dose-response association between cumulative ACEs and cognitive decline. Still, specific ACE metrics are correlated with cognitive trajectories. We can incorporate patients' ACEs into cognitive function assessments for early risk factor identification and tailored interventions. Moreover, recognizing the influence of early-life experiences on cognitive function, we can advocate for nurturing positive family and societal environments to optimize cognitive function.

The influence of night shift work and associated factors on serum uric acid in aircraft maintenance workers

BACKGROUND AND OBJECTIVE

The prevalence of 12-hour shift work is increasing in various occupations. Shift work has been linked to circadian rhythm disruption, which may lead to hormonal changes and metabolic disorders, including alterations in glucose, lipid, and purine metabolism. Despite this, there is limited research on the potential connection between work shifts and abnormal serum uric acid (SUA) levels. Furthermore, the factors that contribute to abnormal SUA levels in shift workers are not well-understood. Therefore, this study aimed to analyze the SUA levels of shift workers employed in an aircraft maintenance company, investigate the potential association between shift work and SUA levels, and explore the factors that may influence abnormal SUA levels in shift workers.

METHODS

A total of 2263 male workers from an aircraft maintenance company were included in this study using the cluster sampling method. The workers were divided into two groups based on their working shifts: night shift (N = 1047, 46.27%) and day working (N = 1216, 53.73%). A survey was conducted between April 1st and June 30th, 2022 to gather information on work, lifestyle, physical examination results, and other relevant factors. The survey included a self-designed demographic information questionnaire to collect data on workers' characteristics, medical history, years of employment, smoking and drinking habits, and main lifestyle behaviors. The workers' SUA levels were measured using uricase colorimetry. One-way ANOVA was used to compare the difference in the abnormal detection rate of SUA between the two groups, and multi-factor logistic regression analysis was used to identify the factors that influence abnormal SUA levels.

RESULTS

The study indicated that 48.9% of night shift workers and 43.8% in the regular day workers had abnormal SUA levels, with a significant difference between the two groups (χ2 = 6.125, P = 0.013). Factors such as circadian rhythm type, shift work, age, the taste of diet, type of diet, smoking, overweight or obesity based on body mass index (BMI), concentration of urine creatinine (CREA), total cholesterol, triglyceride, and low-density lipoprotein cholesterol were found to be correlated with SUA abnormalities (P < 0.05). The risk of developing SUA abnormalities was found to be higher in individuals with an intermittent (OR = 1.34, 95% CI: 0.83-2.12, P < 0.05) or evening circadian rhythm type (OR = 1.45, 95% CI: 0.86-2.43, P > 0.05) compared to those with a morning type. Additionally, factors such as night shift work, a high-sodium diet, smoking, a diet high in meat and low in vegetables, being overweight or obese, and higher levels of CREA were also found to increase the risk of developing SUA abnormalities. The study also revealed a significant dose-response relationship between BMI and abnormal uric acid levels. After controlling for other factors, the risk of developing SUA abnormalities was found to be 1.18 times higher in the night shift work group than in the day work group (OR = 1.18, 95% CI:1.02-1.34, P = 0.01).

CONCLUSION

Shift work has been linked to a higher risk of developing SUA abnormalities, and there are several factors that may contribute to this risk. To prevent diseases, it is recommended that enterprises implement better health monitoring and management practices for shift workers.

The diagnostic agreement of sarcopenic obesity with different definitions in Chinese community-dwelling middle-aged and older adults

Background

In 2022, the European Society for Clinical Nutrition and Metabolism (ESPEN) and the European Association for the Study of Obesity (EASO) launched a consensus on the diagnostic methods for sarcopenic obesity (SO). The study aimed to identify the prevalence and diagnostic agreement of SO using different diagnostic methods in a cohort of subjects from West China aged at least 50 years old.

Methods

A large multi-ethnic sample of 4,155 participants from the West China Health and Aging Trend (WCHAT) study was analyzed. SO was defined according to the newly published consensus of the ESPEN/EASO. Furthermore, SO was diagnosed as a combination of sarcopenia and obesity. The criteria established by the Asian Working Group for Sarcopenia 2019 (AWGS2019) were used to define sarcopenia. Obesity was defined by four widely used indicators: percent of body fat (PBF), visceral fat area (VFA), waist circumference (WC), and body mass index (BMI). Cohen's kappa was used to analyze the diagnostic agreement of the above five diagnostic methods.

Results

A total of 4,155 participants were part of the study, including 1,499 men (63.76 ± 8.23 years) and 2,656 women (61.61 ± 8.20 years). The prevalence of SO was 0.63-7.22% with different diagnostic methods. The diagnosis agreement of five diagnostic methods was poor-to-good (κ: 0.06-0.67). The consensus by the ESPEN/EASO had the poorest agreement with other methods (κ: 0.06-0.32). AWGS+VFA had the best agreement with AWGS+WC (κ = 0.67), and consensus by the ESPEN/EASO had the best agreement with AWGS+ PBF (κ = 0.32).

Conclusion

The prevalence and diagnostic agreement of SO varies considerably between different diagnostic methods. AWGS+WC has the highest diagnostic rate in the diagnosis of SO, whereas AWGS+BMI has the lowest. AWGS+VFA has a relatively good diagnostic agreement with other diagnostic methods, while the consensus of the ESPEN/EASO has a poor diagnostic agreement. AWGS+PBF may be suitable for the alternative diagnosis of the 2022 ESPEN/EASO.

Vitamin D and Cognitive Performance in Older Adults: A Cross-Sectional and Mendelian Randomization Study

Background

Cognitive decline is a prevalent health problem in older adults, and effective treatments remain to be produced. Serum vitamin D, a commonly used biochemical marker, is widely recognized as an indicator of various diseases. Existing research has not fully elucidated the relationship between vitamin D and cognitive function. The aim of this study is to investigate the real relationship between vitamin D and cognitive function and to identify indicators that have a strong predictive effect on cognitive decline.

Methods

At first, we used the dataset of the genome-wide association studies studying vitamin D and cognitive performance to conduct Mendelian randomization analysis. Subsequently, we employed linear regression and smooth curve fitting methods to assess the relationship using the National Health and Nutrition Examination Survey data. Finally, we investigated other predictive features of cognitive performance utilizing a machine learning model.

Results

We found that a 1-unit increase in vitamin D is associated with a 6.51% reduction (P < .001) in the risk of cognitive decline. The correlation between vitamin D and cognitive performance is nonlinear, with the inflection point at 79.9 nmol/L (left: β = 0.043, P < .001; right: β = -0.007, P = .420). In machine learning, the top 5 predictors are vitamin D, weight, height, age, and body mass index.

Conclusion

There is a causal relationship between vitamin D and cognitive performance. 79.9 nmol/L could be the optimal dose for vitamin D supplementation in the elderly. Further consideration of other factors in vitamin D interventions is necessary.

Prediction of Cognitive Impairment Risk among Older Adults: A Machine Learning-Based Comparative Study and Model Development

INTRODUCTION

The prevalence of cognitive impairment and dementia in the older population is increasing, and thereby, early detection of cognitive decline is essential for effective intervention.

METHODS

This study included 2,288 participants with normal cognitive function from the Ma'anshan Healthy Aging Cohort Study. Forty-two potential predictors, including demographic characteristics, chronic diseases, lifestyle factors, anthropometric indices, physical function, and baseline cognitive function, were selected based on clinical importance and previous research. The dataset was partitioned into training, validation, and test sets in a proportion of 60% for training, 20% for validation, and 20% for testing, respectively. Recursive feature elimination was used for feature selection, followed by six machine learning algorithms that were employed for model development. The performance of the models was evaluated using area under the curve (AUC), specificity, sensitivity, and accuracy. Moreover, SHapley Additive exPlanations (SHAP) was conducted to access the interpretability of the final selected model and to gain insights into the impact of features on the prediction outcomes. SHAP force plots were established to vividly show the application of the prediction model at the individual level.

RESULTS

The final predictive model based on the Naive Bayes algorithm achieved an AUC of 0.820 (95% CI, 0.773-0.887) on the test set, outperforming other algorithms. The top ten influential features in the model included baseline Mini-Mental State Examination (MMSE), education, self-reported economic status, collective or social activities, Pittsburgh sleep quality index (PSQI), body mass index, systolic blood pressure, diastolic blood pressure, instrumental activities of daily living, and age. The model demonstrated the potential to identify individuals at a higher risk of cognitive impairment within 3 years from older adults.

CONCLUSION

The predictive model developed in this study contributes to the early detection of cognitive impairment in older adults by primary healthcare staff in community settings.

Mental-somatic multimorbidity in trajectories of cognitive function for middle-aged and older adults

INTRODUCTION

Multimorbidity may confer higher risk for cognitive decline than any single constituent disease. This study aims to identify distinct trajectories of cognitive impairment probability among middle-aged and older adults, and to assess the effect of changes in mental-somatic multimorbidity on these distinct trajectories.

METHODS

Data from the Health and Retirement Study (1998-2016) were employed to estimate group-based trajectory models identifying distinct trajectories of cognitive impairment probability. Four time-varying mental-somatic multimorbidity combinations (somatic, stroke, depressive, stroke and depressive) were examined for their association with observed trajectories of cognitive impairment probability with age. Multinomial logistic regression analysis was conducted to quantify the association of sociodemographic and health-related factors with trajectory group membership.

RESULTS

Respondents (N = 20,070) had a mean age of 61.0 years (SD = 8.7) at baseline. Three distinct cognitive trajectories were identified using group-based trajectory modelling: (1) Low risk with late-life increase (62.6%), (2) Low initial risk with rapid increase (25.7%), and (3) High risk (11.7%). For adults following along Low risk with late-life increase, the odds of cognitive impairment for stroke and depressive multimorbidity (OR:3.92, 95%CI:2.91,5.28) were nearly two times higher than either stroke multimorbidity (OR:2.06, 95%CI:1.75,2.43) or depressive multimorbidity (OR:2.03, 95%CI:1.71,2.41). The odds of cognitive impairment for stroke and depressive multimorbidity in Low initial risk with rapid increase or High risk (OR:4.31, 95%CI:3.50,5.31; OR:3.43, 95%CI:2.07,5.66, respectively) were moderately higher than stroke multimorbidity (OR:2.71, 95%CI:2.35, 3.13; OR: 3.23, 95%CI:2.16, 4.81, respectively). In the multinomial logistic regression model, non-Hispanic Black and Hispanic respondents had higher odds of being in Low initial risk with rapid increase and High risk relative to non-Hispanic White adults.

CONCLUSIONS

These findings show that depressive and stroke multimorbidity combinations have the greatest association with rapid cognitive declines and their prevention may postpone these declines, especially in socially disadvantaged and minoritized groups.

Cognitive Function, Healthy Lifestyle, and All-Cause Mortality among Chinese Older Adults: A Longitudinal Prospective Study

BACKGROUND

Both cognitive decline and unhealthy lifestyles have been linked to an elevated risk of mortality in older people. We aimed to investigate whether a healthy lifestyle might modify the association between cognitive function and all-cause mortality in Chinese older populations.

METHODS

The final analysis included 5124 individuals free of dementia, selected from the Chinese Longitudinal Healthy Longevity Survey from 2011 to 2018. Cognitive function was assessed in 2011 using the Mini-Mental State Examination (MMSE). A lifestyle score was calculated based on five lifestyle factors, including smoking, alcohol consumption, physical activity, diet, and body mass index. Cox proportional hazards models were performed to evaluate the association between baseline cognitive function and the risk of all-cause mortality, with an interaction term of cognitive function and lifestyle score being added to the models.

RESULTS

The average age of participants was 81.87 years old at baseline. During a median follow-up of 6.4 years, 1461 deaths were documented. Both higher cognitive function (HR: 0.96; 95% CI: 0.96-0.97) and a healthier lifestyle (HR: 0.92; 95% CI: 0.87-0.97) were significantly associated with a reduced risk of mortality. We found that lifestyle significantly modified the association of cognitive function with mortality (p for interaction = 0.004). The inverse relation between cognitive function and mortality was found to be more pronounced among participants with a healthier lifestyle. Of note, among the lifestyle scores component, diet showed a significant interaction with mortality (p for interaction = 0.003), and the protective HR of the all-cause mortality associated with higher MMSE scores was more prominent among participants with healthy diets compared with unhealthy diets.

CONCLUSIONS

Our study indicates that cognitive decline is associated with a higher risk of mortality, and such associations are attenuated by maintaining a healthy lifestyle, with a particular emphasis on healthy diet.

Sex difference in the association between BMI and cognitive impairment in Chinese older adults

BACKGROUND

The association between body mass index (BMI) and cognitive impairment (CI) has been the subject of extensive research, yet the precise dose-response effects remain undefined.

METHODS

Older adults were selected from the 2011/2012 survey at baseline and the new recruits from the 2014 and 2018 waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Multiple logistic regression models were used to evaluate the association between BMI categories and CI, and Restricted Cubic Spline (RCS) was used to explore the nonlinear relationship between BMI and CI.

RESULTS

The study included 29,380 older adults aged from 65 to 117 years, with an average age of 82 years. Of these, 13,465 were men, and 5359 exhibited cognitive impairment. The logistic model indicated that in female participants, being underweight was positively correlated with CI (OR:1.32; 95%CI 1.20-1.46), whereas being overweight was inversely correlated with CI (OR:0.86; 95%CI 0.75-0.99), and we didn't find any association between BMI category and CI in male participants. RCS modeling revealed a U-shaped relationship between BMI and CI. When stratified by sex, men exhibited a similar trend, with the lowest risk at a BMI of 22.774 kg/ m2, while women had the lowest risk of CI at a BMI of 24.817 kg/ m2.

LIMITATION

This was a cross-sectional study, it cannot provide information on causal relationships.

CONCLUSION

A U-shaped relationship was observed between BMI and CI in older adults, more pronounced in the male population, suggesting that male older adults may need to manage their BMI more rigorously.

Relationship between cognitive function and weight-adjusted waist index in people ≥ 60 years old in NHANES 2011-2014

BACKGROUND

Widespread attention has been given to the detrimental effects of obesity on cognitive function. However, there is no evidence on the connection between low cognitive performance and the WWI (weight-adjusted waist index). This study looked into the connection between poor cognitive performance and the WWI in senior Americans.

METHODS

A cross-sectional research study was carried out with information from the NHANES 2011-2014. With multivariate linear regression models, the pertinence between the WWI and low cognitive function in persons older than 60 years was examined. The nonlinear link was described using threshold effect analyses and fitted smoothed curves. Interaction tests and subgroup analysis were also conducted.

RESULTS

The study had 2762 individuals in all, and subjects with higher WWI values were at greater risk for low cognitive function. In the completely adjusted model, the WWI was positively connected with low cognitive performance assessed by CERAD W-L (OR = 1.22, 95% CI 1.03-1.45, p = 0.0239), AFT (OR = 1.30, 95% CI 1.09-1.54, p = 0.0029), and DSST (OR = 1.59, 95% CI 1.30-1.94, p < 0.0001). The effect of each subgroup on the positive correlation between the WWI and low cognitive performance was not significant. The WWI and low cognitive performance as determined by CERAD W-L and AFT had a nonlinear connection (log-likelihood ratio < 0.05).

CONCLUSION

Among older adults in the United States, the risk of low cognitive performance may be positively related to the WWI.

Association between obesity, physical activity, and cognitive decline in Chinese middle and old-aged adults: a mediation analysis

BACKGROUND AND OBJECTIVES

Epidemiological evidence on obesity and cognitive decline in middle and old-aged individuals is controversial and the effect of physical activity in this chain is sparse and limited. This study aimed to characterize the association between obesity and cognitive decline and the mediating role of physical activity.

METHODS

Data from China Health and Retirement Longitudinal Study (CHARLS) were used, including 7,392 participants aged ≥ 45 years between 2011 and 2018. Cognitive function was assessed via episodic memory and mental status. The total score of cognitive function was the sum of the above two dimensions (0-31 points). The Group-based trajectory modeling (GBTM) was applied to identify the potential heterogeneity of longitudinal changes in cognitive function. Multivariable ordinal logistic regression was used to investigate associations between obesity and cognitive trajectories, taking body mass index (BMI) as the indicator of obesity. Mediation analysis was performed to examine the potential causal chain in which physical activity mediates the relationship between BMI and cognitive decline.

RESULTS

Of the 7,392 analyzed patients (mean [SD] age, 58.0 [8.5] years; 3,916 [53%] male), the median (interquartile range [IQR]) of BMI was 23.4 (21.1-26.0). Four trajectories were identified by the GBTM model, including the high stable (14.9%), the middle stable (46.0%), the middle decline (29.9%), and the low decline groups (9.2%). After controlling potential confounders, obesity was associated with the low decline groups compared with normal weight (adjusted OR 0.81; 95% CI, 0.70-0.94). Mediation analyses showed that only vigorous physical activity significantly explained 5.94% (95% CI, 0.29-11.60%) of the relationship between obesity and cognitive decline. Sensitivity analyses in different subgroups showed comparable results.

CONCLUSION

This study suggests that vigorous physical activity mediates less than 10% of the association between obesity and cognitive decline in middle and old-aged adults. Further studies are warranted to explore the potential factors related to the obesity paradox in the cognitive field.

The effect of body mass index on self-rated health in middle-aged and older adults: evidence from the China health and retirement longitudinal study

BACKGROUND AND AIMS

Health promotion for middle-aged and older people has received a lot of attention recently in the context of healthy aging. Furthermore, it is unclear how body mass index (BMI) presently affects self-rated health (SRH), a reliable and representative indicator of health.

METHODS

This study used longitudinal follow-up data from the China Health and Retirement Longitudinal Study (CHARLS). Systematic collection of information on the socio-demographic, lifestyle, and health status of the subjects. Binary logistic regression was used to investigate the relationship between BMI and SRH, and gender-specific variations were examined. Subgroup analysis was used to examine interactions, and the results of the research stability were demonstrated.

RESULTS

After adjusting for age, gender, education level, marital status, place of residence, number of chronic diseases, alcohol consumption, smoking, depressive symptoms, and SRH at baseline, it was found that obesity grade 1 and obesity grade 2 were good contributors to SRH compared to normal weight individuals, and this association was different in males and females. According to the results of the subgroup analyses, those under 65 years old, with junior high school or less education, with a spouse, residing in a city, having one chronic disease, and not smoking or drinking, respectively, all had stable positive associations between obesity and SRH.

CONCLUSIONS

Our findings suggest that obesity may be associated with good SRH. Teams of healthcare professionals should revisit the potential impact of obesity among middle-aged and older adults and focus on developing prevention strategies for morbid obesity.

Prenatal exposure to phthalates and child growth trajectories in the first 24 months of life

BACKGROUND

Phthalates are a class of environmental chemicals with endocrine-disrupting properties. Prenatal phthalate exposure has been associated with adverse developmental outcomes in childhood. However, data assessing the effects of prenatal phthalate exposure on postnatal infant growth trajectories are sparse.

OBJECTIVES

To evaluate the associations of prenatal phthalate exposure with child growth trajectories from birth to 24 months old.

METHODS

Within a Chinese birth cohort study, 1051 mother-offspring pairs were included. Seven phthalate metabolites were quantified in maternal urine collected between weeks 33 and 39 of gestation. The trajectories for weight-for-age z-score (WAZ), length-for-age z-score (LAZ), weight-for-length z-score (WLZ) and head-circumference-for-age z-score (HCZ) were determined by group-based trajectory modeling (GBTM). Multinomial logistic regression and the weighted quantile sum approach (WQS) were used to investigate the association between individual and phthalate mixture exposure and the growth trajectories of four anthropometric metrics.

RESULTS

Five trajectory groups were identified for each anthropometric measure using GBTM. Higher prenatal exposure to several phthalate metabolites (MEHP, MEHHP, MEOHP, MECCP, summed DEHP metabolites, as well as MBP) was associated with child growth trajectories, especially for WAZ and LAZ in the first 24 months of life. The associations were further confirmed by a mixture analysis of phthalate metabolites and a sex-specific effect was observed in the WAZ and LAZ trajectories.

CONCLUSION

Prenatal phthalate exposure had heterogeneous associations with postnatal growth trajectories. More studies are warranted to confirm and elucidate the meaning of our findings.

Association between Sarcopenia and Cognitive Trajectories among Middle-Aged and Older Adults in China: A Nationally Representative Cohort Study

OBJECTIVES

The relationship between sarcopenia and cognitive function has been extensively studied, but is usually explored at a single time point. We used repeatedly measured cognitive data to examine the relationship between sarcopenia and cognitive trajectories over time among middle-aged and older Chinese adults.

DESIGN

A nationally representative cohort study.

SETTING AND PARTICIPANTS

Data were from three waves (2011, 2013 and 2015) of the China Health and Retirement Longitudinal Study (CHARLS). A total of 8963 participants with complete baseline data (wave 1) and at least two cognitive function tests (waves 1-3) were enrolled in this study.

MEASUREMENTS

Sarcopenia was diagnosed at baseline (wave 1). The wave 1-3 data were used to analyze cognitive trajectories over time by constructing a latent class trajectory model (LCTM). Logistic regression model was used to analyze the association between sarcopenia and cognitive trajectories.

RESULTS

Among 8693 participants, we identified two trajectories of cognitive function development, including a persistent low trajectory (n= 4856, 55.86%) and a persistent high trajectory (n= 3837, 44.14%). Sarcopenia was associated with persistently low cognitive trajectory of global cognitive (OR: 1.248, 95%CI: 1.046-1.490) after adjustment for other covariates. This association was still observed when stratified by age, gender, educational level, marital status, social activity, smoking status and drinking status. Mediation analysis showed that body mass index (BMI) mediated efficacy accounting for 42.32% of the relationship.

CONCLUSIONS

Our study showed two trajectory groups of global cognitive function. Sarcopenia was associated with a persistent low trajectory over time and BMI mediated the relationship between sarcopenia and cognitive trajectories among middle-aged and older Chinese adults.

A hybrid machine learning model of depression estimation in home-based older adults: a 7-year follow-up study

BACKGROUND

Our aim was to explore whether a two-step hybrid machine learning model has the potential to discover the onset of depression in home-based older adults.

METHODS

Depression data (collected in the year 2011, 2013, 2015 and 2018) of home-based older Chinese (n = 2,548) recruited in the China Health and Retirement Longitudinal Study were included in the current analysis. The long short-term memory network (LSTM) was applied to identify the risk factors of participants in 2015 utilizing the first 2 waves of data. Based on the identified predictors, three ML classification algorithms (i.e., gradient boosting decision tree, support vector machine and random forest) were evaluated with a 10-fold cross-validation procedure and a metric of the area under the receiver operating characteristic curve (AUROC) to estimate the depressive outcome.

RESULTS

Time-varying predictors of the depression were successfully identified by LSTM (mean squared error =0.8). The mean AUCs of the three predictive models had a range from 0.703 to 0.749. Among the prediction variables, self-reported health status, cognition, sleep time, self-reported memory and ADL (activities of daily living) disorder were the top five important variables.

CONCLUSIONS

A two-step hybrid model based on "LSTM+ML" framework can be robust in predicting depression over a 5-year period with easily accessible sociodemographic and health information.