Chinese Longitudinal Healthy Longevity Study Research Articles

Development and validation of machine learning models to predict frailty risk for elderly.

Early identification and intervention of the frailty of the elderly will help lighten the burden of social medical care and improve the quality of life of the elderly. Therefore, we used machine learning (ML) algorithm to develop models to predict frailty risk in the elderly. A prospective cohort study. We collected data on 6997 elderly people from Chinese Longitudinal Healthy Longevity Study wave 6-7 surveys (2011-2012, 2014). After the baseline survey in 1998 (wave 1), the project conducted follow-up surveys (wave 2-8) in 2000-2018. The osteoporotic fractures index was used to assess frailty. Four ML algorithms (random forest [RF], support vector machine, XGBoost and logistic regression [LR]) were used to develop models to identify the risk factors of frailty and predict the risk of frailty. Different ML models were used for the prediction of frailty risk in the elderly and frailty risk was trained on a cohort of 4385 elderly people with frailty (split into a training cohort [75%] and internal validation cohort [25%]). The best-performing model for each study outcome was tested in an external validation cohort of 6997 elderly people with frailty pooled from the surveys (wave 6-7). Model performance was assessed by receiver operating curve and F2-score. Among the four ML models, the F2-score values were similar (0.91 vs. 0.91 vs. 0.88 vs. 0.90), and the area under the curve (AUC) values of RF model was the highest (0.75), followed by LR model (0.74). In the final two models, the AUC values of RF and LR model were similar (0.77 vs. 0.76) and their accuracy was identical (87.4% vs. 87.4%). Our study developed a preliminary prediction model based on two different ML approaches to help predict frailty risk in the elderly. The presented models from this study can be used to inform healthcare providers to predict the frailty probability among older adults and maybe help guide the development of effective frailty risk management interventions. Detecting frailty at an early stage and implementing timely targeted interventions may help to improve the allocation of health care resources and to reduce frailty-related burden. Identifying risk factors for frailty could be beneficial to provide tailored and personalized care intervention for older adults to more accurately prevent or improve their frail conditions so as to improve their quality of life. The study has adhered to STROBE guidelines. No patient or public contribution.

Interpretable prediction models for disability in older adults with hypertension: the Chinese Longitudinal Healthy Longevity and Happy Family Study.

Older adults with hypertension have a high risk of disability, while an accurate risk prediction model is still lacking. This study aimed to construct interpretable disability prediction models for older Chinese with hypertension based on multiple time intervals. Data were collected from the Chinese Longitudinal Healthy Longevity and Happy Family Study for 2008-2018. A total of 1602, 1108, and 537 older adults were included for the periods of 2008-2012, 2008-2014, and 2008-2018, respectively. Disability was measured by basic activities of daily living. Least absolute shrinkage and selection operator (LASSO) was applied for feature selection. Five machine learning algorithms combined with LASSO set and full-variable set were used to predict 4-, 6-, and 10-year disability risk, respectively. Area under the receiver operating characteristic curve was used as the main metric for selection of the optimal model. SHapley Additive exPlanations (SHAP) was used to explore important predictors of the optimal model. Random forest in full-variable set and XGBoost in LASSO set were the optimal models for 4-year prediction. Support vector machine was the optimal model for 6-year prediction on both sets. For 10-year prediction, deep neural network in full variable set and logistic regression in LASSO set were optimal models. Age ranked the most important predictor. Marital status, body mass index, score of Mini-Mental State Examination, and psychological well-being score were also important predictors. Machine learning shows promise in screening out older adults at high risk of disability. Disability prevention strategies should specifically focus on older patients with unfortunate marriage, high BMI, and poor cognitive and psychological conditions.

The Mediating Role of Psychological Balance on the Effects of Dietary Behavior on Cognitive Impairment in Chinese Elderly.

Cognitive impairment, a significant problem in older adults, may be associated with diet. This study aims to examine the association between the dietary diversity score (DDS), dietary pattern (DP), and cognitive impairment in elderly Chinese. This research further explored the role of psychological balance (PB) as a mediator in the relationship between diet and cognitive impairment. A total of 14,318 older adults from the Chinese Longitudinal Healthy Longevity Study (CLHLS) in 2018 were included. Latent class analysis (LCA) was used to identify patterns in seven food varieties. Binary logistic regression models were used to determine factors associated with the DDS, DP, and cognitive impairment. The multiple mediation effect model was evaluated using model 6 in the PROCESS version 3.5 program. Among the participants, 4294 (29.99%) developed cognitive impairment. Compared to people in food variety group two or lower, people with a high dietary diversity score (DDS) had lower odds of cognitive impairment. Compared to DP1, DP2 (OR = 1.24, 95%CI = 1.09 to 1.40) was associated with a higher risk of cognitive impairment, and DP4 (OR = 0.79, 95%CI = 0.69 to 0.89) was associated with a lower risk of cognitive impairment. PB mediated the relationship between DDS, DP, and cognitive impairment, with a mediating effect of 27.24% and 41.00%. A DP that is rich in fruits, vegetables, red meat, fish, eggs, beans, nuts, and milk was related to a lower risk of cognitive impairment. PB has an indirect impact on cognitive impairment. Our findings underscore the importance of promoting a diverse diet, which may contribute to a lower risk of cognitive impairment in older adults. The PB of the elderly should also be taken into consideration.

Long-term exposure to fine particulate matter constituents and cognitive impairment among older adults: An 18-year Chinese nationwide cohort study

BackgroundAlthough growing evidence has shown independent links of long-term exposure to fine particulate matter (PM2.5) with cognitive impairment, the effects of its constituents remain unclear. This study aims to explore the associations of long-term exposure to ambient PM2.5 constituents’ mixture with cognitive impairment in Chinese older adults, and to further identify the main contributor. Methods15,274 adults ≥ 65 years old were recruited by the Chinese Longitudinal Healthy Longevity Study (CLHLS) and followed up through 7 waves during 2000–2018. Concentrations of ambient PM2.5 and its constituents (i.e., black carbon [BC], organic matter [OM], ammonium [NH4+], sulfate [SO42-], and nitrate [NO3-]) were estimated by satellite retrievals and machine learning models. Quantile-based g-computation model was employed to assess the joint effects of a mixture of 5 PM2.5 constituents and their relative contributions to cognitive impairment. Analyses stratified by age group, sex, residence (urban vs. rural), and region (north vs. south) were performed to identify vulnerable populations. ResultsDuring the average 3.03 follow-up visits (89,296.9 person-years), 4294 (28.1%) participants had developed cognitive impairment. The adjusted hazard ratio [HR] (95% confidence interval [CI]) for cognitive impairment for every quartile increase in mixture exposure to 5 PM2.5 constituents was 1.08 (1.05–1.11). BC held the largest index weight (0.69) in the positive direction in the qg-computation model, followed by OM (0.31). Subgroup analyses suggested stronger associations in younger old adults and rural residents. ConclusionLong-term exposure to ambient PM2.5, particularly its constituents BC and OM, is associated with an elevated risk of cognitive impairment onset among Chinese older adults.

Daily Activities of Elder Adults Using Optimized Deep Learning Model in China

The health and happiness of seniors can be influenced by the design of public spaces and buildings. Planning for age-friendly communities requires taking into account the wide variety of daily activities and public facility consumption among older persons. Yet, conventional approaches fall short when it comes to providing comprehensive, objective measures of the actions of the elderly. This research proposes an attention-based recognition approach by decomposing neural network outputs into class-dependent features using dictionary learning to improve discrimination performance. This research aimed to develop an empirical typology of activity of daily living (ADL) and its connection to health in China's ageing population. A deep learning-based model was rummaged to categorise the contributors in the Chinese Longitudinal Healthy Longevity Study (CLHLS) into subgroups based on their abilities with ADL. To be more precise, the study trains a category-based recognition network (CRN) by superimposing a basic but effective specific recognition encoding (SRE) unit on top of convolutional layers. In order to encode attention maps for each class, the SRE module uses the feature maps produced by the Convolutional Neural Networks (CNN) to learn a class-specific vocabulary. Class-wise adaptive feature refining is achieved by multiplying these attention maps by the input features. In this case, a gazelle optimization algorithm (GOA) handles the hyper-parameter tuning process of the proposed model. This case study is the first of its kind in a dense metropolitan region, and it uses objective measurements to evaluate the activity spaces of the elderly. This research provides empirical data for the promotion of healthy ageing in urban areas.

Genetic associations with longevity are on average stronger in females than in males

It is long observed that females tend to live longer than males in nearly every country. However, the underlying mechanism remains elusive. In this study, we discovered that genetic associations with longevity are on average stronger in females than in males through bio-demographic analyses of genome-wide association studies (GWAS) dataset of 2178 centenarians and 2299 middle-age controls of Chinese Longitudinal Healthy Longevity Study (CLHLS). This discovery is replicated across North and South regions of China, and is further confirmed by North-South discovery/replication analyses of different and independent datasets of Chinese healthy aging candidate genes with CLHLS participants who are not in CLHLS GWAS, including 2972 centenarians and 1992 middle-age controls. Our polygenic risk score analyses of eight exclusive groups of sex-specific genes, analyses of sex-specific and not-sex-specific individual genes, and Genome-wide Complex Trait Analysis using all SNPs all reconfirm that genetic associations with longevity are on average stronger in females than in males. Our discovery/replication analyses are based on genetic datasets of in total 5150 centenarians and compatible middle-age controls, which comprises the worldwide largest sample of centenarians. The present study's findings may partially explain the well-known male-female health-survival paradox and suggest that genetic variants may be associated with different reactions between males and females to the same vaccine, drug treatment and/or nutritional intervention. Thus, our findings provide evidence to steer away from traditional view that “one-size-fits-all” for clinical interventions, and to consider sex differences for improving healthcare efficiency. We suggest future investigations focusing on effects of interactions between sex-specific genetic variants and environment on longevity as well as biological function.

Modifiable Factors Associated with Mortality among the Oldest Old with Cognitive Impairment: A Longitudinal Study in China

AbstractBackgroundThe oldest old (aged above 80 years) have the highest death rate, and CI (Cognitive Impairment) significantly increases risk of mortality. Studies investigating mortality associated with dementia mostly identified non‐modifiable factors, providing limited practical implications. This study aims to investigating factors associated with mortality among the oldest old with dementia, with particular focus on modifiable factors.MethodOur study included 6,997 older adults aged 80 years above who were diagnosed with CI. Data were from the Chinese Longitudinal Healthy Longevity Study (CLHLS). CI was measured by the Mini‐mental State Examination (MMSE). Cox proportional hazard regression combined with Least Absolute Shrinkage and Selection Operator (LASSO) was applied to select factors and evaluate their association with mortality of the oldest adults with CI.ResultOf the 6,997 participants, 6,451 died with median survival time of 2.2 years. 21 out of the 56 variables were selected from the LASSO‐penalized Cox regression model. Consistent with previous studies, the hazard of mortality increases by 19% with every incremental 10 years of age (95% CI: 12‐27%). The hazard of morality among women was 23% lower than that of men (95% CI: 18%‐29%). History of diabetes also increases the hazard of mortality. In terms of modifiable factors, daily outdoor activities, daily watching TV or listening to the radio, being married, good chewing ability are associated with lower mortality, while smoking and high systolic blood pressure increases the risk of mortality in a dose‐response manner.ConclusionSeveral modifiable factors are associated with mortality among the oldest adults with CI. Analysis suggests that healthy diet, cognitive stimulating leisure activities, marriage, and good chewing ability may contribute to lowering the risk of mortality among this population.

Predicting mild cognitive impairment among Chinese older adults: a longitudinal study based on long short-term memory networks and machine learning.

Mild cognitive impairment (MCI) is a transitory yet reversible stage of dementia. Systematic, scientific and population-wide early screening system for MCI is lacking. This study aimed to construct prediction models using longitudinal data to identify potential MCI patients and explore its critical features among Chinese older adults. A total of 2,128 participants were selected from wave 5-8 of Chinese Longitudinal Healthy Longevity Study. Cognitive function was measured using the Chinese version of Mini-Mental State Examination. Long- short-term memory (LSTM) and three machine learning techniques, including 8 sociodemographic features and 12 health behavior and health status features, were used to predict individual risk of MCI in the next year. Performances of prediction models were evaluated through receiver operating curve and decision curve analysis. The importance of predictors in prediction models were explored using Shapley Additive explanation (SHAP) model. The area under the curve values of three models were around 0.90 and decision curve analysis indicated that the net benefit of XGboost and Random Forest were approximate when threshold is lower than 0.8. SHAP models showed that age, education, respiratory disease, gastrointestinal ulcer and self-rated health are the five most important predictors of MCI. This screening method of MCI, combining LSTM and machine learning, successfully predicted the risk of MCI using longitudinal datasets, and enables health care providers to implement early intervention to delay the process from MCI to dementia, reducing the incidence and treatment cost of dementia ultimately.

Inequality of opportunity in outpatient expenditure among the elderly with multimorbidity: evidence from China

BackgroundInequality of opportunity (IOp) stemming from social circumstances exists in outpatient service utilization for the multimorbid elderly in China. However, little is known regarding the magnitude of the IOp and its composition. Therefore, this study aims to measure the IOp in outpatient expenditure and provide potential pathways for policy reform by assessing the contribution of each circumstance.MethodsThis study included 3527 elderly aged ≥ 65 years with multimorbidity from the Chinese Longitudinal Healthy Longevity Study conducted in 2017–2018. An ordinary least squares regression model was used to analyze the circumstance-influencing factors of outpatient expenditure. The parametric approach was performed to quantify the IOp in outpatient expenditure and the Shapley value decomposition method was employed to determine the contribution of each circumstance. By extracting heterogeneity in the residual of the circumstance-dependent equation of outpatient expenditure across circumstance groups divided based on cluster analysis, we captured the effect of unobserved circumstances.ResultsExcept for pension and distance to health facilities, all the associations between circumstance and outpatient expenditure were statistically significant. The inequality caused by circumstances accounted for 25.18% of the total inequality. The decomposition results revealed that the reimbursement rate contributed 82.92% of the IOp, followed by education duration (4.55%), household registration (3.21%), household income (3.18%), pension (1.49%), medical insurance (1.26%), physical labor (0.99%), unobserved circumstances (0.86%), distance to health facilities (0.83%) and region (0.71%).ConclusionsThe priority of policy enhancement is to effectively improve the outpatient reimbursement benefit for treating chronic diseases. Additional crucial actions include enhancing the health literacy of the multimorbid elderly to promote the shift from medical needs to demands and accelerating the construction of rural capacity for providing high-quality healthcare to the elderly with multimorbidity.

The Association Between Habitual Tea Consumption and Frailty Transition in Community-Dwelling Older Adults: A Prospective Cohort Study

ObjectivesTo investigate the association between habitual tea consumption and transitions between frailty states among older adults in China. DesignA prospective cohort study based on the Chinese Longitudinal Healthy Longevity Study. Setting and ParticipantsA total of 23,720 older adults aged ≥65 years with complete data regarding frailty status and tea consumption were recruited. MethodsThe frequency and consistency of tea consumption were introduced to evaluate levels of tea consumption. The frailty index was used to define frailty status (frail and nonfrail). Frailty transition was classified into remaining nonfrail, improvement, worsening, and remaining frail groups. Logistic regression models were applied. ResultsThe overall frailty prevalence at baseline was 19.1%, being lower among consistent daily tea drinkers (12.5%) and higher among non-tea drinkers (21.9%). Logistic regression analyses showed that the risk of frailty was significantly reduced among consistent daily tea drinkers after adjusting for all confounders [odds ratio (OR), 0.81; 95% CI, 0.67–0.98]. During the 3-year follow-up, improvement in frailty status was more common among consistent daily tea drinkers (50.9%) than non-tea drinkers (40.9%), and this trend was opposite in participants with worsened frailty status (consistent daily tea drinkers: 12.2%) vs non-tea drinkers: 19.2%). Further analysis showed that consistent daily tea drinkers were significantly associated with improvement in frailty status (OR, 3.24; 95% CI, 1.02–10.31) and remaining in a nonfrail state (OR, 1.35; 95% CI, 1.00–1.83). In addition, daily tea consumption was observed to be positively associated with remaining in a nonfrail state and inversely associated with worsened frailty status in men, but not in women. Conclusions and ImplicationsOlder people consuming tea daily tend to have an improved frailty status in the future. Men with daily tea consumption were less likely to have a worsened frailty status. Advocating for the traditional lifestyle of drinking tea could be a promising way to advance healthy aging for older adults.

Ambient PM2.5, ozone and mortality in Chinese older adults: A nationwide cohort analysis (2005–2018)

BackgroundCohort evidence linking long-term survival with exposure to multiple air pollutants (e.g., fine particulate matter [PM2.5] and ozone) was extensively sparse in low- and middle-income countries, especially among older adults. This study aimed to investigate potential associations of long-term exposures to PM2.5 and ozone with all-cause mortality in Chinese older adults. MethodsA dynamic nationwide prospective cohort comprising 20,352 adults aged ≥65 years were enrolled from the Chinese Longitudinal Healthy Longevity Study and followed up through 2005–2018. Participants’ annual exposures to warm-season ozone and year-round PM2.5 were assigned using satellite-derived spatiotemporal estimates. A directed acyclic graph (DAG) was developed to identify confounding variables. Associations of annual mean exposures to PM2.5 and ozone with mortality were evaluated using single- and two-pollutant Cox proportional hazards models, adjusting for time-dependent individual risk factors and ambient temperature. ResultsDuring 100 thousand person-years of follow-up (median: 3.6 years), a total of 14,313 death events occurred. The participants were averagely aged 87.1 years at baseline and exposed to a wide range of annual average concentrations of warm-season maximum 8-hour ozone (mean, 54.4 ppb; range, 23.3–81.6 ppb) and year-round PM2.5 (mean, 65.5 μg/m3; range, 10.1–162.9 μg/m3). Approximately linear concentration-response relationship was identified for ozone, whereas significant increases in PM2.5-associated mortality risks were observed only when concentrations were above 60 μg/m3. Rises of 10 ppb in ozone and 10 µg/m3 in PM2.5 above 60 µg/m3 were associated with increases in all-cause mortality of 13.2% (95% confidence interval [CI]: 10.2–16.2%) and 6.2% (95% CI: 4.6–7.7%) in DAG-based single-pollutant model, and of 9.7% (95% CI: 6.6–13.0%) and 5.3% (95% CI: 3.7–6.9%) in DAG-based two-pollutant model, respectively. We detected significant effect modification by temperature in associations of mortality with ozone (P <0.001 for interaction), suggesting greater ozone-related risks among participants in warmer locations. ConclusionsThis study provided longitudinal evidence that long-term exposure to ambient PM2.5 and ozone significantly and independently contributed to elevated risks of all-cause mortality among older adults in China.

Effect of frailty status on mortality risk among Chinese community-dwelling older adults: a prospective cohort study

BackgroundFrailty is associated with mortality among older adults. We aimed to determine the appropriate time and frailty index (FI) threshold for frailty intervention in Chinese community-dwelling older adults.MethodsIn this prospective cohort study, we used data from the 2011 wave of the Chinese Longitudinal Healthy Longevity Study. Follow-up was performed for seven years from baseline. Using the FI to evaluate frailty and define frailty status, we explored the best time point and FI score for frailty intervention, by comparing the relationships of FI and frailty status with mortality.ResultsFrom 2011 to 2018, 8642 participants were included and followed-up. A total of 4458 participants died during the study period. After adjusting for variables such as age, sex, marital status, education level, and living conditions, the hazard ratio (HR) of mortality risk based on the FI at baseline was 37.484 (95% confidence interval [CI]: 30.217–46.498; P < 0.001); female sex, living in the city, being married, and living with spouse were found to be protective factors, whereas ageing was a risk factor for frailty. The mortality risk was higher in pre-frail than in frail participants (HR: 3.588, 95% CI: 3.212–4.009, P < 0.001). Piecewise linear regression analysis revealed an FI score threshold of 0.5. When the FI score was > 0.5, the HR of mortality based on the FI was 15.758 (95% CI: 3.656–67.924; P < 0.001); when the FI score was ≤ 0.5, the HR of mortality based on the FI was 48.944 (95% CI: 36.162–66.244; P < 0.001).ConclusionUsing FI as a continuous variable to predict death is more accurate than frailty status. The advancement of early interventions for mortality risk reduction is more beneficial in pre-frail than in frail patients, and an FI score of 0.5 was found to be the threshold for mortality prediction using the FI.

Life expectancy among older adults with or without frailty in China: multistate modelling of a national longitudinal cohort study

BackgroundLittle is known about life expectancy (LE) with or without frailty. We aimed to estimate the total LE and duration of the state of frailty in China.MethodsThis study included older adults aged 65 years and older from the Chinese Longitudinal Healthy Longevity Study (CLHLS). Frailty status was classified into robust, pre-frailty and frailty based on a cumulative deficit model. Total and specific frailty state LEs at 65 years of age were estimated and stratified by demographic characteristics, behaviours, and psychosocial factors using continuous-time multistate modelling.ResultsThe total LE of older adults aged 65 years in China was 14.74 years on average (95% CI: 14.52–14.94), of which 4.18 years (95% CI: 4.05–4.30) were robust, 7.46 years (95% CI: 7.31–7.61) pre-frail and 3.10 years (95% CI: 3.01–3.20) frail. Older adults with higher robust LE included men (4.71 years, 95% CI: 4.56–4.88), married older adults (4.41 years, 95% CI: 4.27–4.56), those engaging in physical activity (4.41 years, 95% CI: 4.23–4.59), those consuming fruits daily (4.48 years, 95% CI: 4.22–4.77) and those with high social participation (4.39 years, 95% CI: 4.26–4.53). Increased educational attainment were gradually associated with increased robust LE.ConclusionsFrailty may lead to a reduced total LE and robust LE of older adults in China. In addition to finding inequalities in total and robust LEs by socioeconomic status, our findings also highlight that healthy behaviours and social participation may ease frailty-related reductions in total and robust LE. Our findings imply that national life-course strategies aimed at frailty screening and psychosocial and behavioural interventions could be important for health aging in China.

Patterns of home care and community support preferences among older adults with disabilities in China: a latent class analysis

BackgroundAgeing in place is the preferred choice for most older adults worldwide. The role of the family as a core care resource has diminished as a result of changes in family structure, thus extending the responsibility for caring for older adults from within the family to outside it and requiring considerably more support from society. However, there is a shortage of formal and qualified caregivers in many countries, and China has limited social care resources. Therefore, it is important to identify home care patterns and family preferences to provide effective social support and reduce government costs.MethodsData were obtained from the Chinese Longitudinal Healthy Longevity Study 2018. Latent class analysis models were estimated using Mplus 8.3. Multinomial logistic regression analysis was adopted to explore the influencing factors with the R3STEP method. Lanza’s method and the chi-square goodness-of-fit test were used to explore community support preferences among different categories of families of older adults with disabilities.ResultsThree latent classes were identified based on older adults with disabilities’ characteristics (degree of disability, demand satisfaction), caregivers’ characteristics (length of providing care, care performance) and living status: Class 1- mild disability and strong care (46.85%); Class 2- severe disability and strong care (43.92%); and Class 3- severe disability and incompetent care (9.24%). Physical performance, geographic region and economic conditions jointly influenced home care patterns (P < 0.05). Home visits from health professionals and health care education were the top two forms of community support that were most preferred by the older adults with disabilities’ families (residual > 0). Families in the Class 3 subgroup preferred personal care support more than those in the other two subgroups (P < 0.05).ConclusionHome care is heterogeneous across families. Older adults’ degrees of disability and care needs may be varied and complex. We classified different families into homogeneous subgroups to reveal differences in home care patterns. The findings can be used by decision-makers in their attempts to design long-term care arrangements for home care and to adjust the distribution of resources for the needs of older adults with disabilities.

Semiparametric copula method for semi-competing risks data subject to interval censoring and left truncation: Application to disability in elderly.

We aim to evaluate the marginal effects of covariates on time-to-disability in the elderly under the semi-competing risks framework, as death dependently censors disability, not vice versa. It becomes particularly challenging when time-to-disability is subject to interval censoring due to intermittent assessments. A left truncation issue arises when the age time scale is applied. We develop a flexible two-parameter copula-based semiparametric transformation model for semi-competing risks data subject to interval censoring and left truncation. The two-parameter copula quantifies both upper and lower tail dependence between two margins. The semiparametric transformation models incorporate proportional hazards and proportional odds models in both margins. We propose a two-step sieve maximum likelihood estimation procedure and study the sieve estimators' asymptotic properties. Simulations show that the proposed method corrects biases in the marginal method. We demonstrate the proposed method in a large-scale Chinese Longitudinal Healthy Longevity Study and provide new insights into preventing disability in the elderly. The proposed method could be applied to the general semi-competing risks data with intermittently assessed disease status.

Dietary diversity and frailty among older Chinese people: evidence from the Chinese Longitudinal Healthy Longevity Study

BackgroundDietary diversity is widely advocated as a means to promote health, but little is known regarding whether the beneficial effects still apply in older adults. ObjectiveTo examine the association between the dietary diversity score (DDS) and frailty among older Chinese adults. MethodsA total of 13,721 adults aged ≥65 y without frailty at baseline were enrolled. The DDS at baseline was constructed based on 9 items of a food frequency questionnaire. We used 39 self-reported health items to construct a frailty index (FI), with FI ≥ 0.25 indicating frailty. Cox models with restricted cubic splines were used to evaluate the dose–response relationships of DDS (continuous) with frailty. In addition, Cox proportional hazard models were used to examine the association between DDS (categorized as scores ≤4, 5–6, 7, and ≥8) and frailty. ResultsDuring the mean follow-up of 5.94 y, 5250 participants met the criteria for frailty. Each 1-unit increase in DDS corresponded to a 5% lower risk of frailty (hazard ratio [HR]; 0.95; 95% CI: 0.94, 0.97]. Compared with participants with DDS ≤4 points, those with a DDS of 5–6, 7, and ≥8 points exhibited a lower frailty risk, with HRs of 0.79 (95% CI: 0.71, 0.87), 0.75 (95% CI: 0.68, 0.83), and 0.74 (95% CI: 0.67, 0.81), respectively (P-trend < 0.001). Protein-rich food items, such as meat; eggs; and beans, were associated with protective effects against frailty. In addition, a significant association was observed between higher consumption of 2 high-frequency foods, tea and fruits, and lower risk of frailty. ConclusionsA higher DDS was associated with a lower risk of frailty among older Chinese adults. This study highlights the importance of a diverse diet as a potential modifiable behavioral factor for preventing frailty in older Chinese adults.

Latent trajectories of frailty and risk prediction models among geriatric community dwellers: an interpretable machine learning perspective

BackgroundThis study aimed to identify long-term frailty trajectories among older adults (≥65) and construct interpretable prediction models to assess the risk of developing abnormal frailty trajectory among older adults and examine significant factors related to the progression of frailty.MethodsThis study retrospectively collected data from the Chinese Longitudinal Healthy Longevity and Happy Family Study between 2002 and 2018 (N = 4083). Frailty was defined by the frailty index. The whole study consisted of two phases of tasks. First, group-based trajectory modeling was used to identify frailty trajectories. Second, easy-to-access epidemiological data was utilized to construct machine learning algorithms including naïve bayes, logistic regression, decision tree, support vector machine, random forest, artificial neural network, and extreme gradient boosting to predict the risk of long-term frailty trajectories. Further, Shapley additive explanations was employed to identify feature importance and open-up the black box model of machine learning to further strengthen decision makers’ trust in the model.ResultsTwo distinct frailty trajectories (stable-growth: 82.54%, rapid-growth: 17.46%) were identified. Compared with other algorithms, random forest performed relatively better in distinguishing the stable-growth and rapid-growth groups. Physical function including activities of daily living and instrumental activities of daily living, marital status, weight, and cognitive function were top five predictors.ConclusionsInterpretable machine learning can achieve the primary goal of risk stratification and make it more transparent in individual prediction beneficial to primary screening and tailored prevention.

Nonparametric inference for reversed mean models with panel count data

Panel count data typically refer to data arising from studies with recurrent events, in which subjects are observed only at discrete time points rather than under continuous observations. We investigate a general situation where a recurrent event process is eventually truncated by an informative terminal event and we are particularly interested in behaviors of the recurrent event process near the terminal event. We propose a reversed mean model for estimating the mean function of the recurrent event process. We develop a two-stage sieve likelihood-based method to estimate the mean function, which overcomes the computational difficulties arising from a nuisance functional parameter involved in the likelihood. The consistency and the convergence rate of the two-stage estimator are established. Allowing for the convergence rate slower than the standard rate, we develop the general weak convergence theory of M-estimators with a nuisance functional parameter, and then apply it to the proposed estimator for deriving the asymptotic normality. Furthermore, a class of two-sample tests is developed. The proposed methods are evaluated with extensive simulation studies and illustrated with panel count data from the Chinese Longitudinal Healthy Longevity Study.

Adherence to High Dietary Diversity and Incident Cognitive Impairment for the Oldest-Old: A Community-Based, Nationwide Cohort Study

Dietary diversity change is associated with cognitive function, however, whether the effect still exists among the oldest-old (80+) is unclear. Our aim was to examine the effect of dietary diversity changes on cognitive impairment for the oldest-old in a large prospective cohort. Within the Chinese Longitudinal Healthy Longevity Study, 6237 adults older than 80 years were included. The dietary diversity score (DDS) was assessed by a simplified food frequency questionnaire (FFQ). Cognitive impairment was defined as a Mini-Mental State Examination (MMSE) score lower than 18 points. Cognitive decline was defined as a reduction of total MMSE score ≥3 points, and cognitive decline of different subdomains was defined as a reduction of ≥15% in the corresponding cognitive domain. The multivariate-adjusted Cox proportional hazard model evaluated the effects of DDS change on cognitive decline. The linear mixed-effect model was used to test subsequent changes in MMSE over the years. During 32,813 person-years of follow-up, 1829 participants developed cognitive impairment. Relative to the high-high DDS change pattern, participants in the low-low and high-low patterns were associated with an increased risk of cognitive impairment with a hazard ratio (95% confidential interval, CI) of 1.43 (1.25, 1.63) and 1.44 (1.24, 1.67), and a faster decline in the MMSE score over the follow-up year. Participants with the low-high pattern had a similar incidence of cognitive impairment with HRs (95% CI) of 1.03 (0.88, 1.20). Compared with the stable DDS status group (-1-1), the risk of cognitive impairment was higher for those with large declines in DDS (≤-5) and the HR was 1.70 (95% CI: 1.44, 2.01). Even for people older than 80, dietary diversity change is a simple method to identify those who had a high risk of cognitive decline. Keeping high dietary diversity is beneficial for cognitive function and its subdomain even in the final phase of life, especially for females and the illiterate oldest-old.

Longitudinal Data to Enhance Dynamic Stroke Risk Prediction.

Stroke risk prediction based on electronic health records is currently an important research topic. Previous research activities have generally used single-time physiological data to build static models and have focused on algorithms to improve prediction accuracy. Few studies have considered historical measurements from a data perspective to construct dynamic models. Since it is a chronic disease, the risk of having a stroke increases and the corresponding risk factors become abnormal when healthy people are diagnosed with a stroke. Therefore, in this paper, we applied longitudinal data, with the backward joint model, to the Chinese Longitudinal Healthy Longevity and Happy Family Study's dataset to monitor changes in individuals' health status precisely on time and to increase the prediction accuracy of the model. The three-year prediction accuracy of our model, considering three measurements of longitudinal parameters, is 0.926. This is higher than the traditional Cox proportional hazard model, which has a 0.833 prediction accuracy. The results obtained in this study verified that longitudinal data improves stroke risk prediction accuracy and is promising for dynamic stroke risk prediction and prevention. Our model also verified that the frequency of fruit consumption, erythrocyte hematocrit, and glucose are potential stroke-related factors.