Prediction of disability-free survival in healthy older people

Outcome risk model development for heterogeneity of treatment effect analyses: a comparison of non-parametric machine learning methods and semi-parametric statistical methods

BACKGROUND

In randomized clinical trials, treatment effects may vary, and this possibility is referred to as heterogeneity of treatment effect (HTE). One way to quantify HTE is to partition participants into subgroups based on individual's risk of experiencing an outcome, then measuring treatment effect by subgroup. Given the limited availability of externally validated outcome risk prediction models, internal models (created using the same dataset in which heterogeneity of treatment analyses also will be performed) are commonly developed for subgroup identification. We aim to compare different methods for generating internally developed outcome risk prediction models for subject partitioning in HTE analysis.

METHODS

Three approaches were selected for generating subgroups for the 2,441 participants from the United States enrolled in the ASPirin in Reducing Events in the Elderly (ASPREE) randomized controlled trial. An extant proportional hazards-based outcomes predictive risk model developed on the overall ASPREE cohort of 19,114 participants was identified and was used to partition United States' participants by risk of experiencing a composite outcome of death, dementia, or persistent physical disability. Next, two supervised non-parametric machine learning outcome classifiers, decision trees and random forests, were used to develop multivariable risk prediction models and partition participants into subgroups with varied risks of experiencing the composite outcome. Then, we assessed how the partitioning from the proportional hazard model compared to those generated by the machine learning models in an HTE analysis of the 5-year absolute risk reduction (ARR) and hazard ratio for aspirin vs. placebo in each subgroup. Cochran's Q test was used to detect if ARR varied significantly by subgroup.

RESULTS

The proportional hazard model was used to generate 5 subgroups using the quintiles of the estimated risk scores; the decision tree model was used to generate 6 subgroups (6 automatically determined tree leaves); and the random forest model was used to generate 5 subgroups using the quintiles of the prediction probability as risk scores. Using the semi-parametric proportional hazards model, the ARR at 5 years was 15.1% (95% CI 4.0-26.3%) for participants with the highest 20% of predicted risk. Using the random forest model, the ARR at 5 years was 13.7% (95% CI 3.1-24.4%) for participants with the highest 20% of predicted risk. The highest outcome risk group in the decision tree model also exhibited a risk reduction, but the confidence interval was wider (5-year ARR = 17.0%, 95% CI= -5.4-39.4%). Cochran's Q test indicated ARR varied significantly only by subgroups created using the proportional hazards model. The hazard ratio for aspirin vs. placebo therapy did not significantly vary by subgroup in any of the models. The highest risk groups for the proportional hazards model and random forest model contained 230 participants each, while the highest risk group in the decision tree model contained 41 participants.

CONCLUSIONS

The choice of technique for internally developed models for outcome risk subgroups influences HTE analyses. The rationale for the use of a particular subgroup determination model in HTE analyses needs to be explicitly defined based on desired levels of explainability (with features importance), uncertainty of prediction, chances of overfitting, and assumptions regarding the underlying data structure. Replication of these analyses using data from other mid-size clinical trials may help to establish guidance for selecting an outcomes risk prediction modelling technique for HTE analyses.

Assessment of frailty, daily life activities, and nutrition of elderly immigrants: A household based cross-sectional study

With the global migrant population on the rise, it's imperative to focus on the health status of more vulnerable groups within these communities. The elderly immigrants face myriad physical and psychosocial challenges that significantly impact their health and quality of life. This study aims to investigate the nutrition, daily life activities, and clinical frailty status of elderly immigrants residing in Türkiye. A cross-sectional design was employed in the Sultanbeyli District, focusing on Syrian immigrants aged 65 and over. Participants were surveyed face-to-face by interpreters proficient in Arabic. The questionnaire comprised sociodemographic details, health status, and scales like Katz Daily Life Activities, Clinical Frailty, and Mini Nutritional Assessment. The data analysis was executed using SPSS 22. Continuous variables were presented as mean ± standard deviation (SD) and median, while categorical ones were expressed in numbers and percentages (%). A significance level of P < .05 was considered for the analyses. The average age of the participants was determined as 71.64 ± 6.20 years. In the study group, 49.7% were female, 75.5% were younger than 75 years old, 47.7% had less than primary school education, 56.3% were married, 42.4% had a low income level, and 56.9% lived in the same household with 5 or more people. Among the participants in the study group, 47% had walking and balance problems, 29.1% had a history of falls in the last year, 10.6% were disabled, 69.5% complained of pain, 82.8% had a chronic illness, and 43% had polypharmacy. The median value of the KATZ Daily Living Activities scale was 6, the mean score of the Clinical Frailty Score scale was 3.25 ± 1.25, and the mean score of the Mini Nutritional Assessment scale was 12.40 ± 2.15. Among immigrant elderly individuals, 88.1% were able to sustain their lives independently, 13.9% were clinically frail, and 3.3% were at risk of malnutrition. Factors such as age, level of education, socioeconomic status, marital status, number of cohabitants in the household, BMI, neurological problems, walking-balance disorders, disability, and presence of chronic diseases are associated with daily life activities, frailty, and malnutrition status. It is believed that broader field research with greater participation would be beneficial for evaluating the nutritional status of immigrant elderly individuals.

Accelerated Aging Induced by an Unhealthy High-Fat Diet: Initial Evidence for the Role of Nrf2 Deficiency and Impaired Stress Resilience in Cellular Senescence

High-fat diets (HFDs) have pervaded modern dietary habits, characterized by their excessive saturated fat content and low nutritional value. Epidemiological studies have compellingly linked HFD consumption to obesity and the development of type 2 diabetes mellitus. Moreover, the synergistic interplay of HFD, obesity, and diabetes expedites the aging process and prematurely fosters age-related diseases. However, the underlying mechanisms driving these associations remain enigmatic. One of the most conspicuous hallmarks of aging is the accumulation of highly inflammatory senescent cells, with mounting evidence implicating increased cellular senescence in the pathogenesis of age-related diseases. Our hypothesis posits that HFD consumption amplifies senescence burden across multiple organs. To scrutinize this hypothesis, we subjected mice to a 6-month HFD regimen, assessing senescence biomarker expression in the liver, white adipose tissue, and the brain. Aging is intrinsically linked to impaired cellular stress resilience, driven by dysfunction in Nrf2-mediated cytoprotective pathways that safeguard cells against oxidative stress-induced senescence. To ascertain whether Nrf2-mediated pathways shield against senescence induction in response to HFD consumption, we explored senescence burden in a novel model of aging: Nrf2-deficient (Nrf2+/-) mice, emulating the aging phenotype. Our initial findings unveiled significant Nrf2 dysfunction in Nrf2+/- mice, mirroring aging-related alterations. HFD led to substantial obesity, hyperglycemia, and impaired insulin sensitivity in both Nrf2+/- and Nrf2+/+ mice. In control mice, HFD primarily heightened senescence burden in white adipose tissue, evidenced by increased Cdkn2a senescence biomarker expression. In Nrf2+/- mice, HFD elicited a significant surge in senescence burden across the liver, white adipose tissue, and the brain. We postulate that HFD-induced augmentation of senescence burden may be a pivotal contributor to accelerated organismal aging and the premature onset of age-related diseases.

Effects of physical activity participation on cognitive impairment in older adults population with disabilities

Background

Existing research on the association between cognitive function and physical activity in the older adults population with disabilities is limited. Additionally, there is a need to explore avenues for enhancing the longevity and quality of life among these individuals.

Objective

This study aimed to investigate the independent and joint associations between cognitive function and levels of physical activity in the older adults population with disabilities.

Methods

A total of 315 older adults adults (men = 182, women = 133), identified with disabilities based on medical evaluation, were recruited from the first survey of the Korean Longitudinal Study of Aging (KLoSA). Participants underwent assessments for cognitive function, physical activity (PA), activities of daily living (ADLs), instrumental activities of daily living (IADLs), and grip strength.

Results

ADLs (p < 0.001) and IADLs (p < 0.001) scores were significantly higher in the male normal cognitive group compared to both the male and female cognitive impairment groups. In an unadjusted model, disabled older adults individuals who did not meet the recommended PA guidelines showed an increased odds ratio for cognitive dysfunction (OR = 2.29, 95% CI = 1.32-3.97). Those participating in PA at least 1 day per week also demonstrated an elevated odds ratio (OR = 1.22, 95% CI = 1.08-1.38) for cognitive dysfunction compared to those who engaged in regular PA. A negative correlation was observed between K-MMSE scores and grip strength (r = 0.448, p < 0.001).

Conclusion

This study provides robust evidence that disabled older adults individuals who do not meet the recommended guidelines for PA or who do not participate in PA at least once a week have an increased likelihood of cognitive impairment compared to those who are regularly active.

Occupational characteristics and disability-free survival after retirement age: an exploratory analysis from the ASPREE study

Background

Certain occupational characteristics have been linked with poor health and reduced longevity. However, the association between occupational characteristics and survival free of disability in a post-retirement age group has not been investigated.

Methods

We investigated outcomes in 12,215 healthy older Australian adults in the Aspirin in Reducing Events in the Elderly (ASPREE) and ASPREE Longitudinal Study of Older Persons (ALSOP) sub-study. The ISCO-88 major occupational groups, settings, and activity levels were assigned based on free-text job descriptions. The Finnish Job Exposure Matrix was used to assign occupational characteristics to the three longest-held jobs. The primary endpoint, disability-free survival, was defined as a composite measure of death, dementia, or persistent physical disability. The endpoint of all-cause mortality was analyzed separately. Because of multiple exploratory analyses, only those associations with a two-sided value of p less than 0.005 were considered statistically significant. Cox proportional hazard models were used to calculate adjusted hazard ratios.

Results

Having worked in an 'elementary' occupation was associated with a reduction in disability-free survival. A specific impact on disability-free survival was observed among those whose work had involved high accident risk and adverse social climate. No significant relationship was identified with those previously exposed to sedentary work, vigorous physical activity, work primarily outdoors, or a range of other occupational characteristics. All-cause mortality was not increased among any of the occupational groups.

Conclusion

This exploratory study found a reduction in disability-free survival among people who worked in 'elementary' occupations, with specific risks associated with occupations involving high accident risks and adverse social climate.

Disability risk prediction model based on machine learning among Chinese healthy older adults: results from the China Health and Retirement Longitudinal Study

Background

Predicting disability risk in healthy older adults in China is essential for timely preventive interventions, improving their quality of life, and providing scientific evidence for disability prevention. Therefore, developing a machine learning model capable of evaluating disability risk based on longitudinal research data is crucial.

Methods

We conducted a prospective cohort study of 2,175 older adults enrolled in the China Health and Retirement Longitudinal Study (CHARLS) between 2015 and 2018 to develop and validate this prediction model. Several machine learning algorithms (logistic regression, k-nearest neighbors, naive Bayes, multilayer perceptron, random forest, and XGBoost) were used to assess the 3-year risk of developing disability. The optimal cutoff points and adjustment parameters are explored in the training set, the prediction accuracy of the models is compared in the testing set, and the best-performing models are further interpreted.

Results

During a 3-year follow-up period, a total of 505 (23.22%) healthy older adult individuals developed disabilities. Among the 43 features examined, the LASSO regression identified 11 features as significant for model establishment. When comparing six different machine learning models on the testing set, the XGBoost model demonstrated the best performance across various evaluation metrics, including the highest area under the ROC curve (0.803), accuracy (0.757), sensitivity (0.790), and F1 score (0.789), while its specificity was 0.712. The decision curve analysis (DCA) indicated showed that XGBoost had the highest net benefit in most of the threshold ranges. Based on the importance of features determined by SHAP (model interpretation method), the top five important features were identified as right-hand grip strength, depressive symptoms, marital status, respiratory function, and age. Moreover, the SHAP summary plot was used to illustrate the positive or negative effects attributed to the features influenced by XGBoost. The SHAP dependence plot explained how individual features affected the output of the predictive model.

Conclusion

Machine learning-based prediction models can accurately evaluate the likelihood of disability in healthy older adults over a period of 3 years. A combination of XGBoost and SHAP can provide clear explanations for personalized risk prediction and offer a more intuitive understanding of the effect of key features in the model.

Expanded statistical analysis plan for legacy and long-term effects of aspirin in the ASPREE-XT observational follow-up study of participants in the ASPREE randomized trial

The ASPREE randomized controlled trial (2010-2017) of 19,114 community-dwelling older adults without cardiovascular disease and significant disability compared daily 100mg aspirin to placebo. A total of 16,317 (93%) of 17,546 surviving and non-withdrawn participants agreed to continue regular study follow-up visits in the post-trial phase, named ASPREE-XT (2017-2024). We present a statistical analysis plan to underpin three main papers to report aspirin effects through to the fourth post-trial ASPREE-XT study visit with focus areas of: (1) death, dementia, and disability, (2) CVD events and bleeding, and (3) cancer. The focus of the plan is to estimate long-term (entire timespan of RCT plus post-trial) and legacy (post-trial period only) effects of aspirin in the setting of primary prevention for older individuals. Preliminary insights to these effects are presented that are based on data that has been reported to the study's observational study monitoring board however formal data lock is not expected until October 2023.

Grip strength affects the association between platelet count and disability-free survival: Evidence from the China Health and Retirement Longitudinal Study

BACKGROUND AND AIMS

Previous studies found elevated platelet count (PLT), especially long-term persist high or increased PLT was associated with less likelihood disability-free survival (DFS). However, whether grip strength affects the relationship between them is still not elucidated.

METHODS

A total of 6252 participants were recruited in the analysis based on the China Health and Retirement Longitudinal Study. The primary outcome was DFS, evaluated by a composite endpoint based on the first occurrence of either disability (having difficulty in at least one of the 6 activities of daily living: namely, dressing, bathing, continence, eating, getting into or out of bed, and toileting) or all-cause mortality.

RESULTS

The association of PLT with primary outcome was significantly modified by grip strength (pinteraction = 0.022). The rates of primary outcome were significantly lower among participants with lower baseline PLT in participants with normal grip strength (multivariable odds ratio [OR], 0.67; 95% confidence interval [CI], 0.54-0.84; ptrend < 0.001), but not in those with low grip strength (multivariable OR, 1.70; 95% CI, 0.88-3.15; ptrend = 0.135), for the lowest quartile vs the highest quartile. Adding baseline PLT (quartiles or continuous) to a model containing conventional risk factors significantly improved risk reclassification for primary outcome among those with normal grip strength (most of p < 0.05).

CONCLUSION

An inverse dose-response association of PLT with DFS was found among participants with normal grip strength, but not among those with low grip strength. Low grip strength might weaken the benefit of low PLT on DFS among middle-aged and older Chinese.

The association between long-term platelet count and disability-free survival among middle-aged and older Chinese: Evidence from the China Health and Retirement Longitudinal Study

BACKGROUND AND AIMS

We aimed to assess the associations of baseline and long-term platelet count (PLT) with disability-free survival (DFS) among middle-aged and older Chinese.

METHODS AND RESULTS

A total of 7296 participants were recruited in the analysis. Updated mean PLT was defined as the mean of the two PLT measurements (4 years between wave 1-3). The long-term status of PLT was defined as persistent low, attenuated, increased and persistent high PLT according to the optimal cut points from the receiver operating characteristic curves of the two PLT measurements, respectively. The primary outcome was DFS, evaluated by the first occurrence of either disability or mortality. During 6-year visit, 1579 participants experienced disability or all-cause mortality. The rates of primary outcome were significantly higher among participants with elevated baseline PLT and updated mean PLT. Multivariable adjusted odds ratios (ORs) and 95% confidence intervals (CIs) of primary outcome were 1.253 (1.049-1.496) for highest baseline PLT tertile and 1.532 (1.124-2.088) for highest updated mean PLT tertile, comparing to the lowest tertiles. Multivariable-adjusted spline regression models showed a linear association of baseline PLT (p_linearity < 0.001) and updated mean PLT (p_linearity = 0.005) with primary outcome. Moreover, participants with persistent high PLT and increased PLT had increased risk of primary outcome (ORs [95% CIs]: 1.825 [1.282-2.597] and 1.767 [1.046-2.985], respectively), compared with the reference of those with persistent low PLT.

CONCLUSION

This study proved elevated baseline PLT, especially long-term persist high or increased PLT was associated with less likelihood of DFS among middle-aged and older Chinese.

Predicting Disability Using a Nomogram of the Tilburg Frailty Indicator (TFI)

Disability is associated with lower quality of life and premature death in older people. Therefore, prevention and intervention targeting older people living with a disability is important. Frailty can be considered a major predictor of disability. In this study, we aimed to develop nomograms with items of the Tilburg Frailty Indicator (TFI) as predictors by using cross-sectional and longitudinal data (follow-up of five and nine years), focusing on the prediction of total disability, disability in activities of daily living (ADL), and disability in instrumental activities of daily living (IADL). At baseline, 479 Dutch community-dwelling people aged ≥75 years participated. They completed a questionnaire that included the TFI and the Groningen Activity Restriction Scale to assess the three disability variables. We showed that the TFI items scored different points, especially over time. Therefore, not every item was equally important in predicting disability. 'Difficulty in walking' and 'unexplained weight loss' appeared to be important predictors of disability. Healthcare professionals need to focus on these two items to prevent disability. We also conclude that the points given to frailty items differed between total, ADL, and IADL disability and also differed regarding years of follow-up. Creating one monogram that does justice to this seems impossible.