Appendicular Lean Mass, Grip Strength, and the Development of Hospital-Associated Activities of Daily Living Disability among Older Adults in the Health ABC Study

Glyphosate exposure, muscular health and functional limitations in middle-aged and older adults

BACKGROUND

Glyphosate is the most widely used herbicide worldwide, both in domestic and industrial settings. Experimental research in animal models has demonstrated changes in muscle physiology and reduced contractile strength associated with glyphosate exposure, while epidemiological studies have shown associations between glyphosate exposure and adverse health outcomes in critical biological systems affecting muscle function.

METHODS

This study used data from a nationally representative survey of the non-institutionalized U.S. general population (NHANES, n = 2132). Urine glyphosate concentrations were determined by ion chromatography with tandem mass spectrometry. Hand grip strength (HGS) was measured using a Takei Dynamometer, and relative strength estimated as the ratio between HGS in the dominant hand and the appendicular lean mass (ALM) to body mass index (ALMBMI) ratio. Low HGS and low relative HGS were defined as 1 sex-, age- and race-specific SD below the mean. Physical function limitations were identified as significant difficulty or incapacity in various activities.

RESULTS

In fully-adjusted models, the Mean Differences (MD) and 95% confidence intervals [95%CI] per doubling increase in glyphosate concentrations were -0.55 [-1.09, -0.01] kg for HGS in the dominant hand, and -0.90 [-1.58. -0.21] kg for HGS/ALMBMI. The Odds Ratios (OR) [95% CI] for low HGS, low relative HGS and functional limitations by glyphosate concentrations were 1.27 [1.03, 1.57] for low HGS; 1.43 [1.05; 1.94] for low relative HGS; 1.33 [1.08, 1.63] for stooping, crouching or kneeling difficulty; 1.17 [0.91, 1.50] for lifting or carrying items weighting up to 10 pounds difficulty; 1.21 [1.01, 1.40] for standing up from armless chair difficulty; and 1.47 [1.05, 2.29] for ascending ten steps without pause difficulty.

CONCLUSIONS

Glyphosate exposure may be a risk factor for decreased grip strength and increased physical functional limitations. More studies investigating the influence of this and other environmental pollutants on functional aging are needed.

Low hemoglobin levels are associated with compromised muscle health: Insights from a post-stroke rehabilitation cohort

AIM

There is limited evidence concerning the association between anemia and alterations in muscle health among hospitalized older patients. We aimed to evaluate the associations between baseline hemoglobin (Hb) levels and changes in muscle function in patients undergoing rehabilitation after stroke.

METHODS

This retrospective cohort study included consecutive hospitalized post-stroke patients. Data on serum Hb level were extracted from medical records on tests performed within 24 h of admission. The main outcomes were discharge score for the skeletal muscle mass index (SMI) obtained through bioimpedance analysis and the corresponding change in SMI during hospitalization. Other outcomes were handgrip strength (HGS) at discharge and the alteration in HGS during hospitalization. Multivariate linear regression analyses were used to determine the association between Hb levels at admission and outcomes of interest, adjusted for potential confounders.

RESULTS

Data from 955 patients (mean age 73.2 years; 53.6% men) were included in the analysis. The median Hb level at admission was 13.3 [11.9, 14.5] g/dL. After fully adjusting for confounding factors, the baseline Hb level was significantly and positively associated with SMI at discharge (β = 0.046, P = 0.039) and with SMI gain (β = 0.010, P = 0.039). Further, the baseline Hb level was independently and positively associated with HGS at discharge (β = 0.058, P = 0.014) and with its change from baseline (β = 0.100, P = 0.014).

CONCLUSION

Diminished baseline Hb levels were demonstrated be correlated with compromised muscle health in patients after stroke. Evaluating anemia at the outset serves as a crucial prognostic indicator. Geriatr Gerontol Int 2024; 24: 305-311.

Genetically Proxied Sarcopenia-Related Muscle Traits and Depression: Evidence from the FinnGen Cohort

BACKGROUND

Sarcopenia and depression are common and often coexist in the elderly. This study aims to determine the impact of sarcopenia-related muscle traits on depression.

METHODS

A two-sample Mendelian randomization (MR) study was performed on the summary-level data from the FinnGen cohort to estimate the causal association of appendicular lean mass (ALM), walking pace, or low hand grip strength with depression. Additionally, multivariable MR (MVMR) was performed to assess the dependence of each muscle trait in the causality and adjust the effect of body mass index (BMI). Supplementary backward MR analyses were performed to estimate the effect of depression on sarcopenia-related muscle traits.

RESULTS

Univariable MR analyses demonstrated that there were causal associations of ALM (odds ratio [OR]: 0.94; 95% confidence interval [CI]: 0.88-0.99), walking pace (OR: 0.53; 95% CI: 0.32-0.88), and low hand grip strength (OR: 1.20; 95% CI: 1.05-1.38) with depression. MVMR analyses showed that ALM was the only trait that had a significant causal relationship with depression (OR: 0.91; 95% CI: 0.85-0.98) after accounting for the other two muscle traits. Moreover, the independent association of ALM with depression remained (OR: 0.92; 95% CI: 0.85-0.99) after being adjusted by BMI. The backward MR analyses showed no causal associations of depression with any sarcopenia-related muscle traits.

CONCLUSION

Low muscle mass independently increases the risk of depression. This study determined the muscle-related risk factors of depression, which may help establish the causality between sarcopenia and depression and provide evidence-based recommendations for improving mental health in the elderly.

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.

Appendicular Lean Mass, Grip Strength, and the Incidence of Dementia Among Older Adults in the Health ABC Study

BACKGROUND

Identification of novel risk factors for dementia in older adults could facilitate development of methods to identify patients most at risk and improve their cognitive outcomes. We aimed to determine whether lower appendicular lean mass (ALM), assessed by dual-energy x-ray absorptiometry (DXA), and lower grip strength are associated with a greater likelihood of incident dementia among older adults in the Health Aging and Body Composition Study (Health ABC).

METHODS

Health ABC data from 1997 to 2008 were analyzed (n = 2 704). Baseline ALM to body mass index (BMI) ratio (ALMBMI) was assessed by DXA. Baseline grip strength was assessed by hand-held dynamometry. Incident dementia diagnosis was defined as either (i) dementia-related hospitalization plus a Modified Mini-Mental State Examination (3MS) score of ≤ 90; or (ii) record of prescription for anti-dementia medication; or (iii) decline of at least 1.5 SDs on the 3MS score compared to baseline. Cox proportional hazard models estimated associations of ALMBMI and grip strength with incident dementia over follow-up with and without adjusting for covariates, stratified by sex.

RESULTS

Among older men, each standard deviation decrement in ALMBMI (adjusted hazard ratio [aHR]: 1.33; 95% confidence interval [CI]: 1.07, 1.65) or grip strength (aHR 1.22; 95% CI: 1.06, 1.41) was associated with increased likelihood of incident dementia.

CONCLUSIONS

Lower ALMBMI and grip strength may be important risk factors for the development of dementia among older men. How these factors may belong to a causal pathway of dementia must be elucidated in future work.

Interpretable Machine Learning for Fall Prediction Among Older Adults in China

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Muscle Evaluation and Hospital-Associated Disability in Acute Hospitalized Older Adults

OBJECTIVES

We aimed to examine the association of muscle evaluation, including muscle ultrasound, with hospital-associated disability (HAD), focusing on ADL categories.

DESIGN

A prospective observational cohort study.

SETTING AND PARTICIPANTS

We recruited patients aged 65 years or older who were admitted to the geriatric ward of an acute hospital between October 2019 and September 2021.

MEASUREMENTS

Handgrip strength, bioimpedance analyzer-determined skeletal muscle mass, bilateral thigh muscle thickness (BATT), and the echo intensity of the rectus femoris on muscle ultrasound were performed as muscle assessments. HAD was evaluated separately for mobility impairments and self-care impairments.

RESULTS

In total, 256 individuals (mean age, 85.2 years; male sex, 41.8%) were analyzed. HAD in mobility was more common than HAD in self-care (37.5% vs. 30.0%). Only BATT was independently associated with HAD in mobility in multiple logistic regression analysis. There was no significant association between muscle indicators and HAD in self-care.

CONCLUSION

A lower BATT was associated with a higher prevalence of HAD in mobility, suggesting the need to reconsider muscle assessment methods in hospitalized older adults. In addition, approaches other than physical may be required, such as psychosocial and environmental interventions to improve HAD in self-care.