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Life sciences · Journal article
BMC Geriatrics · August 6, 2026
Reinforces what was already believed, rather than introducing something new.
In a cross-sectional study of 427 community-dwelling older adults (aged 60–98), sarcopenic obesity prevalence was 8.7% and its risk factors differed substantially by sex and age. Age, BMI, hemoglobin, and white blood cell count emerged as key predictors with marked sex-stratified differences (BMI strong in men; hemoglobin and WBC in women) and age-stratified differences (glucose and WBC in <75 years; BMI in ≥75 years). Multiple analytical approaches (logistic regression, Bayesian models, machine learning) and sensitivity analyses supported the findings.
Cross-sectional study with multivariable logistic regression, Bayesian hierarchical models, and machine learning validation. Community-dwelling adults aged 60–98 years (n=427). n = 427.
PSO prevalence was 8.7% (37/427 participants) Age was a risk factor overall (OR = 1.12, 95%CI:1.05–1.19); hemoglobin was protective (OR = 0.97, 95%CI:0.94–0.99) In men, BMI was a strong risk factor (OR = 1.54, 95%CI:1.19–2.01); in women, low hemoglobin (OR = 0.96, 95%CI:0.92–0.99) and high WBC (OR = 1.43, 95%CI:1.04–1.98) were significant
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Clinicians should recognize that sarcopenic obesity risk factors differ markedly by sex and age in older adults. Targeted interventions—weight control in men and older-old (≥75 years), nutritional and inflammatory monitoring in women, and metabolic management in younger-old (<75 years)—may improve prevention and management strategies.
Cross-sectional study with multivariable analysis and machine learning validation identifying sex- and age-stratified risk factors for sarcopenic obesity in a community sample; provides evidence of differential risk profiles but lacks prospective design and causal inference.
As stated by the source record.
Quoted from the source exactly as published.
Clinicians should recognize that sarcopenic obesity risk factors differ markedly by sex and age in older adults. Targeted interventions—weight control in men and older-old (≥75 years), nutritional and inflammatory monitoring in women, and metabolic management in younger-old (<75 years)—may improve prevention and management strategies.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Possible Sarcopenic obesity (PSO) carries higher risks than either condition alone, but whether its risk factors differ by sex and age remains unclear. We investigated the prevalence, risk factors, and sex/age differences of PSO using traditional regression, Bayesian analysis, and machine learning. This cross-sectional study included 427 community-dwelling adults aged 60–98 years. PSO was defined as concurrent possible sarcopenia (AWGS 2019: low handgrip strength + slow gait speed) and central obesity (waist circumference ≥ 90 cm in men, ≥ 85 cm in women). Multivariable logistic regression, sex- and age-stratified analyses, dose–response analysis, mediation analysis, and E-value sensitivity analysis were performed. Bayesian hierarchical models and XGBoost with SHAP were used for validation. PSO prevalence was 8.7% (37/427). Overall, age (OR = 1.12, 95%CI:1.05–1.19) was a risk factor and hemoglobin (OR = 0.97, 95%CI:0.94–0.99) was protective. Sex-stratified analysis revealed marked differences: in men, BMI was a strong risk factor (OR = 1.54, 95%CI:1.19–2.01); in women, low hemoglobin (OR = 0.96, 95%CI:0.92–0.99) and high white blood cell count (OR = 1.43, 95%CI:1.04–1.98) were significant. Age-stratified analysis showed that in those < 75 years, glucose (OR = 1.26) and white blood cell count (OR = 1.23) were important; in those ≥ 75 years, BMI showed a strong effect (OR = 1.91, 95%CI:1.33–2.76). Bayesian analysis produced consistent findings with narrow credible intervals. The XGBoost model achieved good discrimination (AUC = 0.86), with SHAP confirming age, BMI, and hemoglobin as top predictors. Dose–response analysis showed a linear relationship between hemoglobin and PSO (p for nonlinearity = 0.743). E-value analysis confirmed robustness to unmeasured confounding (E-values ≥ 1.47). Risk factors for PSO differ substantially by sex and age. These findings support targeted prevention strategies: weight control in men, nutritional and inflammatory monitoring in women, metabolic management in younger-old adults, and weight control in the oldest-old.
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