ArticleThe journals of gerontology. Series A, Biological sciences and medical sciences2021
Body Composition by Computed Tomography vs Dual-Energy X-ray Absorptiometry: Long-Term Prediction of All-Cause Mortality in the Health ABC Cohort.
Article in The journals of gerontology. Series A, Biological sciences and medical sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 13 citations in OpenAlex.
- Low lean mass and all-cause mortality risk in the middle-aged and older population: a dose-response meta-analysis of prospective cohort studies.Frontiers in medicine · 2025Pooled it
- Association of muscle wasting with mortality risk among adults: A systematic review and meta-analysis of prospective studies.Journal of cachexia, sarcopenia and muscle · 2023Pooled it
- Clinical Assessment of Obesity Beyond Body Mass Index: Adiposity Evaluation, Body-Composition Phenotyping and Disease Staging: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Clinical Sensitivity of Sub-millimetre-resolution Definite Versus Thin Inter-/Intramuscular Fat Streaks from Calf and Thigh MRI.Calcified tissue international · 2026Article
- Muscle matters: automated CT-based body composition analysis predicts survival in patients with head and neck cancer treated with immunotherapy.Frontiers in oncology · 2026Article
- Deep learning-quantified body composition from positron emission tomography/computed tomography and cardiovascular outcomes: a multicentre study.European heart journal · 2025Article
- Fat-Free Mass: Friend or Foe to Metabolic Health?Journal of cachexia, sarcopenia and muscle · 2025Review
- Thigh muscle by CT images as a predictor of mortality in patients with newly diagnosed colorectal cancer.Scientific reports · 2024Article
- Predictive value of sarcopenia components for all-cause mortality: findings from population-based cohorts.Aging clinical and experimental research · 2024Article
- The Health, Aging, and Body Composition (Health ABC) Study-Ground-Breaking Science for 25 Years and Counting.The journals of gerontology. Series A, Biological sciences and medical sciences · 2023Review
- The Epidemiology and Societal Impact of Aging-Related Functional Limitations: A Looming Public Health Crisis.The journals of gerontology. Series A, Biological sciences and medical sciences · 2023Article
- Sarcopenia: if it looks/walks like a duck, it must be a duck.European journal of clinical nutrition · 2022Article
- Deep learning predicts all-cause mortality from longitudinal total-body DXA imaging.Communications medicine · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundBody composition assessment by computed tomography (CT) predicts health outcomes in diverse populations. However, its performance in predicting mortality has not been directly compared to dual-energy X-ray absorptiometry (DXA). Additionally, the association between different body compartments and mortality, acknowledging the compositional nature of the human body, is not well studied. Compositional data analysis, which is applied to multivariate proportion-type data set, may help to account for the interrelationships of body compartments by constructing log ratios of components. Here, we determined the associations of baseline CT-based measures of mid-thigh cross-sectional areas versus DXA measures of body composition with all-cause mortality in the Health ABC cohort, using both traditional (individual body compartments) and compositional data analysis (using ratios of body compartments) approaches.
methodsThe Health ABC study assessed body composition in 2911 older adults in 1996-1997. We investigated the individual and ratios of (by compositional analysis) body compartments assessed by DXA (lean, fat, and bone masses) and CT (muscle, subcutaneous fat area, intermuscular fat, and bone) on mortality, using Cox proportional hazard models.
resultsLower baseline muscle area by CT (hazard ratio [HR]men = 0.56; 95% confidence interval [95% CI]: 0.48-0.67, HRwomen = 0.60; 95% CI: 0.48-0.74) and fat mass by DXA (HRmen = 0.48; 95% CI: 0.24-0.95) were predictors of mortality in traditional Cox regression analysis. Consistently, compositional data analysis revealed that lower muscle area versus IMF, muscle area versus bone area, and lower fat mass versus lean mass were associated with higher mortality in both sexes.
conclusionBoth CT measure of muscle area and DXA fat mass (either individually or relative to other body compartments) were strong predictors of mortality in both sexes in a community research setting.
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