ArticleClinics (Sao Paulo, Brazil)2026
Cardiometabolic index as a predictor of sarcopenia in U.S. adults aged 20-59: results from a cross-sectional study.
Article in Clinics (Sao Paulo, Brazil), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSarcopenia, characterized by progressive loss of skeletal muscle mass and strength with pathophysiological changes that may originate in early life, and the Cardiometabolic Index (CMI) ‒ a reliable marker for lipid metabolic dysfunction and central adiposity ‒ have associations that are insufficiently studied. This study aimed to investigate the association between CMI and sarcopenia in U.S. adults aged 20-59 and to evaluate the predictive value of CMI for sarcopenia.
methodsUtilizing data from the National Health and Nutrition Examination Survey (NHANES) spanning 2011-2018, which initially encompassed 39,156 participants, the authors applied age-based screening and exclusion of cases with missing data, resulting in a final analytical cohort of 4784 participants for statistical analysis. Sarcopenia was defined per the Foundation for the National Institutes of Health (FNIH) criteria: Appendicular Lean Mass (ALM) adjusted for BMI < 0.512 in men and < 0.461 in women. CMI was calculated as [triglycerides (mg/dL) / high-density lipoprotein cholesterol (mg/dL)] × [waist circumference (cm) / height (m)] / body mass index (kg/m
resultsAmong the 4784 participants, 425 (8.88%) had sarcopenia. Descriptive analysis showed that the median CMI of sarcopenia participants was higher (0.82 vs. 0.43; p < 0.001) compared with non-sarcopenia participants, and they had unfavorable demographic/clinical characteristics (e.g., older age, higher prevalence of diabetes). In the full sample, after adjusting for all confounding factors, Elevated CMI correlated with an increased odds of presenting sarcopenia (Odds Ratio [OR = 1.39], 95% CI = 1.05-1.91, p = 0.005). The multi-dimensional model incorporating CMI demonstrated superior predictive ability (AUC = 0.819). GAM revealed a non-linear association with a threshold of 0.899, which was strongest in males, individuals aged 20-39 years, non-Hispanic Blacks, those with hypertension, and non-obese or non-diabetic individuals.
conclusionCardiometabolic Index (CMI) has a non-linear positive association with sarcopenia in U.S. young and middle-aged adults, with a threshold of 0.899. The multi-dimensional predictive model incorporating CMI demonstrates superior performance in assessing odds of presenting sarcopenia compared to traditional anthropometric indicators, indicating its potential as a novel clinical biomarker for such evaluations.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.