ArticleAnnals of translational medicine2024
Forecasting and validating fat mass ratio models through anthropometric measurements and health-related factors among people with HIV: a cross-sectional investigation.
Article in Annals of translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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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.
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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.
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Sarcopenic Obesity in People With HIV: Diagnostic Criteria and Classification-A Scoping Review With a Meta-Analysis of Prevalence.Journal of obesity · 2026Pooled it
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: There is a limited research on predictive models of fat mass ratio (FMR) in people living with human immunodeficiency virus (HIV) (PWH). This study aimed to develop models considering anthropometric and health-related factors to predict and validate FMR in PWH regardless of sex. Methods: One hundred and six Brazilian PWH (46.4±9.8 years) were evaluated for body composition using dual-energy X-ray absorptiometry (DXA), body circumference (BC), and skinfold thicknesses (SKs). FMR predictive models were developed using stepwise linear regression, and their agreement with DXA was assessed using Bland-Altman plots. Cross-validation was performed using the predicted residual error sum of squares (PRESS) method. Results: Six FMR estimation models were developed for PWH, with adjusted R Conclusions: Our predictive models advance the study of body composition in PWH by consolidating the use of anthropometry for diagnosing and monitoring lipodystrophy regardless of sex.
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