Evidence map›Paper›PMID 35160054›Full record

ArticleJournal of clinical medicine2022

DXA-Based Detection of Low Muscle Mass Using the Total Body Muscularity Assessment Index (TB-MAXI): A New Index with Cutoff Values from the NHANES 1999-2004.

Marco Alessandro Minetto, Maria Giulia Ballatore, Alberto Botter, Chiara Busso, Angelo Pietrobelli, Anita Tabacco

Abstract read
In one paragraph

Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Marco Alessandro MinettoDivision of Physical Medicine and Rehabilitation, Department of Surgical Sciences, University of Turin, 10126 Turin, Italy.ORCID 0000-0001-8434-0341
Maria Giulia BallatoreDepartment of Mathematical Sciences, Politecnico di Torino, 10129 Turin, Italy.ORCID 0000-0002-6216-8939
Alberto BotterLaboratory for Engineering of the Neuromuscular System (LISiN), Department of Electronic and Telecommunications, Politecnico di Torino, 10129 Turin, Italy.
Chiara BussoDivision of Physical Medicine and Rehabilitation, Department of Surgical Sciences, University of Turin, 10126 Turin, Italy.ORCID 0000-0002-3715-6987
Angelo PietrobelliPaediatric Unit, Department of Surgical Sciences, Dentistry, Gynaecology and Paediatrics, University of Verona, 37126 Verona, Italy.ORCID 0000-0002-9912-3232
Anita TabaccoDepartment of Mathematical Sciences, Politecnico di Torino, 10129 Turin, Italy.ORCID 0000-0001-5731-4885

Funding

Fondazione CRT 2021.0566University of Turin Fondo per la Ricerca Locale - ex-60%
6 · The paper itself

Abstract

The aims of this study were to investigate age-related changes in total body skeletal muscle mass (TBSMM) and the between-limb asymmetry in lean mass in a large sample of adults. Demographic, anthropometric, and DXA-derived data of National Health and Nutrition Examination Survey participants were considered. The sample included 10,014 participants of two ethnic groups (Caucasians and African Americans). The age-related decline of TBSMM absolute values was between 5% and 6% per decade in males and between 4.5% and 5.0% per decade in females. The adjustment of TBSMM for body surface area (TB-MAXI) showed that muscle mass peaked in the second decade and decreased progressively during the subsequent decades. The following thresholds were identified to distinguish between low and normal TB-MAXI: (i) 10.0 kg/m

Indexed as

appendicular lean massdual-energy X-ray absorptiometry (DXA)inter- and intra-muscular adipose tissue (IMAT)National Health and Nutrition Examination Survey (NHANES)sarcopenia

Identifiers

PMID35160054
PMCPMC8837094

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Registered trials

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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.