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Cited by 4Nutrition and Health in AgingBody Composition Measurement TechniquesFrailty in Older Adults

Anthropometric Measurements for Predicting Low Appendicular Lean Mass Index for the Diagnosis of Sarcopenia: A Machine Learning Model

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González-Martin, Ana M., Limón-Villegas, Edgar Samid, Reyes-Castillo, Zyanya, Esparza-Ros, Francisco, Hernández-Palma, Luis Alexis, Santillán-Rivera, Minerva Saraí, Herrera-Amante, Carlos Abraham, Ramos-García, César Octavio +1 more·2025·Journal of Functional Morphology and Kinesiology
DOI 10.3390/jfmk10030276PMC PMC12286193
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Abstract

Background: Sarcopenia is a progressive muscle disease that compromises mobility and quality of life in older adults. Although dual-energy X-ray absorptiometry (DXA) is the standard for assessing Appendicular Lean Mass Index (ALMI), it is costly and often inaccessible. This study aims to develop machine learning models using anthropometric measurements to predict low ALMI for the diagnosis of sarcopenia. Methods: A cross-sectional study was conducted on 183 Mexican adults (67.2% women and 32.8% men, ≥60 years old). ALMI was measured using DXA, and anthropometric data were collected following the International Society for the Advancement of Kinanthropometry (ISAK) protocols. Predictive models were developed using Logistic Regression (LR), Decision Trees (DTs), Random Forests (RFs), Artificial Neural Networks (ANNs), and LASSO regression. The dataset was split into training (70%) and testing (30%) sets. Model performance was evaluated using classification performance metrics and the area under the ROC curve (AUC). Results: ALMI indicated strong correlations with BMI, corrected calf girth, and arm relaxed girth. Among models, DT achieved the best performance in females (AUC = 0.84), and ANN indicated the highest AUC in males (0.92). Regarding the prediction of low ALMI, specificity values were highest in DT for females (100%), while RF performed best in males (92%). The key predictive variables varied depending on sex, with BMI and calf girth being the most relevant for females and arm girth for males. Conclusions: Anthropometry combined with machine learning provides an accurate, low-cost approach for identifying low ALMI in older adults. This method could facilitate sarcopenia screening in clinical settings with limited access to advanced diagnostic tools.

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  • Kinanthropometry has refined anthropometry's analytical capacity, enabling the estimation of five-component body composition models (adipose, muscular, skeletal, skin, and residual mass) with standardized ISAK protocols.

    “However, new approaches such as kinanthropometry have refined its analytical capacity, enabling the estimation of five-component body composition models (adipose, muscular, skeletal, skin, and residual mass) with standardized ISAK protocols [150].”

    From The Seven Methods for the Evaluation of Nutritional Status—ABCDEFG: Narrative Review (2026) · cites this paper as [150]

    The Methods (§2.2.2; page unavailable) state that anthropometric measurements followed a standardized ISAK protocol, and the Introduction describes anthropometry as a way to assess body composition. Our copy does not state that kinanthropometry enables estimation of the specified five-component model, so that central part of the claim is unsupported. (LLM, conf=0.84)

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Authors
González-Martin, A. M., Limón-Villegas, E. S., Reyes-Castillo, Z., Esparza-Ros, F., Hernández-Palma, L. A., Santillán-Rivera, M. S., Herrera-Amante, C. A., Ramos-García, C. O., & Righini, N.
Affiliations
  • Universidad de Guadalajara (MX)
  • Instituto de Investigaciones en Comportamiento Alimentario y Nutrición (IICAN), Universidad de Guadalajara, Ciudad Guzmán 49000, Jalisco, Mexico
  • Universidad Católica de Murcia (ES)
Corresponding author
  • González-Martin, Ana M.
  • Righini, Nicoletta
ORCID
Venue
Journal of Functional Morphology and Kinesiology
Publisher
MDPI AG
Published
2025-07-17
Volume
10
Issue
3
Pages
276
ISSN
2411-5142 (ISSN-L)
DOI
10.3390/jfmk10030276
PMC
PMC12286193
OpenAlex
W4412650326
Cited by
4
References
6
Citations by year
2026: 4
FWCI
2.57
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Functional Morphology and Kinesiology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Mexican Secretaría de Ciencia, Humanidades, Tecnología e Innovación (Secihti) (864093)
  • Laboratorio de Evaluación y Cuidado del Estado Nutricio (LECEN) (864093)
  • Instituto de Investigaciones en Comportamiento Alimentario y Nutrición (IICAN) (864093)
  • Doctorado en Ciencia del Comportamiento con orientación en Alimentación y Nutrición (864093)
Topics
  • Health Sciences › Medicine › Physiology › Nutrition and Health in Aging
  • Health Sciences › Medicine › Physiology › Body Composition Measurement Techniques
  • Health Sciences › Medicine › Geriatrics and Gerontology › Frailty in Older Adults
Keywords
sarcopenia, appendicular lean mass index, anthropometry, machine learning prediction, sarcopenia screening, older adults, body mass index, calf circumference, arm circumference, sex differences, low-cost diagnostics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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