{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T09:09:59Z","timestamp":1790327399309,"version":"4.1.0"},"reference-count":70,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Clin. Med.","JCM"],"abstract":"<jats:p>Background\/Objectives: Needle-free measures such as grip strength, gait speed, and anthropometry are attractive as potential substitutes for blood-based biomarkers in low-resource settings, yet whether they can predict an adverse blood-biomarker profile remains unknown. This study aimed to determine whether machine-learning algorithms can predict such a profile from a defined set of needle-free bedside and anthropometric measures in Mexican adults. Methods: Using the Mexican Health and Aging Study (MHAS) 2012 biomarker subsample (n = 2000; mean age 62.4 years; 59.8% women), we defined a blood-based outcome: two or more of four abnormal markers (high CRP, low HDL, high cholesterol, vitamin D deficiency). Needle-free predictors were age, sex, grip strength, gait speed, blood pressure, heart rate, pulse pressure, BMI, waist, hip, waist-to-height ratio, waist-to-hip ratio, knee height, and walking-aid use. A circularity audit confirmed zero overlap. Sixteen algorithms were compared by fivefold cross-validation. Logistic regression and LightGBM were trained on 80% of the data and evaluated on a held-out 20% test set, with bootstrap CIs, calibration, SHAP, decision-curve analysis, permutation tests of the complete pipeline, and continuous regression. Sensitivity analyses varied the outcome definition, class balancing, survey weighting, and medication-related proxies. Results: Cross-validated ROC AUC ranged 0.481 to 0.524. LightGBM achieved AUC 0.539 (95% CI 0.477\u20130.596), and logistic regression 0.584 (0.528\u20130.638) in the held-out set, but the median held-out AUC of logistic regression across 200 random partitions was 0.518. Permutation tests of the complete cross-validated pipelines did not separate performance from chance (p \u2265 0.38). Continuous regression yielded negative R2 values for all markers (\u22120.14 to \u22120.08). No marker, sex stratum (men AUC 0.457; women 0.54), or sensitivity analysis reached useful discrimination (all AUC \u2264 0.58), and no model showed net benefit. SHAP importance was diffuse, with grip and gait ranked below all anthropometric and vital-sign features. Conclusions: In this sample, the evaluated bedside and anthropometric measures carried at most weak information about the predefined blood-biomarker profile, well below the level required for clinical use. They should not replace laboratory measurement of these biomarkers when biochemical assessment is clinically indicated.<\/jats:p>","DOI":"10.3390\/jcm15197454","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T08:09:02Z","timestamp":1790323742000},"page":"7454","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Needle-Free Bedside and Anthropometric Measures Show Weak Discrimination of an Adverse Blood-Biomarker Profile in Middle-Aged and Older Mexican Adults: An Internally Validated Machine-Learning Study"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6190-938X","authenticated-orcid":false,"given":"Exal","family":"Garcia-Carrillo","sequence":"first","affiliation":[{"name":"Department of Physical Activity Sciences, Faculty of Education Sciences, Universidad Cat\u00f3lica del Maule, Talca 3480112, Chile"},{"name":"Department of Physical 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