Towards Precision in Sarcopenia Assessment: The Challenges of Multimodal Data Analysis in the Era of AI
Abstract
1. Introduction
2. Methodology for Selection of Studies
3. AI for Comprehensive Data Integration in Sarcopenia
4. Application of AI for Identifying Molecular Biomarkers
5. Circulating Biomarkers: Update and New Frontiers for AI in Sarcopenia Research
Circulating Signature | Biological Source | Expression Profile | Ref. |
---|---|---|---|
D3-creatinine | urine | downregulation | [57] |
ALDOA | serum | upregulation | [58] |
CTSD | serum | upregulation | [58] |
P3NP | serum | upregulation | [59] |
IL6 | serum | upregulation | [60,61] |
TNF | serum | upregulation | [61] |
CAF | serum | upregulation | [62] |
VCAM1 | serum | upregulation | [63] |
GDF15 | serum | upregulation | [64] |
CETP | serum | upregulation | [65] |
APOA2 | serum | downregulation | [65] |
IGF1 | serum | downregulation | [66] |
GH | serum | downregulation | [66] |
Cf-mtDNA | plasma | high levels | [53] |
miR-28-5p | plasma | upregulation | [45] |
miR-1-3p | plasma | upregulation | [54,67] |
miR-133a | plasma | downregulation | [44] |
miR-133a-3p | serum | downregulation | [68] |
miR-200a-3p | serum | downregulation | [68] |
miR-434-3p | plasma | downregulation | [44] |
miR-455-3p | plasma | downregulation | [44] |
miR-486 | plasma | downregulation | [55] |
miR-146a | plasma | downregulation | [55] |
miR-21 | serum | upregulation | [69] |
traumatic acid | plasma | high levels | [70] |
ceramides | plasma | high levels | [71] |
sphyngomielins | plasma | high levels | [71] |
sphyngomielins | plasma | high and low levels depending on lipid | [72] |
sterol ST(d14:0/25:5) | plasma | high levels | [72] |
phosphatidylcholines | plasma | high and low levels depending on lipid | [72] |
phosphatidylserines | plasma | high and low levels depending on lipid | [72] |
PI 32:1 | plasma | high levels | [73] |
isoleucine | plasma | low levels | [73] |
1-methylhistamine/3-methylhistamine | plasma | high levels | [73] |
carnosine | plasma | low levels | [73] |
creatinine | plasma | low levels | [73] |
arginine | serum | low levels | [43] |
cystin | serum | low levels | [43] |
taurin | serum | high levels | [43] |
hypoxanthine | plasma | high levels | [56] |
hypoxanthine | serum | high levels | [74] |
L-2-amino-3-oxobutanoic acid | plasma | low levels | [56,74] |
PC(14:0/20:2(11Z,14Z)) | plasma | low levels | [56] |
LysoPC(17:0) | plasma | low levels | [56] |
palmitic acid | plasma | low levels | [56] |
mannose | serum | high levels | [74] |
galactose | serum | high levels | [74] |
triethanolamine | serum | low levels | [74] |
homogentisic acid | serum | low levels | [74] |
oleoyl ethanolamide | plasma | high levels | [42] |
stearoyl ethanolamide | plasma | low levels | [42] |
docosahexaenoylethanolamide | plasma | low levels | [42] |
6. Discussion
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Dataset | AI Model | Performance | Key Predictors | Ref. |
---|---|---|---|---|
EHR from 1304 patients | RF, SVM | AUC > 90% | Diagnoses, medications, lab tests | [15] |
166 patients, 99 variables | RBF SVM | Accuracy: 82.5%, F1: 90.2%, precision: 82.8% | Age, hypertension, MNA, sodium | [18] |
133 subjects, BPW signals | LDA, Scoring System | AUC: 0.77 (LDA), 0.83 (scoring) | APS | [17] |
CHARLS | XGBoost | AUROC: 0.759 | MMSE, drinking habits, BUN | [20] |
WCHAT cohort, XMAT validation | Wide and Deep | AUC: 0.97, ACC: 91.1% | MAMC, CC, TSF, AST/ALT ratio | [29] |
Italian ageing populations | RF (3 models) | ACC 89.89%, sensitivity 14.50%, specificity 99.37% | Albumin, CRP, vitamin D, folates | [22] |
1510 patients | LR | S: 0.33, SO: 0.19 OS: 0.267 | BMI | [24] |
KNHANES (4020 patients) | LR, RF, SVM, GBM | AUC [men–women] RF: 0.82–0.78 SVM: 0.8–0.81 GB: 0.81–0.81 LR: 0.82–0.80 | BMI, RBC, nutrient intake, water intake | [25] |
3096 Japanese patients | CNN | ACC: 0.88 | MAMC, CC, TSF, AST/ALT ratio | [16] |
879 oncological patients | PCA + K-means | ACC: PC1 (59%), PC2 (24%), PC3 (15%) | Advanced age, lung, gynecological, gastroint. cancer, diabetes, malnutrition | [32] |
231 post-surgical patients | XGBoost vs. LASSO | AUROC: 0.98 | Serum albumin, diabetes, type surgery, nutritional score, ECOG status | [33] |
Dataset | AI Model | Key Biomarkers | Performance | Ref. |
---|---|---|---|---|
-GSE1428 -GSE136344 | LASSO, SVM-RFE | MYH8, HOXB2, CDKN1A | AUC > 0.7 | [34] |
-GSE111017 | DNN (DSnet-v1) | 27 AI-featured genes (e.g., H4C3, PSMA6, CENPC, VPS35L) | Acc. 0.96 Sens. 1.000 Spec. 0.94 AUC: 0.99 | [14] |
-GSE8479 -GSE9103 -GSE38718 -GSE1428 | RF, ANN | MT1X, CISD1, WISP2 | AUC: 0.999 (train), 0.85 (test) | [35] |
509 Korean males | RFECV, Ensemble ML | 8 CpG sites | AUC: 0.94 | [38] |
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Caputo, V.; Letteri, I.; Santini, S.J.; Sinatti, G.; Balsano, C. Towards Precision in Sarcopenia Assessment: The Challenges of Multimodal Data Analysis in the Era of AI. Int. J. Mol. Sci. 2025, 26, 4428. https://doi.org/10.3390/ijms26094428
Caputo V, Letteri I, Santini SJ, Sinatti G, Balsano C. Towards Precision in Sarcopenia Assessment: The Challenges of Multimodal Data Analysis in the Era of AI. International Journal of Molecular Sciences. 2025; 26(9):4428. https://doi.org/10.3390/ijms26094428
Chicago/Turabian StyleCaputo, Valerio, Ivan Letteri, Silvano Junior Santini, Gaia Sinatti, and Clara Balsano. 2025. "Towards Precision in Sarcopenia Assessment: The Challenges of Multimodal Data Analysis in the Era of AI" International Journal of Molecular Sciences 26, no. 9: 4428. https://doi.org/10.3390/ijms26094428
APA StyleCaputo, V., Letteri, I., Santini, S. J., Sinatti, G., & Balsano, C. (2025). Towards Precision in Sarcopenia Assessment: The Challenges of Multimodal Data Analysis in the Era of AI. International Journal of Molecular Sciences, 26(9), 4428. https://doi.org/10.3390/ijms26094428