Aims: This review aims to evaluate the hypothesis that Volatilomics-the comprehensive analysis of volatile organic compounds (VOCs) from breath, skin, urine, and other biological matrices-can serve as a non-invasive tool to characterize metabolic alterations associated with aging and diabetes mellitus in older adults, supporting precision geriatric medicine. Methods: We conducted a narrative review of experimental and clinical studies investigating VOC signatures in aging individuals with diabetes. The analysis focused on associations between VOC profiles and key biological mechanisms, including oxidative stress, lipid peroxidation, mitochondrial dysfunction, inflammaging, and host–microbiome interactions. We also examined analytical technologies and methodologies, such as advanced mass spectrometry platforms, sensor-based devices, and artificial intelligence-driven pattern recognition approaches. Results: The reviewed evidence suggests that diabetes is associated with distinctive VOC patterns; however, direct evidence specifically derived from geriatric cohorts remains limited. These VOC patterns are associated with glycemic imbalance and age-related metabolic dysfunction and show potential utility for early detection, clinical phenotyping, and individualized monitoring, particularly in frail or multimorbid patients. Technological advances are facilitating translation toward portable and home-based applications. Conclusions: Volatilomics represents a promising, non-invasive approach to improve diabetes management in aging populations. Despite its potential, challenges remain, including methodological heterogeneity, limited reproducibility, confounding effects of comorbidities and polypharmacy, and the lack of large longitudinal geriatric cohorts.

Volatilomics insights in older adults with diabetes: from metabolic mechanisms to precision geriatric medicine

Sommella E.;Campiglia P.;
2026

Abstract

Aims: This review aims to evaluate the hypothesis that Volatilomics-the comprehensive analysis of volatile organic compounds (VOCs) from breath, skin, urine, and other biological matrices-can serve as a non-invasive tool to characterize metabolic alterations associated with aging and diabetes mellitus in older adults, supporting precision geriatric medicine. Methods: We conducted a narrative review of experimental and clinical studies investigating VOC signatures in aging individuals with diabetes. The analysis focused on associations between VOC profiles and key biological mechanisms, including oxidative stress, lipid peroxidation, mitochondrial dysfunction, inflammaging, and host–microbiome interactions. We also examined analytical technologies and methodologies, such as advanced mass spectrometry platforms, sensor-based devices, and artificial intelligence-driven pattern recognition approaches. Results: The reviewed evidence suggests that diabetes is associated with distinctive VOC patterns; however, direct evidence specifically derived from geriatric cohorts remains limited. These VOC patterns are associated with glycemic imbalance and age-related metabolic dysfunction and show potential utility for early detection, clinical phenotyping, and individualized monitoring, particularly in frail or multimorbid patients. Technological advances are facilitating translation toward portable and home-based applications. Conclusions: Volatilomics represents a promising, non-invasive approach to improve diabetes management in aging populations. Despite its potential, challenges remain, including methodological heterogeneity, limited reproducibility, confounding effects of comorbidities and polypharmacy, and the lack of large longitudinal geriatric cohorts.
2026
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4939215
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