NMR metabolomic modeling of age and lifespan: A multicohort analysis

• Published in: Aging cell 2024-07, Vol.23 (7), p.e14164-n/a
• Main Authors: Lau, Chung‐Ho E., Manou, Maria, Markozannes, Georgios, Ala‐Korpela, Mika, Ben‐Shlomo, Yoav, Chaturvedi, Nish, Engmann, Jorgen, Gentry‐Maharaj, Aleksandra, Herzig, Karl‐Heinz, Hingorani, Aroon, Järvelin, Marjo‐Riitta, Kähönen, Mika, Kivimäki, Mika, Lehtimäki, Terho, Marttila, Saara, Menon, Usha, Munroe, Patricia B., Palaniswamy, Saranya, Providencia, Rui, Raitakari, Olli, Schmidt, Amand Floriaan, Sebert, Sylvain, Wong, Andrew, Vineis, Paolo, Tzoulaki, Ioanna, Robinson, Oliver
• Format: Article
• Language: English
• Subjects: Adult; Aged; Aged, 80 and over; Aging; biological age; C-reactive protein; Cardiovascular diseases; Chronic obstructive pulmonary disease; Cohort Studies; cohort study; Dementia disorders; Diabetes mellitus; Female; Finland - epidemiology; Glomerular filtration rate; Humans; Life span; Longevity; Longitudinal studies; Lung cancer; Lung diseases; Magnetic resonance spectroscopy; Magnetic Resonance Spectroscopy - methods; Male; metabolome; Metabolomics; Metabolomics - methods; Middle Aged; molecular epidemiology; Morbidity; Mortality; NMR; Nuclear magnetic resonance; Ovarian cancer; Phenotypes; population aging; Predictions; Regression analysis; Risk Factors; Womens health; Young Adult
• ISSN: 1474-9718; 1474-9726; 1474-9726
• DOI: 10.1111/acel.14164

Metabolomic age models have been proposed for the study of biological aging, however, they have not been widely validated. We aimed to assess the performance of newly developed and existing nuclear magnetic resonance spectroscopy (NMR) metabolomic age models for prediction of chronological age (CA), mortality, and age‐related disease. Ninety‐eight metabolic variables were measured in blood from nine UK and Finnish cohort studies (N ≈31,000 individuals, age range 24–86 years). We used nonlinear and penalized regression to model CA and time to all‐cause mortality. We examined associations of four new and two previously published metabolomic age models, with aging risk factors and phenotypes. Within the UK Biobank (N ≈102,000), we tested prediction of CA, incident disease (cardiovascular disease (CVD), type‐2 diabetes mellitus, cancer, dementia, and chronic obstructive pulmonary disease), and all‐cause mortality. Seven‐fold cross‐validated Pearson's r between metabolomic age models and CA ranged between 0.47 and 0.65 in the training cohort set (mean absolute error: 8–9 years). Metabolomic age models, adjusted for CA, were associated with C‐reactive protein, and inversely associated with glomerular filtration rate. Positively associated risk factors included obesity, diabetes, smoking, and physical inactivity. In UK Biobank, correlations of metabolomic age with CA were modest (r = 0.29–0.33), yet all metabolomic model scores predicted mortality (hazard ratios of 1.01 to 1.06/metabolomic age year) and CVD, after adjustment for CA. While metabolomic age models were only moderately associated with CA in an independent population, they provided additional prediction of morbidity and mortality over CA itself, suggesting their wider applicability. Metabolomic age models have been proposed for the study of biological aging. Performance of newly developed and existing nuclear magnetic resonance spectroscopy (NMR) metabolomic age models for prediction of chronological age (CA), mortality, and age‐related disease were assessed in UK and Finnish populations.