Teemu J Niiranen1,2,3, Danielle M Enserro1,4, Martin G Larson1,4, Ramachandran S Vasan1,5,6,7. 1. National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, Massachusetts. 2. Department of Public Health Solutions, National Institute for Health and Welfare, Turku, Finland. 3. Department of Medicine, Turku University Hospital and University of Turku, Turku, Finland. 4. Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts. 5. Department of Medicine, Section of Preventive Medicine, Boston University School of Medicine, Boston, Massachusetts. 6. Department of Medicine, Section of Cardiology, Boston University School of Medicine, Boston, Massachusetts. 7. Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts.
Abstract
BACKGROUND: Comprehensive conjoint characterization of long-term trajectories representing several biological systems is lacking. METHODS: We measured serially indicators representing 14 distinct biological systems in up to 3,453 participants attending four Framingham Study examinations: bone mineral density, body mass index (BMI), C-reactive protein, glomerular filtration rate, forced vital capacity (FVC), 1 second forced expiratory volume/FVC ratio (FEV1/FVC), gait speed, grip strength, glycosylated hemoglobin (HbA1c), heart rate, left ventricular mass, Mini-Mental State Examination (MMSE), pulse pressure, and total/high-density lipoprotein cholesterol ratio (TC/HDL). RESULTS: We observed that correlations among the 14 sex-specific trajectories were modest (r < .30 for 169 of 182 sex-specific correlations). During follow-up (median 8 years), 232 individuals experienced a cardiovascular disease (CVD) event and 393 participants died. In multivariable regression models, CVD incidence was positively related to trajectories of BMI, HbA1c, TC/HDL, gait time, and pulse pressure (p < .06); mortality risk was related directly to trajectories of gait time, C-reactive protein, heart rate, and pulse pressure but inversely to MMSE and FEV1/FVC (p < .006). A unit increase in the trajectory risk score was associated with a 2.80-fold risk of CVD (95% confidence interval [CI], 2.04-3.84; p < .001) and a 2.71-fold risk of death (95% CI, 2.30-3.20; p < .001). Trajectory risk scores were suggestive of a greater increase in model c-statistic compared with single occasion measures (delta-c compared with age- and sex-adjusted models: .032 vs .026 for CVD; .042 vs .030 for mortality). CONCLUSIONS: Biological systems age differentially over the life course. Longitudinal data on a parsimonious set of biomarkers reflecting key biological systems may facilitate identification of high-risk individuals.
BACKGROUND: Comprehensive conjoint characterization of long-term trajectories representing several biological systems is lacking. METHODS: We measured serially indicators representing 14 distinct biological systems in up to 3,453 participants attending four Framingham Study examinations: bone mineral density, body mass index (BMI), C-reactive protein, glomerular filtration rate, forced vital capacity (FVC), 1 second forced expiratory volume/FVC ratio (FEV1/FVC), gait speed, grip strength, glycosylated hemoglobin (HbA1c), heart rate, left ventricular mass, Mini-Mental State Examination (MMSE), pulse pressure, and total/high-density lipoprotein cholesterol ratio (TC/HDL). RESULTS: We observed that correlations among the 14 sex-specific trajectories were modest (r < .30 for 169 of 182 sex-specific correlations). During follow-up (median 8 years), 232 individuals experienced a cardiovascular disease (CVD) event and 393 participants died. In multivariable regression models, CVD incidence was positively related to trajectories of BMI, HbA1c, TC/HDL, gait time, and pulse pressure (p < .06); mortality risk was related directly to trajectories of gait time, C-reactive protein, heart rate, and pulse pressure but inversely to MMSE and FEV1/FVC (p < .006). A unit increase in the trajectory risk score was associated with a 2.80-fold risk of CVD (95% confidence interval [CI], 2.04-3.84; p < .001) and a 2.71-fold risk of death (95% CI, 2.30-3.20; p < .001). Trajectory risk scores were suggestive of a greater increase in model c-statistic compared with single occasion measures (delta-c compared with age- and sex-adjusted models: .032 vs .026 for CVD; .042 vs .030 for mortality). CONCLUSIONS: Biological systems age differentially over the life course. Longitudinal data on a parsimonious set of biomarkers reflecting key biological systems may facilitate identification of high-risk individuals.
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