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Multisystem Trajectories Over the Adult Life Course and Relations to Cardiovascular Disease and Death

Teemu J Niiranen1,2,3, Danielle M Enserro1,4, Martin G Larson1,4

  • 1National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, Massachusetts.

The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences
|October 26, 2018
PubMed

Insights

Tracking multiple biological system aging trajectories can identify individuals at high risk for cardiovascular disease (CVD) and mortality. This longitudinal analysis reveals key indicators for predicting adverse health outcomes.

Area of Science:

  • Gerontology and Aging Research
  • Biomarker Discovery and Validation
  • Longitudinal Health Studies

Background:

  • Comprehensive characterization of long-term biological system trajectories is currently lacking.
  • Understanding differential aging across biological systems is crucial for predicting health outcomes.

Purpose of the Study:

  • To characterize conjoint, long-term trajectories of 14 distinct biological systems.
  • To assess the association of these trajectories with cardiovascular disease (CVD) incidence and mortality.
  • To evaluate the utility of trajectory risk scores for identifying high-risk individuals.

Main Methods:

  • Serial measurements of 14 biological indicators (e.g., BMI, HbA1c, FVC, gait speed) in 3,453 participants over a median of 8 years.
  • Analysis of sex-specific trajectories and their correlations.
  • Multivariable regression models to link trajectories with CVD events and mortality.

Main Results:

  • Modest correlations (r < .30) were observed among the 14 sex-specific biological trajectories.
  • CVD incidence was associated with trajectories of BMI, HbA1c, TC/HDL, gait time, and pulse pressure.
  • Mortality risk was linked to trajectories of gait time, C-reactive protein, heart rate, and pulse pressure, and inversely to MMSE and FEV1/FVC.
  • A trajectory risk score significantly predicted CVD (OR 2.80) and mortality (OR 2.71).
  • Trajectory risk scores showed improved predictive ability compared to single-time measures for CVD and mortality.

Conclusions:

  • Biological systems exhibit differential aging patterns throughout the lifespan.
  • Longitudinal tracking of a concise set of biomarkers offers a promising approach for identifying individuals at elevated risk.
  • This method can enhance early detection and intervention strategies for age-related diseases.
Abstract

Keywords:
AgingCardiovascular diseaseEpidemiologyTrajectories

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