Composite Measures of Physical Fitness to Discriminate Between Healthy Aging and Heart Failure: The COmPLETE Study

Jonathan Wagner; Raphael Knaier; Karsten Königstein; Christopher Klenk; Justin Carrard; Eric Lichtenstein; Hubert Scharnagl; Winfried März; Henner Hanssen; Timo Hinrichs; Arno Schmidt-Trucksäss; Konstantin Arbeev

doi:10.3389/fphys.2020.596240

Composite Measures of Physical Fitness to Discriminate Between Healthy Aging and Heart Failure: The COmPLETE Study

Front Physiol. 2020 Dec 15:11:596240. doi: 10.3389/fphys.2020.596240. eCollection 2020.

Affiliations

¹ Department of Sport, Exercise and Health, Faculty of Medicine, University of Basel, Basel, Switzerland.
² Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria.
³ Medical Clinic V, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
⁴ SYNLAB Academy, SYNLAB Holding Deutschland GmbH, Mannheim, Germany.
⁵ Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC, United States.

Abstract

Background: Aging and changing age demographics represent critical problems of our time. Physiological functions decline with age, often ending in a systemic process that contributes to numerous impairments and age-related diseases including heart failure (HF). We aimed to analyze whether differences in composite measures of physiological function [health distance (HD)], specifically physical fitness, between healthy individuals and patients with HF, can be observed.

Methods: The COmPLETE Project is a cross-sectional study of 526 healthy participants aged 20-91 years and 79 patients with stable HF. Fifty-nine biomarkers characterizing fitness (cardiovascular endurance, muscle strength, and neuromuscular coordination) and general health were assessed. We computed HDs as the Mahalanobis distance for vectors of biomarkers (all and domain-specific subsets) that quantified deviations of individuals' biomarker profiles from "optimums" in the "reference population" (healthy participants aged <40 years). We fitted linear regressions with HD outcomes and disease status (HF/Healthy) and relevant covariates as predictors and logistic regressions for the disease outcome and sex, age, and age² as covariates in the base model and the same covariates plus combinations of one or two HDs.

Results: Nine out of 10 calculated HDs showed evidence for group differences between Healthy and HF (p ≤ 0.002) and most models presented a negative estimate of the interaction term age by group (p < 0.05 for eight HDs). The predictive performance of the base model for HF cases significantly increased by adding HD General health or HD Fitness [areas under the receiver operating characteristic (ROC) curve (AUCs) 0.63, 0.89, and 0.84, respectively]. HD Cardiovascular endurance alone reached an AUC of 0.88. Further, there is evidence that the combination of HDs Cardiovascular endurance and General health shows superior predictive power compared to single HDs.

Conclusion: HD composed of physical fitness biomarkers differed between healthy individuals and patients with HF, and differences between groups diminished with increasing age. HDs can successfully predict HF cases, and HD Cardiovascular endurance can significantly increase the predictive power beyond classic clinical biomarkers. Applications of HD could strengthen a comprehensive assessment of physical fitness and may present an optimal target for interventions to slow the decline of physical fitness with aging and, therefore, to increase health span.

Keywords: aging; cardiopulmonary exercise test; cardiorespiratory fitness; fitness; heart failure; physiological dysregulation; statistical distance; strength.