A study published in Nature Medicine journal describes how proteomic scores of cardiorespiratory fitness can help predict disease risk as well as all-cause and disease-specific mortality risks beyond genetic risk scores.
Study Design: We developed and validated a circulating proteomic signature of CRF across four cohorts and various exercise modalities. In the UKB, we examined the relationship a proteomic CRF signature with a broad range of clinical endpoints and examined its interaction with polygenic risk. In HERITAGE, we examined the association of the proteomic CRF signature with response to exercise training and correlated changes in signature with changes in CRF. NAFLD, nonalcoholic fatty liver disease. Proteomic analysis of cardiorespiratory fitness for prediction of mortality and multisystem disease risks
Background
Cardiorespiratory fitness (CRF) is a useful prognostic marker of improved health, quality of life, and longevity. Assessing CRF in clinical settings is an effective approach to stratifying disease risk and promoting health.
However, the widespread use of CRF for clinical decision-making is restricted by some factors, including lack of availability and reproducibility of tests and high expenses. Training-responsive biomarkers of CRF is an alternative approach that may address these limitations and enable the identification of pharmacological targets that mimic the effects of exercise.
Exercise induces multiple changes in the metabolic state, which is depicted by changes in blood levels of metabolites associated with CRF. These molecular surrogates of CRF and training responses are associated with clinical prognosis.
In this study, scientists have established, validated, and characterized a proteomic signature of CRF by linking proteomic profiles to CRF in 14,145 individuals across four international cohorts with diverse CRF assessment methods.
They have used data from 22,000 individuals from the UK Biobank to determine the association of the proteomic signature of CRF with a broad range of clinical outcomes (death, cardiovascular, metabolic, malignancy, neurological) and examine its interaction with genetic risk score.
Furthermore, they have used the Health, Risk Factors, Exercise Training, and Genetics (HERITAGE) family study data to evaluate the impact of a 20-week exercise training program on the proteomic signature of CRF.
Proteomic CRF score
The scientists developed a proteomic CRF score in the Coronary Artery Risk Development in Young Adults (CARDIA) study derivation dataset using exercise treadmill test time as the CRF measure. They further validated the proteomic CRF score across approximately 12,500 participants from four international cohorts.
They observed mechanistically reasonable directionality for proteins involved in innate immunity and inflammation, atherosclerosis, neuronal survival and growth, cell physiology, oxidative stress, energy expenditure, and substrate fuel utilization, adiposity, peripheral muscle responses to exercise, and autophagy.
Association between proteomic CRF score and clinical outcomes
The scientists analyzed proteomic profiles and survival data of 21,988 participants from the UK Biobank and observed a significant inverse association between proteomic CRF score and risk of all-cause mortality and disease-specific mortality.
They observed the proteomic CRF score's consistent and robust protective effect on cardiovascular, metabolic, and neurological outcomes. They found that the proteomic CRF score improves risk prediction beyond standard clinical risk factors, with improved risk reclassification and discrimination.
By analyzing the interaction between the proteomic CRF score and genetic risk score of common diseases, they observed a significant additive effect between the proteomic CRF score and each genetic risk score on the corresponding disease outcome. The highest disease risk was observed in participants with the lowest proteomic CRF score and a high genetic risk score.
Impact of exercise on proteomic CRF score
The scientists observed an increase in the proteomic CRF score following a 20-week exercise training program. This change was associated with an alteration in peak oxygen consumption, independent of age, sex, race, body mass index (BMI), pre-training peak oxygen consumption, and pre-training proteomic CRF score.
Specifically, they observed that a higher proteomic CRF score is associated with greater peak oxygen consumption with training, independent of age, sex, and race. However, this association was not sustained after adjusting for BMI.
Furthermore, they observed that the proteomic CRF score components that exhibited significant post-training changes are correlated with a range of metabolic, vascular, and myocardial phenotypes.
Many of these components (proteins) were correlated with adiposity reduction, lipid metabolism, bone morphogenic pathway regulation, and ischemia-reperfusion injury management.
Study significance
The study describes the development of a circulating proteomic signature of CRF using a treadmill exercise test that showed a consistent relation across sub-maximal treadmill exams in 10,320 UK residents and maximal cardiopulmonary exercise exams in 1,587 US residents.
The proteomic signature of CRF exhibits robust and independent associations with a range of metabolic, cardiovascular, and neurological clinical outcomes. These associations seem to be additive to the genetic risk of corresponding diseases. This highlights the utility of proteomic CRF signature for multiomic evaluation of disease and mortality risks.
The study also highlights the dynamicity of proteomic CRF score following a 20-week exercise training program and an association between training-related changes in the score and peak oxygen consumption. This highlights the utility of proteomic CRF score for personalization of exercise recommendations.
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