Details

Autor(en) / Beteiligte

• Perry, Andrew S
• Farber-Eger, Eric
• Gonzales, Tomas
• Tanaka, Toshiko
• Robbins, Jeremy M
• Murthy, Venkatesh L
• Stolze, Lindsey K
• Zhao, Shilin
• Huang, Shi
• Colangelo, Laura A
• Deng, Shuliang
• Hou, Lifang
• Lloyd-Jones, Donald M
• Walker, Keenan A
• Ferrucci, Luigi
• Watts, Eleanor L
• Barber, Jacob L
• Rao, Prashant
• Mi, Michael Y
• Gabriel, Kelley Pettee
• Hornikel, Bjoern
• Sidney, Stephen
• Houstis, Nicholas
• Lewis, Gregory D
• Liu, Gabrielle Y
• Thyagarajan, Bharat
• Khan, Sadiya S
• Choi, Bina
• Washko, George
• Kalhan, Ravi
• Wareham, Nick
• Bouchard, Claude
• Sarzynski, Mark A
• Gerszten, Robert E
• Brage, Soren
• Wells, Quinn S
• Nayor, Matthew
• Shah, Ravi V

Titel

Proteomic analysis of cardiorespiratory fitness for prediction of mortality and multisystem disease risks

Ist Teil von

• Nature medicine, 2024-06, Vol.30 (6), p.1711-1721

Ort / Verlag

New York: Nature Publishing Group

Erscheinungsjahr

2024

Link zum Volltext

Beschreibungen/Notizen

• Abstract Despite the wide effects of cardiorespiratory fitness (CRF) on metabolic, cardiovascular, pulmonary and neurological health, challenges in the feasibility and reproducibility of CRF measurements have impeded its use for clinical decision-making. Here we link proteomic profiles to CRF in 14,145 individuals across four international cohorts with diverse CRF ascertainment methods to establish, validate and characterize a proteomic CRF score. In a cohort of around 22,000 individuals in the UK Biobank, a proteomic CRF score was associated with a reduced risk of all-cause mortality (unadjusted hazard ratio 0.50 (95% confidence interval 0.48–0.52) per 1 s.d. increase). The proteomic CRF score was also associated with multisystem disease risk and provided risk reclassification and discrimination beyond clinical risk factors, as well as modulating high polygenic risk of certain diseases. Finally, we observed dynamicity of the proteomic CRF score in individuals who undertook a 20-week exercise training program and an association of the score with the degree of the effect of training on CRF, suggesting potential use of the score for personalization of exercise recommendations. These results indicate that population-based proteomics provides biologically relevant molecular readouts of CRF that are additive to genetic risk, potentially modifiable and clinically translatable.