Abstract
Objectives
This analysis sought to determine factors (including adiposity-related factors) most associated with HF-type symptoms (fatigue, shortness of breath, and edema) in adults with chronic kidney disease (CKD).
Background
Symptom burden impairs quality of life in CKD, especially symptoms that overlap with HF. These symptoms are common regardless of clinical HF diagnosis, and may be affected by subtle cardiac dysfunction, kidney dysfunction, and other factors. We used machine learning to investigate cross-sectional relationships of clinical variables with symptom scores in a CKD cohort.
Methods
Participants in the Chronic Renal Insufficiency Cohort (CRIC) with a baseline modified Kansas City Cardiomyopathy Questionnaire (KCCQ) score were included, regardless of prior HF diagnosis. The primary outcome was Overall Summary Score as a continuous measure. Predictors were 99 clinical variables representing demographic, cardiac, kidney and other health dimensions. A correlation filter was applied. Random forest regression models were fitted. Variable importance scores and adjusted predicted outcomes are presented.
Results
The cohort included 3426 individuals, 10.3% with prior HF diagnosis. BMI was the most important factor, with BMI 24.3 kg/m2 associated with the least symptoms. Symptoms worsened with higher or lower BMIs, with a potentially clinically relevant 5 point score decline at 35.7 kg/m2 and a 1-point decline at the threshold for low BMI, 18.5 kg/m2. The most important cardiac and kidney factors were heart rate and eGFR, the 4th and 5th most important variables, respectively. Results were similar for secondary analyses.
Conclusions
In a CKD cohort, BMI was the most important feature for explaining HF-type symptoms regardless of clinical HF diagnosis, identifying an important focus for symptom directed investigations.
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Data availability
The data used for this study are available from the National Institute of Diabetes and Digestive and Kidney Diseases Central Repository: https://repository.niddk.nih.gov/studies/cric/.
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Acknowledgements
Dr. Navaneethan is supported by research funding from the Department of Veterans Affairs Health Services Research & Development (1I01HX002917–01A1) and a grant from the National Institutes of Health (NIDDK-R01DK101500). Dr. Gregg is supported in part by the Center for Innovations in Quality, Effectiveness and Safety (CIN 13–413), Michael E. DeBakey VA Medical Center, Houston, TX.
Funding
Funding for this project was obtained through the CRIC Study Opportunity Pool Program. Funding for the CRIC Study was obtained under a cooperative agreement from National Institute of Diabetes and Digestive and Kidney Diseases (U01DK060990, U01DK060984, U01DK061022, U01DK061021, U01DK061028, U01DK060980, U01DK060963, U01DK060902 and U24DK060990). In addition, this work was supported in part by: the Perelman School of Medicine at the University of Pennsylvania Clinical and Translational Science Award NIH/NCATS UL1TR000003, Johns Hopkins University UL1 TR-000424, University of Maryland GCRC M01 RR-16500, Clinical and Translational Science Collaborative of Cleveland, UL1TR000439 from the National Center for Advancing Translational Sciences (NCATS) component of the National Institutes of Health and NIH roadmap for Medical Research, Michigan Institute for Clinical and Health Research (MICHR) UL1TR000433, University of Illinois at Chicago CTSA UL1RR029879, Tulane COBRE for Clinical and Translational Research in Cardiometabolic Diseases P20 GM109036, Kaiser Permanente NIH/NCRR UCSF-CTSI UL1 RR-024131, Department of Internal Medicine, University of New Mexico School of Medicine Albuquerque, NM R01DK119199.
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CPW and SDN contributed to the research idea, study design, and data acquisition. CPW, JSB, LPG, NB, VN, HIF, MGS, and SDN contributed to the data analysis/interpretation. CPW performed the statistical analysis. Each author contributed important intellectual content during manuscript drafting or revision and accepts accountability for the overall work.
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Dr. Walther reports consulting fees from GlaxoSmithKline. Dr. Navaneethan reports personal fees from Bayer, personal fees from Boehringer-Ingelheim, personal fees from REATA, personal fees from Tricida, and grants from Keryx, outside the submitted work. Dr. Nambi has a provisional patent along with Roche and Baylor College of Medicine for use of biomarkers in prediction of heart failure risk and was the site PI for studies sponsored by Merck and Amgen. Dr. Shlipak has reported consulting fees from Cricket Health and Intercept Pharmaceuticals.
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Walther, C.P., Benoit, J.S., Gregg, L.P. et al. Heart failure-type symptom scores in chronic kidney disease: The importance of body mass index. Int J Obes 46, 1910–1917 (2022). https://doi.org/10.1038/s41366-022-01208-x
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DOI: https://doi.org/10.1038/s41366-022-01208-x