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Epidemiology and Population Health

Heart failure-type symptom scores in chronic kidney disease: The importance of body mass index

International Journal of Obesity volume 46, pages 1910–1917 (2022)Cite this article

Subjects

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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Fig. 1: Standardized differences of predictor variables: KCCQ Overall Summary Score < 75 (clinically important symptoms) vs. ≥ 75 (absence of clinically important symptoms).
Fig. 2: Variable importance scores for predicting KCCQ Overall Summary Score, for the 20 most important variables.
Fig. 3: Adjusted* KCCQ Overall Summary Scores over BMI from the primary model (line), and distribution of BMI in the study cohort (bars).
Fig. 4: Adjusted* KCCQ Overall Summary Scores over hemoglobin from the primary model (line), and distribution of hemoglobin in the study cohort (bars).

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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.

Author information

Authors and Affiliations

  1. Selzman Institute for Kidney Health, Section of Nephrology, Baylor College of Medicine, Houston, TX, USA

    Carl P. Walther, L. Parker Gregg & Sankar D. Navaneethan

  2. Texas Institute for Measurement, Evaluation, and Statistics, University of Houston, Houston, TX, USA

    Julia S. Benoit

  3. Section of Nephrology, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA

    L. Parker Gregg & Sankar D. Navaneethan

  4. Kidney Research Institute and Division of Nephrology, University of Washington, Seattle, WA, USA

    Nisha Bansal

  5. Section of Cardiology, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA

    Vijay Nambi

  6. Section of Cardiovascular Research, Baylor College of Medicine, Houston, TX, USA

    Vijay Nambi

  7. Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA, USA

    Harold I. Feldman & Raymond R. Townsend

  8. Kidney Health Research Collaborative, Department of Medicine, University of California San Francisco, San Francisco, CA, USA

    Michael G. Shlipak

  9. General Internal Medicine Division, Medical Service, San Francisco Veterans Affairs Health Care System, San Francisco, CA, USA

    Michael G. Shlipak

  10. Institute of Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA

    Sankar D. Navaneethan

  11. Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA

    Lawrence J. Appel

  12. Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Lawrence J. Appel

  13. Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA

    Jing Chen

  14. Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA

    Jing Chen

  15. Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA, USA

    Alan S. Go

  16. Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA

    Alan S. Go

  17. Department of Medicine and Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA

    Alan S. Go

  18. Department of Medicine, Stanford University, Palo Alto, CA, USA

    Alan S. Go

  19. Department of Medicine, Division of Nephrology, University of Illinois at Chicago, Chicago, IL, USA

    James P. Lash

  20. Chronic Kidney Disease Section, Phoenix Epidemiology & Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ, USA

    Robert G. Nelson

  21. Division of Nephrology and Hypertension, University Hospital Cleveland Medical Center, Case Western Reserve University, Cleveland, OH, USA

    Mahboob Rahman

  22. Division of Nephrology, University of Michigan, Ann Arbor, MI, USA

    Panduranga S. Rao

  23. Department of Internal Medicine and Biochemistry, School of Medicine, University of New Mexico, Albuquerque, NM, USA

    Vallabh O. Shah

  24. Renal, Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA

    Raymond R. Townsend

  25. Department of Medicine, University of New Mexico, Albuquerque, NM, USA

    Mark L. Unruh

Authors
  1. Carl P. Walther
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  2. Julia S. Benoit
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Consortia

CRIC Study Investigators

  • Lawrence J. Appel
  • , Jing Chen
  • , Alan S. Go
  • , James P. Lash
  • , Robert G. Nelson
  • , Mahboob Rahman
  • , Panduranga S. Rao
  • , Vallabh O. Shah
  • , Raymond R. Townsend
  •  & Mark L. Unruh

Contributions

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.

Corresponding author

Correspondence to Carl P. Walther.

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Competing interests

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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