High Fitness Levels Offset The Increased Risk Of Chronic Kidney Disease Due To Low Socioeconomic Status: A Prospective Study

BACKGROUND: Socioeconomic status (SES) and cardiorespiratory fitness (CRF) are each independently associated with chronic kidney disease. The interplay among SES, CRF, and chronic kidney disease is not well understood. We aimed to evaluate the separate and joint associations of SES and CRF with chronic kidney disease risk in a cohort of Caucasian men. METHODS: In 2099 men aged 42-61 years with normal kidney function at baseline, SES was self-reported and CRF was directly measured using a respiratory gas exchange analyzer during cardiopulmonary exercise testing. Hazard ratios (HRs) (95% confidence interval) were estimated for chronic kidney disease. 

RESULTS: A total of 197 chronic kidney disease events occurred during a median follow-up of 25.8 years. Comparing low versus high SES, the multivariable-adjusted HR (95% confidence interval) for chronic kidney disease was 1.55 (1.06-2.25), which remained consistent on further adjustment for CRF 1.53 (1.06- 2.22). Comparing high versus low CRF, the multivariable-adjusted HR for chronic kidney disease was 0.66 (0.45-0.96), which persisted on further adjustment for SES 0.67 (0.46-0.97). Compared with high SES-high CRF, low SES-low CRF was associated with an increased risk of chronic kidney disease 1.88 (1.23-2.87), with no evidence of an association for low SES-high CRF and chronic kidney disease risk 1.32 (0.85-2.05). Positive additive (relative excess risk due to interaction = 0.31) and multiplicative (ratio of HRs = 1.14) interactions were found between SES and CRF in relation to chronic kidney disease risk. 

CONCLUSIONS: In middle-aged and older males, SES and CRF are each independently associated with the risk of incident chronic kidney disease. There exists an interplay among SES, CRF, and chronic kidney disease risk, with high CRF levels appearing to offset the increased chronic kidney disease risk related to low SES. 2022 The Authors. Published by Elsevier Inc. This is an open-access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
The American Journal of Medicine (2022) 135:1247−1254

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KEYWORDS: Cardiorespiratory fitness; Chronic kidney disease; Cohort study; Socioeconomic status

Chronic kidney disease is documented to be associated with an increased risk of cardiovascular disease,1,2 which is also the leading cause of mortality globally.3 In addition to the high healthcare costs associated with treating chronic kidney disease, it is associated with poor quality of life, especially in patients who progress to end-stage renal disease.4 Major risk factors that contribute to chronic kidney disease include diabetes, hypertension, and metabolic syndrome.5,6 Given the substantial global public burden due to chronic kidney disease, there is a need to identify modifiable risk factors that will prevent chronic kidney disease or slow its progression. Targeting multiple risk factors is a more effective approach to reducing the risk of disease compared with single risk factor modification.

Socioeconomic differences in health are well documented. Indeed, individuals with low socioeconomic status (SES) have a higher risk of chronic diseases such as cardiovascular disease and other adverse outcomes, including mortality, than those of higher SES.7,8 Lower SES is also associated with an increased risk of chronic kidney disease.9 The beneficial effects of regular physical activity (PA) and exercise training in preventing cardiovascular disease and promoting overall health are well established. These benefits of PA may also extend to chronic kidney disease incidence and its progression.10 Cardiorespiratory fitness (CRF), an indicator of cardiopulmonary function, which can be improved through increased PA and exercise training,11 is measured by maximal oxygen uptake (VO2max) during cardiopulmonary exercise testing (CPX). CRF is an independent risk marker for chronic disease outcomes including cardiovascular disease and chronic kidney disease, as well as mortality.12-15 There is emerging evidence of an interplay among CRF, known risk markers, and these adverse outcomes. It has been reported that higher levels of CRF can attenuate or offset the adverse impact of other risk factors.16-20 Our group has also shown that higher CRF levels can offset the increased risk of chronic obstructive pulmonary disease, hypertension, heart failure, and mortality due to low SES.16,17,21,22 It is unclear whether the beneficial effects of CRF extend to decreasing the risk of chronic kidney disease among underserved populations. We hypothesized that high CRF levels would attenuate the increased risk of chronic kidney disease due to low SES. To explore this, we sought to evaluate the separate and joint effects of SES and CRF on the risk of incident chronic kidney disease using a population-based prospective cohort of middle-aged and older Caucasian men with normal kidney function baseline. 

METHODS Study Design and Participants 

Reporting of the study conforms to broad Enhancing the Quality and Transparency of Health Research (EQUATOR) guidelines23 and was conducted in accordance with Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for reporting observational studies in epidemiology (Appendix 1, available online). Participants included in the current analysis were part of the Kuopio Ischemic Heart Disease (KIHD) risk factor study, a general population-based prospective cohort study designed to investigate risk factors for atherosclerotic cardiovascular disease and other related chronic disease outcomes among Finnish adults. A detailed description of the study design and recruitment methods have been described previously.24 But briefly, a representative sample of 3433 men aged 42-61 years living in the city of Kuopio and its surrounding communities in eastern Finland, were invited for screening between March 1, 1984, and December 31, 1989, for possible inclusion in the study. Of the 3433 men, 3235 were found to be eligible for inclusion into the study, and of this number, 2682 volunteered to participate and 553 did not respond to the invitation or declined to give informed consent. For this analysis, we excluded men with 1) kidney dysfunction at baseline (n = 56) and 2) missing data on the exposures and potential confounders (n = 527). The current analysis included 2099 men with complete information on both exposures (SES and CRF), relevant covariates, and incident chronic kidney disease events (Appendix 2, available online). The Research Ethics Committee of the University of Kuopio approved the study protocol, and each study participant provided written informed consent. All study procedures adhered to the Declaration of Helsinki.

Assessment of Exposures, Covariates, and Chronic Kidney Disease 

Measurement of blood biomarkers, physical measurements including blood pressure, and assessment of lifestyle characteristics and medical history have been described in detail in previous reports.25 Participants fasted overnight and abstained from drinking alcohol for at least 3 days and from smoking for at least 12 hours before blood samples were taken between 8 a.m. and 10 a.m. Self-administered questionnaires were used to assess medical history and lifestyle characteristics such as smoking, alcohol consumption, and SES.26 The assessment of SES involved the creation of a summary index comprising relevant indicators such as income, education, occupational prestige, material standard of living, and housing conditions.17,21,22 Brieflfly, the items for each indicator were scored and summed. For the material standard of living at baseline assessment, a material possession index was based on self-reports of ownership of 12 items (color TV, video tape recorder, freezer, dishwasher, car, motorcycle, telephone, summer cottage, house trailer, motorboat, sailing boat, and ski mobile). An individual score was derived about the ownership of the 12 items and divided by the total number of item responses. The composite SES index ranged from 0 to 25, with higher values indicating lower SES. Peak oxygen uptake (VO2peak), used as a measure of CRF, was directly assessed using a computerized metabolic measurement system (Medical Graphics) during a maximal symptom-limited exercise-tolerance test on an electrically braked cycle ergometer, which was conducted between 8:00 a.m. and 10:00 a.m.16,27 The standardized testing protocol included a 3-min warmup at 50 watts (W; 1 W = 6.12 kgm/min), followed by 20 W/min increases in workload with direct analyses of expired respiratory gases. The respiratory gas analyzer expressed VO2peak as an average value recorded over 8 seconds. Peak oxygen uptake was defined as the highest attained value for oxygen consumption or a plateau in oxygen uptake at maximal exercise. A history of coronary heart disease was defined as previous myocardial infarction, angina pectoris, the use of nitroglycerin for chest pain once a week or more frequently, or chest pain. The energy expenditure of PA was assessed using the validated KIHD 12-month leisure-time PA questionnaire,28,29 modified from the Minnesota Leisure-Time PA Questionnaire.30 Estimated glomerular filtration rate (GFR) was estimated using the Chronic Kidney Disease Epidemiology Collaboration equation31 using the formula: 141 £ (creatinine in mg/dL/0.9) 1.209 £ 0.993Age.

Chronic kidney disease was defined as kidney damage or estimated GFR lower than 60 mL/min per 1.73 m2 for 3 months or longer. In the KIHD study, participants are under continuous surveillance for the development of new outcomes including chronic kidney disease events. All incident chronic kidney disease cases that occurred from study entry to 2014 were included. There were no losses to follow up. Chronic kidney disease outcomes were collected from the National Hospital Discharge Register data by computer linkage and a comprehensive review of available hospital records, wards of health centers, health practitioner questionnaires, and medicolegal reports.

Statistical Analysis 

Baseline characteristics were presented as means (standard deviation [SD]) or median (interquartile range [IQR]) for continuous variables based on the normality of the distributions and percentages for categorical variables. To assess the cross-sectional associations of CRF with various risk markers, Pearson’s correlation coefficients were estimated using linear regression models adjusted for age. Hazard ratios (HRs) with 95% confidence intervals (CIs) for incident chronic kidney disease were estimated using Cox proportional hazard models after confirmation of no major departure from the proportionality of hazard assumptions using scaled Schoenfeld residuals.32 Adjustment for covariates was based on 3 models: (Model 1) age; (Model 2) Model 1 plus systolic blood pressure, history of type 2 diabetes, smoking status, history of hypertension, history of coronary heart disease, total cholesterol, alcohol consumption, estimated GFR, and PA; and (Model 3) Model 2 plus mutual adjustment (CRF for SES and SES for CRF). These covariates were selected based on their previously established roles as risk factors for chronic kidney disease, evidence from previous research, previously published associations with chronic kidney disease in the KIHD study,33 or their potential as confounders based on known associations with chronic kidney disease and observed associations with the exposures using the available data.34 To evaluate the separate associations of SES and CRF with chronic kidney disease, the exposures were categorized into tertiles to maintain consistency with previous reports.16,27 CRF was also modeled per SD increase given evidence of a linear relationship with chronic kidney disease risk using multivariable restricted cubic spline curves. To explore a potential nonlinear dose-response relationship between CRF and chronic kidney disease risk, we constructed a multivariable restricted cubic spline with knots at the 5th, 35th, 65th, and 95th percentiles of the distribution of CRF as recommended by Harrell.35

For the evaluation of the joint associations, study participants were divided into 4 groups according to median categories of SES and CRF: high SES-high CRF; high SES-low CRF; low SES-low CRF; and low SES-high CRF, as in previous reports.16,17,21,22 Interactions between SES and CRF were examined on both the additive and multiplicative scales in relation to chronic kidney disease risk. Interaction on an additive scale means that the combined effect of two exposures is larger (or smaller) than the sum of the individual effects of the two exposures, whereas interaction on a multiplicative scale means that the combined effect is larger (or smaller) than the product of the individual effects.36 Additive interactions were assessed using the relative excess risk due to interaction (RERI), computed for binary variables as RERIHR = HR11-HR10-HR01+1,37 where HR11 is the HR of the outcome (ie, chronic kidney disease) if both risk factors (low SES and low CRF) are present, HR10 is the HR of the outcome if 1 risk factor is present and the other is absent, with HR01 being vice versa. Multiplicative interactions were assessed using the ratio of HRs = HR11/ (HR10 £ HR01).37 A positive additive interaction is indicated if RERI > 0 and a positive multiplicative interaction is indicated if the ratio of HRs > 1. Stata version MP 17 (Stata Corp) was employed for all analyses.

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