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High Fitness Levels Offset the Increased Risk of Chronic Kidney Disease due to Low Socioeconomic Status: A Prospective Study

  • Setor K. Kunutsor
    Correspondence
    Requests for reprint should be addressed to Setor K. Kunutsor, Translational Health Sciences, Bristol Medical School, University of Bristol, Learning & Research Building (Level 1), Southmead Hospital, Bristol, BS10 5NB, UK.
    Affiliations
    National Institute for Health Research Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol, Bristol, UK

    Musculoskeletal Research Unit, Translational Health Sciences, Bristol Medical School, University of Bristol, Learning & Research Building (Level 1), Southmead Hospital, Bristol, UK

    Diabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, UK
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  • Sae Young Jae
    Affiliations
    Graduate School of Urban Public Health, University of Seoul, Seoul, Republic of Korea

    Department of Sport Science, University of Seoul, Seoul, South Korea

    Department of Urban Big Data Convergence, University of Seoul, Seoul, Republic of Korea
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  • Jussi Kauhanen
    Affiliations
    Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
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  • Jari A. Laukkanen
    Affiliations
    Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland

    Central Finland Health Care District, Department of Medicine, Jyväskylä, Finland District, Jyväskylä, Finland

    Institute of Clinical Medicine, Department of Medicine, University of Eastern Finland, Kuopio, Finland
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Open AccessPublished:July 08, 2022DOI:https://doi.org/10.1016/j.amjmed.2022.06.010

      Abstract

      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 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.

      Keywords

      Clinical Significance
      • Socioeconomic status and fitness are each independently associated with chronic kidney disease.
      • There are significant additive and multiplicative interactions between socioeconomic status and fitness levels in relation to chronic kidney disease.
      • The risk of chronic kidney disease is increased in men with low socioeconomic status and low fitness levels, but high fitness levels appear to offset the increased chronic kidney disease risk related to low socioeconomic status.
      Chronic kidney disease is documented to be associated with an increased risk of cardiovascular disease,
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      which is also the leading cause of mortality globally.

      World Health Organization. Fact sheets. The top 10 causes of death. Available at: https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death. Accessed September 10, 2021.

      In addition to the high health care 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.
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      Major risk factors that contribute to chronic kidney disease include diabetes, hypertension, and metabolic syndrome.
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      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 differentials 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.
      • Hawkins NM
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      Lower SES is also associated with an increased risk of chronic kidney disease.
      • Zeng X
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      Associations between socioeconomic status and chronic kidney disease: a meta-analysis.
      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.
      • Wilund KR
      • Thompson S
      • Viana JL
      • Wang AY.
      Physical activity and health in chronic kidney disease.
      Cardiorespiratory fitness (CRF), an indicator of cardiopulmonary function, which can be improved through increased PA and exercise training,
      • Ross R
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      • et al.
      Importance of assessing cardiorespiratory fitness in clinical practice: a case for fitness as a clinical vital sign: a scientific statement from the American Heart Association.
      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.
      • Kodama S
      • Saito K
      • Tanaka S
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      Cardiorespiratory fitness as a quantitative predictor of all-cause mortality and cardiovascular events in healthy men and women: a meta-analysis.
      • Lee J
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      Association of cardiorespiratory fitness and hemodynamic responses to submaximal exercise testing with the incidence of chronic kidney disease: the Framingham Heart Study.
      • DeFina LF
      • Barlow CE
      • Radford NB
      • Leonard D
      • Willis BL.
      The association between midlife cardiorespiratory fitness and later life chronic kidney disease: the Cooper Center Longitudinal Study.
      • Laukkanen JA
      • Isiozor NM
      • Kunutsor SK.
      Objectively assessed cardiorespiratory fitness and all-cause mortality risk: an updated meta-analysis of 37 cohort studies involving 2,258,029 million participants.
      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.
      • Jae SY
      • Kurl S
      • Bunsawat K
      • et al.
      Impact of cardiorespiratory fitness on survival in men with low socioeconomic status.
      • Kunutsor SK
      • Jae SY
      • Makikallio TH
      • Kurl S
      • Laukkanen JA
      High fitness levels offset the increased risk of chronic obstructive pulmonary disease due to low socioeconomic status: a cohort study.
      • Jae SY
      • Heffernan KS
      • Kurl S
      • et al.
      Cardiorespiratory fitness, inflammation, and the incident risk of pneumonia.
      • Kerrigan DJ
      • Brawner CA
      • Ehrman JK
      • Keteyian S.
      Cardiorespiratory fitness attenuates the impact of risk factors associated with COVID-19 hospitalization.
      • Kokkinos P
      • Faselis C
      • Franklin B
      • et al.
      Cardiorespiratory fitness, body mass index and heart failure incidence.
      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.
      • Jae SY
      • Kurl S
      • Bunsawat K
      • et al.
      Impact of cardiorespiratory fitness on survival in men with low socioeconomic status.
      ,
      • Kunutsor SK
      • Jae SY
      • Makikallio TH
      • Kurl S
      • Laukkanen JA
      High fitness levels offset the increased risk of chronic obstructive pulmonary disease due to low socioeconomic status: a cohort study.
      ,
      • Kunutsor SK
      • Jae SY
      • Makikallio TH
      • Laukkanen JA
      High fitness levels attenuate the increased risk of hypertension due to low socioeconomic status in middle-aged men: a cohort study.
      ,
      • Kunutsor SK
      • Jae SY
      • Makikallio TH
      • Laukkanen JA
      High fitness levels attenuate the increased risk of heart failure due to low socioeconomic status: a cohort study.
      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) guidelines
      • Simera I
      • Moher D
      • Hoey J
      • Schulz KF
      • Altman DG.
      A catalogue of reporting guidelines for health research.
      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.
      • Laukkanen JA
      • Lavie CJ
      • Khan H
      • Kurl S
      • Kunutsor SK.
      Cardiorespiratory fitness and the risk of serious ventricular arrhythmias: a prospective cohort study.
      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 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.
      • Kunutsor SK
      • Makikallio TH
      • Araujo CGS
      • Jae SY
      • Kurl S
      • Laukkanen JA
      Cardiorespiratory fitness is not associated with risk of venous thromboembolism: a cohort study.
      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.
      • Salonen JT
      • Nyyssonen K
      • Korpela H
      • Tuomilehto J
      • Seppanen R
      • Salonen R.
      High stored iron levels are associated with excess risk of myocardial infarction in eastern Finnish men.
      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.
      • Kunutsor SK
      • Jae SY
      • Makikallio TH
      • Kurl S
      • Laukkanen JA
      High fitness levels offset the increased risk of chronic obstructive pulmonary disease due to low socioeconomic status: a cohort study.
      ,
      • Kunutsor SK
      • Jae SY
      • Makikallio TH
      • Laukkanen JA
      High fitness levels attenuate the increased risk of hypertension due to low socioeconomic status in middle-aged men: a cohort study.
      ,
      • Kunutsor SK
      • Jae SY
      • Makikallio TH
      • Laukkanen JA
      High fitness levels attenuate the increased risk of heart failure due to low socioeconomic status: a cohort study.
      Briefly, the items for each indicator were scored and summed. For 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.
      • Jae SY
      • Kurl S
      • Bunsawat K
      • et al.
      Impact of cardiorespiratory fitness on survival in men with low socioeconomic status.
      ,
      • Jae SY
      • Bunsawat K
      • Kurl S
      • et al.
      Cardiorespiratory fitness attenuates the increased risk of sudden cardiac death associated with low socioeconomic status.
      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. Energy expenditure of PA was assessed using the validated KIHD 12-month leisure-time PA questionnaire,
      • Laukkanen JA
      • Laaksonen D
      • Lakka TA
      • et al.
      Determinants of cardiorespiratory fitness in men aged 42 to 60 years with and without cardiovascular disease.
      ,
      • Kunutsor SK
      • Khan H
      • Laukkanen JA.
      Serum albumin concentration and incident type 2 diabetes risk: new findings from a population-based cohort study.
      modified from the Minnesota Leisure-Time PA Questionnaire.
      • Taylor HL
      • Jacobs Jr., DR
      • Schucker B
      • Knudsen J
      • Leon AS
      • Debacker G
      A questionnaire for the assessment of leisure time physical activities.
      Estimated glomerular filtration rate (GFR) was estimated using the Chronic Kidney Disease Epidemiology Collaboration equation
      • Inker LA
      • Schmid CH
      • Tighiouart H
      • et al.
      Estimating glomerular filtration rate from serum creatinine and cystatin C.
      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 hazards assumptions using scaled Schoenfeld residuals.
      • Therneau TM
      • Grambsch PM.
      Modeling Survival Data: Extending the Cox Model.
      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,
      • Kunutsor SK
      • Laukkanen JA.
      Gamma-glutamyltransferase and risk of chronic kidney disease: a prospective cohort study.
      or their potential as confounders based on known associations with chronic kidney disease and observed associations with the exposures using the available data.
      • Groenwold RH
      • Klungel OH
      • Grobbee DE
      • Hoes AW.
      Selection of confounding variables should not be based on observed associations with exposure.
      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.
      • Jae SY
      • Kurl S
      • Bunsawat K
      • et al.
      Impact of cardiorespiratory fitness on survival in men with low socioeconomic status.
      ,
      • Jae SY
      • Bunsawat K
      • Kurl S
      • et al.
      Cardiorespiratory fitness attenuates the increased risk of sudden cardiac death associated with low socioeconomic status.
      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.
      • Harrell Jr, FE
      Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis.
      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.
      • Jae SY
      • Kurl S
      • Bunsawat K
      • et al.
      Impact of cardiorespiratory fitness on survival in men with low socioeconomic status.
      ,
      • Kunutsor SK
      • Jae SY
      • Makikallio TH
      • Kurl S
      • Laukkanen JA
      High fitness levels offset the increased risk of chronic obstructive pulmonary disease due to low socioeconomic status: a cohort study.
      ,
      • Kunutsor SK
      • Jae SY
      • Makikallio TH
      • Laukkanen JA
      High fitness levels attenuate the increased risk of hypertension due to low socioeconomic status in middle-aged men: a cohort study.
      ,
      • Kunutsor SK
      • Jae SY
      • Makikallio TH
      • Laukkanen JA
      High fitness levels attenuate the increased risk of heart failure due to low socioeconomic status: a cohort study.
      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.
      • Knol MJ
      • VanderWeele TJ
      • Groenwold RH
      • Klungel OH
      • Rovers MM
      • Grobbee DE.
      Estimating measures of interaction on an additive scale for preventive exposures.
      Additive interactions were assessed using the relative excess risk due to interaction (RERI), computed for binary variables as RERIHR = HR11-HR10-HR01+1,
      • Li R
      • Chambless L.
      Test for additive interaction in proportional hazards models.
      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).
      • Li R
      • Chambless L.
      Test for additive interaction in proportional hazards models.
      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.

      Results

      Baseline Characteristics

      The overall mean (SD) age, SES, and CRF of study participants at baseline was 53 (5) years, 8.54 (4.24) and 30.3 (7.9) mL/kg/min, respectively (Table). Significant inverse correlations were observed among CRF and SES, age, alcohol consumption, body mass index, blood pressure, total cholesterol, and estimated GFR; whereas, significant positive correlations were observed with PA, high-density lipoprotein cholesterol, and creatinine. Values of CRF were significantly lower in men with preexisting disease such as type 2 diabetes, hypertension, and coronary heart disease compared to those without as well as current smokers compared with nonsmokers.
      TableBaseline participant characteristics and correlates of cardiorespiratory fitness
      Mean (SD), median (IQR), or n (%)Pearson correlation r (95% CI)
      Pearson correlation coefficients between CRF and the row variables.
      Percentage difference (95% CI) in values of CRF per 1 SD higher or compared to reference category of correlate
      Percentage change in values of CRF per 1-SD increase in the row variable (or for categorical variables, the percentage difference in mean values of CRF for the category versus the reference).
      Cardiorespiratory fitness (mL/kg/min)30.3 (7.9)--
      Socioeconomic status8.54 (4.24)−0.13 (−0.17, −0.09)***−0.99% (−1.31, −0.66)***
      Questionnaire/Prevalent conditions
      Age at survey (y)53 (5)−0.35 (−0.38, −0.31)***−2.75% (−3.07, −2.43)***
      Alcohol consumption (g/wk)32.0 (6.4-94.0)−0.10 (−0.14, −0.05)***−0.73% (−1.07, −0.39)***
      History of type 2 diabetes
       No2025 (96.5)-ref
       Yes74 (3.5)-−5.72% (−7.44, −4.00)***
      Smoking status
       Other1439 (68.6)-ref
       Current660 (31.4)-−2.59% (−3.27, −1.91)***
      History of hypertension
       No1475 (70.3)-ref
       Yes624 (29.7)-−3.51% (-−4.20, −2.83)***
      History of coronary heart disease
       No1599 (76.2)ref
       Yes500 (23.8)−5.45% (−6.17, −4.72)***
      Physical measurements
      BMI (kg/m2)26.9 (3.5)−0.37 (−0.41, −0.33)***−2.77% (−3.07, −2.47)***
      SBP (mm Hg)134 (17)−0.14 (−0.18, −0.09)***−1.02% (−1.34, −0.70)***
      DBP (mm Hg)89 (10)−0.15 (−0.19, −0.11)***−1.10% (−1.41, −0.78)***
      Physical activity (KJ/d)1183 (621-1938)0.13 (0.09, 0.18)***1.00% (0.68, 1.31)***
      Blood-based markers
      Total cholesterol (mmol/L)5.92 (1.08)−0.07 (-−0.11, −0.03)**−0.54% (−0.86, −0.22)**
      HDL-C (mmol/L)1.30 (0.30)0.26 (0.22, 0.30)***1.97% (1.66, 2.28)***
      Serum creatinine (µmol/1)88.3 (11.6)0.06 (0.02, 0.10)*0.47% (0.15, 0.78)*
      Estimated GFR (mL/min/1.73 m2)88.0 (16.5)−0.07 (−0.11, −0.02)*−0.50% (−0.83, −0.18)*
      BMI = body mass index; CI = confidence interval; CRF = cardiorespiratory fitness; DBP = diastolic blood pressure; GFR = glomerular filtration rate; HDL-C = high-density lipoprotein-cholesterol; IQR = interquartile range; SD = standard deviation; SBP = systolic blood pressure.
      Asterisks indicate the level of statistical significance: * P < .05; **, P <0.01; ***, P < .001.
      Pearson correlation coefficients between CRF and the row variables.
      Percentage change in values of CRF per 1-SD increase in the row variable (or for categorical variables, the percentage difference in mean values of CRF for the category versus the reference).
      Appendix 1STROBE 2007 Statement—Checklist of items that should be included in reports of cohort studies
      Section/TopicItem #RecommendationReported on page #
      Title and abstract1(a) Indicate the study's design with a commonly used term in the title or the abstractPage 1
      (b) Provide in the abstract an informative and balanced summary of what was done and what was foundPage 2
      Introduction
      Background/rationale2Explain the scientific background and rationale for the investigation being reportedPages 3-4
      Objectives3State specific objectives, including any prespecified hypothesesPage 4
      Methods
      Study design4Present key elements of study design early in the paperMethods
      Setting5Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collectionMethods
      Participants6(a) Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-upMethods
      (b) For matched studies, give matching criteria and number of exposed and unexposedNot applicable
      Variables7Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicableMethods
      Data sources/ measurement8*For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one groupMethods
      Bias9Describe any efforts to address potential sources of biasMethods
      Study size10Explain how the study size was arrived atMethods
      Quantitative variables11Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and whyMethods
      Statistical methods12(a) Describe all statistical methods, including those used to control for confoundingMethods
      (b) Describe any methods used to examine subgroups and interactionsMethods
      (c) Explain how missing data were addressedNot applicable
      (d) If applicable, explain how loss to follow-up was addressedNot applicable
      (e) Describe any sensitivity analysesMethods
      Results
      Participants13*(a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysedMethods
      (b) Give reasons for non-participation at each stageMethods
      (c) Consider use of a flow diagramSupplementary Digital Content 2
      Descriptive data14*(a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential confoundersResults; Table 1
      (b) Indicate number of participants with missing data for each variable of interest
      (c) Summarise follow-up time (eg, average and total amount)Results
      Outcome data15*Report numbers of outcome events or summary measures over timeResults
      Main results16(a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95% confidence interval). Make clear which confounders were adjusted for and why they were includedResults; Figure 1
      (b) Report category boundaries when continuous variables were categorizedResults; Figure 1
      (c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period
      Other analyses17Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analysesResults
      Discussion
      Key results18Summarise key results with reference to study objectivesDiscussion
      Limitations
      Interpretation20Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidenceDiscussion
      Generalisability21Discuss the generalisability (external validity) of the study resultsDiscussion
      Other information
      Funding22Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is basedAfter Discussion

      Interplay Among SES, CRF, and Chronic Kidney Disease Risk

      During a median (interquartile range) follow-up of 25.8 (18.0-28.0) years, 197 incident chronic kidney disease cases were recorded. In analysis adjusted for model 2 covariates: age, 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, low compared with high SES was associated with an increased risk of chronic kidney disease 1.55 (95% CI: 1.06-2.25; Figure 1), which remained similar on further adjustment for CRF (Figure 1). A multivariable restricted cubic spline curve showed that chronic kidney disease risk decreased continuously with increasing CRF across the range 25-46 mL/kg/min (P value for nonlinearity = .19; Figure 2). On adjustment for covariates in model 2, high CRF was associated with a decreased risk of chronic kidney disease compared with low CRF 0.66 (95% CI: 0.45-0.96), which remained similar on additional adjustment for SES (Figure 1). The association was significant when CRF was modeled per 1 SD increment (Figure 1).
      Figure 1
      Figure 1Separate and joint associations of SES and CRF with risk of chronic kidney disease. CRF = cardiorespiratory fitness; HR = hazard ratio; ref, reference; SD = standard deviation; SES = socioeconomic status. For the joint associations, cutoffs for SES and CRF were based on the median values. Model 1: Adjusted for 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 glomerular filtration rate, and physical activity; Model 3: Model 2 plus CRF for SES and SES for CRF.
      Figure 2
      Figure 2Restricted cubic splines of the hazard ratios of chronic kidney disease with CRF. CRF = cardiorespiratory fitness. Dashed lines represent the 95% confidence intervals for the spline model (solid line). Models were adjusted for age, systolic blood pressure, history of type 2 diabetes, smoking status, history of hypertension, history of coronary heart disease, total cholesterol, alcohol consumption, estimated glomerular filtration rate, and physical activity.
      Appendix 2
      Appendix 2Flow diagram. CKD, chronic kidney disease; CRF, cardiorespiratory fitness; SES, socioeconomic status.
      Compared with men with high SES-high CRF, multivariable analysis (model 2) showed that men with low SES-low CRF had an increased risk of chronic kidney disease 1.88 (95% CI: 1.23-2.87), with no evidence of an association for low SES-high CRF and chronic kidney disease risk 1.32 (95% CI: 0.85-2.05; Figure 1). Results of interaction analysis showed the RERI was 0.31 and the ratio of HRs was 1.14, indicating the presence of both additive and multiplicative interactions.

      Discussion

      Our results based on a general population-based prospective cohort study of middle-aged and older Caucasian men confirms the previously reported independent associations of low SES with increased chronic kidney disease risk and high CRF levels with lowered risk of chronic kidney disease. The association between CRF and chronic kidney disease was potentially consistent with a graded dose-response relationship across the range of CRF values 25-46 mL/kg/min. New findings based on the joint associations of SES and CRF with chronic kidney disease risk showed that the risk of chronic kidney disease was increased in men with low SES and low CRF, but the increased risk of chronic kidney disease related to low SES was attenuated to null by high CRF levels. In interaction analysis, the association between the combined exposures (ie, low SES and low CRF) and chronic kidney disease risk exceeded the sum or product of their associations considered separately.
      Biological, behavioral, and psychosocial risk factors prevalent in socioeconomically deprived individuals are known to accentuate the relationship between low SES and chronic disease outcomes.
      • Schultz WM
      • Kelli HM
      • Lisko JC
      • et al.
      Socioeconomic status and cardiovascular outcomes: challenges and interventions.
      These include lower levels of education, unhealthy lifestyles such as excessive alcohol consumption, limited access to health care, higher prevalence of comorbid conditions, and stress and depression. Social deprivation may also be associated with delayed presentation of, and lower rates of treatment, dose, and adherence to therapy for hypertension, diabetes, and metabolic syndrome, which are major risk factors for chronic kidney disease.
      • Saran R
      • Li Y
      • Robinson B
      • et al.
      US renal data system 2014 annual data report: epidemiology of kidney disease in the United States.
      ,
      • Kurella M
      • Lo JC
      • Chertow GM.
      Metabolic syndrome and the risk for chronic kidney disease among nondiabetic adults.
      Though CRF is determined by many nonmodifiable factors such as age, sex, and underlying disease states, with about half of its variation being heritable,
      • Bouchard C.
      Genomic predictors of trainability.
      it largely remains a modifiable risk factor. Increased PA and exercise training are well-documented methods for increasing CRF.
      • Billingsley H
      • Rodriguez-Miguelez P
      • Del Buono MG
      • Abbate A
      • Lavie CJ
      • Carbone S
      Lifestyle interventions with a focus on nutritional strategies to increase cardiorespiratory fitness in chronic obstructive pulmonary disease, heart failure, obesity, sarcopenia, and frailty.
      Both aerobic and resistance training are effective for the beneficial modulation of dysglycemia, high blood pressure, obesity, dyslipidemia, and inflammation,
      • Gould DW
      • Graham-Brown MP
      • Watson EL
      • Viana JL
      • Smith AC.
      Physiological benefits of exercise in pre-dialysis chronic kidney disease.
      which are all involved in the pathophysiology of chronic kidney disease. Other specific mechanisms postulated to underpin the protective effects of habitual PA on chronic kidney disease include improved endothelial dysfunction and alleviation of sympathetic overactivity.
      • Di Francescomarino S
      • Sciartilli A
      • Di Valerio V
      • Di Baldassarre A
      • Gallina S.
      The effect of physical exercise on endothelial function.
      • Hambrecht R
      • Fiehn E
      • Weigl C
      • et al.
      Regular physical exercise corrects endothelial dysfunction and improves exercise capacity in patients with chronic heart failure.
      • Fu Q
      • Levine BD.
      Exercise and the autonomic nervous system.
      Both SES and CRF are powerful determinants of various chronic disease outcomes. The current findings are clinically relevant as they add to the increasing evidence on the plentiful health benefits of CRF and its ability to attenuate or offset the adverse effects of other major risk factors. Previous investigations have shown that higher CRF levels can offset the increased risk of diagnosed chronic obstructive pulmonary disease, hypertension, heart failure, and mortality due to low SES.
      • Jae SY
      • Kurl S
      • Bunsawat K
      • et al.
      Impact of cardiorespiratory fitness on survival in men with low socioeconomic status.
      ,
      • Kunutsor SK
      • Jae SY
      • Makikallio TH
      • Kurl S
      • Laukkanen JA
      High fitness levels offset the increased risk of chronic obstructive pulmonary disease due to low socioeconomic status: a cohort study.
      ,
      • Kunutsor SK
      • Jae SY
      • Makikallio TH
      • Laukkanen JA
      High fitness levels attenuate the increased risk of hypertension due to low socioeconomic status in middle-aged men: a cohort study.
      ,
      • Kunutsor SK
      • Jae SY
      • Makikallio TH
      • Laukkanen JA
      High fitness levels attenuate the increased risk of heart failure due to low socioeconomic status: a cohort study.
      The beneficial effects of PA and exercise are well documented and are observed across most organ systems, and these include enhancement of resilience, the immune system, and longevity.
      • Chow LS
      • Gerszten RE
      • Taylor JM
      • et al.
      Exerkines in health, resilience and disease.
      The World Health Organization recommended in 2020 that that all adults should aim for 150-300 minutes of moderate intensity PA per week or 75-150 minutes of vigorous intensity PA per week or an equivalent combination of moderate intensity and vigorous intensity PA per week.
      • Bull FC
      • Al-Ansari SS
      • Biddle S
      • et al.
      World Health Organization 2020 guidelines on physical activity and sedentary behaviour.
      These guidelines have evolved from previous guidelines.
      • Piercy KL
      • Troiano RP
      • Ballard RM
      • et al.
      The physical activity guidelines for Americans.
      Despite guideline recommendations and population-wide strategies to promote PA levels, most populations do not adhere to PA recommendations. Promoting habitual PA and exercise training (which confers good CRF) is a critical intervention than can reduce the incidence and prevalence of common chronic diseases including chronic kidney disease. Populations at high risk of these chronic diseases including the socioeconomically deprived need more education on the substantial benefits of PA. Furthermore, access to PA resources that are both feasible and attractive for these populations also need to be widened.
      Strengths of the current study include the novelty, being the first evaluation of the separate and joint associations of SES and CRF with chronic kidney disease risk as well as formal investigation of the interactions between SES and CRF levels in relation to chronic kidney disease; the use of a population-based prospective cohort design comprising a relatively homogeneous sample of men with normal kidney function at baseline; the long-term follow-up duration of the cohort that was adequate for the ascertainment of the outcome under investigation; and employment of directly measured CRF as assessed using peak oxygen uptake during CPX (gold standard measure). Limitations deserving consideration included the use of self-administered questionnaires in assessing SES, which may be prone to misclassification bias; inability to generalize findings to women and other populations; lack of data on medications (eg, regular use of nonsteroidal anti-inflammatory drugs) during follow-up, which may have impacted on kidney function; and the potential for biases such as residual confounding, reverse causation, and regression dilution bias because of the observational cohort design.

      Conclusions

      In middle-aged and older Caucasian 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, including significant additive and multiplicative interactions between SES and fitness levels in relation to chronic kidney disease and high fitness levels appearing to offset the increased risk of chronic kidney disease related to low SES.

      Acknowledgments

      We thank the staff of the Kuopio Research Institute of Exercise Medicine and the Research Institute of Public Health and University of Eastern Finland, Kuopio, Finland for the data collection in the study.

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