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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.
National Institute for Health Research Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol, Bristol, UKMusculoskeletal Research Unit, Translational Health Sciences, Bristol Medical School, University of Bristol, Learning & Research Building (Level 1), Southmead Hospital, Bristol, UKDiabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, UK
Graduate School of Urban Public Health, University of Seoul, Seoul, Republic of KoreaDepartment of Sport Science, University of Seoul, Seoul, South KoreaDepartment of Urban Big Data Convergence, University of Seoul, Seoul, Republic of Korea
Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, FinlandCentral Finland Health Care District, Department of Medicine, Jyväskylä, Finland District, Jyväskylä, FinlandInstitute of Clinical Medicine, Department of Medicine, University of Eastern Finland, Kuopio, Finland
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.
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.
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.
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.
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,
Chronic Kidney Disease Prognosis Consortium Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis.
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.
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.
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.
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.
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.
Study Design and Participants
Reporting of the study conforms to broad Enhancing the Quality and Transparency of Health Research (EQUATOR) guidelines
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.
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.
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.
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.
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,
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.
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.
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,
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.
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.
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.
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).
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.
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
Appendix 1STROBE 2007 Statement—Checklist of items that should be included in reports of cohort studies
Reported on page #
Title and abstract
(a) Indicate the study's design with a commonly used term in the title or the abstract
(b) Provide in the abstract an informative and balanced summary of what was done and what was found
Explain the scientific background and rationale for the investigation being reported
State specific objectives, including any prespecified hypotheses
Present key elements of study design early in the paper
Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection
(a) Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up
(b) For matched studies, give matching criteria and number of exposed and unexposed
Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable
Data sources/ measurement
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 group
Describe any efforts to address potential sources of bias
Explain how the study size was arrived at
Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and why
(a) Describe all statistical methods, including those used to control for confounding
(b) Describe any methods used to examine subgroups and interactions
(c) Explain how missing data were addressed
(d) If applicable, explain how loss to follow-up was addressed
(e) Describe any sensitivity analyses
(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 analysed
(b) Give reasons for non-participation at each stage
(c) Consider use of a flow diagram
Supplementary Digital Content 2
(a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential confounders
Results; 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)
Report numbers of outcome events or summary measures over time
(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 included
Results; Figure 1
(b) Report category boundaries when continuous variables were categorized
Results; Figure 1
(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period
Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses
Summarise key results with reference to study objectives
Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence
Discuss the generalisability (external validity) of the study results
Give 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 based
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
The contribution of chronic kidney disease to the global burden of major noncommunicable diseases.