Body Mass Index Modifies The Protective Association of Physical Activity With Incident Chronic Kidney Disease: A Prospective Cohort Study

Summary

This prospective cohort study of over 11,000 adults shows that higher physical activity is associated with lower CKD risk, with stronger effects at higher BMI. Each 1000 MET-min/week increase was linked to greater risk reduction in obesity (HR = 0.72) than in overweight individuals (HR = 0.84).

Abstract

The interaction between physical activity (PA) and body mass index (BMI) on chronic kidney disease (CKD) risk is not well defined. This study aimed to investigate the independent and joint effects of PA and BMI on CKD incidence within the context of public health. This prospective cohort analysis included 11,597 adults with normal renal function from the Shanghai Suburban Adult Cohort and Biobank (SSACB). PA (MET-min/week) and BMI (kg/m²) were assessed at baseline and not updated during follow-up. Incident CKD was defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m² and/or a urine albumin-to-creatinine ratio (ACR) ≥30 mg/g at follow-up, consistent with KDIGO criteria. Cox regression models were used to assess main effects and the PA×BMI interaction. Over a median follow-up of 3.0 years, 485 incident CKD cases occurred. Higher PA was associated with lower CKD risk (per 1000 MET-min/week: HR = 0.91, 95% CI: 0.84–0.98). A significant interaction was observed (P = 0.031), indicating that the protective effect of PA was more pronounced at higher BMI levels. Specifically, each 1000 MET-min/week increase in PA was associated with a greater reduction in CKD risk in individuals with obesity (HR = 0.72) compared with those with overweight (HR ≈ 0.84). The protective effect of PA on CKD risk is modified by BMI, with greater benefits observed in individuals with overweight or obesity. These findings support prioritizing combined PA and weight management strategies for high-risk populations.

Introduction

Chronic kidney disease (CKD) is a major and growing global public health concern, characterized by high prevalence and strong associations with cardiovascular morbidity, mortality, and reduced quality of life¹. The burden is particularly substantial in China², where rapid population aging and increasing prevalence of metabolic disorders, especially obesity and physical inactivity, have contributed to a large CKD population. This trend places considerable pressure on healthcare systems and highlights the need for effective, scalable prevention strategies targeting modifiable risk factors. Such priorities are reflected in national initiatives, which emphasize integrated lifestyle interventions to address obesity and related chronic diseases³.

Among the constellation of risk factors for CKD, two interrelated, modifiable lifestyle elements stand out due to their high prevalence and profound impact: physical inactivity and obesity⁴. Substantial epidemiological evidence has independently linked higher levels of habitual physical activity (PA) with a lower risk of incident CKD and a slower decline in kidney function⁵. The protective mechanisms are multifactorial, involving improvements in blood pressure control, insulin sensitivity, lipid metabolism, and systemic inflammation. Concurrently, elevated body mass index (BMI) and obesity are well-established, potent drivers of kidney disease, primarily through pathways of glomerular hyperfiltration, adipose tissue dysfunction, and the promotion of a pro-inflammatory, pro-fibrotic state, with longitudinal studies confirming the impact of lifelong BMI increase on cardio-renal-metabolic risk⁶. In clinical practice and public health guidance, promoting PA and weight management are often recommended in parallel as cornerstones of cardiorenal health.

From a biological perspective, an interaction between PA and BMI is plausible. Individuals with higher BMI often experience greater metabolic stress, systemic inflammation, endothelial dysfunction, and increased hemodynamic burden on the kidneys, all of which contribute to CKD development. Physical activity has been shown to improve these adverse pathways by enhancing insulin sensitivity, reducing inflammatory signaling, and improving vascular and metabolic function⁷. Because individuals with overweight or obesity typically begin with a greater cardiometabolic burden, the physiological benefits associated with increased physical activity may be more pronounced in this group. This provides a biological rationale for examining whether BMI modifies the association between physical activity and CKD risk.

However, a key gap remains in understanding how PA and BMI jointly influence CKD risk. Most studies have examined these factors as independent or additive contributors contributors^7,8, while their potential interaction has been less frequently evaluated. From a physiological perspective, the effects of PA may vary across BMI levels, particularly in individuals with overweight or obesity. However, existing evidence regarding this potential interaction remains limited and inconsistent, and it is unclear whether the protective effect of PA differs across body weight categories. Clarifying this interaction is important for improving risk stratification and informing more targeted prevention strategies. Based on these considerations, we hypothesized that BMI modifies the association between physical activity and incident CKD risk. The objectives were to (1) examine the independent associations of PA and BMI with incident CKD risk; (2) assess the interaction between PA and BMI; and (3) evaluate the potential application of this interaction for risk stratification and personalized intervention.

Protocol

Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient(s) to publish this paper.

Study Design and Population

This prospective cohort study was based on the Shanghai Suburban Adult Cohort and Biobank (SSACB)⁹, an ongoing community-based study detailed in previous publications. In brief, the SSACB used a multi-stage, stratified, cluster sampling method to recruit adults aged 20–74 years from suburban communities in Shanghai using a multi-stage, stratified, cluster sampling design. The baseline survey was conducted from June 2016 to December 2017, with the first follow-up assessment occurring from June 2019 to August 2020. For the present analysis, we included participants who had complete baseline and follow-up data. We excluded individuals with: (1) baseline estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m², a urine albumin-to-creatinine ratio (ACR) ≥30 mg/g, or a known history of CKD; (2) critical illness (e.g., cancer, stroke, cirrhosis); or (3) missing data on physical activity, BMI, serum creatinine, or key covariates. Participants with missing data were excluded prior to analysis, and a complete-case approach was applied. The proportion of excluded participants due to missing data was low (<5%), minimizing the likelihood of substantial bias. After exclusions, 11,597 participants with normal baseline renal function were included in the longitudinal analysis.

Assessment of Physical Activity (Primary Exposure)

At baseline, habitual PA was assessed using a validated questionnaire adapted from the International Physical Activity Questionnaire (IPAQ)¹⁰. Participants reported the frequency (days per week) and duration (minutes per day) of various activities across domains of work, transportation, housework, and leisure-time exercise during the past 7 days. The metabolic equivalent of task (MET) value for each activity was assigned according to the 2000 Compendium of Physical Activities¹¹. Total weekly PA volume was calculated as the sum of MET-minutes per week (MET-min/week) across all domains. For primary analysis, PA was treated as a continuous variable, expressed per 1000 MET-min/week increment.

Assessment of Body Mass Index (Effect Modifier)

Body weight and height were measured in duplicate by trained staff using standardized protocols. BMI was calculated as weight (kg) divided by height squared (m²). For analysis, BMI was analyzed both as a continuous variable (per 1 kg/m² increase) and as a categorical variable based on Chinese standards: underweight (<18.5 kg/m²), normal weight (18.5–23.9 kg/m²), overweight (24.0–27.9 kg/m²), and obese (≥28.0 kg/m²)^12,13.

Ascertainment of Incident CKD (Primary Outcome)

The primary outcome was incident CKD, defined as the development of either: an eGFR < 60 mL/min/1.73 m², or a urine albumin-to-creatinine ratio (ACR) ≥ 30 mg/g at the follow-up assessment, in participants with normal baseline kidney function. This definition aligns with the Kidney Disease: Improving Global Outcomes (KDIGO) criteria for CKD diagnosis¹⁴. Fasting venous blood and spot urine samples were collected at baseline and follow-up. Serum creatinine was measured using enzymatic methods on a Roche Cobas C702 analyzer. Urine albumin concentration was determined by immunoturbidimetry, and urine creatinine concentration was measured by the enzymatic method, both on a Roche Cobas C501 analyzer. ACR was calculated as urine albumin (mg/dL) / urine creatinine (mg/dL) and expressed in mg/g. The eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation adapted for the Chinese population¹⁵. Participants with baseline eGFR ≥ 60 mL/min/1.73 m² and ACR < 30 mg/g, and no prior CKD diagnosis, were considered at risk. Follow-up time was calculated from the baseline survey date to the date of the follow-up assessment. Participants who met either the eGFR or ACR criterion at follow-up were classified as an incident CKD case.

Assessment of Covariates

Potential confounders were selected a priori based on established literature and assessed at baseline via questionnaire and clinical measurement. These included:

Demographic and Socioeconomic: Age (continuous), sex (male/female), education level (illiterate/no schooling, primary school, middle school, high school or above)^16,17,18.

Lifestyle Behaviors¹⁹: Smoking status (yes/no; defined as >1 cigarette/day for ≥6 months), alcohol consumption (yes/no; defined as drinking >3 times/week for ≥6 months).

Medical History: Self-reported family history of CKD (yes/no), physician-diagnosed hypertension(yes/no; or SBP/DBP ≥140/90 mmHg), type 2 diabetes mellitus (yes/no; or fasting glucose ≥7.0 mmol/L or HbA1c ≥6.5%), and hyperlipidemia (yes/no; or meeting lipid criteria)^20,21. Hypertension, diabetes, and hyperlipidemia were defined based on either self-reported physician diagnosis or clinical measurements obtained during the baseline examination.

Statistical Analysis

Baseline characteristics were compared between participants who developed incident CKD and those who did not. Continuous variables were analyzed using Welch’s t-test, and categorical variables were compared using Pearson’s chi-squared test. Data were presented as mean (SD) or number (percentage), as appropriate. The associations of PA, BMI, and their interaction with incident CKD risk were evaluated using Cox proportional hazards regression models. The proportional hazards assumption was assessed using Schoenfeld residuals.

Two models were constructed: a main effect model, which included PA (per 1000 MET-min/week) and BMI (per 1 kg/m²) as independent variables., and an interaction model, which included the main effects and a multiplicative interaction term (PA × BMI). The statistical significance of the interaction was evaluated using a likelihood ratio test comparing models with and without the interaction term.

All models were adjusted for predefined covariates selected a priori based on clinical relevance and previous literature, including age, sex, education level, smoking status, alcohol consumption, family history of CKD, hypertension, hyperlipidemia, and diabetes. These covariates were selected a priori based on clinical relevance and biological plausibility. Results were reported as hazard ratios (HRs) with 95% confidence intervals (CIs).

To further interpret the interaction between BMI and PA, conditional effects of PA on CKD risk were estimated at selected BMI values (21, 25, and 30 kg/m²), representing normal weight, overweight, and obesity categories, respectively^13,22. These values were chosen based on commonly used reference points in epidemiological studies examining effect modification²³. Predicted hazard ratios (HRs) across a range of PA levels were calculated using the fully adjusted interaction model and visualized accordingly. To further assess the potential non-linear association between BMI and incident CKD, restricted cubic spline (RCS) regression was performed within the Cox proportional hazards framework. BMI was modeled as a continuous variable using three to four knots placed at recommended percentiles. The overall and non-linear associations were evaluated using Wald tests.

For clinical interpretation, participants were categorized into 12 subgroups according to PA levels (<600, 600–3000, ≥3000 MET-min/week) based on WHO physical activity guidelines²⁴ and BMI categories (underweight, normal weight, overweight, obesity). Incidence rates of CKD (per 100 person-years) were calculated for each subgroup. These subgroups were further grouped into three risk tiers (low, intermediate, and high) based on observed incidence patterns.

Absolute risk reduction (ARR) and number needed to treat (NNT) were calculated to estimate the potential impact of increasing PA from insufficient levels (<600 MET-min/week) to higher activity categories. All statistical analyses were performed using R software (version 4.5.2, RRID:SCR_001905; R Foundation for Statistical Computing). A two-sided P-value < 0.05 was considered statistically significant.

Results

Baseline Characteristics of the Study Population

This study ultimately included 11,597 participants with normal baseline renal function, with a median follow-up time of 3.0 years. The participant selection process is illustrated in Figure 1.

Discussion

This prospective cohort study demonstrates a statistical interaction between PA and BMI in the prevention of CKD. Higher PA was associated with a lower risk of CKD, and a significant interaction indicated that this protective association was stronger at higher BMI levels. The apparent attenuation of the association in normal-weight individuals should be interpreted cautiously, as it likely reflects lower baseline risk rather than a true absence of benefit. Translating this interaction into a risk-stratification framework...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Item	Manufacturer / Source	Identifier
Biochemical analyzer	Roche Diagnostics	Cobas C702
Biochemical analyzer	Roche Diagnostics	Cobas C501
Physical activity questionnaire	nternational Physical Activity Questionnaire (IPAQ)	https://sites.google.com/site/theipaq/
Statistical software	R Foundation for Statistical Computing	RRID:SCR_001905