Abstract
Background:
Kidney stone is one of the most common urological conditions in sedentary individuals. Although specific behaviors such as diet and prolonged sitting are known risk factors, the individual and joint effects of metabolic status and lifestyle on kidney stone risk remain understudied.
Materials and Methods:
This cross-sectional study analyzed data from 10 801 sedentary individuals aged over 20 from the U.S. National Health and Nutrition Examination Survey between 2007 and 2018. Metabolic status indicators included central obesity, hyperglycemia, hypertension, and dyslipidemia, assessed using anthropometric and laboratory data and self-reported disease history. Lifestyle factors including physical inactivity, addictive behaviors, and unhealthy diet as well as kidney stone history were also estimated from self-reported questionnaire. We estimated the risk of kidney stone associated with individual and joint factors using weighted multivariable logistic regression, presenting odds ratios (ORs) and 95% confidence intervals (CIs).
Results:
Among the participants, 9.1% reported kidney stone. Individual poor metabolic status and unhealthy lifestyle were associated with higher prevalence of kidney stones. Higher cumulative scores in poor metabolic status or unhealthy lifestyle corresponded with an increased prevalence of kidney stones, rising from 4.37% to 15.59% and 8.01% to 15.39%, respectively. Additionally, participants with “worst metabolic status” or “worst lifestyle” separately had a 1.32-fold and 77% increase for risk of kidney stone, respectively (OR = 2.321, 95% CI: 1.479–3.645; OR = 1.774, 95% CI: 1.260–2.499). Furthermore, when metabolic status was not optimal, the worst lifestyle significantly increased the risk of kidney stone. The risk nearly increased by threefold in participants with both worst metabolic and lifestyle (OR = 3.918, 95% CI: 1.659–9.256).
Conclusions:
Worsening metabolic health combined with an unhealthy lifestyle significantly elevates kidney stone risk in sedentary populations, emphasizing the need to address both factors simultaneously for effective prevention.
Keywords: kidney stone, lifestyle, metabolic status, sedentary population
Introduction
The global prevalence of kidney stones has stayed elevated and continues to rise. Data from the National Health and Nutrition Examination Survey (NHANES) spanning the past 30 years show that the prevalence of kidney stones in adults grew from 3.2% during 1976–1980 to 5.2% between 1988 and 1994, and then further climbed to 8.8% from 2007 to 2010[1,2]. In the United States, the annual health-care costs attributed to kidney stones exceed $2 billion[3]. This trend is not limited to the United States but has been documented in other regions as well[4-6]. The rising prevalence spans all age, gender, racial, and ethnic subgroups[7]. Around 1 in 11 individuals will experience kidney stone[8], with over half suffering a recurrence within 5–10 years[8,9]. The pain, infections, and obstructions caused by kidney stones, combined with a high recurrence rate, significantly impair patients’ physical and psychological well-being, diminishing their quality of life. Therefore, the prevention of kidney stone and the promotion of good health have received increasing attention from both individuals and the health-care system.
The growing prevalence of metabolic disorders and unhealthy lifestyles has led to a notable increase in kidney stone occurrences, especially among working-age adults[8]. Projections suggest that by 2030, obesity alone may increase the prevalence of kidney stones by 0.36%, with an additional annual health-care cost of $157 million, and diabetes may increase prevalence by 0.72%, adding $308 million in annual costs[10]. Previous studies have also shown that individuals with hypertension are at a higher risk of developing kidney stones[11,12], particularly those who are both hypertensive and overweight[12]. Dyslipidemia, such as elevated total cholesterol (TC) and triglycerides (TG), has also been associated with an increased risk of kidney stones[13]. In addition, unhealthy lifestyle behaviors, including smoking, excessive alcohol consumption, physical inactivity, and poor dietary patterns, are linked to a heightened risk of kidney stone[14,15]. Sedentary behavior has become a defining feature of the modern workforce, posing substantial health risks, particularly when compounded by other adverse factors. Given the challenges of reducing sedentary behavior due to work requirements, focusing on other modifiable metabolic and lifestyle factors provides a viable option for preventing kidney stones. The individual and joint associations between modifiable metabolic status and lifestyle factors with kidney stones remain insufficiently studied.
In this study, we used the NHANES data for 2007–2018 to analyze individual and joint associations of modifiable metabolic status and lifestyle behaviors with kidney stone in sedentary populations. Understanding this relationship is crucial for effective public health surveillance, informing future regulatory policies, and designing targeted interventions to minimize exposure risks. We focused on metabolic phenotypes, such as central obesity, hyperglycemia, hypertension, and dyslipidemia, as well as lifestyles factors, such as physical inactivity, addictive behaviors, and unhealthy diet. We hypothesized that across all metabolic status, an unhealthy lifestyle would further increase the risk of kidney stones, with their combination leading to a progressively higher risk. Additionally, we aimed to identify sociodemographic groups at elevated risk for poor metabolic status and unhealthy lifestyle exposures, stratified by age, gender, race, education, poverty, and marriage subgroups.
Material and methods
Study design and population
The NHANES, a program designed to assess the health and nutritional status of individuals in the United States, was approved by the Ethics Review Board of the Centers for Disease Control and Prevention/National Center for Health Statistics. All participants provided written informed consent. As the data are publicly available and de-identified, additional institutional review board approval was not required.
HIGHLIGHTS
First systematic assessment of the individual and joint effects of metabolic status and lifestyle on kidney stone risk.
Cumulative scores of poor metabolic status and unhealthy lifestyle significantly associated with increased kidney stone prevalence.
Risk of kidney stones nearly increased by threefold in individuals with both worst metabolic status and worst lifestyle.
This study utilized data from the NHANES, which conducts biennial surveys of a nationally representative sample of the noninstitutionalized U.S. civilian population, spanning all age-groups. Data are collected in 2-year cycles. For this analysis, six consecutive survey cycles from 2007 to 2018 were selected, which included 59 482 individuals with complete data on the kidney stone questionnaire. A total of 48 681 individuals were excluded based on the following predefined criteria:
(a) A total of 2068 participants were excluded due to a combined survey weight of zero.
(b) A total of 24 002 participants were excluded for being under the age of 20.
(c) A total of 13 756 participants were excluded for reporting less than 30 h of sedentary time per week[16].
(d) A total of 6445 participants were excluded due to missing laboratory or questionnaire data, which prevented classification of metabolic and lifestyle status.
(e) A total of 23 individuals were excluded due to kidney stone questionnaire responses recorded as “Refused,” “Don’t know,” or “Missing.”
(f) A total of 514 participants were excluded for missing covariate data, including age, gender, race, serum creatinine, and serum albumin.
(g) A total of 1430 participants were excluded due to a history of cancer or malignancy.
(h) A total of 123 participants were excluded due to pregnancy.
(i) A total of 320 participants were excluded due to a history of kidney failure.
After applying above criteria, 10 801 sedentary adults were included in the final analysis (Fig. 1). This study followed the Strengthening The Reporting Of Cohort Studies in Surgery (STROCSS) guidelines[17].
Figure 1.
Flowchart of the study. Detailed information on inclusion and exclusion criteria for the study population.
Definition of poor metabolic status
Metabolic status was determined using anthropometric measurements, laboratory results, and self-reported chronic disease history. Therefore, in this study, metabolic status was defined using both objective indicators and questionnaire information, in order to minimize bias caused by subjective factors as much as possible.
Poor metabolic status was defined by the presence of the following metabolic risk factors: central obesity: waist circumference ≥ 102 cm in men or ≥ 88 cm in women; dyslipidemia: TG levels ≥1.69 mmol/L, or TC ≥200 mg/dL, or a history of dyslipidemia; hypertension: blood pressure ≥140/90 mmHg or a history of hypertension; hyperglycemia: fasting plasma glucose levels ≥6.1 mmol/L or a history of diabetes.
A cumulative poor metabolic status score was calculated based on the number of these metabolic risk factors. Participants were categorized into three groups: “best metabolic status” (no risk factors), “moderate metabolic status” (one to three risk factors), and “worst metabolic status” (four risk factors).
Definition of unhealthy lifestyle
Lifestyle information was collected through questionnaires administered to the NHANES participants both at home and in mobile examination centers by trained personnel. An unhealthy lifestyle was defined using three key factors: physical inactivity, addictive behaviors (smoking every day or excessive alcohol consumption), and unhealthy diet.
Physical inactivity was defined as engaging in less than 150 min of non-work-related physical activity per week. This was assessed through responses to six questions covering the frequency and duration of vigorous, moderate, and light physical activities (e.g., walking or cycling). Activities had to be sustained for at least 10 min per session. Vigorous activity was weighted more heavily, being assigned twice the value of moderate activity, due to its higher intensity as indicated by increased sweating, breathing, and heart rate.
Addictive behaviors included current daily smoking or consuming five or more alcoholic drinks per day. Unhealthy diet was determined based on the Healthy Eating Index 2020 (HEI-2020). Participants with an HEI-2020 score below the 25th percentile level were classified as having an unhealthy diet. The HEI-2020 scores range from 0 to 100, with higher scores reflecting better adherence to dietary guidelines.
A cumulative unhealthy lifestyle score was calculated based on the presence of these three risk factors. Participants were categorized into three lifestyle groups: “best lifestyle” (no risk factors), “moderate lifestyle” (one to two risk factors), and “worst lifestyle” (three risk factors).
Identification of kidney stone
Kidney stones were identified through the self-reported questionnaire response to item KIQ026 (“Ever had kidney stone?”) in the Kidney Conditions – Urology (KIQ_U) section.
Covariates
Covariates included age, gender, race, liver function, and kidney function. Racial groups were categorized as “Mexican American,” “Other Hispanic,” “Non-Hispanic Black,” “Non-Hispanic White,” and “Other Race – Including Multi-Racial.” Due to smaller sample sizes, “Mexican American,” “Other Hispanic,” and “Other Race – Including Multi-Racial” were joint into an “Other Race” subgroup. Liver function was assessed via serum albumin levels, a commonly used marker for liver disease diagnosis and management. Kidney function was evaluated through serum creatinine levels, which are critical for diagnosing and managing renal diseases. Both serum albumin and creatinine measurements were part of the Standard Biochemistry Profile, conducted using the Beckman UniCel® DxC800 Synchron system. In the sensitivity analysis, we further incorporated socioeconomic factors, including education level, poverty index, and marital status, as covariates to repeat the main analysis and perform subgroup analysis. Education level is categorized into less than high school, high school, and more than high school (college or AA degree). The poverty index, based on the ratio of family income to the poverty level, is divided into less than 1.3, between 1.3 and 3.5, and greater than 3.5, with a higher ratio indicating a higher income level. Marital status is a binary variable indicating whether or not married.
Statistical analysis
The NHANES employed a complex, multistage probability sampling design. Given this, combined weights from the six 2-year cycles were applied to all phases of the analysis, ensuring nationally representative estimates for baseline calculations, prevalence assessments, and risk estimations. The weighted analysis was conducted using the “survey” package in R software. Continuous variables were reported as medians with interquartile ranges, while categorical variables were presented as counts with percentages. Group comparisons were conducted using the weighted Wilcoxon rank-sum test for continuous variables and the weighted chi-squared test for categorical variables.
By simultaneously fitting all modified metabolic and lifestyle factors, and the covariates as mentioned above into a model, the respective population attributable fraction (PAF) and 95% confidence interval (CI) of each risk factor were calculated for the risk of kidney stones. To evaluate trends across different metabolic status and lifestyle score groups, weighted linear regression models were applied, treating the score or score categories as ordinal variables to assess linear trends. Associations of metabolic status and lifestyle factors with kidney stones risk were analyzed using weighted multivariable logistic regression models, adjusting for covariates. Results are expressed as odds ratios (ORs) with 95% CIs and P-values.
Additionally, stratified risk analyses were conducted by gender, age-groups (<65 and ≥65 years), racial subgroups, and education level, poverty index, and marital status. Statistical significance was defined as a two-sided P-value <0.05. All statistical analyses were performed using R software, version 4.3.2.
Results
Baseline characteristics of the study population
The final analysis included 10 801 participants, of whom 981 (9.1%) reported a history of kidney stones, representing a weighted national estimate of 7 856 168 individuals (9.3%) (Table 1). Compared with individuals without kidney stones, those with kidney stone were older, had a greater proportion of Whites and males, and exhibited higher body mass index (BMI) and waist circumference. No statistically significant difference was observed in levels of TC between the two-group comparison, but significantly higher TG levels were observed in the kidney stone group. The levels of glucose, HbA1c% and systolic blood pressure were higher in individuals with kidney stone. Kidney function was worse in the kidney stone group, as indicated by higher serum creatinine levels. No significant differences in alanine aminotransferase and aspartate aminotransferase levels were observed, but serum albumin levels were lower in the kidney stone group.
Table 1.
Baseline characteristics of the study population who participated in the National Health and Nutrition Examination Survey, 2007–2018
| Characteristics | Unweighted | Weighted | ||||||
|---|---|---|---|---|---|---|---|---|
| Overall | Non-kidney stone group | Kidney stone group | P | Overall | Non-kidney stone group | Kidney stone group | P | |
| N | 10 801 | 9820 | 981 | 84 765 477.34 | 76 909 309.46 | 7 856 167.88 | ||
| Age (years) | 48.00 (33.00, 61.00) | 47.00 (33.00, 61.00) | 54.00 (42.00, 66.00) | <0.001 | 46.00 (33.00, 58.00) | 45.00 (32.00, 58.00) | 52.00 (40.00, 63.00) | <0.001 |
| Race | ||||||||
| White (%) | 5036 (46.63) | 4451 (45.33) | 585 (59.63) | <0.001 | 61 069 204.82 (72.04) | 54 670 212.55 (71.08) | 6 398 992.27 (81.45) | <0.001 |
| Black (%) | 2430 (22.50) | 2290 (23.32) | 140 (14.27) | <0.001 | 8 885 734.57 (10.48) | 8 447 596.83 (10.98) | 438 137.74 (5.58) | <0.001 |
| Other (%) | 3335 (30.88) | 3079 (31.35) | 256 (26.10) | <0.001 | 14 810 537.96 (17.47) | 13 791 500.08 (17.93) | 1 019 037.87 (12.97) | <0.001 |
| Gender (female, %) | 5311 (49.17) | 4884 (49.74) | 427 (43.53) | <0.001 | 42 188 205.62 (49.77) | 38 766 400.63 (50.41) | 3 421 804.99 (43.56) | <0.001 |
| BMI (kg/m2) | 28.44 (24.67, 33.40) | 28.30 (24.50, 33.30) | 29.96 (26.10, 34.70) | <0.001 | 28.30 (24.50, 33.20) | 28.10 (24.32, 33.00) | 30.10 (26.20, 35.40) | <0.001 |
| Wasit (cm) | 99.40 (88.50, 111.20) | 98.80 (88.00, 110.70) | 104.60 (94.80, 116.00) | <0.001 | 99.20 (88.20, 111.00) | 98.50 (87.70, 110.30) | 105.06 (95.00, 116.00) | <0.001 |
| TC (mmol/L) | 4.89 (4.22, 5.61) | 4.89 (4.22, 5.61) | 4.89 (4.22, 5.53) | 0.425 | 4.91 (4.27, 5.64) | 4.91 (4.27, 5.64) | 4.91 (4.27, 5.56) | 0.367 |
| TG (mmol/L) | 1.37 (0.90, 2.10) | 1.35 (0.89, 2.09) | 1.46 (1.00, 2.21) | <0.001 | 1.37 (0.90, 2.09) | 1.35 (0.89, 2.09) | 1.47 (1.00, 2.20) | <0.001 |
| Glucose (mmol/L) | 5.16 (4.72, 5.77) | 5.11 (4.72, 5.72) | 5.33 (4.88, 6.16) | <0.001 | 5.11 (4.72, 5.61) | 5.11 (4.72, 5.61) | 5.27 (4.88, 6.00) | <0.001 |
| HbA1c (%) | 5.50 (5.20, 5.90) | 5.50 (5.20, 5.80) | 5.70 (5.30, 6.10) | <0.001 | 5.40 (5.20, 5.70) | 5.40 (5.20, 5.70) | 5.60 (5.30, 6.00) | <0.001 |
| SBP (mmHg) | 120.67 (111.33, 132.67) | 120.00 (111.00, 132.00) | 123.33 (113.33, 136.00) | <0.001 | 118.67 (110.00, 130.00) | 118.67 (110.00, 129.33) | 121.33 (112.00, 132.67) | 0.002 |
| DBP (mmHg) | 71.33 (64.00, 78.67) | 71.33 (64.00, 78.00) | 72.00 (63.33, 78.67) | 0.454 | 71.33 (64.00, 78.00) | 71.33 (64.00, 78.00) | 72.67 (65.33, 79.33) | 0.019 |
| ALT (U/L) | 21.00 (16.00, 28.00) | 21.00 (16.00, 28.00) | 21.00 (16.00, 29.00) | 0.471 | 21.00 (16.00, 29.00) | 21.00 (16.00, 29.00) | 21.00 (17.00, 30.00) | 0.277 |
| AST (U/L) | 22.00 (19.00, 27.00) | 22.00 (19.00, 27.00) | 23.00 (19.00, 28.00) | 0.559 | 23.00 (19.00, 27.00) | 23.00 (19.00, 27.00) | 23.00 (19.00, 27.91) | 0.812 |
| Albumin (g/dL) | 4.30 (4.00, 4.50) | 4.30 (4.00, 4.50) | 4.20 (4.00, 4.40) | <0.001 | 4.30 (4.10, 4.50) | 4.30 (4.10, 4.50) | 4.20 (4.00, 4.40) | <0.001 |
| Creatinine (µmol/L) | 76.02 (64.53, 89.28) | 76.02 (64.53, 89.28) | 79.56 (66.30, 93.70) | <0.001 | 76.02 (64.53, 88.40) | 75.14 (64.53, 87.52) | 78.68 (66.30, 91.94) | <0.001 |
| UA (µmol/L) | 321.20 (267.70, 380.70) | 321.20 (267.70, 380.70) | 333.10 (279.60, 398.50) | <0.001 | 321.20 (267.70, 380.70) | 321.20 (267.70, 380.70) | 333.10 (279.60, 392.60) | <0.001 |
| BUN (mmol/L) | 4.64 (3.57, 5.71) | 4.28 (3.57, 5.71) | 5.00 (3.93, 6.07) | <0.001 | 4.64 (3.57, 5.71) | 4.64 (3.57, 5.71) | 5.00 (3.93, 6.07) | <0.001 |
| Unhealthy metabolic indicators | ||||||||
| Central obesity (N, %) | 6344 (58.74) | 5646 (57.49) | 698 (71.15) | <0.001 | 49 632 515.92 (58.55) | 43 934 862.16 (57.13) | 5 697 653.76 (72.52) | <0.001 |
| Hypertension (N, %) | 4499 (41.65) | 3965 (40.38) | 534 (54.43) | <0.001 | 31 215 680.84 (36.83) | 27 263 611.24 (35.45) | 3 952 069.60 (50.31) | <0.001 |
| Hyperglycemia (N, %) | 2166 (20.05) | 1871 (19.05) | 295 (30.07) | <0.001 | 14 116 831.62 (16.65) | 12 022 400.68 (15.63) | 2 094 430.94 (26.66) | <0.001 |
| Dyslipidemia (N, %) | 7240 (67.03) | 6523 (66.43) | 717 (73.09) | <0.001 | 56 666 237.09 (66.85) | 51 005 279.04 (66.32) | 5 660 958.05 (72.06) | 0.003 |
| Unhealthy lifestyle indicators | ||||||||
| Addictive behavior (N, %) | 3045 (28.19) | 2718 (27.68) | 327 (33.33) | <0.001 | 21 654 584.35 (25.55) | 19 259 470.57 (25.04) | 2 395 113.78 (30.49) | 0.005 |
| Smoking every day (N, %) | 1985 (37.16) | 1784 (37.35) | 201 (35.51) | 0.417 | 13 593 184.79 (34.19) | 12 168 292.76 (34.14) | 1 424 892.03 (34.68) | 0.84 |
| Excessive drinking (N, %) | 1666 (15.98) | 1475 (15.55) | 191 (20.30) | <0.001 | 11 971 589.51 (14.51) | 10 612 070.78 (14.18) | 1 359 518.73 (17.77) | 0.028 |
| Physical inactivity (N, %) | 6142 (56.87) | 5512 (56.13) | 630 (64.22) | <0.001 | 45 359 082.41 (53.51) | 40 629 772.29 (52.83) | 4 729 310.13 (60.20) | <0.001 |
| Unhealthy diet (N, %) | 2713 (25.12) | 2425 (24.69) | 288 (29.36) | 0.002 | 20 809 675.33 (24.55) | 18 537 633.70 (24.10) | 2 272 041.63 (28.92) | 0.019 |
BMI, body mass index; TC, total cholesterol; TG, triglycerides; HbA1c, hemoglobin A1c; SBP, systolic blood pressure; DBP, diastolic blood pressure; ALT, alanine aminotransferase; AST, aspartate aminotransferase; UA, uric acid; BUN, blood urea nitrogen.The data is presented using median values (25th percentile and 75th percentile).
Regarding metabolic risk factors, participants with kidney stones exhibited a higher weighted prevalence of central obesity, hypertension, hyperglycemia, and dyslipidemia compared with those in the non-kidney stone group. Similarly, individuals with kidney stones were more likely to engage in unhealthy lifestyle, including excessive alcohol consumption, physical inactivity, and unhealthy diet.
Characteristics by gender, age, and race revealed significant differences in clinical measurements and the distribution of metabolic and lifestyle risk factors, and prevalence of kidney stones (Supplementary Digital Content Table 1, available at: http://links.lww.com/JS9/F112, Table 2, available at: http://links.lww.com/JS9/F113, Table 3, available at: http://links.lww.com/JS9/F114).
Weighted prevalence of kidney stones across individual metabolic status and lifestyle factor
A significant difference in the weighted prevalence of kidney stones was shown in pairs of groups with and without each poor metabolic status and unhealthy lifestyle factor (all P-values <0.05, Fig. 2A, B). As scores of poor metabolic status increased, a clear linear trend of rising kidney stones prevalence was observed, with ranging from 4.37% in individuals with “best metabolic status” (score of 0) to 15.59% in those with “worst metabolic status” (score of 4) (P for linear trend <0.001) (Fig. 2C). For scores or categories of unhealthy lifestyle, we observed the similar results (from 8.01% to 15.39%, Fig. 2D).
Figure 2.
Weighted prevalence of kidney stones grouped by individual metabolic and lifestyle risk factor, and by cumulative risk score of poor metabolic status or unhealthy lifestyle. (A) Prevalence of kidney stones grouped by individual metabolic risk factor. (B) Prevalence of kidney stones grouped by individual unhealthy lifestyle risk factor. (C) Prevalence of kidney stones in each group segmented by cumulative risk score of poor metabolic status. (D) Prevalence of kidney stones in each group segmented by cumulative risk score of unhealthy lifestyle.
Stratified by categories of metabolic status, kidney stone prevalence always showed a marked increase with higher unhealthy lifestyle scores (all P-values < 0.05) (Fig. 3). Prevalence increased dramatically, from 3.79% in those with the best metabolic status and the best lifestyle to highest 22.78% in those with the worst combination of metabolic and lifestyle status.
Figure 3.
Prevalence of kidney stones in each group segmented by unhealthy lifestyle risk categories across different metabolic statuses.
Individual association of modified metabolic status and lifestyle with kidney stones
After adjusting for confounding variables, each of metabolic and lifestyle risk factor, except for dyslipidemia and physical inactivity, were significantly and positively associated with an increased risk of kidney stones (Fig. 4A). The above trend remained mostly the same even after adjusting for socioeconomic factors or when extended to the general population (not limited to sedentary individuals) (Supplementary Digital Content Figure 1a, available at: http://links.lww.com/JS9/F111 and Supplementary Digital Content Figure 2a, available at: http://links.lww.com/JS9/F111) Central obesity, hyperglycemia, hypertension, addictive behavior, and unhealthy diet separately increased 68.4%, 48.8%, 43.0%, 23.2%, and 34.6% risk of kidney stones, respectively (all P < 0.05). According to PAF analysis, compared to unhealthy lifestyle, poor metabolic statuses collectively accounted for a larger proportion of the risk of kidney stones, with central obesity showing the highest PAF (20.6%) (Supplementary Digital Content Table 4, available at: http://links.lww.com/JS9/F115).
Figure 4.
Individual and cumulative association of metabolic status and lifestyle with the risk of kidney stones. (A) Adjusted odd ratios with 95% CI of individual metabolic and lifestyle risk factor associated with the risk of kidney stones. (B) Adjusted odd ratios with 95% CI for each cumulative metabolic and lifestyle risk score associated with the risk of kidney stones.
Regarding cumulative scores (from 0 to 4) of poor metabolic statuses, each additional score increase was associated with a 26.7% higher risk of kidney stones (OR = 1.267, 95% CI: 1.160–1.383) (Fig. 4B). Across best, moderate, and worst categories of metabolic status, those with worst metabolic status had a 1.32-fold increased risk of kidney stone (OR = 2.321, 95% CI: 1.479–3.645). Similarly, each additional unit in the unhealthy lifestyle score was associated with a 17.9% increase in kidney stones risk (OR = 1.179, 95% CI: 1.065–1.305). Individuals with worst lifestyle status had a 77.4% higher risk of kidney stone compared to those with best lifestyle status (OR = 1.774, 95% CI: 1.260–2.499) (Fig. 4B). These trends remained largely consistent even after adjusting for socioeconomic factors (Supplementary Digital Content Figure 1b, available at: http://links.lww.com/JS9/F111). However, when generalized to the general population, the correlation between cumulative unhealthy lifestyle scores and kidney stones risk was no longer present (Supplementary Digital Content Figure 2b, available at: http://links.lww.com/JS9/F111). This highlights the necessity of studying unhealthy lifestyles in specific populations such as sedentary individuals.
Subgroup analyses reaffirmed that the increased risk of kidney stones was strongly linked to individual poor metabolic status and unhealthy lifestyle factors to varying degrees (Supplementary Digital Content Figures 3–8, available at: http://links.lww.com/JS9/F111). For cumulative scores, we found that poor metabolic status scores and kidney stones risk were more strongly associated in women, individuals under 65 years old, White and other ethnic groups, those with lower education levels, higher poverty index, and unmarried individuals, while unhealthy lifestyle scores and kidney stones risk were more strongly associated in women, individuals over 65 years old, Black individuals, those with higher education levels, moderate poverty index, and married individuals (Supplementary Digital Content Figures 9–14, available at: http://links.lww.com/JS9/F111).
Effects of joint metabolic status and lifestyle on kidney stones risk
For individuals at best metabolic status, worsening lifestyle did not significantly influence the risk of kidney stones, but worsening lifestyle significantly exacerbated the risk of kidney stone in those with moderate or worst metabolic status (Fig. 5A). Compared to individuals with both best metabolic status and best lifestyle, those with both worst metabolic status and worst lifestyle had a markedly higher risk of kidney stones, experienced nearly a threefold increase in kidney stones risk (OR = 3.918, 95% CI: 1.659–9.256) (Fig. 5B). These trends remained largely consistent even after adjusting for socioeconomic factors or when generalized to the general population (not limited to sedentary individuals) (Supplementary Digital Content Figures 15 and 16, available at: http://links.lww.com/JS9/F111).
Figure 5.
Joint association of poor metabolic status and unhealthy lifestyle with the risk of kidney stone. (A) Adjusted odd ratios with 95% CI of each unhealthy lifestyle risk category associated with the risk of kidney stone across different metabolic statuses. (B) Adjusted odd ratios with 95% CI of combined metabolic status and lifestyle associated with the risk of kidney stones.
Subgroup analysis showed that the joint impact of poor metabolic status and unhealthy lifestyle on kidney stones was more pronounced in women, White and other ethnic groups, individuals with lower education levels, higher poverty index, and unmarried individuals (Supplementary Digital Content Figures 17–22, available at: http://links.lww.com/JS9/F111).
Discussion
Based on data from a nationally representative health survey, our study demonstrated that in sedentary adults, an unhealthy lifestyle further exacerbated the risk of kidney stones for individual with poor metabolic status. With the combination of poor metabolic status and unhealthy lifestyle, the risk increased progressively. These findings underscored the importance of simultaneously modifying metabolic status and lifestyle in sedentary populations.
Although certain unhealthy behaviors, such diets and daily sitting time, are known risk factors[18,19], there is a lack of comprehensive study that evaluate individual and joint effects of metabolic status and lifestyle behaviors on kidney stone risk in sedentary individuals, particularly regarding the stratification of lifestyle risk factors by metabolic health. Understanding lifestyle modifications in metabolic disorders is beneficial for promoting healthy lifestyle choices to reduce kidney stones incidence. To our knowledge, our study is the first to thoroughly assess the joint impact of modified metabolic and lifestyle behaviors on the risk of kidney stones. Among individuals with mildly compromised metabolic health, those with unhealthy lifestyles exhibited a significantly higher risk of developing kidney stones. In individuals with worst metabolic health, the risk of kidney stone due to unhealthy lifestyles escalated dramatically. These findings inferred us that adopting a healthy lifestyle can reduce the risk of kidney stones in individuals with certain metabolic disorders.
Our results also demonstrated that impacts of individual metabolic and lifestyle risk factor on kidney stones vary. Poor metabolic status appears to contribute more to kidney stone risk, as reflected by a higher PAF. Among four of metabolic risk factors, central obesity had the most substantial impact on kidney stones risk, followed by hyperglycemia and hypertension, while dyslipidemia was not significantly associated with the risk of kidney stones. A prospective study with a combined 46-year follow-up across three large cohorts, including the Health Professionals Follow-up Study, the Nurses’ Health Study I, and the Nurses’ Health Study II, reported that increases in body weight, BMI, and waist circumference elevated the risk of kidney stones formation[20]. Furthermore, a study based on data from the UK Biobank and FinnGen found that visceral fat, a marker of central obesity, increases kidney stones risk by elevating serum calcium levels[21], explaining the possible intermediate pathogenic mechanisms through which obesity leads to kidney stones. These previous studies have demonstrated the temporal relationship between obesity and the risk of kidney stones from a longitudinal cohort perspective, which further supports causality and strengthens our findings. This increased risk may be more pronounced in women than in men[22], consistent to our results. Impaired fasting glucose or impaired glucose tolerance has also been linked to a higher risk of kidney stones[23]. Another prospective study identified diabetes mellitus (DM) as a significant risk factor for kidney stone development[24], and the severity of DM further increased the risk of stones formation[25]. Hypertension has been consistently linked to kidney stone risk in both early and recent studies[26–28]. Moreover, lower 24-h urinary potassium excretion has been shown to be harmful against kidney stones in hypertensive individuals[27]. The use of β-blockers and thiazide diuretics could reduce the risk of kidney stones, particularly in older adults[29]. As for dyslipidemia, while some retrospective studies have suggested a positive association of hypercholesterolemia and hypertriglyceridemia with the risk of uric acid stone-independent in diabetes and obesity individual[13], but this was not confirmed in our study. Similarly, another NHANES-based study did not find a significant association between TC and TG with kidney stones[30]. In addition, the use of statins reduced the risk of urinary stone formation, but the underlying mechanism is unrelated to their lipid-lowering effects[31], may explain the lack of a significant association between dyslipidemia and kidney stone risk in our study results. Ye et al studied the relationship between metabolic syndrome-body mass index (MetS-BMI) phenotypes and kidney stone risk, dividing participants into six groups based on metabolic health and weight status. They found that all obesity phenotypes had a higher risk of kidney stones, with the risk increasing significantly in those with metabolic dysfunction, particularly in the metabolically unhealthy overweight and obese groups. Their findings supported that obesity and poor metabolic health together increase the risk of kidney stones[32].
Previous studies on the impact of certain lifestyle factors on kidney stones are controversial. The NHANES-based studies demonstrated an inverse association between physical activity and kidney stones prevalence[33], and physical activity may mitigate the risk of kidney stones in obesity and diabetes[34]. Another recent research reported that physical activity may help counteract genetic predisposition to kidney stone[35]. However, analyses from three large prospective cohorts found that no independent association between physical activity and the incidence of symptomatic kidney stone[36]. Similarly, a meta-analysis did not find a significant relationship between physical activity and kidney stone risk[37]. In contrast with those aforementioned studies, our result revealed that individuals with physical inactivity had a significantly higher prevalence of kidney stones. However, after adjusting for multivariate factors, the association between physical inactivity and the risk of kidney stones was no longer present, suggesting that the effect of physical inactivity on kidney stones is not entirely independent. These inconsistencies may be due to variations in sample size, confounding factors, and differences in the proportion of work-related physical activity across studies. For addictive behaviors, our results show that excessive alcohol consumption, but not smoking, is more prevalent among individuals with a history of kidney stone in our study, highlighting the detrimental effect of excessive drinking. However, past studies on the relationship between alcohol consumption and kidney stone remain inconsistent across studies, potentially due to varying definitions of alcohol use and excessive drinking[38,39]. Association between alcohol consumption and kidney stone warrants further investigation. Additionally, our study assessed diet quality employing the updated HEI-2020 score, which places a greater emphasis on personalized and diverse dietary patterns, particularly focusing on plant-based foods and reducing added sugar intake. The protective role of a healthy diet on the human body has been well-established. Our result shown that lower diet quality is closely associated with an increased risk of kidney stones, consistent with previous studies[39], and for the first time, this has been thoroughly assessed in populations with different metabolic statuses. It is noteworthy that the highest metabolic risk group and the highest lifestyle risk group had similar prevalences of kidney stones. However, the higher OR observed in the highest metabolic risk group can be explained by two factors: first, metabolic dysregulation may directly promote stone formation and therefore confers a stronger risk for kidney stones; second, the prevalence of kidney stones in the best metabolic status group (no risk factors) was 4.37%, which is lower than that in the best lifestyle group (no risk factors), where the prevalence was 8.01%. This difference in the reference groups also helps to explain why the highest metabolic risk group had a higher OR.
This study highlights key innovations, including the identification of sedentary individuals as a vulnerable target population for precision prevention of kidney stones, the first demonstration of the synergistic interplay between metabolic status and lifestyle factors. The strengths of this study include its large sample size, national representativeness in the NHANES dataset, and comprehensive evaluation of both metabolic and lifestyle risk factors. Importantly, this is the first study to thoroughly examine the influence of lifestyle factors on kidney stone risk across different metabolic health strata, as well as the joint cumulative effects of metabolic and lifestyle factors. Additionally, the consistency of our findings across gender, age, race, and socioeconomic subgroups enhances the generalizability and specificity of the results. However, there are several limitations to consider. First, the definitions of kidney stone and several risk factors were based on self-reported data, which may introduce recall bias or underreporting. Moreover, although metabolic status in this study was defined using both objective indicators and questionnaire information, the possibility of reporting bias still cannot be excluded. Second, as this is a cross-sectional study, the ability to infer causal relationships is limited, and further longitudinal studies are necessary to confirm our findings. Third, the combined poor metabolic-lifestyle status for kidney stone risk shows wide CIs, indicating uncertainty. We should interpret the results with caution. Fourth, this study has limitations in terms of the generalizability to sedentary populations and the extrapolation to other ethnic groups. Finally, the coexistence and complex interactions between metabolic and lifestyle risk factors, joint with the subjective determination of risk factor scores, necessitate caution when interpreting their joint effects.
Conclusions
This study, using nationally representative data, provides a comprehensive analysis of the individual and joint effects of modifiable metabolic and lifestyle risk factors on the risk of kidney stones. We propose that prevention strategies should consider both metabolic and lifestyle interventions simultaneously, as effective prevention requires dual management of “internal” factors (metabolic health) and “external” factors (lifestyle behaviors). Following the clinical guidelines of the American Diabetes Association and the American Heart Association for the detection and control of metabolic risk factors, along with their lifestyle recommendations – including dietary improvements, smoking cessation, and alcohol avoidance – may help reduce the burden on both the public health system and affected individuals.
Footnotes
Qihang Li and Xin Huang contributed equally to this work
Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.
Supplemental Digital Content is available for this article. Direct URL citations are provided in the HTML and PDF versions of this article on the journal’s website, www.lww.com/international-journal-of-surgery.
Ethical approval
As the data are publicly available and de-identified, further institutional review board approval was not required.
Consent
All participants agreed to provide medical records for education or nonprofit research. No names, initials, images, or hospital numbers have been used in the paper.
Sources of funding
This work was supported by the National Natural Science Foundation of China (22204090); Shandong Province Natural Science Foundation (ZR2021QB089); and Taishan Scholar Foundation of Shandong Province (to Qiuhui Xuan).
Author contributions
Q.H.X. and Q.H.L.: study concept or design. Q.H.L. and X.H.: data analysis and manuscript writing. Y.T.L. and X.Q.D.: data collection. Q.H.X. and Q.H.L. and X.H: the interpretation of the data. All authors contributed to the article and approved the submitted version.
Conflicts of interest disclosure
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Guarantor
Qiuhui Xuan accept full responsibility for the work.
Research registration unique identifying number (UIN)
None.
Provenance and peer review
Not commissioned, externally peer-reviewed.
Data availability statement
The original contributions presented in this study are included in the article/Supplementary material; further inquiries can be directed to the corresponding authors.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The original contributions presented in this study are included in the article/Supplementary material; further inquiries can be directed to the corresponding authors.