Higher Life’s Essential 8 score is associated with lower risk of kidney stones in Chinese adults: a cross-sectional study

Abstract

Background

Kidney stones are widely prevalent throughout the world and impose a significant economic and medical burden on individuals and society. The aim of this study was to analyze the association between Life’s Essential 8 (LE8) score and kidney stones, to provide more precise interventions for the prevention of kidney stones.

Methods

This was a cross-sectional study that used baseline survey data from the Chinese Multi-Ethnic Cohort (CMEC) with 93,297 eligible participants. Participants’ LE8 score was measured according to the American Heart Association definition. Abdominal ultrasonography was used for the diagnosis of kidney stones. Logistics regression models and restricted cubic spline regression models were used to explore the association between LE8 and its subscale scores and the risk of kidney stones. Subgroup analyses were used to further explore the specific effects of increasing LE8 score on kidney stones in populations with different characteristics.

Results

In this study, a total of 6,327 subjects (6.78%) had kidney stones. Fully adjusted multifactorial logistic regression showed that CVH levels were significantly and negatively correlated with the risk of kidney stones (moderate CVH: OR = 0.76, 95% CI: 0.70-0.82; high CVH: OR = 0.62, 95% CI: 0.56-0.69). Restricted cubic spline regression analysis showed a linear inverse dose-response relationship between both LE8 and its subscale scores and the risk of kidney stones. In addition, subgroup analyses further showed that the negative association between LE8 score and kidney stones risk was more pronounced in males, frequent tea drinkers, and those with higher levels of education.

Conclusions

Higher LE8 and its subscale scores were associated with a lower risk of kidney stones. Adherence to a more optimal CVH will likely be a comprehensive preventive and management measure to reduce the severe disease burden of kidney stones.

Introduction

Kidney stones are crystals that form when the urine is oversaturated with minerals. They are usually deposited in the renal calyces, renal pelvis, or pelvic-ureteral junction, and can cause ureteral obstruction, blood in the urine, urinary tract infections, pain with urination, and other uncomfortable symptoms [1]. For individuals, the formation of kidney stones are painful because it is not just a purely urologic disease but also a systemic disease that can lead to the development of serious complications such as osteoporosis, hypertension, Cardiovascular Disease (CVD), end-stage renal disease, and renal cell carcinoma [2, 3]. Meanwhile, kidney stones are also a highly recurrent Disease. Studies have shown that the recurrence rate of patients with kidney stones within 5-15 Years of stones removal can be up to 20-40%[4], and the recurrence of stones will even accelerate the loss of their kidney function [5]. More worryingly, kidney stones are widespread worldwide. The prevalence of kidney stones has been reported to be 1-5% in Asia, 5-10% in Europe, and as high as 7-13% in North America [6]. The high incidence, high recurrence and unpredictability of kidney stones place a huge burden on individuals and society [7]. Therefore, it is important to carefully consider how to prevent the occurrence of kidney stones.

At present, although a large number of epidemiologic investigations have found that the development of kidney stones are associated with factors such as obesity, sleep, hypertension, hyperglycemia, smoking, and secondhand smoke exposure [8,9,10,11,12], we need to face the fact that all of these previous studies have focused primarily on the effect of a single factor on kidney stones. However, it is worth noting that these factors often coexist and may synergistically influence health outcomes. Thus, we do not know the how combined effects of the coexistence of multiple health behaviors and factors influence the development of kidney stones. Recently, the American Heart Association (AHA) has expanded a new method of assessing Cardiovascular Health (CVH), the Life’s Essential 8 (LE8) [13]. Compared to the Life’s Simple 7 assessment method (including 7 aspects of diet, exercise, smoking, body mass index, blood pressure, blood glucose, and total cholesterol) initially proposed by the AHA in 2010 [14], LE8 not only formalized sleep as the eighth metric for assessing CVH but also developed a more sensitive scoring algorithm for each metric, further improving the identification of both large groups and individual differences in CVH [15]. In conclusion, LE8 is a comprehensive assessment of an individual’s health level of multiple behaviors and factors, which provides us with the conditions to analyze the combined effects of multiple health behaviors and factors on health outcomes. Currently, there is a large body of evidence that obtaining a higher LE8 score not only reduces the risk of CVD as well as non-alcoholic fatty liver disease but also increases life expectancy [16,17,18,19]. Given the strong association between kidney stones and identified CVD risk factors [20], promoting CVH may also be an appropriate prevention and management strategy to reduce the burden of kidney stones. To date, a negative association between LE8 and kidney stones has been found only in the American population [21, 22], but remains unclear for the Chinese population. According to recent evidence, the prevalence of kidney stones has been increasing in China over the past three decades [23, 24]. As a country with a large population, kidney stones have caused a more serious medical and economic burden in China [25]. Therefore, this study utilized baseline survey data from the Chinese Multi-Ethnic Cohort (CMEC) to investigate the association between LE8 score and kidney stones risk in the Chinese population, aiming to provide more detailed and scientific evidence for developing targeted preventive measures for kidney stones in the Chinese population.

Materials and methods

Study design and population

CMEC is a community-based natural population cohort study, and its detailed study design, sampling methods, and baseline population characteristics have been published in the International Journal of Epidemiology and the Chinese Journal of Epidemiology [26, 27]. Briefly, from May 2018 to September 2019, we recruited 99,556 participants aged 30-79 years old in five provinces in Southwest China (i.e., Yunnan, Guizhou, Tibet, Sichuan, and Chongqing) and obtained detailed data on each participant through face-to-face questionnaires, fasting blood tests, and relevant physical examinations (including abdominal ultrasound). In this study, we further excluded participants with no abdominal ultrasound findings (n = 4,572), missing more than two indicators to assess LE8 (n = 1,479), and missing relevant covariates (n = 208). Ultimately, 93,297 participants were included in our main analysis (Fig. 1). This study was conducted in accordance with the Declaration of Helsinki and was approved by the Medical Ethics Review Committee of Sichuan University (K2016038, K2020022). Meanwhile, prior to the survey, all participants signed a written informed consent form.

Fig. 1

Flow chart of the analysis sample in the study

Measurement of LE8

Each participant’s LE8 score was calculated based on four health behaviors (diet, Physical Activity[PA], nicotine exposure, sleep) and four health factors (Body Mass Index[BMI], blood pressure, blood glucose, Non-High Density Lipoprotein Cholesterol Level[Non HDL-CL]) recommended by the AHA [13]. In the CMEC baseline survey, the Food Frequency Questionnaire (FFQ) was used to assess participants’ habitual Dietary intake over the previous 12 months. The FFQ included a List of 13 common food items: tubers, red and processed meats, poultry, fish/seafood, eggs, fresh vegetables, soybean products, preserved vegetables, fresh fruits, dairy products, rice, wheat products, and roughage. Participants reported the consumption of each food group in terms of quantity (how many grams per meal based on standard portion sizes) and frequency (how often they consumed it daily, weekly, monthly, or annually in the past 12 months). For oil and salt, we looked up the household consumption and the number of persons eating each meal in the past month and calculated the daily consumption of cooking oil and salt for each person. Reproducibility and validity of the FFQ have been previously reported [28]. We used the scoring method of diet pattern of the Dietary Approaches to Stop Hypertension (DASH) to evaluate the participants’ diet quality [13]. Specifically, the DASH focuses on eight food components, including fresh fruits, fresh vegetables, nuts and legumes, dairy products, whole grains, salt, oil, and red meat and its products. Participants were divided into quintiles based on their ranking of food intake in each group. The principle of positive scoring was used to assign scores to the intake of five foods, including fresh fruits, fresh vegetables, nuts and legumes, dairy products, and whole grains (e.g., Quintile 1 scored 1 point and quintile 5 scored 5 points) since these foods are recommended for greater intake. However, low intake of salt, oil, and red meat and its products was preferred, and thus they were assigned a reverse score, whereby quintile 1 was given 5 points, whereas quintile 5 was given 1 point. The scores for each component were finally summed to obtain a total Dietary score of 8-40 (In the supplementary material, Table S1 details the DASH diet scoring criteria). For the PA of the study participants, we referred to the validated questionnaire questions from the China Kadoorie Biobank [29]. Briefly, we asked study participants about the intensity and duration of four types of PA in the past Year, including occupation, transportation, household chores, and leisure time. Referring to the 2011 update of the PA compendium, the daily exercise level for each PA was quantified through metabolic equivalent for tasks [30]. Specifically, an individual’s daily exercise level for each PA was calculated by multiplying the assigned Metabolic Equivalent for Task (MET) by the time spent on the activity. Finally, the calculated daily exercise levels for the four types of PA were summed to respond to the participant’s daily total PA level. Similarly, participants were categorized into quintiles based on their rankings of daily total PA levels. Regarding participants’ tobacco use, exposure to secondhand smoke and average sleep time per night were obtained from the“Smoking and Exposure to other environmental factors in the Individual”and“Life Events, Social Support, and Psychological Status” sections of the questionnaire, respectively. During the physical examination, after the subjects had taken a five-minute break, the examiner took three measurements of their blood pressure and measured their height and weight. The average of the three blood pressure measurements was used as the subject’s blood pressure level, and weight (kg) and height (m) were used to calculate BMI and classified according to the BMI criteria for Asian populations [13]. Biochemical markers such as Hemoglobin A1c (HbA1c), Total Cholesterol (TC), and High-Density Lipoprotein Cholesterol Level (HDL-CL) were obtained from fasting blood samples taken from subjects during the physical examination, whereas the non HDL-CL was calculated by subtracting the HDL-CL from the TC. Table S2 in the supplementary material details the specific scoring algorithm for each indicator in LE8, in which we Made appropriate adjustments to the scores for the indicators of Diet, PA, and BMI, taking into account the actual situation in China. In short, each indicator has a score ranging from 0 to 100. Notably, the example provided by the AHA working group for calculating LE8 score allows for one of the eight indicators to be missing [13]. Therefore, in our study, participants who were only able to access seven of the assessment indicators in LE8 were also included in the analysis. Finally, an unweighted average of all indicator scores was calculated to derive the subject’s LE8 score, and based on AHA’s definitions, LE8 score between 80 and 100 were defined as high CVH and 50-79 and 0-49 were defined as moderate and low CVH, respectively [13]. In addition, based on Rong Huai Zhang et al., [31] we used the same cutoff values of LE8 to categorize the two dimensions of health behaviors and health factors to investigate the association of LE8 subscales with kidney stones.

Diagnosis of kidney stones

A portable diagnostic ultrasound device (abdominal probe model C5-1; frequency 3.5-5.5 MHz) of the Volusone model of the American General Corporation was used to diagnose kidney stones in the subjects. Abdominal ultrasound is a very common form of health screening in China. Prior to the investigation, the research team trained experienced sonographers and standardized the diagnostic criteria for kidney stones, i.e., kidney stones were diagnosed when bright echogenic structures were demonstrated in the ultrasound images of the kidneys [32]. In addition, subjects were asked to fast for more than 8 h before the examination to minimize unnecessary interference.

Assessment of covariates

Based on relevant research evidence, in this study we defined some covariates as follows. (a) Sex: male and female; (b) Age group: ≤50 and > 50 years old; [33] (c) Ethnic group: Sichuan Basin and Yunnan-Guizhou and Qinghai-Tibet Plateau; (d) Household registration: rural, urban, and united; (e) Marital status: married/cohabiting, divorced/widowed, and never married; (f) Level of education: junior high school or below, senior high school, junior college, and bachelor degree or above; (g) Household Annual income(Ұ): < 20,000, 20,000-59,999, 60,000-99,999 and ≥ 100,000; (h) Frequency of tea drinking: The questionnaire asked “Have you been drinking tea every week for more than six months” and “If yes, how many days per week on average have you been drinking tea in the past year”, obtaining the tea drinking frequency as “never”, “1-2 days per week”, “3-5 days per week” and “almost every day”.

Statistical analysis

Continuous and categorical variables were statistically described using means and standard deviations, numbers and percentages, respectively, and t-tests and chi-square tests were used to explore differences between subgroups, where appropriate. Multifactorial logistic regression analysis was used to analyze the relationship between single indicator, LE8 and its subscale scores and kidney stones, where multiplicative interaction test studies were further used to explore the interaction between Health behaviors and Health factors. In this process, we built three adjustment models to adjust for known or possible confounders stepwise. Model 1 was unadjusted, Model 2 was adjusted for sex and age, and Model 3 was further adjusted on the basis of Model 2 for ethnic group, household registration, marital status, education, annual household income, and tea drinking frequency. In addition, we further explored the potential dose-response relationship between LE8 and its subscale scores and the risk of kidney stones using restricted cubic spline regression analysis (four knots were chosen, where the knot positions were the 5th, 35th, 65th, and 95th percentiles of the scores, respectively). Subsequently, we stratified the analyses by sex, age group, ethnic group, household registration, marital status, education, annual household income, and tea drinking frequency and similarly used a multiplicative interaction test studies to explore the interaction between the stratification factors and LE8 score to analyze further the specific role of LE8 score on kidney stones among people with different characteristics. Finally, we excluded subjects with a history of CVD (including coronary heart disease and stroke) and incomplete LE8 assessment metrics (i.e., participants who obtained only 7 of the 8 assessment metrics) and performed sensitivity analyses to assess the robustness of the study results. The results of the logistic regression analyses were reported as Odds Ratios (OR) and their corresponding 95% confidence intervals (95% CI). All statistical tests were two-sided and were considered statistically significant when P < 0.05. SPSS 26.0 and R 4.2.3 were used to perform all statistical analyses.

Results

Baseline characterization

The basic information of the participants in this study is described in detail in Table 1. Briefly, of the 93,297 participants included, the mean age was 51.55 ± 11.65 years, female (60.33%) and rural populations (65.75%) were predominant, and the mean LE8 score was 67.17 ± 13.05. In addition, a total of 6,327 participants were diagnosed with kidney stones by ultrasound (6.78%), with the Yunnan-Guizhou and Qinghai-Tibet Plateau populations having the higher prevalence of kidney stones (7.99%). Participants with kidney stones were older, more likely to be male, have a low household income, rarely drink tea, and have a history of marriage compared to participants without kidney stones. For the scores on the indicators, those without kidney stones had higher scores on diet, sleep, nicotine exposure, blood pressure, blood glucose, blood lipid, BMI, LE8, health behavior, and health factor, but lower scores on PA, compared with those with kidney stones. Overall, the prevalence of kidney stones was significantly lower in those assessed as “high” either by LE8 or by its subscales (P < 0.01).

Table 1 Basic characteristics of the study population

Effect of single indicator in LE8 on kidney stones

Fully adjusted multifactorial logistic regression models showed that diet score, nicotine exposure score, sleep score, blood pressure score, blood lipid score, and BMI score were negatively associated with the risk of kidney stones, whereas PA score was positively associated with the risk of kidney stones (all P < 0.05). Specific results are presented in Table S3 in the Supplementary Material.

Relationship between LE8 and its subscale scores and kidney stones

In logistic model that was not adjusted for any covariates, the results indicated that increasing CVH level was a protective factor against kidney stones(For each additional 10 points in LE8: OR = 0.83, 95% CI: 0.81-0.84). The results remained significant even after controlling for possible confounders. In particular, the OR and its 95% CI for developing kidney stones were 0.76 (0.70-0.82) and 0.62 (0.56-0.69) for the moderate and high CVH groups, respectively, compared to the low CVH group. For the subscale scores of LE8, fully adjusted multifactor logistic regression models showed that both health behavior score and health factor score were negatively associated with the risk of kidney stones. In addition, there was no interaction between health behavior score and health factor score with the effect of kidney stones (P for interaction = 0.32) (Table 2). The results of multifactor-corrected restricted cubic spline regression analyses further indicated that LE8, health behavior, and health factor scores showed a linear inverse dose-response relationship with the risk of kidney stones (P for nonlinear were all > 0.05) (Fig. 2).

Table 2 Results of logistic regression of the relationship between LE8 and its subscale scores and kidney stones

Fig. 2

Dose-response relationship between LE8 and its subscale scores and the risk of kidney stones; Adjustments were made for sex, age, ethnic group, household registration, marital status, education, annual household income, and tea drinking frequency (Note: When analyzing the effect of the score of one subscale [Health Behavior Score or Health Factor Score], the score of the other subscale was additionally adjusted)

Subgroup analysis

In all subgroups, the LE8 score was negatively associated with the risk of kidney stones (Fig. 3). Interestingly, Significant interactions were found between LE8 score and gender, education level and frequency of tea consumption with the effect of kidney stones (p for interactions were all < 0.05). The negative association of LE8 score with the risk of kidney stones was more pronounced in males, those with a higher level of education and those who drank tea frequently.

Fig. 3

Subgroup analysis of the association between LE8 score and kidney stones; OR indicates the degree of change in the risk of kidney stones for each 10-point increase in LE8 score; Adjustments were made for sex, age, ethnic group, household registration, marital status, education, annual household income, and tea drinking frequency

Sensitivity analysis

Table 3 and Fig. 4 show the results of the sensitivity analysis in detail. Briefly, we analyzed the remaining 89,008 participants after excluding those with a history of CVD and those with incomplete LE8 assessment metrics, and all results remained virtually unchanged.

Table 3 Sensitivity analysis of logistic regression results between LE8 and its subscale scores and kidney stones

Fig. 4

Sensitivity analysis of dose-response relationship between LE8 and its subscale scores and kidney stones; Adjustments were made for sex, age, ethnic group, household registration, marital status, education, annual household income, and tea drinking frequency (note: when analyzing the effect of the score of one subscale [Health Behavior Score or Health Factor Score], the score of the other subscale was additionally adjusted)

Discussion

This cross-sectional analysis of a population of adults in southwestern China showed that both LE8 and its subscale (two dimensions of health behaviors and health factors) scores were significantly and negatively associated with the risk of kidney stones, which is consistent with previous findings in a U.S. adult population. Our findings further add to the validity and applicability of focusing on and improving the LE8 score to reduce the disease burden of kidney stones in the Chinese adult population.

Although the exact mechanism by which LE8 is associated with kidney stones are unknown, there are several possible causes. The LE8 consists of eight domains: diet, nicotine exposure, sleep, PA, BMI, blood pressure, blood glucose, and blood lipids. In our study, we found that higher DASH diet score, nicotine exposure score, sleep score, BMI score, blood pressure score, and blood lipid score were all beneficial in reducing the risk of kidney stones, which are consistent with previous studies. A follow-up study from the United States [34]suggests that greater adherence to the Mediterranean dietary pattern can be effective in reducing the risk of kidney stones. However, a national survey [35]reported that diet with a higher dietary inflammatory index not only increased the incidence of kidney stones but also led to a significantly higher risk of stone recurrence. In our study, higher DASH diet score emphasized increased intake of fruits, vegetables, whole grains, dairy products, and nuts and legumes, while limiting salt, oil, and red meat and its products. In this regard, a varied, high-quality diet can increase the production of beneficial short-chain fatty acids [36]and optimize the structure of the gut flora [37], ultimately leading to a reduced risk of kidney stones [38, 39]. Getting a normal sleep is a key way to maintain various physiological functions in the body [40]. In 2023, Shan Yin et al. reported for the first time in the American population that normal sleep time is associated with a lower risk of kidney stones compared to short sleep time [8]. It has been found that sleep disorders can cause increased levels of several inflammatory mediators in the peripheral circulation (C-reactive protein, interleukin-6, tumor necrosis factor-α, etc.) [41]. However, high systemic immunoinflammatory index, a novel biomarker representing systemic levels of inflammation, has been associated with a high risk of kidney stones [33]. Thus, elevated levels of inflammation due to sleep disorders may be an important stage to promote the formation of kidney stones, such as an increase in inflammatory mediators such as C-reactive protein [42], interleukin-1β [43] and interleukin-18 [43] in the body. In addition, we noted that nicotine exposure score was also negatively associated with kidney stones risk. Previous studies [11, 12] have also shown that refusing to smoke and reducing exposure to secondhand smoke can help reduce the incidence of kidney stones. Urinary calcium excretion is known to be a protective factor against kidney stones, however smoking causes a decrease in urinary calcium excretion [44]. More importantly, smoking also causes the release of reactive oxygen species from the kidneys, which further leads to kidney damage and creates conditions for the formation of kidney stones [44]. What intrigues us is whether there is an association between PA levels and the development of kidney stones. However, relevant studies are inconclusive. Some previous studies have suggested that increasing PA levels may reduce the risk of kidney stones [45, 46], but others have pointed out that there is no correlation between the two [47]. In our study, we found a positive correlation between increasing PA levels and kidney stones. To investigate the possible reasons for the inconsistency with the findings of others, we considered that it might be related to the geographical environment in which the study population was located. On the one hand, Southwest China is a less developed region of China with lagging economic conditions. Local residents rely on additional occupational PA to meet their livelihood needs [48], and therefore work longer hours and with higher workloads and intensity, resulting in more physical exertion. It is worth mentioning that our team’s previous study also found that occupational PA accounted for a large portion of the total PA of the residents in this region, and the increased level of occupational PA may also be an important risk factor for dyslipidemia [48]. On the other hand, the hot geographical environment [49] may also contribute to the loss of water due to heavy sweating by laborers in the region, which in turn may contribute to the development of kidney stones. Taken together, this may explain to some extent the association of increased total PA with the risk of kidney stones in the present study population. More importantly, the points raised in this study are also similar to the findings of a large number of epidemiological surveys summarized by Paleerath Peerapen et al.[50]. In terms of health factors, among others, obesity leads to many adverse health outcomes, including kidney stones [10]. Studies have shown that obesity-induced adipose dysfunction causes alterations in the expression of adipokines and can further lead to increased oxidative stress and pro-inflammatory states in the body [51, 52]. These conditions may contribute to the fact that obesity independently promotes the development of kidney stone disease [53]. In addition, obesity usually causes metabolic disturbances in the body (e.g., increased blood pressure, dyslipidemia, etc.) [54, 55]. However, it has been demonstrated that metabolic abnormalities also increase the risk of kidney stones, but when coexisting with and obesity further increase the risk of stone formation in the kidney [56]. In summary, attention to LE8 can have a cumulative effect on kidney stones formation in multiple ways. More importantly, an intervention study has shown that setting specific action goals is more helpful in increasing individual improvement and adherence to health behaviors [57]. However, the scoring criteria for each of the LE8 components have clear cutoffs, which helps public health as well as clinical staff to provide clear recommendations for developing interventions for patients. Therefore, our findings reiterate the importance of population-wide health education on LE8 to improve human knowledge and implementation of LE8.

Stratified analyses showed that the negative association between LE8 score and kidney stones was more pronounced in those who drank tea frequently, those with higher education, and men. A prospective cohort study based on 500,000 Chinese people has shown that regular tea consumption significantly reduces the risk of kidney stones [58]. Although the specific mechanism of tea drinking to prevent kidney stones are not clear, caffeine in tea not only has diuretic effect, but also can increase the excretion of calcium, sodium and magnesium in urine [59]. In addition, drinking tea has many protective effects on the formation of kidney stones through the accompanying increase in drinking water and the intake of some components with antioxidant properties [59]. Thus, regular tea drinkers in combination with a more optimal CVH may reduce the risk of kidney stones to a greater extent. More educated study participants will likely have higher adherence to aspects of health other than LE8 compared to less educated participants, and therefore, more pronounced health effects will be produced by increasing LE8 score. With respect to gender, the prevalence of kidney stones are significantly higher in the male population than in the female population. Although previous studies have suggested that gender differences in kidney stones risk can be partially attributed to estrogen [60], a recent longitudinal study that included 268,553 study participants found that lifestyle risk factors explained a portion of the excess risk of kidney stones in men [61]. More importantly, in the present study, we also found that the negative association between a composite score for lifestyle (i.e., the LE8 score) and kidney stones was more pronounced in the male population, further emphasizing the public health importance of strengthening lifestyle modification risk factor interventions in the male population.

Despite the important findings of this study, there are still some limitations. First, this was a cross-sectional study, and further longitudinal or intervention studies are needed in the future to determine the causal relationship between LE8 scores and kidney stones and to investigate the underlying biological mechanisms. Second, abdominal ultrasound was chosen in this study as a diagnostic method for kidney stones rather than non-enhanced computed tomography (a method considered the gold standard for kidney stones diagnosis). However, it should be noted that CMEC, as a large population-based study, requires consideration of the safety, feasibility, and affordability of diagnostic methods. In addition, abdominal ultrasound has been shown to have high sensitivity (70.0%) and specificity (94.4%) in the detection of kidney stones [62]and has been widely utilized in large epidemiological studies on kidney stones [63]. Therefore, the choice of abdominal ultrasound is more appropriate. Moreover, the abdominal ultrasound examinations of all subjects in this study were performed by specialized sonographers with extensive clinical experience, which also improved the detection rate of kidney stones to a certain extent. Nevertheless, in this study, the diagnosis of kidney stones was based only on a single abdominal ultrasound examination, which may have influenced the diagnosis in patients with smaller stones, thus underestimating the effect of the association between LE8 score and kidney stones. Third, the results of the questionnaire survey may be affected by the recall bias of the participants. However, the research team provided specialized training to the investigators prior to the survey, which could ensure the accuracy of the results to a certain extent. Fourth, although our study found a significant interaction between LE8 score and tea drinking frequency on the effect of kidney stones, the specific effects between different tea types have not been measured in detail. However, studies have shown that different tea types have different effects on disease [64]. Therefore, further research is needed in the future to explore in detail the specific effects of tea type and LE8 score on kidney stones, which will help provide more detailed research evidence. Fifth, our study population included only adults in Southwest China, which may limit the generalization of our findings to other regions, ethnic groups, and populations of different ages (especially children and adolescents). Finally, There is a lack of information on the use of diuretics by the study population, which may influence the formation of kidney stones.

Conclusion

In Chinese adults, LE8, health behavior, and health factor score all showed a linear inverse dose-response relationship with kidney stones risk. In particular, the negative association between LE8 score and kidney stones was more pronounced in men, those with higher education, and frequent tea drinkers. Our findings emphasize that focusing on and improving CVH may be an effective preventive measure to reduce the burden of kidney stones. However, more longitudinal or intervention studies are needed to validate our findings in the future.