Abstract
Background
This study aimed to investigate the association between different metabolic syndrome-body mass index (MetS-BMI) phenotypes and the risk of kidney stones.
Materials and Methods
Participants aged 20–80 years from six consecutive cycles of the NHANES 2007–2018 were included in this study. According to their MetS status and BMI, the included participants were allocated into six mutually exclusive groups: metabolically healthy normal weight (MHN)/overweight (MHOW)/obesity (MHO) and metabolically unhealthy normal weight (MUN)/overweight (MUOW)/obesity (MUO). To explore the association between MetS-BMI phenotypes and the risk of kidney stones, binary logistic regression was used to determine the odds ratios (ORs).
Results
A total of 13,589 participants were included. It was revealed that all the phenotypes with obesity displayed higher risks of kidney stones (OR = 1.38, p < 0.01 for MHO & OR = 1.80, p < 0.001 for MUO, in the fully adjusted model). The risk increased significantly when metabolic dysfunction coexisted with overweight and obesity (OR = 1.39, p < 0.05 for MUOW & OR = 1.80, p < 0.001 for MUO, in the fully adjusted model). Of note, the ORs for the MUO and MUOW groups were higher than those for the MHO and MHOW groups, respectively.
Conclusions
Obesity and unhealthy metabolic status can jointly increase the risk of kidney stones. Assessing the metabolic status of all individuals may be beneficial for preventing kidney stones.
1. Introduction
Kidney stones are a common and frequently recurrent condition of the urinary system and refer to stones that occur in the collecting system of the kidney, such as the renal calyx and renal pelvis [Citation1]. Studies have revealed that the prevalence of kidney stones was >10% in Americans [Citation2], 9% in Europeans [Citation1] and 5.8% in Chinese individuals [Citation3]. Unfortunately, the 5-year recurrence rate is as high as 50% [Citation4,Citation5]. In addition, the economic burden of the disease is still increasing, and the annual cost of urinary calculi is approximately $2 billion in the USA, mainly due to kidney stones [Citation6]. Therefore, it is necessary to identify the risk factors for kidney stones and improve early interventions and reduce the occurrence and recurrence of kidney stones.
It is well-known that the risk of kidney stones is increased by factors that facilitate urine concentration and crystallization of solutes. Rule AD et al. reported that risk factors for kidney stones include male sex, age, race, high sodium intake, hypercalcemia, monogenic hereditary disorders, metabolic abnormalities and obesity [Citation2,Citation7]. Of note, cumulative studies have shown that unhealthy metabolic status plays an important role in the onset and recurrence of kidney stones [Citation8,Citation9]. In addition, obesity is generally correlated with poor metabolic status [Citation10–12]. Many obese individuals are diagnosed with MetS [Citation13], but a large percentage of obese people remain metabolically healthy and are identified as having a metabolically healthy obesity (MHO) phenotype [Citation14]. It remains unclear whether the distinctive MHO phenotype is a risk factor for the formation of kidney stones [Citation15]. Several studies have investigated the association between MHO and the risk of chronic kidney disease [Citation16], hypogonadism [Citation17] and prostate cancer [Citation18]. However, the relationship between MHO and kidney stones has not yet been fully elucidated. Furthermore, the risk of kidney stones across different MetS-BMI phenotypes remains unclear. Therefore, the purpose of this study was to evaluate the association between MetS-BMI phenotypes and the risk of kidney stones.
2. Materials and methods
2.1. Data source and analytical sample
Data were extracted from the National Health and Nutrition Examination Survey (NHANES) database (www.cdc.gov/nchs/nhanes/index.htm) of the Centers for Disease Control and Prevention. NHANES uses a complex probability sampling design to sample across the U.S. Through standardized interviews, physical examinations, and sample tests, the health and nutritional status of the Americans were assessed.
From 2007 to 2018 in six study cycles, a total of 34,679 participants aged 20–80 years with a history of kidney stones by responding to the question “Have you ever had kidney stones?” and were screened for this study. Participants with incomplete data on the MetS-BMI components and other covariates were excluded. Ultimately, a total of 13,589 participants were included in the final analyses (). The present study used previously collected public data, and ethical approval was approved by the institutional review board at the National Center for Health Statistics Ethics Review Board [Citation19].
Figure 1. Flowchart identifying process of NHANES 2007 -2018 participant inclusion and exclusion. A total of 34679 participants were surveyed regarding history of kidney stone. Of these subjects, 21090 were excluded due to missing data on MetS-BMI components or other covariates.
2.2. Study variables
Participants who answered “Yes” to the question “Have you ever had kidney stones?” were considered to have a history of kidney stones. Additionally, several covariates, including sociodemographic and lifestyle characteristics, were collected. Sociodemographic characteristics included age, sex (female and male), race and ethnicity (Mexican American, other Hispanic, non-Hispanic white, non-Hispanic black, and other races), marital status (not married or divorced or separated or widowed, and married or living together), family poverty ratio (defined as the ratio of annual household income to the federal poverty line, classified as <1.3, 1.3–3.5, and >3.5), and educational status (less educated than college, and college degree and above). Lifestyle characteristics included alcohol consumption, sleep duration and depression status. In addition, waist circumference, body mass index (BMI), systolic blood pressure, diastolic blood pressure, fasting serum glucose, triglycerides, high-density lipoprotein cholesterol and medical history were collected.
2.3. Assessment of the MetS-BMI phenotypes
Weight and height were collected by trained health technicians according to general guidelines on body measurement procedures [Citation20]. BMI was defined as body weight in kilograms divided by height in meters squared. The standard definition of classification for overweight and obesity was used to categorize BMIs: normal weight (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2) and obesity (>30 kg/m2) [Citation21].
Waist circumference was measured with measuring tape around the trunk in a horizontal plane of the midaxillary line of study participants in a standing position. After the participants had rested quietly in a sitting position for 5 min, their blood pressure was measured by a registered blood pressure examiner at a mobile testing centre. Three consecutive blood pressure readings were obtained, and a fourth reading was taken if the BP measurement was interrupted or incomplete. In this study, average blood pressure was calculated by taking the average of the recorded blood pressure readings. Specimen collection for triglyceride levels, HDL-cholesterol levels, and fasting glucose concentrations performed by trained NHANES laboratory staff at a mobile examination centre. Blood specimens were processed, stored, and shipped to the University of Minnesota, Minneapolis, MN for analysis. Detailed specimen collection and processing instructions are available in the NHANES Laboratory/Medical Technologists Procedures Manual (LPM). In the present study, MetS was defined as a cluster having at least three of the following five parameters according to a previous study [Citation22]: abdominal obesity (waist circumference ≥102 cm in males; ≥88 cm in females), elevated triglycerides (≥150 mg/dL or drug treatment for elevated triglycerides), reduced HDL cholesterol (<40 mg/dL in males; <50 mg/dL in females or drug treatment for reduced HDL cholesterol), elevated blood pressure (systolic pressure ≥130 mmHg or diastolic pressure ≥85 mmHg or drug treatment for hypertension), and elevated fasting glucose (≥100 mg/dL or drug treatment for elevated glucose).
Moreover, based on their BMI classification (normal weight, overweight, and obesity) and MetS status (metabolically healthy, metabolically unhealthy), participants were classified into six mutually exclusive groups: metabolically healthy normal weight (MHN)/overweight (MHOW)/obesity (MHO) and metabolically unhealthy normal weight (MUN)/overweight (MUOW)/obesity (MUO).
2.5. Statistical analyses
In the present study, continuous variables with a normal distribution are displayed as the means and standard deviations, and categoric variables are shown as proportions (%). One-way analysis of variance (ANOVA) was used to detect the differences in continuous data, and the chi-square test was selected for categorical data. To explore the association between MetS-BMI phenotypes and the risk of kidney stones, a set of binary logistic regression models was used to calculate the odds ratios (ORs) and 95% CIs. Model 1 was the crude model. Model 2 was adjusted for age, gender and race. Model 3 was further adjusted for education, marital status and family poverty ratio. model 4 was further adjusted for alcohol consumption, depression status, and sleep duration. The AUC values of four regression models were calculated to assess the accuracy. All analyses were conducted using STATA 16.1 (Stata Corporation, College Station, TX, USA). A two-tailed probability value of p < .05 was considered statistically significant.
3. Results
3.1. Characteristics of the participants
summarizes the characteristics of all included participants according to MetS-BMI phenotypes. Among 13,589 participants, there were 7232 (53.22%) metabolically unhealthy participants and 6357 (46.78%) metabolically healthy participants. A total of 27.26% and 52.26% of the total participants were overweight and obese, respectively. The MHO phenotype accounted for 18.21% of all the enrolled participants and 34.84% of the obese individuals. The MHOW phenotype accounted for 17.48% of all the enrolled participants and 64.15% of the overweight individuals. Moreover, metabolically unhealthy individuals had higher glucose, TC, BP and waist circumference and relatively lower HDL-C than their metabolically healthy counterparts.
Table 1. Characteristics of the participants.
3.2. Association between MetS-BMI phenotypes and the risk of kidney stones
displays the prevalence of kidney stones classified by MetS-BMI phenotypes. Across different MetS-BMI phenotypes, the crude prevalence of kidney stones was 6.68%, 6.86%, 8.93%, 9.23%, 13.48%, and 13.92% in the MHN, MHOW, MHO, MUN, MUOW, and MUO groups, respectively. The prevalence of kidney stones increased gradually with BMI regardless of the presence or absence of MetS (P for trend = 0.002 for metabolically healthy subjects; P for trend = 0.005 for metabolically unhealthy subjects).
Table 2. Prevalence of kidney stones classified by MetS-BMI phenotypes.
shows the association between the phenotypes of MetS and BMI. The AUC values and ROC curves of the four regression models are displayed in Figure S1. We found that all the phenotypes with obesity had a higher risk of kidney stones (OR = 1.41, p = 0.003 for MHO & OR = 2.43, p < 0.001 for MUO, in the crude model). The magnitude and significance remained consistent in all four models (all p < 0.01). In addition, no increased risk was detected for the MUN group after adjusting for covariates (p > 0.05 in model 2 - model 4). However, the risk increased significantly when metabolic dysfunction coexisted with overweight and obesity (OR = 2.15, p < 0.001 for MUOW & OR = 2.43, p < 0.001 for MUO, in the crude model). The magnitude and significance still existed in the other three models (all p < 0.05). Of note, the ORs for the MUO and MUOW groups were higher than those for the MHO and MHOW groups, respectively, thus highlighting the adverse impact of metabolic dysfunction on kidney stones.
Table 3. Association between the phenotypes of MetS-BMI and the prevalence of kidney stones.
4. Discussion
In the present study, we found that individuals with MHO had a higher risk of developing kidney stones than those with MHN and that individuals with MUO had the highest risk among all groups. Our results indicated that MHO was not a benign condition for kidney stones. Moreover, our findings also suggested that the risk of kidney stones in the metabolically unhealthy group was much higher than that in the metabolically healthy group across BMI categories.
An increasing number of studies have shown that being overweight or obese is associated with a higher risk of kidney stones, especially in metabolically unhealthy populations [Citation23–25]. Insulin resistance and other obesity-associated metabolic abnormalities mediate an increased risk of kidney stones in obese patients [Citation23]. Moreover, insulin resistance may be accelerated by obesity, which is usually comorbid with MetS. Taken together, it is difficult to ascertain whether obesity per se is causally linked to the formation of kidney stones or whether obesity and its associated MetS jointly cause kidney stones. Obesity-related metabolic abnormalities are different according to obesity phenotypes. A special subset of the obesity phenotype, referred to as metabolically healthy obesity (MHO), is relatively insulin-sensitive and has fewer accompanying metabolic abnormalities, such as dyslipidaemia, elevated glucose, and hypertension [Citation26]. Few studies have investigated the adverse effect of obesity on kidney stones based on metabolic health status. Furthermore, it is not yet clear whether obesity per se or its related conditions facilitate the development of kidney stones. Evaluating the risk of kidney stones among populations with various BMI-MetS phenotypes may contribute to illustrating the role of obesity in the formation of kidney stones.
A cohort study by Kim et al. explored the relationship between obesity, metabolic health status, and the risk of developing kidney stones [Citation27]. Their findings, similar to ours, indicated that the hazard ratio for kidney stones was 1.36 in the MHO group, compared to the MHN group. These results suggest that MHO is significantly associated with an increased risk of kidney stones. Both our study and the research by Kim et al. further demonstrate that obesity is a crucial determinant of developing kidney stones, independent of metabolic abnormalities, and has a significant adverse impact on kidney stones in adults. Therefore, it is important to distinguish MHO from MHN and MUN individuals, as weight loss interventions may prove to be an effective strategy in preventing kidney stone formation in these metabolically healthy individuals at high risk of developing kidney stones. A rat model study also supports these findings, as a weight loss intervention was shown to reduce the risk of kidney stones [Citation28].
The underlying pathophysiological association between obesity and kidney stones remains unclear, but several potential mechanisms might help to understand why obesity itself causes kidney stones even in individuals with healthy metabolic status who are relatively insulin-sensitive. Obesity promotes conditions of chronic systemic inflammation and oxidative stress, leading to tissue immune cell infiltration and contributing to kidney stone formation. Obesity increases adipokine expression and alters the state of inflammatory molecules, including interleukin-6 and tumour necrosis factor-α [Citation29,Citation30]. Several molecular markers of inflammation and oxidative stress have been detected in both the kidney tissue and urine of patients with kidney stones [Citation31,Citation32]. Individuals with obesity substantially increase total caloric intake or a lithogenic diet, leading to a high risk of kidney stones [Citation33,Citation34]. In addition, obesity alters the chemical composition of urine, thereby increasing the risk of kidney stones [Citation33]. Unfortunately, information on urine composition was not available in the present study.
Previous studies have demonstrated that individuals with MetS have a higher risk of developing kidney stones than populations who are metabolically healthy [Citation27,Citation31,Citation35–37]. No study has clarified the relationship between MetS and kidney stones at the intracellular and molecular levels. However, lipase activation and insulin resistance are possible mechanisms involved in kidney stone formation in individuals with metabolic abnormalities [Citation38,Citation39]. In the present study, metabolically unhealthy individuals were at much higher risk of developing kidney stones than their metabolically healthy counterparts across BMI categories, which is consistent with the results of the study by Kim et al. [Citation27] and further confirms the previous perspective that metabolic status is a valuable indicator for identifying individuals at risk of forming kidney stones. Our findings further suggest that there is a significant difference in the risk of developing kidney stones between the MUO and MHO subsets. Individuals with unhealthy metabolic obesity are associated with a significantly increased risk of kidney stones. One possible mechanism behind this difference is that individuals with MUO have more adequate visceral fat mass and less subcutaneous adipose tissue expansion than individuals with MHO [Citation40], which is associated with a high risk of metabolic abnormalities and possibly a higher risk of kidney stones. While maintaining metabolic health may be quite difficult for overweight and obese people, it remains a key measure to prevent kidney stones. Our findings indicate that the prevention of kidney stones needs to emphasize the importance of assessing the metabolic status and further maintaining metabolic health, even in populations with normal weight.
Our study also has some limitations. First, the cross-sectional design limits the power to determine causal associations. Second, the NHANES data do not provide information on the specific components of kidney stones, which limits further analyses. Moreover, the diagnosis of kidney stones and some covariates are mainly based on self-report, thus reducing the reliability of the conclusion. However, in general, representative population database analysis could also provide a credible reference for clinical research. Last but not least, some drugs (i.e. diuretics) may increase/reduce the risk of kidney stones, and these covariates were not available in the datasets. Further studies should take this aspect into account to reduce the bias.
The present study investigated the association between MetS-BMI phenotypes and the risk of kidney stones and revealed that obesity leads to kidney stones. In addition, an unhealthy metabolic state is also a risk factor for kidney stones, and both of these factors jointly significantly increase the risk of kidney stones. This study provides new insight into the prevention of kidney stones. We should emphasize the importance of assessing the metabolic status and further maintaining metabolic health to prevent kidney stones for all individuals, even those of normal weight. Future high-quality prospective studies should be performed to determine the causal relationships between MetS-BMI phenotypes and the risk of kidney stones.
Our findings indicate that obesity and an unhealthy metabolic state are both risk factors for developing kidney stones, which can jointly increase the risk of kidney stones. Assessing the metabolic status of all individuals may be beneficial for preventing kidney stones.
5. Conclusions
Overweight/obesity and unhealthy metabolic status jointly increase the risk of kidney stones. This study suggests that MHO is not a benign condition for kidney stones. The results of this study also support that preventive interventions for kidney stones should emphasize the importance of assessing the metabolic status and further maintaining metabolic health for all individuals, even those of normal weight, and weight loss should additionally be considered for obese populations.
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References
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