The Relationship Between Metabolic Syndrome and Kidney Stone Disease: A Cross-Sectional Study From the PERSIAN Guilan Cohort Study

Abstract

Background:

Based on the high prevalence of kidney stone disease (KSD) and its possible relationship with metabolic components, the aim of this study was to examine the associations of metabolic syndrome (MetS) and its components with KSD.

Methods:

This is a cross-sectional assessment of the Prospective Epidemiological Research Studies of Iranian Adults (PERSIAN) Guilan cohort study (PGCS), which includes 10,520 participants aged between 35 and 70 in northern Iran from 2014 to 2017. Demographic data and clinical characteristics were filled out. MetS was determined by the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) with the following criteria: hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C), hypertension, abdominal obesity, and hyperglycemia. The association of self-reported KSD with MetS was examined using logistic regression analysis. Odds ratio (OR) and 95% confidence interval (CI) were calculated.

Results:

The prevalence of MetS and KSD was 41.8% and 15.6%, respectively. In the unadjusted model, MetS was associated with 18% increased odds of KSD (OR = 1.18, 95% CI: 1.06–1.31). This association remained significant after adjustment for some demographic characteristics (aOR = 1.30, 95% CI: 1.16–1.46). All MetS components except for low HDL-C were also associated with increased odds of KSD, after adjusting for some demographic variables. In addition, the odds of KSD increased with the number of MetS components, up to an almost 2.2-fold odds among subjects with all five MetS components.

Conclusion:

This study found that the risk of KSD increases with MetS as a whole, all MetS components except for low HDL-C, and the number of MetS components. Our study might provide evidence for individualized management of MetS for preventing KSD.

Introduction

Kidney stone disease (KSD) is a prevalent global health issue, with urinary stones ranking as the third most frequent disease affecting the urinary system. The incidence of this condition has consistently risen in recent decades. Around 10%–12% of males and 5%–6% of females experience symptomatic KSD a minimum of once during their whole life.^1,2 In Iran, the prevalence of KSD ranged from 1.9% to 5.7% in different studies, with a larger occurrence in the west of the country.^3,4 Reports indicate a significant increase in the prevalence and incidence of this condition in Iran. KSD has the potential to cause obstruction in the ureter, which can lead to severe health issues such as septic shock. Additionally, KSD is linked to a higher likelihood of developing chronic kidney diseases, end-stage renal failure, cardiovascular disorders, and hypertension.^5,6 Thus, the identification of the correlated risk variables could provide beneficial in the diagnosis and treatment of etiology.

The pathophysiology of stone formation is a multifactorial combination of genetic, dietary, environmental, and metabolic components, and there is a suggestion that KSD might be a systemic condition associated with metabolic syndrome (MetS).⁷ MetS is a complex status including central obesity, dyslipidemia, hypertension, and high fasting blood sugar (FBS). It has been reported that there is a correlation between MetS and the formation of renal stones, but a few cohort studies have noted this point.^8,9 Therefore, in the present cross-sectional investigation, we surveyed the relationship between MetS and KSD in the Prospective Epidemiological Research Studies of Iranian Adults (PERSIAN) Guilan cohort study (PGCS) population.

Methods

Participants

This is a cross-sectional assessment of the PGCS population, which, as a part of the National Persian Cohort study in Iran, includes 10,520 participants aged between 35 and 70, in Some’e Sara County, Guilan, Iran, from October 8, 2014, to January 20, 2017.¹⁰ Preliminary data related to the Guilan cohort have been published in detail as a cohort profile.¹¹

Data collection

Data collection related to the PERSIAN Guilan Cohort study consists of registration procedures and questionnaire completion filled out using a face-to-face interview.¹¹

Demographic data

Demographic data, including age, sex, education level, marital status, employment, and habitat, were collected.¹¹

Lifestyle information

Lifestyle information, including body mass index (BMI) calculated by weight in kilograms/height in meters squared according to the National Health and Nutrition Examination Survey Manual (underweight: <18.5 kg/m², normal: 18.50–24.99 kg/m², overweight: 25–30 kg/m², obesity: >30 kg/m²), smoking, and alcohol consumption was collected.¹¹

Clinical characteristics

Blood measurements: For blood pressure analysis, the calibrated Richter ausculator mercury sphygmomanometer (MTM, Munich, Germany) was used. The volunteers rested for 10 min in a quiet room. Blood pressure was measured twice in both left and right arms after 5 min. Validity and reliability of blood pressure in the PERSIAN Guilan cohort study performed in previous studies.¹² Hypertension was defined as awareness of high blood pressure by the person who self-reported, diagnosis of blood pressure by a professional center that uses antihypertensive medications, or one having high blood pressure, systolic pressure ≥140 mmHg and/or diastolic pressure ≥90 mmHg.¹³ Diabetes diagnosis: diabetes was defined as FBS equal to or greater than 126 mmol/L, use of antidiabetic drug consumption, or a history of diagnosis of diabetes.¹⁴

Laboratory data

Fasting blood (5 mL) samples were collected by expert technicians and labeled for biochemistry tests, including triglyceride (TG), HDL-C, and FBS. Blood samples were transferred to the cold box and sent to the laboratory of the cohort center.¹¹

Metabolic syndrome (MetS) definition

Based on the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III), the presence of three or more risk factors, including hyperglycemia ≥100 mg/dL or previous diagnosis of type 2 diabetes, hypertension (systolic pressure ≥130 mmHg and diastolic pressure ≥85 mmHg) or previously diagnosed hypertension, abdominal obesity includes waist circumference ≥102 cm for men and ≥88 cm for women, hypertriglyceridemia ≥150 mg/dL and low HDL-cholesterol <40 mg/dL in men and <50 mg/dL in women was defined as MetS.^15,16

Kidney stone disease

In the standard questionnaire, subjects were asked for a self-reported history of KSD with the following question: “have you ever experienced KSD with physician confirmation?” Answers were “Yes, No, and I don’t know.”

Statistical analysis

In the present study, continuous variables were presented as mean ± standard deviation (SD) and categorical variables as number (percentage). We determined the association of KSD with MetS components, number of MetS components, and MetS using logistic regression analysis. Odds ratio (OR) and 95% confidence interval (CI) were calculated. ORs were adjusted for sociodemographic characteristics. Model 0 was unadjusted; Model 1 was adjusted for age; Model 2 was adjusted for age and sex; and Model 3 was adjusted for age and sex, marital status, education, employment, place of residency, physical activity, wealth score index, smoking, and alcohol consumption. All data analyses were carried out using IBM SPSS Statistics for Windows, version 26.0 (IBM Corp., Armonk, NY, USA), and the level of significance was set at 0.05.

The work has been reported in line with the STROCSS criteria (the checklist is presented in Supplementary Table S1).¹⁷

Results

Characteristics of the participants

Table 1 describes the demographic and clinical characteristics of the study population. The study participants had a mean age of 51.52 ± 8.90 years. The majority of the participants were male (46.5%), married (90.6%), had 6–12 years of education (45.9%), were employed (54.6%), and resided in rural areas (56.2%).”

Table 1.

Demographic and Clinical Characteristics of the Participants in the Persian Guilan Cohort Study (n = 10,520)

Variable	Mean (SD) or n (%)
Age (years)	51.52 (8.90)
Sex
Male	4887 (46.5)
Female	5633 (53.5)
Marital status
Single	305 (2.9)
Married	9527 (90.6)
Widow	566 (5.4)
Divorced	122 (1.1)
Education level
Illiterate	1738 (16.5)
1–5	3312 (31.5)
6–12	4832 (45.9)
University	638 (6.1)
Employment
Unemployed	4781 (45.4)
Employed	5739 (54.6)
Habitat
Urban	4613 (43.8)
Rural	5907 (56.2)
BMI (kg/m²)
Normal	2746 (26.1)
Underweight	141 (1.3)
Overweight	4198 (39.9)
Obese	3435 (32.7)
Smoking
Non-smoker	7936 (75.4)
Smoker	2584 (24.6)
Alcohol consumption
No	9125 (86.7)
Yes	1515 (13.3)

SD, standard deviation; BMI, body mass index.

Prevalence of MetS and KSD

The prevalence of KSD was 15.6% in this study and was more prevalent in males than in females (18.5% vs. 13.1%). The prevalence of MetS was 41.8% in this study and was more prevalent in males than in females (56.3% vs. 25.0%).

MetS and the risk of KSD

MetS components and KSD

Table 2 describes the relationship between MetS and its components with KSD among the PGCS population. In the unadjusted model, unexpectedly, participants with abdominal obesity had significantly lower odds of KSD than those without abdominal obesity (OR = 0.83, 95% CI: 0.75–0.93) (Model 0). The similar result was obtained after adjusting for age (OR = 0.83, 95% CI: 0.74–0.92) (Model 1). However, after adjusting for age and sex, abdominal obesity increased the odds of KSD (OR = 1.26, 95% CI: 1.08–1.46), suggesting a confounding effect of the sex (Model 2). In the fully adjusted model, the OR also remained statistically significant (OR = 1.17, 95% CI: 1.01–1.37).

Table 2.

Relationship between MetS and KSD among the Participants in the Persian Guilan Cohort Study

	Prevalence of KSD	Model 0 (unadjusted)		Model 1		Model 2		Model 3
	Prevalence of KSD	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P
Components of MetS
Abdominal obesity
No	753 (17.0%)	1		1		1		1
Yes	891 (14.6%)	0.83 (0.75–0.93)	<0.001	0.83 (0.74–0.92)	<0.001	1.26 (1.08–1.46)	0.003	1.17 (1.01–1.37)	0.041
Hypertriglyceridemia
No	881 (14.7%)	1		1		1		1
Yes	763 (16.8%)	1.17 (1.05–1.30)	0.003	1.17 (1.06–1.30)	0.003	1.16 (1.04–1.28)	0.008	1.12 (1.01–1.25)	0.033
Low HDL-C
No	994 (15.9%)	1		1		1		1
Yes	650 (15.2%)	0.95 (0.86–1.06)	0.371	0.96 (0.87–1.07)	0.506	1.09 (0.97–1.22)	0.147	1.02 (0.91–1.15)	0.697
Hypertension
No	799 (13.4%)	1		1		1		1
Yes	845 (18.6%)	1.48 (1.33–1.65)	<0.001	1.41 (1.27–1.58)	<0.001	1.47 (1.32–1.64)	<0.001	1.48 (1.32–1.66)	<0.001
Hyperglycemia
No	927 (14.3%)	1		1		1		1
Yes	717 (17.8%)	1.30 (1.17–1.45)	<0.001	1.24 (1.12–1.39)	<0.001	1.26 (1.13–1.41)	<0.001	1.24 (1.11–1.38)	0.001
No. of components
0	133 (12.5%)	1		1		1		1
1	325 (14.6%)	1.20 (0.96–1.49)	0.104	1.17 (0.94–1.46)	0.148	1.33 (1.07–1.65)	0.011	1.31 (1.06–1.64)	0.015
2	443 (15.6%)	1.30 (1.05–1.60)	0.014	1.27 (1.03–1.56)	0.027	1.57 (1.27–1.95)	<0.001	1.51 (1.22–1.87)	<0.001
3	414 (16.6%)	1.39 (1.13–1.72)	0.002	1.34 (1.08–1.65)	0.007	1.81 (1.45–2.26)	<0.001	1.71 (1.37–2.14)	<0.001
4	232 (16.6%)	1.40 (1.11–1.76)	0.004	1.32 (1.05–1.66)	0.020	1.90 (1.49–2.43)	<0.001	1.77 (1.39–2.27)	<0.001
5	97 (19.4%)	1.69 (1.27–2.25)	<0.001	1.56 (1.17–2.09)	0.002	2.46 (1.82–3.33)	<0.001	2.21 (1.63–3.01)	<0.001
MetS
No	901 (14.7%)	1		1		1		1
Yes	743 (16.9%)	1.18 (1.06–1.31)	0.002	1.14 (1.03–1.27)	0.015	1.36 (1.21–1.52)	<0.001	1.30 (1.16–1.46)	<0.001

OR, odds ratio; CI, confidence interval; MetS, metabolic syndrome; HDL-C, high-density lipoprotein cholesterol.

Model 0: Unadjusted model.

Model 1: Adjusted for age.

Model 2: Adjusted for age and sex.

Model 3: Adjusted for age and sex, marital status, education, employment, place of residency, wealth score index, physical activity, smoking, and alcohol consumption.

In the unadjusted model (Model 0), the presence of hypertriglyceridemia, hypertension, and hyperglycemia were associated with KSD (OR = 1.17, 95% CI: 1.05–1.30; OR = 1.48, 95% CI: 1.33–1.65; and OR = 1.30, 95% CI: 1.17–1.45, respectively). The similar results were obtained after adjusting for age (Model 1) and for age and sex (Model 2). In the fully adjusted model, the ORs also remained statistically significant (hypertriglyceridemia: OR = 1.12, 95% CI: 1.01–1.25; hypertension: OR = 1.48, 95% CI: 1.32–1.66; and hyperglycemia: OR = 1.24, 95% CI: 1.11–1.38).

The presence of low HDL-C was not associated with KSD in both the unadjusted model (OR = 0.95, 95% CI: 0.86–1.06) (Model 0) and all adjusted models—Model 1 (OR = 0.96, 95% CI: 0.87–1.07), Model 2 (OR = 1.09, 95% CI: 0.97–1.22), and Model 3 (OR = 1.02, 95% CI: 0.91–1.15).

No. of MetS components and KSD

In Model 0 (unadjusted analysis), compared with participants without any MetS component, the OR of KSD was 1.20 (95% CI: 0.96–1.49) for participants with one MetS component, 1.30 (95% CI: 1.05–1.60) for participants with two components, 1.39 (95% CI: 1.13–1.72) for participants with three components, 1.40 (95% CI: 1.11–1.76) for participants with four components, and 1.69 (95% CI: 1.27–2.25) for participants with all five components, with a significant trend in OR with increasing number of MetS components. The similar results were obtained after adjusting for age (Model 1). The same pattern, but with higher ORs, was observed after adjusting for age and sex (Model 2) and for all variables (Model 3) (presented in Table 2).

MetS and KSD

In the unadjusted model, MetS was associated with 18% increased odds of KSD (OR = 1.18, 95% CI: 1.06–1.31, P value = 0.002) (Model 0). This association remained significant after adjustment for age (OR = 1.14, 95% CI: 1.03–1.27) (Model 1). After adjustment for age and sex, the odds ratio was increased markedly to 1.36 (95% CI: 1.21–1.52) (Model 2). The OR also remained statistically significant after adjustment for all variables in Model 3 (OR = 1.30, 95% CI: 1.16–1.46) (presented in Table 2).

Discussion

KSD is a worldwide health issue that is becoming more prevalent. It has been found that MetS, which includes interrelated conditions such as hypertension, obesity, dyslipidemia, and hyperglycemia, is associated with KSD, according to various studies. According to the findings of the current cross-sectional study and consistent with other previous studies, individuals with MetS had a higher likelihood of developing KSD (OR = 1.18), even after adjustment for covariates.^18
–20

Considering the numbers of MetS components, the prevalence of KSD increases from 12.5% in individuals without MetS to 19.4% in those with 5 components. This rise is consistent with the West et al. study,¹⁹ reported that the prevalence of KSD was 3.7% in patients with no components of MetS, 7.5% for 3 components and 9.8% for 5 components. In line with previous studies, our result revealed that from 1–5 components of MetS, the OR of KSD increased from 1.20 to 1.69 in the unadjusted model and 1.31 to 2.21 after adjustment for covariates. So, modifying each component of MetS can reduce the risk of KSD.^8,21

Links between MetS and KSD are multifactorial, and various mechanisms have been suggested to better understand the link between these two. Research has demonstrated that high blood glucose levels, known as hyperglycemia, can cause an increase in the urine release of phosphate, calcium, uric acid, and oxalate, along with causing insulin resistance, which leads to a drop in urine ammonium levels and pH. These factors collectively contribute to the production of kidney stones.^8,22,23 Hyperglycemia was associated with increased odds of KSD in our study, too (OR = 1.30). Vascular dysfunction, as a definitive result of having MetS, is another mechanism that mediates between stone formation through Randall’s plaques.^24,25

Our study revealed that people with hypertension had the highest likelihood of developing KSD compared to the other components of MetS (OR = 1.48). Hypertension is an essential risk factor for heart disease, and consistent with previous cross-sectional studies, there is a potential association between KSD and high blood pressure.^8,26,27 The initial theory on the connection between these two was attributed to excessive calcium in the urine, or hypercalciuria, in patients with high blood pressure and is widely recognized, particularly for stones that contain calcium, specifically calcium oxalate and calcium phosphate.^26,28 The increased excretion of calcium in urine may be referred to either a defect in the renal tubules, known as the renal calcium leak, or to the impact of central volume expansion observed in hypertension. Excessive consumption of salt, sodium chloride, in the diet, due to increasing the blood volume and also the amount of calcium excreted in urine, is recognized to worsen both illnesses.^29,30 So, lifestyle modification with/without medical therapies could be one of the most important ways to prevent KSD in MetS patients, especially in patients with hypertension and hyperglycemia. Physical activity, weight loss, and a diet with lower caloric intake can reduce the risk of KSD.³¹

Unexpectedly, participants with abdominal obesity in our study had significantly lower odds of KSD than those without abdominal obesity, which was similar after adjusting for age but totally changed and was associated with increased odds of KSD after adjusting for age and sex, suggesting a confounding effect of sex. In the fully adjusted model, the OR also remained statistically significant. The aforementioned could be explained by our previously published data, revealing the high prevalence of central obesity in women vs. men in our population,³² along with the lower prevalence of KSD in male participants than in female and the increased likelihood of stone formation in men (OR = 1.51) in our population.³³

The main limitation of our study is the reliance on self-reported data for KSD (based on the following question: “have you ever experienced KSD with physician confirmation?”), without medical record verification, which may be a source of bias. However, previous studies with similar designs that validated self-reported KSD with medical records in random samples have demonstrated high accuracy, with validation rates typically more than 95%.^34
–36 Moreover, results from other Iranian cohort studies using self-reported KSD have shown prevalence rates similar to ours (15.6%); for instance, cohort studies conducted in Iran have shown a self-reported KSD prevalence of 17%–19% among more than 10,000 adults aged 35 to 70 years.^37,38 This similarity in prevalence across studies conducted in different regions of the country strengthens the generalizability and validity of our findings. Results from the National Health and Nutrition Examination Survey (NHANES) of the United States population aged 20 years and older (n = 12,110) showed a slightly lower prevalence of self-reported KSD (8.8%), which could be due to differences in age distributions, genetic backgrounds, dietary and lifestyle patterns, and health care access between populations.^39,40 One other important limitation of the present study is the inability to determine the temporal or causal relationship between MetS and its components with KSD as a result of cross-sectional design of the study. While a significant association between MetS and increased odds of KSD was found, the direction or causality of it is not clear, and longitudinal studies are required in this regard. Some other limitations to be considered in interpreting our results are as follows: we did not have information on the types of kidney stones, which limited our ability to analyze associations based on stone composition; the study population was limited to individuals aged 35–70 years, which may affect the generalizability of the findings to other age groups; 24-h urine collection was not performed, preventing evaluation of urinary metabolic risk factors; and serum uric acid levels were not measured, making it impossible to assess the potential role of hyperuricemia on the study results. Despite all limitations, our study was supportively strengthened by the large sample quantities in the community-based cohort.

Conclusion

Conclusively, this study strengthened the connection between MetS and KSD. Also, the two major components of MetS, including hypertension and hyperglycemia, were strongly allocated to KSD. Therefore, implementing appropriate lifestyle changes and administering effective medication are crucial in the management of MetS to help prevent KSD. Routine assessment for MetS is necessary for more effective prevention of KSD. Also, some prospective studies are suggested for additional information.

Footnotes

Acknowledgments

The authors thank all subjects for participating in this study.

Ethics Approval and Consent to Participate

The research was carried out in adherence to the principles outlined in the Declaration of Helsinki. Ethical approval to perform this study was obtained by the Ethics Committee of Guilan University of Medical Sciences, Rasht, Iran (Ethics Code: IR.GUMS.REC.1403.069), and all participants provided written informed consent.

Availability of Data and Materials

The datasets used and/or analyzed during the present study are available from the corresponding author on reasonable request.

Confirmation Statement

Each author confirms that their research is supported by an institution that is primarily involved in education or research.

Authors’ Contributions

S.M., F.J., A.A., and F.M.-G. conceptualized the study and conducted the data collection. S.M. analyzed the data. S.M., F.S., and S.Y. collected the data and wrote the article. S.M., F.J., M.N., and F.M.-G. revised the article for important intellectual content. All the authors reviewed the article.

Author Disclosure Statement

No competing financial interests exist.

Funding Information

This study was supported by the Guilan University of Medical Science, Rasht, Iran.

Supplementary Material

Supplementary Table S1

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