Hypertriglyceridemia and Low High-Density Lipoprotein Cholesterolemia Are Associated with Increased Hazard for Urolithiasis

Abstract

Purpose:

To assess the association between dyslipidemia and urolithiasis, a propensity score-matching study was performed.

Patients and Methods:

Fasting blood samples were taken, and serum lipid profiles were measured in 655 stone formers (SF) and 1965 propensity score-matched controls between 2005 and 2011. The controls, from a health-screening program, did not have a history of dyslipidemia or statin use and have any evidence of stone disease, as determined by abdominal radiography, ultrasonography examination. Propensity score-matching with respect to age, sex, and body mass index was used to minimize selection bias, and the logistic regression analysis was adjusted for other components of metabolic syndrome.

Results:

Compared with controls, the SF group had significantly higher mean triglyceride and lower total cholesterol, low-density lipoprotein (LDL) cholesterol, and high-density lipoprotein (HDL) cholesterol levels (each P<0.001). The SF group was also more likely to have hypertriglyceridemia and low HDL-cholesterolemia, and less likely to have hypercholesterolemia and high LDL cholesterolemia compared with controls (each P<0.05). When adjusted for other components of metabolic syndrome including obesity, presence of diabetes mellitus or hypertension, the odds ratio (OR) for urinary stones appeared with hypercholesterolemia (OR=0.747, P=0.003), hypertriglyceridemia (OR=1.901, P<0.001), low HDL cholesterolemia (OR=1.886, P<0.001) and high LDL cholesterolemia (OR=0.610, P<0.001).

Conclusions:

Our study implies that dyslipidemia may play a crucial part in urinary stone risk.

Introduction

Metabolic syndrome is composed of several cardiovascular risk factors, including insulin resistance, obesity, dyslipidemia, and hypertension (HTN).¹ Interestingly, the metabolic syndrome components also are associated with an increased prevalence of kidney stones.^2

–7 Numerous studies have detected a significant association between HTN and renal stone disease.^8

–11 Moreover, obesity,^12

–15 diabetes mellitus (DM),^16

–19 and dyslipidemia^3,20
–22 also are suggested as risk factors for stone formation.

Although there is some evidence correlating dyslipidemia with an increased risk of urinary stones, the relationship between dyslipidemia and urolithiasis has not been demonstrated conclusively. In addition, because metabolic syndrome is a systemic disorder that is linked to each component and given the complexity of stone disease, potential confounders should be controlled. Because most evidences in the field of dyslipidemia and urolithiasis have been drawn from heterogeneous populations, the exact role of dyslipidemia on urolithiasis has not yet been sufficiently elucidated.

The aim of this study was to elucidate the relationship between dyslipidemia and urolithiasis using a propensity score-matching analysis.

Patients and Methods

Characteristics of the cases and the controls

We retrospectively reviewed the medical records and imaging studies of 1024 consecutive stone formers (SF) between 2005 and 2011. Patients with staghorn calculi (n=37), recurrent stone episode (n=224), impaired renal function (n=106), history of dyslipidemia or statin use (n=181), urinary tract obstruction (n=36), a single kidney (n=8), or a congenital urologic anomaly (n=6) were excluded from the study (there is some overlap in categories). Of the 1024 consecutive SF, 655 subjects formed the SF group.

The control subjects (n=12,800) were recruited from persons who visited the health promotion center of our hospital during a similar period. The health checkup program involves general body measurements (height, weight, and blood pressure), blood tests (complete blood cell count and chemistries including a lipid battery), urine analyses, abdominal ultrasonography, and a self-report questionnaire about medical history including HTN, dyslipidemia, and DM. The control subjects had a history of stone episodes (n=940), self-reported history of dyslipidemia or use of lipid lowering medications (n=1948), and any evidence of urinary tract calculi, as determined by abdominal radiography, ultrasonography examination (n=2640) (there is some overlap in categories). Of the 6880 eligible populations, 1965 subjects were finally selected via 1:3 propensity score-matched for age, sex, and body mass index (BMI) with SF patients, using three controls for each case.

All patients and controls provided written informed consent to participate in the study, and collection and analysis of all samples was approved by the Institutional Review Board of Chungbuk National University (IRB number: 2006-01-001).

Measurement and definition of variables

Fasting blood samples were taken from the cases and controls to measure their plasma lipid profiles. Thus, their total cholesterol (TC), triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C) were measured by enzymatic methods on a Hitachi 7600 automatic clinical chemistry analyzer (Boehringer Mannheim, Mannheim, Germany) using reagent kits supplied by the manufacturer of the analyzer. Low-density lipoprotein cholesterol (LDL-C) was calculated by using the Friedewald equation. Patients whose TG levels exceeded 400 mg/dL were excluded from the calculation.²³ Dyslipidemia was defined as follows: Hypercholesterolemia (TC >200 mg/dL), hypertriglyceridemia (TG >150 mg/dL), low HDL cholesterolemia (HDL-C <40 mg/dL in men and <50 mg/dL in women), and high LDL cholesterolemia (LDL-C >130 mg/dL).²⁴

Statistical analysis

The continuous variable data are presented as mean±standard deviation. We used propensity score methods to balance observed covariates between the case and control groups. Propensity scores were calculated for each patient using multivariable logistic regression based on the covariates: Age, sex, BMI. Matching for the propensity score was performed using the R package matching. Three controls were matched with the closest propensity with each case (655 cases and 1965 controls).

Lipids level was analyzed as continuous variables with the Mann-Whitney U test, while dyslipidemia status was analyzed as a dichotomous variable with the Fisher exact test. The multivariate logistic regression model was used to estimate the odds ratio (OR) adjusted for other metabolic components. All statistical analyses were performed by using SPSS^® software (SPSS 21.0 for Windows; SPSS Inc., Chicago, IL). All tests were performed with two-tailed analyses, and P<0.05 was considered to indicate statistical significance.

Results

Baseline characteristics of the SF patients and controls

The baseline characteristics of 655 SF patients and 1965 controls are presented in Table 1. The two groups did not differ significantly in terms of mean age, BMI, and sex distribution. The prevalence of DM was higher among SF when compared with non-SF (23.4% vs 19.5%, P=0.038). Although the prevalence of HTN also was higher among SF compared with the control group (31.5% vs 28.7%), this difference did not achieve statistical significance (P=0.181). In the whole cohort, the group with DM showed significantly higher levels of TC (198.7±36.2 vs 194.3±36.5, P=0.005), TG (151.8±72.9 vs 143.6±71.4, P=0.004), LDL-C (117.7±33.2 vs 114.4±34.0, P=0.017) than those in the nondiabetic group. There was no statistical significance in mean level of HDL-C between the diabetic and nondiabetic groups (50.4±12.5 vs 51.6±13.1). In contrast, the hypertensive group showed significantly lower mean HDL-C levels compared with those in the normotensive group (50.5±13.2 vs 51.7±12.9, P=0.017). Other lipid profiles did not differ between hypertensive and normotensive groups (data not shown).

Table 1.

Baseline Characteristics of the Stone-Forming Patients and Their Propensity-Matched Controls

Variables	Controls (n=1965)	Stone-forming patients (n=655)	P value
Age, years (range)	46.8±11.5 (17–82)	46.8±13.2 (20–76)	0.904^‡
BMI, kg/m²	24.4±3.3	24.4±3.5	0.858^*
Obesity, defined as BMI ≥25 (%)	776 (39.5)	256 (39.1)	0.854^†
Sex (%)			0.816^†
Male	1226 (62.4)	405 (61.8)
Female	2739 (37.6)	250 (38.2)
HTN (%)	563 (28.7)	206 (31.5)	0.181^†
DM (%)	383 (19.5)	153 (23.4)	0.038^†

Data are shown as mean±standard deviation unless otherwise indicated.

P values were obtained from ^*Mann-Whitney U test, ^†Fisher exact test, or ^‡Student t test.

BMI=body mass index; HTN=hypertension; DM=diabetes mellitus.

Lipid levels and dyslipidemia status of the SF patients and controls

The SF patients had significantly higher mean TG levels and significantly lower mean TC, LDL-C, and HDL-C levels than the controls (all P<0.001). Compared with the control subjects, the SF group was also more likely to have hypertriglyceridemia (P<0.001) and low HDL-cholesterolemia (P<0.001) and less likely to have hypercholesterolemia (P=0.005) and high LDL-cholesterolemia (P<0.001) (Table 2).

Table 2.

Lipid Levels and Dyslipidemia Status of Stone-Forming Patients and Their Propensity-Matched Controls

Variables	Controls (n=1965)	Stone-forming patients (n=655)	P value
Lipid level
TC	196.9±36.2	190.0±36.6	<0.001^*
TG	138.0±67.2	166.9±80.2	<0.001^*
HDL-C	52.5±13.1	48.1±12.3	<0.001^*
LDL-C	117.5±33.8	107.9±32.9	<0.001^*
Dyslipidemia status
Hypercholesterolemia	672 (34.2)	185 (28.2)	0.005^†
Hypertriglyceridemia	569 (29.0)	287 (43.8)	<0.001^†
Low HDL-cholesterolemia	371 (18.9)	201 (30.7)	<0.001^†
High LDL-cholesterolemia	475 (24.2)	107 (16.3)	<0.001^†

Data are shown as mean±standard deviation or number (%).

P values were obtained from ^*Mann-Whitney U test and ^†Fisher exact test.

TC=total cholesterol; TG=triglyceride; HDL-C=high-density lipoprotein cholesterol; LDL-C=low-density lipoprotein cholesterol.

Dyslipidemia status and risk of stone

After correction for other components of the metabolic syndrome including obesity and presence of DM and HTN, the risk for urinary stones was higher in subjects with hypertriglyceridemia (OR=1.901, P<0.001) and low HDL-cholesterolemia (OR=1.886, P<0.001). In contrast, the risk for urinary stones was lower in subjects with hypercholesterolemia (OR=0.747, P=0.003) and high LDL-cholesterolemia (OR=0.610, P<0.001) (Table 3).

Table 3.

Dyslipidemia Status and Urinary Stone Risk

	OR (95% CI)	P value ^a
Hypercholesterolemia	0.747 (0.615–0.908)	0.003
Hypertriglyceridemia	1.901 (1.583–2.282)	<0.001
Low HDL-C	1.886 (1.542–2.306)	<0.001
High LDL-C	0.610 (0.484–0.769)	<0.001

Logistic regression analysis was used to adjust for other components of the metabolic syndrome, such as obesity, diabetes, and hypertension.

OR=odds ratio; CI=confidence interval; HDL-C=high-density lipoprotein cholesterol; LDL-C=low-density lipoprotein cholesterol.

Discussion

Our study indicates that serum lipid profiles are associated with urinary stone risk that is independent of other components of metabolic syndrome such as obesity, DM, and HTN. Hypertriglyceridemia and low HDL cholesterolemia are associated with increased hazard for urolithiasis, whereas subjects with hypercholesterolemia and high LDL cholesterolemia have lower odds for urinary stone risk adjusting for obesity, HTN, and DM. With regard to the prevention of urinary stone, life-style modification such as weight reduction is worthwhile.

Metabolic syndrome is composed of several cardiovascular risk factors, including insulin resistance, obesity, dyslipidemia, and HTN.²⁵ Interestingly, the metabolic syndrome components also are associated with an increased prevalence of kidney stones,^4,6 and several studies have shown that obesity, HTN, and hyperglycemia are individual risk factors for urolithiasis. For example, Lee and associates⁹ reported that obesity is associated with metabolic alterations and urinary stone recurrence and weight control may be a preventive modality against recurrent stone formation. In addition, Kim and colleagues¹³ found that HTN is an independent predictive determinant for recurrent stone formation. There was no significant difference, however, between the SF and control groups with respect to prevalence of HTN in our study. This inconsistency may be a result in part of different study design and criteria for the inclusion of study subjects.

With regard to insulin resistance, when Weinberg and coworkers²⁶ investigated the association between kidney stone disease and the presence and severity of type 2 DM, glycemic control, and insulin resistance, they found that glycemic control and insulin resistance are important risk factors for kidney stone disease. Similar results were identified in our study. The prevalence of DM was higher among SF when compared with control.

Several studies have also found that increasing numbers of metabolic syndrome features are associated with greater renal stone prevalence. For example, a large cross-sectional study using data from the National Health and Nutrition Examination Survey found that an increasing number of metabolic syndrome features are associated with increased prevalence of self-reported kidney stones.⁴ Similarly, another large cohort cross-sectional analysis demonstrated that metabolic syndrome status and the number of metabolic syndrome components are associated with an increased risk of kidney stones.³ Kohjimoto and associates⁶ also showed that metabolic syndrome trait clustering is associated with increased severity of kidney stone disease.

In terms of dyslipidemia, Torricelli and colleagues²¹ recently reported that the serum lipid profile has a significant influence on 24-hour urine metabolic profiles and stone composition. Another study found that statin medications had a protective effect against stone formation.²⁰ These results supported a link between hyperlipidemia and urinary stone risk.

Although there is some evidence correlating dyslipidemia with an increased risk of urinary stones, the relationship between dyslipidemia and urolithiasis has not been demonstrated conclusively. In addition, because metabolic syndrome is a systemic disorder that is linked to each component and given the complexity of stone disease, potential confounders should be controlled. We also identified the interplay among dyslipidema and the presence of DM and HTN (data not shown).

Consistent with previous studies, our result indicated that serum lipid profiles are associated with urinary stone risk. Interestingly, we first found that individual components of serum lipids (TC, TG, LDL cholesterol, HDL cholesterol) have a contradictory association with urinary stone risk. TG was associated with increased risk for urinary stone, whereas TC, HDL cholesterol, and LDL cholesterol were associated with reduced risk of urinary stones. At present, however, the underlying physiologic mechanism explaining why individual components of dyslipidemia have a contradictory part of urinary stone risk could not be explained. Our study should be viewed as exploratory, and further research is needed to confirm the relationship between individual components of serum lipids and urinary stone risk.

Our study had both strengths and limitations. Propensity score-matching was used to minimize selection bias (e.g., age, sex, and BMI), and the logistic regression analysis was used to adjust for other components of the metabolic syndrome, such as obesity, DM, and HTN. All patients and controls with a history of dyslipidemia or statin use were excluded from the study to minimize selection bias and misclassification of serum lipids profiles. Those with dyslipidemia using statins tended to have a relatively normal lipid profile and thus were classified in the nondyslipidemia group. For that reason, we excluded all statin-treated or those with a history of dyslipidemia from the analysis.

A limitation of the present study is that it had a retrospective design, which means that there may have been some sampling bias. Another concern is that cases and controls were not selected from comparable groups. Whereas the cases were recruited from the general population, controls were selected from a pool of patients who visited the health promotion center. Korea, however, has a mandatory national health insurance system that includes biannual health checkups free of charge. Because this healthcare plan covers almost everyone in the country, a group of patients who visited the health promotion center served as the control group representing the general population.

Moreover, stone composition analysis was not performed in the SF group. Previous study showed a significant association between metabolic syndrome and urine metabolites, stone composition. Thus, the exact role of dyslipidemia in urolithiasis should be validated according to stone composition (noncalcium-containing stones vs calcium-containing stones) in future study.

Conclusion

Our study implies that dyslipidemia may play a crucial part in urinary stone risk. Hypertriglyceridemia and low HDL cholesterolemia are associated with increased hazard for urolithiasis, whereas subjects with hypercholesterolemia and high LDL cholesterolemia have lower risk for urinary stone. Our study should be viewed as exploratory, but it suggests that dyslipidemia may play a role in the risk for urinary stone formation. These results can be used to counsel patients with dyslipidemia with regard to the prevention of urinary stone.

Footnotes

Acknowledgments

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2008-0062611, 2013R1A1A2004740) and by a grant from the Next-Generation BioGreen 21 Program (No. PJ009621), Rural Development Administration, Republic of Korea.

The biospecimens for this study were provided by the Chungbuk National University Hospital, a member of the National Biobank of Korea, which is supported by the Ministry of Health, Welfare and Family Affairs. All samples derived from the National Biobank of Korea were obtained with informed consent under Institutional Review Board-approved protocols.

Disclosure Statement

No competing financial interests exist.

Abbreviations Used

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