Abstract
Introduction: This study aimed to investigate the potential association between the fatty liver index (FLI), metabolic dysfunction-associated steatotic liver disease (MASLD), and the risk of kidney stones using large-scale population-based data. Methods: This study employed a cross-sectional design, utilizing data from the 2007 to 2018 National Health and Nutrition Examination Survey (NHANES) database. A total of 24,342 participants were enrolled in the study, and fatty liver status was assessed by calculating the FLI. MASLD was diagnosed by FLI in conjunction with cardiometabolic criteria. Data on the history of kidney stones were obtained by self-report. We employed logistic regression models to analyze the association between FLI, MASLD, and kidney stone risk and constructed multivariable adjustment models to control for potential confounders. Furthermore, we used restricted cubic spline curve models to investigate the dose-response relationship between FLI and kidney stone risk and conducted subgroup and interaction analyses. Results: The study’s results indicate a strong correlation between increasing FLI quartiles and a notable rise in the prevalence of kidney stones. Specifically, the risk of developing kidney stones was 1.68 times higher among participants in the highest FLI quartile compared to those in the lowest. Furthermore, patients with MASLD exhibited a 1.35-fold increased risk of developing kidney stones compared to those with non-MASLD. Subgroup analyses demonstrated that the correlation between MASLD and kidney stone risk was consistent across multiple subgroups. However, a significant interaction was observed in the subgroups of smoking status, physical activity level, and hypertension (interaction p < 0.05). The restricted cubic spline analysis did not yield a statistically significant nonlinear association between FLI and kidney stone risk. However, the study did identify inflection point values for FLI. Conclusion: This study demonstrated an association between FLI and MASLD and the risk of kidney stones. This suggests that these conditions may be pivotal risk factors for kidney stones. Further investigation is required to elucidate these associations’ underlying mechanisms and develop efficacious interventions to reduce the risk of kidney stones. Also, formulating personalized prevention and treatment strategies for different population subgroups is paramount.
Plain Language Summary
This article examines the relationship between the fatty liver index (FLI), metabolic dysfunction-associated steatotic liver disease (MASLD), and kidney stone risk. The study was based on data from the 2007 to 2018 National Health and Nutrition Examination Survey (NHANES) database, which included 24,342 participants. The results demonstrated a significant correlation between increasing FLI quartiles and the prevalence of kidney stones. Participants in the highest FLI quartile exhibited a 1.68-fold increased risk of developing kidney stones compared to those in the lowest quartile. Furthermore, individuals with MASLD exhibited a 1.35-fold elevated risk of developing kidney stones in comparison to those without MASLD. Although a significant nonlinear relationship between FLI and kidney stone risk was not observed, a significant increase in kidney stone risk was found for FLI values above 58.18. Subgroup analyses demonstrated that the correlation between MASLD and kidney stone risk was consistent across gender, race, education level, and marital status subgroups. However, there was a significant interaction between smoking status, physical activity level, and hypertension. The study concluded that FLI and MASLD are substantial risk factors for kidney stones and suggested that the underlying mechanisms of these relationships require further investigation to develop effective interventions to reduce the risk of kidney stones.
Introduction
Kidney stones, a prevalent urological disorder, are typically characterized by the formation of hard mineral crystals within the kidneys [1]. The composition of these crystals is diverse, encompassing a range of elements, including calcium salts (such as calcium oxalate and calcium phosphate), urates, cystine salts, and combinations thereof [2, 3]. In addition to causing pain and discomfort, kidney stones may also result in serious clinical consequences, including urinary tract infections, renal parenchymal damage, and even renal failure [4]. As a global health issue, the prevalence of kidney stones exhibits considerable variation across different regions, which may be associated with a range of factors, including genetic predisposition, environmental conditions, dietary habits, and lifestyle [5]. In recent years, there has been an observed increase in the incidence of kidney stones in several regions, including North America, Europe, and Asia. This phenomenon may be associated with the rising prevalence of obesity, diabetes, and other metabolic disorders [6]. Furthermore, the development of kidney stones is influenced by some additional factors, including gender differences, advancing age, racial predisposition, dietary habits (high salt, protein, and oxidant intake), inadequate water intake, and the use of certain medications [1, 3]. The interplay of these factors may influence the development and progression of kidney stones.
Nonalcoholic fatty liver disease (NAFLD) is a clinicopathological syndrome that is primarily characterized by the presence of excessive fat deposition in hepatocytes in the absence of alcohol and other well-defined liver-damaging factors [7]. The condition is strongly associated with insulin resistance and genetic susceptibility factors and is considered a metabolic stress liver injury [8]. However, the diagnosis of NAFLD relies heavily on the exclusion of alcohol intake to confirm the disease. This diagnostic approach somewhat ignores the core etiology of metabolic dysfunction behind the disease. Furthermore, the term “nonalcoholic” may also give rise to stigmatization concerns, which could have a detrimental impact on the public perception of the disease and the psychological well-being of patients. In light of the considerations above, in June 2023, several international liver disease associations proposed that this type of liver disease be designated metabolic dysfunction-associated steatotic liver disease (MASLD) [9]. This new designation underscores the pivotal role of metabolic factors in the pathogenesis of fatty liver disease, offering a more comprehensive diagnostic framework and aligning more closely with the demands of clinical practice. The renaming and redefinition of MASLD reflect the advancement of the medical community’s understanding of steatohepatitis liver disease, which is paramount in facilitating the early identification of the disease, ensuring effective management, and enhancing public awareness. The prevalence of MASLD is increasing in parallel with the global epidemics of obesity and metabolic syndrome. The prevalence of MASLD exhibits considerable variation, ranging from 10% to 45% across different regions and populations [10‒12]. This underscores the fact that it is one of the most prevalent chronic liver diseases globally. A further analysis of projections based on current trends indicates that the global prevalence of MASLD is expected to reach 55.7% by 2040 [13].
MASLD can result in a spectrum of hepatic complications, including severe pathologies such as hepatitis, hepatic fibrosis, cirrhosis, and even hepatocellular carcinoma [8, 14‒16]. Furthermore, there is a notable correlation between MASLD and an array of chronic illnesses, particularly those associated with metabolic syndrome, which demonstrate a profound interconnection. The extant literature indicates that patients with MASLD are at an elevated risk of developing cardiovascular disease, chronic kidney disease, and type 2 diabetes [9, 10, 17]. Notably, these diseases tend to co-occur and interact, collectively exacerbating the risk of adverse health events.
In recent years, the potential link between fatty liver and its associated metabolic abnormalities and the risk of kidney stones has been the subject of increasing attention from the research community. Several studies have substantiated the existence of a correlation between the presence of a fatty liver and an increased risk of developing kidney stones [18‒21]. However, the results of studies conducted with different populations exhibit some variability. A large cohort study of a young and middle-aged Korean population revealed a significant association between NAFLD and an increased incidence of kidney stones in men. However, this association was not observed in the female population [22]. In contrast, a recent study employing a two-sample Mendelian randomization analysis indicated insufficient evidence to substantiate a direct causal relationship between NAFLD and kidney stones [23]. Existing studies have focused primarily on the association between NAFLD and kidney stones. Conversely, more research remains to be done on the relationship between MASLD and kidney stone risk. In light of the stronger association between MASLD and metabolic syndrome (MetS), and given that several components of MetS (e.g., obesity, hyperglycemia, hyperlipidemia, etc.) have been demonstrated to be associated with an elevated risk of kidney stones, it is reasonable to hypothesize that there may also be a significant association between MASLD and kidney stone risk.
In light of the background of the research above, the present study aims to investigate the potential association between the fatty liver index (FLI) and MASLD and the risk of kidney stones and analyze the possible influencing factors and mechanisms. The findings of this study are expected to provide new insights and effective strategies for the prevention and treatment of kidney stones. To this end, the present study uses the resources of the National Health and Nutrition Examination Survey (NHANES), a large-scale database covering the period from 2007 to 2018. This study employs a comprehensive and in-depth analysis of fatty liver status, metabolic syndrome characteristics, and kidney stone incidence in many individuals within this database to elucidate potential links between fatty liver, metabolic syndrome, and kidney stones. The findings are anticipated to provide a theoretical foundation and novel perspectives for scientific research.
Methods
Study Population
The data utilized in this study were obtained from the NHANES database from 2007 to 2018. This database encompasses the findings of a cross-sectional survey conducted biennially by the Centers for Disease Control and Prevention (CDC). It is noteworthy that the study protocol for the NHANES database was approved by the National Center for Health Statistics (NCHS) Ethics Review Board, and all participants signed an informed consent form. By relevant National Institutes of Health (NIH) policies, the data in the NHANES database, which was not obtained through direct contact with participants, could be used directly for data analysis without further review by the Institutional Ethics Committee. The study used the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for report writing.
A total of 59,842 participants were recruited during the initial phase of the study, with data drawn from six consecutive cycles of the NHANES survey. However, during the study, we excluded participants who were under 20 years of age and pregnant, as well as those with missing data related to kidney stones, missing data on demographic characteristics (including marital status, education, family income, smoking, and drinking habits), missing data on chronic diseases (including hypertension, coronary artery disease, stroke, and cancer), missing data related to other indices required to calculate the FLI (including body mass index [BMI], waist circumference [WC], γ-glutamyl transpeptidase [GGT], and triglyceride [TG]), as well as participants missing other biochemical indicators (including high-density lipoprotein cholesterol [HDL-C], glycosylated hemoglobin [HbA1c], aspartate aminotransferase [AST], and total bilirubin) were excluded. Following a comprehensive screening process, 24,342 participants were ultimately included in the data analysis phase of this study (Fig. 1).
Participant screening flowchart. BMI, body mass index; WC, waist circumference; GGT, gamma-glutamyl transferase; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; PIR, poverty-to-income ratio; CHD, coronary heart disease; HbA1c, hemoglobin A1c; AST, aspartate aminotransferase; TBIL, total bilirubin.
Participant screening flowchart. BMI, body mass index; WC, waist circumference; GGT, gamma-glutamyl transferase; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; PIR, poverty-to-income ratio; CHD, coronary heart disease; HbA1c, hemoglobin A1c; AST, aspartate aminotransferase; TBIL, total bilirubin.
Disease Assessment
Participants with an FLI of 60 or greater were diagnosed with SLD by the threshold established by prior studies [17]. This criterion has been demonstrated to correlate with an elevated likelihood of hepatic steatosis. Furthermore, the diagnosis of MASLD was based on the presence of any one of the following five cardiometabolic criteria: (1) a BMI of 25 kg/m2 or greater or a WC of 94 cm or greater for males and 80 cm or greater for females; (2) a fasting plasma glucose (FPG) level of 100 mg/dL or greater, or a 2-h postprandial blood glucose level of 140 mg/dL or greater, or an HbA1c level of 5.7% or greater, or a diagnosis of diabetes mellitus (DM) or receipt of glucose-lowering therapy for DM; (3) a blood pressure reading at or above 130/85 mm Hg or the use of antihypertensive medication; (4) TG levels of 150 mg/dL or greater or the use of lipid-lowering therapy; (5) individuals with HDL-C levels below 40 mg/dL (men) and below 50 mg/dL (women) or undergoing lipid-lowering therapy [9].
Data were obtained by querying the “Kidney Conditions – Urology” file in the NHANES database to evaluate kidney stones. Trained interviewers administered a computer-assisted personal interview system to inquire about the presence and characteristics of kidney stones in the subject’s residence. The history of kidney stones was evaluated by asking, “Have you or the sample person (SP) ever had a kidney stone?” Those who responded in the affirmative were classified as having kidney stones, whereas those who responded negatively were classified as not having kidney stones.
Covariate Assessment
Multivariable adjustment models were constructed to thoroughly investigate the effects of confounding variables on the relationship between the insulin resistance index and diabetic nephropathy. This study’s covariates spanned a comprehensive range of sociodemographic characteristics, lifestyle habits, and chronic disease history across multiple dimensions. These included gender (male/female), age (years), race, education level, marital status, family poverty-to-income ratio (PIR), drinking habits (yes/no), smoking status (yes/no), level of physical activity (vigorous/moderate/inactive), and history of chronic diseases such as DM, hypertension, coronary heart disease (CHD), stroke, and cancer.
About the categorization of racial groups, the participants were subdivided into four main groups. The participants were categorized as Mexican American, non-Hispanic White, non-Hispanic Black, or other race. Regarding educational attainment, participants were classified into one of three groups: less than 9th grade, 9th–12th grade, and more than 12th grade, based on their educational level. Marital status was dichotomized into two categories: cohabitation and solitude. To classify family income, the participants were categorized as low income (PIR ≤1.3), medium income (1.3< PIR ≤3.5), or high income (PIR >3.5) based on the PIR criterion as defined in the US government report. About lifestyle habits, smoking status was determined based on whether the participant had smoked at least 100 cigarettes in their lifetime and whether they were currently still smoking. In contrast, alcohol consumption was defined as the consumption of at least 12 alcoholic beverages of any type in any given year. The physical activity assessment was based on the presence of strenuous exercise that resulted in a significant increase in respiration or heart rate, as well as the performance of moderate-intensity exercise that resulted in a slight increase in respiration or heart rate. Regarding medical history, a history of DM was established based on a diagnosis by a medical professional, a FPG level (≥126 mg/dL), an HbA1c level (≥6.5%), and the use of DM medication or insulin. A history of hypertension was established based on whether the participant had been diagnosed with hypertension by a medical professional or was currently taking medication prescribed for hypertension. A history of CHD was identified through reliance on the participant’s self-report of a diagnosis of CHD, angina, or a heart attack by a physician. Similarly, a history of stroke and cancer was determined based on whether a physician had told the participant that they had these conditions.
Statistical Analysis
The Kolmogorov-Smirnov test was employed to ascertain whether the continuous variables exhibited a normal distribution. For variables that exhibited a normal distribution, we expressed them in the form of mean ± standard deviation. Conversely, we expressed variables that did not conform to a normal distribution using the median (and the 25th and 75th percentiles). To compare the differences between these variables, we employed either a one-way ANOVA or a Kruskal-Wallis test, depending on the distributional characteristics of the data. Frequencies and percentages were calculated for categorical variables, and the chi-squared test was employed to assess differences between groups.
To investigate the relationships between FLI, quartiles of FLI, MASLD, and the risk of kidney stones in greater detail, we constructed logistic regression models. We calculated the odds ratio (OR) and its 95% confidence interval (CI). Three multivariable-adjusted models were built to assess these relationships more precisely and address potential confounding variables. Of these, model 1 was unadjusted; model 2 built on model 1 by incorporating age, gender, and race as adjusting factors; and model 3 further built on model 2 by expanding it to adjust for the variables of educational level, marital status, family PIR, smoking, alcohol consumption, physical activity, diabetes, hypertension, CHD, stroke, and cancer. Furthermore, we employed restricted cubic spline curve (RCS) modeling to ascertain the potential existence of a dose-response relationship between FLI and kidney stones. To gain further insight into the relationship between MASLD and kidney stone risk in different subgroups, we conducted a stratification analysis based on the variables of gender, race, education, marital status, family PIR, smoking, alcohol consumption, physical activity, diabetes, hypertension, CHD, stroke, and cancer, and performed interaction analyses.
In all statistical analyses, two-sided tests were employed, and a p value of <0.05 was used to determine whether the results were statistically significant. All statistical analyses were conducted using R 4.4.0 (R Foundation, http://www.R-project.org) and SPSS version 23.0 (IBM, Armonk, NY, USA). The graphs were plotted using GraphPad Prism version 9.0 (GraphPad Software, USA).
Results
Baseline Characteristics of Participants Based on FLI Quartiles
A total of 24,342 participants were included in this study. Based on the quartiles of the FLI, the participants were divided into four groups for in-depth analysis. The study’s results demonstrated a notable increase in the proportion of male participants as the FLI quartiles increased, specifically from 39.34% in the first quartile (quartile 1) to 54.04% in the fourth quartile (quartile 4) (p < 0.001). Additionally, there was a notable shift in the age distribution, with the median age in quartile 1 being 39 years and the median age in quartile 4 increasing to 51 years. Further analysis revealed that Mexican Americans and other race groups were overrepresented in the higher FLI quartiles, whereas the educational level of the participants decreased progressively with increasing FLI quartiles (p < 0.001). Furthermore, individuals with elevated FLIs were more likely to cohabit, exhibit diminished family income status, and engage in more frequent smoking behaviors compared to less prevalent drinking behaviors. These participants showed reduced levels of physical activity and markedly elevated incidences of diabetes, hypertension, CHD, stroke, and cancer (p < 0.001). Further physical measurements and biomarkers analysis revealed associations between FLI and various physiological indicators. Specifically, there was a significant increase in BMI, WC, FPG, HbA1c, total cholesterol, TG, alanine aminotransferase, AST, GGT, and uric acid levels with increasing FLI. In contrast, HDL-C levels significantly decreased (p < 0.001). It is noteworthy that the prevalence of kidney stones demonstrated a substantial increase with increasing FLI quartiles, from 5.92% in the first quartile to 12.98% in the fourth quartile (p < 0.001) (Table 1).
Baseline characteristics of participants based on FLI quartiles
Variables . | FLI . | p value . | |||
---|---|---|---|---|---|
Quartile 1 (n = 6,086) . | Quartile 2 (n = 6,085) . | Quartile 3 (n = 6,085) . | Quartile 4 (n = 6,086) . | ||
Gender, n (%) | <0.001 | ||||
Male | 2,394 (39.34) | 3,159 (51.91) | 3,395 (55.79) | 3,289 (54.04) | |
Female | 3,692 (60.66) | 2,926 (48.09) | 2,690 (44.21) | 2,797 (45.96) | |
Age, years | 39.00 (27.00, 57.00) | 52.00 (36.00, 66.00) | 54.00 (40.00, 66.00) | 51.00 (38.00, 63.00) | <0.001 |
Race, n (%) | <0.001 | ||||
Mexican American | 555 (9.12) | 855 (14.05) | 1,116 (18.34) | 1,070 (17.58) | |
Non-Hispanic White | 2,754 (45.25) | 2,659 (43.70) | 2,592 (42.60) | 2,722 (44.73) | |
Non-Hispanic Black | 1,216 (19.98) | 1,186 (19.49) | 1,169 (19.21) | 1,339 (22.00) | |
Other race | 1,561 (25.65) | 1,385 (22.76) | 1,208 (19.85) | 955 (15.69) | |
Education level, n (%) | <0.001 | ||||
Less than 9th grade | 326 (5.36) | 606 (9.96) | 689 (11.32) | 587 (9.65) | |
9–12th grade | 2,005 (32.94) | 2,148 (35.30) | 2,336 (38.39) | 2,423 (39.81) | |
More than 12th grade | 3,755 (61.70) | 3,331 (54.74) | 3,060 (50.29) | 3,076 (50.54) | |
Marital status, n (%) | <0.001 | ||||
Cohabitation | 3,286 (53.99) | 3,733 (61.35) | 3,805 (62.53) | 3,737 (61.40) | |
Solitude | 2,800 (46.01) | 2,352 (38.65) | 2,280 (37.47) | 2,349 (38.60) | |
Family PIR, n (%) | <0.001 | ||||
Low (≤1.3) | 1,859 (30.55) | 1,803 (29.63) | 1,856 (30.50) | 2,122 (34.87) | |
Medium (1.3–3.5) | 2,187 (35.93) | 2,288 (37.60) | 2,345 (38.54) | 2,377 (39.06) | |
High (>3.5) | 2,040 (33.52) | 1,994 (32.77) | 1,884 (30.96) | 1,587 (26.08) | |
Smoke, n (%) | <0.001 | ||||
Yes | 2,373 (38.99) | 2,761 (45.37) | 2,924 (48.05) | 3,043 (50.00) | |
No | 3,713 (61.01) | 3,324 (54.63) | 3,161 (51.95) | 3,043 (50.00) | |
Alcohol, n (%) | 0.055 | ||||
Yes | 4,280 (70.33) | 4,246 (69.78) | 4,212 (69.22) | 4,146 (68.12) | |
No | 1,806 (29.67) | 1,839 (30.22) | 1,873 (30.78) | 1,940 (31.88) | |
Physical activity, n (%) | <0.001 | ||||
Inactive | 1,136 (18.67) | 1,475 (24.24) | 1,667 (27.40) | 1,833 (30.12) | |
Moderate | 2,181 (35.84) | 2,332 (38.32) | 2,280 (37.47) | 2,347 (38.56) | |
Vigorous | 2,769 (45.50) | 2,278 (37.44) | 2,138 (35.14) | 1,906 (31.32) | |
DM, n (%) | <0.001 | ||||
Yes | 328 (5.39) | 880 (14.46) | 1,316 (21.63) | 2,131 (35.01) | |
No | 5,758 (94.61) | 5,205 (85.54) | 4,769 (78.37) | 3,955 (64.99) | |
Hypertension, n (%) | <0.001 | ||||
Yes | 1,022 (16.79) | 2,012 (33.06) | 2,618 (43.02) | 3,171 (52.10) | |
No | 5,064 (83.21) | 4,073 (66.94) | 3,467 (56.98) | 2,915 (47.90) | |
CHD, n (%) | <0.001 | ||||
Yes | 118 (1.94) | 250 (4.11) | 296 (4.86) | 341 (5.60) | |
No | 5,968 (98.06) | 5,835 (95.89) | 5,789 (95.14) | 5,745 (94.40) | |
Stroke, n (%) | <0.001 | ||||
Yes | 148 (2.43) | 225 (3.70) | 243 (3.99) | 273 (4.49) | |
No | 5,938 (97.57) | 5,860 (96.30) | 5,842 (96.01) | 5,813 (95.51) | |
Cancer, n (%) | <0.001 | ||||
Yes | 446 (7.33) | 634 (10.42) | 648 (10.65) | 617 (10.14) | |
No | 5,640 (92.67) | 5,451 (89.58) | 5,437 (89.35) | 5,469 (89.86) | |
BMI, kg/m2 | 22.50 (20.70, 24.23) | 26.50 (24.84, 28.39) | 30.00 (28.00, 32.15) | 36.48 (33.20, 40.80) | <0.001 |
WC, cm | 81.60 (76.60, 86.40) | 94.20 (89.90, 98.50) | 103.50 (98.60, 108.00) | 118.10 (111.10, 127.00) | <0.001 |
FPG, mg/dL | 87.00 (82.00, 95.00) | 92.00 (86.00, 101.00) | 95.00 (88.00, 107.00) | 100.00 (90.00, 120.00) | <0.001 |
HbA1c, % | 5.30 (5.10, 5.60) | 5.50 (5.20, 5.80) | 5.60 (5.30, 6.00) | 5.80 (5.40, 6.40) | <0.001 |
TC, mg/dL | 179.00 (157.00, 204.00) | 191.00 (165.00, 219.00) | 195.00 (168.00, 223.00) | 194.00 (166.00, 223.00) | <0.001 |
TG, mg/dL | 75.00 (56.00, 102.00) | 112.00 (83.00, 156.00) | 150.00 (105.00, 215.00) | 183.00 (124.00, 278.00) | <0.001 |
HDL-c, mg/dL | 61.00 (51.00, 72.00) | 53.00 (44.00, 63.00) | 47.00 (40.00, 56.00) | 43.00 (36.00, 51.00) | <0.001 |
AST, U/L | 22.00 (18.00, 25.00) | 22.00 (19.00, 27.00) | 23.00 (20.00, 28.00) | 24.00 (20.00, 31.00) | <0.001 |
ALT, U/L | 17.00 (14.00, 21.00) | 20.00 (16.00, 26.00) | 23.00 (17.00, 31.00) | 25.00 (19.00, 37.00) | <0.001 |
GGT, IU/L | 14.00 (11.00, 19.00) | 19.00 (14.00, 26.00) | 23.00 (17.00, 35.00) | 29.00 (20.00, 47.00) | <0.001 |
Total bilirubin, mg/dL | 0.70 (0.50, 0.80) | 0.60 (0.50, 0.80) | 0.60 (0.50, 0.80) | 0.60 (0.40, 0.70) | <0.001 |
Creatinine, mg/dL | 0.81 (0.69, 0.95) | 0.86 (0.72, 1.02) | 0.88 (0.74, 1.04) | 0.87 (0.73, 1.02) | <0.001 |
Uric acid, mg/dL | 4.60 (3.90, 5.50) | 5.30 (4.40, 6.20) | 5.60 (4.80, 6.60) | 6.00 (5.10, 7.00) | <0.001 |
BUN, mg/dL | 12.00 (9.00, 15.00) | 13.00 (10.00, 16.00) | 13.00 (10.00, 17.00) | 13.00 (10.00, 16.00) | <0.001 |
Kidney stones, n (%) | <0.001 | ||||
Yes | 360 (5.92) | 552 (9.07) | 675 (11.09) | 790 (12.98) | |
No | 5,726 (94.08) | 5,533 (90.93) | 5,410 (88.91) | 5,296 (87.02) |
Variables . | FLI . | p value . | |||
---|---|---|---|---|---|
Quartile 1 (n = 6,086) . | Quartile 2 (n = 6,085) . | Quartile 3 (n = 6,085) . | Quartile 4 (n = 6,086) . | ||
Gender, n (%) | <0.001 | ||||
Male | 2,394 (39.34) | 3,159 (51.91) | 3,395 (55.79) | 3,289 (54.04) | |
Female | 3,692 (60.66) | 2,926 (48.09) | 2,690 (44.21) | 2,797 (45.96) | |
Age, years | 39.00 (27.00, 57.00) | 52.00 (36.00, 66.00) | 54.00 (40.00, 66.00) | 51.00 (38.00, 63.00) | <0.001 |
Race, n (%) | <0.001 | ||||
Mexican American | 555 (9.12) | 855 (14.05) | 1,116 (18.34) | 1,070 (17.58) | |
Non-Hispanic White | 2,754 (45.25) | 2,659 (43.70) | 2,592 (42.60) | 2,722 (44.73) | |
Non-Hispanic Black | 1,216 (19.98) | 1,186 (19.49) | 1,169 (19.21) | 1,339 (22.00) | |
Other race | 1,561 (25.65) | 1,385 (22.76) | 1,208 (19.85) | 955 (15.69) | |
Education level, n (%) | <0.001 | ||||
Less than 9th grade | 326 (5.36) | 606 (9.96) | 689 (11.32) | 587 (9.65) | |
9–12th grade | 2,005 (32.94) | 2,148 (35.30) | 2,336 (38.39) | 2,423 (39.81) | |
More than 12th grade | 3,755 (61.70) | 3,331 (54.74) | 3,060 (50.29) | 3,076 (50.54) | |
Marital status, n (%) | <0.001 | ||||
Cohabitation | 3,286 (53.99) | 3,733 (61.35) | 3,805 (62.53) | 3,737 (61.40) | |
Solitude | 2,800 (46.01) | 2,352 (38.65) | 2,280 (37.47) | 2,349 (38.60) | |
Family PIR, n (%) | <0.001 | ||||
Low (≤1.3) | 1,859 (30.55) | 1,803 (29.63) | 1,856 (30.50) | 2,122 (34.87) | |
Medium (1.3–3.5) | 2,187 (35.93) | 2,288 (37.60) | 2,345 (38.54) | 2,377 (39.06) | |
High (>3.5) | 2,040 (33.52) | 1,994 (32.77) | 1,884 (30.96) | 1,587 (26.08) | |
Smoke, n (%) | <0.001 | ||||
Yes | 2,373 (38.99) | 2,761 (45.37) | 2,924 (48.05) | 3,043 (50.00) | |
No | 3,713 (61.01) | 3,324 (54.63) | 3,161 (51.95) | 3,043 (50.00) | |
Alcohol, n (%) | 0.055 | ||||
Yes | 4,280 (70.33) | 4,246 (69.78) | 4,212 (69.22) | 4,146 (68.12) | |
No | 1,806 (29.67) | 1,839 (30.22) | 1,873 (30.78) | 1,940 (31.88) | |
Physical activity, n (%) | <0.001 | ||||
Inactive | 1,136 (18.67) | 1,475 (24.24) | 1,667 (27.40) | 1,833 (30.12) | |
Moderate | 2,181 (35.84) | 2,332 (38.32) | 2,280 (37.47) | 2,347 (38.56) | |
Vigorous | 2,769 (45.50) | 2,278 (37.44) | 2,138 (35.14) | 1,906 (31.32) | |
DM, n (%) | <0.001 | ||||
Yes | 328 (5.39) | 880 (14.46) | 1,316 (21.63) | 2,131 (35.01) | |
No | 5,758 (94.61) | 5,205 (85.54) | 4,769 (78.37) | 3,955 (64.99) | |
Hypertension, n (%) | <0.001 | ||||
Yes | 1,022 (16.79) | 2,012 (33.06) | 2,618 (43.02) | 3,171 (52.10) | |
No | 5,064 (83.21) | 4,073 (66.94) | 3,467 (56.98) | 2,915 (47.90) | |
CHD, n (%) | <0.001 | ||||
Yes | 118 (1.94) | 250 (4.11) | 296 (4.86) | 341 (5.60) | |
No | 5,968 (98.06) | 5,835 (95.89) | 5,789 (95.14) | 5,745 (94.40) | |
Stroke, n (%) | <0.001 | ||||
Yes | 148 (2.43) | 225 (3.70) | 243 (3.99) | 273 (4.49) | |
No | 5,938 (97.57) | 5,860 (96.30) | 5,842 (96.01) | 5,813 (95.51) | |
Cancer, n (%) | <0.001 | ||||
Yes | 446 (7.33) | 634 (10.42) | 648 (10.65) | 617 (10.14) | |
No | 5,640 (92.67) | 5,451 (89.58) | 5,437 (89.35) | 5,469 (89.86) | |
BMI, kg/m2 | 22.50 (20.70, 24.23) | 26.50 (24.84, 28.39) | 30.00 (28.00, 32.15) | 36.48 (33.20, 40.80) | <0.001 |
WC, cm | 81.60 (76.60, 86.40) | 94.20 (89.90, 98.50) | 103.50 (98.60, 108.00) | 118.10 (111.10, 127.00) | <0.001 |
FPG, mg/dL | 87.00 (82.00, 95.00) | 92.00 (86.00, 101.00) | 95.00 (88.00, 107.00) | 100.00 (90.00, 120.00) | <0.001 |
HbA1c, % | 5.30 (5.10, 5.60) | 5.50 (5.20, 5.80) | 5.60 (5.30, 6.00) | 5.80 (5.40, 6.40) | <0.001 |
TC, mg/dL | 179.00 (157.00, 204.00) | 191.00 (165.00, 219.00) | 195.00 (168.00, 223.00) | 194.00 (166.00, 223.00) | <0.001 |
TG, mg/dL | 75.00 (56.00, 102.00) | 112.00 (83.00, 156.00) | 150.00 (105.00, 215.00) | 183.00 (124.00, 278.00) | <0.001 |
HDL-c, mg/dL | 61.00 (51.00, 72.00) | 53.00 (44.00, 63.00) | 47.00 (40.00, 56.00) | 43.00 (36.00, 51.00) | <0.001 |
AST, U/L | 22.00 (18.00, 25.00) | 22.00 (19.00, 27.00) | 23.00 (20.00, 28.00) | 24.00 (20.00, 31.00) | <0.001 |
ALT, U/L | 17.00 (14.00, 21.00) | 20.00 (16.00, 26.00) | 23.00 (17.00, 31.00) | 25.00 (19.00, 37.00) | <0.001 |
GGT, IU/L | 14.00 (11.00, 19.00) | 19.00 (14.00, 26.00) | 23.00 (17.00, 35.00) | 29.00 (20.00, 47.00) | <0.001 |
Total bilirubin, mg/dL | 0.70 (0.50, 0.80) | 0.60 (0.50, 0.80) | 0.60 (0.50, 0.80) | 0.60 (0.40, 0.70) | <0.001 |
Creatinine, mg/dL | 0.81 (0.69, 0.95) | 0.86 (0.72, 1.02) | 0.88 (0.74, 1.04) | 0.87 (0.73, 1.02) | <0.001 |
Uric acid, mg/dL | 4.60 (3.90, 5.50) | 5.30 (4.40, 6.20) | 5.60 (4.80, 6.60) | 6.00 (5.10, 7.00) | <0.001 |
BUN, mg/dL | 12.00 (9.00, 15.00) | 13.00 (10.00, 16.00) | 13.00 (10.00, 17.00) | 13.00 (10.00, 16.00) | <0.001 |
Kidney stones, n (%) | <0.001 | ||||
Yes | 360 (5.92) | 552 (9.07) | 675 (11.09) | 790 (12.98) | |
No | 5,726 (94.08) | 5,533 (90.93) | 5,410 (88.91) | 5,296 (87.02) |
Data are shown as median (25th, 75th percentiles) or number (percentages), and p < 0.05 considered statistically significant.
FLI, fatty liver index; PIR, poverty-to-income ratio; BMI, body mass index; WC, waist circumference; FPG, fasting plasma glucose; HbA1c, hemoglobin A1c; TC, total cholesterol; TG, triglyceride; HDL-c, high-density lipoprotein cholesterol; ALT, alanine transaminase; AST, aspartate aminotransferase; GGT, gamma-glutamyl transferase; BUN, blood urea nitrogen.
Comparison of Baseline Characteristics between MASLD and Non-MASLD Participants
In this study, the total number of participants with MASLD was 11,843 (48.6%), while the total number of participants without MASLD was 12,499 (51.4%). Regarding gender distribution, the study population was slightly more male than female, with 50.27% and 49.73%, respectively (p < 0.001). Notably, the proportion of males was significantly higher than that of females in the MASLD patient group (54.85% compared with 45.93% in the non-MASLD group) (p < 0.001). Patients in the MASLD group were older, with a median age of 52 years, compared with 46 years in the non-MASLD group (p < 0.001). Regarding racial composition, the MASLD group exhibited a higher percentage of Mexican Americans (17.95%) than the non-MASLD group (11.76%) (p < 0.001). Regarding educational attainment, the proportion of patients possessing a high school diploma or higher was lower in the MASLD group (50.39%) than in the non-MASLD group (58.04%) (p < 0.001). Furthermore, the proportion of individuals residing alone was lower in the MASLD group (38.07%) compared to the non-MASLD group (42.18%), and the proportion of economically disadvantaged families was higher in the MASLD group (32.75%) compared to the non-MASLD group (30.09%) (p < 0.001). Regarding health status, the MASLD group exhibited a markedly elevated prevalence of smoking, diabetes, hypertension, CHD, stroke, and cancer in comparison to the non-MASLD group (p < 0.001). The results of the body measurements and biomarker analyses indicated that the MASLD group exhibited elevated levels of BMI, WC, FPG, HbA1c, total cholesterol, TG, AST, alanine aminotransferase, GGT, total bilirubin, creatinine, uric acid, urea nitrogen, and FLI, as well as lower levels of HDL-C (p < 0.001). Finally, regarding kidney stones, the prevalence of patients in the MASLD group was 12.07%, which was markedly higher than that of the non-MASLD group, which was 7.58% (p < 0.001) (Table 2).
Baseline characteristics of participants with and without MASLD
Variables . | Total (n = 24,342) . | Non-MASLD (n = 12,499) . | MASLD (n = 11,843) . | p value . |
---|---|---|---|---|
Gender, n (%) | <0.001 | |||
Male | 12,237 (50.27) | 5,741 (45.93) | 6,496 (54.85) | |
Female | 12,105 (49.73) | 6,758 (54.07) | 5,347 (45.15) | |
Age, years | 49.00 (35.00, 63.00) | 46.00 (31.00, 63.00) | 52.00 (39.00, 64.00) | <0.001 |
Race, n (%) | <0.001 | |||
Mexican American | 3,596 (14.77) | 1,470 (11.76) | 2,126 (17.95) | |
Non-Hispanic White | 10,727 (44.07) | 5,545 (44.36) | 5,182 (43.76) | |
Non-Hispanic Black | 4,910 (20.17) | 2,470 (19.76) | 2,440 (20.60) | |
Other race | 5,109 (20.99) | 3,014 (24.11) | 2,095 (17.69) | |
Education level, n (%) | <0.001 | |||
Less than 9th grade | 2,208 (9.07) | 967 (7.74) | 1,241 (10.48) | |
9–12th grade | 8,912 (36.61) | 4,278 (34.23) | 4,634 (39.13) | |
More than 12th grade | 13,222 (54.32) | 7,254 (58.04) | 5,968 (50.39) | |
Marital status, n (%) | <0.001 | |||
Cohabitation | 14,561 (59.82) | 7,227 (57.82) | 7,334 (61.93) | |
Solitude | 9,781 (40.18) | 5,272 (42.18) | 4,509 (38.07) | |
Family PIR, n (%) | <0.001 | |||
Low (≤1.3) | 7,640 (31.39) | 3,761 (30.09) | 3,879 (32.75) | |
Medium (1.3–3.5) | 9,197 (37.78) | 4,605 (36.84) | 4,592 (38.77) | |
High (>3.5) | 7,505 (30.83) | 4,133 (33.07) | 3,372 (28.47) | |
Smoke, n (%) | <0.001 | |||
Yes | 11,101 (45.60) | 5,289 (42.32) | 5,812 (49.08) | |
No | 13,241 (54.40) | 7,210 (57.68) | 6,031 (50.92) | |
Alcohol, n (%) | 0.022 | |||
Yes | 16,884 (69.36) | 8,752 (70.02) | 8,132 (68.67) | |
No | 7,458 (30.64) | 3,747 (29.98) | 3,711 (31.33) | |
Physical activity, n (%) | <0.001 | |||
Inactive | 6,111 (25.10) | 2,694 (21.55) | 3,417 (28.85) | |
Moderate | 9,140 (37.55) | 4,655 (37.24) | 4,485 (37.87) | |
Vigorous | 9,091 (37.35) | 5,150 (41.20) | 3,941 (33.28) | |
DM, n (%) | <0.001 | |||
Yes | 4,655 (19.12) | 1,265 (10.12) | 3,390 (28.62) | |
No | 19,687 (80.88) | 11,234 (89.88) | 8,453 (71.38) | |
Hypertension, n (%) | <0.001 | |||
Yes | 8,823 (36.25) | 3,159 (25.27) | 5,664 (47.83) | |
No | 15,519 (63.75) | 9,340 (74.73) | 6,179 (52.17) | |
CHD, n (%) | <0.001 | |||
Yes | 1,005 (4.13) | 379 (3.03) | 626 (5.29) | |
No | 23,337 (95.87) | 12,120 (96.97) | 11,217 (94.71) | |
Stroke, n (%) | <0.001 | |||
Yes | 889 (3.65) | 381 (3.05) | 508 (4.29) | |
No | 23,453 (96.35) | 12,118 (96.95) | 11,335 (95.71) | |
Cancer, n (%) | <0.001 | |||
Yes | 2,345 (9.63) | 1,106 (8.85) | 1,239 (10.46) | |
No | 21,997 (90.37) | 11,393 (91.15) | 10,604 (89.54) | |
BMI, kg/m2 | 28.14 (24.41, 32.71) | 24.67 (22.30, 27.00) | 32.79 (29.70, 37.00) | <0.001 |
WC, cm | 98.40 (88.00, 109.50) | 88.50 (81.50, 95.00) | 109.80 (102.80, 119.00) | <0.001 |
FPG, mg/dL | 93.00 (86.00, 105.00) | 90.00 (83.00, 98.00) | 98.00 (89.00, 113.00) | <0.001 |
HbA1c, % | 5.50 (5.20, 5.90) | 5.40 (5.20, 5.70) | 5.70 (5.40, 6.20) | <0.001 |
TC, mg/dL | 189.50 (164.00, 218.00) | 185.00 (160.00, 213.00) | 194.00 (168.00, 223.00) | <0.001 |
TG, mg/dL | 121.00 (81.00, 187.00) | 92.00 (66.00, 131.00) | 166.00 (114.00, 246.00) | <0.001 |
HDL-c, mg/dL | 50.00 (41.00, 61.00) | 56.00 (47.00, 68.00) | 45.00 (38.00, 53.00) | <0.001 |
AST, U/L | 23.00 (19.00, 27.00) | 22.00 (19.00, 26.00) | 24.00 (20.00, 29.00) | <0.001 |
ALT, U/L | 21.00 (16.00, 28.00) | 18.00 (15.00, 24.00) | 24.00 (18.00, 34.00) | <0.001 |
GGT, IU/L | 20.00 (14.00, 31.00) | 16.00 (12.00, 22.00) | 26.00 (19.00, 41.00) | <0.001 |
Total bilirubin, mg/dL | 0.60 (0.50, 0.80) | 0.60 (0.50, 0.80) | 0.60 (0.40, 0.80) | <0.001 |
Creatinine, mg/dL | 0.85 (0.72, 1.01) | 0.83 (0.71, 0.99) | 0.87 (0.73, 1.03) | <0.001 |
Uric acid, mg/dL | 5.40 (4.50, 6.40) | 4.90 (4.10, 5.80) | 5.90 (5.00, 6.80) | <0.001 |
BUN, mg/dL | 13.00 (10.00, 16.00) | 13.00 (10.00, 16.00) | 13.00 (10.00, 16.00) | <0.001 |
FLI | 58.18 (24.11, 86.13) | 25.06 (11.20, 42.28) | 86.75 (74.84, 95.35) | <0.001 |
Kidney stones, n (%) | <0.001 | |||
Yes | 2,377 (9.77) | 947 (7.58) | 1,430 (12.07) | |
No | 21,965 (90.23) | 11,552 (92.42) | 10,413 (87.93) |
Variables . | Total (n = 24,342) . | Non-MASLD (n = 12,499) . | MASLD (n = 11,843) . | p value . |
---|---|---|---|---|
Gender, n (%) | <0.001 | |||
Male | 12,237 (50.27) | 5,741 (45.93) | 6,496 (54.85) | |
Female | 12,105 (49.73) | 6,758 (54.07) | 5,347 (45.15) | |
Age, years | 49.00 (35.00, 63.00) | 46.00 (31.00, 63.00) | 52.00 (39.00, 64.00) | <0.001 |
Race, n (%) | <0.001 | |||
Mexican American | 3,596 (14.77) | 1,470 (11.76) | 2,126 (17.95) | |
Non-Hispanic White | 10,727 (44.07) | 5,545 (44.36) | 5,182 (43.76) | |
Non-Hispanic Black | 4,910 (20.17) | 2,470 (19.76) | 2,440 (20.60) | |
Other race | 5,109 (20.99) | 3,014 (24.11) | 2,095 (17.69) | |
Education level, n (%) | <0.001 | |||
Less than 9th grade | 2,208 (9.07) | 967 (7.74) | 1,241 (10.48) | |
9–12th grade | 8,912 (36.61) | 4,278 (34.23) | 4,634 (39.13) | |
More than 12th grade | 13,222 (54.32) | 7,254 (58.04) | 5,968 (50.39) | |
Marital status, n (%) | <0.001 | |||
Cohabitation | 14,561 (59.82) | 7,227 (57.82) | 7,334 (61.93) | |
Solitude | 9,781 (40.18) | 5,272 (42.18) | 4,509 (38.07) | |
Family PIR, n (%) | <0.001 | |||
Low (≤1.3) | 7,640 (31.39) | 3,761 (30.09) | 3,879 (32.75) | |
Medium (1.3–3.5) | 9,197 (37.78) | 4,605 (36.84) | 4,592 (38.77) | |
High (>3.5) | 7,505 (30.83) | 4,133 (33.07) | 3,372 (28.47) | |
Smoke, n (%) | <0.001 | |||
Yes | 11,101 (45.60) | 5,289 (42.32) | 5,812 (49.08) | |
No | 13,241 (54.40) | 7,210 (57.68) | 6,031 (50.92) | |
Alcohol, n (%) | 0.022 | |||
Yes | 16,884 (69.36) | 8,752 (70.02) | 8,132 (68.67) | |
No | 7,458 (30.64) | 3,747 (29.98) | 3,711 (31.33) | |
Physical activity, n (%) | <0.001 | |||
Inactive | 6,111 (25.10) | 2,694 (21.55) | 3,417 (28.85) | |
Moderate | 9,140 (37.55) | 4,655 (37.24) | 4,485 (37.87) | |
Vigorous | 9,091 (37.35) | 5,150 (41.20) | 3,941 (33.28) | |
DM, n (%) | <0.001 | |||
Yes | 4,655 (19.12) | 1,265 (10.12) | 3,390 (28.62) | |
No | 19,687 (80.88) | 11,234 (89.88) | 8,453 (71.38) | |
Hypertension, n (%) | <0.001 | |||
Yes | 8,823 (36.25) | 3,159 (25.27) | 5,664 (47.83) | |
No | 15,519 (63.75) | 9,340 (74.73) | 6,179 (52.17) | |
CHD, n (%) | <0.001 | |||
Yes | 1,005 (4.13) | 379 (3.03) | 626 (5.29) | |
No | 23,337 (95.87) | 12,120 (96.97) | 11,217 (94.71) | |
Stroke, n (%) | <0.001 | |||
Yes | 889 (3.65) | 381 (3.05) | 508 (4.29) | |
No | 23,453 (96.35) | 12,118 (96.95) | 11,335 (95.71) | |
Cancer, n (%) | <0.001 | |||
Yes | 2,345 (9.63) | 1,106 (8.85) | 1,239 (10.46) | |
No | 21,997 (90.37) | 11,393 (91.15) | 10,604 (89.54) | |
BMI, kg/m2 | 28.14 (24.41, 32.71) | 24.67 (22.30, 27.00) | 32.79 (29.70, 37.00) | <0.001 |
WC, cm | 98.40 (88.00, 109.50) | 88.50 (81.50, 95.00) | 109.80 (102.80, 119.00) | <0.001 |
FPG, mg/dL | 93.00 (86.00, 105.00) | 90.00 (83.00, 98.00) | 98.00 (89.00, 113.00) | <0.001 |
HbA1c, % | 5.50 (5.20, 5.90) | 5.40 (5.20, 5.70) | 5.70 (5.40, 6.20) | <0.001 |
TC, mg/dL | 189.50 (164.00, 218.00) | 185.00 (160.00, 213.00) | 194.00 (168.00, 223.00) | <0.001 |
TG, mg/dL | 121.00 (81.00, 187.00) | 92.00 (66.00, 131.00) | 166.00 (114.00, 246.00) | <0.001 |
HDL-c, mg/dL | 50.00 (41.00, 61.00) | 56.00 (47.00, 68.00) | 45.00 (38.00, 53.00) | <0.001 |
AST, U/L | 23.00 (19.00, 27.00) | 22.00 (19.00, 26.00) | 24.00 (20.00, 29.00) | <0.001 |
ALT, U/L | 21.00 (16.00, 28.00) | 18.00 (15.00, 24.00) | 24.00 (18.00, 34.00) | <0.001 |
GGT, IU/L | 20.00 (14.00, 31.00) | 16.00 (12.00, 22.00) | 26.00 (19.00, 41.00) | <0.001 |
Total bilirubin, mg/dL | 0.60 (0.50, 0.80) | 0.60 (0.50, 0.80) | 0.60 (0.40, 0.80) | <0.001 |
Creatinine, mg/dL | 0.85 (0.72, 1.01) | 0.83 (0.71, 0.99) | 0.87 (0.73, 1.03) | <0.001 |
Uric acid, mg/dL | 5.40 (4.50, 6.40) | 4.90 (4.10, 5.80) | 5.90 (5.00, 6.80) | <0.001 |
BUN, mg/dL | 13.00 (10.00, 16.00) | 13.00 (10.00, 16.00) | 13.00 (10.00, 16.00) | <0.001 |
FLI | 58.18 (24.11, 86.13) | 25.06 (11.20, 42.28) | 86.75 (74.84, 95.35) | <0.001 |
Kidney stones, n (%) | <0.001 | |||
Yes | 2,377 (9.77) | 947 (7.58) | 1,430 (12.07) | |
No | 21,965 (90.23) | 11,552 (92.42) | 10,413 (87.93) |
Data are shown as median (25th, 75th percentiles) or number (percentages), and p < 0.05 considered statistically significant.
MASLD, metabolic dysfunction-associated steatotic liver disease; PIR, poverty-to-income ratio; BMI, body mass index; WC, waist circumference; FPG, fasting plasma glucose; HbA1c, hemoglobin A1c; TC, total cholesterol; TG, triglyceride; HDL-c, high-density lipoprotein cholesterol; ALT, alanine transaminase; AST, aspartate aminotransferase; GGT, gamma-glutamyl transferase; BUN, blood urea nitrogen; FLI, fatty liver index.
Correlation Analysis of FLI, MASLD, and Kidney Stones
Three models were employed in this study to examine the relationship between FLI, MASLD, and kidney stones for assessment: an unadjusted model; a model adjusted for gender, age, and race; and a fully adjusted model. The study’s results demonstrated a statistically significant association between FLI and MASLD and an increased risk of kidney stones in all models (p < 0.001). Specifically, the fully adjusted model revealed that participants in the highest quartile of FLI exhibited an approximately 1.68-fold increased risk of developing kidney stones compared to participants in the lowest quartile (OR = 1.68, 95% CI: 1.46–1.93, p < 0.001). Similarly, patients with MASLD exhibited a 1.35-fold higher risk of developing kidney stones than those with non-MASLD (full-adjusted model OR = 1.35, 95% CI: 1.23–1.48, p < 0.001). These findings consistently indicate that FLI and MASLD are significant risk factors for developing kidney stones (Table 3).
Relationship between FLI, MASLD, and kidney stones in different models
Variables . | Model 1 . | Model 2 . | Model 3 . | |||
---|---|---|---|---|---|---|
OR (95% CI) . | p value . | OR (95% CI) . | p value . | OR (95% CI) . | p value . | |
FLI | 1.01 (1.01–1.01) | <0.001 | 1.01 (1.01–1.01) | <0.001 | 1.01 (1.01–1.01) | <0.001 |
Categories | ||||||
Quartile 1 | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) | |||
Quartile 2 | 1.59 (1.38–1.82) | <0.001 | 1.32 (1.14–1.52) | <0.001 | 1.26 (1.09–1.45) | 0.001 |
Quartile 3 | 1.98 (1.74–2.27) | <0.001 | 1.61 (1.40–1.85) | <0.001 | 1.46 (1.27–1.68) | <0.001 |
Quartile 4 | 2.37 (2.08–2.70) | <0.001 | 2.05 (1.79–2.34) | <0.001 | 1.68 (1.46–1.93) | <0.001 |
MASLD | ||||||
No | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) | |||
Yes | 1.68 (1.54–1.83) | <0.001 | 1.54 (1.41–1.69) | <0.001 | 1.35 (1.23–1.48) | <0.001 |
Variables . | Model 1 . | Model 2 . | Model 3 . | |||
---|---|---|---|---|---|---|
OR (95% CI) . | p value . | OR (95% CI) . | p value . | OR (95% CI) . | p value . | |
FLI | 1.01 (1.01–1.01) | <0.001 | 1.01 (1.01–1.01) | <0.001 | 1.01 (1.01–1.01) | <0.001 |
Categories | ||||||
Quartile 1 | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) | |||
Quartile 2 | 1.59 (1.38–1.82) | <0.001 | 1.32 (1.14–1.52) | <0.001 | 1.26 (1.09–1.45) | 0.001 |
Quartile 3 | 1.98 (1.74–2.27) | <0.001 | 1.61 (1.40–1.85) | <0.001 | 1.46 (1.27–1.68) | <0.001 |
Quartile 4 | 2.37 (2.08–2.70) | <0.001 | 2.05 (1.79–2.34) | <0.001 | 1.68 (1.46–1.93) | <0.001 |
MASLD | ||||||
No | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) | |||
Yes | 1.68 (1.54–1.83) | <0.001 | 1.54 (1.41–1.69) | <0.001 | 1.35 (1.23–1.48) | <0.001 |
The bold values indicated statistically significant.
FLI, fatty liver index; MASLD, metabolic dysfunction-associated steatotic liver disease; PIR, poverty-to-income ratio; OR, odds ratio; CI, confidence interval.
Model 1: crude.
Model 2: adjusted for gender, age, and race.
Model 3: adjusted for gender, age, race, education level, marital status, family PIR, smoke, alcohol, physical activity, MS, hypertension, CHD, stroke, and cancer.
We employed the RCS analysis to investigate further the potential nonlinear relationship between FLI and kidney stone risk. After adjusting for possible confounding variables, including gender, age, race, education, marital status, family PIR, smoking, alcohol consumption, physical activity, diabetes, hypertension, CHD, stroke, and cancer, the results of the analyses demonstrated a significant correlation between FLI and the risk of kidney stones (overall p < 0.001). However, a significant nonlinear relationship was not observed (p = 0.074 for nonlinearity). Threshold analysis revealed an inflection point value for FLI of 58.18. When the FLI exceeded the threshold value, the risk of kidney stones increased significantly with the index (Fig. 2).
Nonlinear relationship of FLI and kidney stones. The solid purple line displays the OR, with the 95% CI represented by purple shading. They were adjusted for gender, age, race, education level, marital status, family PIR, smoke, alcohol, physical activity, DM, hypertension, CHD, stroke, and cancer. FLI, fatty liver index; CI, confidence interval; PIR, poverty-to-income ratio.
Nonlinear relationship of FLI and kidney stones. The solid purple line displays the OR, with the 95% CI represented by purple shading. They were adjusted for gender, age, race, education level, marital status, family PIR, smoke, alcohol, physical activity, DM, hypertension, CHD, stroke, and cancer. FLI, fatty liver index; CI, confidence interval; PIR, poverty-to-income ratio.
Subgroup Analysis of the Relationship between MASLD and Kidney Stones
Further subgroup analyses were conducted to elucidate the relationship between MASLD and kidney stones. The results demonstrated that MASLD was significantly associated with an increased risk of kidney stones in all patients, with an OR of 1.35 and a 95% CI of 1.23–1.48 (p < 0.001). Further subgroup analyses demonstrated that this significant association remained consistent across gender, race, education level, marital status, family PIR, drinking habits, and among patient groups with DM, CHD, stroke, and cancer, with no significant interactions observed (interaction p value >0.05). It is, however, noteworthy that significant interactions were detected in the subgroup analyses of smoking status, physical activity level, and hypertension (interaction p value <0.05). Of particular note was the observation that the strength of the association between MASLD and kidney stone risk appeared to be more pronounced in nonsmokers, in participants with lower and higher levels of physical activity, and in those who were not hypertensive. This finding reinforces the conclusion that MASLD is a significant risk factor for developing kidney stones and suggests that specific lifestyle and clinical characteristics may play an essential moderating role in this association (Fig. 3).
Subgroup analysis of the relationship MASLD and kidney stones. Adjusted variables: gender, age, race, education level, marital status, family PIR, smoke, alcohol, physical activity, DM, hypertension, CHD, stroke, and cancer. The model was not adjusted for the stratification variables themselves in the corresponding stratification analysis. MASLD, metabolic dysfunction-associated steatotic liver disease; PIR, poverty-to-income ratio; OR, odds ratio; CI, confidence interval.
Subgroup analysis of the relationship MASLD and kidney stones. Adjusted variables: gender, age, race, education level, marital status, family PIR, smoke, alcohol, physical activity, DM, hypertension, CHD, stroke, and cancer. The model was not adjusted for the stratification variables themselves in the corresponding stratification analysis. MASLD, metabolic dysfunction-associated steatotic liver disease; PIR, poverty-to-income ratio; OR, odds ratio; CI, confidence interval.
Discussion
The study’s findings indicated a notable correlation between FLI and MASLD and the risk of kidney stones. Specifically, the risk of kidney stones in the highest quartile of FLI was 1.68 times higher than that in the lowest quartile of FLI, and the risk of kidney stones in patients with MASLD was 1.35 times higher than that in people with non-MASLD. The RCS analyses demonstrated a significant linear positive correlation between FLI and the risk of kidney stones. These findings suggest that hepatic steatosis may increase the risk of kidney stone formation by affecting metabolic pathways in the body.
Fatty liver may be a significant contributing factor to the development of kidney stones. As FLI is a reliable indicator for evaluating hepatic steatosis [25], its positive correlation with the risk of kidney stones indicates that the liver’s overall health status may play a significant role in forming kidney stones. As a vital metabolic organ in the human body, an abnormal liver function may significantly impact kidney health through various mechanisms. Precisely, elevated FLI reflects disturbances in hepatic metabolic function and abnormalities in lipid metabolism. These changes may affect the metabolism and excretion of minerals in the urine through direct or indirect pathways, increasing the risk of kidney stone formation [19, 20].
The precise biological mechanisms by which fatty liver increases the incidence of kidney stones remain incompletely understood. However, there is a growing consensus that insulin resistance, oxidative stress, and inflammatory responses represent a few of the critical factors. Specifically, fatty liver induces an increase in insulin resistance, leading to increased urinary excretion of calcium and oxalate, thereby facilitating the formation of kidney stones [26, 27]. Furthermore, insulin resistance may contribute to the development of kidney stones by affecting urinary pH [28]. Moreover, fat accumulation in the liver is frequently accompanied by oxidative stress and inflammatory responses. In this process, the generation of oxygen free radicals, lipid peroxidation, and damage to the mitochondria of hepatocytes are significant contributors to stone formation [29, 30]. Oxidative stress may precipitate hyperoxaluria and increase the concentration of crystals in the urine [31]. Concurrently, inflammatory mediators may also heighten the risk of kidney stones, affecting renal cell function and urine composition [32]. Despite the considerable correlation between FLI and kidney stone risk, this study did not identify a significant nonlinear relationship. This observation leads us to hypothesize that elevated FLI may potentiate the risk of kidney stones across a range.
The diagnostic criteria for MASLD encompass multiple indicators of cardiometabolic abnormalities, which collectively reflect the patient’s overall metabolic health. It is crucial to acknowledge that fatty liver alone is not the sole risk factor for kidney stones. Instead, the interrelationships and interactions between fatty liver and other metabolic abnormalities collectively contribute to the development of kidney stones. Several studies have substantiated a notable correlation between obesity and the prevalence of kidney stones [33‒37]. The extant evidence indicates a positive association between BMI and the occurrence of uric acid stones and calcium oxalate stones [38]. Several recent studies have suggested that elevated levels of the weight-adjusted waist circumference index (WWI) are significantly associated with an increased prevalence of kidney stones in the US adult population. This suggests that increased WWI is a significant risk factor for kidney stones [39‒42]. Furthermore, subcutaneous adipose tissue accumulation has been proposed as an independent risk factor for urinary stones in young people [43]. It is postulated that obesity may increase the risk of kidney stones by increasing the excretion of urinary calcium, uric acid, and other components of the urine, which in turn increases the risk of kidney stones [44‒46]. Individuals with a higher BMI typically consume protein, fat, and sodium-rich foods. These foods interfere with the metabolism and pH balance of the kidneys, which in turn promotes stone formation [47]. Similarly, elevated blood glucose levels represent a significant risk factor for the formation of kidney stones. A positive correlation has been demonstrated between FPG levels of 100 mg/dL or above and changes in the size of kidney stones [48]. It has been suggested that effective glycemic control may reduce urinary risk factors for uric acid stone formation [49]. Hypertension is also a significant risk factor for developing kidney stones [21]. The relationship between hypertension and kidney stones is primarily observed in individuals with a history of stone formation, where hypertension is associated with a notable elevation in urinary calcium [50]. Additionally, hypertension is linked to hypocapnia and hyperoxaluria, both of which may contribute to the development of kidney stones [51]. Similarly, dyslipidemia is acknowledged as a significant contributing factor to kidney stone formation. Several studies have confirmed a robust correlation between dyslipidemia and an elevated risk of kidney stone formation [52, 53]. Elevated serum TG levels have been linked to an elevated risk of urolithiasis [52, 54], while low HDL-C levels have been associated with an increased risk of kidney stones. Lipid dysregulation may alter urine chemistry and pH. Patients with low HDL-C or hypertriglyceridemia have been observed to have higher urinary sodium, oxalate, and uric acid excretion and lower pH, which may affect stone formation [55, 56]. Furthermore, oxidized forms of lipoproteins may facilitate stone formation by promoting oxidative stress and inflammatory responses, thereby altering the environment in which stones are formed [55]. This study’s positive correlation between metabolic syndrome and kidney stone risk further underscores the connection between metabolic syndrome component disorders and kidney stones. It is therefore recommended that particular attention be paid to preventing and treating kidney stones in patients with MASLD to mitigate the potential impact of their metabolic abnormalities on kidney health.
This study observed significant differences between the MASLD and non-MASLD groups regarding age, gender, and racial composition. The MASLD group was observed to have a significantly higher mean age than the non-MASLD group. This may be attributed to the increased prevalence of risk factors for metabolic syndrome and fatty liver with age [57‒59]. As individuals age, they are more likely to be exposed to risk factors for metabolic diseases, such as obesity, diabetes, and hypertension. These risk factors also serve as important triggers for MASLD. Second, the proportion of males in the MASLD group was significantly higher than that of females, which is consistent with the findings of several studies, indicating that metabolic diseases are more prevalent in males [60, 61]. The observed gender differences may be attributed to many factors, including hormonal levels, lifestyle, and social roles, which collectively influence metabolic health. Finally, regarding racial composition, the MASLD group exhibited a higher proportion of Mexican Americans, which may reflect the impact of differences in genetic background, living environment, and cultural practices on metabolic health among different races [62, 63]. Notably, these differences in demographic characteristics may have influenced the assessment of kidney stone risk to some extent. Accordingly, when interpreting the relationship between MASLD and kidney stones, it is essential to consider the potential impact of these factors collectively to ensure the accuracy and reliability of the study findings. Further investigation is required to elucidate the precise associations between MASLD and kidney stone risk in diverse ethnic and gender groups to develop more productive prevention and treatment strategies.
The present study further demonstrated that the strength of the association between MASLD and the risk of developing kidney stones exhibited significant intergroup variability. This important finding indicates that, in addition to the effects of fatty liver and metabolic syndrome on the risk of kidney stones, other lifestyle factors and clinical characteristics may also play a pivotal role in the pathogenesis of kidney stones. Specifically, the effects of smoking on renal metabolism and excretory functions are complex and profound [64]. At the same time, the influence of physical activity levels on the risk of kidney stones is mediated by their impact on urine concentration and dilution [64]. Furthermore, chronic diseases such as hypertension may indirectly contribute to the process of kidney stone development and progression through their potential impact on renal hemodynamics and vascular function [51]. Consequently, when investigating the relationship between MASLD and kidney stone risk in greater depth, it is essential to adopt a comprehensive and integrated approach, taking into account a multitude of potential influencing factors and developing more personalized and precise prevention and treatment strategies tailored to the specific characteristics and needs of different subgroups of the population.
This study makes an innovative contribution to the field by linking FLI, MASLD, and kidney stone risk, thereby addressing a research gap and offering a new perspective on the prevention and treatment of kidney stones. Although the causal relationship was not directly proven, the study initially explored the possible common pathophysiological mechanisms, such as lipid metabolism disorders, oxidative stress, insulin resistance, and so forth. These findings point the way for future research and enhance the academic value of this field. It is important to acknowledge that the study is not without limitations. First, due to the cross-sectional design, only the associations between FLI and MASLD and kidney stones could be revealed; thus, no causal relationship could be established. Second, although the NHANES database offers a substantial data repository, there may be a degree of self-reporting bias inherent to the data collection process. Furthermore, the absence of long-term follow-up data precludes an evaluation of the temporal effects of FLI and MASLD on the risk of kidney stones. Additionally, although studies have considered lifestyle factors such as smoking and alcohol consumption, other lifestyle factors that may influence the risk of kidney stones, such as dietary habits and water intake, have not been subjected to sufficient analysis. Ultimately, the NHANES database is insufficient regarding kidney stones, particularly the absence of data on stone composition, recurrence rates, and other crucial characteristics, constraining the study’s depth and breadth. Consequently, to enhance the precision and dependability of the data, future investigations should employ more objective methods to ascertain the existence and classification of kidney stones.
Conclusion
In conclusion, the present study demonstrated a significant correlation between the FLI, MASLD, and the risk of kidney stones. It provided preliminary insights into the potential influencing factors and mechanisms. These findings offer novel perspectives and strategies for preventing and treating kidney stones and provide new insights into the interconnections between metabolic and urologic diseases. Further investigation is required to elucidate the precise mechanisms underlying these relationships and to develop efficacious interventions to reduce the risk of kidney stones. Furthermore, investigations into distinct demographic groups will furnish crucial scientific evidence for the formulation of tailored prevention and treatment strategies.
Acknowledgments
We thank the NHANES participants and staff for their contributions.
Statement of Ethics
The studies involving humans were approved by National Center for Health Statistics Ethics Review Board. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. As NHANES is a publicly accessible database, the Changzhou Third People’s Hospital Ethics Committee granted approval to waive ethical review and approved the study protocol (02A-A2024018).
Conflict of Interest Statement
The authors have no conflicts of interest to declare.
Funding Sources
This work was supported by the Key Talents Project of Changzhou Third People’s Hospital.
Author Contributions
Conceptualization and methodology; and formal analysis: F.Z. and W.L.; project administration, data curation, and investigation; writing – original draft; and funding acquisition: F.Z.; visualization and supervision; and writing – review and editing: W.L. All authors have read and agreed to the published version of the manuscript.
Data Availability Statement
The National Health and Nutrition Examination Survey dataset is publicly available at the National Center for Health Statistics of the Centers for Disease Control and Prevention (https://www.cdc.gov/nchs/nhanes/index.htm).