ABSTRACT
Background
While many studies have identified steatotic liver disease (SLD) as a risk factor for kidney stone disease (KSD), the impact of the severity of steatosis has not been clearly elucidated in the context of other metabolic risk factors for KSD. This cross‐sectional population‐based study of a large inpatient database sought to investigate the association between KSD and SLD.
Methods
We queried the National Inpatient Database between 2016 and 2020 to identify patients with urolithiasis as well as patients with SLD, and identify other risk factors for stone disease, such as obesity, type II diabetes, and gout using ICD10 codes. Logistic regression was computed for strength and significance of the relationship between both SLD severity levels and KSD, in univariate and multivariate regression adjusted for patient characteristics and comorbidities burden. All statistical analyses were performed using SAS Enterprise Software 9.4.
Results
Odds of being a kidney stone former were significantly higher in patients with MASLD and MASH than in patients without liver injury in the general hospitalized population. Analysis performed in a cohort of hospitalizations that included BMI identifiers showed that this association of both degrees of SLD with KSD was more pronounced than that with diabetes and gout. Finally, comparing both forms of disease severity head‐to‐head, MASLD was found to have a stronger association with KSD than MASH.
Conclusion
Patients with SLD were found to have a higher prevalence of KSD. The more pronounced association in MASLD and the lower‐than‐expected contribution of other conditions involving dysregulation of metabolic homeostasis such as gout or diabetes highlights the central role of SLD in KSD pathogenesis.
Keywords: comorbidities, demographic factors, kidney stone disease (KSD), logistic regression, metabolic dysfunction associated liver disease (MASLD), metabolic dysfunction associated steatohepatitis (MASH), prevalence
1. Introduction
Kidney stone disease (KSD) is a highly recurrent condition [1]. It affects between 10% and 20% of the population and has been increasing in prevalence over the last two decades [2, 3]. The recurrence of KSD poses significant challenges beyond its substantial economic impact, estimated in the billions of dollars [4]. In addition to causing considerable physical discomfort and pain for affected individuals [5], KSD's recurrence can lead to chronic kidney disease and result in kidney failure [6]. Besides the demographic characteristics, multiple independent risk factors underlying nephrolithiasis have been described in large cohorts, such as smoking, hyperuricemia, and proteinuria [7].
Depending on the operational criteria used, metabolic syndrome (MetSd) is characterized by the co‐existence of prediabetes/insulin resistance, visceral obesity, dyslipidemia, and hypertension. It affects approximately one‐third of the U.S. population and continues to increase in prevalence [8]. Multiple components of the MetSd, particularly obesity and T2D, have been associated with an increased risk of KSD [9, 10].
Among the complications of the MetSd is the deposition of fat within the liver parenchyma, called steatotic liver disease (SLD) affecting 27.8% of U.S. adults [11, 12]. Fatty liver disease is defined as the deposition of at least 5% triglycerides in the hepatocytes, and it can range from the simple presence of the lipids within the liver parenchyma (metabolic dysfunction associated liver disease or MASLD) to an inflammatory condition known as metabolic dysfunction associated steatohepatitis (MASH), which is a major risk factor for fibrosis, cirrhosis of the liver, and hepatocellular carcinoma [13].
SLD is associated with an increased risk of urolithiasis [14]. However, Mendelian randomization was unable to ascertain a causal relation in KSD pathogenesis [15]. This discrepancy may be attributed to the limitations of genetic studies in elucidating dietary‐induced metabolic pathologies. Additionally, these studies did not account for established KSD risk factors. The role of metabolic disease of the liver such as SLD in KSD pathogenesis remains understudied, with only small‐scale investigations conducted thus far [14].
The increasing global incidence of kidney stone disease and hepatic steatosis, especially in the US, necessitates a thorough evaluation of both conditions to inform future pathophysiological research and clinical interventions. Our primary objective was to describe the association between kidney stone disease KSD and steatotic liver disease among all hospitalization records and to assess whether this relationship remains significant after adjusting for other metabolic syndrome components identified as KSD risk factors. By stratifying our analysis based on patients' body mass index (BMI) categories, we intended to control for patients' weight and other established metabolic risk factors known to affect KSD pathogenesis. Finally, by comparing MASLD and MASH, we aimed to establish which of the two conditions had a more pronounced association with KSD.
2. Methods
2.1. Data Source
The Healthcare Cost and Utilization Project's (HCUP) National Inpatient Sample (NIS) database is maintained by the Agency for Healthcare Research and Quality. This US database lists multiple aspects of an in‐hospital stay, with variables related to the patients' characteristics (diagnoses and performed procedures) and those related to the hospitals themselves. This study evaluated all hospitalization records from the NIS between 2016 and 2020. The analysis accounted for administrative stratification of participating hospitals, which encompass hospitals' census region location and teaching status (rural, urban non‐teaching and urban teaching) and number of beds (small, medium, large).
Because of the deidentified nature of the NIS database, this study was considered exempt from requiring an IRB review by the Northwell Health Institutional Review Board. This project was conducted in accordance with the Declaration of Helsinki.
2.2. Study Design
Using International Classification of Disease, 10th Edition (ICD‐10 codes), the NIS was queried to identify adult patients (≥ 18 years) between 2016 and 2020 with diagnoses of MASLD or MASH, and KSD. Participants with acute or chronic liver disease as well as hepatic failure were excluded. In other words, a hospitalization record that included both NAFLD and liver fibrosis/cirrhosis was excluded.
In the first part of the study, all records across the five years were pooled and screened to identify hospitalization records encoding KSD. Patients with MASLD and MASH were identified separately based on ICD10 codes. For the sake of this study, the MASLD entity encompassed ICD10 NAFLD encoded patients after excluding NASH, fibrosis, and cirrhosis, which presumably kept us with the least severe form of SLD known as MASLD, which had no identified ICD10 code.
In the second part of the study summarized in a flow diagram (Figure 1), only a subset of all records was extracted based on the presence of an ICD10 code identifier for a BMI class to account for weight. Indeed, each BMI has a separate ICD10 code. This cohort was then subdivided into 5 groups depending on their BMI in kg/m2: < 25, [16, 17, 18, 19, 20, 21], [22, 23, 24, 25, 26, 27], > 41. Presence or absence of KSD, gout, type II diabetes mellitus, and smoking status was ascertained binarily. Finally, SLD status was assessed as a continuum by dividing liver disease into 3 categories: no hepatic steatosis, MASLD, or MASH.
FIGURE 1.
Flow diagram of the second part of the study design. NIS databases were pooled between 2016 and 2020. From this five years records were excluded patients younger than 18 years as well as with any form of acute or chronic liver disease. From this pool of hospitalization were extracted patients with known BMI and subsequently categorized into 5 categories. Using ICD10 codes for NAFLD and NASH, patients with known BMI were classified as having MASH, MASLD or being free of hepatic steatosis. Were finally noted demographic and socio‐economic characteristics as well as presence or absence of kidney stone disease, gout, type II diabetes and smoking status.
In the third part of the study, to compare MASLD and MASH association with KSD, only records of adult patients with either MASLD or MASH were extracted and any records with overlapping diagnoses were excluded. Both groups were compared with each other to evaluate their association with KSD while accounting for gout, type II diabetes mellitus, obesity, and smoking, as well as basic socio‐demographic covariates.
In summary, after exploring the association in the general hospitalized population, subsequent analysis focused on records with known BMIs and then exclusively on records of patients with known hepatic steatosis to compare MASLD to MASH. These analyses accounted for demographic and socioeconomic factors and the comorbidities classically related to an increased risk of KSD.
2.3. Variables of Interests
Patient's variables of interest included age, sex, race, income quartile, primary expected payer, and the admitting hospital's geographic census, which was also included in the analysis.
From the CCSR (Clinical Classifications Software Refined) category relating to liver disease and hepatic failure, any records containing ICD10 codes pertaining to this category were excluded except for two: NAFLD and NASH, which were considered to respectively represent MASLD and MASH in the new nomenclature. KSD status was extracted based on ICD10 codes relative to kidney and genitourinary tract calculi.
In a subset of the population where ICD10 codes for different BMI's were available, we explored the association of BMI with KSD. ICD10 codes specific to obesity were used to identify which records had obesity as an encoded comorbidity in the third part of the study instead of reliance on BMI.
Type II diabetes, gout, and smoking were included as covariates of interest, due to their previously described association with KSD.
All ICD10 codes related to our variables of interest are presented in Table S1.
2.4. Statistical Analysis
Analyses were conducted using the complex sample feature of SAS Software. SAS Survey Procedures allowed us to account for both hospital stratification (NIS_STRATUM) and clustering (HOSP_NIS) and for discharge weights (DISCWT), these factors being provided yearly in the NIS database.
Age is presented as mean with standard errors, and the Kruskal‐Wallis test is used to compare this continuous variable between the groups of interest. Categorical variables are presented as percentages with standard errors and were compared using a second‐order Rao‐Scott Chi‐Square Test.
Binary logistic regressions were computed for strength and significance of the relationship between our predictor variables and our outcome of interest: presence or absence of KSD.
Univariate analysis was initially performed. Multivariate logistic regression was then used to assess whether MASLD or MASH is independently associated with KSD occurrence. Absence of liver steatosis was used as a reference, and adjustments were made for demographic variables such as age, sex, race, socio‐economic markers as well as metabolic comorbidities of interest. Results are presented as Odds Ratio (OR) estimates with 95% confidence intervals.
A 2‐sided p‐value of < 0.05 was considered statistically significant.
All statistical analyses were performed using the SAS Enterprise Software, Version Studio 9.4 (SAS Institute Inc., Cary, NC).
3. Results
3.1. KSD In the General Hospitalized Population
General characteristics of KSD admissions are summarized in Table 1. Individuals with KSD were found to be on average older compared to those without KSD. Males represent a larger proportion of the KSD population (51.06%) compared to females (48.93%). The majority of KSD cases occur in individuals identifying as white (73.39%). Other races also showed differences, with Native Americans and Asians showing lower prevalence rates.
TABLE 1.
Baseline characteristics of the general hospitalized population based on KSD and liver steatosis status.
| Characteristics | KSD (−) | KSD (+) | p | |
|---|---|---|---|---|
| Weighted n | 141 458 993 | 1 233 165 | ||
| Age (years) | 57.87 ± 0.04 | 60.90 ± 0.05 | < 0.0001 | |
| Gender (%) | < 0.0001 | |||
| Male | 41.98 ± 0.06 | 51.06 ± 0.11 | ||
| Female | 58.02 ± 0.06 | 48.93 ± 94 | ||
| Race (%) | < 0.0001 | |||
| White | 66.97 ± 0.21 | 73.39 ± 0.24 | ||
| African American | 15.48 ± 0.14 | 10.18 ± 0.13 | ||
| Hispanic | 11.13 ± 0.14 | 11.02 ± 0.18 | ||
| Asian or Pacific Island | 2.79 ± 0.05 | 2.21 ± 0.07 | ||
| Native American | 0.62 ± 0.02 | 0.47 ± 0.02 | ||
| Other | 2.99 ± 0.06 | 2.73 ± 0.07 | ||
| MASLD (%) | 1.10 ± 0.006 | 3.64 ± 0.041 | < 0.0001 | |
| MASH (%) | 0.12 ± 0.001 | 0.24 ± 0.010 | < 0.0001 |
Note: After pooling NIS databases from 2016 to 2020, hospital records were categorized based on the presence or absence of kidney stone disease (KSD). We present age distributions as means with standard errors and gender, race as well as both forms of steatotic liver disease, metabolic dysfunction associated liver disease (MASLD) or metabolic dysfunction associated steatohepatitis (MASH) as percentages with standard errors of percentages. p‐values are from second order chi‐square (Rao‐Scott Chi‐Square Test).
Logistic regression analysis modeling for the presence of KSD based on hepatic steatosis status is presented in Table 2. A significant association was detected between the presence of KSD and fatty liver disease, using both univariate and multivariate models. The latter showed a strong association with an OR of 3.38 [3.30–3.45], and 1.96 [1.80–2.13] for MASLD and MASH respectively (p < 0.0001).
TABLE 2.
Binary logistic regression models for presence of KSD based on steatotic status.
| MASLD | MASH | |
|---|---|---|
| Univariate regression | 3.39 [3.31–3.46] | 1.99 [1.83–2.16] |
| Multivariate regression | 3.38 [3.30–3.45] | 1.96 [1.80–2.13] |
Note: dds ratios of the association between MASLD and MASH and KSD are the results of binary univariate and multivariate logistic regression. The univariate regression, includes steatotic liver disease as the only variable. SLD Multivariate regression is adjusted to age, gender and race. They are presented with a 95% Confidence Interval.
3.2. KSD in Hospitalized Patients With Known BMI
Extraction of the cohort with known BMI is presented in Figure 1 and its characteristics summarized in Table 3. Patients in higher BMI categories were noted to be younger and more predominantly female. White patients constituted most patients across the 5 categories. As the BMI category increased, so did the prevalence of type II diabetes and gout, ranging respectively from 18.75% ± 0.07% and 0.18% ± 0.0045% when the BMI was less than 25 kg/m2 to 46.41% ± 0.08% and 0.30% ± 0.0048% when the BMI was above 41 kg/m2. Similarly, the prevalence of MASLD rose from 0.79% ± 0.01% to 3.15% ± 0.03% and that of MASH from 0.06% ± 0.003% to 0.43% ± 0.009%.
TABLE 3.
Characteristics of hospital records with known BMI value.
| BMI category | BMI < 25 | BMI [16, 17, 18, 19, 20, 21] | BMI [22, 23, 24, 25, 26] | BMI [27, 37–40] | BMI > 41 | ||
|---|---|---|---|---|---|---|---|
| Weighted n | 5 125 909 | 2 145 540 | 5 235 734 | 4 985 379 | 9 381 903 | ||
| Age (years) | 68.27 ± 0.05 | 65.22 ± 0.06 | 61.01 ± 0.05 | 58.02 ± 0.05 | 54.77 ± 0.04 | < 0.001 | |
| Gender (%) | < 0.001 | ||||||
| Male | 46.26 ± 0.07 | 50.28 ± 0.10 | 47.50 ± 0.09 | 41.54 ± 0.08 | 33.42 ± 0.06 | ||
| Female | 53.74 ± 0.07 | 49.72 ± 0.10 | 52.50 ± 0.09 | 58.46 ± 0.08 | 66.58 ± 0.06 | ||
| Race (%) | < 0.001 | ||||||
| White | 68.74 ± 0.25 | 67.06 ± 0.35 | 68.67 ± 0.26 | 67.72 ± 0.24 | 66.02 ± 0.23 | ||
| African American | 16.95 ± 0.19 | 15.41 ± 0.26 | 15.36 ± 0.19 | 17.08 ± 0.18 | 20.80 ± 0.19 | ||
| Hispanic | 7.47 ± 0.12 | 11.87 ± 0.24 | 11.32 ± 0.18 | 11.02 ± 0.04 | 9.47 ± 0.13 | ||
| Asian or Pacific Island | 3.68 ± 0.08 | 2.21 ± 0.05 | 1.49 ± 0.04 | 1.11 ± 0.04 | 0.82 ± 0.04 | ||
| Native American | 0.49 ± 0.02 | 0.48 ± 0.02 | 0.57 ± 0.02 | 0.63 ± 0.02 | 0.71 ± 0.02 | ||
| Other | 2.66 ± 0.07 | 2.97 ± 0.09 | 2.56 ± 0.06 | 2.44 ± 0.06 | 2.17 ± 0.05 | ||
| Hospital division (%) | < 0.001 | ||||||
| New England | 4.98 ± 0.18 | 5.13 ± 0.27 | 4.57 ± 0.19 | 4.62 ± 0.15 | 4.39 ± 0.12 | ||
| Middle Atlantic | 13.93 ± 0.28 | 13.08 ± 0.52 | 12.93 ± 0.32 | 13.15 ± 0.25 | 13.29 ± 0.23 | ||
| East North Central | 17.39 ± 0.31 | 17.30 ± 0.41 | 19.29 ± 0.33 | 18.79 ± 0.31 | 18.83 ± 0.25 | ||
| West North Central | 6.38 ± 0.19 | 6.04 ± 0.21 | 6.91 ± 0.26 | 7.19 ± 0.20 | 7.72 ± 0.15 | ||
| South Atlantic | 20.61 ± 0.27 | 22.33 ± 0.43 | 20.97 ± 0.33 | 20.64 ± 0.27 | 20.12 ± 0.23 | ||
| East South Central | 6.84 ± 0.17 | 5.55 ± 0.19 | 6.07 ± 0.18 | 6.32 ± 0.15 | 7.74 ± 0.16 | ||
| West South Central | 10.75 ± 0.19 | 11.40 ± 0.26 | 10.74 ± 0.21 | 11.09 ± 0.18 | 11.71 ± 0.16 | ||
| Mountain | 5.20 ± 0.13 | 5.01 ± 0.15 | 5.05 ± 0.13 | 5.21 ± 0.11 | 5.09 ± 0.11 | ||
| Pacific | 13.90 ± 0.24 | 14.13 ± 0.33 | 13.45 ± 0.25 | 12.97 ± 0.22 | 11.09 ± 0.19 | ||
| Medicare Coverage (%) | 67.01 ± 0.13 | 59.43 ± 0.19 | 50.81 ± 0.14 | 45.81 ± 0.12 | 43 ± 0.10 | < 0.001 | |
| Income quartile (%) | < 0.001 | ||||||
| Quartile 1 | 31.53 ± 0.25 | 29.99 ± 0.31 | 29.53 ± 0.25 | 30.58 ± 0.23 | 34.19 ± 0.22 | ||
| Quartile 2 | 25.71 ± 0.17 | 26.06 ± 0.22 | 26.56 ± 0.17 | 27.38 ± 0.16 | 28.20 ± 0.16 | ||
| Quartile 3 | 22.91 ± 0.15 | 23.98 ± 0.20 | 24.70 ± 0.17 | 24.48 ± 0.15 | 23.15 ± 0.15 | ||
| Quartile 4 | 19.85 ± 0.25 | 19.96 ± 0.35 | 19.20 ± 0.25 | 17.55 ± 0.21 | 14.45 ± 0.17 | ||
| Smoking (%) | 23.06 ± 0.10 | 16.79 ± 0.11 | 16.78 ± 0.08 | 15.80 ± 0.06 | 14.37 ± 0.05 | < 0.001 | |
| Gout (%) | 0.18 ± 0.0045 | 0.30 ± 0.0089 | 0.28 ± 0.0058 | 0.28 ± 0.0058 | 0.30 ± 0.0048 | < 0.001 | |
| Type II diabetes (%) | 18.75 ± 0.07 | 34.72 ± 0.16 | 38.57 ± 0.11 | 42.18 ± 0.09 | 46.41 ± 0.08 | < 0.001 | |
| KSD (%) | 0.96 ± 0.01 | 1.11 ± 0.02 | 1.06 ± 0.01 | 1.02 ± 0.01 | 1.03 ± 0.01 | < 0.001 | |
| Liver Status (%) | < 0.001 | ||||||
| No documented steatosis | 99.14 ± 0.01 | 98.07 ± 0.03 | 97.48 ± 0.02 | 96.88 ± 0.03 | 96.40 ± 0.03 | ||
| MASLD | 0.79 ± 0.01 | 1.75 ± 0.02 | 2.28 ± 0.02 | 2.76 ± 0.02 | 3.15 ± 0.03 | ||
| MASH | 0.06 ± 0.003 | 0.17 ± 0.006 | 0.23 ± 0.005 | 0.34 ± 0.009 | 0.43 ± 0.009 | ||
| MASLD + MASH | 0.0006 ± 0.0002 | 0.005 ± 0.001 | 0.007 ± 0.001 | 0.010 ± 0.001 | 0.012 ± 0.001 |
Note: After pooling NIS databases from 2016 to 2020, only hospital records containing an ICD10 identifier for a BMI level were extracted, and the population was categorized into 5 categories of BMIs. We present age distributions, gender, race, geographic location, third payer coverage, household income by quartile among socio‐demographic factors. They are presented along comorbidities of interest in the study of KSD. Continuous variables are presented as mean with standard errors and categorical variables as percentages with standard errors of percentages. p‐values are from second order chi‐square (Rao‐Scott Chi‐Square Test).
Univariate unadjusted binary logistic regression results are presented in Table 4. These analyses showed a strong association of kidney stone disease with MASLD (OR 2.76 [2.66–2.86]) and MASH (OR 1.65 [1.45–1.88]). KSD also had a positive association with BMI, although the strength of association was noticeably not incrementally rising (1.15 [1.11–1.19]) for BMI between 25 and 30 and 1.06 [1.04–1.09] for BMI greater than 41 kg/m2 compared to a BMI less than 25 kg/m2. KSD was also significantly positively associated with gout (1.24 [1.08–1.44]) and T2D (1.10 [1.08–1.12]).
TABLE 4.
Covariate association with KSD on univariate binary logistic regression.
| Covariate | Category | Univariate unadjusted OR [95% CI] | p‐value from analysis of maximum likelihood estimates |
|---|---|---|---|
| Liver steatosis (Ref: No documented steatosis) | MASLD | 2.76 [2.66–2.86] | < 0.0001 |
| MASH | 1.65 [1.45–1.88] | < 0.0001 | |
| MASLD + MASH | 2.40 [1.29–4.48] | 0.0059 | |
| BMI (Ref: BMI < 25) | [16, 17, 18, 19, 20, 21] | 1.15 [1.11–1.19] | < 0.0001 |
| [22, 23, 24, 25, 26] | 1.11 [1.08–1.14] | < 0.0001 | |
| [36–40] | 1.06 [1.03–1.09] | < 0.0001 | |
| > 41 | 1.06 [1.04–1.09] | < 0.0001 | |
| Smoking | 0.90 [0.88–0.93] | < 0.0001 | |
| Gout | 1.24 [1.08–1.44] | 0.0027 | |
| Type II diabetes | 1.10 [1.08–1.12] | < 0.0001 |
Note: In this table are presented the results of the univariate logistic regression between different comorbidities or metabolic risk factors of KSD. Results are presented as odds ratio with 95% confidence intervals. p‐values result from the analysis of maximum likelihood estimates.
A multivariate binary logistic regression evaluated these factors while also adjusting for demographic and socio‐economic factors (Figure 2). White race was found to be positively associated with KSD while female gender had a decreased association (0.85 [0.83–0.86]). South Atlantic and Pacific regions had higher odds of KSD in comparison to the New England census division with OR 1.34 [1.26–1.41] and 1.23 [1.16–1.30] respectively. MASLD was again strongly associated with KSD (OR 2.66 [2.56–2.76]) whereas the odds of KSD with MASH were 1.60 [1.40–1.83]. KSD was negatively associated with smoking (0.90 [0.88–0.92]) and positively associated with gout (1.21 [1.04–1.40]) and with T2D (1.08 [1.06–1.10]).
FIGURE 2.
OR estimated in multivariate binary logistic regression for KSD in the cohort with known BMI. In this figure, we present the results of the multivariate binary logistic regression for the outcome of presence or absence of KSD in the cohort with known BMI. In this model, demographic and socio‐economic factors, as well as the comorbidities and medical conditions that were evaluated individually in univariate logistic regressions, are included. The reference group for the race was white patients, for the primary expected payer (PEP) those covered by Medicare, for the Census Division those living in New England, for the income those in the first quartile, for the BMI those with a BMI < 25, and for the liver steatosis status those that did not have liver steatosis. Results are presented as odds ratio with 95% confidence intervals. The c‐statistic for this model was 0.576.
3.3. Closer Look at Patients With SLD
In the last part of the study, we analyzed extracted records with a diagnosis of SLD (either MASLD or MASH) and the characteristics of this cohort are summarized in Table 5. The average age for patients with MASLD was 53.84 ± 0.05 years, whereas patients with MASH were slightly older, with an average age of 57.21 ± 0.13 years. Gender distribution indicated a higher prevalence of females in both groups, but more pronounced in the MASH group (62.21% ± 0.29% in MASH vs. 52.72% ± 0.11% in MASLD). White individuals constituted the majority in both groups. African Americans and Hispanics were more prevalent in the MASLD group (African American: 11.18% ± 0.13%, Hispanic: 16.47% ± 0.24%) compared to the MASH group (African American: 7.40% ± 0.17%, Hispanic: 12.63% ± 0.33%). Medicare was the primary payer for a significant portion of patients, more so in the MASH group. Private insurance was also a common payer (MAFLD: 35.03% ± 0.17%; MASH: 32.41% ± 0.34%). The distribution across income quartiles was relatively even, with slight variations. Quartile 1 had the highest representation in both groups (MASLD: 29.09% ± 0.26%; MASH: 29.17% ± 0.36%). Type II diabetes and obesity were significantly higher in MASH patients (Type II Diabetes: 51.16% ± 0.33%; Obesity: 45.89% ± 0.43%) than in MASLD patients (Type II Diabetes: 35.43% ± 0.10%; Obesity: 38.82% ± 0.17%). Kidney stone disease (KSD) was more prevalent among MASLD patients (2.80% ± 0.03%) compared to MASH patients (1.70% ± 0.07%).
TABLE 5.
General characteristics of patient with steatotic liver diseas.
| MASLD | MASH | |||
|---|---|---|---|---|
| Weighted N | 1 599 825 | 171 345 | ||
| Age | 53.84 ± 0.05 | 57.21 ± 0.13 | < 0.001 | |
| Gender | < 0.001 | |||
| Male | 47.28 ± 0.11 | 37.78 ± 0.29 | ||
| Female | 52.72 ± 0.11 | 62.21 ± 0.29 | ||
| Race | < 0.001 | |||
| White | 65.71 ± 0.29 | 74.63 ± 0.42 | ||
| African American | 11.18 ± 0.13 | 7.40 ± 0.17 | ||
| Hispanic | 16.47 ± 0.24 | 12.63 ± 0.33 | ||
| Asian or Pacific Island | 2.47 ± 0.06 | 1.76 ± 0.08 | ||
| Native American | 0.84 ± 0.03 | 0.98 ± 0.07 | ||
| Other | 3.31 ± 0.09 | 2.57 ± 0.15 | ||
| Hospital division | < 0.001 | |||
| New England | 4.42 ± 0.16 | 5.45 ± 0.26 | ||
| Middle Atlantic | 12.79 ± 0.27 | 11.99 ± 0.43 | ||
| East North Central | 15.10 ± 0.29 | 17.50 ± 0.39 | ||
| West North Central | 5.58 ± 0.16 | 8.87 ± 0.34 | ||
| South Atlantic | 21.74 ± 0.31 | 18.34 ± 0.41 | ||
| East South Central | 6.11 ± 0.19 | 8.50 ± 0.29 | ||
| West South Central | 10.87 ± 0.21 | 10.55 ± 0.46 | ||
| Mountain | 6.83 ± 0.17 | 6.20 ± 0.22 | ||
| Pacific | 16.55 ± 0.32 | 12.58 ± 0.38 | ||
| Primary expected payer | < 0.001 | |||
| Medicare | 33.68 ± 0.14 | 45.83 ± 0.39 | ||
| Medicaid | 20.74 ± 0.17 | 14.88 ± 0.28 | ||
| Private insurance | 35.03 ± 0.17 | 32.41 ± 0.34 | ||
| Self pay | 6.85 ± 0.09 | 3.89 ± 0.17 | ||
| No charge | 0.67 ± 0.03 | 0.30 ± 0.03 | ||
| Other | 3.03 ± 0.05 | 2.66 ± 0.10 | ||
| Income quartile | < 0.001 | |||
| Quartile 1 | 29.09 ± 0.26 | 29.17 ± 0.36 | ||
| Quartile 2 | 26.52 ± 0.19 | 28.23 ± 0.31 | ||
| Quartile 3 | 24.46 ± 0.18 | 25.23 ± 0.29 | ||
| Quartile 4 | 19.93 ± 0.26 | 17.37 ± 0.31 | ||
| Smoking | 21.82 ± 0.11 | 13.48 ± 0.20 | < 0.001 | |
| Gout | 0.27 ± 0.009 | 0.31 ± 0.03 | 0.1868 | |
| Type II diabetes | 35.43 ± 0.10 | 51.16 ± 0.33 | < 0.001 | |
| Obesity | 38.82 ± 0.17 | 45.89 ± 0.43 | < 0.001 | |
| KSD | 2.80 ± 0.03 | 1.70 ± 0.07 | < 0.001 |
Note: After pooling NIS databases from 2016 to 2020, only hospital records containing an ICD10 identifier for either MASLD or MASH were extracted. We present each group's mean age, gender, race, geographic location, third payer coverage, household income by quartile among socio‐demographic factors. They are presented along comorbidities of interest in the study of KSD such as smoking, gout, type II diabetes, obesity, and finally KSD. p‐values are from second order chi‐square (Rao‐Scott Chi‐Square Test).
Finally, a multivariate binary logistic regression was conducted for KSD in patients with SLD (Figure 3). Age was positively associated with KSD, with an odds ratio (OR) of 1.006 [95% CI: 1.004–1.008], indicating a slight increase in risk with each additional year. Female patients had lower odds of KSD compared to male patients (OR: 0.860 [95% CI: 0.823–0.897]), and non‐white ethnicities had significantly lower odds of KSD. Patients in the South Atlantic area had a higher association with KSD compared to those in the New England division with OR: 1.490 [95% CI: 1.316–1.686]. There were no significant differences in KSD risk across income quartiles 2, 3, and 4 compared to quartile 1. MASLD had a significantly positive association with KSD when compared to MASLD. In this cohort of SLD patients, gout and obesity were not significantly associated with KSD (OR of s1.083 [0.744–1.578] and 0.993 [0.948–1.039] respectively). Only type II diabetes and smoking retained their respective positive and negative associations with KSD (ORs of 1.110 [1.061–1.162] and 0.928 [0.877–0.981]).
FIGURE 3.
OR estimates in multivariate binary logistic regression for KSD with documented SLD. In this figure, we present the results of the multivariate binary logistic regression for the outcome of presence or absence of KSD in the population exclusively comprising patients with steatotic liver disease. The reference group for the race was white patients, for the primary expected payer (PEP) those covered by Medicare, for the Census Division those living in New England, and for the income those in the first quartile. MASLD served as a reference for patients with MASH. In this model were also included gout, type II diabetes, obesity, and smoking. Results are presented as odds ratio with 95% confidence intervals. The c‐statistic for this model is 0.571.
4. Discussion
This inpatient database analysis examined the associations between KSD and various forms of SLD on a large scale. The observed correlations warrant increased clinical vigilance in managing patients with these comorbidities. Furthermore, this study establishes a foundation for future prospective investigations to further elucidate these relationships. Our findings underscore the complexity of KSD, with various demographic factors such as age, gender, race, and conditions like hepatic steatosis, diabetes, and gout playing significant roles in the epidemiology of KSD.
Previous research has demonstrated an association between hepatic steatosis or non‐alcoholic fatty liver disease (NAFLD) and nephrolithiasis. However, these studies were limited by several factors. Notably, many earlier investigations—cross‐sectional studies conducted in Israel, Iran, and Korea—focused on incidental findings of fatty liver disease in patients undergoing imaging for flank pain to diagnose nephrolithiasis, potentially introducing selection bias [28, 29, 30]. Our study, by including anthropometric and demographic characteristics as well as comorbidities of interests, filled part of the knowledge gap and found a significant association between both forms of SLD and KSD, with MASLD exhibiting a particularly strong and consistent relationship to KSD.
Multiple investigations implicate metabolic syndrome‐induced physiological alterations as the principal mechanism underlying this association. These include lipid toxicity, alterations in urinary acidity, inflammatory reactions, and oxidative stress [31]. Oxidative stress and reactive oxygen species generation are hypothesized to directly contribute to nephrolithiasis by inducing membrane‐bound vesicles in damaged cells that can serve as sites for crystal nucleation, initiating stone formation [32, 33]. Epigenetic studies have elucidated the mechanistic link between non‐alcoholic fatty liver disease (NAFLD) and enteric hyperoxaluria. The proposed pathophysiological mechanism involves impaired hepatic oxalate detoxification in NAFLD patients [34]. The stronger association between MASLD and KSD may be attributed to reduced hepatic alanine glyoxylate aminotransferase expression, resulting in systemic hyperoxalemia and subsequent increased renal oxalate excretion, thereby promoting nephrolithiasis [35].
A stronger correlation was found in our study with MASLD than with MASH. This finding contrasts with previous cohort studies [36], though it is important to note the limited sample size of MASH patients in our cohort. Even when adjusted to epidemiological factors such as age, sex, and race, a strong association persisted. A more severe hepatotoxicity, as would be expected in MASH compared with MASLD, might alter cellular metabolism, potentially attenuating the epigenetic changes driving hyperoxaluria. However, this hypothesis warrants cautious interpretation, as no current evidence formally assessed hepatic alanine glyoxylate aminotransferase activity in MASH.
Of note, after adjusting for other variables, our analysis did not demonstrate a consistent incremental increase in the odds of KSD diagnosis with rising BMI values. An analysis of abdominal computed tomography scans revealed that kidney stone disease (KSD) maintained a significant association with fatty liver disease after adjusting for confounding variables. However, the association between KSD and visceral fat deposition lost statistical significance following similar adjustments [28]. While prospective evaluation of 3 large cohorts had demonstrated that BMI and weight gain led to a higher incidence of KSD in both sexes and across multiple age distributions [9], a genome‐wide association study combining two extensive genetic databases demonstrated that visceral adiposity, rather than BMI, elevated kidney stone disease (KSD) risk, mediated by increased serum calcium levels [16].
Aligned with previous studies, female sex was found to be less associated with kidney stone disease, both in the general population and among individuals with fatty liver disease [17]. While MASLD is more common in men, the risk of MASH and subsequent fibrosis was found to be disproportionately higher in women [18, 19]; this partially explains the sex distribution of hepatic steatosis in our hospitalized cohort. We found lower odds of KSD in women, in accordance with previous observations suggesting a potential protective effect of pre‐menopausal estrogen levels. This hormonal influence may mitigate the impact of hepatic steatosis on nephrolithiasis risk in females [20]. Estrogen's protective role against kidney stone formation may be twofold: primarily by inhibiting metabolic syndrome development, and secondarily by reducing bone turnover. These mechanisms result in decreased urinary calcium excretion and increased citrate excretion, collectively lowering lithogenic risk [21, 22]. However, a randomized control trial has shown an increased risk of nephrolithiasis associated with estrogen supplementation in the post‐menopausal population [23]. Finally, geography and climate in the context of global warming are suggested to contribute to KSD [24]. In the hepatic steatosis cohort, the South Atlantic and Mountain census division exhibited higher KSD odds compared to New England, aligning with expectations based on their warmer climates.
This study has multiple limitations, some of which are inherent to the cross‐sectional nature of the NIS, which limits the inferences that can be made. Notably, MASLD and MASH did not have a dedicated ICD10 code at the time of the study [25]. The less severe form of SLD was labeled as MASLD, defined in this study as ICD10 ‘K760’ (NAFLD encoded patients after excluding NASH, Fibrosis, and Cirrhosis) and MASH as ICD10 code ‘K7581’; this study was unable to differentiate the specific associations between MASLD and various kidney stone compositions, such as calcium oxalate, calcium phosphate, or uric acid stones. Some patients with SLD may have that condition not encoded in the database and could have been classified incorrectly as “no hepatic steatosis.” Furthermore, ICD‐10 codes lack sufficient accuracy to distinguish between SLD severity levels. Accurate quantification of hepatic steatosis and fatty involvement, obtainable only through liver biopsy, would be necessary to establish specific thresholds associated with elevated kidney stone disease risk. The acquisition of the variables of interest, based on ICD10 codes, is dependent on the accuracy of providers and could be prone to errors of documentation. Among the limitations inherent to the NIS database, some patients with multiple admissions (either at the same hospital or at two different hospitals) might have counted as multiple separate admission records. The study lacked data on patients' outpatient medication regimens or specific inpatient treatments with potential nephroprotective effects, such as potassium citrate or thiazide diuretics, which could influence kidney stone formation [26]. Similarly, hepatotoxic drugs or medications that could affect liver metabolism could not be assessed. The massive impact of diet on the risk of KSD could explain the modest discriminatory power of the regression models as based on the c‐statistics [27].
5. Conclusion
The findings from this comprehensive analysis highlight the complex interplay between KSD and the various demographic and health factors, particularly MASLD. The robust association between MASLD and KSD further confirms the suspected potential for shared pathophysiological mechanisms. Interestingly, our study found a stronger correlation with MASLD than with MASH and showcased the potential presence of stronger contributing factors to this association than the simple presence of metabolic disease.
This study's clinical significance goes beyond identifying the relationship between SLD and KSD. It adds to an accumulating body of evidence suggesting clinicians go beyond BMI as a surrogate for clinical obesity in the prediction of adverse events in patients. It allows for a reappraisal of demographic factors as factors modulating KSD occurrence. Finally, our study highlights the complex pathophysiology underlying lithogenesis and shows the impact of factors usually unmeasured in clinical practice on KSD; factors such as diet, microbiome, and lifestyle factors such as exercise and sleep are, in our opinion, behind the gap in predictive modeling, as was shown through the c‐statistic's mild discriminatory ability when only accounting for the variables we used. These results underscore the need for tailored screening and management strategies for individuals with SLD, who are at an increased risk of developing KSD. Further research is required to elucidate the complex pathophysiological mechanisms linking these conditions and identify novel biochemical and metabolic contributors to this association.
Consent
The authors have nothing to report.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Table S1: ICD10 codes used for data extraction.
Habib T., Zaidan N., Jaber K., et al., “Association of Kidney Stone Disease With Metabolic Dysfunction Associated Liver Disease and Metabolic Dysfunction Associated Steatohepatitis: A National Inpatient Sample Study,” JGH Open 9, no. 9 (2025): e70280, 10.1002/jgh3.70280.
Funding: The authors received no specific funding for this work.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1: ICD10 codes used for data extraction.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.