
==== Front
Ren Fail
Ren Fail
Renal Failure
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Taylor & Francis

39230050
10.1080/0886022X.2024.2390566
2390566
Version of Record
Research Article
Nephrolithiasis and Urolithiasis
Relationship between the atherogenic index of plasma and the prevalence of kidney stones: insights from a population-based cross-sectional study
D. Wang et al.
Wang Dawei a#
Shi Feng b#
Zhang Dingguo c
Zhang Lin de
Wang Hui f
Zhou Zijian g
Zhu Yu a
a Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
b CAAC East China Aviation Personnel Medical Appraisal Center, Civil Aviation Shanghai Hospital, Shanghai, China
c Department of Urology, Shanghai Pudong New Area People’s Hospital, Shanghai, China
d The School of Public Health and Preventive Medicine, Monash University, Australia
e Suzhou Industrial Park Monash Research Institute of Science and Technology, Monash University, Suzhou, China
f Department of Urology, Shanghai Anting Hospital, Shanghai, China
g Department of Urology, Huashan Hospital, Fudan University, Shanghai, China
# Dawei Wang and Feng Shi contributed equally to this work.

Supplemental data for this article can be accessed online at https://doi.org/10.1080/0886022X.2024.2390566.

CONTACT Yu Zhu zy10478@rjh.com.cn Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197, Ruijiner Road, Shanghai, China
Zijian Zhou zjzhou21@m.fudan.edu.cn Department of Urology, Huashan Hospital, Fudan University, 12 Middle Wulumuqi Road, Shanghai
4 9 2024
2024
4 9 2024
46 2 23905664 6 2024
30 7 2024
6 8 2024
KnowledgeWorks Global Ltd.3 9 2024
published online in a building issue3 9 2024
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
2024
The Author(s)
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

Abstract

Objective

To investigate the association between atherogenic index of plasma (AIP) and kidney stones (KS) occurrence and recurrence.

Methods

Data were obtained from the National Health and Nutrition Examination Survey (NHANES) 2007–2014. Non-pregnant adults who provided complete information on AIP and KS were included in the analyses. AIP was calculated as log (triglyceride/high-density lipoprotein cholesterol). KS was ascertained with questionnaires. Weighted multivariable logistic regression model and restricted cubic spline (RCS) were applied to examine the associations between AIP and KS occurrence and recurrence.

Results

A total of 6488 subjects (weighted mean age 43.19 years and 49.26% male) with a weighted mean AIP of 0.66 were included in this study. The multivariable-adjusted OR for nephrolithiasis occurrence across consecutive tertiles was 1.00 (reference), 1.21 (95% CI: 0.90–1.62), and 1.85 (95% CI: 1.39–2.48), respectively. Moreover, each SD increment of AIP was associated with a 50% (OR:1.50, 95% CI: 1.25–1.81) higher risk of nephrolithiasis recurrence. RCSs showed significant and linear dose-response relationships between AIP and nephrolithiasis occurrence (p-overall = 0.006, p-nonlinear = 0.689) and recurrence (p-overall = 0.001, p-nonlinear = 0.848). The positive associations between AIP and nephrolithiasis occurrence and recurrence persisted in sensitivity analyses, suggesting the robustness of the results.

Conclusion

In the current US nationally representative cross-sectional study, AIP was positively associated with KS occurrence and recurrence.

Keywords

Kidney stone
atherogenic index of plasma
NHANES
cross-sectional study
dyslipidemia
The author(s) reported there is no funding associated with the work featured in this article.
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pmcIntroduction

Kidney stones (KS) are a highly prevalent disease globally, posing financial hardship for individuals and society [1]. Additionally, KS patients suffer a significant recurrence rate, with up to 50% of individuals experiencing a new stone episode within five years of the initial occurrence [2]. Previous studies have shown that KS is considered a systemic disorder closely related to coronary heart disease (CHD), metabolic syndrome (MS), and dyslipidemia [3–5]. For instance, in a study involving two cohorts of women, a history of KS was correlated with a significantly elevated risk of CHD (incidence rate, 754 vs 514 per 100,000 person-years) [6]. Likewise, Bamberger et al. found that MS was independently associated with the risk of stone formation based on a retrospective study including 495 patients [7]. Another study examining the visceral adiposity index (VAI), which incorporates triglycerides (TG), found a significant association with the risk of KS occurrence and recurrence [8]. Also, a case-control study showed that total cholesterol (TC) and TG levels were significantly higher in stone formers compared with the control group [9].

Atherogenic index of plasma (AIP) is a numerical value and a valuable marker that helps in evaluating the risk of CHD and MS, calculated based on the ratio of TG to high-density lipoprotein cholesterol (HDL-C), specifically using the formula: AIP = log(TG/HDL-C) [10]. Compared to traditional single lipid indicators, AIP can more sensitively reflect the extent of CHD, MS, or dyslipidemia [11]. A meta-analysis demonstrated that increased AIP is an independent prognostic factor in patients with CAD [12]. Also, a 9‑year longitudinal study identified AIP as a predictor for MS in Taiwan citizens [13]. Consistently, another 15-year prospective study demonstrated that AIP might be a strong and independent predictor for MS in an urban Chinese population [14].

Given the established association between MS and an increased risk of KS, it’s plausible to consider that AIP might correlate with KS risk. Investigating this association could provide insights into whether AIP could serve as a marker for predicting the risk of KS, especially in individuals with MS or hyperlipidemia. To address this research gap, our study examined the associations between AIP and KS based on the National Health and Nutrition Examination Survey (NHANES).

Methods

Study population

Nation Health and Nutrition Examination Survey (NHANES) is a nationally representative program of surveys examining approximately 5000 non-institutional civilians in the United States each year. We used data from NHANES 2007–2014 cycles and a total of 21,656 non-pregnant adults (aged 20–<80 years) were preliminarily selected. After excluding 58 subjects without kidney stone information and 12,017 subjects without TG/HDL-C measurements, 9581 adults were left for eligibility screening. Afterward, 1,710 subjects taking prescribed medicine for cholesterol, 559 subjects with zero survey weights, and 824 subjects with missing covariates were excluded. Missing numbers and proportions of covariates are shown in Table S1. Finally, 6488 non-pregnant adults were included in the final analyses. The flow of eligible participants selection is displayed in Figure 1.

Figure 1. The Flow of eligible participants selection.

Exposure and outcomes definitions

Blood specimens were processed, stored frozen, and shipped to the University of Minnesota for lipid profile measurements. The atherogenic index of plasma (AIP) was calculated as log [TG (triglyceride)/HDL-C (high-density lipoprotein cholesterol)] with TG and HDL-C expressed in mg/dL [15]. Kidney stones were ascertained according to individual responses to two items, including ‘Have you/Has sample person (SP) ever had kidney stones?’ and ‘How many times have you/has SP passed a kidney stone?’, in the questionnaire. The participants who answered yes to the item ‘Have you/Has sample person (SP) ever had kidney stones?’ were considered to have nephrolithiasis. Participants who have experienced two or more times of passing kidney stones were considered to have a recurrence of kidney stones.

Assessment of covariates

Information on age, sex, race/ethnicity (non-Hispanic white, non-Hispanic black, Mexican American, others), education attainment (under high school, high school, above high school), smoking status (never, former, current), and alcohol drinking status (never, former, current) were obtained with in-home questionaries. Anthropometric information was collected by trained health technicians, and body mass index (BMI) was calculated as weight (kg) divided by height (m) squared. Those with BMI ≥ 30.0 kg/m2 were considered as obese. Hypertension was defined as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, physician-diagnosed hypertension, or currently taking prescribed medicine for hypertension. Diabetes was defined as fasting plasma glucose (FPG) ≥ 126 mg/dL, glycated hemoglobin A1c (HbA1c) ≥ 6.5%, oral glucose tolerance test (OGTT) two-hour glucose ≥ 200 mg/dL, self-reported physician-diagnosed diabetes, use of insulin or oral hypoglycemic medication. Hyperuricemia was defined as serum uric acid ≥ 7 mg/dL for males and ≥ 6 mg/dL for females [16,17].

Statistical methods

Participants were categorized according to the tertiles of AIP. Continuous variables were expressed with weighted means and standard errors (SEs), and categorical variables were expressed with numbers and weighted percentages. Population characteristics across AIP tertiles were compared with linear regression for continuous variables and logistic regression for categorical variables. Weighted mean and 95% confidence intervals (CIs) according to population characteristics were also calculated and compared with linear regression.

Multivariable logistic regression model was applied to investigate the associations of AIP with risks of nephrolithiasis and nephrolithiasis recurrence. Variance inflation factors (VIFs) of independent variables were calculated to assess the multicollinearity, and VIF of 10 was used as the threshold of multicollinearity. As shown in Table S2, all VIFs were <10. Two multivariable models were developed. In model 1, we adjusted age (<50, ≥50 years), sex (male, female), and race/ethnicity (non-Hispanic white, others). In model 2, we further adjusted for obesity (yes, no), education attainment (above high school, high school and below), current smoking (yes, no), current drinking (yes, no), hypertension (yes, no), diabetes (yes, no), hyperuricemia (yes, no), and LDL-C (mg/dL). Linear trend was assessed by introducing medians of AIP tertiles as continuous variables into the model. Multivariable-adjusted ORs with 95% CIs for nephrolithiasis and nephrolithiasis recurrence associated with per SD increment in AIP were additionally calculated. Restricted cubic splines (RCS) with 3 knots (5th, 50th, and 95th percentiles) were further plotted to examine the dose-response relationships between AIP and nephrolithiasis and nephrolithiasis recurrence and the reference value was set at the 10th percentile.

To examine whether confounders, including age, sex, race/ethnicity, obesity, education attainment, smoking, alcohol drinking, hypertension, diabetes, and hyperuricemia modified the associations between AIP and risks of nephrolithiasis and nephrolithiasis recurrence, we performed stratified and interaction analyses. Potential multiplicative interaction between AIP and confounder was evaluated by introducing a multiplicative term between AIP and confounder as continuous variables into the multivariate models. Moreover, we calculated the relative excess risk due to interaction (RERI) to characterize interaction on the additive scale [18].

Five sensitivity analyses were performed to evaluate the robustness of our results. First, we used propensity score adjustment to cope with observed confounding. The propensity score for an individual was defined as the probability of having nephrolithiasis or nephrolithiasis recurrence given a set of covariates, including age, sex, race/ethnicity, obesity, education attainment, smoking, alcohol drinking, hypertension, diabetes, hyperuricemia, and LDL-C. Second, we further adjusted for estimated glomerular filtration rate, which was estimated from serum creatinine by the Modification of Diet in Renal Disease (MDRD) Study equation [19], in multivariable models. Third, we excluded extreme values of AIP (defined as <1st or >99th percentile) to examine whether our results were sensitive to these influential observations. Fourth, missing covariates were imputed under the missing at random assumption using multiple imputation (MI) with fully conditional specification (FCS) and random forest method. Finally, we calculated an assessment of potential residual confounding with E-values, defined as the minimum strength of association on the OR scale that an unmeasured confounder would need to have with both the exposure and the outcome [20,21].

All statistical analyses were conducted with R version 4.3.2 (The R Foundation for Statistical Computing, Vienna, Austria). Weighted survey analyses were conducted with R package ‘survey’ (version 4.4-2), RCS was conducted with R package ‘plotRCS’ (version 0.1.5), MI was conducted with R package ‘mice’ (version 3.16.0), and E-values were calculated with R package ‘EValue’ (version 4.1.3). Two-sided p-values < 0.05 were considered as statistical significance.

Results

Population characteristics

A total of 6488 non-pregnant US adults (weighted mean age 43.19 years and 49.26% male) were included in the current cross-sectional study. The weighted mean (SE) of AIP was 0.66 (0.01) in this population. Population characteristics according to AIP tertiles are shown in Table 1. Participants with higher AIP were older and more likely to be male, non-Hispanic white, poorly educated, current smokers, current nondrinkers, obese, hypertensive, diabetic, hyperuricemic, and had higher values of UA and LDL-C (all p < 0.001). Weighted means and 95% CIs of AIP according to population characteristics were displayed in Table S3. Male adults had much higher AIP values than females (p < 0.001). Moreover, poorly educated, current smoking and current non-drinking adults had higher values of AIP (all p < 0.001). Those with obesity, hypertension, diabetes, and hyperuricemia had much higher AIP values (all p < 0.001).

Table 1. Characteristics of study participants.

Characteristicsa	Total	Tertiles of AIP	p-valueb	
Tertile 1	Tertile 2	Tertile 3	
No. of subjects	6488	2165	2160	2163	 	
Age, years	43.19 (0.32)	42.21 (0.42)	42.82 (0.44)	44.58 (0.45)	<0.001	
BMI, kg/m2	28.51 (0.12)	25.93 (0.18)	28.64 (0.17)	31.05 (0.20)	<0.001	
UA, mg/dL	5.45 (0.03)	4.92 (0.04)	5.44 (0.04)	6.01 (0.05)	<0.001	
LDL-C, mg/dL	117.54 (0.60)	106.79 (0.86)	120.92 (0.91)	125.16 (1.02)	<0.001	
Male, n (%)	3174 (49.26)	761 (35.16)	1094 (50.94)	1319 (62.09)	<0.001	
Race/ethnicity, n (%)	 	 	 	 	<0.001	
 Non-Hispanic white	2802 (66.78)	893 (65.08)	937 (67.58)	972 (67.72)	 	
 Non-Hispanic black	1249 (11.39)	591 (16.23)	403 (10.96)	255 (6.85)	 	
 Mexican American	1067 (9.19)	240 (6.46)	372 (9.40)	455 (11.78)	 	
 Others	1370 (12.64)	441 (12.23)	448 (12.07)	481 (13.65)	 	
Education attainment, n (%)	 	 	 	 	<0.001	
 Under high school	1568 (16.76)	393 (11.79)	536 (17.67)	639 (20.95)	 	
 High school	1420 (20.98)	424 (19.28)	487 (21.07)	509 (22.63)	 	
 Above high school	3500 (62.26)	1348 (68.93)	1137 (61.26)	1015 (56.42)	 	
Smoking status, n (%)	 	 	 	 	<0.001	
 Never	3685 (56.96)	1404 (64.49)	1217 (55.89)	1064 (50.29)	 	
 Former	1373 (21.76)	397 (19.22)	460 (22.03)	516 (24.11)	 	
 Current	1430 (21.28)	364 (16.29)	483 (22.07)	583 (25.60)	 	
Alcohol drinking status, n (%)	 	 	 	 	<0.001	
 Never	809 (10.24)	272 (10.46)	293 (11.32)	244 (8.91)	 	
 Former	1018 (13.08)	253 (9.54)	333 (12.80)	432 (17.01)	 	
 Current	4661 (76.68)	1640 (80.00)	1534 (75.88)	1487 (74.07)	 	
Obesity, n (%)	2277 (33.53)	458 (18.62)	777 (33.60)	1042 (48.83)	<0.001	
Hypertension, n (%)	2104 (29.07)	559 (21.48)	680 (28.53)	865 (37.44)	<0.001	
Diabetes, n (%)	866 (9.39)	155 (5.26)	262 (7.91)	449 (15.16)	<0.001	
Hyperuricemia, n (%)	1249 (19.41)	237 (10.58)	393 (18.39)	619 (29.56)	<0.001	
AIP	0.66 (0.01)	−0.10 (0.01)	0.65 (0.01)	1.46 (0.01)	<0.001	
a Continuous variables were expressed as weighted means and standard errors and categorical variables were expressed as numbers and weighted percentages. The sums of percentages may not reach 100%, owing to the rounding of decimals and missing values.

b Characteristics across AIP tertiles were compared with linear regression for continuous variables and logistic regression for categorical variables.

Abbreviations: AIP: atherogenic index of plasma; BMI: body mass index; LDL-C: LDL-cholesterol; UA: uric acid.

Associations of AIP with nephrolithiasis and nephrolithiasis recurrence

In crude model, the OR for nephrolithiasis across consecutive tertiles was 1.00 (reference), 1.21 (95% CI: 0.90–1.62), and 1.85 (95% CI: 1.39–2.48), respectively (Table 2). After adjusting for age, sex, and race/ethnicity, adults in the highest AIP tertile were confronted with 77% (OR: 1.77, 95% CI: 1.34–2.33) greater risk of nephrolithiasis as compared with those in the lowest tertile (Table 2). In the full-adjustment model, the positive association between AIP and nephrolithiasis persisted, with OR being 1.49 (95% CI: 1.07–2.07) in the last tertile (Table 2). Moreover, each SD increment of AIP was associated with a 20% (OR:1.20, 95% CI: 1.04–1.38) higher risk of nephrolithiasis (Table 2). RCS consistently suggested a significantly positive and linear association between AIP and nephrolithiasis (p-overall = 0.006, p-nonlinear = 0.689) (Figure 2A). For nephrolithiasis recurrence, we also observed that increased AIP was associated with greater risk of nephrolithiasis recurrence (OR:1.62, 95% CI: 1.36–1.92). In the full-adjustment model, each SD increment of AIP was associated with a 50% (OR:1.50, 95% CI: 1.25–1.81) higher risk of nephrolithiasis recurrence (Table 2). We fitted the dose-response relationship between AIP and nephrolithiasis recurrence, and consistently found the positive association of AIP with nephrolithiasis recurrence (p-overall = 0.001) (Figure 2B). Additionally, there was no nonlinear relationship between AIP and nephrolithiasis recurrence (p-nonlinear = 0.848), as suggested by RCS (Figure 2B).

Figure 2. Associations of AIP with risks of (A) nephrolithiasis and (B) nephrolithiasis recurrence. Line represents multivariable-adjusted odds ratio, and shaded area represents 95% CI. Participants with extreme AIP (defined as <1st or >99th percentile) were excluded to minimize the potential impact of outliers. Models were adjusted for age (<50, ≥50 years), sex (male, female), race/ethnicity (non-Hispanic white, others), obesity (yes, no), education attainment (above high school, high school and below), current smoking (yes, no), current drinking (yes, no), hypertension (yes, no), diabetes (yes, no), hyperuricemia (yes, no), and LDL-C (mg/dL, continuous).

Table 2. Associations of AIP with risks of nephrolithiasis and nephrolithiasis recurrence.

Variable	Tertiles of AIP	p-trend	Per SD increment	
Tertile 1 (lowest)	Tertile 2	Tertile 3 (highest)	
Range	<0.34	0.34–<0.97	≥0.97	 	 	
Median	–0.02	0.66	1.39	 	 	
Nephrolithiasis	 	 	 	 	 	
 Case/control (%)	133/2032 (6.14)	158/2002 (7.31)	226/1937 (10.45)	 	 	
 Crude model	1.00 (reference)	1.21 (0.90–1.62)	1.85 (1.39–2.48)	<0.001	1.30 (1.15–1.48)	
 Model 1a	1.00 (reference)	1.18 (0.88–1.57)	1.77 (1.34–2.33)	<0.001	1.29 (1.14–1.45)	
 Model 2b	1.00 (reference)	1.09 (0.80–1.49)	1.49 (1.07–2.07)	0.015	1.20 (1.04–1.38)	
Nephrolithiasis recurrence	 	 	 	 	 	
 Case/control (%)	36/2129 (1.66)	39/2121 (1.81)	92/2071 (4.25)	 	 	
 Crude model	1.00 (reference)	1.21 (0.68–2.13)	3.23 (1.94–5.36)	<0.001	1.64 (1.38–1.95)	
 Model 1a	1.00 (reference)	1.17 (0.67–2.04)	3.09 (1.90–5.01)	<0.001	1.62 (1.36–1.92)	
 Model 2b	1.00 (reference)	1.08 (0.59–1.99)	2.59 (1.44–4.66)	0.001	1.50 (1.25–1.81)	
a Model 1 was adjusted for age (<50, ≥50 years), sex (male, female), and race/ethnicity (non-Hispanic white, others).

b Model 2 was further adjusted for obesity (yes, no), education attainment (above high school, high school and below), current smoking (yes, no), current drinking (yes, no), hypertension (yes, no), diabetes (yes, no), hyperuricemia (yes, no), and LDL-C (mg/dL, continuous).

Abbreviations: AIP: atherogenic index of plasma; CI: confidence interval; LDL-C: LDL-cholesterol; SD: standard deviation.

Furthermore, we conducted ROC analysis for these indicators to establish whether AIP offers distinct advantages over TG or HDL-C alone through area under the curve analysis. As shown in (Table S4), we found that AIP (AUC = 0.572) offers a sight advantage in KS formation. Similarly, AIP (AUC = 0.625) also offers a sight advantage in KS recurrence (Table S4).

Stratified, interaction, and sensitivity analyses

To examine whether confounder, including age, sex, race/ethnicity, obesity, education attainment, smoking, alcohol drinking, hypertension, diabetes, and hyperuricemia modified the associations between AIP and risk of nephrolithiasis and nephrolithiasis recurrence, we performed stratified and interaction analyses. Evidence of the effect modification of smoking in the association between AIP and nephrolithiasis was observed (Table S5). We observed no significant association between AIP and nephrolithiasis in nonsmoking adults (OR: 1.15, 95% CI: 0.92–1.45), while AIP was positively associated with nephrolithiasis in those who smoked when surveyed (OR:1.96, 95% CI: 1.44–2.67) (p-interaction = 0.04). No evidence of effect modification of confounders of interest in the AIP- nephrolithiasis recurrence association was observed (all p-interaction > 0.05) (Table S6). Apart from multiplicative interactions, we also examined whether there were potential additive interactions between AIP and confounders of interest. No additive interactions between AIP and potential modifiers were found, with none RERI achieved statistical significance (Tables S7–S8).

We performed several sensitivity analyses to evaluate the robustness of our results (Table S9–S12). In the model where the propensity score was adjusted, the ORs for nephrolithiasis and nephrolithiasis recurrence in the highest AIP tertile were 1.49 (95% CI: 1.11–1.99) and 2.56 (95% CI: 1.51–4.34), respectively (Table S9). After further adjusting for eGFR in the multivariable model, we observed a positive association between AIP and nephrolithiasis, with OR associated with per SD increment of AIP being 1.20 (95% CI: 1.05–1.38) (Table S10). Moreover, per SD increment of AIP was associated with a 51% (OR: 1.51, 95% CI: 1.25–1.82) greater risk of nephrolithiasis recurrence (Table S10). To examine whether our results were sensitive to influential observations, we excluded outliers of AIP. Subjects in the highest tertile of AIP were confronted with 50% (OR: 1.50, 95% CI: 1.07–2.11) greater risk of nephrolithiasis and 176% (OR: 2.76, 95% CI: 1.47–5.17) greater risk of nephrolithiasis recurrence (Table S11). Moreover, imputing missing covariates values with MI did not negate the significant positive AIP-nephrolithiasis and AIP- AIP-nephrolithiasis recurrence associations (Table S12). Finally, we calculated E-values to evaluate whether our results were sensitive to unmeasured confounding. The observed OR of 1.49 could be explained away by an unmeasured confounder that was associated with both AIP and nephrolithiasis by an OR of at least 2.34 each, above and beyond the measured confounders (Table S13). In addition, the E-value for the association between AIP and nephrolithiasis recurrence was 4.62 (Table S13). E-value sensitivity analyses suggested that it would take very strong confounding to negate the associations observed in our study.

Discussion

To the best of our understanding, this study represents the initial identification of a potential link between AIP values and increased risk of KS occurrence and recurrence, especially after adjusting for demographic factors. The observed positive associations persisted in sensitivity analyses, suggesting the robustness of our results.

Previous studies have found a significant association between TG levels and the risk of KS occurrence and recurrence [22]. A cross-sectional analysis of NHANES data found that higher levels of HDL-C were associated with a lower risk of KS occurrence [23]. Another longitudinal study from Taiwan found that hypertriglyceridemia significantly increased the risk of new KS cases, while high HDL-C offered protection [24]. As a measure reflecting the balance of blood lipids, AIP offers a more holistic assessment by considering all these parameters [25]. In the present study, we observed higher AIP was significantly associated with a greater risk of KS occurrence, even after adjusting confounders. A particularly strong association was observed in smoking adults, suggesting smoking may modify the AIP-KS occurrence relationship. This is consistent with the observation that increased AIP is associated with the risk of CHD and MS, suggesting that CHD/MS and KS might be co-etiological conditions, where smoking plays a vital role [26]. Future cohort studies should assess longitudinal AIP changes and their synergistic impact on KS with other lipid indicators.

Regarding KS recurrence, it has been reported that hypertriglyceridemia is associated with an increased risk for urolithiasis recurrence [27]. Similarly, Qin and others found that a higher triglyceride-glucose index is associated with an increased risk of KS recurrence [28]. Consistently, we identified a linear dose-response relationship between AIP and KS recurrence. In the full-adjustment model, each SD increment of AIP was associated with a 50% (OR:1.50, 95% CI: 1.25–1.81) higher risk of KS recurrence. Previous research on the AIP has highlighted its significance in MS and dyslipidemia across various conditions and patient demographics [29]. Moreover, in kidney disease, Huang et al. found that an elevated AIP index was associated with renal function decline based on the findings from a seven-year cohort study [30]. Dyslipidemia can lead to oxidative stress, inflammation, and hypertension—factors known to influence KS formation, which might be the potential mechanisms underlying AIP and the increased risk of KS occurrence and recurrence [31]. Simultaneously, dyslipidemia affects urinary solutes and chemistry [32]. Higher cholesterol increases urinary potassium and calcium, while lower HDL-C or higher TGs raise urinary sodium, oxalate, and UA, potentially increasing the concentration of stone-forming substances in urine [33,34]. These findings suggest that AIP influences not only metabolic risk but also impacts KS occurrence and recurrence through overall metabolic health, urinary lithogenic substances, and renal physiology.

This study draws data from a large-scale, nationally representative survey, lending the generalizability to the findings. Also, our study concurrently evaluated the occurrence and recurrence of KS, providing insights into the systemic risk assessment. However, the study faces several limitations. First, the study cannot establish causality between AIP and KS by cross-sectional design. Second, reliance on participants’ self-reported history for determining the presence of KS introduces the potential for recall bias, especially for the definition of KS recurrence, because the criterion for recurrence is typically based on imaging evidence rather than self-reported history in clinical practice. KS cases in our study were diagnosed with self-reported questionnaires; hence, the number of KS cases may be underestimated, and the risk estimates may be biased toward null. Third, our study did not include a separate analysis of the Asian population. Further research involving a larger population cohort, potentially in a multicenter study setting, would be beneficial for confirming these findings and ensuring broader applicability across diverse populations and settings.

Conclusions

In conclusion, our research found an independent positive association between AIP and KS, and higher AIP levels are asso­ciated with greater risks of nephrolithiasis occurrence and recurrence in this US nationally representative adult population. This finding provides valuable advice for cases of KS occurrence and recurrence to pay attention to lipid profile assessments.

Ethical approval and consent to participate

The ethics approval was made by the NCHS Research Ethics Review Board (ERB).

Supplementary Material

Supplementary Materials.docx

Authors’ contributions

WDW and ZY designed the study. ZZJ conducted the analysis. SF, WH and ZDG interpreted the results with the help of ZZJ. WDW drafted the paper. ZY and ZL critically revised the paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data of the present study are available from NHANES.
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