Skip to main content
Scientific Reports logoLink to Scientific Reports
. 2024 Nov 16;14:28302. doi: 10.1038/s41598-024-79419-7

Higher dietary insulin index is directly associated with the odd of kidney stones

Niloofar Sadat Maddahi 1,2, Danial Fotros 3, Mohammad Hassan Sohouli 3, Hassan Mozaffari-Khosravi 1,2, Sayyed Saeid Khayyatzadeh 1,2,
PMCID: PMC11569153  PMID: 39550437

Abstract

Kidney stones or Nephrolithiasis are the most common health condition associated with the urinary system. Dietary factors stand as important factors in the occurrence and development of kidney stones. This study aimed to examine the potential link between dietary insulin index (DII) and dietary insulin load (DIL) with prevalence of kidney stones. This cross-sectional study was conducted among adults aged 30 to 75 years in the Shahedieh district of Yazd, Iran, over the period of 2015–2016. DII and DIL were calculated using a validated semi-quantitative food-frequency questionnaire and mathematical formula. Diagnosis of kidney stones is made on the basis of information obtained from self-reported questionnaire (Yes/ No). To explore the association between DII and DIL with the odds of kidney stones, logistic regression was employed in crude and adjusted models. A total of 4,829 participants were included in this study. Individuals in the last quartile of DIL had 214% higher odds of kidney stones in the crude model (OR: 2.14, 95% CI: 1.62–2.83; P-trend < 0.001); this association was remained significant after adjustments for confounding variables (OR: 1.44, 95% CI: 1.04–1.97; P-trend: 0.019). There was a direct significant relationship between DII and odds of kidney stones among third and forth quartiles of DII (OR: 1.52, 95% CI: 1.16–1.98, P-trend = 0.002); but this association disappeared for adjusted models. Higher DII and DIL were associated with an increased odd of renal stones. Large longitudinal study is required to clarify these associations.

Keywords: Nephrolithiasis, Kidney stones, Dietary insulin load, Dietary insulin index, Hyperinsulinemia

Subject terms: Kidney diseases, Biochemistry, Endocrinology, Risk factors

Introduction

Nephrolithiasis, or kidney stones, is one of the most common conditions that affects the urinary system, and its prevalence and recurrence rates are steadily rising worldwide1. This condition can be linked to a higher likelihood of developing chronic kidney disease (CKD)2 and end-stage renal disease (ESRD)3 and incurring substantial healthcare expenses4. The formation of kidney stones is an intricate and multifaceted process that involves both intrinsic elements (age, sex, and inheritance) and external factors1,5, such as climate6, nutrition7, and medication8. Among all the external factors, diet and dietary factors stand as important factors in the occurrence and development of kidney stones9.

One of the aspects of diet is the induced insulin, which is stimulated by the food consumed. Postprandial hyperglycaemia and the resulting hyperinsulinemia are known to contribute to the onset of several chronic conditions, including cardiovascular disease10, diabetes, and metabolic syndrome11. The dietary insulin index (DII) is a new method for classifying foods based on how they affect insulin levels after a meal, compared to a reference food (similar to the glycaemic index, which uses glucose or white bread)12. Likewise, the dietary insulin load (DIL) is another measure that calculates the impact of a food on insulin levels by multiplying its DII value, energy content, and consumption frequency13. Prior research has demonstrated a correlation between DII and DIL with an increased likelihood of developing insulin resistance14 and metabolic syndrome13. Furthermore, researchers have investigated and recognized the relationships between metabolic syndrome, insulin resistance with kidney stones15,16. Nonetheless, the potential correlation between DII and DIL with kidney stones has not been previously investigated. To the best of our knowledge. Its first study to examine the association between DII and DIL with prevalence of kidney stones.

Materials and methods

Participants and study design

This is a cross-sectional analysis of the Shahedieh cohort study, which is a component of the large PERSIAN cohort study being carried out in several parts of Iran. The primary objective of the PERSIAN cohort research was to ascertain the possible risk variables associated with noncommunicable diseases among Iranian participants. This cohort study has been carried out by researchers from local institutions in collaboration with the Ministry of Health and Medical Education. Prior publication has provided comprehensive details on the study’s design, participants, and methods of data collection for the PERSIAN cohort17. To summarize, the Shahedieh cohort study commenced in the Shahedieh district of Yazd, Iran, over the period of 2015–2016. Initially, a letter of invitation was dispatched to 10,194 adult individuals who satisfied the specified requirements and resided in the Shahedieh area. 9,983 people willingly enrolled in the cohort trial. The inclusion criteria for this study were individuals who were of Iranian heritage, aged between 30 and 75 years, and had been living in the Shahedieh region for at least 9 months every year. Individuals were sent an invitation to the healthcare facility located in the Shahedieh area for the purpose of gathering data. The participants were instructed to come in a condition of fasting in order to gather biological samples. Following the collection of a blood sample from each participant, trained interviewers acquired the necessary data on sociodemographic variables, physical activity, and nutritional consumption.

Participants who were missing data for food intake (n = 177), as well as those who under- or over-reported calorie intake beyond the usual range of energy intake (800-4,200 kcal/d; n = 3,025), were eliminated after combining the information. Consequently, out of the original 9,983 participants, a total of 4,829 individuals were ultimately considered in the final analysis. Written informed permission was obtained from all individuals. The Shahedieh cohort research received approval from the Ethics Committee of Shahid Sadoughi University of Medical Sciences, located in Yazd, Iran.

Dietary assessment

The individuals’ dietary intakes were evaluated using a 120-item semi-quantitative food frequency questionnaire (FFQ), which inquired about their dietary habits throughout the previous year. This FFQ, which was specifically designed to assess long-term dietary consumption, was validated for the adult population in Iran18. FFQs were completed by trained interviewers during a face-to-face interview. The participants were asked two types of questions regarding each food item: (1) the frequency of food consumption (number of times per month, week, or day the food was consumed) in the previous year, and (2) the amount of the food that was typically consumed each time (portion size based on the standard serving sizes commonly consumed by Iranians). The stated intakes were converted to grams per day using household estimates of the ingested items. The daily nutrient consumptions for each person were estimated by applying the United States Department of Agriculture’s (USDA) national nutrient databank. The Nutritionist IV program (First Databank, San Bruno, CA, USA - modified for Iranian foods) was utilized to compute nutritional intakes19.

DII and DIL

The food insulin index (FII) is a mathematical formula used to rate different meals based on their effect on insulin levels. It calculates the increase in insulin levels over a two-hour period after consuming a 1000-kJ amount of the test food, compared to the increase in insulin levels after consuming a 1000-kJ portion of the reference food. The FII for each calorie-containing food was obtained from FFQ data using data published by Professor Jennie Brand-Miller of the University of Sydney, Australia20. In order to determine DIL, we initially approximated the insulin load of each item by employing the subsequent equation: Insulin load of a given food = insulin index of that food × amount of that food consumed (g/d) × energy content per 1 g of that food (g/d)21. DIL for each individual was determined by adding up the insulin load of all food items ingested over the course of the last year. Subsequently, the DII for each participant was calculated by dividing the DIL by their total calorie intake.

\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$IL_{{ave}} = \sum\limits_{{a = 1}}^{n} {II_{a} } \times Energy_{a} \times Frequency_{a}$$\end{document}

\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$II_{{ave}} = \frac{{\sum\limits_{{a = 1}}^{n} {\left( {II_{a} \times Energy_{a} \times Frequency_{a} } \right)} }}{{\sum\limits_{{a = 1}}^{n} {\left( {Energy_{a} \times Frequency_{a} } \right)} }}$$\end{document}

Anthropometric assessment

The anthropometric indices were measured in accordance with standard protocol and by trained investigator. Weight was measured using a digital scale (SECA, model 755, Germany) in a state of minimum clothing without shoes to the nearest 100 g. Standing height was assessed using a conventional stadiometer, with the exclusion of footwear, to the closest 0.5 cm. The Body Mass Index (BMI) was computed by dividing the weight in kilograms by the square of the height in meters. In order to minimize any potential inaccuracies or distortions in the measurements, anthropometric data were collected in the morning and after the individual had abstained from eating.

Biochemical assessment

Each participant in a condition of fasting (10–12 h of fasting) provided 25 mL of blood using Vacutainers (Greiner Bio-One International GmbH, Kremsmunster, Austria). The blood was subjected to centrifugation and divided into several portions, which were then labeled and stored in freezers at a temperature of -70 °C. Aside from the preserved samples, a small quantity of blood was utilized to quantify fasting blood glucose (FBG), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), aspartate aminotransferase (AST), and alanine aminotransferase (ALT) levels. The enzymatic colorimetric technique was employed to quantify FBG. TG levels were quantified using enzymatic colorimetric assays involving glycerol phosphate. The determination of HDL-C contents involved the precipitation of apo B-containing lipoproteins using phosphotungstic acid. The enzymatic reagents provided by Pars Azmoon, located in Tehran, Iran, were used for all measurements. These reagents were utilized in conjunction with an autoanalyzer system (Selectra E) manufactured by Vitalab in Holliston, the Netherlands.

Physical activity and other assessment

The International Physical Activity Questionnaire was administered to each participant through a face-to-face interview in order to assess their level of physical activity. The International Physical Activity Questionnaire yielded data that was reported as metabolic equivalents per week (MET/min/week). Diagnosis of kidney stones is made on the basis of information obtained from self-reported questionnaire (Yes/ No). Moreover, data regarding age, gender (male or female), marital status (single, married, widowed or discovered), education level (lower than high school, high school, diploma and associated diploma, bachelors, masters, and higher), history of chronic disease (yes or no), supplement use, and smoking history (never smoker, current smoker, ex-smoker) were obtained through a pretested questionnaire administered during an in-person interview.

Statistical analysis

Individuals were classified into groups based on the quartile thresholds of the DII and DIL scores. The one-way analysis of variance was employed to evaluate variations in quantitative variables across quartiles of DII and DIL. Similarly, the chi-square test was utilized to evaluate the distribution of categorical variables among quartiles of DII and DIL. To explore the association between DII and DIL with odds of kidney stones, logistic regression was conducted in crude and adjusted models. In the first model, adjustments were made for age, gender, and energy intake. The second model underwent additional modifications to account for BMI. The final model additionally incorporated the history of chronic disease (yes/no); marital status (single, married, widow, or discovered); education level (lower than high school, high school, diploma and associated diploma, bachelors, masters, and higher); supplement use; smoking history (never smoker, current smoker, ex-smoker); physical activity level (MET/min/week); and intakes of dietary EPA, DHA, and fiber (continues, g/d). In all models, participants in the lowest quartiles of DIL and DII were designated as the reference group. The statistical analyses were performed using SPSS version 26 (SPSS Inc., Chicago, IL), and in all results, the significance level was determined as p < 0.05.

Results

The mean ± SD for the BMI and age of the study population were 27.73 ± 7.52 kg/m2 and 45.34 ± 8.61 years, respectively. The mean ± SD score for DII and DIL were 59.17 ± 6.15 and 107.41 ± 52.92, respectively. General characteristics and biochemical parameters of participants across quartiles of DIL and DII are presented in Tables 1 and 2. Except for BMI, there were significant differences for age, gender, physical activity, level of education, smoking status, marriage status, and serum concentrations of FBG, TG, TC, LDL-C, HDL-C, AST and ALT among the quartiles of DIL. Furthermore, among DII quartiles, significant differences were observed regarding age, gender, physical activity, level of education, smoking status, marriage status, and serum concentrations of FBG, TG, HDL-C, AST and ALT.

Table 1.

General characteristics of study participants across quartiles of Dietary insulin load.

Variables Quartiles of DIL P
Q1 Q2 Q3 Q4
Age (years) 47.78(9.48) 45.50(8.53) 44.49(8.17) 44.35(8.02) < 0.001
Gender Male 225(25.3%) 449(38.4%) 701(52.8%) 1030(73.0%) < 0.001
Female 863(74.7%) 721(61.6%) 626(47.2%) 381(27.0%)
BMI (kg/m2) 28.09(5.03) 27.60(4.70) 27.80(7.45) 27.58(10.53) 0.397
Physical activity (Met/min/week) 938.17(912.17) 918.71(910.81) 893.51(916.16) 880.29 (850.12) < 0.001
Insulin load 54.58(6.43) 57.47(5.44) 59.68(5.03) 63.00(4.85) < 0.001
Insulin index 48.36(9.73) 74.65(7.25) 105.57(10.89) 173.47(43.08) < 0.001
Education

Lower than high

school

167(18.8%) 137 (11.7%) 81(6.1%) 82(5.8%) < 0.001
High school 446(50.2%) 567(48.5%) 617(46.5%) 707(50.2%)
Diploma and associated diploma 164(18.5%) 280(23.9%) 336(25.3%) 343(24.4%)
Bachelors 93(10.5%) 163(13.9%) 258(19.4%) 243(17.3%)
Masters and higher 18(2.0%) 23(2.0)% 35(2.6%) 33(2.3%)
Smoking status Never smoker 773(90.6%) 965(85.2%) 1015(78.5%) 912(66.6%) < 0.001
Current smoker 52(6.1%) 115(10.2%) 199(15.4%) 321(23.4%)
Ex‑smoker 28(3.3%) 53(4.7%) 80(6.2%) 137(10.0%)
Marriage status Single 4(0.5%) 8 (0.7%) 5(0.4%) 7(0.5%) < 0.001
Married 842(94.8%) 1130(96.6%) 1296(97.7%) 1387(98.3%)
Widowed or divorced 42(4.7%) 32(2.7%) 29(2.0%) 17(1.2%)
Biochemical parameters
FBG (mg/dl) 97.40(24.81) 96.03(32.20) 94.56(19.67) 95.10(19.05) 0.043
TG (mg/dl) 144.68(78.79) 150.37(92.51) 159.73(97.54) 171.07(113.80) < 0.001
TC (mg/dl) 195.19(67.98) 189.24(36.70) 189.95(38.16 192.28(40.44) 0.015
LDL-C (mg/dl) 110.84 (64.00) 105.09(29.86) 106.63(30.55) 109.37 (30.41) 0.003
HDL-C (mg/dl) 56.26(12.80) 54.70(12.32) 52.34(11.98) 50.31(10.94) < 0.001
AST (U/L) 17.93(7.38) 19.16(10.40) 19.25(8.03) 20.58(8.65) < 0.001
ALT (U/L) 19.44(16.33) 22.03(17.63) 23.09(16.83) 26.19(18.39) < 0.001

BMI body mass index, MET metabolic equivalent, TG triglyceride, TC total cholesterol, LDL-C low density lipoprotein-cholestrol, HDL-C high density lipoprotein-cholestrol, AST aspartate aminotransferase, ALT alanine aminotransferase .

Data are presented as mean (standard deviation (SD)) or number (percent).

a Obtained from ANOVA or Chi-square test, where appropriate.

Table 2.

General characteristics of study participants across quartiles of Dietary insulin index.

Variables Quartiles of DII P
Q1 Q2 Q3 Q4
Age (years) 48.99(9.94) 47.42(9.32) 47.83(9.54) 49.74(9.82) < 0.001
Gender Male 645(34.1%) 916(44.4%) 1086(51.4%) 1134(54.4%) < 0.001
Female 1247(65.9%) 1148(55.6%) 1028(48.6%) 950(45.6%)
BMI (kg/m2) 29.00(5.12) 28.60(6.73) 28.61(9.07)) 27.97(4.98) 0.397
Physical activity (Met/min/week) 927.81 (907.17) 912.52(902.32) 898.59 (912.97) 888.32 (887.22) < 0.001
Insulin load 62.46(26.76) 87.41(35.16) 111.11(46.91) 136.72(62.14) < 0.001
Insulin index 49.91(3.84) 56.62(1.30) 60.76(1.14) 66.35(3.58) < 0.001
Education

Lower than high

school

609(32.2%) 615(29.8%) 659(31.2%) 700(33.6%) < 0.001
High school 298 (15.8%) 363(17.6%) 340(16.1%) 327(15.7%)
Diploma and associated diploma 354(18.7%) 429(20.8%) 447(21.2%) 384(18.4%)
Bachelors 232(12.3%) 303(14.7%) 321(15.2%) 220(10.6%)
Masters and higher 36(1.9%) 56(2.7%) 38(1.8%) 29(1.4%)
Smoking status Never smoker 1597(85.6%) 1653(81.5%) 1615(78.0%) 1493(73.1%) < 0.001
Current smoker 150(8.0%) 236(11.6%) 282(13.6%) 370(18.1%)
Ex‑smoker 119(6.4%) 138(6.8%) 174(8.4%) 179(8.8%)
Marriage status Single 7(0.4%) 6(0.3%) 5(0.2%) 12(0.6%) 0.012
Married 1786(94.4%) 1995(96.7%) 2024(95.7%) 1978(94.9%)
Widowed or divorced 99 (5.2%) 63(3.1%) 85(4.0%) 94(4.5%)
Biochemical parameters
FBG (mg/dl) 115.18(52.06) 107.64(42.86) 104.05(37.19) 103.15(33.57) < 0.001
TG (mg/dl) 160.11(93.28) 162.82(102.00) 168.27(100.02) 174.34(113.00) < 0.001
TC (mg/dl) 189.18(55.24) 189.46(42.27) 190.59(40.60) 191.15(41.53) 0.468
LDL-C (mg/dl) 103.65(50.42) 105.05(32.51) 105.73(31.71) 105.97(32.74) 0.220
HDL-C (mg/dl) 54.28(12.62) 53.31(12.28) 52.38(12.18) 51.92(11.91) < 0.001
AST (U/L) 18.50(9.25) 19.37(8.81) 19.73(8.90) 19.47(8.42) < 0.001
ALT (U/L) 18.50(9.25) 19.37(8.81) 19.73(8.90) 19.47(8.42) < 0.001

BMI body mass index, MET metabolic equivalent, TG triglyceride, TC total cholesterol, LDL-C low density lipoprotein-cholestrol, HDL-C high density lipoprotein-cholestrol, AST aspartate aminotransferase, ALT alanine aminotransferase .

Data are presented as mean (standard deviation (SD)) or number (percent).

a Obtained from ANOVA or Chi-square test, where appropriate.

Dietary Food groups and nutrient intakes of study participants across quartiles of DIL and DII are shown in Tables 3 and 4. Compared with those in the first quartiles, individuals in the last quartiles of DIL consumed more energy, protein, carbohydrate, total fat, saturated fatty acid, cholesterol, iron, sodium, vitamin B12 and B9, red meat, processed meat, dairy, and refined grain, as well as a lower intake of vegetables, fruits, legumes, nuts, whole grains, eggs, fish, calcium, zinc, potassium, vitamin E, vitamin D, vitamin C, folate, caffeine, and fiber. Also, people in the highest quartile compared to the lower quartile of DII had a higher intake of carbohydrates, caffein, refined grain, and egga, as well as a lower intake of energy, protein, total fat, saturated fatty acid, cholesterol, iron, sodium, vitamin B12 and B9, red meat, processed meat, dairy, vegetables, fruits, legumes, nuts, whole grains, fish, calcium, zinc, potassium, vitamin E, vitamin D, vitamin C, folate, and fiber.

Table 3.

Dietary food groups and nutrients intakes of study participants across quartiles of Dietary insulin load.

Variables Quartiles of DIL P
Q1 Q2 Q3 Q4
Food groups (g/day)
Fruits 488.68(339.56) 459.56(335.78) 395.11(240.27) 333.51(221.17) < 0.001
Vegetables 242.32(130.38) 232.18(132.68) 213.15(112.42) 181.96(95.43) < 0.001
Red meat 25.74(19.19) 33.67(23.02) 39.53(27.38) 45.79(38.93) < 0.001
Processed meat 0.35(1.21) 0.84(3.20) 0.96(2.73) 1.87(5.63) < 0.001
Dairy product 154.12(114.31) 186.23(128.41) 201.95(150.43) 224.66(175.42) < 0.001
Legumes 40.88(30.86) 36.44(31.51) 32.38(23.54) 25.16(18.50) < 0.001
Nut 25.28(41.03) 19.45(29.92) 16.58(25.34) 10.80(18.68) < 0.001
Whole grains 906.18(591.66) 589.22(529.55) 371.60(438.82) 192.25(280.51) < 0.001
Refined grains 206.65(88.45) 330.97(108.29) 472.55(151.68) 816.55(326.34) < 0.001
Fish 7.02(9.48) 6.20(7.74) 5.22(5.88) 3.92(5.53) < 0.001
Eggs 36.73(26.45) 30.73(21.81) 25.79(18.98) 21.22(15.90) < 0.001
Nutrients
Energy (Kcal/d) 3360.15(1591.21) 3155.75(1319.80) 3304.23(1050.85) 4113.86(944.33) < 0.001
Protein (g/d) 122.11(62.46) 110.39(51.39) 112.65(40.10) 138.36(36.74) < 0.001
Fat (g/d) 88.46(31.76) 90.91(31.50) 97.97(30.80) 114.97(32.79) < 0.001
Carbohydrate (mg/d) 556.96(297.38) 504.11(242.58) 521.95(188.74) 667.13(164.33) < 0.001
Cholesterol 202.88(87.37) 250.44(109.03) 290.85(121.63) 337.23(147.33) < 0.001
SFA (g/d) 25.26(8.31) 26.89(8.43) 29.55(9.27) 34.62(10.59) < 0.001
Calcium (mg/d) 1398.35(494.45) 1175.83(459.48) 1122.06(474.27) 1170.80(532.54) < 0.001
Iron (mg/d) 33.16(20.69) 27.24(16.45) 26.64(11.99) 33.94(9.92) < 0.001
Sodium (mg/d) 6483.43(3327.46) 5534.41(2747.74) 5516.59(2317.11) 6784.41(2135.69) < 0.001
Zinc(mg/d) 24.29(7.68) 19.36(7.94) 19.25(10.17) 21.83(12.21) < 0.001
potassium(mg/d) 5400.36(1551.80) 4558.94(1550.14) 4322.48(1618.56) 4422.00(1833.32) < 0.001
Vitamin B12 (mcg/d) 3.40(2.18) 4.34(2.48) 5.03(3.64) 5.67(3.15) < 0.001
Vitamin E (mg/d) 21.87(9.14) 18.15(10.04) 18.95(8.81) 17.85(8.08) < 0.001
Folate (mcg/d) 692.13(329.12) 658.03(267.19) 683.37(212.45) 846.89(213.54) < 0.001
Vitamin D (mcg/d) 1.28(1.13) 1.12(0.90) 1.00(0.80) 0.85(0.75) < 0.001
Vitamin C (mg/d) 136.37(87.15) 127.90(82.62) 112.38(63.68) 90.50(53.81) < 0.001
Caffein (mg/d) 184.57(185.70) 134.25(98.21) 120.97(89.55) 107.11(86.75) < 0.001
Dietary fibre (g/day) 67.25(43.71) 52.82(34.52) 49.96(25.77) 62.48(21.95) < 0.001

SFA saturated fatty acid.

Data are presented as mean ± standard deviation (SD).

a Obtained from ANOVA.

Table 4.

Dietary food groups and nutrients intakes of study participants across quartiles of Dietary insulin index.

Variables Quartiles of DII P
Q1 Q2 Q3 Q4
Food groups (g/day)
Fruits 481.19(321.96) 452.96(309.58) 435.53(301.75) 391.04(322.92) < 0.001
Vegetables 249.88(152.62) 236.68(132.18) 225.39(129.56) 205.79(118.06) < 0.001
Red meat 33.89(29.95) 36.49(28.38) 36.38(28.76) 32.72(26.09) < 0.001
Processed meat 0.85(4.02) 1.15(5.14) 0.86(2.71) 0.76(2.60) 0.007
Dairy product 195.39(144.28) 195.14(142.10) 196.34(155.20) 181.23(143.21) 0.002
Legumes 33.41(28.42) 33.51(25.65) 33.68(26.51) 31.65(25.77) 0.047
Nut 17.94(28.62) 17.46(27.68) 17.12(29.44) 15.51(29.01) 0.043
Whole grains 835.91(586.33) 572.62(547.64) 436.19(513.99) 398.40(507.42) < 0.001
Refined grains 285.62(165.07) 411.77(231.31) 527.64(305.68) 577.66(371.34) < 0.001
Fish 5.70(7.10) 6.08(8.19) 5.60(7.41) 4.27(7.05) < 0.001
Eggs 25.78(22.68) 27.68(20.95) 27.87(21.35) 25.84(22.48) 0.001
Nutrients
Energy (Kcal/d) 3608.02(1447.35) 3288.00(1264.61) 3385.23(1249.98) 3588.78(1288.41) < 0.001
Protein (g/d) 129.55(57.04) 115.32(48.51) 117.90(46.76) 120.02(47.85) < 0.001
Fat (g/d) 104.60(34.50) 96.20(32.60) 94.94(33.15) 93.26(32.45) < 0.001
Carbohydrate (mg/d) 576.11(269.63) 521.45(227.94) 546.06(220.56) 601.85(234.03) < 0.001
Cholesterol 260.58(130.45) 270.14(124.88) 268.70(127.19) 247.05(134.79) < 0.001
SFA (g/d) 30.83(10.21) 28.81(9.59) 28.44(9.94) 27.22(9.57) < 0.001
Calcium (mg/d) 1290.02(517.57) 1183.24(484.70) 1216.67(500.47) 1211.03(509.32) < 0.001
Iron (mg/d) 33.17(18.91) 28.13(15.25) 29.13(14.09) 30.86(14.51) < 0.001
Sodium (mg/d) 6495.39(3106.19) 5712.20(2686.00) 5847.86(2536.28) 6263.11(2632.23) < 0.001
Zinc(mg/d) 22.75(11.30) 19.86(9.55) 20.48(9.23) 21.27(9.43) < 0.001
potassium(mg/d) 5020.32(1841.54) 4589.78(1665.62) 4645.83(1687.49) 4700.71(1739.64) < 0.001
Vitamin B12 (mcg/d) 4.43(2.66) 4.63(2.95) 4.70(3.14) 4.23(2.66) < 0.001
Vitamin E (mg/d) 19.63(8.58) 18.54(8.46) 18.69(8.72) 19.36(9.84) < 0.001
Folate (mcg/d) 742.41(300.92) 691.74(264.30) 714.64(259.20) 725.49(266.94) < 0.001
Vitamin D (mcg/d) 1.17(1.03) 1.07(0.88) 1.01(0.95) 0.81(0.66) < 0.001
Vitamin C (mg/d) 129.12(84.96) 123.89(73.11) 121.72(76.80) 108.06(89.68) < 0.001
Caffein (mg/d) 111.60(91.75) 122.15(97.31) 135.52(99.07) 194.10(187.66) < 0.001
Dietary fibre (g/day) 67.10(40.53) 54.34(32.19) 55.40(29.69) 59.72(30.59) < 0.001

SFA saturated fatty acid.

Data are presented as mean ± standard deviation (SD).

a Obtained from ANOVA.

Multivariable-adjusted odds ratios (ORs) and 95% CI for kidney stones across quartiles of DII and DIL are shown in Table 5. There was evidence of increased odds of kidney stones for the subjects in the highest compared to the lowest quartile of the DIL (OR = 2.14, 95% CI 1.62–2.83; P-trend < 0.001). These associations were remained significant in all adjusted models (final adjustment model: OR = 1.44, 95% CI 1.0-1.97; P-trend = 0.019). There was a direct significant relationship between DII and odds of kidney stones among third and forth quartiles of DII (OR: 1.52, 95% CI: 1.16–1.98, P-trend = 0.002); but this association disappeared for adjusted models.

Table 5.

Odds ratio (95% CI) and 95% confidence interval (CI) for kidney stones according to Quartiles of Dietary insulin index (DII) and dietary insulin load (DIL).

Quartiles of score P-trend
Q1 Q2 Q3 Q4
DII
Case/Total 93/968 148/1224 170/1330 180/1307
Crude 1.00 (Ref) 1.30(0.98–1.71) 1.40(1.07–1.83) 1.52(1.16–1.98) 0.002
Model 1* 1.00 (Ref) 1.20(0.90–1.59) 1.19(0.90–1.57) 1.20(0.91–1.58) 0.263
Model 2 1.00 (Ref) 1.20(0.90–1.59) 1.19 (0.90–1.57) 1.21(0.92–1.59) 0.250
Model 3 1.00 (Ref) 1.18(0.89–1.58) 1.19(0.89–1.58) 1.22(0.94–1.63) 0.227
DIL
Case/Total 74/903 119/1169 169/1332 229/1425
Crude 1.00 (Ref) 1.26(0.92–1.71) 1.62(1.21–2.16) 2.14(1.62–2.83) < 0.001
Model 1* 1.00 (Ref) 1.15(0.84–1.58) 1.34(0.99–1.81) 1.57(1.16–2.12) 0.001
Model 2 1.00 (Ref) 1.15(0.84–1.58) 1.33(0.98–1.81) 1.56(1.15–2.11) 0.001
Model 3 1.00 (Ref) 1.10(0.81–1.52) 1.23(0.90–1.69) 1.44(1.04–1.97) 0.019

*Model 1: adjusted for age, gender, and intake of energy.

Model 2: adjusted for model 1 and BMI.

Model 3: adjusted for model 2 and history of chronic disease (yes/no); marital status (single, married, widow or discovered); education level (lower than high school, high school, Diploma and associated diploma, Bachelors, Masters and higher); Supplements and drug use, smoking history (never smoker, current smoker, ex-smoker); physical activity level (MET/min/week); intakes of dietary EPA, DHA, water, and fiber (continues, g/d).

Discussion

To the best of our knowledge, the current study is the first to investigate the associations between DII and DIL with odds of kidney stones in a substantial population-based cohort study. Direct associations were observed between higher DII and DIL with odds of renal stones.

According to our search, no study has been done to investigate and find the relationship between DII/DIL and kidney stones. However, there are other approaches to investigate findings related to this relationship. For instance, prior research has demonstrated that a reduced intake of fruits and vegetables2224 and an increased consumption of meat25 and processed food (specifically animal derivatives) are linked to a higher risk of developing kidney stones26. In a study conducted by Turney et al.27, individuals who did not consume meat (specifically fish eaters and vegetarians) or consumed less than 50 g/day of meat experienced a significant reduction in the risk of kidney stones. This reduction ranged between 30% and 50% compared to meat-eaters who consumed 100 g/day or more of total meat products. Overall, their findings indicated that the consumption of red meat and poultry is linked to a higher risk of developing kidney stones. Furthermore, an investigation carried out by Taylor et al.28 revealed that following a DASH-style diet, which includes eating a lot of fruits and vegetables, some low-fat dairy products, and not much animal protein (but a lot of plant protein from legumes and nuts), significantly lowers the risk of developing kidney stones. Similarly, in another prospective cohort study conducted by Leone et al.29, a higher level of adherence to a Mediterranean dietary pattern, which is characterized by a low intake of meat and a focus on plant-based foods, had similar protective effects and was related to a decreased risk of developing nephrolithiasis. Interestingly, our research found that those in the bottom quartiles of the DIL had a similar dietary pattern, with a lower intake of red meat, processed meat, dairy products, and refined grains, and a higher intake of fruits, vegetables, legumes, nuts, whole grains, fish, and eggs. Besides, the results of the study conducted by Meschi et al.30 support the notion that eliminating fruits and vegetables from the diet of healthy individuals results in a decrease in the excretion of potassium, magnesium, citrate, and oxalate in urine while simultaneously increasing the levels of calcium and ammonium. These alterations increase the levels of calcium oxalate and calcium phosphate in the urine.

Our results regarding dairy products align with a previous investigation that suggested individuals with calcium oxalate dehydrate (COD) stones had notably greater consumption of dairy products compared to the control group31. Dairy products are commonly regarded as foods that are abundant in calcium. Nevertheless, we did not detect an elevated calcium intake among those who consumed a greater quantity of dairy products compared to those who consumed a lesser amount. The presence of extra substances in some dairy products, like cured cheese and flavored or sweetened yogurts, such as fats, salt, and added sugars, can explain the association between dairy products and risk31. There is strong evidence to support the association between salt consumption and an increased risk of hypercalciuria, which in turn leads to the occurrence or recurrence of nephrolithiasis32. Furthermore, a recent study has shown that a greater proportion of energy consumed from added sugars is strongly linked to a higher occurrence of kidney stones33.

Another potential mechanism that might account for our result is the presence of insulin resistance and hyperinsulinemia. A study conducted by Cupisti et al.34 demonstrated that insulin resistance might potentially contribute to the production of calcium stones by decreasing the excretion of urine citrate. It has been proposed that the reduced excretion of citrate in individuals with insulin resistance may be attributed to a malfunction in the generation of renal ammonium or alterations in the transport processes of sodium, potassium, and hydrogen ions in the renal tubules35,36. Individuals with insulin resistance exhibit elevated levels of plasma free fatty acids. These fatty acids can penetrate the cells of the proximal tubule and disrupt the utilization of glutamine. The utilization of free fatty acids by proximal tubule cells as an alternative metabolic substrate result in a reduction in ammoniagenesis and glutamine utilization3739. Additionally, in vitro studies that demonstrated insulin’s capacity to promote the production of ammonium from L-glutamine in the kidneys show that insulin resistance can directly impede the process of ammoniagenesis40,41. Insulin may also contribute to the function of the Na/K exchanger in the proximal renal tubule, which is responsible for transporting or trapping ammonium ions in the tubular lumen42. Therefore, individuals with insulin resistance or hyperinsulinemia may have a reduced capacity to eliminate ammonia, resulting in the production of very acidic urine36, which is the primary risk factor for the development of uric acid stones43 and some form of calcium oxalate stones44. Additionally, this condition has the potential to disrupt the renal citrate metabolism34, leading to a reduction in urine citrate levels—a substantial determinant in the development of calcium stones45.

The strengths of this study include the large sample size, the validated dietary assessment method, the adjustment for potential confounders, and the use of novel indicators of dietary insulin. However, this study also has some limitations that should be acknowledged. First, the dietary intake data were based on self-reported FFQs, which may be subject to measurement error and recall bias. Second, the causal relationship between DIL and kidney stones cannot be established due to the observational nature of the study. Therefore, other studies are needed to confirm our findings. Third, no Iranian foods have undergone analysis for the FII. Consequently, there can be a little discrepancy between the FII of the test items listed as references and the actual foods consumed by the participants in the research. Due to the unavailability of the FII for 41% of food items in the FFQ in the reference lists, the insulin index of similar foods was utilized. Hence, it is advisable to use caution when interpreting the results of this study. In addition, future studies should determine the insulin index of Iranian foods. Fourth, the homogeneous characteristics of the study population, which consisted of one regional population, might limit the generalizability of the findings. Furthermore, the study population was from a specific region in Iran, which may limit the generalizability of findings to other populations with different dietary habits and genetic backgrounds. Finally, due to financial limitations due to the large sample size of the study, we were not able to investigate urinary metabolites and also investigate the type of stones.

Conclusion

In conclusion, this study suggests that higher DIL, but not DII, is associated with an increased odds of kidney stones, independent of potential confounders. This finding implies that the insulin potential of the diet may be an important modifiable risk factor or even a predictor of kidney stones. So that, ,the effects of DII and DIL remain an active area of research with the potential to represent modifiable risk factors and to play a role in prevention and management strategies. Further prospective studies are needed to confirm this association and to elucidate the underlying mechanisms.

Author contributions

NM and D.F, contributed to write the manuscript. D.F., MH.S were responsible for the literature search and data collection. M.HS. was responsible for data analysis and interpretation of data. …SSKH.supervised the study and contributed to the conception, design, statistical analyses, data interpretation, and drafting of the article. Final approval of the article before submission was performed by all authors.

Funding

The present study was supported by a grant provided by Shahid Sadoughi University of Medical Sciences.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

The study adhered to the ethical standard outlined in the Declaration of Helsinki for conducting medical research with human beings. The study received ethical approval from the ethics committee of Shahid Sadoughi University of Medical Science (IR.SSU.SPH.REC.1397.161). All participants also provided written informed consent prior to the collection of data.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Alelign, T. & Petros, B. Kidney Stone Disease: an update on current concepts. Adv. Urol.2018, 3068365 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Sigurjonsdottir, V. K., Runolfsdottir, H. L., Indridason, O. S., Palsson, R. & Edvardsson, V. O. Impact of nephrolithiasis on kidney function. BMC Nephrol.16, 149 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.El-Zoghby, Z. M. et al. Urolithiasis and the risk of ESRD. Clin. J. Am. Soc. Nephrology: CJASN. 7 (9), 1409–1415 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Pearle, M. S., Calhoun, E. A. & Curhan, G. C. Urologic diseases in America project: urolithiasis. J. Urol.173 (3), 848–857 (2005). [DOI] [PubMed] [Google Scholar]
  • 5.Moe, O. W. Kidney stones: pathophysiology and medical management. Lancet (London England). 367 (9507), 333–344 (2006). [DOI] [PubMed] [Google Scholar]
  • 6.Maline, G. E. & Goldfarb, D. S. Climate change and kidney stones. Curr. Opin. Nephrol. Hypertens.33 (1), 89–96 (2024). [DOI] [PubMed] [Google Scholar]
  • 7.Siener, R. Nutrition and Kidney Stone Disease. Nutrients. ;13(6). (2021). [DOI] [PMC free article] [PubMed]
  • 8.Matlaga, B. R., Shah, O. D. & Assimos, D. G. Drug-induced urinary calculi. Rev. Urol.5 (4), 227–231 (2003). [PMC free article] [PubMed] [Google Scholar]
  • 9.Ferraro, P. M., Bargagli, M., Trinchieri, A. & Gambaro, G. Risk of kidney stones: influence of dietary factors, dietary patterns, and vegetarian-vegan diets. Nutrients ;12(3). (2020). [DOI] [PMC free article] [PubMed]
  • 10.Lautt, W. W. Postprandial insulin resistance as an early predictor of cardiovascular risk. Ther. Clin. Risk Manag.3 (5), 761–770 (2007). [PMC free article] [PubMed] [Google Scholar]
  • 11.Vaidya, R. A. et al. Hyperinsulinemia: an early biomarker of metabolic dysfunction. Front. Clin. Diabetes Healthc. ;4. (2023). [DOI] [PMC free article] [PubMed]
  • 12.Bell, K. Clinical Application of the Food Insulin Index to Diabetes Mellitus 2014.
  • 13.Sadeghi, O. et al. Dietary insulin index and dietary insulin load in relation to metabolic syndrome: the Shahedieh cohort study. J. Acad. Nutr. Dietetics. 120 (10), 1672–1686 (2020). e4. [DOI] [PubMed] [Google Scholar]
  • 14.Mirmiran, P., Esfandiari, S., Bahadoran, Z., Tohidi, M. & Azizi, F. Dietary insulin load and insulin index are associated with the risk of insulin resistance: a prospective approach in tehran lipid and glucose study. J. Diabetes Metab. Disord.15, 23 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Chen, L. et al. Kidney stones are associated with metabolic syndrome in a health screening population: a cross-sectional study. Translational Androl. Urol.12 (6), 967–976 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Kim, S. et al. Glycemic status, insulin resistance, and the risk of Nephrolithiasis: a Cohort Study. Am. J. Kidney Diseases: Official J. Natl. Kidney Foundation. 76 (5), 658–68e1 (2020). [DOI] [PubMed] [Google Scholar]
  • 17.Poustchi, H. et al. Prospective Epidemiological Research Studies in Iran (the PERSIAN Cohort Study): Rationale, objectives, and design. Am. J. Epidemiol.187 (4), 647–655 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Eghtesad, S. et al. Validity and reproducibility of a food frequency questionnaire assessing food group intake in the PERSIAN Cohort Study. Front. Nutr. ;10. (2023). [DOI] [PMC free article] [PubMed]
  • 19.Bodner-Montville, J. et al. USDA food and nutrient database for dietary studies: released on the web. J. Food Compos. Anal.19, S100–S7 (2006). [Google Scholar]
  • 20.Bao, J., de Jong, V., Atkinson, F., Petocz, P. & Brand-Miller, J. C. Food insulin index: physiologic basis for predicting insulin demand evoked by composite meals. Am. J. Clin. Nutr.90 (4), 986–992 (2009). [DOI] [PubMed] [Google Scholar]
  • 21.Nimptsch, K. et al. Dietary insulin index and insulin load in relation to biomarkers of glycemic control, plasma lipids, and inflammation markers. Am. J. Clin. Nutr.94 (1), 182–190 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Wang, H. et al. Consumption of tea, Alcohol, and fruits and risk of kidney stones: a prospective cohort study in 0.5 million Chinese adults. Nutrients ;13(4). (2021). [DOI] [PMC free article] [PubMed]
  • 23.Barghouthy, Y. & Somani, B. K. Role of Citrus Fruit juices in Prevention of kidney Stone Disease (KSD): a narrative review. Nutrients. 13 (11), 4117 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Sorensen, M. D. et al. Dietary intake of fiber, fruit and vegetables decreases the risk of incident kidney stones in women: a women’s Health Initiative report. J. Urol.192 (6), 1694–1699 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Asoudeh, F. et al. Associations of total protein or animal protein Intake and animal protein sources with risk of kidney stones: a systematic review and dose-response Meta-analysis. Adv. Nutr.13 (3), 821–832 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Coello, I., Sanchis, P., Pieras, E. C. & Grases, F. Diet in different calcium oxalate kidney stones. Nutrients ;15(11). (2023). [DOI] [PMC free article] [PubMed]
  • 27.Turney, B. W. et al. Diet and risk of kidney stones in the Oxford cohort of the European prospective investigation into Cancer and Nutrition (EPIC). Eur. J. Epidemiol.29 (5), 363–369 (2014). [DOI] [PubMed] [Google Scholar]
  • 28.Taylor, E. N., Fung, T. T. & Curhan, G. C. DASH-style diet associates with reduced risk for kidney stones. J. Am. Soc. Nephrology: JASN. 20 (10), 2253–2259 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Leone, A. et al. Adherence to the Mediterranean Dietary Pattern and incidence of Nephrolithiasis in the Seguimiento Universidad De Navarra follow-up (SUN) Cohort. Am. J. Kidney Diseases: Official J. Natl. Kidney Foundation. 70 (6), 778–786 (2017). [DOI] [PubMed] [Google Scholar]
  • 30.Meschi, T. et al. The effect of fruits and vegetables on urinary stone risk factors. Kidney Int.66 (6), 2402–2410 (2004). [DOI] [PubMed] [Google Scholar]
  • 31.Coello, I., Sanchis, P., Pieras, E. C. & Grases, F. Diet in different calcium oxalate kidney stones. Nutrients. 15 (11), 2607 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Ticinesi, A., Nouvenne, A., Maalouf, N. M., Borghi, L. & Meschi, T. Salt and nephrolithiasis. Nephrol. Dialysis Transplantation. 31 (1), 39–45 (2014). [DOI] [PubMed] [Google Scholar]
  • 33.Yin, S. et al. Association between added sugars and kidney stones in U.S. adults: data from National Health and Nutrition Examination Survey 2007–2018. (2023). Frontiers in Nutrition. ;10. [DOI] [PMC free article] [PubMed]
  • 34.Cupisti, A. et al. Insulin resistance and low urinary citrate excretion in calcium stone formers. Biomed. Pharmacotherapy = Biomedecine Pharmacotherapie. 61 (1), 86–90 (2007). [DOI] [PubMed] [Google Scholar]
  • 35.Taylor, E. N., Stampfer, M. J. & Curhan, G. C. Diabetes mellitus and the risk of nephrolithiasis. Kidney Int.68 (3), 1230–1235 (2005). [DOI] [PubMed] [Google Scholar]
  • 36.Abate, N., Chandalia, M., Cabo-Chan, A. V. Jr., Moe, O. W. & Sakhaee, K. The metabolic syndrome and uric acid nephrolithiasis: novel features of renal manifestation of insulin resistance. Kidney Int.65 (2), 386–392 (2004). [DOI] [PubMed] [Google Scholar]
  • 37.Vinay, P., Lemieux, G., Cartier, P. & Ahmad, M. Effect of fatty acids on renal ammoniagenesis in in vivo and in vitro studies. Am. J. Physiol.231 (3), 880–887 (1976). [DOI] [PubMed] [Google Scholar]
  • 38.Lemieux, G., Vinay, P., Gougoux, A., Baverel, G. & Cartier, P. Relationship between the renal metabolism of glutamine, fatty acids and ketone bodies. Curr. Probl. Clin. Biochem.8, 379–388 (1977). [PubMed] [Google Scholar]
  • 39.Bagnasco, S. M., Gaydos, D. S., Risquez, A. & Preuss, H. G. The regulation of renal ammoniagenesis in the rat by extracellular factors. III. Effects of various fuels on in vitro ammoniagenesis. Metab. Clin. Exp.32 (9), 900–905 (1983). [DOI] [PubMed] [Google Scholar]
  • 40.Chobanian, M. C. & Hammerman, M. R. Insulin stimulates ammoniagenesis in canine renal proximal tubular segments. Am. J. Physiol.253 (6 Pt 2), F1171–F1177 (1987). [DOI] [PubMed] [Google Scholar]
  • 41.Krivosíková, Z., Spustová, V. & Dzúrik, R. Participation of P-dependent and P-independent glutaminases in rat kidney ammoniagenesis and their modulation by metabolic acidosis, hippurate and insulin. Physiol. Res.47 (3), 177–183 (1998). [PubMed] [Google Scholar]
  • 42.Klisic, J. et al. Insulin activates na(+)/H(+) exchanger 3: biphasic response and glucocorticoid dependence. Am. J. Physiol. Ren. Physiol.283 (3), F532–F539 (2002). [DOI] [PubMed] [Google Scholar]
  • 43.Moe, O. W., Abate, N. & Sakhaee, K. Pathophysiology of uric acid nephrolithiasis. Endocrinol. Metab. Clin. North Am.31 (4), 895–914 (2002). [DOI] [PubMed] [Google Scholar]
  • 44.Manissorn, J., Fong-Ngern, K., Peerapen, P. & Thongboonkerd, V. Systematic evaluation for effects of urine pH on calcium oxalate crystallization, crystal-cell adhesion and internalization into renal tubular cells. Sci. Rep.7 (1), 1798 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Coe, F. L., Parks, J. H. & Asplin, J. R. The pathogenesis and treatment of kidney stones. N. Engl. J. Med.327 (16), 1141–1152 (1992). [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.


Articles from Scientific Reports are provided here courtesy of Nature Publishing Group

RESOURCES