Background
Chronic kidney disease (CKD) is one of the major contributors to the global burden of disease via increasing cardiovascular disease risk and mortality worldwide [
1]. CKD is characterized by a substantial and progressive change in glomerular filtration rate (GFR) caused by structural and functional damage lasting for more than 3 months. The latest report on the Global Burden of Disease in 2017 indicated that CKD accounts for 1.2 million deaths worldwide; in addition, 7.6% of deaths due to cardiovascular diseases could be attributed to kidney dysfunction [
1,
2]. Various risk factors, including weight gain, hypertension (HTN), type 2 diabetes mellitus (T2DM), and an unhealthy lifestyle, positively influence the occurrence of CKD [
3,
4]. Correspondingly, evidence suggests the protective role of lifestyle modifications such as body fat reduction, increased physical activity, and nutritional manipulations on preventing or reducing CKD progression [
5,
6].
Recently, it has been reported that hyperinsulinemia and insulin resistance (IR), two insulin homeostasis-related disorders, play a destructive role in the pathogenesis of kidney disease and other chronic metabolic diseases. A review of animal studies has shown that hyperinsulinemia and IR may cause kidney damage by increasing albumin excretion, glomerular hyperfiltration, endothelial dysfunction, and incrementing the risk of kidney fibrosis [
7,
8]. Given the importance of hyperinsulinemia and IR as predisposing factors in the incidence of metabolic diseases, several studies have evaluated the role of nutrition and other lifestyle factors, such as physical activity and obesity, in the pathogenesis of these insulin homeostasis-related disorders and metabolic disorders, with different aspects [
9‐
14]. In this regard, some studies have determined the insulinemic potential of dietary pattern and lifestyle and investigated its effects on increasing the risk of IR and hyperinsulinemia and subsequent chronic diseases.
Tabung et al. have recently introduced the insulinemic potential of diet and lifestyle [
15], which has been determined based on four insulinemic indices, including the empirical dietary indexes for hyperinsulinemia (EDIH), the empirical dietary indexes for IR (EDIR), the empirical lifestyle indices for hyperinsulinemia (ELIH), and empirical lifestyle indices for IR (ELIR). To date, no study has examined the association of the insulinemic potential of diet and lifestyle, including EDIH, ELIH, EDIR, and ELIR, with the risk of CKD development, some studies have suggested that adherence to lifestyle and dietary pattern with a higher score of the above-mentioned insulinemic indices may be associated with an increased risk of some metabolic diseases as predisposing factors for CKD risk, such as T2D and obesity, and also various types of cancer [
16‐
22].
Given the possible adverse effect of hyperinsulinemia and IR on the pathogenesis of kidney disease and the lack of data on the role of the above-mentioned insulinemic indices in the development of CKD risk, in the present study, we aimed to investigate the relationship between the insulinemic potential of lifestyle and dietary pattern and the risk of CKD in the adult population.
Results
Study participants’ (54.3% females) mean ± SD age and BMI were 37.8 ± 12.8 and 26.8 ± 4.7, respectively. The median (IQR) ELIH, ELIR, EDIH, and EDIR in participants were 1.31 (1.13–1.50), 4.14 (2.98–5.79), 0.17 (0.08–0.31), and 0.69 (0.47–0.99), respectively. During the 6.03 years of follow-up, 1216 incident cases (20.1%) of CKD was identified (the incidence rate = 260 per 10.000 person-years) among all study population.
The baseline characteristics and dietary intakes of participants according to the quintiles of ELIH score are presented in Table
1. Subjects in the highest ELIH score quintiles were more likely to be female, older, smoked less, and had lower physical activity and academic education levels than those in the lowest quintiles of ELIH (
P < 0.05). Additionally, BMI, serum creatinine, and percentage of T2DM and HTN were increased significantly across ELIH score quintiles, whereas the level of eGFR was decreased (
P < 0.001). Furthermore, participants in the highest quintile of ELIH score had higher intakes of energy, total fat, margarine, butter, and red meat, but lower intakes of fruits juice, whole fruits, coffee, snacks, salad dressing, and high-fat dairy products compared to those in the lowest quintile of ELIH.
Table 1
Baseline characteristics of participants according to quintiles (Q) of the empirical lifestyle index for hyperinsulinemia
Age (years) | 32.8 ± 13.3 | 37.1 ± 13.2 | 39.0 ± 12.1 | 40.1 ± 12.1 | 40.5 ± 11.6 | < 0.001 |
Men (%) | 45.9 | 48.3 | 48.6 | 48.2 | 38.0 | < 0.001 |
Body mass index (kg/m2) | 21.7 ± 2.7 | 24.7 ± 2.4 | 26.6 ± 2.5 | 28.7 ± 2.8 | 32.2 ± 4.7 | < 0.001 |
Smoking (%) | 13.2 | 12.4 | 12.5 | 13.2 | 10.7 | 0.335 |
Physical activity (MET/hour/week) | 82.9 (38.1–125.0) | 71.4 (27.0–107.9) | 64.9 (23.3–103.9) | 59.9 (20.8–102.2) | 51.3 (15.9–90.0) | < 0.001 |
Academic education, (%) | 25.1 | 27.3 | 26.0 | 23.6 | 18.5 | < 0.001 |
Creatinine (mg/dl) | 1.01 ± 0.14 | 1.02 ± 0.14 | 1.02 ± 0.15 | 1.03 ± 0.14 | 1.01 ± 0.14 | 0.154 |
Glomerular filtration rate (mL/min/1.73 m2) | 83.9 ± 12.6 | 80.6 ± 12.0 | 79.2 ± 12.1 | 77.7 ± 11.7 | 77.5 ± 12.1 | < 0.001 |
Hypertension (%) | 4.1 | 6.9 | 7.8 | 13.3 | 15.0 | < 0.001 |
Diabetes (%) | 2.0 | 3.0 | 4.7 | 4.6 | 6.8 | < 0.001 |
Empirical dietary index for hyperinsulinemia | 0.17 ± 0.13 | 0.19 ± 0.17 | 0.20 ± 0.18 | 0.20 ± 0.24 | 0.35 ± 0.30 | < 0.001 |
Empirical lifestyle index for hyperinsulinemia | 0.95 ± 0.11 | 1.16 ± 0.04 | 1.31 ± 0.03 | 1.46 ± 0.05 | 1.75 ± 0.19 | < 0.001 |
Nutrient Intake |
Energy(Kcal/d) | 2387 ± 724 | 2329 ± 696 | 2306 ± 702 | 2310 ± 700 | 2431 ± 748 | < 0.001 |
Carbohydrate(% of energy) | 59.3 ± 6.7 | 59.0 ± 6.8 | 58.6 ± 11.8 | 58.2 ± 6.7 | 56.1 ± 6.9 | < 0.001 |
Protein(% of energy) | 14.0 ± 2.7 | 14.4 ± 2.9 | 15.0 ± 1.8 | 14.5 ± 2.7 | 14.6 ± 3.0 | 0.066 |
Fat(% of energy) | 29.8 ± 6.0 | 29.9 ± 6.0 | 31.0 ± 6.0 | 30.3 ± 6.7 | 32.3 ± 7.0 | < 0.001 |
Food groups |
Red meat (serving/week) | 0.49 (0.28–0.77) | 0.56 (0.28–0.91) | 0.63 (0.35–1.19) | 0.70 (0.42–1.26) | 0.91 (0.49–1.89) | < 0.001 |
Fruit juice(serving/week) | 0.12 (0.07–0.91) | 0.28 (0.07–0.70) | 0.21 (0.02–0.54) | 0.28 (0.07–0.70) | 0.21 (0.03–0.70) | 0.352 |
Coffee(serving/d) | 0.00 (0.00–0.03) | 0.00 (0.00–0.03) | 0.00 (0.00–0.02) | 0.00 (0.00–0.02) | 0.00 (0.00–0.02) | 0.030 |
Butter and Margarine (serving/d) | 0.16 (0.00–0.71) | 0.16 (0.01–0.71) | 0.25 (0.01–1.07) | 0.41 (0.02–1.42) | 0.71 (0.04–2.50) | < 0.001 |
Whole fruit(serving/d) | 1.65 (0.83–2.72) | 1.54 (0.80–2.65) | 1.50 (0.78–2.40) | 1.39 (0.74–2.30) | 1.41 (0.75–2.32) | < 0.001 |
High-fat dairy products(serving/d) | 1.31 (0.85–2.12) | 1.23 (0.82–1.96) | 1.17 (0.72–1.79) | 1.11 (0.69–1.65) | 1.09 (0.66–1.59) | < 0.001 |
Snacks(serving/d) | 0.17 (0.03–0.57) | 0.14 (0.02–0.41) | 0.14 (0.02–0.36) | 0.13 (0.01–0.31) | 0.14 (0.02–0.35) | < 0.001 |
Salad dressing(serving/d) | 0.16 (0.06–0.42) | 0.15 (0.06–0.34) | 0.14 (0.04–0.31) | 0.14 (0.04–0.29) | 0.14 (0.05–0.31) | < 0.001 |
We also showed individuals’ baseline characteristics and dietary intakes according to the quintiles of the ELIR score in Table
2. The mean BMI, eGFR, physical activity, % of male subjects, and % of smoking were increased across quintiles of the ELIR score, whereas the mean age was reduced across quintiles of this score. Total energy, dietary intakes of carbohydrates, protein, refined grains, red meats, margarine, tomatoes, and potatoes significantly increased across quintiles of ELIR score (
P < 0.001). However, the intakes of total fats, tea, high-fat dairy products, and green leafy vegetables were decreased across quintiles of this score (
P < 0.001).
Table 2
Baseline characteristics of participants according to quintiles (Q) of the empirical lifestyle index for insulin resistance
Age (years) | 40.0 ± 13.4 | 38.4 ± 12.8 | 36.9 ± 12.5 | 37.7 ± 12.3 | 36.5 ± 12.6 | < 0.001 |
Men (%) | 37.8 | 40.2 | 42.8 | 48.3 | 60.0 | < 0.001 |
Body mass index (kg/m2) | 25.9 ± 4.1 | 27.0 ± 4.7 | 26.7 ± 4.8 | 27.2 ± 4.8 | 27.8 ± 5.0 | < 0.001 |
Smoking (%) | 10.3 | 10.5 | 11.6 | 13.2 | 16.2 | < 0.001 |
Physical activity (MET/hour/week) | 63.5 (20.8–105.7) | 62.5 (22.2–102.7) | 63.1 (23.6–105.9) | 68.6 (26.9–107.8) | 71.4 (27.7–108.3) | 0.001 |
Academic education, (%) | 23.1 | 22.7 | 27.2 | 23.1 | 24.5 | 0.078 |
Creatinine (mg/dl) | 1.00 ± 0.14 | 1.01 ± 0.14 | 1.02 ± 0.14 | 1.03 ± 0.15 | 1.06 ± 0.14 | < 0.001 |
Glomerular filtration rate (mL/min/1.73 m2) | 78.4 ± 12.6 | 79.2 ± 12.5 | 80.4 ± 12.3 | 79.7 ± 12.1 | 81.1 ± 12.2 | < 0.001 |
Hypertension (%) | 8.2 | 10.2 | 8.6 | 9.4 | 10.7 | 0.227 |
Diabetes (%) | 4.6 | 4.3 | 4.0 | 4.1 | 4.1 | 0.933 |
Empirical dietary index for insulin resistance | 0.37 ± 0.23 | 0.58 ± 0.27 | 0.71 ± 0.28 | 0.89 ± 0.30 | 1.38 ± 0.47 | < 0.001 |
Empirical lifestyle index for insulin resistance | 2.31 ± 0.34 | 3.20 ± 0.24 | 4.17 ± 0.32 | 5.43 ± 0.42 | 8.88 ± 2.62 | < 0.001 |
Nutrient Intake | | | | | | < 0.001 |
Energy(Kcal/d) | 1984 ± 634 | 2239 ± 666 | 2307 ± 687 | 2431 ± 664 | 2801 ± 670 | < 0.001 |
Carbohydrate(% of energy) | 57.3 ± 7.4 | 57.9 ± 6.8 | 57.4 ± 6.5 | 58.7 ± 11.9 | 59.8 ± 6.4 | < 0.001 |
Protein(% of energy) | 15.5 ± 3.7 | 14.9 ± 3.6 | 14.4 ± 2.8 | 14.3 ± 11.4 | 13.2 ± 2.1 | < 0.001 |
Fat(% of energy) | 31.4 ± 6.9 | 30.7 ± 6.4 | 31.3 ± 6.3 | 31.0 ± 6.0 | 28.9 ± 6.4 | < 0.001 |
Food groups |
Refined grains(serving/d) | 1.31 (0.96–1.61) | 2.30 (1.92–2.68) | 3.59 (3.20–4.03) | 5.17 (4.73–5.72) | 8.62 (7.32–11.01) | < 0.001 |
Red meat (serving/week) | 0.21 (0.28–0.84) | 0.56 (0.35–0.98) | 0.70 (0.35–1.26) | 0.77 (0.42–1.26) | 0.84 (0.56–1.47) | < 0.001 |
Tomatoes(serving/d) | 0.47 (0.31–1.11) | 0.63 (0.31–1.11) | 0.63 (0.31–1.11) | 0.63 (0.31–1.11) | 0.63 (0.31–1.11) | < 0.001 |
Fruit juice(serving/d) | 0.03 (0.00–0.09) | 0.04 (0.01–0.10) | 0.04 (0.01–0.10) | 0.04 (0.01–0.10) | 0.04 (0.01–0.11) | 0.644 |
Potatoes(serving/d) | 0.06 (0.01–0.09) | 0.06 (0.02–0.12) | 0.06 (0.02–0.12) | 0.06 (0.03–0.12) | 0.06 (0.04–0.15) | < 0.001 |
Processed meat(serving/week) | 0.06 (0.01–0.14) | 0.10 (0.02–0.24) | 0.13 (0.04–0.30) | 0.13 (0.05–0.32) | 0.16 (0.08–0.42) | < 0.001 |
Other vegetables(serving/d) | 1.70 (1.01–3.12) | 2.00 (1.28–2.97) | 1.91 (1.15–2.87) | 1.91 (1.10–3.12) | 1.84 (1.08–2.74) | 0.079 |
Tea(serving/d) | 2.08 (1.04–3.12) | 2.08 (1.04–3.12) | 2.08 (1.04–3.12) | 2.08 (1.04–3.12) | 2.08 (1.04–3.12) | < 0.001 |
Coffee(serving/week) | 0.01 (0.00–0.13) | 0.02 (0.00–0.13) | 0.03 (0.00–0.24) | 0.02 (0.00–0.24) | 0.03 (0.00–0.25) | 0.600 |
High-fat dairy products(serving/d) | 1.27 (0.85–1.97) | 1.19 (0.78–1.88) | 1.18 (0.72–1.91) | 1.14 (0.72–1.85) | 1.10 (0.66–1.61) | < 0.001 |
Green leafy vegetables(serving/d) | 0.33 (0.15–0.63) | 0.34 (0.17–0.64) | 0.29 (0.14–0.56) | 0.27 (0.13–0.52) | 0.24 (0.11–0.48) | < 0.001 |
Table
3 shows the results on the HR of CKD according to quintiles of EDIH, EDIR, ELIH, and ELIR. Based on the age and sex-adjusted model, compared to participants in the first quintile of EDIR, ELIH, and ELIR, participants in the fifth quintile of these indices had a higher risk of incident CKD by 28, 34, and 24%, respectively [EDIR (HR = 1.28; 95%CI: 1.07–1.52), ELIH (HR = 1.34; 95%CI: 1.09–1.64), and ELIR (HR = 1.24; 95%CI: 1.04–1.48)]. Also, we observed a significant increase in HR of CKD per unit increase in the quintile of EDIR (P for trend: 0.006), ELIH (P for trend: 0.005), and ELIR (P for trend: 0.015) based on the age and sex-adjusted model. However, there was no significant association between the higher score of EDIH and the risk of developing CKD (HR = 1.06; 95%CI: 0.87–1.25).
Table 3
The association between the lifestyle and dietary insulinemic indices and incidence of chronic kidney disease: the Tehran Lipid and Glucose Study
EDIH |
Median score | 0.009 | 0.096 | 0.170 | 0.272 | 0.516 |
Follow up period | 7.66 ± 2.84 | 7.87 ± 2.68 | 7.67 ± 2.75 | 7.82 ± 2.66 | 7.77 ± 2.67 |
person-years | 9263.2 | 9500.2 | 9274.0 | 9456.8 | 9395.4 |
Case/Total | 304/1209 | 247/1207 | 243/1209 | 221/1208 | 200/1208 |
Incidence rate (10.000 person year) | 328 | 259 | 262 | 233 | 212 |
Model 1a | 1.00 (Ref) | 0.87 (0.73–1.03) | 1.02 (0.86–1.20) | 1.09 (0.91–1.29) | 1.06 (0.87–1.25) |
Model 2b | 1.00 (Ref) | 0.87 (0.73–1.05) | 1.03 (0.86–1.23) | 1.11 (0.92–1.33) | 1.08 (0.89–1.31) |
EDIR |
Median score | 0.312 | 0.517 | 0.695 | 0.918 | 1.386 |
Follow up period | 7.81 ± 2.86 | 7.76 ± 2.71 | 7.70 ± 2.73 | 7.85 ± 2.62 | 7.66 ± 2.69 |
person-years | 9441.5 | 9393.1 | 9311.5 | 9490.4 | 9256.6 |
Case/Total | 242/1208 | 218/1208 | 238/1209 | 251/1209 | 266/1208 |
Incidence rate (10.000 person year) | 261 | 229 | 255 | 267 | 281 |
Model 1a | 1.00 (Ref) | 1.03 (0.86–1.22) | 1.08 (0.91–1.29) | 1.05 (0.87–1.25) | 1.28 (1.07–1.52) |
Model 2b | 1.00 (Ref) | 1.01 (0.84–1.21) | 1.14 (0.95–1.38) | 1.02 (0.84–1.29) | 1.29 (1.06–1.57) |
ELIH |
Median score | 0.98 | 1.17 | 1.31 | 1.45 | 1.69 |
Follow up period | 7.79 ± 3.04 | 7.87 ± 2.64 | 7.77 ± 2.81 | 7.63 ± 2.76 | 7.47 ± 2.79 |
person-years | 9552.1 | 9335.2 | 9237.9 | 9051.7 | 8877.0 |
Case/Total | 145/1187 | 225/1186 | 248/1188 | 278/1186 | 303/1187 |
Incidence rate (10.000 person year) | 151 | 241 | 268 | 307 | 341 |
Model 1a | 1.00 (Ref) | 1.13 (0.92–1.39) | 1.23 (1.00–1.51) | 1.18 (0.97–1.44) | 1.34 (1.09–1.64) |
Model 2c | 1.00 (Ref) | 1.14 (0.91–1.41) | 1.25 (1.01–1.55) | 1.20 (0.97–1.48) | 1.35 (1.10–1.67) |
ELIR |
Median score | 2.37 | 3.20 | 4.14 | 5.38 | 8.06 |
Follow up period | 7.83 ± 3.01 | 7.85 ± 2.81 | 7.76 ± 2.85 | 7.73 ± 2.68 | 7.67 ± 2.65 |
person-years | 9323.1 | 9212.6 | 9187.3 | 9100.0 | 9226.0 |
Case/Total | 219/1187 | 242/1186 | 226/1188 | 234/1186 | 277/1187 |
Incidence rate (10.000 person year) | 237 | 262 | 245 | 257 | 297 |
Model 1a | 1.00 (Ref) | 1.06 (0.89–1.26) | 1.18 (0.98–1.40) | 1.13 (0.95–1.35) | 1.24 (1.04–1.48) |
Model 2c | 1.00 (Ref) | 1.07 (0.90–1.29) | 1.17 (0.97–1.41) | 1.20 (1.00–1.45) | 1.24 (1.02–1.51) |
In the multivariable-adjusted model, after adjusting for potential confounding factors, individuals in the highest quintile of EDIR (HR = 1.29; 95% CI: 1.06–1.57), ELIH (HR = 1.35; 95%CI: 1.10–1.67), and ELIR (HR = 1.24;95%CI:1.02–1.51) had significantly higher risk of incident CKD than those in the lowest quintile of these indices. Also, based on the final cox regression model, our findings showed that there is a significant increase in HR of CKD per unit increase in the quintile of EDIR (P for trend: 0.016), ELIH (P for trend: 0.006), and ELIR (P for trend: 0.026). However, no significant relationship was observed between EDIH and CKD risk, based on a fully adjusted model (HR = 1.08; 95%CI: 0.89–1.31).
Comparing the spline with linear modes showed no significant non-linear association between insulin indices and CKD incidence. Also, except for ELIR, other indices showed any significant linear relationship with CKD [EDIH (P-nonlinearity = 0.739 and P-linearity = 0.567), EDIR (P-nonlinearity = 0.147 and P-linearity = 0.174), ELIH (P-nonlinearity = 0.649 and P-linearity = 0.228), and ELIR (P-nonlinearity = 0.281 and P-linearity = 0.004)]. Models were adjusted for age, sex, BMI (only for EDIH and EDIR), physical activity (only for EDIH and EDIR), smoking, education level, baseline eGFR, energy intake, hypertension, and type 2 diabetes.
Discussion
In this population-based cohort study, we determined the insulinemic potential of diet and lifestyle indices, including EDIH, EDIR, ELIH, and ELIR, and assessed their relationship with the risk of developing CKD, independent of potential confounders, among the adult population. We showed that higher EDIR, ELIH, and ELIR scores were associated with a higher risk of incident CKD by 29, 35, and 24%, respectively, whereas no significant association was found between EDIH and risk of CKD.
A growing body of evidence suggests that insulin metabolism-related disorders such as central obesity, IR, and hyperinsulinemia can contribute to the progression and development of kidney dysfunction and an increased risk of CKD [
31]. On the other hand, some reports revealed that higher dietary and lifestyle insulinemic potential is associated with an increased risk of adiposity, IR, hyperinsulinemia, and type 2 diabetes [
19,
22]. Considering that the above-mentioned metabolic outcomes are each predisposing factors for an increased risk of kidney impairment, we hypothesized that a high insulinemic diet and lifestyle could also play a significant role in the pathogenesis of CKD. Although there is no study on the association of the insulinemic potential of diet and lifestyle with the risk of CKD, our findings are in agreement with the results of most previous studies supporting a direct link between a higher insulinemic diet and lifestyle with the risk of chronic metabolic diseases. A cohort study in the framework of TLGS indicated that higher scores of EDIR, ELIR, and ELIH were associated with an increased risk of T2DM, while no significant association was observed between EDIH and the risk of T2DM [
21]. Mokhtari et al. suggested that adherence to a lifestyle with a higher score of ELIH may be associated with an increment in the risk of IR and hyperinsulinemia. However, no significant association was found between a high insulinemic diet and the risk of insulin-related disorders [
19]. Additionally, a cohort study on a large sample of US female nurses showed that higher EDIH and ELIH were related to a higher risk of colorectal cancer in the young population [
16]. Furthermore, the Nurses’ Health Study findings reported that adherence to a dietary pattern with higher insulinemic potential was associated with a higher risk of T2DM [
22]. In general, the results of previous studies indicate that dietary and lifestyle patterns contributing to IR and hyperinsulinemia can play a remarkable role in the pathogenesis of metabolic disorders and their related chronic diseases, therefore, our findings on the positive association of high insulinemic diet and lifestyle with CKD risk can be logical and valuable findings.
Based on our main results, there is no significant association between a higher score of EDIH and the risk of CKD. Non-significant results regarding EDIH score with risk of metabolic disorders such as IR and T2D incident have also been seen in previous studies conducted on the Iranian population. Contrary to the results of studies conducted on other people, among the Iranian people, the EDIH score has indicated low potency in predicting the risk of metabolic disorders such as IR [
19], T2DM [
21], and CKD. This inconsistency in results can be mainly justified by the low consumption of dietary components of EDIH in our study population, which subsequently leads to lower estimated scores for the EDIH index among individuals. Also, in the current study, individuals’ intakes for the food components of EDIH were close to each other and did not have high dispersion, therefore, the estimated EDIH score in our study population had a narrow range. Moreover, the EDIH index was initially developed and validated in different populations. Therefore variations in diet patterns and genetic background exist in comparison to our population, which could potentially be responsible for this consistency in our results with others.
Our results suggest that hyperinsulinemia may be a potential mechanism linking dietary and lifestyle insulinemic indices to CKD development. The insulinemic effect of inappropriate food choices such as higher consumption of red and processed meat, margarine, refined grains, and sweetened beverages and lower consumption of vegetables, legumes, whole grain, and dairy products in combination with high body fat and sedentary life, as main parts of lifestyle, may play a key role in increasing chronic insulin secretion. It has been suggested that high chronic insulin secretion leads to beta cell dysfunction, increased central obesity, and a higher risk of IR [
19,
32]. Consequently, hyperinsulinemia, increased adiposity, and IR could result in developing kidney dysfunction and increased risk of CKD during a long period through the increments in glomerular hyperfiltration, endothelial dysfunction, albumin excretion, and inducing vascular permeability [
7,
8,
33]. Lastly, IR may cause glomerulosclerosis or atherosclerosis-related kidney impairment in the elderly via inducing oxidative stress and endothelial dysfunction [
34,
35].
Our study has several main strengths. To the best of our knowledge, this is the first study to investigate the association of the insulinemic potential of diet and lifestyle indices, including EDIH, EDIR, ELIH, and ELIR, with the risk of CKD in the adult population. The prospective design, long-term follow-up time, and as well as relatively large sample size are the other major strengths of this study. Also, in the current study, we used valid and reliable questionnaires to assess the data on participants’ dietary intakes and physical activity levels. Despite these strengths, this study is not without limitations. First, some measurement errors are inevitable because of using FFQ for dietary assessment; however, similar to other epidemiological studies, we have used a valid and reliable questionnaire, which minimizes this error. Second, Similar to most epidemiologic studies, the serum creatinine was measured only once in our study to detection of CKD; however, it has been recommended that creatinine be measured 3 times to enhance the accuracy of detecting CKD. We did not have data for the measurement of microalbuminuria, which could help us determine early kidney damage in participants based on insulin index scores, however, we used serum creatinine and eGFR to determine the occurrence of CKD in participants, which is a common method to determine CKD in epidemiological studies. Also, the level of plasma insulin and its related indicators did not measure in the participants, which could help determine the insulinemic effect of diet and lifestyle. Furthermore, although we controlled the effects of major confounding variables in our final statistical analysis, there may still be residual or unmeasured confounders, such as fluid intake and family history of CKD effects which cannot be ruled out.
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