Association of insulin resistance with chronic kidney disease in individuals without diabetes in a community population in South China

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Methods: We performed a cross-sectional survey among 2142 community-based participants without diabetes from southern China from June to October 2012 and excluded the incomplete data. We divided all the participants into four groups according to the quartiles of homeostasis model assessment of IR (HOMA-IR). Logistic regression models were used to explore the associations of IR with CKD in these subjects. Results: In the unadjusted model, compared with the quartile one group, IR was significantly associated with CKD (odds ratio [OR] = 2.24, P < 0.001; OR = 4.46, P < 0.001) in the quartile three and four groups, and the association was still significant (OR = 2.08, P = 0.005; OR = 3.89, P < 0.001 ) after adjusting for potential confounders (including age, current smoker, current alcohol use, physical inactivity, education level, systolic blood pressure, diastolic blood pressure, serum triglyceride, and body mass index). The area under the receiver operating characteristic curve (95% confidence interval) of HOMA-IR for diagnosing CKD was 0.67 (0.64, 0.71). The cut-off value was 2.5, the sensitivity was 75.2%, and the specificity was 56.4%. Conclusions: HOMA-IR is associated with CKD in participants without diabetes. Early intervention for IR is important for reducing the incidence of CKD. insulin resistance chronic kidney disease participants without diabetes Figures Figure 1 Background Chronic kidney disease (CKD) has become an important risk factor for global public health and has caused significant economic burden to patients and families. Studies have shown that the global prevalence of CKD is 9.1% [ 1 ], and the prevalence of CKD in China is 10.8% [ 2 ]. CKD has many aetiologies, such as metabolic syndrome (hypertension, hyperlipidaemia, hyperglycaemia, and obesity), which is closely related to the pathogenesis of CKD. Insulin resistance (IR) is the central link to metabolic syndrome and refers to a pathological state in which insulin promotes glucose uptake and utilisation and reduces the body’s responsiveness and sensitivity to the physiological effects of insulin because of genetic and environmental factors [ 3 ]. Studies have shown that IR is a common lesion in the early stage of CKD even when the estimated glomerular filtration rate (eGFR) is still within the normal range. IR is closely related to and interacts with CKD; therefore, IR is a risk factor for CKD progression. The severity of IR is directly related to the risk of CKD, and the incidence of CKD increases significantly with the increase in serum insulin level and IR [ 4 – 6 ]. There are still some controversies about the relationship between IR and CKD in individuals without diabetes. At present, few studies with large samples have been conducted on IR and CKD in individuals without diabetes in China. This study is the first to investigate the relationship between IR and CKD in a population in the southern community of China (the original residents of Wanzai community in Zhuhai City). Methods Research object A total of 2142 residents over 18 years old who have lived in Wanzai community of Zhuhai City for more than 10 years and had a local registered residence from June to October 2012 were selected to the stratified random cross-sectional survey. All participants gave their informed consent. This study adhered to the principles of the Declaration of Helsinki and was approved by the ethics committee of the Third Affiliated Hospital of Southern Medical University. The participants underwent oral glucose tolerance test (OGTT) to understand the glucose metabolism of patients, and patients who had no history of diabetes and had fasting plasma glucose (FPG) < 6.1 mmol/L or OGTT 2 h plasma glucose (2-hPG) < 11.1 mmol/L were included in this study. The exclusion criteria were as follows: (1) patients with a history of diabetes and currently taking hypoglycaemic drugs; (2) those with no history of diabetes and had FPG > 6.1 mmol/L or OGTT 2-HPG > 11.1 mmol / L; (3) and those with incomplete data on fasting blood glucose, insulin, blood creatinine, and blood triglycerides. A total of 1691 people were included in the study. Research methods A questionnaire survey was conducted by professional medical staff to record the sex, age, medical history, smoking, and drinking history of all subjects. Residents who completed the information registration were scheduled for the next physical examination, blood collection, and morning urine collection. On the day of the physical examination, the height, body mass, waist circumference, hip circumference, and blood pressure of the residents were measured. The morning urine was collected to assess the urine albumin to creatinine ratio (uACR). In addition, fasting blood sampling was used to measure serum creatinine, uric acid, blood sugar, insulin, blood lipids, etc. The collected samples were uniformly transported to the Experimental Center of the Third Affiliated Hospital of Southern Medical University within 3 h and were stored at 4°C until detection and application. The CKD-EPI equation was used to calculate the eGFR. Diagnostic criteria The CKD diagnostic criteria were as follows [ 7 ]: eGFR < 60 mL/min/1.73 m 2 or uACR ≥ 30 mg/g (3 mg/mmol) and lasts for three months or more. eGFR was used to calculate the glomerular filtration rate according to the CKD Epidemiology Collaboration (CKD-EPI) equation of the KDIGO guidelines in the United States. Formulas used for calculations IR diagnostic criteria IR was measured using the homeostasis model assessment of IR (HOMA-IR): HOMA-IR = fasting blood glucose × fasting insulin/22.5. At present, no normal range has been set for HOMA-IR, but its upper limit ranges from two to three in different populations. CKD-EPI formula: Male Scr ≤ 0.7mg/dL eGFR = 144 × (Scr/0.7) −0.329 × 0.993 Age Scr > 0.7mg/dL eGFR = 144 × (Scr/0.7) −1.209 × 0.993 Age Female Scr ≤ 0.9mg/dL eGFR = 141 × (Scr/0.9) −0.411 × 0.993 Age Scr > 0.9mg/dL eGFR = 141 × (Scr/0.9) −1.209 × 0.993 Age Statistical treatment The population was divided into the CKD and non-CKD groups. According to the HOMA-IR quartile, the population was then further divided into four groups (Q1, Q2, Q3, and Q4). Statistical analysis was conducted using SPSS 19.0 software. The measurement data of normal distribution are expressed in mean ± standard deviation. The skewed measurement data are represented by median and interquartile intervals. Enumeration data are expressed as percentages. Analysis of variance was used for the comparison between groups of continuous variables, and the chi-square test was used for the comparison of categorical variables. A multivariate logistic regression model was established to investigate the correlation between IR and CKD. The results are expressed using odds ratio (OR) and 95% confidence interval (CI). Three models were established: model 1 was uncorrected; model 2 was corrected for age, smoking, drinking, and exercise; and model 3 was corrected for age, smoking, drinking, exercise, blood pressure, triglyceride, and body mass index. The performance of the HOMA-IR index in the diagnosis of CKD was analysed using the ROC curve. P < 0.05 was considered statistically significant. Results Ordinary circumstances A total of 1691 participants were included in the study, and the average age of the participants was 50.84 ± 14.70 years. The prevalence of CKD in the total population was 15.5%. Table 1 shows that the interquartile spacings of HOMA-IR were 2.42 for Q1, Q2, Q3, and Q4, respectively. The prevalence rates of CKD were 13.7%, 11.1%, 27.9%, and 47.3% for Q1, Q2, Q3, and Q4, respectively. The prevalence rate of CKD in the HOMA-IR quartile group was statistically different (P < 0.05). Table 1 Baseline characteristics of the subjects according to HOMA-IR quartiles in participants without diabetes Characteristics HOMA-IR P Quartile one 2.42 (n = 423) Age (year) History of hypertension (%) 47 (11.21) 44 (10.40) 69 (16.31) 114 (26.95) < 0.001 Physical inactivity (%) 154 (36.50) 151 (35.70) 134 (31.68) 159 (37.59) 0.358 Current smoker (%) 67(15.8) 46 (10.87) 44 (10.40) 42 (9.93) 0.149 Current alcohol use (%) 120(28.43) 109 (25.77) 92 (21.75) 101 (23.88) 0.080 SBP (mm Hg) 122.93 ± 20.95 122.35 ± 18.90 130.87 ± 70.42 132.26 ± 18.80 0.001 DBP (mm Hg) 74.36 ± 10.83 77.08 ± 37.37 77.91 ± 11.06 80.95 ± 10.02 0.001 BMI (kg/m 2 ) 21.24 ± 2.75 21.91 ± 2.93 23.53 ± 3.18 25.07 ± 3.25 < 0.001 Scr (µmol/L) 72.70 ± 16.32 71.50 ± 15.20 71.90 ± 19.50 74.61 ± 16.51 0.044 FBG (mmol/L) 4.47 ± 0.38 4.60 ± 0.34 4.68 ± 0.38 4.86 ± 0.40 < 0.001 uric acid (µmol/L) 331.82 ± 91.03 328.95 ± 82.93 344.92 ± 92.92 381.15 ± 102.75 < 0.001 Insulin (pmol/L) 4.20 ± 0.92 6.75 ± 0.88 9.57 ± 1.30 16.55 ± 5.20 < 0.001 eGFR (mL/min) 101.55 ± 22.58 102.34 ± 21.06 102.70 ± 23.83 98.15 ± 23.92 0.015 uACR (mg/mmoL) 1.35 ± 1.62 1.16 ± 1.32 1.64 ± 2.13 2.30 ± 3.19 < 0.001 TG (mmol/L) 1.08 ± 0.60 1.23 ± 0.70 1.43 ± 0.78 1.95 ± 1.18 < 0.001 CKD (%) 36 (13.7) 29 (11.1) 73 (27.9) 124 (47.3) < 0.001 SBP: systolic blood pressure; DBP: diastolic blood pressure; BMI: body mass index; Scr: serum creatinine; FBG: fasting blood glucose; eGFR: estimated glomerular filtration; uACR: urinary albumin to creatinine ratio; TG: serum triglyceride. Basic characteristics of grouping with HOMA-IR interquartile interval The median HOMA-IR was 1.64. The systolic blood pressure, diastolic blood pressure, body mass index, serum creatinine, fasting blood glucose, blood uric acid, insulin, triglyceride, uACR, and other markers of water level in Q4 of HOMA-IR were higher than those in the other three groups, and the difference was statistically significant (P < 0.05). However, the level of eGFR in Q4 was lower than that in the other three groups (P < 0.05; Table 1 ). Relationship between HOMA-IR and CKD In the multifactor logistic regression model, the presence or absence of CKD was used as the secondary dependent variable, and HOMA-IR quartile was used as the rank variable into the regression model. Model 1 is the uncorrected model; model 2 was adjusted for age, smoking, drinking, and exercise; and model 3 was adjusted for age, smoking, drinking, exercise, blood pressure, triglyceride, and body mass index. As shown in Table 2 , compared with Q1, the prevalence of CKD in Q3 and Q4 increased significantly (OR = 2.24, P < 0.001; OR = 4.46, P < 0.001). After adjusting for age, smoking, drinking, and exercise, the prevalence rates of CKD in Q3 and Q4 were still higher than Q1 (OR = 2.54, P < 0.001; OR = 4.61, P < 0.001). After adjusting for systolic blood pressure, diastolic blood pressure, triglyceride, and body mass index, the prevalence of CKD in the third and fourth groups still increased significantly compared with the lowest quartile array (OR = 2.08, P = 0.005; OR = 3.89, P < 0.001). The results suggest that HOMA-IR is independently related to CKD in participants without diabetes. Table 2 Association between HOMA-IR and CKD in participants without diabetes Quartiles of HOMA-IR Model one a Model two b Model three c OR (95% CI) P OR (95% CI) P OR (95% CI) p Quartile one Reference Reference Reference Quartile two 0.79 (0.47–1.31) 0.362 0.87 (0.52–1.49) 0.629 0.83(0.46–1.51) 0.547 Quartile three 2.24 (1.46–3.43) < 0.001 2.54 (1.62–3.99) 0.000 2.08 (1.25–3.49) 0.005 Quartile four 4.46 (2.99–6.66) < 0.001 4.61 (3.00–7.07) 0.000 3.89 (2.36–6.41) < 0.001 a Unadjusted b Adjusted for age, current smoker, alcohol use, physical inactivity, education c Adjusted for above + systolic blood pressure, diastolic blood pressure, serum triglyceride, and body mass index. Figure legends ROC curve evaluation of the efficacy of HOMA-IR in predicting CKD in participants without diabetes By using HOMA-IR as the detection variable, the area under the curve (95% CI) for CKD diagnosis was 0.67 (0.64, 0.71), the sensitivity was 75.2%, and the specificity was 54.6%. The breakpoint value of HOMA-IR diagnosis for CKD is 2.5 (Fig. 1 ). Discussion IR is a pathological state caused by genetic and environmental factors in which insulin promotes glucose uptake and utilisation and causes the body to be less responsive and sensitive to the physiological action of insulin. It mainly acts on the liver, fat, and muscle tissue. The resulting glucose and lipid metabolism disorder can lead to diabetes, coronary heart disease, obesity, metabolic syndrome, and other metabolic disorders [ 3 ]. IR not only exists in diabetes but also in patients with non-diabetes kidney disease, thus increasing the risk of early death in CKD patients [ 8 , 9 ]. IR can exist at any stage of CKD and is a common complication of patients undergoing haemodialysis and peritoneal dialysis [ 10 ]. IR in CKD patients is caused by multiple factors, such as exercise, chronic inflammation, oxidative stress, vitamin D deficiency, metabolic acidosis, anaemia, lipid metabolism disorder, and intestinal flora disorder [ 11 – 14 ]. The occurrence and progress of CKD caused by IR are also affected by multiple pathogenic mechanisms. Hyperinsulinemia can lead to glomerular ultrafiltration, endothelial dysfunction, and increased vascular permeability via the insulin-like growth factor-1 pathway, thus leading to proteinuria. At the same time, it can also promote renal fibrosis via transforming growth factor beta. In patients without diabetes, even short-term insulin injections will increase urinary protein excretion. On the contrary, the protein in the renal tubule may cause tubulointerstitial damage and fibrosis [ 15 – 18 ]. In the current study, we found that a higher HOMA-IR index is correlated with a higher risk of CKD. IR is an independent risk factor for CKD in people without diabetes in southern China. However, the relationship between IR and CKD is still controversial in people without diabetes. A study in the United States found that the risk of CKD increased with the increase in the HOMA-IR index in middle-aged individuals without diabetes [ 9 ]. Landau et al. [ 19 ] found that HOMA-IR was negatively correlated with eGFR in individuals without diabetes with eGFR < 60 ml/min/1.73 m. Wang et al. [ 20 ] observed 286 patients without diabetes but with stage 1–3 CKD and found that HOMA-IR was positively correlated with urea nitrogen and serum creatinine levels in patients with early renal insufficiency and was negatively correlated with eGFR. A nine-year follow-up study found that the incidence of CKD increased significantly with the increase in serum insulin and IR levels in slightly overweight patients without diabetes [ 11 ]. The results of a single-centre study in Japan show that IR can predict the risk of death in 170 patients without diabetes who are undergoing dialysis [ 10 ]. All the above studies indicate that IR is a risk factor for CKD, which is consistent with the conclusion of the current study. However, a cross-sectional study of 574 participants without diabetes in the United States showed that the prevalence of CKD in a population with metabolic syndrome was high; however, only hypertension was associated with the prevalence of CKD in each component of metabolic syndrome (P < 0.05), and IR was not an independent risk factor for CKD [ 21 ]. They believed that the main reason for the difference between this study and the previous study was that this study diagnosed IR by using the direct detection method of the insulin inhibition test. The vast majority of IR detection uses indirect methods, such as HOMA-IR, to diagnose IR. Compared with direct detection methods, the variability of HOMA-IR detection methods is less than 40%. This may be the main reason for the inconsistent results. Conclusions The results of this study indicate that the early intervention of IR is of great significance in reducing CKD in individuals without diabetes. At present, few studies with large sample sizes have been conducted on IR and CKD in individuals without diabetes in China. This study is the first to conduct an epidemiological survey in the community population of southern China (the original residents of Wanchai community in Zhuhai City) to explore the relationship between IR and CKD. This study provides evidence regarding the relationship between IR and CKD in individuals without diabetes from the perspective of epidemiology and provides guidance for the prevention and treatment of chronic diseases. However, given that this study is a cross-sectional survey, the sampling population was limited, and the random sample is only representative. It is necessary to conduct long-term prospective research to further clarify the value of IR in predicting CKD in individuals without diabetes. Abbreviations IR: insulin resistance CKD: chronic kidney disease HOMA-IR: homeostasis model assessment of IR eGFR: estimated glomerular filtration rate OGTT: oral glucose tolerance test FPG: fasting plasma glucose 2-hPG: 2 h plasma glucose uACR: urine albumin to creatinine ratio Scr: serum creatine Declarations Ethics approval and consent to participate The experimental scheme was approved by the ethics committee of the Third Affiliated Hospital of Southern Medical University (201,708,011). Consent for publication Not Applicable. Availability of data and materials The datasets during and/or analysed during the current study available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was supported by the National Natural Science Foundation of China (81270840) and the Clinical Research Cultivation Project of Southern Medical University (LC2016PY047). Authors’ contributions LJ and ZQ reviewed the literature, drafted and revised the manuscript, created the figures, and translated the manuscript. LJ and LZ collected the samples and data and revised the manuscript. ZH provided critical comments. Acknowledgements Experimental Center of the Third Affiliated Hospital of Southern Medical University. References Wang V, Vilme H, Maciejewski ML, Boulware LE. The Economic Burden of Chronic Kidney Disease and End-Stage Renal Disease[J]. Semin Nephrol. 2016;36(4):319-30. Zhang L, Zhao MH, Zuo L, Wang Y, Yu F, Zhang H, et al. China Kidney Disease Network (CK-NET) 2016 Annual Data Report[J]. Kidney Int Suppl (2011). 2020;10(2):e97-185. Jung SH, Jung CH, Reaven GM, Kim SH. Adapting to insulin resistance in obesity: role of insulin secretion and clearance[J]. Diabetologia. 2018;61(3):681-7. Chen J, Muntner P, Hamm LL, Fonseca V, Batuman V, Whelton PK, et al. Insulin resistance and risk of chronic kidney disease in nondiabetic US adults[J]. J Am Soc Nephrol. 2003;14(2):469-77. Conkar S, Mir S. Relationship of insulin resistance to vitamin d status in children with nondiabetic chronic kidney disease[J]. Saudi J Kidney Dis Transpl. 2017;28(5):1078-84. Wakino S, Minakuchi H, Miya K, Takamatsu N, Tada H, Tani E, et al. Aldosterone and Insulin Resistance: Vicious Combination in Patients on Maintenance Hemodialysis[J]. Ther Apher Dial. 2018;22(2):142-51. Kumar BV, Mohan T. Retrospective Comparison of Estimated GFR using 2006 MDRD, 2009 CKD-EPI and Cockcroft-Gault with 24 Hour Urine Creatinine Clearance[J]. J Clin Diagn Res. 2017;11(5):C9-12. de Boer IH, Zelnick L, Afkarian M, Ayers E, Curtin L, Himmelfarb J, et al. Impaired Glucose and Insulin Homeostasis in Moderate-Severe CKD[J]. J Am Soc Nephrol. 2016;27(9):2861-71. Cao W, Shi M, Wu L, Yang Z, Yang X, Liu H, et al. A renal-cerebral-peripheral sympathetic reflex mediates insulin resistance in chronic kidney disease[J]. EBioMedicine. 2018,37:281-93. Xu H, Carrero JJ. Insulin resistance in chronic kidney disease[J]. Nephrology (Carlton). 2017;22 Suppl 4:31-4. Chen J, Muntner P, Hamm LL, Fonseca V, Batuman V, Whelton PK, et al. Insulin resistance and risk of chronic kidney disease in nondiabetic US adults[J]. J Am Soc Nephrol. 2003;14(2):469-77. Juszczak F, Caron N, Mathew AV, Declèves AE. Critical Role for AMPK in Metabolic Disease-Induced Chronic Kidney Disease[J]. Int J Mol Sci. 2020;21(21):7994. Guthoff M, Wagner R, Vosseler D, Peter A, Nadalin S, Häring HU, et al. Impact of end-stage renal disease on glucose metabolism-a matched cohort analysis[J]. Nephrol Dial Transplant. 2017;32(4):670-6. Tan Z, Ye Z, Zhang J, Chen Y, Cheng C, Wang C, et al. Serum irisin levels correlated to peritoneal dialysis adequacy in nondiabetic peritoneal dialysis patients[J]. PLoS One. 2017;12(4):e176137. Tahar A, Zerdoumi F, Saidani M, Griene L, Koceir EA. [Effects of oral vitamin D(3) supplementation in stage 3 chronic kidney disease subjects: insulin resistance syndrome and hormonal disturb interactions][J]. Ann Biol Clin (Paris). 2018;76(3):313-25. Roshanravan B, Zelnick LR, Djucovic D, Gu H, Alvarez JA, Ziegler TR, et al. Chronic kidney disease attenuates the plasma metabolome response to insulin[J]. JCI Insight. 2018,3(16):e122219. Pammer LM, Lamina C, Schultheiss UT, Kotsis F, Kollerits B, Stockmann H, et al. Association of the metabolic syndrome with mortality and major adverse cardiac events: A large chronic kidney disease cohort[J]. J Intern Med. 2021;290(6):1219-32. Chalupsky M, Goodson DA, Gamboa JL, Roshanravan B. New insights into muscle function in chronic kidney disease and metabolic acidosis[J]. Curr Opin Nephrol Hypertens. 2021;30(3):369-76. Landau M, Kurella-Tamura M, Shlipak MG, Kanaya A, Strotmeyer E, Koster A, et al. Correlates of insulin resistance in older individuals with and without kidney disease[J]. Nephrol Dial Transplant. 2011;26(9):2814-19. Wang CJ, Bao XR, Du GW, Wang Y, Chen K, Shen ML, et al. Effects of insulin resistance on left ventricular hypertrophy in patients with CKD stage 1-3[J]. Int Urol Nephrol. 2014;46(8):1609-17. Johns BR, Pao AC, Kim SH. Metabolic syndrome, insulin resistance and kidney function in non-diabetic individuals[J]. Nephrol Dial Transplant. 2012;27(4):1410-5. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Nov, 2024 Read the published version in BMC Nephrology → Version 1 posted Editorial decision: Revision requested 04 Sep, 2024 Reviews received at journal 02 Sep, 2024 Reviewers agreed at journal 19 Aug, 2024 Reviews received at journal 09 Jul, 2024 Reviewers agreed at journal 05 Jul, 2024 Reviews received at journal 30 Jun, 2024 Reviewers agreed at journal 30 Jun, 2024 Reviewers invited by journal 09 Jun, 2024 Editor assigned by journal 08 Jun, 2024 Editor invited by journal 12 Apr, 2024 Submission checks completed at journal 12 Apr, 2024 First submitted to journal 06 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4229443","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291383959,"identity":"a0048ab2-9083-4863-ad60-faa4de40ae71","order_by":0,"name":"Jiamin Li","email":"","orcid":"","institution":"Nanfang Hospital Baiyun Branch of Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiamin","middleName":"","lastName":"Li","suffix":""},{"id":291383960,"identity":"1904ca54-ba1d-4ec3-85cc-a188b1c491af","order_by":1,"name":"Qin Zhou","email":"","orcid":"","institution":"The Central Hospital of Wuhan","correspondingAuthor":false,"prefix":"","firstName":"Qin","middleName":"","lastName":"Zhou","suffix":""},{"id":291383961,"identity":"4c646dc3-667e-4832-bd81-e19ae022b8a3","order_by":2,"name":"Zhen Liu","email":"","orcid":"","institution":"Nanfang Hospital Baiyun Branch of Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Liu","suffix":""},{"id":291383962,"identity":"03847656-eaa5-44a6-953c-8f5d68234aa4","order_by":3,"name":"Hequn Zou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYBACAxiDXwJMScgQr0VyBgNjA1ALD/FaDG6AtTAQ1mLO3rxNuuCPTeLm283HH92oseBhYD98dAM+LZY9x8qkZ/CkJW67cyyxOecY0GE8aWk38DrsRo6ZNI/E4cRtN3IMm3PYgFokeMzwa7n/BqjF4HDi5hkgLf+I0XKDB6gl4XDiBgmgltw2YrScSSu25jmQZjzjRlri7Nw+CR42gn45fnjjbZ4/NrL9M5IPfM75VifHz374GF4tDEhRAwFsBJRj0TIKRsEoGAWjAB0AAMfiRidpAVRjAAAAAElFTkSuQmCC","orcid":"","institution":"Pinghu Hospital, Shenzhen University","correspondingAuthor":true,"prefix":"","firstName":"Hequn","middleName":"","lastName":"Zou","suffix":""}],"badges":[],"createdAt":"2024-04-07 03:44:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4229443/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4229443/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12882-024-03866-7","type":"published","date":"2024-11-30T15:57:28+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55006413,"identity":"98e21f65-59f1-4516-93d8-2dfa74f9be1c","added_by":"auto","created_at":"2024-04-19 18:57:56","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":25522,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analysis of HOMA-IR in predicting CKD.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4229443/v1/1898282400f496fb8c238e38.jpg"},{"id":70388637,"identity":"6f0531ea-21af-494a-87e1-97854c1a8202","added_by":"auto","created_at":"2024-12-02 17:26:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":468255,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4229443/v1/d6485ec3-923d-4847-9bd3-644f294a0a4d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of insulin resistance with chronic kidney disease in individuals without diabetes in a community population in South China","fulltext":[{"header":"Background","content":"\u003cp\u003eChronic kidney disease (CKD) has become an important risk factor for global public health and has caused significant economic burden to patients and families. Studies have shown that the global prevalence of CKD is 9.1% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], and the prevalence of CKD in China is 10.8% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. CKD has many aetiologies, such as metabolic syndrome (hypertension, hyperlipidaemia, hyperglycaemia, and obesity), which is closely related to the pathogenesis of CKD. Insulin resistance (IR) is the central link to metabolic syndrome and refers to a pathological state in which insulin promotes glucose uptake and utilisation and reduces the body\u0026rsquo;s responsiveness and sensitivity to the physiological effects of insulin because of genetic and environmental factors [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Studies have shown that IR is a common lesion in the early stage of CKD even when the estimated glomerular filtration rate (eGFR) is still within the normal range. IR is closely related to and interacts with CKD; therefore, IR is a risk factor for CKD progression. The severity of IR is directly related to the risk of CKD, and the incidence of CKD increases significantly with the increase in serum insulin level and IR [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are still some controversies about the relationship between IR and CKD in individuals without diabetes. At present, few studies with large samples have been conducted on IR and CKD in individuals without diabetes in China. This study is the first to investigate the relationship between IR and CKD in a population in the southern community of China (the original residents of Wanzai community in Zhuhai City).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eResearch object\u003c/p\u003e \u003cp\u003eA total of 2142 residents over 18 years old who have lived in Wanzai community of Zhuhai City for more than 10 years and had a local registered residence from June to October 2012 were selected to the stratified random cross-sectional survey. All participants gave their informed consent. This study adhered to the principles of the Declaration of Helsinki and was approved by the ethics committee of the Third Affiliated Hospital of Southern Medical University. The participants underwent oral glucose tolerance test (OGTT) to understand the glucose metabolism of patients, and patients who had no history of diabetes and had fasting plasma glucose (FPG)\u0026thinsp;\u0026lt;\u0026thinsp;6.1 mmol/L or OGTT 2 h plasma glucose (2-hPG)\u0026thinsp;\u0026lt;\u0026thinsp;11.1 mmol/L were included in this study. The exclusion criteria were as follows: (1) patients with a history of diabetes and currently taking hypoglycaemic drugs; (2) those with no history of diabetes and had FPG\u0026thinsp;\u0026gt;\u0026thinsp;6.1 mmol/L or OGTT 2-HPG\u0026thinsp;\u0026gt;\u0026thinsp;11.1 mmol / L; (3) and those with incomplete data on fasting blood glucose, insulin, blood creatinine, and blood triglycerides. A total of 1691 people were included in the study.\u003c/p\u003e \u003cp\u003eResearch methods\u003c/p\u003e \u003cp\u003eA questionnaire survey was conducted by professional medical staff to record the sex, age, medical history, smoking, and drinking history of all subjects. Residents who completed the information registration were scheduled for the next physical examination, blood collection, and morning urine collection. On the day of the physical examination, the height, body mass, waist circumference, hip circumference, and blood pressure of the residents were measured. The morning urine was collected to assess the urine albumin to creatinine ratio (uACR). In addition, fasting blood sampling was used to measure serum creatinine, uric acid, blood sugar, insulin, blood lipids, etc. The collected samples were uniformly transported to the Experimental Center of the Third Affiliated Hospital of Southern Medical University within 3 h and were stored at 4\u0026deg;C until detection and application. The CKD-EPI equation was used to calculate the eGFR.\u003c/p\u003e \u003cp\u003eDiagnostic criteria\u003c/p\u003e \u003cp\u003eThe CKD diagnostic criteria were as follows [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]: eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e or uACR\u0026thinsp;\u0026ge;\u0026thinsp;30 mg/g (3 mg/mmol) and lasts for three months or more. eGFR was used to calculate the glomerular filtration rate according to the CKD Epidemiology Collaboration (CKD-EPI) equation of the KDIGO guidelines in the United States.\u003c/p\u003e \u003cp\u003eFormulas used for calculations\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eIR diagnostic criteria\u003c/h2\u003e \u003cp\u003eIR was measured using the homeostasis model assessment of IR (HOMA-IR): HOMA-IR\u0026thinsp;=\u0026thinsp;fasting blood glucose \u0026times; fasting insulin/22.5. At present, no normal range has been set for HOMA-IR, but its upper limit ranges from two to three in different populations.\u003c/p\u003e \u003cp\u003eCKD-EPI formula:\u003c/p\u003e \u003cp\u003eMale Scr\u0026thinsp;\u0026le;\u0026thinsp;0.7mg/dL eGFR\u0026thinsp;=\u0026thinsp;144 \u0026times; (Scr/0.7)\u003csup\u003e\u0026minus;0.329\u003c/sup\u003e \u0026times; 0.993\u003csup\u003eAge\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eScr\u0026thinsp;\u0026gt;\u0026thinsp;0.7mg/dL eGFR\u0026thinsp;=\u0026thinsp;144 \u0026times; (Scr/0.7)\u003csup\u003e\u0026minus;1.209\u003c/sup\u003e \u0026times; 0.993\u003csup\u003eAge\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eFemale Scr\u0026thinsp;\u0026le;\u0026thinsp;0.9mg/dL eGFR\u0026thinsp;=\u0026thinsp;141 \u0026times; (Scr/0.9)\u003csup\u003e\u0026minus;0.411\u003c/sup\u003e \u0026times; 0.993\u003csup\u003eAge\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eScr\u0026thinsp;\u0026gt;\u0026thinsp;0.9mg/dL eGFR\u0026thinsp;=\u0026thinsp;141 \u0026times; (Scr/0.9)\u003csup\u003e\u0026minus;1.209\u003c/sup\u003e \u0026times; 0.993\u003csup\u003eAge\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eStatistical treatment\u003c/p\u003e \u003cp\u003eThe population was divided into the CKD and non-CKD groups. According to the HOMA-IR quartile, the population was then further divided into four groups (Q1, Q2, Q3, and Q4). Statistical analysis was conducted using SPSS 19.0 software. The measurement data of normal distribution are expressed in mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. The skewed measurement data are represented by median and interquartile intervals. Enumeration data are expressed as percentages. Analysis of variance was used for the comparison between groups of continuous variables, and the chi-square test was used for the comparison of categorical variables. A multivariate logistic regression model was established to investigate the correlation between IR and CKD. The results are expressed using odds ratio (OR) and 95% confidence interval (CI). Three models were established: model 1 was uncorrected; model 2 was corrected for age, smoking, drinking, and exercise; and model 3 was corrected for age, smoking, drinking, exercise, blood pressure, triglyceride, and body mass index. The performance of the HOMA-IR index in the diagnosis of CKD was analysed using the ROC curve. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOrdinary circumstances\u003c/p\u003e \u003cp\u003eA total of 1691 participants were included in the study, and the average age of the participants was 50.84\u0026thinsp;\u0026plusmn;\u0026thinsp;14.70 years. The prevalence of CKD in the total population was 15.5%. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that the interquartile spacings of HOMA-IR were \u0026lt;\u0026thinsp;1.13, 1.13\u0026ndash;1.64, 1.64\u0026ndash;2.42, and \u0026gt;\u0026thinsp;2.42 for Q1, Q2, Q3, and Q4, respectively. The prevalence rates of CKD were 13.7%, 11.1%, 27.9%, and 47.3% for Q1, Q2, Q3, and Q4, respectively. The prevalence rate of CKD in the HOMA-IR quartile group was statistically different (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the subjects according to HOMA-IR quartiles in participants without diabetes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuartile one\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.13\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;422)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuartile two\u003c/p\u003e \u003cp\u003e1.13\u0026ndash;1.64\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;423)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuartile three\u003c/p\u003e \u003cp\u003e1.64\u0026ndash;2.42\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;423)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQuartile four\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2.42\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;423)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of hypertension (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47 (11.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44 (10.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69 (16.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e114 (26.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical inactivity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e154 (36.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e151 (35.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e134 (31.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e159 (37.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67(15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46 (10.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44 (10.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42 (9.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent alcohol use (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120(28.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109 (25.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92 (21.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e101 (23.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e122.93\u0026thinsp;\u0026plusmn;\u0026thinsp;20.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122.35\u0026thinsp;\u0026plusmn;\u0026thinsp;18.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e130.87\u0026thinsp;\u0026plusmn;\u0026thinsp;70.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e132.26\u0026thinsp;\u0026plusmn;\u0026thinsp;18.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.36\u0026thinsp;\u0026plusmn;\u0026thinsp;10.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.08\u0026thinsp;\u0026plusmn;\u0026thinsp;37.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.91\u0026thinsp;\u0026plusmn;\u0026thinsp;11.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80.95\u0026thinsp;\u0026plusmn;\u0026thinsp;10.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.24\u0026thinsp;\u0026plusmn;\u0026thinsp;2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.53\u0026thinsp;\u0026plusmn;\u0026thinsp;3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.07\u0026thinsp;\u0026plusmn;\u0026thinsp;3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScr (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.70\u0026thinsp;\u0026plusmn;\u0026thinsp;16.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.50\u0026thinsp;\u0026plusmn;\u0026thinsp;15.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.90\u0026thinsp;\u0026plusmn;\u0026thinsp;19.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.61\u0026thinsp;\u0026plusmn;\u0026thinsp;16.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003euric acid (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e331.82\u0026thinsp;\u0026plusmn;\u0026thinsp;91.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e328.95\u0026thinsp;\u0026plusmn;\u0026thinsp;82.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e344.92\u0026thinsp;\u0026plusmn;\u0026thinsp;92.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e381.15\u0026thinsp;\u0026plusmn;\u0026thinsp;102.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin (pmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.55\u0026thinsp;\u0026plusmn;\u0026thinsp;5.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (mL/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e101.55\u0026thinsp;\u0026plusmn;\u0026thinsp;22.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102.34\u0026thinsp;\u0026plusmn;\u0026thinsp;21.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e102.70\u0026thinsp;\u0026plusmn;\u0026thinsp;23.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98.15\u0026thinsp;\u0026plusmn;\u0026thinsp;23.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003euACR (mg/mmoL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.30\u0026thinsp;\u0026plusmn;\u0026thinsp;3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36 (13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73 (27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e124 (47.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSBP: systolic blood pressure; DBP: diastolic blood pressure; BMI: body mass index; Scr: serum creatinine; FBG: fasting blood glucose; eGFR: estimated glomerular filtration; uACR: urinary albumin to creatinine ratio; TG: serum triglyceride.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBasic characteristics of grouping with HOMA-IR interquartile interval\u003c/p\u003e \u003cp\u003eThe median HOMA-IR was 1.64. The systolic blood pressure, diastolic blood pressure, body mass index, serum creatinine, fasting blood glucose, blood uric acid, insulin, triglyceride, uACR, and other markers of water level in Q4 of HOMA-IR were higher than those in the other three groups, and the difference was statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, the level of eGFR in Q4 was lower than that in the other three groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRelationship between HOMA-IR and CKD\u003c/p\u003e \u003cp\u003eIn the multifactor logistic regression model, the presence or absence of CKD was used as the secondary dependent variable, and HOMA-IR quartile was used as the rank variable into the regression model. Model 1 is the uncorrected model; model 2 was adjusted for age, smoking, drinking, and exercise; and model 3 was adjusted for age, smoking, drinking, exercise, blood pressure, triglyceride, and body mass index. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, compared with Q1, the prevalence of CKD in Q3 and Q4 increased significantly (OR\u0026thinsp;=\u0026thinsp;2.24, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; OR\u0026thinsp;=\u0026thinsp;4.46, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjusting for age, smoking, drinking, and exercise, the prevalence rates of CKD in Q3 and Q4 were still higher than Q1 (OR\u0026thinsp;=\u0026thinsp;2.54, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; OR\u0026thinsp;=\u0026thinsp;4.61, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjusting for systolic blood pressure, diastolic blood pressure, triglyceride, and body mass index, the prevalence of CKD in the third and fourth groups still increased significantly compared with the lowest quartile array (OR\u0026thinsp;=\u0026thinsp;2.08, P\u0026thinsp;=\u0026thinsp;0.005; OR\u0026thinsp;=\u0026thinsp;3.89, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The results suggest that HOMA-IR is independently related to CKD in participants without diabetes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between HOMA-IR and CKD in participants without diabetes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eQuartiles of HOMA-IR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel one\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel two\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel three\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile two\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79 (0.47\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87 (0.52\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.83(0.46\u0026ndash;1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile three\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.24 (1.46\u0026ndash;3.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.54 (1.62\u0026ndash;3.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.08 (1.25\u0026ndash;3.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile four\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.46 (2.99\u0026ndash;6.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.61 (3.00\u0026ndash;7.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.89 (2.36\u0026ndash;6.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003eUnadjusted\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003eb\u003c/sup\u003eAdjusted for age, current smoker, alcohol use, physical inactivity, education\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ec\u003c/sup\u003eAdjusted for above +\u0026thinsp;systolic blood pressure, diastolic blood pressure, serum triglyceride, and body mass index.\u003cb\u003eFigure legends\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eROC curve evaluation of the efficacy of HOMA-IR in predicting CKD in participants without diabetes\u003c/p\u003e \u003cp\u003eBy using HOMA-IR as the detection variable, the area under the curve (95% CI) for CKD diagnosis was 0.67 (0.64, 0.71), the sensitivity was 75.2%, and the specificity was 54.6%. The breakpoint value of HOMA-IR diagnosis for CKD is 2.5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIR is a pathological state caused by genetic and environmental factors in which insulin promotes glucose uptake and utilisation and causes the body to be less responsive and sensitive to the physiological action of insulin. It mainly acts on the liver, fat, and muscle tissue. The resulting glucose and lipid metabolism disorder can lead to diabetes, coronary heart disease, obesity, metabolic syndrome, and other metabolic disorders [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. IR not only exists in diabetes but also in patients with non-diabetes kidney disease, thus increasing the risk of early death in CKD patients [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. IR can exist at any stage of CKD and is a common complication of patients undergoing haemodialysis and peritoneal dialysis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. IR in CKD patients is caused by multiple factors, such as exercise, chronic inflammation, oxidative stress, vitamin D deficiency, metabolic acidosis, anaemia, lipid metabolism disorder, and intestinal flora disorder [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The occurrence and progress of CKD caused by IR are also affected by multiple pathogenic mechanisms. Hyperinsulinemia can lead to glomerular ultrafiltration, endothelial dysfunction, and increased vascular permeability via the insulin-like growth factor-1 pathway, thus leading to proteinuria. At the same time, it can also promote renal fibrosis via transforming growth factor beta. In patients without diabetes, even short-term insulin injections will increase urinary protein excretion. On the contrary, the protein in the renal tubule may cause tubulointerstitial damage and fibrosis [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the current study, we found that a higher HOMA-IR index is correlated with a higher risk of CKD. IR is an independent risk factor for CKD in people without diabetes in southern China. However, the relationship between IR and CKD is still controversial in people without diabetes. A study in the United States found that the risk of CKD increased with the increase in the HOMA-IR index in middle-aged individuals without diabetes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Landau et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] found that HOMA-IR was negatively correlated with eGFR in individuals without diabetes with eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 ml/min/1.73 m. Wang et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] observed 286 patients without diabetes but with stage 1\u0026ndash;3 CKD and found that HOMA-IR was positively correlated with urea nitrogen and serum creatinine levels in patients with early renal insufficiency and was negatively correlated with eGFR. A nine-year follow-up study found that the incidence of CKD increased significantly with the increase in serum insulin and IR levels in slightly overweight patients without diabetes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The results of a single-centre study in Japan show that IR can predict the risk of death in 170 patients without diabetes who are undergoing dialysis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. All the above studies indicate that IR is a risk factor for CKD, which is consistent with the conclusion of the current study. However, a cross-sectional study of 574 participants without diabetes in the United States showed that the prevalence of CKD in a population with metabolic syndrome was high; however, only hypertension was associated with the prevalence of CKD in each component of metabolic syndrome (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and IR was not an independent risk factor for CKD [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. They believed that the main reason for the difference between this study and the previous study was that this study diagnosed IR by using the direct detection method of the insulin inhibition test. The vast majority of IR detection uses indirect methods, such as HOMA-IR, to diagnose IR. Compared with direct detection methods, the variability of HOMA-IR detection methods is less than 40%. This may be the main reason for the inconsistent results.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe results of this study indicate that the early intervention of IR is of great significance in reducing CKD in individuals without diabetes. At present, few studies with large sample sizes have been conducted on IR and CKD in individuals without diabetes in China. This study is the first to conduct an epidemiological survey in the community population of southern China (the original residents of Wanchai community in Zhuhai City) to explore the relationship between IR and CKD. This study provides evidence regarding the relationship between IR and CKD in individuals without diabetes from the perspective of epidemiology and provides guidance for the prevention and treatment of chronic diseases. However, given that this study is a cross-sectional survey, the sampling population was limited, and the random sample is only representative. It is necessary to conduct long-term prospective research to further clarify the value of IR in predicting CKD in individuals without diabetes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eIR: insulin resistance\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCKD: chronic kidney disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHOMA-IR: homeostasis model assessment of IR\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eeGFR: estimated glomerular filtration rate\u003c/p\u003e\n\u003cp\u003eOGTT: oral glucose tolerance test\u003c/p\u003e\n\u003cp\u003eFPG: fasting plasma glucose\u003c/p\u003e\n\u003cp\u003e2-hPG: 2 h plasma glucose\u003c/p\u003e\n\u003cp\u003euACR: urine albumin to creatinine ratio\u003c/p\u003e\n\u003cp\u003eScr: serum creatine\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental scheme was approved by the ethics committee of the Third Affiliated Hospital of Southern Medical University (201,708,011).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets during and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (81270840) and the Clinical Research Cultivation Project of Southern Medical University (LC2016PY047).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLJ and ZQ reviewed the literature, drafted and revised the manuscript, created the figures, and translated the manuscript. LJ and LZ collected the samples and data and revised the manuscript. ZH provided critical comments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExperimental Center of the Third Affiliated Hospital of Southern Medical University.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWang V, Vilme H, Maciejewski ML, Boulware LE. The Economic Burden of Chronic Kidney Disease and End-Stage Renal Disease[J]. Semin Nephrol. 2016;36(4):319-30.\u003c/li\u003e\n\u003cli\u003eZhang L, Zhao MH, Zuo L, Wang Y, Yu F, Zhang H, et al. China Kidney Disease Network (CK-NET) 2016 Annual Data Report[J]. Kidney Int Suppl (2011). 2020;10(2):e97-185.\u003c/li\u003e\n\u003cli\u003eJung SH, Jung CH, Reaven GM, Kim SH. Adapting to insulin resistance in obesity: role of insulin secretion and clearance[J]. Diabetologia. 2018;61(3):681-7.\u003c/li\u003e\n\u003cli\u003eChen J, Muntner P, Hamm LL, Fonseca V, Batuman V, Whelton PK, et al. Insulin resistance and risk of chronic kidney disease in nondiabetic US adults[J]. J Am Soc Nephrol. 2003;14(2):469-77.\u003c/li\u003e\n\u003cli\u003eConkar S, Mir S. Relationship of insulin resistance to vitamin d status in children with nondiabetic chronic kidney disease[J]. Saudi J Kidney Dis Transpl. 2017;28(5):1078-84.\u003c/li\u003e\n\u003cli\u003eWakino S, Minakuchi H, Miya K, Takamatsu N, Tada H, Tani E, et al. Aldosterone and Insulin Resistance: Vicious Combination in Patients on Maintenance Hemodialysis[J]. Ther Apher Dial. 2018;22(2):142-51.\u003c/li\u003e\n\u003cli\u003eKumar BV, Mohan T. Retrospective Comparison of Estimated GFR using 2006 MDRD, 2009 CKD-EPI and Cockcroft-Gault with 24 Hour Urine Creatinine Clearance[J]. J Clin Diagn Res. 2017;11(5):C9-12.\u003c/li\u003e\n\u003cli\u003ede Boer IH, Zelnick L, Afkarian M, Ayers E, Curtin L, Himmelfarb J, et al. Impaired Glucose and Insulin Homeostasis in Moderate-Severe CKD[J]. J Am Soc Nephrol. 2016;27(9):2861-71.\u003c/li\u003e\n\u003cli\u003eCao W, Shi M, Wu L, Yang Z, Yang X, Liu H, et al. A renal-cerebral-peripheral sympathetic reflex mediates insulin resistance in chronic kidney disease[J]. EBioMedicine. 2018,37:281-93.\u003c/li\u003e\n\u003cli\u003eXu H, Carrero JJ. Insulin resistance in chronic kidney disease[J]. Nephrology (Carlton). 2017;22 Suppl 4:31-4.\u003c/li\u003e\n\u003cli\u003eChen J, Muntner P, Hamm LL, Fonseca V, Batuman V, Whelton PK, et al. Insulin resistance and risk of chronic kidney disease in nondiabetic US adults[J]. J Am Soc Nephrol. 2003;14(2):469-77.\u003c/li\u003e\n\u003cli\u003eJuszczak F, Caron N, Mathew AV, Decl\u0026egrave;ves AE. Critical Role for AMPK in Metabolic Disease-Induced Chronic Kidney Disease[J]. Int J Mol Sci. 2020;21(21):7994.\u003c/li\u003e\n\u003cli\u003eGuthoff M, Wagner R, Vosseler D, Peter A, Nadalin S, H\u0026auml;ring HU, et al. Impact of end-stage renal disease on glucose metabolism-a matched cohort analysis[J]. Nephrol Dial Transplant. 2017;32(4):670-6.\u003c/li\u003e\n\u003cli\u003eTan Z, Ye Z, Zhang J, Chen Y, Cheng C, Wang C, et al. Serum irisin levels correlated to peritoneal dialysis adequacy in nondiabetic peritoneal dialysis patients[J]. PLoS One. 2017;12(4):e176137.\u003c/li\u003e\n\u003cli\u003eTahar A, Zerdoumi F, Saidani M, Griene L, Koceir EA. [Effects of oral vitamin D(3) supplementation in stage 3 chronic kidney disease subjects: insulin resistance syndrome and hormonal disturb interactions][J]. Ann Biol Clin (Paris). 2018;76(3):313-25.\u003c/li\u003e\n\u003cli\u003eRoshanravan B, Zelnick LR, Djucovic D, Gu H, Alvarez JA, Ziegler TR, et al. Chronic kidney disease attenuates the plasma metabolome response to insulin[J]. JCI Insight. 2018,3(16):e122219.\u003c/li\u003e\n\u003cli\u003ePammer LM, Lamina C, Schultheiss UT, Kotsis F, Kollerits B, Stockmann H, et al. Association of the metabolic syndrome with mortality and major adverse cardiac events: A large chronic kidney disease cohort[J]. J Intern Med. 2021;290(6):1219-32.\u003c/li\u003e\n\u003cli\u003eChalupsky M, Goodson DA, Gamboa JL, Roshanravan B. New insights into muscle function in chronic kidney disease and metabolic acidosis[J]. Curr Opin Nephrol Hypertens. 2021;30(3):369-76.\u003c/li\u003e\n\u003cli\u003eLandau M, Kurella-Tamura M, Shlipak MG, Kanaya A, Strotmeyer E, Koster A, et al. Correlates of insulin resistance in older individuals with and without kidney disease[J]. Nephrol Dial Transplant. 2011;26(9):2814-19.\u003c/li\u003e\n\u003cli\u003eWang CJ, Bao XR, Du GW, Wang Y, Chen K, Shen ML, et al. Effects of insulin resistance on left ventricular hypertrophy in patients with CKD stage 1-3[J]. Int Urol Nephrol. 2014;46(8):1609-17.\u003c/li\u003e\n\u003cli\u003eJohns BR, Pao AC, Kim SH. Metabolic syndrome, insulin resistance and kidney function in non-diabetic individuals[J]. Nephrol Dial Transplant. 2012;27(4):1410-5.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"insulin resistance, chronic kidney disease, participants without diabetes","lastPublishedDoi":"10.21203/rs.3.rs-4229443/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4229443/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e To explore the relationship of insulin resistance (IR) with chronic kidney disease (CKD) in individuals without diabetes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe performed a cross-sectional survey among 2142 community-based participants without diabetes from southern China from June to October 2012 and excluded the incomplete data. We divided all the participants into four groups according to the quartiles of homeostasis model assessment of IR (HOMA-IR). Logistic regression models were used to explore the associations of IR with CKD in these subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e In the unadjusted model, compared with the quartile one group, IR was significantly associated with CKD (odds ratio [OR] = 2.24, P \u0026lt; 0.001; OR = 4.46, P \u0026lt; 0.001) in the quartile three and four groups, and the association was still significant (OR = 2.08, P = 0.005; OR = 3.89, P \u0026lt; 0.001 ) after adjusting for potential confounders (including age, current smoker, current alcohol use, physical inactivity, education level, systolic blood pressure, diastolic blood pressure, serum triglyceride, and body mass index). The area under the receiver operating characteristic curve (95% confidence interval) of HOMA-IR for diagnosing CKD was 0.67 (0.64, 0.71). The cut-off value was 2.5, the sensitivity was 75.2%, and the specificity was 56.4%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e HOMA-IR is associated with CKD in participants without diabetes. Early intervention for IR is important for reducing the incidence of CKD.\u003c/p\u003e","manuscriptTitle":"Association of insulin resistance with chronic kidney disease in individuals without diabetes in a community population in South China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-19 18:57:51","doi":"10.21203/rs.3.rs-4229443/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-04T13:21:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-02T09:06:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179347177571538859376510271964343601027","date":"2024-08-20T00:36:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-10T02:51:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254095198223279709545699363016296506653","date":"2024-07-06T01:34:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-01T00:47:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255017650436709457533921215874779749754","date":"2024-06-30T23:21:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-09T16:17:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-08T10:00:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-12T12:22:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-12T12:21:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2024-04-07T03:32:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dde6f1f0-5296-41d4-bc93-4dc7ab3ff613","owner":[],"postedDate":"April 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-02T17:21:54+00:00","versionOfRecord":{"articleIdentity":"rs-4229443","link":"https://doi.org/10.1186/s12882-024-03866-7","journal":{"identity":"bmc-nephrology","isVorOnly":false,"title":"BMC Nephrology"},"publishedOn":"2024-11-30 15:57:28","publishedOnDateReadable":"November 30th, 2024"},"versionCreatedAt":"2024-04-19 18:57:51","video":"","vorDoi":"10.1186/s12882-024-03866-7","vorDoiUrl":"https://doi.org/10.1186/s12882-024-03866-7","workflowStages":[]},"version":"v1","identity":"rs-4229443","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4229443","identity":"rs-4229443","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}