HDL-3 Subfractions and IL-6 Dynamics: A Novel Biomarker Panel for Precision Prediction of Prediabetes in High-Risk Populations of Hunan, China

HDL-3 Subfractions and IL-6 Dynamics: A Novel Biomarker Panel for Precision Prediction of Prediabetes in High-Risk Populations of Hunan, China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article HDL-3 Subfractions and IL-6 Dynamics: A Novel Biomarker Panel for Precision Prediction of Prediabetes in High-Risk Populations of Hunan, China Yunlai Liang, Kun Wang, Kangkang Huang, Jingzhong Liao, Qizhuo Hou, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7065733/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Develop and evaluate a nomogram to predict prediabetes risk in Hunan province, China. Methods A retrospective cohort study was conducted with 124 prediabetic and 112 healthy individuals recruited from Xiangya Hospital between June and December 2023. Sample size was calculated based on an expected odds ratio of 2.0 for key predictors with 80% power (α = 0.05).. Analyzed baseline data using T test, Wilcoxon rank sum test or Chi-square test. Selected independent risk factors via univariate or multivariate logistic regression. Used 5-fold cross-validation and random forest algorithm. Results Insulin, HOMA-IR score, and levels of blood glucose, TG, TC, LDL-C, ALT, TBA, HbA1c, Hb, RBC and IL-6 were higher in prediabetes; HDL-3, HOMA-β, HOMA-IS and Scr were lower. HDL-3, IL-6 and LDL-C identified as independent factors. The nomogram demonstrated perfect discrimination in the training set (C-index 1.00, 95% CI 1.00–1.00), likely due to model overfitting, and acceptable performance in the validation set (C-index 0.78, 95% CI 0.73–0.84). AUC was 1.00 and 0.78 respectively. Well-calibrated and DCA verified clinical value. Conclusion HDL-3, IL-6 and LDL-C are strong predictors. Nomogram is a reliable tool for prediabetes prediction, beneficial for clinicians in prevention and individualized treatment. prediabetes prediction HDL-3 nomogram risk factor Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Prediabetes, an intermediate stage of glucose dysregulation that may precede type 2 diabetes mellitus (T2DM), affected approximately 720 million individuals worldwide in 2021 and will affect an estimated 1 billion people by 2045 [ 1 ] . In the US, around 10% of the people with prediabetes progress to diabetes each year [ 2 ] . The results of the Chinese epidemiological survey from 2007 to 2008 showed that the prevalence of prediabetes was 15.5% (about 148 million people ) according to the WHO 1999 standard, while from 2015 to 2017, it climbed to 35.2% in terms of the ADA 2018 standard [ 3 ] . Every year, nearly 5–10% of the prediabetic population develop toward diabetes mellitus (DM), with 70% of them eventually becoming DM [ 4 ] . Prediabetes is considered as a marker or watershed, which indicates that individuals have an increased risk of diabetes, cardiovascular and cerebrovascular diseases, microangiopathy, tumors, dementia and other diseases in the future. Early intervention can dramatically block prediabetes transformation into worse abnormalities. Therefore, differentiating individuals with a high-risk of prediabetes from the normal, and accordingly, intervention efforts are the key to prevent diabetes [ 3 ] . Nomogram plays a vital role by graphically representing the effect of each predictor on the involved disease outcome [ 5 ] . Even though previous studies on predictive models of prediabetes included age as an indicator [ 6 – 8 ] , their diagnostic performances were not prominent, where non-elderly patients might be excluded. In this study, five commonly used indicators were adopted to establish a prediction model, and the new nomogram plot identified risk factors for prediabetes development from a normal state of blood glucose in central south region of China. Moreover, some unnecessary clinical examinations would be avoided by using this model. 2 Materials and methods 2.1 The main workflow of this study The main workflow of this study summarized in Fig. 1 . 2.2 Study population A total of 124 patients with prediabetes in Department of Endocrinology and Nephrology, Xiangya Hospital of Central South University from January 2023 to December 2023 were enrolled, including 71 males and 53 females, aged 22–78 years with an average age of 57.76 ± 9.04 years. All prediabetes patients were defined according to the American Diabetes Association (ADA) 2020 diagnostic criteria, fulfilling at least one of the following criteria: ① Impaired fasting glucose (IFG), 100 mg/dL (5.6 mmol/L) ≤ FPG ≤ 125 mg /dL (6.9 mmol/ L); ② impaired glucose tolerance (IGT), 140 mg /dL (7.8 mmol /L) ≤ 2-hour post-OGTT glucose ≤ 199 mg/dL (11.0 mmol /L); and ③ HbA1c ranged from 5.7 to 6.4% (39–47 mmol/mol) [ 9 ] . The insulin resistance was determined by the Homeostasis Model Assessment for Insulin Resistance (HOMA-IR) index ≥ 3, otherwise there was no IR [ 10 ] . Meanwhile, 112 healthy subjects were recruited as the normal control group (NC), including 67 males and 45 females, aged 23–67 years old, averaged 57.97 ± 6.59 years. Participants meeting any of the following conditions were excluded: ① severe hyperlipidemia; ② acute coronary syndrome or coronary artery bypass surgery history; ③ pulmonary heart disease or chronic heart failure; ④ chronic disease toxicity or bacterial infection; ⑤ immune system disease cachexia or cancer; and ⑥ non-residents of Hunan Province, China. This study was approved by the Ethics Committee of Xiangya Hospital of Central South University (NO. 202203082). All subjects signed the informed consents. All patient data were anonymized prior to analysis, with identifiers removed by an independent data manager. Data collection included sex, age and blood biochemical indicators of TB, DB, ALB, ALT, Scr, HDL-3, IL-6, TNFα and insulin, as well as HOMA-IR, HOMA-β and HOMA-S. The biochemical tests were carried out on an automatic biochemical analyzer (Beckman AU580, Beckman Coulter, Brea, CA, USA). HDL-3 kits were purchased from Anhui Daqian Biological Engineering Co., Ltd., Anhui, China. TB, DB, ALB, ALT, Scr, insulin and other conventional biochemical kits were offered by the Beckman Kurt Experimental System (Changsha) Co., Ltd. IL-6 and TNF-α kits were provided by Jiangsu Kote Biological Co., Ltd. Jiangsu, China; HOMA-IR, HOMA-β and HOMA-S were calculated with the formulas of HOMA-IR = FPG (mmol / L) ×FINS (µU/mL) / 22.5, HOMA-S = 1 / HOMA-IR and HOMA-β %= 20 × FINS / (FPG-3.5) [ 11 ] . 2.3 Statistical Analysis All statistical analyses and graphics were performed with the SPSS software, version 25.0 (SPSS Inc., Chicago, IL) and the Deepwise &Beckman Coulter DxAI platform ( https://dxonline.deepwise.com ). Data with a normal distribution were presented as the mean ± standard deviation (SD) and compared with the Student’s independent t-test, while non-normally distributed data were presented as medians and interquartile ranges (IQRs) and analyzed with the Wilcoxon test. Categorical variables were displayed as numbers and percentages, and their differences were determined using the chi-squared test. Correlation estimation was illustrated through a heat map incorporated with Pearson's correlation analysis. Variables with p < 0.10 in univariate analysis were entered into stepwise multivariate logistic regression (entry criteria: p 0.10) to identify independent predictors for prediabetes and construction of the nomogram. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCA) were performed to validate the nomogram. For all the tests, a two-tailed P value of < 0.05 was considered to be statistically significant. 3 Results 3.1 Baseline characteristics of the participants The baseline characteristics of the studied cohorts were shown in Table 1 . There was no significant difference in age or gender between the prediabetes and the normal control group ( P > 0.05). The values of IR, serum insulin, blood glucose, HOMA-IR score, TG, TC, LDL-C, ALT, TBA, HbA1c, Hb, RBC and IL-6 levels in the prediabetes group were significantly high, while HDL-3, HOMA-β, HOMA-IS and Scr, significantly low, in contrast to those in the NC group. Although significant differences in ALT and Scr were found between the two groups, their mean values were within the normal reference range of the Chinese population. Table 1 Baseline Characteristics of the Individuals Variables Prediabetes(n = 124) NC(n = 112) W/t P Age (years) 56.500(51.000–65.000) 57.000(52.000–62.000) 6971.5 0.959 Gender female 49 (39.5%) 43 (38.4%) 0.860 male 75 (60.5%) 69 (61.6%) IR or not no 88 (71.0%) 105 (93.8%) < 0.01 yes 36 (29.0%) 7 (6.2%) Insulin (pmol/L) 49.700(33.075–73.400) 36.900(26.625–51.825) 8904 < 0.01 HDL3 (mg/mL) 35.745(31.872–40.275) 37.195(34.635–39.485) 5855.5 0.038 Glu (mmol/L) 6.410(6.218–6.620) 5.300(4.970–5.595) 13421 < 0.01 HOMA-IR 2.030(1.317–3.125) 1.210(0.882–1.740) 10132 < 0.01 HOMA-β 47.785(33.943–74.088) 61.590(45.078–79.390) 5711 0.019 HOMA-IS 0.490(0.320–0.760) 0.810(0.575–1.115) 3747.5 < 0.01 TG (umol/L) 1.690(1.100-2.583) 1.210(0.965–1.520) 9389.5 < 0.01 TC (umol/L) 5.445(4.795–6.210) 4.770(4.360–5.200) 9799.5 < 0.01 LDL-C (mmol/l) 3.515(2.930–3.980) 2.970(2.625–3.270) 9909 < 0.01 HDL-C (mmol/l) 1.275(1.107–1.475) 1.310(1.185–1.490) 6174.5 0.174 TB (umol/L) 5.100(3.950–6.950) 5.500(4.300-6.325) 6671.5 0.678 DB (umol/L) 21.700(16.375–35.175) 20.950(15.100-26.375) 7935.5 0.058 ALT (U/L) 24.350(19.650-29.775) 22.800(19.975–26.200) 8045 0.036 AST (U/L) 3.500(2.100-5.925) 3.000(2.000-5.125) 7423.5 0.360 TBA (umol/L) 79.179 ± 14.492 75.062 ± 13.355 2.262 0.025 Scr (umol/L) 86.580(77.987–98.888) 95.840(79.728–110.620) 5568.5 < 0.01 HbA1c (%) 5.950 ± 0.262 5.477 ± 0.309 11.023 < 0.01 2h-Glu (mmol/l) 9.109 ± 1.372 7.592 ± 2.378 2.095 0.039 RBC (10^12/L) 4.735(4.457–5.112) 4.555(4.277–4.890) 8581.5 < 0.01 PLT (10^12/L) 207.500(168.250–258.000) 199.000(166.000-238.000) 7603 0.209 Hb (g/L) 145.000(136.000-157.000) 139.000(131.000-150.250) 8449 < 0.01 IL-6 (pg/ml) 22.621(16.699–29.075) 13.669(7.386–19.134) 3763 < 0.01 TNF-α (pg/ml) 354.643(165.682-436.429) 315.500(221.833–414.500) 2537.5 0.715 Abbreviations: HDL-3, high density lipoprotein 3; Glu, glucose; HOMA, homeostasis model assessment; TG, triglyceride; TC, total cholesterol; LDL-C, low density lipoprotein-cholesterol; HDL-C, high-density lipoprotein-cholesterol; TB, total bilirubin; DB, direct bilirubin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; TBA, total bile acid; Scr, serum creatinine; HbA1c, hemoglobin A1c; 2h-Glu, 2-hour glucose; RBC, erythrocyte; Hb, hemoglobin; IL-6, interleukin 6; TNF-α, tumor necrosis factor α. 3.2 Correlation analysis between each index and the prediabetes Pearson / Spearman correlation was used to analyze the correlations between each index and the prediabetes. The results revealed that the individuals with prediabetes were significantly positively correlated with HDL-3, HOMA-β, HOMA-IS and Scr and negatively correlated with insulin level, blood glucose level, HOMA-IR score, TG, TC, LDL-C, ALT, HbA1c, RBC, Hb and IL-6 level, whereas those with insulin resistance had significantly positive relations with HDL-3, HOMA-β, HOMA-IS and Scr levels (Fig. 2 and supplementary table 1). 3.3 Characteristics Selection The clinical variables of prediabetes were selected by the multiple linear and multivariate logistic regression methods (Table 2 and Table 3 ). The multiple linear regression analysis revealed that HDL-3, HOMA-IS, IL-6, insulin and LDL-C were related to prediabetes occurrence. Subsequently, these five indicators were included in the multivariate logistic regression to screen for risk factors for prediabetes, and the determined indicators were further adopted for the stepwise regression analysis, resulting in that HDL-3(OR = 0.858, 95% CI 0.776–0.948, P = 0.003), IL-6(OR = 1.053, 95% CI 1.017–1.091, P = 0.004) and LDL-C(OR = 3.44, 95% CI 1.760–6.725, P < 0.001) were independent factors for prediabetes development through the healthy states and associated with the occurrence of prediabetes as well ( P < 0.05). Table 2 Multiple linear regression analysis Univariate analysis Multivariate analysis Dependent variable Variables Statistic P Regression Coefficient Standard Regression Coefficient SE Statistic P Group insulin -0.244 < 0.01 0.043 5.666 0.006 7.434 < 0.01 HDL3 0.136 0.037 0.014 0.149 0.006 2.425 0.017 TG -0.315 < 0.01 TC -0.367 < 0.01 LDL-C -0.38 < 0.01 -0.118 -0.166 0.044 -2.688 < 0.01 ALT -0.137 0.035 Scr 0.171 < 0.01 RBC -0.204 < 0.01 Hb -0.188 < 0.01 IL-6 -0.464 < 0.01 -0.003 -0.17 0.001 -2.85 < 0.01 HOMA-IR -0.397 < 0.01 -1.055 -5.846 0.136 -7.732 < 0.01 HOMA-β 0.154 0.018 HOMA-IS 0.394 < 0.01 0.217 0.226 0.069 3.147 < 0.01 Constant / / 2.142 0.256 8.352 < 0.01 Table 3 Multivariate logistic regression analysis Regression Coefficient SE z P OR OR 95%CI HDL3 -0.154 0.051 -3.007 0.003 0.858 0.776–0.948 HOMA-IS -0.775 0.673 -1.153 0.249 0.461 0.123–1.722 IL-6 0.052 0.018 2.874 0.004 1.053 1.017–1.091 insulin 0.015 0.009 1.725 0.084 1.015 0.998–1.033 LDL-C 1.235 0.342 3.612 0.000 3.44 1.760–6.725 3.4 Creation of the Nomogram The nomogram model for prediabetes prediction was developed using the risk factors independently (Fig. 3 ). The scores for each factor were obtained following the scale above the nomogram, and the total scores were acquired by summing the individual scores. The probability for predicting prediabetes was calculated from the total score, which was ranged from 0 to 160, and the corresponding risk rate ranged from 0.05 to 0.99. A higher total score indicates a greater risk of prediabetes. 3.5 Validation of the Nomogram In order to build a valid, robust model based on the data included in this study, a 5-fold cross-validation was implemented, with a random seed of 1. The cross-validation method divided the data set into k mutually exclusive subsets of similar sizes and maintained the consistency of the data distribution during the division. The union of K-1 subsets was used as the training set each time, and the rest was taken as the test set for the k times of training. Finally, the mean value of the k times of results is acquired. In this research, a machine learning model called random forest was performed, and its performance was assessed by the area under the receiver operating characteristic curve (AUC) and calibration curves was applied to assess calibration. The AUC of the operating characteristic curve (ROC) for this nomogram was 1.00 (95% CI, 1.00–1.00) (Fig. 4 A) and 0.78 (95% CI, 0.73–0.84) (Fig. 4 B) for the training and validation sets, respectively. The C-index of the training set achieved as high as 1.00 (95% CI, 1.00–1.00), reflecting an excellent discriminative ability. Similarly, the C-index of the validation set harvested 0.78 (95% CI, 0.73, 0.84), demonstrating its satisfactory predictability. In the calibration plots, the blue line represents the performance of the nomogram; a closer fit to the diagonal dotted line indicates a better prediction. The calibration plots in the training set (Fig. 5 A) and validation set (Fig. 5 B) revealed relatively good consistency between the predictions and the actual observations. As suggested in the DCA, the nomogram was of clinical significance for predicting the risk of prediabetes development over a considerable range of threshold probabilities in both of the training and validation (Fig. 5 C) sets. 4 Discussion The present study aimed to develop and validate a novel nomogram for prediction of prediabetes and identification of the involved predictive risk factors in Hunan Province, China, where the unique dietary habits (high salt and fat intake) have been linked to dyslipidemia and insulin resistance in recent studies [ 12 , 13 ] . In this study, analyses of logistic regression and stepwise regression on clinical data of the prediabetes patients determined HDL-3, IL-6 and LDL-C as independent risk factors for prediabetes, with which a nomogram was built and the relative ROC curves were generated, as a result, the AUC values of the training and validation sets reached 1.00 (95% CI, 1.00–1.00) and 0.78 (95% CI, 0.73–0.84), respectively. Similarly, the C-indexes for the training and validation sets were 1.00 (95% CI, 1.00–1.00) and 0.78 (95% CI, 0.73–0.84), respectively, implying that the nomogram had excellent discriminative power. The calibration curve proved the prominent performance of the nomogram and the prediction model. The DCA decision curves verified the protuberant predictability of the model. The perfect discrimination in the training set (AUC = 1.00) may reflect overfitting due to the limited sample size. To mitigate this, we applied 5-fold cross-validation and rigorous variable selection, yet external validation in larger cohorts remains essential. The present research confirmed for the first time that decreased HDL-3 level was a risk factor and a predictive indicator for prediabetes. There is growing evidence that HDL-3 is the key component for HDL-C in its functional capacity of anti-inflammation, anti-oxidation and anti-atherosclerosis [ 14 , 15 ] . The specific anti-inflammatory mechanisms include that ox-LDL may induce inhibition on interaction or adhesion between monocytes, endothelial cells and smooth muscle cells [ 16 ] , oppress the expression of VCAM-1, ICAM-1 and E-selectin induced by cytokines, curb the release of inflammatory factors of TNF-α and IL-1β [ 17 ] , and suppress the oxidative activity of myeloperoxidase (MPO) [ 18 ] . Some investigations demonstrated that inflammatory stress was one of the important pathogeneses of diabetes and its complications [ 19 , 20 ] . Other epidemiologic studies revealed an association between the inflammatory biomarkers and the occurrence of T2DM and its complications [ 21 – 23 ] . Low HDL2-C and HDL3-C levels were found in those with normal glucose tolerance (NGT) but who were the first-degree relatives of T2D patients in South Asian families, and their levels predominantly linked to beta cell function deterioration [ 24 ] . A study recruiting 2049 subjects in Taiwan aimed at assessing lipoprotein subfractions using novel assays concluded that women with prediabetes had significantly lower levels of HDL-C, HDL2-C, HDL3-C, and apoE HDL-C than the normal subjects [ 25 ] . HDL-3 exerts anti-inflammatory effects by suppressing NF-κB signaling and reducing endothelial adhesion molecule expression [ 14 ] . A recent study by Lund et al [ 26 ] further demonstrated that HDL-3 enhances mitochondrial energy metabolism in skeletal muscle, potentially counteracting insulin resistance. Combined with our findings that serum HDL-3 levels were significantly reduced in prediabetic patients, it is reasonable to assume that decreased HDL-3 level may aggravate inflammation by impairing anti-inflammatory activity in the development of T2DM. As a prototypical cytokine for maintaining homeostasis, IL-6 can respond to systemic inflammation [ 27 ] . The pathogenesis of T2DM is associated with a low-grade chronic inflammatory state [ 28 ] . In recent years, there have been a number of studies focusing on IL-6 and T2DM. Yi et al found that protein concentrations of IL-6 and IL-8 in Gestational diabetes mellitus (GDM) were increased in both maternal and umbilical arterial blood [ 29 ] ; A Canagliflozin Cardiovascular Assessment Study (CANVAS) in Japan manifested that in T2DM patients with high cardiovascular (CV) risk, baseline IL-6 and its 1-year change were associated with the CV and kidney outcomes [ 30 ] . Using liquid chromatography-tandem mass spectrometry (LC-MS/MS) to analyze murine vitreous fluid, Rebekah et al disclosed that the diabetes-induced alterations in the murine vitreous proteome were mitigated by IL-6 trans-signaling inhibition, implying that IL-6 trans-signaling may be an important therapeutic target for the treatment of diabetic retinopathy (DR) [ 31 ] . Our previous research confirmed that IL-6 levels distinctly varied in the healthy, prediabetics and T2DM cohorts, suggesting that IL-6 may be functional during the progression from a healthy condition toward diabetes via prediabetes [ 32 ] . In this study, we reconfirmed that IL-6 levels were significantly increased in prediabetes versus the controls; moreover, we found that IL-6 was an independent predictor of prediabetes, and combining it with other indicators, we succeeded in constructing a predictive model for prediabetes prediction, which may be of help for clinical application in prevention of prediabetes prior to T2DM. Dyslipidemia is a powerful risk factor for diabetes and the associated cardiovascular complications [ 33 ] ; although guidelines advocate aggressive management of lipid parameters in diabetes, most of them do not address dyslipidemia in prediabetes [ 33 ] . Some investigations preliminarily probed the relationship between LDL-C and prediabetes. Kaveh et al. found that ginseng supplements could change the levels of TC and LDL-C in patients with prediabetes and T2D [ 34 ] . A research based on 28476 patients diagnosed with coronary heart disease proclaimed that the LDL-C / HDL-C ratio was correlative to the risk of prediabetes and T2D [ 35 ] . Another cross-sectional study with 1584 school students (aged 14–19 years) enrolled demonstrated that participants classified as obese and having prediabetes had elevated levels of LDL-C compared to those with normal BMI and without prediabetes, but prediabetes alone was associated with reduced levels of LDL-C and increased levels of HDL-C among females [ 36 ] . Our work not only obtained the similar results as the above-mentioned studies did, but also further identified LDL-C as a risk factor and a warning indicator for prediabetes. In view of the importance of the above three indicators in prediabetes, we constructed a prediabetes prediction model. As far as we know, such predictive models on the basis of Hunan population, China have not been reported, where the local cuisine characterized by high salt and fat may contribute to the prevalence of prediabetes and T2DM. Compared with Hu et al.’s study, our model used fewer indicators (three vs. five), but had stronger predictive ability with the AUC in the training set of 1.00 (95% CI, 1.00–1.00) vs. 0.734 (95% CI 0.7290–0.7392) and the AUC in the validation set of 0.78 (95% CI, 0.73–0.84 ) vs . 0.7336 (95% CI 0.7285–0.7387) [ 8 ] ; Using LASSO regression algorithm, Ou et al. established a practical nomogram for predicting the risk incidence of prediabetes and the conversion of prediabetes to diabetes [ 37 ] , while their model yielded less AUC and lower predictivity compared to ours. Another retrospective study conducted in Japan using data from 2004 to 2015 constructed a clinical prediction model based on age, waist circumference, smoking history, the presence of fatty liver, fasting blood glucose (FBG) and glycated hemoglobin (HbA1c) level [ 7 ] , which attained both high AUC in the training cohort (0.87) and the validation set (0.87) for predicting incident T2DM and prediabetes; nevertheless, this model did not take the specific number of years of smoking into account, nor the occurrence of the fatty liver of the subjects, which may cause bias. The present study employed the multivariate logistic regression analysis to determine risk indicators for prediabetes, with which a novel nomogram was constructed and acquired good predictive performance, which we believe would benefit clinicians for distinguishing individuals with high-risk prediabetes from the healthy states. In addition, the predictors adopted in our nomogram are routinely available variables, and putting them in clinical practice may help save time and effort in preventing and treating prediabetes before the disease gets worse. It must be noted that besides the knowledge of HDL-3 acting as an early warning indicator and potential biomarker for early diagnosis of diabetes, more attention should be put into its important role of chronic low-grade inflammation in the development of diabetes, when taking into account its anti-inflammatory and antioxidant properties [ 26 , 38 ] . Hunan Province is a populous region in central southern China. Its special eating habits make the prevention and treatment of T2D in this area very difficult but urgent. Conclusively, compared with other clinical prediction models, our model incorporates HDL-3 as a new indicator, and in combination with IL-6 and LDL-C, all of which are common indexes, and offers clinicians a convenient choice for prediabetes prediction. It needs to be emphasized that the subjects included in this study are permanent residents of Hunan Province, so the model is more suitable for prediabetes prediction and prevention in this area. Our study has several potential limitations. First, some clinical indicators, such as drug use history and smoking history, were not introduced in this study owing to the restriction of electronic medical records. Second, selection bias seems unavoidable due to the retrospective design and single-center nature of the study. Third, unmeasured confounders such as smoking history, physical activity, and dietary patterns were not included in the model, which may affect the generalizability of our findings. Finally, the sample size was small because of the short period of enrollment time within a year, and larger samples are required to make the conclusion more convincible. 5 Conclusion We developed and validated a personalized nomogram to predict the risk of incident prediabetes in the residents of Hunan Province, China, which exhibited excellent performance both in the training and validation cohorts by introducing clinically easily available indexes of HDL-3, LDL-C and IL-6 as risk factors. Indeed, our model has advantages of low cost and high convenience to be generalized in practice. For all this, lifestyle, physical activity, and mental health should be considered to improve the reliability of the prediction model; and also, more clinically relevant evaluation and other work are inescapable before it finds wide-spread application. Declarations Acknowledgements The authors thank the Clinical Mass Spectrometry Platform of the Clinical Laboratory of Xiangya Hospital for their assistance during the sample detection. Author Contributions All authors made a significant contribution to this work, whether it was in the conception, study design, execution, acquisition of data, analysis, interpretation, or in all these areas; took part in drafting, revising, or critically reviewing the article; gave final approval of the version to be published; agreed on the journal to which the article would submit and were accountable for all aspects of the work. Ethics and Consent to Participate declarations This study was approved by the Ethics Committee of Xiangya Hospital of Central South University (NO. 202203082). All participants provided written informed consent to participate in the study prior to data collection. All patient data were anonymized before analysis to protect individual privacy. Consent to Publish declaration Not applicable. Data sharing statement Raw data available upon reasonable request to the corresponding author Fundings This work was funded by the Natural Science Foundation of Hunan Province (2023JJ40962; 2023JJ30965; 2023JJ40949), National Key Research and Development Program of China (2022YFC2009604) and The Hunan Provincial Department of Finance's Health Project (Xiangcai Shezhi [2021] No. 86). Disclosure The authors report no conflicts of interest in this work. 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and abuse of HOMA modeling[J].Diabetes Care,2004,27(6):1487-1495.Doi:10.2337/diacare.27.6.1487 Kerem G,Yu X,Ismayi A, et al.Small intestinal microbiota composition altered in obesity-T2DM mice with high salt fed[J].Sci Rep,2023,13(1):8256.Doi:10.1038/s41598-023-33909-2 He X,Gao X,Hong Y, et al.High Fat Diet and High Sucrose Intake Divergently Induce Dysregulation of Glucose Homeostasis through Distinct Gut Microbiota-Derived Bile Acid Metabolism in Mice[J].J Agric Food Chem,2024,72(1):230-244.Doi:10.1021/acs.jafc.3c02909 Kim DS,Burt AA,Rosenthal EA, et al.HDL-3 is a superior predictor of carotid artery disease in a case-control cohort of 1725 participants[J].J Am Heart Assoc,2014,3(3):e000902.Doi:10.1161/JAHA.114.000902 Kontush A,Chantepie S, Chapman MJ.Small, dense HDL particles exert potent protection of atherogenic LDL against oxidative stress[J].Arterioscler Thromb Vasc Biol,2003,23(10):1881-1888.Doi:10.1161/01.ATV.0000091338.93223.E8 Seetharam D,Mineo C,Gormley AK, et al.High-density lipoprotein promotes endothelial cell migration and reendothelialization via scavenger receptor-B type I[J].Circ Res,2006,98(1):63-72.Doi:10.1161/01.RES.0000199272.59432.5b Liu D,Ji L,Zhang D, et al.Nonenzymatic glycation of high-density lipoprotein impairs its anti-inflammatory effects in innate immunity[J].Diabetes Metab Res Rev,2012,28(2):186-195.Doi:10.1002/dmrr.1297 Vivekanandan-Giri A,Slocum JL,Byun J, et al.High density lipoprotein is targeted for oxidation by myeloperoxidase in rheumatoid arthritis[J].Ann Rheum Dis,2013,72(10):1725-1731.Doi:10.1136/annrheumdis-2012-202033 Al-Aubaidy HA,Dayan A,Deseo MA, et al.Twelve-Week Mediterranean Diet Intervention Increases Citrus Bioflavonoid Levels and Reduces Inflammation in People with Type 2 Diabetes Mellitus[J].Nutrients,2021,13(4).Doi:10.3390/nu13041133 Mahjabeen W,Khan DA,Mirza SA, et al.Effects of delta-tocotrienol supplementation on Glycemic Control, oxidative stress, inflammatory biomarkers and miRNA expression in type 2 diabetes mellitus: A randomized control trial[J].Phytother Res,2021,35(7):3968-3976.Doi:10.1002/ptr.7113 Rayego-Mateos S,Rodrigues-Diez RR,Fernandez-Fernandez B, et al.Targeting inflammation to treat diabetic kidney disease: the road to 2030[J].Kidney Int,2023,103(2):282-296.Doi:10.1016/j.kint.2022.10.030 Lontchi-Yimagou E,Sobngwi E,Matsha TE, et al.Diabetes mellitus and inflammation[J].Curr Diab Rep,2013,13(3):435-444.Doi:10.1007/s11892-013-0375-y Guo W,Song Y,Sun Y, et al.Systemic immune-inflammation index is associated with diabetic kidney disease in Type 2 diabetes mellitus patients: Evidence from NHANES 2011-2018[J].Front Endocrinol (Lausanne),2022,13:1071465.Doi:10.3389/fendo.2022.1071465 Yahya R,Jainandunsing S,Rashid M, et al.HDL associates with insulin resistance and beta-cell dysfunction in South Asian families at risk of type 2 diabetes[J].J Diabetes Complications,2021,35(10):107993.Doi:10.1016/j.jdiacomp.2021.107993 Hsu H,Hsu P,Cheng MH, et al.Lipoprotein Subfractions and Glucose Homeostasis in Prediabetes and Diabetes in Taiwan[J].J Atheroscler Thromb,2019,26(10):890-914.Doi:10.5551/jat.48330 Lund J,Lähteenmäki E,Eklund T, et al.Human HDL subclasses modulate energy metabolism in skeletal muscle cells[J].J Lipid Res,2024,65(1):100481.Doi:10.1016/j.jlr.2023.100481 Tanaka T,Narazaki M, Kishimoto T.Interleukin (IL-6) Immunotherapy[J].Cold Spring Harb Perspect Biol,2018,10(8).Doi:10.1101/cshperspect.a028456 Akbari M, Hassan-Zadeh V.IL-6 signalling pathways and the development of type 2 diabetes[J].Inflammopharmacology,2018,26(3):685-698.Doi:10.1007/s10787-018-0458-0 Li YX,Long DL,Liu J, et al.Gestational diabetes mellitus in women increased the risk of neonatal infection via inflammation and autophagy in the placenta[J].Medicine (Baltimore),2020,99(40):e22152.Doi:10.1097/md.0000000000022152 Koshino A,Schechter M,Sen T, et al.Interleukin-6 and Cardiovascular and Kidney Outcomes in Patients With Type 2 Diabetes: New Insights From CANVAS[J].Diabetes Care,2022,45(11):2644-2652.Doi:10.2337/dc22-0866 Robinson R,Youngblood H,Iyer H, et al.Diabetes Induced Alterations in Murine Vitreous Proteome Are Mitigated by IL-6 Trans-Signaling Inhibition[J].Invest Ophthalmol Vis Sci,2020,61(11):2.Doi:10.1167/iovs.61.11.2 Huang K,Liang Y,Ma Y, et al.The Variation and Correlation of Serum Adiponectin, Nesfatin-1, IL-6, and TNF-α Levels in Prediabetes[J].Front Endocrinol (Lausanne),2022,13:774272.Doi:10.3389/fendo.2022.774272 Neves JS,Newman C,Bostrom JA, et al.Management of dyslipidemia and atherosclerotic cardiovascular risk in prediabetes[J].Diabetes Res Clin Pract,2022,190:109980.Doi:10.1016/j.diabres.2022.109980 Naseri K,Saadati S,Sadeghi A, et al.The Efficacy of Ginseng (Panax) on Human Prediabetes and Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis[J].Nutrients,2022,14(12).Doi:10.3390/nu14122401 Yang T,Liu Y,Li L, et al.Correlation between the triglyceride-to-high-density lipoprotein cholesterol ratio and other unconventional lipid parameters with the risk of prediabetes and Type 2 diabetes in patients with coronary heart disease: a RCSCD-TCM study in China[J].Cardiovasc Diabetol,2022,21(1):93.Doi:10.1186/s12933-022-01531-7 Almari M,Mohammad A,Abubaker J, et al.Obesity and Prediabetes are Jointly Associated with Lipid Abnormalities Among Adolescents: A Cross-Sectional Study[J].Diabetes Metab Syndr Obes,2021,14:345-353.Doi:10.2147/dmso.S290383 Ou Q,Jin W,Lin L, et al.LASSO-based machine learning algorithm to predict the incidence of diabetes in different stages[J].Aging Male,2023,26(1):2205510.Doi:10.1080/13685538.2023.2205510 Meng Q,Yang J,Wang F, et al.Development and External Validation of Nomogram to Identify Risk Factors for CHD in T2DM in the Population of Northwestern China[J].Diabetes Metab Syndr Obes,2023,16:1271-1282.Doi:10.2147/dmso.S404683 Supplementary Table Supplementary Table 1 is not available with this version. 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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-7065733","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485189166,"identity":"a5bd83e8-e69e-48ce-9145-71ff8c581c12","order_by":0,"name":"Yunlai Liang","email":"","orcid":"","institution":"Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Yunlai","middleName":"","lastName":"Liang","suffix":""},{"id":485189167,"identity":"7dd4442a-e3fc-4952-b2ef-c0176e3b7a18","order_by":1,"name":"Kun Wang","email":"","orcid":"","institution":"Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Wang","suffix":""},{"id":485189168,"identity":"f5fb5de8-5536-4944-a594-9f9915e8519a","order_by":2,"name":"Kangkang Huang","email":"","orcid":"","institution":"Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Kangkang","middleName":"","lastName":"Huang","suffix":""},{"id":485189169,"identity":"b277757d-d2e6-470b-9600-65f01ed1be8f","order_by":3,"name":"Jingzhong Liao","email":"","orcid":"","institution":"Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Jingzhong","middleName":"","lastName":"Liao","suffix":""},{"id":485189170,"identity":"ebf63c63-d0e7-4799-bbc0-b435afec8729","order_by":4,"name":"Qizhuo Hou","email":"","orcid":"","institution":"Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Qizhuo","middleName":"","lastName":"Hou","suffix":""},{"id":485189171,"identity":"08b7919d-aa43-4735-8cd6-b5f8001a4900","order_by":5,"name":"Wenze Yu","email":"","orcid":"","institution":"Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Wenze","middleName":"","lastName":"Yu","suffix":""},{"id":485189172,"identity":"6c68e2f7-4fdb-4d2d-a2fe-27998ce02258","order_by":6,"name":"Shiyang Qiu","email":"","orcid":"","institution":"Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Shiyang","middleName":"","lastName":"Qiu","suffix":""},{"id":485189173,"identity":"968d84a4-1a12-46cf-aeb9-11c792616b4f","order_by":7,"name":"Bin Yi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYJCCwwwGDHJs7O0HSNNizMdzJoF4LcxAnDhPwsGAOOUGN3IMDxcU3Elvk2BIYPhRsY2wFskZOQaHZxg8y22TbjzA2HPmNmEt/BJALTwGh3PbZA4kMDO2EaGFDaolnU0iwYA4LTBbEojXItnzrADol8OGbcBAPkiUXwyOJ2/+XPDnsLx8e/vBBz8qiNDCIJCBiI4DRKgHAv7jD4hTOApGwSgYBSMXAABYJD0EBq0AswAAAABJRU5ErkJggg==","orcid":"","institution":"Xiangya Hospital of Central South University","correspondingAuthor":true,"prefix":"","firstName":"Bin","middleName":"","lastName":"Yi","suffix":""}],"badges":[],"createdAt":"2025-07-07 13:08:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7065733/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7065733/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86917604,"identity":"8e3e9fe6-f5d0-4a96-b996-46a86dbb8666","added_by":"auto","created_at":"2025-07-17 07:04:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104399,"visible":true,"origin":"","legend":"\u003cp\u003eillustrates the study workflow: (1) recruitment of 236 participants with prediabetes (n=124) and healthy controls (n=112); (2) exclusion of individuals with severe comorbidities; (3) random allocation of 80% to the training set and 20% to the validation set; (4) multivariate logistic regression for predictor selection.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7065733/v1/64c7a618cdcd56e96aa77fe9.png"},{"id":86918968,"identity":"2c3cffcb-a7b6-4f6a-b7aa-1d9f15867439","added_by":"auto","created_at":"2025-07-17 07:12:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":124482,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation Heatmap\u003c/p\u003e\n\u003cp\u003eRed represents a positive correlation; blue indicates a negative correlation, and the darker the color, the stronger the correlation.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7065733/v1/26fd7b933f74a02480e2e8a0.png"},{"id":86917605,"identity":"57fd6982-421d-4732-940b-9c37314cb627","added_by":"auto","created_at":"2025-07-17 07:04:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":43801,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of the nomogram model to predict the prediabetes occurrence\u003c/p\u003e\n\u003cp\u003eEach risk predictor’s score is plotted on the appropriate scale. A vertical line is drawn from each patient’s score on the appropriate scale to the top points scale in order to determine the patient’s score for each risk predictor. All scores are summed to obtain the total points score. Using the bottom portion of the total points scale, we can predict the probability of prediabetes occurring. Example calculation: For a patient with HDL-3=35 mg/mL (score=40), IL-6=20 pg/mL (score=30), and LDL-C=3.5 mmol/L (score=50), the total score=120 corresponds to a 75% risk of prediabetes.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7065733/v1/7ab156b467b678a08281cdee.png"},{"id":86917607,"identity":"7537baee-09b3-4ae3-9b71-fbea0eb5977e","added_by":"auto","created_at":"2025-07-17 07:04:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":43127,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for prediabetes prediction in the training set and the validation set. (A) ROC curve of the nomogram in the training set. (B) ROC curve of the nomogram in the validation set.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7065733/v1/0815b83c1a051665ee00753d.png"},{"id":86917608,"identity":"171256b9-abca-49f9-a633-27512a131e8a","added_by":"auto","created_at":"2025-07-17 07:04:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":123739,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration and DCA curves of the nomogram. (A) Calibration curve of the training set; (B) Calibration curve of the validation set; (C) DCA curve of the training set and the validation set.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7065733/v1/2a7276baa62b8964383bdfa9.png"},{"id":89708370,"identity":"222caeb6-633c-462b-b758-c9108497701d","added_by":"auto","created_at":"2025-08-23 02:01:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1247545,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7065733/v1/d0e02297-4b66-4bf6-9c32-ccf876641df5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"HDL-3 Subfractions and IL-6 Dynamics: A Novel Biomarker Panel for Precision Prediction of Prediabetes in High-Risk Populations of Hunan, China","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003ePrediabetes, an intermediate stage of glucose dysregulation that may precede type 2 diabetes mellitus (T2DM), affected approximately 720\u0026nbsp;million individuals worldwide in 2021 and will affect an estimated 1\u0026nbsp;billion people by 2045\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. In the US, around 10% of the people with prediabetes progress to diabetes each year\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The results of the Chinese epidemiological survey from 2007 to 2008 showed that the prevalence of prediabetes was 15.5% (about 148\u0026nbsp;million people ) according to the WHO 1999 standard, while from 2015 to 2017, it climbed to 35.2% in terms of the ADA 2018 standard\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Every year, nearly 5\u0026ndash;10% of the prediabetic population develop toward diabetes mellitus (DM), with 70% of them eventually becoming DM\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Prediabetes is considered as a marker or watershed, which indicates that individuals have an increased risk of diabetes, cardiovascular and cerebrovascular diseases, microangiopathy, tumors, dementia and other diseases in the future. Early intervention can dramatically block prediabetes transformation into worse abnormalities. Therefore, differentiating individuals with a high-risk of prediabetes from the normal, and accordingly, intervention efforts are the key to prevent diabetes\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNomogram plays a vital role by graphically representing the effect of each predictor on the involved disease outcome\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Even though previous studies on predictive models of prediabetes included age as an indicator\u003csup\u003e[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e, their diagnostic performances were not prominent, where non-elderly patients might be excluded. In this study, five commonly used indicators were adopted to establish a prediction model, and the new nomogram plot identified risk factors for prediabetes development from a normal state of blood glucose in central south region of China. Moreover, some unnecessary clinical examinations would be avoided by using this model.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 The main workflow of this study\u003c/h2\u003e\u003cp\u003eThe main workflow of this study summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Study population\u003c/h2\u003e\u003cp\u003eA total of 124 patients with prediabetes in Department of Endocrinology and Nephrology, Xiangya Hospital of Central South University from January 2023 to December 2023 were enrolled, including 71 males and 53 females, aged 22\u0026ndash;78 years with an average age of 57.76\u0026thinsp;\u0026plusmn;\u0026thinsp;9.04 years. All prediabetes patients were defined according to the American Diabetes Association (ADA) 2020 diagnostic criteria, fulfilling at least one of the following criteria: ① Impaired fasting glucose (IFG), 100 mg/dL (5.6 mmol/L)\u0026thinsp;\u0026le;\u0026thinsp;FPG\u0026thinsp;\u0026le;\u0026thinsp;125 mg /dL (6.9 mmol/ L); ② impaired glucose tolerance (IGT), 140 mg /dL (7.8 mmol /L)\u0026thinsp;\u0026le;\u0026thinsp;2-hour post-OGTT glucose\u0026thinsp;\u0026le;\u0026thinsp;199 mg/dL (11.0 mmol /L); and ③ HbA1c ranged from 5.7 to 6.4% (39\u0026ndash;47 mmol/mol)\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. The insulin resistance was determined by the Homeostasis Model Assessment for Insulin Resistance (HOMA-IR) index\u0026thinsp;\u0026ge;\u0026thinsp;3, otherwise there was no IR\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Meanwhile, 112 healthy subjects were recruited as the normal control group (NC), including 67 males and 45 females, aged 23\u0026ndash;67 years old, averaged 57.97\u0026thinsp;\u0026plusmn;\u0026thinsp;6.59 years. Participants meeting any of the following conditions were excluded: ① severe hyperlipidemia; ② acute coronary syndrome or coronary artery bypass surgery history; ③ pulmonary heart disease or chronic heart failure; ④ chronic disease toxicity or bacterial infection; ⑤ immune system disease cachexia or cancer; and ⑥ non-residents of Hunan Province, China. This study was approved by the Ethics Committee of Xiangya Hospital of Central South University (NO. 202203082). All subjects signed the informed consents. All patient data were anonymized prior to analysis, with identifiers removed by an independent data manager.\u003c/p\u003e\u003cp\u003eData collection included sex, age and blood biochemical indicators of TB, DB, ALB, ALT, Scr, HDL-3, IL-6, TNFα and insulin, as well as HOMA-IR, HOMA-β and HOMA-S. The biochemical tests were carried out on an automatic biochemical analyzer (Beckman AU580, Beckman Coulter, Brea, CA, USA). HDL-3 kits were purchased from Anhui Daqian Biological Engineering Co., Ltd., Anhui, China. TB, DB, ALB, ALT, Scr, insulin and other conventional biochemical kits were offered by the Beckman Kurt Experimental System (Changsha) Co., Ltd. IL-6 and TNF-α kits were provided by Jiangsu Kote Biological Co., Ltd. Jiangsu, China; HOMA-IR, HOMA-β and HOMA-S were calculated with the formulas of HOMA-IR\u0026thinsp;=\u0026thinsp;FPG (mmol / L) \u0026times;FINS (\u0026micro;U/mL) / 22.5, HOMA-S\u0026thinsp;=\u0026thinsp;1 / HOMA-IR and HOMA-β %= 20 \u0026times; FINS / (FPG-3.5)\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses and graphics were performed with the SPSS software, version 25.0 (SPSS Inc., Chicago, IL) and the Deepwise \u0026amp;Beckman Coulter DxAI platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dxonline.deepwise.com\u003c/span\u003e\u003cspan address=\"https://dxonline.deepwise.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Data with a normal distribution were presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and compared with the Student\u0026rsquo;s independent t-test, while non-normally distributed data were presented as medians and interquartile ranges (IQRs) and analyzed with the Wilcoxon test. Categorical variables were displayed as numbers and percentages, and their differences were determined using the chi-squared test. Correlation estimation was illustrated through a heat map incorporated with Pearson's correlation analysis. Variables with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10 in univariate analysis were entered into stepwise multivariate logistic regression (entry criteria: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, removal criteria: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.10) to identify independent predictors for prediabetes and construction of the nomogram. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCA) were performed to validate the nomogram. For all the tests, a two-tailed \u003cem\u003eP\u003c/em\u003e value of \u0026lt;\u0026thinsp;0.05 was considered to be statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline characteristics of the participants\u003c/h2\u003e\u003cp\u003eThe baseline characteristics of the studied cohorts were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There was no significant difference in age or gender between the prediabetes and the normal control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The values of IR, serum insulin, blood glucose, HOMA-IR score, TG, TC, LDL-C, ALT, TBA, HbA1c, Hb, RBC and IL-6 levels in the prediabetes group were significantly high, while HDL-3, HOMA-β, HOMA-IS and Scr, significantly low, in contrast to those in the NC group. Although significant differences in ALT and Scr were found between the two groups, their mean values were within the normal reference range of the Chinese population.\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 Individuals\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrediabetes(n\u0026thinsp;=\u0026thinsp;124)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNC(n\u0026thinsp;=\u0026thinsp;112)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eW/t\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56.500(51.000\u0026ndash;65.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57.000(52.000\u0026ndash;62.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6971.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.959\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49 (39.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43 (38.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.860\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75 (60.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69 (61.6%)\u003c/p\u003e\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\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eIR or not\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eno\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88 (71.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e105 (93.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eyes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36 (29.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7 (6.2%)\u003c/p\u003e\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\u003eInsulin (pmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49.700(33.075\u0026ndash;73.400)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36.900(26.625\u0026ndash;51.825)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL3 (mg/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.745(31.872\u0026ndash;40.275)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37.195(34.635\u0026ndash;39.485)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5855.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlu (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.410(6.218\u0026ndash;6.620)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.300(4.970\u0026ndash;5.595)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHOMA-IR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.030(1.317\u0026ndash;3.125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.210(0.882\u0026ndash;1.740)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHOMA-β\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47.785(33.943\u0026ndash;74.088)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.590(45.078\u0026ndash;79.390)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHOMA-IS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.490(0.320\u0026ndash;0.760)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.810(0.575\u0026ndash;1.115)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3747.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG (umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.690(1.100-2.583)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.210(0.965\u0026ndash;1.520)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9389.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC (umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.445(4.795\u0026ndash;6.210)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.770(4.360\u0026ndash;5.200)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9799.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C (mmol/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.515(2.930\u0026ndash;3.980)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.970(2.625\u0026ndash;3.270)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C (mmol/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.275(1.107\u0026ndash;1.475)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.310(1.185\u0026ndash;1.490)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6174.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTB (umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.100(3.950\u0026ndash;6.950)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.500(4.300-6.325)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6671.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.678\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDB (umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21.700(16.375\u0026ndash;35.175)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.950(15.100-26.375)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7935.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.350(19.650-29.775)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.800(19.975\u0026ndash;26.200)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.500(2.100-5.925)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.000(2.000-5.125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7423.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.360\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTBA (umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79.179\u0026thinsp;\u0026plusmn;\u0026thinsp;14.492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e75.062\u0026thinsp;\u0026plusmn;\u0026thinsp;13.355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScr (umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e86.580(77.987\u0026ndash;98.888)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e95.840(79.728\u0026ndash;110.620)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5568.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.950\u0026thinsp;\u0026plusmn;\u0026thinsp;0.262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.477\u0026thinsp;\u0026plusmn;\u0026thinsp;0.309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2h-Glu (mmol/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.109\u0026thinsp;\u0026plusmn;\u0026thinsp;1.372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.592\u0026thinsp;\u0026plusmn;\u0026thinsp;2.378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRBC (10^12/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.735(4.457\u0026ndash;5.112)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.555(4.277\u0026ndash;4.890)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8581.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT (10^12/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e207.500(168.250\u0026ndash;258.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e199.000(166.000-238.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.209\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHb (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e145.000(136.000-157.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e139.000(131.000-150.250)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIL-6 (pg/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.621(16.699\u0026ndash;29.075)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.669(7.386\u0026ndash;19.134)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNF-α (pg/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e354.643(165.682-436.429)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e315.500(221.833\u0026ndash;414.500)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2537.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.715\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: HDL-3, high density lipoprotein 3; Glu, glucose; HOMA, homeostasis model assessment; TG, triglyceride; TC, total cholesterol; LDL-C, low density lipoprotein-cholesterol; HDL-C, high-density lipoprotein-cholesterol; TB, total bilirubin; DB, direct bilirubin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; TBA, total bile acid; Scr, serum creatinine; HbA1c, hemoglobin A1c; 2h-Glu, 2-hour glucose; RBC, erythrocyte; Hb, hemoglobin; IL-6, interleukin 6; TNF-α, tumor necrosis factor α.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Correlation analysis between each index and the prediabetes\u003c/h2\u003e\u003cp\u003ePearson / Spearman correlation was used to analyze the correlations between each index and the prediabetes. The results revealed that the individuals with prediabetes were significantly positively correlated with HDL-3, HOMA-β, HOMA-IS and Scr and negatively correlated with insulin level, blood glucose level, HOMA-IR score, TG, TC, LDL-C, ALT, HbA1c, RBC, Hb and IL-6 level, whereas those with insulin resistance had significantly positive relations with HDL-3, HOMA-β, HOMA-IS and Scr levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and supplementary table 1).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Characteristics Selection\u003c/h2\u003e\u003cp\u003eThe clinical variables of prediabetes were selected by the multiple linear and multivariate logistic regression methods (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The multiple linear regression analysis revealed that HDL-3, HOMA-IS, IL-6, insulin and LDL-C were related to prediabetes occurrence. Subsequently, these five indicators were included in the multivariate logistic regression to screen for risk factors for prediabetes, and the determined indicators were further adopted for the stepwise regression analysis, resulting in that HDL-3(OR\u0026thinsp;=\u0026thinsp;0.858, 95% CI 0.776\u0026ndash;0.948, P\u0026thinsp;=\u0026thinsp;0.003), IL-6(OR\u0026thinsp;=\u0026thinsp;1.053, 95% CI 1.017\u0026ndash;1.091, P\u0026thinsp;=\u0026thinsp;0.004) and LDL-C(OR\u0026thinsp;=\u0026thinsp;3.44, 95% CI 1.760\u0026ndash;6.725, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were independent factors for prediabetes development through the healthy states and associated with the occurrence of prediabetes as well (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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\u003eMultiple linear regression analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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=\"left\" 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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c9\" namest=\"c5\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDependent\u003c/p\u003e\u003cp\u003evariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStatistic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRegression Coefficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStandard Regression Coefficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStatistic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003einsulin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.244\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHDL3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.315\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.367\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLDL-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.118\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.166\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-2.688\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.137\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eScr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.204\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.188\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIL-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.464\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-2.85\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHOMA-IR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.397\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.055\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-5.846\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-7.732\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHOMA-β\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHOMA-IS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.394\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate logistic regression analysis\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=\"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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRegression Coefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOR 95%CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.154\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.007\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.776\u0026ndash;0.948\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHOMA-IS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.775\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.673\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.153\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.123\u0026ndash;1.722\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIL-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.017\u0026ndash;1.091\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003einsulin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.725\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.998\u0026ndash;1.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.760\u0026ndash;6.725\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Creation of the Nomogram\u003c/h2\u003e\u003cp\u003eThe nomogram model for prediabetes prediction was developed using the risk factors independently (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The scores for each factor were obtained following the scale above the nomogram, and the total scores were acquired by summing the individual scores. The probability for predicting prediabetes was calculated from the total score, which was ranged from 0 to 160, and the corresponding risk rate ranged from 0.05 to 0.99. A higher total score indicates a greater risk of prediabetes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Validation of the Nomogram\u003c/h2\u003e\u003cp\u003eIn order to build a valid, robust model based on the data included in this study, a 5-fold cross-validation was implemented, with a random seed of 1. The cross-validation method divided the data set into k mutually exclusive subsets of similar sizes and maintained the consistency of the data distribution during the division. The union of K-1 subsets was used as the training set each time, and the rest was taken as the test set for the k times of training. Finally, the mean value of the k times of results is acquired. In this research, a machine learning model called random forest was performed, and its performance was assessed by the area under the receiver operating characteristic curve (AUC) and calibration curves was applied to assess calibration.\u003c/p\u003e\u003cp\u003eThe AUC of the operating characteristic curve (ROC) for this nomogram was 1.00 (95% CI, 1.00\u0026ndash;1.00) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) and 0.78 (95% CI, 0.73\u0026ndash;0.84) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) for the training and validation sets, respectively. The C-index of the training set achieved as high as 1.00 (95% CI, 1.00\u0026ndash;1.00), reflecting an excellent discriminative ability. Similarly, the C-index of the validation set harvested 0.78 (95% CI, 0.73, 0.84), demonstrating its satisfactory predictability. In the calibration plots, the blue line represents the performance of the nomogram; a closer fit to the diagonal dotted line indicates a better prediction. The calibration plots in the training set (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) and validation set (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) revealed relatively good consistency between the predictions and the actual observations. As suggested in the DCA, the nomogram was of clinical significance for predicting the risk of prediabetes development over a considerable range of threshold probabilities in both of the training and validation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC) sets.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe present study aimed to develop and validate a novel nomogram for prediction of prediabetes and identification of the involved predictive risk factors in Hunan Province, China, where the unique dietary habits (high salt and fat intake) have been linked to dyslipidemia and insulin resistance in recent studies\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. In this study, analyses of logistic regression and stepwise regression on clinical data of the prediabetes patients determined HDL-3, IL-6 and LDL-C as independent risk factors for prediabetes, with which a nomogram was built and the relative ROC curves were generated, as a result, the AUC values of the training and validation sets reached 1.00 (95% CI, 1.00\u0026ndash;1.00) and 0.78 (95% CI, 0.73\u0026ndash;0.84), respectively. Similarly, the C-indexes for the training and validation sets were 1.00 (95% CI, 1.00\u0026ndash;1.00) and 0.78 (95% CI, 0.73\u0026ndash;0.84), respectively, implying that the nomogram had excellent discriminative power. The calibration curve proved the prominent performance of the nomogram and the prediction model. The DCA decision curves verified the protuberant predictability of the model. The perfect discrimination in the training set (AUC\u0026thinsp;=\u0026thinsp;1.00) may reflect overfitting due to the limited sample size. To mitigate this, we applied 5-fold cross-validation and rigorous variable selection, yet external validation in larger cohorts remains essential.\u003c/p\u003e\u003cp\u003eThe present research confirmed for the first time that decreased HDL-3 level was a risk factor and a predictive indicator for prediabetes. There is growing evidence that HDL-3 is the key component for HDL-C in its functional capacity of anti-inflammation, anti-oxidation and anti-atherosclerosis\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. The specific anti-inflammatory mechanisms include that ox-LDL may induce inhibition on interaction or adhesion between monocytes, endothelial cells and smooth muscle cells\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, oppress the expression of VCAM-1, ICAM-1 and E-selectin induced by cytokines, curb the release of inflammatory factors of TNF-α and IL-1β\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, and suppress the oxidative activity of myeloperoxidase (MPO)\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Some investigations demonstrated that inflammatory stress was one of the important pathogeneses of diabetes and its complications\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Other epidemiologic studies revealed an association between the inflammatory biomarkers and the occurrence of T2DM and its complications\u003csup\u003e[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Low HDL2-C and HDL3-C levels were found in those with normal glucose tolerance (NGT) but who were the first-degree relatives of T2D patients in South Asian families, and their levels predominantly linked to beta cell function deterioration\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. A study recruiting 2049 subjects in Taiwan aimed at assessing lipoprotein subfractions using novel assays concluded that women with prediabetes had significantly lower levels of HDL-C, HDL2-C, HDL3-C, and apoE HDL-C than the normal subjects\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. HDL-3 exerts anti-inflammatory effects by suppressing NF-κB signaling and reducing endothelial adhesion molecule expression\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. A recent study by Lund et al\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e further demonstrated that HDL-3 enhances mitochondrial energy metabolism in skeletal muscle, potentially counteracting insulin resistance. Combined with our findings that serum HDL-3 levels were significantly reduced in prediabetic patients, it is reasonable to assume that decreased HDL-3 level may aggravate inflammation by impairing anti-inflammatory activity in the development of T2DM.\u003c/p\u003e\u003cp\u003eAs a prototypical cytokine for maintaining homeostasis, IL-6 can respond to systemic inflammation\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. The pathogenesis of T2DM is associated with a low-grade chronic inflammatory state \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. In recent years, there have been a number of studies focusing on IL-6 and T2DM. Yi et al found that protein concentrations of IL-6 and IL-8 in Gestational diabetes mellitus (GDM) were increased in both maternal and umbilical arterial blood\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e; A Canagliflozin Cardiovascular Assessment Study (CANVAS) in Japan manifested that in T2DM patients with high cardiovascular (CV) risk, baseline IL-6 and its 1-year change were associated with the CV and kidney outcomes\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Using liquid chromatography-tandem mass spectrometry (LC-MS/MS) to analyze murine vitreous fluid, Rebekah et al disclosed that the diabetes-induced alterations in the murine vitreous proteome were mitigated by IL-6 trans-signaling inhibition, implying that IL-6 trans-signaling may be an important therapeutic target for the treatment of diabetic retinopathy (DR)\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Our previous research confirmed that IL-6 levels distinctly varied in the healthy, prediabetics and T2DM cohorts, suggesting that IL-6 may be functional during the progression from a healthy condition toward diabetes via prediabetes\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn this study, we reconfirmed that IL-6 levels were significantly increased in prediabetes versus the controls; moreover, we found that IL-6 was an independent predictor of prediabetes, and combining it with other indicators, we succeeded in constructing a predictive model for prediabetes prediction, which may be of help for clinical application in prevention of prediabetes prior to T2DM.\u003c/p\u003e\u003cp\u003eDyslipidemia is a powerful risk factor for diabetes and the associated cardiovascular complications\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e; although guidelines advocate aggressive management of lipid parameters in diabetes, most of them do not address dyslipidemia in prediabetes\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Some investigations preliminarily probed the relationship between LDL-C and prediabetes. Kaveh et al. found that ginseng supplements could change the levels of TC and LDL-C in patients with prediabetes and T2D\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. A research based on 28476 patients diagnosed with coronary heart disease proclaimed that the LDL-C / HDL-C ratio was correlative to the risk of prediabetes and T2D\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Another cross-sectional study with 1584 school students (aged 14\u0026ndash;19 years) enrolled demonstrated that participants classified as obese and having prediabetes had elevated levels of LDL-C compared to those with normal BMI and without prediabetes, but prediabetes alone was associated with reduced levels of LDL-C and increased levels of HDL-C among females\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Our work not only obtained the similar results as the above-mentioned studies did, but also further identified LDL-C as a risk factor and a warning indicator for prediabetes.\u003c/p\u003e\u003cp\u003eIn view of the importance of the above three indicators in prediabetes, we constructed a prediabetes prediction model. As far as we know, such predictive models on the basis of Hunan population, China have not been reported, where the local cuisine characterized by high salt and fat may contribute to the prevalence of prediabetes and T2DM. Compared with Hu et al.\u0026rsquo;s study, our model used fewer indicators (three \u003cem\u003evs.\u003c/em\u003e five), but had stronger predictive ability with the AUC in the training set of 1.00 (95% CI, 1.00\u0026ndash;1.00) \u003cem\u003evs.\u003c/em\u003e 0.734 (95% CI 0.7290\u0026ndash;0.7392) and the AUC in the validation set of 0.78 (95% CI, 0.73\u0026ndash;0.84 ) \u003cem\u003evs\u003c/em\u003e. 0.7336 (95% CI 0.7285\u0026ndash;0.7387)\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e; Using LASSO regression algorithm, Ou et al. established a practical nomogram for predicting the risk incidence of prediabetes and the conversion of prediabetes to diabetes\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e, while their model yielded less AUC and lower predictivity compared to ours. Another retrospective study conducted in Japan using data from 2004 to 2015 constructed a clinical prediction model based on age, waist circumference, smoking history, the presence of fatty liver, fasting blood glucose (FBG) and glycated hemoglobin (HbA1c) level \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, which attained both high AUC in the training cohort (0.87) and the validation set (0.87) for predicting incident T2DM and prediabetes; nevertheless, this model did not take the specific number of years of smoking into account, nor the occurrence of the fatty liver of the subjects, which may cause bias.\u003c/p\u003e\u003cp\u003eThe present study employed the multivariate logistic regression analysis to determine risk indicators for prediabetes, with which a novel nomogram was constructed and acquired good predictive performance, which we believe would benefit clinicians for distinguishing individuals with high-risk prediabetes from the healthy states. In addition, the predictors adopted in our nomogram are routinely available variables, and putting them in clinical practice may help save time and effort in preventing and treating prediabetes before the disease gets worse. It must be noted that besides the knowledge of HDL-3 acting as an early warning indicator and potential biomarker for early diagnosis of diabetes, more attention should be put into its important role of chronic low-grade inflammation in the development of diabetes, when taking into account its anti-inflammatory and antioxidant properties\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHunan Province is a populous region in central southern China. Its special eating habits make the prevention and treatment of T2D in this area very difficult but urgent. Conclusively, compared with other clinical prediction models, our model incorporates HDL-3 as a new indicator, and in combination with IL-6 and LDL-C, all of which are common indexes, and offers clinicians a convenient choice for prediabetes prediction. It needs to be emphasized that the subjects included in this study are permanent residents of Hunan Province, so the model is more suitable for prediabetes prediction and prevention in this area. Our study has several potential limitations. First, some clinical indicators, such as drug use history and smoking history, were not introduced in this study owing to the restriction of electronic medical records. Second, selection bias seems unavoidable due to the retrospective design and single-center nature of the study. Third, unmeasured confounders such as smoking history, physical activity, and dietary patterns were not included in the model, which may affect the generalizability of our findings. Finally, the sample size was small because of the short period of enrollment time within a year, and larger samples are required to make the conclusion more convincible.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eWe developed and validated a personalized nomogram to predict the risk of incident prediabetes in the residents of Hunan Province, China, which exhibited excellent performance both in the training and validation cohorts by introducing clinically easily available indexes of HDL-3, LDL-C and IL-6 as risk factors. Indeed, our model has advantages of low cost and high convenience to be generalized in practice. For all this, lifestyle, physical activity, and mental health should be considered to improve the reliability of the prediction model; and also, more clinically relevant evaluation and other work are inescapable before it finds wide-spread application.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the Clinical Mass Spectrometry Platform of the Clinical Laboratory of Xiangya Hospital for their assistance during the sample detection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors made a significant contribution to this work, whether it was in the conception, study design, execution, acquisition of data, analysis, interpretation, or in all these areas; took part in drafting, revising, or critically reviewing the article; gave final approval of the version to be published; agreed on the journal to which the article would submit and were accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics and Consent to Participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Xiangya Hospital of Central South University (NO. 202203082). All participants provided written informed consent to participate in the study prior to data collection. All patient data were anonymized before analysis to protect individual privacy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sharing statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw data available upon reasonable request to the corresponding author\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Natural Science Foundation of Hunan Province (2023JJ40962; 2023JJ30965; 2023JJ40949), National Key Research and Development Program of China (2022YFC2009604) and The Hunan Provincial Department of Finance\u0026apos;s Health Project\u0026nbsp;(Xiangcai Shezhi [2021] No. 86).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no conflicts of interest in this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSun H,Saeedi P,Karuranga S, et al.IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045[J].Diabetes Res Clin Pract,2022,183:109119.Doi:10.1016/j.diabres.2021.109119\u003c/li\u003e\n\u003cli\u003eEchouffo-Tcheugui JB,Perreault L,Ji L, et al.Diagnosis and Management of Prediabetes: A Review[J].Jama,2023,329(14):1206-1216.Doi:10.1001/jama.2023.4063\u003c/li\u003e\n\u003cli\u003eEndocrinology CSo,Society CD, Association CE.Intervention for adults with pre-diabetes: a Chinese expert consensus (2023 edition)[J].Chinese journal of diabetes,2023(06):484-494.Doi:10.3760/cma.j.cn115791-20230509-00188\u003c/li\u003e\n\u003cli\u003eTab\u0026aacute;k AG,Herder C,Rathmann W, et al.Prediabetes: a high-risk state for diabetes development[J].Lancet,2012,379(9833):2279-2290.Doi:10.1016/s0140-6736(12)60283-9\u003c/li\u003e\n\u003cli\u003ePark SY.Nomogram: 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Systematic Review and Meta-Analysis[J].Nutrients,2022,14(12).Doi:10.3390/nu14122401\u003c/li\u003e\n\u003cli\u003eYang T,Liu Y,Li L, et al.Correlation between the triglyceride-to-high-density lipoprotein cholesterol ratio and other unconventional lipid parameters with the risk of prediabetes and Type 2 diabetes in patients with coronary heart disease: a RCSCD-TCM study in China[J].Cardiovasc Diabetol,2022,21(1):93.Doi:10.1186/s12933-022-01531-7\u003c/li\u003e\n\u003cli\u003eAlmari M,Mohammad A,Abubaker J, et al.Obesity and Prediabetes are Jointly Associated with Lipid Abnormalities Among Adolescents: A Cross-Sectional Study[J].Diabetes Metab Syndr Obes,2021,14:345-353.Doi:10.2147/dmso.S290383\u003c/li\u003e\n\u003cli\u003eOu Q,Jin W,Lin L, et al.LASSO-based machine learning algorithm to predict the incidence of diabetes in different stages[J].Aging Male,2023,26(1):2205510.Doi:10.1080/13685538.2023.2205510\u003c/li\u003e\n\u003cli\u003eMeng Q,Yang J,Wang F, et al.Development and External Validation of Nomogram to Identify Risk Factors for CHD in T2DM in the Population of Northwestern China[J].Diabetes Metab Syndr Obes,2023,16:1271-1282.Doi:10.2147/dmso.S404683\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary Table","content":"\u003cp\u003eSupplementary Table 1 is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"prediabetes, prediction, HDL-3, nomogram, risk factor","lastPublishedDoi":"10.21203/rs.3.rs-7065733/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7065733/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eDevelop and evaluate a nomogram to predict prediabetes risk in Hunan province, China.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA retrospective cohort study was conducted with 124 prediabetic and 112 healthy individuals recruited from Xiangya Hospital between June and December 2023. Sample size was calculated based on an expected odds ratio of 2.0 for key predictors with 80% power (α\u0026thinsp;=\u0026thinsp;0.05).. Analyzed baseline data using T test, Wilcoxon rank sum test or Chi-square test. Selected independent risk factors via univariate or multivariate logistic regression. Used 5-fold cross-validation and random forest algorithm.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eInsulin, HOMA-IR score, and levels of blood glucose, TG, TC, LDL-C, ALT, TBA, HbA1c, Hb, RBC and IL-6 were higher in prediabetes; HDL-3, HOMA-β, HOMA-IS and Scr were lower. HDL-3, IL-6 and LDL-C identified as independent factors. The nomogram demonstrated perfect discrimination in the training set (C-index 1.00, 95% CI 1.00\u0026ndash;1.00), likely due to model overfitting, and acceptable performance in the validation set (C-index 0.78, 95% CI 0.73\u0026ndash;0.84). AUC was 1.00 and 0.78 respectively. Well-calibrated and DCA verified clinical value.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eHDL-3, IL-6 and LDL-C are strong predictors. Nomogram is a reliable tool for prediabetes prediction, beneficial for clinicians in prevention and individualized treatment.\u003c/p\u003e","manuscriptTitle":"HDL-3 Subfractions and IL-6 Dynamics: A Novel Biomarker Panel for Precision Prediction of Prediabetes in High-Risk Populations of Hunan, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-17 07:04:48","doi":"10.21203/rs.3.rs-7065733/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d385a122-f243-4582-b752-43515fa6eab1","owner":[],"postedDate":"July 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-23T01:53:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-17 07:04:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7065733","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7065733","identity":"rs-7065733","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}