Evaluating Multiple Metabolic Indicators to Predict Gastric Intestinal Metaplasia Risk

Evaluating Multiple Metabolic Indicators to Predict Gastric Intestinal Metaplasia Risk | 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 Evaluating Multiple Metabolic Indicators to Predict Gastric Intestinal Metaplasia Risk Chieh Lee, Chia-Yu Lai, Ta-Sen Yeh, Ming-Ling Chang, Tsung-Hsing Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4016440/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 Metabolic syndrome is highly associated with gastric cancer (GC) formation, although the reliability of individual indices for predicting IM (intestinal metaplasia) risk remains inconsistent. This retrospective cohort study applied univariate and multivariate analyses using Python and its statistical packages to analyze the relationships between multiple metabolic indicators and IM, including the Atherogenic Index of Plasma (AIP), the Triglyceride-Glucose Index (TyG), and levels of fasting (TC, AC: Fasting) blood glucose (AC), postprandial blood glucose (PC), total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), and very-low-density lipoprotein (VLDL).Our analysis of the metabolic indicators revealed that TyG and AIP were not predictors of IM. However, across all ages and genders, LDL was a significant predictor of IM. Moreover, we found that the accuracy associated with certain metabolic indicators of IM can vary according to age and gender. More specifically, HDL was a significant indicator of IM in young males, while TC was significant in young females. Additionally, for middle-aged individuals, PC was a significant indicator in males, while AC was significant in females. In elderly males, LDL, VLDL, and TyG were significant indicators, while TC and LDL were significant in elderly females. Furthermore, the AUC of elder individuals (> 60%) was significantly higher compared to young individuals (54.7%, males; 56.5%, females) and middle-aged individuals (53.6%, males; 52.5%, females). By conducting a comprehensive analysis of multiple metabolic indicators, our study reveals that significance varies according to gender and age, although LDL is a significant predictor of IM across all groups. gastric intestinal metaplasia Atherogenic Index of Plasma Triglyceride-Glucose Index Metabolic Indicators Figures Figure 1 What is already known on this topic Metabolic syndrome strongly correlates with gastric cancer development, yet the reliability of individual metabolic indices in predicting intestinal metaplasia (IM) risk remains uncertain. Previous studies have not comprehensively evaluated various metabolic indicators' predictive value for IM across different age and gender groups. What this study adds: This study identifies LDL as a consistent predictor of IM across all age and gender groups, enhancing understanding of metabolic indicators for IM risk. It unveils variations in metabolic indicator significance based on age and gender, such as HDL in young males and TC in young females, offering insights for personalized risk assessment. How this study might affect research, practice, or policy: The findings underscore the importance of considering age and gender-specific metabolic indicators in IM risk assessment, potentially enabling more targeted screening and prevention strategies. Clinicians could benefit from integrating LDL levels into GC risk assessment protocols, potentially enhancing early detection and management. Policymakers and healthcare providers should consider advocating for routine metabolic screenings, particularly among individuals with metabolic syndrome, to mitigate GC and related complications. Introduction Metabolic syndrome (MetS) is a complex medical condition characterized by a cluster of interrelated conditions, including obesity, hypertension, elevated blood sugar levels, and abnormal lipid profiles. Extensive research has demonstrated strong associations between MetS and chronic diseases, such as cardiovascular disorders and diabetes[ 1 ]. Moreover, MetS has emerged as a valuable predictor for postoperative complications, cancer recurrence, and increased overall mortality rates among patients with GC[ 2 ]. The relationship between MetS and gastric precancerous lesions remains unclear, although recent studies have suggested the Triglyceride-Glucose Index (TyG) as a novel serum biomarker with predictive potential for gastric carcinogenesis[ 3 ]. Meanwhile, gastric intestinal metaplasia (IM) has been firmly established as a precancerous lesion in the development of GC, and the severity of gastric IM is closely associated with the risk of GC development[ 4 ]. The primary objective of the present study was to investigate the potential association between MetS and gastric IM, with a specific focus on evaluating the predictive capabilities of two metabolic indices: the Atherogenic Index of Plasma (AIP) and TyG. AIP, which integrates arterial lipids and blood sugar levels, offers valuable insights into the risk of atherosclerosis and cardiovascular diseases. On the other hand, TyG serves as a reflective marker of insulin resistance, and may be closely correlated with MetS. Through a comprehensive examination of the relationship between metabolic indicators and gastric IM, we aim to identify biomarkers that can predict IM risk, thus facilitating early clinical intervention and further clarifying our understanding of the links between MetS and the development of GC. Methods We performed univariate analysis and multivariate analysis to characterize the relationship between metabolic indicators and IM. In addition, Python packages were used to build a database and conduct statistical analyses. In this section, we first present our database inclusion and exclusion criteria, and subsequently present our statistical methods and measurements of the relationships between the metabolic indicators and IM. This study was approved by the Institutional Review Board (IRB) under protocol number 202300866B0. Data Collection and Preprocessing From 2010 to 2014, 59,143 subjects were enrolled in this retrospective cohort study, 1,355 of whom had undergone endoscopic biopsy and underwent further analysis. After eliminating cases with incomplete blood test data, the sample size was narrowed to 10,380 subjects. The analysis then segregated these individuals into two groups, 2,088 subjects with IM and 8,292 subjects without IM, as illustrated in Fig. 1 . This section comprehensively outlines the study design and the methodology we used to investigate IM and the associations with metabolic indicators. We collected data related to the metabolic indicators from the 10,380 study subjects. The metabolic indicators encompassed a broader spectrum of factors, including pre-prandial blood glucose (AC), postprandial blood glucose (PC), total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), very-low-density lipoprotein (VLDL), the Atherogenic Index of Plasma (AIP), and the Triglyceride-Glucose (TyG) index. Several studies, such as those by Khaw et al[ 5 ] and Tseng et al[ 6 ], have shown that hemoglobin A1C (HbA1C), representing blood sugar (glucose) level, is a significant risk factor for IM, after adjusting for various factors including fasting blood sugar, supporting the hypothesis that HbA1C and fasting glucose may be putative risk factors. In addition, previous studies by Liu et al[ 7 ] and van der Poorten et al[ 8 ] indicate that predictors of liver-related diseases are associated with metabolic syndromes, including cardiovascular diseases and stroke. The studies focused on subjects with non-alcoholic fatty liver disease (NAFLD), showing that visceral fat is an independent predictive factor positively associated with serum triglycerides, HDL, LDL, and interleukin-6 (IL-6), lipid-related parameters. These factors are known to be directly related to inflammatory processes, thereby validating their integration into analytical models. Similarly, the AIP and TyG indexes, as reported by Cheong et al.[ 9 ], influence disease, and are independent predictors of disease incidence and mortality rates, thus justifying their suitability for inclusion in the predictive indicators studied here. We used the following equations to calculate the AIP and TyG: AIP = log ( \(\frac{\text{T}\text{r}\text{i}\text{g}\text{l}\text{y}\text{c}\text{e}\text{r}\text{i}\text{d}\text{e}}{\text{H}\text{D}\text{L}}\) ) TyG = \(\frac{\text{log}\left(\frac{\text{T}\text{r}\text{i}\text{g}\text{l}\text{y}\text{c}\text{e}\text{r}\text{i}\text{d}\text{e}}{2.2}\right) \text{*} \text{G}\text{l}\text{u}\text{c}\text{o}\text{s}\text{e}\left(\text{A}\text{C}\right)}{2}\) The exclusion criteria for this study at the subjects’ annual health checkup were as follows: Uncooperative, unwilling, or individuals with impaired consciousness. Individuals with conditions such as pregnancy or systemic diseases that may affect anesthesia and safety. Individuals who have experienced bleeding or ischemic stroke within the last six months. Those with cardiovascular or pulmonary diseases that pose a risk during the checkup. Individuals with abnormal liver function, bilirubin, or platelet levels. Individuals with abnormal thyroid function or poorly controlled diabetes. People who have undergone major surgery in the last six months. Individuals with drug or alcohol addiction. Individuals with severe ankylosing spondylitis or expected airway difficulties. Obesity with severe obstructive sleep apnea or a BMI (Body Mass Index) greater than 35. Abnormal potassium levels (K 5.0). Nail polish should ideally be completely removed or at least one fingernail on each hand for anesthesia safety. If not removed, anesthesia should be canceled. Statistical Methods Our statistical methodology adopted a heuristic approach to analyze the incidence of IM. We used a two-stage process with Python and its statistical packages. In the first stage, we conducted a univariate analysis using the t-test and z-sample proportion test to evaluate the impact of individual variables on IM. This approach provided us with a preliminary understanding of each factor's influence. In the second stage, we analyzed multivariate logistic regression by integrating the variables using Python's robust tools including Scipy. Stats, ttest_ind, and sklearn.linear_model. We used a stepwise selection process in our logistic regression model to identify significant variables and determine the strength and direction of their associations with IM. By combining these heuristic techniques, our analysis captured the complexities surrounding IM in a practical and insightful way, striking a balance between assessing individual variables and their collective dynamics. To determine the optimal cut-off value of the index for predicting IM, we employed the receiver operating characteristic (ROC) curve analysis[ 10 ]. The cut-off value was identified as the point on the ROC curve that maximized the Youden index within the area prioritizing sensitivity. This summary metric serves as a valuable instrument for appraising a model's capacity to differentiate between two diagnostic categories (affected/normal), supplying a thorough assessment that takes into account all conceivable classification thresholds. It functions as a dependable and comprehensive indicator of overall performance, offering a valuable understanding of the capabilities of the model. In the logistic regression analysis, we computed two performance indicators for the model, namely, sensitivity and specificity, as outlined by the equations below: $$Sensitivity=\frac{\text{T}\text{r}\text{u}\text{e} \text{P}\text{o}\text{s}\text{i}\text{t}\text{i}\text{v}\text{e}}{\text{T}\text{r}\text{u}\text{e} \text{P}\text{o}\text{s}\text{i}\text{t}\text{i}\text{v}\text{e}+\text{F}\text{a}\text{l}\text{s}\text{e} \text{P}\text{o}\text{s}\text{i}\text{t}\text{i}\text{v}\text{e}}$$ $$Specificty =\frac{\text{T}\text{r}\text{u}\text{e} \text{N}\text{e}\text{g}\text{a}\text{t}\text{i}\text{v}\text{e}}{\text{T}\text{r}\text{u}\text{e} \text{N}\text{e}\text{g}\text{a}\text{t}\text{i}\text{v}\text{e}+\text{F}\text{a}\text{l}\text{s}\text{e} \text{N}\text{e}\text{g}\text{a}\text{t}\text{i}\text{v}\text{e}}$$ Our analysis aimed to identify the threshold value that maximizes the Youden index, which serves as a criterion for selecting the most effective threshold within a range that favors sensitivity. The Youden index was calculated using the following formula: Youden Index = Sensitivity + Specificity − 1 The cut-off value was then determined at the point where the index reached its highest value. Sensitivity, also known as the true positive rate, represents the proportion of actual positive cases correctly identified, while specificity, or the true negative rate, signifies the proportion of actual negative cases correctly identified. We assessed the Youden index for various potential cut-off values, and the one yielding the highest value was considered the optimal cut-off point. Results The basic information for all individuals are listed in Table 1 , which shows that the age, BMI, and blood pressure presented similar mean and standard deviation values between the control group (without IM) and the IM group. However, the TC and LDL values exhibited notably different mean values between the control and IM groups. Thus, we further implemented the univariate and multivariate analyses to investigate the impact of metabolic indicators on IM classification. To compare the impact of metabolic indicators between different gender and age groups, we repeated both the univariate and multivariate analyses for males and females in three different age groups. Those three age groups were young ( 70 years) subjects. Table 1 Individual’s basic information Variable Control N = 8292(95%CI) IM N = 2088(95%CI) gender(male/female) 4732/ 3560 1447/ 641 Age 51.33 ± 10.93 52.9 ± 9.2 Waistline 85.6 ± 12.9 86.0 ± 8.2 BMI 24.3 ± 3.45 24.3 ± 2.9 Glucose(AC) 101.1 ± 22.2 100.9 ± 19.2 Glucose(PC) 110.1 ± 41.3 109.4 ± 34.5 Diastolic blood pressure 81.7 ± 11.8 82.1 ± 10.4 Systolic blood pressure 133.8 ± 19.7 133.9 ± 17.4 Triglyceride(TG) 136.6 ± 96.3 142.0 ± 103.5 Total cholesterol(TC) 199.6 ± 36.5 203.4 ± 35.3 HDL 49.7 ± 13.2 48.2 ± 11.9 low-density lipoprotein(LDL) 122.9 ± 32.9 126.5 ± 33.0 Very-low-density(VLDL) 27.0 ± 17.6 28.0 ± 17.3 AC: Fasting Blood Glucose; PC: Postprandial Blood Glucose HDL: High-Density Lipoprotein As shown in Table 2 , the univariate analysis revealed that all metabolic indicators, except for PC, significantly impacted the classification of IM patients. However, the multivariate analysis showed that only HDL and LDL were significant indicators of IM. The overall area under the ROC curve (AUC) was 55.5%, showing that the MetS may discriminate non-IM from IM individuals. To further investigate the discriminability of the metabolic indicators, we applied the univariate and multivariate analyses to the different age groups. Table 2 The overall results for metabolic indicators Variable Univariate Multivariate Control N = 8292 (95%CI) IM N = 2088 (95%CI) P value P value Cut-off point AC 101.1 ± 22.2 100.9 ± 19.2 0.07 PC 110.1 ± 41.3 110.7 ± 37.0 0.30 TC 199.6 ± 36.5 203.4 ± 35.0 0.0000 TG 136.6 ± 96.3 140.0 ± 95.4 0.022 HDL 49.7 ± 13.2 48.2 ± 11.9 0.0000 0.0000 0.511 LDL 122.9 ± 32.9 127.3 ± 33.1 0.0000 0.0000 0.511 VLDL 27.0 ± 17.6 28.1 ± 17.5 0.0000 AIP 0.38 ± 0.31 0.41 ± 0.3 0.0000 TyG 0.16 ± 0.52 0.2 ± 0.5 0.0000 AC: Fasting Blood Glucose; PC: Postprandial Blood Glucose; TG: Triglyceride; TC: total Cholesterol HDL: High-Density Lipoprotein; LDL: Low-Density Lipoprotein; VLDL: Very-Low-Density Lipoprotein; AIP: Atherogenic Index of Plasma; TyG: Triglyceride-Glucose The results for the different gender groups are presented in Tables 3 and 4 . As shown in Table 3 , the metabolic indicators exhibited different impacts in the different gender groups. However, LDL remained the most pronounced indicator. Furthermore, AC was a significant indicator of IM for females but not males. The AUC of the male group was 53.6%, and for the female group was 52.5%. The AUCs for both groups were lower than the overall result, where the AUC equaled 55.5%. The difference may be due to the size of the data. Table 3 The metabolic indicators in males vs. females Male Female Variable Univariate Multivariate Univariate Multivariate Control N = 4732 (95%CI) IM N = 1447 (95%CI) P value P value Cut-off point Control N = 3560 (95%CI) IM N = 641 (95%CI) P value P value Cut-off point AC 102.9 ± 23.6 102.2 ± 20.4 0.14 98.0 ± 16.4 0.08 0.0300 0.504 PC 110.7 ± 44.1 110.2 ± 36.3 0.58 109.4 ± 37.2 109.5 ± 33.6 0.87 TC 199.2 ± 36.8 203.0 ± 36.4 0.0000 200.1 ± 36.2 203.5 ± 36.0 0.0001 TG 154.9 ± 108.3 157.1 ± 120.4 0.33 112.3 ± 13.5 111.1 ± 61.7 0.45 HDL 45.1 ± 10.9 44.6 ± 10.0 0.05 0.0257 0.503 55.8 ± 10.9 56.4 ± 12.8 0.07 LDL 123.6 ± 33.4 127.5 ± 33.4 0.0000 0.0000 0.503 121.9 ± 32.2 124.9 ± 33.2 0.0000 0.0000 0.504 VLDL 30.5 ± 19.3 30.7 ± 19.7 0.6 22.4 ± 13.8 22.2 ± 12.2 0.44 AIP 0.48 ± 0.3 0.49 ± 0.3 0.32 0.26 ± 0.3 0.25 ± 0.3 0.28 TyG 0.27 ± 0.53 0.28 ± 0.52 0.38 0.01 ± 0.46 0.01 ± 0.45 0.99 AC: Fasting Blood Glucose; PC: Postprandial Blood Glucose; TG: Triglyceride; TC: total Cholesterol HDL: High-Density Lipoprotein; LDL: Low-Density Lipoprotein; VLDL: Very-Low-Density Lipoprotein; AIP: Atherogenic Index of Plasma; TyG: Triglyceride-Glucose The LDL value was identified as the only significant indicator for both the male and female groups. For the female group, the TC value was also a significant indicator, indicating that the TC level is important for females but not for males. The AUC of using the metabolic indicators to predict for the young males was 54.7%, and for the young females was 56.5%. The AUCs of the young subjects were higher than the overall result, indicating that the impacts of the metabolic indicators vary by age group. Table 4 The metabolic indicators in young males vs. females Male Female Variable Univariate Multivariate Univariate Multivariate Control N = 1319 (95%CI) IM N = 310 (95%CI) P value P value Cut-off point Control N = 922 (95%CI) IM N = 143 (95%CI) P value P value Cut-off point AC 102.9 ± 23.6 102.2 ± 20.4 0.14 92.4 ± 12.1 93.1 ± 8.5 0.13 PC 110.7 ± 44.1 110.2 ± 36.3 0.58 96.1 ± 21.15 97.5 ± 17.4 0.15 TC 199.2 ± 36.8 203.0 ± 36.4 0.0000 182.6 ± 30.4 184.2 ± 28.2 0.23 0.0007 0.502 TG 154.9 ± 108.3 157.1 ± 120.4 0.33 91.45 ± 48.2 92.0 ± 41.1 0.78 HDL 45.1 ± 10.9 44.6 ± 10.0 0.05 0.0257 0.503 55.8 ± 12.9 56.9 ± 11.6 0.08 LDL 123.6 ± 33.4 127.5 ± 33.4 0.0000 0.0000 0.503 108.6 ± 27.4 109.4 ± 24.7 0.5 0.0093 0.502 VLDL 30.5 ± 19.3 30.7 ± 19.7 0.6 18.2 ± 9.3 18.7 ± 8.4 0.46 AIP 0.48 ± 0.3 0.49 ± 0.3 0.32 0.18 ± 0.25 0.19 ± 0.24 0.2 TyG 0.27 ± 0.53 0.28 ± 0.52 0.38 -0.1 ± 0.43 -0.06 ± 0.38 0.2 AC: Fasting Blood Glucose; PC: Postprandial Blood Glucose; TG: Triglyceride; TC: total Cholesterol HDL: High-Density Lipoprotein; LDL: Low-Density Lipoprotein; VLDL: Very-Low-Density Lipoprotein; AIP: Atherogenic Index of Plasma; TyG: Triglyceride-Glucose As shown in Table 5 , for middle-aged individuals, LDL remained the common risk indictor, while blood sugar was also a common risk indicator. The TG level was significant only for males. Note that the AUC of middle-aged males was 53.4%, similar to the overall male result. However, for females, the AUC of the metabolic indicator exhibited a higher value than the overall female result (54.5% vs. 52.3%). Table 5 The metabolic indicators in middle-aged males vs. females Male Female Variable Univariate Multivariate Univariate Multivariate Control N = 3166 (95%CI) IM N = 1060 (95%CI) P value P value Cut-off point Control N = 2453 (95%CI) IM N = 461 (95%CI) P value P value Cut-off point AC 105.2 ± 25.9 104.0 ± 20.74 0.01 100.6 ± 21.2 99.0 ± 17.0 0.0009 0.0018 0.511 PC 115.5 ± 46.9 112.6 ± 37.8 0.008 0.0173 0.503 112.5 ± 38.0 110.2 ± 31.4 0.02 TC 200.9 ± 37.2 203.5 ± 35.3 0.05 206.9 ± 35.9 210.3 ± 35.9 0.001 TG 151.6 ± 93.5 153.1 ± 104.6 0.56 119.2 ± 76.4 115.5 ± 64.3 0.07 HDL 45.4 ± 11.0 44.8 ± 9.8 0.004 0.0021 0.503 55.9 ± 13.7 55.9 ± 12.6 0.93 LDL 125.4 ± 34.1 127.1 ± 33.3 0.0000 0.0002 0.503 127.2 ± 32.4 130.0 ± 33.0 0.002 0.0000 0.511 VLDL 30.0 ± 17.4 30.4 ± 18.0 0.46 23.8 ± 14.9 23.4 ± 13.0 0.36 AIP 0.47 ± 0.29 0.48 ± 0.28 0.02 0.28 ± 0.29 0.26 ± 0.28 0.07 TyG 0.24 ± 0.52 0.26 ± 0.49 0.3 0.05 ± 0.47 0.03 ± 0.45 0.09 AC: Fasting Blood Glucose; PC: Postprandial Blood Glucose; TG: Triglyceride; TC: total Cholesterol HDL: High-Density Lipoprotein; LDL: Low-Density Lipoprotein; VLDL: Very-Low-Density Lipoprotein For elder individuals, shown in Table 6 , the AUC was 60.1% for elderly males, and 60.3% for elderly females. Hence, the AUCs of elderly individuals were over 60% regardless of gender. This result indicates that the metabolic indicators have a higher discriminability to classify non-IM and IM individuals. Note that the VLDL value was significant in predicting IM in elderly males, while LDL remained the most pronounced metabolic indicator across all age and gender groups. The metabolic indicators showed the highest discriminability in the elderly groups. Table 6 The metabolic indicators in elderly males vs. females Male Female Variable Univariate Multivariate Univariate Multivariate Control N = 247 (95%CI) IM N = 77 (95%CI) P value P value Cut-off point Control N = 185 (95%CI) IM N = 37 (95%CI) P value P value Cut-off point AC 103.0 ± 23.5 100.5 ± 19.4 0.19 104.0 ± 27.4 101.0 ± 15.2 0.17 PC 131.1 ± 54.2 128.4 ± 43.7 0.6 133.6 ± 61.5 122.6 ± 41.1 0.04 TC 187.0 ± 35.1 192.0 ± 35.0 0.12 198.0 ± 35.6 204.0 ± 25.6 0.06 0.0021 0.503 TG 126.2 ± 71.8 114.6 ± 48.4 0.04 124.7 ± 61.5 122.4 ± 64.9 0.72 HDL 47.37 ± 11.9 49.4 ± 11.5 0.05 55.0 ± 14.1 57.3 ± 13.9 0.12 LDL 114.3 ± 30.3 120.4 ± 31.2 0.03 0.0066 0.492 118.0 ± 31.3 122.2 ± 26.8 0.16 0.0271 0.503 VLDL 25.3 ± 14.7 23.0 ± 9.8 0.01 0.0008 0.492 25.0 ± 12.6 27.0 ± 15.4 0.19 AIP 0.38 ± 0.27 0.33 ± 0.22 0.06 0.32 ± 0.27 0.36 ± 0.31 0.28 TyG 0.1 ± 0.47 0.08 ± 0.4 0.62 0.0037 0.492 0.1 ± 0.44 0.1 ± 0.5 0.67 AC: Fasting Blood Glucose; PC: Postprandial Blood Glucose; TG: Triglyceride; TC: total Cholesterol HDL: High-Density Lipoprotein; LDL: Low-Density Lipoprotein; VLDL: Very-Low-Density Lipoprotein; AIP: Atherogenic Index of Plasma; TyG: Triglyceride-Glucose Discussion Previous studies have reported inconsistent findings regarding the relationships between lipids and various types of cancers, including GC[ 11 ]. Similarly, TC has been found to exhibit diverse associations with gastric neoplasms, including negative[ 12 , 13 ], positive[ 14 ], and no correlation[ 15 ]. Meanwhile, elevated levels of LDL cholesterol (LDL-C) and low levels of HDL cholesterol (HDL-C) have been linked to increased inflammation, and certain genes associated with the LDL receptor have been shown to be involved in regulating tumors, including GC[ 16 ]. High LDL levels have been associated with the development of GC[ 17 , 18 ] via suppression of the host immune system[ 19 ]. Dyslipidemia is one component of MetS, and a significant body of literature has explored the relationship between MetS and GC; however, there is limited research on the correlation with gastric IM. In our study, we investigate this association, focusing on the roles of two metabolic indices, AIP and TyG. Furthermore, we consider their applications in other medical contexts, including cancer[ 20 ], with a particular emphasis on gastric precancerous lesions[ 3 ]. MetS is characterized by chronic low-grade inflammation, with elevated levels of pro-inflammatory cytokines. This inflammatory environment may contribute to changes in gastric mucosa, as chronic inflammation has been recognized as a precursor to cancer initiation and progression[ 21 ]. Both AIP and TyG are indicative of insulin resistance, a key component of MetS, while insulin resistance can lead to hyperinsulinemia and increased levels of insulin-like growth factors, which have been implicated in gastric carcinogenesis[ 22 , 23 ]. The AIP index, a marker of atherogenicity, reflects the balance between pro-atherogenic and anti-atherogenic lipoproteins. Dyslipidemia may promote endothelial dysfunction and atherosclerosis, and has also been used to predict colon cancer[ 20 ]. While GC is highly correlated with metabolism[ 24 ], limited research has explored the association between AIP and GC. According to the results of our study, AIP is not associated with the development of gastric IM. Meanwhile, the TyG index has been reported as a novel predictive biomarker for gastric carcinogenesis[ 3 ]. However, our study results show this association only in our older male subjects, which is inconsistent with other studies. Metabolic indicators, except PC, significantly impact IM. HDL and LDL are significant, consistent with prior findings across genders and ages.[ 25 , 26 ]. The expression of the VLDL receptor, which plays a significant role in TG metabolism, has been reported to be associated with the differentiation of gastrointestinal cancer[ 27 ]. Herein, we hypothesize that gastric mucosal metaplasia may follow a similar mechanism. According to our investigation, LDL plays a significant role in IM individuals, aside from TyG[ 3 ] and AIP; hence, using LDL-C as a predictor for gastric precancerous lesions in the general population may facilitate early intervention and improve patient outcomes. Regular monitoring of LDL-C in individuals with or without MetS may help to identify those at higher risk, enabling timely endoscopic evaluations and preventive measures. Our study offers a comprehensive analysis of metabolic indicators, revealing gender and age variations in their significance for IM. LDL emerges as the sole consistent predictor, guiding clinical risk assessment. Yet, limitations include sample specificity and the challenge of establishing a universal predictive model due to the complex MetS-IM relationship. Declarations Conflict of Interest All authors had no conflict Author Contribution Ta-Sen Yeh :Lead and provide informationChieh Lee and Chia-Yu Lain: Responsible for statistics and analysisMing-Ling Chang:Provide administrative resourcesTsung-Hsing Chen : References Handelsman Y, Butler J, Bakris GL, DeFronzo RA, Fonarow GC, Green JB, Grunberger G, Januzzi JL, Jr., Klein S, Kushner PR et al : Early intervention and intensive management of patients with diabetes, cardiorenal, and metabolic diseases. J Diabetes Complications 2023, 37(2):108389. Huang Z, Zhou J, Chen L, Zhang Y: Metabolic Syndrome and Clinical Outcomes of Patients with Gastric Cancer: A Meta-Analysis. Horm Metab Res 2023, 55(5):333–342. Kim YM, Kim JH, Park JS, Baik SJ, Chun J, Youn YH, Park H: Association between triglyceride-glucose index and gastric carcinogenesis: a health checkup cohort study. Gastric Cancer 2022, 25(1):33–41. 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Caruso MG, Notarnicola M, Cavallini A, Di Leo A: 3-Hydroxy-3-methylglutaryl coenzyme A reductase activity and low-density lipoprotein receptor expression in diffuse-type and intestinal-type human gastric cancer. J Gastroenterol 2002, 37(7):504–508. Kim HY: Metabolic syndrome is associated with gastric dysplasia. Eur J Gastroenterol Hepatol 2011, 23(10):871–875. Jung MK, Jeon SW, Cho CM, Tak WY, Kweon YO, Kim SK, Choi YH, Bae HI, Lee JY, Chung JM: Hyperglycaemia, hypercholesterolaemia and the risk for developing gastric dysplasia. Dig Liver Dis 2008, 40(5):361–365. Bigler RD, Khoo M, Lund-Katz S, Scerbo L, Esfahani M: Identification of low density lipoprotein as a regulator of Fc receptor-mediated phagocytosis. Proc Natl Acad Sci U S A 1990, 87(13):4981–4985. Han M, Wang H, Yang S, Zhu S, Zhao G, Shi H, Li P: Triglyceride glucose index and Atherogenic index of plasma for predicting colorectal neoplasms in patients without cardiovascular diseases. Front Oncol 2022, 12:1031259. Karra S, Gurushankari B, Rajalekshmy MR, Elamurugan TP, Mahalakshmy T, Kate V, Nanda N, Rajesh NG, Shankar G: Diagnostic Utility of NLR, PLR and MLR in Early Diagnosis of Gastric Cancer: an Analytical Cross-Sectional Study. J Gastrointest Cancer 2023. Guevara-Aguirre J, Peña G, Acosta W, Pazmiño G, Saavedra J, Soto L, Lescano D, Guevara A, Gavilanes AWD: Cancer in growth hormone excess and growth hormone deficit. Endocr Relat Cancer 2023, 30(10). Wang K, Yu Y, Wang W, Jiang Y, Li Y, Jiang X, Qiao Y, Chen L, Zhao X, Liu J et al : Targeting the E3 ligase NEDD4 as a novel therapeutic strategy for IGF1 signal pathway-driven gastric cancer. Oncogene 2023, 42(14):1072–1087. Huang D, Shin WK, De la Torre K, Lee HW, Min S, Shin A, Lee JK, Kang D: Association between metabolic syndrome and gastric cancer risk: results from the Health Examinees Study. Gastric Cancer 2023, 26(4):481–492. Nam SY, Jeong J, Jeon SW: Constant Association between Low High-Density Lipoprotein Cholesterol and Gastric Cancer Regardless of Site. J Obes Metab Syndr 2023, 32(2):141–150. Pih GY, Gong EJ, Choi JY, Kim MJ, Ahn JY, Choe J, Bae SE, Chang HS, Na HK, Lee JH et al : Associations of Serum Lipid Level with Gastric Cancer Risk, Pathology, and Prognosis. Cancer Res Treat 2021, 53(2):445–456. Chen T, Wu F, Chen FM, Tian J, Qu S: Variations of very low-density lipoprotein receptor subtype expression in gastrointestinal adenocarcinoma cells with various differentiations. World J Gastroenterol 2005, 11(18):2817–2821. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-4016440","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":277157984,"identity":"9addb7a3-5853-4849-a6de-44c683221727","order_by":0,"name":"Chieh Lee","email":"","orcid":"","institution":"National Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Chieh","middleName":"","lastName":"Lee","suffix":""},{"id":277157985,"identity":"ba9133a4-90b6-4458-8a2f-4a6f53e2b8ab","order_by":1,"name":"Chia-Yu Lai","email":"","orcid":"","institution":"National Pingtung University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Chia-Yu","middleName":"","lastName":"Lai","suffix":""},{"id":277157986,"identity":"140f3bad-f857-47b7-80b0-1d584399c994","order_by":2,"name":"Ta-Sen Yeh","email":"","orcid":"","institution":"Chang Gung Memorial Hospital, Chang Gung University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ta-Sen","middleName":"","lastName":"Yeh","suffix":""},{"id":277157987,"identity":"73958800-9392-4e14-b5c0-d311a01dc752","order_by":3,"name":"Ming-Ling Chang","email":"","orcid":"","institution":"Chang Gung Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ming-Ling","middleName":"","lastName":"Chang","suffix":""},{"id":277157989,"identity":"787753ea-081f-4701-b233-b0658b6737c3","order_by":4,"name":"Tsung-Hsing Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIie3PMUvDQBTA8Xc8uCyxri2V9itEAsXBD/OCkA4SEVwydDgQzsXYNR+jXZxTAuly0FW4pdDFRcjmIQWNmVS4qJvg/Xkc7w2/4QBcrr8Y4mpn0tkIgNqbvicePwtBVeEX0iUP/cmQSYzEj0mvhGB4qfh0fnNOYF7uL8DLdo/MlFYyKIHCPD1KcvW0YNmdvgJ/PTkBspOghIJ8xRPxkCyQ3epI9GMeAOkOwkRxIHE6/gVBdt0QCt4JmJbgtosMSo4sV9XxsvnLKhM6kn7FgeJXK+ltNs+mTmfj0TpZbs1eR3NPYl2fxlYCxcedSQDeTJ/s4DMB2Lcv1l3C5XK5/l1vfSJgLNaYVBUAAAAASUVORK5CYII=","orcid":"","institution":"Chang Gung Memorial Hospital","correspondingAuthor":true,"prefix":"","firstName":"Tsung-Hsing","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-03-05 10:16:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4016440/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4016440/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52450044,"identity":"b875c2f9-1b60-413e-91e0-b2bfa7e468cc","added_by":"auto","created_at":"2024-03-11 19:03:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":29122,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of study population selection\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4016440/v1/cf766b1ceb34b9ec2337dd8a.png"},{"id":52924563,"identity":"396abc81-59af-4a5a-933a-b84db1c4cd4a","added_by":"auto","created_at":"2024-03-18 17:52:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":541911,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4016440/v1/e05f8685-26d7-4e9b-8fd2-20fc83413fd3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating Multiple Metabolic Indicators to Predict Gastric Intestinal Metaplasia Risk","fulltext":[{"header":"What is already known on this topic","content":"\u003cp\u003e\u003cstrong\u003eMetabolic syndrome strongly correlates with gastric cancer development, yet the reliability of individual metabolic indices in predicting intestinal metaplasia (IM) risk remains uncertain.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrevious studies have not comprehensively evaluated various metabolic indicators' predictive value for IM across different age and gender groups.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat this study adds:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis study identifies LDL as a consistent predictor of IM across all age and gender groups, enhancing understanding of metabolic indicators for IM risk.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIt unveils variations in metabolic indicator significance based on age and gender, such as HDL in young males and TC in young females, offering insights for personalized risk assessment.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHow this study might affect research, practice, or policy:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe findings underscore the importance of considering age and gender-specific metabolic indicators in IM risk assessment, potentially enabling more targeted screening and prevention strategies.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinicians could benefit from integrating LDL levels into GC risk assessment protocols, potentially enhancing early detection and management.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolicymakers and healthcare providers should consider advocating for routine metabolic screenings, particularly among individuals with metabolic syndrome, to mitigate GC and related complications.\u003c/strong\u003e\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eMetabolic syndrome (MetS) is a complex medical condition characterized by a cluster of interrelated conditions, including obesity, hypertension, elevated blood sugar levels, and abnormal lipid profiles. Extensive research has demonstrated strong associations between MetS and chronic diseases, such as cardiovascular disorders and diabetes[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Moreover, MetS has emerged as a valuable predictor for postoperative complications, cancer recurrence, and increased overall mortality rates among patients with GC[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe relationship between MetS and gastric precancerous lesions remains unclear, although recent studies have suggested the Triglyceride-Glucose Index (TyG) as a novel serum biomarker with predictive potential for gastric carcinogenesis[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Meanwhile, gastric intestinal metaplasia (IM) has been firmly established as a precancerous lesion in the development of GC, and the severity of gastric IM is closely associated with the risk of GC development[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe primary objective of the present study was to investigate the potential association between MetS and gastric IM, with a specific focus on evaluating the predictive capabilities of two metabolic indices: the Atherogenic Index of Plasma (AIP) and TyG. AIP, which integrates arterial lipids and blood sugar levels, offers valuable insights into the risk of atherosclerosis and cardiovascular diseases. On the other hand, TyG serves as a reflective marker of insulin resistance, and may be closely correlated with MetS. Through a comprehensive examination of the relationship between metabolic indicators and gastric IM, we aim to identify biomarkers that can predict IM risk, thus facilitating early clinical intervention and further clarifying our understanding of the links between MetS and the development of GC.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe performed univariate analysis and multivariate analysis to characterize the relationship between metabolic indicators and IM. In addition, Python packages were used to build a database and conduct statistical analyses. In this section, we first present our database inclusion and exclusion criteria, and subsequently present our statistical methods and measurements of the relationships between the metabolic indicators and IM. This study was approved by the Institutional Review Board (IRB) under protocol number 202300866B0.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Collection and Preprocessing\u003c/h2\u003e \u003cp\u003eFrom 2010 to 2014, 59,143 subjects were enrolled in this retrospective cohort study, 1,355 of whom had undergone endoscopic biopsy and underwent further analysis. After eliminating cases with incomplete blood test data, the sample size was narrowed to 10,380 subjects. The analysis then segregated these individuals into two groups, 2,088 subjects with IM and 8,292 subjects without IM, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This section comprehensively outlines the study design and the methodology we used to investigate IM and the associations with metabolic indicators.\u003c/p\u003e \u003cp\u003eWe collected data related to the metabolic indicators from the 10,380 study subjects. The metabolic indicators encompassed a broader spectrum of factors, including pre-prandial blood glucose (AC), postprandial blood glucose (PC), total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), very-low-density lipoprotein (VLDL), the Atherogenic Index of Plasma (AIP), and the Triglyceride-Glucose (TyG) index. Several studies, such as those by Khaw et al[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and Tseng et al[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], have shown that hemoglobin A1C (HbA1C), representing blood sugar (glucose) level, is a significant risk factor for IM, after adjusting for various factors including fasting blood sugar, supporting the hypothesis that HbA1C and fasting glucose may be putative risk factors. In addition, previous studies by Liu et al[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and van der Poorten et al[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] indicate that predictors of liver-related diseases are associated with metabolic syndromes, including cardiovascular diseases and stroke. The studies focused on subjects with non-alcoholic fatty liver disease (NAFLD), showing that visceral fat is an independent predictive factor positively associated with serum triglycerides, HDL, LDL, and interleukin-6 (IL-6), lipid-related parameters. These factors are known to be directly related to inflammatory processes, thereby validating their integration into analytical models. Similarly, the AIP and TyG indexes, as reported by Cheong et al.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], influence disease, and are independent predictors of disease incidence and mortality rates, thus justifying their suitability for inclusion in the predictive indicators studied here. We used the following equations to calculate the AIP and TyG:\u003c/p\u003e \u003cp\u003eAIP\u0026thinsp;=\u0026thinsp;log (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\text{T}\\text{r}\\text{i}\\text{g}\\text{l}\\text{y}\\text{c}\\text{e}\\text{r}\\text{i}\\text{d}\\text{e}}{\\text{H}\\text{D}\\text{L}}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eTyG = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\text{log}\\left(\\frac{\\text{T}\\text{r}\\text{i}\\text{g}\\text{l}\\text{y}\\text{c}\\text{e}\\text{r}\\text{i}\\text{d}\\text{e}}{2.2}\\right) \\text{*} \\text{G}\\text{l}\\text{u}\\text{c}\\text{o}\\text{s}\\text{e}\\left(\\text{A}\\text{C}\\right)}{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eThe exclusion criteria for this study at the subjects\u0026rsquo; annual health checkup were as follows:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eUncooperative, unwilling, or individuals with impaired consciousness.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIndividuals with conditions such as pregnancy or systemic diseases that may affect anesthesia and safety.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIndividuals who have experienced bleeding or ischemic stroke within the last six months.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThose with cardiovascular or pulmonary diseases that pose a risk during the checkup.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIndividuals with abnormal liver function, bilirubin, or platelet levels.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIndividuals with abnormal thyroid function or poorly controlled diabetes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePeople who have undergone major surgery in the last six months.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIndividuals with drug or alcohol addiction.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIndividuals with severe ankylosing spondylitis or expected airway difficulties.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eObesity with severe obstructive sleep apnea or a BMI (Body Mass Index) greater than 35.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAbnormal potassium levels (K\u0026thinsp;\u0026lt;\u0026thinsp;3.0 or K\u0026thinsp;\u0026gt;\u0026thinsp;5.0).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eNail polish should ideally be completely removed or at least one fingernail on each hand for anesthesia safety. If not removed, anesthesia should be canceled.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Methods\u003c/h2\u003e \u003cp\u003eOur statistical methodology adopted a heuristic approach to analyze the incidence of IM. We used a two-stage process with Python and its statistical packages. In the first stage, we conducted a univariate analysis using the t-test and z-sample proportion test to evaluate the impact of individual variables on IM. This approach provided us with a preliminary understanding of each factor's influence. In the second stage, we analyzed multivariate logistic regression by integrating the variables using Python's robust tools including Scipy. Stats, ttest_ind, and sklearn.linear_model. We used a stepwise selection process in our logistic regression model to identify significant variables and determine the strength and direction of their associations with IM. By combining these heuristic techniques, our analysis captured the complexities surrounding IM in a practical and insightful way, striking a balance between assessing individual variables and their collective dynamics.\u003c/p\u003e \u003cp\u003eTo determine the optimal cut-off value of the index for predicting IM, we employed the receiver operating characteristic (ROC) curve analysis[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The cut-off value was identified as the point on the ROC curve that maximized the Youden index within the area prioritizing sensitivity. This summary metric serves as a valuable instrument for appraising a model's capacity to differentiate between two diagnostic categories (affected/normal), supplying a thorough assessment that takes into account all conceivable classification thresholds. It functions as a dependable and comprehensive indicator of overall performance, offering a valuable understanding of the capabilities of the model. In the logistic regression analysis, we computed two performance indicators for the model, namely, sensitivity and specificity, as outlined by the equations below:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$Sensitivity=\\frac{\\text{T}\\text{r}\\text{u}\\text{e} \\text{P}\\text{o}\\text{s}\\text{i}\\text{t}\\text{i}\\text{v}\\text{e}}{\\text{T}\\text{r}\\text{u}\\text{e} \\text{P}\\text{o}\\text{s}\\text{i}\\text{t}\\text{i}\\text{v}\\text{e}+\\text{F}\\text{a}\\text{l}\\text{s}\\text{e} \\text{P}\\text{o}\\text{s}\\text{i}\\text{t}\\text{i}\\text{v}\\text{e}}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$Specificty =\\frac{\\text{T}\\text{r}\\text{u}\\text{e} \\text{N}\\text{e}\\text{g}\\text{a}\\text{t}\\text{i}\\text{v}\\text{e}}{\\text{T}\\text{r}\\text{u}\\text{e} \\text{N}\\text{e}\\text{g}\\text{a}\\text{t}\\text{i}\\text{v}\\text{e}+\\text{F}\\text{a}\\text{l}\\text{s}\\text{e} \\text{N}\\text{e}\\text{g}\\text{a}\\text{t}\\text{i}\\text{v}\\text{e}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eOur analysis aimed to identify the threshold value that maximizes the Youden index, which serves as a criterion for selecting the most effective threshold within a range that favors sensitivity. The Youden index was calculated using the following formula:\u003c/p\u003e \u003cp\u003eYouden Index\u0026thinsp;=\u0026thinsp;Sensitivity\u0026thinsp;+\u0026thinsp;Specificity \u0026minus;\u0026thinsp;1\u003c/p\u003e \u003cp\u003eThe cut-off value was then determined at the point where the index reached its highest value. Sensitivity, also known as the true positive rate, represents the proportion of actual positive cases correctly identified, while specificity, or the true negative rate, signifies the proportion of actual negative cases correctly identified. We assessed the Youden index for various potential cut-off values, and the one yielding the highest value was considered the optimal cut-off point.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe basic information for all individuals are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which shows that the age, BMI, and blood pressure presented similar mean and standard deviation values between the control group (without IM) and the IM group. However, the TC and LDL values exhibited notably different mean values between the control and IM groups. Thus, we further implemented the univariate and multivariate analyses to investigate the impact of metabolic indicators on IM classification. To compare the impact of metabolic indicators between different gender and age groups, we repeated both the univariate and multivariate analyses for males and females in three different age groups. Those three age groups were young (\u0026lt;\u0026thinsp;45 years), middle-aged (45 to 70 years), and elderly (\u0026gt;\u0026thinsp;70 years) subjects.\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\u003eIndividual\u0026rsquo;s basic information\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;8292(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIM\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2088(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\u003egender(male/female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4732/ 3560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1447/ 641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.33\u0026thinsp;\u0026plusmn;\u0026thinsp;10.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaistline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose(AC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.1\u0026thinsp;\u0026plusmn;\u0026thinsp;22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.9\u0026thinsp;\u0026plusmn;\u0026thinsp;19.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose(PC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110.1\u0026thinsp;\u0026plusmn;\u0026thinsp;41.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109.4\u0026thinsp;\u0026plusmn;\u0026thinsp;34.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.7\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133.8\u0026thinsp;\u0026plusmn;\u0026thinsp;19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133.9\u0026thinsp;\u0026plusmn;\u0026thinsp;17.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride(TG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136.6\u0026thinsp;\u0026plusmn;\u0026thinsp;96.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142.0\u0026thinsp;\u0026plusmn;\u0026thinsp;103.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol(TC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199.6\u0026thinsp;\u0026plusmn;\u0026thinsp;36.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e203.4\u0026thinsp;\u0026plusmn;\u0026thinsp;35.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elow-density lipoprotein(LDL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122.9\u0026thinsp;\u0026plusmn;\u0026thinsp;32.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126.5\u0026thinsp;\u0026plusmn;\u0026thinsp;33.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery-low-density(VLDL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.0\u0026thinsp;\u0026plusmn;\u0026thinsp;17.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAC: Fasting Blood Glucose; PC: Postprandial Blood Glucose HDL: High-Density Lipoprotein\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the univariate analysis revealed that all metabolic indicators, except for PC, significantly impacted the classification of IM patients. However, the multivariate analysis showed that only HDL and LDL were significant indicators of IM. The overall area under the ROC curve (AUC) was 55.5%, showing that the MetS may discriminate non-IM from IM individuals. To further investigate the discriminability of the metabolic indicators, we applied the univariate and multivariate analyses to the different age groups.\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\u003eThe overall results for metabolic indicators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;8292\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIM\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2088\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCut-off point\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e101.1\u0026thinsp;\u0026plusmn;\u0026thinsp;22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e100.9\u0026thinsp;\u0026plusmn;\u0026thinsp;19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\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\u003ePC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e110.1\u0026thinsp;\u0026plusmn;\u0026thinsp;41.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e110.7\u0026thinsp;\u0026plusmn;\u0026thinsp;37.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.30\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\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e199.6\u0026thinsp;\u0026plusmn;\u0026thinsp;36.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e203.4\u0026thinsp;\u0026plusmn;\u0026thinsp;35.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0000\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\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e136.6\u0026thinsp;\u0026plusmn;\u0026thinsp;96.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e140.0\u0026thinsp;\u0026plusmn;\u0026thinsp;95.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\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\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e49.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e48.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e122.9\u0026thinsp;\u0026plusmn;\u0026thinsp;32.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e127.3\u0026thinsp;\u0026plusmn;\u0026thinsp;33.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e28.1\u0026thinsp;\u0026plusmn;\u0026thinsp;17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0000\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\u003eAIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0000\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\u003eTyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0000\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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAC: Fasting Blood Glucose; PC: Postprandial Blood Glucose; TG: Triglyceride; TC: total Cholesterol HDL: High-Density Lipoprotein; LDL: Low-Density Lipoprotein; VLDL: Very-Low-Density Lipoprotein; AIP: Atherogenic Index of Plasma; TyG: Triglyceride-Glucose\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results for the different gender groups are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the metabolic indicators exhibited different impacts in the different gender groups. However, LDL remained the most pronounced indicator. Furthermore, AC was a significant indicator of IM for females but not males. The AUC of the male group was 53.6%, and for the female group was 52.5%. The AUCs for both groups were lower than the overall result, where the AUC equaled 55.5%. The difference may be due to the size of the data.\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\u003eThe metabolic indicators in males vs. females\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;4732\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIM\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1447\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCut-off point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;3560\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIM\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;641\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCut-off point\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102.9\u0026thinsp;\u0026plusmn;\u0026thinsp;23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102.2\u0026thinsp;\u0026plusmn;\u0026thinsp;20.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\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 \u003cp\u003e98.0\u0026thinsp;\u0026plusmn;\u0026thinsp;16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110.7\u0026thinsp;\u0026plusmn;\u0026thinsp;44.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110.2\u0026thinsp;\u0026plusmn;\u0026thinsp;36.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\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 \u003cp\u003e109.4\u0026thinsp;\u0026plusmn;\u0026thinsp;37.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e109.5\u0026thinsp;\u0026plusmn;\u0026thinsp;33.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199.2\u0026thinsp;\u0026plusmn;\u0026thinsp;36.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e203.0\u0026thinsp;\u0026plusmn;\u0026thinsp;36.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0000\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 \u003cp\u003e200.1\u0026thinsp;\u0026plusmn;\u0026thinsp;36.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e203.5\u0026thinsp;\u0026plusmn;\u0026thinsp;36.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e154.9\u0026thinsp;\u0026plusmn;\u0026thinsp;108.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157.1\u0026thinsp;\u0026plusmn;\u0026thinsp;120.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\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 \u003cp\u003e112.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e111.1\u0026thinsp;\u0026plusmn;\u0026thinsp;61.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123.6\u0026thinsp;\u0026plusmn;\u0026thinsp;33.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127.5\u0026thinsp;\u0026plusmn;\u0026thinsp;33.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e121.9\u0026thinsp;\u0026plusmn;\u0026thinsp;32.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e124.9\u0026thinsp;\u0026plusmn;\u0026thinsp;33.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.5\u0026thinsp;\u0026plusmn;\u0026thinsp;19.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.7\u0026thinsp;\u0026plusmn;\u0026thinsp;19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\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 \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\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 \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eAC: Fasting Blood Glucose; PC: Postprandial Blood Glucose; TG: Triglyceride; TC: total Cholesterol HDL: High-Density Lipoprotein; LDL: Low-Density Lipoprotein; VLDL: Very-Low-Density Lipoprotein; AIP: Atherogenic Index of Plasma; TyG: Triglyceride-Glucose\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe LDL value was identified as the only significant indicator for both the male and female groups. For the female group, the TC value was also a significant indicator, indicating that the TC level is important for females but not for males. The AUC of using the metabolic indicators to predict for the young males was 54.7%, and for the young females was 56.5%. The AUCs of the young subjects were higher than the overall result, indicating that the impacts of the metabolic indicators vary by age group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe metabolic indicators in young males vs. females\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1319\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIM\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;310\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCut-off point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;922\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIM\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;143\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCut-off point\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102.9\u0026thinsp;\u0026plusmn;\u0026thinsp;23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102.2\u0026thinsp;\u0026plusmn;\u0026thinsp;20.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\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 \u003cp\u003e92.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e93.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110.7\u0026thinsp;\u0026plusmn;\u0026thinsp;44.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110.2\u0026thinsp;\u0026plusmn;\u0026thinsp;36.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\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 \u003cp\u003e96.1\u0026thinsp;\u0026plusmn;\u0026thinsp;21.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97.5\u0026thinsp;\u0026plusmn;\u0026thinsp;17.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199.2\u0026thinsp;\u0026plusmn;\u0026thinsp;36.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e203.0\u0026thinsp;\u0026plusmn;\u0026thinsp;36.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0000\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 \u003cp\u003e182.6\u0026thinsp;\u0026plusmn;\u0026thinsp;30.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e184.2\u0026thinsp;\u0026plusmn;\u0026thinsp;28.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e154.9\u0026thinsp;\u0026plusmn;\u0026thinsp;108.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157.1\u0026thinsp;\u0026plusmn;\u0026thinsp;120.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\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 \u003cp\u003e91.45\u0026thinsp;\u0026plusmn;\u0026thinsp;48.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e92.0\u0026thinsp;\u0026plusmn;\u0026thinsp;41.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123.6\u0026thinsp;\u0026plusmn;\u0026thinsp;33.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127.5\u0026thinsp;\u0026plusmn;\u0026thinsp;33.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e108.6\u0026thinsp;\u0026plusmn;\u0026thinsp;27.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e109.4\u0026thinsp;\u0026plusmn;\u0026thinsp;24.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.5\u0026thinsp;\u0026plusmn;\u0026thinsp;19.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.7\u0026thinsp;\u0026plusmn;\u0026thinsp;19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\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 \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\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 \u003cp\u003e-0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eAC: Fasting Blood Glucose; PC: Postprandial Blood Glucose; TG: Triglyceride; TC: total Cholesterol HDL: High-Density Lipoprotein; LDL: Low-Density Lipoprotein; VLDL: Very-Low-Density Lipoprotein; AIP: Atherogenic Index of Plasma; TyG: Triglyceride-Glucose\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, for middle-aged individuals, LDL remained the common risk indictor, while blood sugar was also a common risk indicator. The TG level was significant only for males. Note that the AUC of middle-aged males was 53.4%, similar to the overall male result. However, for females, the AUC of the metabolic indicator exhibited a higher value than the overall female result (54.5% vs. 52.3%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe metabolic indicators in middle-aged males vs. females\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;3166\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIM\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1060\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCut-off point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2453\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIM\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;461\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCut-off point\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105.2\u0026thinsp;\u0026plusmn;\u0026thinsp;25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104.0\u0026thinsp;\u0026plusmn;\u0026thinsp;20.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\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 \u003cp\u003e100.6\u0026thinsp;\u0026plusmn;\u0026thinsp;21.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e99.0\u0026thinsp;\u0026plusmn;\u0026thinsp;17.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115.5\u0026thinsp;\u0026plusmn;\u0026thinsp;46.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112.6\u0026thinsp;\u0026plusmn;\u0026thinsp;37.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e112.5\u0026thinsp;\u0026plusmn;\u0026thinsp;38.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e110.2\u0026thinsp;\u0026plusmn;\u0026thinsp;31.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200.9\u0026thinsp;\u0026plusmn;\u0026thinsp;37.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e203.5\u0026thinsp;\u0026plusmn;\u0026thinsp;35.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\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 \u003cp\u003e206.9\u0026thinsp;\u0026plusmn;\u0026thinsp;35.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e210.3\u0026thinsp;\u0026plusmn;\u0026thinsp;35.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151.6\u0026thinsp;\u0026plusmn;\u0026thinsp;93.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153.1\u0026thinsp;\u0026plusmn;\u0026thinsp;104.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56\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 \u003cp\u003e119.2\u0026thinsp;\u0026plusmn;\u0026thinsp;76.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e115.5\u0026thinsp;\u0026plusmn;\u0026thinsp;64.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55.9\u0026thinsp;\u0026plusmn;\u0026thinsp;13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e55.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125.4\u0026thinsp;\u0026plusmn;\u0026thinsp;34.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127.1\u0026thinsp;\u0026plusmn;\u0026thinsp;33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e127.2\u0026thinsp;\u0026plusmn;\u0026thinsp;32.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e130.0\u0026thinsp;\u0026plusmn;\u0026thinsp;33.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.0\u0026thinsp;\u0026plusmn;\u0026thinsp;17.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.4\u0026thinsp;\u0026plusmn;\u0026thinsp;18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46\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 \u003cp\u003e23.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\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 \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3\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 \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eAC: Fasting Blood Glucose; PC: Postprandial Blood Glucose; TG: Triglyceride; TC: total Cholesterol HDL: High-Density Lipoprotein; LDL: Low-Density Lipoprotein; VLDL: Very-Low-Density Lipoprotein\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor elder individuals, shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the AUC was 60.1% for elderly males, and 60.3% for elderly females. Hence, the AUCs of elderly individuals were over 60% regardless of gender. This result indicates that the metabolic indicators have a higher discriminability to classify non-IM and IM individuals. Note that the VLDL value was significant in predicting IM in elderly males, while LDL remained the most pronounced metabolic indicator across all age and gender groups. The metabolic indicators showed the highest discriminability in the elderly groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe metabolic indicators in elderly males vs. females\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;247\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIM\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;77\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCut-off point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;185\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIM\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;37\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCut-off point\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103.0\u0026thinsp;\u0026plusmn;\u0026thinsp;23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.5\u0026thinsp;\u0026plusmn;\u0026thinsp;19.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\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 \u003cp\u003e104.0\u0026thinsp;\u0026plusmn;\u0026thinsp;27.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e101.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131.1\u0026thinsp;\u0026plusmn;\u0026thinsp;54.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128.4\u0026thinsp;\u0026plusmn;\u0026thinsp;43.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e133.6\u0026thinsp;\u0026plusmn;\u0026thinsp;61.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e122.6\u0026thinsp;\u0026plusmn;\u0026thinsp;41.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e187.0\u0026thinsp;\u0026plusmn;\u0026thinsp;35.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192.0\u0026thinsp;\u0026plusmn;\u0026thinsp;35.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\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 \u003cp\u003e198.0\u0026thinsp;\u0026plusmn;\u0026thinsp;35.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e204.0\u0026thinsp;\u0026plusmn;\u0026thinsp;25.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126.2\u0026thinsp;\u0026plusmn;\u0026thinsp;71.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114.6\u0026thinsp;\u0026plusmn;\u0026thinsp;48.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\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 \u003cp\u003e124.7\u0026thinsp;\u0026plusmn;\u0026thinsp;61.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e122.4\u0026thinsp;\u0026plusmn;\u0026thinsp;64.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.37\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\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 \u003cp\u003e55.0\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114.3\u0026thinsp;\u0026plusmn;\u0026thinsp;30.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120.4\u0026thinsp;\u0026plusmn;\u0026thinsp;31.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e118.0\u0026thinsp;\u0026plusmn;\u0026thinsp;31.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e122.2\u0026thinsp;\u0026plusmn;\u0026thinsp;26.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\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 \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eAC: Fasting Blood Glucose; PC: Postprandial Blood Glucose; TG: Triglyceride; TC: total Cholesterol HDL: High-Density Lipoprotein; LDL: Low-Density Lipoprotein; VLDL: Very-Low-Density Lipoprotein; AIP: Atherogenic Index of Plasma; TyG: Triglyceride-Glucose\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrevious studies have reported inconsistent findings regarding the relationships between lipids and various types of cancers, including GC[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Similarly, TC has been found to exhibit diverse associations with gastric neoplasms, including negative[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], positive[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and no correlation[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Meanwhile, elevated levels of LDL cholesterol (LDL-C) and low levels of HDL cholesterol (HDL-C) have been linked to increased inflammation, and certain genes associated with the LDL receptor have been shown to be involved in regulating tumors, including GC[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. High LDL levels have been associated with the development of GC[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] via suppression of the host immune system[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDyslipidemia is one component of MetS, and a significant body of literature has explored the relationship between MetS and GC; however, there is limited research on the correlation with gastric IM. In our study, we investigate this association, focusing on the roles of two metabolic indices, AIP and TyG. Furthermore, we consider their applications in other medical contexts, including cancer[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], with a particular emphasis on gastric precancerous lesions[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMetS is characterized by chronic low-grade inflammation, with elevated levels of pro-inflammatory cytokines. This inflammatory environment may contribute to changes in gastric mucosa, as chronic inflammation has been recognized as a precursor to cancer initiation and progression[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Both AIP and TyG are indicative of insulin resistance, a key component of MetS, while insulin resistance can lead to hyperinsulinemia and increased levels of insulin-like growth factors, which have been implicated in gastric carcinogenesis[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe AIP index, a marker of atherogenicity, reflects the balance between pro-atherogenic and anti-atherogenic lipoproteins. Dyslipidemia may promote endothelial dysfunction and atherosclerosis, and has also been used to predict colon cancer[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. While GC is highly correlated with metabolism[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], limited research has explored the association between AIP and GC. According to the results of our study, AIP is not associated with the development of gastric IM. Meanwhile, the TyG index has been reported as a novel predictive biomarker for gastric carcinogenesis[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, our study results show this association only in our older male subjects, which is inconsistent with other studies.\u003c/p\u003e \u003cp\u003eMetabolic indicators, except PC, significantly impact IM. HDL and LDL are significant, consistent with prior findings across genders and ages.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The expression of the VLDL receptor, which plays a significant role in TG metabolism, has been reported to be associated with the differentiation of gastrointestinal cancer[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Herein, we hypothesize that gastric mucosal metaplasia may follow a similar mechanism. According to our investigation, LDL plays a significant role in IM individuals, aside from TyG[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and AIP; hence, using LDL-C as a predictor for gastric precancerous lesions in the general population may facilitate early intervention and improve patient outcomes. Regular monitoring of LDL-C in individuals with or without MetS may help to identify those at higher risk, enabling timely endoscopic evaluations and preventive measures.\u003c/p\u003e \u003cp\u003eOur study offers a comprehensive analysis of metabolic indicators, revealing gender and age variations in their significance for IM. LDL emerges as the sole consistent predictor, guiding clinical risk assessment. Yet, limitations include sample specificity and the challenge of establishing a universal predictive model due to the complex MetS-IM relationship.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eAll authors had no conflict\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eTa-Sen Yeh :Lead and provide informationChieh Lee and Chia-Yu Lain: Responsible for statistics and analysisMing-Ling Chang:Provide administrative resourcesTsung-Hsing Chen :\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHandelsman Y, Butler J, Bakris GL, DeFronzo RA, Fonarow GC, Green JB, Grunberger G, Januzzi JL, Jr., Klein S, Kushner PR \u003cem\u003eet al\u003c/em\u003e: Early intervention and intensive management of patients with diabetes, cardiorenal, and metabolic diseases. 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Endocr Relat Cancer 2023, 30(10).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang K, Yu Y, Wang W, Jiang Y, Li Y, Jiang X, Qiao Y, Chen L, Zhao X, Liu J \u003cem\u003eet al\u003c/em\u003e: Targeting the E3 ligase NEDD4 as a novel therapeutic strategy for IGF1 signal pathway-driven gastric cancer. Oncogene 2023, 42(14):1072\u0026ndash;1087.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang D, Shin WK, De la Torre K, Lee HW, Min S, Shin A, Lee JK, Kang D: Association between metabolic syndrome and gastric cancer risk: results from the Health Examinees Study. Gastric Cancer 2023, 26(4):481\u0026ndash;492.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNam SY, Jeong J, Jeon SW: Constant Association between Low High-Density Lipoprotein Cholesterol and Gastric Cancer Regardless of Site. J Obes Metab Syndr 2023, 32(2):141\u0026ndash;150.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePih GY, Gong EJ, Choi JY, Kim MJ, Ahn JY, Choe J, Bae SE, Chang HS, Na HK, Lee JH \u003cem\u003eet al\u003c/em\u003e: Associations of Serum Lipid Level with Gastric Cancer Risk, Pathology, and Prognosis. Cancer Res Treat 2021, 53(2):445\u0026ndash;456.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen T, Wu F, Chen FM, Tian J, Qu S: Variations of very low-density lipoprotein receptor subtype expression in gastrointestinal adenocarcinoma cells with various differentiations. World J Gastroenterol 2005, 11(18):2817\u0026ndash;2821.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"gastric intestinal metaplasia, Atherogenic Index of Plasma, Triglyceride-Glucose Index, Metabolic Indicators","lastPublishedDoi":"10.21203/rs.3.rs-4016440/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4016440/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMetabolic syndrome is highly associated with gastric cancer (GC) formation, although the reliability of individual indices for predicting IM (intestinal metaplasia) risk remains inconsistent. This retrospective cohort study applied univariate and multivariate analyses using Python and its statistical packages to analyze the relationships between multiple metabolic indicators and IM, including the Atherogenic Index of Plasma (AIP), the Triglyceride-Glucose Index (TyG), and levels of fasting (TC, AC: Fasting) blood glucose (AC), postprandial blood glucose (PC), total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), and very-low-density lipoprotein (VLDL).Our analysis of the metabolic indicators revealed that TyG and AIP were not predictors of IM. However, across all ages and genders, LDL was a significant predictor of IM. Moreover, we found that the accuracy associated with certain metabolic indicators of IM can vary according to age and gender. More specifically, HDL was a significant indicator of IM in young males, while TC was significant in young females. Additionally, for middle-aged individuals, PC was a significant indicator in males, while AC was significant in females. In elderly males, LDL, VLDL, and TyG were significant indicators, while TC and LDL were significant in elderly females. Furthermore, the AUC of elder individuals (\u0026gt;\u0026thinsp;60%) was significantly higher compared to young individuals (54.7%, males; 56.5%, females) and middle-aged individuals (53.6%, males; 52.5%, females). By conducting a comprehensive analysis of multiple metabolic indicators, our study reveals that significance varies according to gender and age, although LDL is a significant predictor of IM across all groups.\u003c/p\u003e","manuscriptTitle":"Evaluating Multiple Metabolic Indicators to Predict Gastric Intestinal Metaplasia Risk","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-11 19:03:11","doi":"10.21203/rs.3.rs-4016440/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":"c4cda242-53e8-4b24-9a40-11093c96609b","owner":[],"postedDate":"March 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-18T17:44:38+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-11 19:03:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4016440","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4016440","identity":"rs-4016440","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}