Association of lipid accumulation product and the risk of type 2 diabetes; a cross sectional population-based study

Association of lipid accumulation product and the risk of type 2 diabetes; a cross sectional population-based study | 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 Association of lipid accumulation product and the risk of type 2 diabetes; a cross sectional population-based study Sepehr Sadafi, Ali Azizi, Farid Najafi, Yahya Pasdar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3875246/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 Background The Lipid Accumulation Product (LAP) is a measure that indicates the presence of excessive fat accumulation in the body. LAP have been the focus of research in epidemiological studies aimed at forecasting chronic and metabolic illnesses. This study was carried out to examine the association between LAP and type 2 diabetes mellitus (T2DM) in the adult population of western Iran. Methods The study included 9,065 adults between the ages of 35 and 65 who were registered baseline phase of the Ravansar non-communicable diseases study (RaNCD) cohort study. Multiple logistic regression models were employed to explore the association between LAP and T2DM. The receiver operating characteristic (ROC) curve was used to evaluate the predictive capability of the LAP for T2DM. Results The average LAP was 53.10 ± 36.60 in the healthy group and 75.51 ± 51.34 in the diabetic group (P < 0.001). The multiple regression model indicated that, after controlling for potentially confounding variables, the odds of T2DM in the second quartile of lipids is 1.60 (95%CI: 1.17, 2.18) times higher than in the first quartile. Additionally, in the third and fourth quartiles, it is 2.43 (95%CI: 1.80, 3.28) and 3.36 (95%CI: 2.47, 4.56) times higher than in the first quartile, respectively. The results of ROC analysis for predicting T2DM indicated that the LAP index has (AUC: 0.66, 95%CI: 0.64, 0.68). Conclusion The association between high LAP levels and the T2DM was found to be strong in the adult population of western Iran. LAP is suggested as a tool in diabetes susceptibility screening. Type 2 diabetes lipid accumulation product waist circumference triglyceride Figures Figure 1 Figure 2 Introduction The World Health Organization (WHO) states that non-communicable diseases (NCDs) are the main reason for global mortality [ 1 ]. Type 2 diabetes mellitus (T2DM) is a significant NCD and accounts for 2.74% of all global deaths. In Iran, 3% of all deaths are associated with T2DM [ 2 ]. The age-standardized mortality rate for T2DM in Iran has been steadily increasing since 2015 and is expected to rise slightly by 2030 [ 3 ]. Diabetes is a long-term condition that can lead to microvascular and microvascular complications, such as cardio-cerebrovascular disease (CVD), diabetic nephropathy, and diabetic retinopathy. Additionally, T2DM can result in disability and a decrease in quality of life [ 4 ]. In the early stages of T2DM, there are no specific symptoms, making it easy to overlook [ 5 ]. Absolutely, early diagnosis of T2DM can lead to early intervention, which in turn can help reduce or prevent the development of complications. This can ultimately lessen the burden of the disease for individuals and healthcare systems [ 5 ]. Obesity, especially abdominal obesity, is a risk factor for T2DM, as it results from the accumulation of fat in the central part of the body [ 6 – 8 ]. Disorders in the metabolism of adipose tissue directly effect on the balance of lipids and glucose [ 9 ]. It has been suggested that T2DM may be the result of complex metabolic effects caused by excessive accumulation of abnormal lipids or liver fat [ 10 – 12 ]. Furthermore, scientific evidence indicates that waist circumference (WC), as a common indicator of abdominal obesity, and elevated triglyceride (TG) levels are associated with an increased risk of developing T2DM [ 13 – 16 ]. Recently, the lipid accumulation product (LAP) index has emerged as a tool for predicting metabolic diseases. LAP is a combined index of WC and TG levels, used to assess visceral fat. Research conducted in some populations suggests that the LAP index is a reliable predictor for chronic conditions such as metabolic syndrome, CVD, hypertension, and T2DM [ 17 – 20 ]. It appears that there has not been a thorough study in Iran to examine the effectiveness of LAP in predicting T2DM. If LAP has a good predictive power in Iranian populations, it can be used as a simple, cost-effective and accurate tool for T2DM screening in large populations. Therefore, the present study was conducted to investigate the association between the LAP and T2DM in a large population of adults in western Iran. Methods Study design and participants The current study is a cross-sectional analysis and the data for this study were obtained from the baseline phase of the Ravansar non-communicable diseases (RaNCD) cohort study [ 21 ]. The RaNCD study is a component of the PERSIAN (Prospective Epidemiological Research Studies in Iran) studies [ 22 ]. The RaNCD study is being conducted with the aim of investigating NCD and their risk factors in western Iran (Ravansar city of Kermanshah province). The RaNCD study includes 10,000 adults from the urban and rural areas of Ravansar, who will be followed for 15 years. Data for the RaNCD study were collected through medical examinations, biochemical and anthropometric measurements, as well as information from a comprehensive questionnaire. In the present study, participants with cancer and fatty liver, along with pregnant women and subjects with incomplete information, were excluded from the study. Ultimately, 9065 people were included in the study ( Fig. 1 ). Data collection and measurements All data was gathered at the RaNCD cohort center in accordance with the standard protocol of Persian studies [ 21 ]. The socioeconomic status (SES) was assessed through principal component analysis (PCA), considering factors like education level, asset ownership (e.g., house, car, motorcycle, computer, and internet), household amenities (e.g., washing machine, dishwasher, refrigerator, and freezer), type and frequency of trips, educational attainment, household size, and number of rooms. Following this analysis, the SES was categorized into three groups (poor, medium, and good) [ 23 ]. Anthropometric indices including body mass index (BMI), visceral fat area (VFA), and waist circumference (WC) were measured using a Bio-Impedance Analyzer BIA (Inbody 770, Inbody Co, Seoul, Korea). In addition, WC was measured using a flexible measuring tape at a point midway between the lower rib margin and the iliac crest, with measurements recorded to the nearest 0.5 cm. Fasting blood samples of participants were used to evaluate fasting blood sugar (FBS), liver enzymes including aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT) and Alkaline phosphatase (ALP), and lipid profile including triglyceride (TG), Total cholesterol (TC), high-density lipoprotein cholesterol (HDL), low-density lipoprotein (LDL). Diabetic patients were considered as individuals with a FBS level of 126 mg/dL and/or a history of using medication to manage T2DM [ 21 ]. The LAP was calculated using the following formula: [WC (cm) – 65] × TG concentration (mmol/L) for men, and [WC (cm) – 58] × TG concentration (mmol/L) for women [ 24 ]. The participants' systolic and diastolic blood pressure (SBP and DBP) was measured while they were seated on a chair using the standard method, following a 10-minute rest period, and measurements were taken for both the right and left arm [ 25 ]. The physical activity was evaluated using the standard questionnaire of the PERSIAN cohort. This questionnaire includes inquiries about sports, work, and leisure activities over a 24-hour period, measured in MET/hour per day [ 21 ]. Individuals who reported that they had smoked over 100 cigarettes in their lifetime were categorized as current smokers [ 26 ]. Statistical analysis The analysis in this study was performed using Stata version 14.2 software (Stata Corp, College Station, TX, USA). For the report of the descriptive information of the participants, we used the mean ± standard deviation (SD) for quantitative variables, and for the categorical variables, we used frequency (percentage). T-test and Chi-square tests were used to investigate the difference between the basic characteristics of the two diabetic and non-diabetic groups. To compare participant characteristics between LAP quartiles, one-way ANOVA and chi-square tests were conducted. Univariate and multiple binary logistic regression models were used to evaluate the relationship between T2DM and LAP quartiles. The multiple model incorporated adjustments for age, family history of T2DM, BMI, physical activity, SBP, DBP and SES variables. Receiver operating characteristic (ROC) curves were created to assess LAP, WC and TG ability to predict T2DM based on the area under the curves (AUC) with a 95% confidence interval. All reported P-values were two-sided, and statistical significance was defined as p < 0.05. Results A total of 9,065 participants, comprising 49.30% men and 50.70% women, with an average age of 47.24 ± 8.27 years, were included in the study. Among the participants, 7.82% had T2DM, 11.97% were smokers, and 5.02% reported alcohol consumption. Anthropometric indices, including BMI, WC, and VFA, were significantly higher in diabetic individuals (P < 0.001). Additionally, liver enzymes, systolic and diastolic blood pressure were significantly higher in diabetic subjects (P < 0.001). Except for LDL, all lipid profile indices were significantly different between the two diabetic and non-diabetic groups (P < 0.001). The average LAP was 53.10 ± 36.60 in the healthy group and 75.51 ± 51.34 in the diabetic group (P < 0.001) ( Table 1 ) . Table 1 Baseline characteristics of participants this study, (n = 9,065) Parameters Total (n = 9,065) Non-T2DM (n = 8,356) T2DM (n = 709) P value* Gender, n (%) Men 4469 (49.30) 4126 (92.32) 343 (7.68) 0.609 Women 4596 (50.70) 4230 (92.04) 366 (7.96) Age (year) 47.24 ± 8.27 46.89 ± 8.24 51.44 ± 7.45 < 0.001 Current smoker, n (%) 1080 (11.97) 1006 (12.09) 74 (10.45) < 0.001 Alcohol drinking, n (%) 455 (5.02) 425 (5.09) 30 (4.23) 0.317 Physical activity, n (%) Low 2682 (29.59) 2431 (29.09) 251 (35.40) < 0.001 Moderate 4287 (47.29) 3952 (47.30) 335 (47.25) Vigorous 2096 (23.12) 1973 (23.61) 123 (17.35) Socioeconomic status, n (%) Low 3031 (33.44) 2803 (33.54) 228 (32.16) 0.230 Moderate 3010 (33.20) 2754 (32.96) 256 (36.11) Good 3024 (33.36) 2799 (33.50) 225 (31.73) Body mass index (kg/m 2 ) 27.19 ± 4.49 27.10 ± 4.50 28.47 ± 4.14 < 0.001 Waist circumference (cm) 96.63 ± 10.29 96.35 ± 10.30 100.01 ± 9.56 < 0.001 Visceral fat area (cm 2 ) 118.83 ± 50.28 117.51 ± 50.21 134.51 ± 48.39 < 0.001 Fasting blood sugar (mg/dl) 96.48 ± 29.46 90.23 ± 9.72 170.04 ± 64.14 < 0.001 Triglycerides (mg/dl) 135.01 ± 82.28 131.34 ± 75.22 178.10 ± 133.64 < 0.001 High-density lipoprotein cholesterol (mg/dl) 46.48 ± 11.37 46.7 ± 11.36 44.19 ± 11.20 < 0.001 Low-density lipoprotein cholesterol (mg/dl) 111.37 ± 31.12 111.38 ± 30.85 111.21 ± 34.21 0.443 Total cholesterol (mg/dl) 184.81 ± 37.66 184.30 ± 37.10 190.73 ± 43.48 < 0.001 Systolic blood pressure (mmHg) 108.10 ± 17.04 107.47 ± 16.77 115.23 ± 18.54 < 0.001 Diastolic blood pressure (mmHg) 69.78 ± 9.92 69.52 ± 9.80 72.80 ± 10.83 < 0.001 Alkaline phosphatase (mg/dl) 196.81 ± 62.80 195.01 ± 61.88 218.11 ± 69.25 < 0.001 Aspartate aminotransferase (mg/dl) 21.31 ± 8.95 21.41 ± 8.91 20.24 ± 9.43 < 0.001 Alanine aminotransferase (mg/dl) 24.65 ± 14.65 24.42 ± 14.59 27.37 ± 15.10 < 0.001 Gamma-glutamyl transpeptidase (mg/dl) 24.20 ± 19.35 23.53 ± 18.84 32.10 ± 23.21 < 0.001 Lipid accumulation product 54.82 ± 38.43 53.10 ± 36.60 75.51 ± 51.34 < 0.001 Family history of T2DM, n (%) 2213 (24.42) 1900 (22.74) 313 (44.15) < 0.001 *P value < 0.005 by t-test or chi 2 test The study found that the mean of BMI was significantly higher in the fourth quartile of the LAP compared to the first quartile (23.63 ± 3.48 vs. 30.17 ± 4.31, Ptrend < 0.001). Additionally, WC, TG, LDL and TC showed an increasing trend from the first to the fourth quartile of LAP (Ptrend < 0.001). The average FBS in the fourth quartile of LAP was significantly higher compared to the first quartile (89.80 ± 24.20 vs. 103.85 ± 34.78, Ptrend < 0.001). Across quartiles of LAP, vigorous physical activity has decreased and low physical activity has increased. Liver enzymes were significantly higher in the fourth quartile of LAP compared to the first quartile (P < 0.001) ( Table 2 ). Table 2 Baseline characteristics of participants according to lipid accumulation product quartiles *P value < 0.005 by one-way ANOVA or chi 2 tests Variables Quartiles of lipid accumulation product Q1 (n = 2,267) Q2 (n = 2,266) Q3 (n = 2,267 ) Q4 (n = 2,265 ) P value* P value trend* Age (year) 46.05 ± 8.34 47.24 ± 8.34 47.68 ± 8.15 48.19 ± 8.10 < 0.001 < 0.001 Body mass index (kg/m 2 ) 23.63 ± 3.48 26.91 ± 3.55 28.59 ± 3.78 30.17 ± 4.31 < 0.001 < 0.001 Waist circumference (cm) 87.01 ± 8.28 95.45 ± 7.27 99.88 ± 7.80 104.18 ± 8.94 < 0.001 < 0.001 Triglycerides (mg/dl) 72.55 ± 24.37 101.64 ± 27.44 138.67 ± 58.97 227.20 ± 93.44 < 0.001 < 0.001 High-density lipoprotein cholesterol (mg/dl) 50.70 ± 11.79 47.67 ± 11.10 45.21 ± 10.93 42.32 ± 9.68 < 0.001 < 0.001 Low-density lipoprotein cholesterol (mg/dl) 100.99 ± 28.27 111.49 ± 29.60 116.74 ± 30.31 116.27 ± 33.22 < 0.001 < 0.001 Total cholesterol (mg/dl) 166.20 ± 32.10 179.45 ± 34.14 190.21 ± 34.75 203.88 ± 39.47 < 0.001 < 0.001 Fasting blood sugar (mg/dl) 89.80 ± 24.20 93.77 ± 25.89 98.52 ± 30.95 103.85 ± 34.78 < 0.001 < 0.001 Systolic blood pressure (mmHg) 103.20 ± 16.28 107.10 ± 15.01 109.55 ± 16.95 112.46 ± 17.55 < 0.001 < 0.001 Diastolic blood pressure (mmHg) 67.39 ± 9.35 69.21 ± 9.47 70.49 ± 10.10 72.10 ± 10.20 < 0.001 < 0.001 Alkaline phosphatase (mg/dl) 184.20 ± 56.36 192.83 ± 56.64 201.99 ± 66.34 208.25 ± 67.62 < 0.001 < 0.001 Aspartate aminotransferase (mg/dl) 21.23 ± 9.24 20.94 ± 10.02 21.13 ± 7.60 21.94 ± 8.60 0.003 0.002 Alanine aminotransferase (mg/dl) 21.01 ± 13.05 23.41 ± 12.72 26.14 ± 12.50 28.76 ± 16.21 < 0.001 < 0.001 Gamma-glutamyl transpeptidase (mg/dl) 19.21 ± 13.51 22.19 ± 17.11 25.73 ± 20.90 30.34 ± 23.65 < 0.001 < 0.001 Current smoker, n (%) 351 (15.57) 254 (11.25) 234 (10.36) 241 (10.68) 0.001 0.090 Alcohol drinking, n (%) 130 (5.73) 108 (4.77) 112 (4.94) 105 (4.64) 0.325 0.128 Physical activity, n (%) Low 548 (24.17) 634 (27.98) 726 (32.02) 774 (34.17) 0.002 0.001 Moderate 1014 (44.73) 1053 (46.47) 1101 (48.57) 1119 (49.40) Vigorous 705 (31.10) 579 (25.55) 440 (19.41) 372 (16.42) Socioeconomic status, n (%) Low 803 (35.42) 754 (33.27) 679 (29.95) 795 (35.10) 0.001 0.373 Moderate 733 (32.33) 768 (33.89) 761 (33.57) 748 (33.02) Good 731 (32.25) 744 (32.83) 827 (36.48) 722 (31.88) *P value<0.005 by one-way ANOVA or chi 2 tests The crude model revealed that the odds of T2DM in the second quartile of LAP is 1.9 times, in the third quartile it is 3.20 times and in the fourth quartile it is 4.77 times higher than in the first quartile (P < 0.001). After adjusting for age and history of diabetes in first-degree relatives, it was observed that the odds of diabetes in the second quartile of LAP is 1.74 (95%CI: 1.29, 2.35) times, in the third quartile 2.82 (95% CI: 2.13, 3.74) times, and in the fourth quartile 4.05 (95% CI: 3.10, 5.3) times higher than the first quartile. The multiple regression model indicated that, after controlling for potentially confounding variables, the odds of T2DM in the second quartile of lipids is 1.60 (95% CI: 1.17, 2.18) times higher than in the first quartile. Additionally, in the third and fourth quartiles, it is 2.43 (95% CI: 1.80, 3.28) and 3.36 (95% CI: 2.47, 4.56) times higher than in the first quartile, respectively ( Table 3 ). Table 3 Association of lipid accumulation product and type 2 diabetes mellitus by logistic regression analysis Lipid accumulation product quartiles Model 1 Model 2 Model 3 OR (95% CI) P value OR (95% CI) P value OR (95% CI) P value Q1 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Q2 1.91 (1.42, 2.57) < 0.001 1.74 (1.29, 2.35) < 0.001 1.60 (1.17, 2.18) 0.03 Q3 3.20 (2.43, 4.22) < 0.001 2.82 (2.13, 3.74) < 0.001 2.43 (1.80, 3.28) < 0.001 Q4 4.77 (3.65, 6.23) < 0.001 4.05 (3.10, 5.3) < 0.001 3.36 (2.47, 4.56) < 0.001 P value trend < 0.001 < 0.001 < 0.001 Model 1: Unadjusted; Model 2: Adjusted for age and family history of T2DM; Model 3: Adjusted for age, family history of T2DM, BMI, physical activity, SBP, DBP and SES Table 4 ROC curve analysis of lipid accumulation product and its components for predicting of type 2 diabetes mellitus risk Variables Cut-points AUC ROC (95% CI) Sensitivity (%) Specificity (%) Youden J-index Lipid accumulation product 46.24 0.66 (0.64, 0.68) 0.70 0.57 27% Triglycerides 120.40 0.64 (0.62, 0.66) 0.65 0.54 19% Waist circumference 100.2 0.60 (0.58, 0.62) 0.78 0.41 19% The ROC curve in Fig. 2 illustrates the prediction of T2DM based on LAP, TG, and WC in the population under study. The results of ROC analysis for predicting T2DM indicated that the LAP index has higher predictive power (AUC: 0.66, 95% CI: 0.64, 0.68) than TG (AUC: 0.64, 95% CI: 0.62, 0.66) and WC (AUC:0.60, 95% CI: 0.58, 0.62) in the studied population ( Table 3 ). Discussion In this population-based study, we observed a significant increase in the odds of T2DM with the rise in LAP levels in the adult population of western Iran. Specifically, in the second to fourth quartiles of LAP levels, the odds of T2DM was 1.60, 2.43, and 3.36 times higher than in the first quartile, respectively. The association between LAP and T2DM has been found to be positive in several demographic and ethnic subgroups. Briefly, in the Japanese population, high LAP have been shown to increase the risk of T2DM by 76% over time [ 20 ]. Similar results have been reported in adults Chinese and middle-aged Koreans [ 27 , 28 ]. Studies have shown that obesity is a risk factor for T2DM [ 29 ]. The excessive accumulation of lipids or liver fat leads to a series of complex metabolic consequences, such as non-alcoholic fatty liver disease (NAFLD) and insulin resistance (IR), which ultimately contribute to the development and progression of T2DM [ 11 , 12 , 30 ]. Additionally, WC, relative to BMI, is a more accurate indicator for assessing excess visceral fat in the body. WC does not clearly distinguish between subcutaneous and visceral fat in the abdominal cavity [ 30 ]. Furthermore, dyslipidemia, hypertriglyceridemia, and low HDL cholesterol levels have been observed to be related to T2DM [ 31 ]. Therefore, LAP, as a composite index of WC and TG, reflects the state of visceral fat and blood lipids. A strong relationship between LAP and metabolic diseases such as CVD and hypertension has also been observed [ 19 , 32 ]. Generally, the positive relationship between LAP and diabetes is acceptable. In individuals who are overweight, excessive fat tissue in the body, combined with overeating and chronic inflammation, can result in IR and a disorder in glucose metabolism [ 33 ]. IR is a crucial stage in the progression of diabetes [ 34 ], and chronic inflammation and mitochondrial dysfunction caused by obesity can both worsen IR [ 35 – 37 ]. Additionally, prolonged exposure to fatty acids in β-cells can decrease glucose-induced insulin secretion, disrupt insulin gene expression, and increase cell death [ 38 , 39 ]. The onset of T2DM is gradual and may lead to complications for an individual before it is diagnosed, making early diagnosis and prevention particularly important. Considering the change in lifestyle and the increasing trend of T2DM as well as serious complications, reduction in quality of life and heavy economic burden caused by this disease, it is very important to provide a low-cost, easy and calculable index to identify T2DM as soon as possible. Previous studies have shown that LAP has a higher diagnostic value for predicting T2DM than indicators such as BMI, WC, A Body Shape Index (ABSI), Visceral adiposity index (VAI) and waist/height ratio (WHtR) [ 20 , 40 ]. Similarly, the present study also showed that LAP (AUC = 0.66) has higher diagnostic value than WC (AUC = 0.60) and TG (AUC = 0.64). Furthermore, LAP has been identified as a useful indicator for predicting metabolic diseases like hypertension and CVD [ 18 , 19 ]. Based on the results of this study in a population of 9,065 participants including men and women and residents of cities and villages, and the results of previous studies, we suggest LAP as an accurate and comprehensive index for T2DM screening in large populations. The study had a few limitations. It was a cross-sectional analysis, which means that the observed relationships are not causal. However, causal relationships in longitudinal studies have been observed in other population groups. Additionally, the study was conducted on adults in western Iran, so the results should be cautiously generalized to other population groups. It is recommended to carry out this study in other population subgroups. Lastly, the study did not measure HbA1c and oral glucose tolerance test variables. The large sample size is a key advantage of the current study. Additionally, the comprehensive collection of lifestyle, anthropometric, and biochemical variables, as well as the adjustment of a large number of confounding variables, is strength of this study. Conclusion The findings of this study revealed a significant increase in the risk of T2DM with rising LAP levels in the adult population of western Iran. Additionally, based on Rock curve analysis, LAP was found to be an accessible and cost-effective tool for screening and monitoring diabetes in the adults, with a higher predictive value than TG levels and obesity. Declarations Authors’ contributions SS and AA conceived the idea of the study. FN developed the statistical analysis plan and conducted statistical analyses. YP, SS and AA contributed to the interpretation of the results. SS and AA drafted the original manuscript. AA, YP and FN supervised the conduct of this study. All authors reviewed the manuscript draft and revised it critically on intellectual content. All authors approved the final version of the manuscript to be published. Ethics approval and consent to participate The study was approved by the ethics committee of Kermanshah University of Medical Sciences (KUMS.REC.1394.318). All methods were carried out in accordance with relevant guidelines and regulations. All the participants were provided oral and written informed consent. All methods were carried out by relevant guidelines and regulations. This study was conducted by the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials The data analyzed in the study are available from the corresponding author upon reasonable request. Competing interests The authors declare no conflicts of interest. Funding This research was supported by Kermanshah University of Medical Sciences (grant number: 92472). The Iranian Ministry of Health and Medical Education has also contributed to the funding used in the PERSIAN Cohort through Grant no 700/534. Acknowledgements The authors thank the PERSIAN cohort Study collaborators and of Kermanshah University of Medical Sciences. References Budreviciute A, Damiati S, Sabir DK, Onder K, Schuller-Goetzburg P, Plakys G, et al. Management and prevention strategies for non-communicable diseases (NCDs) and their risk factors. Frontiers in public health. 2020;8:788. Institute for Health Metrics and Evaluation (IHME). GBD Compare Seattle, WA: IHME,University of Washington2015 [Available from: https://vizhub.healthdata.org/gbd-compare/. Khosravi Shadmani F, Farzadfar F, Larijani B, Mirzaei M, Haghdoost AA. Trend and projection of mortality rate due to non-communicable diseases in Iran: A modeling study. PloS one. 2019;14(2):e0211622. Valencia WM, Florez H. How to prevent the microvascular complications of type 2 diabetes beyond glucose control. Bmj. 2017;356. Gong Q, Zhang P, Wang J, Ma J, An Y, Chen Y, et al. Morbidity and mortality after lifestyle intervention for people with impaired glucose tolerance: 30-year results of the Da Qing Diabetes Prevention Outcome Study. The lancet Diabetes & endocrinology. 2019;7(6):452-61. Klein S, Sheard NF, Pi-Sunyer X, Daly A, Wylie-Rosett J, Kulkarni K, et al. Weight management through lifestyle modification for the prevention and management of type 2 diabetes: rationale and strategies. A statement of the American Diabetes Association, the North American Association for the Study of Obesity, and the American Society for Clinical Nutrition. The American journal of clinical nutrition. 2004;80(2):257-63. Zhang F-L, Ren J-X, Zhang P, Jin H, Qu Y, Yu Y, et al. Strong Association of waist circumference (WC), body mass index (BMI), waist-to-height ratio (WHtR), and waist-to-hip ratio (WHR) with diabetes: A population-based cross-sectional study in Jilin province, China. Journal of diabetes research. 2021;2021. Wei J, Liu X, Xue H, Wang Y, Shi Z. Comparisons of visceral adiposity index, body shape index, body mass index and waist circumference and their associations with diabetes mellitus in adults. Nutrients. 2019;11(7):1580. Guilherme A, Virbasius JV, Puri V, Czech MP. Adipocyte dysfunctions linking obesity to insulin resistance and type 2 diabetes. Nature reviews Molecular cell biology. 2008;9(5):367-77. Kahn HS, Cheng YJ, Thompson TJ, Imperatore G, Gregg EW. Two risk-scoring systems for predicting incident diabetes mellitus in US adults age 45 to 64 years. Annals of internal medicine. 2009;150(11):741-51. Sattar N, Wannamethee S, Forouhi N. Novel biochemical risk factors for type 2 diabetes: pathogenic insights or prediction possibilities? Diabetologia. 2008;51:926-40. Unger RH. Reinventing type 2 diabetes: pathogenesis, treatment, and prevention. Jama. 2008;299(10):1185-7. Cassano PA, Rosner B, Vokonas PS, Weiss ST. Obesity and body fat distribution in relation to the incidence of non-lnsulin-dependent diabetes mellitus: A prospective cohort study of men in the normative aging study. American journal of epidemiology. 1992;136(12):1474-86. Schulze MB, Heidemann C, Schienkiewitz A, Bergmann MM, Hoffmann K, Boeing H. Comparison of anthropometric characteristics in predicting the incidence of type 2 diabetes in the EPIC-Potsdam study. Diabetes care. 2006;29(8):1921-3. Yang W, Lu J, Weng J, Jia W, Ji L, Xiao J, et al. Prevalence of diabetes among men and women in China. New England journal of medicine. 2010;362(12):1090-101. Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nature reviews endocrinology. 2018;14(2):88-98. Ray L, Ravichandran K, Nanda SK. Comparison of lipid accumulation product index with body mass index and waist circumference as a predictor of metabolic syndrome in Indian population. Metabolic syndrome and related disorders. 2018;16(5):240-5. Muheiyati G, Mei Y, Tao N. Association of lipid accumulation product and visceral adiposity index with the risk of hypertension among oil workers in Xinjiang, China. PeerJ. 2023;11:e15273. Vieira JN, Braz MAD, Gomes FO, Silva PRd, Santos OTdM, Rocha IMGd, et al. Cardiovascular risk assessment using the lipid accumulation product index among primary healthcare users: a cross-sectional study. Sao Paulo Medical Journal. 2019;137:126-31. Liu T, Lu W, Zhao X, Yao T, Song B, Fan H, et al. Relationship between lipid accumulation product and new-onset diabetes in the Japanese population: a retrospective cohort study. Frontiers in Endocrinology. 2023;14:1181941. Pasdar Y, Najafi F, Moradinazar M, Shakiba E, Karim H, Hamzeh B, et al. Cohort profile: Ravansar Non-Communicable Disease cohort study: the first cohort study in a Kurdish population. International journal of epidemiology. 2019;48(3):682-3f. Poustchi H, Eghtesad S, Kamangar F, Etemadi A, Keshtkar A-A, Hekmatdoost A, et al. Prospective epidemiological research studies in Iran (the PERSIAN Cohort Study): rationale, objectives, and design. American journal of epidemiology. 2018;187(4):647-55. Najafi F, Rezaei S, Hajizadeh M, Soofi M, Salimi Y, Kazemi Karyani A, et al. Decomposing socioeconomic inequality in dental caries in Iran: cross-sectional results from the PERSIAN cohort study. Archives of Public Health. 2020;78(1):1-11. Ching YK, Chin YS, Appukutty M, Gan WY, Chan YM. Comparisons of conventional and novel anthropometric obesity indices to predict metabolic syndrome among vegetarians in Malaysia. Scientific reports. 2020;10(1):20861. Chobanian AV. National heart, lung, and blood institute joint national committee on prevention, detection, evaluation, and treatment of high blood pressure; national high blood pressure education program coordinating committee. The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure: the JNC 7 report. Jama. 2003;289:2560-72. Ryan H, Trosclair A, Gfroerer J. Adult current smoking: differences in definitions and prevalence estimates—NHIS and NSDUH, 2008. Journal of environmental and public health. 2012;2012. Yan G, Li F, Elia C, Zhao Y, Wang J, Chen Z, et al. Association of lipid accumulation product trajectories with 5-year incidence of type 2 diabetes in Chinese adults: a cohort study. Nutrition & metabolism. 2019;16(1):1-8. Lee J-W, Lim N-K, Park H-YJBed. The product of fasting plasma glucose and triglycerides improves risk prediction of type 2 diabetes in middle-aged Koreans. 2018;18:1-10. Rohm TV, Meier DT, Olefsky JM, Donath MY. Inflammation in obesity, diabetes, and related disorders. Immunity. 2022;55(1):31-55. Kang PS, Neeland IJ. Body Fat Distribution, Diabetes Mellitus, and Cardiovascular Disease: an Update. Current Cardiology Reports. 2023;25(11):1555-64. Kane JP, Pullinger CR, Goldfine ID, Malloy MJ. Dyslipidemia and diabetes mellitus: Role of lipoprotein species and interrelated pathways of lipid metabolism in diabetes mellitus. Current Opinion in Pharmacology. 2021;61:21-7. Song J, Zhao Y, Nie S, Chen X, Wu X, Mi J. The effect of lipid accumulation product and its interaction with other factors on hypertension risk in Chinese Han population: a cross-sectional study. PLoS One. 2018;13(6):e0198105. Lu J, Zhao J, Meng H, Zhang XJFii. Adipose tissue-resident immune cells in obesity and type 2 diabetes. 2019;10:1173. Hanson P, Weickert MO, Barber TMJTAiE, Metabolism. Obesity: novel and unusual predisposing factors. 2020;11:2042018820922018. Di Meo S, Iossa S, Venditti PJJoE. Skeletal muscle insulin resistance: role of mitochondria and other ROS sources. 2017;233(1):R15-R42. Kim O-K, Jun W, Lee JJAoN, Metabolism. Mechanism of ER stress and inflammation for hepatic insulin resistance in obesity. 2015;67(4):218-27. Shoelson SE, Herrero L, Naaz AJG. Obesity, inflammation, and insulin resistance. 2007;132(6):2169-80. Jacqueminet S, Briaud I, Rouault C, Reach G, Poitout VJM. Inhibition of insulin gene expression by long-term exposure of pancreatic β cells to palmitate is dependent on the presence of a stimulatory glucose concentration. 2000;49(4):532-6. Maedler K, Spinas G, Dyntar D, Moritz W, Kaiser N, Donath MYJD. Distinct effects of saturated and monounsaturated fatty acids on β-cell turnover and function. 2001;50(1):69-76. Yang SH, Yoon J, Lee Y-J, Park B, Jung D-HJD, Metabolic Syndrome, Targets O, et al. Lipid accumulation product index predicts new-onset type 2 diabetes among non-obese koreans: a 12-year longitudinal study. 2022:3729-37. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3875246","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268088335,"identity":"aa6ce55a-ecc4-4869-85a0-ab5f06e7f331","order_by":0,"name":"Sepehr Sadafi","email":"","orcid":"","institution":"Kermanshah University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sepehr","middleName":"","lastName":"Sadafi","suffix":""},{"id":268088336,"identity":"41448f9c-6120-4ade-9b25-cc90452eae38","order_by":1,"name":"Ali Azizi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIie3OsQqCQBzH8b8c6PKPWy+IfIW/SAct9SqCL9AUQWCGYIvQs7Q0B4IuRmviEvQEbTc05GBLg+fYcN/l4ODD7w9gMv1jjhUDECDH7w/TEdaRcTacdC9VQ+/iCdu/1Cqa+FU1OylYuOCMHr1E5FYiBNkor5lsEEIvZg71z+RWLIgQ5Q1l094ZALP7hduuqIAE+keUtYKdnlBupeLSztAok3eEXE+8lsxjClBUxbpBKr1UR6bl4Vmrd7TkWXiu1Wbrcl70k987ATQbJpPJZBrSBwy1Ni6Ccpc9AAAAAElFTkSuQmCC","orcid":"","institution":"Kermanshah University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Ali","middleName":"","lastName":"Azizi","suffix":""},{"id":268088337,"identity":"9b21f154-eda6-4403-9a1f-ebdac5e2ccc1","order_by":2,"name":"Farid Najafi","email":"","orcid":"","institution":"Kermanshah University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Farid","middleName":"","lastName":"Najafi","suffix":""},{"id":268088338,"identity":"7ebb699d-e4ba-468e-af56-1a24a792c546","order_by":3,"name":"Yahya Pasdar","email":"","orcid":"","institution":"Kermanshah University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yahya","middleName":"","lastName":"Pasdar","suffix":""}],"badges":[],"createdAt":"2024-01-18 08:44:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3875246/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3875246/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49969129,"identity":"90d64ef9-a430-4f48-ac50-a9a341c31958","added_by":"auto","created_at":"2024-01-22 12:59:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":126751,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study participants\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3875246/v1/d384963eb0f2e2e317dc0f3d.png"},{"id":49969128,"identity":"16e56a28-7d96-454f-8d2d-98d868de2888","added_by":"auto","created_at":"2024-01-22 12:59:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":16343,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analysis of lipid accumulation product and its components for predicting of type 2 diabetes mellitus risk\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3875246/v1/2647f004d9c2982c8ce9df36.png"},{"id":50245517,"identity":"95d657af-da53-4946-999a-df3a5d678b47","added_by":"auto","created_at":"2024-01-27 10:52:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":502606,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3875246/v1/be216367-d6d1-42ea-8b90-8e03e736b545.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of lipid accumulation product and the risk of type 2 diabetes; a cross sectional population-based study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe World Health Organization (WHO) states that non-communicable diseases (NCDs) are the main reason for global mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Type 2 diabetes mellitus (T2DM) is a significant NCD and accounts for 2.74% of all global deaths. In Iran, 3% of all deaths are associated with T2DM [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The age-standardized mortality rate for T2DM in Iran has been steadily increasing since 2015 and is expected to rise slightly by 2030 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Diabetes is a long-term condition that can lead to microvascular and microvascular complications, such as cardio-cerebrovascular disease (CVD), diabetic nephropathy, and diabetic retinopathy. Additionally, T2DM can result in disability and a decrease in quality of life [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In the early stages of T2DM, there are no specific symptoms, making it easy to overlook [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAbsolutely, early diagnosis of T2DM can lead to early intervention, which in turn can help reduce or prevent the development of complications. This can ultimately lessen the burden of the disease for individuals and healthcare systems [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Obesity, especially abdominal obesity, is a risk factor for T2DM, as it results from the accumulation of fat in the central part of the body [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Disorders in the metabolism of adipose tissue directly effect on the balance of lipids and glucose [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. It has been suggested that T2DM may be the result of complex metabolic effects caused by excessive accumulation of abnormal lipids or liver fat [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Furthermore, scientific evidence indicates that waist circumference (WC), as a common indicator of abdominal obesity, and elevated triglyceride (TG) levels are associated with an increased risk of developing T2DM [\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecently, the lipid accumulation product (LAP) index has emerged as a tool for predicting metabolic diseases. LAP is a combined index of WC and TG levels, used to assess visceral fat. Research conducted in some populations suggests that the LAP index is a reliable predictor for chronic conditions such as metabolic syndrome, CVD, hypertension, and T2DM [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. It appears that there has not been a thorough study in Iran to examine the effectiveness of LAP in predicting T2DM. If LAP has a good predictive power in Iranian populations, it can be used as a simple, cost-effective and accurate tool for T2DM screening in large populations. Therefore, the present study was conducted to investigate the association between the LAP and T2DM in a large population of adults in western Iran.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThe current study is a cross-sectional analysis and the data for this study were obtained from the baseline phase of the Ravansar non-communicable diseases (RaNCD) cohort study [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The RaNCD study is a component of the PERSIAN (Prospective Epidemiological Research Studies in Iran) studies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The RaNCD study is being conducted with the aim of investigating NCD and their risk factors in western Iran (Ravansar city of Kermanshah province). The RaNCD study includes 10,000 adults from the urban and rural areas of Ravansar, who will be followed for 15 years. Data for the RaNCD study were collected through medical examinations, biochemical and anthropometric measurements, as well as information from a comprehensive questionnaire. In the present study, participants with cancer and fatty liver, along with pregnant women and subjects with incomplete information, were excluded from the study. Ultimately, 9065 people were included in the study \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection and measurements\u003c/h2\u003e \u003cp\u003eAll data was gathered at the RaNCD cohort center in accordance with the standard protocol of Persian studies [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The socioeconomic status (SES) was assessed through principal component analysis (PCA), considering factors like education level, asset ownership (e.g., house, car, motorcycle, computer, and internet), household amenities (e.g., washing machine, dishwasher, refrigerator, and freezer), type and frequency of trips, educational attainment, household size, and number of rooms. Following this analysis, the SES was categorized into three groups (poor, medium, and good) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnthropometric indices including body mass index (BMI), visceral fat area (VFA), and waist circumference (WC) were measured using a Bio-Impedance Analyzer BIA (Inbody 770, Inbody Co, Seoul, Korea). In addition, WC was measured using a flexible measuring tape at a point midway between the lower rib margin and the iliac crest, with measurements recorded to the nearest 0.5 cm. Fasting blood samples of participants were used to evaluate fasting blood sugar (FBS), liver enzymes including aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT) and Alkaline phosphatase (ALP), and lipid profile including triglyceride (TG), Total cholesterol (TC), high-density lipoprotein cholesterol (HDL), low-density lipoprotein (LDL).\u003c/p\u003e \u003cp\u003eDiabetic patients were considered as individuals with a FBS level of 126 mg/dL and/or a history of using medication to manage T2DM [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The LAP was calculated using the following formula: [WC (cm) \u0026ndash; 65] \u0026times; TG concentration (mmol/L) for men, and [WC (cm) \u0026ndash; 58] \u0026times; TG concentration (mmol/L) for women [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe participants' systolic and diastolic blood pressure (SBP and DBP) was measured while they were seated on a chair using the standard method, following a 10-minute rest period, and measurements were taken for both the right and left arm [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The physical activity was evaluated using the standard questionnaire of the PERSIAN cohort. This questionnaire includes inquiries about sports, work, and leisure activities over a 24-hour period, measured in MET/hour per day [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Individuals who reported that they had smoked over 100 cigarettes in their lifetime were categorized as current smokers [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe analysis in this study was performed using Stata version 14.2 software (Stata Corp, College Station, TX, USA). For the report of the descriptive information of the participants, we used the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for quantitative variables, and for the categorical variables, we used frequency (percentage). T-test and Chi-square tests were used to investigate the difference between the basic characteristics of the two diabetic and non-diabetic groups. To compare participant characteristics between LAP quartiles, one-way ANOVA and chi-square tests were conducted. Univariate and multiple binary logistic regression models were used to evaluate the relationship between T2DM and LAP quartiles. The multiple model incorporated adjustments for age, family history of T2DM, BMI, physical activity, SBP, DBP and SES variables. Receiver operating characteristic (ROC) curves were created to assess LAP, WC and TG ability to predict T2DM based on the area under the curves (AUC) with a 95% confidence interval. All reported P-values were two-sided, and statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 9,065 participants, comprising 49.30% men and 50.70% women, with an average age of 47.24\u0026thinsp;\u0026plusmn;\u0026thinsp;8.27 years, were included in the study. Among the participants, 7.82% had T2DM, 11.97% were smokers, and 5.02% reported alcohol consumption. Anthropometric indices, including BMI, WC, and VFA, were significantly higher in diabetic individuals (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, liver enzymes, systolic and diastolic blood pressure were significantly higher in diabetic subjects (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Except for LDL, all lipid profile indices were significantly different between the two diabetic and non-diabetic groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The average LAP was 53.10\u0026thinsp;\u0026plusmn;\u0026thinsp;36.60 in the healthy group and 75.51\u0026thinsp;\u0026plusmn;\u0026thinsp;51.34 in the diabetic group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBaseline characteristics of participants this study, (n\u0026thinsp;=\u0026thinsp;9,065)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;9,065)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-T2DM\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;8,356)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;709)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eGender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4469 (49.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4126 (92.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e343 (7.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4596 (50.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4230 (92.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e366 (7.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.24\u0026thinsp;\u0026plusmn;\u0026thinsp;8.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.89\u0026thinsp;\u0026plusmn;\u0026thinsp;8.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.44\u0026thinsp;\u0026plusmn;\u0026thinsp;7.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent smoker, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1080 (11.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1006 (12.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 (10.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol drinking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e455 (5.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e425 (5.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (4.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003ePhysical activity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2682 (29.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2431 (29.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e251 (35.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4287 (47.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3952 (47.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e335 (47.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVigorous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2096 (23.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1973 (23.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e123 (17.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocioeconomic status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3031 (33.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2803 (33.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e228 (32.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3010 (33.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2754 (32.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e256 (36.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3024 (33.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2799 (33.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e225 (31.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody mass index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.19\u0026thinsp;\u0026plusmn;\u0026thinsp;4.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.10\u0026thinsp;\u0026plusmn;\u0026thinsp;4.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.47\u0026thinsp;\u0026plusmn;\u0026thinsp;4.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWaist circumference (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.63\u0026thinsp;\u0026plusmn;\u0026thinsp;10.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.35\u0026thinsp;\u0026plusmn;\u0026thinsp;10.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.01\u0026thinsp;\u0026plusmn;\u0026thinsp;9.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVisceral fat area (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118.83\u0026thinsp;\u0026plusmn;\u0026thinsp;50.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117.51\u0026thinsp;\u0026plusmn;\u0026thinsp;50.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134.51\u0026thinsp;\u0026plusmn;\u0026thinsp;48.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFasting blood sugar (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.48\u0026thinsp;\u0026plusmn;\u0026thinsp;29.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.23\u0026thinsp;\u0026plusmn;\u0026thinsp;9.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170.04\u0026thinsp;\u0026plusmn;\u0026thinsp;64.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTriglycerides (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135.01\u0026thinsp;\u0026plusmn;\u0026thinsp;82.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131.34\u0026thinsp;\u0026plusmn;\u0026thinsp;75.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178.10\u0026thinsp;\u0026plusmn;\u0026thinsp;133.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-density lipoprotein cholesterol (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.48\u0026thinsp;\u0026plusmn;\u0026thinsp;11.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.7\u0026thinsp;\u0026plusmn;\u0026thinsp;11.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.19\u0026thinsp;\u0026plusmn;\u0026thinsp;11.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow-density lipoprotein cholesterol (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111.37\u0026thinsp;\u0026plusmn;\u0026thinsp;31.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111.38\u0026thinsp;\u0026plusmn;\u0026thinsp;30.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111.21\u0026thinsp;\u0026plusmn;\u0026thinsp;34.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal cholesterol (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184.81\u0026thinsp;\u0026plusmn;\u0026thinsp;37.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184.30\u0026thinsp;\u0026plusmn;\u0026thinsp;37.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190.73\u0026thinsp;\u0026plusmn;\u0026thinsp;43.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108.10\u0026thinsp;\u0026plusmn;\u0026thinsp;17.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.47\u0026thinsp;\u0026plusmn;\u0026thinsp;16.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.23\u0026thinsp;\u0026plusmn;\u0026thinsp;18.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.78\u0026thinsp;\u0026plusmn;\u0026thinsp;9.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.52\u0026thinsp;\u0026plusmn;\u0026thinsp;9.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.80\u0026thinsp;\u0026plusmn;\u0026thinsp;10.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlkaline phosphatase (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e196.81\u0026thinsp;\u0026plusmn;\u0026thinsp;62.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e195.01\u0026thinsp;\u0026plusmn;\u0026thinsp;61.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e218.11\u0026thinsp;\u0026plusmn;\u0026thinsp;69.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAspartate aminotransferase (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.31\u0026thinsp;\u0026plusmn;\u0026thinsp;8.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.41\u0026thinsp;\u0026plusmn;\u0026thinsp;8.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.24\u0026thinsp;\u0026plusmn;\u0026thinsp;9.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlanine aminotransferase (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.65\u0026thinsp;\u0026plusmn;\u0026thinsp;14.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.42\u0026thinsp;\u0026plusmn;\u0026thinsp;14.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.37\u0026thinsp;\u0026plusmn;\u0026thinsp;15.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGamma-glutamyl transpeptidase (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.20\u0026thinsp;\u0026plusmn;\u0026thinsp;19.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.53\u0026thinsp;\u0026plusmn;\u0026thinsp;18.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.10\u0026thinsp;\u0026plusmn;\u0026thinsp;23.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLipid accumulation product\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.82\u0026thinsp;\u0026plusmn;\u0026thinsp;38.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.10\u0026thinsp;\u0026plusmn;\u0026thinsp;36.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.51\u0026thinsp;\u0026plusmn;\u0026thinsp;51.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFamily history of T2DM, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2213 (24.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1900 (22.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e313 (44.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e*P value\u0026thinsp;\u0026lt;\u0026thinsp;0.005 by t-test or chi\u003csup\u003e2\u003c/sup\u003e test\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe study found that the mean of BMI was significantly higher in the fourth quartile of the LAP compared to the first quartile (23.63\u0026thinsp;\u0026plusmn;\u0026thinsp;3.48 vs. 30.17\u0026thinsp;\u0026plusmn;\u0026thinsp;4.31, Ptrend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, WC, TG, LDL and TC showed an increasing trend from the first to the fourth quartile of LAP (Ptrend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The average FBS in the fourth quartile of LAP was significantly higher compared to the first quartile (89.80\u0026thinsp;\u0026plusmn;\u0026thinsp;24.20 vs. 103.85\u0026thinsp;\u0026plusmn;\u0026thinsp;34.78, Ptrend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Across quartiles of LAP, vigorous physical activity has decreased and low physical activity has increased. Liver enzymes were significantly higher in the fourth quartile of LAP compared to the first quartile (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBaseline characteristics of participants according to lipid accumulation product quartiles *P value\u0026thinsp;\u0026lt;\u0026thinsp;0.005 by one-way ANOVA or chi\u003csup\u003e2\u003c/sup\u003e tests\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eQuartiles of lipid accumulation product\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2,267)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2,266)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2,267 )\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2,265 )\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value trend*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.05\u0026thinsp;\u0026plusmn;\u0026thinsp;8.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.24\u0026thinsp;\u0026plusmn;\u0026thinsp;8.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.68\u0026thinsp;\u0026plusmn;\u0026thinsp;8.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.19\u0026thinsp;\u0026plusmn;\u0026thinsp;8.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody mass index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.63\u0026thinsp;\u0026plusmn;\u0026thinsp;3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.91\u0026thinsp;\u0026plusmn;\u0026thinsp;3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.59\u0026thinsp;\u0026plusmn;\u0026thinsp;3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.17\u0026thinsp;\u0026plusmn;\u0026thinsp;4.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWaist circumference (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.01\u0026thinsp;\u0026plusmn;\u0026thinsp;8.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.45\u0026thinsp;\u0026plusmn;\u0026thinsp;7.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.88\u0026thinsp;\u0026plusmn;\u0026thinsp;7.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104.18\u0026thinsp;\u0026plusmn;\u0026thinsp;8.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTriglycerides (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.55\u0026thinsp;\u0026plusmn;\u0026thinsp;24.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101.64\u0026thinsp;\u0026plusmn;\u0026thinsp;27.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138.67\u0026thinsp;\u0026plusmn;\u0026thinsp;58.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e227.20\u0026thinsp;\u0026plusmn;\u0026thinsp;93.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-density lipoprotein cholesterol (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.70\u0026thinsp;\u0026plusmn;\u0026thinsp;11.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.67\u0026thinsp;\u0026plusmn;\u0026thinsp;11.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.21\u0026thinsp;\u0026plusmn;\u0026thinsp;10.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.32\u0026thinsp;\u0026plusmn;\u0026thinsp;9.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow-density lipoprotein cholesterol (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.99\u0026thinsp;\u0026plusmn;\u0026thinsp;28.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111.49\u0026thinsp;\u0026plusmn;\u0026thinsp;29.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116.74\u0026thinsp;\u0026plusmn;\u0026thinsp;30.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116.27\u0026thinsp;\u0026plusmn;\u0026thinsp;33.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal cholesterol (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166.20\u0026thinsp;\u0026plusmn;\u0026thinsp;32.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e179.45\u0026thinsp;\u0026plusmn;\u0026thinsp;34.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190.21\u0026thinsp;\u0026plusmn;\u0026thinsp;34.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e203.88\u0026thinsp;\u0026plusmn;\u0026thinsp;39.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFasting blood sugar (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.80\u0026thinsp;\u0026plusmn;\u0026thinsp;24.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.77\u0026thinsp;\u0026plusmn;\u0026thinsp;25.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.52\u0026thinsp;\u0026plusmn;\u0026thinsp;30.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103.85\u0026thinsp;\u0026plusmn;\u0026thinsp;34.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103.20\u0026thinsp;\u0026plusmn;\u0026thinsp;16.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.10\u0026thinsp;\u0026plusmn;\u0026thinsp;15.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109.55\u0026thinsp;\u0026plusmn;\u0026thinsp;16.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112.46\u0026thinsp;\u0026plusmn;\u0026thinsp;17.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.39\u0026thinsp;\u0026plusmn;\u0026thinsp;9.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.21\u0026thinsp;\u0026plusmn;\u0026thinsp;9.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.49\u0026thinsp;\u0026plusmn;\u0026thinsp;10.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.10\u0026thinsp;\u0026plusmn;\u0026thinsp;10.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlkaline phosphatase (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184.20\u0026thinsp;\u0026plusmn;\u0026thinsp;56.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192.83\u0026thinsp;\u0026plusmn;\u0026thinsp;56.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201.99\u0026thinsp;\u0026plusmn;\u0026thinsp;66.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e208.25\u0026thinsp;\u0026plusmn;\u0026thinsp;67.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAspartate aminotransferase (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.23\u0026thinsp;\u0026plusmn;\u0026thinsp;9.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.94\u0026thinsp;\u0026plusmn;\u0026thinsp;10.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.13\u0026thinsp;\u0026plusmn;\u0026thinsp;7.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.94\u0026thinsp;\u0026plusmn;\u0026thinsp;8.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlanine aminotransferase (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.01\u0026thinsp;\u0026plusmn;\u0026thinsp;13.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.41\u0026thinsp;\u0026plusmn;\u0026thinsp;12.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.14\u0026thinsp;\u0026plusmn;\u0026thinsp;12.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.76\u0026thinsp;\u0026plusmn;\u0026thinsp;16.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGamma-glutamyl transpeptidase (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.21\u0026thinsp;\u0026plusmn;\u0026thinsp;13.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.19\u0026thinsp;\u0026plusmn;\u0026thinsp;17.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.73\u0026thinsp;\u0026plusmn;\u0026thinsp;20.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.34\u0026thinsp;\u0026plusmn;\u0026thinsp;23.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent smoker, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e351 (15.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e254 (11.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e234 (10.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e241 (10.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol drinking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130 (5.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108 (4.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112 (4.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105 (4.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhysical activity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e548 (24.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e634 (27.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e726 (32.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e774 (34.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1014 (44.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1053 (46.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1101 (48.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1119 (49.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVigorous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e705 (31.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e579 (25.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e440 (19.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e372 (16.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocioeconomic status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e803 (35.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e754 (33.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e679 (29.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e795 (35.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e733 (32.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e768 (33.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e761 (33.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e748 (33.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e731 (32.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e744 (32.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e827 (36.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e722 (31.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e*P value\u0026lt;0.005 by one-way ANOVA or chi\u003csup\u003e2\u003c/sup\u003e tests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe crude model revealed that the odds of T2DM in the second quartile of LAP is 1.9 times, in the third quartile it is 3.20 times and in the fourth quartile it is 4.77 times higher than in the first quartile (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjusting for age and history of diabetes in first-degree relatives, it was observed that the odds of diabetes in the second quartile of LAP is 1.74 (95%CI: 1.29, 2.35) times, in the third quartile 2.82 (95% CI: 2.13, 3.74) times, and in the fourth quartile 4.05 (95% CI: 3.10, 5.3) times higher than the first quartile. The multiple regression model indicated that, after controlling for potentially confounding variables, the odds of T2DM in the second quartile of lipids is 1.60 (95% CI: 1.17, 2.18) times higher than in the first quartile. Additionally, in the third and fourth quartiles, it is 2.43 (95% CI: 1.80, 3.28) and 3.36 (95% CI: 2.47, 4.56) times higher than in the first quartile, respectively \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eAssociation of lipid accumulation product and type 2 diabetes mellitus by logistic regression analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLipid accumulation product quartiles\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.91 (1.42, 2.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.74 (1.29, 2.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.60 (1.17, 2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.20 (2.43, 4.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.82 (2.13, 3.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.43 (1.80, 3.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.77 (3.65, 6.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.05 (3.10, 5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.36 (2.47, 4.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eModel 1: Unadjusted; Model 2: Adjusted for age and family history of T2DM; Model 3: Adjusted for age, family history of T2DM, BMI, physical activity, SBP, DBP and SES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eROC curve analysis of lipid accumulation product and its components for predicting of type 2 diabetes mellitus risk\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCut-points\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUC ROC\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSensitivity (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecificity (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYouden J-index\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLipid accumulation product\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66 (0.64, 0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTriglycerides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64 (0.62, 0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWaist circumference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60 (0.58, 0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe ROC curve in Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e illustrates the prediction of T2DM based on LAP, TG, and WC in the population under study. The results of ROC analysis for predicting T2DM indicated that the LAP index has higher predictive power (AUC: 0.66, 95% CI: 0.64, 0.68) than TG (AUC: 0.64, 95% CI: 0.62, 0.66) and WC (AUC:0.60, 95% CI: 0.58, 0.62) in the studied population \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this population-based study, we observed a significant increase in the odds of T2DM with the rise in LAP levels in the adult population of western Iran. Specifically, in the second to fourth quartiles of LAP levels, the odds of T2DM was 1.60, 2.43, and 3.36 times higher than in the first quartile, respectively.\u003c/p\u003e \u003cp\u003eThe association between LAP and T2DM has been found to be positive in several demographic and ethnic subgroups. Briefly, in the Japanese population, high LAP have been shown to increase the risk of T2DM by 76% over time [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Similar results have been reported in adults Chinese and middle-aged Koreans [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Studies have shown that obesity is a risk factor for T2DM [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The excessive accumulation of lipids or liver fat leads to a series of complex metabolic consequences, such as non-alcoholic fatty liver disease (NAFLD) and insulin resistance (IR), which ultimately contribute to the development and progression of T2DM [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Additionally, WC, relative to BMI, is a more accurate indicator for assessing excess visceral fat in the body. WC does not clearly distinguish between subcutaneous and visceral fat in the abdominal cavity [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Furthermore, dyslipidemia, hypertriglyceridemia, and low HDL cholesterol levels have been observed to be related to T2DM [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Therefore, LAP, as a composite index of WC and TG, reflects the state of visceral fat and blood lipids. A strong relationship between LAP and metabolic diseases such as CVD and hypertension has also been observed [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Generally, the positive relationship between LAP and diabetes is acceptable.\u003c/p\u003e \u003cp\u003eIn individuals who are overweight, excessive fat tissue in the body, combined with overeating and chronic inflammation, can result in IR and a disorder in glucose metabolism [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. IR is a crucial stage in the progression of diabetes [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and chronic inflammation and mitochondrial dysfunction caused by obesity can both worsen IR [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Additionally, prolonged exposure to fatty acids in β-cells can decrease glucose-induced insulin secretion, disrupt insulin gene expression, and increase cell death [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The onset of T2DM is gradual and may lead to complications for an individual before it is diagnosed, making early diagnosis and prevention particularly important. Considering the change in lifestyle and the increasing trend of T2DM as well as serious complications, reduction in quality of life and heavy economic burden caused by this disease, it is very important to provide a low-cost, easy and calculable index to identify T2DM as soon as possible. Previous studies have shown that LAP has a higher diagnostic value for predicting T2DM than indicators such as BMI, WC, A Body Shape Index (ABSI), Visceral adiposity index (VAI) and waist/height ratio (WHtR) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Similarly, the present study also showed that LAP (AUC\u0026thinsp;=\u0026thinsp;0.66) has higher diagnostic value than WC (AUC\u0026thinsp;=\u0026thinsp;0.60) and TG (AUC\u0026thinsp;=\u0026thinsp;0.64). Furthermore, LAP has been identified as a useful indicator for predicting metabolic diseases like hypertension and CVD [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Based on the results of this study in a population of 9,065 participants including men and women and residents of cities and villages, and the results of previous studies, we suggest LAP as an accurate and comprehensive index for T2DM screening in large populations.\u003c/p\u003e \u003cp\u003eThe study had a few limitations. It was a cross-sectional analysis, which means that the observed relationships are not causal. However, causal relationships in longitudinal studies have been observed in other population groups. Additionally, the study was conducted on adults in western Iran, so the results should be cautiously generalized to other population groups. It is recommended to carry out this study in other population subgroups. Lastly, the study did not measure HbA1c and oral glucose tolerance test variables. The large sample size is a key advantage of the current study. Additionally, the comprehensive collection of lifestyle, anthropometric, and biochemical variables, as well as the adjustment of a large number of confounding variables, is strength of this study.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe findings of this study revealed a significant increase in the risk of T2DM with rising LAP levels in the adult population of western Iran. Additionally, based on Rock curve analysis, LAP was found to be an accessible and cost-effective tool for screening and monitoring diabetes in the adults, with a higher predictive value than TG levels and obesity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSS\u0026nbsp;and\u0026nbsp;AA\u0026nbsp;conceived the idea of the study.\u0026nbsp;FN\u0026nbsp;developed the statistical analysis plan and conducted statistical analyses.\u0026nbsp;YP, SS\u0026nbsp;and\u0026nbsp;AA\u0026nbsp;contributed to the interpretation of the results. \u0026nbsp;SS\u0026nbsp;and\u0026nbsp;AA\u0026nbsp;drafted the original manuscript.\u0026nbsp;AA,\u0026nbsp;YP\u0026nbsp;and\u0026nbsp;FN\u0026nbsp;supervised the conduct of this study. All authors reviewed the manuscript draft and revised it critically on intellectual content. All authors approved the final version of the manuscript to be published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the ethics committee of Kermanshah University of Medical Sciences\u0026nbsp;(KUMS.REC.1394.318).\u0026nbsp;All methods were carried out in accordance with relevant guidelines and regulations. All the participants were provided oral and written informed consent.\u0026nbsp;All methods were carried out by relevant guidelines and regulations.\u0026nbsp;This study was conducted by the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data analyzed in the study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by\u0026nbsp;Kermanshah University of Medical Sciences (grant number: 92472).\u0026nbsp;The Iranian Ministry of Health and Medical Education has also contributed to the funding used in the PERSIAN Cohort through Grant no 700/534.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the PERSIAN cohort Study collaborators and of Kermanshah University of Medical Sciences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBudreviciute A, Damiati S, Sabir DK, Onder K, Schuller-Goetzburg P, Plakys G, et al. Management and prevention strategies for non-communicable diseases (NCDs) and their risk factors. Frontiers in public health. 2020;8:788.\u003c/li\u003e\n\u003cli\u003eInstitute for Health Metrics and Evaluation (IHME). GBD Compare Seattle, WA: IHME,University of Washington2015 [Available from: https://vizhub.healthdata.org/gbd-compare/.\u003c/li\u003e\n\u003cli\u003eKhosravi Shadmani F, Farzadfar F, Larijani B, Mirzaei M, Haghdoost AA. Trend and projection of mortality rate due to non-communicable diseases in Iran: A modeling study. PloS one. 2019;14(2):e0211622.\u003c/li\u003e\n\u003cli\u003eValencia WM, Florez H. How to prevent the microvascular complications of type 2 diabetes beyond glucose control. Bmj. 2017;356.\u003c/li\u003e\n\u003cli\u003eGong Q, Zhang P, Wang J, Ma J, An Y, Chen Y, et al. Morbidity and mortality after lifestyle intervention for people with impaired glucose tolerance: 30-year results of the Da Qing Diabetes Prevention Outcome Study. The lancet Diabetes \u0026amp; endocrinology. 2019;7(6):452-61.\u003c/li\u003e\n\u003cli\u003eKlein S, Sheard NF, Pi-Sunyer X, Daly A, Wylie-Rosett J, Kulkarni K, et al. Weight management through lifestyle modification for the prevention and management of type 2 diabetes: rationale and strategies. A statement of the American Diabetes Association, the North American Association for the Study of Obesity, and the American Society for Clinical Nutrition. The American journal of clinical nutrition. 2004;80(2):257-63.\u003c/li\u003e\n\u003cli\u003eZhang F-L, Ren J-X, Zhang P, Jin H, Qu Y, Yu Y, et al. Strong Association of waist circumference (WC), body mass index (BMI), waist-to-height ratio (WHtR), and waist-to-hip ratio (WHR) with diabetes: A population-based cross-sectional study in Jilin province, China. Journal of diabetes research. 2021;2021.\u003c/li\u003e\n\u003cli\u003eWei J, Liu X, Xue H, Wang Y, Shi Z. Comparisons of visceral adiposity index, body shape index, body mass index and waist circumference and their associations with diabetes mellitus in adults. Nutrients. 2019;11(7):1580.\u003c/li\u003e\n\u003cli\u003eGuilherme A, Virbasius JV, Puri V, Czech MP. Adipocyte dysfunctions linking obesity to insulin resistance and type 2 diabetes. Nature reviews Molecular cell biology. 2008;9(5):367-77.\u003c/li\u003e\n\u003cli\u003eKahn HS, Cheng YJ, Thompson TJ, Imperatore G, Gregg EW. Two risk-scoring systems for predicting incident diabetes mellitus in US adults age 45 to 64 years. Annals of internal medicine. 2009;150(11):741-51.\u003c/li\u003e\n\u003cli\u003eSattar N, Wannamethee S, Forouhi N. Novel biochemical risk factors for type 2 diabetes: pathogenic insights or prediction possibilities? Diabetologia. 2008;51:926-40.\u003c/li\u003e\n\u003cli\u003eUnger RH. Reinventing type 2 diabetes: pathogenesis, treatment, and prevention. Jama. 2008;299(10):1185-7.\u003c/li\u003e\n\u003cli\u003eCassano PA, Rosner B, Vokonas PS, Weiss ST. Obesity and body fat distribution in relation to the incidence of non-lnsulin-dependent diabetes mellitus: A prospective cohort study of men in the normative aging study. American journal of epidemiology. 1992;136(12):1474-86.\u003c/li\u003e\n\u003cli\u003eSchulze MB, Heidemann C, Schienkiewitz A, Bergmann MM, Hoffmann K, Boeing H. Comparison of anthropometric characteristics in predicting the incidence of type 2 diabetes in the EPIC-Potsdam study. Diabetes care. 2006;29(8):1921-3.\u003c/li\u003e\n\u003cli\u003eYang W, Lu J, Weng J, Jia W, Ji L, Xiao J, et al. Prevalence of diabetes among men and women in China. New England journal of medicine. 2010;362(12):1090-101.\u003c/li\u003e\n\u003cli\u003eZheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nature reviews endocrinology. 2018;14(2):88-98.\u003c/li\u003e\n\u003cli\u003eRay L, Ravichandran K, Nanda SK. Comparison of lipid accumulation product index with body mass index and waist circumference as a predictor of metabolic syndrome in Indian population. Metabolic syndrome and related disorders. 2018;16(5):240-5.\u003c/li\u003e\n\u003cli\u003eMuheiyati G, Mei Y, Tao N. Association of lipid accumulation product and visceral adiposity index with the risk of hypertension among oil workers in Xinjiang, China. PeerJ. 2023;11:e15273.\u003c/li\u003e\n\u003cli\u003eVieira JN, Braz MAD, Gomes FO, Silva PRd, Santos OTdM, Rocha IMGd, et al. Cardiovascular risk assessment using the lipid accumulation product index among primary healthcare users: a cross-sectional study. Sao Paulo Medical Journal. 2019;137:126-31.\u003c/li\u003e\n\u003cli\u003eLiu T, Lu W, Zhao X, Yao T, Song B, Fan H, et al. Relationship between lipid accumulation product and new-onset diabetes in the Japanese population: a retrospective cohort study. Frontiers in Endocrinology. 2023;14:1181941.\u003c/li\u003e\n\u003cli\u003ePasdar Y, Najafi F, Moradinazar M, Shakiba E, Karim H, Hamzeh B, et al. Cohort profile: Ravansar Non-Communicable Disease cohort study: the first cohort study in a Kurdish population. International journal of epidemiology. 2019;48(3):682-3f.\u003c/li\u003e\n\u003cli\u003ePoustchi H, Eghtesad S, Kamangar F, Etemadi A, Keshtkar A-A, Hekmatdoost A, et al. Prospective epidemiological research studies in Iran (the PERSIAN Cohort Study): rationale, objectives, and design. American journal of epidemiology. 2018;187(4):647-55.\u003c/li\u003e\n\u003cli\u003eNajafi F, Rezaei S, Hajizadeh M, Soofi M, Salimi Y, Kazemi Karyani A, et al. Decomposing socioeconomic inequality in dental caries in Iran: cross-sectional results from the PERSIAN cohort study. Archives of Public Health. 2020;78(1):1-11.\u003c/li\u003e\n\u003cli\u003eChing YK, Chin YS, Appukutty M, Gan WY, Chan YM. Comparisons of conventional and novel anthropometric obesity indices to predict metabolic syndrome among vegetarians in Malaysia. Scientific reports. 2020;10(1):20861.\u003c/li\u003e\n\u003cli\u003eChobanian AV. National heart, lung, and blood institute joint national committee on prevention, detection, evaluation, and treatment of high blood pressure; national high blood pressure education program coordinating committee. The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure: the JNC 7 report. Jama. 2003;289:2560-72.\u003c/li\u003e\n\u003cli\u003eRyan H, Trosclair A, Gfroerer J. Adult current smoking: differences in definitions and prevalence estimates\u0026mdash;NHIS and NSDUH, 2008. Journal of environmental and public health. 2012;2012.\u003c/li\u003e\n\u003cli\u003eYan G, Li F, Elia C, Zhao Y, Wang J, Chen Z, et al. Association of lipid accumulation product trajectories with 5-year incidence of type 2 diabetes in Chinese adults: a cohort study. Nutrition \u0026amp; metabolism. 2019;16(1):1-8.\u003c/li\u003e\n\u003cli\u003eLee J-W, Lim N-K, Park H-YJBed. The product of fasting plasma glucose and triglycerides improves risk prediction of type 2 diabetes in middle-aged Koreans. 2018;18:1-10.\u003c/li\u003e\n\u003cli\u003eRohm TV, Meier DT, Olefsky JM, Donath MY. Inflammation in obesity, diabetes, and related disorders. Immunity. 2022;55(1):31-55.\u003c/li\u003e\n\u003cli\u003eKang PS, Neeland IJ. Body Fat Distribution, Diabetes Mellitus, and Cardiovascular Disease: an Update. Current Cardiology Reports. 2023;25(11):1555-64.\u003c/li\u003e\n\u003cli\u003eKane JP, Pullinger CR, Goldfine ID, Malloy MJ. Dyslipidemia and diabetes mellitus: Role of lipoprotein species and interrelated pathways of lipid metabolism in diabetes mellitus. Current Opinion in Pharmacology. 2021;61:21-7.\u003c/li\u003e\n\u003cli\u003eSong J, Zhao Y, Nie S, Chen X, Wu X, Mi J. The effect of lipid accumulation product and its interaction with other factors on hypertension risk in Chinese Han population: a cross-sectional study. PLoS One. 2018;13(6):e0198105.\u003c/li\u003e\n\u003cli\u003eLu J, Zhao J, Meng H, Zhang XJFii. Adipose tissue-resident immune cells in obesity and type 2 diabetes. 2019;10:1173.\u003c/li\u003e\n\u003cli\u003eHanson P, Weickert MO, Barber TMJTAiE, Metabolism. Obesity: novel and unusual predisposing factors. 2020;11:2042018820922018.\u003c/li\u003e\n\u003cli\u003eDi Meo S, Iossa S, Venditti PJJoE. Skeletal muscle insulin resistance: role of mitochondria and other ROS sources. 2017;233(1):R15-R42.\u003c/li\u003e\n\u003cli\u003eKim O-K, Jun W, Lee JJAoN, Metabolism. Mechanism of ER stress and inflammation for hepatic insulin resistance in obesity. 2015;67(4):218-27.\u003c/li\u003e\n\u003cli\u003eShoelson SE, Herrero L, Naaz AJG. Obesity, inflammation, and insulin resistance. 2007;132(6):2169-80.\u003c/li\u003e\n\u003cli\u003eJacqueminet S, Briaud I, Rouault C, Reach G, Poitout VJM. Inhibition of insulin gene expression by long-term exposure of pancreatic \u0026beta; cells to palmitate is dependent on the presence of a stimulatory glucose concentration. 2000;49(4):532-6.\u003c/li\u003e\n\u003cli\u003eMaedler K, Spinas G, Dyntar D, Moritz W, Kaiser N, Donath MYJD. Distinct effects of saturated and monounsaturated fatty acids on \u0026beta;-cell turnover and function. 2001;50(1):69-76.\u003c/li\u003e\n\u003cli\u003eYang SH, Yoon J, Lee Y-J, Park B, Jung D-HJD, Metabolic Syndrome, Targets O, et al. Lipid accumulation product index predicts new-onset type 2 diabetes among non-obese koreans: a 12-year longitudinal study. 2022:3729-37.\u003c/li\u003e\n\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":"Type 2 diabetes, lipid accumulation product, waist circumference, triglyceride","lastPublishedDoi":"10.21203/rs.3.rs-3875246/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3875246/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe Lipid Accumulation Product (LAP) is a measure that indicates the presence of excessive fat accumulation in the body. LAP have been the focus of research in epidemiological studies aimed at forecasting chronic and metabolic illnesses. This study was carried out to examine the association between LAP and type 2 diabetes mellitus (T2DM) in the adult population of western Iran.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe study included 9,065 adults between the ages of 35 and 65 who were registered baseline phase of the Ravansar non-communicable diseases study (RaNCD) cohort study. Multiple logistic regression models were employed to explore the association between LAP and T2DM. The receiver operating characteristic (ROC) curve was used to evaluate the predictive capability of the LAP for T2DM.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe average LAP was 53.10\u0026thinsp;\u0026plusmn;\u0026thinsp;36.60 in the healthy group and 75.51\u0026thinsp;\u0026plusmn;\u0026thinsp;51.34 in the diabetic group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The multiple regression model indicated that, after controlling for potentially confounding variables, the odds of T2DM in the second quartile of lipids is 1.60 (95%CI: 1.17, 2.18) times higher than in the first quartile. Additionally, in the third and fourth quartiles, it is 2.43 (95%CI: 1.80, 3.28) and 3.36 (95%CI: 2.47, 4.56) times higher than in the first quartile, respectively. The results of ROC analysis for predicting T2DM indicated that the LAP index has (AUC: 0.66, 95%CI: 0.64, 0.68).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe association between high LAP levels and the T2DM was found to be strong in the adult population of western Iran. LAP is suggested as a tool in diabetes susceptibility screening.\u003c/p\u003e","manuscriptTitle":"Association of lipid accumulation product and the risk of type 2 diabetes; a cross sectional population-based study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-22 12:59:48","doi":"10.21203/rs.3.rs-3875246/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":"089b659a-6646-4bc3-872b-c8e15cd81b86","owner":[],"postedDate":"January 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-27T10:44:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-22 12:59:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3875246","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3875246","identity":"rs-3875246","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}