Metabolic syndrome modulates risk of endometrial cancer regardless of menopause status- A UK BIOBANK Study

Metabolic syndrome modulates risk of endometrial cancer regardless of menopause status- A UK BIOBANK 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 Article Metabolic syndrome modulates risk of endometrial cancer regardless of menopause status- A UK BIOBANK Study Rebecca Karkia, Gideon Maccarthy, Annette Payne, Emmanouil Karteris, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4812894/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 This study investigates the association between endometrial cancer (EC) risk and features of metabolic syndrome (MetS) using the UK Biobank. Methods Univariate and multivariate analysis of EC risk and features of MetS including serum biochemistry were analysed. Subgroup analysis was also undertaken for pre- and post-menopausal participants. Results 203,644 females from the UK Biobank were included in this study. 49,071 (43.8%) met the met the International Diabetes Federation (IDF) definition of MetS and in these females the risk of EC was almost threefold higher (OR = 2.67; 95%CI:2.41–2.96, P 80cm, BMI > 30kg/m 2 , hypertension > 130/80mmHg and hyperlipidaemia or diabetes were significantly associated with increased risk of EC. BMI > 30 kg/m 2 alone was associated with threefold higher risk and BMI > 40 kg/m 2 a ninefold higher risk. Associations remained significant in pre and postmenopausal subgroups. Treatment for hypertension, hyperlipidaemia or diabetes was associated with EC risk in univariate analysis but did not remain significant in multivariate analysis. Having abnormal lipid profile, fasting hyperglycaemia or hypertension significantly increased the risk of EC after correction for confounding factors. Conclusion Features of MetS, both independently and in combination, significantly increase the risk of EC. Screening those with obesity for MetS, in pre-menopausal years may help to identify those at highest risk. Figures Figure 1 Introduction Excess weight and adiposity are associated with a number of metabolic and hormonal dysregulations that include increased peripheral aromatisation of androgens into oestrogens within white adipose tissue, as well as hyperinsulinemia, hyperglycemia, and chronic inflammation (Dossus and Kaaks, 2008). Clinically, the effects of these obesity related changes are regarded as metabolic syndrome (MetS). Given that MetsS represents a cluster of disorders, there are a number of different definitions but the most commonly employed definitions are the International Diabetes Federation (IDF) criteria (2005), the National Cholesterol Education Program (NCEP) ATP3 2005 and the World health Organisation (1999) definitions (Table 1)(Alberti et al., 2006; Consultation, 1999; Grundy et al., 2005). Table 1. Female definitions of MetS according to the International Diabetes Federation (IDF), National Cholesterol Education Program (NCEP) and World Health Organisation (WHO) Waist (cm) Glucose (mmol/L) HDL (mmol/L) Triglycerides (mmol/L) Blood pressure (mm/Hg) IDF (Waist+ Any 2 others) >80cm (Europeans) >5.6 or diabetes 1.7 or medicated >130/85 or medicated NCEP (Any 3) >88cm 5.6 or diabetes medicated 1.7 or medicated >130/85 or medicated WHO (insulin resistance / glucose + any 2 others Waist-Hip ratio>0.85 or BMI>30 > 6.1 mmol/L, 2 h glucose> 7.8 mmol 1.7 >140/90 The pathogenesis of MetS encompasses multiple genetic and acquired entities that fall under the umbrella of insulin resistance and chronic low-grade inflammation. If left untreated, MetS is significantly associated with an increased risk of developing diabetes and its vascular complications, ischaemic heart disease and cerebrovascular disease (5,6). It is now also evident that MetS is associated with a number of cancers including colorectal cancer, postmenopausal breast, renal and uterine cancer (5,7,8). Endometrial Cancer (EC) appears to be one of the cancers most associated with MetS development. Up to one third of endometrial cancers are thought to be preventable and secondary to obesity (9). In the UK, there is around 10,000 new cases of EC diagnosed annually (10). In contrast, there are 200,000 new cases of diabetes mellitus diagnosed annually and latest figures from the National Health Service (NHS) digital suggest that up to 26% of adults in the UK are now obese, with a further 38% falling in the overweight category (11). Despite this, relatively few who have the top risk factors for EC development such as obesity or diabetes will go on to develop EC and as such strategies for risk prediction need to be more nuanced in targeting those at highest risk (12). As a direct consequence of the rise in diagnosis of EC, the mortality from EC is also rising, despite improvements in overall survival (OS). There are many modifiable risk factors implicated in EC pathogenesis, in fact, a meta-analysis of the top risk factors in EC development found that BMI is the largest predictor, with diabetes mellitus and polycystic ovarian syndrome (PCOS) other top risk factors (12). Oral contraceptive use and continuous hormone replacement therapy (HRT) are also known to be risk reducing factors (12). Despite this, at present, there are no nationally recommended primary prevention strategies in place for those at highest risk of EC. Similarly, despite published data showing strong associations between components of MetS and EC risk, there are as yet no nationally recommended screening systems or risk reduction strategies in place. Given the high rates of obesity in the UK and the drive towards precision medicine, it would be beneficial to understand if metabolic syndrome or it’s component features can be used to identify those at highest risk of EC, over and above the risk that BMI adds and also importantly how these factors are altered by menopausal status. We therefore sought to investigate the association between EC risk and individual features of MetS, including serum biochemistry variables that pertain to, inflammation, insulin resistance and hyperlipidaemia using one of the largest UK based prospectively collected datasets, the UK Biobank. Secondary outcomes will be to conduct subgroup analysis of pre and post-menopausal females to determine whether pre-menopausal women with features of MetS have a similar risk of EC development as their post-menopausal counterparts. Methods Study Population The UK Biobank is a major national and international health resource, created to improve the prevention, diagnosis and treatment of serious and life-threatening illnesses, including cancer (13). The female cohort consists of individuals aged between 39 and 71 years included in the database between March 2006 and October 2010. Detailed ethnicity, health, demographic and anthropometry was collected as well blood samples. Cancer diagnoses were ascertained through the International Classification of Diseases (ICD) ICD-10 records recorded by NHS England in England and Wales and the NHS Central Register, National Records of Scotland. The latest cancer registry record linked to UK Biobank data was the 13 May, 2022. Inpatient data acquired through hospital episode statistics (HES), the Scottish morbidity record (SMR) or the patient episode database for Wales was available and complete up until October, August and May 2022, respectively. Full details can be found at https://www.ukbiobank.ac.uk . The study was approved by the Northwest Multi-Centre Research Ethics Committee (16/NW/0274), Patient Information Advisory Group (England and Wales) and the Community Health Index Advisory Group (Scotland). All participants provided written informed consent. The study was conducted in accordance with the Declaration of Helsinki. Identification of EC cases was performed using ICD-10, of which there were 9 follow-up time point records during the 2006-2010 recruitment period. Only incident cancer cases diagnosed after the 9 th follow- up time point were included in the study and no patients with an active diagnosis of cancer or women who had previously undergone a hysterectomy were included into either control or cancer group when they attended the UK Biobank centre, to minimize the risk of reverse causality. For all other data, only data collected at the initial visit was analysed. Self-reported data was used for menopausal status. All missing data was excluded from statistical analysis. From a starting number of 273 298 female participants, there were 203, 644 remaining for analysis after exclusion of those with a previous cancer diagnosis, those with previous hysterectomy or those withdrawing from the UK Biobank study (Figure A). Statistical Analysis Continuous and categorical variables in the UK Biobank dataset were compared using a Mann-Whitney U or Chi-square test, respectively. Univariate and multivariate backwards stepwise logistic regression analyses were used to determine which of the baseline characteristics, medical co-morbidities and biochemistry studies had a significant contribution in prediction of EC. Subgroup analysis was undertaken for pre- and post-menopausal female participants. The WHO 1999 definition and the IDF 2005 definition were used to define the number of participants meeting the criteria for a diagnosis of MetS (2). Subsequently the risk of EC development for any woman who met the diagnostic criteria for MetS was ascertained. In line with the most common MetS definitions, further regression analysis was conducted to ascertain which specific components of MetS are the strongest predictors of EC development. Following individual component analysis of all the risk factors pertaining to a MetS, subsequent regression analysis was undertaken to assess the overall risk of EC development in females who met the criteria for MetS diagnosis. Given the similarity of the NCEP ATP 3 definition and the IDF definition, only the IDF and the WHO definitions were used for regression analysis. As detailed in table 1, individuals can be diagnosed with MetS based on fulfilling different criteria, and as such different models were created to assess risk of EC development. The effect size of characteristics associated with EC development was expressed as odds ratio (OR) with 95% confidence interval (CI). Significance was assumed at 5% and a posthoc Bonferroni correction was used to adjust for multiple comparisons when necessary. R statistical packages were used in statistical analyses with a p value <0.05 considered as significant. Results Basic cohort characteristics The baseline characteristics of cases and controls are described in Table 2. The control group was formed of 202,012 females who at the time of baseline investigations and subsequent visits had no cancer diagnosis and no previous hysterectomy. The EC group was formed of 1632 individuals who following their first assessment went on to develop EC. The median time to EC diagnosis following first assessment at the UK biobank was 2022 days (5.5 years). As can be seen from Table 2, there were 190, 058 (94.1%) that reported being of white ethnicity in the control group and 1538 (94.2%) in the EC group, with no significant difference seen in any of the ethnic groups represented. Those females who developed EC following recruitment into the study tended to be older at recruitment with a median age of 60.0 as compared to controls with a median age of 56.0 (p<0.0001). Females who went on to develop EC tended to be taller, heavier, with higher waist circumference and BMI. There was no significant difference in age at menarche (a known contributor to EC risk) however women who went on to develop EC tended towards later menopause with the median age being 52.0 versus 50.0 years of age, respectively. Nulliparity, was more common amongst females who went on to develop EC (24.0% of the EC group were nulliparous versus 20.0% in the control group, respectively, p<0.0001. The rate of previous oral contraceptive pill use was significantly lower in the EC group as compared to the control group (69.6 % versus 82.0%, respectively, p<0.0001). Similarly, the EC group had a much lower rate of hormone replacement therapy (HRT) use, with 64.0% reporting being never users versus 30.4% in the control group (p<0.001). Table 2. Baseline characteristics table of females in UK Biobank. NS: not significant Control n=202,012 EC n =1,632 P-Value (Mann-U) Ethnicity n (%) 1- White 190,058 (94.1) 1538 (94.2) NS 2- Mixed 1,468 (0.73) 9 (0.6) NS 3- Asian/Asian British 3,644 (1.8) 36 (2.2) NS 4- Black/Black British 3,450 (1.7) 19 (1.2) NS 5- East Asian 815 (0.4) 5 (0.3) NS 6- Other 2,008 (1.0) 19 (1.2) NS NA – Not answered n (%) 569 (0.3) 6 (0.4) NS Index of deprivation (England) median (IQR) 12.9 (7.4-23.3) 14.0 (8.0-23.6) <0.001 Age at recruitment median (IQR) 56.0 (39.1-71.0) 60.0 (54.0-64.0) <0.0001 Height (cm) median (IQR) 163.0 (158.0-167.0) 162.0 (158.0-166.0) 0.0003 Weight (kg) median (IQR) 70.9 (61.2-77.9) 76.5 (66.9-90.7) <0.0001 Waist Circumference (cm) median (IQR) 82.0 (75.0-91.0) 90.0 (81.0-102.0) <0.0001 BMI (kg/m 2 ) median (IQR) 25.9 (23.2-29.4) 29.1 (25.3-34.7) <0.0001 Age at Menarche median (IQR) 13.0 (12.0-13.0) 13.0 (11.0-14.0) <0.0001 Pre-Menopausal, n (%) 61,130 (30.3) 286 (17.5) <0.0001 Post-Menopause, n (%) 130,215 (64.5) 1281 (78.5) <0.0001 Age at Menopause median (IQR) 50.0 (47.0-53.0) 52.0 (49.0-54.0) <0.0001 Nulliparous n (%) 40,361 (20.0) 421 (25.4) <0.0001 Oral contraceptive pill (OCP) used n (%) 165,638 (82.0) 1136 (69.6) <0.0001 OCP never used n (%) 35,872 (17.8) 492 (30.1) <0.0001 Hormone replacement therapy (HRT) used n (%) 139,950 (69.3) 580 (35.5) <0.0001 HRT never used n (%) 61,437 (30.4) 1044 (64.0) <0.0001 Never smoked n (%) 121,875 (60.3) 1076 (65.9) <0.0001 Ex-smoker n (%) 61,646 (30.5) 466 (28.6) NS Current smoker n (%) 17,842 (8.8) 84 (5.1) <0.0001 Diabetes Mellitus (DM)- any n (%) 6,600 (3.3) 126 (7.7) <0.0001 Taking insulin currently n (%) 273 (0.1) 3 (0.2) NS Gestational DM only n (%) 782 (0.4) 4 (0.2) NS Taking cholesterol lowering medication n (%) 21,450 (10.6) 309 (19.0) <0.0001 Anti-hypertensives n (%) 18,305 (9.1) 253 (15.5) <0.0001 Polycystic Ovarian Syndrome n (%) 312 (0.15) 4 (0.24) <0.001 Aspirin use n (%) 13,450 (6.6) 172 (10.5) <0.001 Biochemical comparison Table 3 displays the serum biochemistry taken at the time of the primary assessment. The analysis showed that alkaline phosphatase (ALP), aspartate aminotransferase (AST), gammaglutamyl transferase (GGT), C-reactive protein (CRP), low density lipoprotein (LDL), Lipoprotein A, and triglycerides were all significantly increased in the EC group when compared to controls (Table 3). Inversely, albumin levels, high density lipoprotein (HDL), Insulin-like growth factor 1 (IGF-1) and sex hormone binding globulin (SHBG) levels were significantly lower in the EC group. Regression analyses Univariate and multivariate analyses was carried out to ascertain the strength of independent contributors of MetS as well as the overall effect of diagnosis of MetS on EC risk (Table 4, Supplementary table 6). BMI, waist circumference, hip circumference, arterial blood pressure (both systolic and diastolic), diabetes, and use of antihypertensive or cholesterol lowering medication were all significant independent predictors of EC risk. The risk of developing EC in those that were overweight was significantly increased, nearly three-fold higher in those with a BMI > 30kg/m 2 and 9-fold higher in those with a BMI > 40kg/m 2 . The same trends were observed with waist circumference. In those with a waist circumference > 80 cm, the risk of EC development was also increased. In those with a waist circumference > 88cm the risk of EC doubled and in those with a waist circumference > 100cm risk was 5-fold higher. East Asian ethnic origin, hormonal contraceptive use, and smoking were all significantly associated with lower risk of EC development. Subsequent multivariate analysis showed a strong association of EC with high BMI (OR=1.69; 95%CI: 1.31-2.19 for BMI 30.0-39.9kg/m2 and OR=3.04 95%CI: 2.09-4.44 for BMI >40.0kg/m2, respectively), waist circumference above 80cm (OR=1.21 95%CI: 1.01-1.44), hip circumference above 131cm (OR=1.69 95%CI: 1.18-2.42), increased systolic blood pressure (OR=1.41; 95%CI: 1.19-1.68 for BP 140 – 179mmHg and OR=1.72 95%CI: 1.25-2.34 for BP > 180mmHg, respectively). Use of hormonal contraception remained significant as a protective factor, reducing the likelihood of EC development 11.1 times (OR=0.09, 95%CI: 0.02-0.29). Looking specifically at features diagnostic for MetS, univariate analysis revealed that waist circumference >94cm and BMI >30 kg/m 2 were equally associated with EC development (OR=2.85, p 88cm and triglycerides >1.7mmol/L (OR=2.66, 95%CI: 2.41-.93 and OR=1.96, 95% CI: 1.77-2.17, respectively). All factors associated with a diagnosis of MetS remained significant after multivariate regression. HbA1c was also included in the analysis as although not formally a feature in the diagnostic criteria, it is widely accepted that an HbA1c value above 48 mmol/mol is diagnostic of diabetes. This was a stronger independent predictor of EC development than fasting glucose > 5.6 mmol/L. Additional information can be found in supplementary tables. Table 3. Baseline blood biochemistry results of females in UK Biobank Control n=202,012 Median (IQR) EC n =1,632 Median (IQR) P-Value (Mann-U ) Liver enzymes Albumin (g/L) 45.0 (43.3-46.7) 44.4 (42.8-46.1) <0.0001 Alkaline phosphatase (U/L) 80.2 (65.9-96.6) 88.7(71.7-102.4) <0.0001 Apolipoprotein A (g/L) 1.62 (1.5-1.8) 1.55 (1.4-1.7) NS Apolipoprotein B (g/L) 1.0 (0.9-1.2) 1.0 (0.9-1.2) NS Asp Aminotransferase (U/L) 22.8 (19.8-26.6) 23.4 (20.2-27.4) <0.0001 Gamma GT (U/L) 20.8 (15.7-29.0) 24.9 (18.2-37.9) <0.0001 CRP (mg/L) 1.3 (0.6-2.7) 2.2 (0.9-4.4) <0.0001 Glucose metabolism Glucose (mmol/L) 4.9 (4.6-5.3) 5.0 (4.7-5.4) NS HbA1c (mmol/mol) 35.0 (32.5-37.5) 36.2 (33.7-39.2) <0.0001 IGF-1 (nmol/L) 21.3 (17.5-24.9) 19.9 (16.1-23.7) <0.0001 Fatty acids metabolism Total cholesterol (mmol/L) 5.8 (5.0-6.5) 5.8 (5.1-6.6) NS HDL (mmol/L) 1.6 (1.3-1.8) 1.4 (1.2-1.7) <0.0001 LDL (mmol/L) 3.5 (3.0-4.1) 3.7 (3.0-4.3) 0.0002 Lipoprotein A (nmol/L) 22.1 (9.9-62.2) 24.9 (10.7-62.8) 0.04888 Triglycerides (mmol/L) 1.3 (0.9-1.8) 1.6 (1.1-2.2) <0.0001 Sex hormones Oestradiol (pmol/L) 404.6 (270.1-645.8) 373.4 (248.7-593.4) 0.03753 SHBG (nmol/L) 57.0 (40.6-77.4) 44.4 (31.3-60.3) <0.0001 Free oestradiol index (pmol/L) 0.71 0.84 <0.0001 Testosterone (nmol/L) 1.0 (0.7-1.4) 1.1 (0.8-1.5) NS Free androgen index (nmol/L) 1.8 2.5 <0.0001 Table 4. Regression analysis of risk factors associated with EC development Univariate Analysis Multivariate analysis OR (95%CI) P Value OR (95%CI) P Value Ethnic Background White 1 - 1 Mixed - - Asian/Asian British - - Black/Black British - - East Asian 0.15 0.011 - Other 0.24 0.022 - Body Mass Index (Kg/m2) BMI <18.5 - - BMI 18.5-24.9 1 BMI 25.0-29.9 1.69 (1.32-1.63) <0.0001 1.26 (1.04-1.52) 0.014 BMI 30-39.9 3.17 (2.37-1.93) <0.0001 1.69 (1.31-2.19) 40 9.06 (6.17-8.31) <0.0001 3.04 (2.09-4.44) <0.0001 Waist circumference (cm) Waist circumference < 80 1 Waist circumference 80-80 1.58 (1.36-1.83) <0.0001 1.21 (1.01-1.44) 0.037 Waist circumference 88-100 2.34 (2.04-2.69) 100 4.96 (4.33-4.68) <0.0001 1.82 (1.40-2.36) <0.0001 Hip Circumference (cm) Hip circumference <100cm 1 Hip circumference 100-110 1.70 (1.50-1.93) <0.0001 - Hip circumference 111-130 2.82 (2.47-3.21) 131 8.60 (7.11-10.31) <0.0001 1.69 (1.18-2.42) 0.004 Waist Hip Ratio <0.80 1 0.81-0.85 1.46 (1.28-1.66) 0.86 2.10 (1.87-2.35) <0.0001 1.26 <0.0004 PCOS - Systolic Blood Pressure (mmHg) <120 1 120-129 1.22 0.0369 - 130-139 1.44 <0.0001 - 140-179 1.89 180 2.41 <0.0001 1.72 (1.25-2.34) 0.0006 Diastolic Blood Pressure (mmHg) <80 1 80-89 1.21 (1.07-1.36) 0.0020 0.85 (0.74-0.97) 0.016 90-120 1.57 (1.39-1.78) 120 4.90 (1.50-11.68) 0.002 - Diabetes diagnosed by Physician 2.48 (2.06-2.97) <0.0001 - Insulin use 1.37 (0.33-3.57) 0.591 Cholesterol lowering medication 2.17 (1.90-2.46) <0.0001 - Antihypertensive medication 2.08 (1.81-2.39) <0.0001 - HRT 0.65 (0.33-1.53) 0.264 - COCP/POP 0.10 (0.03-0.24) <0.0001 0.09 (0.02-0.29) <0.0001 Ever smoked 0.85 (0.76-0.95) 0.0053 - Current smoker 0.53 (0.42-0.66) <0.0001 - Subgroup analysis Sub-analysis of pre-and post-menopausal females was also undertaken. In the pre and postmenopausal subgroup, the waist circumference, BMI and index of deprivation were significantly higher in the EC group as compared to controls (Supplementary table 1 & 2). The protective effect of contraception remained significant even after exclusion of the premenopausal females suggesting a sustained protective effect of contraception beyond the years at which it is required for contraception. The subgroup analysis of serum biochemistry showed that the liver enzymes, CRP, Lipoprotein A and triglycerides were significantly higher in the EC group regardless of the menopausal status when compared with controls (Table 5). Interestingly, LDL levels were significantly higher in the EC group, but the effect was only present in pre-menopause (3.4, 95%CI: 2.9-5.9 vs 3.2 95%CI: 2.8-3.7, p<0.001). Oestradiol levels were marginally lower in the EC group than the control group however when grouped by menopausal status, levels in pre-menopausal controls and the EC group were very similar (438 vs 432 pmol/L). In the post-menopausal group, oestradiol levels were again marginally lower in the EC group versus the control group (239 vs 278 pmol/L). However, as expected, when the SHBG results were accounted for, the bioavailable oestradiol levels were higher in the EC group versus the control group in both pre and post-menopausal females. To confirm the strength of the associations between features of MetS and EC in premenopausal women, regression analysis was undertaken looking at the individual components of MetS. The strongest predictors of EC development in pre-menopausal women were HbA1c>48 mmol/L and waist circumference >94cm where risk was increased nearly three-fold (OR=2.74, 95%CI:1.30-5.04 and OR=2.73, 95%CI 2.13-3.49, respectively) (Table 5). Table 5. Regression analysis of MetS parameters associated in pre and post-menopausal women Pre-menopausal Post-menopausal OR (95%CI) P Value OR (95%CI) P Value Waist circumference >88 (cm) 2.37 (1.87-2.98) <0.0001 2.48 (2.22-2.77) 94 (cm) 2.73 (2.13-3.49) <0.0001 2.68 (2.40-3.00) 30 (kg/m 2 ) 2.49 (1.96-3.16) <0.0001 2.80 (2.51-3.13) 1.7 (mmol/L) 1.95 (1.50-2.52) <0.0001 1.70 (1.52-1.90) 5.6 (mmol/L) 1.68 (1.14-2.41) 0.006 1.43 (1.23-1.65) 48 (mmol/mol) 2.74 (1.30-5.04) 0.003 1.97 (1.52-2.51) <0.0001 HDL <1 (mmol/L) 1.72 (0.99-2.76) 0.038 1.84 (1.38-1.41) <0.0001 HDL <1.3 (mmol/L) 2.29 (1.77-2.96) <0.0001 1.72 (1.51-1.96) 80cm, diabetes (diagnosed by doctor or HbA1C>48, or fasting glucose>5.6mmol/L) and biochemical or treated hypertriglyceridemia had the highest risk of development of EC (OR=2.76, 95% CI 2.39-3.17, P<0.0001) (Supplementary table 3 & 4). Overall, there was an almost three-fold increased risk of developing EC in any female who met any criteria for diagnosis of MetS by the IDF definition (OR=2.67,95%CI 2.41-2.96, P<0.0001) (Supplementary table 3). Discussion Principal Findings The principal findings of the study show that high BMI, increased waist and hip circumference, increased arterial blood pressure, abnormal lipid profile and deranged glucose metabolism are all associated with an increased risk of developing EC, regardless of whether they are identified in pre-menopause or post-menopause. Furthermore, all features of MetS are significant independent predictors of EC development. Females who develop EC are also more likely to have mildly abnormal liver function tests and mildly raised inflammatory markers. This supports the hypothesis that metabolic syndrome significantly increases the odds of developing EC and thus in effect may be used as a predictor of EC development, even in the pre-menopausal period. Comparison with other studies The distribution of body fat, in particular central adiposity or visceral adipose tissue is linked to several metabolic abnormalities including insulin resistance and inflammation that are associated with EC development. There is evidence that excess visceral adipose tissue in particular is associated with adverse metabolic, dyslipidemic, and atherogenic obesity as compared to subcutaneous fat (14). As such, central adiposity, typically assessed by waist circumference or waist to hip ratio, has been studied in numerous studies in relation to risk of EC (15–18). In the California Teachers’ Study, waist circumference and waist to hip ratio were positively associated with EC risk after adjustment for BMI (19). In contrast, the Nurses’ Health Study did not report an independent association with these measures, while the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort reported an independent association with waist circumference but not waist to hip ratio (17,20). A 2015 meta-analysis also found an independent association with waist circumference but not waist to hip ratio (21). In the UK Biobank cohort, waist circumference and waist-hip ratio were both significantly independent predictors of EC risk with waist circumference being the strongest predictor out of the three (waist circumference, hip circumference and waist-hip ratio) with an OR of 4.96 for waist circumference >100cm. The Epidemiology of Endometrial Cancer Obesity and Endometrial Cancer Consortium (E2C2) is a national cancer institute supported consortium of over 45 worldwide studies investigating the associations of obesity and EC (22). Pooled estimates of EC risk where both waist-to-hip ratio and abdominal circumference were assessed demonstrated an OR of 1.92 (95%CI: 1.57–2.35))(22,23). Body mass index (BMI) reflects both fat and fat-free mass, rather than fat distribution, which varies considerably among individuals with a similar BMI. Despite this, BMI was the best predictor of EC risk in our study amongst all of the anthropometric measures assessed. Similarly, in the E2C2 study, the overall pooled estimate for obesity and EC risk was 2.65 (95%CI: 2.43–2.90) in those with a BMI of 30-35kg/m 2 and 4.66 (95%CI: 3.78–5.75) in individuals with severe obesity (BMI>35kg/m 2 ) which is similar to findings in this cohort (22,23). Diabetes/Insulin resistance Insulin resistance and hyperinsulinemia are associated with increased EC risk independently of obesity (24–26). Insulin may act directly on endometrial tissue as a mitogenic and antiapoptotic growth factor (27,28). Insulin can also increase IGF-I bioactivity, and increase the bioavailability of free estrogens and androgens through downregulation of SHBG and upregulation of ovarian sex steroid production (25,29). Amongst the UK Biobank cohort, a self-reported diagnosis of diabetes did not remain a significant independent predictor of EC after multivariate regression. However, when we assessed individual biochemical parameters in keeping with a diagnosis of diabetes such as glucose levels > 5.6 mmol/L and HbA1c >48 mmol/L, these were both significant independent risk factors for EC. These findings are in keeping with a population-based prospective cohort study where women with type 2 diabetes had a 2-fold higher risk of EC development (30). Whilst there are a number of studies which claim the independent contribution of diabetes as a risk factor for EC, one large population study conducted by Attner et al. found positive associations but no independent effect after controlling for confounding factors such as BMI (31). A potential explanation for the discrepancy in findings is that glycaemic treatment such as metformin may modify risk. Whilst in-vitro studies have been promising, larger trials and cohort studies have failed to find a significant benefit. A recent Cochrane review which included only two small randomised controlled trials did not find sufficient evidence to confirm whether metformin lowers risk of EC development (32). More recently, randomised trials such as the feMME trial have also found no significant benefit (33). Similarly to self-reported diabetes, the reported use of cholesterol lowering medication was not associated with EC, however, having hypertryglyceridaemia (>1.7 mmol/L) and or low HDL <1.3 mmol/l was. This is in keeping with the published literature in which a number of epidemiological studies indicated that abnormal lipid metabolism is strongly associated with EC. In a cohort study of 13,061 patients, dyslipidemia was an independent risk factor for the development of EC, and it significantly increased the incidence of EC. The risk of EC increased by 1.34-fold in patients with elevated triglycerides (TGs) > 1.7mmol/l and 1.65-fold in patients with reduced HDL (HDL < 0.56 mmol/L) (34). Another study found that dyslipidemia significantly increased the risk of EC, with a summary OR of 1.62 (95%CI: 1.32–1.99) for hypercholesterolemia (≥ 5.55 mmol/L), 1.25 (95%CI: 1.05–1.49) for hypertriglyceridemia (≥ 1.71 mmol/L), 1.45 (95%CI: 1.16–1.79) for high LDL-C level (≥ 5.50 mmol/L), and 2.40 (95%CI: 1.90–3.03) for low HDL-C level (≤ 1.10 mmol/L) (35). In the present study, hypertriglyceridemia was the most important marker of dyslipidaemia and EC risk, followed by HDL level. LDL levels were non-significantly associated with EC risk (data not shown). The fact that those individuals using cholesterol lowering medication was not a significant independent contributor to EC risk after multi-variate analysis, but having biochemical hyperlipidaemia was raises the possibility that treatment is associated with risk recuction. The association between statin use and EC risk has been the subject of much debate with a number of conflicting outcomes and wide heterogeneity between studies (36–38). Hypertension Soler et al. found significant association between EC and hypertension. The 2 factors (BMI and hypertension) appeared to have a synergistic effect on the risk of EC and produced appreciably elevated risk (almost 5-fold increase) in women with both, high BMI and hypertension (39). After multivariate analysis, the estimated effect of hypertension alone remained significant (OR=1.60, 95%CI: 1.30-1.90) for EC, which is very similar to our findings. This is consistent with other estimates in the literature with the OR ranging from 1.20 to 2.10 (40–42). A meta-analysis more recently evaluated the effect of hypertension and other confounders such as exercise levels and BMI and concluded that whilst studies controlling for confounding had lower overall relative risk, hypertension remains a significant factor after exclusion of other potential confounding factors (43). The mechanisms behind this remain unclear, with Hamet suggesting that hypertension may increase the cancer risk by blocking and subsequently modifying apoptosis, thereby affecting the regulation of cell turnover but very little published literature looking at the distinct biological mechanisms implicated in EC since (44). There have been multiple studies linking MetS with cancer. A recent 2022 analysis examined the relationship between MetS and the risk of thirteen IARC obesity-associated cancers (8). It pooled the results of 63 studies conducted in the United States, Europe, Asia, Canada, Israel and Australia of the cancer risk in adults (all age groups > 18 years age) without MetS versus with MetS. The effect estimates for the risk of cancer (adjusted for alcohol consumption or cigarette smoking in 68% of studies) were: 1.13 to 6.73 for breast; 1.14 to 2.61 for colorectal; 1.18 to 2.50 for gastric; 1.37 to 2.20 for endometrial; 1.59 to 2.13 for pancreas; 2.13 to 5.06 for hepatocellular carcinoma (8). The proposed mechanisms of MetS related carcinogenesis in EC centre around endogenous sex steroid production, insulin resistance and inflammation. The ‘unopposed oestrogen’ theory suggests that increases in the synthesis of endogenous estrogen by adipose tissue, coupled with decreased SHBG production by the liver, leads to increased plasma levels of bioavailable estrogen, increasing the likelihood of malignant cell development in the postmenopausal female no longer producing the counterregulatory hormone progesterone. Several case– control studies have reported increased total and bioavailable estrogen levels and decreased plasma SHBG level in postmenopausal women with EC compared to controls (25,45–47). This was evident in the UK Biobank cohort and in the sub-group analysis was lower SHBG levels in the pre and post-menopausal EC groups as compared to the control group. Interestingly, in the UK Biobank cohort, oestradiol levels were marginally higher in the control group as compared to the EC group, and this remained the case in both pre and post-menopausal subgroups. However, higher free oestradiol was seen in both the pre and post-menopausal EC group. In addition to that, low serum HDL-C levels and a high LDL-C/HDL-C ratio have been found to predict increased bioavailable estradiol levels more strongly among overweight than normal-weight women (48). Thus, overweight women may experience a heightened physiologic response to the presence of other metabolic factors compared with leaner women. Insulin like growth factor-1 (IGF-1) levels were lower than seen in the control group. Lower IGF-1 levels may correlate inversely with adiposity and T2DM (49) Interestingly, both CRP and GGT were significantly higher in patients who developed EC when compared with controls (20.0 vs 17.9 U/L in pre-menopausal group and 25.9 vs 22.2U/L for GGT, respectively and 1.7 vs. 1.0 mg/L in pre-menopausal group and 2.3 vs 1.4 mg/L for CRP, respectively). Recent studies demonstrated that GGT is involved in the pathogenesis of cardiovascular disease and MetS through promotion of oxidative stress and pro-inflammatory response(50,51). A previous study on coronary artery disease suggested that increased levels of serum GGT may serve as early marker of oxidative stress and predict increased levels of CRP as well as development of diabetes and hypertension(52). When assessed by univariate analysis, both markers were independent and significantly associated with EC and these associations held even in the pre-menopausal population (OR=1.24, 95%CI: 1.11-1.39, P<0.0001). Looking at this UK Biobank cohort, which is amongst the largest study to have looked at the MetS associations with EC risk in a British population, there were 8,852 women who fulfilled the WHO criteria of MetS and 49,071 who fulfilled IDF criteria of a diagnosis of MetS. Regardless of the diagnostic criteria, the risk of EC was around 2.5-fold higher when compared with non-metabolic syndrome controls. This is very similar to the risk of having a BMI>30 in univariate analysis, both in the pre and post-menopausal population. Strikingly, 24% of the entire cohort included in this analysis met the criteria for a diagnosis of MetS. Amongst the control group this was also 24% but amongst the cohort who went on to develop EC the prevalence was 44%. It is also high probabilty that a large proportion of the UK Biobank participants met the biochemical criteria for elements of METs diagnosis but hadn’t been clinically diagnosed in the primary care setting. Supplementary table 5 details the disparity between diagnosed and treated cases of hypertension, hypercholesterolaemia and diabetes against the number of cases diagnosable biochemically. It is possible that recognition and treatment of MetS would lower the risk of EC and other obesity driven cancers. Given that national schemes such as the National Diabetes prevention program already exist and are freely available to those recognised as pre-diabetic, early recognition is likely to be beneficial (53). Study strengths and limitations and future work One of the major strengths of this study are the large size and the fact that it is the only study using the UK Biobank to assess the association between MetS and EC individually. The prospective design minimizes selection bias arising from inappropriate selection of control subjects. No participant developed cancer during the assessment period, thus minimising the risk of reverse causality. The risk factors identified in this study remained true risk factors regardless of menopausal status offering an opportunity for risk reduction strategies prior to the average age of onset of disease in the seventh decade. One of the limitations is that the UK Biobank include the use of self-reported data. In this study, there was a disparity in risk amongst those self-reporting as diabetic, hypertensive or hypercholesterolaemic and the risk demonstrated by assessment of serum biochemistry. The exact reason for this is unclear. One important explanation is that treatment of such conditions may reduce risk of EC development. There is growing evidence to suggest aspirin may be effective in risk reduction of EC, particularly for those with genetic predisposition to EC such as Lynch Syndrome (54). Similarly, the evidence for the benefits of metformin for risk reduction is unclear. However, the other plausible explanation is that misreporting results in confounding and misclassification of these variables. Likewise, as with similar sorts of epidemiological datasets using self-reporting there is always potential for recall and selection bias. This is in part why biochemical variables were assessed against self-reported ones. Similarly, to avoid further exacerbating this risk, multiple imputation was avoided for cases of missing data with all missing data excluded from analysis. High quality studies which directly assess the impact of medications such as glycaemic agents, antihypertensives and lipid lowering medications are needed to better understand the effects they have on EC pathogenesis. A further limitation of the study was the inability to determine the association of PCOS and EC risk. As in the study of Hutt et al we know from meta-analysis that PCOS is a major contributing risk factor to EC development and is closely interlinked with insulin resistance (12). Unfortunately, in the UK Biobank very few patients were reported as having had a diagnosis of PCOS. It is likely this is due to their age at the time of UK Biobank study and the fact that PCOS is diagnosed in the first few decades of life. Conclusion This study supports the hypothesis that MetS and it’s individual features significantly increase the odds of developing EC and thus in effect may be used as a predictor of EC development, even in the pre-menopausal period. Despite published data showing strong associations between components of MetS and EC risk, there are as yet no nationally recommended screening systems or risk reduction strategies in place. MetS was diagnosable in up to 24% of this entire female cohort, but strikingly in 44% of patients who went on to develop EC. This is likely due to the huge overlap between the two conditions. In this cohort, 55 % of those that had a BMI of > 30 met the criteria for diagnosis of MetS and in those with a BMI of >40 this rose to 68%. Screening those with obesity for MetS may be a strategy for screening those at higher risk of EC over that and above the risk of obesity alone, especially in the premenopausal period. Furthermore, medicating the conditions associated with MetS may modify risk of EC as well as potentially a number of other obesity driven cancers and thus more attention should be placed on primary prevention strategies. Declarations Authors' contributions RK – Study conception, data analysis and manuscript writing GM – Bioinformatics guidance and statistical advice AP – Bioinformatics guidance and statistical advice RP – Bioinformatics guidance and statistical advice EK – Study conception, manuscript editing JC – Study conception, manuscript editing Ethics approval and consent to participate The UK Biobank was approved by the Northwest Multi-Centre Research Ethics Committee (16/NW/0274), Patient Information Advisory Group (England and Wales) and the Community Health Index Advisory Group (Scotland). All participants provided written informed consent. This study is performed using the UKB data under application number 60549. 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Available from: /pmc/articles/PMC7294238/ 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-4812894","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":349438390,"identity":"4c3663c1-807a-47f6-9138-a9ed77a8534e","order_by":0,"name":"Rebecca Karkia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYBAC+wYeCEOCgYGZIYHBhsGAkBaDA6ha0qBaEojVwsBwmAgtx3sPPvhRY8cg2X72scGDmvN55vwH2B58/IHHLz3nkg17jiUzSPOkGyckHLtdbDkjgd1wBh5b7CRyzKQZG5gZ5BjSmA8ksN1O3HCDgU2aB48WY/k35r8ZG+oZ5PifAbX8O5e44fwBNuk/eLQYzuAxY2ZsOMwgLZHGnJDYdiBxA9AuabzeP5OXLNlz7DiP5IxnzAaJfclAhyW2Sfak4dFy/OzBDz9qquUkzqcxS/74Zgd02OFjEj9scGuBAR4kNmMDYfWjYBSMglEwCvACAO/fUU0fWCjhAAAAAElFTkSuQmCC","orcid":"","institution":"Brunel University London","correspondingAuthor":true,"prefix":"","firstName":"Rebecca","middleName":"","lastName":"Karkia","suffix":""},{"id":349438391,"identity":"48e9a00e-4eec-4a67-ae2b-3e3abc934747","order_by":1,"name":"Gideon Maccarthy","email":"","orcid":"","institution":"Brunel University London","correspondingAuthor":false,"prefix":"","firstName":"Gideon","middleName":"","lastName":"Maccarthy","suffix":""},{"id":349438392,"identity":"1f96bc58-8084-4f8e-aac3-4ea74cdead3f","order_by":2,"name":"Annette Payne","email":"","orcid":"","institution":"Brunel University London","correspondingAuthor":false,"prefix":"","firstName":"Annette","middleName":"","lastName":"Payne","suffix":""},{"id":349438393,"identity":"6f585b6f-ab44-4247-bfb9-e24709699f13","order_by":3,"name":"Emmanouil Karteris","email":"","orcid":"","institution":"Brunel University London","correspondingAuthor":false,"prefix":"","firstName":"Emmanouil","middleName":"","lastName":"Karteris","suffix":""},{"id":349438394,"identity":"119b221e-1b7f-44d1-a7f5-6e4443ab3188","order_by":4,"name":"Raha Pazoki","email":"","orcid":"","institution":"Brunel University London","correspondingAuthor":false,"prefix":"","firstName":"Raha","middleName":"","lastName":"Pazoki","suffix":""},{"id":349438395,"identity":"45fcc66e-040d-415f-824b-5810e9277ed5","order_by":5,"name":"Jayanta Chatterjee","email":"","orcid":"","institution":"Brunel University London","correspondingAuthor":false,"prefix":"","firstName":"Jayanta","middleName":"","lastName":"Chatterjee","suffix":""},{"id":349438396,"identity":"31f3bfc3-08f3-4f2c-933b-838bf6f42572","order_by":6,"name":"Rebecca Karkia","email":"","orcid":"","institution":"Brunel University London","correspondingAuthor":false,"prefix":"","firstName":"Rebecca","middleName":"","lastName":"Karkia","suffix":""}],"badges":[],"createdAt":"2024-07-27 11:47:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4812894/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4812894/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64713618,"identity":"e3435efe-4902-4ec2-a3db-17b3cb850246","added_by":"auto","created_at":"2024-09-18 02:16:07","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":124531,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of inclusions into study from UK biobank cohort.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4812894/v1/696d853031ccb09e142b1403.jpeg"},{"id":66509236,"identity":"88e3031f-ec40-4c4f-96e4-69b1f4ab4567","added_by":"auto","created_at":"2024-10-13 20:31:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":906323,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4812894/v1/79ebca81-4346-4742-a866-4edc6956c6b5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metabolic syndrome modulates risk of endometrial cancer regardless of menopause status- A UK BIOBANK Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eExcess weight and adiposity are associated with a number of metabolic and hormonal dysregulations that include increased peripheral aromatisation of androgens into oestrogens within white adipose tissue, as well as hyperinsulinemia, hyperglycemia, and chronic inflammation (Dossus and Kaaks, 2008). Clinically, the effects of these obesity related changes are regarded as metabolic syndrome (MetS). Given that MetsS represents a cluster of disorders, there are a number of different definitions but the most commonly employed definitions are the International Diabetes Federation (IDF) criteria (2005), the National Cholesterol Education Program (NCEP) ATP3 2005 and the World health Organisation (1999) definitions (Table 1)(Alberti et al., 2006; Consultation, 1999; Grundy et al., 2005).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eFemale definitions of MetS according to the International Diabetes Federation (IDF), National\u0026nbsp;Cholesterol\u0026nbsp;Education\u0026nbsp;Program (NCEP) and World Health Organisation (WHO)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964664310954063%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.784452296819786%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.664310954063604%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlucose (mmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.84452296819788%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(mmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.137809187279153%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriglycerides\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(mmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.604240282685513%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood pressure\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(mm/Hg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964664310954063%\" valign=\"top\"\u003e\n \u003cp\u003eIDF\u003c/p\u003e\n \u003cp\u003e(Waist+ Any 2 others)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.784452296819786%\"\u003e\n \u003cp\u003e\u0026gt;80cm (Europeans)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.664310954063604%\"\u003e\n \u003cp\u003e\u0026gt;5.6 or diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.84452296819788%\"\u003e\n \u003cp\u003e\u0026lt;1.3 or medicated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.137809187279153%\"\u003e\n \u003cp\u003e\u0026gt;1.7 or medicated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.604240282685513%\"\u003e\n \u003cp\u003e\u0026gt;130/85 or medicated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964664310954063%\" valign=\"top\"\u003e\n \u003cp\u003eNCEP (Any 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.784452296819786%\"\u003e\n \u003cp\u003e\u0026gt;88cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.664310954063604%\"\u003e\n \u003cp\u003e5.6 or diabetes medicated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.84452296819788%\"\u003e\n \u003cp\u003e\u0026lt;1.3 or medicated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.137809187279153%\"\u003e\n \u003cp\u003e\u0026gt;1.7 or medicated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.604240282685513%\"\u003e\n \u003cp\u003e\u0026gt;130/85 or medicated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.964664310954063%\" valign=\"top\"\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003cp\u003e(insulin resistance / glucose + any 2 others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.784452296819786%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eWaist-Hip ratio\u0026gt;0.85 or BMI\u0026gt;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.664310954063604%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;6.1\u0026nbsp;mmol/L, 2\u0026nbsp;h glucose\u0026gt;\u0026thinsp;7.8\u0026nbsp;mmol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.84452296819788%\"\u003e\n \u003cp\u003e\u0026lt;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.137809187279153%\"\u003e\n \u003cp\u003e\u0026gt;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.604240282685513%\"\u003e\n \u003cp\u003e\u0026gt;140/90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe pathogenesis of MetS encompasses multiple genetic and acquired entities that fall under the umbrella of insulin resistance and chronic low-grade inflammation. If left untreated, MetS is significantly associated with an increased risk of developing diabetes and its vascular complications, ischaemic heart disease and cerebrovascular disease (5,6). It is now also evident that MetS is associated with a number of cancers including colorectal cancer, postmenopausal breast, renal and uterine cancer (5,7,8). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEndometrial Cancer (EC) appears to be one of the cancers most associated with MetS development. Up to one third of endometrial cancers are thought to be preventable and secondary to obesity (9). In the UK, there is around 10,000 new cases of EC diagnosed annually (10). In contrast, there are 200,000 new cases of diabetes mellitus diagnosed annually and latest figures from the National Health Service (NHS) digital suggest that up to 26% of adults in the UK are now obese, with a further 38% falling in the overweight category (11). Despite this, relatively few who have the top risk factors for EC development such as\u0026nbsp;obesity\u0026nbsp;or \u0026nbsp;diabetes\u0026nbsp;will go on to develop EC and as such strategies for risk prediction need to be more nuanced in targeting those at highest risk (12). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs a direct consequence of the rise in diagnosis of EC, the mortality from EC is also rising, despite improvements in overall survival (OS). There are many modifiable risk factors implicated in EC pathogenesis, in fact, a \u0026nbsp;meta-analysis of the top risk factors in EC development found that BMI is the largest predictor, with \u0026nbsp;diabetes mellitus and polycystic ovarian syndrome (PCOS) other top risk factors (12). Oral contraceptive use and continuous hormone replacement therapy (HRT) are also known to be risk reducing factors (12). Despite this, at present, there are no nationally recommended primary prevention strategies in place for those at highest risk of EC. \u0026nbsp;Similarly, despite published data showing strong associations between components of MetS and EC risk, there are as yet no nationally recommended screening systems or risk reduction strategies in place. Given the high rates of obesity in the UK and the drive towards precision medicine, it would be beneficial to understand if metabolic syndrome or it\u0026rsquo;s component features can be used to identify those at highest risk of EC, over and above the risk that BMI adds and also importantly how these factors are altered by menopausal status. \u0026nbsp;We therefore sought to investigate the association between EC risk and individual features of MetS, including serum biochemistry variables that pertain to, inflammation, insulin resistance and hyperlipidaemia using one of the largest UK based prospectively collected datasets, the UK Biobank. Secondary outcomes will be to conduct subgroup analysis of pre and post-menopausal females to determine whether pre-menopausal women with features of MetS have a similar risk of EC development as their post-menopausal counterparts. \u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy Population\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe UK Biobank is a major national and international health resource, created to improve the prevention, diagnosis and treatment of serious and life-threatening illnesses, including cancer (13). The female cohort consists of individuals aged between 39 and 71 years included in the database between March 2006 and October 2010. Detailed ethnicity, health, demographic and anthropometry was collected as well blood samples. Cancer diagnoses were ascertained through the International Classification of Diseases (ICD) ICD-10 records recorded by NHS England in England and Wales and the NHS Central Register, National Records of Scotland. \u0026nbsp;The latest cancer registry record linked to UK Biobank data was the 13 May, 2022. Inpatient data acquired through hospital episode statistics (HES), the Scottish morbidity record (SMR) or the patient episode database for Wales was available and complete up until October, August and May 2022, respectively. Full details can be found at\u003ca href=\"https://www.ukbiobank.ac.uk/\"\u003e\u0026nbsp;\u003c/a\u003ehttps://www.ukbiobank.ac.uk\u003ca href=\"https://www.ukbiobank.ac.uk/\"\u003e.\u003c/a\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Northwest Multi-Centre Research Ethics Committee (16/NW/0274), Patient Information Advisory Group (England and Wales) and the Community Health Index Advisory Group (Scotland). All participants provided written informed consent. The study was conducted in accordance with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIdentification of EC cases was performed using ICD-10, of which there were 9 follow-up time point records during the 2006-2010 recruitment period. Only incident cancer cases diagnosed after the 9\u003csup\u003eth\u003c/sup\u003e follow- up time point were included in the study and no patients with an active diagnosis of cancer or women who had previously undergone a hysterectomy were included into either control or cancer group when they attended the UK Biobank centre, to minimize the risk of reverse causality. For all other data, only data collected at the initial visit was analysed. Self-reported data was used for menopausal status. All missing data was excluded from statistical analysis. From a starting number of 273 298 female participants, there were 203, 644 remaining for analysis after exclusion of those with a previous cancer diagnosis, those with previous hysterectomy or those withdrawing from the UK Biobank study (Figure A).\u003c/p\u003e\n\u003ch2\u003eStatistical Analysis\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eContinuous and categorical variables in the UK Biobank dataset were compared using a Mann-Whitney U or Chi-square test, respectively. Univariate and multivariate backwards stepwise logistic regression analyses were used to determine which of the baseline characteristics, medical co-morbidities and biochemistry studies had a significant contribution in prediction of EC. Subgroup analysis was undertaken for pre- and post-menopausal female participants. The WHO 1999 definition and the IDF 2005 definition were used to define the number of participants meeting the criteria for a diagnosis of MetS (2). Subsequently the risk of EC development for any woman who met the diagnostic criteria for MetS was ascertained. In line with the most common MetS definitions, further regression analysis was conducted to ascertain which specific components of MetS are the strongest predictors of EC development. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFollowing individual component analysis of all the risk factors pertaining to a MetS, subsequent regression analysis was undertaken to assess the overall risk of EC development in females who met the criteria for MetS diagnosis. Given the similarity of the NCEP ATP 3 definition and the IDF definition, only the IDF and the WHO definitions were used for regression analysis. As detailed in table 1, individuals can be diagnosed with MetS based on fulfilling different criteria, and as such different models were created to assess risk of EC development. The effect size of characteristics associated with EC development was expressed as odds ratio (OR) with 95% confidence interval (CI). Significance was assumed at 5% and a posthoc Bonferroni correction was used to adjust for multiple comparisons when necessary. R statistical packages were used in statistical analyses with a p value \u0026lt;0.05 considered as significant. \u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eBasic cohort characteristics\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe baseline characteristics of cases and controls are described in Table 2. The control group was formed of 202,012 females who at the time of baseline investigations and subsequent visits had no cancer diagnosis and no previous hysterectomy. The EC group was formed of 1632 individuals who following their first assessment went on to develop EC. The median time to EC diagnosis following first assessment at the UK biobank was 2022 days (5.5 years). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs can be seen from Table 2, there were 190, 058 (94.1%) that reported being of white ethnicity in the control group and 1538 (94.2%) in the EC group, \u0026nbsp;with no significant difference seen in any of the ethnic groups represented. Those females who developed EC following recruitment into the study tended to be older at recruitment with a median age of 60.0 as compared to controls with a median age of 56.0 (p\u0026lt;0.0001). Females who went on to develop EC tended to be taller, heavier, with higher waist circumference and BMI. There was no significant difference in age at menarche (a known contributor to EC risk) however women who went on to develop EC tended towards later menopause with the median age being 52.0 versus 50.0 years of age, respectively. Nulliparity, was more common amongst females who went on to develop EC (24.0% of the EC group were nulliparous versus 20.0% in the control group, respectively, p\u0026lt;0.0001. The rate of previous oral contraceptive pill use was significantly lower in the EC group as compared to the control group (69.6 % versus 82.0%, respectively, p\u0026lt;0.0001). Similarly, the EC group had a much lower rate of hormone replacement therapy (HRT) use, with 64.0% reporting being never users versus 30.4% in the control group (p\u0026lt;0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2.\u0026nbsp;Baseline characteristics table of females in UK Biobank. NS: not significant\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"609\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003cp\u003en=202,012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003cp\u003en =1,632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003eP-Value (Mann-U)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003eEthnicity n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003e1- White\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e190,058 (94.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e1538 (94.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003e2- Mixed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e1,468 (0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e9 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003e3- Asian/Asian British\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e3,644 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e36 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003e4- Black/Black British\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e3,450 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e19 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003e5- East Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e815 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e5 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003e6- Other\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e2,008 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e19 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003eNA \u0026ndash; Not answered n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e569 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e6 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003eIndex of deprivation (England) median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e12.9 (7.4-23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e14.0 (8.0-23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003eAge at recruitment median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e56.0 (39.1-71.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e60.0 (54.0-64.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003eHeight (cm) median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e163.0 (158.0-167.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e162.0 (158.0-166.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003eWeight (kg) median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e70.9 (61.2-77.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e76.5 (66.9-90.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003eWaist Circumference (cm) median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e82.0 (75.0-91.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e90.0 (81.0-102.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e) median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e25.9 (23.2-29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e29.1 (25.3-34.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003eAge at Menarche median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e13.0 (12.0-13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e13.0 (11.0-14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003ePre-Menopausal, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e61,130 (30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e286 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003ePost-Menopause, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e130,215 (64.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e1281 (78.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003eAge at Menopause median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e50.0 (47.0-53.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e52.0 (49.0-54.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003eNulliparous n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e40,361 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e421 (25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003eOral contraceptive pill (OCP) used n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e165,638 (82.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e1136 (69.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003eOCP never used n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e35,872 (17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e492 (30.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003eHormone replacement therapy (HRT) used n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e139,950 (69.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e580 (35.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003eHRT never used n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e61,437 (30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e1044 (64.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003eNever smoked n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e121,875 (60.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e1076 (65.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003eEx-smoker n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e61,646 (30.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e466 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003eCurrent smoker n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e17,842 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e84 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003eDiabetes Mellitus (DM)- any n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e6,600 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e126 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003eTaking insulin currently n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e273 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e3 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003eGestational DM only n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e782 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e4 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003eTaking cholesterol lowering medication n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e21,450 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e309 (19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003eAnti-hypertensives \u0026nbsp;n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e18,305 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e253 (15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003ePolycystic Ovarian Syndrome n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e312 (0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e4 (0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.141215106732346%\"\u003e\n \u003cp\u003eAspirin use n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.032840722495894%\" style=\"width: 20.0329%;\"\u003e\n \u003cp\u003e13,450 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.361247947454842%\" style=\"width: 20.3612%;\"\u003e\n \u003cp\u003e172 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.464696223316913%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003eBiochemical comparison\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eTable 3 displays the serum biochemistry taken at the time of the primary assessment. The analysis showed that alkaline phosphatase (ALP), aspartate aminotransferase (AST), gammaglutamyl transferase (GGT), C-reactive protein (CRP), low density lipoprotein (LDL), Lipoprotein A, and triglycerides were all significantly increased in the EC group when compared to controls (Table 3). Inversely, albumin levels, high density lipoprotein (HDL), Insulin-like growth factor 1 (IGF-1) and sex hormone binding globulin (SHBG) levels were significantly lower in the EC group. \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eRegression analyses\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eUnivariate and multivariate analyses was carried out to ascertain the strength of independent contributors of MetS as well as the overall effect of diagnosis of MetS on EC risk (Table 4, Supplementary table 6). BMI, waist circumference, hip circumference, arterial blood pressure (both systolic and diastolic), diabetes, and use of antihypertensive or cholesterol lowering medication were all significant independent predictors of EC risk. The risk of developing EC in those that were overweight was significantly increased, nearly three-fold higher in those with a BMI\u003cu\u003e\u0026gt;\u003c/u\u003e30kg/m\u003csup\u003e2\u003c/sup\u003e and 9-fold higher in those with a BMI\u003cu\u003e\u0026gt;\u003c/u\u003e40kg/m\u003csup\u003e2\u003c/sup\u003e. The same trends were observed with waist circumference. In those with a waist circumference \u003cu\u003e\u0026gt;\u003c/u\u003e80 cm, the risk of EC development was also increased. In those with a waist circumference \u003cu\u003e\u0026gt;\u003c/u\u003e88cm the risk of EC doubled and in those with a waist circumference \u003cu\u003e\u0026gt;\u003c/u\u003e100cm risk was 5-fold higher. East Asian ethnic origin, hormonal contraceptive use, and smoking were all significantly associated with lower risk of EC development. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSubsequent multivariate analysis showed a strong association of EC with high BMI (OR=1.69; 95%CI: 1.31-2.19 for BMI 30.0-39.9kg/m2 and OR=3.04 95%CI: 2.09-4.44 for BMI \u0026gt;40.0kg/m2, respectively), waist circumference above 80cm (OR=1.21 95%CI: 1.01-1.44), hip circumference above 131cm (OR=1.69 95%CI: 1.18-2.42), increased systolic blood pressure (OR=1.41; 95%CI: 1.19-1.68 for BP 140 \u0026ndash; 179mmHg and OR=1.72 95%CI: 1.25-2.34 for BP \u0026gt; 180mmHg, respectively). Use of hormonal contraception remained significant as a protective factor, reducing the likelihood of EC development 11.1 times (OR=0.09, 95%CI: 0.02-0.29). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLooking specifically at features diagnostic for MetS, univariate analysis revealed that waist circumference \u0026gt;94cm and BMI \u0026gt;30 kg/m\u003csup\u003e2\u003c/sup\u003e were equally associated with EC development (OR=2.85, p\u0026lt;0.0001). The next strongest predictor was waist circumference \u0026gt; 88cm and triglycerides \u0026gt;1.7mmol/L (OR=2.66, 95%CI: 2.41-.93 and OR=1.96, 95% CI: 1.77-2.17, respectively). All factors associated with a diagnosis of MetS remained significant after multivariate regression. HbA1c was also included in the analysis as although not formally a feature in the diagnostic criteria, it is widely accepted that an HbA1c value above 48 mmol/mol is diagnostic of diabetes. This was a stronger independent predictor of EC development than fasting glucose \u0026gt; 5.6 mmol/L. Additional information can be found in supplementary tables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3.\u0026nbsp;Baseline blood biochemistry results of females in UK Biobank\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"519\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.53846153846154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003cp\u003en=202,012\u003c/p\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.423076923076923%\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003cp\u003en =1,632\u003c/p\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.961538461538462%\"\u003e\n \u003cp\u003eP-Value (Mann-U )\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.53846153846154%\"\u003e\n \u003cp\u003eLiver enzymes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.38461538461539%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.53846153846154%\"\u003e\n \u003cp\u003eAlbumin (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e45.0 (43.3-46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.423076923076923%\"\u003e\n \u003cp\u003e44.4 (42.8-46.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.961538461538462%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.53846153846154%\"\u003e\n \u003cp\u003eAlkaline phosphatase (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e80.2 (65.9-96.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.423076923076923%\"\u003e\n \u003cp\u003e88.7(71.7-102.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.961538461538462%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.53846153846154%\"\u003e\n \u003cp\u003eApolipoprotein A (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e1.62 (1.5-1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.423076923076923%\"\u003e\n \u003cp\u003e1.55 (1.4-1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.961538461538462%\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.53846153846154%\"\u003e\n \u003cp\u003eApolipoprotein B (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e1.0 (0.9-1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.423076923076923%\"\u003e\n \u003cp\u003e1.0 (0.9-1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.961538461538462%\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.53846153846154%\"\u003e\n \u003cp\u003eAsp Aminotransferase (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e22.8 (19.8-26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.423076923076923%\"\u003e\n \u003cp\u003e23.4 (20.2-27.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.961538461538462%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.53846153846154%\"\u003e\n \u003cp\u003eGamma GT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e20.8 (15.7-29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.423076923076923%\"\u003e\n \u003cp\u003e24.9 (18.2-37.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.961538461538462%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.53846153846154%\"\u003e\n \u003cp\u003eCRP\u0026nbsp;(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e1.3 (0.6-2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.423076923076923%\"\u003e\n \u003cp\u003e2.2 (0.9-4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.961538461538462%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.53846153846154%\"\u003e\n \u003cp\u003eGlucose metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.423076923076923%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.961538461538462%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.53846153846154%\"\u003e\n \u003cp\u003eGlucose (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e4.9 (4.6-5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.423076923076923%\"\u003e\n \u003cp\u003e5.0 (4.7-5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.961538461538462%\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.53846153846154%\"\u003e\n \u003cp\u003eHbA1c (mmol/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e35.0 (32.5-37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.423076923076923%\"\u003e\n \u003cp\u003e36.2 (33.7-39.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.961538461538462%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.53846153846154%\"\u003e\n \u003cp\u003eIGF-1 (nmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e21.3 (17.5-24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.423076923076923%\"\u003e\n \u003cp\u003e19.9 (16.1-23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.961538461538462%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.53846153846154%\"\u003e\n \u003cp\u003eFatty acids metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.423076923076923%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.961538461538462%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.53846153846154%\"\u003e\n \u003cp\u003eTotal cholesterol (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e5.8 (5.0-6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.423076923076923%\"\u003e\n \u003cp\u003e5.8 (5.1-6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.961538461538462%\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.53846153846154%\"\u003e\n \u003cp\u003eHDL (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e1.6 (1.3-1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.423076923076923%\"\u003e\n \u003cp\u003e1.4 (1.2-1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.961538461538462%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.53846153846154%\"\u003e\n \u003cp\u003eLDL (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e3.5 (3.0-4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.423076923076923%\"\u003e\n \u003cp\u003e3.7 (3.0-4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.961538461538462%\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.53846153846154%\"\u003e\n \u003cp\u003eLipoprotein A (nmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e22.1 (9.9-62.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.423076923076923%\"\u003e\n \u003cp\u003e24.9 (10.7-62.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.961538461538462%\"\u003e\n \u003cp\u003e0.04888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.53846153846154%\"\u003e\n \u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e1.3 (0.9-1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.423076923076923%\"\u003e\n \u003cp\u003e1.6 (1.1-2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.961538461538462%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.53846153846154%\"\u003e\n \u003cp\u003eSex hormones\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.38461538461539%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.53846153846154%\"\u003e\n \u003cp\u003eOestradiol (pmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e404.6 (270.1-645.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.423076923076923%\"\u003e\n \u003cp\u003e373.4 (248.7-593.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.961538461538462%\"\u003e\n \u003cp\u003e0.03753\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.53846153846154%\"\u003e\n \u003cp\u003eSHBG (nmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e57.0 (40.6-77.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.423076923076923%\"\u003e\n \u003cp\u003e44.4 (31.3-60.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.961538461538462%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.53846153846154%\"\u003e\n \u003cp\u003eFree oestradiol index (pmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.423076923076923%\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.961538461538462%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.53846153846154%\"\u003e\n \u003cp\u003eTestosterone (nmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e1.0 (0.7-1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.423076923076923%\"\u003e\n \u003cp\u003e1.1 (0.8-1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.961538461538462%\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.53846153846154%\"\u003e\n \u003cp\u003eFree androgen index (nmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.423076923076923%\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.961538461538462%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4.\u0026nbsp;Regression analysis of risk factors associated with EC development\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"560\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.607142857142858%\" colspan=\"2\"\u003e\n \u003cp\u003eUnivariate Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.535714285714285%\" colspan=\"2\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eEthnic Background\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eWhite\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eMixed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eAsian/Asian British\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eBlack/Black British\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eEast Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eOther\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eBody Mass Index (Kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.142857142857146%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eBMI \u0026lt;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eBMI 18.5-24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eBMI 25.0-29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e1.69 (1.32-1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e1.26 (1.04-1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eBMI 30-39.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e3.17 (2.37-1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e1.69 (1.31-2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eBMI \u0026gt;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e9.06 (6.17-8.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e3.04 (2.09-4.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eWaist circumference (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eWaist circumference \u0026lt; 80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eWaist circumference 80-80\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e1.58 (1.36-1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e1.21 (1.01-1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eWaist circumference 88-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e2.34 (2.04-2.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e1.39 (1.13-1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eWaist circumference \u0026gt; 100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e4.96 (4.33-4.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e1.82 (1.40-2.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eHip Circumference (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eHip circumference \u0026lt;100cm\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eHip circumference 100-110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e1.70 (1.50-1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eHip circumference 111-130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e2.82 (2.47-3.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eHip circumference \u0026gt;131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e8.60 (7.11-10.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e1.69 (1.18-2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eWaist Hip Ratio\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003e\u0026lt;0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003e0.81-0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e1.46 (1.28-1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003e\u0026gt;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e2.10 (1.87-2.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026lt;0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003ePCOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eSystolic Blood Pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003e\u0026lt;120\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003e120-129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e0.0369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003e130-139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003e140-179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e1.41 (1.19-1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003e\u0026gt;180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e1.72 (1.25-2.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eDiastolic Blood Pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003e\u0026lt;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003e80-89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e1.21 (1.07-1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e0.0020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e0.85 (0.74-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003e90-120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e1.57 (1.39-1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e0.84 (0.72-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003e\u0026gt;120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e4.90 (1.50-11.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eDiabetes diagnosed by Physician\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e2.48 (2.06-2.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eInsulin use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e1.37 (0.33-3.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eCholesterol lowering medication\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e2.17 (1.90-2.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eAntihypertensive medication\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e2.08 (1.81-2.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eHRT\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e0.65 (0.33-1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eCOCP/POP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e0.10 (0.03-0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e0.09 (0.02-0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eEver smoked\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e0.85 (0.76-0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e0.0053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.857142857142854%\"\u003e\n \u003cp\u003eCurrent smoker\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e0.53 (0.42-0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.928571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.607142857142858%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003cu\u003eSubgroup analysis\u0026nbsp;\u003c/u\u003e\u003c/h2\u003e\n\u003cp\u003eSub-analysis of pre-and post-menopausal females was also undertaken. In the pre and postmenopausal subgroup, the waist circumference, BMI and index of deprivation were significantly higher in the EC group as compared to controls (Supplementary table 1 \u0026amp; 2). The protective effect of contraception remained significant even after exclusion of the premenopausal females suggesting a sustained protective effect of contraception beyond the years at which it is required for contraception. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe subgroup analysis of serum biochemistry showed that the liver enzymes, CRP, Lipoprotein A and triglycerides were significantly higher in the EC group regardless of the menopausal status when compared with controls (Table 5). Interestingly, LDL levels were significantly higher in the EC group, but the effect was only present in pre-menopause (3.4, 95%CI: 2.9-5.9 vs 3.2 95%CI: 2.8-3.7, p\u0026lt;0.001). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOestradiol levels were marginally lower in the EC group than the control group however when grouped by menopausal status, levels in pre-menopausal controls and the EC group were very similar (438 vs 432 pmol/L). In the post-menopausal group, oestradiol levels were again marginally lower in the EC group versus the control group (239 vs 278 pmol/L). However, as expected, when the SHBG results were accounted for, the bioavailable oestradiol levels were higher in the EC group versus the control group in both pre and post-menopausal females. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo confirm the strength of the associations between features of MetS and EC in premenopausal women, regression analysis was undertaken looking at the individual components of MetS. The strongest predictors of EC development in pre-menopausal women were HbA1c\u0026gt;48 mmol/L and waist circumference \u0026gt;94cm where risk was increased nearly three-fold (OR=2.74, 95%CI:1.30-5.04 and OR=2.73, 95%CI 2.13-3.49, respectively) (Table 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5.\u0026nbsp;Regression analysis of MetS parameters associated in pre and post-menopausal women\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"591\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.7972972972973%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.263513513513516%\" colspan=\"2\"\u003e\n \u003cp\u003ePre-menopausal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.939189189189186%\" colspan=\"2\"\u003e\n \u003cp\u003ePost-menopausal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.85617597292724%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.4585448392555%\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.304568527918782%\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.85617597292724%\"\u003e\n \u003cp\u003eWaist circumference \u0026gt;88 (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.4585448392555%\"\u003e\n \u003cp\u003e2.37 (1.87-2.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.304568527918782%\"\u003e\n \u003cp\u003e2.48 (2.22-2.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.85617597292724%\"\u003e\n \u003cp\u003eWaist circumference \u0026gt; 94 (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.4585448392555%\"\u003e\n \u003cp\u003e2.73 (2.13-3.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.304568527918782%\"\u003e\n \u003cp\u003e2.68 (2.40-3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.85617597292724%\"\u003e\n \u003cp\u003eBMI \u0026gt;30 (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.4585448392555%\"\u003e\n \u003cp\u003e2.49 (1.96-3.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.304568527918782%\"\u003e\n \u003cp\u003e2.80 (2.51-3.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.85617597292724%\"\u003e\n \u003cp\u003eTriglycerides \u0026gt;1.7 (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.4585448392555%\"\u003e\n \u003cp\u003e1.95 (1.50-2.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.304568527918782%\"\u003e\n \u003cp\u003e1.70 (1.52-1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.85617597292724%\"\u003e\n \u003cp\u003eGlucose \u0026gt; 5.6 (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.4585448392555%\"\u003e\n \u003cp\u003e1.68 (1.14-2.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.304568527918782%\"\u003e\n \u003cp\u003e1.43 (1.23-1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.85617597292724%\"\u003e\n \u003cp\u003eHbA1c \u0026gt;48 (mmol/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.4585448392555%\"\u003e\n \u003cp\u003e2.74 (1.30-5.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.304568527918782%\"\u003e\n \u003cp\u003e1.97 (1.52-2.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.85617597292724%\"\u003e\n \u003cp\u003eHDL \u0026lt;1 (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.4585448392555%\"\u003e\n \u003cp\u003e1.72 (0.99-2.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.304568527918782%\"\u003e\n \u003cp\u003e1.84 (1.38-1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.85617597292724%\"\u003e\n \u003cp\u003eHDL \u0026lt;1.3 (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.4585448392555%\"\u003e\n \u003cp\u003e2.29 (1.77-2.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.304568527918782%\"\u003e\n \u003cp\u003e1.72 (1.51-1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.690355329949238%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThose females with MetS diagnosed by having an abdominal circumference \u0026gt; 80cm, diabetes (diagnosed by doctor or HbA1C\u0026gt;48, or fasting glucose\u0026gt;5.6mmol/L) and biochemical or treated hypertriglyceridemia had the highest risk of development of EC (OR=2.76, 95% CI 2.39-3.17, P\u0026lt;0.0001) (Supplementary table 3 \u0026amp; 4). Overall, there was an almost three-fold increased risk of developing EC in any female who met any criteria for diagnosis of MetS by the IDF definition (OR=2.67,95%CI 2.41-2.96, P\u0026lt;0.0001) (Supplementary table 3). \u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003ch2\u003ePrincipal Findings\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe principal findings of the study show that high BMI, increased waist and hip circumference, increased arterial blood pressure, abnormal lipid profile and deranged glucose metabolism are all associated with an increased risk of developing EC, regardless of whether they are identified in pre-menopause or post-menopause. Furthermore, all features of MetS are significant independent predictors of EC development. Females who develop EC are also more likely to have mildly abnormal liver function tests and mildly raised inflammatory markers. This supports the hypothesis that metabolic syndrome significantly increases the odds of developing EC and thus in effect may be used as a predictor of EC development, even in the pre-menopausal period. \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eComparison with other studies\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe distribution of body fat, in particular central adiposity or visceral adipose tissue is linked to several metabolic abnormalities including insulin resistance and inflammation that are associated with EC development. There is evidence that excess visceral adipose tissue in particular is associated with adverse metabolic, dyslipidemic, and atherogenic obesity as compared to subcutaneous fat (14). As such, central adiposity, typically assessed by waist circumference or waist to hip ratio, has been studied in numerous studies in relation to risk of EC (15\u0026ndash;18). In the California Teachers\u0026rsquo; Study, waist circumference and waist to hip ratio were positively associated with EC risk after adjustment for BMI (19). In contrast, the Nurses\u0026rsquo; Health Study did not report an independent association with these measures, while the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort reported an independent association with waist circumference but not waist to hip ratio (17,20). A 2015 meta-analysis also found an independent association with waist circumference but not waist to hip ratio (21). In the UK Biobank cohort, waist circumference and waist-hip ratio were both significantly independent predictors of EC risk with waist circumference being the strongest predictor out of the three (waist circumference, hip circumference and waist-hip ratio) with an OR of 4.96 for waist circumference \u0026gt;100cm. The Epidemiology of Endometrial Cancer Obesity and Endometrial Cancer Consortium (E2C2) is a national cancer institute supported consortium of over 45 worldwide studies investigating the associations of obesity and EC (22). Pooled estimates of EC risk where both waist-to-hip ratio and abdominal circumference were assessed demonstrated an OR of 1.92 (95%CI: 1.57\u0026ndash;2.35))(22,23). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBody mass index (BMI) reflects both fat and fat-free mass, rather than fat distribution, which varies considerably among individuals with a similar BMI. Despite this, BMI was the best predictor of EC risk in our study amongst all of the anthropometric measures assessed. Similarly, in the E2C2 study, the overall pooled estimate for obesity and EC risk was 2.65 (95%CI: 2.43\u0026ndash;2.90) in those with a BMI of 30-35kg/m\u003csup\u003e2\u003c/sup\u003e and 4.66 (95%CI: 3.78\u0026ndash;5.75) in individuals with severe obesity (BMI\u0026gt;35kg/m\u003csup\u003e2\u003c/sup\u003e) which is similar to findings in this cohort (22,23).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eDiabetes/Insulin resistance \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eInsulin resistance and hyperinsulinemia are associated with increased EC risk independently of obesity (24\u0026ndash;26). Insulin may act directly on endometrial tissue as a mitogenic and antiapoptotic growth factor (27,28). Insulin can also increase IGF-I bioactivity, and increase the bioavailability of free estrogens and androgens through downregulation of SHBG and upregulation of ovarian sex steroid production (25,29). \u0026nbsp;Amongst the UK Biobank cohort, a self-reported diagnosis of diabetes did not remain a significant independent predictor of EC after multivariate regression. However, when we assessed individual biochemical parameters in keeping with a diagnosis of diabetes such as glucose levels \u0026gt; 5.6 mmol/L and HbA1c \u0026gt;48 mmol/L, these were both significant independent risk factors for EC. These findings are in keeping with a population-based prospective cohort study where women with type 2 diabetes had a 2-fold higher risk of EC development (30). \u0026nbsp;Whilst there are a number of studies which claim the independent contribution of diabetes as a risk factor for EC, one large population study conducted by Attner et al. found positive associations but no independent effect after controlling for confounding factors such as BMI (31). \u0026nbsp;A potential explanation for the discrepancy in findings is that glycaemic treatment such as metformin may modify risk. Whilst in-vitro studies have been promising, larger trials and cohort studies have failed to find a significant benefit. A recent Cochrane review which included only two small randomised controlled trials did not find sufficient evidence to confirm whether metformin lowers risk of EC development (32). More recently, randomised trials such as the feMME trial have also found no significant benefit (33). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimilarly to self-reported diabetes, the reported use of cholesterol lowering medication was not associated with EC, however, having hypertryglyceridaemia (\u0026gt;1.7 mmol/L) and or low HDL \u0026lt;1.3 mmol/l was. This is in keeping with the published literature in which a number of epidemiological studies indicated that abnormal lipid metabolism is strongly associated with EC. In a cohort study of 13,061 patients, dyslipidemia was an independent risk factor for the development of EC, and it significantly increased the incidence of EC. The risk of EC increased by 1.34-fold in patients with elevated triglycerides (TGs) \u0026gt;\u0026thinsp;1.7mmol/l and 1.65-fold in patients with reduced HDL (HDL\u0026thinsp;\u0026lt;\u0026thinsp;0.56 mmol/L) (34). Another study found that dyslipidemia significantly increased the risk of EC, with a summary OR of 1.62 (95%CI: 1.32\u0026ndash;1.99) for hypercholesterolemia (\u0026ge;\u0026thinsp;5.55 mmol/L), 1.25 (95%CI: 1.05\u0026ndash;1.49) for hypertriglyceridemia (\u0026ge;\u0026thinsp;1.71 mmol/L), 1.45 (95%CI: 1.16\u0026ndash;1.79) for high LDL-C level (\u0026ge;\u0026thinsp;5.50 mmol/L), and 2.40 (95%CI: 1.90\u0026ndash;3.03) for low HDL-C level (\u0026le;\u0026thinsp;1.10 mmol/L) (35). In the present study, hypertriglyceridemia was the most important marker of dyslipidaemia and EC risk, followed by HDL level. LDL levels were non-significantly associated with EC risk (data not shown). The fact that those individuals using cholesterol lowering medication was not a significant independent contributor to EC risk after multi-variate analysis, but having biochemical hyperlipidaemia was raises the possibility that treatment is associated with risk recuction. The association between statin use and EC risk has been the subject of much debate with a \u0026nbsp;number of conflicting outcomes and wide heterogeneity between studies (36\u0026ndash;38). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eHypertension \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eSoler et al. found significant association between EC and hypertension. The 2 factors (BMI and hypertension) appeared to have a synergistic effect on the risk of EC and produced appreciably elevated risk (almost 5-fold increase) in women with both, high BMI and hypertension (39). After multivariate analysis, the estimated effect of hypertension alone remained significant (OR=1.60, 95%CI: 1.30-1.90) for EC, which is very similar to our findings. This is consistent with other estimates in the literature with the OR ranging from 1.20 to 2.10 (40\u0026ndash;42). \u0026nbsp;A meta-analysis more recently evaluated the effect of hypertension and other confounders such as exercise levels and BMI and concluded that whilst studies controlling for confounding had lower overall relative risk, hypertension remains a significant factor after exclusion of other potential confounding factors (43). The mechanisms behind this remain unclear, with Hamet suggesting that hypertension may increase the cancer risk by blocking and subsequently modifying apoptosis, thereby affecting the regulation of cell turnover but very little published literature looking at the distinct biological mechanisms implicated in EC since (44). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere have been multiple studies linking MetS with cancer. A recent 2022 analysis examined the relationship between MetS and the risk of thirteen IARC obesity-associated cancers (8). It pooled the results of 63 studies conducted in the United States, Europe, Asia, Canada, Israel and Australia of the cancer risk in adults (all age groups \u0026gt; 18 years age) without MetS versus with MetS. The effect estimates for the risk of cancer (adjusted for alcohol consumption or cigarette smoking in 68% of studies) were: 1.13 to 6.73 for breast; 1.14 to 2.61 for colorectal; 1.18 to 2.50 for gastric; 1.37 to 2.20 for endometrial; 1.59 to 2.13 for pancreas; 2.13 to 5.06 for hepatocellular carcinoma (8).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe proposed mechanisms of MetS related carcinogenesis in EC centre around endogenous sex steroid production, insulin resistance and inflammation. The \u0026lsquo;unopposed oestrogen\u0026rsquo; theory suggests that increases in the synthesis of endogenous estrogen by adipose tissue, coupled with decreased SHBG production by the liver, leads to increased plasma levels of bioavailable estrogen, increasing the likelihood of malignant cell development in the postmenopausal female no longer producing the counterregulatory hormone progesterone. Several case\u0026ndash; control studies have reported increased total and bioavailable estrogen levels and decreased plasma SHBG level in postmenopausal women with EC compared to controls (25,45\u0026ndash;47). This was evident in the UK Biobank cohort and in the sub-group analysis was lower SHBG levels in the pre and post-menopausal EC groups as compared to the control group. Interestingly, in the UK Biobank cohort, oestradiol levels were marginally higher in the control group as compared to the EC group, and this remained the case in both pre and post-menopausal subgroups. However, higher free oestradiol was seen in both the pre and post-menopausal EC group. In addition to that, low serum HDL-C levels and a high LDL-C/HDL-C ratio have been found to predict increased bioavailable estradiol levels more strongly among overweight than normal-weight women (48). Thus, overweight women may experience a heightened physiologic response to the presence of other metabolic factors compared with leaner women. Insulin like growth factor-1 (IGF-1) levels were lower than seen in the control group. Lower IGF-1 levels may correlate inversely with adiposity and T2DM (49)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInterestingly, both CRP and GGT were significantly higher in patients who developed EC when compared with controls (20.0 vs 17.9 U/L in pre-menopausal group and 25.9 vs 22.2U/L for GGT, respectively and 1.7 vs. 1.0 mg/L in pre-menopausal group and 2.3 vs 1.4 mg/L for CRP, respectively). Recent studies demonstrated that GGT is involved in the pathogenesis of cardiovascular disease and MetS through promotion of oxidative stress and pro-inflammatory response(50,51). A previous study on coronary artery disease suggested that increased levels of serum GGT may serve as early marker of oxidative stress and predict increased levels of CRP as well as development of diabetes and hypertension(52). When assessed by univariate analysis, both markers were independent and significantly associated with EC and these associations held even in the pre-menopausal population (OR=1.24, 95%CI: 1.11-1.39, P\u0026lt;0.0001). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLooking at this UK Biobank cohort, which is amongst the largest study to have looked at the MetS associations with EC risk in a British population, there were 8,852 women who fulfilled the WHO criteria of MetS and 49,071 who fulfilled IDF criteria of a diagnosis of MetS. Regardless of the diagnostic criteria, the risk of EC was around 2.5-fold higher when compared with non-metabolic syndrome controls. This is very similar to the risk of having a BMI\u0026gt;30 in univariate analysis, both in the pre and post-menopausal population. Strikingly, 24% of the entire cohort included in this analysis met the criteria for a diagnosis of MetS. Amongst the control group this was also 24% but amongst the cohort who went on to develop EC the prevalence was 44%. It is also high probabilty that a large proportion of the UK Biobank participants met the biochemical criteria for elements of METs diagnosis but hadn\u0026rsquo;t been clinically diagnosed in the primary care setting. Supplementary table 5 details the disparity between diagnosed and treated cases of hypertension, hypercholesterolaemia and diabetes against the number of cases diagnosable biochemically. It is possible that recognition and treatment of MetS would lower the risk of EC and other obesity driven cancers. Given that national schemes such as the National Diabetes prevention program already exist and are freely available to those recognised as pre-diabetic, early recognition is likely to be beneficial\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(53). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eStudy strengths and limitations and future work\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eOne of the major strengths of this study are the large size and the fact that it is the only study using the UK Biobank to assess the association between MetS and EC individually. The prospective design minimizes selection bias arising from inappropriate selection of control subjects. No participant developed cancer during the assessment period, thus minimising the risk of reverse causality. \u0026nbsp;The risk factors identified in this study remained true risk factors regardless of menopausal status offering an opportunity for risk reduction strategies prior to the average age of onset of disease in the seventh decade. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOne of the limitations is that the UK Biobank include the use of self-reported data. In this study, there was a disparity in risk amongst those self-reporting as diabetic, hypertensive or hypercholesterolaemic and the risk demonstrated by assessment of serum biochemistry. The exact reason for this is unclear. One important explanation is that treatment of such conditions may reduce risk of EC development. There is growing evidence to suggest aspirin may be effective in risk reduction of EC, particularly for those with genetic predisposition to EC such as Lynch Syndrome (54). Similarly, the evidence for the benefits of metformin for risk reduction is unclear. However, the other plausible explanation is that misreporting results in confounding and misclassification of these variables. Likewise, as with similar sorts of epidemiological datasets using self-reporting there is always potential for recall and selection bias. This is in part why biochemical variables were assessed against self-reported ones. Similarly, to avoid further exacerbating this risk, multiple imputation was avoided for cases of missing data with all missing data excluded from analysis. High quality studies which directly assess the impact of medications such as glycaemic agents, antihypertensives and lipid lowering medications are needed to better understand the effects they have on EC pathogenesis. \u0026nbsp;A further limitation of the study was the inability to determine the association of PCOS and EC risk. As in the study of Hutt et al we know from meta-analysis that PCOS is a major contributing risk factor to EC development and is closely interlinked with insulin resistance (12). Unfortunately, in the UK Biobank very few patients were reported as having had a diagnosis of PCOS. It is likely this is due to their age at the time of UK Biobank study and the fact that PCOS is diagnosed in the first few decades of life.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study supports the hypothesis that MetS and it\u0026rsquo;s individual features significantly increase the odds of developing EC and thus in effect may be used as a predictor of EC development, even in the pre-menopausal period. Despite published data showing strong associations between components of MetS and EC risk, there are as yet no nationally recommended screening systems or risk reduction strategies in place. MetS was diagnosable in up to 24% of this entire female cohort, but strikingly in 44% of patients who went on to develop EC. This is likely due to the huge overlap between the two conditions. In this cohort, \u0026nbsp;55 % of those that had a BMI of \u0026gt; 30 met the criteria for diagnosis of MetS and in those with a BMI of \u0026gt;40 this rose to 68%. Screening those with obesity for MetS may be a strategy for screening those at higher risk of EC over that and above the risk of obesity alone, especially in the premenopausal period. Furthermore, medicating the conditions associated with MetS may modify risk of EC as well as potentially a number of other obesity driven cancers and thus more attention should be placed on primary prevention strategies. \u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRK \u0026ndash; Study conception, data analysis and manuscript writing \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGM \u0026ndash; Bioinformatics guidance and statistical advice \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAP \u0026ndash; Bioinformatics guidance and statistical advice\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRP \u0026ndash; Bioinformatics guidance and statistical advice\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEK \u0026ndash; Study conception, manuscript editing \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJC \u0026ndash; Study conception, manuscript editing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe UK Biobank was approved by the Northwest Multi-Centre Research Ethics Committee (16/NW/0274), Patient Information Advisory Group (England and Wales) and the Community Health Index Advisory Group (Scotland). All participants provided written informed consent. This study is performed using the UKB data under application number 60549.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study is available upon request to the UK Biobank. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding to access to the UK\u0026nbsp;Biobank data was\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003esupported by Brunel University London BRIEF AWARDS 2020/21 to Dr Raha Pazoki.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDossus L, Kaaks R. Nutrition, metabolic factors and cancer risk. 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Available from: /pmc/articles/PMC7294238/ \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":"","lastPublishedDoi":"10.21203/rs.3.rs-4812894/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4812894/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study investigates the association between endometrial cancer (EC) risk and features of metabolic syndrome (MetS) using the UK Biobank.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUnivariate and multivariate analysis of EC risk and features of MetS including serum biochemistry were analysed. Subgroup analysis was also undertaken for pre- and post-menopausal participants.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e203,644 females from the UK Biobank were included in this study. 49,071 (43.8%) met the met the International Diabetes Federation (IDF) definition of MetS and in these females the risk of EC was almost threefold higher (OR\u0026thinsp;=\u0026thinsp;2.67; 95%CI:2.41\u0026ndash;2.96, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Of those participants who developed EC (n\u0026thinsp;=\u0026thinsp;1632), Waist circumference\u0026thinsp;\u0026gt;\u0026thinsp;80cm, BMI\u0026thinsp;\u0026gt;\u0026thinsp;30kg/m\u003csup\u003e2\u003c/sup\u003e, hypertension\u0026thinsp;\u0026gt;\u0026thinsp;130/80mmHg and hyperlipidaemia or diabetes were significantly associated with increased risk of EC. BMI\u0026thinsp;\u0026gt;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e alone was associated with threefold higher risk and BMI\u0026thinsp;\u0026gt;\u0026thinsp;40 kg/m\u003csup\u003e2\u003c/sup\u003e a ninefold higher risk. Associations remained significant in pre and postmenopausal subgroups. Treatment for hypertension, hyperlipidaemia or diabetes was associated with EC risk in univariate analysis but did not remain significant in multivariate analysis. Having abnormal lipid profile, fasting hyperglycaemia or hypertension significantly increased the risk of EC after correction for confounding factors.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eFeatures of MetS, both independently and in combination, significantly increase the risk of EC. Screening those with obesity for MetS, in pre-menopausal years may help to identify those at highest risk.\u003c/p\u003e","manuscriptTitle":"Metabolic syndrome modulates risk of endometrial cancer regardless of menopause status- A UK BIOBANK Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-18 02:16:01","doi":"10.21203/rs.3.rs-4812894/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":"7843f81b-990f-41ab-b417-3ad54c861033","owner":[],"postedDate":"September 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-13T20:23:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-18 02:16:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4812894","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4812894","identity":"rs-4812894","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}