Six anthropometric indicators and their association with myocardial infarction and death in male Swedish automotive industry workers followed for 28 years

Six anthropometric indicators and their association with myocardial infarction and death in male Swedish automotive industry workers followed for 28 years | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Six anthropometric indicators and their association with myocardial infarction and death in male Swedish automotive industry workers followed for 28 years Lala Joulha Ian, Lennart Dimberg This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4529247/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background: Overweight, Body Mass Index (BMI, kg/m2) 25-30, and obesity (BMI 30+) have historically been associated with risk of myocardial infarction and death. Several indexes have been introduced to better reflect the abdominal adiposity. The risk for each index does vary in different populations. This project aimed to determine the correlations between six common different body measurements and their association with cardiovascular disease risk factors, myocardial infarction, and death in middle-aged workers in the Swedish automotive industry. Methods: In 1993, 1,000 randomly selected Swedish men from the automotive industry were surveyed at a nurse-led health examination. Body measures were recorded together with blood pressure, and a fasting blood test was analyzed. A 28-year follow-up was conducted using the national myocardial infarction register data from the Swedeheart and death data from the Swedish Cause of Death Register. Multiple logistic regression models were used to compare BMI with the other five body measurements. The measure of obesity was calculated for the following risk indexes: Body Mass Index (BMI), Waist Circumference, Waist-to-Height Ratio, Waist-to-Hip Ratio, Sagittal Abdominal Diameter, and Sagittal Abdominal diameter-to-height ratio. Outcomes of accumulated first-time myocardial infarction and death were assessed as odds ratios (OR) between the fourth and first quartiles, before and after adjustment for traditional risk factors. Results: Data were recorded for 959 men. Significant correlations between the six body measurements were shown. All were also associated with CVD risk factors, myocardial infarction, and death. However, when adjusted for traditional risk factors the association with cardiovascular endpoints almost disappeared. Before adjustment the highest odds ratio with myocardial infarction was calculated for sagittal abdominal diameter to height ratio, OR=3.60; 95% Confidence Interval (CI) 1.78-7.29; p=0.0016. Correspondingly, the highest OR with all-cause death was calculated for the sagittal abdominal diameter, OR=1.85; 95% CI=1.17-2.92; p=0.0117. The predictive ability measured by Nagelkerke’s R2 was comparable for each measure. Conclusions: For this population, after adjustment for traditional risk factors, no index was significantly preferred as a risk factor for myocardial infarction or death. obesity body measurements longitudinal cohort myocardial infarction mortality risk factor Figures Figure 1 Figure 2 Background The worldwide obesity epidemic (Body Mass Index 30 and above), so-called “globesity,” is becoming an increasing concern all over the world. In 2019, according to the Global Burden of Disease, about half of men 40 + years old were found to be overweight or obese, of whom about 15% were obese ( 1 ). Obesity is a serious medical condition that is defined as abnormal or excess body fat accumulation to such an extent that it affects health negatively. The pathophysiology behind obesity is complex. Adiposity includes subcutaneous and visceral fat, and visceral obesity is considered more hazardous than generalized obesity and more strongly associated with chronic diseases ( 2 ). Visceral obesity is associated with various medical conditions such as hypertension, diabetes, insulin resistance, and metabolic syndrome, all of which increase the risk for cardiovascular disease ( 3 ). Cardiovascular diseases (CVDs) include stroke, heart failure, hypertensive heart diseases, and coronary artery diseases (CAD) such as angina and myocardial infarction (MI). Cardiovascular diseases are globally the leading cause of death ( 4 ). According to the Centers for Disease Control and Prevention (CDC), an estimated 80% of cardiovascular diseases are preventable or delayable. Preventive measures include avoiding smoking, limiting alcohol intake, healthy eating and exercise, as well as treating hypertension, hyperlipidemia, and diabetes ( 5 , 6 , 7 ). The Framingham Heart Study is an ongoing cardiovascular cohort study. It was initiated in 1948 in Framingham, Massachusetts. The study is directed by the National Heart, Lung, and Blood Institute (NHLBI), and it established risk factors that contribute to cardiovascular diseases, such as older age, male sex, smoking status, diabetes, high blood pressure, abnormal lipid levels, obesity (BMI 30 and above), and physical inactivity. The Framingham Risk Score (FRS) is the first CVD risk assessment instrument for primary prevention; it calculates a 10-year risk for cardiovascular events and includes BMI ( 8 ). Though commonly used, BMI has its limitations. It doesn’t consider important factors like age, gender, muscle mass, and the location or distribution of body fat. In 2006, a large meta-analysis suggested that we need to include other measurement methods to predict cardiovascular mortality because BMI is only a significant predictor of cardiovascular mortality in patients with severe obesity ( 9 ). Alternate indexes such as waist circumference (WC), waist-to-height ratio (WHtR) waist-to-hip ratio (WHR), sagittal abdominal diameter (SAD), and sagittal abdominal diameter to height ratio (SADHtR) have been introduced and argued to correlate better with abdominal obesity, which is considered causally related to the pathophysiology of cardiac structure and function ( 10 , 11 ). In 2021, a meta-analysis of body weight and different obesity indicators found 31 cohort studies with acceptable follow-up duration for evaluation. None of these included industrial workers in an occupational health setting. The Renault-Volvo Coeur project is a prospective longitudinal investigation initiated in 1992 in collaboration with the occupational health clinics of two automotive companies. The study was designed to compare the national differences in cardiovascular disease between the French and Swedish workers at Renault and Volvo ( 12 ). The main objective of the study was to understand why Frenchmen had significantly lower rates of coronary heart disease (CHD) compared to men in other European countries when the French lifestyle itself contained risk aspects such as high consumption of cheese and wine and a high proportion of smokers, which has been called the “French Paradox” ( 13 ). National differences included a higher proportion of married/cohabitant Frenchmen (90%) compared to Swedish men (76%), potentially linked with a healthier diet and social support associated with lower cardiovascular risk ( 14 ). Another finding in the Swedish cohort was an association between living alone and a significantly increased risk of MI and all-cause death ( 15 ). Likewise, blue-collar workers as compared to white-collar workers displayed an increased risk of MI and all-cause death, whereas the opposite association was seen for education. The results of our findings have sparked interest in these organizations to start various tailored prevention programs. At the start of the Coeur project, standardized measurements for all six common obesity markers were recorded. Using Swedish register data from the subsequent 28 years, we can now report the accumulated incidence of first MI and death. This project aims to identify correlations between the different commonly used body measures of obesity, as well as to determine the association between these body measures and other CVD risk factors. This study also investigates which body measurement, if any, has the strongest associations with MI and death. Methods Study population In 1993, one thousand middle-aged men (45-50 years of age) from each automotive company were recruited. The study aimed to investigate coronary health among French and Swedish employees and to initiate preventive measures in cooperation with the respective occupational health services. This study is focused on the Swedish cohort. To reach 1000 Swedish white male employees since this reflected the demographics of the employees at the time, born between 1943 and 1948, 1144 randomly selected individuals were asked to participate; 144 employees declined due to lack of interest or long-term sickness. The participants were surveyed at a nurse-led health examination with a self-administered questionnaire including questions about marital status, smoking habits, alcohol intake, lifestyle, mental health, working conditions, stress, exercise, and more. The methods have been previously described by Simon et al. (12). Weight was measured by digital scales and height by fixed stadiometers. Cardiovascular risk was estimated using the Framingham risk score, age, smoking habits, systolic blood pressure, diabetes mellitus, left ventricular hypertrophy, and the ratio of total cholesterol to high-density lipoprotein concentration (8). Outcome measures Data on the first myocardial infarction in the Swedish cohort were obtained from the National Swedish Register of MI, Swedeheart (16). To define MI, we used the International Statistical Classification of Diseases: code 410 in the ninth revision and codes I21-I23 in the tenth revision (17). Death data was collected from the Swedish Cause of Death Registry (18). Body measures Body mass index (BMI kg/ m 2 ) is calculated by dividing weight in kilograms by the square of height in meters (10). BMI classification according to WHO is presented in Table 1, along with cut-off points for WC, WHtR, and WHR. Waist circumference (WC; cm) is measured in a standing position, with the measurement tape placed at the midpoint between the iliac crest and the lower margin of the last palpable rib (19). To classify abdominal obesity, we used threshold values suggested by the National Institutes of Health (NIH) (Table 1) (20). Waist-to-Height Ratio (WHtR) is calculated by dividing WC (cm), as measured above, by height (m). There is no standard reference value for WHtR; however, an ideal WHtR has been suggested to be less than or equal to 0.5 (21) Waist-to-Hip Ratio (WHR) is calculated by dividing the waist circumference (cm) by the hip circumference (cm). The ratio determines the amount of stored fat on the waist, hips, and buttocks (22). WHR cut-off points were determined according to WHO guidelines (Table 1). Sagittal Abdominal Diameter (SAD) is measured in a supine position, as the distance (cm) from the lower back to the highest point of the abdomen. The National Health and Nutrition Examination Survey (NHANES) describes in detail how to measure the sagittal abdominal diameter (23). A 2010 population-based cross-sectional study of Swedish subjects aged 60 calculated the cardiometabolic risk score. According to their analyses, the optimal cut-offs for SAD were 22 cm in men and 20 cm in women (24). Sagittal Abdominal Diameter to Height Ratio (SADHtR) is another screening method to measure visceral fat. It was first suggested in an earlier phase of the Coeur project (25,26). It’s comprised of the sagittal diameter (cm) divided by the height (m). The lack of reference ranges and cutpoints to assign risk categories is a limitation of SADHtR. Table 1. Anthropometric measurements cut-off points of CVD risks. BMI classification. Classification BMI (kg/m 2 ) Underweight <18.5 Normal weight 18.5-24.9 Overweight 25.0-29.9 Obese Class I 30-34.9 Class II 35-39.9 Class III ≥40 Waist circumference cut-off points. Low-risk High-risk Increased higher risk Men 102 cm Women 88 cm Waist-to-hip ratio cut-off points. Health risk: Low Moderate High Men 0.95 or lower 0.96-1.0 1.0 or higher Women 0.80 or lower 0.81-0.85 0.85 or higher Waist-to-height ratio cut-off points. An ideal waist-to-height ratio has been suggested to be less than or equal to 0.5 Sagittal Abdominal Diameter cut-off points. The optimal cutoffs for sagittal abdominal diameter are 22 cm in men and 20 cm in women. There isn’t an optimal cut-off point for sagittal abdominal diameter by height. Abbreviations: BMI, body mass index; CVD, cardiovascular disease; cm, centimeter; m, meter; kg, kilogram. Statistical analysis Baseline variables and their collection have been described in detail by Simon et al. (12). Baseline data for categorical (qualitative, dichotomous) variables was described as numbers and percentages. The distribution of continuous (quantitative) variables was described as mean value with standard deviation in parentheses. The association between each anthropometric measurement and each cardiovascular risk factor was assessed as follows. For cardiovascular risk factors represented by numeric variables, Spearman’s correlation coefficient between each obesity measure and the respective variable was calculated. Spearman’s correlation ranges from -1 to +1. Values close to -1 indicate a negative monotonic relationship between the variables, zero indicates no correlation, and +1 indicates a positive monotonic relationship. If the associated 95% confidence interval (constructed by z-transformation) contained 0, the association was not statistically significant at the 5% level. For categorical variables, the distribution of the values of each obesity variable was compared across the categories by the Mann-Whitney-Wilcoxon (two categories) or Kruskal-Wallis (more categories) test. We were not able to reliably study the risk factor “diabetes” due to only 13 participants with this condition at baseline. The Aalen-Johansen and Kaplan-Meier methods were used to estimate the cumulative incidences of MI and death over the 28 years of follow-up. Unadjusted and adjusted logistic regression analyses were conducted to quantify the association of the six body measurements with the association of MI, and with death within 28 years of follow-up in the form of odds ratios and their 95% confidence intervals. The body measurements were categorized according to quartiles, and their statistical significance was assessed by the likelihood ratio test. We used Nagelkerke’s R2 to assess the predictive ability of the fitted models. For pairs of anthropometric measures (for example, measure M1 and M2), we judged their relative predictive strength by comparing their adequacy measures (27). Higher adequacy in the pair indicates a higher predictive ability of the corresponding measure (given the model). The adequacy of M1 (M2) expresses the proportion of log-likelihood explained by a model containing only M1 (M2) as compared to a model containing both measures as explanatory variables. It is calculated as the ratio (%) of likelihood ratio statistics from the full and the nested models. Adjustments for age, systolic blood pressure, the ratio of total cholesterol/ HDL-C, and blue/white collar status were included in the models. In case of death, the models were additionally adjusted for marital status. Double-sided P values below 0.05 were considered statistically significant. Missing values were omitted from the analyses. Analysis was performed in R (version 4.3.1, with RStudio, version 2023.03.0), employing packages rms (version 6.7-0), Hmisc (version 4.7-2) (28), survival (version 3.5-5) (29), and cmprsk (version 2.2-11) (30) Explanatory (independent) variables included components of the Framingham risk index (FRI): age (years), systolic blood pressure (mmHg), total cholesterol/HDL-C (mmol/l), smoking (No/Past/Current), and diabetes (Yes/No; excluded in the present study due to low number [n=13] at baseline). Additional explanatory variables are presented in Table 2. Outcomes include deaths from all causes, deaths due to cardiovascular causes, deaths due to MI, and first MI. Table 2. Additional explanatory variables and their codings Additional explanatory variables Coding Type of work Blue collar/white collar Married or co-habiting Yes/no University education Yes/no Exercise level Less than 1 hour/week 1-3 hours per week More than 3 hours per week Athlete Diastolic blood pressure mm Hg Resting heart rate Beats per minute Triglycerides mmol/l Fasting plasma glucose mmol/l Total alcohol consumption g/week Body mass index, BMI kg/m2 Waist circumference cm Waist-to-height ratio, WHtR cm/m Sagittal abdominal diameter, SAD cm Sagittal abdominal diameter to height ratio, SADHtR cm/m Abbreviations: Hg, mercury; mm, millimeter; m, meter; g, gram; mmol, millimoles; l, liter; cm, centimeter: kg, kilogram. Ethical approval and informed consent This study was performed in line with the principles of the Declaration of Helsinki and included written, informed consent from all participants. The Research Ethics Committee of Gothenburg University approved the original study protocol on Feb 11, 1993 (Dnr. 23-93), and amendments on Feb 19, 2019. Applications to use data from the National Myocardial Infarction Register, Swedeheart, and the National Cause of Death Register were also approved. Results In total, 959 men had full information on the six body measurements. Of those, 60% performed clerical work and 38% manual work; 76% were married/living together with a partner; and nearly one-third were current smokers (Table 3). An overview of the extent of missing values is presented (Additional File 1: Table S1). Missing values vary between 0-8%. Table 3. Baseline characteristics and end-point data of first myocardial infarction and all-cause death. Variable All analyzed participants (n=959) Participants with an MI (n = 90) Participants deceased (n= 194) Age at baseline (years), mean (SD) 47.7(1.48) 47.9(1.39) 47.9(1.42) Blue-collar workers, n (%) * 367(38%) 42(47%) 97(50%) University education, n (%) * 227(24%) 25(28%) 49(25%) Married or cohabitant, n (%) * 729 (76%) 67 (74%) 124 (64%) Systolic blood pressure (mmHg), mean (SD) 117(15) 121(16) 122(18) Diastolic blood pressure (mmHg), mean (SD) * 75(12) 76(11) 74(11) Pulse rate (beats per minute), mean (SD) * 63 (10) 63(10) 63(9) B-Cholesterol (mmol/l), mean (SD) * 5.6(1.01) 6.2(1.04) 5.9(1.02) HDL-C (mmol/l), mean (SD) * 1.2(0.29) 1.1(0.26) 1.2(0.27) Triglycerides (mmol/l), mean (SD)* 1.5(0.93) 1.7(0.93) 1.6(1.03) B-glucose (mmol/l), mean (SD) * 5.5(1.02) 5.5(0.84) 5.6(1.07) Body mass index (kg/m2), mean (SD) 25.6(3.37) 26.7(3.22) 26.1(3.96) Waist (cm), mean (SD) 92.4(9.08) 95.7(8.20) 94.2(10.44) Waist-hip ratio, mean (SD) 0.*93(0.06) 0.95(0.05) 0.94(0.06) Sagittal abdominal diameter (cm), mean (SD) 20.3(2.69) 21.0(2.27) 20.8(3.10) Sagittal abdominal diameter to height ratio, mean (SD) 11.3(1.53) 11.8(1.30) 11.6(1.81) Waist to height ratio, mean (SD) 0.52(0.05) 0.54(0.05) 0.53(0.06) Alcohol consumption gram/ week, mean (SD) * 51.4(58.40) 54.4(58.10) 52.1(58.65) Smoking * Never, n (%) 259(27%) 9(10%) 45(23%) Past, n (%) 371(39%) 34(38%) 57(29%) Current, n (%) 270(28%) 44(49%) 83(43%) Exercise * Less than 1 hour per week, n (%) 292(30%) 28(31%) 63(32%) 1 - 3 hours of exercise per week, n (%) 450(47%) 42(47%) 94(48%) More than 3 hours of exercise per week, n (%) 163(17%) 16(18%) 25(13%) Active as an athlete, n (%) 28(3%) 4(4%) 9(5%) The extent of missing values for variables denoted with * is described in the supplementary material (S1); in all cases, the proportion of missing values did not exceed 8%. Abbreviations: N, number of participants; SD, standard deviations; MI, myocardial infarction; HDL-C, high-density lipoprotein cholesterol; B-glucose (glucose level in blood); B-cholesterol (cholesterol level in blood). Over the 28 years of follow-up, 90 individuals suffered at least one MI, and 194 had died. Of those who were deceased, 53 persons died due to cardiovascular causes, of which 19 died due to MI. The average BMI was 25.6, which was higher in both the MI and death groups (Table 3). WC at baseline was also higher in the MI and death groups. WHR, SAD, and SADHtR were slightly higher in both MI and death groups, but still within normal ranges. Both the mean systolic and diastolic blood pressure were higher in the MI group, though within normal ranges. The mean for systolic blood pressure was 121 compared to 117 in the overall group; for diastolic pressure, it was 76 compared to 75. Total cholesterol, high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), and fasting plasma glucose were similar between the groups. Associations between the six body measures of obesity To see how well the six different measures of obesity correlated with each other, we used Spearman’s correlation coefficient (Table 4). The calculated correlations were moderate-to-high, with WHtR being less strongly associated with the others. Table 4. Spearman’s correlation coefficient and 95% confidence interval of six different obesity measurements. Spearman’s correlation (95%CI) Waist circumference Waist-to-hip ratio Waist-to- height ratio SAD SADHtR BMI 0.88 (0.86 – 0.89) 0.64 (0.60 – 0.68) 0.90 (0.88 - 0.91) 0.82 (0.80 -0.84) 0.84 (0.82 – 0.86) Waist circumference -- 0.79 (0.77 – 0.82) 0.93 (0.92 – 0.94) 0.85 (0.84 - 0.87) 0.81 (0.79 – 0.83) Waist-to-hip ratio -- 0.83 (0.81 – 0.85) 0.63 (0.59 - 0.67) 0.66 (0.63 – 0.70) Waist-to- height ratio -- 0.80 (0.78 – 0.82) 0.86 (0.84 – 0.88) SAD -- 0.96 (0.95 - 0.96) SADHtR -- Abbreviations: CI, confidence interval; BMI, body mass index; SAD, sagittal abdominal diameter; SADHtR, sagittal abdominal diameter to height ratio. The division of probands into quartile categories according to the values of the anthropometric measurements are specified (Additional File 1: Table S2). The number of probands in the various high-risk groups varies between BMI>30 (n=95) and WHR>0.9 (n=687). How the normal cut-off values of the variables relate to the usual BMI categories is shown (Additional File 1:Table S3). Association between each anthropometric measurement with each cardiovascular risk factor A significant association (measured by the Kruskal-Wallis test for categorical risk factors, and Spearman’s correlation coefficient for continuous risk factors) was found between the anthropometric measurements (except for waist circumference), and type of work, smoking, fasting blood glucose levels, triglycerides, HDL cholesterol, total cholesterol, and SBP. The p-values were, however, not adjusted for multiple testing. Due to missing values, the number of probands used in the analysis differs slightly between the variables; this analysis is available (additional file 1: Table S4, and Table S5). Traditional cardiovascular risk factors as continuous variables: smoking (pack-years) b-lipids, b-glucose, and systolic blood pressure are significantly correlated with all six indexes. The same is found for the following categorical variables: diabetes (yes/no), smoker (never/past/current), and collar (blue/white). However, WHR is only borderline significantly associated with diabetes (p=0.0515). Only 13 persons had diabetes when the study started. Association between the body measurement variables and MI over time Among 959 participants, there were 90 MIs and 194 deaths. Cumulative incidences of MI for the first and the fourth quantiles of the body measurement variables are presented (Figure 1). The differences between the curves are statistically significant for all body measurement variables. The analysis accounts for the fact that death before MI is a competing event for MI. To measure the association between the body measurement variables and the probability of developing MI within 28 years of follow-up, we used a logistic regression model (Table 5 and Figure 1). Table 5. Body measures’ association with developing MI within 28 years of follow-up. Events/ Total Unadjusted OR 95% CI p-value (likelihood ratio test) Events/ Total Adjusted OR 95% CI p-value (likelihood ratio test) BMI 1 st quartile 2 nd quartile 3 rd quartile 4 th quartiles 15/240 11/241 32/244 32/234 Ref. 0.72 (0.32 – 1.60) 2.26 (1.19 – 4.30) 2.38 (1.25 – 4.52) 0.0003 13/206 10/206 28/208 39/202 Ref. 0.65 (0.27 - 1.56) 1.66 (0.79 - 3.47) 1.73 (0.82 - 3.67) 0.0404 Waist circumference 1 st quartile 2 nd quartile 3 rd quartile 4 th quartiles 10/254 23/265 28/213 29/227 Ref. 2.32 (1.08 – 4.98) 3.69 (1.75 – 7.79) 3.57 (1.7 – 7.51) 0.0006 8/218 20/224 27/187 25/193 Ref. 2.22 (0.94 - 5.26) 3.34 (1.43 - 7.8) 2.52 (1.06 - 6.02) 0.0293 Waist-to-hip ratio 1 st quartile 2 nd quartile 3 rd quartile 4 th quartiles 13/241 16/239 28/241 33/238 Ref. 1.26 (0.59 – 2.68) 2.34 (1.16 – 4.57) 2.82 (1.45 – 5.51) 0.0032 12/203 12/199 27/217 29/203 Ref. 0.80 (0.34 - 1.89) 1.84 (0.88 - 3.85) 1.64 (0.78 - 3.46) 0.0720 Waist-to-height ratio 1 st quartile 2 nd quartile 3 rd quartile 4 th quartiles 12/242 12/242 30/236 36/239 Ref. 1.00 (0.44 – 2.27) 2.79 (1.39 – 5.59) 3.40 (1.72 – 6.71) <0.0001 11/205 10/214 27/201 32/202 Ref. 0.80 (0.33 - 1.96) 2.03 (0.94 - 4.38) 2.11 (0.98 - 4.54) 0.0225 SAD 1 st quartile 2 nd quartile 3 rd quartile 4 th quartiles 9/249 30/312 31/222 20/176 Ref. 2.84 (1.32 – 6.09) 4.33 (2.01 – 9.31) 3.42 (1.52 – 7.7) 0.0004 8/204 26/279 29/192 17/147 Ref. 1.93 (0.83 - 4.45) 2.93 (1.25 - 6.87) 1.84 (0.72 - 4.72) 0.0653 SADHtR 1 st quartile 2 nd quartile 3 rd quartile 4 th quartiles 11/250 21/243 24/227 34/239 Ref. 2.06 (0.97 – 4.36) 2.57 (1.23 – 5.37) 3.60 (1.78 – 7.29) 0.0016 10/209 17/212 22/199 31/202 Ref. 1.20 (0.52 - 2.77) 1.78 (0.79 - 4.02) 2.11 (0.93 - 4.78) 0.1955 *Adjusted OR were adjusted for age, smoker status (never, past, current), cholesterol ratio, blue/white collar status, and the analysis is based on complete cases. Abbreviations: CI, confidence interval: OR, odds ratio: BMI, body mass index: SAD, sagittal abdominal diameter; SADHtR, sagittal abdominal diameter to height ratio; Ref, reference. p-value, probability value; MI, myocardial infarction. For each variable of interest (BMI, WHR, WC, WHtR, SAD, SADHtR), we used logistic regression to model the odds of experiencing an MI within 28 years of follow-up, the complimentary event being no record of MI (either dead or alive). In the unadjusted analysis, large baseline values of all six variables of interest were associated with larger odds of MI within 28 years of follow-up. All variables were statistically significant when tested by a likelihood ratio test. Nagelkerke’s R2 for the unadjusted models ranged from WHR: 0.031 to WHtR:0.053. When adjusting for some of the baseline variables, the odds ratios were attenuated and the variables WHR and SADHtR did not pass the formal significance test. Nagelkerke’s R2 for the adjusted models ranged from 0.136 (SADHtR) to 0.148 (WHtR), indicating a weak relationship between predictors and outcomes. In pairwise comparisons of predictive information contained in each of the anthropometric measures, in our data, and in combination with the chosen model and chosen categorization of the body measures, we found a slight dominance for waist-to-height ratio (Additional file 1: Table S6). Association between the body measurement variables and death over time A logistic regression model was used to measure the association between the body measurement variables (BMI, WHR, WC, WHtR, SAD, SADHtR) and the probability of dying within 28 years of follow-up. Of 959 participants, 194 died within this period. We saw higher cumulative incidences of death for the 4th quartiles of the overweight variables as compared to the 1st quartiles, but the difference was not significant for BMI and waist circumference (Figure 2). Similar results were obtained from unadjusted logistic regressions (Table 6). Table 6. Body measures’ association with all-cause mortality within 28 years of follow-up. Events/ Total Unadjusted OR 95% CI p-value (likelihood ratio test) Events/ Total Adjusted OR 95% CI p-value (likelihood ratio test) BMI 1 st quartile 2 nd quartile 3 rd quartile 4 th quartiles 50/240 41/241 45/244 58/234 Ref. 0.78 (0.49 – 1.23) 0.86 (0.55 – 1.35) 1.25 (0.81 – 1.93) 0.1703 44/206 38/206 38/207 49/202 Ref. 0.81 (0.48 - 1.36) 0.80 (0.47 - 1.37) 1.13 (0.66 - 1.93) 0.4773 Waist circumference 1 st quartile 2 nd quartile 3 rd quartile 4 th quartiles 50/254 46/265 38/213 60/227 Ref. 0.86 (0.55 – 1.34) 0.89 (0.56 – 1.41) 1.47 (0.96 – 2.25) 0.0633 45/218 38/224 37/186 49/193 Ref. 0.75 (0.45 - 1.25) 0.98 (0.58 - 1.66) 1.26 (0.74 - 2.14) 0.2651 Waist to hip ratio 1 st quartile 2 nd quartile 3 rd quartile 4 th quartiles 43/241 31/239 56/241 64/238 Ref. 0.69 (0.42 – 1.13) 1.39 (0.89 – 2.18) 1.69 (1.09 – 2.62) 0.0007 37/203 27/199 54/216 51/203 Ref. 0.75 (0.43 - 1.33) 1.57 (0.94 - 2.6) 1.44 (0.85 - 2.44) 0.0235 Waist-to-height ratio 1 st quartile 2 nd quartile 3 rd quartile 4 th quartiles 43/242 42/242 44/236 65/239 Ref. 0.97 (0.61 – 1.55) 1.06 (0.67 – 1.69) 1.73 (1.12 –2.67) 0.0259 39/205 37/214 38/200 55/202 Ref. 0.89 (0.53 - 1.51) 1.05 (0.61 - 1.81) 1.49 (0.88 - 2.55) 0.2387 SAD 1 st quartile 2 nd quartile 3 rd quartile 4 th quartiles 46/249 58/312 38/222 52/176 Ref. 1.01 (0.66 – 1.55) 0.91 (0.57 – 1.46) 1.85 (1.17 – 2.92) 0.0117 39/204 54/278 32/192 44/147 Ref. 1.01 (0.62 - 1.66) 0.80 (0.45 - 1.42) 1.85 (1.03 - 3.32) 0.0239 SADHtR 1 st quartile 2 nd quartile 3 rd quartile 4 th quartiles 47/250 44/243 39/227 64/239 Ref. 0.95 (0.61 – 1.51) 0.90 (0.56 – 1.43) 1.58 (1.03 – 2.42) 0.0403 41/209 40/211 33/199 55/202 Ref. 0.83 (0.49 - 1.4) 0.77 (0.44 - 1.34) 1.42 (0.82 - 2.46) 0.0828 *Adjusted ORs are adjusted for age, smoker status (never, past, current), cholesterol ratio, blue/white collar status, and marital status. The analysis is based on complete cases. Abbreviations: CI, confidence interval; OR, odds ratio. BMI, body mass index; SAD, sagittal abdominal diameter; SADHtR, sagittal abdominal diameter to height ratio; Ref, reference; p-value, probability value. When adjusting for age, SBP, the ratio total cholesterol/HDL-C, marital status, and blue/white collar status, the odds ratios were non-significant, or significant but attenuated (WHR), or wider CI (SAD). Nagelkerke's R2 ranged from 0.138 (BMI) to 0.150 (SAD and WHR). In pairwise comparisons of adequacies (Additional file 1: Table S7), we saw larger differences in predictive ability between the anthropometric measures (e.g., WHR dominated clearly in all relevant pairs), but the differences were strongly attenuated after adjusting for other important predictors, making the body measurements very comparable. Odds ratios (ORs) with 95% confidence intervals from univariable and multivariable logistic regression models with MI within 28 years of follow-up as an outcome and the anthropometric measures represented by quartiles represented (Additional file: Table S8). In the unadjusted model, the SAD to height ratio shows the highest OR=3.6; 95% CI 1.78-7.29. However, after adjustment, all confidence intervals except WC override 1, and R2 are comparable for all six indexes (0.136-0.148). For completeness, the full model information for all-cause death is displayed in (Additional file 1: Table S9. In the unadjusted model SAD displays the highest OR=1.85; 95% CI 1.17-1.92. After adjustment, the 95% CI overrides 1 for all indexes except SAD. Nevertheless, R2 is comparable for all indexes: 0.138-0.150: If cut-offs from Table 1 are used for myocardial infarction all body measurements except sagittal abdominal diameter were significant after adjustment (Additional file: Table S10). However, for all-cause death, significant findings after adjustment were only seen for WHR and SAD (Additional file 1: Table S11). Correspondingly, R-squared were weak:0.147 and 0.148, Comparison of distribution of the anthropometric measures between those who died due to cardiovascular causes (including MI) and those who died due to other causes is displayed in (Additional file 1: Table S12). Statistically significant associations were seen for all body measurements except WHR. Under our chosen approach to modeling the associations between the body measurements and the events of death and MI over the 28-year follow-up, only minor differences between the indicators were found. The predictive ability of the models measured by Nagelkerke’s R2 was comparable for all the body measures. Discussion In the unadjusted analyses, all body measurements had significant odds ratios for MI. The highest OR comparing 4th to 1st quartile was displayed by SADHtR; OR=3.6; 95% CI 1.78-7.29; p=0.0016. Some studies report similar results (31,32). After adjustment, SADHtR was non-significant, and WC showed the highest OR (4th vs 1st quartile): 2.52; 95% CI 1.06-6.02; p=0.0293. However, the predictive ability of the models was comparable for all measurements, as indicated by Nagelkerke’s R2. Three meta-analyses of cardiovascular risk indicators demonstrated conflicting findings: Cao et al. reported WHR as the best obesity cardiovascular risk indicator; Xue et al. found that WC, WHR, and WHtR, had the best predictive ability; and de Koning et al. gave equal credence to WHR and WC (33,34,35). In our study, the unadjusted OR for WHR was 2.82; 95% CI 1.45-5.51; p=0.0032. As for mortality, in the unadjusted analysis, WHR, WHtR, SAD, and SADHtR were significantly associated with mortality. The highest OR (4th vs 1st quartile) was seen for SAD= 1.85; 95% CI 1.17-2.92; p=0.0117. Most ORs were attenuated after adjustment for traditional risk factors. However, Nagelkerke’s R2 again indicated that predictive ability was comparable for all measurements. All body measurements were significantly correlated with one another. However, WHR was somewhat weaker, with an r=0.64; 95%CI 0.60-0.68 to BMI. This finding is mirrored in a study of 4,504 participants from 29 different countries (36). A significant correlation appeared between all six indicators and all covariates except total alcohol consumption, pulse, and DBP. (These comparisons were, however, not adjusted for multiple testing.) Other studies have shown similar associations with risk factors and debate which body measures are the strongest associated (37,38). A previous finding of the Coeur study suggested the SADHtR was strongest (24), though a systematic review found that WTHtR was a better screening tool than WC and BMI (39). It is generally understood that visceral obesity is the most dangerous cardiovascular risk factor, especially extreme obesity (40, 2, 3, 32, 41). Theoretically, there are arguments for cardiovascular risk via inflammation and the secretion of inflammatory markers, as well as the pathophysiology of cardiac structure and function (3,11). Our study does not support these theories, since the odds ratios for the six obesity risk indicators were attenuated or non-significant for MI and all-cause death after adjustment for age, SBP, total cholesterol/HDL-C, marital status, and blue/white collar status. Our findings are in harmony with several other major studies, such as a study of 855 Swedish men born in 1913 and followed for 13 years that investigated stroke, ischemic disease, and all-cause death as endpoints. In that study, WHR was used as a risk indicator and associations were non-significant when adjusted for smoking, SBP, and serum cholesterol (42). A prospective study of 62,223 Norwegians with death as the endpoint found that WHR was the strongest of 5 obesity indicators, but the association was not significant after adjustment (37). This phenomenon was also found in a study of 6,529 British civil servants, which showed that neither BMI nor WC was significantly associated with death after adjustment (43). Our finding may be relevant to the “fat but fit” thesis in that obesity may be of little relevance for non-diabetic, non-smoking, and fit men with normal BP, blood sugar, and blood lipids (44). Basketball star Michael Jordan’s BMI reportedly varied between 27-29, yet his waist size was less than 30. For him, muscle mass, not fat mass, was the causal factor of the high BMI, and WC would have been a more relevant measure than BMI (45). It is still unclear why the human body is designed to store extra energy in fat depots. It may have been a survival mechanism for early hunters with serious variations in food intake and periods of starvation. Contrary to this, extreme obesity (BMI>35) is likely a separate condition and is no doubt a strong mortality predictor (46). The quartile ORs mortality is displayed in this study (Table 5) showing a J-shaped association of adiposity with mortality with the lowest ORs in the second or third quartiles for all body measures. This shape was also reported by Elagizi in 2018 in an overview of the so-called obesity paradox. (47) Study strength The original cohort was randomly selected from a well-characterized population of male workers followed over 28 years. Very few refused to participate indicating a high level of cooperation. The nurses and health professionals were specially trained and well-defined methods were used (12). Accurate information about endpoints (death and MI) was obtained through well-established national registers that can be considered reliable sources. The internal validity and the data of the current project have been carefully secured throughout the study. Study weaknesses and limitations The cohort of the study was limited to an occupational sample of middle-aged men working in a particular industry (automotive) and in a particular country (Sweden), therefore the external validity for other populations, such as women and in other contexts to the research question was another limitation; thus, the outcomes of the events were restricted. Some data was incomplete due to manual entry or missing lab results. Confounding factors and biases In longitudinal studies, both confounding factors and biases may occur because it is difficult to consider how the participants’ lifestyles and health change over time. For example, over 28 years, the participant may lose or gain weight, or quit smoking, which may change the prognosis of some endpoints. There are always risks for biases, especially when some answers in the questionnaire are self-reported. For example, people may tend to underestimate their smoking and drinking habits which leads to systematic errors. Body measurements might be biased due to incorrect measurement techniques, though comprehensive training of health professionals was mandatory before the study. Conclusion We found a significant correlation between the six different body measurements, and they were all associated with CVD risk factors such as SBP, diabetes, and dyslipidemia, as well as smoking. All body measurements were significantly associated with MI. After adjustment, all but SADHtR and WHR remained significant. SADHtR, SAD, WHtR, and WHR were significantly associated with mortality before adjustment. This association remained for SAD and WHR after adjustment. Compared to other body measurements, BMI showed weaker associations with mortality. Nagelkerke’s R2 was rather comparable for all the indexes, which means that no index stood out as superior to the others. Future studies should investigate these body measurements as risk factors in larger cohorts of industrial workers and women. The success of occupational health services to mitigate risk behaviors should also be evaluated. Visual Abstract (additional file 2) in here. Abbreviations BMI Body mass index CAD Coronary artery disease CDC Centers for Disease Control and Prevention CHD Coronary heart diseases CI Confidence interval CVD Cardiovascular disease DBP diastolic blood pressure FRI Framingham Risk Index FRS Framingham Risk Score HDL-C High-density lipoprotein cholesterol ICD International classification of disease LDL Low-density lipoprotein MI Myocardial infarction NIH The National Institutes of Health NHANES National Health and Nutrition Examination Survey NHLBI National Heart, Lung, and Blood Institute OR Odds ratio P-Value Probability value SAD Sagittal abdominal diameter SADHtR Sagittal abdominal diameter to height ratio SBP systolic blood pressure TG triglycerides WC Waist circumference WHO World Health Organization WHR Waist-to-hip ratio WHtR Waist-to-Height Ratio Declarations Ethics approval and consent to participate This study was performed in line with the principles of the Declaration of Helsinki and included written, informed consent from all participants. The Research Ethics Committee of Gothenburg University approved the original study protocol on Feb 11, 1993 (Dnr. 23-93), and amendments on Feb 19, 2019. Applications to use data from the National Myocardial Infarction Register, Swedeheart, and the National Cause of Death Register were also approved. Consent for publication Consent for publication has been given by the participants and the University of Gothenburg. Availability of data and materials Unidentified data is available upon request to the corresponding author. Competing interests The authors declare no conflict of interest. Lennart Dimberg was employed as an occupational physician for Volvo Flygmotor at the start of the project. Volvo had no influence on the study design, analysis, or interpretation of the data. Funding This work was supported by the Volvo Research Fund and the Mary von Sydow Foundation. Authors contributions Authors’ contributions Lennart Dimberg conceived the study and designed the research. Lala Joulha Ian did the literature search. Both authors contributed to revising and approving the final version of the manuscript. Acknowledgements We would like to acknowledge the original Renault-Volvo Coeur project group 1993-1998 (additional file 1) and all participants of the studies. Furthermore, we are deeply indebted to the masterly Barbora Kessel for her excellent statistical work and to Rebecca Elfast for her superb drawing of the visual abstract. Reghan Borer, for her skillful language revision. References Boutari C, Mantzoros CS. A 2022 update on the epidemiology of obesity and a call to action: as its twin COVID-19 pandemic appears to be receding, the obesity and dysmetabolism pandemic continues to rage on. Metabolism. 2022;133:155217. Björntorp P. Visceral obesity: a civilization syndrome. Obes Res. 1993;1(3):206–22. Poirier P, Giles TD, Bray GA, et al. American Heart Association; Obesity Committee of the Council on Nutrition, Physical Activity, and Metabolism. 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Supplementary Files SupportinginformationBMC2024.docx Visualabstractobesity50x60mmcopy.tif Additional file 2 Visual abstract Caption is imbedded in the picture. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Jul, 2024 Reviews received at journal 02 Jul, 2024 Reviews received at journal 28 Jun, 2024 Reviews received at journal 19 Jun, 2024 Reviewers agreed at journal 13 Jun, 2024 Reviewers agreed at journal 10 Jun, 2024 Reviewers agreed at journal 10 Jun, 2024 Reviewers invited by journal 10 Jun, 2024 Editor invited by journal 10 Jun, 2024 Editor assigned by journal 09 Jun, 2024 Submission checks completed at journal 09 Jun, 2024 First submitted to journal 04 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4529247","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":317128632,"identity":"07a8189f-c101-4c1a-be6d-65af455093da","order_by":0,"name":"Lala Joulha Ian","email":"","orcid":"","institution":"University of Gothenburg","correspondingAuthor":false,"prefix":"","firstName":"Lala","middleName":"Joulha","lastName":"Ian","suffix":""},{"id":317128635,"identity":"83c94c2d-4f8d-4456-9308-f02cc9c4d289","order_by":1,"name":"Lennart Dimberg","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYBACA4bEBmYQg5+BgfEAaVokGxgYiNWSwADWYnCAaC3sya2bCyru2W0+fvjAAYYam2jCWngett2ecaY4eduZtIQDDMfSchsIapFIbLvN25aQbHYgx+AAY8NhYrX8S0g27n9DkpaGBDsDCaJtAfvlWEKCxI1nCQcSiPGLfXv6s9sFNQn2/P3JBx98qLEhrAUGEsEqE4hVDraNFMWjYBSMglEwwgAAYL1F413JErkAAAAASUVORK5CYII=","orcid":"","institution":"University of Gothenburg","correspondingAuthor":true,"prefix":"","firstName":"Lennart","middleName":"","lastName":"Dimberg","suffix":""}],"badges":[],"createdAt":"2024-06-04 15:45:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4529247/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4529247/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58953428,"identity":"1a2740d5-7011-4762-9dba-2a68b86f2f77","added_by":"auto","created_at":"2024-06-24 14:27:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":96021,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 1 shows cumulative incidences of MI for the first and the fourth quantiles of Sagittal Abdominal diameter to Height, which has the highest OR=3.60, 95% CI 1.78-7.29; p=0.0016, and BMI for reference with OR=2.38, 95% CI 1.25-4.52; P=0.0003. The analysis accounts for the fact that death before MI is a competing event for MI.\u003c/p\u003e\n\u003cp\u003e*BMI, body mass index; SADHtR, sagittal abdominal diameter to height ratio; MI, myocardial infarction; n, number; p, probability value; OR, odds ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4529247/v1/f6cc801a9a41bd665a27ab32.png"},{"id":58953426,"identity":"1daf753f-3420-4a2f-ba24-52771f4dd91e","added_by":"auto","created_at":"2024-06-24 14:27:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":104718,"visible":true,"origin":"","legend":"\u003cp\u003eThe graphs shown in Figure 2 include only SAD and BMI. They are selected because the SAD had the highest OR=1.85, 95% CI 1.17-2.92;p= 0.117. BMI is shown for reference (OR=1.25, 95% CI 0.81-1.93; p=0.1703). Complete results were obtained from unadjusted logistic regressions (Table 6).\u003c/p\u003e\n\u003cp\u003e*BMI, body mass index; SAD, sagittal abdominal diameter; MI, myocardial infarction; n, number; p, probability value; OR, odds ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4529247/v1/abc9bb70c63f4655eac94135.png"},{"id":58954069,"identity":"efd0a7f1-3c07-4c15-b2a3-a35c829e3f38","added_by":"auto","created_at":"2024-06-24 14:35:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1195619,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4529247/v1/777a10bd-3acc-47e0-858a-d2761800353a.pdf"},{"id":58953427,"identity":"d36db67d-0444-4d95-a5b3-5f82b2efe988","added_by":"auto","created_at":"2024-06-24 14:27:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":70525,"visible":true,"origin":"","legend":"","description":"","filename":"SupportinginformationBMC2024.docx","url":"https://assets-eu.researchsquare.com/files/rs-4529247/v1/5e994bc4ce1bae1006287857.docx"},{"id":58953430,"identity":"dde2b821-fe3e-4808-ac23-052d7a983999","added_by":"auto","created_at":"2024-06-24 14:27:51","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1288924,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2\u003c/p\u003e\n\u003cp\u003eVisual abstract\u003c/p\u003e\n\u003cp\u003eCaption is imbedded in the picture.\u003c/p\u003e","description":"","filename":"Visualabstractobesity50x60mmcopy.tif","url":"https://assets-eu.researchsquare.com/files/rs-4529247/v1/8b8efb02f0123f302a9971ce.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Six anthropometric indicators and their association with myocardial infarction and death in male Swedish automotive industry workers followed for 28 years","fulltext":[{"header":"Background","content":"\u003cp\u003eThe worldwide obesity epidemic (Body Mass Index 30 and above), so-called \u0026ldquo;globesity,\u0026rdquo; is becoming an increasing concern all over the world. In 2019, according to the Global Burden of Disease, about half of men 40\u0026thinsp;+\u0026thinsp;years old were found to be overweight or obese, of whom about 15% were obese (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eObesity is a serious medical condition that is defined as abnormal or excess body fat accumulation to such an extent that it affects health negatively. The pathophysiology behind obesity is complex. Adiposity includes subcutaneous and visceral fat, and visceral obesity is considered more hazardous than generalized obesity and more strongly associated with chronic diseases (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Visceral obesity is associated with various medical conditions such as hypertension, diabetes, insulin resistance, and metabolic syndrome, all of which increase the risk for cardiovascular disease (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCardiovascular diseases (CVDs) include stroke, heart failure, hypertensive heart diseases, and coronary artery diseases (CAD) such as angina and myocardial infarction (MI). Cardiovascular diseases are globally the leading cause of death (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). According to the Centers for Disease Control and Prevention (CDC), an estimated 80% of cardiovascular diseases are preventable or delayable. Preventive measures include avoiding smoking, limiting alcohol intake, healthy eating and exercise, as well as treating hypertension, hyperlipidemia, and diabetes (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Framingham Heart Study is an ongoing cardiovascular cohort study. It was initiated in 1948 in Framingham, Massachusetts. The study is directed by the National Heart, Lung, and Blood Institute (NHLBI), and it established risk factors that contribute to cardiovascular diseases, such as older age, male sex, smoking status, diabetes, high blood pressure, abnormal lipid levels, obesity (BMI 30 and above), and physical inactivity. The Framingham Risk Score (FRS) is the first CVD risk assessment instrument for primary prevention; it calculates a 10-year risk for cardiovascular events and includes BMI (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThough commonly used, BMI has its limitations. It doesn\u0026rsquo;t consider important factors like age, gender, muscle mass, and the location or distribution of body fat. In 2006, a large meta-analysis suggested that we need to include other measurement methods to predict cardiovascular mortality because BMI is only a significant predictor of cardiovascular mortality in patients with severe obesity (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Alternate indexes such as waist circumference (WC), waist-to-height ratio (WHtR) waist-to-hip ratio (WHR), sagittal abdominal diameter (SAD), and sagittal abdominal diameter to height ratio (SADHtR) have been introduced and argued to correlate better with abdominal obesity, which is considered causally related to the pathophysiology of cardiac structure and function (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 2021, a meta-analysis of body weight and different obesity indicators found 31 cohort studies with acceptable follow-up duration for evaluation. None of these included industrial workers in an occupational health setting.\u003c/p\u003e \u003cp\u003eThe Renault-Volvo Coeur project is a prospective longitudinal investigation initiated in 1992 in collaboration with the occupational health clinics of two automotive companies. The study was designed to compare the national differences in cardiovascular disease between the French and Swedish workers at Renault and Volvo (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The main objective of the study was to understand why Frenchmen had significantly lower rates of coronary heart disease (CHD) compared to men in other European countries when the French lifestyle itself contained risk aspects such as high consumption of cheese and wine and a high proportion of smokers, which has been called the \u0026ldquo;French Paradox\u0026rdquo; (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNational differences included a higher proportion of married/cohabitant Frenchmen (90%) compared to Swedish men (76%), potentially linked with a healthier diet and social support associated with lower cardiovascular risk (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Another finding in the Swedish cohort was an association between living alone and a significantly increased risk of MI and all-cause death (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Likewise, blue-collar workers as compared to white-collar workers displayed an increased risk of MI and all-cause death, whereas the opposite association was seen for education. The results of our findings have sparked interest in these organizations to start various tailored prevention programs.\u003c/p\u003e \u003cp\u003eAt the start of the Coeur project, standardized measurements for all six common obesity markers were recorded. Using Swedish register data from the subsequent 28 years, we can now report the accumulated incidence of first MI and death.\u003c/p\u003e \u003cp\u003eThis project aims to identify correlations between the different commonly used body measures of obesity, as well as to determine the association between these body measures and other CVD risk factors. This study also investigates which body measurement, if any, has the strongest associations with MI and death.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn 1993, one thousand middle-aged men (45-50 years of age) from each automotive company were recruited. The study aimed to investigate coronary health among French and Swedish employees and to initiate preventive measures in cooperation with the respective occupational health services.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study is focused on the Swedish cohort. To reach 1000 Swedish white male employees since this reflected the demographics of the employees at the time,\u0026nbsp;born between 1943 and 1948, 1144 randomly selected individuals were asked to participate; 144 employees declined due to lack of interest or long-term sickness.\u003c/p\u003e\n\u003cp\u003eThe participants were surveyed at a nurse-led health examination with a self-administered questionnaire including questions about marital status, smoking habits, alcohol intake, lifestyle, mental health, working conditions, stress, exercise, and more. The methods have been previously described by Simon et al. (12).\u003c/p\u003e\n\u003cp\u003eWeight was measured by digital scales and height by fixed stadiometers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCardiovascular risk was estimated using the Framingham risk score, age, smoking habits, systolic blood pressure, diabetes mellitus, left ventricular hypertrophy, and the ratio of total cholesterol to high-density lipoprotein concentration (8).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData on the first myocardial infarction in the Swedish cohort were obtained from the National Swedish Register of MI, Swedeheart (16).\u003c/p\u003e\n\u003cp\u003eTo define MI, we used the International Statistical Classification of Diseases: code 410 in the ninth revision and codes I21-I23 in the tenth revision (17).\u003c/p\u003e\n\u003cp\u003eDeath data was collected from the Swedish Cause of Death Registry (18).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBody measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBody mass index (BMI kg/ m\u003csup\u003e2\u003c/sup\u003e) is calculated by dividing weight in kilograms by the square of height in meters (10). BMI classification according to WHO is presented in Table 1, along with cut-off points for WC, WHtR, and WHR.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWaist circumference (WC; cm) is measured in a standing position, with the measurement tape placed at the midpoint between the iliac crest and the lower margin of the last palpable rib (19). To classify abdominal obesity, we used threshold values suggested by the National Institutes of Health (NIH) (Table 1) (20).\u003c/p\u003e\n\u003cp\u003eWaist-to-Height Ratio (WHtR) is calculated by dividing WC (cm), as measured above, by height (m). There is no standard reference value for WHtR; however, an ideal WHtR has been suggested to be less than or equal to 0.5 (21)\u003c/p\u003e\n\u003cp\u003eWaist-to-Hip Ratio (WHR) is calculated by dividing the waist circumference (cm) by the hip circumference (cm). The ratio determines the amount of stored fat on the waist, hips, and buttocks (22). WHR cut-off points were determined according to WHO guidelines (Table 1).\u003c/p\u003e\n\u003cp\u003eSagittal Abdominal Diameter (SAD) is measured in a supine position, as the distance (cm) from the lower back to the highest point of the abdomen. The National Health and Nutrition Examination Survey (NHANES) describes in detail how to measure the sagittal abdominal diameter (23). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA 2010 population-based cross-sectional study of Swedish subjects aged 60 calculated the cardiometabolic risk score. According to their analyses, the optimal cut-offs for SAD were 22\u0026thinsp;cm in men and 20\u0026thinsp;cm in women (24).\u003c/p\u003e\n\u003ch3\u003eSagittal Abdominal Diameter to Height Ratio (SADHtR) is another screening method to measure visceral fat. It was first suggested in an earlier phase of the Coeur project (25,26).\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eIt\u0026rsquo;s comprised of the sagittal diameter (cm) divided by the height (m).\u003c/p\u003e\n\u003cp\u003eThe lack of reference ranges and cutpoints to assign risk categories is a limitation of SADHtR.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Anthropometric measurements cut-off points of CVD risks.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBMI classification.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eClassification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eNormal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e18.5-24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e25.0-29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eObese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eClass I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e30-34.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eClass II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e35-39.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eClass III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026ge;40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Waist circumference cut-off points.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eLow-risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHigh-risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eIncreased higher risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;94 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e94-102 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;102 cm\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;80 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e80-88 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;88 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWaist-to-hip ratio cut-off points.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHealth risk:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.95 or lower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.96-1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e1.0 or higher\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.80 or lower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.81-0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0.85 or higher\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWaist-to-height ratio cut-off points.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;An ideal waist-to-height ratio has been suggested to be less than or equal to 0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSagittal Abdominal Diameter cut-off points.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\"\u003e\n \u003cp\u003eThe optimal cutoffs for sagittal abdominal diameter are 22\u0026thinsp;cm in men and 20\u0026thinsp;cm in women.\u003c/p\u003e\n \u003cp\u003eThere isn\u0026rsquo;t an optimal cut-off point for sagittal abdominal diameter by height.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: BMI, body mass index; CVD, cardiovascular disease; cm, centimeter; m, meter; kg, kilogram.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline variables and their collection have been described in detail by Simon et al. (12). Baseline data for categorical (qualitative, dichotomous) variables was described as numbers and percentages. The distribution of continuous (quantitative) variables was described as mean value with standard deviation in parentheses. The association between each anthropometric measurement and each cardiovascular risk factor was assessed as follows. For cardiovascular risk factors represented by numeric variables, Spearman\u0026rsquo;s correlation coefficient between each obesity measure and the respective variable was calculated. Spearman\u0026rsquo;s correlation ranges from -1 to +1. Values close to -1 indicate a negative monotonic relationship between the variables, zero indicates no correlation, and +1 indicates a positive monotonic relationship. If the associated 95% confidence interval (constructed by z-transformation) contained 0, the association was not statistically significant at the 5% level. For categorical variables, the distribution of the values of each obesity variable was compared across the categories by the Mann-Whitney-Wilcoxon (two categories) or Kruskal-Wallis (more categories) test. We were not able to reliably study the risk factor \u0026ldquo;diabetes\u0026rdquo; due to only 13 participants with this condition at baseline.\u003c/p\u003e\n\u003cp\u003eThe Aalen-Johansen and Kaplan-Meier methods were used to estimate the cumulative incidences of MI and death over the 28 years of follow-up. Unadjusted and adjusted logistic regression analyses were conducted to quantify the association of the six body measurements with the association of MI, and with death within 28 years of follow-up in the form of odds ratios and their 95% confidence intervals. The body measurements were categorized according to quartiles, and their statistical significance was assessed by the likelihood ratio test. We used Nagelkerke\u0026rsquo;s R2 to assess the predictive ability of the fitted models. For pairs of anthropometric measures (for example, measure M1 and M2), we judged their relative predictive strength by comparing their adequacy measures (27). Higher adequacy in the pair indicates a higher predictive ability of the corresponding measure (given the model). The adequacy of M1 (M2) expresses the proportion of log-likelihood explained by a model containing only M1 (M2) as compared to a model containing both measures as explanatory variables. It is calculated as the ratio (%) of likelihood ratio statistics from the full and the nested models. Adjustments for age, systolic blood pressure, the ratio of total cholesterol/ HDL-C, and blue/white collar status were included in the models. In case of death, the models were additionally adjusted for marital status.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDouble-sided P values below 0.05 were considered statistically significant. Missing values were omitted from the analyses. Analysis was performed in R (version 4.3.1, with RStudio, version 2023.03.0), employing packages rms (version 6.7-0), Hmisc (version 4.7-2) (28), survival (version 3.5-5) (29), and cmprsk (version 2.2-11) (30)\u003c/p\u003e\n\u003cp\u003eExplanatory (independent) variables included components of the Framingham risk index (FRI): age (years), systolic blood pressure (mmHg), total cholesterol/HDL-C (mmol/l), smoking (No/Past/Current), and diabetes (Yes/No; excluded in the present study due to low number [n=13] at baseline). Additional explanatory variables are presented in Table 2.\u003c/p\u003e\n\u003cp\u003eOutcomes include deaths from all causes, deaths due to cardiovascular causes, deaths due to MI, and first MI.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Additional explanatory variables and their codings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eAdditional explanatory variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eCoding\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eType of work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eBlue collar/white collar\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eMarried or co-habiting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eYes/no\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eUniversity education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eYes/no\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eExercise level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eLess than 1 hour/week\u003c/p\u003e\n \u003cp\u003e1-3 hours per week\u003c/p\u003e\n \u003cp\u003eMore than 3 hours per week\u003c/p\u003e\n \u003cp\u003eAthlete\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eDiastolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003emm Hg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eResting heart rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eBeats per minute\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eTriglycerides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003emmol/l\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eFasting plasma glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003emmol/l\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eTotal alcohol consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eg/week\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eBody mass index, BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003ekg/m2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eWaist circumference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003ecm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eWaist-to-height ratio, WHtR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003ecm/m\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eSagittal abdominal diameter, SAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003ecm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eSagittal abdominal diameter to height ratio, SADHtR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003ecm/m\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: Hg, mercury; mm, millimeter; m, meter; g, gram; mmol, millimoles; l, liter; cm, centimeter: kg, kilogram.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and informed consent\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki and included written, informed consent from all participants. The Research Ethics Committee of Gothenburg University approved the original study protocol on Feb 11, 1993 (Dnr. 23-93), and amendments on Feb 19, 2019.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eApplications to use data from the National Myocardial Infarction Register, Swedeheart, and the National Cause of Death Register were also approved.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn total, 959 men had full information on the six body measurements. Of those, 60% performed clerical work and 38% manual work; 76% were married/living together with a partner; and nearly one-third were current smokers (Table 3).\u003c/p\u003e\n\u003cp\u003eAn overview of the extent of missing values is presented (Additional File 1: Table S1). Missing values vary between 0-8%.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Baseline characteristics and end-point data of first myocardial infarction and all-cause death.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003eVariable\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\" valign=\"top\"\u003e\n \u003cp\u003eAll analyzed participants\u003c/p\u003e\n \u003cp\u003e(n=959)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\" valign=\"top\"\u003e\n \u003cp\u003eParticipants with an MI\u003c/p\u003e\n \u003cp\u003e(n = 90)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\" valign=\"top\"\u003e\n \u003cp\u003eParticipants deceased\u003c/p\u003e\n \u003cp\u003e(n= 194)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003eAge at baseline (years), mean (SD)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e47.7(1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e47.9(1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e47.9(1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003eBlue-collar workers, n (%) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e367(38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e42(47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e97(50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003eUniversity education, n (%) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e227(24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e25(28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e49(25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003eMarried or cohabitant, n (%) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e729 (76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e67 (74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e124 (64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003eSystolic blood pressure (mmHg), mean (SD)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e117(15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e121(16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e122(18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003eDiastolic blood pressure (mmHg), mean (SD)\u0026nbsp;*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e75(12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e76(11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e74(11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003ePulse rate (beats per minute), mean (SD)\u0026nbsp;*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e63 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e63(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e63(9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003eB-Cholesterol (mmol/l), mean (SD)\u0026nbsp;*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e5.6(1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e6.2(1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e5.9(1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003eHDL-C (mmol/l), mean (SD)\u0026nbsp;*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e1.2(0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e1.1(0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e1.2(0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003eTriglycerides (mmol/l), mean (SD)* \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e1.5(0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e1.7(0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e1.6(1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003eB-glucose (mmol/l), mean (SD)\u0026nbsp;*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e5.5(1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e5.5(0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e5.6(1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003eBody mass index (kg/m2), mean (SD) \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e25.6(3.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e26.7(3.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e26.1(3.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003eWaist (cm), mean (SD) \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e92.4(9.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e95.7(8.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e94.2(10.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003eWaist-hip ratio, mean (SD) \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e0.*93(0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e0.95(0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e0.94(0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003eSagittal abdominal diameter (cm), mean (SD) \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e20.3(2.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e21.0(2.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e20.8(3.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003eSagittal abdominal diameter to height ratio, mean (SD) \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e11.3(1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e11.8(1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e11.6(1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003eWaist to height ratio, mean (SD) \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e0.52(0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e0.54(0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e0.53(0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003eAlcohol consumption gram/\u0026nbsp;week, mean (SD)\u0026nbsp;*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e51.4(58.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e54.4(58.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e52.1(58.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking\u0026nbsp;*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Never, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e259(27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e9(10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e45(23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Past, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e371(39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e34(38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e57(29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Current, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e270(28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e44(49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e83(43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003eExercise\u0026nbsp;*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Less than 1 hour per week, n (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e292(30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e28(31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e63(32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;1 - 3 hours of exercise per week, n (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e450(47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e42(47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e94(48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;More than 3 hours of exercise per week, n (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e163(17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e16(18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e25(13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.60399334442596%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Active as an athlete, n (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.630615640599%\"\u003e\n \u003cp\u003e28(3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.965058236272878%\"\u003e\n \u003cp\u003e4(4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.800332778702163%\"\u003e\n \u003cp\u003e9(5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe extent of missing values for variables denoted with * is described in the supplementary material (S1); in all cases, the proportion of missing values did not exceed 8%.\u003c/p\u003e\n\u003cp\u003eAbbreviations: N, number of participants; SD, standard deviations; MI, myocardial infarction; HDL-C, high-density lipoprotein cholesterol; B-glucose (glucose level in blood); B-cholesterol (cholesterol level in blood).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOver the 28 years of follow-up, 90 individuals suffered at least one MI, and 194 had died. Of those who were deceased, 53 persons died due to cardiovascular causes, of which 19 died due to MI.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe average BMI was 25.6, which was higher in both the MI and death groups (Table 3). WC at baseline was also higher in the MI and death groups. WHR, SAD, and SADHtR were slightly higher in both MI and death groups, but still within normal ranges.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBoth the mean systolic and diastolic blood pressure were higher in the MI group, though within normal ranges. The mean for systolic blood pressure was 121 compared to 117 in the overall group; for diastolic pressure, it was 76 compared to 75.\u003c/p\u003e\n\u003cp\u003eTotal cholesterol, high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), and fasting plasma glucose were similar between the groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociations between the six body measures of obesity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo see how well the six different measures of obesity correlated with each other, we used Spearman\u0026rsquo;s correlation coefficient (Table 4). The calculated correlations were moderate-to-high, with WHtR being less strongly associated with the others.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Spearman\u0026rsquo;s correlation coefficient and 95% confidence interval of six different obesity measurements.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.970049916805323%\" valign=\"top\"\u003e\n \u003cp\u003eSpearman\u0026rsquo;s correlation (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.970049916805323%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist circumference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.139767054908486%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist-to-hip ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.973377703826955%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist-to-\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eheight ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.640599001663894%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.306156405990016%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSADHtR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.970049916805323%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.970049916805323%\" valign=\"top\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003cp\u003e(0.86 \u0026ndash; 0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.139767054908486%\" valign=\"top\"\u003e\n \u003cp\u003e0.64\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.60 \u0026ndash; 0.68)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.973377703826955%\" valign=\"top\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003cp\u003e(0.88 - 0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.640599001663894%\" valign=\"top\"\u003e\n \u003cp\u003e0.82\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.80 -0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.306156405990016%\" valign=\"top\"\u003e\n \u003cp\u003e0.84\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.82 \u0026ndash; 0.86)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.970049916805323%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist circumference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.970049916805323%\" valign=\"top\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.139767054908486%\" valign=\"top\"\u003e\n \u003cp\u003e0.79\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.77 \u0026ndash; 0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.973377703826955%\" valign=\"top\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003cp\u003e(0.92 \u0026ndash; 0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.640599001663894%\" valign=\"top\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003cp\u003e(0.84 - 0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.306156405990016%\" valign=\"top\"\u003e\n \u003cp\u003e0.81\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.79 \u0026ndash; 0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.970049916805323%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist-to-hip ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.970049916805323%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.139767054908486%\" valign=\"top\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.973377703826955%\" valign=\"top\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003cp\u003e(0.81 \u0026ndash; 0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.640599001663894%\" valign=\"top\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003cp\u003e(0.59 - 0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.306156405990016%\" valign=\"top\"\u003e\n \u003cp\u003e0.66\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.63 \u0026ndash; 0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.970049916805323%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist-to-\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eheight ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.970049916805323%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.139767054908486%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.973377703826955%\" valign=\"top\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.640599001663894%\" valign=\"top\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003cp\u003e(0.78 \u0026ndash; 0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.306156405990016%\" valign=\"top\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003cp\u003e(0.84 \u0026ndash; 0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.970049916805323%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.970049916805323%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.139767054908486%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.973377703826955%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.640599001663894%\" valign=\"top\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.306156405990016%\" valign=\"top\"\u003e\n \u003cp\u003e0.96\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.95 - 0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.970049916805323%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSADHtR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.970049916805323%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.139767054908486%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.973377703826955%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.640599001663894%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.306156405990016%\" valign=\"top\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: CI, confidence interval; BMI, body mass index; SAD, sagittal abdominal diameter; SADHtR, sagittal abdominal diameter to height ratio.\u003c/p\u003e\n\u003cp\u003eThe division of probands into quartile categories according to the values of the anthropometric measurements are specified (Additional File 1: Table S2). The number of probands in the various high-risk groups varies between BMI\u0026gt;30 (n=95) and WHR\u0026gt;0.9 (n=687).\u003c/p\u003e\n\u003cp\u003eHow the normal cut-off values of the variables relate to the usual BMI categories is shown (Additional File 1:Table S3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between each anthropometric measurement with each cardiovascular risk factor\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA significant association (measured by the Kruskal-Wallis test for categorical risk factors, and Spearman\u0026rsquo;s correlation coefficient for continuous risk factors) was found between the anthropometric measurements (except for waist circumference), and type of work, smoking, fasting blood glucose levels, triglycerides, HDL cholesterol, total cholesterol, and SBP. The p-values were, however, not adjusted for multiple testing.\u003c/p\u003e\n\u003cp\u003eDue to missing values, the number of probands used in the analysis differs slightly between the variables; this analysis is available (additional file 1: Table S4, and Table S5). Traditional cardiovascular risk factors as continuous variables: smoking (pack-years) b-lipids, b-glucose, and systolic blood pressure are significantly correlated with all six indexes. The same is found for the following categorical variables: diabetes (yes/no), smoker (never/past/current), and collar (blue/white). However, WHR is only borderline significantly associated with diabetes (p=0.0515). Only 13 persons had diabetes when the study started.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between the body measurement variables and MI over time\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 959 participants, there were 90 MIs and 194 deaths. Cumulative incidences of MI for the first and the fourth quantiles of the body measurement variables are presented (Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe differences between the curves are statistically significant for all body measurement variables. The analysis accounts for the fact that death before MI is a competing event for MI.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo measure the association between the body measurement variables and the probability of developing MI within 28 years of follow-up, we used a logistic regression model (Table 5 and Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Body measures\u0026rsquo; association with developing MI within 28 years of follow-up.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.943521594684384%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.132890365448505%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvents/\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.940199335548172%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnadjusted OR\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.289036544850498%\" valign=\"top\"\u003e\n \u003cp\u003ep-value (likelihood ratio test)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.299003322259136%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvents/\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.60797342192691%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted OR\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.787375415282392%\" valign=\"top\"\u003e\n \u003cp\u003ep-value (likelihood ratio test)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.943521594684384%\" valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e3\u003csup\u003erd\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e4\u003csup\u003eth\u003c/sup\u003e quartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.132890365448505%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15/240\u003c/p\u003e\n \u003cp\u003e11/241\u003c/p\u003e\n \u003cp\u003e32/244\u003c/p\u003e\n \u003cp\u003e32/234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.940199335548172%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e0.72 (0.32 \u0026ndash; 1.60)\u003c/p\u003e\n \u003cp\u003e2.26 (1.19 \u0026ndash; 4.30)\u003c/p\u003e\n \u003cp\u003e2.38 (1.25 \u0026ndash; 4.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.289036544850498%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.299003322259136%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13/206\u003c/p\u003e\n \u003cp\u003e10/206\u003c/p\u003e\n \u003cp\u003e28/208\u003c/p\u003e\n \u003cp\u003e39/202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.60797342192691%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e0.65 (0.27 - 1.56)\u003c/p\u003e\n \u003cp\u003e1.66 (0.79 - 3.47)\u003c/p\u003e\n \u003cp\u003e1.73 (0.82 - 3.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.787375415282392%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0404\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.943521594684384%\" valign=\"top\"\u003e\n \u003cp\u003eWaist circumference\u003c/p\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e3\u003csup\u003erd\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e4\u003csup\u003eth\u003c/sup\u003e quartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.132890365448505%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10/254\u003c/p\u003e\n \u003cp\u003e23/265\u003c/p\u003e\n \u003cp\u003e28/213\u003c/p\u003e\n \u003cp\u003e29/227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.940199335548172%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e2.32 (1.08 \u0026ndash; 4.98)\u003c/p\u003e\n \u003cp\u003e3.69 (1.75 \u0026ndash; 7.79)\u003c/p\u003e\n \u003cp\u003e3.57 (1.7 \u0026ndash; 7.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.289036544850498%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.299003322259136%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8/218\u003c/p\u003e\n \u003cp\u003e20/224\u003c/p\u003e\n \u003cp\u003e27/187\u003c/p\u003e\n \u003cp\u003e25/193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.60797342192691%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e2.22 (0.94 - 5.26)\u003c/p\u003e\n \u003cp\u003e3.34 (1.43 - 7.8)\u003c/p\u003e\n \u003cp\u003e2.52 (1.06 - 6.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.787375415282392%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0293\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.943521594684384%\" valign=\"top\"\u003e\n \u003cp\u003eWaist-to-hip ratio\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e3\u003csup\u003erd\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e4\u003csup\u003eth\u003c/sup\u003e quartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.132890365448505%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13/241\u003c/p\u003e\n \u003cp\u003e16/239\u003c/p\u003e\n \u003cp\u003e28/241\u003c/p\u003e\n \u003cp\u003e33/238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.940199335548172%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e1.26 (0.59 \u0026ndash; 2.68)\u003c/p\u003e\n \u003cp\u003e2.34 (1.16 \u0026ndash; 4.57)\u003c/p\u003e\n \u003cp\u003e2.82 (1.45 \u0026ndash; 5.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.289036544850498%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0032\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.299003322259136%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12/203\u003c/p\u003e\n \u003cp\u003e12/199\u003c/p\u003e\n \u003cp\u003e27/217\u003c/p\u003e\n \u003cp\u003e29/203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.60797342192691%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e0.80 (0.34 - 1.89)\u003c/p\u003e\n \u003cp\u003e1.84 (0.88 - 3.85)\u003c/p\u003e\n \u003cp\u003e1.64 (0.78 - 3.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.787375415282392%\" valign=\"top\"\u003e\n \u003cp\u003e0.0720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.943521594684384%\" valign=\"top\"\u003e\n \u003cp\u003eWaist-to-height ratio\u003c/p\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e3\u003csup\u003erd\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e4\u003csup\u003eth\u003c/sup\u003e quartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.132890365448505%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12/242\u003c/p\u003e\n \u003cp\u003e12/242\u003c/p\u003e\n \u003cp\u003e30/236\u003c/p\u003e\n \u003cp\u003e36/239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.940199335548172%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e1.00 (0.44 \u0026ndash; 2.27)\u003c/p\u003e\n \u003cp\u003e2.79 (1.39 \u0026ndash; 5.59)\u003c/p\u003e\n \u003cp\u003e3.40 (1.72 \u0026ndash; 6.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.289036544850498%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.299003322259136%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11/205\u003c/p\u003e\n \u003cp\u003e10/214\u003c/p\u003e\n \u003cp\u003e27/201\u003c/p\u003e\n \u003cp\u003e32/202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.60797342192691%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e0.80 (0.33 - 1.96)\u003c/p\u003e\n \u003cp\u003e2.03 (0.94 - 4.38)\u003c/p\u003e\n \u003cp\u003e2.11 (0.98 - 4.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.787375415282392%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0225\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.943521594684384%\" valign=\"top\"\u003e\n \u003cp\u003eSAD\u003c/p\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e3\u003csup\u003erd\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e4\u003csup\u003eth\u003c/sup\u003e quartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.132890365448505%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9/249\u003c/p\u003e\n \u003cp\u003e30/312\u003c/p\u003e\n \u003cp\u003e31/222\u003c/p\u003e\n \u003cp\u003e20/176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.940199335548172%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e2.84 (1.32 \u0026ndash; 6.09)\u003c/p\u003e\n \u003cp\u003e4.33 (2.01 \u0026ndash; 9.31)\u003c/p\u003e\n \u003cp\u003e3.42 (1.52 \u0026ndash; 7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.289036544850498%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.299003322259136%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8/204\u003c/p\u003e\n \u003cp\u003e26/279\u003c/p\u003e\n \u003cp\u003e29/192\u003c/p\u003e\n \u003cp\u003e17/147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.60797342192691%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e1.93 (0.83 - 4.45)\u003c/p\u003e\n \u003cp\u003e2.93 (1.25 - 6.87)\u003c/p\u003e\n \u003cp\u003e1.84 (0.72 - 4.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.787375415282392%\" valign=\"top\"\u003e\n \u003cp\u003e0.0653\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.943521594684384%\" valign=\"top\"\u003e\n \u003cp\u003eSADHtR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e3\u003csup\u003erd\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e4\u003csup\u003eth\u003c/sup\u003e quartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.132890365448505%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11/250\u003c/p\u003e\n \u003cp\u003e21/243\u003c/p\u003e\n \u003cp\u003e24/227\u003c/p\u003e\n \u003cp\u003e34/239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.940199335548172%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e2.06 (0.97 \u0026ndash; 4.36)\u003c/p\u003e\n \u003cp\u003e2.57 (1.23 \u0026ndash; 5.37)\u003c/p\u003e\n \u003cp\u003e3.60 (1.78 \u0026ndash; 7.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.289036544850498%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.299003322259136%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10/209\u003c/p\u003e\n \u003cp\u003e17/212\u003c/p\u003e\n \u003cp\u003e22/199\u003c/p\u003e\n \u003cp\u003e31/202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.60797342192691%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e1.20 (0.52 - 2.77)\u003c/p\u003e\n \u003cp\u003e1.78 (0.79 - 4.02)\u003c/p\u003e\n \u003cp\u003e2.11 (0.93 - 4.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.787375415282392%\" valign=\"top\"\u003e\n \u003cp\u003e0.1955\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Adjusted OR were adjusted for age, smoker status (never, past, current), cholesterol ratio, blue/white collar status, and the analysis is based on complete cases.\u003c/p\u003e\n\u003cp\u003eAbbreviations: CI, confidence interval: OR, odds ratio: BMI, body mass index: SAD, sagittal abdominal diameter; SADHtR, sagittal abdominal diameter to height ratio; Ref, reference. p-value, probability value; MI, myocardial infarction.\u003c/p\u003e\n\u003cp\u003eFor each variable of interest (BMI, WHR, WC, WHtR, SAD, SADHtR), we used logistic regression to model the odds of experiencing an MI within 28 years of follow-up, the complimentary event being no record of MI (either dead or alive).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the unadjusted analysis, large baseline values of all six variables of interest were associated with larger odds of MI within 28 years of follow-up. All variables were statistically significant when tested by a likelihood ratio test. Nagelkerke\u0026rsquo;s R2 for the unadjusted models ranged from WHR: 0.031 to WHtR:0.053.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen adjusting for some of the baseline variables, the odds ratios were attenuated and the variables WHR and SADHtR did not pass the formal significance test. Nagelkerke\u0026rsquo;s R2 for the adjusted models ranged from 0.136 (SADHtR) to 0.148 (WHtR), indicating a weak relationship between predictors and outcomes. In pairwise comparisons of predictive information contained in each of the anthropometric measures, in our data, and in combination with the chosen model and chosen categorization of the body measures, we found a slight dominance for waist-to-height ratio (Additional file 1: Table S6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between the body measurement variables and death over time\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA logistic regression model was used to measure the association between the body measurement variables (BMI, WHR, WC, WHtR, SAD, SADHtR) and the probability of dying within 28 years of follow-up. Of 959 participants, 194 died within this period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe saw higher cumulative incidences of death for the 4th quartiles of the overweight variables as compared to the 1st quartiles, but the difference was not significant for BMI and waist circumference (Figure 2). Similar results were obtained from unadjusted logistic regressions (Table 6).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. Body measures\u0026rsquo; association with all-cause mortality within 28 years of follow-up.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.306156405990016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.816971713810316%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvents/\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.13477537437604%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnadjusted OR\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.311148086522463%\" valign=\"top\"\u003e\n \u003cp\u003ep-value (likelihood ratio test)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.650582362728786%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvents/\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.302828618968388%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted OR\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\" valign=\"top\"\u003e\n \u003cp\u003ep-value (likelihood ratio test)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.306156405990016%\" valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e3\u003csup\u003erd\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e4\u003csup\u003eth\u003c/sup\u003e quartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.816971713810316%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e50/240\u003c/p\u003e\n \u003cp\u003e41/241\u003c/p\u003e\n \u003cp\u003e45/244\u003c/p\u003e\n \u003cp\u003e58/234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.13477537437604%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e0.78 (0.49 \u0026ndash; 1.23)\u003c/p\u003e\n \u003cp\u003e0.86 (0.55 \u0026ndash; 1.35)\u003c/p\u003e\n \u003cp\u003e1.25 (0.81 \u0026ndash; 1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.311148086522463%\" valign=\"top\"\u003e\n \u003cp\u003e0.1703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.650582362728786%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e44/206\u003c/p\u003e\n \u003cp\u003e38/206\u003c/p\u003e\n \u003cp\u003e38/207\u003c/p\u003e\n \u003cp\u003e49/202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.302828618968388%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e0.81 (0.48 - 1.36)\u003c/p\u003e\n \u003cp\u003e0.80 (0.47 - 1.37)\u003c/p\u003e\n \u003cp\u003e1.13 (0.66 - 1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\" valign=\"top\"\u003e\n \u003cp\u003e0.4773\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.306156405990016%\" valign=\"top\"\u003e\n \u003cp\u003eWaist circumference\u003c/p\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e3\u003csup\u003erd\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e4\u003csup\u003eth\u003c/sup\u003e quartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.816971713810316%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e50/254\u003c/p\u003e\n \u003cp\u003e46/265\u003c/p\u003e\n \u003cp\u003e38/213\u003c/p\u003e\n \u003cp\u003e60/227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.13477537437604%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e0.86 (0.55 \u0026ndash; 1.34)\u003c/p\u003e\n \u003cp\u003e0.89 (0.56 \u0026ndash; 1.41)\u003c/p\u003e\n \u003cp\u003e1.47 (0.96 \u0026ndash; 2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.311148086522463%\" valign=\"top\"\u003e\n \u003cp\u003e0.0633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.650582362728786%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45/218\u003c/p\u003e\n \u003cp\u003e38/224\u003c/p\u003e\n \u003cp\u003e37/186\u003c/p\u003e\n \u003cp\u003e49/193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.302828618968388%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e0.75 (0.45 - 1.25)\u003c/p\u003e\n \u003cp\u003e0.98 (0.58 - 1.66)\u003c/p\u003e\n \u003cp\u003e1.26 (0.74 - 2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\" valign=\"top\"\u003e\n \u003cp\u003e0.2651\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.306156405990016%\" valign=\"top\"\u003e\n \u003cp\u003eWaist to hip ratio\u003c/p\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e3\u003csup\u003erd\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e4\u003csup\u003eth\u003c/sup\u003e quartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.816971713810316%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e43/241\u003c/p\u003e\n \u003cp\u003e31/239\u003c/p\u003e\n \u003cp\u003e56/241\u003c/p\u003e\n \u003cp\u003e64/238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.13477537437604%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e0.69 (0.42 \u0026ndash; 1.13)\u003c/p\u003e\n \u003cp\u003e1.39 (0.89 \u0026ndash; 2.18)\u003c/p\u003e\n \u003cp\u003e1.69 (1.09 \u0026ndash; 2.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.311148086522463%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.650582362728786%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e37/203\u003c/p\u003e\n \u003cp\u003e27/199\u003c/p\u003e\n \u003cp\u003e54/216\u003c/p\u003e\n \u003cp\u003e51/203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.302828618968388%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e0.75 (0.43 - 1.33)\u003c/p\u003e\n \u003cp\u003e1.57 (0.94 - 2.6)\u003c/p\u003e\n \u003cp\u003e1.44 (0.85 - 2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0235\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.306156405990016%\" valign=\"top\"\u003e\n \u003cp\u003eWaist-to-height ratio\u003c/p\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e3\u003csup\u003erd\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e4\u003csup\u003eth\u003c/sup\u003e quartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.816971713810316%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e43/242\u003c/p\u003e\n \u003cp\u003e42/242\u003c/p\u003e\n \u003cp\u003e44/236\u003c/p\u003e\n \u003cp\u003e65/239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.13477537437604%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e0.97 (0.61 \u0026ndash; 1.55)\u003c/p\u003e\n \u003cp\u003e1.06 (0.67 \u0026ndash; 1.69)\u003c/p\u003e\n \u003cp\u003e1.73 (1.12 \u0026ndash;2.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.311148086522463%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0259\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.650582362728786%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e39/205\u003c/p\u003e\n \u003cp\u003e37/214\u003c/p\u003e\n \u003cp\u003e38/200\u003c/p\u003e\n \u003cp\u003e55/202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.302828618968388%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e0.89 (0.53 - 1.51)\u003c/p\u003e\n \u003cp\u003e1.05 (0.61 - 1.81)\u003c/p\u003e\n \u003cp\u003e1.49 (0.88 - 2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\" valign=\"top\"\u003e\n \u003cp\u003e0.2387\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.306156405990016%\" valign=\"top\"\u003e\n \u003cp\u003eSAD\u003c/p\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e3\u003csup\u003erd\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e4\u003csup\u003eth\u003c/sup\u003e quartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.816971713810316%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e46/249\u003c/p\u003e\n \u003cp\u003e58/312\u003c/p\u003e\n \u003cp\u003e38/222\u003c/p\u003e\n \u003cp\u003e52/176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.13477537437604%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e1.01 (0.66 \u0026ndash; 1.55)\u003c/p\u003e\n \u003cp\u003e0.91 (0.57 \u0026ndash; 1.46)\u003c/p\u003e\n \u003cp\u003e1.85 (1.17 \u0026ndash; 2.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.311148086522463%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0117\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.650582362728786%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e39/204\u003c/p\u003e\n \u003cp\u003e54/278\u003c/p\u003e\n \u003cp\u003e32/192\u003c/p\u003e\n \u003cp\u003e44/147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.302828618968388%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e1.01 (0.62 - 1.66)\u003c/p\u003e\n \u003cp\u003e0.80 (0.45 - 1.42)\u003c/p\u003e\n \u003cp\u003e1.85 (1.03 - 3.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0239\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.306156405990016%\" valign=\"top\"\u003e\n \u003cp\u003eSADHtR\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e3\u003csup\u003erd\u003c/sup\u003e quartile\u003c/p\u003e\n \u003cp\u003e4\u003csup\u003eth\u003c/sup\u003e quartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.816971713810316%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e47/250\u003c/p\u003e\n \u003cp\u003e44/243\u003c/p\u003e\n \u003cp\u003e39/227\u003c/p\u003e\n \u003cp\u003e64/239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.13477537437604%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e0.95 (0.61 \u0026ndash; 1.51)\u003c/p\u003e\n \u003cp\u003e0.90 (0.56 \u0026ndash; 1.43)\u003c/p\u003e\n \u003cp\u003e1.58 (1.03 \u0026ndash; 2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.311148086522463%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0403\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.650582362728786%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e41/209\u003c/p\u003e\n \u003cp\u003e40/211\u003c/p\u003e\n \u003cp\u003e33/199\u003c/p\u003e\n \u003cp\u003e55/202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.302828618968388%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e0.83 (0.49 - 1.4)\u003c/p\u003e\n \u003cp\u003e0.77 (0.44 - 1.34)\u003c/p\u003e\n \u003cp\u003e1.42 (0.82 - 2.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\" valign=\"top\"\u003e\n \u003cp\u003e0.0828\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Adjusted ORs are adjusted for age, smoker status (never, past, current), cholesterol ratio, blue/white collar status, and marital status. The analysis is based on complete cases.\u003c/p\u003e\n\u003cp\u003eAbbreviations: CI, confidence interval; OR, odds ratio. BMI, body mass index; SAD, sagittal abdominal diameter; SADHtR, sagittal abdominal diameter to height ratio; Ref, reference; p-value, probability value.\u003c/p\u003e\n\u003cp\u003eWhen adjusting for age, SBP, the ratio total cholesterol/HDL-C, marital status, and blue/white collar status, the odds ratios were non-significant, or significant but attenuated (WHR), or wider CI (SAD). \u0026nbsp;Nagelkerke\u0026apos;s R2 ranged from 0.138 (BMI) to 0.150 (SAD and WHR). In pairwise comparisons of adequacies (Additional file 1: Table S7), we saw larger differences in predictive ability between the anthropometric measures (e.g., WHR dominated clearly in all relevant pairs), but the differences were strongly attenuated after adjusting for other important predictors, making the body measurements very comparable. Odds ratios (ORs) with 95% confidence intervals from univariable and multivariable logistic regression models with MI within 28 years of follow-up as an outcome and the anthropometric measures represented by quartiles represented (Additional file: Table S8). In the unadjusted model, the SAD to height ratio shows the highest OR=3.6; 95% CI 1.78-7.29. However, after adjustment, all confidence intervals except WC override 1, and R2 are comparable for all six indexes (0.136-0.148).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor completeness, the full model information for all-cause death is displayed in (Additional file 1: Table S9.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the unadjusted model SAD displays the highest OR=1.85;\u0026nbsp;95% CI\u0026nbsp;1.17-1.92. After adjustment, the 95% CI overrides 1 for all indexes except SAD. Nevertheless, R2 is comparable for all indexes: 0.138-0.150:\u003c/p\u003e\n\u003cp\u003eIf cut-offs from Table 1 are used for myocardial infarction all body measurements except sagittal abdominal diameter were significant after adjustment (Additional file: Table S10). However, for all-cause death, significant findings after adjustment were only seen for WHR and SAD (Additional file 1: Table S11). Correspondingly, R-squared were weak:0.147 and 0.148, Comparison of distribution of the anthropometric measures between those who died due to cardiovascular causes (including MI) and those who died due to other causes is displayed in (Additional file 1: Table S12). Statistically significant associations were seen for all body measurements except WHR.\u003c/p\u003e\n\u003cp\u003eUnder our chosen approach to modeling the associations between the body measurements and the events of death and MI over the 28-year follow-up, only minor differences between the indicators were found. The predictive ability of the models measured by Nagelkerke\u0026rsquo;s R2 was comparable for all the body measures.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the unadjusted analyses, all body measurements had significant odds ratios for MI. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe highest OR comparing 4th to 1st quartile was displayed by SADHtR; OR=3.6; 95% CI 1.78-7.29; p=0.0016. Some studies report similar results (31,32). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter adjustment, SADHtR was non-significant, and WC showed the highest OR (4th vs 1st quartile): 2.52; 95% CI 1.06-6.02; p=0.0293. However, the predictive ability of the models was comparable for all measurements, as indicated by Nagelkerke\u0026rsquo;s R2.\u003c/p\u003e\n\u003cp\u003eThree meta-analyses of cardiovascular risk indicators demonstrated conflicting findings: Cao et al. reported WHR as the best obesity cardiovascular risk indicator; Xue et al. found that WC, WHR, and WHtR, had the best predictive ability; and de Koning et al. gave equal credence to WHR and WC (33,34,35). In our study, the unadjusted OR for WHR was 2.82; 95% CI 1.45-5.51; p=0.0032. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs for mortality, in the unadjusted analysis, WHR, WHtR, SAD, and SADHtR were significantly associated with mortality. The highest OR (4th vs 1st quartile) was seen for SAD= 1.85; 95% CI 1.17-2.92; p=0.0117. Most ORs were attenuated after adjustment for traditional risk factors. However, Nagelkerke\u0026rsquo;s R2 again indicated that predictive ability was comparable for all measurements.\u003c/p\u003e\n\u003cp\u003eAll body measurements were significantly correlated with one another. However, WHR was somewhat weaker, with an r=0.64; 95%CI 0.60-0.68 to BMI. This finding is mirrored in a study of 4,504 participants from 29 different countries (36).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA significant correlation appeared between all six indicators and all covariates except total alcohol consumption, pulse, and DBP. \u0026nbsp;(These comparisons were, however, not adjusted for multiple testing.) Other studies have shown similar associations with risk factors and debate which body measures are the strongest associated (37,38). A previous finding of the Coeur study suggested the SADHtR was strongest (24), though a systematic review found that WTHtR was a better screening tool than WC and BMI (39).\u003c/p\u003e\n\u003cp\u003eIt is generally understood that visceral obesity is the most dangerous cardiovascular risk factor, especially extreme obesity (40, 2, 3, 32, 41).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTheoretically, there are arguments for cardiovascular risk via inflammation and the secretion of inflammatory markers, as well as the pathophysiology of cardiac structure and function (3,11). Our study does not support these theories, since the odds ratios for the six obesity risk indicators were attenuated or non-significant for MI and all-cause death after adjustment for age, SBP, total cholesterol/HDL-C, marital status, and blue/white collar status. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur findings are in harmony with several other major studies, such as a study of 855 Swedish men born in 1913 and followed for 13 years that investigated stroke, ischemic disease, and all-cause death as endpoints. In that study, WHR was used as a risk indicator and associations were non-significant when adjusted for smoking, SBP, and serum cholesterol (42).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA prospective study of 62,223 Norwegians with death as the endpoint found that WHR was the strongest of 5 obesity indicators, but the association was not significant after adjustment (37).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis phenomenon was also found in a study of 6,529 British civil servants, which showed that neither BMI nor WC was significantly associated with death after adjustment\u0026nbsp;(43). Our finding may be relevant to the \u0026ldquo;fat but fit\u0026rdquo; thesis in that obesity may be of little relevance for non-diabetic, non-smoking, and fit men with normal BP, blood sugar, and blood lipids (44).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBasketball star Michael Jordan\u0026rsquo;s BMI reportedly varied between 27-29, yet his waist size was less than 30. For him, muscle mass, not fat mass, was the causal factor of the high BMI, and WC would have been a more relevant measure than BMI (45).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt is still unclear why the human body is designed to store extra energy in fat depots.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt may have been a survival mechanism for early hunters with serious variations in food intake and periods of starvation. Contrary to this, extreme obesity (BMI\u0026gt;35) is likely a separate condition and is no doubt a strong mortality predictor (46). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe quartile ORs mortality is displayed in this study (Table 5) showing a J-shaped association of adiposity with mortality with the lowest ORs in the second or third quartiles for all body measures. This shape was also reported by Elagizi in 2018 in an overview of the so-called obesity paradox. (47)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy strength\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original cohort was randomly selected from a well-characterized population of male workers followed over 28 years. Very few refused to participate indicating a high level of cooperation. The nurses and health professionals were specially trained and well-defined methods were used (12). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccurate information about endpoints (death and MI) was obtained through well-established national registers that can be considered reliable sources. The internal validity and the data of the current project have been carefully secured throughout the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy weaknesses and limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cohort of the study was limited to an occupational sample of middle-aged men working in a particular industry (automotive) and in a particular country (Sweden), therefore the external validity for other populations, such as women and in other contexts to the research question was another limitation; thus, the outcomes of the events were restricted. Some data was incomplete due to manual entry or missing lab results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConfounding factors and biases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn longitudinal studies, both confounding factors and biases may occur because it is difficult to consider how the participants\u0026rsquo; lifestyles and health change over time. For example, over 28 years, the participant may lose or gain weight, or quit smoking, which may change the prognosis of some endpoints.\u003c/p\u003e\n\u003cp\u003eThere are always risks for biases, especially when some answers in the questionnaire are self-reported. For example, people may tend to underestimate their smoking and drinking habits which leads to systematic errors. Body measurements might be biased due to incorrect measurement techniques, though comprehensive training of health professionals was mandatory before the study.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe found a significant correlation between the six different body measurements, and they were all associated with CVD risk factors such as SBP, diabetes, and dyslipidemia, as well as smoking.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll body measurements were significantly associated with MI. After adjustment, all but SADHtR and WHR remained significant. SADHtR, SAD, WHtR, and WHR were significantly associated with mortality before adjustment. This association remained for SAD and WHR after adjustment. Compared to other body measurements, BMI showed weaker associations with mortality.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNagelkerke\u0026rsquo;s R2 was rather comparable for all the indexes, which means that no index stood out as superior to the others.\u003c/p\u003e\n\u003cp\u003eFuture studies should investigate these body measurements as risk factors in larger cohorts of industrial workers and women. The success of occupational health services to mitigate risk behaviors should also be evaluated.\u003c/p\u003e\n\u003cp\u003eVisual Abstract (additional file 2) in here.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoronary artery disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCDC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCenters for Disease Control and Prevention\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoronary heart diseases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCVD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiovascular disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ediastolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFramingham Risk Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFRS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFramingham Risk Score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational classification of disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow-density lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMyocardial infarction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNIH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe National Institutes of Health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNHANES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Health and Nutrition Examination Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNHLBI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Heart, Lung, and Blood Institute\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eP-Value\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProbability value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSagittal abdominal diameter\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSADHtR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSagittal abdominal diameter to height ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esystolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etriglycerides\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWaist circumference\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWaist-to-hip ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHtR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWaist-to-Height Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki and included written, informed consent from all participants. The Research Ethics Committee of Gothenburg University approved the original study protocol on Feb 11, 1993 (Dnr. 23-93), and amendments on Feb 19, 2019.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eApplications to use data from the National Myocardial Infarction Register, Swedeheart, and the National Cause of Death Register were also approved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent for publication has been given by the participants and the University of Gothenburg.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnidentified data is available upon request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest. Lennart Dimberg was employed as an occupational physician for Volvo Flygmotor at the start of the project. Volvo had no influence on the study design, analysis, or interpretation of the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Volvo Research Fund and the Mary von Sydow Foundation.\u003c/p\u003e\n\u003cp\u003eAuthors contributions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLennart Dimberg conceived the study and designed the research. Lala Joulha Ian did the literature search. Both authors contributed to revising and approving the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the original\u0026nbsp;Renault-Volvo\u0026nbsp;Coeur\u0026nbsp;project\u0026nbsp;group\u0026nbsp;1993-1998\u003c/p\u003e\n\u003cp\u003e(additional file 1) and all participants of the studies. Furthermore, we are deeply indebted to the masterly Barbora Kessel for her excellent statistical work and to Rebecca Elfast for her superb drawing of the visual abstract. Reghan Borer, for her skillful language revision.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBoutari C, Mantzoros CS. A 2022 update on the epidemiology of obesity and a call to action: as its twin COVID-19 pandemic appears to be receding, the obesity and dysmetabolism pandemic continues to rage on. 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Accessed Nov 14, 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.webmd.com/diet/features/how-accurate-body-mass-index-bmi\u003c/span\u003e\u003cspan address=\"https://www.webmd.com/diet/features/how-accurate-body-mass-index-bmi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSj\u0026ouml;str\u0026ouml;m LV. Mortality of severely obese subjects. Am J Clin Nutr. 1992;55(2 Suppl):S516\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElagizi A, Kachur S, Lavie CJ, et al. An Overview and Update on Obesity and the Obesity Paradox in Cardiovascular Diseases. Prog Cardiovasc Dis. 2018;61(2):142\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"obesity, body measurements, longitudinal cohort, myocardial infarction, mortality, risk factor","lastPublishedDoi":"10.21203/rs.3.rs-4529247/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4529247/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Overweight, Body Mass Index (BMI, kg/m2) 25-30, and obesity (BMI 30+) have historically been associated with risk of myocardial infarction and death.\u003c/p\u003e\n\u003cp\u003eSeveral indexes have been introduced to better reflect the abdominal adiposity. The risk for each index does vary in different populations.\u003c/p\u003e\n\u003cp\u003eThis project aimed to determine the correlations between six common different body measurements and their association with cardiovascular disease risk factors, myocardial infarction, and death in middle-aged workers in the Swedish automotive industry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e In 1993, 1,000 randomly selected Swedish men from the automotive industry were surveyed at a nurse-led health examination. Body measures were recorded together with blood pressure, and a fasting blood test was analyzed. A 28-year follow-up was conducted using the national myocardial infarction register data from the Swedeheart and death data from the Swedish Cause of Death Register. Multiple logistic regression models were used to compare BMI with the other five body measurements. The measure of obesity was calculated for the following risk indexes: Body Mass Index (BMI), Waist Circumference, Waist-to-Height Ratio, Waist-to-Hip Ratio, Sagittal Abdominal Diameter, and Sagittal Abdominal diameter-to-height ratio. Outcomes of accumulated first-time myocardial infarction and death were assessed as odds ratios (OR) between the fourth and first quartiles, before and after adjustment for traditional risk factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Data were recorded for 959 men. Significant correlations between the six body measurements were shown. All were also associated with CVD risk factors, myocardial infarction, and death. However, when adjusted for traditional risk factors the association with cardiovascular endpoints almost disappeared. Before adjustment the highest odds ratio with myocardial infarction was calculated for sagittal abdominal diameter to height ratio, OR=3.60; 95% Confidence Interval (CI) 1.78-7.29; p=0.0016. Correspondingly, the highest OR with all-cause death was calculated for the sagittal abdominal diameter, OR=1.85; 95% CI=1.17-2.92; p=0.0117.\u003c/p\u003e\n\u003cp\u003eThe predictive ability measured by Nagelkerke’s R2 was comparable for each measure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e For this population, after adjustment for traditional risk factors, no index was significantly preferred as a risk factor for myocardial infarction or death.\u003c/p\u003e","manuscriptTitle":"Six anthropometric indicators and their association with myocardial infarction and death in male Swedish automotive industry workers followed for 28 years","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-24 14:27:46","doi":"10.21203/rs.3.rs-4529247/v1","editorialEvents":[{"type":"communityComments","content":1},{"type":"decision","content":"Revision requested","date":"2024-07-03T04:39:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-02T18:53:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-28T08:06:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-19T06:06:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105621533751707644410720865890711540009","date":"2024-06-13T08:06:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314584261246280577651385406356750317684","date":"2024-06-10T16:43:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178927040142650717283065640141370899733","date":"2024-06-10T08:07:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-10T07:19:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-10T06:54:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-10T01:23:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-10T01:22:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-06-04T15:44:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e6e1b788-0566-40f0-ab1e-c2a3ec114c3d","owner":[],"postedDate":"June 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-30T17:08:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-24 14:27:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4529247","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4529247","identity":"rs-4529247","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}