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Obesity attenuates gender differences in cardiovascular mortality

Cardiovascular Diabetology volume 13, Article number: 144 (2014) Cite this article

Abstract

Background

To estimate cardiovascular disease (CVD) mortality in relation to obesity and gender.

Methods

Data from 11 prospective cohorts from four European countries including 23 629 men and 21 965 women, aged 24 to 99 years, with a median follow-up of 7.9 years were analyzed. Hazards ratios (HR) for CVD mortality in relation to baseline body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) were estimated using Cox proportional hazards models with age as the timescale.

Results

Men had higher CVD mortality than women in all four BMI categories (<25.0, 25.0-29.9, 30.0-34.9 and ≥35.0 kg/m2). Compared with the lowest BMI category in women, multivariable adjusted HRs (95% confidence intervals) for higher BMI categories are 1.0 (0.8-1.4), 1.6 (1.1-2.1) and 2.8 (2.0-3.8) in women and 2.8 (2.2-3.6), 3.1 (2.5-3.9), 3.8 (2.9-4.9) and 5.4 (3.8-7.7) in men, respectively. Similar findings were observed for abdominal obesity defined by WC, WHR or WHtR. The gender difference was slightly smaller in obese than in non-obese individuals; but the interaction was statistically significant only between gender and WC (p = 0.02), and WHtR (p = 0.01). None of the interaction terms was significant among non-diabetic individuals.

Conclusions

Men had higher CVD mortality than women across categories of anthropometric measures of obesity. The gender difference was attenuated in obese individuals, which warrants further investigation.

Background

Cardiovascular diseases (CVD) are still the leading cause of mortality in both men and women [1]. Further, women are known to have a much lower risk for CVD mortality than men. There is substantial evidence of a gender difference in cardiac autonomic modulation [2]-[5], lipid and glucose metabolism [6]-[9], sex hormones [4],[10]-[14] and cytokines [15]-[19]. On average, women would have augmented sympathetic inhibition, higher cardiac vagal tone, higher heart rate variability, lower susceptibility to arrhythmias, and decreased myocardial contractility than men [2],[3],[20], leading to a preponderance of vagal over sympathetic control of cardiac function [2]-[5]. Moreover, before menopause, women generally have lower levels of serum total and low-density lipoprotein cholesterol (TC and LDL-C), triglycerides and apolipoprotein B and higher levels of high-density lipoprotein cholesterol (HDL-C) and apolipoprotein A-I than their male counterparts [6],[21]-[23] although TC and LDL-C increase in women after menopause [21],[22]. However, this female advantage is abrogated with diabetes and aging [24]-[32], perhaps as a consequence of diabetes inducing higher levels of inflammatory mar-kers and higher rates of nitric oxide release in women compared with men, resulting in reduced protective effects of estrogen on body fat distribution and insulin action, or a more impaired endothelial function in women than in men [10],[33]. The prevalence of obesity is rising worldwide, leading to an increased risk for diabetes and CVD [34],[35]. It is not clear, however, whether the gender difference in CVD mortality remains after accounting for the increased development of obesity.

The aim of this study was to assess the risk of CVD mortality in relation to obesity and gender [1] in the general population and [2] separately for those with or without diabetes at baseline.

Subjects and methods

Study population

This analysis was based on 11 cohorts from the DECODE (Diabetes Epidemiology: Collaborative analysis Of Diagnostic criteria in Europe) study including 45 594 individuals (48.2% women) aged 24–99 at baseline, from four European countries (Finland, Sweden, Turkey and UK). Information on weight, height, waist and hip circumferences, self-reported smoking status, leisure-time physical activity, and history of diabetes mellitus were available. In addition, 20 270 individuals (44.7% women) had data on fasting or 2-h plasma glucose (FPG and 2hPG) from a 75 g oral glucose tolerance test. Individual participant data from each cohort were sent to the National Institute for Health and Welfare in Helsinki, Finland for collaborative data analyses. Studies included were approved by local ethics committees, and the analysis plan was approved by the ethics committee of the National Institute for Health and Welfare, Helsinki, Finland.

Definition of covariates

Height and weight were measured without shoes and with light clothing. Waist circumference (WC) was measured midway between the lower rib margin and iliac crest. Hip circumference was measured at the level of the widest circumference over the greater trochanters. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. Waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) were calculated as waist circumference divided by hip circumference or height, respectively. Participants were classified into five categories: underweight (BMI <18.5 kg/m2), normal weight (BMI 18.5-24.9 kg/m2), overweight (BMI 25.0-29.9 kg/m2), obesity (BMI 30.0-34.9 kg/m2) and severe obesity (BMI ≥35.0 kg/m2), according to the WHO classification criteria [36]. Due to the low number of participants in the underweight category in this study population, the underweight and normal weight groups were combined for analysis. Abdominal obesity was defined as the sex-specific top quartile of WC, WHR or WHtR (WC ≥99 cm, WHR ≥0.97, and WHtR ≥0.57 for males; WC ≥90 cm, WHR ≥0.85, and WHtR ≥0.56 for females, respectively). Participants reporting reading, watching TV, housework, sewing and walking <1 km daily were defined as physically inactive; all those engaging in higher levels of physical activity were defined as physically active. Based on responses to the questionnaire, smoking status at baseline was classified into three categories of never, former and current smokers. Diabetes was defined as either a history of diabetes at baseline or an FPG level ≥7.0 mmol/L and/or a 2hPG level ≥11.1 mmol/L [37].

Definition of fatal events

Vital status and causes of death were recorded for all participants. CVD mortality was defined according to the International Classification of Disease codes 401–448 (9th revision) or I10–I79 (10th revision).

Statistical analyses

The absolute CVD mortality rate per 10 000 person-years of follow-up for each obesity and gender category was calculated. Cox proportional hazards models were used to estimate hazard ratios (HR) and 95% confidence intervals (CIs) for CVD mortality, adjusting for baseline smoking status, leisure-time physical activity and cohort, using attained age as the timescale and non-obese women as the reference group. To investigate whether the gender difference in CVD mortality was different in non-obese from obese participants, we checked for the interaction between sex and obesity in the Cox models, using a chi-squared log-likelihood ratio test. STATA version 11 (StataCorp, College Station, TX, USA) was used.

Results

Table 1 provides baseline characteristics of the cohorts. Median follow-up time varied between 2.5 and 21.8 years in the different cohorts. Lean females were younger, more abdominally obese and had low prevalence of diabetes, while obese females were older, less abdominally obese and had higher prevalence of diabetes at baseline, compared with their respective male counterparts (Table 2). More men than women were smokers and physically active. Men tended to have higher mean values of systolic blood pressure and fasting plasma glucose, and a worse lipid profile than women, regardless of BMI categories. Similar characteristics were observed among non-diabetic individuals (Additional file 1: Table S1). But diabetic men tended to be older and less physically active. Similar gender specific baseline characteristics were observed within the categories of abdominal obesity defined by sex-specific quartiles of anthropometric measures of WC, WHR and WHtR (Additional file 1: Table S2).

Table 1 Baseline characteristics and follow-up information of the survey participants
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Table 2 Baseline characteristics of participants by body mass index and sex
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During the median follow-up of 7.9 years, 945 (4.0%) men and 339 (1.5%) women died from CVD. Absolute rates and age- or multivariate-adjusted HRs for CVD mortality are shown across BMI categories or sex-specific quartiles of anthropometric measures of abdominal obesity (Table 3). Males had higher CVD mortality rates and higher hazard ratios across BMI categories, and categories of abdominal obesity than females, but the sex difference tended to be attenuated in the obese categories. The interaction was statistically significant between gender and WC (p = 0.02), and WHtR (p = 0.01), but not significant with BMI and WHR (Table 3). For most studies, study-specific HRs were within 10% of the pooled estimate, although there was heterogeneity by some studies for the HRs of sex-ratio within BMI 25.0-29.9 kg/m2 (I2 = 95.5%, P <0.05, Additional file 1: Table S3). However, exclusion of any study in the analysis had little overall influence on the main results (Additional file 1: Table S4). The finding persisted when the analysis was restricted to individuals without baseline diabetes, but the gender-obesity interaction was no longer significant in the non-diabetic population (Table 4). In addition, the gender difference among diabetic individuals diminished especially for non-obese ones.

Table 3 Mortality rate per 10 000 person-years and multivariate-adjusted hazard ratios for cardiovascular disease mortality in men and women by body mass index categories or sex-specific quartiles of anthropometric measures of abdominal obesity
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Table 4 Mortality rate per 10 000 person-years and multivariate adjusted hazard ratios for cardiovascular disease mortality among non-diabetic and diabetic individuals by sex and body mass index categories or sex-specific quartiles of anthropometric measures of abdominal obesity
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The findings for BMI categories were not substantially altered when the analysis was repeated with additional adjusting for abdominal obesity (Additional file 1: Table S5). Multivariate adjustment for other CVD risk factors such as systolic blood pressure, fasting plasma glucose, triglyceride, high-density lipoprotein cholesterol or total cholesterol decreased the HRs in each obesity category for both men and women but the gender difference remained unchanged (Additional file 1: Table S6). A sensitivity analysis that excluded the first 5 years of follow-up did not alter the results (Additional file 1: Table S7).

Discussion

Men had a higher CVD mortality than women across all categories of anthropometric measures of obesity after adjusting for age, smoking status, leisure-time physical activity and cohort. The gender difference diminished somewhat in obese individuals especially among diabetic participants.

There is substantial evidence of a gender difference in cardiac autonomic modulation [2]-[5], lipid and glucose metabolism [6]-[9], sex hormones [4],[10]-[14] and cytokines [15]-[19], that might explain part of the observed differences of CVD mortality between women and men. Besides, males tend to have more fat in the abdominal region, even among normal weight or non-obese men, which may be predominantly due to the accumulation of more visceral fat in males than females during the puberty [38].

In addition, sex hormones might play important roles in determining body fat mass and its distribution [11],[14], exert multiple direct and indirect effects on insulin and glucose homeostasis or on cardiovascular physiology [4],[10],[12],[13]. Specifically, estrogen increases fat deposition whereas testosterone inhibits fat deposition, and accordingly, men tend to have less overall body fat than women [14], however, the distribution differs between the sexes as discussed below. Women tend to accumulate more subcutaneous fat but less intra-abdominal fat than men probably due to the effects of estrogen by preventing androgen effects [39]. Intra-abdominal fat is believed to be the main pathogenic fat depot that has the clinical relevance to CVD [40], in particular being more metabolically active than adipose depots located in the hip, thigh or buttocks [41]. Clinical studies showed that there was higher intra-abdominal fat accumulation in men than in women for a given level of BMI, WC or WHR [42]-[44]. In contrast, adult women tend to have a larger hip circumference than men. This is partly due to a difference in bone structure of pelvis, but also in subcutaneous fat mass, that through the metabolically protective physiology of gluteofemoral subcutaneous fat mass, perhaps by trapping excess fatty acids and preventing chronic exposure to elevated lipid levels, or through a beneficial adipokine profile (leptin and adiponectin) [45]. It remains unclear whether CVD risk differs by site of subcutaneous fat accumulation. In addition, estrogen might also play a role in the maintenance of glucose homeostasis and substrate metabolism [46]. Men had significantly higher fasting and significantly lower post-challenge insulin levels than women did [8],[47], which is not fully explained by differences in fasting and post-challenge glucose levels between sexes [47]. Additionally, adults have tended to show greater insulin resistance in men than in women [7],[9].

Adipose tissue is also a highly active metabolic and endocrine organ, which expresses and secretes a variety of bioactive factors including leptin, adiponectin, and other cytokines [19], which might contribute to the gender difference in CVD mortality. Women have higher circulating leptin levels and higher adiponectin levels than men [15]-[18]. Hyperleptinemia could be a sign of resistance to normal leptin signaling regulating food intake and satiety. This is believed to be a non-physiological state, which has been associated with an increased risk of diabetes, hypertension and CVD in men but not in women [48],[49]. Additionally, hypoadiponectinemia has been found to be associated with an increased risk of both diabetes and CVD [50],[51].

Obesity is associated with increased sympathetic activity and decreased vagal activity [52],[53], hyperglycemia, insulin resistance [35],[54], and is accompanied by chronic low-grade inflammation [19],[55], hypertension and dyslipidemia [6],[56],[57], all of which might predispose to CVD. Abdominal obesity, in particular, is also associated with deficiency of estrogens or testosterone [58],[59], although a causal link still needs to be established [14],[60]. Deficiency of estrogens or testosterone has been consistently found to be associated with increased risk of CVD [60],[61]. Increased leptin and decreased adiponectin levels were observed in obese individuals [48],[49],[62], but the expression of these cytokines differed between subcutaneous and intra-abdominal fat depots [17],[63],[64].

Interestingly, the gender difference in CVD mortality appears to somewhat diminish in obese individuals, although misclassification bias between obese and non-obese individuals might occur due to the gender difference of fat distribution, probably enhanced by disturbances of glucose metabolism. Causes of gender difference in CVD mortality with obesity are poorly understood. In our study, the attenuation of the gender difference in CVD mortality among obese individuals remained after adjustment of baseline age or other conventional CVD risk factors, or among non-diabetic individuals even when using other measures of abdominal obesity. The interactions of gender with anthropometric measures were, however, statistically significant only with WC and WHtR in the whole study population and not significant with any of the anthropometric measures in non-diabetic individuals. This suggests an effect modification by diabetes. Studies have indeed shown an attenuation of gender difference in CVD risk once women getting diabetes [24]-[27],[29]. Still, there could be other potential unknown CVD risk factors clustering in obese women due to their older age that contributed to the increased CVD risk in obese women.

Since CVD previously has been considered a `male disease’ because of an earlier debut age on the average in men as compared with women, most epidemiological studies have been conducted primarily on men, leading to lesser prevention and treatment efforts in women [65]. Also, obese European women appear to be at greater risk of psychological dysfunction than obese men probably due to increased societal pressures on women to be thin in Europe [66]. In addition, obese women tend to have left ventricular concentric and eccentric hypertrophy, whereas obese men have predominantly concentric hypertrophy, the latter probably being more strongly related to cardiovascular mortality than eccentric hypertrophy, a finding noted recently [30].

Our study was based on several European population-based or occupational studies, with sufficient power to investigate the association between obesity indicators and CVD mortality risk. Yet, limitations of the study exist. The study does not have data on changes in anthropometric measurements before baseline and during follow-up, which makes it impossible to exclude the possibility of `reverse causation’ [34], a consequence of underlying diseases before enrolment rather than a cause of deaths. This was examined by excluding the first five years of follow-up, and the results were little affected. Data are based on surveys of Caucasian Europeans, the results may not be applicable to other ethnic groups or races due to difference in percentage of body fat across ethnic groups [67]. Moreover, we do not have data on changes in anthropometric measurements after menopause. Rapid accumulation of abdominal fat after menopause has been suggested to reduce the gender difference in body fat distribution [11],[68]. In addition, we do not have information on the use of hormone replacement therapy, which decreases weight gain and the accumulation of intra-abdominal fat in postmenopausal women [69]. Since this is a collaborative data analysis, certain lifestyle variables such as physical activity were recorded differently in different studies. Despite the efforts to “harmonize” variables and to adjust for the different studies in the data analysis, discrepancies exist. The discrepancies in study design and methodologies have been taken into account in our data analysis by fitting “study cohort” into the models as a co-variable, and by making a meta-analysis based on risk estimate of each single study. The effect of each study was also examined by removing each study from the pooled analysis. Exclusion of any study in the analysis had little overall influence on the main findings.

In summary, men had higher CVD mortality than women across all categories of anthropometric measures of obesity, but this gender difference in CVD mortality somewhat diminished in obese individuals, which warrants further investigation to understand the underlying mechanism.

Appendix

Studies and investigators in this collaborative study are:

Finland National FINRISK 1987, 1992 and 1997 Cohorts: J. Tuomilehto1,2,3, P. Jousilahti1, J. Lindström1, 1. Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki; 2. Center for Vascular Prevention, Danube University Krems, Krems, Austria; 3 King Abdulaziz University, Jeddah, Saudi Arabia.

National FINRISK 2002 Study: J. Tuomilehto1,2,3, T. Laatikainen1,4,5, M. Peltonen1, J. Lindström1, 1. Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki; 2. Center for Vascular Prevention, Danube University Krems, Krems, Austria; 3 King Abdulaziz University, Jeddah, Saudi Arabia; 4. Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland; 5. Hospital District of North Karelia, Joensuu, Finland.

Sweden Northern Sweden MONICA Survey: S. Söderberg1,2, M. Eliasson1, 1. Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden; 2. Baker IDI Heart and Diabetes Institute, Melbourne, Australia.

Turkey Turkish Adult Risk Factor Study (TARFS): A Onat. Department of Cardiology, Cerrahpaşa Medical Faculty, Istanbul University, Istanbul, Turkey.

United Kingdom Whitehall II Study: M.G.Marmot1, A.G. Tabák1,2, M. Kivimäki1,3, E.J. Brunner1, D.R. Witte1,4, 1. Department of Epidemiology and Public Health, University College London, London, UK; 2. Semmelweis University Faculty of Medicine, 1st Department of Medicine, Budapest, Hungary; 3. Finnish Institute of Occupational Health, Helsinki, Finland; 4. Steno Diabetes Center, Gentofte, Denmark.

Authors’ contributions

XS initiated the study, prepared and analyzed the data, interpreted the results, drafted the manuscript, and contributed to the final version of the manuscript. QQ initiated the study, interpreted the results, and contributed to the final version of the manuscript. All coauthors contributed, revised and edited the manuscript. QQ is guarantor of this work and had full access to all the data in the study. All authors read and approved the final manuscript.

Additional file

References

  1. 1.

    World Health Organization: The Top 10 Causes of Death. Updated May 2014; Available at: http://who.int/mediacentre/factsheets/fs310/en/index.html. Accessed May 20, 2014.

  2. 2.

    Ramaekers D, Ector H, Aubert AE, Rubens A, Van de Werf F: Heart rate variability and heart rate in healthy volunteers. Is the female autonomic nervous system cardioprotective?. Eur Heart J. 1998, 19 (9): 1334-1341. 10.1053/euhj.1998.1084.

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    Dart AM, Du XJ, Kingwell BA: Gender, sex hormones and autonomic nervous control of the cardiovascular system. Cardiovasc Res. 2002, 53 (3): 678-687. 10.1016/S0008-6363(01)00508-9.

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Vaccarino V, Badimon L, Corti R, de Wit C, Dorobantu M, Hall A, Koller A, Marzilli M, Pries A, Bugiardini R: Ischaemic heart disease in women: are there sex differences in pathophysiology and risk factors? Position paper from the working group on coronary pathophysiology and microcirculation of the European Society of Cardiology. Cardiovasc Res. 2011, 90 (1): 9-17. 10.1093/cvr/cvq394.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  5. 5.

    Moodithaya S, Avadhany ST: Gender differences in age-related changes in cardiac autonomic nervous function. J Aging Res. 2012, 2012: 679345-10.1155/2012/679345.

    PubMed Central  Article  PubMed  Google Scholar 

  6. 6.

    Freedman DS, Jacobsen SJ, Barboriak JJ, Sobocinski KA, Anderson AJ, Kissebah AH, Sasse EA, Gruchow HW: Body fat distribution and male/female differences in lipids and lipoproteins. Circulation. 1990, 81 (5): 1498-1506. 10.1161/01.CIR.81.5.1498.

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Donahue RP, Prineas RJ, DeCarlo DR, Bean JA, Skyler JS: The female `insulin advantage’ in a biracial cohort: results from the Miami Community Health Study. Int J Obes Relat Metab Disord. 1996, 20 (1): 76-82.

    CAS  PubMed  Google Scholar 

  8. 8.

    Garaulet M, Perex-Llamas F, Fuente T, Zamora S, Tebar FJ: Anthropometric, computed tomography and fat cell data in an obese population: relationship with insulin, leptin, tumor necrosis factor-alpha, sex hormone-binding globulin and sex hormones. Eur J Endocrinol. 2000, 143 (5): 657-666. 10.1530/eje.0.1430657.

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Magkos F, Wang X, Mittendorfer B: Metabolic actions of insulin in men and women. Nutrition. 2010, 26 (7–8): 686-693. 10.1016/j.nut.2009.10.013.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  10. 10.

    Steinberg HO, Paradisi G, Cronin J, Crowde K, Hempfling A, Hook G, Baron AD: Type II diabetes abrogates sex differences in endothelial function in premenopausal women. Circulation. 2000, 101 (17): 2040-2046. 10.1161/01.CIR.101.17.2040.

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Mayes JS, Watson GH: Direct effects of sex steroid hormones on adipose tissues and obesity. Obes Rev. 2004, 5 (4): 197-216. 10.1111/j.1467-789X.2004.00152.x.

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    D’Eon TM, Souza SC, Aronovitz M, Obin MS, Fried SK, Greenberg AS: Estrogen regulation of adiposity and fuel partitioning. Evidence of genomic and non-genomic regulation of lipogenic and oxidative pathways. J Biol Chem. 2005, 280 (43): 35983-35991. 10.1074/jbc.M507339200.

    Article  PubMed  Google Scholar 

  13. 13.

    Relationship of body size and shape to the development of diabetes in the diabetes prevention program. Obesity (Silver Spring). 2006, 14 (11): 2107-2117. 10.1038/oby.2006.246.

  14. 14.

    Singh R, Artaza JN, Taylor WE, Braga M, Yuan X, Gonzalez-Cadavid NF, Bhasin S: Testosterone inhibits adipogenic differentiation in 3 T3-L1 cells: nuclear translocation of androgen receptor complex with beta-catenin and T-cell factor 4 may bypass canonical Wnt signaling to down-regulate adipogenic transcription factors. Endocrinology. 2006, 147 (1): 141-154. 10.1210/en.2004-1649.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  15. 15.

    Saad MF, Damani S, Gingerich RL, Riad-Gabriel MG, Khan A, Boyadjian R, Jinagouda SD, el-Tawil K, Rude RK, Kamdar V: Sexual dimorphism in plasma leptin concentration. J Clin Endocrinol Metab. 1997, 82 (2): 579-584.

    CAS  PubMed  Google Scholar 

  16. 16.

    Licinio J, Negrao AB, Mantzoros C, Kaklamani V, Wong ML, Bongiorno PB, Negro PP, Mulla A, Veldhuis JD, Cearnal L, Flier JS, Gold PW: Sex differences in circulating human leptin pulse amplitude: clinical implications. J Clin Endocrinol Metab. 1998, 83 (11): 4140-4147.

    CAS  PubMed  Google Scholar 

  17. 17.

    Cnop M, Havel PJ, Utzschneider KM, Carr DB, Sinha MK, Boyko EJ, Retzlaff BM, Knopp RH, Brunzell JD, Kahn SE: Relationship of adiponectin to body fat distribution, insulin sensitivity and plasma lipoproteins: evidence for independent roles of age and sex. Diabetologia. 2003, 46 (4): 459-469.

    CAS  PubMed  Google Scholar 

  18. 18.

    Steffes MW, Gross MD, Schreiner PJ, Yu X, Hilner JE, Gingerich R, Jacobs DR: Serum adiponectin in young adults–interactions with central adiposity, circulating levels of glucose, and insulin resistance: the CARDIA study. Ann Epidemiol. 2004, 14 (7): 492-498. 10.1016/j.annepidem.2003.10.006.

    Article  PubMed  Google Scholar 

  19. 19.

    Kershaw EE, Flier JS: Adipose tissue as an endocrine organ. J Clin Endocrinol Metab. 2004, 89 (6): 2548-2556. 10.1210/jc.2004-0395.

    CAS  Article  PubMed  Google Scholar 

  20. 20.

    Barnett SR, Morin RJ, Kiely DK, Gagnon M, Azhar G, Knight EL, Nelson JC, Lipsitz LA: Effects of age and gender on autonomic control of blood pressure dynamics. Hypertension. 1999, 33 (5): 1195-1200. 10.1161/01.HYP.33.5.1195.

    CAS  Article  PubMed  Google Scholar 

  21. 21.

    Tremollieres FA, Pouilles JM, Cauneille C, Ribot C: Coronary heart disease risk factors and menopause: a study in 1684 French women. Atherosclerosis. 1999, 142 (2): 415-423. 10.1016/S0021-9150(98)00252-4.

    CAS  Article  PubMed  Google Scholar 

  22. 22.

    Peters HW, Westendorp IC, Hak AE, Grobbee DE, Stehouwer CD, Hofman A, Witteman JC: Menopausal status and risk factors for cardiovascular disease. J Intern Med. 1999, 246 (6): 521-528. 10.1046/j.1365-2796.1999.00547.x.

    CAS  Article  PubMed  Google Scholar 

  23. 23.

    Couillard C, Bergeron J, Despres JP, Gagnon J, Rankinen T, Leon AS, Rao DC, Skinner JS, Wilmore JH, Bouchard C: Apolipoprotein AI- and AI:AII-containing lipoproteins in white men and women of the HERITAGE Family study: Associations with metabolic risk profile variables. Metabolism. 2003, 52 (12): 1530-1536. 10.1016/j.metabol.2003.07.003.

    CAS  Article  PubMed  Google Scholar 

  24. 24.

    Jousilahti P, Vartiainen E, Tuomilehto J, Puska P: Sex, age, cardiovascular risk factors, and coronary heart disease: a prospective follow-up study of 14 786 middle-aged men and women in Finland. Circulation. 1999, 99 (9): 1165-1172. 10.1161/01.CIR.99.9.1165.

    CAS  Article  PubMed  Google Scholar 

  25. 25.

    Hu G: Gender difference in all-cause and cardiovascular mortality related to hyperglycaemia and newly-diagnosed diabetes. Diabetologia. 2003, 46 (5): 608-617.

    CAS  PubMed  Google Scholar 

  26. 26.

    Roche MM, Wang PP: Sex differences in all-cause and cardiovascular mortality, hospitalization for individuals with and without diabetes, and patients with diabetes diagnosed early and late. Diabetes Care. 2013, 36 (9): 2582-2590. 10.2337/dc12-1272.

    PubMed Central  Article  PubMed  Google Scholar 

  27. 27.

    Mascarenhas-Melo F, Marado D, Palavra F, Sereno J, Coelho A, Pinto R, Teixeira-Lemos E, Teixeira F, Reis F: Diabetes abrogates sex differences and aggravates cardiometabolic risk in postmenopausal women. Cardiovasc Diabetol. 2013, 12: 61-2840-12-61. 10.1186/1475-2840-12-61.

    Article  Google Scholar 

  28. 28.

    Kahn HS, Bullard KM, Barker LE, Imperatore G: Differences between adiposity indicators for predicting all-cause mortality in a representative sample of United States non-elderly adults. PLoS One. 2012, 7 (11): e50428-10.1371/journal.pone.0050428.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  29. 29.

    Del Gobbo LC, Song Y, Poirier P, Dewailly E, Elin RJ, Egeland GM: Low serum magnesium concentrations are associated with a high prevalence of premature ventricular complexes in obese adults with type 2 diabetes. Cardiovasc Diabetol. 2012, 11: 23-2840-11-23.

    Article  Google Scholar 

  30. 30.

    Rider OJ, Lewandowski A, Nethononda R, Petersen SE, Francis JM, Pitcher A, Holloway CJ, Dass S, Banerjee R, Byrne JP, Leeson P, Neubauer S: Gender-specific differences in left ventricular remodelling in obesity: insights from cardiovascular magnetic resonance imaging. Eur Heart J. 2013, 34 (4): 292-299. 10.1093/eurheartj/ehs341.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  31. 31.

    Martins RA, Jones JG, Cumming SP, Silva MJ C e, Teixeira AM, Verissimo MT: Glycated hemoglobin and associated risk factors in older adults. Cardiovasc Diabetol. 2012, 11: 13-2840-11-13. 10.1186/1475-2840-11-13.

    Article  Google Scholar 

  32. 32.

    Shelton NJ: Regional risk factors for health inequalities in Scotland and England and the “Scottish effect”. Soc Sci Med. 2009, 69 (5): 761-767. 10.1016/j.socscimed.2009.06.044.

    Article  PubMed  Google Scholar 

  33. 33.

    Han TS, Sattar N, Williams K, Gonzalez-Villalpando C, Lean ME, Haffner SM: Prospective study of C-reactive protein in relation to the development of diabetes and metabolic syndrome in the Mexico City Diabetes Study. Diabetes Care. 2002, 25 (11): 2016-2021. 10.2337/diacare.25.11.2016.

    CAS  Article  PubMed  Google Scholar 

  34. 34.

    Willett WC, Dietz WH, Colditz GA: Guidelines for healthy weight. N Engl J Med. 1999, 341 (6): 427-434. 10.1056/NEJM199908053410607.

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Kahn SE, Hull RL, Utzschneider KM: Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature. 2006, 444 (7121): 840-846. 10.1038/nature05482.

    CAS  Article  PubMed  Google Scholar 

  36. 36.

    Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. 2000, 894 (i-xii): 1-253.

  37. 37.

    Alberti KG, Zimmet PZ: Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med. 1998, 15 (7): 539-553. 10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S.

    CAS  Article  PubMed  Google Scholar 

  38. 38.

    Suliga E: Visceral adipose tissue in children and adolescents: a review. Nutr Res Rev. 2009, 22 (2): 137-147. 10.1017/S0954422409990096.

    Article  PubMed  Google Scholar 

  39. 39.

    Wajchenberg BL: Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome. Endocr Rev. 2000, 21 (6): 697-738. 10.1210/edrv.21.6.0415.

    CAS  Article  PubMed  Google Scholar 

  40. 40.

    Klein S: The case of visceral fat: argument for the defense. J Clin Invest. 2004, 113 (11): 1530-1532. 10.1172/JCI200422028.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  41. 41.

    Carey DG: Abdominal obesity. Curr Opin Lipidol. 1998, 9 (1): 35-40. 10.1097/00041433-199802000-00008.

    CAS  Article  PubMed  Google Scholar 

  42. 42.

    Ross R, Shaw KD, Rissanen J, Martel Y, de Guise J, Avruch L: Sex differences in lean and adipose tissue distribution by magnetic resonance imaging: anthropometric relationships. Am J Clin Nutr. 1994, 59 (6): 1277-1285.

    CAS  PubMed  Google Scholar 

  43. 43.

    Kuk JL, Lee S, Heymsfield SB, Ross R: Waist circumference and abdominal adipose tissue distribution: influence of age and sex. Am J Clin Nutr. 2005, 81 (6): 1330-1334.

    CAS  PubMed  Google Scholar 

  44. 44.

    Camhi SM, Bray GA, Bouchard C, Greenway FL, Johnson WD, Newton RL, Ravussin E, Ryan DH, Smith SR, Katzmarzyk PT: The relationship of waist circumference and BMI to visceral, subcutaneous, and total body fat: sex and race differences. Obesity (Silver Spring). 2011, 19 (2): 402-408. 10.1038/oby.2010.248.

    Article  Google Scholar 

  45. 45.

    Cameron AJ, Magliano DJ, Söderberg S: A systematic review of the impact of including both waist and hip circumference in risk models for cardiovascular diseases, diabetes and mortality. Obes Rev. 2013, 14 (1): 86-94. 10.1111/j.1467-789X.2012.01051.x.

    CAS  Article  PubMed  Google Scholar 

  46. 46.

    Louet JF, LeMay C, Mauvais-Jarvis F: Antidiabetic actions of estrogen: insight from human and genetic mouse models. Curr Atheroscler Rep. 2004, 6 (3): 180-185. 10.1007/s11883-004-0030-9.

    Article  PubMed  Google Scholar 

  47. 47.

    Ferrara A, Barrett-Connor E, Wingard DL, Edelstein SL: Sex differences in insulin levels in older adults and the effect of body size, estrogen replacement therapy, and glucose tolerance status. The Rancho Bernardo Study, 1984–1987. Diabetes Care. 1995, 18 (2): 220-225. 10.2337/diacare.18.2.220.

    CAS  Article  PubMed  Google Scholar 

  48. 48.

    Söderberg S, Stegmayr B, Stenlund H, Sjöström LG, Agren A, Johansson L, Weinehall L, Olsson T: Leptin, but not adiponectin, predicts stroke in males. J Intern Med. 2004, 256 (2): 128-136. 10.1111/j.1365-2796.2004.01351.x.

    Article  PubMed  Google Scholar 

  49. 49.

    Lilja M, Rolandsson O, Norberg M, Söderberg S: The impact of leptin and adiponectin on incident type 2 diabetes is modified by sex and insulin resistance. Metab Syndr Relat Disord. 2012, 10 (2): 143-151. 10.1089/met.2011.0123.

    CAS  Article  PubMed  Google Scholar 

  50. 50.

    Rabin KR, Kamari Y, Avni I, Grossman E, Sharabi Y: Adiponectin: linking the metabolic syndrome to its cardiovascular consequences. Expert Rev Cardiovasc Ther. 2005, 3 (3): 465-471. 10.1586/14779072.3.3.465.

    CAS  Article  PubMed  Google Scholar 

  51. 51.

    Oh DK, Ciaraldi T, Henry RR: Adiponectin in health and disease. Diabetes Obes Metab. 2007, 9 (3): 282-289. 10.1111/j.1463-1326.2006.00610.x.

    CAS  Article  PubMed  Google Scholar 

  52. 52.

    Arone LJ, Mackintosh R, Rosenbaum M, Leibel RL, Hirsch J: Autonomic nervous system activity in weight gain and weight loss. Am J Physiol. 1995, 269 (1 Pt 2): R222-R225.

    CAS  PubMed  Google Scholar 

  53. 53.

    Karason K, Molgaard H, Wikstrand J, Sjostrom L: Heart rate variability in obesity and the effect of weight loss. Am J Cardiol. 1999, 83 (8): 1242-1247. 10.1016/S0002-9149(99)00066-1.

    CAS  Article  PubMed  Google Scholar 

  54. 54.

    Flier JS: Obesity wars: molecular progress confronts an expanding epidemic. Cell. 2004, 116 (2): 337-350. 10.1016/S0092-8674(03)01081-X.

    CAS  Article  PubMed  Google Scholar 

  55. 55.

    Onat A, Can G: Enhanced proinflammatory state and autoimmune activation: a breakthrough to understanding chronic diseases. Curr Pharm Des. 2014, 20 (4): 575-584. 10.2174/138161282004140213145551.

    CAS  Article  PubMed  Google Scholar 

  56. 56.

    Despres JP, Moorjani S, Lupien PJ, Tremblay A, Nadeau A, Bouchard C: Regional distribution of body fat, plasma lipoproteins, and cardiovascular disease. Arteriosclerosis. 1990, 10 (4): 497-511. 10.1161/01.ATV.10.4.497.

    CAS  Article  PubMed  Google Scholar 

  57. 57.

    Grundy SM, Brewer HB, Cleeman JI, Smith SC, Lenfant C: Definition of metabolic syndrome: report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Arterioscler Thromb Vasc Biol. 2004, 24 (2): e13-e18. 10.1161/01.ATV.0000111245.75752.C6.

    CAS  Article  PubMed  Google Scholar 

  58. 58.

    Tchernof A, Poehlman ET, Despres JP: Body fat distribution, the menopause transition, and hormone replacement therapy. Diabetes Metab. 2000, 26 (1): 12-20.

    CAS  PubMed  Google Scholar 

  59. 59.

    Stanworth RD, Jones TH: Testosterone in obesity, metabolic syndrome and type 2 diabetes. Front Horm Res. 2009, 37: 74-90. 10.1159/000176046.

    CAS  Article  PubMed  Google Scholar 

  60. 60.

    Jones TH: Testosterone deficiency: a risk factor for cardiovascular disease?. Trends Endocrinol Metab. 2010, 8 (21(8)): 496-503. 10.1016/j.tem.2010.03.002.

    Article  Google Scholar 

  61. 61.

    Bulow B, Hagmar L, Eskilsson J, Erfurth EM: Hypopituitary females have a high incidence of cardiovascular morbidity and an increased prevalence of cardiovascular risk factors. J Clin Endocrinol Metab. 2000, 85 (2): 574-584.

    CAS  PubMed  Google Scholar 

  62. 62.

    Havel PJ: Update on adipocyte hormones: regulation of energy balance and carbohydrate/lipid metabolism. Diabetes. 2004, 53 (Suppl 1): S143-S151. 10.2337/diabetes.53.2007.S143.

    CAS  Article  PubMed  Google Scholar 

  63. 63.

    Montague CT, Prins JB, Sanders L, Digby JE, O’Rahilly S: Depot- and sex-specific differences in human leptin mRNA expression: implications for the control of regional fat distribution. Diabetes. 1997, 46 (3): 342-347. 10.2337/diab.46.3.342.

    CAS  Article  PubMed  Google Scholar 

  64. 64.

    Westerbacka J, Cornér A, Tiikkainen M, Tamminen M, Vehkavaara S, Häkkinen AM, Fredriksson J, Yki-Järvinen H: Women and men have similar amounts of liver and intra-abdominal fat, despite more subcutaneous fat in women: implications for sex differences in markers of cardiovascular risk. Diabetologia. 2004, 47 (8): 1360-1369. 10.1007/s00125-004-1460-1.

    CAS  Article  PubMed  Google Scholar 

  65. 65.

    Stramba-Badiale M, Fox KM, Priori SG, Collins P, Daly C, Graham I, Jonsson B, Schenck-Gustafsson K, Tendera M: Cardiovascular diseases in women: a statement from the policy conference of the European Society of Cardiology. Eur Heart J. 2006, 27 (8): 994-1005. 10.1093/eurheartj/ehi819.

    Article  PubMed  Google Scholar 

  66. 66.

    Carpenter KM, Hasin DS, Allison DB, Faith MS: Relationships between obesity and DSM-IV major depressive disorder, suicide ideation, and suicide attempts: results from a general population study. Am J Public Health. 2000, 90 (2): 251-257. 10.2105/AJPH.90.2.251.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  67. 67.

    Expert WHO: Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004, 363 (9403): 157-163. 10.1016/S0140-6736(03)15268-3.

    Article  Google Scholar 

  68. 68.

    Kotani K, Tokunaga K, Fujioka S, Kobatake T, Keno Y, Yoshida S, Shimomura I, Tarui S, Matsuzawa Y: Sexual dimorphism of age-related changes in whole-body fat distribution in the obese. Int J Obes Relat Metab Disord. 1994, 18 (4): 207-202.

    CAS  PubMed  Google Scholar 

  69. 69.

    Sites CK, Brochu M, Tchernof A, Poehlman ET: Relationship between hormone replacement therapy use with body fat distribution and insulin sensitivity in obese postmenopausal women. Metabolism. 2001, 50 (7): 835-840. 10.1053/meta.2001.24878.

    CAS  Article  PubMed  Google Scholar 

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Acknowledgements

This study was supported by a grant from EC Health-2011-F2-279074-Project.

Disclaimer

Results and views of the presented study represent the authors’ own and are not necessarily any official views of the Swedish Medical Products Agency where BZ is employed.

Author information

Affiliations

  1. Department of Public Health, Hjelt Institute, University of Helsinki, Helsinki, Finland

    Xin Song & Qing Qiao

  2. Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland

    Xin Song, Tiina Laatikainen, Pekka Jousilahti, Jaakko Tuomilehto & Qing Qiao

  3. Department of Epidemiology and Public Health, University College London, London, UK

    Adam G Tabák

  4. 1st Department of Medicine, Faculty of Medicine, Semmelweis University, Budapest, Hungary

    Adam G Tabák

  5. Department of Public Health and Caring Sciences/Geriatrics, Uppsala University, Uppsala, Sweden

    Björn Zethelius

  6. Medical Products Agency, Uppsala, Sweden

    Björn Zethelius

  7. Department of Medicine, University College London, London, UK

    John S Yudkin

  8. Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden

    Stefan Söderberg

  9. Baker IDI Heart and Diabetes Institute, Melbourne, Australia

    Stefan Söderberg

  10. Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland

    Tiina Laatikainen

  11. Hospital District of North Karelia, Joensuu, Finland

    Tiina Laatikainen

  12. Department of Internal Medicine and Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Centre, Maastricht, The Netherlands

    Coen DA Stehouwer

  13. Unit for Cardiovascular Epidemiology, The Gertner Institute, Sheba Medical Center, Tel Hashomer, Israel

    Rachel Dankner

  14. Division of Epidemiology and Prevention, School of Public Health, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel

    Rachel Dankner

  15. Department of Cardiology, Cerrahpaşa Medical Faculty, Istanbul, Turkey

    Altan Onat

  16. Department of Clinical Sciences, Lund University, Malmö, Sweden

    Peter M Nilsson

  17. Center of Emergency Medicine, Skåne University Hospital, Malmö, Sweden

    Peter M Nilsson

  18. Istanbul University, Istanbul Medical Faculty, Istanbul, Turkey

    Ilhan Satman

  19. Department of Clinical Medicine and Surgery, Federico II University, Naples, Italy

    Olga Vaccaro

  20. Center for Vascular Prevention, Danube University Krems, Krems, Austria

    Jaakko Tuomilehto

  21. King Abdulaziz University, Jeddah, Saudi Arabia

    Jaakko Tuomilehto

  22. Instituto de Investigacion Sanitaria del Hospital Universario LaPaz (IdiPAZ), Madrid, Spain

    Jaakko Tuomilehto

  23. R&D AstraZeneca AB, Mölndal, Sweden

    Qing Qiao

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  1. Xin Song
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  2. Adam G Tabák
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  3. Björn Zethelius
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  11. Peter M Nilsson
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  15. Qing Qiao
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for the DECODE Study Group

Corresponding author

Correspondence to Xin Song.

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The authors declare that they have no competing interests.

Electronic supplementary material

Table S2.

Additional file 1: Table S1.: Baseline characteristics among non-diabetic and diabetic subjects by body mass index and sex. Baseline characteristics of participants by sex-specific anthropometric measures of abdominal obesity*. Table S3. Multivariate-adjusted hazard ratios for cardiovascular disease mortality in men and women by body mass index categories, stratified by cohort*. Table S4. Multivariate-adjusted hazard ratios for cardiovascular disease mortality in men and women by body mass index categories: impact of omitting each cohort from the analysis*. Table S5. Multivariate-adjusted hazard ratios for cardiovascular disease mortality in men and women by body mass index categories. Table S6. Multivariate-adjusted hazard ratios and their 95% confidence intervals for cardiovascular disease (CVD) mortality in men and women by body mass index categories or sex-specific categories of anthropometric measures of abdominal obesity. Table S7. Mortality rate per 10 000 person-years and multivariate-adjusted hazard ratios for cardiovascular disease mortality in men and women by body mass index categories or sex-specific categories of anthropometric measures of abdominal obesity, excluding the first five years of follow-up (n= 42 273). (DOCX 148 KB)

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Song, X., Tabák, A.G., Zethelius, B. et al. Obesity attenuates gender differences in cardiovascular mortality. Cardiovasc Diabetol 13, 144 (2014). https://doi.org/10.1186/s12933-014-0144-5

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Keywords

  • Obesity
  • Gender
  • Cardiovascular disease mortality

Cardiovascular Diabetology

ISSN: 1475-2840