Published online before print March 17, 2003, doi: 10.1161/01.CIR.0000057974.74060.68
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(Circulation. 2003;107:1626.)
© 2003 American Heart Association, Inc.
Clinical Investigation and Reports |
Peripheral Adiposity Exhibits an Independent Dominant Antiatherogenic Effect in Elderly Women
From the Center for Clinical and Basic Research A/S, Ballerup and Aalborg, and Rheoscience A/S, Rødovre (P.J.L.), Denmark.
Correspondence to László B. Tankó, MD, PhD, Center for Clinical and Basic Research, Ballerup byvej 222, DK-2750 Ballerup, Denmark. E-mail lbt{at}ccbr.dk
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Abstract |
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Background— Although several lines of evidence point to an atherogenic role of central fat mass (CFM), few data are available to address the specific role played by peripheral fat mass (PFM).
Methods and Results— This study was a cross-sectional analysis of 1356 women aged 60 to 85 years. Study variables were physical measures, CFM and PFM measured by DEXA, aortic calcification (AC) graded on lateral radiographs, lipid and glucose metabolites, blood pressure, and information on lifestyle factors and coronary disease. Peripheral fat mass showed independent negative correlation with both atherogenic metabolic risk factors and AC (P<0.001). The most severe insulin resistance-dyslipidemic syndrome and AC (score 5.10±0.76) was found in women with high central fat percentage (CF%, 21.7±0.2%) and low peripheral fat percentage (PF%, 18.3±0.2%, n=48). The least severe AC (score 2.45±0.31) was found in obese women with high CF% (21.6±0.1%) and high PF% (27.3±0.14%, n=112). The insulin resistance-dyslipidemic syndrome was also less severe compared with those with the same CF% but low PF%. The most favorable metabolic profile characterized women with low CF% (11.56±0.16%) and high PF% (26.86±0.33%, n=44). In women with a history of myocardial infarct (18.41±0.55%, n=45), CF% was significantly higher compared with women with no manifest coronary disease (16.48±0.12%, n=1210) without differences in PF%.
Conclusions— In elderly women, localization of fat mass is apparently more important for atherosclerosis than obesity per se; although CFM is associated with atherogenic tendencies, PFM seems to exhibit an independent dominant antiatherogenic effect.
Key Words: atherosclerosis • epidemiology • obesity • risk factors • women
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Introduction |
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Obesity constitutes a cardiovascular risk factor.1 The contribution of body fatness per se to atherogenesis and cardiovascular risk, however, shows conflicting results. Body mass index (BMI) shows strong association with the progression of atherosclerosis and cardiovascular mortality in men.2,3 In contrast, most studies found no correlation between BMI and the severity of atherosclerosis in women.3–7 Moreover, among the elderly, obesity compared with thinness is associated with lower mortality rates, suggesting a protective influence of fat mass.8,9
Although the atherogenic role of central fat mass (CFM) is well established,10 there are limited data to address the specific role of peripheral fat mass (PFM). Large hip circumference is an independent predictor of lower cardiovascular and diabetes-related mortality.11 Furthermore, hip circumference and fat on the legs show strong negative association with atherogenic lipid and glucose metabolites.12–14 However, at the present time, there are no large population-based studies on postmenopausal women relating direct measures of PFM to direct measures of atherosclerosis that could confirm this apparent antiatherogenic influence of PFM.
In the present study, we have combined DEXA scanning and radiographic assessment of aortic calcification (AC)15 to study the depot-specific relation of fat mass to AC atherosclerosis in 1356 elderly women aged 60 to 85 years. Risk factors for coronary heart disease (CHD) and glucose intolerance, including type 2 diabetes, cluster,16 suggesting a common underlying pathophysiological mechanism. Therefore, we have also studied the association between insulin resistance as estimated by homeostasis model assessment17 and AC.
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Methods |
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The study population consisted of 1356 women aged 60 to 85 years who previously participated in epidemiologic cohorts followed by the Center for Clinical and Basic Research in Aalborg, Denmark. The original population was recruited by questionnaire surveys. In 2000 to 2001, these women were invited for a follow-up examination. Demographic characteristics and frequency of risk factors in the study population are indicated in Table 1. Those receiving lipid-lowering medication (n=39) were excluded from all analyses, except for the comparison of healthy women with infarct patients. Borderline cases of self-reported angina pectoris without angiographic confirmation of coronary atherosclerosis were excluded from this comparison (n=101).
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All women gave informed consent to participation, and the study was carried out in accordance with the Helsinki Declaration II and the European Standards for Good Clinical Practice. The local ethics committee approved the study protocol.
Cardiovascular Risk Factors
Body weight and height were measured to the closest 0.1 kg and 0.1 cm, and BMI was calculated (kg/m2). Arterial blood pressure was measured with a digital blood pressure monitor (UA-777, A&D Instruments LTD). Information on level of education (primary/secondary/university), smoking habits (never/ex/current), daily alcohol and coffee consumption, weekly fitness activities (1, never; 2, once weekly; 3, twice weekly; 4, more than twice weekly), current use of hormone replacement therapy, and presence of treated diabetes mellitus, hypertension, hypercholesterinemia, or CHD was gathered during personal interviews. Confirmed presence of myocardial infarction (MI) in 45 participants was given by hospital discharge summaries (ECG changes and cardiac enzymes).
Glucose, total cholesterol, and triglyceride were measured by a Vitros-250 automatic blood analyzer (Johnson & Johnson). LDL-C and HDL-C were determined by enzymatic assay for direct determination, whereas lipoprotein a [Lp(a)], apolipoprotein (apo)-A1, and apo-B were determined by immunoturbidimetry using a Hitachi-912 analyzer (Hoffman-La Roche Ltd). White blood cell (WBC) count was measured by a Sysmex K-1000 analyzer (Sysmex Corporation). Insulin was measured by radioimmunoassay using a commercially available kit (Linco). The estimate of insulin resistance by HOMAIR was calculated with the following formula: fasting insulin (µU/mL)xfasting glucose (mmol/L)/22.5.17 As an estimate of ß cell function, we used the HOMAF index, as follows: 20xfasting insulin (mmol/L)/fasting glucose (mmol/L)-3.5.17
Body Adiposity
Body adiposity was measured by DEXA using a Hologic QDR4500A scanner (Hologic Inc, software version 9.03D). Fat mass of the trunk (in kilograms), here termed as CFM, included both subcutaneous and visceral fat of this anatomical region. The sum of fat mass on the legs and arms (in kg) was termed as PFM. When used for comparative purposes, CFM and PFM were expressed as percentage of the sum of total body fat and lean mass. Precision of the measurement is 1.5%, 0.8%, and 1.1% for the arms, legs, and trunk, respectively.18
Aortic Calcification
Aortic calcification was assessed on lateral radiographs, as previously described in detail.15 Calcified deposits in the lumbar aorta adjacent to each lumbar vertebra (L1-L4) were assessed separately for the anterior and posterior wall of the aorta using the midpoint of the intervertebral space as the boundaries. Severity of AC was described by 3 scores: the affected segments score (scale 0 to 4), the anterior and posterior affected score (scale 0 to 8), and the anteroposterior severity score (scale 0 to 24). The same investigator, who was blinded for all other results of the individual participants, carried out the evaluations. Intrarater correlations were in the range of r=0.92 to 0.98, similarly to published results.15
Data Analysis
Data shown are mean±SEM. Statistical analysis was carried out using the SPSS data analysis software (version 10.01, SPSS Inc). Because risk factors, both categorical and numerical, were not normally distributed, Spearman rank-order correlation was used to determine the bivariate associations between measures of fat mass or AC and the selected risk factors. The independent correlation between selected variables was determined using partial regression analysis controlling for selected contributors or by establishing general linear models (multivariate).
To isolate women with different extremes of fat distribution, we stratified the participants into 4 groups according to percentiles of PF% and CF%. Different combinations of the respective lowest (<25th) and highest (>75th) percentiles of PF% and CF% pointed out individuals belonging to the 4 extreme groups (Figure 1). Characteristics of these groups were compared by one-way ANOVA combined with Scheffe’s test or by Kruskal-Wallis test. Characteristics of women with MI or no CHD were compared using Student’s 2-tailed t test for unpaired observations or by Mann-Whitney test. Correlations and differences were considered significant if P<0.05.
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Results |
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Aortic Calcification and Cardiovascular Risk Factors
Figure 2 illustrates significant associations between AC and cardiovascular risk factors (n=1317). In multivariate analysis, age, BMI, total cholesterol, triglyceride, current and previous smoking, and treated hypertension contributed independently to the variation in AC (all P<0.05). In addition, serum glucose and WBC were independent contributors to the anterior or posterior affected score (<0.05).
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Aortic Calcification and CHD
In women with MI, AC was significantly more severe compared with women with no CHD. Thus, the anterior and posterior severity scores of AC in the no CHD (n=1210) and MI (n=45) groups were 4.40±0.13 and 5.29±0.79, the anterior-posterior affected scores were 2.30±0.06 and 3.40±0.36, and the affected segments scores were 1.51±0.04 and 2.16±0.21, respectively (all P<0.01).
There were no differences in age (72.3±0.7 versus 71.5±0.2 years, P=0.216). Both lipid-lowering (17.8% versus 2.4%, P<0.001) and antihypertensive (57.8% versus 27.4%, P<0.001) therapies were more frequent in women with a history of MI. Serum glucose, triglyceride, WBC count, and the prevalence of smoking and diabetes mellitus tended to be higher in the MI group; however, the differences did not reach statistical significance.
Body Adiposity and Cardiovascular Risk Factors
Figure 3 illustrates the direct and independent associations of PFM and CFM with cardiovascular risk factors. In contrast to CFM, the independent correlations of PFM with cardiovascular risk factors was negative. Current and previous smoking were also independently and negatively related to PFM and CFM.
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Body Adiposity and Aortic Calcification
Peripheral fat mass showed significant negative correlation with AC (r=-0.106 to 0.125, P<0.001), whereas no association with CFM was observed. Similar results were found in a multivariate analysis, where PFM, but not CFM, contributed independently to AC (P<0.001).
Women with Extremes of Fat Distribution
To obtain additional insight into the role of PFM and CFM, we compared the severity of AC in 4 groups of women with different extremes of body fat distribution (Figure 1). There were no differences in mean age (P=0.316). Mean BMI in group 1 (lean women) was 22.6±3.2 kg/m2, in group 2 (peripheral adiposity) was 24.9±3.2 kg/m2, in group 3 (central adiposity) was 28.2±2.2 kg/m2, and in group 4 (general adiposity) was 30.8±3.1 kg/m2.
Indexes of body fat distribution are shown in Figure 4A, whereas scores of AC are shown in Figure 4B. The lowest scores of AC were found in group 4. In group 3, displaying the same CF% but lower PF%, significantly higher scores of AC were observed. Group 1 also showed significantly higher scores of AC compared with generally obese women, whereas AC in group 2 was similar to that seen in group 4.
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Aortic Calcification in Women With Extreme Body Fat Distribution in Relation to Cardiovascular Risk Factors
The 4 subgroups showed comparable levels of arterial blood pressure and similar frequencies of treated hypertension (group 1, 25.3%; group 2, 31.8%; group 3, 29.2%; and group 4, 27.7%; P=0.856). Frequency of smokers among women with high CF% was lower (group 3, 4.2%; group 4, 8.0%) compared with women with low CF% (group 1, 25.3%; group 2, 29.5%). There were, however, no differences in the representation of ex-smokers (P=0.616). Removal of current smokers from groups 1 and 2 led to more prominent decreases in AC in the latter group (data not shown).
Table 2 indicates the metabolic risk profile in the 4 groups. In general, groups 3 and 4 displayed a characteristic atherogenic lipid profile, ie, significantly lower HDL-C and higher triglyceride, apo-A1, VLDL, non-HDL-C, LDL-C/HDL-C, and triglyceride/HDL-C ratios. LDL-C was similar to those in groups 1 and 2. Most of these parameters were significantly higher in group 3 compared with group 4. Glucose metabolism in group 3 was characterized by a combination of impaired fasting glucose and impaired insulin sensitivity. All 4 groups displayed comparable ß cell function as assessed by HOMAF. Within the low CF% groups, group 1 had significantly higher apo-B concentrations compared with group 2.
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Based on these findings, the contribution of CFM to AC was additionally investigated in group 3, where the concomitant influence of PFM is likely to be minimal. The different scores of AC showed a positive correlation with CFM (r=0.324, n=48; P<0.05), which remained significant even after adjustment for traditional risk factors (r=0.377, P=0.025). Adjustment for HOMAIR did not change this result (r=0.399, P=0.021). Furthermore, we found higher CF% in women with verified MI (18.41±0.55%, n=45) compared with women with no CHD (16.48±0.12%, n=1210, P<0.001), whereas there were no differences in PF% (22.86±0.57 versus 22.30±0.11, P=0.344).
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Discussion |
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Our main observation is the apparent independent and dominant antiatherogenic influence of PFM, suggesting a protective effect against cardiovascular disease.
Body Fat Distribution and Metabolic Risk Factors
In accordance with previous reports,12–14 CFM showed positive correlation with an atherogenic lipid profile. In contrast, PFM showed an independent negative correlation with glucose and lipid metabolites. Furthermore, the lowest atherogenic lipids were observed in women characterized by low CF% and high PF%. Thus, these results indicate that different fat depots exhibit different influences on lipid metabolism, with CFM promoting and PFM counteracting atherogenic tendencies. In most studies, visceral adiposity is associated with elevated levels of small-sized LDL-C particles, as evidenced by high apo-B levels.19 In absence of other risk factors, only elevated levels of apo-B can induce development of atherosclerosis in humans.20 In the present study, women with low CFM displayed a lipoprotein profile opposite to the expected, with high apo-B and low apo-A1. However, this observation suggests that underlying causes of AC are multiple, with lipoprotein size as a considerable contributor in lean women, whereas other factors may predominate in women with isolated central obesity.
HOMAIR is a useful surrogate variable for the prediction of cardiovascular disease, even after adjustment for covariates, including dyslipidemia.21 In the present study, impaired HOMAIR and impaired fasting glucose coexisted in women with isolated central adiposity. In contrast, isolated peripheral adiposity has no apparent effect on glucose homeostasis, as evidenced by unaffected HOMAIR and HOMAF values. Additional support of adverse metabolic consequences of visceral fat on glucose homeostasis comes from experimental22 and epidemiological data.23 Thus, the strong correlation between CFM and AC in women with low PFM may be attributable to dyslipidemia or to insulin resistance associated with central adiposity. It is widely held that obesity-related dyslipidemia constitutes the causal atherogenic factors. However, in the present study, atherogenic risk posed by CFM persisted even after adjustment for lipids, suggesting that other factors associated with visceral adiposity contribute to atherogenesis. The independent influence of factors other than lipids on impaired vascular function is exaggerated in glucose-intolerant individuals. It has been shown that the lipid-lowering effects of statins are not accompanied by expected improvement of endothelial function in patients with type 2 diabetes.24
A central question is whether insulin resistance associated with visceral adiposity contributes to AC to a similar extent as dyslipidemia. Insulin resistance relates to several other cardiovascular risk factors, but it remains to be established whether the independent effect of insulin resistance on atherosclerosis is mediated directly via a vascular target. Insulin exerts several antiatherogenic actions on the vessel wall,25 effects that are all impaired in insulin-resistant individuals. The adverse metabolic consequences of central adiposity on glucose homeostasis include increased hepatic glucose output and decreased peripheral lipid clearance, both of which may also promote atherogenesis.
It is interesting that a relative lack of PF% leads to significantly poorer insulin sensitivity, also observed by other groups.26,27 Although we cannot exclude the possibility that low PF% is a late epiphenomenon of insulin resistance, these observations could also be explained by an active inhibitory influence of PFM, which can overrule the atherogenic tendencies caused by high CFM. Arad et al28 recently provided strong evidence for the independent contribution of CFM to the insulin resistance syndrome and coronary calcification, even among otherwise asymptomatic nondiabetic men and women. Visceral obesity antedates type 2 diabetes, but its causal relation to other components of the metabolic syndrome is less clear.29
Different Body Fat Depots and Their Association With Aortic Calcification
Peripheral fat mass showed negative correlation with AC, a direct measure of atherosclerosis; however, no significant association with CFM was observed. Central fat mass determined by DEXA comprises both subcutaneous and visceral compartments. Therefore, it cannot be excluded that our results are partly attributable to the opposing effects of the subcutaneous and visceral fat within this anatomic region. This hypothesis is also supported by the observation that atherosclerosis was markedly more pronounced in central obesity (low PF%) compared with general obesity (high PF%). Furthermore, the significant negative correlation between AC and BMI, taking into account the larger contribution of PFM versus CFM to total fat mass, also seems to corroborate the dominant antiatherogenic influence of PFM. Similar to the findings on metabolic factors, the severity of atherosclerosis was significantly lower in generally obese women compared with those with predominant central obesity. Thus, it is likely that visceral adipocytes release a variety of yet-to-be-identified factors that promote systemic atherogenesis, whereas peripheral adipocytes could release factors constituting protection against atherosclerosis. Peripheral adipocytes secrete the hormone adiponectin, which correlates negatively to insulin resistance and has putative antiatherogenic properties that are relevant for the prevention of formation of atherosclerotic plaques.30
Smoking and Fat Mass
Smoking showed the strongest positive correlate for AC but also showed independent negative correlations with both types of fat mass. This is in accordance with previous31 studies demonstrating that female smokers tend to be leaner compared with nonsmokers. Because of the complex interactions between smoking, fat mass, and AC, it is somewhat difficult to clearly interpret atherosclerosis in women with low CF%. However, the favorable metabolic risk profile of these groups suggested that smoking might have contributed significantly to atherosclerosis. When attempting to adjust for current smoking, women with high PFM showed more prominent decreases in AC compared with lean women, suggesting a role played by PFM. However, the two groups with high CF% showed virtually similar frequencies of current and previous smoking. Thus, differences between these groups could not be attributed to differences in smoking habits. Because smoking-related loss of PFM might be an important amplifier of the direct atherogenic effects of smoking, clarification of this issue deserves due attention in future longitudinal studies.
Conclusions
In conclusion, the present study demonstrated that in elderly women localization of fat mass is more important for atherogenesis than obesity per se. Genetic and environmental determinants of body fat distribution as well as hormones and secretory factors from visceral and subcutaneous adipocytes that hypothetically may mediate the atherogenic and antiatherogenic effects of CFM and PFM, respectively, warrant additional investigation.
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Acknowledgments |
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The authors gratefully acknowledge the staff of the Department of Clinical Research Chemistry (CCBR, Ballerup) for technical assistance in the laboratory analyses. Grete Hoffmeister and Merete Christensen (CCBR, Aalborg) are thanked for assessment of the radiographs and assistance in establishing the database, respectively.
Received October 28, 2002; revision received December 31, 2002; accepted January 7, 2003.
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 J. Mukuddem-Petersen, M. B Snijder, R. M van Dam, J. M Dekker, L. M Bouter, C. D. Stehouwer, R. J Heine, G. Nijpels, and J. C Seidell Sagittal abdominal diameter: no advantage compared with other anthropometric measures as a correlate of components of the metabolic syndrome in elderly from the Hoorn Study. Am. J. Clinical Nutrition, November 1, 2006; 84(5): 995 - 1002. [Abstract] [Full Text] [PDF]
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 M. Snijder, R. van Dam, M Visser, and J. Seidell What aspects of body fat are particularly hazardous and how do we measure them? Int. J. Epidemiol., February 1, 2006; 35(1): 83 - 92. [Full Text] [PDF]
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D. Canoy, N. Wareham, A. Welch, S. Bingham, R. Luben, N. Day, and K.-T. Khaw Plasma ascorbic acid concentrations and fat distribution in 19 068 British men and women in the European Prospective Investigation into Cancer and Nutrition Norfolk cohort study Am. J. Clinical Nutrition, December 1, 2005; 82(6): 1203 - 1209. [Abstract] [Full Text] [PDF]
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 L. B. Tanko and C. Christiansen Can the Obesity Paradox Be Explained by the Protective Effects of Peripheral Adiposity? Arch Intern Med, August 8, 2005; 165(15): 1796 - 1797. [Full Text] [PDF]
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R. E. Van Pelt, C. M. Jankowski, W. S. Gozansky, R. S. Schwartz, and W. M. Kohrt Lower-Body Adiposity and Metabolic Protection in Postmenopausal Women J. Clin. Endocrinol. Metab., August 1, 2005; 90(8): 4573 - 4578. [Abstract] [Full Text] [PDF]
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 S. A. Lear Waist Circumference Is Simpler than Body Mass Index Clin. Chem., June 1, 2005; 51(6): 1082 - 1084. [Full Text] [PDF]
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 Y. Iwashima, T. Katsuya, K. Ishikawa, I. Kida, M. Ohishi, T. Horio, N. Ouchi, K. Ohashi, S. Kihara, T. Funahashi, et al. Association of Hypoadiponectinemia With Smoking Habit in Men Hypertension, June 1, 2005; 45(6): 1094 - 1100. [Abstract] [Full Text] [PDF]
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L. B. Tanko, Y. Z. Bagger, G. Qin, P. Alexandersen, P. J. Larsen, and C. Christiansen Enlarged Waist Combined With Elevated Triglycerides Is a Strong Predictor of Accelerated Atherogenesis and Related Cardiovascular Mortality in Postmenopausal Women Circulation, April 19, 2005; 111(15): 1883 - 1890. [Abstract] [Full Text] [PDF]
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 D. R. Jacobs Jr. and M. A. Pereira Physical Activity, Relative Body Weight, and Risk of Death among Women N. Engl. J. Med., December 23, 2004; 351(26): 2753 - 2755. [Full Text] [PDF]
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M. B. Snijder, M. Visser, J. M. Dekker, and J. C. Seidell RE: "CHANGES IN BODY WEIGHT AND BODY FAT DISTRIBUTION AS RISK FACTORS FOR CLINICAL DIABETES IN US MEN" Am. J. Epidemiol., November 15, 2004; 160(11): 1133 - 1134. [Full Text] [PDF]
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P. Koh-Banerjee, Y. Wang, and W. Willett THREE AUTHORS REPLY Am. J. Epidemiol., November 15, 2004; 160(11): 1134 - 1135. [Full Text] [PDF]
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L. B. Tanko, J. M. Bruun, P. Alexandersen, Y. Z. Bagger, B. Richelsen, C. Christiansen, and P. J. Larsen Novel Associations Between Bioavailable Estradiol and Adipokines in Elderly Women With Different Phenotypes of Obesity: Implications for Atherogenesis Circulation, October 12, 2004; 110(15): 2246 - 2252. [Abstract] [Full Text] [PDF]
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I. Ferreira, M. B. Snijder, J. W. R. Twisk, W. van Mechelen, H. C. G. Kemper, J. C. Seidell, and C. D. A. Stehouwer Central Fat Mass Versus Peripheral Fat and Lean Mass: Opposite (Adverse Versus Favorable) Associations with Arterial Stiffness? The Amsterdam Growth and Health Longitudinal Study J. Clin. Endocrinol. Metab., June 1, 2004; 89(6): 2632 - 2639. [Abstract] [Full Text] [PDF]
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I. Lemieux Energy Partitioning in Gluteal-Femoral Fat: Does the Metabolic Fate of Triglycerides Affect Coronary Heart Disease Risk? Arterioscler Thromb Vasc Biol, May 1, 2004; 24(5): 795 - 797. [Full Text]
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T. Okura, Y. Nakata, K. Yamabuki, and K. Tanaka Regional Body Composition Changes Exhibit Opposing Effects on Coronary Heart Disease Risk Factors Arterioscler Thromb Vasc Biol, May 1, 2004; 24(5): 923 - 929. [Abstract] [Full Text]
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 M. B. Snijder, J. M. Dekker, M. Visser, L. M. Bouter, C. D.A. Stehouwer, J. S. Yudkin, R. J. Heine, G. Nijpels, and J. C. Seidell Trunk Fat and Leg Fat Have Independent and Opposite Associations With Fasting and Postload Glucose Levels: The Hoorn Study Diabetes Care, February 1, 2004; 27(2): 372 - 377. [Abstract] [Full Text] [PDF]
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H. S. Kahn, L. B. Tanko, Y. Z. Bagger, P. Alexandersen, C. Christiansen, for the Prospective Epidemiological Risk Factors (, and P. J. Larsen Peripheral Adiposity and Cardiovascular Risk * Response Circulation, December 9, 2003; 108 (23): e164 - e164. [Full Text] [PDF]
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D. J. Brotman, J. P. Girod, L. B. Tanko, Y. Z. Bagger, P. Alexandersen, C. Christiansen, and P. J. Larsen Can Glucocorticoid Homeostasis Explain the Antiatherogenic Effect of Peripheral Adiposity? * Reply Circulation, August 26, 2003; 108 (8): e61 - e61. [Full Text] [PDF]
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