© 1996 American Heart Association, Inc.
Articles |
Impact of Body Mass Index on Coronary Heart Disease Risk Factors in Men and Women
The Framingham Offspring Study
the Lipid Metabolism Laboratory (S.L.-F., E.J.S.), Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, Boston, and the Framingham Study (P.W.F.W.), Epidemiology Biometry Program, Framingham, Mass.
Correspondence to Stefania Lamon-Fava, MD, PhD, Lipid Metabolism Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, 711 Washington St, Boston, MA 02111. E-mail Fava LI@HNRC.Tufts.edu.
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Abstract |
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Increased body weight has been associated with an increased risk of morbidity and mortality from coronary heart disease (CHD) in several populations. We studied the distribution of body mass index (BMI, kg/m2) in men (n=1566; mean age, 49±10 years) and women (n=1627; mean age, 49±10 years) participating in the third examination cycle of the Framingham Offspring Study and the association of BMI with known CHD risk factors. In men, BMI increased with age until age 50 years, when it reached a plateau. In women, there was a trend toward an increase in BMI with age up to the seventh decade of life. Seventy-two percent of men and 42% of women had a BMI
25.00, the cutoff point for the definition of overweight. In age-adjusted analyses, BMI was significantly and linearly associated with systolic blood pressure, fasting glucose levels, plasma total cholesterol, VLDL cholesterol, and LDL cholesterol levels and was inversely and linearly associated with HDL cholesterol levels (P<.001) in nonsmoking men and women. The association between BMI and apolipoprotein B and A-I was similar to that of LDL and HDL cholesterol, respectively. LDL size was also linearly associated with BMI: subjects with higher BMI had smaller LDL particles. Lipoprotein(a) levels were not associated with BMI in this population. Of all these risk factors for CHD, reduced HDL cholesterol levels and hypertension were those more strongly associated with higher BMI in both men and women. Elevated triglyceride levels and small LDL particles, and diabetes in women, were also strongly associated with higher BMI values in this population. Our results indicate that a high prevalence of adult Americans are overweight and support the concept that increased BMI is associated with an adverse effect on all major CHD risk factors. These results emphasize the importance of excess body fat as a public health issue.
Key Words: body mass index • coronary heart disease • lipoproteins • cholesterol • blood pressure • diabetes mellitus
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Introduction |
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The National Cholesterol Education Program (NCEP) Adult Treatment Panel II has defined age, family history of premature CHD, hypertension, diabetes mellitus, smoking, LDL cholesterol levels
4.14 mmol/L, and HDL cholesterol levels <0.91 mmol/L as major independent risk factors for CHD.1 Some of these factors, such as age and family history of premature CHD, are nonmodifiable. However, the other risk factors can be modified through changes in lifestyle and/or drug treatment. Several epidemiological studies have demonstrated that individuals with body weight above their ideal body weight, defined by standard tables of desirable body weight such as those provided by the Metropolitan Life Insurance Company,2 3 are at increased risk of mortality from CHD.4 5 6 7 8 9 While these studies have clearly shown the positive association between increased body weight and risk of CHD, most of them did not provide clear evidence of the contribution of increased body weight to each of the major risk factors for CHD. It is thought that at least part of the increased risk of CHD conferred by an increased body weight is explained by the effects of body weight on blood pressure, glucose tolerance, and plasma lipid metabolism.10
BMI, expressed as body weight in kilograms divided by height in square meters (kg/m2), is highly correlated with body weight and poorly correlated with height11 and is frequently used as a measure of body fatness in large epidemiological studies.
In the literature, the definition of overweight and obesity, on the basis of BMI values, is somewhat confusing because different studies have used different criteria for it. For example, a body weight 20% above the ideal body weight, defined as the midpoint of the desirable weight range at a specified height, usually has been considered overweight: this corresponds to a BMI of 26.40 in men and 25.80 in women when using the 1959 Metropolitan Life Insurance tables and a BMI of 27.20 in men and 26.90 in women when using the 1983 tables.12 On the other hand, the National Health and Nutrition Examination Surveys (NHANES) studies define as overweight men with a BMI 27.80 and women with BMI 27.30 or more, corresponding to the 85th percentile in the 20- to 29-year-old subjects in that population.13 14 According to the World Health Organization, subjects with BMI between 25.00 and 30.00 are overweight, and subjects with BMI 30.00 are obese.15 Recently, the Dietary Guidelines for Americans released in 1995 define BMI >25.00 as moderate overweight and BMI >29.00 as severe overweight.16
The purpose of this study was to evaluate the distribution of BMI in a large population of white men and women and relate BMI to the prevalence of the major risk factors for CHD in this population.
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Methods |
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Population
Subjects were participants in the Framingham Offspring Study, whose purpose and design have been previously described.17 Subjects were the offspring (and their spouses) of the participants to the original Framingham Heart Study. Data were collected during the third examination cycle, from 1984 to 1987. All subjects were white. Only subjects with complete cholesterol lipoprotein measurements (1566 men: age range, 23 to 76 years; 1627 women: age range, 19 to 78 years) were included in our analyses.
Body weight was measured to the nearest 0.25 lb with the use of a standing beam balance and with subjects wearing only examination robes. This measurement was then converted to kilograms. Height was measured with the use of a stadiometer (to the nearest 0.25 in, and then converted to meters). BMI was calculated as the weight in kilograms divided by the square of the height in meters (kg/m2).
Hypertension was defined as the presence of a systolic blood pressure >140 mm Hg or a diastolic blood pressure >95 mm Hg or use of antihypertensive medications. Diabetes mellitus was defined as the presence of fasting serum glucose levels 7.77 mmol/L or the use of insulin or hypoglycemic medications.
Laboratory Measurements
Blood was drawn from each subject, after a 12- to 14-hour fast, in 0.1% EDTA tubes. Plasma was separated by centrifugation at 2500 rpm for 30 minutes at 4°C. Plasma was then subjected to ultracentrifugation in a Beckman 50 Ti rotor at 39 000 rpm for 18 hours at 4°C, at a density (d) of 1.006 g/mL, to separate VLDL, according to the Lipid Research Clinics methodology.18 Plasma HDL cholesterol levels were measured after the precipitation of apolipoprotein B–containing lipoproteins by the dextran sulfate–MgCl2 method, as previously described.19 Triglyceride levels in total plasma and cholesterol levels in total plasma, the HDL supernatant, and the d 1.006 g/mL infranatant were measured by automated enzymatic techniques.20 VLDL cholesterol and LDL cholesterol levels were calculated as
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Plasma aliquots for the measurement of apo A-I and apo B and lipoprotein(a) levels were stored at -70°C. Apo A-I and B levels were measured by noncompetitive enzyme-linked immunosorbent assays with the use of specific affinity-purified polyclonal anti–apo A-I and anti–apo B antibodies, respectively.21 22
Lp(a) levels in plasma were measured with the use of a commercially available immunoassay [Macra Lp(a), Strategic Diagnostics], which uses a capturing monoclonal anti–apo(a) antibody and a detecting polyclonal antibody, as previously described.23 Lp(a) was measured only in, and analyses shown for, 1228 men and 1250 women.
LDL particle size was determined (in 1433 men and 1484 women) by gradient gel electrophoresis as previously described24 and then expressed as LDL particle score, taking into account the relative areas under the peak of the major and of all minor LDL bands present in each individual.25 LDL particles were also expressed as pattern A (LDL particle score <3.5) and pattern B (LDL particle score 3.5).
The CHD risk score was calculated in each nonsmoking subject on the basis of the coronary heart disease risk factor prediction chart developed from the Framingham Heart Study26 and included individual scoring for age, total cholesterol levels, HDL cholesterol levels, systolic blood pressure, and diabetes. Scoring from left ventricular hypertrophy was not included in the final calculations. The 10-year risk of CHD in men and women was derived from the CHD risk score, as indicated in the CHD risk prediction chart.26
Statistical Analyses
Statistical analyses were performed with the SAS statistical program (SAS Institute Inc, Cary, NC). Means were calculated for all parameters in both men and women and in different age groups. The age group categories were: <30 years, 30 to 39 years, 40 to 49 years, 50 to 59 years, and 60 years. The majority of the individuals in the <30 years category were between 20 and 29 years of age, and the majority of the individuals in the 60 years category were between 60 and 69 years of age in both men and women. Subjects were also divided into 6 groups according to their BMI: <21.00, 21.00 to 22.99, 23.00 to 24.99, 25.00 to 27.49, 27.50 to 29.99, and 30.00 kg/m2. These ranges were selected because they are similar to those selected in other large epidemiological studies of men and women.5 9 27 To achieve normal distribution, a logarithmic transformation was applied to BMI, triglyceride, total cholesterol, VLDL cholesterol, HDL cholesterol, apo A-I, apo B, and Lp(a) levels in men and women and to LDL cholesterol levels in women. The PROC REG procedure was used to test the association of BMI (as a continuous variable) with blood pressure, glucose, and plasma lipid levels after adjustment for age effects and exclusion of smokers. The odds ratios for each unit of BMI increase were determined using PROC LOGIST, after the exclusion of smokers from the analysis to avoid residual effects of smoking.
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Results |
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Characteristics of men and women in our population are shown in Table 1
. The mean BMI in men was higher than in women (P<.0001) and in both sexes was above the value of 25.00, recommended by both the 1995 dietary guidelines and the World Health Organization as the upper limit of the desirable range. The cumulative distribution of BMI in both men and women, by age, is shown in Fig 1. In men, with the exception of the youngest age group, in which BMI distribution was shifted toward lower values, the distribution of BMI was similar in different age groups. In women, there was a shift in the BMI distribution toward higher values with aging. In addition, the BMI distribution in women was more skewed toward lower BMI values than in men.
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The age-related pattern of increase in BMI in men and women is also evident in Table 2. With the use of a BMI of 25.00 as a cutoff point, there was a progressive increase in the number of overweight men and women with age. In addition, the percentage of subjects with BMI 30.00 was twofold in men and threefold in women in the 50- to 59-year age range compared with that of subjects younger than 30 years of age. The relation of BMI with age was also observed after subjects were separated by smoking habits (Fig 2). In both smokers and nonsmokers, there was an increase of BMI with age. With the exception of the group of women <30 years of age, mean BMI was always lower in smokers than in nonsmokers in both sexes and in each age category.
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When subjects were divided into six categories according to their BMI, there was a trend toward a decreased prevalence of smokers from the group with BMI <21.00 to that with BMI 30.00 (men, 50% to 24%; women, 38% to 23%).
Table 3 shows characteristics and plasma lipid levels in nonsmoking men and women according to levels of BMI. After adjustment for age effects and use of BMI as a continuous variable, there was a significant and positive linear association between BMI and blood pressure, glucose levels, and plasma triglyceride, total cholesterol, VLDL cholesterol, and LDL cholesterol levels in both men and women (Table 3). Plasma HDL cholesterol levels significantly decreased (P<.0001) by increasing BMI, also in a linear fashion (Table 3). Plasma apo B and apo A-I levels followed the same trend observed for LDL cholesterol and HDL cholesterol levels, respectively. Lp(a) levels were not related to BMI in this population. This was also true after Lp(a) levels were controlled for triglyceride levels (data not shown). In both men and women, the LDL size, expressed as LDL type score, decreased significantly and linearly by increasing BMI (a higher LDL score represents a smaller LDL size) (Table 3).
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According to their mean CHD risk score calculated from the CHD risk factor prediction chart, men and women in the group with the highest BMI were predicted to have approximately threefold the likelihood to develop CHD within 10 years than men and women in the lowest BMI group, respectively (Table 3
). After adjustment for age effects, subjects in the highest BMI group were still twofold more likely to develop CHD than subjects in the lowest BMI group (data not shown).
We determined in men and women by logistic analysis the likelihood of presenting with a CHD risk factor by increasing BMI. Smokers were excluded from the analysis to eliminate residual effects of smoking after adjustment. Table 4 shows that the prevalence of most CHD risk factors increased significantly by an increase in each one unit of BMI. The effect of BMI on high-risk total cholesterol and LDL cholesterol levels was not as strong as that on hypertension, diabetes, elevated triglycerides, and low HDL cholesterol levels. Lp(a) levels in the CHD risk range (0.30 g/L) were not related to BMI. The likelihood of an LDL score >3.5 (or LDL type "pattern B") was strongly and significantly related to an increase in BMI. This was true also after adjustment for triglyceride levels.
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Discussion |
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The percentage of overweight subjects in the US population has been steadily increasing over the past decades.14 28 A similar trend has been observed in other industrialized countries.29 30 Our results indicate that a high proportion of men and women in the Framingham Offspring population are overweight as defined by the new dietary guidelines criteria. The overall prevalence of overweight in the Framingham Offspring Study population compared with that in other populations is shown in Table 5
. Only 28% of women (age range, 30 to 55 years) participating in the Nurses' Health Study were overweight compared with 37.4% of women in a similar age range in the Framingham Offspring Study. Since the BMI in the Nurses' study was reported in 1976, the difference in excess body weight between these two populations may be explained partly by secular trends for increasing BMI. In addition, a higher level of education and a healthier lifestyle in nurses than in the general population may contribute to a lower prevalence of overweight in the Nurses' Health study. A greater percentage of men and a lower percentage of women in the Framingham Offspring Study were overweight than in the NHANES III population.
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It is now clear that both genetic and environmental factors play an important role in the development and maintenance of obesity. The genetic component of obesity has become apparent from studies comparing monozygotic and dizygotic twins,31 and studies of monozygotic twins reared apart.32 However, genetic factors play a role in obesity within the context of energy imbalance. Energy imbalance may be caused by an increase in energy intake, a decrease in energy expenditure, or both. It is important to point out that the increase in body weight in white Americans over the past decades very likely may be caused by energy imbalance and not by genetic selection. Studies of Japanese men in Japan, Hawaii, and California have indicated that a change in diet composition toward an increase in both total fat and saturated fatty acids correlates with an increase in body weight and an increase in mortality from CHD.33 An increase in energy intake within the context of an increase in dietary fat and the same level of physical activity resulted in a significant increase in body weight over a 4- to 5-week period in Tarahumara Indians.34 Similarly, in a recent study in 27 subjects, consumption of a low fat diet (15% total fat, 5% saturated fat, 17 mg/1000 kJ cholesterol) first under isocaloric conditions for 4 to 5 weeks and then under ad libitum conditions for 10 to 12 weeks resulted in an average weight loss of 3.6 kg during the ad libitum phase.35 Weight loss in these subjects was accompanied by a significant decrease in triglyceride and LDL cholesterol levels compared with the levels during the isocaloric phase of the study.35 Therefore, diet composition may affect caloric intake and energy balance. However, it should be noted that despite a decrease in the dietary fat intake over the last decades,36 the average body weight in the US population has been increasing steadily. This probably is explained by a higher proportion of older subjects in the population, a more sedentary lifestyle, and the fact that dietary fat has been increasingly replaced by high-carbohydrate, high-energy foods, mostly provided by the food industry and not by natural foods such as fruits, vegetables, and grains.
In our population, BMI was strongly related to age, and in men the increase in BMI with age reached a plateau at age 50 years, but in women there was a constant increase of BMI with age through all age groups. Similar sex-related trends have been observed in other studies.14 Smoking is known to be a strong determinant of BMI but did not affect the association between age and BMI, in both men and women.
With the exception of Lp(a), an increase in BMI values was associated with a significant increase in the prevalence of all CHD risk factors. A positive association of body weight with blood pressure, glucose levels, and cholesterol levels has been described in previous studies.12 37 38 The lack of association between Lp(a) and BMI is not surprising, since Lp(a) levels are poorly controlled by environmental factors and are mostly genetically determined.39 We observed an association between BMI and LDL particle size. Our results indicate that LDL particle size was smaller in subjects with higher BMI both in men and women. This association was independent of triglyceride levels, which have been shown to be one of the strongest determinants of LDL size in this population.24 25 After controlling for age and smoking effects, our analysis showed that the relationship between all lipoprotein parameters and BMI is linear, indicating that the "optimal" levels of BMI are toward lower values. With the use of the CHD risk prediction chart, there was approximately a twofold age-adjusted increase in the 10-year risk of CHD in subjects in the group with BMI 30.00 kg/m2 than in those with BMI <21.00 kg/m2. This is consistent with the observed increased risk of CHD by BMI reported in other studies.5 6 10 40 41
Our results clearly indicate that the effects of increasing BMI on total cholesterol and LDL cholesterol levels, though significant, are relatively modest in both men and women. Of all known risk factors for CHD, reduced HDL cholesterol levels, and hypertension were those more strongly associated with higher BMI in both men and women. In addition, diabetes in women and elevated triglyceride levels and small LDL particles were also strongly associated with higher BMI values in this population.
BMI is widely used as a measure of fatness in epidemiological studies because it has been shown that this index is highly correlated with body fat and is nearly independent of height.11 Densitometry is currently considered the gold standard in the measurement of body fatness, and correlations of BMI and densitometry approximate 0.6.11 Body composition in some of the participants in the Framingham Offspring Study also has been measured by bioelectric impedance, a more precise method of estimating body fat, and BMI, though correlated with the bioelectric impedance measurement, did not explain all variability in fatness in this population.42 This indicates that BMI may not reliably represent body fat in individuals. This is particularly true in men and is due to the fact that BMI is mostly a measure of weight and therefore does not discriminate between body fat and lean mass. Taking this into account, it is likely that the contribution of body fatness to cardiovascular risk factors in the Framingham Offspring population is even greater than that derived by our analyses.
Our data indicate that BMI is highly correlated with most risk factors for CHD and that prevention of overweight is an important public heath issue. Treatment of excess body weight through lifestyle changes, such as an increase in physical exercise and a decrease in energy intake and fat intake, is advisable for the general population. In addition, addressing the trend toward an increase in body weight observed in several affluent societies through early education during childhood may be the most important step in the prevention of future CHD.
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Selected Abbreviations and Acronyms |
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Acknowledgments |
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This work was supported by contract HV-83-03 from the National Institutes of Health and contract 53-3K06-5-10 from the US Department of Agriculture Research Service. The authors thank Dr Gerard E. Dallal for assistance in the statistical analyses.
Received January 17, 1996;
revision received May 7, 1996;
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K. Oh, F. B. Hu, J. E. Manson, M. J. Stampfer, and W. C. Willett Dietary Fat Intake and Risk of Coronary Heart Disease in Women: 20 Years of Follow-up of the Nurses' Health Study Am. J. Epidemiol., April 1, 2005; 161(7): 672 - 679. [Abstract] [Full Text] [PDF]
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 C. L Pelkman, V. K Fishell, D. H Maddox, T. A Pearson, D. T Mauger, and P. M Kris-Etherton Effects of moderate-fat (from monounsaturated fat) and low-fat weight-loss diets on the serum lipid profile in overweight and obese men and women Am. J. Clinical Nutrition, February 1, 2004; 79(2): 204 - 212. [Abstract] [Full Text] [PDF]
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R. Wolk, P. Berger, R. J. Lennon, E. S. Brilakis, and V. K. Somers Body Mass Index: A Risk Factor for Unstable Angina and Myocardial Infarction in Patients With Angiographically Confirmed Coronary Artery Disease Circulation, November 4, 2003; 108(18): 2206 - 2211. [Abstract] [Full Text] [PDF]
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 L Shapo, J Pomerleau, and M McKee Epidemiology of hypertension and associated cardiovascular risk factors in a country in transition: a population based survey in Tirana City, Albania J Epidemiol Community Health, September 1, 2003; 57(9): 734 - 739. [Abstract] [Full Text] [PDF]
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 D. N. Reeds, B. Mittendorfer, B. W. Patterson, W. G. Powderly, K. E. Yarasheski, and S. Klein Alterations in lipid kinetics in men with HIV-dyslipidemia Am J Physiol Endocrinol Metab, September 1, 2003; 285(3): E490 - E497. [Abstract] [Full Text] [PDF]
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S. Zhu, Z. Wang, W. Shen, S. B Heymsfield, and S. Heshka Percentage body fat ranges associated with metabolic syndrome risk: results based on the third National Health and Nutrition Examination Survey (1988-1994) Am. J. Clinical Nutrition, August 1, 2003; 78(2): 228 - 235. [Abstract] [Full Text] [PDF]
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B. Mittendorfer, B. W. Patterson, S. Klein, and L. S. Sidossis VLDL-triglyceride kinetics during hyperglycemia-hyperinsulinemia: effects of sex and obesity Am J Physiol Endocrinol Metab, April 1, 2003; 284(4): E708 - E715. [Abstract] [Full Text] [PDF]
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B. Mittendorfer, B. W Patterson, and S. Klein Effect of sex and obesity on basal VLDL-triacylglycerol kinetics Am. J. Clinical Nutrition, March 1, 2003; 77(3): 573 - 579. [Abstract] [Full Text] [PDF]
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B. Mittendorfer, B. W. Patterson, and S. Klein Effect of weight loss on VLDL-triglyceride and apoB-100 kinetics in women with abdominal obesity Am J Physiol Endocrinol Metab, March 1, 2003; 284(3): E549 - E556. [Abstract] [Full Text] [PDF]
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 P. W. F. Wilson, R. B. D'Agostino, L. Sullivan, H. Parise, and W. B. Kannel Overweight and Obesity as Determinants of Cardiovascular Risk: The Framingham Experience Arch Intern Med, September 9, 2002; 162(16): 1867 - 1872. [Abstract] [Full Text] [PDF]
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 C.F. Ebenbichler, M. Laimer, S. Kaser, A. Ritsch, A. Sandhofer, H. Weiss, F. Aigner, and J.R. Patsch Relationship Between Cholesteryl Ester Transfer Protein and Atherogenic Lipoprotein Profile in Morbidly Obese Women Arterioscler Thromb Vasc Biol, September 1, 2002; 22(9): 1465 - 1469. [Abstract] [Full Text] [PDF]
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E. J Schaefer Lipoproteins, nutrition, and heart disease Am. J. Clinical Nutrition, February 1, 2002; 75(2): 191 - 212. [Abstract] [Full Text] [PDF]
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 T. R. Gaunt, J. A. Cooper, G. J. Miller, I. N.M. Day, and S. D. O'Dell Positive associations between single nucleotide polymorphisms in the IGF2 gene region and body mass index in adult males Hum. Mol. Genet., July 1, 2001; 10(14): 1491 - 1501. [Abstract] [Full Text] [PDF]
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P. Holvoet, A. Mertens, P. Verhamme, K. Bogaerts, G. Beyens, R. Verhaeghe, D. Collen, E. Muls, and F. Van de Werf Circulating Oxidized LDL Is a Useful Marker for Identifying Patients With Coronary Artery Disease Arterioscler Thromb Vasc Biol, May 1, 2001; 21(5): 844 - 848. [Abstract] [Full Text] [PDF]
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D. L. Rainwater, B. D. Mitchell, A. G. Comuzzie, J. L. VandeBerg, M. P. Stern, and J. W. MacCluer Associations among 5-Year Changes in Weight, Physical Activity, and Cardiovascular Disease Risk Factors in Mexican Americans Am. J. Epidemiol., November 15, 2000; 152(10): 974 - 982. [Abstract] [Full Text] [PDF]
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M. B. Olson, S. F. Kelsey, V. Bittner, S. E. Reis, N. Reichek, E. M. Handberg, C. N. Bairey Merz, and for the Women's Ischemia Syndrome Evaluation (WISE Weight cycling and high-density lipoprotein cholesterol in women: evidence of an adverse effect: A report from the NHLBI-sponsored WISE study J. Am. Coll. Cardiol., November 1, 2000; 36(5): 1565 - 1571. [Abstract] [Full Text] [PDF]
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C. N. Bairey Merz, S. F. Kelsey, C. J. Pepine, N. Reichek, S. E. Reis, W. J. Rogers, B. L. Sharaf, G. Sopko, and for the WISE Study Group The Women's Ischemia Syndrome Evaluation (WISE) Study: protocol design, methodology and feasibility report J. Am. Coll. Cardiol., May 1, 1999; 33(6): 1453 - 1461. [Abstract] [Full Text] [PDF]
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A. Pietrobelli, R. C Lee, E. Capristo, R. J Deckelbaum, and S. B Heymsfield An independent, inverse association of high-density-lipoprotein-cholesterol concentration with nonadipose body mass Am. J. Clinical Nutrition, April 1, 1999; 69(4): 614 - 620. [Abstract] [Full Text] [PDF]
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F. Borson-Chazot, C. Harthe, F. Teboul, F. Labrousse, C. Gaume, L. Guadagnino, B. Claustrat, F. Berthezene, and P. Moulin Occurrence of Hyperhomocysteinemia 1 Year after Gastroplasty for Severe Obesity J. Clin. Endocrinol. Metab., February 1, 1999; 84(2): 541 - 545. [Abstract] [Full Text]
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T. Rönnemaa, J. Marniemi, M. J. Savolainen, Y. A. Kesäniemi, C. Ehnholm, C. Bouchard, and M. Koskenvuo Serum Lipids, Lipoproteins, and Lipid Metabolizing Enzymes in Identical Twins Discordant for Obesity J. Clin. Endocrinol. Metab., August 1, 1998; 83(8): 2792 - 2799. [Abstract] [Full Text]
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