Upper-Body Fat Distribution: A Hyperinsulinemia-Independent Predictor of Coronary Heart Disease Mortality. The Paris Prospective Study

Transcription

1 1387 Upper-Body Fat Distribution: A Hyperinsulinemia-Independent Predictor of Coronary Heart Disease Mortality The Paris Prospective Study Philippe Casassus, Annick Fontbonne, Nadine Thibult, Pierre Ducimetiere, Jacques L. Richard, Jean-Roger Claude, Jean-Michel Warnet, Gabriel Rosselin, and Eveline Eschwege The Paris Prospective Study is a long-term Investigation of the factors predicting coronary heart disease in a large population of middle-aged men. The first follow-up examination involved 7,152 subjects, who were natives of metropolitan France and were free of any cardiovascular history. At that time, the usual cardiovascular risk factors and plasma insulin levels were recorded. An index of body fat distribution, the iliac-to-thigh ratio, was entered into the list of predictive variables, despite the fact that it had been measured 1 year before the first follow-up examination. After 11 years of mean follow-up, 129 of the men had died of coronary heart disease. Univariate analysis showed that the iliac-to-thigh ratio (p<) and plasma insulin level (both fasting \p<0.003] and 2-hour postload [/>]), as well as the four major risk factors of coronary heart disease (age, smoking, blood pressure, and plasma cholesterol level) were significantly higher in subjects who died of coronary heart disease compared with those who had died of another cause or were alive at the end of follow-up. In multivariate stepwise logistic regression, the iliac-to-thigh ratio appeared as an independent predictor of coronary heart disease death, thereby causing the removal of fasting insulin level from the list of significant independent predictors. Nevertheless, in a model that entered 2-hour postload insulin in two classes (high or low), both the insulin level and iliac-to-thigh ratio were found as significant independent predictors. These results suggest that much of the already demonstrated predictive power of plasma insulin levels could be mediated through their association with upper-body fat distribution. However, 2-hour postload insulin concentration appears to improve the prediction and may denote the existence of metabolic abnormalities that are not entirely summarized by the type of fat distribution. {Arteriosclerosis and Thrombosis 1992;12: ) KEY WORDS epidemiology Paris Prospective Study risk factors coronary heart disease mortality body-fat distribution plasma insulin level alcohol cigarette smoking The fact that hyperinsulinemia is an independent predictor for coronary heart disease (CHD) mortality has been ascertained by three large prospective studies 1-3 in the last 10 years. This result remained unchanged in the Paris Prospective Study over a longer follow-up period. 4 This epidemiological finding is consistent with the hypothesis of cardiovascular risk attached to "syndrome X" as defined by Reaven, 5 i.e., that hyperinsulinemia is linked to insulin resistance and potentially atherogenic abnormalities (high very low density lipoprotein triglyceride level, low high density lipoprotein cholesterol level, elevated From IERM Unite 21 (Ph.C, A.F., N.T., E.E.), Villejuif; IERM U.258 (P.D., J.L.R.), H6pital Broussais, Paris; Centre de Prevention des Maladies Cardiovasculaires (J.-R.C, J.-M.W.), Direction of Social Affairs, Childhood and Health (DASES), Paris; and IERM U.55 (G.R.), Paris, France. The Paris Prospective Study is coordinated by the Study Group for the Epidemiology of Atherosclerosis (GREA) and is supported by the French National Institute of Health and Medical Research (IERM U.21, U-55, U.169, and U.258) and DASES, Paris, France. Address for reprints: Dr. Ph. Casassus, IERM U.21, 16 Avenue Paul Vaillant Couturier, F Villejuif Cedex, France. Received February 17, 1992; revision accepted July 28, blood pressure, and glucose intolerance). Stout 6 also proposed some physiological hypotheses that could give credibility to a causal role of hyperinsulinemia per se. In parallel during the last decade, upper-body fat distribution has emerged as another "new" independent risk factor for CHD and as a correlate of angiographically documented coronary atherosclerosis This prospectively confirmed Jean Vague's observations, published in 1956, 11 that women with cardiovascular complications or non-insulin-dependent diabetes tended to have android (upper-body) rather than gynoid obesity. Some epidemiological results suggest that android fat distribution is closely linked to hyperinsulinemia, and there are physiological hypotheses to explain this relation, mostly those involving free fatty acid metabolism. Thus, it seemed interesting to compare the respective roles of hyperinsulinemia and upper-body fat distribution as predictors of CHD mortality. The Paris Prospective Study provided the means of performing this comparison, since baseline variables included measurements of both anthropometric indices and plasma insulin concentrations.

2 1388 Arteriosclerosis and Thrombosis Vol 12, No 12 December 1992 Methods The present analysis was performed on data from the Paris Prospective Study, of which the general aims, detailed methodology, and population characteristics have been previously described. 4 Only relevant data are summarized here. The inclusion session took place between 1967 and The first of four consecutive annual follow-up examinations, performed during the period , involved 7,152 male employees of the Paris police, aged years, who were included after having given consent to participate and who were assessed with respect to the following conditions: place of birth in metropolitan France (i.e., excluding Caribbean natives) and freedom from any cardiovascular history. Among this population, the iliac and the thigh circumferences had been measured 1 year before, i.e., at the initial entry examination, in 7,141 subjects. Anthropometric indices were measured by three specially trained technicians, with the subjects in a standing position (the arms at rest along the body) and in apnea fixed at the midrespiratory phase. Iliac (at the iliac crest level) and left thigh (middle distance between the trochanter and external condylus) circumferences to the nearest millimeter were obtained with a flexible measuring tape. The ratio of these two circumferences, or the iliac-to-thigh ratio (UK), was used as an estimate of fat distribution. The first annual examination included a 0-2-hour 75-g oral glucose tolerance test, with measurement of glucose levels in 7,003 men; of fasting plasma insulin level in (FPI) in 6,937 men; and of postload insulin (PI) levels in 6, The major CHD mortality risk factors were recorded: blood pressure, smoking habits, body mass index (BMI, weight/height 2, in kilograms per square meter), and plasma cholesterol and trigryceride levels. Diabetes prevalence was estimated by grouping subjects with newly diagnosed diabetes (2-hour plasma glucose equal to or above 11.1 mmol/1) and subjects with known diabetes. Also, because alcohol is supposed to increase intra-abdominal fat deposits, 15 erythrocyte mean corpuscular volume TABLE 1. Mean Values (±2 SEM) of Baseline Variables According to CHD Death at 11-Year Follow-up Variable (years) arettes (NoVday) Systolic blood pressure (mm Hg) Body mass index (kg/m 2 ) Iliac-to-thigh ratio Cholesterol level (mmol/1) Trigryceride level (mmol/1)* Fasting glucose level (mmol/1) 2-Hour glucose level (mmol/1) Diabetes prevalence (%) Fasting insulin level (pmol/1)* 2-Hour insulin level (pmol/1)* Mean corpuscular volume (fl) CHD death (n = 129) 48.9±2.1 13±10 157± ± ± ± ± ± ±14 277± ±6.0 (MCV), a marker of alcohol consumption, 18 was calculated (MCV=hematocrit/red blood cell count, fl). All deaths up to January 1,1983 were counted (mean follow-up, 11 years), and the present analysis was performed at this point, where the predictive power of insulin concentrations was shown to be the strongest. 4 ' 19 Nevertheless, the major results were checked at the next reckoning point, January 1, 1987 (mean follow-up, 15 years). Deaths were systematically reported by the different administrative departments of the Paris Police Service. Complementary inquiries to families, health practitioners, and hospitals were organized to obtain information regarding the circumstances and causes of death. The underlying cause of death was ascertained and coded by a panel of physicians. Coding was performed according to the International Classification of Diseases (8th revision); in the present analysis, only deaths caused by CHD to a large extent (codes , myocardial infarction; 795.0, sudden death; , 427.0, 427.1, and 519.1, heart failure) were considered. For statistical analyses, subjects of the baseline cohort with missing values for one or more variables used in the test were excluded. All testing methods were the usual parametric ones. Multivariate analyses of the predictive power of baseline variables toward CHD death used the stepwise logistic-regression model. In the case of a nonlinear relation between a variable and CHD mortality, the variable was either logarithmically transformed or categorized to ensure a better fit to statistical modeling. Four interaction variables were created by calculating the product of with FPI and 2-hour postload insulinemia (2hPI), either continuously or in two classes, and were expressed in standard deviation units. Because of the narrow age range of the population, no adjustment for age was made for univariate analyses. However, age was systematically included in multivariate analyses. All statistical analyses were done using the SAS statistical package. 20 Results At 11-year follow-up, 129 men of the initial cohort had died of CHD. They differed significantly on all Other (n=7,023) 48.4±2.0 9±10 144± ± ± ± ± ± ± ± ± ±5.3 CHD, coronary heart disease. 'These values were computed in log values and then transformed into the original units. P < < < < <0.004 < < < <

3 Casassus et al Fat Distribution, Hyperinsulinemia, and Coronary Risk FIGURE 1. Bar graph of 11-year coronary heart disease (CHD) mortality rates according to classes of iliac-to-thigh ratio () below or above the median of 1.77 and of fasting plasma insulin (FPI) below or above the lower limit of the fifth quintile of 108 pmol/l. Number of deaths, from left to right, are 40/3,122, 38/2,174, 6/632, and 39/ < 1.77 > 1.77 FPI < 108 pmol/l < 1.77 > 1.77 FPI > 108 pmol/l was observed concurrently with a high PI level, CHD mortality was 4.1 times higher (for FPI) and 2.4 times higher (for 2hPI) than in the respective low- group. In Figure 2, CHD mortality rate appeared more or less equal in the high--low-2hpi group and the low--high-2hpi group; this result indicates the possibility that both variables are independent CHD risk factors. The results of several multivariate analyses are given in Table 2. The four major risk factors of CHD were added as explanatory variables to and PI levels with four modalities (FPI taken continuously, FPI in two classes as defined above, 2hPI taken continuously, and 2hPI in two classes as defined above). In all four models, appeared as an independent predictor of CHD death. FPI level, whether continuous or dichotomized, did not reach statistical significance as an independent predictor of CHD death when was taken into account. However, the dichotomized (but not the continuous) 2hPI level retained a significant predictive power together with. BMI, plasma glucose, and triglyceride levels never reached a significant probability value in any of the models. The results were similar at 15-year follow-up. baseline variables listed in Table 1 from those who were alive or who had died of another cause. CHD mortality was significantly associated with older age; higher cigarette use; and higher levels of blood pressure, BMI, plasma cholesterol, triglyceride, fasting and postload blood glucose, and PI. Moreover, subjects who died of CHD had a significantly higher (/7<) and a more elevated MCV (p<). All of these variables were interrelated to some extent. Specifically, was correlated with FPI (r=0.22, p<) and 2hPI (r=0.18,p<). To study the interrelation of fat distribution and insulin concentration to CHD risk, two classes were created for each variable: for, classes were above and below 1.77, which was the median of the distribution, and for FPI and 2hPI levels, the classes were the last quintile versus first four quintiles, in accordance with previous reports. 419 The "high-" group had approximately twice the annual CHD mortality rate of the "low-" group: 2.22% versus 1.15% (/?<0.0002). Figures 1 and 2 illustrate CHD mortality in relation to classes and FPI and 2hPI levels. Each factor, when isolated, either had little influence on CHD death rates or was inoperative (e.g., FPI). However, when a high T 4 o d W 01 a FIGURE 2. Bar graph of 11-year coronary heart disease (CHD) mortality rates according to classes of iliac-to-thigh ratio () below or above the median of 1.77 and of 2-hour postload plasma insulin (2h PI) below or above the lower limit of the fifth quintile of 452 pmol/l. Number of deaths, from left to right, are 38/3,226, 42/2,329, 9/512, and 35/ = > 1.77 < 1.77 g < h PI < 452 pmol/l > h PI > 452 pmol/l

4 1390 Arteriosclerosis and Thrombosis Vol 12, No 12 December 1992 TABLE 2. Stepwise Logistic-Regression Models for CHD Mortality at 11-Year Follow-up, With Six Variables Including Diac-to-Thigb Ratio and Plasma Insulin Level Model T LogFPI pr < Model 2* Cl(FPI) Pt < <0.03 Model 3* Log (2bPI) /»t <0.002 <0.009 Model 4* Q (2hPI) P\ <0.002 <0.003 <0.03 <0.03 CHD, coronary heart disease,, systolic blood pressure;, cigarette smoking;, plasma cholesterol;, iliac-to-thigh ratio; FPI, fasting plasma insulin; Cl (2hPI), classes of 2hPI (below or above 452 pmol/l);, not significant; Cl (FPI), classes of FPI (below or above 108 pmol/l); 2hPI, 2-hour postload plasma insulin. 'Variables ordered as they were entered in the equation. tminimum value needed to propose a variable for entry into the model. To complete this analysis, a multivariate stepwise logistic regression was performed with an interaction variable between insulin (in the four previously described modalities) and. In no model was the interaction variable entered, and it never affected the coefficient for and insulin. To find some explanation for the independent predictive roles of 2hPI level and fat distribution, we compared the values of various parameters in quintiles of 2hPI and (Tables 3 and 4, respectively). For BMI, systolic blood pressure, plasma cholesterol level, proportion of diabetes cases, and CHD mortality, there was a steady significant increase of values along the quintiles of both 2hPI and. By contrast, cigarette use and MCV increased with increasing, whereas they decreased with increasing 2hPI. Discussion The present analysis confirms a previous analysis of the Paris Prospective Study that showed an association of 2hPI level with CHD mortality risk. 4 However, upper-body fat distribution estimated by the is shown here to be another powerful independent predictor of CHD mortality. It even replaces FPI, which predicted CHD death in a model that did not control for body-fat distribution. 1 This result is in accordance with those of other prospective studies, 7-9 which demonstrated a link between various measures of upper-body fat and cardiovascular outcomes. Moreover, the data set of the Paris Prospective Study allowed us to separate the predictive power of upper-body fat distribution and of PI levels, a question that had been raised 10 because of the physiological relations between these two parameters. The ratio of waist-to-hip circumferences is more frequently used than the as an estimate of fat distribution. In the Paris Prospective Study, waist and hip circumferences were not initially measured in contrast to those of the iliac and thigh. Nevertheless, can be considered a good marker of upper-body obesity. 21 This ratio is clearly related to BMI (Table 4) and to skinfold thicknesses. 8 Within this male population the iliac circumference must be influenced more by differences in fat deposition rather than by different skeletal characteristics. Moreover, if differential muscle mass in the thigh can also be suspected to contribute to CHD prediction with this index, 14 we found that iliac circumference alone was strongly predictive of CHD death (p<), whereas the thigh circumference was not (p=0.30). Using the ratio of the two circumferences greatly enhanced the predictive power. The correlation of hyperinsulinemia and upper-body (or male-type) fat distribution is well established and was also confirmed in the present study. In contrast to what had been found previously, 1 FPI did not reach a significant probability value as a predictor of CHD in multivariate analyses after was entered in the model. Because it has been hypothesized that hyperin- TABLE 3. Mean Values (±SD) of Baseline Variables According to Quintiles of 2-Hour Postload Plasma Insulin Level at Entry to Follow-up and Unlvarlate Correlation Coefficients Between These Variables and 2-Hour Plasma Insulin Levels Quintiles of 2-hour postload insulin (No./day) (mm Hg) BMI (kg/m 2 ) (mmol/1) FPI (pmol/l) MCV(fl) Diabetes prevalence (%) CHD mortality (%o) ±2 11±11 139± ±3 1.74± ± ± ± J± ± ±3 1.75±0.15 5J5± ± ± ±2 9.4±11 142± ±3 1.75± ± ± ± ±2 8.3±10 146± ±3 1.78± ± ± ± ± ± ± ± r P , cigarette smoking;, systolic blood pressure; BMI, body mass index;, iliac-to-thigh ratio;, plasma cholesterol level; FPI, fasting plasma insulin level; MCV, mean corpuscular volume; CHD, coronary heart disease.

5 Casassus et al Fat Distribution, Hyperinsulinemia, and Coronary Risk 1391 TABU: 4. Mean Values (±SD) of Baseline Variables According to Quintiles of Illac-to-Thigh Ratio at Entry to Follow-up and Unrvariate Correlatioa Coefficients Between These Variables and Illac-to-Thigh Ratio Quintiles of iliac-to-thigh ratio r P (NoVday) (mm Hg) 48.7±2 7.8±10 137± ±2 9±10 140± ±2 9±10 145± ± ± ±2 10.8±ll 154± BMI (IcgAn 2 ) 24.3 ±3 25.3±3 26.2±3 27.0±3 28.0± (mmol/l) FPI (pmol/1) 5.29± ± ± ± ± ± ± ± ± ± Hour postload insulin (pmol/1) 229.4± ± ± ± ± MCV(fl) 96.5± ±5 96.8±5 97.2±6 97.4± Diabetes prevalence (%) CHD mortality (%o) ; cigarette smoking;, systolic blood pressure; BMI, body mass index; 1'1'K, iliac-to-thigh ratio;, plasma cholesterol level; FPI, fasting plasma insulin level; MCV, mean corpuscular volume; CHD, coronary heart disease. sulinemia is a feature of syndrome X as described initially by Reaven 5 and that this syndrome could be the explanation of the epidemiological finding of the relation between insulin levels and CHD, 19 then it is probably correct to think, as was suggested by Zimmet, 23 that upper-body fat distribution should be added to the metabolic syndrome. This latter variable, as another indicator of the same syndrome, could then replace insulin level as a predictor of future CHD death. If this hypothesis is true, then fat distribution could represent the clinical parameter and PI level the biological marker of the same CHD risk syndrome. Actually, several authors have proposed physiological explanations to account for the role of abdominal fat deposits in hepatic insulin resistance first and hyperinsulinemia second. Our results support the idea of a greater predictive power for 1'1'K, which may involve alcohol consumption and cigarette use, because they increase with increasing, whereas they decrease with increasing PI level. However, a second important finding of this analysis is the independent predictive power of dichotomized 2hPI level even when a marker of fat distribution and the four major coronary risk factors (blood pressure, smoking, age, and plasma cholesterol level) are controlled for in the model. This latter result could be consistent with a direct effect of the highest PI levels on the vessel walls, as was previously proposed, 616 or with other specific metabolic effects, such as a possible relation to very low density lipoprotein triglyceride, that are independent of fat distribution. 27 Thus, our analysis confirms that hyperinsulinemia and male-type fat distribution are two interrelated predictors of CHD mortality. This may be explained in part because both factors are markers of a metabolic syndrome associated with insulin resistance. However, some part of their predictive power seems to be independent. Pathophysiological explanations are still needed to account for this fact. References 1. Eschwege E, Richard JL, Thibult N, Ducimetiere P, Warnet JM, Claude JR, Rosselin GE: Coronary heart disease mortality in relation with diabetes, blood glucose and plasma insulin levels: The Paris Prospective Study, ten years later. Horm Metab Res 1985; 15(suppl): Pyorala K, Savolainen E, Kaukola S, Haapakoski J: Plasma insulin as coronary heart disease risk factor Relationship to other risk factors and predictive value during 9Vtyear follow-up of the Helsinki Policemen Study population. Ada Med Scand 1985; 701(suppl): Welborn TA, Wearne K: Coronary heart disease and cardiovascular mortality in Busselton with reference to glucose and insulin concentrations. Diabetes Care 1979;2: Fontbonne A, Charles MA, Thibult N, Richard JL, Claude JM, Warnet JM, Rosselin GE, Eschwege E: Hyperinsulinaemia as a predictor of coronary heart disease mortality in a healthy population: The Paris Prospective Study, 15-year follow-up. Diabetologia 1991^4: Reaven GM: Role of insulin resistance in human disease. Diabetes 1988;37: O7 6. Stout RW: Insulin and atheroma 20-Year perspective. Diabetes Can 199O;13: Larsson B, Svardsudd K, Welin L, Wilhelmsen L, Bjorntorp P, Tibblin G: Abdominal adipose tissue distribution, obesity, and risk of cardiovascular disease and death: 13 Year follow up of participants in the study of men born in Br J Med 1984^88: Ducimetiere P, Richard JL: The relationship between subsets of anthropometric upper versus lower body measurements and coronary heart disease risk in middle aged men: The Paris Prospective Study. Int J Obes 1989;13: Donahue RP, Abbott RD, Bloom E, Reed DM, Yano K: Central obesity and coronary heart disease in men. Lancet 1987;l: Thompson CJ, Ryu JE, Craven TE, Kahl FR, Crouse JR III: Central adipose distribution is related to coronary atherosclerosis. Arterioscler Thmmb 1991;ll: Vague J: The degree of masculine differentiation of obesities: A factor determining predisposition to diabetes, atherosclerosis, gout and uric calculous disease. Am J din Nutr 1956;4:2O Haffner SM, Fong D, Hazuda HP, Pugh JA, Patterson JK: Hyperinsulinemia, upper-body adiposity, and cardiovascular risk factors in non-diabetics. Metabolism 1988^37: McKeigue PM, Shah B, Marmot MG: Relation of central obesity and insulin resistance with high diabetes prevalence and cardiovascular risk in South Asians. Lancet 1991^337: Bjorntorp P: Abdominal obesity and the development of non insulin-dependent diabetes mellitus. Diabetes Metab Rev 1988;4: Bjorntorp P: "Portal" adipose tissue as a generator of risk factors for cardiovascular disease and diabetes. Arteriosclerosis 1990;10: Bouchard C, Bray GA, Hubbard VS: Basic and clinical aspects of regional fat distribution. Am] CUn Nutr : Rosselin GE, Assan R, Yalow RS, Berson SA: Separation of antibody bound and unbound peptide hormone labelled with

6 1392 Arteriosclerosis and Thrombosis Vol 12, No 12 December 1992 iodine 131 by talcum powder and precipitates silica. Nature 1966; Unger KW, Johnson D Jr Red blood cell mean corpuscular volume: A potential indicator of alcohol usage in a working population. Am J Med Sci 1974;267: Fontbonne A, Eschwege E: Insulin and cardiovascular disease: Paris Prospective Study. Diabetes Care 1991;14: SAS Institute Inc: SAS User's Guide: Statistics, Version 6. Cary, NC, SAS Institute Inc, Mueller WH, Wear ML, Hanis CL, Emerson JB, Barton SA, Hewett- Emmett D, Schull WJ: Which measure of body fat distribution is best for epidemiologic research! Am J Epidemiol 1991;133: Pasquali R, Casimirri F, Cantobelli S, Melchionda N, Morselli Labate AM, Fabbri R, Capelli M, Bortoluzzi L: Effect of obesity and body fat distribution on sex hormones and insulin in men. Metabolism 1991;4O:101-lO4 23. Zimmet P: Non-insulin-dependent (type 2) diabetes mellitus: Does it really exist? Diabetic Med 1989;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-,6: Despres JP, Moorjani S, Lupien PJ, Tremblay A, Nadeau A, Bouchard C: Regional distribution of body fat, plasma lipoproteins, and cardiovascular disease. Arteriosclerosis 199O;10: Kissebah AH: Insulin resistance in visceral obesity. Int J Obes 1991;15: Ferland M, Despres JP, Nadeau A, Moorjani S, Tremblay A, Lupien PJ, The'riault G, Bouchard C: Contribution of glucose tolerance and plasma insulin levels to the relationships between body fat distribution and plasma lipoprotein levels in women. IntJ Obes 1991;15: