Relation of Body Mass Index to Mortality Among Men With Coronary Heart Disease
Article Outline
Reports among patients with coronary heart disease regarding the association between body mass index (BMI) and long-term mortality are inconsistent, ranging among linear, U-shaped, or inverse (the “obesity paradox”) associations. BMI and mortality data were available for 12,466 men with chronic coronary heart disease. BMI was classified as <20 (lean), 20.0 to 22.99, 23.0 to 24.99 (reference), 25.0 to 26.99, 27.0 to 29.99, and ≥30 kg/m2 (obese). Age-adjusted (direct methods) mortality was investigated within risk factor categories. Adjusted hazard ratios compared with the reference group were estimated using a Cox proportional-hazards model. Two thirds of the patients had BMIs ≥25 kg/m2. A number of risk factors were progressively more frequent with increasing BMI (age, diabetes, past smoking, and metabolic components). Over a median follow-up period of 12 years, adjusted mortality rates per 1,000 patient-years followed a U-shaped association with BMI. The highest risk was noted in 148 lean (hazard ratio 1.41, 95% confidence interval 1.08 to 1.85) and 1,788 obese (hazard ratio 1.28, 95% confidence interval 1.15 to 1.42) patients. Mortality hazard in patients with BMIs of 20.0 to 29.99 kg/m2 (84% of patients) did not significantly differ from the reference group (lowest risk). Risk factor presence was associated with higher mortality in every BMI category. Lean patients had a particularly poor prognosis in the presence of past myocardial infarction, smoking, or renal insufficiency. A U-shaped association was found in most subgroups examined. In conclusion, BMI ≥25 kg/m2 is common in patients with coronary heart disease. A U-shaped association, with highest risk among lean and obese patients, is persistent regardless of risk factor presence. Further data are required to support the need of aggressive weight reduction in patients with BMIs <30 kg/m2.
The aim of our study was to evaluate the long-term association of body mass index (BMI) and mortality in a large cohort of patients with coronary heart disease (CHD) over a long follow-up period. In addition, we aimed to study the possible interaction between BMI and known cardiovascular risk factors or co-morbidities in association to mortality.
Methods
A total of 15,524 patients (of whom 12,529 [81%] were men) with histories of CHD aged 45 to 74 years were screened from February 1990 to October 1992 for participation in the Bezafibrate Infarction Prevention (BIP) study. BIP was a placebo-controlled, secondary prevention randomized trial assessing the effect of bezafibrate on the risk for recurrent events and mortality.1, 2 Institutional ethics committees at each of the participating centers and the central national committee approved the study.
Height and weight measurements were available for 12,466 men (99%) screened (including 2,851 of 2,854 patients who participated in the clinical trial1). Patients with total cholesterol ≤270 mg/dl (7.0 mmol/L), high-density lipoprotein (HDL) cholesterol ≤45 mg/dl (1.16 mmol/L), and triglycerides ≤300 mg/dl (3.39 mmol/L) on the first screening visit (9,170 men) received dietary consultation and were invited to the next screening visit. Long-term mortality data were obtained through July 2004 from the Israeli population registry (data were complete for 11,992 men [96.2%]). Data regarding diagnoses of cancer were obtained from the Israel National Cancer Registry, a population-based registry established in 1960. Since 1982, reporting to the registry has been mandatory by law and includes all medical facilities in the country.
Patients' weight and height were measured during the first screening visit. BMI was calculated as the ratio between weight (in kilograms) and squared height (in meters). The definition of hypertension was based on self-report by patients and assessment by screening physicians. Diabetes was defined on the basis of self-report of the disease or treatment with hypoglycemic drugs. Patients who had quit smoking ≥1 month before screening were categorized as past smokers. Patients who smoked at the time of screening or had quit <1 before screening were categorized as smokers.
Functional capacity was classified according to the New York Heart Association classification. Metabolic syndrome was defined on the basis of the Adult Treatment Panel III report classification as ≥3 of the following: HDL cholesterol >40 mg/dl (1.04 mmol/L), triglycerides >150 mg/dl (1.69 mmol/L), blood pressure >135/85 mm Hg, and glucose >110 mg/dl (6.11 mmol/L), and replacing waist circumference criteria (not available) with BMI > 28 kg/m2. BMI classification was based on World Health Organization criteria, with further division of the normal and overweight ranges. Because the study group included only 32 men with BMIs <18.5 kg/m2, they were included in the lowest subgroup of the normal range (<20 kg/m2).
Glomerular filtration rate (ml/min/1.73 m2) was estimated using the formula derived by the Modification of Diet in Renal Disease (MDRD) study group and calculated as 186 × (serum creatinine [mg/dl]−1.154) × (age [years]−0.203) × 1.21 (if black).3 Serum creatinine was available for 6,279 men of 12,466, who were invited to the second screening visit.
For the purpose of multivariate hazard ratio estimation, the 23.00 to 24.99 kg/m2 BMI group was used as the reference group. This choice was based on previous reports from population studies that pointed to a BMI of about 25 kg/m2 as the nadir of the BMI-mortality risk association.
Blood samples, drawn after ≥12-hour fasting, were collected using standardized equipment and procedures.1 Serum analysis was carried out at a central laboratory using standard automated procedures with commercially available diagnostic kits (Boehringer-Mannheim GmbH, Mannheim, Germany). Accuracy and precision for lipid measurements were under periodic surveillance by the Centers for Disease Control and Prevention and National Heart, Lung, and Blood Institute's Lipids Standardization Program and by the Wellcome-Murex Diagnostic Clinical Chemistry Quality Assessment Program. Internal quality control applied 2 levels of control sera (Precinor Lipid and Precipath Lipid; Boehringer-Mannheim) for lipids and lipoproteins. Internal quality control samples were run at the beginning of each shift, and ≥4 repeated runs were made during the analytic process.
Data were analyzed using SAS version 8.2 (SAS Institute Inc., Cary, North Carolina). Characteristics of patients are presented as frequencies or as mean ± SD unless otherwise specified, and were compared using chi-square tests for categorical variables and analysis of variance for normally distributed continuous variables. Triglyceride values, which were not normally distributed, are presented as geometric mean (95% confidence interval) and were compared using the nonparametric Kruskal-Wallis test.
Test of trend in mean was performed applying the CONTRAST statement with the SAS GLM procedure. For geometric means, the test was performed on the log-transformed values. Trend in proportions was assessed by the Mantel-Haenszel chi-square test. Direct adjustment using the entire group included in the analysis as the reference group was used for computation of age adjusted mortality rates per 1,000 patient-years by BMI groups.
The cumulative probability of mortality by BMI groups was calculated applying the Kaplan-Meier method. Curves were compared using the log-rank test.
The age- and multivariate-adjusted mortality hazard associated with each BMI stratum, compared with the 23.0 to 24.99 kg/m2 group, was estimated using a Cox proportional-hazards model. A fixed set of variables selected on the basis of previous knowledge was introduced into models. Variables included in the models were age, history of myocardial infarction, diabetes, peripheral vascular disease, smoking, chronic obstructive pulmonary disease (COPD), HDL cholesterol level (continuous), non-HDL cholesterol level (continuous), and systolic blood pressure. Participation in the clinical trial was also included in the model to account for possible differences due to the selection of patients for participation in the BIP study. To study possible interactions between risk factors and BMI, we ran the models separately for patients with or without the risk factor tested (omitting the stratification risk factor from the variable list included in the model). The significance of possible interaction was tested by running a model with interaction product terms of risk factor existence and BMI group in the model in addition to the variables listed previously. The predictive discrimination ability of each model was evaluated using a C-statistic4 corresponding to the area under a receiver-operating characteristic curve. C-statistics ranged from 0.63 to 0.68 for all models.
The validity of the proportional-hazards assumption was tested by running a model including BMI groups and time-dependent explanatory variables for each group to test the assumption of no time-dependent effect. No significant deviation from the proportional-hazards assumption was detected.
Results
Most of the 12,466 men with CHD included in the analysis had BMIs ranging from 23.0 to 29.99 kg/m2 (23.0 to 24.99 kg/m2: 22%; 25.0 to 26.99 kg/m2: 26%; and 27.0 to 29.99 kg/m2: 26%). Ten percent had BMIs of 20.0 to 22.99 kg/m2, and 14% were obese, with BMIs ≥30 kg/m2. Only 148 men (1%) had BMIs <20 kg/m2 (32 had BMIs <18 kg/m2).
The characteristics of patients by BMI group are listed in Table 1, Table 2. The mean age was 59.4 years in lean patients (BMI <20 kg/m2), 60.1 years in those with BMIs of 20 to 24.9 kg/m2, and decreased linearly thereafter with increasing BMI (p for linear trend = 0.001). A number of risk factors were progressively more frequent in patients with higher BMIs. The increase in the prevalence of hypertension (p for trend <0.0001) was paralleled in measured blood pressure (systolic and diastolic), and the increasing frequency of diabetes (p for trend <0.0001) was paralleled by glucose level, ranging from 103 mg/dl (5.72 mmol/L) in lean patients to 124 mg/dl (6.88 mmol/L) in obese patients (Table 2). Past smokers were monotonically more prevalent with higher BMI. Similar linear relations with BMI were noted for total cholesterol, triglycerides, and HDL cholesterol (inverse) (Table 2). The proportion of patients with metabolic syndrome increased with increasing BMI alongside the trends noted for its components (Table 1).
Table 1. Characteristic of 12,466 men with coronary heart disease by body mass index group
| BMI (kg/m2) | |||||||
|---|---|---|---|---|---|---|---|
| <20.0 | 20.0–22.9 | 23.0–24.9 | 25.0–26.9 | 27.0–29.9 | ≥30 | ||
| Variable | (n = 148) | (n = 1,304) | (n = 2,709) | (n = 3,256) | (n = 3,261) | (n = 1,788) | p Value |
| BMI (kg/m2) | 19 ±1.0 | 22±0.8 | 24±0.6 | 26±0.6 | 28±0.8 | 32±2.4 | |
| Age (years) | 59±6.7 | 60±7.0 | 60±7.0 | 60±7.2 | 59±7.2 | 58±7.1 | <0.0001 |
| Medical history | |||||||
| Myocardial infarction | 110(74%) | 974(75%) | 2,087(77%) | 2,411(74%) | 2,427(75%) | 1,342(75%) | 0.14 |
| Peripheral vascular disease | 6(4.1%) | 69(5.3%) | 119(4.4%) | 109(3.4%) | 153(4.7%) | 71(4.0%) | 0.03 |
| Stroke | 5(3.4%) | 25(1.9%) | 48(1.8%) | 51(1.6%) | 52(1.6%) | 35(2.0%) | 0.54 |
| Hypertension | 29(20%) | 303(23%) | 736(27%) | 923(28%) | 1,089(33%) | 675(38%) | <0.0001 |
| Diabetes | 17(11%) | 195(15%) | 451(17%) | 504(15%) | 619(19%) | 430(24%) | <0.0001 |
| Cancer⁎ | 7(4.7%) | 63(4.8%) | 100(3.7%) | 109(3.3%) | 121(3.7%) | 59(3.3%) | 0.003 |
| COPD | 9(6.1%) | 61(4.7%) | 77(2.9%) | 80(2.5%) | 102(3.1%) | 69(3.9%) | 0.0004 |
| New York Heart Association class >I | 37(26%) | 333(26%) | 681(26%) | 796(25%) | 882(28%) | 556(32%) | <0.0001 |
| Metabolic syndrome | 21(14%) | 308(24%) | 854(32%) | 1,150(35%) | 2,029(62%) | 1,490(83%) | <0.0001 |
| Smoking | |||||||
| Past | 64(43%) | 684(52%) | 1,514(56%) | 1,961(60%) | 1,963(60%) | 1,141(64%) | <0.0001 |
| Current | 42(28%) | 184(14%) | 282(10%) | 351(11%) | 428(13%) | 252(14%) | <0.0001 |
| Treatment | |||||||
| β blockers | 41(28%) | 373(27%) | 859(32%) | 1,140(35%) | 1,175(36%) | 680(38%) | <0.0001 |
| Antidiabetic | 7(4.7%) | 113(8.7%) | 256(9.4%) | 289(8.9%) | 367(11.3%) | 249(13.9%) | <0.0001 |
| Digitalis | 13(8.8%) | 87(6.7%) | 145(5.4%) | 157(4.8%) | 102(3.1%) | 59(3.3%) | <0.0001 |
| Blood pressure (mm Hg) | |||||||
| Systolic | 126±18 | 130±19 | 133±19 | 133±18 | 134±19 | 137±19 | <0.0001 |
| Diastolic | 77±9 | 79±9 | 80±9 | 81±9 | 82±10 | 83±10 | <0.0001 |
⁎Cancer before inclusion or diagnosis within 6 months of inclusion. |
Table 2. Biochemical measurements by body mass index groups
| BMI (kg/m2) | |||||||
|---|---|---|---|---|---|---|---|
| <20.0 | 20.0–22.9 | 23.0–24.9 | 25.0–26.9 | 27.0–29.9 | ≥30 | ||
| Variable | (n = 148) | (n = 1,304) | (n = 2,709) | (n = 3,256) | (n = 3,261) | (n = 1,788) | p Value |
| Total cholesterol (mg/dl) | 210±38 | 217±38 | 219±37 | 221±37 | 221±37 | 221±38 | <0.0001 |
| HDL cholesterol (mg/dl) | 41.0±11.9 | 38.9±10.2 | 37.2±9.1 | 36.2±8.8 | 35.2±8.4 | 34.5±8.3 | <0.0001 |
| Low-density lipoprotein cholesterol (mg/dl) | 145±33 | 151±34 | 154±33 | 154±33 | 154±33 | 152±34 | 0.0009 |
| Triglycerides (mg/dl) | 109(101–117) | 119(116–122) | 129(127–131) | 138(136–141) | 148(146–151) | 161(158–165) | <0.0001 |
| Glucose (mg/dl) | 103±48 | 109.1±47 | 110±41 | 111±41 | 115±44 | 124±51 | <0.0001 |
The median number of components of the metabolic syndrome ranged from 1 in patients with BMIs <20 kg/m2 (interquartile range 1 to 2) to 4 in obese patients.3, 4 Other risk factors, particularly COPD, stroke history, and smoking at the time of screening, were most likely to be found in lean patients, followed by obese patients, than in patients in the middle BMI range (a J-shaped association).
No significant differences between subgroups were noted for history of previous atherosclerotic disease (myocardial infarction or stroke). However, although patients with BMIs <25 kg/m2 were as likely to be functionally impaired (New York Heart Association class ≥II), the frequency of functional impairment increased thereafter with increasing BMI. The frequency of cancer diagnosis before or within 6 months of screening was slightly higher in patients with BMIs <23.0 kg/m2 compared to those with BMIs ≥23.0 kg/m2 (Table 1).
Patients were followed for a median period of 12 years (interquartile range 9.9 to 13.2), corresponding to a total of 128,995 patient-years. Figures 1 and 2 depict age-adjusted mortality rates per 1,000 patient-years for the entire group and by the existence of risk factors and BMI category, respectively. Age-adjusted rates in the entire group of men followed a U-shaped pattern, with the highest rates observed in lean and obese patients and the lowest risk in those with BMIs of 23.0 to 24.99 kg/m2. The corresponding adjusted hazards in patients with BMIs of 20.0 to 22.99 or 25.0 to 26.99 kg/m2 were similar to the low-risk reference category (Table 3). Further adjustment for additional possible confounders had little impact on these results, with only a slight attenuation of risk in the highest 2 BMI categories. After multivariate adjustment, the cumulative mortality risk was similar in all subgroups studied, in patients with BMIs of 20.0 to 29.99 kg/m2 (Figure 3). Men with BMIs <23 kg/m2 had the worst survival probability over most of the follow-up period. Men who were obese at baseline (BMI ≥30 kg/m2) had similar survival to those with BMIs of 23 to 29.99 kg/m2 over the first years of follow-up but had an increasing disadvantage later on (hazard ratio for 12 years 1.28, 95% confidence interval 1.15 to 1.42).
Figure 1.
Age-adjusted mortality rate per 1,000 patient-years (PY) by BMI class in 12,466 men with CHD.
Figure 2.
Age-adjusted mortality rate per 1,000 patient-years (PY) by risk factors and BMI class. GFR = glomerular filtration rate; MI = myocardial infarction.
Table 3. Adjusted all-cause mortality by body mass index group and by risk factors
| BMI (kg/m2) | ||||||
|---|---|---|---|---|---|---|
| <20.0 | 20.0–22.9 | 23.0–24.9 | 25.0–26.9 | 27.0–29.9 | ≥30 | |
| Variable | (n = 148) | (n = 1,304) | (n = 2,709) | (n = 3,256) | (n = 3,261) | (n = 1,788) |
| Rate per 1,000 patient-years | 40.8 | 30.8 | 28.9 | 29.7 | 32.3 | 41.7 |
| Hazard ratio adjusted for | ||||||
| Age | 1.40(1.07–1.81) | 1.05(0.94–1.18) | 1(reference) | 1.02(0.93–1.12) | 1.11(1.01–1.22) | 1.43(1.29–1.58) |
| Multivariate | 1.41(1.08–1.85) | 1.07(0.95–1.20) | 1(reference) | 1.04(0.95–1.14) | 1.06(0.97–1.16) | 1.28(1.15–1.42) |
| Multivariate by risk factor existence | ||||||
| No history of myocardial infarction | 0.56(0.23–1.38) | 1.01(0.76–1.34) | 1(reference) | 1.12(0.91–1.38) | 1.11(0.90–1.38) | 1.20(0.94–1.53) |
| History of myocardial infarction | 1.64(1.23–2.17) | 1.08(0.95–1.23) | 1(reference) | 1.01(0.92–1.12) | 1.04(0.94–1.16) | 1.29(1.15–1.45) |
| Nondiabetic | 1.48(1.10–1.99) | 1.03(0.90–1.17) | 1(reference) | 1.07(0.97–1.19) | 1.08(0.97–1.20) | 1.31(1.16–1.49) |
| Diabetic | 1.29(0.68–2.44) | 1.17(0.92–1.48) | 1(reference) | 0.91(0.76–1.09) | 0.98(0.82–1.17) | 1.17(0.97–1.41) |
| Normotensive | 1.48(1.10–2.00) | 1.09(0.95–1.26) | 1(reference) | 1.07(0.96–1.19) | 1.06(0.95–1.19) | 1.29(1.13–1.47) |
| Hypertensive | 1.13(0.60–2.13) | 1.02(0.83–1.15 | 1(reference) | 0.98(0.83–1.15) | 1.03(0.88–1.20) | 1.23(1.03–1.46) |
| Nonsmokers/past smokers | 1.31(0.94–1.81) | 1.06(0.93–1.20) | 1(reference) | 1.03(0.94–1.14) | 1.05(0.95–1.16) | 1.31(1.18–1.47) |
| Smokers | 1.69(1.03–2.78) | 1.12(0.82–1.52) | 1(reference) | 1.05(0.80–1.36) | 1.06(0.82–1.36) | 1.08(0.81–1.44) |
| No metabolic syndrome | 1.39(1.03–1.88) | 1.11(0.96–1.28) | 1(reference) | 1.06(0.94–1.19) | 1.08(0.94–1.24) | 1.43(1.14–1.78) |
| Metabolic syndrome | 1.53(0.81–2.87) | 1.01(0.82–1.25) | 1(reference) | 1.00(0.87–1.16) | 0.97(0.85–1.11) | 1.15(1.00–1.32) |
| Estimated glomerular filtration rate ≥60 ml/min/1.73 m2 | 1.29(0.74–2.25) | 1.14(0.92–1.40) | 1(reference) | 1.00(0.86–1.18) | 1.18(1.01–1.37) | 1.33(1.11–1.59) |
| Estimated glomerular filtration rate <60 ml/min/1.73 m2 | 2.20(0.96–5.06) | 1.03(0.72–1.49) | 1(reference) | 0.90(0.70–1.16) | 1.04(0.81–1.33) | 0.99(0.72–1.35) |
Figure 3.
Multivariate-adjusted 12-year mortality among 12,466 men with CHD by BMI.
As a rule, in every BMI category, age-adjusted mortality risk was higher in the presence of each risk factor examined compared to its absence. Lean patients had a particularly poor prognosis in the presence of past myocardial infarction, smoking, or renal insufficiency (almost threefold risk vs. glomerular filtration rate ≥60 ml/min/1.73 m2). The association between BMI and mortality was U shaped in most subgroups. A history of myocardial infarction seemed to modify the association between BMI and mortality, with a quadratic association observed in patients with histories of myocardial infarction and a linear one in those with histories of angina pectoris only. Higher mortality rates were associated with BMI < 20 kg/m2, decreasing thereafter toward the middle of the range and increasing again. Mortality risk in patients with BMIs in the range of 25.0 to 26.99 kg/m2 was close to the risk observed for the reference group (BMI 23.0 to 24.99 kg/m2), which was in most subgroups the lowest risk recorded.
Discussion
Most (67%) patients with chronic CHD studied were “overweight” or obese. The large group enabled us to examine subcategories of the division suggested by the World Health Organization. BMI exhibited a monotonic cross-sectional association with a number of risk factors (age, diabetes, past smoking, and components of the metabolic syndrome). The lowest degree of risk (low prevalence or level of risk factor) was found in lean patients and the highest in obese counterparts. Despite these associations, we found a U-shaped relation between BMI and 12-year age-adjusted mortality rates. In the entire group, BMIs of 23.0 to 24.99 kg/m2 were associated with the lowest mortality rate. After multivariate adjustment, similar risk was found in patients with BMIs of 20.0 to 29.99 kg/m2, without significant differences between subgroups in this range.
These results are in line with a recent meta-analysis of data from 388,622 patients from 26 observational studies.5 In this meta-analysis comprising mainly population studies, McGee et al5 found little increase in all-cause mortality in overweight subjects and a moderately increased rate associated with obesity. Similar results were recently reported by the Prospective Studies Collaboration after analysis of data from 900,000 generally healthy adults from 57 prospective studies.6
Although our data may suggest extreme leanness to be detrimental, the increased mortality in lean patients could also be due to reverse causality or a temporal bias, as low weight might reflect a preexisting adverse condition such as cardiac cachexia associated with congestive heart failure7, 8 or COPD.6 COPD, often associated with weight loss in advanced stages, was reported to be highly prevalent in lean patients with peripheral artery disease.9 In our study, cancer diagnosed before screening or within the first 6 months from screening was only slightly more prevalent in lean patients. Although we found the highest prevalence of COPD and smoking at the time of screening in lean patients, adjustment for these variables did not diminish the risk associated with low weight.
A number of reports have shown an inverse relation between BMI and mortality in patients with CHD.10, 11, 12, 13 The reason for differences in reports in patients CHD is unclear. It is possible that obesity is protective in the period following an acute event or procedure (to which most reports pertain) but associated with augmented risk in the long term or in a chronic phase of the disease, as demonstrated in a number of studies.14, 15 It might be postulated that the short-term benefit of elevated weight arises from some protective effect of fat mass in the setting of an acute event. For example, adipose tissue was shown to be a major reservoir of adult stem cells with potential to develop into cardiomyocytes or endothelium.16 In addition, the perception of increased risk associated with overweight and obesity might lead to more aggressive treatment, which results in a better short-term prognosis for these patients. Indeed, in patients referred to angiography, obesity was reported to be associated with younger age and a higher proportion of concomitant disease as well as with a lower probability of high-risk coronary anatomy, indicating that obese patients were referred to angiography at an earlier stage of the disease.17 In the latter study, there was no difference in long-term (30- to 36-month) mortality according to BMI. In the Get With the Guidelines study the observed paradox of hospital mortality in patients hospitalized with CHD was associated with better use of guideline recommended medical treatment and invasive management of CHD.18 Nevertheless, an inverse relation was also reported by Galal et al11 between BMI and mortality or cardiac death or myocardial infarction during a mean follow-up period of 6 years.
The possibility that the difference in association between BMI and mortality originates from interaction with other risk factors was tested in the present study. The existence of every factor examined had an adverse effect on survival. This is particularly true for lean patients, who fared much worse in the presence of smoking, renal insufficiency, or a history of myocardial infarction, demonstrating their fragility compared to patients with higher BMIs. Disease-induced cachexia has been suggested as a possible explanation to the obesity paradox in patients with CHD.19 Although a history of myocardial infarction seemed to change the shape of the association from linear to quadratic, after multivariate adjustment, a significant interaction was noted only in lean patients. None of the subgroups studied displayed an inverse association, as reported by others.
Weight loss shortly after myocardial infarction was reported to be associated with increased mortality risk.20, 21 However these and our results do not preclude a possible benefit from controlled weight loss through recommended lifestyle modifications. Eilat-Adar et al22 reported lower CHD incidence rate associated with voluntary weight loss in overweight subjects with only 1 CHD risk factor but free of overt CHD and cancer at entry. Similarly, voluntary weight loss was also correlated with reductions in risk factors in obese patients with CHD.23 Whether these positive changes translate into reduced long-term mortality risk in patients with chronic CHD is unclear. Yaari and Goldbourt24 reported an increase in mortality risk due to voluntary as well as involuntary weight loss in a large general male population group.
Our study had the advantage of being a large prospective study with long-term follow-up. In addition, BMI estimates were based on measured weight and height rather than self-reported by patients. A number of limitations are worth notice. Although BMI may be a useful risk predictor on a population level, it is not a direct measure of body composition (muscle mass vs fat mass). In addition, BMI does not allow a distinction between fat location or type (such as brown fat), which may attenuate its risk prediction ability. Data relating to fat distribution such as thigh diameter, waist or hip circumference (as an indirect measure of visceral fat), or a measure of liver fat are not available for the BIP registry patients. The results of the INTERHEART multinational cross-sectional case-control study suggest that waist/hip ratio is most strongly associated with the prevalence of nonfatal myocardial infarction.25 These results are limited by the INTERHEART cross-sectional study design.
Although it is a good prognosticator in populations, BMI may better predict risk at both extremes of its distribution (lean and obese). Information on fat distribution and type may be important to individual risk prediction, particularly at the middle range of BMI distribution. In contrast, BMI has the advantage of being easy to estimate, highly accurate, and more generally available, whereas waist circumference is more prone to interobserver variation.8 Moreover, waist circumference does not provide a distinction between intra-abdominal visceral fat associated with increased risk and benign subcutaneous fat.
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PII: S0002-9149(10)00941-0
doi:10.1016/j.amjcard.2010.03.078
© 2010 Elsevier Inc. All rights reserved.
