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Mediators of the Effect of Body Mass Index on Coronary Heart Disease

Fritz, Josef; Strohmaier, Susanne; Nagel, Gabriele; Concin, Hans; Ulmer, Hanno

doi: 10.1097/EDE.0000000000000442
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Department of Medical Statistics, Informatics and Health Economics, Innsbruck Medical University, Innsbruck, Austria

Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway

Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany

Agency for Preventive and Social Medicine, Bregenz, Austria

Department of Medical Statistics, Informatics and Health Economics, Innsbruck Medical University, Innsbruck, Austria, hanno.ulmer@i-med.ac.at

The authors report no conflicts of interest.

Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com).

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To the Editor:

The question of how much of the harmful effect of increased body mass index (BMI) on cardiovascular events is mediated through cardiovascular risk factors is of high interest for clinical understanding, public health, and preventive health counseling. Therefore, we appreciate the studies of Lu et al.1,2 where this topic has been addressed. Major strengths of the study in The Lancet1 are the large number of cohorts included, of the study in EPIDEMIOLOGY2 the sophisticated methodology. However, we think that the results deserve further discussion.

Since it is well established that the effects of the major metabolic risk factors, including BMI, on cardiovascular diseases decrease with age,3 we wonder why this interaction effect was not considered in either study. Analyzing data of the Vorarlberg Health Monitoring & Promotion Programme (VHM&PP)4 which is also part of the earlier study,1 the interaction term BMI*age suggested a submultiplicative effect of the continuous variables BMI and age on the outcome death from coronary heart disease (CHD, defined via ICD-10 codes I20 to I25). Consequently, we performed mediation analyses assessing the combined contribution of systolic blood pressure, total cholesterol, and blood glucose controlling for confounders age, sex, and smoking status separately for younger (<65 years) and older (≥65 years) individuals. We opted for these two age groups because an age of ≥65 years is a common definition of an elderly person. For our analyses, we used the two-stage regression method proposed by VanderWeele5,6 allowing decomposition of the total effects of the exposure variable BMI into natural direct effects and natural indirect effects. The same method was applied by Lu et al.2 in their article in EPIDEMIOLOGY. A technical description and R code of the statistical analysis can be found in the eAppendix (http://links.lww.com/EDE/B7).

Our results can be seen below (Table). Our study cohort comprised 111,303 individuals, of whom 2,127 suffered from death due to CHD during a median follow-up of 14.5 years; we excluded individuals with a BMI < 20 kg/m2. Referring to the interaction mentioned above, age group specific estimates expressed as hazard ratios of overweight/obese versus normal weight individuals were markedly lower in the older age group (see total effects in the Table). For younger individuals, our analyses confirmed the findings of Lu et al.1,2 that approximately half of the excess risk can be attributed to the risk factors systolic blood pressure, total cholesterol, and blood glucose. For elderly individuals, the total effect of overweight as compared with normal weight was 1.06 (95% confidence interval: 0.93, 1.21) which was fully mediated by the three risk factors. For obesity, there was a total effect of 1.35 (95% confidence interval: 1.15, 1.58); however, the major part (80%) of the excess risk was not explained via the risk factors.

TABLE

TABLE

The results of our analyses clearly demonstrate that the analyzed metabolic risk factors play a distinctly different role in explaining the risk of increased BMI on CHD between the younger and elderly population. In higher ages, elevated risk factor levels are not only restricted to overweight or obese individuals but also increase in normal weight individuals7 (e.g., in our study mean systolic blood pressure was 122.8 mmHg in normal weight individuals <65 years, in ≥65 years 147.7 mmHg). The smaller difference in risk factor levels between normal weight and overweight/obese elderly individuals is the main reason for the reduced relevance of risk factors acting as mediators. In the elderly, obesity presents with an excess risk for CHD mediated only to a small part by the major metabolic risk factors. More than in younger individuals, it appears that risk constellations other than the established pathways (e.g., chronic inflammation) are in play that merit further investigation.8

Josef Fritz

Department of Medical Statistics,

Informatics and Health Economics

Innsbruck Medical University

Innsbruck, Austria

Susanne Strohmaier

Oslo Centre for Biostatistics and Epidemiology

University of Oslo

Oslo, Norway

Gabriele Nagel

Institute of Epidemiology and Medical Biometry

Ulm University

Ulm, Germany

Hans Concin

Agency for Preventive and Social Medicine

Bregenz, Austria

Hanno Ulmer

Department of Medical Statistics,

Informatics and Health Economics

Innsbruck Medical University

Innsbruck, Austria

hanno.ulmer@i-med.ac.at

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REFERENCES

1. Lu Y, Hajifathalian K, Ezzati M, Woodward M, Rimm EB, Danaei G.. Metabolic mediators of the effects of body-mass index, overweight, and obesity on coronary heart disease and stroke: a pooled analysis of 97 prospective cohorts with 1·8 million participants. Lancet. 2014;383:970–983
2. Lu Y, Hajifathalian K, Rimm EB, Ezzati M, Danaei G.. Mediators of the effect of body mass index on coronary heart disease: decomposing direct and indirect effects. Epidemiology. 2015;26:153–162
3. Singh GM, Danaei G, Farzadfar F, et al.Global Burden of Metabolic Risk Factors of Chronic Diseases Collaborating Group; Asia-Pacific Cohort Studies Collaboration (APCSC); Diabetes Epidemiology: Collaborative analysis of Diagnostic criteria in Europe (DECODE); Emerging Risk Factor Collaboration (ERFC); Prospective Studies Collaboration (PSC). The age-specific quantitative effects of metabolic risk factors on cardiovascular diseases and diabetes: a pooled analysis. PLoS One. 2013;8:e65174
4. Ulmer H, Kelleher C, Diem G, Concin H.. Long-term tracking of cardiovascular risk factors among men and women in a large population-based health system: the Vorarlberg Health Monitoring & Promotion Programme. Eur Heart J. 2003;24:1004–1013
5. VanderWeele TJ.. Causal mediation analysis with survival data. Epidemiology. 2011;22:582–585
6. Vanderweele TJ.. Mediation analysis with multiple versions of the mediator. Epidemiology. 2012;23:454–463
7. Brown CD, Higgins M, Donato KA, et al. Body mass index and the prevalence of hypertension and dyslipidemia. Obes Res. 2000;8:605–619
8. Dorner TE, Rieder A.. Obesity paradox in elderly patients with cardiovascular diseases. Int J Cardiol. 2012;155:56–65

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