Conceived and designed the experiments: BGN NJT GDS. Analyzed the data: NJT TMP MB. Wrote the first draft of the manuscript: NJT. Contributed to the writing of the manuscript: NJT TMP BGN GDS JZ ATH MB.
George Davey Smith is on the
A Mendelian randomization analysis conducted by Børge G. Nordestgaard and colleagues using data from observational studies supports a causal relationship between body mass index and risk for ischemic heart disease.
Adiposity, assessed as elevated body mass index (BMI), is associated with increased risk of ischemic heart disease (IHD); however, whether this is causal is unknown. We tested the hypothesis that positive observational associations between BMI and IHD are causal.
In 75,627 individuals taken from two population-based and one case-control study in Copenhagen, we measured BMI, ascertained 11,056 IHD events, and genotyped
For every 4 kg/m2 increase in BMI, observational estimates suggested a 26% increase in odds for IHD while causal estimates suggested a 52% increase. These data add evidence to support a causal link between increased BMI and IHD risk, though the mechanism may ultimately be through intermediate factors like hypertension, dyslipidemia, and type 2 diabetes. This work has important policy implications for public health, given the continuous nature of the BMI-IHD association and the modifiable nature of BMI. This analysis demonstrates the value of observational studies and their ability to provide unbiased results through inclusion of genetic data avoiding confounding, reverse causation, and bias.
Ischemic heart disease (IHD; also known as coronary heart disease) is the leading cause of death among adults in developed countries. In the US alone, IHD kills nearly half a million people every year. With age, fatty deposits (atherosclerotic plaques) build up in the walls of the coronary arteries, the blood vessels that supply the heart with oxygen and nutrients. The resultant reduction in the heart's blood supply causes shortness of breath, angina (chest pains that are usually relieved by rest), and potentially fatal heart attacks (myocardial infarctions). Risk factors for IHD include smoking, high blood pressure (hypertension), abnormal amounts of cholesterol and other fat in the blood (dyslipidemia), type 2 diabetes, and being overweight or obese (having excess body fat). Treatments for IHD include lifestyle changes (for example, losing weight) and medications that lower blood pressure and blood cholesterol levels. The narrowed arteries can also be widened using a device called a stent or surgically bypassed.
Prospective observational studies have shown an association between a high body mass index (BMI, a measure of body fat that is calculated by dividing a person's weight in kilograms by their height in meters squared; a BMI greater than 30 kg/m2 indicates obesity) and an increased risk of IHD. Observational studies, which ask whether people who are exposed to a suspected risk factor develop a specific disease more often than people who are not exposed to the risk factor, cannot prove, however, that changes in BMI/adiposity cause IHD. Obese individuals may share other characteristics that cause both IHD and obesity (confounding) or, rather than obesity causing IHD, IHD may cause obesity (reverse causation). Here, the researchers use “Mendelian randomization” to examine whether elevations in BMI across the lifecourse have a causal impact on IHD risk. Three common genetic variants—
The researchers analyzed data from two population-based studies in which adults were physically examined and answered a lifestyle questionnaire before being followed to see how many developed IDH. They also analyzed data from a case-control study on IDH (in a case-control study, people with a disease are matched with similar people without the disease and the occurrence of risk factors in the patients and controls is compared). Overall, the researchers measured the BMI of 75,627 white individuals, among whom 11,056 already had IDH or developed it, and determined which of the BMI-increasing genetic variants each participant carried. On the basis of the observational data, every 4 kg/m2 increase in BMI increased the odds of IDH by 26% (an odds ratio of 1.26). Using a score derived from the combination of the three genetic variants, the researchers confirmed an association between each BMI increasing allele and both BMI (as expected) and IHD (0.28 kg/m2 and an odds ratio for IHD of 1.03, respectively). On average, compared to people carrying no BMI-increasing gene variants, people carrying six BMI-increasing gene variants had a 1.68 kg/m2 increase in BMI and an 18% increase in IHD risk. To extend this and to essentially reassess the original, observational, relationship between BMI and IHD risk, an “instrumental variable analysis” was used to examine the causal effect of a lifetime change in BMI on the risk of IDH. In this, it was found that for every 4 kg/m2 increase in BMI increased the odds of IDH by 52%.
These findings support a causal link between increased BMI and IDH risk, although it may be that BMI affects IDH through intermediate factors such as hypertension, dyslipidemia, and diabetes. The findings also show that observational studies into the impact of elevated BMI on IHD risk were consistent with this, but also that the inclusion of genetic data increases the value of observational studies by making it possible to avoid issues such as confounding and reverse causation. Finally, these findings and those of recent, observational studies have important implications for public-health policy because they show that the association between BMI (which is modifiable by lifestyle changes) and IHD is continuous. That is, any increase in BMI increases the risk of IHD; there is no threshold below which a BMI increase has no effect on IDH risk. Thus, public-health policies that aim to reduce BMI by even moderate levels could substantially reduce the occurrence of IDH in populations.
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Observational examination of the prospective association between body mass index (BMI) and ischemic heart disease (IHD) has been undertaken in a range of populations
Observational association of BMI with IHD is impaired by confounding
Genetic variation at the fat mass and obesity related locus (
The aim here was to test the hypothesis that known positive observational relationships between BMI and IHD are causal in two well-sized general population studies (the Copenhagen General Population Study [CGPS];
All participants were white and of Danish descent; this information is available through the national Danish Central Person Registry. No participants appeared in more than one of the three studies. The studies were approved by Danish ethical committees and Herlev Hospital.
This general population study was initiated in 2003 with ongoing enrolment
This prospective general population study was initiated in 1976–1978 with follow-up examinations in 1981–1983, 1991–1994, and 2001–2003
This case-control study comprises 5,270 patients from the greater Copenhagen area referred for coronary angiography to Copenhagen University Hospital during the period 1991–2009 and 5,270 unmatched controls without IHD randomly sampled from the CGPS. Beside a diagnosis of IHD as described below, these patients also had stenosis/atherosclerosis on coronary angiography and/or a positive exercise electrocardiography test.
In all three studies, information on diagnosis of IHD (World Health Organization International Classification of Diseases: ICD8 410–414; ICD10 I20–I25) was collected and verified from existing data from 1976 until May 2009 by reviewing all hospital admissions and diagnoses entered in the national Danish Patient Registry and all causes of death entered in the national Danish Causes of Death Registry. Even though some individuals entered into our studies after 1976, we have complete information on all participants on any hospitalisation or death from IHD from 1976 through 2009 through these registries. IHD was angina pectoris and/or myocardial infarction (ICD8 410; ICD10 I21–I22), based on characteristic chest pain, electrocardiographic changes, and/or elevated cardiac enzymes. Follow-up was 100% complete, that is, no individual was lost to follow-up in any of the studies.
Genotyping was conducted blind to phenotypic data. In the absence of genomewide data, the ABI PRISM 7900HT Sequence Detection system (Applied Biosystems Inc.) was used to genotype the BMI instrument loci:
BMI was calculated as measured weight (kg) divided by measured height squared (m2). To exclude influence of age and sex on our results, BMI was standardised into age- and sex-adjusted Z-scores within each study separately (
Smoking was categorized from self-reported data as ever versus never smoked, alcohol consumption was categorized as <14/21 or ≥14/21 units per week for women/men (1 unit = 12 g), education as schooling for <10, ≥10 to <13, and ≥13 y, and annual income as <100,000DKK, 100,000DKK–400,000DKK, 400,000DKK–600,000DKK, and >600,000DKK. Use of statins was recorded at examination. Measured systolic and diastolic blood pressure was recorded as described previously
All analyses were performed in Stata version 11 (StataCorp). For genotypes a deviation from Hardy-Weinberg equilibrium was investigated using a Pearson chi-squared test. Observational estimates of odds ratios (ORs) of IHD per unit increase in standardised BMI, allele score, and potential confounders were estimated using logistic regression. Estimates of the association of standardised BMI with allele score and potential confounders were performed using linear regression. Associations of potential confounders by genotype and allele score were estimated using logistic regression, linear regression, and Pearson's chi-squared test.
Instrumental variable estimates of causal ORs were derived using the Wald-type estimator
In a case-control study like the CIHDS, the allele score-IHD association is valid as in cohort studies like the CGPS and the CCHS because it is not affected by IHD status and represents lifecourse BMI-associated IHD risk. BMI may be affected by IHD, because some patients loose weight after an IHD diagnosis. Therefore, the allele score-BMI association entering into the instrumental variable analyses is best derived from people in the general population as done in the present study. The advantage of including the CIHDS together with the CGPS and the CCHS is that it adds considerable statistical power to the combined analyses, as done by ourselves in previous studies
Meta-analysis pooled estimates were obtained using the random effects meta-analysis model implemented in the user-written Stata command “metan”
The numbers of IHD patients were 3,780 of 54,613 participants in the CGPS (79% prevalent and 21% incident), 2,006 of 10,474 participants in the CCHS (22% prevalent and 78% incident), and 5,270 of 10,540 participants in the CIHDS (100% prevalent). Baseline characteristics of participants in the three studies are shown in
Characteristics | CGPS | CCHS | CIHDS | ||||||||||
Tertile 1 | Tertile 2 | Tertile 3 | Overall | Tertile 1 | Tertile 2 | Tertile 3 | Overall | Tertile 1 Controls | Tertile 2 Controls | Tertile 3 Controls | Controls | Overall | |
|
18,063 | 18,060 | 18,060 | 54,613 | 3,488 | 3,488 | 3,488 | 10,474 | 1,758 | 1,756 | 1,756 | 5,270 | 10,540 |
BMI (kg/m2) | 22.3 (21.1–23.2) | 25.6 (24.8–26.4) | 29.9 (28.5–32.3) | 25.6 (23.2–28.5) | 21.0 (20.0–21.8) | 23.9 (23.3–24.7) | 27.9 (26.7–30.0) | 23.9 (21.8–26.7) | 22.2 (21.1–23.2) | 25.4 (24.6–26.2) | 29.5 (28.1–31.8) | 25.4 (23.2–28.1) | 25.5 (23.3–28.1) |
Waist hip ratio | 0.81 (0.77–0.87) | 0.88 (0.82–0.93) | 0.93 (0.86–0.98) | 0.87 (0.81–0.93) | 0.81 (0.76–0.87) | 0.87 (0.81–0.93) | 0.93 (0.86–0.99) | 0.87 (0.80–0.94) | 0.81 (0.77–0.87) | 0.87 (0.82–0.92) | 0.92 (0.86–0.97) | 0.87 (0.80–0.93) | 0.87 (0.80–0.93) |
Women (%) | 70.3 | 48.4 | 47.7 | 55.6 | 70.6 | 51.8 | 44.3 | 55.6 | 72.8 | 49.8 | 47.3 | 56.6 | 43.0 |
Age (y) | 54 (44–65) | 58 (48–67) | 60 (50–68) | 57 (47–67) | 54 (40–67) | 60 (48–70) | 64 (54–72) | 60 (47–70) | 54 (45–64) | 56 (46–66) | 59 (49–69) | 56 (46–66) | 60 (51–69) |
Ever smoked (%) | 56.4 | 60.7 | 61.3 | 59.3 | 58.0 | 56.0 | 53.2 | 55.7 | 56.5 | 59.7 | 60.5 | 58.9 | 59.5 |
Drinking (%) | 73.4 | 75.2 | 68.5 | 72.1 | 50.9 | 56.0 | 53.3 | 53.4 | 74.6 | 75.5 | 69.5 | 73.2 | 59.4 |
Education (%) |
20.3 | 27.2 | 37.6 | 28.4 | 38.2 | 50.8 | 66.0 | 51.6 | 20.1 | 27.5 | 36.1 | 27.9 | 27.9 |
Education (%) |
58.0 | 55.5 | 48.9 | 54.1 | 46.1 | 36.8 | 27.9 | 36.9 | 58.3 | 55.7 | 50.1 | 54.7 | 54.7 |
Education (%) |
21.8 | 17.3 | 13.5 | 17.5 | 15.8 | 12.4 | 6.1 | 11.4 | 21.6 | 16.8 | 13.8 | 17.4 | 17.4 |
Income (%) |
2.0 | 1.6 | 2.1 | 1.9 | 19.9 | 16.4 | 18.1 | 18.1 | 2.7 | 1.4 | 1.9 | 2.0 | 2.0 |
Income (%) |
35.4 | 36.2 | 43.9 | 38.6 | 52.5 | 53.9 | 56.4 | 54.3 | 35.8 | 35.9 | 41.6 | 37.7 | 37.7 |
Income (%) |
38.8 | 40.7 | 38.9 | 39.4 | 24.3 | 26.0 | 22.9 | 24.4 | 37.8 | 41.9 | 41.0 | 40.3 | 40.3 |
Income (%) |
23.8 | 21.5 | 15.1 | 20.1 | 3.3 | 3.7 | 2.7 | 3.2 | 23.7 | 20.8 | 15.5 | 20.0 | 20.0 |
IHD (%) | 4.3 | 6.9 | 9.6 | 6.9 | 12.4 | 17.8 | 27.3 | 19.2 | NA | NA | NA | NA | 50.0 |
Event time (y) | 2.1 (1.2–3.6) | 2.3 (1.1–3.7) | 2.4 (1.0–3.7) | 2.3 (1.1–3.7) | 15.6 (11.8–16.5) | 15.3 (9.3–16.2) | 13.5 (6.5–15.9) | 15.2 (8.7–16.2) | 2.5 (1.5–3.9) | 2.6 (1.3–3.9) | 2.6 (1.2–3.9) | 2.6 (1.3–3.9) | NA |
SBP (mmHg) | 132 (120–148) | 140 (126–154) | 145 (132–160) | 140 (125–155) | 120 (111–132) | 127 (117–139) | 134 (123–147) | 127 (116–140) | 130 (120–145) | 140 (125–155) | 145 (132–160) | 140 (125–154) | 140 (125–154) |
DBP (mmHg) | 80 (72–86) | 82 (75–90) | 86 (80–95) | 82 (75–90) | 75 (69–83) | 80 (73–87) | 85 (78–93) | 80 (72–88) | 80 (72–86) | 83 (76–90) | 87 (80–95) | 83 (75–90) | 81 (75–90) |
Hypertension (%) | 56.5 | 70.2 | 83.0 | 70.0 | 19.2 | 32.1 | 51.1 | 34.2 | 54.4 | 67.8 | 81.7 | 68.0 | 52.7 |
AH medicine (%) | 11.3 | 17.8 | 28.3 | 19.1 | 1.8 | 2.4 | 6.0 | 3.4 | 10.9 | 13.8 | 24.5 | 16.4 | 23.6 |
Adjusted SBP (mmHg) | 132 (120–150) | 140 (128–156) | 149 (135–164) | 140 (126–157) | 120 (111–132) | 128 (117–140) | 135 (123–148) | 127 (116–140) | 131 (120–148) | 140 (125–156) | 147 (134–162) | 140 (125–156) | 140 (126–158) |
Adjusted DBP (mmHg) | 80 (72–87) | 84 (76–90) | 88 (80–95) | 84 (76–91) | 75 (69–83) | 80 (73–87) | 85 (78–93) | 80 (73–88) | 84 (76–92) | 80 (73–87) | 84 (76–91) | 84 (76–92) | 83 (75–91) |
Glucose (mmol/l) | 5.0 (4.6–5.5) | 5.1 (4.7–5.6) | 5.2 (4.8–5.8) | 5.1 (4.7–5.6) | 5.7 (5.1–6.3) | 5.9 (5.3–6.5) | 6.1 (5.6–6.8) | 5.9 (5.3–6.6) | 5.0 (4.6–5.5) | 5.0 (4.7–5.5) | 5.2 (4.8–5.8) | 5.1 (4.7–5.6) | 5.1 (4.7–5.6) |
LDL cholesterol (mmol/l) | 3.0 (2.4–3.6) | 3.2 (2.6,3.9) | 3.4 (2.7–4.0) | 3.2 (2.6–3.9) | 3.3 (2.6–4.2) | 3.6 (2.9–4.4) | 3.8 (3.1–4.6) | 3.6 (2.9–4.4) | 3.0 (2.4–3.6) | 3.2 (2.7–3.9) | 3.4 (2.8–4.0) | 3.2 (2.6–3.9) | 3.1 (2.4–3.8) |
HDL cholesterol (mmol/l) | 1.8 (1.5–2.2) | 1.6 (1.3–1.9) | 1.4 (1.1–1.7) | 1.6 (1.3–1.9) | 1.6 (1.3–1.9) | 1.5 (1.2–1.7) | 1.3 (1.1–1.6) | 1.5 (1.2–1.7) | 1.8 (1.5–2.2) | 1.6 (1.3–1.9) | 1.4 (1.1–1.7) | 1.6 (1.3–2.0) | 1.4 (1.1–1.8) |
Triglycerides (mmol/l) | 1.1 (0.8–1.5) | 1.4 (1.0–2.1) | 1.8 (1.3–2.6) | 1.4 (1.0–2.1) | 1.1 (0.8–1.5) | 1.3 (0.9–1.8) | 1.7 (1.2–2.5) | 1.3 (0.9–1.9) | 1.1 (0.8–1.5) | 1.4 (1.0–2.1) | 1.8 (1.3–2.6) | 1.4 (1.0–2.1) | 1.5 (1.0–2.2) |
Data are from study enrolment in 2003–2009 in the CGPS, from the 1991–1994 or 2001–2003 examinations of the CCHS when DNA was collected, and from study enrolment in 1991–2009 in the CIDHS. Values are median and interquartile range or number of participants and percentages. Drinking alcohol represented by <14/21; ≥14/21 units per week for women/men at the time of examination. Event time is the absolute value of the difference between age at IHD event and age at measurement of BMI (y). SBP and DBP adjusted by adding 10 and 5 mmHg to each, respectively, if patient on AH medication. To convert glucose values in mmol/l to mg/dl, multiply by 18. To convert LDL and HDL cholesterol values in mmol/l to mg/dl, multiply by 38.6. To convert triglyceride values in mmol/l to mg/dl, multiply by 88. In CIHDS, education and income are only available in controls.
<10 y of schooling.
≥10 to <13 y of schooling.
≥13 y of schooling.
<100,000DKK annual income.
100,000DKK–400,000DKK annual income.
400,000–600,000DKK annual income.
>600,000DKK annual income.
AH, antihypertensive; DBP, diastolic blood pressure; HDL, high density lipoprotein; LDL, low density lipoprotein; SBP, systolic blood pressure.
Genotype or Allele Score | 0 | 1 | 2 | 3 | 4 | 5 | 6 |
|
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17,846 (35.2) | 24,595 (48.5) | 8,307 (16.4) | |||||
28,797 (56.7) | 18,862 (37.2) | 3,092 (6.1) | |||||
1,517 (2.9) | 15,036 (28.4) | 36,435 (68.9) | |||||
|
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3,744 (35.8) | 4,981 (47.6) | 1,748 (16.7) | |||||
5,998 (57.3) | 3,841 (36.7) | 635 (6.1) | |||||
316 (3.0) | 2,975 (28.4) | 7,180 (68.6) | |||||
|
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3,613 (34.3) | 5,159 (49.0) | 1,768 (16.8) | |||||
5,913 (56.1) | 3,930 (37.3) | 697 (6.6) | |||||
279 (2.7) | 2,955 (28.0) | 7,306 (69.3) | |||||
|
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58 (0.6) | 738 (7.1) | 2,782 (26.6) | 3,768 (36.0) | 2,386 (22.8) | 658 (6.3) | 80 (0.8) | |
294 (0.6) | 3,452 (6.8) | 13,203 (26.0) | 18,578 (36.6) | 11,556 (22.8) | 3,283 (6.5) | 349 (0.7) | |
55 (0.5) | 682 (6.5) | 2,672 (25.4) | 3,831 (36.4) | 2,506 (23.8) | 721 (6.8) | 73 (0.7) |
All SNPs adhere to Hardy-Weinberg Equilibrium (
In the general population, for one standard deviation (4 kg/m2) increase in BMI, the observational ORs for IHD were 1.23 (95% CI 1.19–1.28) for the CGPS and 1.31 (95% CI 1.23–1.39) for the CCHS (
The ORs are for a 4 kg/m2 increase in BMI.
Study | OR (95% CI) | OR (95% CI) |
OR (95% CI) |
CGPS | 1.23 (1.19–1.28) | 1.21 (1.17–1.24) | 1.22 (1.18–1.26) |
CCHS | 1.31 (1.23–1.39) | 1.31 (1.24–1.38) | 1.22 (1.15–1.29) |
Pooled | 1.27 (1.19–1.34) | 1.25 (1.16–1.36) | 1.22 (1.18–1.26) |
Pooled estimates from fixed effects meta-analysis.
Adjusted for sex, smoking status, drinking status, years of education, income level.
Adjusted for sex, smoking status, drinking status, years of education, income level, and event time.
Pooled across the three studies, each additional adiposity-related allele from the allele score was associated with a 0.28 kg/m2 (95% CI 0.22–0.34) increase in BMI (
Analyses are stratified by IHD status.
In an analysis such as this one, it is important to examine whether potential confounding factors could be part of the explanation behind an observational BMI-IHD association, or behind a causal BMI-IHD association estimated using allele score in instrumental variable analysis. For a potential confounder to be part of the explanation behind an association, the factor in question needs to associate both with the exposure and the outcome.
We therefore examined both the association between potentially confounding factors (sex, age, smoking status, drinking status, education, income, and event time) and our primary exposure (BMI), our primary outcome (IHD), and our instrumental variables (genotypes and allele score). For both our primary outcome and exposure, there was strong evidence for association between both BMI, IHD, and drinking, education, income, and event time (
Potential Confounders with Genotypes or Allele Score | OR (95% CI) on Logistic Regression |
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CGPS | CCHS | CIHDS | |
|
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1.02 (0.99–1.04) | 0.95 (0.90–1.00) | 1.01 (0.95–1.06) | |
0.99 (0.96–1.01) | 0.95 (0.89–1.01) | 1.06 (1.00–1.13) | |
0.99 (0.96–1.03) | 0.95 (0.89–1.03) | 1.02 (0.94–1.09) | |
Allele score | 1.00 (0.98–1.02) | 0.95 (0.92–0.99) | 1.03 (0.99–1.07) |
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1.02 (1.00–1.05) | 0.95 (0.90–1.01) | 1.03 (0.97–1.09) | |
1.01 (0.98–1.04) | 1.02 (0.95–1.08) | 0.98 (0.91–1.04) | |
1.03 (1.00–1.06) | 1.05 (0.97–1.12) | 1.00 (0.92–1.08) | |
Allele score | 1.02 (1.00–1.04) | 1.00 (0.96–1.03) | 1.00 (0.96–1.04) |
|
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0.97 (0.95–1.00) | 0.93 (0.88–0.99) | 1.00 (0.94–1.06) | |
0.99 (0.96–1.03) | 0.95 (0.89–1.01) | 0.99 (0.92–1.06) | |
0.98 (0.95–1.02) | 1.03 (0.96–1.11) | 1.01 (0.93–1.10) | |
Allele score | 0.98 (0.97–1.00) | 0.96 (0.93–1.00) | 1.00 (0.96–1.04) |
|
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−0.02 (−0.19 to 0.15) | 0.06 (−0.38 to 0.50) | 0.09 (−0.26 to 0.44) | |
0.10 (−0.10 to 0.30) | 0.15 (−0.36 to 0.65) | 0.25 (−0.14 to 0.63) | |
−0.05 (−0.27 to 0.16) | 0.69 (0.11–1.27) | 0.04 (−0.41 to 0.50) | |
Allele score | 0.02 (−0.10 to 0.13) | 0.24 (−0.05 to 0.53) | 0.13 (−0.09 to 0.36) |
|
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0.00 (−0.03 to 0.03) | 0.01 (−0.14 to 0.15) | 0.02 (−0.04 to 0.08) | |
−0.01 (−0.04 to 0.02) | 0.05 (−0.12 to 0.21) | −0.03 (−0.09 to 0.04) | |
0.02 (−0.02 to 0.05) | −0.16 (−0.35 to 0.03) | 0.05 (−0.03 to 0.12) | |
Allele score | 0.00 (−0.02 to 0.02) | −0.02 (−0.12 to 0.08) | 0.01 (−0.03 to 0.05) |
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0.99 (0.96–1.02) | 0.92 (0.85–1.01) | 0.95 (0.86–1.04) | |
1.00 (0.97–1.04) | 0.93 (0.84–1.03) | 0.94 (0.84–1.05) | |
1.00 (0.96–1.04) | 0.95 (0.85–1.06) | 0.91 (0.80–1.04) | |
Allele score | 0.99 (0.97–1.02) | 0.93 (0.88–0.99) | 0.94 (0.88–1.00) |
|
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0.99 (0.96–1.03) | 0.98 (0.85–1.13) | 0.99 (0.93–1.06) | |
0.98 (0.95–1.02) | 0.96 (0.81–1.13) | 1.03 (0.95–1.11) | |
0.94 (0.91–0.98) | 0.90 (0.75–1.08) | 1.00 (0.91–1.10) | |
Allele score | 0.98 (0.96–1.00) | 0.96 (0.87–1.05) | 1.01 (0.96–1.05) |
Drinking represented by <14/21; ≥14/21 units per week for women/men at the time of examination. Education represented by less or more than 13 y schooling. Income represented by annual income less or more than 400,000DKK. Event time is absolute difference between age at IHD event and age at measurement of BMI (y).
In aggregate, these data suggest that several factors likely could confound the observational BMI-IHD association. In contrast, it is unlikely that these same factors should confound the instrumental variable analyses assessing the causal BMI-IHD association, as these factors were not associated with genotype or allele score.
In the CGPS the OR for IHD per risk allele, that is per 0.28 kg/m2 increase, was 1.02 (95% CI 0.98–1.05), in the CCHS 1.05 (95% CI 1.01–1.11), and in the CIHDS 1.03 (95% CI 1.00–1.07). The meta-analysed OR per risk allele was 1.03 (95% CI 1.01–1.05), with no evidence of between-study heterogeneity (
Left
An instrumental variable analysis using allele score examines the causal effect of a lifecourse change in BMI on the risk of IHD. The instrumental variable estimate of the causal relationship between a 4 kg/m2 increase in BMI and IHD showed an OR of 1.31 (95% CI 0.76–2.26) in the CGPS, 2.11 (95% CI 1.05–4.24) in the CCHS, and 1.46 (95% CI 0.96–2.24) in the CIHDS. The meta-analysed causal OR was 1.52 (95% CI 1.12–2.05), with no evidence of between-study heterogeneity (
Instrumental variable estimates based on the use of individual SNPs gave broadly similar results, but with reduced statistical power (
As the relationships between BMI and health outcomes can be attenuated with age, we performed subgroup analyses in individuals above and below age 60 y. There was evidence of smaller observational ORs for IHD in those above versus below 60 y (
In summary, in observational analyses for every 4 kg/m2 increase in BMI, the OR for IHD was 1.26 (95% CI 1.19–1.34), corresponding to a 26% increased IHD risk (
In two large studies of the general population, observational estimates suggested a 26% increase in risk of IHD for every 4 kg/m2 increase in BMI. A unit change of an allelic score combining genotypes from three established genetic associates of BMI was associated with a 0.28 kg/m2 change in BMI, a change neither correlated with classic confounding features nor affected by reverse causation. Using this as an instrument for lifecourse BMI difference in 75,627 Danish individuals from the same general population studies and a further case-control series, instrumental variable analysis was employed to re-estimate the causal relationship between BMI and IHD risk. In doing this, estimates suggest that the same increase in BMI is causally related to an increased risk of IHD consistent with observational estimates, if not greater. Whilst features such as statin use appear to impact observational estimates, those based on genetic instruments for BMI appear consistent across sub-analyses. Importantly, we not only qualify the likely causal role of BMI (rather than just an observational associate), we are able to quantify this effect. It should be mentioned that the causal relationship between increased BMI and increased risk of IHD may be realised through intermediate factors like hypertension, dyslipidemia, and type 2 diabetes.
The design of this study and the use of genetic variation as a proxy marker for elevated BMI lead to additional discussion points. Firstly, the use of effective instruments for BMI has allowed for forms of study bias to be effectively accounted for. For example, observational relationships between BMI and IHD risk tend to increase with time from BMI measurement largely as a result of tracking and the natural progression of BMI with age
Despite these benefits, there are a number of potential limitations in Mendelian randomisation studies like the present one
In the context of available evidence concerning the causal role of BMI as an intermediate risk factor for IHD, we can speculate that the explanation for the causal association is straightforward: increased BMI contributes causally to well-known cardiovascular risk factors including hypertension, dyslipidemia, and type 2 diabetes, factors that may then go on to cause the observed increased risk of IHD. Similar evidence supporting the role of elevated BMI in the generation of a common risk profile is emerging
In conclusion, for every 4 kg/m2 increase in BMI, observational estimates suggested a 26% increase in IHD risk with instrumental variable analysis suggesting a causal 52% increase in IHD risk. These data add novel evidence to support a causal link between increased BMI and increased IHD risk, while the mechanism of this effect is likely to be operating through intermediate factors. In the context of recent, high impact, observational findings, this work has important policy implications for public health given the continuous nature of the BMI-IHD association, the modifiable nature of BMI, and the likely benefits of reducing BMI even by moderate levels. Finally, this analysis demonstrates the value of observational studies and their ability to provide essentially unbiased results because of inclusion of genetic data avoiding confounding, reverse causation, and bias.
Meta-analysis forest plots of observational and instrumental variable estimates using a weighted allele score of the relationship between IHD and standardised BMI.
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Meta-analysis forest plots of instrumental variable causal estimates of the relationship between IHD and BMI stratified by genotype.
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Meta-analysis of logistic structural mean model causal OR estimates of IHD risk per 4 kg/m2 increase in BMI in the CGPS and CCHS. Logistic structural mean models fitted using
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Means and standard deviations of BMI by 5-y age band and sex used to generate standardised BMI in the CGPS and CCHS.
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Associations of potential confounders with standardised BMI in the three studies.
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Associations of potential confounders with IHD in the three studies.
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We thank Dorthe Uldall Andersen and Anne Bank for technical assistance.
body mass index
Copenhagen City Heart Study
Copenhagen General Population Study
Copenhagen Ischaemic Heart Disease Study
ischemic heart disease