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1.  Effects of BMI, Fat Mass, and Lean Mass on Asthma in Childhood: A Mendelian Randomization Study 
PLoS Medicine  2014;11(7):e1001669.
In this study, Granell and colleagues used Mendelian randomization to investigate causal effects of BMI, fat mass, and lean mass on current asthma at age 7½ years in the Avon Longitudinal Study of Parents and Children (ALSPAC) and found that higher BMI increases the risk of asthma in mid-childhood.
Please see later in the article for the Editors' Summary
Background
Observational studies have reported associations between body mass index (BMI) and asthma, but confounding and reverse causality remain plausible explanations. We aim to investigate evidence for a causal effect of BMI on asthma using a Mendelian randomization approach.
Methods and Findings
We used Mendelian randomization to investigate causal effects of BMI, fat mass, and lean mass on current asthma at age 7½ y in the Avon Longitudinal Study of Parents and Children (ALSPAC). A weighted allele score based on 32 independent BMI-related single nucleotide polymorphisms (SNPs) was derived from external data, and associations with BMI, fat mass, lean mass, and asthma were estimated. We derived instrumental variable (IV) estimates of causal risk ratios (RRs). 4,835 children had available data on BMI-associated SNPs, asthma, and BMI. The weighted allele score was strongly associated with BMI, fat mass, and lean mass (all p-values<0.001) and with childhood asthma (RR 2.56, 95% CI 1.38–4.76 per unit score, p = 0.003). The estimated causal RR for the effect of BMI on asthma was 1.55 (95% CI 1.16–2.07) per kg/m2, p = 0.003. This effect appeared stronger for non-atopic (1.90, 95% CI 1.19–3.03) than for atopic asthma (1.37, 95% CI 0.89–2.11) though there was little evidence of heterogeneity (p = 0.31). The estimated causal RRs for the effects of fat mass and lean mass on asthma were 1.41 (95% CI 1.11–1.79) per 0.5 kg and 2.25 (95% CI 1.23–4.11) per kg, respectively. The possibility of genetic pleiotropy could not be discounted completely; however, additional IV analyses using FTO variant rs1558902 and the other BMI-related SNPs separately provided similar causal effects with wider confidence intervals. Loss of follow-up was unlikely to bias the estimated effects.
Conclusions
Higher BMI increases the risk of asthma in mid-childhood. Higher BMI may have contributed to the increase in asthma risk toward the end of the 20th century.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
The global burden of asthma, a chronic (long-term) condition caused by inflammation of the airways (the tubes that carry air in and out of the lungs), has been rising steadily over the past few decades. It is estimated that, nowadays, 200–300 million adults and children worldwide are affected by asthma. Although asthma can develop at any age, it is often diagnosed in childhood—asthma is the most common chronic disease in children. In people with asthma, the airways can react very strongly to allergens such as animal fur or to irritants such as cigarette smoke, becoming narrower so that less air can enter the lungs. Exercise, cold air, and infections can also trigger asthma attacks, which can be fatal. The symptoms of asthma include wheezing, coughing, chest tightness, and shortness of breath. Asthma cannot be cured, but drugs can relieve its symptoms and prevent acute asthma attacks.
Why Was This Study Done?
We cannot halt the ongoing rise in global asthma rates without understanding the causes of asthma. Some experts think obesity may be one cause of asthma. Obesity, like asthma, is increasingly common, and observational studies (investigations that ask whether individuals exposed to a suspected risk factor for a condition develop that condition more often than unexposed individuals) in children have reported that body mass index (BMI, an indicator of body fat calculated by dividing a person's weight in kilograms by their height in meters squared) is positively associated with asthma. Observational studies cannot prove that obesity causes asthma because of “confounding.” Overweight children with asthma may share another unknown characteristic (confounder) that actually causes both obesity and asthma. Moreover, children with asthma may be less active than unaffected children, so they become overweight (reverse causality). Here, the researchers use “Mendelian randomization” to assess whether BMI has a causal effect on asthma. In Mendelian randomization, causality is inferred from associations between genetic variants that mimic the effect of a modifiable risk factor and the outcome of interest. Because gene variants are inherited randomly, they are not prone to confounding and are free from reverse causation. So, if a higher BMI leads to asthma, genetic variants associated with increased BMI should be associated with an increased risk of asthma.
What Did the Researchers Do and Find?
The researchers investigated causal effects of BMI, fat mass, and lean mass on current asthma at age 7½ years in 4,835 children enrolled in the Avon Longitudinal Study of Parents and Children (ALSPAC, a long-term health project that started in 1991). They calculated an allele score for each child based on 32 BMI-related genetic variants, and estimated associations between this score and BMI, fat mass and lean mass (both measured using a special type of X-ray scanner; in children BMI is not a good indicator of “fatness”), and asthma. They report that the allele score was strongly associated with BMI, fat mass, and lean mass, and with childhood asthma. The estimated causal relative risk (risk ratio) for the effect of BMI on asthma was 1.55 per kg/m2. That is, the relative risk of asthma increased by 55% for every extra unit of BMI. The estimated causal relative risks for the effects of fat mass and lean mass on asthma were 1.41 per 0.5 kg and 2.25 per kg, respectively.
What Do These Findings Mean?
These findings suggest that a higher BMI increases the risk of asthma in mid-childhood and that global increases in BMI toward the end of the 20th century may have contributed to the global increase in asthma that occurred at the same time. It is possible that the observed association between BMI and asthma reported in this study is underpinned by “genetic pleiotropy” (a potential limitation of all Mendelian randomization analyses). That is, some of the genetic variants included in the BMI allele score could conceivably also increase the risk of asthma. Nevertheless, these findings suggest that public health interventions designed to reduce obesity may also help to limit the global rise in asthma.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001669.
The US Centers for Disease Control and Prevention provides information on asthma and on all aspects of overweight and obesity (in English and Spanish)
The World Health Organization provides information on asthma and on obesity (in several languages)
The UK National Health Service Choices website provides information about asthma, about asthma in children, and about obesity (including real stories)
The Global Asthma Report 2011 is available
The Global Initiative for Asthma released its updated Global Strategy for Asthma Management and Prevention on World Asthma Day 2014
Information about the Avon Longitudinal Study of Parents and Children is available
MedlinePlus provides links to further information on obesity in children, on asthma, and on asthma in children (in English and Spanish
Wikipedia has a page on Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
doi:10.1371/journal.pmed.1001669
PMCID: PMC4077660  PMID: 24983943
2.  Genetic Variants at Chromosome 9p21 and Risk of First Versus Subsequent Coronary Heart Disease Events 
Objectives
The purpose of this analysis was to compare the association between variants at the chromosome 9p21 locus (Ch9p21) and risk of first versus subsequent coronary heart disease (CHD) events through systematic review and meta-analysis.
Background
Ch9p21 is a recognized risk factor for a first CHD event. However, its association with risk of subsequent events in patients with established CHD is less clear.
Methods
We searched PubMed and EMBASE for prospective studies reporting association of Ch9p21 with incident CHD events and extracted information on cohort type (individuals without prior CHD or individuals with established CHD) and effect estimates for risk of events.
Results
We identified 31 cohorts reporting on 193,372 individuals. Among the 16 cohorts of individuals without prior CHD (n = 168,209), there were 15,664 first CHD events. Ch9p21 was associated with a pooled hazard ratio (HR) of a first event of 1.19 (95% confidence interval: 1.17 to 1.22) per risk allele. In individuals with established CHD (n = 25,163), there were 4,436 subsequent events providing >99% and 91% power to detect a per-allele HR of 1.19 or 1.10, respectively. The pooled HR for subsequent events was 1.01 (95% confidence interval: 0.97 to 1.06) per risk allele. There was strong evidence of heterogeneity between the effect estimates for first and subsequent events (p value for heterogeneity = 5.6 × 10−11). We found no evidence for biases to account for these findings.
Conclusions
Ch9p21 shows differential association with risk of first versus subsequent CHD events. This has implications for genetic risk prediction in patients with established CHD and for mechanistic understanding of how Ch9p21 influences risk of CHD.
doi:10.1016/j.jacc.2014.01.065
PMCID: PMC4035794  PMID: 24607648
coronary heart disease; genomics; incident; subsequent; 9p21; Ch9p21, chromosome 9p21 locus; CHD, coronary heart disease; CI, confidence interval; HR, hazard ratio; MI, myocardial infarction
3.  Lack of Identification in Semiparametric Instrumental Variable Models With Binary Outcomes 
American Journal of Epidemiology  2014;180(1):111-119.
A parameter in a statistical model is identified if its value can be uniquely determined from the distribution of the observable data. We consider the context of an instrumental variable analysis with a binary outcome for estimating a causal risk ratio. The semiparametric generalized method of moments and structural mean model frameworks use estimating equations for parameter estimation. In this paper, we demonstrate that lack of identification can occur in either of these frameworks, especially if the instrument is weak. In particular, the estimating equations may have no solution or multiple solutions. We investigate the relationship between the strength of the instrument and the proportion of simulated data sets for which there is a unique solution of the estimating equations. We see that this proportion does not appear to depend greatly on the sample size, particularly for weak instruments (ρ2 ≤ 0.01). Poor identification was observed in a considerable proportion of simulated data sets for instruments explaining up to 10% of the variance in the exposure with sample sizes up to 1 million. In an applied example considering the causal effect of body mass index (weight (kg)/height (m)2) on the probability of early menarche, estimates and standard errors from an automated optimization routine were misleading.
doi:10.1093/aje/kwu107
PMCID: PMC4070936  PMID: 24859275
Avon Longitudinal Study of Parents and Children; generalized method of moments; identifiability; identification; instrumental variables; semiparametric methods; structural mean model; weak instruments
4.  Secretory Phospholipase A2-IIA and Cardiovascular Disease 
Holmes, Michael V. | Simon, Tabassome | Exeter, Holly J. | Folkersen, Lasse | Asselbergs, Folkert W. | Guardiola, Montse | Cooper, Jackie A. | Palmen, Jutta | Hubacek, Jaroslav A. | Carruthers, Kathryn F. | Horne, Benjamin D. | Brunisholz, Kimberly D. | Mega, Jessica L. | van Iperen, Erik P.A. | Li, Mingyao | Leusink, Maarten | Trompet, Stella | Verschuren, Jeffrey J.W. | Hovingh, G. Kees | Dehghan, Abbas | Nelson, Christopher P. | Kotti, Salma | Danchin, Nicolas | Scholz, Markus | Haase, Christiane L. | Rothenbacher, Dietrich | Swerdlow, Daniel I. | Kuchenbaecker, Karoline B. | Staines-Urias, Eleonora | Goel, Anuj | van 't Hooft, Ferdinand | Gertow, Karl | de Faire, Ulf | Panayiotou, Andrie G. | Tremoli, Elena | Baldassarre, Damiano | Veglia, Fabrizio | Holdt, Lesca M. | Beutner, Frank | Gansevoort, Ron T. | Navis, Gerjan J. | Mateo Leach, Irene | Breitling, Lutz P. | Brenner, Hermann | Thiery, Joachim | Dallmeier, Dhayana | Franco-Cereceda, Anders | Boer, Jolanda M.A. | Stephens, Jeffrey W. | Hofker, Marten H. | Tedgui, Alain | Hofman, Albert | Uitterlinden, André G. | Adamkova, Vera | Pitha, Jan | Onland-Moret, N. Charlotte | Cramer, Maarten J. | Nathoe, Hendrik M. | Spiering, Wilko | Klungel, Olaf H. | Kumari, Meena | Whincup, Peter H. | Morrow, David A. | Braund, Peter S. | Hall, Alistair S. | Olsson, Anders G. | Doevendans, Pieter A. | Trip, Mieke D. | Tobin, Martin D. | Hamsten, Anders | Watkins, Hugh | Koenig, Wolfgang | Nicolaides, Andrew N. | Teupser, Daniel | Day, Ian N.M. | Carlquist, John F. | Gaunt, Tom R. | Ford, Ian | Sattar, Naveed | Tsimikas, Sotirios | Schwartz, Gregory G. | Lawlor, Debbie A. | Morris, Richard W. | Sandhu, Manjinder S. | Poledne, Rudolf | Maitland-van der Zee, Anke H. | Khaw, Kay-Tee | Keating, Brendan J. | van der Harst, Pim | Price, Jackie F. | Mehta, Shamir R. | Yusuf, Salim | Witteman, Jaqueline C.M. | Franco, Oscar H. | Jukema, J. Wouter | de Knijff, Peter | Tybjaerg-Hansen, Anne | Rader, Daniel J. | Farrall, Martin | Samani, Nilesh J. | Kivimaki, Mika | Fox, Keith A.A. | Humphries, Steve E. | Anderson, Jeffrey L. | Boekholdt, S. Matthijs | Palmer, Tom M. | Eriksson, Per | Paré, Guillaume | Hingorani, Aroon D. | Sabatine, Marc S. | Mallat, Ziad | Casas, Juan P. | Talmud, Philippa J.
Objectives
This study sought to investigate the role of secretory phospholipase A2 (sPLA2)-IIA in cardiovascular disease.
Background
Higher circulating levels of sPLA2-IIA mass or sPLA2 enzyme activity have been associated with increased risk of cardiovascular events. However, it is not clear if this association is causal. A recent phase III clinical trial of an sPLA2 inhibitor (varespladib) was stopped prematurely for lack of efficacy.
Methods
We conducted a Mendelian randomization meta-analysis of 19 general population studies (8,021 incident, 7,513 prevalent major vascular events [MVE] in 74,683 individuals) and 10 acute coronary syndrome (ACS) cohorts (2,520 recurrent MVE in 18,355 individuals) using rs11573156, a variant in PLA2G2A encoding the sPLA2-IIA isoenzyme, as an instrumental variable.
Results
PLA2G2A rs11573156 C allele associated with lower circulating sPLA2-IIA mass (38% to 44%) and sPLA2 enzyme activity (3% to 23%) per C allele. The odds ratio (OR) for MVE per rs11573156 C allele was 1.02 (95% confidence interval [CI]: 0.98 to 1.06) in general populations and 0.96 (95% CI: 0.90 to 1.03) in ACS cohorts. In the general population studies, the OR derived from the genetic instrumental variable analysis for MVE for a 1-log unit lower sPLA2-IIA mass was 1.04 (95% CI: 0.96 to 1.13), and differed from the non-genetic observational estimate (OR: 0.69; 95% CI: 0.61 to 0.79). In the ACS cohorts, both the genetic instrumental variable and observational ORs showed a null association with MVE. Instrumental variable analysis failed to show associations between sPLA2 enzyme activity and MVE.
Conclusions
Reducing sPLA2-IIA mass is unlikely to be a useful therapeutic goal for preventing cardiovascular events.
doi:10.1016/j.jacc.2013.06.044
PMCID: PMC3826105  PMID: 23916927
cardiovascular diseases; drug development; epidemiology; genetics; Mendelian randomization; ACS, acute coronary syndrome(s); CI, confidence interval; LDL-C, low-density lipoprotein cholesterol; MI, myocardial infarction; MVE, major vascular events; OR, odds ratio; RCT, randomized clinical trial; SNP, single-nucleotide polymorphism; sPLA2, secretory phospholipase A2
5.  The Effect of Elevated Body Mass Index on Ischemic Heart Disease Risk: Causal Estimates from a Mendelian Randomisation Approach 
PLoS Medicine  2012;9(5):e1001212.
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.
Background
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.
Methods and Findings
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 FTO(rs9939609), MC4R(rs17782313), and TMEM18(rs6548238). Using genotypes as a combined allele score in instrumental variable analyses, the causal odds ratio (OR) between BMI and IHD was estimated and compared with observational estimates. The allele score-BMI and the allele score-IHD associations used to estimate the causal OR were also calculated individually. In observational analyses the OR for IHD was 1.26 (95% CI 1.19–1.34) for every 4 kg/m2 increase in BMI. A one-unit allele score increase associated with a 0.28 kg/m2 (95 CI% 0.20–0.36) increase in BMI and an OR for IHD of 1.03 (95% CI 1.01–1.05) (corresponding to an average 1.68 kg/m2 BMI increase and 18% increase in the odds of IHD for those carrying all six BMI increasing alleles). In instrumental variable analysis using the same allele score the causal IHD OR for a 4 kg/m2 increase in BMI was 1.52 (95% CI 1.12–2.05).
Conclusions
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.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
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.
Why Was This Study Done?
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—FTO(rs9939609), MC4R(rs17782313), and TMEM18(rs6548238)—which have the largest single genetic variant associations with BMI were used in this study. Given that gene variants are inherited essentially randomly with respect to conventional confounding factors and are not subject reverse causation, use of these as instruments (or proxy measures) for variation in BMI as a risk factor (as opposed to measuring BMI directly) allows researchers to comment on whether obesity is causally involved in IHD.
What Did the Researchers Do and Find?
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%.
What Do These Findings Mean?
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.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001212.
The American Heart Association provides information about IHD and tips on keeping the heart healthy, including weight management; it also provides personal stories about IHD
The UK National Health Service Choices website provides information about IHD, including information on prevention and personal stories about IHD
Information is available from the British Heart Foundation on heart disease and keeping the heart healthy
The US National Heart Lung and Blood Institute also provides information on IHD (in English and Spanish)
MedlinePlus provides links to many other sources of information on IHD (in English and Spanish)
Wikipedia has a page on Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
doi:10.1371/journal.pmed.1001212
PMCID: PMC3341326  PMID: 22563304
6.  Association Between Genetic Variants on Chromosome 15q25 Locus and Objective Measures of Tobacco Exposure 
Background
Two single-nucleotide polymorphisms, rs1051730 and rs16969968, located within the nicotinic acetylcholine receptor gene cluster on chromosome 15q25 locus, are associated with heaviness of smoking, risk for lung cancer, and other smoking-related health outcomes. Previous studies have typically relied on self-reported smoking behavior, which may not fully capture interindividual variation in tobacco exposure.
Methods
We investigated the association of rs1051730 and rs16969968 genotype (referred to as rs1051730–rs16969968, because these are in perfect linkage disequilibrium and interchangeable) with both self-reported daily cigarette consumption and biochemically measured plasma or serum cotinine levels among cigarette smokers. Summary estimates and descriptive statistical data for 12 364 subjects were obtained from six independent studies, and 2932 smokers were included in the analyses. Linear regression was used to calculate the per-allele association of rs1051730–rs16969968 genotype with cigarette consumption and cotinine levels in current smokers for each study. Meta-analysis of per-allele associations was conducted using a random effects method. The likely resulting association between genotype and lung cancer risk was assessed using published data on the association between cotinine levels and lung cancer risk. All statistical tests were two-sided.
Results
Pooled per-allele associations showed that current smokers with one or two copies of the rs1051730–rs16969968 risk allele had increased self-reported cigarette consumption (mean increase in unadjusted number of cigarettes per day per allele = 1.0 cigarette, 95% confidence interval [CI] = 0.57 to 1.43 cigarettes, P = 5.22 × 10−6) and cotinine levels (mean increase in unadjusted cotinine levels per allele = 138.72 nmol/L, 95% CI = 97.91 to 179.53 nmol/L, P = 2.71 × 10−11). The increase in cotinine levels indicated an increased risk of lung cancer with each additional copy of the rs1051730–rs16969968 risk allele (per-allele odds ratio = 1.31, 95% CI = 1.21 to 1.42).
Conclusions
Our data show a stronger association of rs1051730–rs16969968 genotype with objective measures of tobacco exposure compared with self-reported cigarette consumption. The association of these variants with lung cancer risk is likely to be mediated largely, if not wholly, via tobacco exposure.
doi:10.1093/jnci/djs191
PMCID: PMC3352832  PMID: 22534784
7.  Mendelian Randomization Studies Do Not Support a Role for Raised Circulating Triglyceride Levels Influencing Type 2 Diabetes, Glucose Levels, or Insulin Resistance 
Diabetes  2011;60(3):1008-1018.
OBJECTIVE
The causal nature of associations between circulating triglycerides, insulin resistance, and type 2 diabetes is unclear. We aimed to use Mendelian randomization to test the hypothesis that raised circulating triglyceride levels causally influence the risk of type 2 diabetes and raise normal fasting glucose levels and hepatic insulin resistance.
RESEARCH DESIGN AND METHODS
We tested 10 common genetic variants robustly associated with circulating triglyceride levels against the type 2 diabetes status in 5,637 case and 6,860 control subjects and four continuous outcomes (reflecting glycemia and hepatic insulin resistance) in 8,271 nondiabetic individuals from four studies.
RESULTS
Individuals carrying greater numbers of triglyceride-raising alleles had increased circulating triglyceride levels (SD 0.59 [95% CI 0.52–0.65] difference between the 20% of individuals with the most alleles and the 20% with the fewest alleles). There was no evidence that the carriers of greater numbers of triglyceride-raising alleles were at increased risk of type 2 diabetes (per weighted allele odds ratio [OR] 0.99 [95% CI 0.97–1.01]; P = 0.26). In nondiabetic individuals, there was no evidence that carriers of greater numbers of triglyceride-raising alleles had increased fasting insulin levels (SD 0.00 per weighted allele [95% CI −0.01 to 0.02]; P = 0.72) or increased fasting glucose levels (0.00 [−0.01 to 0.01]; P = 0.88). Instrumental variable analyses confirmed that genetically raised circulating triglyceride levels were not associated with increased diabetes risk, fasting glucose, or fasting insulin and, for diabetes, showed a trend toward a protective association (OR per 1-SD increase in log10 triglycerides: 0.61 [95% CI 0.45–0.83]; P = 0.002).
CONCLUSIONS
Genetically raised circulating triglyceride levels do not increase the risk of type 2 diabetes or raise fasting glucose or fasting insulin levels in nondiabetic individuals. One explanation for our results is that raised circulating triglycerides are predominantly secondary to the diabetes disease process rather than causal.
doi:10.2337/db10-1317
PMCID: PMC3046819  PMID: 21282362
8.  Using multiple genetic variants as instrumental variables for modifiable risk factors 
Mendelian randomisation analyses use genetic variants as instrumental variables (IVs) to estimate causal effects of modifiable risk factors on disease outcomes. Genetic variants typically explain a small proportion of the variability in risk factors; hence Mendelian randomisation analyses can require large sample sizes. However, an increasing number of genetic variants have been found to be robustly associated with disease-related outcomes in genome-wide association studies. Use of multiple instruments can improve the precision of IV estimates, and also permit examination of underlying IV assumptions. We discuss the use of multiple genetic variants in Mendelian randomisation analyses with continuous outcome variables where all relationships are assumed to be linear. We describe possible violations of IV assumptions, and how multiple instrument analyses can be used to identify them. We present an example using four adiposity-associated genetic variants as IVs for the causal effect of fat mass on bone density, using data on 5509 children enrolled in the ALSPAC birth cohort study. We also use simulation studies to examine the effect of different sets of IVs on precision and bias. When each instrument independently explains variability in the risk factor, use of multiple instruments increases the precision of IV estimates. However, inclusion of weak instruments could increase finite sample bias. Missing data on multiple genetic variants can diminish the available sample size, compared with single instrument analyses. In simulations with additive genotype-risk factor effects, IV estimates using a weighted allele score had similar properties to estimates using multiple instruments. Under the correct conditions, multiple instrument analyses are a promising approach for Mendelian randomisation studies. Further research is required into multiple imputation methods to address missing data issues in IV estimation.
doi:10.1177/0962280210394459
PMCID: PMC3917707  PMID: 21216802
causal inference; econometrics; epidemiology; genetics; instrumental variables; Mendelian randomisation
9.  The causal roles of vitamin B12 and transcobalamin in prostate cancer: can Mendelian randomization analysis provide definitive answers? 
Circulating vitamin B12 (cobalamin/B12) and total transcobalamin (tTC) have been associated with increased and reduced risk, respectively, of prostate cancer. Mendelian randomization has the potential to determine whether these are causal associations. We estimated associations of single nucleotide polymorphisms in B12-related genes (MTR, MTRR, FUT2, TCN2, TCN1, CUBN, and MUT) with plasma concentrations of B12, tTC, holo-transcobalamin, holo-haptocorrin, folate, and homocysteine and with prostate cancer risk in a case-control study (913 cases, 895 controls) nested within the UK-wide population-based ProtecT study of prostate cancer in men age 45-69 years. Instrumental variable (IV) analysis was used to estimate odds ratios for effects of B12 and tTC on prostate cancer. We observed that B12 was lower in men with FUT2 204G>A (rs492602), CUBN 758C>T (rs1801222) and MUT 1595G>A (rs1141321) alleles (Ptrend<0.001); tTC was lower in men with the TCN2 776C>G (rs1801198) allele (Ptrend<0.001). FUT2 204G>A and CUBN 758C>T were selected as instruments for B12; TCN2 776C>G for tTC. Conventional and IV estimates for the association of loge(B12) with prostate cancer were: OR=1.17 (95% CI 0.90-1.51), P=0.2 and OR=0.60 (0.16-2.15), P=0.4, respectively. Conventional and IV estimates for the association of loge(tTC) with prostate cancer were: OR=0.81 (0.54-1.20), P=0.3 and OR=0.41 (0.13-1.32), P=0.1, respectively. Confidence intervals around the IV estimates in our study were too wide to allow robust inference. Sample size estimates based on our data indicated that Mendelian randomization in this context requires much larger studies or multiple genetic variants that explain all of the variance in the intermediate phenotype.
PMCID: PMC3243448  PMID: 22199995
Vitamin B12/cobalamin; transcobalamin; prostate cancer; Mendelian Randomization
10.  Mendelian Randomization Studies do not Support a Role for Raised Circulating Triglyceride Levels influencing Type 2 Diabetes, Glucose Levels, or Insulin Resistance 
Diabetes  2011;60(3):1008-1018.
Objective
The causal nature of associations between circulating triglycerides, insulin resistance and type 2 diabetes is unclear. We aimed to use Mendelian randomization to test the hypothesis that raised circulating triglyceride levels causally influence the risk of type 2 diabetes, raised normal fasting glucose levels, and hepatic insulin resistance.
Research design and methods
We tested 10 common genetic variants robustly associated with circulating triglyceride levels against type 2 diabetes status in 5637 cases, 6860 controls, and four continuous outcomes (reflecting glycemia and hepatic insulin resistance) in 8271 non-diabetic individuals from four studies.
Results
Individuals carrying greater numbers of triglyceride-raising alleles had increased circulating triglyceride levels (0.59 SD [95% CI: 0.52, 0.65] difference between the 20% of individuals with the most alleles and the 20% with the fewest alleles). There was no evidence that carriers of greater numbers of triglyceride-raising alleles were at increased risk of type 2 diabetes (per weighted allele odds ratio (OR) 0.99 [95% CI: 0.97, 1.01]; P = 0.26). In non-diabetic individuals, there was no evidence that carriers of greater numbers of triglyceride-raising alleles had increased fasting insulin levels (0.00 SD per weighted allele [95% CI: −0.01, 0.02]; P = 0.72) or increased fasting glucose levels (0.00 SD per weighted allele [95% CI: −0.01, 0.01]; P = 0.88). Instrumental variable analyses confirmed that genetically raised circulating triglyceride levels were not associated with increased diabetes risk, fasting glucose or fasting insulin, and, for diabetes, showed a trend towards a protective association (OR per 1 SD increase in log10-triglycerides: 0.61 [95% CI: 0.45, 0.83]; P = 0.002).
Conclusion
Genetically raised circulating triglyceride levels do not increase the risk of type 2 diabetes, or raise fasting glucose or fasting insulin levels in non-diabetic individuals. One explanation for our results is that raised circulating triglycerides are predominantly secondary to the diabetes disease process rather than causal.
doi:10.2337/db10-1317
PMCID: PMC3046819  PMID: 21282362
11.  Novel methods to deal with publication biases: secondary analysis of antidepressant trials in the FDA trial registry database and related journal publications 
Objective To assess the performance of novel contour enhanced funnel plots and a regression based adjustment method to detect and adjust for publication biases.
Design Secondary analysis of a published systematic literature review.
Data sources Placebo controlled trials of antidepressants previously submitted to the US Food and Drug Administration (FDA) and matching journal publications.
Methods Publication biases were identified using novel contour enhanced funnel plots, a regression based adjustment method, Egger’s test, and the trim and fill method. Results were compared with a meta-analysis of the gold standard data submitted to the FDA.
Results Severe asymmetry was observed in the contour enhanced funnel plot that appeared to be heavily influenced by the statistical significance of results, suggesting publication biases as the cause of the asymmetry. Applying the regression based adjustment method to the journal data produced a similar pooled effect to that observed by a meta-analysis of the FDA data. Contrasting journal and FDA results suggested that, in addition to other deviations from study protocol, switching from an intention to treat analysis to a per protocol one would contribute to the observed discrepancies between the journal and FDA results.
Conclusion Novel contour enhanced funnel plots and a regression based adjustment method worked convincingly and might have an important part to play in combating publication biases.
doi:10.1136/bmj.b2981
PMCID: PMC2723217  PMID: 19666685
12.  Association of plasma uric acid with ischaemic heart disease and blood pressure: mendelian randomisation analysis of two large cohorts  
Objectives To assess the associations between both uric acid levels and hyperuricaemia, with ischaemic heart disease and blood pressure, and to explore the potentially confounding role of body mass index.
Design Mendelian randomisation analysis, using variation at specific genes (SLC2A9 (rs7442295) as an instrument for uric acid; and FTO (rs9939609), MC4R (rs17782313), and TMEM18 (rs6548238) for body mass index).
Setting Two large, prospective cohort studies in Denmark.
Participants We measured levels of uric acid and related covariables in 58 072 participants from the Copenhagen General Population Study and 10 602 from the Copenhagen City Heart Study, comprising 4890 and 2282 cases of ischaemic heart disease, respectively.
Main outcome Blood pressure and prospectively assessed ischaemic heart disease.
Results Estimates confirmed known observational associations between plasma uric acid and hyperuricaemia with risk of ischaemic heart disease and diastolic and systolic blood pressure. However, when using genotypic instruments for uric acid and hyperuricaemia, we saw no evidence for causal associations between uric acid, ischaemic heart disease, and blood pressure. We used genetic instruments to investigate body mass index as a potentially confounding factor in observational associations, and saw a causal effect on uric acid levels. Every four unit increase of body mass index saw a rise in uric acid of 0.03 mmol/L (95% confidence interval 0.02 to 0.04), and an increase in risk of hyperuricaemia of 7.5% (3.9% to 11.1%).
Conclusion By contrast with observational findings, there is no strong evidence for causal associations between uric acid and ischaemic heart disease or blood pressure. However, evidence supports a causal effect between body mass index and uric acid level and hyperuricaemia. This finding strongly suggests body mass index as a confounder in observational associations, and suggests a role for elevated body mass index or obesity in the development of uric acid related conditions.
doi:10.1136/bmj.f4262
PMCID: PMC3715134  PMID: 23869090
13.  Alcohol consumption and cognitive performance: a Mendelian randomization study 
Addiction (Abingdon, England)  2014;109(9):1462-1471.
Aims
To use Mendelian randomization to assess whether alcohol intake was causally associated with cognitive function.
Design
Mendelian randomization using a genetic variant related to alcohol intake (ADH1B rs1229984) was used to obtain unbiased estimates of the association between alcohol intake and cognitive performance.
Setting
Europe.
Participants
More than 34 000 adults.
Measurements
Any versus no alcohol intake and units of intake in the previous week was measured by questionnaire. Cognitive function was assessed in terms of immediate and delayed word recall, verbal fluency and processing speed.
Findings
Having consumed any versus no alcohol was associated with higher scores by 0.17 standard deviations (SD) [95% confidence interval (CI) = 0.15, 0.20] for immediate recall, 0.17 SD (95% CI = 0.14, 0.19) for delayed recall, 0.17 SD (95% CI = 0.14, 0.19) for verbal fluency and 0.12 SD (95% CI = 0.09, 0.15) for processing speed. The minor allele of rs1229984 was associated with reduced odds of consuming any alcohol (odds ratio = 0.87; 95% CI = 0.80, 0.95; P = 0.001; R2 = 0.1%; F-statistic = 47). In Mendelian randomization analysis, the minor allele was not associated with any cognitive test score, and instrumental variable analysis suggested no causal association between alcohol consumption and cognition: −0.74 SD (95% CI = −1.88, 0.41) for immediate recall, −1.09 SD (95% CI = −2.38, 0.21) for delayed recall, −0.63 SD (95% CI = −1.78, 0.53) for verbal fluency and −0.16 SD (95% CI = −1.29, 0.97) for processing speed.
Conclusions
The Mendelian randomization analysis did not provide strong evidence of a causal association between alcohol consumption and cognitive ability.
doi:10.1111/add.12568
PMCID: PMC4309480  PMID: 24716453
ADH1B; alcohol intake; cognition; memory; processing speed; verbal fluency
14.  Association between alcohol and cardiovascular disease: Mendelian randomisation analysis based on individual participant data 
Holmes, Michael V | Dale, Caroline E | Zuccolo, Luisa | Silverwood, Richard J | Guo, Yiran | Ye, Zheng | Prieto-Merino, David | Dehghan, Abbas | Trompet, Stella | Wong, Andrew | Cavadino, Alana | Drogan, Dagmar | Padmanabhan, Sandosh | Li, Shanshan | Yesupriya, Ajay | Leusink, Maarten | Sundstrom, Johan | Hubacek, Jaroslav A | Pikhart, Hynek | Swerdlow, Daniel I | Panayiotou, Andrie G | Borinskaya, Svetlana A | Finan, Chris | Shah, Sonia | Kuchenbaecker, Karoline B | Shah, Tina | Engmann, Jorgen | Folkersen, Lasse | Eriksson, Per | Ricceri, Fulvio | Melander, Olle | Sacerdote, Carlotta | Gamble, Dale M | Rayaprolu, Sruti | Ross, Owen A | McLachlan, Stela | Vikhireva, Olga | Sluijs, Ivonne | Scott, Robert A | Adamkova, Vera | Flicker, Leon | van Bockxmeer, Frank M | Power, Christine | Marques-Vidal, Pedro | Meade, Tom | Marmot, Michael G | Ferro, Jose M | Paulos-Pinheiro, Sofia | Humphries, Steve E | Talmud, Philippa J | Leach, Irene Mateo | Verweij, Niek | Linneberg, Allan | Skaaby, Tea | Doevendans, Pieter A | Cramer, Maarten J | van der Harst, Pim | Klungel, Olaf H | Dowling, Nicole F | Dominiczak, Anna F | Kumari, Meena | Nicolaides, Andrew N | Weikert, Cornelia | Boeing, Heiner | Ebrahim, Shah | Gaunt, Tom R | Price, Jackie F | Lannfelt, Lars | Peasey, Anne | Kubinova, Ruzena | Pajak, Andrzej | Malyutina, Sofia | Voevoda, Mikhail I | Tamosiunas, Abdonas | Maitland-van der Zee, Anke H | Norman, Paul E | Hankey, Graeme J | Bergmann, Manuela M | Hofman, Albert | Franco, Oscar H | Cooper, Jackie | Palmen, Jutta | Spiering, Wilko | de Jong, Pim A | Kuh, Diana | Hardy, Rebecca | Uitterlinden, Andre G | Ikram, M Arfan | Ford, Ian | Hyppönen, Elina | Almeida, Osvaldo P | Wareham, Nicholas J | Khaw, Kay-Tee | Hamsten, Anders | Husemoen, Lise Lotte N | Tjønneland, Anne | Tolstrup, Janne S | Rimm, Eric | Beulens, Joline W J | Verschuren, W M Monique | Onland-Moret, N Charlotte | Hofker, Marten H | Wannamethee, S Goya | Whincup, Peter H | Morris, Richard | Vicente, Astrid M | Watkins, Hugh | Farrall, Martin | Jukema, J Wouter | Meschia, James | Cupples, L Adrienne | Sharp, Stephen J | Fornage, Myriam | Kooperberg, Charles | LaCroix, Andrea Z | Dai, James Y | Lanktree, Matthew B | Siscovick, David S | Jorgenson, Eric | Spring, Bonnie | Coresh, Josef | Li, Yun R | Buxbaum, Sarah G | Schreiner, Pamela J | Ellison, R Curtis | Tsai, Michael Y | Patel, Sanjay R | Redline, Susan | Johnson, Andrew D | Hoogeveen, Ron C | Hakonarson, Hakon | Rotter, Jerome I | Boerwinkle, Eric | de Bakker, Paul I W | Kivimaki, Mika | Asselbergs, Folkert W | Sattar, Naveed | Lawlor, Debbie A | Whittaker, John | Davey Smith, George | Mukamal, Kenneth | Psaty, Bruce M | Wilson, James G | Lange, Leslie A | Hamidovic, Ajna | Hingorani, Aroon D | Nordestgaard, Børge G | Bobak, Martin | Leon, David A | Langenberg, Claudia | Palmer, Tom M | Reiner, Alex P | Keating, Brendan J | Dudbridge, Frank | Casas, Juan P
Objective To use the rs1229984 variant in the alcohol dehydrogenase 1B gene (ADH1B) as an instrument to investigate the causal role of alcohol in cardiovascular disease.
Design Mendelian randomisation meta-analysis of 56 epidemiological studies.
Participants 261 991 individuals of European descent, including 20 259 coronary heart disease cases and 10 164 stroke events. Data were available on ADH1B rs1229984 variant, alcohol phenotypes, and cardiovascular biomarkers.
Main outcome measures Odds ratio for coronary heart disease and stroke associated with the ADH1B variant in all individuals and by categories of alcohol consumption.
Results Carriers of the A-allele of ADH1B rs1229984 consumed 17.2% fewer units of alcohol per week (95% confidence interval 15.6% to 18.9%), had a lower prevalence of binge drinking (odds ratio 0.78 (95% CI 0.73 to 0.84)), and had higher abstention (odds ratio 1.27 (1.21 to 1.34)) than non-carriers. Rs1229984 A-allele carriers had lower systolic blood pressure (−0.88 (−1.19 to −0.56) mm Hg), interleukin-6 levels (−5.2% (−7.8 to −2.4%)), waist circumference (−0.3 (−0.6 to −0.1) cm), and body mass index (−0.17 (−0.24 to −0.10) kg/m2). Rs1229984 A-allele carriers had lower odds of coronary heart disease (odds ratio 0.90 (0.84 to 0.96)). The protective association of the ADH1B rs1229984 A-allele variant remained the same across all categories of alcohol consumption (P=0.83 for heterogeneity). Although no association of rs1229984 was identified with the combined subtypes of stroke, carriers of the A-allele had lower odds of ischaemic stroke (odds ratio 0.83 (0.72 to 0.95)).
Conclusions Individuals with a genetic variant associated with non-drinking and lower alcohol consumption had a more favourable cardiovascular profile and a reduced risk of coronary heart disease than those without the genetic variant. This suggests that reduction of alcohol consumption, even for light to moderate drinkers, is beneficial for cardiovascular health.
doi:10.1136/bmj.g4164
PMCID: PMC4091648  PMID: 25011450

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