Aroon D. Hingorani

Pre-eclampsia and risk of cardiovascular disease and cancer in later life: systematic review and meta-analysis

Objective To quantify the risk of future cardiovascular diseases, cancer, and mortality after pre-eclampsia. Design Systematic review and meta-analysis. Data sources Embase and Medline without language restrictions, including papers published between 1960 and December 2006, and hand searching of reference lists of relevant articles and reviews for additional reports. Review methods Prospective and retrospective cohort studies were included, providing a dataset of 3u2009488u2009160 women, with 198u2009252 affected by pre-eclampsia (exposure group) and 29u2009495 episodes of cardiovascular disease and cancer (study outcomes). Results After pre-eclampsia women have an increased risk of vascular disease. The relative risks (95% confidence intervals) for hypertension were 3.70 (2.70 to 5.05) after 14.1 years weighted mean follow-up, for ischaemic heart disease 2.16 (1.86 to 2.52) after 11.7 years, for stroke 1.81 (1.45 to 2.27) after 10.4 years, and for venous thromboembolism 1.79 (1.37 to 2.33) after 4.7 years. No increase in risk of any cancer was found (0.96, 0.73 to 1.27), including breast cancer (1.04, 0.78 to 1.39) 17 years after pre-eclampsia. Overall mortality after pre-eclampsia was increased: 1.49 (1.05 to 2.14) after 14.5 years. Conclusions A history of pre-eclampsia should be considered when evaluating risk of cardiovascular disease in women. This association might reflect a common cause for pre-eclampsia and cardiovascular disease, or an effect of pre-eclampsia on disease development, or both. No association was found between pre-eclampsia and future cancer.

Homocysteine and stroke: evidence on a causal link from mendelian randomisation

BACKGROUNDnIndividuals homozygous for the T allele of the MTHFR C677T polymorphism have higher plasma homocysteine concentrations (the phenotype) than those with the CC genotype, which, if pathogenetic, should put them at increased risk of stroke. Since this polymorphism is distributed randomly during gamete formation, its association with stroke should not be biased or confounded. We investigated consistency between the expected odds ratio for stroke among TT homozygotes, extrapolated from genotype-phenotype and phenotype-disease studies, and the observed odds ratio from a meta-analysis of genotype-disease association studies.nnnMETHODSnWe searched MEDLINE and EMBASE up to June, 2003, for all relevant studies on the association between homocysteine concentration and the MTHFR polymorphism, and until December, 2003, for those on the association between the polymorphism and the risk of stroke. Pooled odds ratios and 95% CI were calculated by random-effects and fixed-effects models. Consistency between expected and observed odds ratios was assessed by interaction test.nnnFINDINGSn111 studies met the selection criteria. Among 15635 people without cardiovascular disease, the weighted mean difference in homocysteine concentration between TT and CC homozygotes was 1.93 micromol/L (95% CI 1.38 to 2.47). The expected odds ratio for stroke corresponding to this difference based on previous observational studies was 1.20 (1.10 to 1.31). In our genetic meta-analysis (n=13928) the odds ratio for stroke was 1.26 (1.14 to 1.40) for TT versus CC homozygotes, similar to the expected odds ratio (p=0.29). Consistency between the odds ratios was preserved in analyses by age-group, ethnic background, and geographical location.nnnINTERPRETATIONnThe observed increase in risk of stroke among individuals homozygous for the MTHFR T allele is close to that predicted from the differences in homocysteine concentration conferred by this variant. This concordance is consistent with a causal relation between homocysteine concentration and stroke.

Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study

Objectives To assess the performance of a panel of common single nucleotide polymorphisms (genotypes) associated with type 2 diabetes in distinguishing incident cases of future type 2 diabetes (discrimination), and to examine the effect of adding genetic information to previously validated non-genetic (phenotype based) models developed to estimate the absolute risk of type 2 diabetes. Design Workplace based prospective cohort study with three 5 yearly medical screenings. Participants 5535 initially healthy people (mean age 49 years; 33% women), of whom 302 developed new onset type 2 diabetes over 10 years. Outcome measures Non-genetic variables included in two established risk models—the Cambridge type 2 diabetes risk score (age, sex, drug treatment, family history of type 2 diabetes, body mass index, smoking status) and the Framingham offspring study type 2 diabetes risk score (age, sex, parental history of type 2 diabetes, body mass index, high density lipoprotein cholesterol, triglycerides, fasting glucose)—and 20 single nucleotide polymorphisms associated with susceptibility to type 2 diabetes. Cases of incident type 2 diabetes were defined on the basis of a standard oral glucose tolerance test, self report of a doctor’s diagnosis, or the use of anti-diabetic drugs. Results A genetic score based on the number of risk alleles carried (range 0-40; area under receiver operating characteristics curve 0.54, 95% confidence interval 0.50 to 0.58) and a genetic risk function in which carriage of risk alleles was weighted according to the summary odds ratios of their effect from meta-analyses of genetic studies (area under receiver operating characteristics curve 0.55, 0.51 to 0.59) did not effectively discriminate cases of diabetes. The Cambridge risk score (area under curve 0.72, 0.69 to 0.76) and the Framingham offspring risk score (area under curve 0.78, 0.75 to 0.82) led to better discrimination of cases than did genotype based tests. Adding genetic information to phenotype based risk models did not improve discrimination and provided only a small improvement in model calibration and a modest net reclassification improvement of about 5% when added to the Cambridge risk score but not when added to the Framingham offspring risk score. Conclusion The phenotype based risk models provided greater discrimination for type 2 diabetes than did models based on 20 common independently inherited diabetes risk alleles. The addition of genotypes to phenotype based risk models produced only minimal improvement in accuracy of risk estimation assessed by recalibration and, at best, a minor net reclassification improvement. The major translational application of the currently known common, small effect genetic variants influencing susceptibility to type 2 diabetes is likely to come from the insight they provide on causes of disease and potential therapeutic targets.

Critical appraisal of CRP measurement for the prediction of coronary heart disease events: new data and systematic review of 31 prospective cohorts

BACKGROUNDnNon-uniform reporting of relevant relationships and metrics hampers critical appraisal of the clinical utility of C-reactive protein (CRP) measurement for prediction of later coronary events.nnnMETHODSnWe evaluated the predictive performance of CRP in the Northwick Park Heart Study (NPHS-II) and the Edinburgh Artery Study (EAS) comparing discrimination by area under the ROC curve (AUC), calibration and reclassification. We set the findings in the context of a systematic review of published studies comparing different available and imputed measures of prediction. Risk estimates per-quantile of CRP were pooled using a random effects model to infer the shape of the CRP-coronary event relationship.nnnRESULTSnNPHS-II and EAS (3441 individuals, 309 coronary events): CRP alone provided modest discrimination for coronary heart disease (AUC 0.61 and 0.62 in NPHS-II and EAS, respectively) and only modest improvement in the discrimination of a Framingham-based risk score (FRS) (increment in AUC 0.04 and -0.01, respectively). Risk models based on FRS alone and FRS + CRP were both well calibrated and the net reclassification improvement (NRI) was 8.5% in NPHS-II and 8.8% in EAS with four risk categories, falling to 4.9% and 3.0% for 10-year coronary disease risk threshold of 15%. Systematic review (31 prospective studies 84 063 individuals, 11 252 coronary events): pooled inferred values for the AUC for CRP alone were 0.59 (0.57, 0.61), 0.59 (0.57, 0.61) and 0.57 (0.54, 0.61) for studies of <5, 5-10 and >10 years follow up, respectively. Evidence from 13 studies (7201 cases) indicated that CRP did not consistently improve performance of the Framingham risk score when assessed by discrimination, with AUC increments in the range 0-0.15. Evidence from six studies (2430 cases) showed that CRP provided statistically significant but quantitatively small improvement in calibration of models based on established risk factors in some but not all studies. The wide overlap of CRP values among people who later suffered events and those who did not appeared to be explained by the consistently log-normal distribution of CRP and a graded continuous increment in coronary risk across the whole range of values without a threshold, such that a large proportion of events occurred among the many individuals with near average levels of CRP.nnnCONCLUSIONSnCRP does not perform better than the Framingham risk equation for discrimination. The improvement in risk stratification or reclassification from addition of CRP to models based on established risk factors is small and inconsistent. Guidance on the clinical use of CRP measurement in the prediction of coronary events may require updating in light of this large comparative analysis.

Separating the Mechanism-Based and Off-Target Actions of Cholesteryl Ester Transfer Protein Inhibitors With CETP Gene Polymorphisms

Background— Cholesteryl ester transfer protein (CETP) inhibitors raise high-density lipoprotein (HDL) cholesterol, but torcetrapib, the first-in-class inhibitor tested in a large outcome trial, caused an unexpected blood pressure elevation and increased cardiovascular events. Whether the hypertensive effect resulted from CETP inhibition or an off-target action of torcetrapib has been debated. We hypothesized that common single-nucleotide polymorphisms in the CETP gene could help distinguish mechanism-based from off-target actions of CETP inhibitors to inform on the validity of CETP as a therapeutic target. Methods and Results— We compared the effect of CETP single-nucleotide polymorphisms and torcetrapib treatment on lipid fractions, blood pressure, and electrolytes in up to 67 687 individuals from genetic studies and 17 911 from randomized trials. CETP single-nucleotide polymorphisms and torcetrapib treatment reduced CETP activity and had a directionally concordant effect on 8 lipid and lipoprotein traits (total, low-density lipoprotein, and HDL cholesterol; HDL2; HDL3; apolipoproteins A-I and B; and triglycerides), with the genetic effect on HDL cholesterol (0.13 mmol/L, 95% confidence interval [CI] 0.11 to 0.14 mmol/L) being consistent with that expected of a 10-mg dose of torcetrapib (0.13 mmol/L, 95% CI 0.10 to 0.15). In trials, 60 mg of torcetrapib elevated systolic and diastolic blood pressure by 4.47 mm Hg (95% CI 4.10 to 4.84 mm Hg) and 2.08 mm Hg (95% CI 1.84 to 2.31 mm Hg), respectively. However, the effect of CETP single-nucleotide polymorphisms on systolic blood pressure (0.16 mm Hg, 95% CI −0.28 to 0.60 mm Hg) and diastolic blood pressure (−0.04 mm Hg, 95% CI −0.36 to 0.28 mm Hg) was null and significantly different from that expected of 10 mg of torcetrapib. Conclusions— Discordance in the effects of CETP single-nucleotide polymorphisms and torcetrapib treatment on blood pressure despite the concordant effects on lipids indicates the hypertensive action of torcetrapib is unlikely to be due to CETP inhibition or shared by chemically dissimilar CETP inhibitors. Genetic studies could find a place in drug-development programs as a new source of randomized evidence for drug-target validation in humans.

Bayesian Meta-Analysis of Genetic Association Studies with Different Sets of Markers

Robust assessment of genetic effects on quantitative traits or complex-disease risk requires synthesis of evidence from multiple studies. Frequently, studies have genotyped partially overlapping sets of SNPs within a gene or region of interest, hampering attempts to combine all the available data. By using the example of C-reactive protein (CRP) as a quantitative trait, we show how linkage disequilibrium in and around its gene facilitates use of Bayesian hierarchical models to integrate informative data from all available genetic association studies of this trait, irrespective of the SNP typed. A variable selection scheme, followed by contextualization of SNPs exhibiting independent associations within the haplotype structure of the gene, enhanced our ability to infer likely causal variants in this region with population-scale data. This strategy, based on data from a literature based systematic review and substantial new genotyping, facilitated the most comprehensive evaluation to date of the role of variants governing CRP levels, providing important information on the minimal subset of SNPs necessary for comprehensive evaluation of the likely causal relevance of elevated CRP levels for coronary-heart-disease risk by Mendelian randomization. The same method could be applied to evidence synthesis of other quantitative traits, whenever the typed SNPs vary among studies, and to assist fine mapping of causal variants.

Primary Prevention of Cardiovascular Disease: Time to Get More or Less Personal?

IN THE 1980S, ROSE COINED THE TERM PREVENTION PARAdox to describe the fact that a large proportion of cardiovascular disease (CVD) events occur among the many individuals with average risk factor values. He distinguished between 2 approaches to CVD prevention. The highrisk strategy, which aims to truncate the upper tail of the normal distribution of risk factors, focuses on individuals who are most likely to benefit personally from preventive treatment. By contrast, the population-based strategy aims to shift the entire risk distribution. At the time, the available lipid-lowering therapies were limited, none was well tolerated, and the risk-benefit profile for clofibrate, for instance, argued against its widespread use. Soon, the high-risk approach came to be synonymous with the use of drugs, and the population approach was identified with efforts to shift norms about diet, physical activity, or smoking. Modest lifestyle changes could be recommended to the population at large because evolutionarily sensible interventions such as a low-salt diet may be “presumed to be safe.” The real-world effects of such recommendations have been limited. Targeting high-risk individuals with preventive drug therapies optimizes the benefits for the individuals concerned but does little to reduce the overall burden of CVD in the population. In the 1990s, evidence emerged documenting the efficacy of a new class of potent low-density lipoprotein (LDL)–cholesterol-lowering agents, the 3-hydroxy-3methylglutaryl coenzyme A reductase inhibitors (statins), first in patients with coronary disease and subsequently in primary prevention. Meta-analyses of large statin trials have demonstrated an approximately 25% relative reduction in risk of CVD events in both primary and secondary prevention populations and in patients with a varying risk factor profile. For this reason, the number needed to treat to prevent 1 CVD event depends largely on the baseline absolute risk. When evidence of the benefits of statins first emerged and the potential for high-volume use was recognized, a strategy was necessary to maximize benefit and to limit any potential harm because of the high initial cost of these agents and the uncertainty about their long-term safety. In the United States, individuals considered for statin therapy were originally identified according to an agreed LDL cholesterol threshold. Because LDL cholesterol does not perform well as a CVD screening test, a threshold based on absolute risk was adopted in the United Kingdom. This absolute risk– based approach reduces the number needed to treat to prevent 1 event and maximizes the health benefits for a given cost. In the United Kingdom, it is now routine for family practitioners to use tables or computer programs based on validated risk equations to estimate individual risk at the point of care. The widely used risk models accurately assign individuals to different categories of risk in such a way that the observed event rates for the risk groups are close to the predicted rates. It is not widely appreciated, however, that risk models fail to efficiently distinguish or discriminate between patients who will or will not experience events. Recent research efforts have therefore focused on new biomarkers and vascular imaging tools to improve discrimination and risk stratification. Of these, C-reactive protein (CRP) has been associated with risk of CVD events, and a US consensus statement suggests a CRP cut point of 3 mg/L may aid the identification of high-risk patients. The fact that mendelian randomization studies suggest that CRP is not a cause of vascular disease is not important for the purpose of prediction. Nevertheless, when assessed with appropriate metrics of predictive performance, a CRP measurement, like LDL cholesterol alone, is a poor discriminator of future CVD events and only marginally enhances risk stratification using established risk factors. Vascular imaging techniques are costly and some involve radiation exposure, which may limit their applicability for primary prevention. New metrics for assessing predictive performance have been advocated based on reclassification. These assess the extent to which the addition of a new marker to an estab-

Translating genomics into improved healthcare

#### Summary pointsnnSince the work of Mendel,1 genetic research has been punctuated by key, shifting advances (box 1), which culminated in the first draft sequence of the human genome in 2001.2 3 4 Although most of the human genome sequence is shared by everyone, a small proportion varies between individuals. This variation, acting together with environmental factors, is thought to underlie differences in physiology, susceptibility to disease, and responses to drugs. We summarise the recent discoveries and review the implications of newly acquired knowledge for medical practice and public health.nn#### Sources and selection criteriannBecause of the wide remit of this article, we did not attempt a systematic search covering the whole of translational genetics. Instead, we used personal collections of major journal articles and reviews accumulated individually by all authors over several years of academic work in translational genetics.nn#### Box 1 Key early milestones in genetic research