@article {984, title = {The Emerging Risk Factors Collaboration: analysis of individual data on lipid, inflammatory and other markers in over 1.1 million participants in 104 prospective studies of cardiovascular diseases.}, journal = {Eur J Epidemiol}, volume = {22}, year = {2007}, month = {2007}, pages = {839-69}, abstract = {

Many long-term prospective studies have reported on associations of cardiovascular diseases with circulating lipid markers and/or inflammatory markers. Studies have not, however, generally been designed to provide reliable estimates under different circumstances and to correct for within-person variability. The Emerging Risk Factors Collaboration has established a central database on over 1.1 million participants from 104 prospective population-based studies, in which subsets have information on lipid and inflammatory markers, other characteristics, as well as major cardiovascular morbidity and cause-specific mortality. Information on repeat measurements on relevant characteristics has been collected in approximately 340,000 participants to enable estimation of and correction for within-person variability. Re-analysis of individual data will yield up to approximately 69,000 incident fatal or nonfatal first ever major cardiovascular outcomes recorded during about 11.7 million person years at risk. The primary analyses will involve age-specific regression models in people without known baseline cardiovascular disease in relation to fatal or nonfatal first ever coronary heart disease outcomes. This initiative will characterize more precisely and in greater detail than has previously been possible the shape and strength of the age- and sex-specific associations of several lipid and inflammatory markers with incident coronary heart disease outcomes (and, secondarily, with other incident cardiovascular outcomes) under a wide range of circumstances. It will, therefore, help to determine to what extent such associations are independent from possible confounding factors and to what extent such markers (separately and in combination) provide incremental predictive value.

}, keywords = {Albumins, Biomarkers, Cardiovascular Diseases, Databases, Factual, Far East, Humans, Inflammation, Leukocyte Count, Lipids, Lipoproteins, HDL, Prospective Studies, Risk Factors, Triglycerides}, issn = {0393-2990}, doi = {10.1007/s10654-007-9165-7}, author = {Danesh, J and Erqou, S and Walker, M and Thompson, S G and Tipping, R and Ford, C and Pressel, S and Walldius, G and Jungner, I and Folsom, A R and Chambless, L E and Knuiman, M and Whincup, P H and Wannamethee, S G and Morris, R W and Willeit, J and Kiechl, S and Santer, P and Mayr, A and Wald, N and Ebrahim, S and Lawlor, D A and Yarnell, J W G and Gallacher, J and Casiglia, E and Tikhonoff, V and Nietert, P J and Sutherland, S E and Bachman, D L and Keil, J E and Cushman, M and Psaty, B M and Tracy, R P and Tybjaerg-Hansen, A and Nordestgaard, B G and Frikke-Schmidt, R and Giampaoli, S and Palmieri, L and Panico, S and Vanuzzo, D and Pilotto, L and Simons, L and McCallum, J and Friedlander, Y and Fowkes, F G R and Lee, A J and Smith, F B and Taylor, J and Guralnik, J and Phillips, C and Wallace, R and Blazer, D and Khaw, K T and Jansson, J H and Donfrancesco, C and Salomaa, V and Harald, K and Jousilahti, P and Vartiainen, E and Woodward, M and D{\textquoteright}Agostino, R B and Wolf, P A and Vasan, R S and Pencina, M J and Bladbjerg, E M and Jorgensen, T and Moller, L and Jespersen, J and Dankner, R and Chetrit, A and Lubin, F and Rosengren, A and Wilhelmsen, L and Lappas, G and Eriksson, H and Bjorkelund, C and Cremer, P and Nagel, D and Tilvis, R and Strandberg, T and Rodriguez, B and Bouter, L M and Heine, R J and Dekker, J M and Nijpels, G and Stehouwer, C D A and Rimm, E and Pai, J and Sato, S and Iso, H and Kitamura, A and Noda, H and Goldbourt, U and Salomaa, V and Salonen, J T and Nyyss{\"o}nen, K and Tuomainen, T-P and Deeg, D and Poppelaars, J L and Meade, T and Cooper, J and Hedblad, B and Berglund, G and Engstrom, G and D{\"o}ring, A and Koenig, W and Meisinger, C and Mraz, W and Kuller, L and Selmer, R and Tverdal, A and Nystad, W and Gillum, R and Mussolino, M and Hankinson, S and Manson, J and De Stavola, B and Knottenbelt, C and Cooper, J A and Bauer, K A and Rosenberg, R D and Sato, S and Naito, Y and Holme, I and Nakagawa, H and Miura, H and Ducimetiere, P and Jouven, X and Crespo, C and Garcia-Palmieri, M and Amouyel, P and Arveiler, D and Evans, A and Ferrieres, J and Schulte, H and Assmann, G and Shepherd, J and Packard, C and Sattar, N and Cantin, B and Lamarche, B and Despr{\'e}s, J-P and Dagenais, G R and Barrett-Connor, E and Wingard, D and Bettencourt, R and Gudnason, V and Aspelund, T and Sigurdsson, G and Thorsson, B and Trevisan, M and Witteman, J and Kardys, I and Breteler, M and Hofman, A and Tunstall-Pedoe, H and Tavendale, R and Lowe, G D O and Ben-Shlomo, Y and Howard, B V and Zhang, Y and Best, L and Umans, J and Onat, A and Meade, T W and Njolstad, I and Mathiesen, E and Lochen, M L and Wilsgaard, T and Gaziano, J M and Stampfer, M and Ridker, P and Ulmer, H and Diem, G and Concin, H and Rodeghiero, F and Tosetto, A and Brunner, E and Shipley, M and Buring, J and Cobbe, S M and Ford, I and Robertson, M and He, Y and Ibanez, A M and Feskens, E J M and Kromhout, D and Collins, R and Di Angelantonio, E and Kaptoge, S and Lewington, S and Orfei, L and Pennells, L and Perry, P and Ray, K and Sarwar, N and Scherman, M and Thompson, A and Watson, S and Wensley, F and White, I R and Wood, A M} } @article {1171, title = {Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables.}, journal = {Stat Med}, volume = {29}, year = {2010}, month = {2010 May 30}, pages = {1298-311}, abstract = {

Genetic markers can be used as instrumental variables, in an analogous way to randomization in a clinical trial, to estimate the causal relationship between a phenotype and an outcome variable. Our purpose is to extend the existing methods for such Mendelian randomization studies to the context of multiple genetic markers measured in multiple studies, based on the analysis of individual participant data. First, for a single genetic marker in one study, we show that the usual ratio of coefficients approach can be reformulated as a regression with heterogeneous error in the explanatory variable. This can be implemented using a Bayesian approach, which is next extended to include multiple genetic markers. We then propose a hierarchical model for undertaking a meta-analysis of multiple studies, in which it is not necessary that the same genetic markers are measured in each study. This provides an overall estimate of the causal relationship between the phenotype and the outcome, and an assessment of its heterogeneity across studies. As an example, we estimate the causal relationship of blood concentrations of C-reactive protein on fibrinogen levels using data from 11 studies. These methods provide a flexible framework for efficient estimation of causal relationships derived from multiple studies. Issues discussed include weak instrument bias, analysis of binary outcome data such as disease risk, missing genetic data, and the use of haplotypes.

}, keywords = {Bayes Theorem, Biostatistics, C-Reactive Protein, Fibrinogen, Genetic Markers, Humans, Meta-Analysis as Topic, Models, Statistical, Phenotype, Polymorphism, Single Nucleotide}, issn = {1097-0258}, doi = {10.1002/sim.3843}, author = {Burgess, Stephen and Thompson, Simon G and Burgess, S and Thompson, S G and Andrews, G and Samani, N J and Hall, A and Whincup, P and Morris, R and Lawlor, D A and Davey Smith, G and Timpson, N and Ebrahim, S and Ben-Shlomo, Y and Davey Smith, G and Timpson, N and Brown, M and Ricketts, S and Sandhu, M and Reiner, A and Psaty, B and Lange, L and Cushman, M and Hung, J and Thompson, P and Beilby, J and Warrington, N and Palmer, L J and Nordestgaard, B G and Tybjaerg-Hansen, A and Zacho, J and Wu, C and Lowe, G and Tzoulaki, I and Kumari, M and Sandhu, M and Yamamoto, J F and Chiodini, B and Franzosi, M and Hankey, G J and Jamrozik, K and Palmer, L and Rimm, E and Pai, J and Psaty, B and Heckbert, S and Bis, J and Anand, S and Engert, J and Collins, R and Clarke, R and Melander, O and Berglund, G and Ladenvall, P and Johansson, L and Jansson, J-H and Hallmans, G and Hingorani, A and Humphries, S and Rimm, E and Manson, J and Pai, J and Watkins, H and Clarke, R and Hopewell, J and Saleheen, D and Frossard, R and Danesh, J and Sattar, N and Robertson, M and Shepherd, J and Schaefer, E and Hofman, A and Witteman, J C M and Kardys, I and Ben-Shlomo, Y and Davey Smith, G and Timpson, N and de Faire, U and Bennet, A and Sattar, N and Ford, I and Packard, C and Kumari, M and Manson, J and Lawlor, Debbie A and Davey Smith, George and Anand, S and Collins, R and Casas, J P and Danesh, J and Davey Smith, G and Franzosi, M and Hingorani, A and Lawlor, D A and Manson, J and Nordestgaard, B G and Samani, N J and Sandhu, M and Smeeth, L and Wensley, F and Anand, S and Bowden, J and Burgess, S and Casas, J P and Di Angelantonio, E and Engert, J and Gao, P and Shah, T and Smeeth, L and Thompson, S G and Verzilli, C and Walker, M and Whittaker, J and Hingorani, A and Danesh, J} } @article {1213, title = {Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies.}, journal = {Lancet}, volume = {375}, year = {2010}, month = {2010 Jun 26}, pages = {2215-22}, abstract = {

BACKGROUND: Uncertainties persist about the magnitude of associations of diabetes mellitus and fasting glucose concentration with risk of coronary heart disease and major stroke subtypes. We aimed to quantify these associations for a wide range of circumstances.

METHODS: We undertook a meta-analysis of individual records of diabetes, fasting blood glucose concentration, and other risk factors in people without initial vascular disease from studies in the Emerging Risk Factors Collaboration. We combined within-study regressions that were adjusted for age, sex, smoking, systolic blood pressure, and body-mass index to calculate hazard ratios (HRs) for vascular disease.

FINDINGS: Analyses included data for 698 782 people (52 765 non-fatal or fatal vascular outcomes; 8.49 million person-years at risk) from 102 prospective studies. Adjusted HRs with diabetes were: 2.00 (95\% CI 1.83-2.19) for coronary heart disease; 2.27 (1.95-2.65) for ischaemic stroke; 1.56 (1.19-2.05) for haemorrhagic stroke; 1.84 (1.59-2.13) for unclassified stroke; and 1.73 (1.51-1.98) for the aggregate of other vascular deaths. HRs did not change appreciably after further adjustment for lipid, inflammatory, or renal markers. HRs for coronary heart disease were higher in women than in men, at 40-59 years than at 70 years and older, and with fatal than with non-fatal disease. At an adult population-wide prevalence of 10\%, diabetes was estimated to account for 11\% (10-12\%) of vascular deaths. Fasting blood glucose concentration was non-linearly related to vascular risk, with no significant associations between 3.90 mmol/L and 5.59 mmol/L. Compared with fasting blood glucose concentrations of 3.90-5.59 mmol/L, HRs for coronary heart disease were: 1.07 (0.97-1.18) for lower than 3.90 mmol/L; 1.11 (1.04-1.18) for 5.60-6.09 mmol/L; and 1.17 (1.08-1.26) for 6.10-6.99 mmol/L. In people without a history of diabetes, information about fasting blood glucose concentration or impaired fasting glucose status did not significantly improve metrics of vascular disease prediction when added to information about several conventional risk factors.

INTERPRETATION: Diabetes confers about a two-fold excess risk for a wide range of vascular diseases, independently from other conventional risk factors. In people without diabetes, fasting blood glucose concentration is modestly and non-linearly associated with risk of vascular disease.

FUNDING: British Heart Foundation, UK Medical Research Council, and Pfizer.

}, keywords = {Adult, Aged, Blood Glucose, Coronary Disease, Diabetes Complications, Diabetes Mellitus, Fasting, Female, Humans, Male, Middle Aged, Risk Factors, Stroke}, issn = {1474-547X}, doi = {10.1016/S0140-6736(10)60484-9}, author = {Sarwar, N and Gao, P and Seshasai, S R Kondapally and Gobin, R and Kaptoge, S and Di Angelantonio, E and Ingelsson, E and Lawlor, D A and Selvin, E and Stampfer, M and Stehouwer, C D A and Lewington, S and Pennells, L and Thompson, A and Sattar, N and White, I R and Ray, K K and Danesh, J} }