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Genetic Profiling Using Genome-Wide Significant Coronary Artery Disease Risk Variants Does Not Improve the Prediction of Subclinical Atherosclerosis: The Cardiovascular Risk in Young Finns Study, the Bogalusa Heart Study and the Health 2000 Survey – A Meta-Analysis of Three Independent Studies  [PDF]
Jussi A. Hernesniemi, Ilkka Sepp?l?, Leo-Pekka Lyytik?inen, Nina Mononen, Niku Oksala, Nina Hutri-K?h?nen, Markus Juonala, Leena Taittonen, Erin N. Smith, Nicholas J. Schork, Wei Chen, Sathanur R. Srinivasan, Gerald S. Berenson, Sarah S. Murray, Tomi Laitinen, Antti Jula, Johannes Kettunen, Samuli Ripatti, Reijo Laaksonen, Jorma Viikari, Mika K?h?nen, Olli T. Raitakari, Terho Lehtim?ki
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0028931
Abstract: Background Genome-wide association studies (GWASs) have identified a large number of variants (SNPs) associating with an increased risk of coronary artery disease (CAD). Recently, the CARDIoGRAM consortium published a GWAS based on the largest study population so far. They successfully replicated twelve already known associations and discovered thirteen new SNPs associating with CAD. We examined whether the genetic profiling of these variants improves prediction of subclinical atherosclerosis – i.e., carotid intima-media thickness (CIMT) and carotid artery elasticity (CAE) – beyond classical risk factors. Subjects and Methods We genotyped 24 variants found in a population of European ancestry and measured CIMT and CAE in 2001 and 2007 from 2,081, and 2,015 subjects (aged 30–45 years in 2007) respectively, participating in the Cardiovascular Risk in Young Finns Study (YFS). The Bogalusa Heart Study (BHS; n = 1179) was used as a replication cohort (mean age of 37.5). For additional replication, a sub-sample of 5 SNPs was genotyped for 1,291 individuals aged 46–76 years participating in the Health 2000 population survey. We tested the impact of genetic risk score (GRS24SNP/CAD) calculated as a weighted (by allelic odds ratios for CAD) sum of CAD risk alleles from the studied 24 variants on CIMT, CAE, the incidence of carotid atherosclerosis and the progression of CIMT and CAE during a 6-year follow-up. Results CIMT or CAE did not significantly associate with GRS24SNP/CAD before or after adjusting for classical CAD risk factors (p>0.05 for all) in YFS or in the BHS. CIMT and CAE associated with only one SNP each in the YFS. The findings were not replicated in the replication cohorts. In the meta-analysis CIMT or CAE did not associate with any of the SNPs. Conclusion Genetic profiling, by using known CAD risk variants, should not improve risk stratification for subclinical atherosclerosis beyond conventional risk factors among healthy young adults.
Improvement in Prediction of Coronary Heart Disease Risk over Conventional Risk Factors Using SNPs Identified in Genome-Wide Association Studies  [PDF]
Jennifer L. Bolton, Marlene C. W. Stewart, James F. Wilson, Niall Anderson, Jackie F. Price
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0057310
Abstract: Objective We examined whether a panel of SNPs, systematically selected from genome-wide association studies (GWAS), could improve risk prediction of coronary heart disease (CHD), over-and-above conventional risk factors. These SNPs have already demonstrated reproducible associations with CHD; here we examined their use in long-term risk prediction. Study Design and Setting SNPs identified from meta-analyses of GWAS of CHD were tested in 840 men and women aged 55–75 from the Edinburgh Artery Study, a prospective, population-based study with 15 years of follow-up. Cox proportional hazards models were used to evaluate the addition of SNPs to conventional risk factors in prediction of CHD risk. CHD was classified as myocardial infarction (MI), coronary intervention (angioplasty, or coronary artery bypass surgery), angina and/or unspecified ischaemic heart disease as a cause of death; additional analyses were limited to MI or coronary intervention. Model performance was assessed by changes in discrimination and net reclassification improvement (NRI). Results There were significant improvements with addition of 27 SNPs to conventional risk factors for prediction of CHD (NRI of 54%, P<0.001; C-index 0.671 to 0.740, P = 0.001), as well as MI or coronary intervention, (NRI of 44%, P<0.001; C-index 0.717 to 0.750, P = 0.256). ROC curves showed that addition of SNPs better improved discrimination when the sensitivity of conventional risk factors was low for prediction of MI or coronary intervention. Conclusion There was significant improvement in risk prediction of CHD over 15 years when SNPs identified from GWAS were added to conventional risk factors. This effect may be particularly useful for identifying individuals with a low prognostic index who are in fact at increased risk of disease than indicated by conventional risk factors alone.
The Construction of Risk Prediction Models Using GWAS Data and Its Application to a Type 2 Diabetes Prospective Cohort  [PDF]
Daichi Shigemizu, Testuo Abe, Takashi Morizono, Todd A. Johnson, Keith A. Boroevich, Yoichiro Hirakawa, Toshiharu Ninomiya, Yutaka Kiyohara, Michiaki Kubo, Yusuke Nakamura, Shiro Maeda, Tatsuhiko Tsunoda
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0092549
Abstract: Recent genome-wide association studies (GWAS) have identified several novel single nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2D). Various models using clinical and/or genetic risk factors have been developed for T2D risk prediction. However, analysis considering algorithms for genetic risk factor detection and regression methods for model construction in combination with interactions of risk factors has not been investigated. Here, using genotype data of 7,360 Japanese individuals, we investigated risk prediction models, considering the algorithms, regression methods and interactions. The best model identified was based on a Bayes factor approach and the lasso method. Using nine SNPs and clinical factors, this method achieved an area under a receiver operating characteristic curve (AUC) of 0.8057 on an independent test set. With the addition of a pair of interaction factors, the model was further improved (p-value 0.0011, AUC 0.8085). Application of our model to prospective cohort data showed significantly better outcome in disease-free survival, according to the log-rank trend test comparing Kaplan-Meier survival curves (). While the major contribution was from clinical factors rather than the genetic factors, consideration of genetic risk factors contributed to an observable, though small, increase in predictive ability. This is the first report to apply risk prediction models constructed from GWAS data to a T2D prospective cohort. Our study shows our model to be effective in prospective prediction and has the potential to contribute to practical clinical use in T2D.
Periodontal Disease and Risk of Preeclampsia: A Meta-Analysis of Observational Studies  [PDF]
Ben-Juan Wei, Yi-Jun Chen, Li Yu, Bin Wu
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0070901
Abstract: Background Many epidemiological studies have found a positive association between periodontal disease (PD) and the risk of preeclampsia, but the magnitude of this association varies and independent studies have reported conflicting findings. We performed a meta-analysis to ascertain the relationship between PD and preeclampsia. Methods The PubMed database was searched up to January 12, 2013, for relevant observational studies on an association between PD and the risk of preeclampsia. Data were extracted and analyzed independently by two authors. The meta-analysis was performed using comprehensive meta-analysis software. Results Thirteen observational case-control studies and two cohort studies, involving 1089 preeclampsia patients, were identified. Based on a random-effects meta-analysis, a significant association between PD and preeclampsia was identified (odds ratio = 2.79, 95% confidence interval CI, 2.01–3.01, P<0.0001). Conclusions Although the causality remains unclear, the association between PD and preeclampsia may reflect the induction of PD by the preeclamptic state, or it may be part of an overall exaggerated inflammatory response to pregnancy. Larger randomized controlled trials with preeclampsia as the primary outcome and pathophysiological studies are required to explore causality and to dissect the biological mechanisms involved.
Framingham Risk Score and Alternatives for Prediction of Coronary Heart Disease in Older Adults  [PDF]
Nicolas Rodondi, Isabella Locatelli, Drahomir Aujesky, Javed Butler, Eric Vittinghoff, Eleanor Simonsick, Suzanne Satterfield, Anne B. Newman, Peter W. F. Wilson, Mark J. Pletcher, Douglas C. Bauer, for the Health ABC Study
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0034287
Abstract: Background Guidelines for the prevention of coronary heart disease (CHD) recommend use of Framingham-based risk scores that were developed in white middle-aged populations. It remains unclear whether and how CHD risk prediction might be improved among older adults. We aimed to compare the prognostic performance of the Framingham risk score (FRS), directly and after recalibration, with refit functions derived from the present cohort, as well as to assess the utility of adding other routinely available risk parameters to FRS. Methods Among 2193 black and white older adults (mean age, 73.5 years) without pre-existing cardiovascular disease from the Health ABC cohort, we examined adjudicated CHD events, defined as incident myocardial infarction, CHD death, and hospitalization for angina or coronary revascularization. Results During 8-year follow-up, 351 participants experienced CHD events. The FRS poorly discriminated between persons who experienced CHD events vs. not (C-index: 0.577 in women; 0.583 in men) and underestimated absolute risk prediction by 51% in women and 8% in men. Recalibration of the FRS improved absolute risk prediction, particulary for women. For both genders, refitting these functions substantially improved absolute risk prediction, with similar discrimination to the FRS. Results did not differ between whites and blacks. The addition of lifestyle variables, waist circumference and creatinine did not improve risk prediction beyond risk factors of the FRS. Conclusions The FRS underestimates CHD risk in older adults, particularly in women, although traditional risk factors remain the best predictors of CHD. Re-estimated risk functions using these factors improve accurate estimation of absolute risk.
Meta-Analyses of 8 Polymorphisms Associated with the Risk of the Alzheimer’s Disease  [PDF]
Xuting Xu, Yunliang Wang, Lingyan Wang, Qi Liao, Lan Chang, Leiting Xu, Yi Huang, Huadan Ye, Limin Xu, Cheng Chen, Xiaowei Shen, Fuqiang Zhang, Meng Ye, Qinwen Wang, Shiwei Duan
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0073129
Abstract: Aims The aim of this study was to evaluate the combined contribution of 8 polymorphisms to the risk of Alzheimer's disease (AD). Methods Through a comprehensive literature search for genetic variants involved in the AD association study, we harvested a total of 6 genes (8 polymorphisms) for the current meta-analyses. These genes consisted of A2M (5bp I/D and V1000I), ABCA2 (rs908832), CHAT (1882G >A, 2384G >A), COMT (Val158Met), HTR6 (267C >T) and LPL (Ser447Ter). Results A total of 33 studies among 9,453 cases and 10,833 controls were retrieved for the meta-analyses of 8 genetic variants. It was showed that A2M V1000I (odd ratio (OR) = 1.26, 95% confidence interval (CI) = 1.07–1.49, P = 0.007), rs908832 allele of ABCA2 (OR = 1.55, 95% CI = 1.12–2.16, P = 0.009), 2384G >A of CHAT (OR = 1.22, 95% CI = 1.00–1.49, P = 0.05) and Ser447Ter of LPL in the Northern-American population (OR = 0.56, 95% CI = 0.35–0.91, P = 0.02) were significantly associated with the risk of AD. No association was found between the rest of the 5 polymorphisms and the risk of AD. Conclusion Our results showed that A2M V1000I polymorphism in German, Korean, Chinese, Spanish, Italian and Polish populations, rs90883 of ABCA2 gene in French, American, Swiss, Greek and Japanese populations, 2384G >A of CHAT gene in British and Korean populations and LPL Ser447Ter in the Northern-American population were associated with the risk of AD.
Family-based genetic risk prediction of multifactorial disease
Douglas M Ruderfer, Joshua Korn, Shaun M Purcell
Genome Medicine , 2010, DOI: 10.1186/gm123
Abstract: Although whole-genome association studies have detected dozens of common variants for a broad range of complex diseases, and are likely to detect many more, the total variance explained by the known variants is typically modest [1,2]. As such, realising the goals of accurate genetic risk prediction and the subsequent opportunities of personalised medicine remains difficult [3,4]. Indeed, it has often been noted that family history alone will perform substantially better as a predictor of risk, compared to genotype data for known risk variants [5]. It is true that a positive family history will likely remain an important factor in prediction for the many complex diseases with substantial heritabilties and shared familial environmental components. (A caveat is that family history information might sometimes not be straightforwardly available, for example, for phenotypes such as response to a particular drug treatment.) However, analogous to clinical genetic testing for Mendelian disease, it is plausible that in many cases a positive family history will itself be a motivating factor for pursuing a genetic test. For example, an individual whose older sibling developed a particular disease might be particularly concerned with their own personal risk, which they assume will be higher than average. In this context, in which a genetic test is sought because a first-degree relative has disease, we developed a family-based model for risk prediction incorporating genotype data from both the index individual and a relative of known phenotype. As such, we do not ask "how well do SNPs predict disease compared to family history", but rather, "how well do SNPs predict disease given a positive family history, and to what extent does including genotype data from the affected relatives help?".For diseases with polygenic and shared environmental components of risk, the genotype of a relative of known phenotype can be informative for an individual's disease risk, over and above the indi
Periodontal Disease and Risk of Chronic Obstructive Pulmonary Disease: A Meta-Analysis of Observational Studies  [PDF]
Xian-Tao Zeng, Ming-Li Tu, Dong-Yan Liu, Dong Zheng, Jing Zhang, WeiDong Leng
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0046508
Abstract: Background Many epidemiological studies have found a positive association between periodontal disease (PD) and risk of chronic obstructive pulmonary disease (COPD), but this association is varied and even contradictory among studies. We performed a meta-analysis to ascertain the relationship between PD and COPD. Methods PubMed and Embase database were searched up to January 10, 2012, for relevant observational studies on the association between PD and risk of COPD. Data from the studies selected were extracted and analyzed independently by two authors. The meta-analysis was performed using the Comprehensive Meta-Analysis software. Results Fourteen observational studies (one nested case-control, eight case-control, and five cross-sectional) involving 3,988 COPD patients were yielded. Based on random-effects meta-analysis, a significant association between PD and COPD was identified (odds ratio = 2.08, 95% confidence interval = 1.48–2.91; P<0.001), with sensitivity analysis showing that the result was robust. Subgroups analyses according to study design, ethnicity, assessment of PD/COPD, and adjusted/unadjusted odds ratios also revealed a significant association. Publication bias was detected. Conclusions Based on current evidence, PD is a significant and independent risk factor of COPD. However, whether a causal relationships exists remains unclear. Morever, we suggest performing randomized controlled trails to explore whether periodontal interventions are beneficial in regulating COPD pathogenesis and progression.
How to evaluate the calibration of a disease risk prediction tool  [PDF]
V. Viallon,J. Benichou,F. Clavel-Chapelon,S. Ragusa
Statistics , 2007,
Abstract: To evaluate the calibration of a disease risk prediction tool, the quantity $E/O$, i.e., the ratio of the expected number of events to the observed number of events, is generally computed. However, because of censoring, or more precisely because of individuals who drop out before the termination of the study, this quantity is generally unavailable for the complete population study and an alternative estimate has to be computed. In this paper, we present and compare four methods to do this. We show that two of the most commonly used methods generally lead to biased estimates. Our arguments are first based on some theoretic considerations. Then, we perform a simulation study to highlight the magnitude of the previously mentioned biases. As a concluding example, we evaluate the calibration of an existing predictive model for breast cancer on the E3N-EPIC cohort.
Circulating Biomarkers for Predicting Cardiovascular Disease Risk; a Systematic Review and Comprehensive Overview of Meta-Analyses  [PDF]
Thijs C. van Holten, Leonie F. Waanders, Philip G. de Groot, Joost Vissers, Imo E. Hoefer, Gerard Pasterkamp, Menno W. J. Prins, Mark Roest
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0062080
Abstract: Background Cardiovascular disease is one of the major causes of death worldwide. Assessing the risk for cardiovascular disease is an important aspect in clinical decision making and setting a therapeutic strategy, and the use of serological biomarkers may improve this. Despite an overwhelming number of studies and meta-analyses on biomarkers and cardiovascular disease, there are no comprehensive studies comparing the relevance of each biomarker. We performed a systematic review of meta-analyses on levels of serological biomarkers for atherothrombosis to compare the relevance of the most commonly studied biomarkers. Methods and Findings Medline and Embase were screened on search terms that were related to “arterial ischemic events” and “meta-analyses”. The meta-analyses were sorted by patient groups without pre-existing cardiovascular disease, with cardiovascular disease and heterogeneous groups concerning general populations, groups with and without cardiovascular disease, or miscellaneous. These were subsequently sorted by end-point for cardiovascular disease or stroke and summarized in tables. We have identified 85 relevant full text articles, with 214 meta-analyses. Markers for primary cardiovascular events include, from high to low result: C-reactive protein, fibrinogen, cholesterol, apolipoprotein B, the apolipoprotein A/apolipoprotein B ratio, high density lipoprotein, and vitamin D. Markers for secondary cardiovascular events include, from high to low result: cardiac troponins I and T, C-reactive protein, serum creatinine, and cystatin C. For primary stroke, fibrinogen and serum uric acid are strong risk markers. Limitations reside in that there is no acknowledged search strategy for prognostic studies or meta-analyses. Conclusions For primary cardiovascular events, markers with strong predictive potential are mainly associated with lipids. For secondary cardiovascular events, markers are more associated with ischemia. Fibrinogen is a strong predictor for primary stroke.
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