Search

Whereas most people with diabetes mellitus have type 1 (T1DM) or type 2 (T2DM) diabetes, there are a number of other inherited forms of diabetes and insulin resistance syndromes, which represent up to about 5% of all cases of diabetes. Monogenic forms of pancreatic β-cell dysfunction include maturity-onset diabetes of the young (MODY) and neonatal diabetes (NDM), with MODY being the most common form of inherited diabetes. The long-term response to sulfonylurea drugs in MODY caused by HNF1A and HNF4A mutations and NDM caused by KCNJ11 and ABCC8 mutations is an excellent example of pharmacogenetics. Mitochondrial diabetes is maternally inherited and usually associated with sensorineural deafness and other neurological features. Monogenic severe insulin resistance can be divided into adipose tissue defects (lipodystrophies, characterized by abnormal fat distribution) and disorders of insulin signalling. Molecular diagnosis of inherited diabetes has important implications for patients, allowing personalized management and screening of their relatives. Misclassification of monogenic diabetes or severe insulin resistance as T1DM and T2DM is common, and new tools for prioritizing suspected cases for genetic testing are needed.

Background: Diabetes mellitus has a multifactorial pathogenesis with a strong genetic component as well as many environmental and lifestyle influences. Emerging evidence suggests that environmental contaminants, including pesticides, might play an important role in the pathogenesis of diabetes. Objectives: We performed a systematic review and meta-analysis of observational studies that assessed the association between exposure to pesticides and diabetes and we examined the presence of heterogeneity and biases across available studies. Methods: A comprehensive literature search of peer-reviewed original research pertaining to pesticide exposure and diabetes, published until 30st May 2015, with no language restriction, was conducted. Eligible studies were those that investigated potential associations between pesticides and diabetes without restrictions on diabetes type. We included cohort studies, case–control studies and cross-sectional studies. We extracted information on study characteristics, type of pesticide assessed, exposure assessment, outcome definition, effect estimate and sample size. Results: We identified 22 studies assessing the association between pesticides and diabetes. The summary OR for the association of top vs. bottom tertile of exposure to any type of pesticide and diabetes was 1.58 (95% CI: 1.32–1.90, p = 1.21 × 10−6), with large heterogeneity (I2 = 66.8%). Studies evaluating Type 2 diabetes in particular (n = 13 studies), showed a similar summary effect comparing top vs. bottom tertiles of exposure: 1.61 (95% CI 1.37–1.88, p = 3.51 × 10−9) with no heterogeneity (I2 = 0%). Analysis by type of pesticide yielded an increased risk of diabetes for DDE, heptachlor, HCB, DDT, and trans-nonachlor or chlordane. Conclusions: The epidemiological evidence, supported by mechanistic studies, suggests an association between exposure to organochlorine pesticides and Type 2 diabetes. Keywords: Diabetes mellitus, Heterogeneity, Meta-analysis, Organochlorines, Pesticides, Type 2 diabetes

Response to anti-diabetic medications is not always predictable or favourable even in phenotypically similar type-2 diabetes (T2D) cases. This is not only due to patient's compliance and access to care but is also considered to be an effect of idiosyncratic differences among individuals, stemming from the combination of their unique genetic background and environmental exposures. In this systematic review, we aimed to summarise the available evidence on pharmacogenetic and pharmacogenomic studies of oral agents for T2D that are currently in the market and describe the agents studied, the targeted loci in regards to the efficacy and the toxicity profile of the agents included. We included 53 studies published between 2003-2012, which examined the following anti-diabetic classes: sulphonylureas, metformin, metiglinides and thiazolidenediones. There were no published studies on newer agents (e.g. incretin based treatments). Forty-nine studies (92.5%) examined the therapeutic response to oral antiglycaemic agents. Outcomes assessed included changes in metabolic markers (fasting or postprandial blood glucose, fasting or postprandial insulin, HbA1c), Homeostasis Assessment Model (HOMA)-Insulin Resistance (IR) or HOMA-B-cell function (HOMA-B), and time to monotherapy failure. Regarding side effects, hypoglycaemia and TZD-related oedema were the most commonly assessed. In the vast majority of the studies included (n=38, 71.7%), more than one outcomes (n=27, 50.9%) and/or more than one SNPs (n=21, 39.6%) were evaluated in the same publication, but most studies examined one drug (n=50, 94.3%). A considerable number of the proposed genes seem to be related to beta-cell development and function, but there are several genes whose underlying pathway linked to diabetes pharmacotherapy remains poorly understood. Pharmacogenomics are still not in pace with the wealth of information provided by GWAS in the genetics of T2D and related traits and the proposed associations need further validation in well-characterized large studies of varying ancestral origins.

Type-2 diabetes is a complex disease modified by a number of environmental and genetic factors that contribute at varying degrees to the final phenotype and possibly interact with each other. Deciphering the genetic background of the disease serves multiple goals ranging from expanding our knowledge on the disease pathogenesis and identifying future targets for drug development to successfully personalizing clinical disease prediction and prognosis. In the present review, we aimed to systematically appraise the current evidence from genome-wide association studies (GWAS) on type- 2 diabetes, identify the gene targets that have been assessed to-date, and discuss issues that stem from the rapid growth of this literature. Our search identified more than 60 recently identified loci implicated with type-2 diabetes and related traits assessed in populations of European and Asian ancestry. A considerable number of the proposed genes seem to be related to beta-cell development and function, but there are several genes identified as "diabetes-genes" whose underlying pathway linked to diabetes remains poorly understood. Despite the increasing numbers of identified genetic markers, a large proportion of the observed type-2 diabetes heritability remains unexplained; larger GWAS on enhanced genotyping platforms, refined ascertainment of the characteristics of the populations under study and additional information from whole-genome sequencing will contribute to a more comprehensive view of the genetic architecture of the disease. This information is also anticipated to improve the predictive ability of multiple-loci genetic risk scores that will eventually be able to identify disease susceptibility over and above the traditional non-genetic risk factors.

Bone mineral density (BMD) is a heritable complex trait used in the clinical diagnosis of osteoporosis and the assessment of fracture risk. We performed meta-analysis of five genome-wide association studies of femoral neck and lumbar spine BMD in 19,195 subjects of Northern European descent. We identified 20 BMD loci that reached genome-wide significance (GWS; P < 5 x 10(-8)), of which 13 map to regions not previously associated with this trait: 1p31.3 (GPR177), 2p21 (SPTBN1), 3p22 (CTNNB1), 4q21.1 (MEPE), 5q14 (MEF2C), 7p14 (STARD3NL), 7q21.3 (FLJ42280), 11p11.2 (LRP4, ARHGAP1, F2), 11p14.1 (DCDC5), 11p15 (SOX6), 16q24 (FOXL1), 17q21 (HDAC5) and 17q12 (CRHR1). The meta-analysis also confirmed at GWS level seven known BMD loci on 1p36 (ZBTB40), 6q25 (ESR1), 8q24 (TNFRSF11B), 11q13.4 (LRP5), 12q13 (SP7), 13q14 (TNFSF11) and 18q21 (TNFRSF11A). The many SNPs associated with BMD map to genes in signaling pathways with relevance to bone metabolism and highlight the complex genetic architecture that underlies osteoporosis and variation in BMD.

In an effort to pinpoint potential genetic risk factors for schizophrenia, research groups worldwide have published over 1,000 genetic association studies with largely inconsistent results. To facilitate the interpretation of these findings, we have created a regularly updated online database of all published genetic association studies for schizophrenia ('SzGene'). For all polymorphisms having genotype data available in at least four independent case-control samples, we systematically carried out random-effects meta-analyses using allelic contrasts. Across 118 meta-analyses, a total of 24 genetic variants in 16 different genes (APOE, COMT, DAO, DRD1, DRD2, DRD4, DTNBP1, GABRB2, GRIN2B, HP, IL1B, MTHFR, PLXNA2, SLC6A4, TP53 and TPH1) showed nominally significant effects with average summary odds ratios of approximately 1.23. Seven of these variants had not been previously meta-analyzed. According to recently proposed criteria for the assessment of cumulative evidence in genetic association studies, four of the significant results can be characterized as showing 'strong' epidemiological credibility. Our project represents the first comprehensive online resource for systematically synthesized and graded evidence of genetic association studies in schizophrenia. As such, it could serve as a model for field synopses of genetic associations in other common and genetically complex disorders.

Meta-analysis offers the opportunity to combine evidence from retrospectively accumulated or prospectively generated data. Meta-analyses may provide summary estimates and can help in detecting and addressing potential inconsistency between the combined datasets. Application of meta-analysis in genetic associations presents considerable potential and several pitfalls. In this review, we present basic principles of meta-analytic methods, adapted for human genome epidemiology. We describe issues that arise in the retrospective or the prospective collection of relevant data through various sources, common traps to consider in the appraisal of evidence and potential biases that may interfere. We describe the relative merits and caveats for common methods used to trace inconsistency across studies along with possible reasons for non-replication of proposed associations. Different statistical models may be employed to combine data and some common misconceptions may arise in the process. Several meta-analysis diagnostics are often applied or misapplied in the literature, and we comment on their use and limitations. An alternative to overcome limitations arising from retrospective combination of data from published studies is to create networks of research teams working in the same field and perform collaborative meta-analyses of individual participant data, ideally on a prospective basis. We discuss the advantages and the challenges inherent in such collaborative approaches. Meta-analysis can be a useful tool in dissecting the genetics of complex diseases and traits, provided its methods are properly applied and interpreted.

CONTEXT: Cytotoxic T-lymphocyte associated antigen 4 (CTLA-4) polymorphisms have been widely examined for their associations with autoimmune thyroid diseases [Graves' disease (GD) and Hashimoto thyroiditis (HT)], but their relative population effect remains unclear. OBJECTIVE: The aim was to generate large-scale evidence on whether the CTLA-4 polymorphisms (A49G and CT60) and haplotypes thereof increase the susceptibility to GD and/or HT. DESIGN, SETTING, AND PARTICIPANTS: Meta-analyses of group-level data were reviewed from 32 (11,019 subjects) and 12 (4,479) published and unpublished studies for the association of the A49G polymorphism with GD and HT, respectively (PubMed and HuGeNet search until July 2006). There were 15 (n = 7246) and six (n = 3086) studies available for the CT60 polymorphism, respectively. Meta-analyses of individual-level data from 10 (4906 subjects) and five (2386) collaborating teams for GD and HT, respectively, were also reviewed. MAIN OUTCOME MEASURES: Association of gene variants and haplotypes with GD and HT was measured. RESULTS: Group-level data suggested significant associations with GD and HT for both A49G [odds ratios 1.49 (P = 6 x 10(-14)) and 1.29 (P = 0.001) per G allele, respectively] and CT60 [1.45 (P = 2 x 10(-9)) and 1.64 (P = 0.003) per G allele, respectively]. Results were consistent between Asian and Caucasian descent subjects. Individual-level data showed that compared with the AA haplotype, the risk conferred by the GG haplotype was 1.49 (95% confidence interval 1.31,1.70) and 1.36 (95% confidence interval 1.16,1.59) for GD and HT, respectively. Data were consistent with a dose-response effect for the G allele of CT60. CONCLUSION: The CT60 polymorphism of CTLA-4 maps an important genetic determinant for the risk of both GD and HT across diverse populations.

Background: Maturity-onset diabetes of the young (MODY) is uncommon; however, accurate diagnosis facilitates personalized management and informs prognosis in probands and relatives. Objective: The objective of the study was to highlight that the appropriate use of genetic and nongenetic investigations leads to the correct classification of diabetes etiology. Case Discussion: A 30-year-old European female was diagnosed with insulin-treated gestational diabetes. She discontinued insulin after delivery; however, her fasting hyperglycemia persisted. β-Cell antibodies were negative and C-peptide was 0.79 nmol/L. Glucokinase (GCK)-MODY was suspected and confirmed by the identification of a GCK mutation (p.T206M). Methods: Systematic clinical and biochemical characterization and GCK mutational analysis were implemented to determine the diabetes etiology in five relatives. Functional characterization of GCK mutations was performed. Results: Identification of the p.T206M mutation in the proband's sister confirmed a diagnosis of GCK-MODY. Her daughter was diagnosed at 16 weeks with permanent neonatal diabetes (PNDM). Mutation analysis identified two GCK mutations that were inherited in trans-p. [(R43P);(T206M)], confirming a diagnosis of GCK-PNDM. Both mutations were shown to be kinetically inactivating. The proband's mother, other sister, and daughter all had a clinical diagnosis of type 1 diabetes, confirmed by undetectable C-peptide levels and β-cell antibody positivity. GCK mutations were not detected. Conclusions: Two previously misclassified family members were shown to have GCK-MODY, whereas another was shown to have GCK-PNDM. A diagnosis of type 1 diabetes was confirmed in three relatives. This family exemplifies the importance of careful phenotyping and systematic evaluation of relatives after discovering monogenic diabetes in an individual.

CONTEXT: A number of small data sets have suggested a potential role for skewed X chromosome activation (XCI), away from the expected 50:50 parent of origin ratio, as an explanation for the strong female preponderance seen in the common autoimmune thyroid diseases (AITD), Graves' disease (GD), and Hashimoto's thyroiditis (HT). OBJECTIVE: The objective of the study was to confirm a role for XCI skewing as a potential explanation for the strong female preponderance seen in AITD. DESIGN: The design of the study was to screen XCI in the largest GD, HT, and control case-control cohort and family cohort to date and undertake a meta-analysis of previous AITD XCI reports. SETTING: The study was conducted at a research laboratory. PATIENTS: Three hundred and nine GD, 490 HT, and 325 female UK Caucasians controls, 273 UK Caucasian GD families, and a meta-analysis of 454 GD, 673 HT, and 643 female Caucasian controls were included in the study. MAIN OUTCOME MEASURES: Case-control and family-based association studies and meta-analysis were measured. RESULTS: Skewed XCI was observed with GD [odds ratio (OR) 2.17 [95% confidence interval (CI) 1.43-3.30], P=2.1×10(-4)] and a trend toward skewing with HT (P=.08) compared with the control cohort. A meta-analysis of our UK data and that of four previous non-UK Caucasian studies confirmed significant skewing of XCI with GD [OR 2.54 (95% CI 1.58-4.10), P=1.0×10(-4), I2=30.2%] and HT [OR 2.40 (95% CI 1.10-5.26), P=.03, I2=74.3%]. CONCLUSIONS: Convincing evidence exists to support a role for skewed XCI in female subjects with AITD, which may, in part, explain the strong female preponderance observed in this disease.

Maturity Onset Diabetes of the Young (MODY), represents a rare cause of diabetes (1% of all cases), commonly misdiagnosed as Type 1 Diabetes (T1D) or Type 2 Diabetes (T2D). Clinical characteristics of MODY include age of onset before 45 years, absence of beta-cell autoimmunity and features of metabolic syndrome, sustained endogenous insulin production and strong family history. Common reasons for misdiagnosis are limitations in physicians' awareness and restrictions in performing genetic testing. In an attempt to improve diagnosis rates, recent research efforts have focused on the discovery of non-genetic biomarkers for prioritising individuals for genetic testing, with some promising progress (identification of high-sensitivity CRP, plasma glycan profile as HNF1A-MODY). The information provided is relevant to physicians dealing with young adults but details on pediatric populations are also included. Raising awareness about MODY and making the diagnosis more accessible will improve prognostication and management of these patients and their relatives.

Background: Osteoporosis is a highly heritable trait. Many candidate genes have been proposed as being involved in regulating bone mineral density (BMD). Few of these findings have been replicated in independent studies.Objective: To assess the relationship between BMD and fracture and all common single-nucleotide polymorphisms (SNPs) in previously proposed osteoporosis candidate genes.Design: Large-scale meta-analysis of genome-wide association data.Setting: 5 international, multicenter, population-based studies.Participants: Data on BMD were obtained from 19 195 participants (14 277 women) from 5 populations of European origin. Data on fracture were obtained from a prospective cohort (n = 5974) from the Netherlands.Measurements: Systematic literature review using the Human Genome Epidemiology Navigator identified autosomal genes previously evaluated for association with osteoporosis. We explored the common SNPs arising from the haplotype map of the human genome (HapMap) across all these genes. BMD at the femoral neck and lumbar spine was measured by dual-energy x-ray absorptiometry. Fractures were defined as clinically apparent, site-specific, validated nonvertebral and vertebral low-energy fractures.Results: 150 candidate genes were identified and 36 016 SNPs in these loci were assessed. SNPs from 9 gene loci (ESR1, LRP4, ITGA1, LRP5, SOST, SPP1, TNFRSF11A, TNFRSF11B, and TNFSF11) were associated with BMD at either site. For most genes, no SNP was statistically significant. For statistically significant SNPs (n = 241), effect sizes ranged from 0.04 to 0.18 SD per allele. SNPs from the LRP5, SOST, SPP1, and TNFRSF11A loci were significantly associated with fracture risk; odds ratios ranged from 1.13 to 1.43 per allele. These effects on fracture were partially independent of BMD at SPP1 and SOST.Limitation: Only common polymorphisms in linkage disequilibrium with SNPs in HapMap could be assessed, and previously reported associations for SNPs in some candidate genes could not be excluded.Conclusion: In this large-scale collaborative genome-wide meta-analysis, 9 of 150 candidate genes were associated with regulation of BMD, 4 of which also significantly affected risk for fracture. However, most candidate genes had no consistent association with BMD.

BACKGROUND: Several genes encoding for DNA repair molecules implicated in maintaining genomic integrity have been proposed as cancer-susceptibility genes. Although efforts have been made to create synopses for specific fields that summarize the data from genetic association studies, such an overview is not available for genes involved in DNA repair. METHODS: We have created a regularly updated database of studies addressing associations between DNA repair gene variants (excluding highly penetrant mutations) and different types of cancer. Using 1087 datasets and publicly available data from genome-wide association platforms, meta-analyses using dominant and recessive models were performed on 241 associations between individual variants and specific cancer types that had been tested in two or more independent studies. The epidemiological strength of each association was graded with Venice criteria that assess amount of evidence, replication, and protection from bias. All statistical tests were two-sided. RESULTS: Thirty-one nominally statistically significant (ie, P < .05 without adjustment for multiple comparisons) associations were recorded for 16 genes in dominant and/or recessive model analyses (BRCA2, CCND1, ERCC1, ERCC2, ERCC4, ERCC5, MGMT, NBN, PARP1, POLI, TP53, XPA, XRCC1, XRCC2, XRCC3, and XRCC4). XRCC1, XRCC2, TP53, and ERCC2 variants were each nominally associated with several types of cancer. Three associations were graded as having "strong" credibility, another four had modest credibility, and 24 had weak credibility based on Venice criteria. Requiring more stringent P values to account for multiplicity of comparisons, only the associations of ERCC2 codon 751 (recessive model) and of XRCC1 -77 T>C (dominant model) with lung cancer had P This work was made possible by a grant to ECNIS (Environmental Cancer Risk, Nutrition and Individual Susceptibility), a network of excellence operating within the European Union 6th Framework Program, Priority 5: “Food Quality and Safety” (contract no. 513943), and by a grant of the compagnia di San Paolo, of the Italian Association for Cancer Research, Italy, and of the Piedmont Region Progetti di Ricerca Sanitaria Finalizzata. F. K. Kavvoura is supported by a PENED training grant cofi nanced by EU — European Social Fund (75%) and the Greek Ministry of Development – General Secretariat of Research and Technology (25%).

Genetic epidemiology is a rapidly evolving field, and the number of published reports is increasing geometrically (1). Many “significant” genetic associations are proposed, but several of them may represent false-positive results (2–5). As we have moved into large-scale testing of associations (6–8), understanding biases in statistically significant results is becoming increasingly useful. Evaluation of observational associations has traditionally rested on P values and hypothesis-testing using nominal levels of statistical significance. This widespread practice may bias the accumulated evidence on single associations or in large fields of research (9). Various types of biases have been described. In publication bias, studies with nominally statistically significant (“positive”) results are more likely to be published than other (“negative”) findings (10–13). The latter findings may be published after a delay (time-lag bias) (14) or may remain unpublished. Selective analysis and outcome reporting bias also lead to overrepresentation of nominally statistically significant outcomes and findings and concealment of non-statistically significant results within the same study (15–17). Finally, some statistically significant results may be fabricated (fake data), hopefully uncommonly (18–20). All of these biases converge towards increasing the relative proportion of nominally statistically significant findings in a body of evidence as compared with what would be expected in the absence of bias. Several tests have been proposed for assessing the presence of publication bias, but they have limitations (21–24). They are designed to be applied to data from single meta-analyses of studies that all pertain to the same research question. Inferences derived from single meta-analyses are limited by the fact that usually few studies are available; thus, these tests are underpowered (25, 26). Recently, Ioannidis and Trikalinos (27) proposed an exploratory test that examines whether there is an excess of significant results in a whole domain of research. Identification of an excess of significant findings with this test can result either from significance-chasing biases or from excessive heterogeneity (diversity of effects) between studies on the same research question. Between-study heterogeneity could be due either to genuine diversity or to biases. This test can be applied not only to single meta-analyses but also to whole domains of research across collections of many meta-analyses. Application in meta-analyses of clinical trials shows an overrepresentation of statistically significant treatment effects in some randomized clinical research, such as research on the effectiveness of diverse neuroleptic treatments (27). Some data suggest a possible excess of significant findings in genetic epidemiology, such as the Chinese-language literature on genetic associations or genetic associations in myocardial infarction (28, 29). Empirical surveys also corroborate the preference for reporting “positive” results in genetic epidemiology (30), and various investigators have reported evidence for publication bias in selected associations (31, 32). However, solid evidence requires the examination of large domains of genetic research, encompassing many hundreds of studies. Such large-scale evaluations may also help us evaluate whether excesses of statistically significant findings are due mostly to bias or to genuine heterogeneity. Here, we applied this exploratory test to cumulative data contained in the AlzGene database (33). Alzheimer's disease is a prolific field in genetic association studies. With over 1,000 individual publications included to date, AlzGene offers the unique opportunity to study a large domain of observational research for a potential excess of statistically significant findings and to try to understand the reasons for such an excess.

Background Several thousand human genome epidemiology association studies are published every year investigating the relationship between common genetic variants and diverse phenotypes. Transparent reporting of study methods and results allows readers to better assess the validity of study findings. Here, we document reporting practices of human genome epidemiology studies. Methods Articles were randomly selected from a continuously updated database of human genome epidemiology association studies to be representative of genetic epidemiology literature. The main analysis evaluated 315 articles published in 2001–2003. For a comparative update, we evaluated 28 more recent articles published in 2006, focusing on issues that were poorly reported in 2001–2003. Results During both time periods, most studies comprised relatively small study populations and examined one or more genetic variants within a single gene. Articles were inconsistent in reporting the data needed to assess selection bias and the methods used to minimize misclassification (of the genotype, outcome, and environmental exposure) or to identify population stratification. Statistical power, the use of unrelated study participants, and the use of replicate samples were reported more often in articles published during 2006 when compared with the earlier sample. Conclusion We conclude that many items needed to assess error and bias in human genome epidemiology association studies are not consistently reported. Although some improvements were seen over time, reporting guidelines and online supplemental material may help enhance the transparency of this literature.

Background Ranking of universities and institutions has attracted wide attention recently. Several systems have been proposed that attempt to rank academic institutions worldwide. Methods We review the two most publicly visible ranking systems, the Shanghai Jiao Tong University 'Academic Ranking of World Universities' and the Times Higher Education Supplement 'World University Rankings' and also briefly review other ranking systems that use different criteria. We assess the construct validity for educational and research excellence and the measurement validity of each of the proposed ranking criteria, and try to identify generic challenges in international ranking of universities and institutions. Results None of the reviewed criteria for international ranking seems to have very good construct validity for both educational and research excellence, and most don't have very good construct validity even for just one of these two aspects of excellence. Measurement error for many items is also considerable or is not possible to determine due to lack of publication of the relevant data and methodology details. The concordance between the 2006 rankings by Shanghai and Times is modest at best, with only 133 universities shared in their top 200 lists. The examination of the existing international ranking systems suggests that generic challenges include adjustment for institutional size, definition of institutions, implications of average measurements of excellence versus measurements of extremes, adjustments for scientific field, time frame of measurement and allocation of credit for excellence. Conclusion Naïve lists of international institutional rankings that do not address these fundamental challenges with transparent methods are misleading and should be abandoned. We make some suggestions on how focused and standardized evaluations of excellence could be improved and placed in proper context.

Background Epidemiological studies may be subject to selective reporting, but empirical evidence thereof is limited. We empirically evaluated the extent of selection of significant results and large effect sizes in a large sample of recent articles. Methods and Findings We evaluated 389 articles of epidemiological studies that reported, in their respective abstracts, at least one relative risk for a continuous risk factor in contrasts based on median, tertile, quartile, or quintile categorizations. We examined the proportion and correlates of reporting statistically significant and nonsignificant results in the abstract and whether the magnitude of the relative risks presented (coined to be consistently ≥1.00) differs depending on the type of contrast used for the risk factor. In 342 articles (87.9%), ≥1 statistically significant relative risk was reported in the abstract, while only 169 articles (43.4%) reported ≥1 statistically nonsignificant relative risk in the abstract. Reporting of statistically significant results was more common with structured abstracts, and was less common in US-based studies and in cancer outcomes. Among 50 randomly selected articles in which the full text was examined, a median of nine (interquartile range 5–16) statistically significant and six (interquartile range 3–16) statistically nonsignificant relative risks were presented (p = 0.25). Paradoxically, the smallest presented relative risks were based on the contrasts of extreme quintiles; on average, the relative risk magnitude was 1.41-, 1.42-, and 1.36-fold larger in contrasts of extreme quartiles, extreme tertiles, and above-versus-below median values, respectively (p < 0.001). Conclusions Published epidemiological investigations almost universally highlight significant associations between risk factors and outcomes. For continuous risk factors, investigators selectively present contrasts between more extreme groups, when relative risks are inherently lower., An evaluation of published articles reporting epidemiological studies found that they almost universally highlight significant associations between risk factors and outcomes., Editors' Summary Background. Medical and scientific researchers use statistical tests to try to work out whether their observations—for example, seeing a difference in some characteristic between two groups of people—might have occurred as a result of chance alone. Statistical tests cannot determine this for sure, rather they can only give a probability that the observations would have arisen by chance. When researchers have many different hypotheses, and carry out many statistical tests on the same set of data, they run the risk of concluding that there are real differences where in fact there are none. At the same time, it has long been known that scientific and medical researchers tend to pick out the findings on which to report in their papers. Findings that are more interesting, impressive, or statistically significant are more likely to be published. This is termed “publication bias” or “selective reporting bias.” Therefore, some people are concerned that the published scientific literature might contain many false-positive findings, i.e., findings that are not true but are simply the result of chance variation in the data. This would have a serious impact on the accuracy of the published scientific literature and would tend to overestimate the strength and direction of relationships being studied. Why Was This Study Done? Selective reporting bias has already been studied in detail in the area of randomized trials (studies where participants are randomly allocated to receive an intervention, e.g., a new drug, versus an alternative intervention or “comparator,” in order to understand the benefits or safety of the new intervention). These studies have shown that very many of the findings of trials are never published, and that statistically significant findings are more likely to be included in published papers than nonsignificant findings. However, much medical research is carried out that does not use randomized trial methods, either because that method is not useful to answer the question at hand or is unethical. Epidemiological research is often concerned with looking at links between risk factors and the development of disease, and this type of research would generally use observation rather than experiment to uncover connections. The researchers here were concerned that selective reporting bias might be just as much of a problem in epidemiological research as in randomized trials research, and wanted to study this specifically. What Did the Researchers Do and Find? In this investigation, searches were carried out of PubMed, a database of biomedical research studies, to extract epidemiological studies that were published between January 2004 and October 2005. The researchers wanted to specifically look at studies reporting the effect of continuous risk factors and their effect on health or disease outcomes (a continuous risk factor is something like age or glucose concentration in the blood, is a number, and can have any value on a sliding scale). Three hundred and eighty-nine original research studies were found, and the researchers pulled out from the abstracts and full text of these papers the relative risks that were reported along with the results of statistical tests for them. (Relative risk is the chance of getting an outcome, say disease, in one group as compared to another group.) The researchers found that nearly 90% of these studies had one or more statistically significant risks reported in the abstract, but only 43% reported one or more risks that were not statistically significant. When looking at all of the findings reported anywhere in the full text for 50 of these studies, the researchers saw that papers overall reported more statistically significant risks than nonsignificant risks. Finally, it seemed that in the set of papers studied here, the way in which statistical analyses were done produced a bias towards more extreme findings: for datasets showing small relative risks, papers were more likely to report a comparison between extreme subsets of the data so as to report larger relative risks. What Do These Findings Mean? These findings suggest that there is a tendency among epidemiology researchers to highlight statistically significant findings and to avoid highlighting nonsignificant findings in their research papers. This behavior may be a problem, because many of these significant findings could in future turn out to be “false positives.” At present, registers exist for researchers to describe ongoing clinical trials, and to set out the outcomes that they plan to analyze for those trials. These registers will go some way towards addressing some of the problems described here, but only for clinical trials research. Registers do not yet exist for epidemiological studies, and therefore it is important that researchers and readers are aware of and cautious about the problem of selective reporting in epidemiological research. Additional Information. Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0040079. Wikipedia entry on publication bias (note: Wikipedia is an internet encyclopedia that anyone can edit) The International Committee of Medical Journal Editors gives guidelines for submitting manuscripts to its member journals, and includes comments about registration of ongoing studies and the obligation to publish negative studies ClinicalTrials.gov and the ISRCTN register are two registries of ongoing clinical trials

Purpose: We aimed to assess whether epidemiological evidence on genetic associations for complex diseases concord with in vitro functional data. Methods: We examined 36 studies on bi-allelic markers and 23 studies on haplotypes where investigators had addressed both epidemiological associations and the functional effect of the same gene variants in luciferase reporter systems in vitro. Results: There was no correlation between epidemiological odds ratios and luciferase activity ratios (−0.09, P = 0.60). Luciferase activity ratios could not tell whether a probed epidemiologic association would be significant or not (area under receiver operating characteristics curve, 0.52). Luciferase results usually were qualitatively similar across cell lines and experimental conditions, with some exceptions. A luciferase activity ratio of 1.44 adequately separated statistically significant from non-significant functional differences (area under receiver operating characteristics curve, 0.95). Binary and continuous disease outcomes usually gave concordant results; other in vitro methods, in particular EMSA, agreed with luciferase results. Selective reporting and use of different variants and contrasts between functional and epidemiological analyses were common in these studies. Conclusions: In vitro biological data and epidemiology provide independent lines of evidence on complex diseases. We provide suggestions for improving the design and reporting of studies addressing both in vitro and epidemiological effects.

Several treatment options exist for many conditions. Randomized trial evidence on the relative merits of various options may be missing or biased.To examine the patterns of trial evidence (network geometry) and explain their implications for the interpretation of the existing evidence on a treatment's relative effectiveness.PubMed and Thompson ISI Web of Knowledge (last search April 2007).Published networks of randomized trials that included at least 4 treatments were identified.For each network, data on the number of studies per treatment comparison were extracted by one investigator and verified by a second investigator.Indices were adopted from the ecological literature that measure diversity (number of treatments and how often they were tested) and co-occurrence (whether some treatment comparisons were preferred and others avoided). Eighteen eligible treatment networks were identified for different diseases, involving 4 to 16 alternative treatments and 10 to 84 trials. Networks in which 1 option (placebo or no treatment) was the typical comparator were star-shaped, even though several treatments might have had proven effectiveness. Other networks had different shapes. Some showed important co-occurrence that avoided specific head-to-head comparisons. Comparison choices sometimes seemed justified, such as when newer treatments were not compared with older ones already shown to be inferior, whereas other choices seemed to reflect preference bias.Networks evolve over time as new trials accumulate, and their geometry may change. Statistical testing for co-occurrence is underpowered when few trials exist.Evaluation of the geometry of a treatment network can offer valuable insights for the interpretation of total evidence when many treatment options are available.

Background Postulated epidemiological associations are subject to several biases. We evaluated whether the Chinese literature on human genome epidemiology may offer insights on the operation of selective reporting and language biases. Methods and Findings We targeted 13 gene-disease associations, each already assessed by meta-analyses, including at least 15 non-Chinese studies. We searched the Chinese Journal Full-Text Database for additional Chinese studies on the same topics. We identified 161 Chinese studies on 12 of these gene-disease associations; only 20 were PubMed-indexed (seven English full-text). Many studies (14–35 per topic) were available for six topics, covering diseases common in China. With one exception, the first Chinese study appeared with a time lag (2–21 y) after the first non-Chinese study on the topic. Chinese studies showed significantly more prominent genetic effects than non-Chinese studies, and 48% were statistically significant per se, despite their smaller sample size (median sample size 146 versus 268, p < 0.001). The largest genetic effects were often seen in PubMed-indexed Chinese studies (65% statistically significant per se). Non-Chinese studies of Asian-descent populations (27% significant per se) also tended to show somewhat more prominent genetic effects than studies of non-Asian descent (17% significant per se). Conclusion Our data provide evidence for the interplay of selective reporting and language biases in human genome epidemiology. These biases may not be limited to the Chinese literature and point to the need for a global, transparent, comprehensive outlook in molecular population genetics and epidemiologic studies in general.