The Estimation Power of Alternative Comorbidity Indices

Objective:  Health-care expenditures are strongly influenced by overall illness burden. Appropriate risk adjustment is required for correct policy analysis. We compared three risk adjustment methods: the Charlson comorbidity index (CCI), the chronic disease score (CDS), and the Agency for Healthcare Research and Quality's comorbidity index (AHRQCI) in terms of their estimation power in analyzing health-care expenditures. Method:  Data from the Thomson MarketScan ® Research Databases (Thomson Healthcare, Ann Arbor, MI) were used to estimate total health-care expenditures of migraine patients treated by a triptan. Seven distinct multivariate models were evaluated for model fit (CCI only, CDS only, AHRQCI only, CCI + CDS, CCI + AHRQCI, CDS + AHRQCI, and CCI + CDS + AHRQCI). The estimation power of these indices (alone and in combination) was evaluated using Bayesian and Akaike information criteria, log-likelihood scores, and pseudo R 2 values. Results:  Confirming results from previous studies, when comorbidity indices were considered individually the results were inconclusive. Statistically the best performance was observed in the model that included all three of the comorbidity measures (CCI + CDS + AHRQCI); however, the practical differences in the estimated values were small. Conclusion:  Low correlation between these comorbidity indices shows that it is possible to have potential risk factors that are not captured in the single comorbidity index. Each comorbidity measure considers different risks, and the collinearity of the three measures is not strong enough to preclude using them simultaneously in the same model.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/71610/1/j.1524-4733.2008.00343.x.pd

Insomnia medication use and the probability of an accidental event in an older adult population

ObjectiveThis study examined the risk of accidental events in older adults prescribed a sedating antidepressant, long-acting benzodiazepine, short-acting benzodiazepine, and nonbenzodiazepine, relative to a reference group (selective melatonin receptor agonist).MethodsThis was a retrospective cohort analysis of older adults (≥65 years) with newly initiated pharmacological treatment of insomnia. Data were collected from the Thomson MarketScan(®) Medicare Supplemental and Coordination of Benefits databases (January 1, 2000, through June 30, 2006). Probit models were used to evaluate the probability of an accidental event.ResultsData were analyzed for 445,329 patients. Patients taking a long-acting benzodiazepine (1.21 odds ratio [OR]), short-acting benzodiazepine (1.16 OR), or nonbenzodiazepine (1.12 OR) had a significantly higher probability of experiencing an accidental event during the first month following treatment initiation compared with patients taking the reference medication (P < 0.05 for all). A significantly higher probability of experiencing an accidental event was also observed during the 3-month period following the initiation of treatment (1.62 long-acting benzodiazepine, 1.60 short-acting benzodiazepine, 1.48 nonbenzodiazepine, and 1.56 sedating antidepressant; P < 0.05).ConclusionsOlder adults taking an SAD or any of the benzodiazepine receptor agonists appear to have a greater risk of an accidental event compared with a reference group taking an MR

Hundreds of variants clustered in genomic loci and biological pathways affect human height

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

Parallel adaptation of rabbit populations to myxoma virus.

In the 1950s the myxoma virus was released into European rabbit populations in Australia and Europe, decimating populations and resulting in the rapid evolution of resistance. We investigated the genetic basis of resistance by comparing the exomes of rabbits collected before and after the pandemic. We found a strong pattern of parallel evolution, with selection on standing genetic variation favoring the same alleles in Australia, France, and the United Kingdom. Many of these changes occurred in immunity-related genes, supporting a polygenic basis of resistance. We experimentally validated the role of several genes in viral replication and showed that selection acting on an interferon protein has increased the protein's antiviral effect.This work was supported by grants from the Programa Operacional Potencial Humano–Quadro de Referência Estratégica Nacional funds from the European Social Fund and Portuguese Ministério da Ciência, Tecnologia e Ensino Superior to M.C. (IF/00283/2014/CP1256/CT0012), to P.J.E. (IF/00376/2015) and to J.M.A. (SFRH/BD/72381/2010). AM was supported by the European Research Council (grant 647787-LocalAdaptation). FJ was supported by the European Research Council (grant 281668). LL was supported by the European Research Council grant (339941-ADAPT). McFadden Lab is supported by National Institute of Health (NIH) grant R01 AI080607. S.C.G. holds a Sir Henry Dale Fellowship, co-funded by the Wellcome Trust and the Royal Society (098406/Z/12/Z)

New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk.

Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes

Meta-analysis of Genome-Wide Association Studies for Extraversion: Findings from the Genetics of Personality Consortium

Extraversion is a relatively stable and heritable personality trait associated with numerous psychosocial, lifestyle and health outcomes. Despite its substantial heritability, no genetic variants have been detected in previous genome-wide association (GWA) studies, which may be due to relatively small sample sizes of those studies. Here, we report on a large meta-analysis of GWA studies for extraversion in 63,030 subjects in 29 cohorts. Extraversion item data from multiple personality inventories were harmonized across inventories and cohorts. No genome-wide significant associations were found at the single nucleotide polymorphism (SNP) level but there was one significant hit at the gene level for a long non-coding RNA site (LOC101928162). Genome-wide complex trait analysis in two large cohorts showed that the additive variance explained by common SNPs was not significantly different from zero, but polygenic risk scores, weighted using linkage information, significantly predicted extraversion scores in an independent cohort. These results show that extraversion is a highly polygenic personality trait, with an architecture possibly different from other complex human traits, including other personality traits. Future studies are required to further determine which genetic variants, by what modes of gene action, constitute the heritable nature of extraversion

Subsequent Event Risk in Individuals with Established Coronary Heart Disease:Design and Rationale of the GENIUS-CHD Consortium

BACKGROUND: The "GENetIcs of sUbSequent Coronary Heart Disease" (GENIUS-CHD) consortium was established to facilitate discovery and validation of genetic variants and biomarkers for risk of subsequent CHD events, in individuals with established CHD. METHODS: The consortium currently includes 57 studies from 18 countries, recruiting 185,614 participants with either acute coronary syndrome, stable CHD or a mixture of both at baseline. All studies collected biological samples and followed-up study participants prospectively for subsequent events. RESULTS: Enrollment into the individual studies took place between 1985 to present day with duration of follow up ranging from 9 months to 15 years. Within each study, participants with CHD are predominantly of self-reported European descent (38%-100%), mostly male (44%-91%) with mean ages at recruitment ranging from 40 to 75 years. Initial feasibility analyses, using a federated analysis approach, yielded expected associations between age (HR 1.15 95% CI 1.14-1.16) per 5-year increase, male sex (HR 1.17, 95% CI 1.13-1.21) and smoking (HR 1.43, 95% CI 1.35-1.51) with risk of subsequent CHD death or myocardial infarction, and differing associations with other individual and composite cardiovascular endpoints. CONCLUSIONS: GENIUS-CHD is a global collaboration seeking to elucidate genetic and non-genetic determinants of subsequent event risk in individuals with established CHD, in order to improve residual risk prediction and identify novel drug targets for secondary prevention. Initial analyses demonstrate the feasibility and reliability of a federated analysis approach. The consortium now plans to initiate and test novel hypotheses as well as supporting replication and validation analyses for other investigators