Combination therapy as a potential risk factor for the development of type 2 diabetes in patients with schizophrenia: the GOMAP study.

BACKGROUND: Schizophrenia (SCZ) is associated with increased risk of type 2 diabetes (T2D). The potential diabetogenic effect of concomitant application of psychotropic treatment classes in patients with SCZ has not yet been evaluated. The overarching goal of the Genetic Overlap between Metabolic and Psychiatric disease (GOMAP) study is to assess the effect of pharmacological, anthropometric, lifestyle and clinical measurements, helping elucidate the mechanisms underlying the aetiology of T2D. METHODS: The GOMAP case-control study (Genetic Overlap between Metabolic and Psychiatric disease) includes hospitalized patients with SCZ, some of whom have T2D. We enrolled 1653 patients with SCZ; 611 with T2D and 1042 patients without T2D. This is the first study of SCZ and T2D comorbidity at this scale in the Greek population. We retrieved detailed information on first- and second-generation antipsychotics (FGA, SGA), antidepressants and mood stabilizers, applied as monotherapy, 2-drug combination, or as 3- or more drug combination. We assessed the effects of psychotropic medication, body mass index, duration of schizophrenia, number of hospitalizations and physical activity on risk of T2D. Using logistic regression, we calculated crude and adjusted odds ratios (OR) to identify associations between demographic factors and the psychiatric medications. RESULTS: Patients with SCZ on a combination of at least three different classes of psychiatric drugs had a higher risk of T2D [OR 1.81 (95% CI 1.22-2.69); p = 0.003] compared to FGA alone therapy, after adjustment for age, BMI, sex, duration of SCZ and number of hospitalizations. We did not find evidence for an association of SGA use or the combination of drugs belonging to two different classes of psychiatric medications with increased risk of T2D [1.27 (0.84-1.93), p = 0.259 and 0.98 (0.71-1.35), p = 0.885, respectively] compared to FGA use. CONCLUSIONS: We find an increased risk of T2D in patients with SCZ who take a combination of at least three different psychotropic medication classes compared to patients whose medication consists only of one or two classes of drugs

Evaluating the glucose raising effect of established loci via a genetic risk score.

Recent genome-wide association studies have identified several single nucleotide polymorphisms (SNPs) associated with glucose levels. We tested the hypothesis here whether the cumulative effect of glucose raising SNPs, assessed via a score, is associated with glucose levels. A total of 1,434 participants of Greek descent from the THISEAS study and 1,160 participants form the GOMAP study were included in this analysis. We developed a genetic risk score (GRS), based on the known glucose-raising loci, in order to investigate the cumulative effect of known glucose loci on glucose levels. In the THISEAS study, the GRS score was significantly associated with increased glucose levels (mmol/L) (β ± SE: 0.024 ± 0.004, P = 8.27e-07). The effect of the genetic risk score was also significant in the GOMAP study (β ± SE: 0.011 ± 0.005, P = 0.031). In the meta-analysis of the two studies both scores were significantly associated with higher glucose levels GRS: β ± SE: 0.019 ± 0.003, P = 1.41e-09. Also, variants at the SLC30A8, PROX1, MTNR1B, ADRA2A, G6PC2, LPIN3 loci indicated nominal evidence for association with glucose levels (p < 0.05). We replicate associations of the established glucose raising variants in the Greek population and confirm directional consistency of effects (binomial sign test p = 6.96e-05). We also demonstrate that the cumulative effect of the established glucose loci yielded a significant association with increasing glucose levels

Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations

Osteoarthritis affects over 300 million people worldwide. Here, we conduct a genome-wide association study meta-analysis across 826,690 individuals (177,517 with osteoarthritis) and identify 100 independently associated risk variants across 11 osteoarthritis phenotypes, 52 of which have not been associated with the disease before. We report thumb and spine osteoarthritis risk variants and identify differences in genetic effects between weight-bearing and non-weight-bearing joints. We identify sex-specific and early age-at-onset osteoarthritis risk loci. We integrate functional genomics data from primary patient tissues (including articular cartilage, subchondral bone, and osteophytic cartilage) and identify high-confidence effector genes. We provide evidence for genetic correlation with phenotypes related to pain, the main disease symptom, and identify likely causal genes linked to neuronal processes. Our results provide insights into key molecular players in disease processes and highlight attractive drug targets to accelerate translation

Evaluation of shared genetic aetiology between osteoarthritis and bone mineral density identifies SMAD3 as a novel osteoarthritis risk locus

Osteoarthritis (OA) is a common complex disease with high public health burden and no curative therapy. High bone mineral density (BMD) is associated with an increased risk of developing OA, suggesting a shared underlying biology. Here, we performed the first systematic overlap analysis of OA and BMD on a genome wide scale. We used summary statistics from the GEFOS consortium for lumbar spine (n = 31,800) and femoral neck (n = 32,961) BMD, and from the arcOGEN consortium for three OA phenotypes (hip, ncases=3,498; knee, ncases=3,266; hip and/or knee, ncases=7,410; ncontrols=11,009). Performing LD score regression we found a significant genetic correlation between the combined OA phenotype (hip and/or knee) and lumbar spine BMD (rg=0.18, P = 2.23 × 10-2), which may be driven by the presence of spinal osteophytes. We identified 143 variants with evidence for cross-phenotype association which we took forward for replication in independent large-scale OA datasets, and subsequent meta-analysis with arcOGEN for a total sample size of up to 23,425 cases and 236,814 controls. We found robustly replicating evidence for association with OA at rs12901071 (OR 1.08 95% CI 1.05-1.11, Pmeta=3.12 × 10-10), an intronic variant in the SMAD3 gene, which is known to play a role in bone remodeling and cartilage maintenance. We were able to confirm expression of SMAD3 in intact and degraded cartilage of the knee and hip. Our findings provide the first systematic evaluation of pleiotropy between OA and BMD, highlight genes with biological relevance to both traits, and establish a robust new OA genetic risk locus at SMAD3.This work was funded by the Wellcome Trust (WT098051)

The Genetics of Osteoarthritis

Introduction: Osteoarthritis (OA) is the most prevalent musculoskeletal disease and a leading cause of disability worldwide. It affects 40% of individuals over the age of 70. The health economic burden of OA is rising, commensurate with longevity and obesity rates, and there is currently no curative therapy. The genetic component of OA is ~50% and previous genetic studies have identified associated polymorphisms, traversing hip, knee and hand OA with limited overlap. The heritability explained by these loci is relatively low. Aims: The aim of the work conducted in this thesis was to unravel the genetic component of OA by conducting a series of GWAS followed by additional analyses and by examining established OA loci in so far non-studied populations. Methods: Initially a knee OA GWAS in a Greek population is described. Further, GWAS were performed using genotype data across 16.5 million variants from UK Biobank, followed by gene-based and gene-set analyses. OA was defined based on both self-reported status and through linkage to Hospital Episode Statistics data, and on joint-specificity of disease (knee and/or hip OA). In silico replication and meta-analysis of promising signals was carried out in further cases and controls. Finally, a hip and/or knee OA GWAS was conducted in an independent Greek ethnic group. Results: Confirmatory evidence for previously reported OA risk loci was found in the Greek population datasets, while three loci showed suggestive evidence for association. The UK Biobank analyses identified nine novel OA loci, located within TGFA, ANXA3, PLEC, MAP2K6, JPH3, ZNF345, near SLC30A10, between MPB3B and EQTN and near LTN1 respectively. Most of these signals are common at frequency and reside in or near genes reported to be associated with skeletal and OA relevant phenotypes by functional and animal model studies. Gene and gene based analyses identified genes and biological processes associated with OA. Conclusions: My findings contribute to a better understanding of OA pathophysiology taking the number of established OA loci to twenty nine and pointing to novel biological insights. Larger sample sizes are required to detect reported and novel OA risk loci at genome wide significance in the Greek population. Going forward, large-scale whole genome sequencing studies of well-phenotyped individuals across diverse populations will capture the full allele frequency and variation type spectrum, and afford us further insights into the causes of this debilitating disease

Personal non-commercial use only

ABSTRACT. Osteoarthritis (OA) is a complex disease that affects the whole joint, with multiple biological and environmental factors contributing to its development. The heritable component for primary OA accounts for ~50% of susceptibility. So far, candidate gene studies and genome-wide association scans have established 18 OA-associated loci. These findings account for 11% of the heritability, explaining a rather small fraction of the genetic component. To further unravel the genetic architecture of OA, the field needs to facilitate more precise phenotypic definitions, high genome coverage, and large sample metaanalyses, expecting the identification of rare and low frequency variants with potentially higher penetrance, and more accurate methods for calculating phenotype-genotype correlation. Expression analysis, epigenetics, and investigation of interactions can also help clarify the implicated transcriptional regulatory pathways and provide insights into further novel pathogenic OA mechanisms leading to diagnostic biomarker identification and new, more focused therapeutic disease approaches

:2; Personal non-commercial use only

ABSTRACT. Osteoarthritis (OA) is a complex disease that affects the whole joint, with multiple biological and environmental factors contributing to its development. The heritable component for primary OA accounts for ~50% of susceptibility. So far, candidate gene studies and genome-wide association scans have established 18 OA-associated loci. These findings account for 11% of the heritability, explaining a rather small fraction of the genetic component. To further unravel the genetic architecture of OA, the field needs to facilitate more precise phenotypic definitions, high genome coverage, and large sample metaanalyses, expecting the identification of rare and low frequency variants with potentially higher penetrance, and more accurate methods for calculating phenotype-genotype correlation. Expression analysis, epigenetics, and investigation of interactions can also help clarify the implicated transcriptional regulatory pathways and provide insights into further novel pathogenic OA mechanisms leading to diagnostic biomarker identification and new, more focused therapeutic disease approaches

Widespread epigenomic, transcriptomic and proteomic differences between hip osteophytic and articular chondrocytes in osteoarthritis.

OBJECTIVES: To identify molecular differences between chondrocytes from osteophytic and articular cartilage tissue from OA patients. METHODS: We investigated genes and pathways by combining genome-wide DNA methylation, RNA sequencing and quantitative proteomics in isolated primary chondrocytes from the cartilaginous layer of osteophytes and matched areas of low- and high-grade articular cartilage across nine patients with OA undergoing hip replacement surgery. RESULTS: Chondrocytes from osteophytic cartilage showed widespread differences to low-grade articular cartilage chondrocytes. These differences were similar to, but more pronounced than, differences between chondrocytes from osteophytic and high-grade articular cartilage, and more pronounced than differences between high- and low-grade articular cartilage. We identified 56 genes with significant differences between osteophytic chondrocytes and low-grade articular cartilage chondrocytes on all three omics levels. Several of these genes have known roles in OA, including ALDH1A2 and cartilage oligomeric matrix protein, which have functional genetic variants associated with OA from genome-wide association studies. An integrative gene ontology enrichment analysis showed that differences between osteophytic and low-grade articular cartilage chondrocytes are associated with extracellular matrix organization, skeletal system development, platelet aggregation and regulation of ERK1 and ERK2 cascade. CONCLUSION: We present a first comprehensive view of the molecular landscape of chondrocytes from osteophytic cartilage as compared with articular cartilage chondrocytes from the same joints in OA. We found robust changes at genes relevant to chondrocyte function, providing insight into biological processes involved in osteophyte development and thus OA progression.Wellcome Trust [grant number WT098051]; European Research Council [grant number ERC-2011-StG 280559-SEPI to E Zeggini]; RAB was funded by the National Institute for Health Research (Cambridge Biomedical Research Centre)

Distribution of individuals in every genotype score group and cumulative effects of the risk alleles from the 36 SNPs for glucose levels.

<p>The bar plots show the average and standard error of glucose in mmol/L for each genotype score group distribution of the genetic risk score.</p

Associations of the unweighted Genetic Risk Score (GRS) and the weighted Genetic Risk Score (wGRS) with glucose levels for the meta-analysis of the GOMAP and THISEAS studies ^a.

<p>Associations of the unweighted Genetic Risk Score (GRS) and the weighted Genetic Risk Score (wGRS) with glucose levels for the meta-analysis of the GOMAP and THISEAS studies <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0186669#t001fn001" target="_blank">^a</a>.</p