ORCID Profile
0000-0003-3581-8980
Current Organisations
University of Oxford
,
University of Exeter
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Publisher: Cold Spring Harbor Laboratory
Date: 21-01-2022
DOI: 10.1101/2022.01.21.22269629
Abstract: To assess how accurately a genetic risk score (GRS) can identify incident prostate cancer in men seeing their general practitioner with lower urinary tract symptoms. Cohort study. UK Biobank data linked to primary care records. Men registered with the UK Biobank, eligible for the primary care data linkage, with a record showing that they consulted their general practitioner with lower urinary tract symptoms (LUTS) that could indicate possible undiagnosed prostate cancer. A diagnosis of prostate cancer within two years of the patient’s first consultation with their general practitioner for LUTS. A GRS is associated with prostate cancer in men with symptoms (OR=2.54 [2.16 to 2.99] p=5e-29). An integrated risk model including age and GRS applied to symptomatic men predicted prostate cancer with an AUC of 0.768 (0.739 to 0.796). Men aged 40 years and under in the bottom four GRS quintiles, aged 50 years and under in the bottom two GRS quintiles, and aged 50 to 60 years in the bottom GRS quintile had a two-year prostate cancer incidence below 1%, despite the presence of symptoms. The negative predictive value of the combined model exceeded 99%. This study is the first to apply a genetic risk score in a clinical setting to improve the triage of men with symptoms of prostate cancer. It demonstrates the added benefit of incorporating an estimate of genetic risk of prostate cancer into the clinical assessment of symptomatic men in primary care. Assessment of prostate cancer risk in men with LUTS is currently based on presenting clinical features alone. Men with the lowest genetic risk of developing prostate cancer could safely avoid invasive investigation, with adequate safety netting, whilst those identified with the greatest risk could be fast-tracked for further investigation.
Publisher: Cold Spring Harbor Laboratory
Date: 19-04-2023
DOI: 10.1101/2023.04.18.23288756
Abstract: Heterogeneity exists in type 1 diabetes (T1D) development and presentation. Islet autoantibodies form the foundation for T1D diagnostic and staging efforts. We hypothesized that autoantibodies can be used to identify heterogeneity in T1D before, at, and after diagnosis, and in response to disease modifying therapies. at clinically relevant timepoints throughout T1D progression. We performed a systematic review assessing 10 years of original research studies examining relationships between autoantibodies and heterogeneity during disease progression, at the time of diagnosis, after diagnosis, and in response to disease modifying therapies in in iduals at risk for T1D or within 1 year of T1D diagnosis. 10,067 papers were screened. Out of 151 that met data extraction criteria, 90 studies characterized heterogeneity before clinical diagnosis. Autoantibody type/target was most commonly examined, followed by autoantibody number, titer, order of seroconversion, affinity, and novel islet autoantibodies/epitopes. Recurring themes included positive relationships of autoantibody number and specific types and titers with disease progression, differing clinical phenotypes based on the order of autoantibody seroconversion, and interactions with age and genetics. Overall, reporting of autoantibody assay performance was commonly included however, only 43% (65/151) included information about autoantibody assay standardization efforts. Populations studied were almost exclusively of European ancestry. Current evidence most strongly supports the application of autoantibody features to more precisely define T1D before clinical diagnosis. Our findings support continued use of pre-clinical staging paradigms based on autoantibody number and suggest that additional autoantibody features, particularly when considered in relation to age and genetic risk, could offer more precise stratification. Increased participation in autoantibody standardization efforts is a critical step to improving future applicability of autoantibody-based precision medicine in T1D. We performed a systematic review to ascertain whether islet autoantibodies, biomarkers of autoimmunity against insulin-producing cells, could aid in stratifying in iduals with different clinical presentations of type 1 diabetes. We found existing evidence most strongly supporting the application of these biomarkers to the period before clinical diagnosis, when certain autoantibody features (number, type) and the age when they develop, can provide important information for patients and care providers on what to expect for future type 1 diabetes progression.
Publisher: Public Library of Science (PLoS)
Date: 03-04-2018
Publisher: American Diabetes Association
Date: 18-01-2022
DOI: 10.2337/DB21-0694
Abstract: We aim to assess the long-term impact of acute kidney injury (AKI) on progression of diabetic kidney disease (DKD) and all-cause mortality and investigate determinants of AKI in Chinese patients with type 2 diabetes (T2D). A consecutive cohort of 9,096 Chinese patients with T2D from the Hong Kong Diabetes Register was followed for 12 years (mean ± SD age 57 ± 13.2 years 46.9% men median duration of diabetes 5 years). AKI was defined based on the Kidney Disease: Improving Global Outcomes (KDIGO) criteria using serum creatinine. Estimated glomerular filtration rate measurements were used to identify the first episode with chronic kidney disease (CKD) and end-stage renal disease (ESRD). Polygenic risk score (PRS) composed of 27 single nucleotide polymorphisms (SNPs) known to be associated with serum uric acid (SUA) in European populations was used to examine the role of SUA in pathogenesis of AKI, CKD, and ESRD. Validation was sought in an independent cohort including 6,007 patients (age 61.2 ± 10.9 years 59.5% men median duration of diabetes 10 years). Patients with AKI had a higher risk for developing incident CKD (hazard ratio 14.3 [95% CI 12.69–16.11]), for developing ESRD (12.1 [10.74–13.62]), and for all-cause death (7.99 [7.31–8.74]) compared with those without AKI. Incidence rate for ESRD among patients with no episodes of AKI and one, two, and three or more episodes of AKI was 7.1, 24.4, 32.4, and 37.3 per 1,000 person-years, respectively. Baseline SUA was a strong independent predictor for AKI. A PRS composed of 27 SUA-related SNPs was associated with AKI and CKD in both discovery and replication cohorts but not ESRD. Elevated SUA may increase the risk of DKD through increasing AKI. The identification of SUA as a modifiable risk factor and PRS as a nonmodifiable risk factor may facilitate the identification of in iduals at high risk to prevent AKI and its long-term impact in T2D.
Publisher: American Diabetes Association
Date: 12-07-2018
DOI: 10.2337/DC18-0087
Abstract: We tested the ability of a type 1 diabetes (T1D) genetic risk score (GRS) to predict progression of islet autoimmunity and T1D in at-risk in iduals. We studied the 1,244 TrialNet Pathway to Prevention study participants (T1D patients’ relatives without diabetes and with one or more positive autoantibodies) who were genotyped with Illumina ImmunoChip (median [range] age at initial autoantibody determination 11.1 years [1.2–51.8], 48% male, 80.5% non-Hispanic white, median follow-up 5.4 years). Of 291 participants with a single positive autoantibody at screening, 157 converted to multiple autoantibody positivity and 55 developed diabetes. Of 953 participants with multiple positive autoantibodies at screening, 419 developed diabetes. We calculated the T1D GRS from 30 T1D-associated single nucleotide polymorphisms. We used multivariable Cox regression models, time-dependent receiver operating characteristic curves, and area under the curve (AUC) measures to evaluate prognostic utility of T1D GRS, age, sex, Diabetes Prevention Trial–Type 1 (DPT-1) Risk Score, positive autoantibody number or type, HLA DR3/DR4-DQ8 status, and race/ethnicity. We used recursive partitioning analyses to identify cut points in continuous variables. Higher T1D GRS significantly increased the rate of progression to T1D adjusting for DPT-1 Risk Score, age, number of positive autoantibodies, sex, and ethnicity (hazard ratio [HR] 1.29 for a 0.05 increase, 95% CI 1.06–1.6 P = 0.011). Progression to T1D was best predicted by a combined model with GRS, number of positive autoantibodies, DPT-1 Risk Score, and age (7-year time-integrated AUC = 0.79, 5-year AUC = 0.73). Higher GRS was significantly associated with increased progression rate from single to multiple positive autoantibodies after adjusting for age, autoantibody type, ethnicity, and sex (HR 2.27 for GRS & .295, 95% CI 1.47–3.51 P = 0.0002). The T1D GRS independently predicts progression to T1D and improves prediction along T1D stages in autoantibody-positive relatives.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Richard Oram.