Publications by authors named "Jennifer A Collister"

Article Synopsis
  • Previous research shows that adding a polygenic risk score (PRS) improves breast cancer risk prediction models, but its effect on risk categorization needed further exploration.
  • The study analyzed data from over 126,000 post-menopausal women and combined PRS with existing risk models (Tyrer-Cuzick and Gail) to assess changes in risk classification.
  • Results indicated that including PRS significantly enhanced prediction accuracy, particularly for identifying cases, suggesting that PRS could be valuable in breast cancer screening and risk assessment.
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Aims: We evaluated whether incorporating information on ethnic background and polygenic risk enhanced the Leicester Risk Assessment (LRA) score for predicting 10-year risk of type 2 diabetes.

Methods: The sample included 202,529 UK Biobank participants aged 40-69 years. We computed the LRA score, and developed two new risk scores using training data (80% sample): LRArev, which incorporated additional information on ethnic background, and LRAprs, which incorporated polygenic risk for type 2 diabetes.

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The UK Biobank has made general practitioner (GP) data (censoring date 2016-2017) available for approximately 45% of the cohort, whilst hospital inpatient and death registry (referred to as "HES/Death") data are available cohort-wide through 2018-2022 depending on whether the data comes from England, Wales or Scotland. We assessed the importance of case ascertainment via different data sources in UKB for three diseases that are usually first diagnosed in primary care: Parkinson's disease (PD), type 2 diabetes (T2D), and all-cause dementia. Including GP data at least doubled the number of incident cases in the subset of the cohort with primary care data (e.

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Background: Associations between kidney function and dementia risk are inconclusive. Chronic kidney disease (CKD) severity is determined by levels of both estimated glomerular filtration rate (eGFR) and the urine albumin to creatinine ratio (ACR). However, whether there is a graded increase in dementia risk for worse eGFR in each ACR category is unclear.

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Objective: To externally evaluate the performance of QRISK3 for predicting 10 year risk of cardiovascular disease (CVD) in the UK Biobank cohort.

Methods: We used data from the UK Biobank, a large-scale prospective cohort study of 403 370 participants aged 40-69 years recruited between 2006 and 2010 in the UK. We included participants with no previous history of CVD or statin treatment and defined the outcome to be the first occurrence of coronary heart disease, ischaemic stroke or transient ischaemic attack, derived from linked hospital inpatient records and death registrations.

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Context: Early diagnosis of type 2 diabetes is crucial to reduce severe comorbidities and complications. Current screening recommendations for type 2 diabetes include traditional risk factors, primarily body mass index (BMI) and family history, however genetics also plays a key role in type 2 diabetes risk. It is important to understand whether genetic predisposition to type 2 diabetes modifies the effect of these traditional factors on type 2 diabetes risk.

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Polygenic risk scores (PRS) are proposed for use in clinical and research settings for risk stratification. However, there are limited investigations on how different PRS diverge from each other in risk prediction of individuals. We compared two recently published PRS for each of three conditions, breast cancer, hypertension and dementia, to assess the stability of using these algorithms for risk prediction in a single large population.

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Introduction: There is inconsistent evidence on whether genetic risk for dementia modifies the association between hypertension and dementia.

Methods: In 198,965 dementia-free participants aged ≥60 years, Cox proportional-hazards models were used to investigate the association between hypertension and incident dementia. A polygenic risk score (PRS) based on 38 non-apolipoprotein E (APOE) single nucleotide polymorphisms and APOE ε4 status were used to determine genetic risk for dementia.

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A polygenic risk score estimates the genetic risk of an individual for some disease or trait, calculated by aggregating the effect of many common variants associated with the condition. With the increasing availability of genetic data in large cohort studies such as the UK Biobank, inclusion of this genetic risk as a covariate in statistical analyses is becoming more widespread. Previously this required specialist knowledge, but as tooling and data availability have improved it has become more feasible for statisticians and epidemiologists to calculate existing scores themselves for use in analyses.

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