The Baker Biobank: Understanding Cardiovascular Outcomes

Haregu, Tilahun Nigatu; Nanayakkara, Shane; Kingwell, Bronwyn A.; Jennings, G.; Dart, Anthony M.; Carrington, M.; Kaye, David M.

doi:10.1016/j.hlc.2019.08.014

Cited by 4 publications

(7 citation statements)

References 5 publications

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“…The Baker Biobank is a CVD database in Australia that collected data on 6531 individuals aged 18–69 years between January 2000 and December 2011. Participants were recruited from hospital and health check clinics and included those with established CVD or with at least one risk factor for CVD (including obesity) 14 . The database includes baseline data regarding sociodemographic profile, medical history, family history, medication use, physical measurements, and biomarkers.…”

Section: Methodsmentioning

confidence: 99%

“…Follow‐up data were determined via linkage with the Victorian Admitted Episodes Dataset (VAED), a state‐wide administrative database that includes discharge diagnostic data, and the National Death Index (NDI), including all admissions or deaths until May 2018. Further details regarding the Baker Biobank study methods are detailed in previous studies 14–16 …”

Section: Methodsmentioning

confidence: 99%

“…Participants were recruited from hospital and health check clinics and included those with established CVD or with at least one risk factor for CVD (including obesity). 14 The database includes baseline data regarding sociodemographic profile, medical history, family history, medication use, physical measurements, and biomarkers. Follow‐up data were determined via linkage with the Victorian Admitted Episodes Dataset (VAED), a state‐wide administrative database that includes discharge diagnostic data, and the National Death Index (NDI), including all admissions or deaths until May 2018.…”

Section: Methodsmentioning

confidence: 99%

“…Further details regarding the Baker Biobank study methods are detailed in previous studies. [14][15][16] Given that the purpose of the present study was to examine factors associated with development of incident HF, patients with a previous history of HF were excluded from the analysis. Patients not matched to the VAED or NDI datasets and therefore without any follow-up data were also excluded.…”

Section: Baker Biobank and Study Participantsmentioning

confidence: 99%

“…17 Total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides, and fasting glucose were measured immediately following baseline blood sampling as previously described. 14 Systolic and diastolic blood pressures (SBP and DBP) were measured by study staff at the baseline study visit using the average of three recordings with an automated sphygmomanometer (Omron HEM-907) in the seated position after 10 min of rest.…”

Section: Baseline Characteristics and Atherosclerotic Cardiovascular ...mentioning

confidence: 99%

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Differences in predictors of incident heart failure according to atherosclerotic cardiovascular disease status

Dawson,

Carrington,

Haregu

et al. 2023

ESC Heart Failure

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AimsHeart failure (HF) is a common cause of morbidity and mortality, related to a broad range of sociodemographic, lifestyle, cardiometabolic, and comorbidity risk factors, which may differ according to the presence of atherosclerotic cardiovascular disease (ASCVD). We assessed the association between incident HF with baseline status across these domains, overall and separated according to ASCVD status.Methods and resultsWe included 5758 participants from the Baker Biobank cohort without HF at baseline enrolled between January 2000 and December 2011. The primary endpoint was incident HF, defined as hospital admission or HF‐related death, determined through linkage with state‐wide administrative databases (median follow‐up 12.2 years). Regression models were fitted adjusted for sociodemographic variables, alcohol intake, smoking status, measures of adiposity, cardiometabolic profile measures, and individual comorbidities. During 65 987 person‐years (median age 59 years, 38% women), incident HF occurred among 784 participants (13.6%) overall. Rates of incident HF were higher among patients with ASCVD (624/1929, 32.4%) compared with those without ASCVD (160/3829, 4.2%). Incident HF was associated with age, socio‐economic status, alcohol intake, smoking status, body mass index (BMI), waist circumference, waist–hip ratio, systolic blood pressure (SBP), and low‐ and high‐density lipoprotein cholesterol (LDL‐C and HDL‐C), with non‐linear relationships observed for age, alcohol intake, BMI, waist circumference, waist–hip ratio, SBP, LDL‐C, and HDL‐C. Risk factors for incident HF were largely consistent regardless of ASCVD status, although diabetes status had a greater association with incident HF among patients without ASCVD.ConclusionsIncident HF is associated with a broad range of baseline sociodemographic, lifestyle, cardiometabolic, and comorbidity factors, which are mostly consistent regardless of ASCVD status. These data could be useful in efforts towards developing risk prediction models that can be used in patients with ASCVD.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Baker Biobank and Study Participantsmentioning

confidence: 99%

Section: Baseline Characteristics and Atherosclerotic Cardiovascular ...mentioning

confidence: 99%

See 3 more Smart Citations

Differences in predictors of incident heart failure according to atherosclerotic cardiovascular disease status

Dawson,

Carrington,

Haregu

et al. 2023

ESC Heart Failure

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Morocco’s First Biobank: Establishment, Ethical Issues, Biomedical Research Opportunities, and Challenges

Lhousni

Belmokhtar

et al. 2020

BioMed Research International

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Background. Biobanks are highly organized infrastructures that allow the storage of human biological specimens associated with donors’ personal and clinical data. These infrastructures play a key role in the development of translational medical research. In this context, we launched, in November 2015, the first biobank in Morocco (BRO Biobank) in order to promote biomedical research and provide opportunities to include Moroccan and North African ethnic groups in international biomedical studies. Here, we present the setup and the sample characteristics of BRO Biobank. Methods. Patients were recruited at several departments of two major health-care centers in the city of Oujda. Healthy donors were enrolled during blood donation campaigns all over Eastern Morocco. From each participant, personal, clinical, and biomedical data were collected, and several biospecimens were stored. Standard operating procedures have been established in accordance with international guidelines on human biobanks. Results. Between November 2015 and July 2020, 2446 participants were recruited into the BRO Biobank, of whom 2013 were healthy donors, and 433 were patients. For healthy donors, the median age was 35 years with a range between 18 and 65 years and the consanguinity rate was 28.96%. For patients, the median age was 11 years with a range between 1 day and 83 years. Among these patients, 55% had rare diseases (hemoglobinopathies, intellectual disabilities, disorders of sex differentiation, myopathies, etc.), 13% had lung cancer, 4% suffered from hematological neoplasms, 3% were from the kidney transplantation project, and 25% had unknown diagnoses. The BRO Biobank has collected 5092 biospecimens, including blood, white blood cells, plasma, serum, urine, frozen tissue, FFPE tissue, and nucleic acids. A sample quality control has been implemented and suggested that samples of the BRO Biobank are of high quality and therefore suitable for high-throughput nucleic acid analysis. Conclusions. The BRO Biobank is the largest sample collection in Morocco, and it is ready to provide samples to national and international research projects. Therefore, the BRO Biobank is a valuable resource for advancing translational medical research.

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Ten‐Year Risk Equations for Incident Heart Failure in Established Atherosclerotic Cardiovascular Disease Populations

Dawson,

Carrington,

Haregu

et al. 2024

JAHA

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Background Ten‐year risk equations for incident heart failure (HF) are available for the general population, but not for patients with established atherosclerotic cardiovascular disease (ASCVD), which is highly prevalent in HF cohorts. This study aimed to develop and validate 10‐year risk equations for incident HF in patients with known ASCVD. Methods and Results Ten‐year risk equations for incident HF were developed using the United Kingdom Biobank cohort (recruitment 2006–2010) including participants with established ASCVD but free from HF at baseline. Model performance was validated using the Australian Baker Heart and Diabetes Institute Biobank cohort (recruitment 2000–2011) and compared with the performance of general population risk models. Incident HF occurred in 13.7% of the development cohort (n=31 446, median 63 years, 35% women, follow‐up 10.7±2.7 years) and in 21.3% of the validation cohort (n=1659, median age 65 years, 25% women, follow‐up 9.4±3.7 years). Predictors of HF included in the sex‐specific models were age, body mass index, systolic blood pressure (treated or untreated), glucose (treated or untreated), cholesterol, smoking status, QRS duration, kidney disease, myocardial infarction, and atrial fibrillation. ASCVD‐HF equations had good discrimination and calibration in development and validation cohorts, with superior performance to general population risk equations. Conclusions ASCVD‐specific 10‐year risk equations for HF outperform general population risk models in individuals with established ASCVD. The ASCVD‐HF equations can be calculated from readily available clinical data and could facilitate screening and preventative treatment decisions in this high‐risk group.

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The Baker Biobank: Understanding Cardiovascular Outcomes

Cited by 4 publications

References 5 publications

Differences in predictors of incident heart failure according to atherosclerotic cardiovascular disease status

Differences in predictors of incident heart failure according to atherosclerotic cardiovascular disease status

Morocco’s First Biobank: Establishment, Ethical Issues, Biomedical Research Opportunities, and Challenges

Ten‐Year Risk Equations for Incident Heart Failure in Established Atherosclerotic Cardiovascular Disease Populations

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