Gemma Cadby

Australian higher education equity policy has traditionally relied on demographic indicators to identify students at risk. While these classifications remain essential for addressing structural disadvantage, they may obscure variation in how students experience university. Using a person-centred Latent Class Analysis (LCA), this study examined multidimensional patterns of underlying student experience and their associations with intention to leave and academic performance. Drawing on nationally representative data from the Quality Indicators for Learning and Teaching (QILT) Student Experience Survey (SES) from 2019 to 2022 (approximately 230,000 domestic undergraduate students), five latent classes were identified, characterised by distinct configurations of belonging, engagement, institutional support, satisfaction, and external pressures. These classes were stable across survey years and formed a clear hierarchy of risk. Students in the “Disengaged” and “Engaged but under-supported” classes consistently showed the highest likelihood of considering leaving university or reporting a fail grade, whereas those in the “Happy overall” class demonstrated the lowest risk. Each equity group contained a heterogeneous mix of experience classes. Findings suggest that experience-based approaches can complement existing equity frameworks by enabling more nuanced identification of student vulnerability.

Background In Australia, surveillance of adverse events following immunisation is primarily conducted by states and territories, with each jurisdiction only able to view and analyse reports originating from their own population. Distributed data models (aka federated data models) are a form of decentralised collaboration, with each site maintaining ownership of its data from end-to-end including data collection, storage and analysis. The primary benefit of this model is that it maintains independence and autonomy while enabling interdependence, collaboration and scalability. Objective We aimed to investigate statistical methods for a multi-jurisdictional collaboration when conducting a rigorous assessment of rare adverse events following immunisation at a national level. Methods Victoria and Western Australia have independently established routine data linkage for vaccine safety surveillance. A data collaboration model is proposed, whereby each jurisdiction can generate de-identified population-level data for adverse events following immunisation, using agreed case definitions and analytical methods. To demonstrate its utility, Victoria and Western Australia combined data from a self-controlled case series via a meta-analysis approach using aggregate data and a pooled approach using individual-level data to investigate the association between coronavirus disease 2019 vaccines and Guillain–Barré syndrome. Results There were 519 and 176 new Guillain–Barré syndrome International Classification of Diseases, Tenth Revision, Australian Modification coded admissions in Victoria and Western Australia, respectively, between 01/01/2020 and 31/12/2023. Combining data using a fixed-effect meta-analysis method (relative incidence: 2.64, 95% confidence interval 1.90, 3.66) and a pooled method (relative incidence: 2.45, 95% confidence interval 1.76, 3.41) confirmed the known increased incidence in the 42 days following a coronavirus disease 2019 Vaxzevria^® vaccination. Both methods resulted in a decreased standard error when compared with either state alone. Conclusions This project represents an ongoing successful collaboration between two Australian jurisdictions using data linkage to investigate rare adverse events following immunisation and inform accurate benefit-risk analyses. The decision to use meta-analysis and pooled analysis methods should be considered on a case-by-case basis and may depend on data-sharing agreements, the ease of pooling potentially discordant data variables and underlying population characteristics.

Obesity is a risk factor for type 2 diabetes and cardiovascular disease. However, a substantial proportion of patients with these conditions have a seemingly normal body mass index (BMI). Conversely, not all obese individuals present with metabolic disorders giving rise to the concept of “metabolically healthy obese”. We use lipidomic-based models for BMI to calculate a metabolic BMI score (mBMI) as a measure of metabolic dysregulation associated with obesity. Using the difference between mBMI and BMI (mBMIΔ), we identify individuals with a similar BMI but differing in their metabolic health and disease risk profiles. Exercise and diet associate with mBMIΔ suggesting the ability to modify mBMI with lifestyle intervention. Our findings show that, the mBMI score captures information on metabolic dysregulation that is independent of the measured BMI and so provides an opportunity to assess metabolic health to identify “at risk” individuals for targeted intervention and monitoring.

Obesity is a risk factor for type 2 diabetes and cardiovascular disease. However, a substantial proportion of patients with these conditions have a seemingly normal body mass index (BMI). Conversely, not all obese individuals present with metabolic disorders giving rise to the concept of “metabolically healthy obese”. Using comprehensive lipidomic datasets from two large independent population cohorts in Australia (n = 14,831), we developed models that predicted BMI and calculated a metabolic BMI score (mBMI) as a measure of metabolic dysregulation associated with obesity. We postulated that the mBMI score would be an independent metric for defining obesity and help identify a hidden risk for metabolic disorders regardless of the measured BMI. Based on the difference between mBMI and BMI (mBMI delta; “mBMIΔ”), we identified individuals with a similar BMI but differing in their metabolic health profiles. Participants in the top quintile of mBMIΔ (Q5) were more than four times more likely to be newly diagnosed with T2DM (OR = 4.5; 95% CI = 3.1–6.6), more than two times more likely to develop T2DM over a five year follow up period (OR = 2.5; CI = 1.5–4.1) and had higher odds of cardiovascular disease (heart attack or stroke) (OR = 2.1; 95% CI = 1.5–3.1) relative to those in the bottom quintile (Q1). Exercise and diet were associated with mBMIΔ suggesting the ability to modify mBMI with lifestyle intervention. In conclusion, our findings show that, the mBMI score captures information on metabolic dysregulation that is independent of the measured BMI and so provides an opportunity to assess metabolic health to identify individuals at risk for targeted intervention and monitoring.

Background: Breast density is a strong and potentially modifiable breast cancer risk factor. Almost everything we know about breast density has been derived from mammography, and therefore, very little is known about breast density in younger women aged <40. This study examines the acceptability and performance of two alternative breast density measures, Optical Breast Spectroscopy (OBS) and Dual X-ray Absorptiometry (DXA), in women aged 18–40. Methods: Breast tissue composition (percent water, collagen, and lipid content) was measured in 539 women aged 18–40 using OBS. For a subset of 169 women, breast density was also measured via DXA (percent fibroglandular dense volume (%FGV), absolute dense volume (FGV), and non-dense volume (NFGV)). Acceptability of the measurement procedures was assessed using an adapted validated questionnaire. Performance was assessed by examining the correlation and agreement between the measures and their associations with known determinants of mammographic breast density. Results: Over 93% of participants deemed OBS and DXA to be acceptable. The correlation between OBS-%water + collagen and %FGV was 0.48. Age and BMI were inversely associated with OBS-%water + collagen and %FGV and positively associated with OBS-%lipid and NFGV. Conclusions: OBS and DXA provide acceptable and viable alternative methods to measure breast density in younger women aged 18–40 years.

We integrated lipidomics and genomics to unravel the genetic architecture of lipid metabolism and identify genetic variants associated with lipid species putatively in the mechanistic pathway for coronary artery disease (CAD). We quantified 596 lipid species in serum from 4,492 individuals from the Busselton Health Study. The discovery GWAS identified 3,361 independent lipid-loci associations, involving 667 genomic regions (479 previously unreported), with validation in two independent cohorts. A meta-analysis revealed an additional 70 independent genomic regions associated with lipid species. We identified 134 lipid endophenotypes for CAD associated with 186 genomic loci. Associations between independent lipid-loci with coronary atherosclerosis were assessed in ∼456,000 individuals from the UK Biobank. Of the 53 lipid-loci that showed evidence of association (P < 1 × 10⁻³), 43 loci were associated with at least one lipid endophenotype. These findings illustrate the value of integrative biology to investigate the aetiology of atherosclerosis and CAD, with implications for other complex diseases.

Mammographic breast density is a strong breast cancer risk factor, and its routine clinical measurement could potentially be used to identify women at higher risk of breast cancer and/or monitor primary prevention strategies. Previous reports of optical breast spectroscopy (OBS), a novel approach to measuring breast density, demonstrated that it is safe (no ionizing radiation), portable, low-cost, and does not require image interpretation but have been limited to small, single-center studies. Reference measurements taken on a phantom breast prior to and after each woman’s OBS assessment are required for the calibration of the system transfer function as a part of processing participant data. To inform the validity of participant data, a detailed description of the reference measurements and a repeatability analysis of these measurements taken before and after participant assessment is presented. Reference measurements for OBS from 539 women aged 18–40 years were obtained as a part of a high-throughput epidemiological pilot study. Of these, measurements from 20 women with no useable data due to device failure (3.7%) were excluded and from another 12 women due to user error. The intra-class correlation (ICC) within complete pairs of reference data (taken before and after assessment) was high (all ICC > 0.84). The analysis presented here confirms the OBS participant data as valid for use in ongoing epidemiological research, providing further supporting evidence of OBS as a measure of breast density. A novel method of measuring breast density is needed to bridge large gaps in the knowledge of breast density in younger women and its relation to later-life breast cancer risk.

Mammographic breast density (MBD) is a strong and highly heritable predictor of breast cancer risk and a biomarker for the disease. This study systematically assesses MBD as an endophenotype for breast cancer—a quantitative trait that is heritable and genetically correlated with disease risk. Using data from the family-based kConFab Study and the 1994/1995 cross-sectional Busselton Health Study, participants were divided into three status groups—cases, relatives of cases and controls. Participant’s mammograms were used to measure absolute dense area (DA) and percentage dense area (PDA). To address each endophenotype criterion, linear mixed models and heritability analysis were conducted. Both measures of MBD were significantly associated with breast cancer risk in two independent samples. These measures were also highly heritable. Meta-analyses of both studies showed that MBD measures were higher in cases compared to relatives (β = 0.48, 95% CI = 0.10, 0.86 and β = 0.41, 95% CI = 0.06, 0.78 for DA and PDA, respectively) and in relatives compared to controls (β = 0.16, 95% CI = −0.24, 0.56 and β = 0.16, 95% CI = −0.21, 0.53 for DA and PDA, respectively). This study formally demonstrates, for the first time, that MBD is an endophenotype for breast cancer.