Modeling Joint Reference Regions for Omics Biomarkers in UK Biobank Proteomics
- 1Hasselt University
Abstract
Conventional univariate reference intervals (UniRIs) are widely used to identify abnormal biomarker values, but they evaluate each biomarker independently and do not account for coordinated deviations between biomarkers. We developed and evaluated a joint reference region (JRR) framework for plasma proteomics data using the Olink proteomics dataset generated by the UK Biobank Pharma Proteomics Project, covering approximately 3,000 plasma proteins. JRRs were estimated for selected protein pairs in a healthy reference subset, while UniRIs were estimated separately for individual proteins using the nonparametric method. Both approaches were then evaluated in ICD-defined disease subsets. Biomarker discovery revealed sparse and heterogeneous disease-protein associations, with some proteins recurring across multiple phenotypes and others showing more disease-specific patterns. The added value of JRRs varied across diseases and protein pairs. Across evaluated protein pairs, 56.5% showed higher sensitivity under the JRR framework than the UniRI of the first protein, and 47.3% showed higher sensitivity than the UniRI of the second protein. At the disease level, the median proportion of protein pairs with improved JRR sensitivity was 0.57. JRRs were most informative when univariate detection was limited but a subset of diseased observations was flagged only by the joint region.
These findings suggest that JRRs provide a complementary approach to UniRIs by capturing abnormal joint biomarker configurations in high-dimensional proteomics data.
Related
- Identification of hepatitis B virus-associated membrane proteins as prognostic biomarkers for hepatocellular carcinoma based on retrospective analysis of integrated membrane proteomics
- Lifespan EEG Reference Charts Reveal Aperiodic Confounds in Beta-Band Biomarkers Across Neurological and Neuropsychiatric Disorders
- Integrating proteomics to map immune landscape and protein biomarkers for risk stratification in childhood Langerhans cell histiocytosis
- Population-scale plasma proteomics reveals pre-diagnostic biomarkers and shared pathways of cancer risk across 25 cancers
- Plasma proteomics identifies four candidate biomarkers associated with macrovascular complications in type 2 diabetes: evidence from a clinical cohort and an animal model
BibTeX
@misc{pusparum2026modeling,
title = {{Modeling Joint Reference Regions for Omics Biomarkers in UK Biobank Proteomics}},
author = {M. Pusparum and O. Thas and G. Ertaylan},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.01.26361504},
url = {https://www.medrxiv.org/content/10.64898/2026.09.01.26361504v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Modeling Joint Reference Regions for Omics Biomarkers in UK Biobank Proteomics
AU - M. Pusparum
AU - O. Thas
AU - G. Ertaylan
PY - 2026
DA - 2026/09/01
PB - medRxiv
DO - 10.64898/2026.09.01.26361504
UR - https://www.medrxiv.org/content/10.64898/2026.09.01.26361504v1
AB - Conventional univariate reference intervals (UniRIs) are widely used to identify abnormal biomarker values, but they evaluate each biomarker independently and do not account for coordinated deviations between biomarkers. We developed and evaluated a joint reference region (JRR) framework for plasma proteomics data using the Olink proteomics dataset generated by the UK Biobank Pharma Proteomics Project, covering approximately 3,000 plasma proteins. JRRs were estimated for selected protein pairs in a healthy reference subset, while UniRIs were estimated separately for individual proteins using the nonparametric method. Both approaches were then evaluated in ICD-defined disease subsets. Biomarker discovery revealed sparse and heterogeneous disease-protein associations, with some proteins recurring across multiple phenotypes and others showing more disease-specific patterns. The added value of JRRs varied across diseases and protein pairs. Across evaluated protein pairs, 56.5% showed higher sensitivity under the JRR framework than the UniRI of the first protein, and 47.3% showed higher sensitivity than the UniRI of the second protein. At the disease level, the median proportion of protein pairs with improved JRR sensitivity was 0.57. JRRs were most informative when univariate detection was limited but a subset of diseased observations was flagged only by the joint region. These findings suggest that JRRs provide a complementary approach to UniRIs by capturing abnormal joint biomarker configurations in high-dimensional proteomics data.
ER -