Predicting the probability of death using proteomics

Eiriksdottir, Thjodbjorg; Ardal, Steinthor; Jónsson, Benedikt Atli; Lund, Sigrún H.; Ívarsdóttir, Erna V.; Norland, Kristjan; Ferkingstad, Egil; Stefánsson, Hreinn; Jónsdóttir, Ingileif; Hólm, Hilma; Rafnar, Thorunn; Saemundsdottir, Jona; Norddahl, Gudmundur L.; Þorgeirsson, Guðmundur; Guðbjartsson, Daníel F.; Sulem, Patrick; Þorsteinsdóttir, Unnur; Stefánsson, Kári; Ulfarsson, Magnus O.

doi:10.1038/s42003-021-02289-6

Cited by 17 publications

(16 citation statements)

References 54 publications

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“…Comparative analyses in this article include only other DNA methylation measures of aging. Recent reports suggest valuable information about aging and healthspan may also be captured in metabolomic and proteomic datasets ( Deelen et al, 2019 ; Eiriksdottir et al, 2021 ; Jansen et al, 2021 ; Lehallier et al, 2019 ; Tanaka et al, 2020 ). Future studies should compare DunedinPACE to measures derived from these biological levels of analysis.…”

Section: Discussionmentioning

confidence: 99%

DunedinPACE, a DNA methylation biomarker of the pace of aging

Belsky

Caspi

Corcoran

et al. 2022

eLife

432

385

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Background: Measures to quantify changes in the pace of biological aging in response to intervention are needed to evaluate geroprotective interventions for humans. Previously we showed that quantification of the pace of biological aging from a DNA-methylation blood test was possible (Belsky et al. 2020). Here we report a next-generation DNA-methylation biomarker of Pace of Aging, DunedinPACE (for Pace of Aging Calculated from the Epigenome).Methods: We used data from the Dunedin Study 1972-3 birth cohort tracking within-individual decline in 19 indicators of organ-system integrity across four time points spanning two decades to model Pace of Aging. We distilled this two-decade Pace of Aging into a single-time-point DNA-methylation blood-test using elastic-net regression and a DNA-methylation dataset restricted to exclude probes with low test-retest reliability. We evaluated the resulting measure, named DunedinPACE, in five additional datasets.Results: DunedinPACE showed high test-retest reliability, was associated with morbidity, disability, and mortality, and indicated faster aging in young adults with childhood adversity. DunedinPACE effect-sizes were similar to GrimAge Clock effect-sizes. In analysis of incident morbidity, disability, and mortality, DunedinPACE and added incremental prediction beyond GrimAge.Conclusions: DunedinPACE is a novel blood biomarker of the pace of aging for gerontology and geroscience.Funding: This research was supported by US-National Institute on Aging grants AG032282, AG061378, AG066887, and UK Medical Research Council grant MR/P005918/1.

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Section: Discussionmentioning

confidence: 99%

DunedinPACE, a DNA methylation biomarker of the pace of aging

Belsky

Caspi

Corcoran

et al. 2022

eLife

432

385

View full text Add to dashboard Cite

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“…13 Also, there is an overlap in proteins (KIM1, GDF-15, REN, OPG) between our model and studies that identified proteins for predicting recurrent atherosclerotic cardiovascular disease 14 and predicting all-cause mortality. 15 Overall, these overlaps suggest that these proteins are markers of pathological processes that are shared among many conditions. Interestingly, our study included the immune response panel, which was not included in any of the other studies.…”

Section: Discussionmentioning

confidence: 99%

Combining Polygenic and Proteomic Risk Scores With Clinical Risk Factors to Improve Performance for Diagnosing Absence of Coronary Artery Disease in Patients With de novo Chest Pain

Møller,

Rohde,

Dahl

et al. 2023

Circ: Genomic and Precision Medicine

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Background: Patients with de novo chest pain, referred for evaluation of possible coronary artery disease (CAD), frequently have an absence of CAD resulting in millions of tests not having any clinical impact. The objective of this study was to investigate whether polygenic risk scores and targeted proteomics improve the prediction of absence of CAD in patients with suspected CAD, when added to the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) minimal risk score (PMRS). Methods: Genotyping and targeted plasma proteomics (N=368 proteins) were performed in 1440 patients with symptoms suspected to be caused by CAD undergoing coronary computed tomography angiography. Based on individual genotypes, a polygenic risk score for CAD (PRS CAD ) was calculated. The prediction was performed using combinations of PRS CAD , proteins, and PMRS as features in models using stability selection and machine learning. Results: Prediction of absence of CAD yielded an area under the curve of PRS CAD -model, 0.64±0.03; proteomic-model, 0.58±0.03; and PMRS model, 0.76±0.02. No significant correlation was found between the genetic and proteomic risk scores (Pearson correlation coefficient, −0.04; P =0.13). Optimal predictive ability was achieved by the full model (PRS CAD +protein+PMRS) yielding an area under the curve of 0.80±0.02 for absence of CAD, significantly better than the PMRS model alone ( P <0.001). For reclassification purpose, the full model enabled down-classification of 49% (324 of 661) of the 5% to 15% pretest probability patients and 18% (113 of 611) of >15% pretest probability patients. Conclusions: For patients with chest pain and low-intermediate CAD risk, incorporating targeted proteomics and polygenic risk scores into the risk assessment substantially improved the ability to predict the absence of CAD. Genetics and proteomics seem to add complementary information to the clinical risk factors and improve risk stratification in this large patient group. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02264717

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“…Scores developed through statistical learning stratify where individuals lie on the disease-risk continuum for a population. While proteomic and metabolomics scores have been developed for certain time-to-event outcomes in isolation 9,[16][17][18][19][20] , these predictors are rarely developed and tested at scale. Proteomic predictors have been trained using the SomaScan platform for diabetes and cardiovascular event risk and multiple lifestyle and health indicators 21 .…”

Section: Introductionmentioning

confidence: 99%

Blood protein levels predict leading incident diseases and mortality in UK Biobank

Gadd

Hillary

Kuncheva

et al. 2023

Preprint

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The circulating proteome offers insights into the biological pathways that underlie disease. Here, we test relationships between 1,468 Olink protein levels and the incidence of 23 age-related diseases and mortality, ascertained over 16 years of electronic health linkage in the UK Biobank (N=49,234). We report 3,123 associations between 1,052 protein levels and incident diseases (PBonferroni < 5.4x10-6). Forty four proteins are indicators of eight or more morbidities. Next, protein-based scores (ProteinScores) are developed using penalised Cox regression. When applied to test sets, eight ProteinScores improve Area Under the Curve (AUC) estimates for the 10-year onset of incident outcomes (PBonferroni < 0.0025) beyond age, sex and additional health and lifestyle covariates. The type 2 diabetes ProteinScore outperforms HbA1c (P = 5.7x10-12), a clinical marker used to monitor and diagnose type 2 diabetes. A maximal type 2 diabetes model including the ProteinScore, HbA1c and a polygenic risk score has AUC = 0.90 and Precision-Recall AUC = 0.76. These data characterise early proteomic contributions to major age-related disease.

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Predicting the probability of death using proteomics

Cited by 17 publications

References 54 publications

DunedinPACE, a DNA methylation biomarker of the pace of aging

DunedinPACE, a DNA methylation biomarker of the pace of aging

Combining Polygenic and Proteomic Risk Scores With Clinical Risk Factors to Improve Performance for Diagnosing Absence of Coronary Artery Disease in Patients With de novo Chest Pain

Blood protein levels predict leading incident diseases and mortality in UK Biobank

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