Qingwen Chen

In the United Kingdom, population health worsened significantly during the period of austerity that was implemented following the 2008 Financial Crash. When analysing these trends in older adults, the field of social gerontology has increasingly used the concept of Later Life Precarity to understand the exposure to numerous, intersecting risks that individuals face when ageing in this social and political environment. However, there has been limited quantitative analysis of Later Life Precarity and its effects on health. Accordingly, this study uses data on 8553 adults aged 50 + from the English Longitudinal Study of Ageing to examine the forms that precarity takes in later life and its associations with health. We firstly describe configurations of risk that make up precarity experienced by older adults across the domains of finances, pensions, employment, housing, relationships and caregiving. We then use Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) to identify precarious intersections of risk associated with especially elevated frailty levels. The MAIHDA model identified a number of forms of precarity that were associated with particularly high frailty, with the strongest random effect being observed for women who are divorced, widowed and/or living alone and who lack financial and pensions security. In the context of the ongoing cost-of-living crisis, these findings demonstrate how frailty emerges at the intersection of numerous social risks and, furthermore, highlight the cost (both to the individual and to healthcare systems) of policies that fail to provide a secure and equitable environment for our ageing population.

Rationale: Idiopathic Pulmonary Fibrosis (IPF) is characterized by chronic progressive pulmonary fibrosis and high mortality. Genetic markers, summarized into a polygenic risk score (PRS), associate with IPF in well-phenotyped research cohorts. Objectives: To evaluate the performance of the PRS using real-world data from routinely captured electronic healthcare records. Methods: We conducted an observational study evaluating the association of a PRS for IPF with electronic healthcare record diagnosis of IPF as well as lung transplant-free survival in four independent cohorts; the Mass General Brigham Biobank (MGBB), Mayo Clinic Biobank (MCBB), Mayo Clinic Tapestry Cohort (Tapestry), and U.K. Biobank (UKBB). We used multivariable logistic regression and multivariable Cox proportional hazards models adjusting for age, gender, and principal components of ancestry. The cohorts then underwent fixed and random effects meta-analysis. Measurements and Main Results: Of 37,709; 44,195; 43,202; and 447,422 participants from MGBB, MCBB, Tapestry and UKBB respectively, 1,015 (2.7%) 2,879 (6.5%), 1,310 (3.0%), and 2,742 (0.6%) participants had an IPF diagnosis. Meta-analysis demonstrated a high-risk PRS associated with IPF diagnosis, OR 2.88 (95%CI 2.41-3.44) compared to all other individuals. A high-risk PRS also associated with the composite endpoint of mortality or lung transplant among those with an IPF diagnosis, HR 1.23(95%CI 1.11-1.35) compared to all other individuals. Conclusions: A PRS can identify those at risk for an IPF diagnosis and mortality in biobank-scale data, which may have implications for clinical decisions. Further work is necessary to evaluate the utility of adding genetics in clinical settings.

Herpesviruses infect nearly all humans and have long been implicated in autoimmune and chronic diseases, yet their immune interactions with host proteins have not been systematically characterized at population scale. We profiled immunoglobulin G reactivities to >4,600 herpesvirus peptides and >15,000 human proteins using multiplexed protein display in two Mass General Brigham Biobank cohorts (discovery n=1,289; replication n=763), with longitudinal electronic health record follow-up. We identified and replicated 3,943 FDR-significant associations across 93 autoantigens, including previously uncharacterized viral–autoantigen axes, such as PHLDA1 and ZNF550. Eleven autoantigens were predicted by viral peptide reactivities with >85% accuracy in independent validation; some exhibited shared viral–host sequence homology, consistent with possible molecular mimicry. Integrating immune reactivities with incident disease outcomes revealed virus-specific network architectures: cytomegalovirus formed the largest multimorbid network, Epstein–Barr virus converged on pleiotropic autoimmune hubs, and herpes simplex viruses formed smaller, partially overlapping autoreactive networks. These findings define a herpesvirus–autoantigen–disease network atlas and prioritize candidate viral–host immune axes for mechanistic investigation.

Biological aging reflects complex cellular and biochemical processes that can be measured across multiple omic layers. Using routine clinical laboratory data from ~31,000 participants in the Mass General Brigham Biobank, we developed EMRAge, a biomarker of mortality risk that can be broadly recapitulated across electronic medical records. Here we show that EMRAge can be modeled using elastic net regression with DNA methylation and multi-omics to generate DNAmEMRAge and OMICmAge, respectively. Both biomarkers are strongly associated with incident and prevalent chronic diseases and mortality, performing comparably or better than current biomarkers across discovery (Massachusetts General Brigham Aging Biobank Cohort, n = 3,451) and validation cohorts (TruDiagnostic, n = 14,213; Generation Scotland, n = 18,672). Importantly, OMICmAge leverages epigenetic biomarker proxies to integrate proteomic, metabolomic and clinical domains while remaining quantifiable from DNA methylation alone. This framework establishes an accessible, scalable measure of biological aging with potential to reveal molecular interconnections that shape healthspan and disease risk.

BACKGROUND: We constructed multi-trait polygenic risk scores (PRSs) predicting chronic obstructive pulmonary disease (COPD) and exacerbations, validated their performance in diverse cohorts, and identified PRS-related proteins for potential therapeutic targeting. METHODS: PRSmix ⁺ , a multi-trait PRS framework, is used to train a composite PRS (PRS _multi ) in COPDGene non-Hispanic White participants ( n = 6,647). Associations of PRS _multi with COPD status (GOLD 2–4 vs. GOLD 0 or ICD) and exacerbation frequency were tested in COPDGene African American ( n = 2,466), ECLIPSE ( n = 1,858), Mass General Brigham Biobank ( n = 15,152), and All of Us ( n = 118,566). Protein prediction models were applied to GWAS summary statistics from traits contributing to PRS _multi and were validated with proteomic data in COPDGene ( n = 5,173) and UK Biobank ( n = 5,012). RESULTS: PRSmix ⁺ selected 7 traits for PRS _multi . In multivariable models, PRS _multi was associated with COPD status (meta-analysis random effects [RE] OR 1.58 [95% CI: 1.28–1.94]) and exacerbation frequency (meta-analysis RE β 0.21 [95% CI: 0.11–0.31]), with higher effect sizes observed in smoking-enriched cohorts. PRS _multi outperformed traditional single-trait PRS in all tested cohorts. Using protein prediction models, we identified 73 proteins associated with the PRSs that were also validated with measured protein levels in COPDGene and UK Biobank. Of these proteins, 25 were linked to approved or investigational drugs. Notable targets include RAGE/sRAGE, IL1RL1, and SCARF2, all implicated in COPD pathogenesis and exacerbations. CONCLUSIONS: Multi-trait PRS improves prediction of COPD and exacerbation risk. Integration with proteomic data identifies druggable protein targets, offering a promising avenue for precision medicine in COPD management. TRIAL REGISTRATION: COPDGene: ClinicalTrials.gov NCT00608764; ECLIPSE: ClinicalTrials.gov NCT00292552.

Recent epigenome-wide studies have identified a large number of genomic regions that consistently exhibit changes in their methylation status with aging across diverse populations, but the functional consequences of these changes are largely unknown. On the other hand, transcriptomic changes are more easily interpreted than epigenetic alterations, but previously identified age-related gene expression changes have shown limited replicability across populations. Here, we develop an approach that leverages high-resolution multi-omic data for an integrative analysis of epigenetic and transcriptomic age-related changes and identify genomic regions associated with both epigenetic and transcriptomic age-dependent changes in blood. Our results show that these multi-omic aging genes in blood are enriched for adaptive immune functions, replicate more robustly across diverse populations and are more strongly associated with aging-related outcomes compared to the genes identified using epigenetic or transcriptomic data alone. These multi-omic aging genes may serve as targets for epigenetic editing to facilitate cellular rejuvenation.

The lack of biomarkers to identify individuals at risk of asthma exacerbations remains a significant limitation to improving patient outcomes. To address this need, we analyze data from three asthma cohorts, combining up to 25 years of electronic medical records with sequential metabolomics studies, to develop and replicate a predictive model for asthma exacerbation risk. We identify asthma-associated biochemical pathways via global circulatory metabolomics and then apply targeted mass spectrometry methods to quantify selected steroids, sphingolipids, and microbial-derived metabolites. The sphingolipid-to-steroid ratios robustly associate with 5-year exacerbation risk (discovery p value = 1.63×10⁻²⁶-0.029; replication p value = 1.89×10⁻³⁶-0.033). Based upon these findings, we derive and replicate a simple 5-year predictive model of asthma exacerbations using 21 sphingolipid-to-steroid ratios that outperforms current clinical measures (discovery AUC = 0.90; replication AUC = 0.89). These findings underscore the value of metabolomic profiling to develop a practical, cost-effective clinical assay for asthma exacerbation risk that may improve patient care.

Most studies to date of protein quantitative trait loci (pQTLs) have relied on affinity proteomics platforms, which provide only limited information about the targeted protein isoforms and may be affected by genetic variation in their epitope binding. Here we show that mass spectrometry (MS)-based proteomics can complement these studies and provide insights into the role of specific protein isoform and epitope-altering variants. Using the Seer Proteograph nanoparticle enrichment MS platform, we identified and replicated new pQTLs in a genome-wide association study of proteins in blood plasma samples from two cohorts and evaluated previously reported pQTLs from affinity proteomics platforms. We found that >30% of the evaluated pQTLs were confirmed by MS proteomics to be consistent with the hypothesis that genetic variants induce changes in protein abundance, whereas another 30% could not be replicated and are possibly due to epitope effects, although alternative explanations for nonreplication need to be considered on a case-by-case basis.

Asthma poses a significant public health burden. Despite identifying more than a hundred genetic risk loci in genome-wide association studies (GWAS), the underlying functional mechanisms remain poorly understood. Studying omics, especially microRNAs (miRNAs), is a promising approach to facilitate our understanding of the biological pathways of asthma. Here, we performed miRNA expression quantitative trait loci (miRNA-QTL) analyses using whole-genome sequencing and serum-based miRNA expression data from two independent cohorts of children with asthma (Genetic Epidemiology of Asthma in Costa Rica Study (GACRS), (NCT00021840, 2005-06-23) (N = 980, Discovery) and the Childhood Asthma Management Program (CAMP) (NCT00000575, 2005-06-23) (N = 354, Replication)). Our robust discovery analysis identified 28 significant cis-miRNA-QTL associations, where 12 were not reported in three independent miRNA-QTL studies. Three of these 12 signals were replicated in CAMP. The QTLs colocalize with expression and splicing QTL in asthma-relevant tissues and cells, and overlap with asthma-related and blood cell trait GWAS hits.

The promise of integrating Electronic Medical Records (EMR) and genetic data for precision medicine has largely fallen short due to its omission of environmental context over time. Post-genomic data can bridge this gap by capturing the real-time dynamic relationship between underlying genetics and the environment. This perspective highlights the pivotal role of integrating EMR and post-genomics for personalized health, reflecting on lessons from past efforts, and outlining a roadmap of challenges and opportunities that must be addressed to realize the potential of precision medicine.