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PubMed2026

Population proteomics for equitable precision medicine.

Population proteomics is emerging as a new framework for equitable precision medicine. By studying protein variation across populations, this field bridges population genomics and conventional proteomics to capture the functional molecular states through which genetic ancestry, environmental exposures and other contextual factors shape human health. Here, we discuss how recent advances are moving population proteomics beyond biomarker discovery toward equitable clinical translation through cross-population validation, mechanistic multiomics and global research infrastructures. Its ultimate promise is not to classify populations as fixed biological categories, but to make human diversity measurable, interpretable and clinically actionable for equitable precision medicine.

Journal Article↗

PubMed2026

Characterizing the impact of plasma protein levels on human brain structure and disorders leveraging integrative multi-omics analysis.

With recent advances in high-throughput proteomic technologies, population-scale plasma proteomics datasets, often linked to extensive genetic and phenotypic information, have become increasingly accessible. Yet the relationships between circulating protein levels, brain imaging phenotypes, and risk for neurological and psychiatric disorders remain largely unexplored. Proteome-wide association studies offer a promising approach for elucidating biological mechanisms that connect genetic variation to complex brain-related traits and diseases. In this study, we integrated protein quantitative trait loci (pQTLs) from the two largest plasma proteomic resources (the UK Biobank Pharma Proteomics Project [UKB-PPP] and Ferkingstad et al. [deCODE]) with genome-wide association studies of brain imaging-derived phenotypes in UK Biobank using Mendelian randomization and colocalization analyses. We identified 120 cis and 20 trans associations between plasma proteins and imaging phenotypes and validated these findings using brain tissue-derived proteomic and transcriptomic datasets. Multivariable Mendelian randomization revealed eleven plasma proteins (coding genes APOE, ARL3, MICB, NSF, RHOC, RSPO3, ENPP2, BTN2A1, EIF2AK3, MRVI1, and OPLAH) with significant direct effects on the risk of Alzheimer's disease, Parkinson's disease, multiple sclerosis, bipolar disorder, and schizophrenia. Single-cell expression and pathway enrichment analyses further revealed cell-type-specific effects and distinct biological processes underlying these protein-disease associations. Together, these findings demonstrate robust links between plasma protein variation and brain structure, delineate protein-disease pathways, and highlight the cellular and molecular mechanisms that contribute to neurobiological diversity and pathology.

Journal Article↗

PubMed2026

Reframing Proteomics Measurement: Super Mass Spectrometry Framework and the Role of Delayed Electrospray Ionization Technique.

Dynamic range, repeatability, and reproducibility remain the central limitations of data-independent acquisition (DIA) proteomics. Current workflows emphasize protein group identification counts and throughput, but these metrics mask the fundamental measurement challenge: generating a repeatable, reproducible, high-fidelity, and relatively complete digital representation of complex proteomes. In particular, plasma proteomics spans more than 10 orders of magnitude in protein abundance, far exceeding the capacity and dynamic range of any single mass spectrometer. Incremental advances have not closed this gap. In this Perspectives article, I introduce the Super Mass Spectrometry framework and then highlight the Delayed Electrospray Ionization (Delayed-ESI) technique, as a practical approach to address these limitations. By producing compositionally identical but temporally staggered ion beams, the Delayed-ESI technique enables deterministic remeasurement of the same analyte profile, supporting various novel strategies to improve analytical figures of merit. While recent implementations of the Delayed-ESI technique have emphasized throughput, I argue that the broader value of the Delayed-ESI technique lies in extending dynamic range and improving repeatability and reproducibility─objectives that should take precedence if proteomics is to evolve into a robust measurement science capable of supporting population-scale proteomics studies.

Proteomics↗

PubMed2026

From Peaks to Power: Systematic Evaluation of Chromatographic Sampling Reveals Determinants of Quantification and Biological Discovery in DIA Proteomics.

Modern DIA proteomics increasingly emphasizes throughput and depth for large-cohort studies, but methods are often optimized using proxy metrics that can mask losses in quantifiable signal and statistical power. Here, we evaluate how data points per peak and other chromatographic features jointly contribute to quantification and downstream biological discovery. Using a matrix-matched calibration curve dataset, we checked how the number of data points per peak (DPPP) affects the limits of detection and quantification (LOD/LOQ). Reduced DPPP minimally affected LOD but substantially degraded LOQ. Feature modeling and nonparametric association analyses identified precursor peak area as the strongest feature-level predictor of LOQ, whereas DPPP showed weaker and context-dependent effects. Simulations of chromatographic peak integration recapitulated these trends, showing that increased sampling primarily improves integration precision, while quantitative accuracy is strongly governed by peak height and peak shape. Finally, when comparing 20 cancer vs 20 control plasma samples processed with Seer Proteograph, the decrease in DPPP led to a loss of statistical significance for proteins with low-abundance precursors. These findings argue that DIA optimization should prioritize LOQ and statistical power metrics─not identifications alone─by balancing sampling density with chromatographic peak height and quality to maximize useful biological signal.

Proteomics↗

PubMed2026

Auditing bacterial dark-gene screens for superimposed open reading frame artefacts: A multi-layer analysis of Rv2438A in Mycobacterium tuberculosis.

Essentiality and knockdown-vulnerability screens can promote spurious bacterial open reading frames when those frames overlap essential genes, because such a frame inherits its neighbour's signals undiluted and therefore satisfies the screen's criteria better than a genuine small gene. We present a multi-layer audit that tests this failure mode across genome annotation, transposon mutagenesis, CRISPR interference, homology, transcript mapping, proteomics, and population variation. We apply it to Rv2438A, a 92-codon conserved hypothetical open reading frame of Mycobacterium tuberculosis ranked first by our own dark-gene target screen. Rv2438A is superimposed on the essential NAD synthetase locus nadE: 44% lies within its coding sequence on the opposite strand, and the remainder covers its promoter and transcription start site. Consequently, three of five Himar1 sites lie within nadE, no CRISPRi guide can target Rv2438A without binding nadE, and the cross-species hit maps to the same nadE start junction. Rv2438A lacks its own transcription start site and is absent from every proteomic dataset that detects nadE. A genome-wide scan identifies six short, overlapping, uncharacterised loci among 3907 annotated genes, but only Rv2438A combines overlap and essentiality with non-detection across all proteomic datasets; rare genome-wide, it ranked first among screen hits. We provide an implementable audit workflow and a codon-position control, but measure the control's sensitivity as only two of five genes with attested protein, limiting it to confirmatory use. Overlap coordinates and neighbour-specific experimental resolution should therefore be reported before bacterial dark genes are prioritised.

CRISPR interference↗

PubMed2025

Alterations in DNA Methylation, Proteomic, and Metabolomic Profiles in African Ancestry Populations with APOL1 Risk Alleles.

KEY POINTS: We aimed to elucidate potential methylation, proteomic, and metabolomic mechanisms by which APOL1 variants may be linked to kidney disease. We report distinct methylation profiling between APOL1 risk allele carriers and noncarriers, many near APOL gene family. We report higher APOL1 protein and lower C18:1 cholesteryl ester in two risk allele carriers. BACKGROUND: The APOL1 high-risk haplotype has been associated with CKD and the deterioration of kidney function, particularly in populations with West African ancestry. However, the mechanisms by which APOL1 risk variants increase the risk for kidney disease and its progression have not been fully elucidated. METHODS: We compared methylation (N=3191; 715 [22%] carriers), proteomic (N=1240; 169 [14%] carriers), and metabolomic (N=6309; 674 [11%] carriers) profiles in African and Hispanic/Latino carriers of two APOL1 high-risk alleles (G1/G1, G2/G2, G1/G2) and noncarriers (G0/G0), excluding heterozygotes (G0/G1, G0/G2), from the Population Architecture using Genomics and Epidemiology Consortium and UK Biobank. In each study, the associations between the APOL1 high-risk haplotype and up to 722,719 cytosine-phosphate-guanine (CpG) sites, 2923 proteins, or 836 metabolites were estimated using covariate-adjusted linear regression models, followed by fixed-effects sample size–weighted meta-analyses. RESULTS: Significant associations were observed between APOL1 high-risk haplotype and methylation at 52 CpG sites, with 48 located on chromosome 22 and 18 in the vicinity of APOL1–4 and MYH9. All significant CpG sites near APOL2 were hypomethylated, whereas those near APOL3 and APOL4 were hypermethylated. APOL1-associated CpG sites were also identified in genes involved in ion transport and mitochondrial stress pathways. Sensitivity analyses indicated consistent yet attenuated effects among heterozygotes, supporting an additive effect of APOL1 risk alleles. Further analyses of the 52 CpG sites identified two near APOL4 exhibiting G1-specific effects, eight associated with CKD but none with eGFR, and three showing heterogeneity by CKD status. In addition, carrying two APOL1 risk alleles was associated with higher plasma APOL1 protein (β=1.12, PFDR = 2.26e-70) and lower C18:1 cholesteryl ester metabolite (Z=−4.50, PFDR = 4.83e-3). CONCLUSIONS: Our results demonstrate differential methylation, proteomic, and metabolomic profiles associated with APOL1 high-risk haplotypes.

APOL1↗

PubMed2026

Omics signature of new-onset mild cognitive impairment and dementia in a population-based study.

Plasma proteomics and metabolomics snapshots reveal a molecular signature in circulation delineating pathophysiology of major and minor neurocognitive disorder. To identify new cues to disease aetiology and diagnostic approach, we applied plasma proteomics and metabolomics profiling platforms to samples collected in a population-based study of the Singapore Longitudinal Ageing Studies Wave 2 (SLAS-2). In this longitudinal study, blood samples were analysed with standard clinical chemistry, plasma proteomics (Sengenics) and metabolomics (Nightingale) panels. Participants were followed up for the development of mild cognitive impairment (MCI) and dementia for 3-5 years. Of the total 1,892 molecules in all assay types, 463 demonstrated significant associations with baseline prevalent MCI and dementia. We trained an automatic linear modelling of predictors for follow-up new-onset MCI and dementia. The best model consists of 10 variables including ZSCAN18, PRKD3, SPANXN4, DDX43, saturated fatty acids, PPP3CA, NFATC4, IL-8, PAK6, and PDGFB. In terms of molecular function, these molecular markers are involved in immunological dysfunction and inflammatory reaction, protein coding, lipids, DNA-binding transcription factor activity, and nervous system development. In conclusion, our current research has identified an omics signature linked to new-onset mild cognitive disorder and dementia, which we hope can help enhance the accuracy of their diagnosis using circulating blood samples.

Humans↗

PubMed2025

Proteome-wide association study of prostate cancer risk across populations.

There is insufficient understanding of the molecular basis of prostate cancer (PCa) across different populations. We perform a large-scale proteome-wide association study (PWAS) to identify proteins with genetically regulated expression in plasma to be associated with PCa risk across populations. We develop genetic prediction models for expression of 1578, 1993, 1218, and 1390 proteins for African (n = 450), European (n = 758), Asian (n = 289), and Hispanic/Latino (n = 474) males, respectively, and evaluate associations of genetically regulated protein expression with PCa risk in 19,391 PCa cases and 61,608 controls of African population, 122,188 cases and 604,640 controls of European population, 10,809 cases and 95,790 controls of Asian population, and 3931 cases and 26,405 controls of Hispanic/Latino population. We identify three, four, 15, and 73 PCa-associated proteins in African, Hispanic/Latino, Asian, and European populations, respectively, and 83 in trans-population meta-analysis. There are both pan-population and population-specific associations. Our findings provide valuable insights into etiology of PCa.

Humans↗

PubMed2026

Proteomics-driven discovery of intervention windows and risk subtypes in osteoporosis: A prospective cohort study.

Given the limited feasibility of population-wide bone mineral density screening and the infrequency of long-term monitoring in healthy individuals, identifying the window for early intervention and the populations to be prioritized for screening is critical. This study aimed to identify intervention windows for osteoporosis and to determine potential high-risk subtypes within the healthy population. Based on proteomic data from 41,408 healthy adults, we conducted the DE-SWAN method to identify change peaks in plasma protein during the pre-diagnostic osteoporosis phase, and employed finite Gaussian mixture model-based clustering to delineate high-risk subtypes of osteoporosis. We identified 122 protein biomarkers significantly associated with osteoporosis risk throughout the follow-up period. Importantly, we identified two critical peaks occurring approximately 10 and 6 years before diagnosis, with the former enriched in immune-related pathways and the latter prominently involving responses to retinoic acid and glucocorticoids. Furthermore, one high-risk subtype for osteoporosis was identified in both males and females, termed the Frailty and Obesity Subtype. This subtype is characterized by a high degree of frailty and obesity, accompanied by a significantly elevated risk of both osteoporosis and fractures. Finally, we developed a predictive model comprising 10 proteins for identifying high-risk subtypes of osteoporosis, which demonstrated better performance than the traditional risk factor model (AUC: 0.743 vs. 0.680). Our findings demonstrate that proteomic profiling can reveal early molecular changes and identify high-risk subtypes years before clinical onset, providing a foundation for screening and precision prevention of osteoporosis.

Proteomics↗

PubMed2025

Proteome organ aging and cardiometabolic risk in a population at risk for heart failure.

BACKGROUND: Biological aging varies across individuals and tissues, influencing chronic diseases, including heart failure (HF). Emerging proteome techniques enable quantification of organ-specific aging acceleration (OAA), but whether OAA relates to HF severity and differs by sex remains unclear. We aim to assess the sex-related association between OAA of heart, artery and kidneys and HF severity, and to investigate relevant cardiometabolic risk factors of organ aging. METHODS: In 556 participants from the HELPFul cohort, we estimated predicted biological age for heart, artery, and kidneys using plasma proteomics and calculated OAA as the deviation from chronological age. Associations between OAA and HF stage, echocardiographic parameters, and cardiometabolic risk factors were evaluated using regression models. Composite indices, including triglyceride-glucose body mass index (TyG-BMI), c-reactive protein-triglyceride glucose index and triglyceride-to-HDL cholesterol ratio were assessed for associations with advanced OAA. RESULTS: Mean age was 63 ± 9 years; 65% were women. Patients were classified as HF stage A (35%), B (29%) and C/D (36%). Heart OAA was significantly associated with advanced HF (Stage C/D) in both sexes (OR = 1.12, 95% CI 1.03 to 1.23 in women; OR = 1.18, 95% CI 1.05 to 1.32 in men), while artery OAA was linked to HF only in women (OR = 1.10, 95% CI 1.01 to 1.18). Multi-organ aging (≥ 2 organs with advanced OAA) conferred over three-fold higher odds of being in Stage C/D. Heart OAA correlated with impaired cardiac structure and function, particularly reduced ejection fraction in men and increased left ventricular mass index in both sexes. Diabetes emerged as the most relevant factor of artery and kidney OAA. TyG-BMI was significantly associated with advanced kidney OAA, only in women (z-scored OR = 1.88, 95% CI 1.45 to 2.45). CONCLUSIONS: Proteome-derived organ aging correlates with HF severity, with possible sex-related patterns. Diabetes and higher TyG-BMI are associated with faster organ aging, which may reflect shared aging mechanisms between metabolic dysfunction and HF.

Humans↗

PubMed2024

Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast.

Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein coexpression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship.

Saccharomyces cerevisiae↗

PubMed2025

Proteomic discovery analysis of quantitatively assessed emphysema in the general population. The MESA Lung Study.

BACKGROUND: Pulmonary emphysema occurs frequently in older adults, often without airflow limitation. Its presence predicts symptoms, respiratory hospitalizations and deaths, and all-cause mortality. Proteomics may provide further insights into emphysema pathogenesis and inform therapeutic targets. OBJECTIVE: We performed a proteomic discovery analysis of percent emphysema on computed tomography (CT) in a population-based, multiethnic sample from the Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study. Replication was performed in two chronic obstructive pulmonary disease (COPD)-based studies, the SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS) and the Genetic Epidemiology of COPD (COPDGene) Study. METHODS: MESA recruited participants from the general population in 2000-02. The MESA Lung Study performed full-lung CT scans in 2010-12. Percent emphysema was defined as the percentage of lung voxels < -950 Hounsfield units. Over 7,200 plasma aptamers were measured via SomaScan. Cross-sectional linear and least absolute shrinkage and selection operator (LASSO) regression models were adjusted for demographics, anthropometrics, smoking, renal function, and scanner parameters. Statistical significance was defined as a false discovery rate p-value < 0.05. Gene Ontology (GO)/Reactome enrichment analyses were performed. LASSO-selected proteins' predictive performance was evaluated. RESULTS: Among 2,504 participants in the MESA Lung Study, mean age was 69.4 years, 1,291 had ever smoked, and median percent emphysema-like lung was 1.4%. In total, 1,234 aptamers were significantly associated with percent emphysema in the MESA Lung Study, and 35 replicated in the SPIROMICS and COPDGene Studies. Novel associations included protein family with sequence similarity (FAM) 177A1, syntenin-2, ubiquitin carboxyl-terminal hydrolase 25, and uncharacterized protein C20orf173. Previously identified emphysema-associated proteins included soluble advanced glycosylation end product-specific receptor (sRAGE), protein S100-A12, high mobility group protein B1, and roundabout homolog 2. Enrichment analyses identified 40 GO biological processes, including chemokine production and regulation and cell-cell adhesion and regulation, and two Reactome pathways, including RAGE signaling. In tenfold cross-validation, novel proteins were largely retained by LASSO (R2 = 5.4%), improved overall model performance (R2 = 24.8%), and uniquely explained greater variance in percent emphysema. CONCLUSIONS: This analysis in a general population sample identified novel and previously characterized proteins whose functional roles were validated by GO/Reactome enriched pathways, offering new insights into emphysema pathophysiology and therapeutics.

Humans↗

PubMed2025

Admixture-mapping analysis reveals genetic determinants of the human plasma proteome.

Protein profiling and genetic findings can be integrated to define the genetic architecture of the circulating proteome in chronic diseases. Most self-identified African American (AA) individuals have both African and European genetic ancestry. Admixture mapping can detect genomic association regions in which causal variants exist with substantial differences in allele frequency or effect sizes between genetic ancestries. We performed admixture mapping of the circulating proteome in 1,989 participants from the Jackson Heart Study (JHS), investigating the relation of local African ancestry within genomic regions with levels of circulating proteins. We conditioned protein-local ancestry association models on variants previously found to be associated with those proteins in genome-wide association studies (GWASs). We replicated findings in 196 AA participants from the Multi-Ethnic Study of Atherosclerosis (MESA). 62 proteins were associated with local African ancestry. 21 of 62 remained statistically significant after conditioning on protein-associated variants observed in previous GWASs. 48 of 54 available protein-local ancestry associations were replicated in the MESA. Proteins associated with local African ancestry included chemokines, factors associated with vascular biology and inflammation, and other biologically interesting proteins. Admixture associations unexplained by previously reported protein-associated variants in conditional analysis suggest the existence of causal variants missed by standard GWAS techniques.

Aged↗

PubMed2026

Beyond ion channel dysfunction: Integration of the transcriptome and proteome from patient-specific re-engineered cardiac cells, and population-level QT genome-wide association study reveals broad cellular dysfunction.

BACKGROUND: Congenital long QT syndrome (LQTS) is a cardiac channelopathy with increased risk of cardiac-triggered syncope/seizures, sudden cardiac arrest, and sudden cardiac death. OBJECTIVE: This study aimed to describe the transcriptomic and proteomic profiles in patient-derived inducible pluripotent stem cell-derived cardiomyocyte (iPSC-CM) models of the 3 canonical genotypes of congenital LQTS: LQT1, LQT2, and LQT3 and integrate these omics-level findings with each other and with population/clinical level QT-genome-wide association study (GWAS) data. METHODS: LQT1, LQT2, LQT3 and respective isogenic control iPSC-CMs were cultured, and RNA and protein samples were collected. RNA sequencing and mass spectrometry-enabled proteomic analysis was performed. PrediXcan analysis was performed using QT GWAS summary statistics and transcriptome expression data. Differential gene and protein expression and ingenuity pathway analysis (IPA) was performed comparing each LQT genotype with its respective isogenic control. RESULTS: 1645 differentially expressed genes (DEGs) were identified; 13 were altered in all 3 LQTS genotypes. IPA analysis of DEGs revealed 301 altered pathways; 47 were altered in all LQTS genotypes. Proteomic analysis identified 2561 differentially expressed proteins (DEPs); 30 were altered in all 3 genotypes. IPA analysis of DEPs identified 646 altered pathways. 306 genes/proteins were identified as significantly altered in both the transcriptome and proteome; pathway analysis of these 301 genes identified 201 altered pathways. 7 pathways were altered in all 3 LQTS genotypes in both the transcriptome and proteome. Integration of the population-level PrediXcan results and the cardiomyocyte-derived omics results identified multiple shared pathways. CONCLUSION: Multi-omics analysis of LQTS and integration of omics results with QT GWAS data reveals that primary LQTS-causative ion channel defects precipitate secondary alterations in a wide range of cellular pathways. Our findings suggest more broad molecular level changes throughout the cell. This study lays the foundation for further exploration of broad cellular changes resulting from ion channel disturbances and how they contribute to disease mechanism.

Humans↗

PubMed2025

T cell population size control by coronin 1 uncovered: from a spot identified by two-dimensional gel electrophoresis to quantitative proteomics.

INTRODUCTION: Recent work identified members of the evolutionarily conserved coronin protein family as key regulators of cell population size. This work originated ~25 years ago through the identification, by two-dimensional gel electrophoresis, of coronin 1 as a host protein involved in the virulence of Mycobacterium tuberculosis. We here describe the journey from a spot on a 2D gel to the recent realization that coronin proteins represent key controllers of eukaryotic cell population sizes, using ever more sophisticated proteomic techniques. AREAS COVERED: We discuss the value of 'old school' proteomics using relatively simple and cost-effective technologies that allowed to gain insights into subcellular proteomes and describe how label-free quantitative (phospho)proteomics using mass spectrometry allowed to disentangle the role for coronin 1 in eukaryotic cell population size control. Finally, we mention potential implications of coronin-mediated cell population size control for health and disease. EXPERT OPINION: Proteome analysis has been revolutionized by the advent of modern-day mass spectrometers and is indispensable for a better understanding of biology. Here, we discuss how careful dissection of physio-pathological processes by a combination of proteomics, genomics, biochemistry and cell biology may allow to zoom in on the unexplored, thereby possibly tackling hitherto unasked questions and defining novel mechanisms.

Proteomics↗

PubMed2026

Molecular Signature of Prediabetes With High-Risk of Diabetes Revealed by Deep Plasma Proteome.

AIMS: Prediabetes is biologically heterogeneous, but molecular subtypes linked to diabetes progression remain poorly defined. We aimed to identify plasma proteome-based subtypes of impaired fasting glucose (IFG), characterise their molecular features and assess their association with future diabetes risk. MATERIALS AND METHODS: We quantified 2584 plasma proteins using liquid chromatography-mass spectrometry in 538 IFG participants from a prospective discovery cohort (Nutrition and Health of Aging Population in China, NHAPC). Proteomic subtypes were defined by consensus clustering, linked to longitudinal changes in insulin sensitivity and incident type 2 diabetes mellitus (T2DM), which were further validated in an independent Shanghai Brain Aging Study (SBAS) cohort. RESULTS: Two reproducible IFG molecular subtypes based on plasma proteomics were identified. The high-risk subtype showed higher incident diabetes and a greater 6-year decline in insulin sensitivity and was characterised by enrichment of glycolysis/gluconeogenesis, insulin signalling and neutrophil degranulation, together with a dyslipidemic lipidomic profile indicating co-dysregulation of glucose and lipid homeostasis. The low-risk subtype demonstrated a higher complement cascade and high-density lipoprotein particle remodelling signature. In the high-risk subtype, key proteins and lipids showed stronger associations with longitudinal declines in insulin sensitivity, including PPBP, PGK1 and ALDOA, as well as PE-P 18:0/20:3 and PE-P 18:1/20:3. CONCLUSIONS: Proteome-based molecular subtyping stratifies IFG individuals with similar fasting glucose levels but distinct biology and future diabetes risk, supporting earlier and more targeted prevention.

Humans↗

PubMed2026

Proteomics-Based Soluble Urokinase Plasminogen Activator Receptor Levels Are Associated With Adverse Cardiovascular Outcomes in the General Population: Insights From the UK Biobank.

BACKGROUND: Elevated soluble urokinase plasminogen activator receptor (suPAR) levels are associated with inflammation, immune activation, and major adverse cardiovascular events in coronary artery disease. Encoded by the PLAUR gene, suPAR levels are influenced by the rs4760 genetic variant. Whether proteomics-based suPAR levels predict adverse outcomes in the general population remains unknown. METHODS: Proteomics-based suPAR levels were measured using the Olink Immunoassay in 33 963 UK Biobank participants without known coronary artery disease. Fine-Gray and Cox proportional hazards models assessed associations between suPAR and major adverse cardiovascular events (primary outcome: cardiovascular mortality, nonfatal myocardial infarction, or stroke), cardiovascular mortality, and all-cause mortality (secondary outcomes), after adjustment for demographic and clinical risk factors, hs-CRP (high-sensitivity C-reactive protein), and the rs4760 variant. Incremental discrimination was evaluated using C-statistics. RESULTS: Participants were aged 56.4 (SD, 8.2) years; 45% were men, and 93.4% were White. Over a median follow-up of 14 years (476 177 person-years), 10.7% experienced major adverse cardiovascular events, 2.6% experienced cardiovascular mortality, and 9.4% experienced all-cause mortality. Each 1-SD increment in proteomics-based suPAR was associated with significantly higher risk of major adverse cardiovascular events (hazard ratio [HR], 3.2 [95% CI, 2.9-4.5]), cardiovascular mortality (HR, 5.9 [95% CI, 5.1-6.9]), and all-cause mortality (HR, 5.0 [95% CI, 4.6-5.5]), independent of clinical risk factors and hs-CRP. Additional adjustment for rs4760 did not attenuate these associations. Proteomics-based suPAR significantly improved discrimination beyond clinical risk factors (C-statistic: 0.719 versus 0.732; P<0.001). CONCLUSIONS: Proteomics-based suPAR independently predicts adverse cardiovascular outcomes in the general population, beyond conventional risk factors, hs-CRP, and genetic predisposition to elevated suPAR levels.

Humans↗

PubMed2026

Proteomic analysis identifies pathways related to immune dysregulation in patients with hematologic malignancies after COVID-19 infection.

Patients with hematologic malignancies (HMs) are particularly vulnerable to coronavirus disease 2019 (COVID-19) because of underlying immune dysfunction and treatment-related immunosuppression. However, proteomic features associated with different clinical trajectories in this population remain insufficiently characterized. We performed serum proteomic analysis in 40 HM patients with COVID-19 and 15 healthy controls. Compared with controls, HM patients showed impaired immune-related responses during the acute phase of COVID-19. Acute-phase proteomic patterns differed across outcome groups; however, because outcome groups were closely intertwined with initial COVID-19 severity, ICU admission, and systemic illness, and because multivariable adjustment was not performed due to the limited sample size, these patterns should be interpreted as severity- and outcome-associated profiles rather than independent trajectory-specific markers. Fatal cases showed evidence of dysregulated immune activation, whereas patients later classified as having long COVID exhibited broader suppression of immune-related pathways. In addition to immune alterations, pathways related to platelet activation and cardiac-related dysfunction were associated with adverse clinical trajectories. Enzyme-linked immunosorbent assay validation supported the association of selected proteins with outcome groups during acute infection. These findings provide a proteomic overview of COVID-19 in HM patients and offer a basis for future mechanistic studies and larger external validation cohorts.IMPORTANCEPatients with hematologic malignancies are highly vulnerable to severe coronavirus disease 2019 (COVID-19), acute death, and long COVID due to preexisting immune dysfunction. However, the proteomic signatures linked to adverse clinical trajectories remain poorly understood. Our serum proteomic study identifies distinct acute-phase immune profiles associated with different outcomes: broad immune suppression characterizes long COVID, while dysregulated immune activation is associated with fatal cases. Platelet activation and cardiac-related pathways are also linked to poor outcomes. These findings provide key molecular insights for this high-risk population, supporting future biomarker development, risk stratification, and targeted clinical management.CLINICAL TRIALSThis study is registered with ClinicalTrials.gov as NCT05683353.

Humans↗