Objective To delineate organ-specific and systemic drivers of metabolic dysfunction-associated steatotic liver disease (MASLD), we applied integrative causal inference across clinical, imaging, and proteomic domains in individuals with and without type 2 diabetes (T2D). Methods Bayesian network analyses and complementary two-sample Mendelian randomization were used to quantify causal pathways linking adipose distribution, glycemia, and insulin dynamics with liver fat in the IMI-DIRECT prospective cohort study. Data included frequently sampled metabolic challenge tests, MRI-derived abdominal and hepatic fat content, serological biomarkers, and Olink plasma proteomics from 331 adults with new-onset T2D and 964 adults without diabetes, with harmonized protocols enabling replication. Results High basal insulin secretion rate (BasalISR), estimated via C-peptide deconvolution, emerged as the primary potential causal driver of liver fat accumulation in both cohorts. BasalISR, a clearance-independent measure of β-cell insulin output distinct from peripheral insulin levels, was independently linked to hepatic steatosis. Visceral adipose tissue exhibited bidirectional associations with liver fat, suggesting a self-reinforcing metabolic loop. Of 446 analyzed proteins, 34 mapped to these metabolic networks (27 in the non-diabetes network, 18 in the T2D network, and 11 shared). Key proteins directly associated with liver fat included GUSB, ALDH1A1, LPL, IGFBP1/2, CTSD, HMOX1, FGF21, AGRP, and ACE2. Sex-stratified analyses identified GUSB in females and LEP in males as the strongest protein predictors of liver fat. Conclusions BasalISR may better capture early β-cell-driven disturbances contributing to MASLD. These findings outline a multifactorial, sex- and disease stage–specific proteo-metabolic architecture of hepatic steatosis and identify potential biomarkers or therapeutic targets.

Despite extensive research, molecular differences in human populations and the influence of ancestry, age, geography, and diet are poorly understood. We performed comprehensive multiomics profiling (including genomics, transcriptomics, proteomics, metabolomics, lipidomics, metallomics, glycomics, and microbiomics) on samples from 322 healthy individuals of European, East Asian, and South Asian ancestry across multiple continents. We identified ethnicity-associated molecular features linked to host metabolism, autoimmune disease risk, drug metabolism, and neurodegenerative pathways. We uncovered ancestry-and geography-related molecular changes affecting metabolism, immune function, microbiome composition, and biological aging. Specific genetic variants and gene expression differences were associated with lipid metabolism and immune regulation. Geography influenced biological age: East Asians showed lower biological age in their ancestral regions, whereas individuals of European ancestry exhibited lower biological age in the US/Canada than in Europe. Diet-microbiome metabolism interactions displayed ethnicity-specific patterns, many related to health. This open access resource advances understanding of ethnicity-environment interactions and supports precision medicine.

The mechanisms by which temperature exposure affects health outcomes remain unclear. In this study, we conducted three repeated measurements (2020-2022) by collecting self-sampled volumetric dry blood spots (DBSs) from 807 participants from the Swedish BAMSE cohort (mean age 25.9 years). By estimating individual-address level daily temperature using a high-resolution spatiotemporal model, we found that 58 (16%) of the 365 studied inflammation-related proteins were significantly associated with short-term exposure to ambient temperatures. The impact of temperature exposure was modified by sex, smoking, asthma, and concurrent exposure to air pollution. The temperature-associated proteins were linked to lung function, blood pressure, and HbA1c with validations in the UK Biobank. Furthermore, peak temperature exposure (both cold and heat) was associated with significantly increased proteomic age acceleration. Our findings suggest that ambient temperature exposure may cause adverse health effects through perturbating inflammation-related proteins.

The circulating blood proteome comprises soluble and cellular components that reflect physiological and pathological states across tissues. Advances in mass spectrometry and affinity-based proteomics have improved sensitivity and throughput, enabling the generation of public blood proteomics resources. However, comprehensive assessments of these datasets remain limited. This work reviews the cellular and molecular complexity of publicly available blood proteomics data, recent methodological developments, and the complementarity of diverse data sources across the abundance range, while outlining remaining challenges for translating blood proteomics into personalized medicine.

Blood proteins have provided essential insights into how humans responded to the recent pandemic. To expand our understanding beyond patients seeking medical care, we conducted a citizen-centric survey with 2,000 random residents (age: 18–69 years) from Sweden's two largest cities in 2021. With self-sampled dried blood spots (DBS) and health information from 437 (22%) volunteers, we performed multi-analyte COVID-19 serology, measured autoantibodies (AAbs) against 22 interferons, and quantified 502 circulating low-abundant immune-related blood proteins. Antibody assays confirmed self-reported infections (26%) and vaccinations (40%), showed timing-dependent discrepancies in the immune response, and revealed anti-type I interferon AAbs co-occurring frequently alongside natural infections. Proteomics data added plausible mechanistic insights into cell-mediated processes: data-driven analyses revealed 24% of participants presented deviating immune phenotypes linked to infections, immunity, respiratory effects, and age. Multi-molecular DBS analysis of random layperson samples captured the broader spectrum of immune system states, adding relevant insights for clinical and public health investigations.

Understanding the genetic regulation of circulating protein levels can provide new insights into disease mechanisms. Here, we present the largest proteogenomic study to date (n = 78,664 participants across 38 studies), identifying >24,000 protein quantitative trait loci (QTLs) associated with 1,116 proteins, acting near to (n = 5,040) or distant (n = 19,698) from the cognate gene. Using machine learning-guided effector gene assignment, we provide genetic evidence for pathways, cell types, and tissues that modulate circulating protein levels, highlighting N-linked glycosylation as an important regulatory pathway. We demonstrate that genetic instruments of protein production/function (''cis'') versus modulation (''trans'') reveal distinct phenotypic insights. We identify proteins as candidates for drug targets and engagement (e.g., plasma furin and cardiovascular diseases) by comparing cis-based genetic evidence with protein-disease associations. Systematic triangulation of trans-protein QTLs (pQTLs) with genetic and protein associations across many diseases highlights potential drug repurposing opportunities, e.g., tyrosine kinase 2 (TYK2) inhibitors for rheumatoid arthritis. Our multi-cohort meta-analyses generate proteogenomic insights into disease mechanisms and new treatment opportunities.

Prostate cancer (PCa) is common world-wide. Current diagnostics based on testing for circulating levels of prostate specific antigen (PSA) is unspecific, and novel prognostic markers are needed for personalized therapeutic strategies. Pro-neuropeptide Y (pro-NPY) has been reported as a tissue marker for PCa related to poor prognosis. This study explored the prognostic value of circulating pro-NPY in PCa. Plasma samples were obtained from two patient cohorts: (1) men examined due to increased PSA levels in 2003–2011 (n = 796) and (2) patients treated for PCa in 2013–2016 (n = 92). Cohort 2 also provided plasma samples collected ~ 3 months after therapy. For plasma pro-NPY assessment, a sandwich immunoassay was developed. In cohort 1, 315 patients were diagnosed with PCa at the time for blood sampling, 137 were diagnosed during follow-up, and 344 remained disease-free. Plasma pro-NPY provided independent prognostic information from PSA regarding time to metastasis and PCa death. In cohort 2, high plasma pro-NPY levels were confirmed associated with metastatic disease and poor survival. Plasma pro-NPY levels were normalized after androgen-deprivation therapy, suggesting androgen-regulation. In conclusion, high circulating pro-NPY levels are associated with metastasis and poor outcome in PCa. Prospective validation is needed before suggesting pro-NPY for clinical use. The underlying biology and consequences of pro-NPY overexpression remain to be understood.

The translation of blood-based proteomics into healthcare is no longer constrained primarily by technological limitations, but by unresolved challenges in standardization, validation, and implementation. In this Commentary, we identify four structural bottlenecks at the current translational inflection point that limit the opportunities for the adoption of multi-protein blood biomarkers in routine clinical care: lack of harmonized reference frameworks, uncertainty around fit-for-purpose biological resolution, complexity in validating multi-analyte and algorithm-based tests, and misalignment between the design of discovery workflows and clinical requirements. We argue that progress will depend on shifting from exploratory profiling toward decision-oriented proteome analytics, with early alignment across academic, regulatory, clinical, and technical domains. Establishing coherent validation pathways for clinically actionable use cases will be essential to enable reliable integration of proteomics into healthcare.

Blood-based proteomics is approaching a translational inflection point. Driven by advances in measurement technologies, rapid expansion of analytical capabilities, and growing adoption across research and medical communities, there is increasing demand for clinically actionable biomarkers. As the field transitions away from purely large-scale discovery-oriented studies toward more informed, targeted, application-driven analyses, the generation of proteomic data is no longer the bottleneck. Instead, the central challenge is to translate these measurements into robust, reproducible, and clinically meaningful insights. In this Review, we assess recent technological and methodological developments, evaluate persistent preanalytical and interpretative limitations, and outline the key steps required for clinical translation. We focus on three deeply interconnected dimensions: the capabilities and constraints of current measurement platforms, the role of computational and machine learning approaches in extracting biological and clinical signals, and the emergence of large-scale population studies that create new opportunities for validation and generalization. Finally, we discuss a forward-looking vision in which proteomics plays a central role in dynamic, multilayered omics frameworks, where integration with genomics, temporal profiling, and imaging can deepen our understanding of health, disease, and therapeutic response.

The human blood proteome provides a holistic readout of health states through the assessment of thousands of circulating proteins. In this study, we present a pan-disease resource to enable the study of diverse disease phenotypes within a harmonized proteomics dataset. By profiling protein concentrations across 59 diseases and healthy cohorts, we identified proteins associated with age, sex, and body mass index, as well as disease-specific signatures. This study highlights shared and distinct protein patterns across conditions, demonstrating the power of a unified proteomics approach to uncover biological insights. The dataset, covering 8262 individuals and up to 5416 proteins, serves as an online resource for exploring disease-specific protein profiles and advancing precision medicine research.