Biology subjects

Bergström, G.

Publications and source records attributed to Bergström, G..

4 recordsLinked to original sources

bioRxiv2026

Machine learning uncovers circulating biomarkers and molecular heterogeneity in obesity and type 2 diabetes

Obesity and Type 2 Diabetes (T2D) are heterogeneous metabolic disorders whose molecular diversity is incompletely defined. We analyzed circulating proteomic profiles from 129 individuals belonging to Control, Obesity, and T2D groups and applied complementary machine-learning approaches, including random forest, multinomial logistic regression with LASSO regularization, support vector machines, and ensemble voting to identify proteins distinguishing the clinical groups. Convergent model outputs revealed a partially overlapping panel of discriminative proteins. Model performance was evaluated in an independent dataset from the Human Protein Atlas (n=834) comprising healthy individuals, patients with Obesity, T2D, or other metabolic diseases. Unsupervised clustering further identified multiple proteomic subgroups within each clinical category, indicating substantial intragroup heterogeneity. Bootstrap random forest with null-model benchmarking highlighted stable cluster-discriminative proteins. These findings demonstrate that integrating circulating proteomics with machine learning can resolve molecular heterogeneity in Obesity and T2D and nominate candidate biomarkers for metabolic disease stratification.

biochemistry↗

bioRxiv2026

Systems-level longitudinal immune profiling reveals individualized immunotypes and genetic associations

The human immune system exhibits substantial inter-individual variation, yet how this variability is coordinated across the system and persists over time remains incompletely defined. Here, we integrated whole-genome sequencing with longitudinal multi-omics immune profiling in 101 healthy individuals followed over two years, combining mass cytometry-based immune cell profiling, PBMC transcriptomics, plasma proteomics, and clinical measurements. Coordinated variation across cellular composition, gene expression, and circulating proteins revealed reproducible immune patterns within individuals across visits. Integrative analyses identified three major immunotypes: adaptive lymphoid (CD4 T cell and B cell enriched), myeloid-inflammation (monocyte and dendritic cell-driven), and cytotoxic (CD8 T cell-dominated), each associated with distinct metabolic and inflammatory profiles. Genome-wide association analyses identified cell-type-specific quantitative trait loci primarily affecting memory lymphocytes, and a polygenic score for memory B cells correlated with both cellular abundance and transcriptional activity. Together, these findings provide a systems-level view for understanding baseline immune heterogeneity in human populations. TeaserLongitudinally stable immune individuality reflects coordinated modules shaped by systemic physiology and genetics

systems biology↗

bioRxiv2024

Longitudinal Exposomics in a Multiomic Wellness Cohort Reveals Distinctive and Dynamic Environmental Chemical Mixtures in Blood

Chemical exposomes can now be comprehensively measured in human blood, but robust application of chemical exposomics in cohort studies requires knowledge of the longitudinal stability and interindividual variability of exogenous molecular profiles. Here we applied chemical exposomics to plasma of 46 adults, each sampled six times over two years in a multiomic wellness cohort. New chemicals were discovered, distinctive co-exposure patterns were observed, and intra-class correlation coefficients (ICC) for 519 confidently annotated substances are reported to support study design. Longitudinal stability of the chemical exposome (mean ICC 0.30) was significantly lower than the proteome, metabolome, lipidome or microbiome, and must be measured more frequently than other molecular profiles in health studies. Mixed-effects models nevertheless revealed significant associations between testosterone and perfluoroalkyl substances, and significant time-trends for low and high stability exposures alike. Complex exposome data structures were visualized and explored, demonstrating great potential for longitudinal exposomics in precision health research. TeaserThe first cohort-level application of longitudinal exposomics revealed novel and dynamic co-exposures in blood of relevance to precision health.

pharmacology and toxicology↗

bioRxiv2020

Facets of individual-specific health signatures determined from longitudinal plasma proteome profiling

BackgroundPrecision medicine approaches aim to tackle diseases on an individual level through molecular profiling. Despite the growing knowledge about diseases and the reported diversity of molecular phenotypes, the descriptions of human health on an individual level have been far less elaborate. MethodsTo provide insights into the longitudinal protein signatures of well-being, we profiled blood plasma collected over one year from 101 clinically healthy individuals using multiplexed antibody assays. After applying an antibody validation scheme, we utilized > 700 protein profiles for in-depth analyses of the individuals short-term health trajectories. FindingsWe found signatures of circulating proteomes to be highly individual-specific. Considering technical and longitudinal variability, we observed both stable and fluctuating proteins in the circulation, as well as networks of proteins that covaried over time. For each participant, there were unique protein profiles and some of these could be explained by associations to genetic variants. InterpretationThis study demonstrates that there was noticeable diversity among clinically healthy subjects, and facets of individual-specific signatures emerged by monitoring the variability of the circulating proteomes over time. Longitudinal profiling of circulating proteomes has the potential to enable a more personal hence precise assessment of health states, and thereby provide a valuable component of precision medicine approaches. FundingThis work was supported by the Erling Persson Foundation for the KTH Centre for Precision Medicine and the Swedish Heart and Lung Foundation for the SCAPIS project. We also acknowledge the Knut and Alice Wallenberg Foundation for funding the Human Protein Atlas project, Science for Life Laboratory for Plasma Profiling Facility, and the Swedish Research Council (Grant no 2017-00641).

biochemistry↗