SEARCH · Search PubMed

Results for “Biologic pathways”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5Linked to original sources

PubMed1993

Normal cellular processing of the beta-amyloid precursor protein results in the secretion of the amyloid beta peptide and related molecules.

Alzheimer's disease is characterized by the extracellular deposition in the brain and its blood vessels of insoluble aggregates of the amyloid beta peptide (A beta). This peptide is derived from a large integral membrane protein, the beta-amyloid precursor protein (beta APP), by proteolytic processing. The A beta has previously been found only in the brains of patients with Alzheimer's disease or advanced aging. We describe here the finding that A beta is produced continuously by normal processing in tissue culture cells. A beta and closely related peptides were identified in the media of cells transfected with cDNAs coding for beta APP in a variety of cell lines and primary tissue cultured cells. The identity of these peptides was confirmed by epitope mapping and radiosequencing. Peptides of a molecular weight of approximately 3 and approximately 4 kDa are described. The 4 kDa range contains mostly the A beta and two related peptides starting N-terminal to the beginning of A beta. In the 3 kDa range, the majority of peptides start at the secretase site; in addition, two longer peptides were found starting at amino acid F(4) and E(11) of the A beta sequence. To identify the processing pathways which lead to the secretion of these peptides, we used a variety of drugs known to interfere with certain cell biological pathways. We conclude that lysosomes may not play a predominant role in the formation of 3 and 4 kDa peptides. We show that an acidic environment is necessary to create the N-terminus of the A beta and postulate that alternative secretory cleavage might result in the formation of the N-terminus of A beta and related peptides. This cleavage takes place either in the late Golgi, at the cell-surface or in early endosomes, but not in lysosomes. The N-terminus of most of the 3 kDa peptides is created by secretory cleavage on the cell surface or within late Golgi.

Amino Acid Sequence↗

PubMed2025

Transcriptomic analysis at 48 h postmortem: a proof of concept for the identification of biomarkers to estimate time since death.

BACKGROUND: The postmortem interval (PMI) refers to the time elapsed between an individual's death and the examination of the body. Tissues undergo a sequence of anatomical changes following death, which are routinely used to estimate the PMI. METHODS: To determine if these anatomical changes are associated with identifiable genomic adaptations that could characterize the PMI more accurately, we analyzed the rat skeletal muscle transcriptome at 0 and 48 h postmortem using Clariom™ S arrays. This study investigates whether specific transcriptomic changes correlate with PMI progression, offering a potential molecular tool to complement established anatomical methods. RESULTS: A total of 3,873 differentially expressed mRNAs were identified, of which 2,787 downregulated and 1,086 upregulated transcripts. The most significantly downregulated mRNA was Tnni1 (FC = -30.95, p = 1 × 10-3), while the most upregulated were mt-ATP6, mt-ATP8, and mt-CO3 (FC > 7.78, p < 1.36 × 10-12). Gene ontology (GO) enrichment analyses revealed that mRNAs upregulated at 48 h in the PMI were primarily associated with vascular and endothelial processes, including nitric oxide transport and angiogenesis. Conversely, downregulated mRNAs were linked to mitochondrial activity and cellular metabolism, reflecting both a transient vascular response and metabolic pathway shutdown in the rat skeletal muscle. CONCLUSION: Our results demonstrate significant transcriptomic changes at 48 h postmortem, highlighting specific genes and biological pathways that may serve as candidate biomarkers for PMI estimation.

Animals↗

PubMed1999

Computational methods for the identification of differential and coordinated gene expression.

With the first complete 'draft' of the human genome sequence expected for Spring 2000, the three basic challenges for today's bioinformatics are more than ever: (i) finding the genes; (ii) locating their coding regions; and (iii) predicting their functions. However, our capacity for interpreting vertebrate genomic and transcript (cDNA) sequences using experimental or computational means very much lags behind our raw sequencing power. If the performances of current programs in identifying internal coding exons are good, the precise 5'-->3' delineation of transcription units (and promoters) still requires additional experiments. Similarly, functional predictions made with reference to previously characterized homologues are leaving >50% of human genes unannotated or classified in uninformative categories ('kinase', 'ATP-binding', etc.). In the context of functional genomics, large-scale gene expression studies using massive cDNA tag sequencing, two-dimensional gel proteome analysis or microarray technologies are the only approaches providing genome-scale experimental information at a pace consistent with the progress of sequencing. Given the difficulty and cost of characterizing genes one by one, academic and industrial researchers are increasingly relying on those methods to prioritize their studies and choose their targets. The study of expression patterns can also provide some insight into the function, reveal regulatory pathways, indicate side effects of drugs or serve as a diagnostic tool. In this article, I review the theoretical and computational approaches used to: (i) identify genes differentially expressed (across cell types, developmental stages, pathological conditions, etc.); (ii) identify genes expressed in a coordinated manner across a set of conditions; and (iii) delineate clusters of genes sharing coherent expression features, eventually defining global biological pathways.

Animals↗

PubMed2025

Histology-Based Virtual RNA Inference Identifies Pathways Associated With Metastasis Risk in Colorectal Cancer.

Colorectal cancer (CRC) remains a major health concern, with >150,000 new diagnoses and >50,000 deaths annually in the United States, underscoring an urgent need for improved screening, prognostication, disease management, and therapeutic approaches. The tumor microenvironment (TME)-comprising cancerous and immune cells interacting within the tumor's spatial architecture-plays a critical role in disease progression and treatment outcomes, reinforcing its importance as a prognostic marker for metastasis and recurrence risk. However, traditional methods for TME characterization, such as bulk transcriptomics and multiplex protein assays, lack sufficient spatial resolution. Although spatial transcriptomics (ST) allows for the high-resolution mapping of whole transcriptomes at near-cellular resolution, current ST technologies (eg, Visium and Xenium) are limited by high costs, low throughput, and issues with reproducibility, preventing their widespread application in large-scale molecular epidemiology studies. In this study, we refined and implemented virtual RNA inference (VRI) to derive ST-level molecular information directly from hematoxylin and eosin (H&E)-stained tissue images. Our VRI models were trained on the largest matched CRC ST data set to date, comprising 45 patients and >300,000 Visium spots from primary tumors. Using state-of-the-art deep learning models (UNI, ResNet-50, Vision Transformer, and Vision Mamba), we achieved a median Spearman's correlation coefficient of 0.546 between predicted and measured spot-level expression. As validation, VRI-derived gene signatures linked to specific tissue regions (tumor, interface, submucosa, stroma, serosa, muscularis, and inflammation) showed strong concordance with signatures generated via direct ST, and VRI performed accurately in estimating cell-type proportions spatially from H&E slides. In an expanded CRC cohort controlling for tumor invasiveness and clinical factors, we further identified VRI-derived gene signatures significantly associated with key prognostic outcomes, including metastasis status. Although certain tumor-related pathways are not fully captured by histology alone, our findings highlight the ability of VRI to infer a wide range of "histology-associated" biological pathways at near-cellular resolution without requiring ST profiling. Future efforts will extend this framework to expand TME phenotyping from standard H&E tissue images, with the potential to accelerate translational CRC research at scale.

Humans↗

PubMed2026

Rare genetic variant risks in patients with sepsis-associated acute respiratory distress syndrome.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a complex, heterogeneous, and deadly condition often resulting from pulmonary lesions due to sepsis, among other causes. There is a lack of targeted therapies to specifically treat the patients. Common genetic factors in the population (frequency > 1%) have been associated with ARDS susceptibility, but systematic genetic screens of the role of rare genetic variants are lacking. We used the network of known molecular interactions to identify ARDS risks from clusters of biologically related genes containing qualifying variants (QVs) with frequency < 1% likely affecting function. METHODS: We conducted whole-exome sequencing in sepsis patients from the GEN-SEP cohort (n = 822, of which 272 developed ARDS). A network-based heterogeneity clustering algorithm was used to discover significant gene clusters (p < 1 × 10–5). Gene-set enrichment analysis and logistic regression models aggregating QVs were used for cross-verification to confirm consistency and deepen understanding of the effect sizes of gene clusters. RESULTS: We identified 19 significant clusters (plowest = 3.29 × 10–10), each containing an average of 102 genes (11.6% mean similarity). QVs in nine gene clusters were associated with sepsis-associated ARDS (plowest = 1 × 10–5) but were not associated with 28-day survival. Clusters were enriched in several biological pathways, notably the Toll-like receptor cascades. CONCLUSIONS: These results support a marked genetic heterogeneity underlying ARDS susceptibility and the presence of rare risk variants involving multiple biological processes that are associated with sepsis outcomes. Particularly, they underscore the importance of rare variants in genes of the Toll-like receptor cascades in the risk for sepsis-associated ARDS.

Humans↗

PubMed1998

[Mechanism of cellular cholesterol removal: a communication system between extracellular cholesterol transport and intracellular cholesterol homeostasis].

Cholesterol efflux is one of the essential events in cellular cholesterol homeostasis since peripheral cells do not catabolize the cholesterol molecule. There are two distinct mechanisms for the efflux. One is the non-specific classical pathway mediated by physicochemical diffusion of cholesterol through the aqueous phase and its esterification on high density lipoprotein (HDL) by lecithin: cholesterol acyltransferase (LCAT). The other is the specific and biological pathway in which new HDL particles are generated from cellular lipid by the direct interaction of cell membrane and amphiphilic apolipoproteins that have dissociated from HDL. The latter reaction consists of binding of apolipoprotein to the specific binding site of the cellular surface and subsequent mobilization of intracellular cholesterol for the HDL generation mediated by intracellular signal transduction. This reaction seems to be a major source of plasma HDL.

Animals↗

PubMed1998

Inherited breast cancer: an emerging picture.

A role for BRCA1 and BRCA2 in the control of genome integrity easily fits a tumor suppressor model. It is well established that mutations in DNA repair genes lead to genomic instability (138). Genomic instability may directly lead to tumorigenesis by allowing for the accumulation of mutations in key cell cycle regulators (139). The studies summarized here suggest that BRCA1, BRCA2, RAD51. and BARD1 function as a biochemical complex. This complex apparently plays a role in one or more of the DNA damage response pathways. Experimental data suggest that BRCA1 and BRCA2 function as regulators of transcription. These observations highlight some of the fundamental questions that remain to be addressed in the study of the biology of these genes. Are the DNA repair and transcriptional regulatory functions of BRCA1 and BRCA2 related? BRCA1 and BRCA2 may maintain the integrity of the genome by regulating expression of genes directly involved in this process. Alternatively, if the functions are not related, which is required for suppression of tumorigenesis? Researchers also are grappling with another paradox. If BRCA1 and BRCA2 are ubiquitously expressed, why do mutations in BRCA1 and BRCA2 lead specifically to tumors primarily of the breast and ovary, as well as a limited number of other tissues to a lesser degree? Nothing to date has been revealed that would explain how alteration of the transcriptional regulatory function and or the DNA repair function ascribed to BRCA1 and BRCA2 would result in tumor specificity as both of these functions are essential to a broad spectrum of tissues. It is possible that BRCAI and BRCA2 may regulate genes expressed only in the breast and ovary. Similarly, there may be unidentified BRCA1 and BRCA2 co-factors that are active only in the breast and ovary and, therefore, are critical to tumorigenesis. All breast cancer is genetic, although only a small fraction of cases are attributable to inherited genetic predisposition. Most breast cancer is due to genetic alterations that are specific to breast epithelial cells, many of which remain unknown. Integration of genetic approaches into research designed to elucidate biological pathways of breast cancer tumorigenesis will ultimately lead to new information critical to the development of new tools for the diagnosis and treatment of disease.

BRCA1 Protein↗

PubMed2026

Refining the Genetic Contribution to Type 2 Diabetes Subtypes.

BACKGROUND: Type 2 diabetes (T2D) is a complex and highly heterogeneous disease driven in part by genetic predisposition and can be stratified into clinical subgroups to aid disease management. We recently grouped T2D subjects in the Qatar Biobank (QBB) cohort into Severe Insulin-Deficient Diabetes (SIDD), Severe Insulin-Resistant Diabetes (SIRD), Mild Obesity-Related Diabetes (MOD) and Mild Age-Related Diabetes (MARD) subtypes. Herein, we focused on the genetic makeup of these subtypes. METHODS: We used the QBB cohort (n = 13,808), of whom 2687 were with T2D, and comprehensively assessed polygenic risk scores (PGS) across T2D subtypes, investigated genetic loci associated with each subtype by leveraging the most recent and largest GWAS for T2D, evaluated SNP associations across T2D genetic clusters, and identified protein interaction pathways associated with these distinct T2D subtypes. RESULTS: MOD showed consistently lower PGS compared with other T2D subtypes across all tested scores. SIDD showed more associations with SNPs mapping to residual glycemic cluster compared with other T2D subtypes. The incremental analysis of PGS004838 demonstrated a high ΔAUC of 0.101 for SIDD and a moderate ΔAUC of 0.068 for SIRD, but not for MOD and MARD. Protein interaction analyses identified candidate subtype-associated gene networks linked to pathways related to glucose homeostasis in SIDD, insulin signalling and hepatic metabolism in SIRD, body fat distribution in MOD and vascular-related processes in MARD. CONCLUSION: We found heterogeneous genetic architectures across clinically defined T2D subtypes in a Middle Eastern population. Our findings provide evidence supporting differential polygenic burden, subtype genetic associations and subtype-associated biological pathways across T2D subtypes. These observations support the utility of subtype-based genetic analyses for improving biological understanding of T2D heterogeneity.

Humans↗

PubMed2025

Unique signatures of highly constrained genes across publicly available genomic databases.

PURPOSE: Publicly available genomic databases are critical in understanding human genetic variation. They also provide unique insights into patterns of genetic constraints and their relationship with human disease. METHODS: We utilized one of the largest publicly available databases, Genome Aggregate Database, to determine genes that are highly constrained for only loss-of-function, only missense, and both loss-of-function/missense variants. We identified their unique signatures and explored their causal relationship with human diseases. Those genes were also evaluated for chromosomal location, tissue-level expression, Gene Ontology analysis, and gene family categorization using multiple publicly available databases. RESULTS: We identified unique patterns of inheritance, protein size, and enrichment in distinct molecular pathways for those constrained genes associated with human disease. In addition, we identified genes that are currently not known to cause human disease, which may be excellent gene discovery candidates. CONCLUSION: We elucidate biological pathways of highly constrained genes that expand our understanding of critical cellular proteins. The findings can also advance research in rare diseases.

Humans↗

PubMed2026

Differential DNA methylation in blood as potential mediator of the association between ambient PM2.5 and cerebrospinal fluid biomarkers of Alzheimer's disease among a cognitively normal population-based cohort.

Fine particulate matter (PM2.5) is a known risk factor for Alzheimer's disease (AD), with emerging evidence showing its effects detectable in the pre-clinical stage through cerebrospinal fluid (CSF) biomarkers of AD. While studies have linked PM2.5 exposure and AD to DNA methylation (DNAm) alterations, the role of DNAm as potential mediator in the association between PM2.5 and AD biomarkers in cognitively normal individuals remains largely unexplored, and formal mediation analyses addressing this question are scarce. Genome-wide DNAm profiles (Illumina EPIC BeadChips) in whole blood and CSF Aβ42 concentrations were assessed in 536 cognitively normal individuals from the Emory Healthy Brain Study (EHBS). Residential PM2.5 exposure for the year preceding participants' blood collection was estimated. A multi-stage analytical pipeline, incorporating single-mediator analysis, high-dimensional mediation analysis, and causal mediation analysis, was applied. Nine CpG sites were identified as noteworthy mediators of the relationship between PM2.5 and decreased CSF Aβ42 concentrations. Causal mediation analysis confirmed significant natural indirect effects (NIE) for eight CpGs, with effect estimates ranging from -0.015--0.029 per 1 ug/m3 increase in PM2.5 exposure. The proportion mediated ranging from 14-43%. Six CpGs are annotated to genes implicated in neuroinflammatory pathways. These findings suggest that differential DNAm, particularly in genes related to neuroinflammation, mediates the association between PM2.5 exposure and CSF Aβ42 concentrations, highlighting the utility of blood DNAm in detecting and studying biological pathways underlying PM2.5 toxicity in the pre-clinical stages of AD.

Humans↗

PubMed2025

Comorbidity alters the genetic relationship between anxiety disorders and major depression.

BACKGROUND: Comorbid anxiety disorders (ANX) and major depression (MD) have worse clinical outcomes than either disorder alone. Analysis of genomic data based on comorbidity status may reveal more precise biological pathways and causal relationships with potential clinical implications. We investigated the genetic relationship between ANX and MD with and without mutual comorbidity. METHODS: We leveraged data from UK Biobank to perform disorder-specific genome-wide association studies (GWAS) of ANX-only (n=189,422) and MD-only (n=194,339) and generate polygenic risk scores (PRS). The Norwegian Mother, Father, and Child Cohort (MoBa, n = 130,992) served to test the associations of PRS with diagnoses. MD and ANX GWAS, including comorbidities (MD-comorbid and ANX-comorbid), were used for comparison. Genetic correlations were compared by comorbidity status, and Mendelian randomization was employed to assess causal relationships. RESULTS: The MD-only PRS showed a stronger association with MD-only compared to ANX-only cases (Z=3.74; Padjusted=0.002); however, MD-comorbid PRS did not show a significant difference (Z=2.71; Padjusted=0.08). The genetic correlation between ANX-only and MD-only was 0.53, lower than between ANX-comorbid and MD-comorbid (0.90). ANX-only showed a causal relationship with MD-only (Padjusted=0.015), but not vice versa, and contrasted the bidirectional causal relationship (Padjusted=2.9e-12, and Padjusted=9.3e-06) when comorbidity was included. Gene sets of MD-comorbid, ANX-comorbid, and MD-only, but not of ANX-only, were enriched for immune regulation pathways such as interleukin production. CONCLUSIONS: ANX and MD show more distinct genetics when comorbid cases are excluded, and ANX may be causal for MD. Disorder-specific genetic studies help uncover more relevant biological mechanisms and guide tailored clinical interventions.

Journal Article↗

PubMed2026

CeLLTra: aligning cell names with gene expression via a pathway-informed transformer.

MOTIVATION: Single-cell RNA sequencing (scRNA-Seq) technology enables detailed exploration of gene expression at the individual cell level, crucial for annotating cell types and understanding cellular diversity. Traditional methods for cell type annotation often rely on marker genes and manual labeling, posing challenges due to low data quality and incomplete reference datasets. RESULTS: We developed CeLLTra, a novel contrastive learning framework that leverages a Transformer-based model integrating biological pathway information to group genes into super tokens, effectively capturing comprehensive gene expression from scRNA-Seq data. By combining this pathway-informed Transformer with a pretrained domain-specific language model, CeLLTra accurately aligns cell-type annotations with gene expression profiles. Evaluations on a large-scale human scRNA-Seq dataset showed that CeLLTra significantly outperformed state-of-the-art methods in supervised and zero-shot cell-type prediction. Additionally, CeLLTra generalized well to external datasets, improving clustering performance and enabling better characterization of cancerous cell states in tumor-infiltrating myeloid cells from non-small cell lung cancer patients. AVAILABILITY AND IMPLEMENTATION: CeLLTra is freely available on GitHub (https://github.com/WJZheng-group/CeLLTra) and Zenodo (https://doi.org/10.5281/zenodo.17666735). The datasets underlying this article are the following: GSE201333 and GSE127465. All these datasets are publicly available and can be freely accessed on the Gene Expression Omnibus repository.

Humans↗

PubMed2026

Large language models in bioinformatics: a comprehensive survey.

The emergence of foundation models with trillion-level parameters has redefined the landscape of artificial intelligence. Various fields are developing their own large-scale models, which can solve many problems within the field and improve work efficiency. Biological large-scale models are a cross-disciplinary research field that combines mathematics, computer science, and biology, aiming to simulate and understand the structure, function, and dynamic changes of biological systems through the establishment of complex computational models. This field covers multiple levels such as biological pathways, population dynamics, protein folding, etc., providing us with tools for deep exploration of the mysteries of life and applications in medicine, ecology, and other fields. This article reviews the background and research status of biological large-scale models, and discusses future directions. Large language models (LLMs) and other large-scale foundation models have rapidly advanced in recent years, enabling powerful representation learning and generation across text, sequences, and multimodal data. In bioinformatics and biomedicine, these models are increasingly used to analyze genomic sequences, infer protein properties and structures, support drug discovery, and integrate heterogeneous biomedical evidence. This survey reviews the basic principles of LLMs and summarizes representative applications in (i) gene and genome sequence analysis, (ii) protein structure and function prediction, and (iii) drug design, including virtual screening and personalized medicine. We also discuss emerging multi-model modeling approaches, as well as key challenges such as data quality and privacy, interpretability, generalization to new organisms and tasks, and responsible deployment in health-related settings. Finally, we outline future directions for developing reliable, scalable, and explainable bioinformatics foundation models.

bioinformatics↗

PubMed1994

Psychoneuroimmunology of HIV infection.

The biological pathways exist that could allow psychological factors to alter immune status in HIV-positive individuals. It yet remains to be determined whether such factors can, in fact, act as cofactors in HIV progression. The biology of AIDS is complex, and a multitude of processes may act on HIV progression and complicate studies in this area. The search for modifiable host factors that may alter the progression of HIV infection, however, is an important part of AIDS research and deserves the careful attention of behavioral and biological scientists.

Antigens, CD↗

PubMed1990

Psychosocial effects on immune function: neuroendocrine pathways.

Psychoneuroimmunology represents the newest interdisciplinary endeavor relevant to psychosomatic medicine. Work in this area is particularly exciting because it promises to reveal a more unified view of the individual and the complex interactions between social, psychological, neural, endocrinological, immunological, and genetic factors that contribute to disease. This article reviews the major biological pathways implicated in the psychosocial modulation of immune function and disease resistance.

Autonomic Nervous System↗

PubMed2026

Multiregion profiling of genomic and transcriptional heterogeneity in head and neck squamous-cell carcinoma.

BACKGROUND: Intratumoral heterogeneity (ITH) is thought to contribute to tumour evolution and treatment resistance but its biological and clinical significance in localised head and neck squamous-cell carcinoma (HNSCC) remains incompletely understood. PATIENTS AND METHODS: In the prospective SCANDARE study, we analysed 87 patients with resectable HNSCC treated with upfront surgery. Two to five spatially distinct tumour regions per patient underwent pathological evaluation, targeted DNA sequencing, and bulk RNA sequencing. Genomic ITH (gITH) was quantified using clonal deconvolution and Shannon diversity indices, whereas transcriptional heterogeneity (tITH) was assessed using the intratumour expression distance metric. Associations between ITH, molecular features, tumour microenvironment composition, and clinical outcomes were explored using multivariable statistical models. RESULTS: Pathology-based spatial heterogeneity showed limited prognostic value. gITH was common, with 37% of tumours displaying regionally heterogeneous pathogenic variants, including spatially actionable alterations in 10% of patients. In an initial multivariable Cox model, higher gITH was associated with shorter disease-free survival. However, after Ridge-penalised modelling and bootstrap internal validation, the effect size was attenuated [corrected hazard ratio 1.42, 95% confidence interval (CI) 0.91-2.75]. The overall model retained moderate discriminative performance (optimism-corrected C-index 0.69, 95% CI 0.59-0.79). gITH was associated with tumour cellularity, reduced estimated endothelial cell infiltration, and alterations in KMT2C and PIK3CA. tITH differed according to human papillomavirus (HPV) status, with lower tITH in HPV-positive tumours, and was associated with distinct biological pathways and genomic alterations. Genomic and tITH were not correlated. CONCLUSIONS: This prospective multiregion study provides a comprehensive characterisation of genomic and tITH in localised HNSCC. Our findings highlight substantial spatial molecular diversity within primary tumours and suggest potential associations between heterogeneity, tumour biology, and clinical outcome that warrant validation in independent cohorts.

head and neck squamous-cell carcinoma (HNSCC)↗

PubMed2026

Ultra-processed Foods, Cancer, and Early-onset Cancer: A Comprehensive Review.

The classification of foods according to their degree of processing, and particularly the concept of ultra-processed foods, is relatively new. Consumption of ultra-processed foods has increased markedly worldwide in recent decades. Their growing consumption has coincided with a rising global burden of cancer, including marked increases in several cancers diagnosed before age 50 years. In this comprehensive review, we summarize trends in ultra-processed food consumption and the sociodemographic, psychological, and behavioral characteristics associated with higher intake. We further review the epidemiological evidence linking ultra-processed foods with cancer incidence and mortality, with particular attention to the limited but emerging evidence relevant to early-onset cancer. Potential mechanisms linking ultra-processed foods to cancer include unfavorable nutrient displacement, changes in body composition and fat deposition, and increased exposure to additives, processing by-products, and other chemicals. These influences may converge on a range of biological pathways, including metabolic dysfunction, chronic inflammation, immune dysregulation, gut microbiome disruption, DNA damage and genomic instability, and epigenetic alterations. Substantial uncertainties remain, including heterogeneous exposure definitions and classification practices, limitations in dietary assessment and temporal exposure capture, residual confounding, and the complexity of putative biological mechanisms. We conclude by highlighting key research challenges and future directions, along with considerations related to policy, regulation, and industry practices.

Ultra-processed foods↗

PubMed2026

Genetic overlap between depression and C-reactive protein levels: Evidence from a cross-trait analysis.

Inflammation and depression have been consistently associated, with elevated C-reactive protein (CRP) levels observed in a significant subset of affected individuals. However, the genetic mechanisms underlying this association remain poorly understood. We integrated results from large-scale genome-wide association studies (GWAS) of depression and CRP levels in a cross-trait analysis specifically focusing on identifying horizontally pleiotropic loci. Identified variants were stratified as concordant versus discordant based on their direction of effects on the two traits and followed up using functional annotation, gene set enrichment, and colocalization analyses. We also explored causal relationships using Mendelian Randomization (MR) analysis with extensive sensitivity analyses, including adjustment for body mass index (BMI). We identified 9 novel loci. Functional analyses revealed that concordant loci were enriched in genes linked to immune and inflammatory processes, while discordant loci mostly mapped to metabolic pathways, including lipid regulation. MR provided strong evidence for body mass index driving a causal relationship between the genetic liability of depression on CRP levels. Our findings suggest that the association between depression and CRP levels is partly driven by shared genetic influences, pointing to different biological pathways depending on whether genetic effects are concordant or discordant. These results underscore the importance of considering effect direction when assessing the genetic overlap between depression and inflammatory processes. In addition, they highlight BMI as a key factor in the causal relationship between depression and systemic inflammation.

C-Reactive Protein↗