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Carlo Maj

Publications and source records attributed to Carlo Maj.

3 recordsLinked to original sources

Tissue origins of the plasma proteomic response to glucose ingestion in humans.

AIMS/HYPOTHESIS: Circulating proteins act as important hormonal signals of nutrient intake. We aimed to systematically characterise the time-resolved proteomic response to glucose ingestion in humans, and to assess its robustness following prolonged complete caloric restriction. METHODS: We conducted oral glucose tolerance tests (OGTTs) in 11 healthy volunteers before and after 7 days of complete caloric restriction and measured the response of >2900 targets through high-resolution plasma protein profiling. RESULTS: We identified a signature of 44 proteins that changed significantly following glucose ingestion, which was reproducible after 7 days without food, and was strongly (20-fold) enriched for 'stomach-specific' proteins. We report that annexin A10 (ANXA10) shows the most significant post-glucose change observed, similar to the trajectories of secreted hormones. We present observational human evidence from multiple sources suggesting that ANXA10 is secreted upon sensing an increase in gastric pH, with the stomach as the major contributing tissue. Despite a profound metabolic shift after 7 days of complete caloric restriction, characterised by delayed insulin secretion and postprandial hyperglycaemia, only four proteins showed robust evidence for a differential trajectory during both OGTTs. This included plasma levels of tryptophanyl-tRNA synthetase 1 (WARS), for which we found a genetic association with glucose homeostasis and coronary artery disease. CONCLUSIONS/INTERPRETATION: Our exploratory study identifies the proteomic response to glucose ingestion and demonstrates its reproducibility despite major shifts in glucose homeostasis. We characterise the gastrointestinal origin of these changes, and hypothesise a hitherto under-recognised role for sensing of changes in gastric pH on the plasma proteome.

Humans

An Updated Polygenic Index Repository: Expanded Phenotypes, New Cohorts, and Improved Causal Inference.

Polygenic indexes (PGIs) - DNA-based predictors of individual phenotypes - have become essential tools across biomedical and social sciences. We introduce Version 2 of the Polygenic Index Repository, which expands phenotype coverage from 47 to 61, increases the number of participating datasets from 11 to 20, and adopts a more consistent and improved methodology for PGI construction. For 16 phenotypes, we leverage summary statistics from an updated GWAS meta-analysis with greater statistical power compared to the original release, thereby improving the PGI's predictive power. To improve power for family-based analyses, we provide imputed parental PGIs in all datasets with first-degree relatives and offer a framework for interpreting results from analyses that control for parental PGIs. We illustrate the utility of parental PGIs using two applications: (1) comparing PGI associations with and without parental PGI controls for all phenotypes in two Repository datasets with family data, and (2) for BMI and diastolic blood pressure, exploring the contribution of causal versus non-causal components of PGI associations to the imperfect portability of PGIs across subgroups within a genetic ancestry. Collectively, the updates enhance predictive performance, broaden the Repository's scope, and introduce novel resources that reduce confounding bias and improve interpretability.

Journal Article

Distinct Genetic Risk Profile in Aortic Stenosis Compared With Coronary Artery Disease.

IMPORTANCE: Aortic stenosis (AS) and coronary artery disease (CAD) frequently coexist. However, it is unknown which genetic and cardiovascular risk factors might be AS-specific and which could be shared between AS and CAD. OBJECTIVE: To identify genetic risk loci and cardiovascular risk factors with AS-specific associations. DESIGN, SETTING, AND PARTICIPANTS: This was a genomewide association study (GWAS) of AS adjusted for CAD with participants from the European Consortium for the Genetics of Aortic Stenosis (EGAS) (recruited 2000-2020), UK Biobank (recruited 2006-2010), Estonian Biobank (recruited 1997-2019), and FinnGen (recruited 1964-2019). EGAS participants were collected from 7 sites across Europe. All participants were of European ancestry, and information on comorbid CAD was available for all participants. Follow-up analyses with GWAS data on cardiovascular traits and tissue transcriptome data were also performed. Data were analyzed from October 2022 to July 2023. EXPOSURES: Genetic variants. MAIN OUTCOMES AND MEASURES: Cardiovascular traits associated with AS adjusted for CAD. Replication was performed in 2 independent AS GWAS cohorts. RESULTS: A total of 18 792 participants with AS and 434 249 control participants were included in this GWAS adjusted for CAD. The analysis found 17 AS risk loci, including 5 loci with novel and independently replicated associations (RNF114A, AFAP1, PDGFRA, ADAMTS7, HAO1). Of all 17 associated loci, 11 were associated with risk specifically for AS and were not associated with CAD (ALPL, PALMD, PRRX1, RNF144A, MECOM, AFAP1, PDGFRA, IL6, TPCN2, NLRP6, HAO1). Concordantly, this study revealed only a moderate genetic correlation of 0.15 (SE, 0.05) between AS and CAD (P = 1.60 × 10-3). Mendelian randomization revealed that serum phosphate was an AS-specific risk factor that was absent in CAD (AS: odds ratio [OR], 1.20; 95% CI, 1.11-1.31; P = 1.27 × 10-5; CAD: OR, 0.97; 95% CI 0.94-1.00; P = .04). Mendelian randomization also found that blood pressure, body mass index, and cholesterol metabolism had substantially lesser associations with AS compared with CAD. Pathway and transcriptome enrichment analyses revealed biological processes and tissues relevant for AS development. CONCLUSIONS AND RELEVANCE: This GWAS adjusted for CAD found a distinct genetic risk profile for AS at the single-marker and polygenic level. These findings provide new targets for future AS research.

Humans