Daniela Zanetti

Lipid traits are known to be sex-differential, but the underlying molecular players are largely unknown. Herein, we aim to highlight differential molecular signatures in lipid metabolism between males and females by identifying proteins that are causally linked to lipids in a sex-specific or sex-differentiated manner. Since protein levels are downstream products of gene expression, proteomics data can be crucial to understand etiology of sex-differences in lipids metabolism. We used sex-specific pQTL summary statistics for 2,923 circulating proteins measured in the UK Biobank Pharma Proteomics Project, along with sex-specific summary statistics of lipids from the Global Lipids Genetics Consortium. We combined these using two-sample Mendelian Randomization analyses and applied stringent multiple testing p-values correction and sensitivity analyses. We identified several sex-specific significant causal links between protein levels and lipid phenotypes: 83 were exclusive to females and 82 to males, 46% of which would not have been identified using combined-sex GWASs. The estimated causal effect was instead significant in both sexes but substantially different in size for 39 causal relationships (p < 0.05). Intriguingly, several of these proteins were previously shown to be involved in inflammation and cardiometabolic diseases, such as Apolipoprotein(a) and lipoprotein lipase. Furthermore, two sex-specific proteins — LEPR and LPA — are molecular targets of emerging cardiometabolic therapies, highlighting the need for future investigations to determine whether these protein-level differences translate into sex-specific efficacy or safety in clinical trials and routine treatments. The discovery of these sex-differences can provide important etiological insights into the management of lipid metabolism. Such knowledge may in turn improve the predictive use of sex-specific pQTLs and point to new therapeutic gender specific strategies to prevent cardiovascular disease.

Sex hormones exert widespread effects on immune, metabolic, and cardiovascular physiology, yet their systemic impact in healthy women remains poorly characterised due to the scarcity of longitudinal molecular data across the menstrual cycle. Here, we carried out the first longitudinal, high-throughput plasma proteomics analysis of the menstrual cycle. We profiled 159 naturally cycling women from the Women4Health cohort, integrating plasma levels of 2,453 circulating proteins, 5 sex hormones, and 10 serum cardiometabolic phenotypes across four menstrual phases, enabling high-resolution mapping of dynamic proteomic changes. We identified 43 proteins differentially abundant across phases and hundreds associated with sex hormones and cardiometabolic phenotypes. These included proteins involved in extracellular matrix remodelling, immune regulation and hormone metabolism, such as beta-glucuronidase, previously described mainly as part of the microbial estrobolome and here for the first time identified in plasma. To investigate directional relationships, leveraging longitudinal measurements, we applied a cross-lagged structural equation modelling framework and validated its robustness through extensive simulations. With this approach we recovered expected links such as progesterone-mediated regulation of PROK1, as well as novel connections, including those linking IGFBP1 to both hormone and lipid levels. Many of the identified proteins have established causal roles in complex diseases. Our study provides the first proteomic atlas of the menstrual cycle and reveals mechanistic pathways underlying sex-hormone-driven regulation of inflammatory and cardiometabolic circuits.

Lipid traits are known to be sex-differential, but the underlying molecular players are largely unknown. Since protein levels are downstream products of gene expression, proteomics data can be crucial to understand etiology of sex-differences in lipids metabolism. We used sex-specific pQTL summary statistics for 2,923 circulating proteins measured in the UK Biobank Pharma Proteomics Project, along with sex-specific summary statistics of lipids from the Global Lipids Genetics Consortium. We combined these using two-sample Mendelian Randomization analyses and applied stringent multiple testing p-values correction and sensitivity analyses. We identified several sex-specific significant causal links between protein levels and lipid phenotypes: 82 were exclusive to females and 82 to males. The estimated causal effect was instead significant in both sexes but substantially different in size for 38 causal relationships (p <0.05). In the Lifelines Cohort, we replicated 16 sex-specific causal relationships and 4 sex-differentiated relationships also including GWASs restricted to non-statin users, ruling out the potential confounding of lipid-lowering medications more often prescribed in males. Intriguingly, several of these proteins were previously shown to be involved in inflammation and cardiometabolic disease, such as Apolipoprotein(a) and lipoprotein lipase. These results provide insight into potential genetic drivers of sex dimorphism in lipid metabolism.

In the past 20 years, the involvement of gut microbiome in human health has received particular attention, but its contribution to age-related diseases remains unclear. To address this, we performed a comprehensive two-sample Mendelian randomization investigation, testing 55130 potential causal relationships between 37 traits representing gut microbiome composition and function and age-related phenotypes, including 1472 inflammatory and cardiometabolic circulating plasma proteins from UK Biobank Pharma Proteomic Project and 18 complex traits. A total of 91 causal relationships remained significant after multiple testing correction (false discovery rate p-valueFaecalibacterium prausnitzii we found evidence of regulation of six proteins by its function as mucosal-A antigen utilization. These results support the role of gut microbiome as modulator of the inflammatory and cardiometabolic circuits, that may contribute to the onset of age-related diseases, albeit future studies are needed to investigate the underlying biological mechanisms.

Aims: Epidemiological research has shown relevant differences between sexes in clinical manifestations, severity, and progression of cardiovascular and metabolic disorders. To date, the mechanisms underlying these differences remain unknown. Given the rising incidence of such diseases, gender-specific research on established and emerging risk factors, such as dysfunction of glycaemic and/or lipid metabolism, of sex hormones and of gut microbiome, is of paramount importance. The relationships between sex hormones, gut microbiome, and host glycaemic and/or lipid metabolism are largely unknown even in the homoeostasis status. Yet this knowledge gap would be pivotal to pinpoint to key mechanisms that are likely to be disrupted in disease context. Methods and results: Here we present the Women4Health (W4H) cohort, a unique cohort comprising up to 300 healthy women followed up during a natural menstrual cycle, set up with the primary goal to investigate the combined role of sex hormones and gut microbiota variations in regulating host lipid and glucose metabolism during homoeostasis, using a multi-omics strategy. Additionally, the W4H cohort will take into consideration another ecosystem that is unique to women, the vaginal microbiome, investigating its interaction with gut microbiome and exploring—for the first time—its role in cardiometabolic disorders. Conclusion: The W4H cohort study lays a foundation for improving current knowledge of women-specific mechanisms in cardiometabolic regulation. It aspires to transform insights on host–microbiota interactions into prevention and therapeutic approaches for personalized health care.

Background: Proteomic profiling could potentially disclose new pathophysiological pathways for cardiovascular diseases (CVD) and improve prediction at the individual level. We therefore aimed to study the plasma protein profile associated with the incidence of different CVDs. Methods: Plasma levels of 245 proteins suspected to be linked to CVD or metabolism were measured in 4 Swedish prospective population-based cohorts (SIMPLER [Swedish Infrastructure for Medical Population-Based Life-Course and Environmental Research], ULSAM (Uppsala Longitudinal Study of Adult Men), EpiHealth, and POEM [Prospective Investigation of Obesity, Energy Production, and Metabolism]) comprising 11 869 individuals, free of CVD diagnoses at baseline. Our primary CVD outcome was defined by a combined end point that included either incident myocardial infarction, stroke, or heart failure. Results: Using a discovery/validation approach, 42 proteins were associated with our primary composite end point occurring in 1163 subjects. In separate meta-analyses for each of the 3 CVD outcomes, 49 proteins were related to myocardial infarction, 34 to ischemic stroke, and 109 to heart failure. Thirteen proteins were related to all 3 outcomes. Of those, urokinase plasminogen activator surface receptor, adrenomedullin, and KIM-1 (kidney injury molecule 1) were also related to several markers of subclinical CVD in Prospective Investigation of Obesity, Energy production and Metabolism, reflecting myocardial or arterial pathologies. In prediction analysis, a lasso selection of 11 proteins in ULSAM improved the discrimination of CVD by 3.3% ( P <0.0001) in SIMPLER when added to traditional risk factors. Conclusions: Protein profiling in multiple samples disclosed several new proteins to be associated with subsequent myocardial infarction, stroke, and heart failure, suggesting common pathophysiological pathways for these diseases. KIM-1, urokinase plasminogen activator surface receptor, and adrenomedullin were novel early markers of CVD. A selection of 11 proteins improved the discrimination of CVD.

To efficiently distribute blood flow to cardiac muscle, the coronary artery tree must follow a specific branching pattern over the heart. How this pattern arises in humans is unknown due to the limitations of studying human heart development. Here, we leveraged a natural variation of coronary artery anatomy, known as coronary dominance, in genetic association studies to identify the first known driver of human coronary developmental patterning. Coronary dominance refers to whether the right, left, or both coronary arteries branch over the posterior left ventricle, but whether this variability is heritable and how it would be genetically regulated was completely unknown. By conducting the first large-scale, multi-ancestry genome-wide association study (GWAS) of coronary dominance in 61,043 participants of the VA Million Veteran Program, we observed moderate heritability (27.7%) with ten loci reaching genome wide significance. An exceptionally strong association mapped DNA variants to a non-coding region near the chemokine CXCL12 in both European and African ancestries, which overlapped with variants associated with coronary artery disease. Genomic analyses predicted these variants to impact CXCL12 levels, and imaging revealed dominance to develop during fetal life coincident with CXCL12 expression. Reducing Cxcl12 in mice to model the human genetics altered septal artery dominance patterns and caused coronary branches to develop away from Cxcl12 expression domains. Cxcl12 heterozygosity did not compromise overall artery coverage as seen with full deletion, but instead changed artery patterning, reminiscent of the human scenario. Together, our data support CXCL12 as a critical determinant of human coronary artery growth and patterning and lay a foundation for the utilization of developmental pathways to guide future precision ‘medical revascularization’ therapeutics.

Aims/hypothesis: The euglycaemic–hyperinsulinaemic clamp (EIC) is the reference standard for the measurement of whole-body insulin sensitivity but is laborious and expensive to perform. We aimed to assess the incremental value of high-throughput plasma proteomic profiling in developing signatures correlating with the M value derived from the EIC. Methods: We measured 828 proteins in the fasting plasma of 966 participants from the Relationship between Insulin Sensitivity and Cardiovascular disease (RISC) study and 745 participants from the Uppsala Longitudinal Study of Adult Men (ULSAM) using a high-throughput proximity extension assay. We used the least absolute shrinkage and selection operator (LASSO) approach using clinical variables and protein measures as features. Models were tested within and across cohorts. Our primary model performance metric was the proportion of the M value variance explained (R²). Results: A standard LASSO model incorporating 53 proteins in addition to routinely available clinical variables increased the M value R² from 0.237 (95% CI 0.178, 0.303) to 0.456 (0.372, 0.536) in RISC. A similar pattern was observed in ULSAM, in which the M value R² increased from 0.443 (0.360, 0.530) to 0.632 (0.569, 0.698) with the addition of 61 proteins. Models trained in one cohort and tested in the other also demonstrated significant improvements in R² despite differences in baseline cohort characteristics and clamp methodology (RISC to ULSAM: 0.491 [0.433, 0.539] for 51 proteins; ULSAM to RISC: 0.369 [0.331, 0.416] for 67 proteins). A randomised LASSO and stability selection algorithm selected only two proteins per cohort (three unique proteins), which improved R² but to a lesser degree than in standard LASSO models: 0.352 (0.266, 0.439) in RISC and 0.495 (0.404, 0.585) in ULSAM. Reductions in improvements of R² with randomised LASSO and stability selection were less marked in cross-cohort analyses (RISC to ULSAM R² 0.444 [0.391, 0.497]; ULSAM to RISC R² 0.348 [0.300, 0.396]). Models of proteins alone were as effective as models that included both clinical variables and proteins using either standard or randomised LASSO. The single most consistently selected protein across all analyses and models was IGF-binding protein 2. Conclusions/interpretation: A plasma proteomic signature identified using a standard LASSO approach improves the cross-sectional estimation of the M value over routine clinical variables. However, a small subset of these proteins identified using a stability selection algorithm affords much of this improvement, especially when considering cross-cohort analyses. Our approach provides opportunities to improve the identification of insulin-resistant individuals at risk of insulin resistance-related adverse health consequences. Graphical

Obesity is a pre-disposing condition for chronic obstructive pulmonary disease, asthma, and pulmonary arterial hypertension. Accumulating evidence suggests that metabolic influences during development can determine chronic lung diseases (CLD). We demonstrate that maternal obesity causes early metabolic disorder in the offspring. Here, interleukin-6 induced bronchial and microvascular smooth muscle cell (SMC) hyperproliferation and increased airway and pulmonary vascular resistance. The key anti-proliferative transcription factor FoxO1 was inactivated via nuclear exclusion. These findings were confirmed using primary SMC treated with interleukin-6 and pharmacological FoxO1 inhibition as well as genetic FoxO1 ablation and constitutive activation. In vivo, we reproduced the structural and functional alterations in offspring of obese dams via the SMC-specific ablation of FoxO1. The reconstitution of FoxO1 using IL-6-deficient mice and pharmacological treatment did not protect against metabolic disorder but prevented SMC hyperproliferation. In human observational studies, childhood obesity was associated with reduced forced expiratory volume in 1 s/forced vital capacity ratio Z-score (used as proxy for lung function) and asthma. We conclude that the interleukin-6-FoxO1 pathway in SMC is a molecular mechanism by which perinatal obesity programs the bronchial and vascular structure and function, thereby driving CLD development. Thus, FoxO1 reconstitution provides a potential therapeutic option for preventing this metabolic programming of CLD.

Background: SARS-CoV-2, the causal agent of COVID-19, enters human cells using the ACE2 (angiotensin-converting enzyme 2) protein as a receptor. ACE2 is thus key to the infection and treatment of the coronavirus. ACE2 is highly expressed in the heart and respiratory and gastrointestinal tracts, playing important regulatory roles in the cardiovascular and other biological systems. However, the genetic basis of the ACE2 protein levels is not well understood. Methods: We have conducted the largest genome-wide association meta-analysis of plasma ACE2 levels in >28 000 individuals of the SCALLOP Consortium (Systematic and Combined Analysis of Olink Proteins). We summarize the cross-sectional epidemiological correlates of circulating ACE2. Using the summary statistics–based high-definition likelihood method, we estimate relevant genetic correlations with cardiometabolic phenotypes, COVID-19, and other human complex traits and diseases. We perform causal inference of soluble ACE2 on vascular disease outcomes and COVID-19 severity using mendelian randomization. We also perform in silico functional analysis by integrating with other types of omics data. Results: We identified 10 loci, including 8 novel, capturing 30% of the heritability of the protein. We detected that plasma ACE2 was genetically correlated with vascular diseases, severe COVID-19, and a wide range of human complex diseases and medications. An X-chromosome cis–protein quantitative trait loci–based mendelian randomization analysis suggested a causal effect of elevated ACE2 levels on COVID-19 severity (odds ratio, 1.63 [95% CI, 1.10–2.42]; P =0.01), hospitalization (odds ratio, 1.52 [95% CI, 1.05–2.21]; P =0.03), and infection (odds ratio, 1.60 [95% CI, 1.08–2.37]; P =0.02). Tissue- and cell type–specific transcriptomic and epigenomic analysis revealed that the ACE2 regulatory variants were enriched for DNA methylation sites in blood immune cells. Conclusions: Human plasma ACE2 shares a genetic basis with cardiovascular disease, COVID-19, and other related diseases. The genetic architecture of the ACE2 protein is mapped, providing a useful resource for further biological and clinical studies on this coronavirus receptor.