Associations of polygenic risk scores for preeclampsia and blood pressure with hypertensive disorders of pregnancy.

BACKGROUND: Preexisting hypertension increases risk for preeclampsia. We examined whether a generic blood pressure polygenic risk score (BP-PRS), compared with a preeclampsia-specific polygenic risk score (PE-PRS), could better predict hypertensive disorders of pregnancy. METHODS: Our study sample included 141 298 genotyped FinnGen study participants with at least one childbirth and followed from 1969 to 2021. We calculated PRSs for SBP and preeclampsia using summary statistics for greater than 1.1 million single nucleotide polymorphisms. RESULTS: We observed 8488 cases of gestational hypertension (GHT) and 6643 cases of preeclampsia. BP-PRS was associated with GHT [multivariable-adjusted hazard ratio for 1SD increase in PRS (hazard ratio 1.38; 95% CI 1.35-1.41)] and preeclampsia (1.26, 1.23-1.29), respectively. The PE-PRS was also associated with GHT (1.16; 1.14-1.19) and preeclampsia (1.21, 1.18-1.24), but with statistically more modest magnitudes of effect (P = 0.01). The model c-statistic for preeclampsia improved when PE-PRS was added to clinical risk factors (P = 4.6 × 10-15). Additional increment in the c-statistic was observed when BP-PRS was added to a model already including both clinical risk factors and PE-PRS (P = 1.1 × 10-14). CONCLUSION: BP-PRS is strongly associated with hypertensive disorders of pregnancy. Our current observations suggest that the BP-PRS could capture the genetic architecture of preeclampsia better than the current PE-PRSs. These findings also emphasize the common pathways in the development of all BP disorders. The clinical utility of a BP-PRS for preeclampsia prediction warrants further investigation.

Stroke genetics informs drug discovery and risk prediction across ancestries.

Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.

Multi-Trait Genetic Analysis Reveals Clinically Interpretable Hypertension Subtypes.

BACKGROUND: Hypertension comprises a heterogeneous range of phenotypes. We asked whether underlying genetic structure could explain a part of this heterogeneity. METHODS: Our study sample comprised N=198 148 FinnGen participants (56% women, mean age 58 years) and N=21 168 well-phenotyped FINRISK participants (53% women, mean age 50 years). First, we identified genetic hypertension components with an unsupervised Bayesian non-negative matrix factorization algorithm using public genome-wide association data for 144 genetic hypertension variants and 16 clinical traits. For these components, we computed their (1) cross-sectional associations with clinical traits in FINRISK using linear regression and (2) longitudinal associations with incident adverse outcomes in FinnGen using Cox regression. RESULTS: We observed 4 genetic hypertension components corresponding to recognizable clinical phenotypes: obesity (high body mass index), dyslipidemia (low high-density lipoprotein cholesterol and high triglycerides), hypolipidemia (low low-density lipoprotein cholesterol and low total cholesterol), and short stature. In FINRISK, all hypertension components had robust associations with their respective clinical characteristics. In FinnGen, the Obesity component was associated with increased diabetes risk (hazard ratio per 1 SD increase 1.08 [Bonferroni corrected CI, 1.05-1.10]) and the Hypolipidemia component with increased autoimmune disease risk (hazard ratio per 1 SD increase 1.05 [Bonferroni corrected CI, 1.03-1.07]). In addition, all hypertension components were related to both hypertension and cardiovascular disease. CONCLUSIONS: Our unsupervised analysis demonstrates that the genetic basis of hypertension can be understood as a mixture of 4 broad, clinically interpretable components capturing disease heterogeneity. These components could be used to stratify individuals into specific genetic subtypes and, therefore, to benefit personalized health care and pharmaceutical research.

Genetic, Molecular, and Cellular Determinants of Sex-Specific Cardiovascular Traits.

Despite the well-known sex dimorphism in cardiovascular disease traits, the exact genetic, molecular, and cellular underpinnings of these differences are not well understood. A growing body of evidence currently points at the links between cardiovascular disease traits and the genome, epigenome, transcriptome, and metabolome. However, the sex-specific differences in these links remain largely unstudied due to challenges in bioinformatic methods, inadequate statistical power, analytic costs, and paucity of valid experimental models. This review article provides an overview of the literature on sex differences in genetic architecture, heritability, epigenetic changes, transcriptomic signatures, and metabolomic profiles in relation to cardiovascular disease traits. We also review the literature on the associations between sex hormones and cardiovascular disease traits and discuss the potential mechanisms underlying these associations, focusing on human studies.

Polygenic Risk Scores for Predicting Adverse Outcomes After Coronary Revascularization.

Coronary procedures predispose patients to adverse events. To improve our understanding of the genetic factors underlying postoperative prognosis, we studied the association of polygenic risk scores (PRSs) with postprocedural complications in coronary patients who underwent revascularization. The study sample comprised 8,296, 6,132, and 13,082 patients who underwent percutaneous coronary intervention, coronary artery bypass grafting, or any revascularization, respectively. We genotyped all subjects and identified adverse events during follow-up of up to 30 years by record linkage with nationwide healthcare registers. We computed PRSs for each postoperative adverse outcome (atrial fibrillation [AF], myocardial infarction, stroke, and bleeding complications) for all participants. Cox proportional hazards models were used to examine the association between PRSs and outcomes. A 1-SD increase in AF-PRS was associated with greater risk of postoperative AF with hazard ratios of 1.22 (95% confidence interval [CI] 1.16 to 1.28), 1.15 (95% CI 1.10 to 1.20) and 1.18 (95% CI 1.14 to 1.22) after percutaneous coronary intervention, coronary artery bypass grafting, and any revascularization, respectively. In contrast, the association of each PRSs with other postoperative complications was nonexistent to marginal. Inclusion of the AF-PRS in a model with a clinical risk score resulted in significant model improvement (increase in model c-statistic 0.0059 to 0.0098 depending on procedure; p <0.0002 for all). In conclusion, our results demonstrate that PRS can be used for AF risk-prediction in patients who underwent revascularization. The AF-PRS could potentially be used to improve AF prevention and outcomes in patients who underwent revascularization.

Systemic Blockade of Clever-1 Elicits Lymphocyte Activation Alongside Checkpoint Molecule Downregulation in Patients with Solid Tumors: Results from a Phase I/II Clinical Trial.

PURPOSE: Macrophages are critical in driving an immunosuppressive tumor microenvironment that counteracts the efficacy of T-cell-targeting therapies. Thus, agents able to reprogram macrophages toward a proinflammatory state hold promise as novel immunotherapies for solid cancers. Inhibition of the macrophage scavenger receptor Clever-1 has shown benefit in inducing CD8+ T-cell-mediated antitumor responses in mouse models of cancer, which supports the clinical development of Clever-1-targeting antibodies for cancer treatment. PATIENTS AND METHODS: In this study, we analyzed the mode of action of a humanized IgG4 anti-Clever-1 antibody, FP-1305 (bexmarilimab), both in vitro and in patients with heavily pretreated metastatic cancer (n = 30) participating in part 1 (dose-finding) of a phase I/II open-label trial (NCT03733990). We studied the Clever-1 interactome in primary human macrophages in antibody pull-down assays and utilized mass cytometry, RNA sequencing, and cytokine profiling to evaluate FP-1305-induced systemic immune activation in patients with cancer. RESULTS: Our pull-down assays and functional studies indicated that FP-1305 impaired multiprotein vacuolar ATPase-mediated endosomal acidification and improved the ability of macrophages to activate CD8+ T-cells. In patients with cancer, FP-1305 administration led to suppression of nuclear lipid signaling pathways and a proinflammatory phenotypic switch in blood monocytes. These effects were accompanied by a significant increase and activation of peripheral T-cells with indications of antitumor responses in some patients. CONCLUSIONS: Our results reveal a nonredundant role played by the receptor Clever-1 in suppressing adaptive immune cells in humans. We provide evidence that targeting macrophage scavenging activity can promote an immune switch, potentially leading to intratumoral proinflammatory responses in patients with metastatic cancer.

Novel Biomarkers for Diagnosing Periprosthetic Joint Infection from Synovial Fluid and Serum.

BACKGROUND: Synovial fluid bacterial culture is the cornerstone of confirmation or exclusion of periprosthetic joint infection (PJI). The aim of this study was to assess synovial fluid and serum biomarker patterns of patients with total joint arthroplasty (TJA), and the association of these patterns with PJI. METHODS: Synovial fluid and serum samples were collected from 35 patients who were admitted to the Arthroplasty Unit of the Department of Orthopaedics and Traumatology at Turku University Hospital. Of the 25 patients who were included in the study, 10 healthy patients with an elective TJA for osteoarthritis served as the control group, and 15 patients who were admitted due to clinical suspicion of PJI with local redness, swelling, wound drainage, pain, and/or fever and who had a positive synovial fluid bacterial culture served as the study group. Logistic regression was used to assess the ability of 37 biomarkers (including cytokines, chemokines, and growth factors) with commercially available tests to detect PJIs. RESULTS: In synovial fluid, the concentrations of sTNF-R1 and sTNF-R2 (soluble tumor necrosis factor receptors 1 and 2) and BAFF (B-cell activating factor, also known as TNFSF13B) were significantly higher in the PJI group (p < 0.002). In serum, the sTNF-R1 concentration was significantly higher in the PJI group, whereas the TWEAK (tumor necrosis factor-like weak inducer of apoptosis) and osteocalcin concentrations were significantly lower (p < 0.002). The sensitivity for detecting PJI using synovial fluid was 1.00 for sTNF-R2, 0.93 for sTNF-R1, and 0.87 for BAFF/TNFSF13B. The specificity of all 3 synovial markers was 1.00. The sensitivity using serum was 0.80 for TWEAK, 0.73 for sTNF-R1, and 0.80 for osteocalcin. The specificity of all 3 serum markers was 1.00. CONCLUSIONS: Synovial sTNF-R2 is a promising new biomarker for detecting PJI. We are not aware of any previous reports of the use of sTNF-R2 in PJI diagnosis. More research is needed to assess the clinical importance of our findings. LEVEL OF EVIDENCE: Diagnostic Level II. See Instructions for Authors for a complete description of levels of evidence.

Polygenic Risk Scores Predict Hypertension Onset and Cardiovascular Risk.

Although genetic risk scores have been used to predict hypertension, their utility in the clinical setting remains uncertain. Our study comprised N=218 792 FinnGen participants (mean age 58 years, 56% women) and N=22 624 well-phenotyped FINRISK participants (mean age 50 years, 53% women). We used public genome-wide association data to compute polygenic risk scores (PRSs) for systolic and diastolic blood pressure (BP). Using time-to-event analysis, we then assessed (1) the association of BP PRSs with hypertension and cardiovascular disease (CVD) in FinnGen and (2) the improvement in model discrimination when combining BP PRSs with the validated 4- and 10-year clinical risk scores for hypertension and CVD in FINRISK. In FinnGen, compared with having a 20 to 80 percentile range PRS, a PRS in the highest 2.5% conferred 2.3-fold (95% CI, 2.2­2.4) risk of hypertension and 10.6 years (95% CI, 9.9­11.4) earlier hypertension onset. In subgroup analyses, this risk was only 1.6-fold (95% CI, 1.5­1.7) for late-onset hypertension (age ≥55 years) but 2.8-fold (95% CI, 2.6­2.9) for early-onset hypertension (age <55 years). Elevated systolic BP PRS also conferred 1.3-fold (95% CI, 1.2­1.4) risk of CVD and 2.3 years (95% CI, 1.6­3.1) earlier onset. In FINRISK, systolic and diastolic BP PRSs improved clinical risk prediction of hypertension (but not CVD), increasing the C statistics by 0.7% (95% CI, 0.3­1.1). We demonstrate that genetic information improves hypertension risk prediction. BP PRSs together with traditional risk factors could improve prediction of hypertension and particularly early-onset hypertension, which confers substantial CVD risk.

Unsupervised hierarchical clustering identifies a metabolically challenged subgroup of hypertensive individuals.

The current classification of hypertension does not reflect the heterogeneity in characteristics or cardiovascular outcomes of hypertensive individuals. Our objective was to identify distinct phenotypes of hypertensive individuals with potentially different cardiovascular risk profiles using data-driven cluster analysis. We performed clustering, a procedure that identifies groups with similar characteristics, in 3726 individuals (mean age 59.4 years, 49% women) with grade 2 hypertension (blood pressure ≥160/100 mmHg or antihypertensive medication) selected from FINRISK 1997, 2002, and 2007 cohorts. We computed clusters based on eight factors associated with hypertension: mean arterial pressure, pulse pressure, non-high-density lipoprotein cholesterol, blood glucose, BMI, C-reactive protein, estimated glomerular filtration rate, and alcohol. After that, we used Cox regression models adjusted for age and sex to assess the relative risk of cardiovascular disease (CVD) outcomes between the clusters and a reference group of 11 020 individuals. We observed two comparable clusters in both men and women. The Metabolically Challenged (MC) cluster was characterized by high blood glucose (Z-score 4.4 ± 1.1 vs 0.2 ± 0.8, men; 3.5 ± 1.1 vs 0.0 ± 0.6, women) and elevated BMI (30.4 ± 4.1 vs 28.9 ± 4.3, men; 32.7 ± 4.9 vs 29.3 ± 5.5, women). Over a 10-year follow-up (1034 CVD events), MC had 1.6-fold (95% CI 1.1-2.4) CVD risk compared to non-MC and 2.5-fold (95% CI 1.7-3.7) CVD risk compared to the reference group (P ≤ .009 for both). Using unsupervised hierarchical clustering, we found two phenotypically distinct hypertension subgroups with different risks of CVD complications. This substratification could be used to design studies that explore the differential effects of antihypertensive therapies among subgroups of hypertensive individuals.