Discovery and refinement of loci associated with lipid levels

Genetically determined increase in apolipoprotein C-III (APOC3 gain-of-function) delays very low-density lipoprotein clearance in humans

AIM

Apolipoprotein C-III (apoC-III) is an important regulator of triglyceride (TG) metabolism and a target for intervention. The present study examined the effects of gain-of-function (GOF) variants in APOC3 on apolipoprotein B kinetics to understand further how changes in the synthesis of this apolipoprotein impact triglyceride-rich lipoprotein (TRL) metabolism.

METHODS

Two groups of subjects were recruited by population screening, 9 carriers of known APOC3 GOF variants and 9 age-, sex- and BMI-matched non-carriers. The kinetics of TRL were determined using stable isotope tracers of apoprotein and triglyceride metabolism in a non-steady-state protocol involving administration of a fat-rich meal.

RESULTS

APOC3 GOF carriers had 47 % higher plasma apoC-III levels compared to non-carriers (P = 0.022) and higher production rates for the apolipoprotein. Post-prandial response (total area-under-curve) for plasma TG was 108 % greater in GOF carriers compared to non-carriers (P = 0.002) due specifically to higher levels of VLDL1. In contrast, no difference was seen in the chylomicron apoB48 response. Comparison of TRL kinetics between groups showed that APOC3 GOF carriers had lower fractional clearance rates for VLDL1-apoB100 and VLDL1-apoB48-containing particles (P < 0.02), but no difference in VLDL1-apoB100 or chylomicron apoB48 production rates. Both the rate of VLDL lipolysis and the rate of clearance of VLDL particles from the circulation were lower in APOC3 GOF carriers than in non-carriers. In contrast, chylomicron apoB clearance rates did not differ between APOC3 GOF carriers and non-carriers.

CONCLUSION

APOC3 GOF carriers showed specific alterations in TRL metabolism (compared to matched non-carriers), namely slower lipolysis and delayed clearance of VLDL1-sized particles, but no difference in chylomicron metabolism. Our findings suggest that intervention to reduce apoC-III production can be modelled as a reduction in TRL, particularly VLDL particle levels, without deleterious effects on fat absorption or hepatic VLDL production.

Associations of Lipid-Lowering Drugs With Blood Pressure and Fasting Glucose: A Mendelian Randomization Study

BACKGROUND

Observational studies have linked LDL-C (low-density lipoprotein-cholesterol)-lowering drugs with lower blood pressure (BP) and higher fasting glucose, but the causality remains unclear. We conducted a drug target Mendelian randomization study to assess the causal associations of genetically proxied inhibition of HMGCR (3-hydroxy-3-methylglutaryl coenzyme A reductase), PCSK9 (proprotein convertase subtilisin/kexin type 9), and NPC1L1 (Niemann-Pick C1-Like 1) with BP and fasting glucose.

METHODS

Single-nucleotide polymorphisms in HMGCR, NPC1L1, and PCSK9 associated with LDL-C in a genome-wide association study meta-analysis from the Global Lipid Genetics Consortium (173 082 European individuals) were used to proxy LDL-C-lowering drug targets. BP and fasting glucose data were obtained from genome-wide association studies conducted by the International Consortium of Blood Pressure (757 601 European participants) and the Glucose and Insulin-related Traits Consortium (58 074 European participants). We used the inverse-variance weighted method and a series of sensitivity analyses for assessment.

RESULTS

Genetically proxied inhibition of HMGCR was negatively associated with systolic BP (β, -0.81 [95% CI, -1.26 to -0.37 mm Hg]; P=3.72×10-4) and diastolic BP (β, -1.58 [95% CI, -2.24 to -0.91 mm Hg]; P=3.23×10-6). Conversely, we observed a positive association between genetically proxied inhibition of HMGCR and high fasting glucose (β, 0.13 [95% CI, 0.08-0.17 mmol/L]; P=4.25×10-8). However, there was no association of PCSK9 and NPC1L1 inhibition with BP or fasting glucose.

CONCLUSIONS

Genetically proxied inhibition of HMGCR was significantly associated with low BP and high fasting glucose, while there was no effect of PCSK9 and NPC1L1 inhibition on BP or fasting glucose.

Inhibition of Putative Ibrutinib Targets Promotes Atrial Fibrillation, Conduction Blocks, and Proarrhythmic Electrocardiogram Indices: A Mendelian Randomization Analysis

Background

The mechanism by which ibrutinib, a Bruton's tyrosine kinase inhibitor, can elevate the risk of arrhythmias is not fully elucidated. In this study, we explored how inhibition of off-target kinases can contribute to this phenomenon.

Methods

We performed a Mendelian randomization analysis to examine the causal associations between genetically proxied inhibition of six putative ibrutinib drug targets (ErbB2/HER2, CSK, JAK3, TEC, BLK, and PLCG2) and the atrial fibrillation (AF) risk, proarrhythmic ECG indices, and cardiometabolic traits and diseases. Inverse-variance weighted random-effects models and Wald ratio were used to examine the associations between genetically proxied inhibition of these drug targets and the risk of outcomes. Colocalization analyses were employed to examine the robustness of the causally significant findings. ELISAs were used to measure ErbB2 levels in intracardiac plasma samples.

Results

Genetically proxied ErbB2 inhibition was associated with an increased AF risk, higher P wave terminal force, and prolonged QTc interval. Patients with AF had significantly higher intracardiac ErbB2 levels compared with patients with paroxysmal supraventricular tachycardia. CSK inhibition prolonged the QRS duration, decreased the QTc interval, and was potentially linked to conduction blocks. PLCG2 inhibition led to decreased P wave terminal force, shorter QTc interval, and increased risk of left bundle branch block. BLK inhibition shortened the QTc interval and was also associated with atrioventricular block.

Conclusion

The off-target effects and downstream targets of ibrutinib, including CSK, PLCG2, ERBB2, TEC, and BLK, may lead to cardiac electrical homeostasis imbalances and lethal cardiovascular diseases. Using drugs that inhibit these targets should be given extra caution.

Potential Causal Relationship Between Extensive Lipid Profiles and Various Hair Loss Diseases: Evidence From Univariable and Multivariable Mendelian Randomization Analyses

BACKGROUND

Hair loss disorders, including non-cicatricial forms such as alopecia areata (AA) and androgenetic alopecia (AGA), as well as cicatricial forms, represent significant dermatological concerns influenced by various factors, including lipid metabolism. While observational studies and clinical trials have suggested a link between lipid levels and hair loss, the causal relationship remains unclear.

METHODS

We conducted a comprehensive analysis of 983 lipid variables [including triglycerides (TG), fatty acids, cholesterol, cholesterol esters, phospholipids, and lipoproteins] and 4 hair loss disorders. Two-sample univariable Mendelian randomization (UVMR) and multivariable Mendelian randomization (MVMR) analyses were employed to investigate the causal effects of lipids on hair loss disorders. Sensitivity analyses were performed to ensure the robustness of our findings.

RESULTS

The UVMR analysis identified 56 significant causal associations between lipid levels and hair loss disorders, with cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), TG, apolipoprotein A1, apolipoprotein B, and lipoprotein(a) emerging as key contributors. The MVMR analysis evaluated the independent effects of HDL-C, LDL-C, and TG on alopecia disorders, identifying significant associations only between HDL-C, TG, and AA. Sensitivity analyses confirmed the consistency and robustness of these results.

CONCLUSION

This study provides strong evidence for potential causal associations between lipids and hair loss disorders, highlighting potential therapeutic targets and the importance of lipid management in affected patients.

Assessing the effect of modifiable risk factors on hepatocellular carcinoma: evidence from a bidirectional Mendelian randomization analysis

BACKGROUND

The pathogenesis of hepatocellular carcinoma (HCC) involves a variety of environmental risk factors, some of which have yet to be fully clarified. Using the Mendelian randomization (MR) approach, this study comprehensively investigates the causal effect of genetically predicted modifiable risk factors on HCC.

METHODS

Genetic variants related to the 50 risk factors that had been identified in previous research were derived from genome-wide association studies. Summary statistics for the discovery cohort and validation cohort of HCC were sourced from the FinnGen consortium and the UK Biobank, respectively. Bidirectional MR analysis and sensitivity analysis were performed to establish causative risk factors for HCC.

RESULTS

Through the inverse variance weighted method, the results of the discovery cohort indicated that waist circumference, nonalcoholic fatty liver disease (NAFLD), alanine aminotransferase (ALT) levels, and aspartate aminotransferase (AST) levels were significantly linked to HCC occurrence risk. Furthermore, body fat percentage, glycated hemoglobin (HbA1c), obesity class 1-3, waist-to-hip ratio, iron, ferritin, transferrin saturation, and urate had suggestive associations with HCC. The validation cohort further confirmed that NAFLD and ALT levels were strongly related to HCC. Reverse MR indicated that genetic susceptibility to HCC was connected to NAFLD and transferrin saturation. Sensitivity analyses showed that most of the findings were robust.

CONCLUSION

This MR study delivers evidence of the complex causal relationship between modifiable risk factors and HCC. These findings offer new insights into potential prevention and treatment strategies for HCC.

Epigenome-wide methylation analysis shows phosphonoethylamine alleviates aberrant DNA methylation in NASH caused by Pcyt2 deficiency

BACKGROUND

Aberrant DNA methylation can lead to the onset of pathological phenotypes and is increasingly being implicated in age-related metabolic diseases. In our preceding study we show that the heterozygous ablation of Pcyt2, the rate limiting enzyme in phosphatidylethanolamine (PE) synthesis, causes an age-dependent development of non-alcoholic steatohepatitis (NASH), and that treatment with the Pcyt2 substrate phosphonoethylamine (PEA) can attenuate phenotypic NASH pathologies. Here, we hypothesize that abnormal DNA methylation patterns underly the development of Pcyt2 + /- NASH. In this study, we conduct an epigenome-wide methylation analysis to characterize the differential methylation of Pcyt2 + /- livers and investigate whether the attenuation of NASH with PEA treatment is associated with changes in DNA methylation.

RESULTS

Pcyt2 + /- NASH liver experiences significant alterations in DNA methylation pattens relative to Pcyt2 + / + . Differentially methylated genes belong to pathways including PI3K-Akt signalling pathway, Foxo signalling pathway, oxidative phosphorylation and insulin signalling/secretion, indicating that epigenetic regulation underlies many of our previously established functional pathological mechanisms of Pcyt2 + /- NASH. Previously unidentified pathways during Pcyt2 deficiency are highlighted, such as cell cycle regulation and cellular senescence that may contribute to NASH development. Treatment with PEA dramatically attenuates aberrant total and protein-coding DNA methylation patterns by 96%. PEA treatment restored the methylation status of key genes involved in epigenetic modifications and induced differential methylation of genes associated with obesity and T2DM such as Adyc3, Celsr2, Fam63b.

CONCLUSION

The Pcyt2 + /- liver methylome and transcriptome is altered and likely underlies much of the pathology in Pcyt2 + /- NASH phenotype. The treatment with PEA significantly attenuates aberrant DNA methylation in Pcyt2 + /- liver and corrects the DNA methylation of genes involved in the pathogenesis of NASH, indicating its therapeutic potential. This analysis provides critical insight into the epigenetic basis of NASH pathophysiology and suggests diagnostic markers and therapeutic targets.

Examining the link between 179 lipid species and 7 diseases using genetic predictors

BACKGROUND

Genome-wide association studies of lipid species have identified several loci shared with various diseases, however, the relationship between lipid species and disease risk remains poorly understood. Here we investigated whether the plasma levels of lipid species are causally linked to disease risk.

METHODS

We built genetic predictors of 179 lipid species, measured in 7174 Finnish individuals, by utilising either 11 high-impact genomic loci or genome-wide polygenic scores (PGS). We assessed the impact of the lipid species on seven diseases by performing disease association across FinnGen (n = 500,348), UK Biobank (n = 420,531), and Generation Scotland (n = 20,032). We performed univariable Mendelian randomisation (MR) and multivariable MR (MVMR) analyses to examine whether lipid species impact disease risk independently of standard lipids.

FINDINGS

PGS explained >4% of the variance for 34 lipid species but variants outside the high-impact loci had only a marginal contribution. Variants within the high-impact loci showed association with all seven diseases. MVMR supported a causal role of ApoB in ischaemic heart disease after accounting for lipid species. Phosphatidylethanolamine-increasing LIPC variants seemed to lower age-related macular degeneration risk independently of HDL-cholesterol. MVMR suggested a protective effect of four lipid species containing arachidonic acid on cholelithiasis risk independently of Total Cholesterol.

INTERPRETATION

Our study demonstrates how genetic predictors of lipid species can be utilised to gain insights into disease risk. We report potential links between lipid species and age-related macular degeneration and cholelithiasis risk, which can be explored for their utility in disease risk prediction and therapy.

FUNDING

The funders had no role in the study design, data analyses, interpretation, or writing of this article.

Druggable genome-wide Mendelian randomization identifies therapeutic targets for metabolic dysfunction-associated steatotic liver disease

BACKGROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD) affects > 25% of the global population, potentially leading to severe hepatic and extrahepatic complications, including metabolic dysfunction-associated steatohepatitis. Given that the pathophysiology of MASLD is incompletely understood, identifying therapeutic targets and optimizing treatment strategies are crucial for addressing this severe condition.

METHODS

Mendelian randomization (MR) analysis was conducted using two genome-wide association study datasets: a European meta-analysis (8,434 cases; 770,180 controls) and an additional study (3,954 cases; 355,942 controls), identifying therapeutic targets for MASLD. Of 4302 drug-target genes, 2,664 genetic instrument variables were derived from cis-expression quantitative trait loci (cis-eQTLs). Colocalization analyses assessed shared causal variants between MASLD-associated single nucleotide polymorphisms and eQTLs. Using the drug target gene cis-eQTL of liver tissue from the genotype-tissue expression project, we performed MR and summary MR to validate the significance of the gene results of the blood eQTL MR. RNA-sequencing data from liver biopsies were validated using immunohistochemistry and quantitative polymerase chain reaction (qPCR) tests to confirm gene expression findings.

RESULT

MR analysis across both datasets identified significant MR associations between MASLD and two drug targets-milk fat globule-EGF factor 8 (MFGE8) (odds ratio [OR] 0.89, 95% confidence interval [CI] 0.85-0.94; P = 2.15 × 10-6) and cluster of differentiation 33 (CD33) (OR 1.17, 95% CI 1.10-1.25; P = 1.39 × 10-6). Both targets exhibited strong colocalization with MASLD. Genetic manipulation indicating MFGE8 activation and CD33 inhibition did not increase the risk for other metabolic disorders. RNA-sequencing, qPCR, and immunohistochemistry validation demonstrated consistent differential expressions of MFGE8 and CD33 in MASLD.

CONCLUSION

CD33 inhibition can reduce MASLD risk, while MFGE8 activation may offer therapeutic benefits for MASLD treatment.

Leveraging pleiotropic clustering to address high proportion correlated horizontal pleiotropy in Mendelian randomization studies

Mendelian randomization harnesses genetic variants as instrumental variables to infer causal relationships between exposures and outcomes. However, certain genetic variants can affect both the exposure and the outcome through a shared factor. This phenomenon, called correlated horizontal pleiotropy, may result in false-positive causal findings. Here, we propose a Pleiotropic Clustering framework for Mendelian randomization, PCMR. PCMR detects correlated horizontal pleiotropy and extends the zero modal pleiotropy assumption to enhance causal inference in trait pairs with correlated horizontal pleiotropic variants. Simulations show that PCMR can effectively detect correlated horizontal pleiotropy and avoid false positives in the presence of correlated horizontal pleiotropic variants, even when they constitute a high proportion of the variants connecting both traits (e.g., 30-40%). In datasets consisting of 48 exposure-common disease pairs, PCMR detects horizontal correlated pleiotropy in 7 out of the exposure-common disease pairs, and avoids detecting false positive causal links. Additionally, PCMR can facilitate the integration of biological information to exclude correlated horizontal pleiotropic variants, enhancing causal inference. We apply PCMR to study causal relationships between three common psychiatric disorders as examples.

Genetic association of lipids and lipid-lowering drug target genes with breast cancer

BACKGROUND

Although several preclinical and epidemiological studies have shown that blood lipids and lipid-lowering drugs can reduce the risk of breast cancer, this finding remains controversial. This study aimed to explore the causal relationship between dyslipidemia,lipid-lowering drugs, and breast cancer. We also aimed to evaluate the potential impact of lipid-lowering drug targets on breast cancer.

METHOD

Data of 431 lipid- and lipid-related phenotypes were obtained from genome-wide association study (GWAS), and mendelian randomization (MR) analyses were performed using two independent breast cancer datasets as endpoints. Genetic variants associated with genes encoding lipid-lowering drug targets were extracted from the Global Lipid Genetics Consortium. Expression quantitative trait loci data in relevant tissues were used to further validate lipid-lowering drug targets that reached significance and combined with bioinformatics approaches for molecular expression and prognostic exploration. Further mediation analyses were performed to explore potential mediators.

RESULT

In two independent datasets, phosphatidylcholine (18:1_0:0 levels) was associated with breast cancer risk (discovery: odds ratio (OR) = 1.255 [95% confidence interval (CI) 1.120-1.406]; p = 8.936 × 10-5, replication: OR = 1.016 [95% CI, 1.003-1.030]; p = 0.017), HMG- CoA reductase (HMGCR) inhibition was genetically modeled and associated with a reduced risk of breast cancer (discovery: OR = 0.833 [95% CI 0.752-0.923], p = 5.12 × 10-4; replication: OR = 0.975 [95% CI 0.960-0.990], p = 1.65 × 10-3). There was a significant MR correlation between HMGCR expression in whole blood and breast cancer (OR = 1.11 [95% 1.01-1.22] p = 0.04). Bioinformatics analysis revealed that HMGCR expression higher in breast cancer tissues than in normal tissues, along with poor overall survival and relapse-free survival, and was associated with multiple immune cell infiltration. Finally, the mediation analysis showed that HMGCR inhibitors affected breast cancer through different immune cell phenotypes and C-reactive protein levels.

CONCLUSION

In this study, we found for the first time that phosphatidylcholine (18:1_0:0) levels are associated with breast cancer risk. We found that HMGCR inhibitors are associated with a reduced risk of breast cancer, and part of their action may be through pathways other than lipid-lowering, including modulation of immune function and reduction of inflammation represented by C-reactive protein levels.

Comparison of Patients With Familial Chylomicronemia Syndrome and Multifactorial Chylomicronemia Syndrome

CONTEXT

Patients with rare familial chylomicronemia syndrome (FCS) and relatively common multifactorial chylomicronemia syndrome (MCS) both express severe hypertriglyceridemia, defined as plasma triglyceride concentration ≥10 mmol/L (≥885 mg/dL). Clinically there can be confusion between the 2 conditions.

OBJECTIVE

To compare clinical and biochemical phenotypes in patients with genotypically characterized FCS and MCS.

METHODS

We performed targeted sequencing of DNA from 193 patients with severe hypertriglyceridemia, classified them as having either FCS or MCS, and compared clinical and biochemical characteristics.

RESULTS

Patients with FCS were significantly younger than patients with MCS (31.4 ± 16.7 vs 51.0 ± 11.3 years; P = .003), with earlier age at symptom onset (15.0 ± 15.8 vs 37.8 ± 8.8 years; P = .00066), lower body mass index (23.3 ± 3.1 vs 30.7 ± 5.0 kg/m2; P = .000016), and higher prevalence of pancreatitis events (81.8% vs 35.2%; P = .003). Furthermore, patients with FCS had a higher ratio of triglyceride to total cholesterol (ie, 4.18 ± 0.92 vs 1.08 ± 0.51; P < .0001) and lower plasma apolipoprotein B (ie, 0.56 ± 0.15 vs 1.02 ± 0.43 g/L; P < .0001) than patients with MCS. Patients with MCS with heterozygous pathogenic variants had a relatively more severe clinical presentation than other MCS genetic subgroups.

CONCLUSION

Patients with FCS have notable phenotypic differences from patients with MCS, although there is overlap. While genetic analysis of patients with persistent severe hypertriglyceridemia can definitively diagnose FCS, 8.8% of patients with MCS with sustained refractory hypertriglyceridemia behave functionally as if they have FCS, which should influence their eligibility for novel therapies for severe hypertriglyceridemia.

Systematic interrogation of functional genes underlying cholesterol and lipid homeostasis

BACKGROUND

Dyslipidemia or hypercholesterolemia are among the main risk factors for cardiovascular diseases. Unraveling the molecular basis of lipid or cholesterol homeostasis would help to identify novel drug targets and develop effective therapeutics.

RESULTS

Here, we adopt a systematic approach to catalog the genes underlying lipid and cholesterol homeostasis by combinatorial use of high-throughput CRISPR screening, RNA sequencing, human genetic variant association analysis, and proteomic and metabolomic profiling. Such integrative multi-omics efforts identify gamma-glutamyltransferase GGT7 as an intriguing potential cholesterol and lipid regulator. As a SREBP2-dependent target, GGT7 positively regulates cellular cholesterol levels and affects the expression of several cholesterol metabolism genes. Furthermore, GGT7 interacts with actin-dependent motor protein MYH10 to control low-density lipoprotein cholesterol (LDL-C) uptake into the cells. Genetic ablation of Ggt7 in mice leads to reduced serum cholesterol levels, supporting an in vivo role of Ggt7 during cholesterol homeostasis.

CONCLUSIONS

Our study not only provides a repertoire of lipid or cholesterol regulatory genes from multiple angles but also reveals a causal link between a gamma-glutamyltransferase and cholesterol metabolism.

Causal association between metabolic syndrome and ovarian dysfunction: a bidirectional two-sample mendelian randomization

BACKGROUND

The relationship between Metabolic Syndrome (MetS) and ovarian dysfunction has been widely reported in observational studies, yet it remains not fully understood. This study employs genetic prediction methods and utilizes summary data from genome-wide association studies (GWAS) to investigate this causal link.

METHODS

We employed a bidirectional two-sample Mendelian Randomization (MR) analysis utilizing MetS and ovarian dysfunction summary data from GWAS. Inverse variance weighted (IVW) was employed as the primary MR method, supplemented by Weighted Median, Weighted Mode, and MR-Egger methods. The robustness of the results was further assessed through sensitivity analyses including MR-Egger regression, MR-PRESSO, Cochran's Q, and leave-one-out test.

RESULTS

Our MR analysis identified a causal relationship between genetically determined insulin resistance (OR = 0.26, 95% CI: 0.08-0.89, P = 0.03), waist circumference (OR = 2.14, 95% CI: 1.45-3.15, P < 0.001), BMI (OR = 2.1, 95% CI: 1.56-2.83, P < 0.001) and ovarian dysfunction. Conversely, reverse MR analysis confirmed causal effects of ovarian dysfunction on metabolic syndrome (OR = 0.98, 95% CI: 0.97-0.99, P < 0.001) and waist circumference (OR = 0.99, 95% CI: 0.98-0.99, P = 0.02). The results of MR-Egger regression test indicated that the whole analysis was not affected by horizontal pleiotropy. Additionally, the MR-PRESSO test identified outliers in SNPs, but after removal of outliers, results remained unchanged.

CONCLUSION

This study reveals a bidirectional causal connection between metabolic syndrome and ovarian dysfunction via genetic prediction methods. These findings are crucial for advancing our understanding of the interactions between these conditions and developing strategies for prevention and treatment.

Evaluating lipid-lowering drug targets for full-course diabetic retinopathy

BACKGROUND

Implementing lipid control in patients with diabetes is regarded as a potential strategy for halting the advancement of diabetic retinopathy (DR). This study seeks to use Mendelian randomisation (MR) to assess the causal relationship between lipid traits and lipid-lowering drug targets and full-course DR (background DR, severe non-proliferative DR (NPDR) and proliferative DR (PDR)).

METHODS

A two-sample MR and drug target MR to decipher the causal effects of lipid traits and lipid-lowering drug targets on full-course DR, including background DR, severe NPDR and PDR, was conducted in the study. Genetic variants associated with lipid traits and genes encoding the protein targets of lipid-lowering drugs were extracted from the Global Lipids Genetics Consortium and UK Biobank. Summary-level data of full-course DR are obtained from FinnGen.

RESULTS

No significant causal relationship was found between lipid traits and full-course DR. However, in drug target MR analysis, peroxisome proliferator-activated receptor gamma (PPARG) enhancement was associated with lower risks of background DR (OR=0.12, p=0.005) and PDR (OR=0.25, p=0.006). Additionally, mediation MR analysis showed that lowering fasting insulin (p=0.015) and HbA1c (p=0.005) levels mediated most of the association between PPARG and full-course DR.

CONCLUSIONS

This study reveals PPARG may be a promising drug target for full-course DR. The activation of PPARG could reduce the risk of full-course DR, especially background DR and PDR. The mechanism of the PPARG agonists' protection of full-course DR may be dependent on the glucose-lowering effect.

Mendelian randomization-based observational cohort study on drug targets: Impact of antihypertensive and lipid-lowering therapies on inflammatory cytokines

This study assesses the causal effects of antihypertensive and lipid-lowering drugs on inflammatory cytokines using a Mendelian randomization (MR) approach. We conducted a drug-targeted MR analysis using data from large-scale genome-wide association studies and eQTL datasets. SNPs near drug target genes served as instrumental variables to investigate the impact of antihypertensive (angiotensin-converting enzyme inhibitors [ACEIs], ARBs) and lipid-lowering drugs (HMGCR inhibitors, proprotein convertase subtilisin/Kexin type 9 [PCSK9] inhibitors, Niemann-Pick C1-like 1 inhibitors) on inflammatory cytokines. Sensitivity analyses, including leave-one-out and MR-Egger tests, were performed to confirm the robustness of the findings. ACEIs were associated with decreased levels of IL-1β, TNF-α, and CRP. ARBs did not show significant effects on inflammatory cytokines. HMGCR inhibitors significantly reduced MCP-1, MIP-1β, TNF-α, and IFN-γ, while PCSK9 inhibitors were linked to reductions in IL-1β and IL-6. Sensitivity analyses supported the reliability of these findings. The study demonstrated distinct anti-inflammatory effects of ACEIs, HMGCR inhibitors, and PCSK9 inhibitors. These findings support the potential use of these drugs to mitigate inflammation-related complications in patients with chronic conditions.

Cholesterol 7 alpha-hydroxylase (CYP7A1) gene polymorphisms are associated with increased LDL-cholesterol levels and the incidence of subclinical atherosclerosis

The cholesterol 7 alpha-hydroxylase (CYP7A1) enzyme plays an important role in the conversion of cholesterol to bile acid, contributing to the reduction of cholesterol plasma levels in normal conditions. Nonetheless, recent studies have shown that some genetic variants in the enhancer and promoter regions of the CYP7A1 gene reduce the expression of the CYP7A1 enzyme, increasing plasma lipid levels, as well as the risk of developing coronary heart disease. The aim of this work was to explore whether the genetic variants (rs2081687, rs9297994, rs10107182, rs10504255, rs1457043, rs8192870, and rs3808607) of the CYP7A1 gene are involved in subclinical atherosclerosis and plasma lipid levels. We included 416 patients with subclinical atherosclerosis (SA) with coronary artery calcium (CAC) greater than zero, and 1046 controls with CAC = 0. According to the inheritance models (co-dominant, dominant, recessive, over-dominant and additive), the homozygosity of the minor allele frequencies of 7 analyzed polymorphisms showed a high incidence of SA (P < 0.05). In a sub-analysis performed including only the patients with SA, the same SNPs were associated with increased low-density lipoprotein cholesterol (LDL-C) levels. On the other hand, our findings showed that the haplotype (TGCGCTG) increases the risk of developing SA (P < 0.05). In conclusion, the rs2081687, rs9297994, rs10107182, rs10504255, rs1457043, rs8192870, and rs3808607 polymorphisms of CYP7A1 confer a risk of developing SA and elevated LDL-C levels. Our results suggest that the CYP7A1 is involved in the incidence of SA through the increase in the plasma lipid profile.

Causal Relationship Between Intelligence, Noncognitive Education, Cognition and Urinary Tract or Kidney Infection: A Mendelian Randomization Study

Background

The occurrence of urinary tract or kidney infection is correlated with intelligence, noncognitive education and cognition, but the causal relationship between them remains uncertain, and which risk factors mediate this causal relationship remains unknown.

Methods

The intelligence (n=269,867), noncognitive education (n=510,795) and cognition data (n=257,700) were obtained from genome-wide association studies (GWAS) conducted in individuals of European ethnicities. The genetic associations between these factors and urinary tract or kidney infection (UK Biobank, n=397,867) were analyzed using linkage disequilibrium score regression. We employed a two-sample univariate and multivariate Mendelian randomization to evaluate the causal relationship, and utilized a two-step Mendelian randomization to examine the involvement of 28 potential mediators and their respective mediating proportions.

Results

The genetic correlation coefficients of intelligence, noncognitive education, cognition, and urinary tract or kidney infection were -0.338, -0.218, and -0.330. The Mendelian randomization using the inverse variance weighted method revealed each 1-SD increase in intelligence, the risk of infection decreased by 15.9%, while after adjusting for noncognitive education, the risk decreased by 20%. For each 1-SD increase in noncognitive education, the risk of infection decreased by 8%, which further reduced to 7.1% after adjusting for intelligence and to 6.7% after adjusting for cognition. For each 1-SD increase in cognition, the risk of infection decreased by 10.8%, increasing to 11.9% after adjusting for noncognitive education. The effects of intelligence and cognition are interdependent. 2 out of 28 potential mediating factors exhibited significant mediation effects in the causal relationship between noncognitive education and urinary tract or kidney infection, with body mass index accounting for 12.1% of the mediation effect and smoking initiation accounting for 14.7%.

Conclusion

Enhancing intelligence, noncognitive education, and cognition can mitigate the susceptibility to urinary tract or kidney infection. Noncognitive education exhibited independent effect, while body mass index and smoking initiation assuming a mediating role.

Sex differences in predicting dyslipidemia using polygenic risk score with fatty liver index and fibrotic nonalcoholic steatohepatitis index

Non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) are recognized risk factors for dyslipidemia. Current prediction models that rely solely on dyslipidemia polygenic risk score (PRS) have certain limitations. We aimed to validate simple indexes for NAFLD and NASH as predictors of dyslipidemia using the PRS. This study utilized cohort data from an urban population-based dataset comprising 48,263 South Koreans. The incidence of dyslipidemia was higher in men than in women (32.4% and 27.8%; p < 0.001). The PRS model predicted dyslipidemia more accurately in men (AUROC [95% confidence intervals]: 0.645 [0.636-0.754]). Notably, integrating the fatty liver index (FLI) and fibrotic NASH index (FNI) with the PRS model resulted in the highest accuracy in diagnosing dyslipidemia, particularly in men (AUROC [95% confidence intervals]: 0.704 [0.698-0.711]). In conclusion, a predictive model combining the PRS with FLI and FNI was validated. This model offers more accurate predictive value for diagnosing dyslipidemia, particularly in East Asian men. Thus, our study has the clinical potential for identifying high-risk individuals and determining preventive measures for dyslipidemia in a sex-specific manner.

Precision Medicine in Cardiovascular Disease Prevention: Clinical Validation of Multi-Ancestry Polygenic Risk Scores in a U.S. Cohort

BACKGROUND

Polygenic risk score (PRS) quantifies the cumulative effects of common genetic variants across the genome, including both coding and non-coding regions, to predict the risk of developing common diseases. In cardiovascular medicine, PRS enhances risk stratification beyond traditional clinical risk factors, offering a precision medicine approach to coronary artery disease (CAD) prevention. This study evaluates the predictive performance of a multi-ancestry PRS framework for cardiovascular risk assessment using the All of Us (AoU) short-read whole-genome sequencing dataset comprising over 225,000 participants.

METHODS

We developed PRSs for lipid traits (LDL-C, HDL-C, triglycerides) and cardiometabolic conditions (type 2 diabetes, hypertension, atrial fibrillation) and constructed two metaPRSs: one integrating lipid and cardiometabolic PRSs (risk factor metaPRS) and another incorporating CAD PRSs in addition to these risk factors (risk factor + CAD metaPRS). Predictive performance was evaluated separately for each trait-specific PRS and for both metaPRSs to assess their effectiveness in CAD risk prediction across diverse ancestries. Model predictive performance, including calibration, was assessed separately for each ancestry group, ensuring that all metrics were ancestry-specific and that PRSs remain generalizable across diverse populations Results: PRSs for lipids and cardiometabolic conditions demonstrated strong predictive performance across ancestries. The risk factors metaPRS predicted CAD risk across multiple ancestries. The addition of a CAD-specific PRS to the risk factors metaPRS improved predictive performance, highlighting a genetic component in CAD etiopathology that is not fully captured by traditional risk factors, whether clinically measured or genetically inferred. Model calibration and validation across ancestries confirmed the broad applicability of PRS-based approaches in multi-ethnic populations.

CONCLUSION

PRS-based risk stratification provides a reliable, ancestry-inclusive framework for personalized cardiovascular disease prevention, enabling better targeted interventions such as pharmacological therapy and lifestyle modifications. By incorporating genetic information from both coding and non-coding regions, PRSs refine risk prediction across diverse populations, advancing the integration of genomics into precision medicine for common diseases.

Systematic Evaluation of the Impact of a Wide Range of Dietary Habits on Myocardial Infarction: A Two-Sample Mendelian Randomization Analysis

BACKGROUND

Myocardial infarction is a cardiovascular disease that significantly contributes to global morbidity and disability. Given the significant role of diet in the pathogenesis and prevention of cardiovascular diseases, this study rigorously investigates the causal relationship between dietary habits and myocardial infarction.

METHODS AND RESULTS

This study used large-scale genome-wide association studies with pooled UK Biobank data to explore associations between 9 dietary categories (83 types) and myocardial infarction. A 2-sample Mendelian randomization approach was applied to assess these associations, while multivariate Mendelian randomization and mediation analyses investigated the role of lipids in mediating the effects of diet on myocardial infarction. Univariate Mendelian analyses revealed genetic associations among 9 categories of dietary habits (83 types) and myocardial infarction. Notably, robust evidence indicates the "tablespoons of cooked vegetables per day" as the most significant risk factor for myocardial infarction development. "Coffee consumption(cups per day)" and "frequency of adding salt to food" were also identified as supplementary risk factors. In contrast, "overall alcohol intake" showed a protective effect, potentially by increasing high-density lipoprotein cholesterol (4.48% mediation) and reducing triglycerides (6.24% mediation). Cereal category, particularly "cereal consumption (bowls per week)" was associated with reduced myocardial infarction risk, contributing by raising high-density lipoprotein cholesterol (3.69% mediation) and lowering total cholesterol (8.33% mediation). Additionally, "overall cheese consumption" was also protective against myocardial infarction.

CONCLUSIONS

Our findings elucidate the influence of dietary habits on myocardial infarction, showing underlying genetic mechanisms and emphasizing the regulatory role of lipids as an intermediate.

Sirolimus as a repurposed drug for tendinopathy: A systems biology approach combining computational and experimental methods

BACKGROUND

Effective drugs for tendinopathy are lacking, resulting in significant morbidity and re-tearing rate after operation. Applying systems biology to identify new applications for current pharmaceuticals can decrease the duration, expenses, and likelihood of failure associated with the development of new drugs.

METHODS

We identify tendinopathy signature genes employing a transcriptomics database encompassing 154 clinical tendon samples. We then proposed a systems biology based drug prediction strategy that encompassed multiplex transcriptional drug prediction, systematic review assessment, deep learning based efficacy prediction and Mendelian randomization (MR). Finally, we evaluated the effects of drug target using gene knockout mice.

RESULTS

We demonstrate that sirolimus is a repurposable drug for tendinopathy, supported by: 1) Sirolimus achieves top ranking in drug-gene signature-based multiplex transcriptional drug efficacy prediction, 2) Consistent evidence from systematic review substantiates the efficacy of sirolimus in the management of tendinopathy, 3) Genetic prediction indicates that plasma proteins inhibited by mTOR (the target of sirolimus) are associated with increased tendinopathy risk. The effectiveness of sirolimus is further corroborated through in vivo testing utilizing tendon tissue-specific mTOR gene knockout mice. Integrative pathway enrichment analysis suggests that mTOR inhibition can regulate heterotopic ossification-related pathways to ameliorate clinical tendinopathy.

CONCLUSIONS

Our study assimilates knowledge of system-level responses to identify potential drugs for tendinopathy, and suggests sirolimus as a viable candidate. A systems biology approach could expedite the repurposing of drugs for human diseases that do not have well-defined targets.

Evaluation of polygenic scores for hypertrophic cardiomyopathy in the general population and across clinical settings

Hypertrophic cardiomyopathy (HCM) is an important cause of morbidity and mortality, with pathogenic variants found in about a third of cases. Large-scale genome-wide association studies (GWAS) demonstrate that common genetic variation contributes to HCM risk. Here we derive polygenic scores (PGS) from HCM GWAS and genetically correlated traits and test their performance in the UK Biobank, 100,000 Genomes Project, and clinical cohorts. We show that higher PGS significantly increases the risk of HCM in the general population, particularly among pathogenic variant carriers, where HCM penetrance differs 10-fold between those in the highest and lowest PGS quintiles. Among relatives of HCM probands, PGS stratifies risks of developing HCM and adverse outcomes. Finally, among HCM cases, PGS strongly predicts the risk of adverse outcomes and death. These findings support the broad utility of PGS across clinical settings, enabling tailored screening and surveillance and stratification of risk of adverse outcomes.

Sex differences in risk factor relationships with subarachnoid haemorrhage and intracranial aneurysms: A Mendelian randomization study

Background

The prevalence of intracranial aneurysms (IAs) and incidence of aneurysmal subarachnoid haemorrhage (aSAH) is higher in women than in men. Although several cardiometabolic and lifestyle factors have been related to the risk of IAs or aSAH, it is unclear whether there are sex differences in causal relationships of these risk factors.

Aims

The aim of this study was to determine sex differences in causal relationships between cardiometabolic and lifestyle factors and risk of aSAH and IA.

Methods

We conducted a sex-specific two-sample Mendelian randomization study using summary-level data from genome-wide association studies. We analysed low-density lipoprotein cholesterol, high-density lipoprotein cholesterol [HDL-C], triglycerides, non-HDL-C, total cholesterol, fasting glucose, systolic and diastolic blood pressure, smoking initiation, and alcohol use as exposures, and aSAH and IA (i.e. aSAH and unruptured IA combined) as outcomes.

Results

We found statistically significant sex differences in the relationship between genetically proxied non-HDL-C and aSAH risk, with odds ratios (ORs) of 0.72 (95% confidence interval 0.58, 0.88) in women and 1.01 (0.77, 1.31) in men (p-value for sex difference 0.044). Moreover, genetic liability to smoking initiation was related to a statistically significantly higher risk of aSAH in men compared to women (p-value for sex difference 0.007) with ORs of 3.81 (1.93, 7.52) and 1.12 (0.63, 1.99), respectively, and to a statistically significantly higher IA risk in men compared to women (p-value for sex difference 0.036) with ORs of 3.58 (2.04, 6.27) and 1.61 (0.98, 2.64), respectively. In addition, higher genetically proxied systolic and diastolic blood pressure were related to a higher risk of aSAH and IA in both women and men.

Conclusions

Higher genetically proxied non-HDL-C was related to a lower risk of aSAH in women compared to men. Moreover, genetic liability to smoking initiation was associated with a higher risk for aSAH and IA in men compared to women. These findings may help improve understanding of sex differences in the development of aSAH and IA.

Unsupervised Learning-Derived Complex Metabolic Signatures Refine Cardiometabolic Risk

BACKGROUND

Cardiometabolic diseases have become a leading cause of morbidity and mortality globally. Nuclear magnetic resonance metabolomics represents a precise tool for assessing metabolic individuality.

OBJECTIVES

This study aimed to use unsupervised learning to decode plasma metabolomic profiles, providing new insights into the etiology of cardiometabolic diseases.

METHODS

We applied unsupervised learning to generate robust metabolic signatures from the plasma profiles of 118,001 UK Biobank participants. Phenome-wide and genome-wide association studies were conducted to reveal their phenomic and genetic architectures. Integrated prospective cohort analyses and Mendelian randomization clarified their roles in cardiometabolic risks.

RESULTS

Eleven metabolic clusters were identified, linked to 101 loci and 445 phenotypes, mostly cardiometabolic diseases. These novel signatures partially outperformed traditional lipids in cardiometabolic risk prediction. Triglyceride-rich lipoproteins demonstrated superior predictive power for ischemic heart disease, type 2 diabetes, and hypertension, compared with apolipoprotein B and lipoprotein(a). Non-high-density lipoprotein cholesterol was found to increase the risk of hyperlipidemia and ischemic heart disease while offering a protective effect against type 2 diabetes. Furthermore, different high-density lipoprotein clusters showed heterogeneous associations with cardiometabolic diseases, with high-density lipoprotein subpopulations enriched in free cholesterol or triglyceride increasing risk, and those enriched in cholesterol esters providing protection.

CONCLUSIONS

These metabolic signatures extract comprehensive information from the metabolomic profile while maintaining clarity and interpretability, facilitating clinical translation. The findings emphasize the crucial roles of lipid subpopulations in cardiometabolic risks, encouraging clinicians to take a more nuanced approach to managing blood lipids and balancing different disease risks.

A multi-ancestry genome-wide association study and evaluation of polygenic scores of LDL-C levels

The genetic determinants of low-density lipoprotein cholesterol (LDL-C) levels in blood have been predominantly explored in European populations and remain poorly understood in Middle Eastern populations. We investigated the genetic architecture of LDL-C variation in Qatar by conducting a genome-wide association study (GWAS) on serum LDL-C levels using whole genome sequencing data of 13,701 individuals (discovery; n = 5,939, replication; n = 7,762) from the population-based Qatar Biobank (QBB) cohort. We replicated 168 previously reported loci from the largest LDL-C GWAS by the Global Lipids Genetics Consortium (GLGC), with high correlation in allele frequencies (R2 = 0.77) and moderate correlation in effect sizes (Beta; R2 = 0.53). We also performed a multi-ancestry meta-analysis with the GLGC study using MR-MEGA (Meta-Regression of Multi-Ethnic Genetic Association) and identified one novel LDL-C-associated locus; rs10939663 (SLC2A9; genomic control-corrected P = 1.25 × 10-8). Lastly, we developed Qatari-specific polygenic score (PGS) panels and tested their performance against PGS derived from other ancestries. The multi-ancestry-derived PGS (PGS000888) performed best at predicting LDL-C levels, whilst the Qatari-derived PGS showed comparable performance. Overall, we report a novel gene associated with LDL-C levels, which may be explored further to decipher its potential role in the etiopathogenesis of cardiovascular diseases. Our findings also highlight the importance of population-based genetics in developing PGS for clinical utilization.

A framework for detecting causal effects of risk factors at an individual level based on principles of Mendelian randomisation: applications to modelling individualised effects of lipids on coronary artery disease

BACKGROUND

Mendelian Randomisation (MR) has been widely used to study the causal effects of risk factors. However, almost all MR studies concentrate on the population's average causal effects. With the advent of precision medicine, the individualised treatment effect (ITE) is often of greater interest. For instance, certain risk factors may pose a higher risk to some individuals than others, and the benefits of treatments may vary across individuals. This study proposes a framework for estimating individualised causal effects in large-scale observational studies where unobserved confounding factors may be present.

METHODS

We propose a framework (MR-ITE) that expands the scope of MR from estimating average causal effects to individualised causal effects. We present several approaches for estimating ITEs within this MR framework, primarily grounded on the principles of the "R-learner". To evaluate the presence of causal effect heterogeneity, we also proposed two permutation testing methods. We employed polygenic risk score (PRS) as instruments and proposed methods to improve the accuracy of ITE estimates by removal of potentially pleiotropic single nucleotide polymorphisms (SNPs). The validity of our approach was substantiated through comprehensive simulations. The proposed framework also allows the identification of important effect modifiers contributing to individualised differences in treatment effects. We applied our framework to study the individualised causal effects of various lipid traits, including low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), and total cholesterol (TC), on the risk of coronary artery disease (CAD) based on the UK-Biobank (UKBB). We also studied the ITE of C-reactive protein (CRP) and insulin-like growth factor 1 (IGF-1) on CAD as secondary analyses.

FINDINGS

Simulation studies demonstrated that MR-ITE outperformed traditional causal forest approaches in identifying ITEs when unobserved confounders were present. The integration of the contamination mixture (ConMix) approach to remove invalid pleiotropic SNPs further enhanced MR-ITE's performance. In real-world applications, we identified positive causal associations between CAD and several factors (LDL-C, Total Cholesterol, and IGF-1 levels). Our permutation tests revealed significant heterogeneity in these causal associations across individuals. Using Shapley value analysis, we identified the top effect modifiers contributing to this heterogeneity.

INTERPRETATION

We introduced a new framework, MR-ITE, capable of inferring individualised causal effects in observational studies based on the MR approach, utilizing PRS as instruments. MR-ITE extends the application of MR from estimating the average treatment effect to individualised treatment effects. Our real-world application of MR-ITE underscores the importance of identifying ITEs in the context of precision medicine.

FUNDING

This work was supported partially by a National Natural Science Foundation of China grant (NSFC; grant number 81971706), the KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, Kunming Institute of Zoology and The Chinese University of Hong Kong, China, and the Lo Kwee Seong Biomedical Research Fund from The Chinese University of Hong Kong.

Genetic Correlates of Treatment-Resistant Depression

Importance

Treatment-resistant depression (TRD) is a major challenge in mental health, affecting a significant number of patients and leading to considerable burdens. The etiological factors contributing to TRD are complex and not fully understood.

Objective

To investigate the genetic factors associated with TRD using polygenic scores (PGS) across various traits and explore their potential role in the etiology of TRD using large-scale genomic data from the All of Us (AoU) Research Program.

Design, Setting, and Participants

This study was a cohort design with observational data from participants in the AoU Research Program who have both electronic health records and genomic data. Data analysis was performed from March 27 to October 24, 2024.

Exposures

PGS for 61 unique traits from 7 domains.

Main Outcomes and Measures

Logistic regressions to test if PGS was associated with treatment-resistant depression (TRD) compared with treatment-responsive major depressive disorder (trMDD). Cox proportional hazard model was used to determine if the progressions from MDD to TRD were associated with PGS.

Results

A total of 292 663 participants (median [IQR] age, 57 (41-69) years; 175 981 female [60.1%]) from the AoU Research Program were included in this analysis. In the discovery set (124 945 participants), 11 of the selected PGS were found to have stronger associations with TRD than with trMDD, encompassing PGS from domains in education, cognition, personality, sleep, and temperament. Genetic predisposition for insomnia (odds ratio [OR], 1.11; 95% CI, 1.07-1.15) and specific neuroticism (OR, 1.11; 95% CI, 1.07-1.16) traits were associated with increased TRD risk, whereas higher education (OR, 0.88; 95% CI, 0.85-0.91) and intelligence (OR, 0.91; 95% CI, 0.88-0.94) scores were protective. The associations held across different TRD definitions (meta-analytic R2 >83%) and were consistent across 2 other independent sets within AoU (the whole-genome sequencing Diversity dataset, 104 388, and Microarray dataset, 63 330). Among 28 964 individuals followed up over time, 3854 developed TRD within a mean of 944 days (95% CI, 883-992 days). All 11 previously identified and replicated PGS were found to be modulating the conversion rate from MDD to TRD.

Conclusions and Relevance

Results of this cohort study suggest that genetic predisposition related to neuroticism, cognitive function, and sleep patterns had a significant association with the development of TRD. These findings underscore the importance of considering psychosocial factors in managing and treating TRD. Future research should focus on integrating genetic data with clinical outcomes to enhance understanding of pathways leading to treatment resistance.

Beyond guilty by association at scale: searching for causal variants on the basis of genome-wide summary statistics

Understanding the causal genetic architecture of complex phenotypes will fuel future research into disease mechanisms and potential therapies. Here, we illustrate the power of a novel framework: it detects, starting from summary statistics, and across the entire genome, sets of variants that carry non-redundant information on the phenotypes and are therefore more likely to be causal in a biological sense. The approach, implemented in open-source software, is also computationally efficient, requiring less than 15 minutes on a single CPU to perform genome-wide analysis. Through extensive genome-wide simulation studies, we show that the method can substantially outperform existing methods in false discovery rate control, statistical power and various fine-mapping criteria. In applications to a meta-analysis of ten large-scale genetic studies of Alzheimer's disease (AD), we identified 82 loci associated with AD, including 37 additional loci missed by conventional GWAS pipeline. Massively parallel reporter assays and CRISPR-Cas9 experiments have confirmed the functionality of the putative causal variants our method points to. Finally, we retrospectively analyzed summary statistics from 67 large-scale GWAS for a variety of phenotypes. Results reveal the method's capacity to robustly discover additional loci for polygenic traits and pinpoint potential causal variants underpinning each locus beyond conventional GWAS pipeline, contributing to a deeper understanding of complex genetic architectures in post-GWAS analyses.

Metabolic syndrome including both elevated blood pressure and elevated fasting plasma glucose is associated with higher mortality risk: a prospective study

BACKGROUND

Metabolic syndrome (MetS) encompasses a collection of metabolic abnormalities. This study aims to determine which combination of MetS components has the highest mortality risk, and to investigate the causal relationships between MetS components and longevity.

METHODS

Prospective analyses were conducted on 340,196 participants from the MJ cohort at baseline, and 121,936 participants had follow-up MetS information. We defined MetS according to the NCEP ATP III criteria. The study's outcomes included mortality from cardiovascular disease (CVD), cancer, and all causes combined. We employed Cox proportional hazard models to calculate hazard ratios (HRs) and 95% confidence intervals. Multivariable Mendelian randomization (MVMR) was employed to infer causality using the genetic data of MetS components and longevity.

RESULTS

Elevated blood pressure (BP) was the initial split for all-cause mortality, cancer mortality, and CVD mortality. Participants with MetS, especially those with elevated BP and elevated fasting plasma glucose (FPG), had higher mortality risks than those with other types of MetS. In the MJ cohort, participants with elevated BP and FPG (BG-type MetS) had a 44% (HR = 1.44, 95% CI = 1.37-1.51), 73% (HR = 1.73, 95% CI = 1.62-1.84), and 34% (HR = 1.34, 95% CI = 1.27-1.42) increased risk of all-cause mortality, cancer mortality, and CVD mortality, respectively, compared with non-BG-type MetS (12%, 24%, 5%). The highest mortality rate and mortality risk were observed in participants with BG-type MetS at baseline and follow-up (mortality rate/1000 person years = 9.73, 95% CI = 8.81-10.74; HR = 1.52, 95% CI = 1.35-1.72). SBP and FPG increases that were genetically proxied to a 1-standard deviation higher level decreased the probabilities of living to the 90th percentile age by 41% (OR = 0.59, 95% CI = 0.40-0.86) and 32% (OR = 0.68, 95% CI = 0.48-0.98) in MVMR, respectively.

CONCLUSIONS

Individuals with BG-type MetS are at a higher risk of death than those with other types of MetS. Therefore, these individuals should be targeted to improve MetS outcomes.

2025 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

BACKGROUND

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2025 AHA Statistical Update is the product of a full year's worth of effort in 2024 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. This year's edition includes a continued focus on health equity across several key domains and enhanced global data that reflect improved methods and incorporation of ≈3000 new data sources since last year's Statistical Update.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Dual-functional probe for sensitive detection of MCF-7 cells and mendelian randomization analysis of MUC1 association with multiple cancers

The present study successfully developed a method based on a dual-functional probe for detecting breast cancer cells MCF-7 by recognizing the MUC1 protein on the cell surface. This method integrated inductively coupled plasma mass spectrometry (ICP-MS) and fluorescence imaging technology, enhancing the sensitivity, specificity, and accuracy of breast cancer cell detection. Additionally, through two-sample Mendelian randomization (MR) analysis, we verified a potential correlation between breast cancer and MUC1 (PIVW<0.05), while also proving no potential correlation between liver cancer and MUC1 (PIVW>0.05). Furthermore, this study explored the relationship between other cancers and MUC1, indicating a potential correlation between ovarian cancer and colorectal cancer with MUC1 (PIVW<0.05). In summary, this study provides new strategies for the early diagnosis and treatment of breast cancer and offers new insights into the potential of MUC1 as a biomarker for the detection of other cancers.

EHBP1 suppresses liver fibrosis in metabolic dysfunction-associated steatohepatitis

Excess cholesterol accumulation contributes to fibrogenesis in metabolic dysfunction-associated steatohepatitis (MASH), but how hepatic cholesterol metabolism becomes dysregulated in MASH is not completely understood. We show that human fibrotic MASH livers have decreased EH-domain-binding protein 1 (EHBP1), a genome-wide association study (GWAS) locus associated with low-density lipoprotein (LDL) cholesterol, and that EHBP1 loss- and gain-of-function increase and decrease MASH fibrosis in mice, respectively. Mechanistic studies reveal that EHBP1 promotes sortilin-mediated PCSK9 secretion, leading to LDL receptor (LDLR) degradation, decreased LDL uptake, and reduced TAZ, a fibrogenic effector. At a cellular level, EHBP1 deficiency affects the intracellular localization of retromer, a protein complex required for sortilin stabilization. Our therapeutic approach to stabilizing retromer is effective in mitigating MASH fibrosis. Moreover, we show that the tumor necrosis factor alpha (TNF-α)/peroxisome proliferator-activated receptor alpha (PPARα) pathway suppresses EHBP1 in MASH. These data not only provide mechanistic insights into the role of EHBP1 in cholesterol metabolism and MASH fibrosis but also elucidate an interplay between inflammation and EHBP1-mediated cholesterol metabolism.

Genetic association of circulating lipids and lipid-lowering drug targets with vascular calcification

BACKGROUND AND AIMS

Vascular calcification (VC) significantly increases the incidence and mortality of many diseases. The causal relationships of dyslipidaemia and lipid-lowering drug use with VC severity remain unclear. This study explores the genetic causal associations of different circulating lipids and lipid-lowering drug targets with coronary artery calcification (CAC) and abdominal aortic artery calcification (AAC).

METHODS

We obtained single-nucleotide polymorphisms (SNPs) and expression quantitative trait loci (eQTLs) associated with seven circulating lipids and 13 lipid-lowering drug targets from publicly available genome-wide association studies and eQTL databases. Causal associations were investigated by univariable, multivariable, drug-target, and summary data-based Mendelian randomization (MR) analyses. Potential mediation effects of metabolic risk factors were evaluated.

RESULTS

MR analysis revealed that genetic proxies for low-density lipoprotein cholesterol (LDL-C), triglycerides (TC) and Lipoprotein (a) (Lp(a)) were causally associated with CAC severity, and apolipoprotein B (apoB) level was causally associated with AAC severity. A significant association was detected between hepatic Lipoprotein(A) (LPA) gene expression and CAC severity. Colocalisation analysis supported the hypothesis that the association between LPA expression and CAC quantity is driven by different causal variant sites within the ±1 Mb flanking region of LPA. Serum calcium and phosphorus had causal associations with CAC severity.

CONCLUSIONS

Inhibitors targeting LPA might represent CAC drug candidates. Moreover, T2DM, hypercalcemia, and hyperphosphatemia are positively causally associated with CAC severity, while chronic kidney disease and estimated glomerular filtration rate are not.

Remnant cholesterol and risk of aortic aneurysm and dissection: a prospective cohort Study from the UK biobank study and mendelian randomization analysis

AIM

This study aimed to examine the relationships between remnant cholesterol (RC) and the risk of aortic aneurysm and dissection (AAD).

METHODS

This prospective cohort study included 368,139 European adults from the UK Biobank. Additionally, the causal relationship between RC and AAD was investigated using Mendelian randomization (MR) analyses.

RESULTS

During a median follow-up of 13.65 years, 1,634 cases of abdominal aortic aneurysm (AAA), 698 cases of thoracic aortic aneurysm (TAA), and 184 cases of aortic dissection (AD) were identified. Elevated RC levels were associated with an increased risk of AAA compared to the reference group ([highest vs. lowest RC levels]: adjusted hazard ratio (HR) = 1.65, 95% CI: 1.36-1.99). However, no significant association was observed between high RC levels and the risk of either TAA or AD. Two-sample MR analyses supported a significant causal effect of RC on AAA risk (odds ratio (OR) = 2.08, 95% CI: 1.70-2.56). The association between RC and AAA persisted after adjusting for the effects of RC-associated genetic variants on low-density lipoprotein cholesterol (LDL-C). In contrast, MR analyses did not indicate any causal associations between RC and TAA or AD.

CONCLUSIONS

Elevated RC was linked to a greater risk of developing AAA, with MR analyses confirming a causal relationship. These findings suggest that RC may function as a new biomarker for AAA and could be integral to strategies aimed at preventing AAA.

A study into rare GPR146 gene variants in humans and mice

BACKGROUND AND AIMS

G-protein coupled receptor 146 (GPR146)-deficient mice exhibit a moderate 21 % reduction in plasma cholesterol. This is associated with decreased phosphorylation of ERK1/2 and reduced SREBP2 activity in the liver, which leads to lower VLDL secretion. Insight into the role of GPR146 in humans is however limited. We therefore set out to study rare genetic variants in GPR146 to improve our understanding of this new player in lipid metabolism.

METHODS

We used whole genome sequencing data from UK Biobank participants to search for rare coding variants in GPR146. We first carried out gene-based burden tests (using SAIGE-GENE-framework) and examined the association of individual variants with plasma cholesterol levels. One of the variants (P62L) was also studied using the Global Lipids Genetics Consortium (GLGC) data set and in a knock-in mouse model.

RESULTS

We found that the combination of rare genetic variants identified in GPR146 is significantly associated with plasma cholesterol levels. Three rare variants, i.e. P62L, I129I, and A175T were individually associated with reduced plasma cholesterol. In the GLGC cohort, the P62L variant was associated with reductions in both HDL and LDL cholesterol. Follow-up experiments show lower plasma cholesterol levels in GPR146P61L male and female mice (-13 %, p < 0.05 and -15 %, p < 0.005, respectively) when compared to controls due to a reduction in HDL cholesterol. The GPR146P61L mice did not exhibit a change in VLDL secretion. In line, the ERK1/2 signalling pathway and Srebp2 mRNA expression in liver homogenates, and the secretion of apoB by primary hepatocytes of GPR146P61L and wild-type mice were unchanged.

CONCLUSIONS

This study shows that rare GPR146 gene variants are associated with lower plasma cholesterol levels in humans. One of these variants, P62L is associated with reductions of HDL cholesterol and LDL cholesterol in humans while the ortholog in mice confers a loss of GPR146 function leading to only reduced HDL cholesterol. How GPR146 affects HDL metabolism in humans and mice remains to be resolved.

Factorial Mendelian randomization of lipoprotein (a) lowering, low-density lipoprotein cholesterol lowering, and lifestyle improvements: joint associations with cardiovascular risk

BACKGROUND

High levels of lipoprotein(a) [Lp(a)] have been associated with an increased risk of cardiovascular disease (CVD); however, the effects of Lp(a)-lowering therapy in combination with low-density lipoprotein cholesterol (LDL-C)-lowering treatment or lifestyle improvements on CVD risk remain unexplored.

METHODS

We conducted a factorial Mendelian randomization study among 385 917 participants in the UK Biobank. Separate genetic scores were constructed to proxy the effects of Lp(a) lowering, LDL-C lowering through different targets [HMG-CoA reductase, NPC1-like intracellular cholesterol transporter 1, proprotein convertase subtilisin/kexin Type 9, and low-density lipoprotein receptor (LDLR)], as well as improvements in body mass index (BMI), systolic blood pressure (SBP), and lifestyle factors (cigarette smoking, alcohol consumption, and physical activity).

RESULTS

Genetically predicted lower Lp(a) levels were associated with a decreased risk of CVD and CVD-specific mortality. Per 50-mg/dl, the hazard ratio ranged from 0.73 [95% confidence interval (CI): 0.73, 0.73] for peripheral artery disease (PAD) to 0.95 (95% CI: 0.92, 0.99) for venous thromboembolism. In factorial analyses exploring combined exposure to low-level Lp(a) and low-level LDL-C, there was no consistent evidence for departure from an additive model for any outcome (Pinteraction > .05), with the exception of the analysis using the LDLR score and PAD (Pinteraction = .006). In factorial analyses exploring combination therapies integrating Lp(a) lowering with interventions on BMI, SBP, and lifestyle factors, there was no evidence for departure from an additive model in any analysis (Pinteraction > .05).

CONCLUSIONS

Our study suggests that Lp(a) lowering will have a similar magnitude for reducing cardiovascular events whether it is considered alone, or in conjunction with LDL-C reduction or lifestyle improvements.

Identification of tacrolimus-related genes in familial combined hyperlipidemia and development of a diagnostic model using bioinformatics analysis

Background

Clinical observations have revealed that patients undergoing organ transplantation administered tacrolimus often experience abnormal lipid metabolism with serious consequences. Thus, the intricate interplay between tacrolimus and lipid metabolism must be addressed to develop targeted therapeutic interventions. Our ongoing research aims to develop precision medicine approaches that not only alleviate the immediate repercussions for organ transplant patients but also enhance their long-term outcomes. To this end, we investigated the potential genes associated with tacrolimus metabolism in familial combined hyperlipidemia (FCHL) to identify relevant biomarkers of FCHL, develop predictive diagnostic models for hyperlipidemia, and reveal potential therapeutic targets for FCHL.

Methods

Dataset GSE1010 containing information on patients diagnosed with FCHL was obtained from the Gene Expression Omnibus (GEO), and an ensemble of tacrolimus-related genes (TRGs) was retrieved from the GeneCards, STITCH, and Molecular Signatures Database databases. A thorough weighted gene co-expression network analysis was conducted, including a differential expression analysis of the GSE1010 and TRG datasets, to identify intricate patterns of gene co-expression and provide insights on the underlying molecular dynamics within the datasets. Key genes were screened, diagnostic models were constructed, and all genes associated with logFC values were assessed using gene set variation and enrichment analyses. Upregulated genes were identified by a positive logFC (>0) and P < 0.05, while downregulated genes were characterized by a negative logFC (<0) and P < 0.05. These criteria facilitated a more nuanced categorization of gene expression changes within the analyzed datasets. Given tacrolimus's immunosuppressive impact, the gene expression matrix data obtained from dataset GSE1010 was submitted to CIBERSORT to assess immune cell infiltration outcomes. Finally, we examined the regulatory network of screened key genes that interact with RNA-binding proteins, potential drugs, small-molecule compounds, and transcription factors.

Results

We screened 14 statistically significant key genes, built a reliable risk model, and grouped the dataset into categories at high and low risk for hyperlipidemia development. FCHL was linked to memory B and immature B immune cells. The gene set variation analysis revealed two pathways associated with cholesterol homeostasis and the complement system that were closely associated with the potential functions of FCHL and tacrolimus-related differentially expressed genes.

Conclusions

Our research offers a better understanding of FCHL and the TRGs involved in lipid metabolism. Additionally, it provides research directions for identifying potential targets for clinical therapies.

A Comprehensive Review of the Genetics of Dyslipidemias and Risk of Atherosclerotic Cardiovascular Disease

Dyslipidemias are often diagnosed based on an individual's lipid panel that may or may not include Lp(a) or apoB. But these values alone omit key information that can underestimate risk and misdiagnose disease, which leads to imprecise medical therapies that reduce efficacy with unnecessary adverse events. For example, knowing whether an individual's dyslipidemia is monogenic can granularly inform risk and create opportunities for precision therapeutics. This review explores the canonical and non-canonical causes of dyslipidemias and how they impact atherosclerotic cardiovascular disease (ASCVD) risk. This review emphasizes the multitude of genetic causes that cause primary hypercholesterolemia, hypertriglyceridemia, and low or elevated high-density lipoprotein (HDL)-cholesterol levels. Within each of these sections, this review will explore the evidence linking these genetic conditions with ASCVD risk. Where applicable, this review will summarize approved therapies for a particular genetic condition.

Insights Into Causal Effects of Genetically Proxied Lipids and Lipid-Modifying Drug Targets on Cardiometabolic Diseases

BACKGROUND

The differential impact of serum lipids and their targets for lipid modification on cardiometabolic disease risk is debated. This study used Mendelian randomization to investigate the causal relationships and underlying mechanisms.

METHODS

Genetic variants related to lipid profiles and targets for lipid modification were sourced from the Global Lipids Genetics Consortium. Summary data for 10 cardiometabolic diseases were compiled from both discovery and replication data sets. Expression quantitative trait loci data from relevant tissues were employed to evaluate significant lipid-modifying drug targets. Comprehensive analyses including colocalization, mediation, and bioinformatics were conducted to validate the results and investigate potential mediators and mechanisms.

RESULTS

Significant causal associations were identified between lipids, lipid-modifying drug targets, and various cardiometabolic diseases. Notably, genetic enhancement of LPL (lipoprotein lipase) was linked to reduced risks of myocardial infarction (odds ratio [OR]1, 0.65 [95% CI, 0.57-0.75], P1=2.60×10-9; OR2, 0.59 [95% CI, 0.49-0.72], P2=1.52×10-7), ischemic heart disease (OR1, 0.968 [95% CI, 0.962-0.975], P1=5.50×10-23; OR2, 0.64 [95% CI, 0.55-0.73], P2=1.72×10-10), and coronary heart disease (OR1, 0.980 [95% CI, 0.975-0.985], P1=3.63×10-14; OR2, 0.64 [95% CI, 0.54-0.75], P2=6.62×10-8) across 2 data sets. Moreover, significant Mendelian randomization and strong colocalization associations for the expression of LPL in blood and subcutaneous adipose tissue were linked with myocardial infarction (OR, 0.918 [95% CI, 0.872-0.967], P=1.24×10-3; PP.H4, 0.99) and coronary heart disease (OR, 0.991 [95% CI, 0.983-0.999], P=0.041; PP.H4=0.92). Glucose levels and blood pressure were identified as mediators in the total effect of LPL on cardiometabolic outcomes.

CONCLUSIONS

The study substantiates the causal role of lipids in specific cardiometabolic diseases, highlighting LPL as a potent drug target. The effects of LPL are suggested to be influenced by changes in glucose and blood pressure, providing insights into its mechanism of action.

Genetically mimicked effects of evinacumab on psoriasis: a drug target Mendelian randomization study

BACKGROUND AND OBJECTIVES

Dyslipidemia has been reported to contribute to the psoriasis pathogenesis. Thus, evinacumab, a novel lipid-lowering drug targeting angiopoietin-like 3, may have therapeutic potential to treat and/or manage psoriasis.

METHODS AND STUDY DESIGN

Summary statistics were obtained from genome-wide association studies addressing psoriasis (FinnGen Consortium; n=216,752) and serum lipid concentrations (United Kingdom Biobank; n=403,943-440,546). Two-sample Mendelian randomization analyses were conducted to evaluate the associations of serum lipid concentrations and genetically mimicked effects of evinacumab, respectively, with the risks of psoriasis and its subtypes.

RESULTS

Genetically determined per standard deviation increase in triglyceride concentrations was associated with increased risk of psoriasis (OR: 1.17, 95% CI: 1.03-1.32, p=0.018), whereas that in low-density lipoprotein-cholesterol (LDL-C) was associated with both psoriasis (OR: 1.22, 95% CI: 1.05-1.43, p=0.011) and its subtypes, including arthropathic psoriasis (OR: 1.30, 95% CI: 1.02-1.65, p=0.032), psoriasis vulgaris (OR: 1.87, 95% CI: 1.16-2.99, p=0.0095), and guttate psoriasis (OR: 2.19, 95% CI: 1.17-4.07, p=0.014). Moreover, genetically mimicked effects of evinacumab, via angiopoietin-like 3 inhibition, significantly reduced the risk of psoriasis (OR: 0.752 per standard deviation reduction in triglycerides, 95% CI: 0.577-0.982, p=0.036) and arthropathic psoriasis (OR: 0.266 per standard deviation reduction in LDL-C, 95% CI: 0.0886-0.799, p=0.018).

CONCLUSIONS

The genetically mimicked effect of evinacumab has the potential to reduce the risk of psoriasis and arthropathic psoriasis by lowering circulating triglyceride and LDL-C concentrations, respectively. These findings suggest that evinacumab may help prevent psoriasis and psoriatic arthritis progression in clinical practice.

Sphingoid Base Diversity

Sphingolipids (SL) are crucial components of cellular membranes and play pivotal roles in various biological processes, including cell growth, differentiation, apoptosis, and stress responses. All SL contain a sphingoid base (SPB) backbone which is the shared and class-defining element. SPBs are heterogeneous in length and structure. This review summarizes our current understanding on minor SPBs and the role of the serine palmitoyltransferase (SPT) in particular of its subunits SPTLC3 and SPTSSA/B in forming a spectrum of structurally and metabolically distinct SPBs. Some minor SPBs, such as 1-deoxysphingolipids (1-deoxySL) are neurotoxic and associated with neurological disorders such as hereditary sensory neuropathy type 1 (HSAN1) and diabetic neuropathy. Furthermore, the review discusses the pathological implications of atypical SPBs in cardiometabolic conditions such as obesity, type 2 diabetes or cardiomyopathy, where the induction of the SPTLC3 subunit alters the SPB profile and contributes to disease progression. Understanding these, often neglected aspects of the sphingolipid metabolism provides potential therapeutic targets for metabolic and neurodegenerative diseases, emphasizing the need for continued research in this area.

Genetic association of lipid-lowering drug target genes with pancreatic cancer: a Mendelian randomization study

Previous studies have found that dyslipidemia is a risk factor for pancreatic cancer (PC), and that lipid-lowering drugs may reduce the risk of PC. However, it is not clear whether dyslipidemia causes PC. The Mendelian randomization (MR) study aimed to investigate the causal role of lipid traits in pancreatic cancer and to assess the potential impact of lipid-lowering drug targets on pancreatic cancer. Genetic variants associated with lipid traits and variants of genes encoding lipid-lowering drug targets were extracted from the Global Lipids Genetics Consortium genome-wide association study (GWAS). Summary statistics for PC were obtained from an independent GWAS datasets. Colocalization analyses were performed to validate the robustness of the results. No significant effect of lipid-lowering drug targets on PC risk was found. Genetic mimicry of lipoprotein lipase (LPL) was potentially associated with PC risks. Significant MR associations were observed in the discovery dataset (OR 1.64 [95% CI 1.24-2.16], p = 4.48*10-4) with PC in one dataset. However, the finding was not verified in the replication dataset. Our findings do not support dyslipidemia as a causal factor for PC. Among lipid-lowering drug targets, LPL is the potential drug target in PC.

An interracial Mendelian analysis revealed a link between lipid-lowering drugs and renal failure

Lipid-lowering drugs have been used in clinics widely. It is unclear whether the drugs have an effect on renal failure. We chose high-density lipoprotein cholesterol (ieu-b-109), low-density lipoprotein cholesterol (ieu-a-300), triglyceride (ieu-b-111), and total cholesterol (ebi-a-GCST90038690) as exposures. SNPs near drug genes served as instrumental variables. Acute renal failure (ARF) and chronic renal failure (CRF) in Europeans from the GWAS catalog were selected as outcomes. Datasets on renal failure in East Asians and South Asians were used for validation. Inverse variance weighted (IVW) was the primary method for drug-targeted Mendelian randomization. In the Europeans, people who used PPARG reduced ARF risk by 69.3% (OR: 0.307, 95% CI: 0.171-0.553, p = 0.015). NPC1L1 inhibitors increased ARF risk by 2.684 times (OR: 2.684, 95% CI: 2.027-3.341, p = 0.003). APOE increased ARF risk by 1.987 times (OR: 1.987, 95% CI: 1.062-3.716, p = 0.032) but decreased CRF risk by 49.7% (OR: 0.503, 95% CI: 0.283-0.894, p = 0.019). TNFSF12 increased CRF risk by 3.866 times (OR: 3.866, 95% CI: 1.174-12.729, p = 0.026). In the East Asians, PPARG reduced CRF risk by 85.8% (OR: 0.142, 95% CI: 0.054-0.371, p < 0.001). And in the South Asians, APOE decreased ARF risk by 99.8% (OR: 0.002, 95% CI: 2.12e-05-0.179, p = 0.007). We revealed that PPARG could reduce the risk of renal failure in Europeans and Asians. APOE could cause ARF in the Europeans, but it was protective in the South Asians. Clinicians need to consider the characteristics of the local population before administering drugs to patients of different ethnicities.

Glo1 reduction in mice results in age- and sex-dependent metabolic dysfunction

Objectives

Advanced glycation end products (AGEs) have been implicated as an important mediator of metabolic disorders including obesity, insulin resistance, and coronary artery disease. Glyoxalase 1 (Glo1) is a critical enzyme in the clearance of toxic dicarbonyl such as methylglyoxal, precursors of AGEs. The role of AGE-independent mechanisms that underly Glo1-induced metabolic disorders have yet to be elucidated.

Methods

We performed a longitudinal study of female and male Glo1 heterozygous knockdown (Glo1 +/- ) mice with ~50% gene expression and screened metabolic phenotypes such as body weight, adiposity, glycemic control and plasma lipids. We also evaluated atherosclerotic burden, AGE levels, and gene expression profiles across cardiometabolic tissues (liver, adipose, muscle, kidney and aorta) to identify pathway perturbations and potential regulatory genes of Glo1 actions.

Results

Partial loss of Glo1 resulted in obesity, hyperglycemia, dyslipidemia, and alterations in lipid metabolism in metabolic tissues in an age- and sex-dependent manner. Glo1 +/- females displayed altered glycemic control and increased plasma triglycerides, which aligned with significant perturbations in genes involved in adipogenesis, PPARg, insulin signaling, and fatty acid metabolism pathways in liver and adipose tissues. Conversely, Glo1 +/- males developed increased skeletal muscle mass and visceral adipose depots along with changes in lipid metabolism pathways. For both cohorts, most phenotypes manifested after 14 weeks of age. Evaluation of methylglyoxal-derived AGEs demonstrated changes in only male skeletal muscle but not in female tissues, which cannot explain the broad metabolic changes observed in Glo1 +/- mice. Transcriptional profiles suggest that altered glucose and lipid metabolism may be partially explained by alternative detoxification of methylglyoxal to metabolites such as pyruvate. Moreover, transcription factor (TF) analysis of the tissue-specific gene expression data identified TFs involved in cardiometabolic diseases such as Hnf4a (all tissues) and Arntl (aorta, liver, and kidney) which are female-biased regulators and whose targets are altered in response to Glo1 +/- .

Conclusions

Our results indicate that Glo1 reduction perturbs metabolic health and metabolic pathways in a sex- and age-dependent manner without significant changes in AGEs across metabolic tissues. Rather, tissue-specific gene expression analysis suggests that key transcription factors such as Hfn4a and Arntl as well as metabolite changes from alternative methylglyoxal detoxification such as pyruvate, likely contribute to metabolic dysregulation in Glo1 +/- mice.

Associations between lipid-lowering drugs and urate and gout outcomes: a Mendelian randomization study

Objective

Gout is a common inflammatory arthritis and lipid metabolism plays a crucial role in urate and gout. Potential associations between urate and gout and lipid-lowering drugs have been revealed in observational studies. However, the effects of lipid-lowering drugs on urate and gout remain controversial. The aim of this study was to investigate the genetic association between lipid-lowering drugs and urate and gout.

Methods

In this study, two genetic proxies were employed to approximate lipid-lowering drug exposure: expression quantitative trait loci (eQTL) associated with drug-target genes and genetic variations proximal to or within genes targeted by these drugs, which are linked to low-density lipoprotein cholesterol (LDL-C) levels. The study's exposures encompassed genetic variants within drug target genes (HMGCR, PCSK9, NPC1L1), each representing distinct lipid-lowering mechanisms. Causal effects were estimated using the inverse variance weighting (IVW) method, while the Summary Data-based Mendelian Randomization (SMR) method, leveraging pooled data, was applied to compute effect estimates. These estimates were further refined through various approaches including MR-Egger, the weighted median method, simple and weighted models, and leave-one-out analyses to conduct sensitivity analyses.

Result

The analytical outcomes utilizing the IVW method indicated that inhibitors of HMGCR were correlated with an elevated risk of developing gout (IVW: OR [95%CI] = 1.25 [1.03, 1.46], p=0.0436), while PCSK9 inhibitors were linked to heightened levels of urate (IVW: OR [95%CI] = 1.06 [1.01,1.10], p=0.0167). Conversely, no significant correlation between NPC1L1 inhibitors and the levels of urate or the risk of gout was established. Furthermore, the SMR analysis failed to identify significant associations between the expression levels of the HMGCR, PCSK9, and NPC1L1 genes and the risk of gout or elevated urate levels (SMR method: all P values >0.05). Sensitivity analyses further confirmed the robustness of these results, with no significant heterogeneity or pleiotropy found.

Conclusion

This study furnishes novel causal evidence supporting the potential genetic correlation between the use of lipid-lowering drugs and the incidence of gout as well as urate levels. The findings indicate that inhibitors targeting HMGCR may elevate the risk associated with the development of gout, while inhibitors targeting PCSK9 are likely to increase urate concentrations.

Association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio and serum thyroid function measures: Recent Findings from NHANES 2007-2012 and Mendelian randomization

Objective

There is limited epidemiological data regarding the association of blood lipids with thyroid hormones. Thus, the present article aims to explore whether there is an association between non-high-density to high-density lipoprotein cholesterol ratio (NHHR) and thyroid hormones.

Methods

We analyzed samples from 3,881 adults aged 20 years and above who took part in the National Health and Nutrition Examination Survey (NHANES) spanning 2007 to 2012. The study tested for thyroid hormones, including total triiodothyronine (TT3), free triiodothyronine (FT3), total thyroxine (TT4), free thyroxine (FT4), as well as thyroid-stimulating hormone (TSH). Survey-weighted linear regression and restricted cubic spline (RCS) models were employed to investigate the relationship between NHHR and thyroid hormones. Subsequently, subgroup analyses were conducted. In Mendelian randomization (MR), the inverse variance weighting method (IVW) is used as the primary analytical approach.

Results

This study finally comprised 3,881 adults aged 20 years and older. After extensive adjustments for covariables, the regression analysis revealed significant negative associations between NHHR and FT4 (β: -0.11, 95% confidence interval [CI]: -0.18, -0.04), FT4/FT3 (β: -0.06, 95% CI: -0.08, -0.04), and TT4/TT3 (β: -0.001, 95% CI: -0.001, 0.000). Both observational and Mendelian randomization studies suggest that high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and total cholesterol may not significantly influence the risk of hyperthyroidism or hypothyroidism.

Conclusions

The study indicates negative associations between NHHR and FT4, as well as the ratios of FT4/FT3 and TT4/TT3. This suggests that NHHR may reflect changes in thyroid function, highlighting its potential clinical significance in assessing thyroid function and metabolic health.

Lipoprotein(a) in familial dyslipidemias: The effect on cardiovascular prognosis in patients with familial hypercholesterolemia or familial combined hyperlipidemia

BACKGROUND AND AIMS

Familial dyslipidemias are associated with increased cardiovascular risk. Increased lipoprotein(a) [Lp(a)] is considered as the most prevalent monogenic lipid disorder. The objective of the study was to identify the cardiovascular prognosis of patients with familial dyslipidemias (heterozygous familial hypercholesterolemia (FH) or familial combined hyperlipidemia (FCH)), without cardiovascular disease at baseline, investigating in parallel the effect of Lp(a).

METHODS AND RESULTS

909 patients with FH (n = 433, mean age 44.2 ± 12.8 years) or FCH (n = 476, mean age 49.0 ± 11.1 years) were evaluated during a mean period of 10 years. The main endpoint was the composite of major cardiovascular events. The incidence of major cardiovascular events in the total population was 6.6 %, while greater in patients with FH compared to patients with FCH (8.1 % vs 5.5 %, p = 0.03). Multiple Cox regression analysis revealed that FH patients had greater cardiovascular risk compared to FCH patients (HR 2.17, 95 % CI 1.10-4.26, p = 0.02). In FH patients, increased baseline Lp(a) (≥30 mg/dl) was an independent predictor of adverse cardiovascular events (HR 2.37 95 % CI 1.41-4.90, p = 0.02), whereas in FCH patients was not. In FCH patients the presence of diabetes at baseline was a strong independent prognosticator of adverse cardiovascular events (HR 3.56 95 % CI 1.19-11.33, p = 0.03), after adjustment for confounders.

CONCLUSIONS

FH patients demonstrate double cardiovascular risk compared to FCH patients. In FH patients increased Lp(a) doubles the cardiovascular risk, beyond low density lipoprotein cholesterol. In FCH patients the presence of diabetes triples the cardiovascular risk, beyond Lp(a) which does not seem to convey an independent prognostic value.

The causal association between lipid-lowering strategies and risk of intracranial aneurysms: A drug-target Mendelian randomization study

BACKGROUND

Observational studies have suggested potential correlations between unfavorable lipid profiles and the occurrence of intracranial aneurysms (IAs), proposing that lipid-lowering therapies might curb IA progression and prevent rupture. This study aimed to explore the causal impacts of lipid-reducing strategies on the risk of IAs.

METHODS

We employed 3 genetic tools as proxies for our exposures and assessed causal effects using outcome genome-wide association study data from the FinnGen Biobank. Single nucleotide polymorphisms strongly associated with low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol, and triglycerides, located within ±100 kb of the region of target genes, were selected as instrumental variables for drug-target Mendelian randomization (MR). Additionally, gene expression and protein MR analyses were conducted to elucidate the causal effects of lipid levels from transcriptional and translational perspectives, using two-sample MR (TSMR) and summary-data-based MR (SMR).

RESULTS

Drug-target MR analysis revealed that proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibition-mediated LDL-C reduction was associated with an increased risk of IA development (OR = 1.406, P = 3.28E-09). In contrast, protein MR demonstrated that higher PCSK9 expression had protective effects against IA incidence (ORTSMR = 0.896, P = 1.79E-03; ORSMR = 0.881, P = 1.78E-02). Subgroup analyses further suggested that PCSK9 might reduce the risk of IA rupture (ORTSMR = 0.893, P = 1.08E-02; ORSMR = 0.866, P = 3.39E-02).

CONCLUSION

Our MR analyses indicated a potential causal relationship between higher PCSK9 expression and a reduced risk of both IA formation and rupture, highlighting the dual role of PCSK9 inhibitors in cerebrovascular disease. Hence, careful consideration is warranted when prescribing PCSK9 inhibitors, particularly in patients at risk for developing IAs.

Genetic Links Between Metabolic Syndrome and Osteoarthritis: Insights From Cross-Trait Analysis

CONTEXT

Previous observational studies have indicated a bidirectional association between metabolic syndrome (MetS) and osteoarthritis (OA). However, it remains unclear whether these bidirectional associations reflect causal relationships or shared genetic factors, and the underlying biological mechanisms of this association are not fully understood.

OBJECTIVE

We aimed to explore the genetic connection between MetS and OA using genome-wide association study (GWAS) summary data.

METHODS

Leveraging summary statistics from GWAS conducted by the UK Biobank and the Glucose and Insulin-related Traits Consortium (MAGIC), we performed global genetic correlation analyses, genome-wide cross-trait meta-analyses, and a bidirectional two-sample Mendelian randomization analyses using summary statistics from GWAS to comprehensively assess the relationship of MetS and OA.

RESULTS

We first detected an extensive genetic correlation between MetS and OA (rg = 0.393, P = 1.52 × 10-18), which was consistent in 4 MetS components, including waist circumference, triglycerides, hypertension, and high-density lipoprotein cholesterol and OA with rg ranging from -0.229 to 0.490. We then discovered 32 variants jointly associated with MetS and OA through Multi-Trait Analysis of GWAS (MTAG). Co-localization analysis found 12 genes shared between MetS and OA, with functional implications in several biological pathways. Finally, Mendelian randomization analysis suggested genetic liability to MetS significantly increased the risk of OA, but no reverse causality was found.

CONCLUSION

Our results illustrate a common genetic architecture, pleiotropic loci, as well as causality between MetS and OA, potentially enhancing our knowledge of high comorbidity and genetic processes that overlap between the 2 disorders.

Missing Regulation Between Genetic Association and Transcriptional Abundance for Hypercholesterolemia Genes

Background: Low-density lipoprotein cholesterol (LDL-C) is a well-established risk factor for cardiovascular disease, and it plays a causal role in the development of atherosclerosis. Genome-wide association studies (GWASs) have successfully identified hundreds of genetic variants associated with LDL-C. Most of these risk loci fall in non-coding regions of the genome, and it is unclear how these non-coding variants affect circulating lipid levels. One hypothesis is that genetically mediated variation in transcript abundance, detected via the analysis of expressed quantitative trait loci (eQTLs), is key to the biologic function of causal variants. Here, we investigate the hypothesis that non-coding GWAS risk variants affect the homeostatic expression of a nearby putatively causal gene for serum LDL-C levels. Methods: We establish a set of twenty-one expert-curated and validated genes implicated in hypercholesterolemia via dose-dependent pharmacologic modulation in human adults, for which the relevant tissue type has been established. We show that the expression of these LDL-C genes is impacted by eQTLs in relevant tissues and that there are significant genomic-risk loci in LDL-GWAS near these causal genes. We evaluate, using statistical colocalization, whether a single variant or set of variants in each genetic locus is responsible for the GWAS and eQTL signals. Results: Genome-wide association study results for serum LDL-C levels demonstrate that the 402 identified genomic-risk loci for LDL-C are highly enriched for known causal genes for LDL-C (OR 527, 95% CI 126-5376, p < 2.2 × 10-16). However, we find limited evidence for colocalization between GWAS signals near validated hypercholesterolemia genes and eQTLs in relevant tissues (colocalization rate of 26% at a locus-level colocalization probability > 50%). Conclusions: Our results highlight the complexity of genetic regulatory effects for causal hypercholesterolemia genes; we suggest that context-responsive eQTLs may explain the effects of non-coding GWAS hits that do not overlap with standard eQTLs.