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Background: Lipoprotein(a) (Lp(a)) is now established as a causal risk factor for cardiovascular disease (CVD) and accurate laboratory measurement is of pivotal importance in reducing Lp(a) associated risk. The consensus statement by HEART UK in 2019 included recommendations to improve standardisation of clinical laboratory measurement and reporting of Lp(a). Methods: A 16 question, electronic audit survey was circulated to 190 accredited clinical biochemistry laboratories to assess the adoption of these recommendations in the UK. Results: Responses were received from 65 of 190 laboratories (34%). Only 5 (8%) did not offer Lp(a) measurement. Of those providing the test, 23% (n = 14) offered an in-house service (IHS), the remaining laboratories (77%; n = 46) used an external referral service (ERS). The majority (10 of 14 or 71%) of IHS laboratories responded with details of their method, stating whether it minimised sensitivity to the effect of Lp(a) isoform size and used calibrators certified for traceability to the WHO/IFCC reference material, however, only a minority ERS laboratories (13 of the 46 or 28%) were able to specify the method used by their referral laboratory. Of the laboratories who specified their reporting units, 6 of 10 IHS and 7 of 23 ERS laboratories reported in nmol/L. Among the 60 laboratories who responded, the HEART UK recommendations appear to have been adopted in full by only 3 IHS laboratories. Conclusions: Further efforts are needed to standardise the measurement and reporting of Lp(a) so that results and interpretation are comparable across clinical biochemistry laboratories in the UK. [ABSTRACT FROM AUTHOR]

Purpose: Coffee intake and apolipoprotein B levels have been linked to gastric, colorectal, and esophageal cancers in numerous recent studies. However, whether these associations are all causal remains unestablished. This study aimed to assess the potential causal associations of apolipoprotein B and coffee intake with the risk of gastric, colorectal, and esophageal cancers using Mendelian randomization analysis. Methods: In this study, we utilized a two-sample Mendelian randomization analysis to access the causal effects of coffee intake and apolipoprotein B on gastric, colorectal, and esophageal cancers. The summary statistics of coffee intake (n = 428,860) and apolipoprotein B (n = 439,214) were obtained from the UK Biobank. In addition, the summary statistics of gastric cancer, colorectal cancer, and esophageal cancer were obtained from the FinnGen biobank (n = 218,792). Inverse variance weighted, MR–Egger, weighted median, and weighted mode were applied to examine the causal relationship between coffee intake, apolipoprotein B and gastric, colorectal, and esophageal cancers. MR–Egger intercept test, Cochran's Q test, and leave-one-out analysis were performed to evaluate possible heterogeneity and pleiotropy. Steiger filtering and bidirectional mendelian randomization analysis were performed to evaluate the possible reverse causality. Results: The result of the inverse variance weighted method indicated that apolipoprotein B levels were significantly associated with a higher risk of gastric cancer (OR = 1.392, 95% CI 1.027–1.889, P = 0.0333) and colorectal cancer (OR = 1.188, 95% CI 1.001–1.411, P = 0.0491). Furthermore, multivariable Mendelian randomization analysis also revealed a positive association between apolipoprotein B levels and colorectal cancer risk, but the effect of apolipoprotein B on gastric cancer risk disappeared after adjustment of coffee intake, body mass index or lipid-related traits. However, we did not discover any conclusive evidence linking coffee intake to gastric, colorectal, or esophageal cancers. Conclusions: This study suggested a causal association between genetically increased apolipoprotein B levels and higher risk of colorectal cancer. No causal relationship was observed between coffee intake and gastric, colorectal, or esophageal cancers. [ABSTRACT FROM AUTHOR]

Background: This study aims to comprehensively investigate the phenotypic and genetic relationships between four common lipids (high-density lipoprotein cholesterol, HDL-C; low-density lipoprotein cholesterol, LDL-C; total cholesterol, TC; and triglycerides, TG), chronic kidney disease (CKD), and estimated glomerular filtration rate (eGFR). Methods: We first investigated the observational association of lipids (exposures) with CKD (primary outcome) and eGFR (secondary outcome) using data from UK Biobank. We then explored the genetic relationship using summary statistics from the largest genome-wide association study of four lipids (N = 1,320,016), CKD (Ncase = 41,395, Ncontrol = 439,303), and eGFR(N = 567,460). Results: There were significant phenotypic associations (HDL-C: hazard ratio (HR) = 0.76, 95%CI = 0.60–0.95; TG: HR = 1.08, 95%CI = 1.02–1.13) and global genetic correlations (HDL-C: r g = − 0.132, P = 1.00 × 10–4; TG: r g = 0.176; P = 2.66 × 10–5) between HDL-C, TG, and CKD risk. Partitioning the whole genome into 2353 LD-independent regions, twelve significant regions were observed for four lipids and CKD. The shared genetic basis was largely explained by 29 pleiotropic loci and 36 shared gene-tissue pairs. Mendelian randomization revealed an independent causal relationship of genetically predicted HDL-C (odds ratio = 0.91, 95%CI = 0.85–0.98), but not for LDL-C, TC, or TG, with the risk of CKD. Regarding eGFR, a similar pattern of correlation and pleiotropy was observed. Conclusions: Our work demonstrates a putative causal role of HDL-C in CKD and a significant biological pleiotropy underlying lipids and CKD in populations of European ancestry. Management of low HDL-C levels could potentially benefit in reducing the long-term risk of CKD. [ABSTRACT FROM AUTHOR]

Objective: To prospectively investigate population‐based metabolomics for incident gout and reproduce the findings for recurrent flares, accounting for serum urate. Methods: We conducted a prediagnostic metabolome‐wide analysis among 105,615 UK Biobank participants with nuclear magnetic resonance metabolomic profiling data (168 total metabolites) from baseline blood samples collected 2006–2010 in those without history of gout. We calculated hazard ratios (HRs) for incident gout, adjusted for gout risk factors, excluding and including serum urate levels, overall and according to fasting duration before sample collection. Potential causal effects were tested with 2‐sample Mendelian randomization. Poisson regression was used to calculate rate ratios (RRs) for the association with recurrent flares among incident gout cases. Results: Correcting for multiple testing, 88 metabolites were associated with risk of incident gout (N = 1,303 cases) before serum urate adjustment, including glutamine and glycine (inversely), and lipids, branched‐chain amino acids, and most prominently, glycoprotein acetyls (GlycA; P = 9.17 × 10−32). Only GlycA remained associated with incident gout following urate adjustment (HR 1.52 [95% confidence interval (95% CI) 1.22–1.88] between extreme quintiles); the HR increased progressively with fasting duration before sample collection, reaching 4.01 (95% CI 1.36–11.82) for ≥8 hours of fasting. Corresponding HRs per SD change in GlycA levels were 1.10 (95% CI 1.04–1.17) overall and 1.54 (95% CI 1.21–1.96) for ≥8 hours of fasting. GlycA levels were also associated with recurrent gout flares among incident gout cases (RR 1.90 [95% CI 1.27–2.85] between extreme quintiles) with larger associations with fasting. Mendelian randomization corroborated a potential causal role for GlycA on gout risk. Conclusion: This prospective, population‐based study implicates GlycA, a stable long‐term biomarker reflecting neutrophil overactivity, in incident and recurrent gout flares (central manifestation from neutrophilic synovitis) beyond serum urate. [ABSTRACT FROM AUTHOR]

Background: Statin use for cancer prevention has raised wide attention but the conclusions are still controversial. Whether statins use have exact causal effects on cancer prevention remains unclear. Methods: Based on the Genome-Wide Association Studies (GWAS) datasets from the large prospective UK Biobank and other consortium databases, two-sample mendelian randomization (MR) analysis was conducted to explore the causal effects of statins use on varied site-specific cancer risks. Five MR methods were applied to investigate the causality. The stability, heterogeneity, and pleiotropy of MR results were also evaluated. Results: The atorvastatin use could increase the risk of colorectal cancer (odd ratio (OR) = 1.041, p = 0.035 by fixed-effects inverse variance weighted (IVW) method (IVWFE), OR = 1.086, p = 0.005 by weighted median; OR = 1.101, p = 0.048 by weighted mode, respectively). According to the weighted median and weighted mode, atorvastatin could modestly decrease the risk of liver cell cancer (OR = 0.989, p = 0.049, and OR = 0.984, p = 0.004, respectively) and head and neck cancer (OR = 0.972, p = 0.020). Besides, rosuvastatin use could reduce the bile duct cancer risk by 5.2% via IVWEF method (OR = 0.948, p = 0.031). No significant causality was determined in simvastatin use and pan-cancers via the IVWFE or multiplicative random-effects IVW (IVWMRE) method if applicable (p > 0.05). There was no horizontal pleiotropy observed in the MR analysis and the leave-one-out analysis proved the stability of the results. Conclusion: The causalities between statin use and cancer risk were only observed in colorectal cancer and bile duct cancer in the European ancestry population. Future works are warranted to provide more robust evidence for supporting statin repurposing for cancer prevention. [ABSTRACT FROM AUTHOR]

Background: Observational studies have shown inconsistent findings of the relationships between aspirin use and the risk of Alzheimer's disease (AD). Objective: Since residual confounding and reverse causality were challenging issues inherent in observational studies, we conducted a 2-sample Mendelian randomization analysis (MR) to investigate whether aspirin use was causally associated with the risk of AD. Methods: We conducted 2-sample MR analyses utilizing summary genetic association statistics to estimate the potential causal relationship between aspirin use and AD. Single-nucleotide variants associated with aspirin use in a genome-wide association study (GWAS) of UK Biobank were considered as genetic proxies for aspirin use. The GWAS summary-level data of AD were derived from a meta-analysis of GWAS data from the International Genomics of Alzheimer's Project (IGAP) stage I. Results: Univariable MR analysis based on these two large GWAS data sources showed that genetically proxied aspirin use was associated with a decreased risk of AD (Odds Ratio (OR): 0.87; 95%CI: 0.77–0.99). In multivariate MR analyses, the causal estimates remained significant after adjusting for chronic pain, inflammation, heart failure (OR = 0.88, 95%CI = 0.78–0.98), or stroke (OR = 0.87, 95%CI = 0.77–0.99), but was attenuated when adjusting for coronary heart disease, blood pressure, and blood lipids. Conclusion: Findings from this MR analysis suggest a genetic protective effect of aspirin use on AD, possibly influenced by coronary heart disease, blood pressure, and lipid levels. [ABSTRACT FROM AUTHOR]

Large-scale molecular profiling and genotyping provide a unique opportunity to systematically compare the genetically predicted effects of therapeutic targets on the human metabolome. We firstly constructed genetic risk scores for 8 drug targets on the basis that they primarily modify low-density lipoprotein (LDL) cholesterol (HMGCR, PCKS9, and NPC1L1), high-density lipoprotein (HDL) cholesterol (CETP), or triglycerides (APOC3, ANGPTL3, ANGPTL4, and LPL). Conducting mendelian randomisation (MR) provided strong evidence of an effect of drug-based genetic scores on coronary artery disease (CAD) risk with the exception of ANGPTL3. We then systematically estimated the effects of each score on 249 metabolic traits derived using blood samples from an unprecedented sample size of up to 115,082 UK Biobank participants. Genetically predicted effects were generally consistent among drug targets, which were intended to modify the same lipoprotein lipid trait. For example, the linear fit for the MR estimates on all 249 metabolic traits for genetically predicted inhibition of LDL cholesterol lowering targets HMGCR and PCSK9 was r2 = 0.91. In contrast, comparisons between drug classes that were designed to modify discrete lipoprotein traits typically had very different effects on metabolic signatures (for instance, HMGCR versus each of the 4 triglyceride targets all had r2 < 0.02). Furthermore, we highlight this discrepancy for specific metabolic traits, for example, finding that LDL cholesterol lowering therapies typically had a weak effect on glycoprotein acetyls, a marker of inflammation, whereas triglyceride modifying therapies assessed provided evidence of a strong effect on lowering levels of this inflammatory biomarker. Our findings indicate that genetically predicted perturbations of these drug targets on the blood metabolome can drastically differ, despite largely consistent effects on risk of CAD, with potential implications for biomarkers in clinical development and measuring treatment response. [ABSTRACT FROM AUTHOR]

Background: Numerous epidemiological studies have investigated the role of blood lipids in prostate cancer (PCa) risk, though findings remain inconclusive to date. The ongoing research has mainly involved observational studies, which are often prone to confounding. This study aimed to identify the relationship between genetically predicted blood lipid concentrations and PCa.Methods and Findings: Data for low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides (TG), apolipoprotein A (apoA) and B (apoB), lipoprotein A (Lp(a)), and PCa were acquired from genome-wide association studies in UK Biobank and the PRACTICAL consortium, respectively. We used a two-sample summary-level Mendelian randomisation (MR) approach with both univariable and multivariable (MVMR) models and utilised a variety of robust methods and sensitivity analyses to assess the possibility of MR assumptions violation. No association was observed between genetically predicted concentrations of HDL, TG, apoA and apoB, and PCa risk. Genetically predicted LDL concentration was positively associated with total PCa in the univariable analysis, but adjustment for HDL, TG, and Lp(a) led to a null association. Genetically predicted concentration of Lp(a) was associated with higher total PCa risk in the univariable (ORweighted median per standard deviation (SD) = 1.091; 95% CI 1.028 to 1.157; P = 0.004) and MVMR analyses after adjustment for the other lipid traits (ORIVW per SD = 1.068; 95% CI 1.005 to 1.134; P = 0.034). Genetically predicted Lp(a) was also associated with advanced (MVMR ORIVW per SD = 1.078; 95% CI 0.999 to 1.163; P = 0.055) and early age onset PCa (MVMR ORIVW per SD = 1.150; 95% CI 1.015,1.303; P = 0.028). Although multiple estimation methods were utilised to minimise the effect of pleiotropy, the presence of any unmeasured pleiotropy cannot be excluded and may limit our findings.Conclusions: We observed that genetically predicted Lp(a) concentrations were associated with an increased PCa risk. Future studies are required to understand the underlying biological pathways of this finding, as it may inform PCa prevention through Lp(a)-lowering strategies. [ABSTRACT FROM AUTHOR]