Genetic evidence for causality of late chronotype on metabolic syndrome in East Asians and Europeans.

A novel Mendelian randomisation framework unravels one gene expression component, correlated with proliferation and genome stability-related features, associated with telomere length in lung adenocarcinoma tumours, which provides insights into how telomere length influences the genetic basis of lung cancer aetiology.

A novel Mendelian randomisation framework unravels one gene expression component, correlated with proliferation and genome stability-related features, associated with telomere length in lung adenocarcinoma tumours, which provides insights into how telomere length influences the genetic basis of lung cancer aetiology.

Background:Loneliness is a risk factor of morbidity and mortality. Although the association of loneliness with osteoarthritis (OA) has been reported among young adults, the causal effect of loneliness on OA...

BackgroundGout is a highly hereditary disease, but not all those carrying well-known risk variants have developing gout attack even in hyperuricemia status. We performed a genome-wide association study (GWAS) and polygenic risk score (PRS) analysis to illustrate the new genetic architectures of gout and asymptomatic hyperuricemia (AH).MethodsGWAS was performed to identify variants associated with gout/AH compared with normouricemia. The participants were males, enrolled from the Taiwan Biobank and China Medical University, and divided into discovery (n=39,594) and replication (n=891) cohorts for GWAS. For PRS analysis, the discovery cohort was grouped as base (n=21,814) and target (n=17,780) cohorts, and the score was estimated by grouping the polymorphisms into protective or not for the phenotypes in the base cohort.ResultsThe genes ABCG2 and SLC2A9 were found as the major genetic factors governing gouty and AH, and even in those carrying the rs2231142 (ABCG2) wild-genotype. Surprisingly, variants on chromosome 1, such as rs7546668 (DNAJC16), rs10927807 (AGMAT), rs9286836 (NUDT17), rs4971100 (TRIM46), rs4072037 (MUC1), and rs2974935 (MTX1), showed significant associations with gout in both discovery and replication cohorts (all p-values < 1e−8). Concerning the PRS, the rates of gout and AH increased with increased quartile PRS in those SNPs having risk effects on the phenotypes; on the contrary, gout/AH rates decreased with increased quartile PRS in those protective SNPs.ConclusionsWe found new variants on chromosome 1 significantly relating to gout, and PRS predicts the risk of developing gout/AH more robustly based on the SNPs’ effect types on the trait.

Mendelian randomisation allows for the testing of causal effects in situations where clinical trials are challenging to do. In this hypothesis-free, data-driven phenome-wide association study (PheWAS), we sought to assess possible associations of high body-mass index (BMI) with multiple disease outcomes. For this registry-based case-control PheWAS, we used genome-wide data available from the UK Biobank to construct a genetic risk score of 76 variants related to BMI. Eligible UK Biobank participants were aged 37-73 years during recruitment, were white British, were unrelated to each other, and had available genetic information. Disease outcomes from these participants were mapped to a phenotype code (phecode). Participants with a phecode of interest were recoded as cases, whereas participants without a phecode of interest or any codes under a parent phecode were classified as controls. We did a PheWAS to analyse possible associations between the BMI genetic risk score and a range of disease outcomes. Disease associations passing stringent correction for multiple testing (Bonferroni corrected threshold p<5·4 × 10-5, false discovery rate corrected p<0·0074) were assessed for causal association with use of inverse-variance weighted mendelian randomisation. We did sensitivity analyses to assess pleiotropy and stability of estimation with use of weighted median, weighted mode, Egger regression, and mendelian randomisation pleiotropy residual sum and outlier methods. Our study population comprised 337 536 UK Biobank participants, and analyses were done for 925 unique phecodes from 17 different disease categories. After Bonferroni correction, PheWAS identified that BMI genetic risk score was associated with hospital-diagnosed obesity and 58 other outcomes; 30 distinct disease associations were supported by the mendelian randomisation analyses. 30 distinct disease associations were supported by the mendelian randomisation analyses. In inverse-variance weighted mendelian randomisation, genetically determined BMI was associated with endocrine disorders (odds ratio per one SD or 4·1 kg/m2 higher BMI 2·72, 95% CI 2·33-3·29 for type 2 diabetes; 2·11, 1·62-2·76 for type 1 diabetes; and 1·46, 1·25-1·70 for hypothyroidism), circulatory diseases (1·96, 1·53-2·51 for phlebitis and thrombophlebitis; 1·89, 1·39-2·57 for cardiomegaly; 1·68, 1·35-2·09 for congestive heart failure; 1·55, 1·37-1·76 for hypertension; 1·31, 1·13-1·52 for ischaemic heart disease; and 1·25, 1·14-1·37 for cardiac dysrhythmias), and inflammatory or dermatological conditions (2·00, 1·72-2·23 for superficial cellulitis and abscess; 3·37, 2·17-5·25 for chronic ulcers of leg and foot; 4·99, 2·54-9·82 for gangrene; and 2·24, 1·53-3·28 for atopy). Mendelian randomisation analyses provided further support for a causal effect of BMI on renal failure, osteoarthrosis, neurological (insomnia and peripheral nerve disorders) and respiratory diseases (asthma and chronic bronchitis), structural problems (hernias and knee derangement), and chemotherapy treatment. Mendelian randomisation with Egger regression produced consistently wider CIs compared with those of other methods. 26 of 72 distinct diseases detected under false discovery rate correction produced consistent estimates across at least four mendelian randomisation methods, and consistent evidence across all five approaches was obtained for 14 diseases. Our data-driven approach identified a range of diseases as possibly affected by high BMI. This population-level screening approximated the accumulated consequences of high BMI, whereas the true effects might be more complex and vary by life stage. Our results highlight the importance of obesity prevention and effective management of obesity-related comorbidities. National Health and Medical Research Council of Australia.

Higher segregation of functional networks in the brain has been associated with better cognitive abilities in aging (Chan, PNAS, 2014) and higher cognitive resilience against Alzheimer's disease (Ewers, Brain, in press). Here, we elucidated for the first time the genetic and environmental (i.e. cardiovascular) determinants of system segregation (SyS) in two large population-based cohorts. We included 16,635 UK Biobank (UKB) participants (45-81y, discovery-sample) and 2,414 Rotterdam-Study participants (52-90y, replication-sample). Resting-state-fMRI SyS was computed as the ratio of between-network to within-network connectivity, where networks (N=55) were defined by independent component analysis. Genome-wide association study (GWAS) of SyS was performed in UKB, controlling for twenty principal components, age, sex, genotype-array and assessment center. For out-of-sample prediction, a polygenic risk score (PRS) of SyS was tested in Rotterdam-Study participants. In both cohorts, we determined the effect of cardiovascular health (adherence to Life's simple 7) on SyS. We estimated age-dependent and -independent effects of SyS on cognition (multi-domain factor score) in both cohorts and, in a subsample of 2,113 UKB participants, on cognitive decline over time. To explore causal effects, Mendelian Randomization (MR) analyses were performed. GWAS of SyS in UKB yielded 659 genome-wide significant single-nucleotide polymorphisms (SNPs; P<5e-08, h2 =0.144, Fig. 1). Nine independent risk loci were detected implicated 59 genes. Lead SNPs with highest likelihood to have deleterious consequences were located near the INPP5A and PLCE1 genes. In Rotterdam-Study participants, PRS explained 1.4% of variance in SyS. We found overall better cardiovascular health to be associated with higher SyS, while higher blood pressure alone was a significant predictor of lower SyS (UKB:βstd =-0.059(0.007), P<0.001; Rotterdam-Study:βstd (SE)=-0.060(0.020), P=0.003; Fig. 2). MR analysis in UKB confirmed that genetically elevated levels of blood pressure were associated with lower SyS (β(CI95%)=-0.003(-0.002,-0.001), P=0.002). We found higher SyS to be associated with better cognition across all ages in UKB (βstd (SE)=-0.031(0.012), P=0.005) and in older, but not younger Rotterdam-Study participants (βstd (SE)=0.038(0.016), P=0.0026, Fig. 3). In MR analysis, genetically elevated levels of SyS were associated with better cognition (β(CI95)=0.104(0.03-0.18), P=0.008). The current study highlights the importance of cardiovascular health for maintaining segregated brain systems what in turn was shown to benefit cognitive functions during aging.

Epidemiological evidence suggests a link between noise pollution exposure and Alzheimer's disease (AD); however, its causal relationship remains unclear. This study aimed to evaluate the potential genetic correlation and causal association between noise exposure and AD risk using comprehensive genetic approaches. Genome-wide association study (GWAS) data for noise pollution from the UK Biobank were used to calculate polygenic scores (PGSs) in AD cases and controls. Logistic regression analyses were conducted in the discovery cohort (ADc1234ADA: 2,651 cases, 2,768 controls) and the replication cohort (ADNI: 679 cases, 645 controls) using PRSice-2 software. The PGS associations with AD were adjusted for covariates including sex, age, and APOE ε4 allele count. Meta-analyses were performed across datasets, with Bonferroni-corrected p-values < 0.05 considered significant. Mendelian Randomization (MR) analyses using the inverse-variance weighted (IVW) method further evaluated the causal relationship. PGSs for noise pollution at average 24-hour sound levels were significantly associated with AD risk (OR = 1.198; 95% CI: 1.100-1.235; p=3.65 × 10⁻⁵) from the meta-analysis. MR analyses indicated that genetically predicted frequent exposure to very noisy workplaces was associated with a higher risk of AD (OR = 3.251; 95% CI: 1.645-7.214; p<0.004) and a lower likelihood of longevity (OR = 0.635; 95% CI: 0.500-0.806; p<1.92 × 10⁻⁴). Sensitivity tests supported these causal effects. This study provides robust evidence supporting a causal link between noise pollution and AD risk. The findings highlight the importance of reducing noise exposure, particularly in noisy workplaces, as a public health strategy to lower AD risk and enhance longevity.

Late chronotype (LC) correlates with reduced metabolic insulin sensitivity and cardiovascular disease. It is unclear if insulin action on aortic waveforms and inflammation is altered in LC versus early chronotype (EC). Adults with metabolic syndrome (n = 39, MetS) were classified as either EC (Morning‐Eveningness Questionnaire [MEQ] = 63.5 ± 1.2) or LC (MEQ = 45.5 ± 1.3). A 120 min euglycemic clamp (40 mU/m2/min, 90 mg/dL) with indirect calorimetry was used to determine metabolic insulin sensitivity (glucose infusion rate [GIR]) and nonoxidative glucose disposal (NOGD). Aortic waveforms via applanation tonometry and inflammation by blood biochemistries were assessed at 0 and 120 min of the clamp. LC had higher fat‐free mass and lower VO2max, GIR, and NOGD (between groups, all p ≤ 0.05) than EC. Despite no difference in 0 min waveforms, both groups had insulin‐stimulated elevations in pulse pressure amplification with reduced AIx75 and augmentation pressure (AP; time effect, p ≤ 0.05). However, EC had decreased forward pressure (Pf; interaction effect, p = 0.007) with insulin versus rises in LC. Although LC had higher tumor necrosis factor‐α (TNF‐α; group effect, p ≤ 0.01) than EC, both LC and EC had insulin‐stimulated increases in TNF‐α and decreases in hs‐CRP (time effect, both p ≤ 0.01). Higher MEQ scores related to greater insulin‐stimulated reductions in AP (r = −0.42, p = 0.016) and Pf (r = −0.41, p = 0.02). VO2max correlated with insulin‐mediated reductions in AIx75 (r = −0.56, p < 0.01) and AP (r = −0.49, p < 0.01). NOGD related to decreased AP (r = −0.44, p = 0.03) and Pf (r = −0.43, p = 0.04) during insulin infusion. LC was depicted by blunted forward pressure waveform responses to insulin and higher TNF‐α in MetS. More work is needed to assess endothelial function across chronotypes.

Idiopathic pulmonary fibrosis (IPF) and telomere length are both strongly linked to rare and common genetic variants. Shortened telomere length might itself be causal for IPF. We aimed to evaluate whether rare and common variants compete or cooperate to confer genetic risk of IPF uniformly. In this genetic analysis, we used whole-genome sequencing (WGS) data from a discovery case-control cohort sequenced at Columbia University and validated findings using WGS data from Trans-Omics for Precision Medicine (TOPMed) and UK Biobank. In all cohorts, we identified rare damaging variants in disease-associated genes and computed control-normalised non-overlapping polygenic risk scores (PRS) for IPF and telomere length. We assessed the MUC5B rs35705950 single-nucleotide polymorphism (SNP), an IPF common risk variant with a large effect, independently from the polygenic scores. Telomere length in blood leukocytes was measured using a quantitative PCR assay for the discovery cohort and UK Biobank validation cohort. We conducted logistic regression (adjusting for age, sex, and principal components of ancestry) to evaluate the association between IPF risk and the MUC5B SNP, the IPF PRS excluding MUC5B (IPF-PRS-noMUC5B), and the PRS for telomere length in the overall cohort and analysed their effects in patient subgroups for IPF endotypes (carriers and non-carriers of rare variants stratified by telomere length cutoffs). To assess disease prediction, we calculated cross-validated area under the receiver operating receiver operating curve (AUC). We also compared the liability of IPF explained by genetic variables. The discovery cohort was recruited between April 23, 2003 and June 19, 2019 and included 777 patients with IPF and 2905 controls. We replicated the analyses in the TOPMed (1148 patients with IPF and 5202 controls) and UK Biobank (2739 patients with IPF and 395 331 controls) cohorts. 23-43% of patients with IPF had damaging rare variants or telomeres shorter than the tenth percentile. Analysis of the association of genetic variables with IPF diagnosis yielded odds ratios of 1·63 (95% CI 1·47-1·81) for telomere length PRS and 1·60 (1·44-1·77) for IPF-PRS-noMUC5B in the discovery cohort, with similar effect sizes for the two variables in the replication cohorts (1·47, 1·36-1·59 vs 1·37, 1·25-1·50 in TOPMed; 1·24, 1·19-1·29 vs 1·25, 1·21-1·30 in UK Biobank). The telomere length PRS had the greatest effect on disease risk in patients with IPF not harbouring rare variants and with telomere length shorter than the tenth percentile in the discovery cohort (2·02, 1·76-2·33) and UK Biobank replication cohort (1·70, 1·56-1·85). Accounting for clinical variables and all genetic variables (rare variants, MUC5B SNP, IPF PRS, and telomere length PRS) led to the best disease prediction in the discovery cohort (combined AUC 0·89), TOPMed cohort (0·89), and UK Biobank cohort (0·77). Rare and common variants contributed jointly to the genetic liability of IPF. The telomere length PRS accounted for 13% of the explained genetic liability of IPF in the discovery cohort and 8% and 13% in the TOPMed and UK Biobank cohorts, respectively. Common and rare genetic variation confer context-specific genetic risk in patients with IPF both competitively and cooperatively. In contrast to known IPF common risk variants, the telomere length PRS, which includes more than 180 genetic loci not previously associated with IPF, is associated with increased risk of disease in patients with specific IPF endotypes. Polygenic risk from telomere-associated common variants is a key feature of genetic heterogeneity in IPF. US National Institutes of Health, UK Medical Research Council, and UK National Institute for Health and Care Research.

Genetic variants used as instruments for exposures in Mendelian randomisation (MR) analyses may have horizontal pleiotropic effects (i.e., influence outcomes via pathways other than through the exposure), which can undermine the validity of results. We examined the extent of this using smoking behaviours as an example. We first ran a phenome‐wide association study in UK Biobank, using a smoking initiation genetic instrument. From the most strongly associated phenotypes, we selected those we considered could either plausibly or not plausibly be caused by smoking. We examined associations between genetic instruments for smoking initiation, smoking heaviness and lifetime smoking and these phenotypes in UK Biobank and the Avon Longitudinal Study of Parents and Children (ALSPAC). We conducted negative control analyses among never smokers, including children. We found evidence that smoking‐related genetic instruments were associated with phenotypes not plausibly caused by smoking in UK Biobank and (to a lesser extent) ALSPAC. We observed associations with phenotypes among never smokers. Our results demonstrate that smoking‐related genetic risk scores are associated with unexpected phenotypes that are less plausibly downstream of smoking. This may reflect horizontal pleiotropy in these genetic risk scores, and we would encourage researchers to exercise caution this when using these and genetic risk scores for other complex behavioural exposures. We outline approaches that could be taken to consider this and overcome issues caused by potential horizontal pleiotropy, for example, in genetically informed causal inference analyses (e.g., MR) it is important to consider negative control outcomes and triangulation approaches, to avoid arriving at incorrect conclusions.

Background Currently, the treatment and prevention of rheumatoid arthritis (RA) face significant challenges. In the pursuit of new therapeutic avenues, Mendelian randomization (MR) analysis has emerged as a crucial research method. Building on this, we conducted a comprehensive genome-wide analysis of MR of drug targets to identify potential therapeutic intervention points for RA.MethodS In this study, we constructed a comprehensive analytical framework aimed at identifying and validating potential biomarkers for RA. The framework begins with a two-sample MR study utilizing two large plasma protein datasets. Building upon this foundation, we conducted an in-depth exploration of the identified positive proteins using the summary data-based Mendelian randomization (SMR) method, combined with Bayesian co-localization analysis of coding genes. This approach allowed us to reveal RA multi-omics biomarkers, and we employed the LDSC analysis method to investigate the genetic correlation between the identified genes and complex diseases. Additionally, a phenome-wide association study (PheWAS) was performed on the positive genes mapped by the identified proteins, alongside an exploration of their expression in various tissues. Subsequently, we expanded our analysis to include protein-protein interaction (PPI) network analysis, gene ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Finally, we conducted drug prediction and molecular docking studies. The purpose of these comprehensive analytical methods is to thoroughly investigate the biological functions and mechanisms of action of these biomarkers, thereby providing a scientific basis for the development of more effective and targeted therapeutic drugs. Our findings encompass RA and its multiple subtypes, including seropositive RA, seronegative RA, and juvenile RA.Results This study presents a multidimensional analysis of plasma proteins in relation to RA and its subtypes. In the MR analysis of Icelandic plasma protein - Quantitative Trait Loci(pQTLs) associated with RA, the findings revealed 137, 150, 95, and 69 positive associations for RA, seropositive RA, seronegative RA, and juvenile RA, respectively. Additionally, the MR analysis of plasma pQTLs from the UK Biobank database identified 156, 167, 106, and 81 positive plasma proteins for the same conditions. After applying false discovery rate (FDR) correction, the MR analysis of plasma pQTLs and RA in Iceland identified PPA2, JUND, AGER, F2, and PMEL as significantly positive proteins. In the MR analysis of plasma pQTLs and RA within the UK Biobank database, the significantly positive proteins included AIF1, ARG2, ATP5IF1, CCL19, CDSN, CEP43, MXRA8, PADI2, RPA2, SLC16A1, TNF, and TNFRSF14. For the MR analysis of plasma pQTLs and seropositive RA in Iceland, TGFBR3, FCGR3B, TIMP4, and PMEL were identified as significantly positive proteins. In the UK Biobank MR analysis of plasma pQTLs and seropositive RA, the following proteins were significantly positive: AIF1, APOBR, ATP6V1G2, BCL2L15, C1QTNF6, CCL19, CD40, CDSN, CEP43, CX3CL1, FCGR2B, FCRL1, IL6R, MXRA8, TGFBR3, TNF, and TNFRSF14. The MR analysis of plasma pQTLs and seronegative RA in the UK Biobank identified AIF1, CEP43, and TNF as significantly positive proteins. Following Bonferroni correction, the MR analysis of UK Biobank plasma pQTLs and RA highlighted AIF1, CCL19, CDSN, CEP43, and TNF as significantly positive proteins. For seropositive RA in the UK Biobank, AIF1, ATP6V1G2, BCL2L15, CCL19, CDSN, CEP43, IL6R, and TNF were identified as significantly positive proteins. Lastly, the MR analysis of plasma pQTLs and seronegative RA in the UK Biobank confirmed AIF1 and TNF as significantly positive proteins.In the context of single-gene SMR analysis, the examination of Icelandic plasma pQTLs in relation to RA—specifically seropositive RA, seronegative RA, and juvenile RA—identified 28, 34, 21, and 15 positive plasma associations, respectively. For proteins, MR analysis of UK Biobank plasma pQTLs revealed 38, 37, 21, and 12 positive plasma proteins, respectively. Building on the findings from the previous two-sample MR analysis, Bayesian co-localization was subsequently performed. Among the Icelandic plasma pQTLs, F2 emerged as a significantly positive gene associated with RA. In the UK Biobank plasma pQTLs, the genes ATP5IF1, CCL19, CX3CL1, HDGF, MXRA8, and TNFRSF14 were identified as significantly positive. LDSC analysis demonstrated a significant positive genetic correlation between CCL19 and both RA and seropositive RA, as well as a significant positive genetic correlation between TNFRSF14 and both RA and seropositive RA. These results suggest that FCGR3A, ADAM15, CCL19, CX3CL1, NFKBIE, TNFRSF14, F2, ATP5IF1, HDGF, and MXRA8 may serve as key therapeutic targets for RA. Notably, TNFRSF14 and CCL19 warrant further investigation as important genes for understanding the pathogenesis and potential therapeutic strategies for RA and its subtypes.Conclusion Through a comprehensive analysis of plasma proteomic and transcriptomic data, we successfully identified key therapeutic targets for RA and its three clinical subtypes. Specifically, we identified FCGR3A, ADAM15, CCL19, CX3CL1, NFKBIE, TNFRSF14, F2, ATP5IF1, HDGF, and MXRA8 as potential therapeutic targets for RA. By integrating genetic relatedness scores, we further elucidated the significance of these findings. Notably, TNFRSF14 and CCL19 emerged as critical genes warranting in-depth exploration regarding the pathogenesis of RA and its subtypes, as well as their potential as therapeutic targets. These results provide a scientific basis for the development of new immunotherapy approaches, combination treatment regimens, or targeted intervention strategies, and are anticipated to advance research progress in the treatment of RA.

Associations of muscle mass and grip strength with severe NAFLD: A prospective study of 333,295 UK Biobank participants.

Genetic analysis from multiple cohorts implies causality between 2200 druggable genes, telomere length, and leukemia

BackgroundExamining sex differences on the impact of modifiable risk factors in Alzheimer’s disease (AD) risk can help us better understand the mechanisms underlying sex differences in the prevalence and incidence of AD. Here, we used polygenic risk scores (PRS) and Mendelian randomization (MR) to investigate sex‐specific effects of sleep duration, insomnia, blood pressure, diabetes, alcohol intake, smoking, body mass index, high cholesterol, and education on AD risk.MethodWe obtained combined and sex‐stratified genome‐wide association study (GWAS) summary statistics for each risk factor from the UK Biobank and used them as the base dataset for constructing PRS and exposure datasets in the MR analysis. Linkage disequilibrium clumping was performed to identify independent genome‐wide significant single nucleotide polymorphisms (SNPs) across the sex‐combined, male‐specific, and female‐specific GWAS. The combined lead SNPs were then weighted by their strata‐specific effect sizes in the stratified PRS and MR analyses. PRS were constructed for each risk factor in participants from the Alzheimer’s Disease Genetics Consortium. Linear regression was used to investigate the association of each PRS with AD risk, adjusting for age, principal components, and cohort. MR was used to estimate sex‐stratified causal effects of each risk factor on AD. Sex differences in PRS associations and MR causal estimates were determined using Fisher’s Z score method.ResultsAssociation testing of the PRS with AD risk in sex stratified cohorts revealed that the BMI PRS was associated with differential effects in men and women (OR [95%CI]: males: 1.05 [1.00, 1.10] vs females: 0.96 [0.93, 0.99], p = 0.003). Furthermore, the university completion PRS was non‐significant in men but was associated with reduced risk in women (OR [95%CI]: males: 0.96 [0.92, 1.01] vs females: 0.92 [0.89, 0.96], p = 0.12). In the follow‐up MR analysis, university completion was also causally associated with reduced risk in women only (OR [95%CI]: males: 1.09 [0.77, 1.53] vs females: 0.55 [0.42, 0.72], p = 0.002) (Figure 1). No other risk factors showed evidence of sex‐differences.ConclusionOur study found sex‐specific effects of genetically predicted BMI and educational attainment on AD risk. These findings suggest the need for sex‐specific approaches to AD prevention and management.

Hyperuricemia is closely associated with insulin resistance syndrome (and its many cardiometabolic sequelae); however, whether they are causally related has long been debated. We undertook this study to investigate the potential causal nature and direction between insulin resistance and hyperuricemia, along with gout, by using bidirectional Mendelian randomization (MR) analyses. We used genome-wide association data (n = 288,649 for serum urate [SU] concentration; n = 763,813 for gout risk; n = 153,525 for fasting insulin) to select genetic instruments for 2-sample MR analyses, using multiple MR methods to address potential pleiotropic associations. We then used individual-level, electronic medical record-linked data from the UK Biobank (n = 360,453 persons of European ancestry) to replicate our analyses via single-sample MR analysis. Genetically determined SU levels, whether inferred from a polygenic score or strong individual loci, were not associated with fasting insulin concentrations. In contrast, genetically determined fasting insulin concentrations were positively associated with SU levels (0.37 mg/dl per log-unit increase in fasting insulin [95% confidence interval (95% CI) 0.15, 0.58]; P = 0.001). This persisted in outlier-corrected (β = 0.56 mg/dl [95% CI 0.45, 0.67]) and multivariable MR analyses adjusted for BMI (β = 0.69 mg/dl [95% CI 0.53, 0.85]) (P < 0.001 for both). Polygenic scores for fasting insulin were also positively associated with SU level among individuals in the UK Biobank (P < 0.001). Findings for gout risk were bidirectionally consistent with those for SU level. These findings provide evidence to clarify core questions about the close association between hyperuricemia and insulin resistance syndrome: hyperinsulinemia leads to hyperuricemia but not the other way around. Reducing insulin resistance could lower the SU level and gout risk, whereas lowering the SU level (e.g., allopurinol treatment) is unlikely to mitigate insulin resistance and its cardiometabolic sequelae.