Urate, the terminal product of purine metabolism, manifests an imbalance between its synthesis and excretion, potentially leading to increased serum urate concentrations and the genesis of monosodium urate crystals (1). Gout represents an inflammatory condition, instigated by the deposition of monosodium urate crystals within joints and soft tissues, and stands as the globally most widespread variant of inflammatory arthritis (2).This condition places a significant burden on individual health and the healthcare system (3). Gout afflicts approximately 4% of the adult population in developed nations, with over 7 million new cases emerging globally each year (4), and its prevalence is notably increasing alongside economic development (5). The disease is typified by recurrent acute episodes, which may culminate in severe pain and significant dysfunction (6). Severe gout can lead to chronic kidney diseases or urinary tract stones, potentially resulting in kidney failure (7). Studies show that dyslipidemia increases the risk of gout and higher serum urate levels (8). Dyslipidemia involves high levels of total cholesterol, triacylglycerols, and low-density lipoprotein cholesterol (LDL-C), which are all positively linked to serum urate levels (9). Dyslipidemia occurs more frequently in gout patients than in those with silent high urate levels (10) and gout patients often have a history of dyslipidemia (11).

Statins are preferred for the treatment of dyslipidemia due to their proven efficacy in reducing LDL-C levels and mitigating the risk of atherosclerotic cardiovascular disease (ASCVD) (12). Inhibitors of 3-Hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), including simvastatin and rosuvastatin, represent prevalent statin types. Additionally, other lipid-lowering agents, including FDA-approved inhibitors of the preprotein convertase subtilisin/kexin type 9 (PCSK9) and ezetimibe targeting Niemann-Pick C1-like 1 (NPC1L1), are employed to augment the lipid-reducing efficacy (13, 14). PCSK9, identified as a serine protease, occupies a pivotal role in the regulation of LDL-C metabolism, contributing to the onset of dyslipidemia and atherosclerosis through the inhibition of LDLR recycling to the cellular surface, thus elevating LDL-C concentrations (15). NPC1L1, a transmembrane protein prevalent in diverse cells, notably within the parietal membrane of intestinal epithelial cells and the renal tubular membrane of hepatocytes, plays a crucial role in mediating cholesterol absorption and overseeing hepatic cholesterol excretion (16), significantly influencing LDL-C metabolism regulation (17). Extant research highlights that both statin and non-statin lipid-lowering medications may contribute to increased urate levels and a heightened risk of gout development (12, 18), While certain investigations have probed into the correlation between serum urate and lipid concentrations, the findings remain contentious (19). Therefore, it has become particularly important to thoroughly investigate the causal relationship between lipid-lowering drugs and urate levels and gout.

Drug target Mendelian randomization analyses utilize genetic variation that mimics the pharmacological inhibitory effects of a pharmacogenetic target as an instrumental variable (IV). This approach aims to clarify the consequences of drug utilization via regression techniques, thereby augmenting the comprehension of the causal nexus between drug targets and the potential repercussions on urate levels and gout manifestations (15). In accordance with Mendel’s laws, genetic material undergoes random distribution during meiosis and is transmitted from parents to offspring during fertilization, thereby reducing the likelihood that the outcomes of MR studies are influenced by potential confounding factors or reverse causation (20). Consequently, MR analyses have furnished a tier of evidence that is second only to that provided by randomized controlled trials (21). In the current investigation, we employed a two-sample Mendelian Randomization analysis approach to explore the relationship between lipid-lowering agents (HMGCR inhibitors, PCSK9 inhibitors, and NPC1L1 inhibitors) and outcomes related to urate and gout.

In this investigation, we employed a comprehensive approach to explore the impact of lipid-lowering drug targets on gout and urate through drug-target Mendelian Randomization and SMR analysis. Utilizing genetic instruments, our goal was to surmount the constraints inherent in observational studies and to furnish more robust evidence for the potential role of HMGCR, PCSK9, and NPC1L1 in the onset of gout and urate. The findings from our analysis indicated no significant associations between SMR-based gene expression levels of HMGCR, PCSK9, and NPC1L1 and the risks of developing gout and elevated urate levels. However, through IVW-MR analysis, we identified a positive relationship between HMGCR inhibition and gout, albeit without a significant correlation to hyperuricemia. Conversely, a positive relationship was observed between PCSK9 inhibition and hyperuricemia, while no genetic association with gout was detected. The analysis indicated no significant causal link between the gene expression of NPC1L1 inhibitors and the onset of gout and urate levels, implying that the side effects associated with NPC1L1 inhibitors in patients suffering from gout and urate might be inferior to those arising from HMGCR and PCSK9 inhibitors. These results offer theoretical backing for tailoring hyperlipidemia treatment approaches in individuals with gout and hyperuricemia. The rising concern of pharmacologically induced hyperuricemia and gout in clinical settings is noteworthy. Various medications, particularly diuretics, antituberculosis agents, and immunosuppressants, have been implicated in triggering hyperuricemia accompanied by gout (38). Observational research has indicated that instances of hyperuricemia and gout are prevalent among individuals suffering from dyslipidemia (39–41), A comprehensive randomized, double-blind, placebo-controlled trial involving 13,970 participants revealed that individuals on lipid-lowering medication exhibited an increased risk of developing gout and hyperuricemia in comparison to those in the placebo group (42). Nevertheless, current investigations into its underlying mechanisms remain inadequate, and the conclusions drawn from various studies are inconsistent. For instance, a retrospective analysis discovered that patients administering a combination of allopurinol, febuxostat, and fenofibrate experienced a more significant reduction in serum urate concentrations (43). Conversely, another observational study highlighted that fenofibrate frequently correlates with nephrotoxicity among gout patients, underscoring the necessity for additional investigations into the selection of lipid-lowering medications in the treatment of gout (44). The reliability of these findings is controversial due to the scarcity of randomized controlled trials and limited cohort studies. A meta-analysis of Phase 2 and Phase 3 clinical studies shows that bempedoic acid raises the risk of hyperuricemia and gout (45). In clinical practice, patients with hyperuricemia or acute gout are advised to be monitored while using bempedoic acid (46).

From an etiological standpoint, the genesis of gout and hyperuricemia is attributable to various factors, notably including a definitive correlation with body mass. A research indicated that bariatric surgery markedly decreased body weight and serum urate concentrations in individuals suffering from obesity (47). The Mendelian randomization analysis further substantiates obesity as a contributory risk factor for the onset of gout and hyperuricemia (48). Multiple pathways may interfere in the mechanisms by which lipid-lowering drugs affect gout and urate. In a study of gout and urate and lipid profiles, gout and urate showed characteristic changes at different stages of the disease, and gout and hyperuricemia were associated with alterations in plasma lipid profiles, with reductions in LPC, LPC O- and LPCP- (LPC class as the mainpart ofoxidized LDL), underscoring the significance of monitoring LDL concentrations in individuals with gout and hyperuricemia. conversely, a significant association exists between gout and diabetes, as evidenced by studies demonstrating that individuals with diabetes exhibit a reduced risk of developing gout compared to non-diabetics (49, 50). Concurrently, lipid-lowering medications demonstrate both anti-inflammatory and pro-inflammatory properties (51), soluble urate and urate nanocrystals enhance NF-κB and IL-1β expression through NLRP3 inflammasome activation (52). PCSK9 promotes oxLDL-induced inflammation and TLR4 expression by increasing LOX expression, thereby initiating inflammation through NF-κB activation (53). This implication suggests that lipid-lowering drugs could modulate urate concentrations and gout symptoms via inflammatory routes, yet this hypothesis demands additional empirical substantiation. Concurrently, the dualistic character of statins, manifesting both anti-inflammatory and pro-inflammatory properties, intimates that inflammation might play a role in the genesis of gout, a hypothesis that requires further empirical validation (51).

This investigation showcased numerous notable strengths. Primarily, this research represents the inaugural systematic application of a drug-target Mendelian Randomization approach to elucidate the causal dynamics between lipid-lowering medications and both gout and urate. In the context of the prevailing absence of randomized controlled trials providing direct evidence, MR analysis serves to significantly diminish the impact of confounding variables typically present in observational studies by emulating a natural experiment akin to a randomized trial scenario, consequently enhancing the reliability of the conclusions.

Nevertheless, this study encompasses certain limitations. Firstly, the GWAS data utilized in this study predominantly originated from individuals of European descent. The limited diversity in current public GWAS databases restricts the availability of sufficient data from non-European populations, preventing this study from conducting an analysis across broader ethnic groups. Consequently, this limits the generalizability of our findings to other populations. Secondly, despite the application of both SMR and IVW-MR methods, only the IVW-MR presented a significant correlation, potentially due to the influence of various factors. Thirdly, our study primarily reflects the effects of lifetime suppression of drug targets on disease outcomes. Regarding long-term effects, the relationship between short-term drug use and disease risk remains uncertain. Mendelian Randomization studies may not fully capture the real-world effects of medication use due to factors such as dosage, mechanisms of action, individual variability, and duration of drug exposure. Consequently, there is a pressing need for high-quality randomized controlled trials to delve deeper into the specific impacts of lipid-lowering medications on gout and hyperuricemia, along with their underlying mechanisms. Fourthly, the absence of eQTL data for the target genes in the liver (a critical site for lipid metabolism) diminished the credibility of the observed correlations. Specifically, the limited sample size of NPC1L1 eQTL within the GTEx program, coupled with the absence of NPC1L1 eQTL data in blood samples, might have resulted in an underestimation of the efficacy of NPC1L1 inhibitors in managing gout and urate.

In conclusion, this research furnishes empirical evidence suggesting that HMGCR inhibitors elevate the risk of developing gout, while PCSK9 inhibitors heighten the risk of urate. Despite its limitations, this study offers significant insights into evaluating their application in personalized treatment strategies.