Abstract：
BACKGROUND: Previous studies have highlighted the association between cannabis use and diabetes and its complications; however, the causality remains ambiguous.
METHODS: Univariate Mendelian randomization (MR), multivariate MR, mediation MR, and linkage disequilibrium score (LDSC) analysis to assess the causal relationship between cannabis use and 12 diabetic phenotypes. Summary statistics for lifetime cannabis use (N = 184,765) and cannabis use disorder (CUD) (N = 374,287) from genome-wide association studies. The primary method used was inverse-variance-weighted (IVW). A range of sensitivity analyses ensured the robustness of the results.
RESULTS: LDSC analysis revealed a significant genetic correlation between CUD and T2DM, as well as between lifetime cannabis use and four diabetic phenotypes (P < 0.05). After correction by false discovery rate (FDR), the primary IVW analysis indicates that the genetically predicted CUD is positively associated with the risk of diabetic hypoglycemia (OR = 1.11, 95% CI 1.04-1.20, P = 0.003, PFDR = 0.04) and proliferative diabetic retinopathy (PDR) (OR = 1.12, 95% CI 1.04-1.19, P = 4.89×10-4, PFDR =0.01). Additionally, suggestive evidence links CUD with increased risks of diabetic nephropathy, type 1 diabetes mellitus (T1DM), diabetic retinopathy, and T1DM associated with diabetic ketoacidosis (P < 0.05 & PFDR > 0.05). No causal relationship was detected between lifetime cannabis use and diabetic phenotypes (P > 0.05 & PFDR > 0.05). Multivariable and mediation MR analyses revealed that glycated hemoglobin A1c partially mediates the causal effect of CUD on PDR and diabetic hypoglycemia.
CONCLUSIONS: This MR study suggests that CUD may have a causal role in several diabetic disease phenotypes.
METHODS: Univariate Mendelian randomization (MR), multivariate MR, mediation MR, and linkage disequilibrium score (LDSC) analysis to assess the causal relationship between cannabis use and 12 diabetic phenotypes. Summary statistics for lifetime cannabis use (N = 184,765) and cannabis use disorder (CUD) (N = 374,287) from genome-wide association studies. The primary method used was inverse-variance-weighted (IVW). A range of sensitivity analyses ensured the robustness of the results.
RESULTS: LDSC analysis revealed a significant genetic correlation between CUD and T2DM, as well as between lifetime cannabis use and four diabetic phenotypes (P < 0.05). After correction by false discovery rate (FDR), the primary IVW analysis indicates that the genetically predicted CUD is positively associated with the risk of diabetic hypoglycemia (OR = 1.11, 95% CI 1.04-1.20, P = 0.003, PFDR = 0.04) and proliferative diabetic retinopathy (PDR) (OR = 1.12, 95% CI 1.04-1.19, P = 4.89×10-4, PFDR =0.01). Additionally, suggestive evidence links CUD with increased risks of diabetic nephropathy, type 1 diabetes mellitus (T1DM), diabetic retinopathy, and T1DM associated with diabetic ketoacidosis (P < 0.05 & PFDR > 0.05). No causal relationship was detected between lifetime cannabis use and diabetic phenotypes (P > 0.05 & PFDR > 0.05). Multivariable and mediation MR analyses revealed that glycated hemoglobin A1c partially mediates the causal effect of CUD on PDR and diabetic hypoglycemia.
CONCLUSIONS: This MR study suggests that CUD may have a causal role in several diabetic disease phenotypes.
摘要：
背景：先前的研究强调了大麻使用与糖尿病及其并发症之间的关联；然而，因果关系仍然模棱两可。
方法：单变量孟德尔随机化（MR），多变量MR，调解先生，和连锁不平衡评分（LDSC）分析，以评估大麻使用与12种糖尿病表型之间的因果关系。来自全基因组关联研究的终生大麻使用（N=184,765）和大麻使用障碍（CUD）（N=374,287）的汇总统计数据。使用的主要方法是逆方差加权(IVW)。一系列的灵敏度分析确保了结果的鲁棒性。
结果：LDSC分析显示CUD和T2DM之间存在显著的遗传相关性，以及终生使用大麻和四种糖尿病表型之间（P<0.05）。通过错误发现率(FDR)校正后，主要IVW分析表明,遗传预测的CUD与糖尿病性低血糖(OR=1.11,95%CI1.04-1.20,P=0.003,PFDR=0.04)和增生性糖尿病性视网膜病变(PDR)(OR=1.12,95%CI1.04-1.19,P=4.89×10-4,PFDR=0.01)的风险呈正相关.此外,提示证据表明CUD与糖尿病肾病的风险增加，1型糖尿病（T1DM），糖尿病视网膜病变，T1DM与糖尿病酮症酸中毒相关(P<0.05,PFDR>0.05)。终生使用大麻与糖尿病表型之间未检测到因果关系（P>0.05和PFDR>0.05）。多变量和中介MR分析显示，糖化血红蛋白A1c部分介导了CUD对PDR和糖尿病性低血糖的因果关系。
结论：这项MR研究表明，CUD可能在几种糖尿病疾病表型中具有因果关系。
方法：单变量孟德尔随机化（MR），多变量MR，调解先生，和连锁不平衡评分（LDSC）分析，以评估大麻使用与12种糖尿病表型之间的因果关系。来自全基因组关联研究的终生大麻使用（N=184,765）和大麻使用障碍（CUD）（N=374,287）的汇总统计数据。使用的主要方法是逆方差加权(IVW)。一系列的灵敏度分析确保了结果的鲁棒性。
结果：LDSC分析显示CUD和T2DM之间存在显著的遗传相关性，以及终生使用大麻和四种糖尿病表型之间（P<0.05）。通过错误发现率(FDR)校正后，主要IVW分析表明,遗传预测的CUD与糖尿病性低血糖(OR=1.11,95%CI1.04-1.20,P=0.003,PFDR=0.04)和增生性糖尿病性视网膜病变(PDR)(OR=1.12,95%CI1.04-1.19,P=4.89×10-4,PFDR=0.01)的风险呈正相关.此外,提示证据表明CUD与糖尿病肾病的风险增加，1型糖尿病（T1DM），糖尿病视网膜病变，T1DM与糖尿病酮症酸中毒相关(P<0.05,PFDR>0.05)。终生使用大麻与糖尿病表型之间未检测到因果关系（P>0.05和PFDR>0.05）。多变量和中介MR分析显示，糖化血红蛋白A1c部分介导了CUD对PDR和糖尿病性低血糖的因果关系。
结论：这项MR研究表明，CUD可能在几种糖尿病疾病表型中具有因果关系。
