HIV and risk of hypertension: a two-sample Mendelian randomization study.
Previous studies have shown that human immunodeficiency virus (HIV) infection is associated with hypertension; however, the results of these studies are affected by a variety of confounding factors. There is no definite evidence to prove a causal relationship between these two factors. This study aimed to investigate the causal relationship between HIV infection and hypertension.
A two-sample Mendelian randomization (MR) study was conducted using genome-wide association study (GWAS) statistics published online. The data were collected mainly from the OpenGWAS and FinnGen databases. The HIV database contained 357 HIV patients and 218,435 control patients; the hypertension database contained 54,358 patients and 408,652 control patients; and the blood pressure database contained 436,424 samples. Random effect inverse variance weighting (IVW) was used as the main analysis method, weighted median and Mr-Egger analysis methods were used to ensure the accuracy of the results, and Cochran's Q test and Mr-Egger regression methods were used to detect heterogeneity and correct multiple horizontal effects. Finally, the leave-one-out method was used to analyse the reliability of the test results. In order to further verify the research results, different databases were used and the same statistical method was used for a replication analysis. In order to prevent false positive results caused by multiple tests, Bonferroni correction is used to correct the statistical results.
After screening, a total of 9 SNPs (single-nucleotide polymorphisms) were selected as the instrumental variable (IV) used in this study. The IVW MR analysis results showed a causal relationship between HIV infection and the risk of hypertension (IVW: OR = 1.001, P = 0.03). When systolic blood pressure was the outcome, the IVW method results were positive (OR = 1.004, P = 0.01280), and when diastolic blood pressure was the outcome, the weighted median method results were positive (OR = 1.004, P = 0.04570). According to the sensitivity analysis, the results of this study were unlikely to be affected by heterogeneity and horizontal pleiotropy. The leave-one-out analysis showed that the results of this study did not change significantly with the elimination of a single SNP. In replication analysis, when diastolic blood pressure was taken as the outcome, the weighted median method was positive (OR = 1.042, P = 0.037). Sensitivity analysis shows that there is heterogeneity, but there is no horizontal pleiotropy. The leave-one-out analysis showed that the results of this study did not change significantly with the elimination of a single SNP.
As the first exploratory study using MR method to study the causal relationship between HIV infection and hypertension and blood pressure, this study found that HIV infection may increase systolic and diastolic blood pressure and increase the risk of hypertension. PLWH, as a high-risk group of cardiovascular and cerebrovascular diseases, should prevent the occurrence of hypertension in order to further improve their quality of life. However, this study also has some limitations. The results of the relationship between HIV infection and hypertension and blood pressure may be affected by the lack of statistical efficacy. In order to further confirm this conclusion, more large-scale RCT or genetic studies should be carried out.
Zhu RW
，Guo HY
，Niu LN
，Deng M
，Li XF
，Jing L
... -
《BMC INFECTIOUS DISEASES》
Global and regional genetic association analysis of ulcerative colitis and type 2 diabetes mellitus and causal validation analysis of two-sample two-way Mendelian randomization.
Clinical co-occurrence of UC (Ulcerative Colitis) and T2DM (Type 2 Diabetes Mellitus) is observed. The aim of this study is to investigate the potential causal relationship between Ulcerative Colitis (UC) and Type 2 Diabetes Mellitus (T2DM) using LDSC and LAVA analysis, followed by genetic verification through TSMR, providing insights for clinical prevention and treatment.
Genetic loci closely related to T2DM were extracted as instrumental variables from the GWAS database, with UC as the outcome variable, involving European populations. The UC data included 27,432 samples and 8,050,003 SNPs, while the T2DM data comprised 406,831 samples and 11,914,699 SNPs. LDSC and LAVA were used for quantifying genetic correlation at both global (genome-wide) and local (genomic regions) levels. MR analysis was conducted using IVW, MR-Egger regression, Weighted median, and Weighted mode, assessing the causal relationship between UC and diabetes with OR values and 95% CI. Heterogeneity and pleiotropy were tested using Egger-intercept, MR-PRESSO, and sensitivity analysis through the "leave-one-out" method and Cochran Q test. Subsequently, a reverse MR operation was conducted using UC as the exposure data and T2DM as the outcome data for validation.
Univariable and bivariable LDSC calculated the genetic correlation and potential sample overlap between T2DM and UC, resulting in rg = -0.0518, se = 0.0562, P = 0.3569 with no significant genetic association found for paired traits. LAVA analysis identified 9 regions with local genetic correlation, with 6negative and 3 positive associations, indicating a negative correlation between T2DM and UC. MR analysis, with T2DM as the exposure and UC as the outcome, involved 34 SNPs as instrumental variables. The OR values and 95% CI from IVW, MR-Egger, Weighted median, and Weighted mode were 0.917 (0.848~0.992), 0.949 (0.800~1.125), 0.881 (0.779~0.996), 0.834(0.723~0.962) respectively, with IVW P-value < 0.05, suggesting a negative causal relationship between T2DM and UC. MR-Egger regression showed an intercept of -0.004 with a standard error of 0.009, P = 0.666, and MR-PRESSO Global Test P-value > 0.05, indicating no pleiotropy and no outliers detected. Heterogeneity tests showed no heterogeneity, and the "leave-one-out" sensitivity analysis results were stable. With UC as the exposure and T2DM as the outcome, 32 SNPs were detected, but no clear causal association was found.
There is a causal relationship between T2DM and UC, where T2DM reduces the risk of UC, while no significant causal relationship was observed from UC to T2DM.
Hu YZ
，Chen Z
，Zhou MH
，Zhao ZY
，Wang XY
，Huang J
，Li XT
，Zeng JN
... -
《Frontiers in Immunology》
Genetically proxied glucose-lowering drug target perturbation and risk of cancer: a Mendelian randomisation analysis.
Epidemiological studies have generated conflicting findings on the relationship between glucose-lowering medication use and cancer risk. Naturally occurring variation in genes encoding glucose-lowering drug targets can be used to investigate the effect of their pharmacological perturbation on cancer risk.
We developed genetic instruments for three glucose-lowering drug targets (peroxisome proliferator activated receptor γ [PPARG]; sulfonylurea receptor 1 [ATP binding cassette subfamily C member 8 (ABCC8)]; glucagon-like peptide 1 receptor [GLP1R]) using summary genetic association data from a genome-wide association study of type 2 diabetes in 148,726 cases and 965,732 controls in the Million Veteran Program. Genetic instruments were constructed using cis-acting genome-wide significant (p<5×10-8) SNPs permitted to be in weak linkage disequilibrium (r2<0.20). Summary genetic association estimates for these SNPs were obtained from genome-wide association study (GWAS) consortia for the following cancers: breast (122,977 cases, 105,974 controls); colorectal (58,221 cases, 67,694 controls); prostate (79,148 cases, 61,106 controls); and overall (i.e. site-combined) cancer (27,483 cases, 372,016 controls). Inverse-variance weighted random-effects models adjusting for linkage disequilibrium were employed to estimate causal associations between genetically proxied drug target perturbation and cancer risk. Co-localisation analysis was employed to examine robustness of findings to violations of Mendelian randomisation (MR) assumptions. A Bonferroni correction was employed as a heuristic to define associations from MR analyses as 'strong' and 'weak' evidence.
In MR analysis, genetically proxied PPARG perturbation was weakly associated with higher risk of prostate cancer (for PPARG perturbation equivalent to a 1 unit decrease in inverse rank normal transformed HbA1c: OR 1.75 [95% CI 1.07, 2.85], p=0.02). In histological subtype-stratified analyses, genetically proxied PPARG perturbation was weakly associated with lower risk of oestrogen receptor-positive breast cancer (OR 0.57 [95% CI 0.38, 0.85], p=6.45×10-3). In co-localisation analysis, however, there was little evidence of shared causal variants for type 2 diabetes liability and cancer endpoints in the PPARG locus, although these analyses were likely underpowered. There was little evidence to support associations between genetically proxied PPARG perturbation and colorectal or overall cancer risk or between genetically proxied ABCC8 or GLP1R perturbation with risk across cancer endpoints.
Our drug target MR analyses did not find consistent evidence to support an association of genetically proxied PPARG, ABCC8 or GLP1R perturbation with breast, colorectal, prostate or overall cancer risk. Further evaluation of these drug targets using alternative molecular epidemiological approaches may help to further corroborate the findings presented in this analysis.
Summary genetic association data for select cancer endpoints were obtained from the public domain: breast cancer ( https://bcac.ccge.medschl.cam.ac.uk/bcacdata/ ); and overall prostate cancer ( http://practical.icr.ac.uk/blog/ ). Summary genetic association data for colorectal cancer can be accessed by contacting GECCO (kafdem at fredhutch.org). Summary genetic association data on advanced prostate cancer can be accessed by contacting PRACTICAL (practical at icr.ac.uk). Summary genetic association data on type 2 diabetes from Vujkovic et al (Nat Genet, 2020) can be accessed through dbGAP under accession number phs001672.v3.p1 (pha004945.1 refers to the European-specific summary statistics). UK Biobank data can be accessed by registering with UK Biobank and completing the registration form in the Access Management System (AMS) ( https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access ).
Yarmolinsky J
，Bouras E
，Constantinescu A
，Burrows K
，Bull CJ
，Vincent EE
，Martin RM
，Dimopoulou O
，Lewis SJ
，Moreno V
，Vujkovic M
，Chang KM
，Voight BF
，Tsao PS
，Gunter MJ
，Hampe J
，Pellatt AJ
，Pharoah PDP
，Schoen RE
，Gallinger S
，Jenkins MA
，Pai RK
，PRACTICAL consortium
，VA Million Veteran Program
，Gill D
，Tsilidis KK
... -
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ResearchGate
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