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Background: Blood counts and biochemical markers are among the most common tests performed in hospitals and most readily accepted by patients, and are widely regarded as reliable biomarkers in the literature. The aim of this study was to assess the causal relationship between blood counts, biochemical indicators and pulmonary arterial hypertension (PAH). Methods: A two-sample Mendelian randomization (MR) analysis was performed to assess the causal relationship between blood counts and biochemical indicators with PAH. The genome-wide association study (GWAS) for blood counts and biochemical indicators were obtained from the UK Biobank (UKBB), while the GWAS for PAH were sourced from the FinnGen Biobank. Inverse variance weighting (IVW) was used as the primary analysis method, supplemented by three sensitivity analyses to assess the robustness of the results. And we conducted an observational study using data from National Health and Nutrition Examination Survey (NHANES) 2003–2018 to verify the relationship. Results: The MR analysis primarily using the IVW method revealed genetic variants of platelet count (OR=2.51, 95% CI 1.56-4.22, P<0.001), platelet crit(OR=1.87, 95% CI1.17-7.65, P=0.022), direct bilirubin (DBIL)(OR=1.71, 95%CI 1.18-2.47,P=0.004), insulin-like growth factor (IGF-1)(OR=0.51, 95% CI 0.27-0.96, P=0.038), Lipoprotein A (Lp(a))(OR=0.66, 95% CI 0.45-0.98, P=0.037) and total bilirubin (TBIL)(OR=0.51, 95% CI 0.27-0.96, P=0.038) were significantly associated with PAH. In NHANES, multivariate logistic regression analyses revealed a significant positive correlation between platelet count and volume and the risk of PAH, and a significant negative correlation between total bilirubin and PAH. [ABSTRACT FROM AUTHOR]

To investigate whether the number of years of schooling are causally associated traumatic brain injury (TBI). We aimed to investigate whether the number of years of schooling are causally associated TBI. We investigate the prospective causal effect of years of schooling on TBI using summary statistical data. The statistical dataset comprising years of schooling (n = 293,723) from genome-wide association studies (GWASs) deposited in the UK Biobank was used for exposure. We used the following GWAS available in the FinnGen dataset: individuals with TBI (total = 13,165; control = 136,576; number of single nucleotide polymorphisms [SNPs] = 16,380,088). Seventy significant genome-wide SNPs from GWAS datasets with annotated years of schooling were selected as instrumental variables. The inverse variance weighted method results supported a causal relationship between years of schooling and TBI (odds ratio (OR), 0.78; 95 % confidence interval (CI), 0.62–0.98; P = 0.029). MR-Egger regression showed that polydirectionality was unlikely to bias the results (intercept = 0.007, SE = 0.01, P = 0.484) and demonstrated no causal relationship between years of schooling and TBI (OR, 0.52; 95%CI, 0.17–1.64; P = 0.270). The weighted median method revealed a causal relationship with TBI (OR, 0.73; 95%CI, 0.55–0.98; P = 0.047). A Cochran's Q test and funnel plot did not show heterogeneity nor asymmetry, indicating no directional pleiotropy. The current investigation yields substantiation of a causal association between years of schooling and TBI development. More years of schooling may be causally associated with a reduced risk of TBI, which has implications for clinical and public health practices and policies. • To explore the causal association between years of schooling and traumatic brain injury with Mendelian randomization. • More years of schooling results in a lower risk of traumatic brain injury. • Our data provides the evidence required to develop clinical and public health policies. [ABSTRACT FROM AUTHOR]

Background: A mounting body of evidence suggests a strong connection between gut microbiota and the risk of frailty. However, the question of causality remains unanswered. In this study, we employed a Mendelian randomization (MR) approach to assess potential causal relationships between gut microbiota and the risk of frailty. Materials and methods: Summary statistics for the gut microbiome were obtained from a genome wide association study (GWAS) meta-analysis of the MiBioGen consortium (N = 18,340). Summary statistics for frailty were obtained from a GWAS meta-analysis, including the UK Biobank and TwinGene (N = 175,226). Our primary analysis utilized the inverse variance weighted (IVW) method. To enhance the robustness of our results, we also applied weighted median methods, MR Egger regression, and MR pleiotropy residual sum and outlier test. Finally, we conducted reverse MR analysis to investigate the potential for reverse causality. Results: IVW method identified 7 bacterial taxa nominally associated with the risk of FI. Class Bacteroidia (p = 0.033) and genus Eubacterium ruminantium group (p = 0.028) were protective against FI. In addition, class Betaproteobacteria (p = 0.042), genus Allisonella (p = 0.012), genus Bifidobacterium (p = 0.013), genus Clostridium innocuum group (p = 0.036) and genus Eubacterium coprostanoligenes group (p = 0.003) were associated with a higher risk of FI. No pleiotropy or heterogeneity were found. Conclusion: The MR analysis indicates a causal relationship between specific gut microbiota and FI, offering new insights into the mechanisms underlying FI mediated by gut microbiota. [ABSTRACT FROM AUTHOR]

Background: In this study we used Mendelian randomization (MR) to investigate the potential causal association of lipoprotein (a) [Lp(a)] levels with pulse wave velocity (PWV). Methods: Genetic variants associated with Lp(a) were retrieved from the UK Biobank GWAS (N = 290,497). A non‐ overlapping GWAS based on a European cohort (N = 7,000) was used to obtain genetic associations with PWV (outcome) and utilized two different measures for the same trait, brachial–ankle (baPWV) and carotid–femoral (cfPWV) PWV. We applied a two‐sample MR using the inverse variance weighting method (IVW) and a series of sensitivity analyses for 170 SNPs that were selected as instrumental variables (IVs). Results: Our analyses do not support a causal association between Lp(a) and PWV for neither measurement [βiwv(baPWV) = −.0005, p =.8 and βiwv(cfPWV) = −.006, p =.16]. The above findings were consistent across sensitivity analyses including weighted median, mode‐based estimation, MR‐Egger regression and MR‐PRESSO. Conclusion: We did not find evidence indicating that Lp(a) is causally associated with PWV, the gold standard marker of arterial stiffness. [ABSTRACT FROM AUTHOR]

Background: Migraine has an increased prevalence in several immune disorders, but genetic cause-effect relationships remain unclear. Mendelian randomization (MR) was used in this study to explore whether immune diseases are causally associated with migraine and its subtypes. Methods: We conducted a two-sample bidirectional multivariate Mendelian randomization study. Single-nucleotide polymorphisms (SNP) for six immune diseases, including rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), type 1 diabetes mellitus (T1D), allergic rhinitis (AR), asthma and psoriasis, were used as genetic instrumental variables. Summary statistics for migraine were obtained from 3 databases: the International Headache Genetics Consortium (IHGC), UK Biobank, and FinnGen study. MR analyses were performed per outcome database for each exposure and subsequently meta-analyzed. Reverse MR analysis was performed to determine whether migraine were risk factors for immune diseases. In addition, we conducted a genetic correlation to identify shared genetic variants for these two associations. Results: No significant causal relationship was found between immune diseases and migraine and its subtypes. These results were robust with a series of sensitivity analyses. Using the linkage disequilibrium score regression method (LDSC), we detected no genetic correlation between migraine and immune diseases. Conclusion: The evidence from our study does not support a causal relationship between immune diseases and migraine. The mechanisms underlying the frequent comorbidity of migraine and several immune diseases need to be further elucidated. [ABSTRACT FROM AUTHOR]

Background: Previous observational studies have shown that a potential relationship between anti-Helicobacter pylori (H. pylori) IgG levels and Myocardial Infarction (MI). Nevertheless, the evidence for the causal inferences remains disputable. To further clarify the relationship between anti-H. pylori IgG levels and MI and explore its pathogenesis, we conducted a Mendelian randomization (MR) analysis. Methods: In this study, we used two-sample Mendelian Randomization (MR) to assess the causality of anti-H. pylori IgG levels on MI and potential pathogenesis, 12 single nucleotide polymorphisms (SNPs) related to anti-H. pylori IgG levels were obtained from the European Bioinformatics Institute (EBI). Summary data from a large-scale GWAS meta-analysis of MI was utilized as the outcome dataset. Summary data of mediators was obtained from the FinnGen database, the UK Biobank, the EBI database, MRC-IEU database, the International Consortium of Blood Pressure, the Consortium of Within family GWAS. Inverse variance weighted (IVW) analysis under the fixed effect model was identified as our main method. To ensure the reliability of the findings, many sensitivity analyses were performed. Results: Our study revealed that increases of anti-H. pylori IgG levels were significantly related to an increased risk of MI (OR, 1.104; 95% CI,1.042-1.169; p = 7.084 × 10-4) and decreases in HDL cholesterol levels (β, -0.016; 95% CI, -0.026 to -0.006; p = 2.02 × 10-3). In addition, there was no heterogeneity or pleiotropy in our findings. Conclusion: This two-sample MR analysis revealed the causality of anti-H. pylori IgG levels on MI, which might be explained by lower HDL cholesterol levels. Further research is needed to clarify the results. [ABSTRACT FROM AUTHOR]

Background: Stroke is a major cause of mortality and long-term disability worldwide. Whether the associations between brain imaging-derived phenotypes (IDPs) and stroke are causal is uncertain. Methods: We performed two-sample bidirectional Mendelian randomization (MR) analyses to explore the causal associations between IDPs and stroke. Summary data of 587 brain IDPs (up to 33,224 individuals) from the UK Biobank and five stroke types (sample size range from 301,663 to 446,696, case number range from 5,386 to 40,585) from the MEGASTROKE consortium were used. Results: Forward MR indicated 14 IDPs belong to projection fibers or association fibers were associated with stroke. For example, higher genetically determined mean diffusivity (MD) in the right external capsule was causally associated with an increased risk of small vessel stroke (IVW OR = 2.76, 95% CI 2.07 to 3.68, P = 5.87 × 10−12). Reverse MR indicated that genetically determined higher risk of any ischemic stroke was associated with increased isotropic or free water volume fraction (ISOVF) in body of corpus callosum (IVW β = 0.23, 95% CI 0.14 to 0.33, P = 3.22 × 10−7). This IDP is a commissural fiber and it is not included in the IDPs identified by forward MR. Conclusions: We identified 14 IDPs with statistically significant evidence of causal effects on stroke or stroke subtypes. We also identified potential causal effects of stroke on one IDP of commissural fiber. These findings might guide further work toward identifying preventative strategies at the brain imaging levels. [ABSTRACT FROM AUTHOR]

Background: Multiple risk factors of stroke have been identified in previous studies; however, the causal role of snoring in the onset of stroke is less investigated. To clarify the causal association of snoring on stroke and its subtypes, this study is performed. Methods: The single nucleotide polymorphisms in relation to snoring were retrieved from the UK biobank cohort with 408,317 participants. The data for stroke and its subtypes of European ancestry (67,162 cases and 453,702 controls) were obtained fromtheMEGASTROKE consortium. In single-variable Mendelian randomization (SVMR) and multivariable MR (MVMR) analyses, inverse variance weighting was used as the primary estimate, complemented with sensitivity analyses more robust to pleiotropy. Results: Genetically predicted snoring increased the risk of stroke (odds ratio [OR] = 2.69, 95% confidence interval [CI] = 1.19-6.08, P = 0.016) and ischemic stroke (IS) (OR = 2.82, 95% CI = 1.23-6.44, P = 0.013), but not large artery stroke (LAS) (OR = 3.02, 95% CI = 0.31-29.44, P = 0.339), cardioembolic stroke (CES) (OR = 1.51, 95% CI = 0.58-3.92, P = 0.395). We provide novel genetic evidence that snoring increases the risk of stroke and IS, but not LAS, CES, and SVS. Conclusion: Our findings provide novel genetic evidence that snoring increases the risk of stroke and IS, but not LAS, CES, and SVS. [ABSTRACT FROM AUTHOR]

Purpose: Observational research has indicated the presence of a causal relationship between sleep disturbances and low back pain (LBP). However, the link may have been biased by confounding factors. The purpose of this study was to examine the potential causal association of insomnia and daytime sleepiness with LBP by using mendelian randomization (MR). Methods: Genome-wide association study (GWAS) summary statistics of insomnia were obtained from a large-scale GWAS meta-analysis (n = 1,331,010; individuals from UK Biobank and 23andMe) or UK Biobank alone (n = 453,379). The summary statistics of daytime sleepiness were from UK Biobank (n = 452,071) and LBP were provided by the FinnGen Release 6 (210,645 individuals with 16,356 LBP cases and 194,289 controls) or UK Biobank (5,423 cases versus 355,771 controls). Linkage disequilibrium score (LDSC) regression and bidirectional MR analysis was employed to estimate genetic correlation and causal relationship. In the MR analysis, the inverse variance weighted method (IVW) was utilized as the main analysis procedure, while MR-Egger, Weighted median and Robust adjusted profile score (RAPS) were utilized for supplementary analyses. Results: LDSC analysis showed that LBP were significantly genetically correlated with insomnia (rg = 0.57, p = 2.26e-25) and daytime sleepiness (rg = 0.18, p = 0.001). The MR analysis revealed that genetically predicted insomnia was significantly associated with an increased risk of LBP (OR = 1.250, 95% CI: 1.186-1.318; p = 1.69e-16). However, the reverse causality was not confirmed. No evidence was identified supporting causality of daytime sleepiness and LBP. Conclusion: This study demonstrates a putative causal link of insomnia on LBP and a null causal effect of LBP on insomnia. Furthermore, a causal link between daytime sleepiness and LBP were not reported. This finding may stimulate new strategies for patient management in clinical practice, benefiting public health. [ABSTRACT FROM AUTHOR]

Backgrounds: Epidemiological studies have repeatedly investigated the association between obesity related anthropometric indicators and body compositions and osteoarthritis (OA). However, the results have remained inconsistent. This work aimed to investigate the genetic correlation and causal associations of obesity related anthropometric indicators and body compositions with knee and hip OA. Methods: Single-nucleotide polymorphisms associated with the exposures were searched from the recent genome-wide association studies (GWAS) to obtain full statistics. Summary-level results of knee and hip OA were from the UK Biobank and arcOGEN. First, linkage disequilibrium score regression (LD score regression) was applied to detect the genetic correlation (rg). We further performed a series of sensitivity analyses as validation of primary mendelian randomization (MR) results and the specific evidence of potential causal effects was defined. Results: We found that genetic components in OA had significant correlation with obesity related traits, except waist-to-hip ratio. In the univariable MR analysis, with the exception of waist-to-hip ratio, obesity related anthropometric indicators were causally associated with increased risks of knee and hip OA. For obesity related body compositions, higher fat-free mass in arm, leg, and whole body increased the risk of knee OA but only fat-free mass in leg showed a significant association with hip OA. Meanwhile trunk fat mass and trunk fat percentage, were associated with knee but not with hip OA. Higher fat mass, and fat percentage in arm, leg, and whole body increased the risk of both knee and hip OA. After adjusting for BMI, the multivariable MR showed maintained results in knee OA. However, in hip OA, only fat mass and fat-free mass in arm, leg, trunk and whole body were significantly associated with the risk of hip OA. Conclusion: The present study suggests genetic evidence for certain causal associations of obesity related anthropometric indicators and body compositions with knee and hip OA, which may provide important insights for the prevention and treatment on OA. [ABSTRACT FROM AUTHOR]

Background: The association reported between tea intake and type 2 diabetes (T2D) is inconsistent in previous studies and remains controversial. We aimed to explore the causal relationship between tea intake, T2D, and glycemic traits including hemoglobin A1c (HbA1c), fasting plasma glucose (FPG), fasting serum insulin (FSI), and homeostasis model of insulin resistance (HOMA-IR) levels. Methods: A 2-sample Mendelian randomization (MR) was performed using summary statistics from large-scale genome-wide association studies of tea intake from the UK Biobank, T2D from the DIAGRAM consortium, and glycemic traits from the Magic consortium. The findings were verified through sensitivity analyses using various MR methods with different model assumptions and by comprehensively evaluating the influence of pleiotropy effects and outliers. Results: With the use of a two-sample MR with inverse variance-weighted method, the odds ratio per unit SD change of tea intake (SD: 2.85 cups/day) for T2D, HbA1c, FPG, FSI, and HOMA-IR levels was 0.949 (95% CI 0.844–1.067, p = 0.383), 0.994 (95% CI 0.975–1.013, p = 0.554), 0.996 (95% CI 0.978–1.015, p = 0.703), 0.968 (95% CI 0.948–0.986, p = 0.001), and 0.953 (95% CI 0.900–1.009, p = 0.102), respectively. The results were consistent with those of the other six methods that we used with different model assumptions, suggesting that the findings were robust and convincing. We also performed various sensitivity analyses for outlier removal, pleiotropy detection, and leave-one-out analysis. Conclusion: Our MR results did not support the causal effect of tea intake on T2D and crucial glycemic traits. These findings suggest that previous observational studies may have been confounded. [ABSTRACT FROM AUTHOR]

Evidence from observational studies for the effect of tea consumption on obesity is inconclusive. This study aimed to verify the causal association between tea consumption and obesity through a two-sample Mendelian randomization (MR) analysis in general population-based datasets. The genetic instruments, single nucleotide polymorphisms (SNPs) associated with tea consumption habits, were obtained from genome-wide association studies (GWAS): UK Biobank, Nurses' Health Study, Health Professionals Follow-up Study, and Women's Genome Health Study. The effect of the genetic instruments on obesity was analyzed using the UK Biobank dataset (among ∼500,000 participants). The causal relationship between tea consumption and obesity was analyzed by five methods of MR analyses: inverse variance weighted (IVW) method, MR-Egger regression method, weighted median estimator (WME), weighted mode, and simple mode. Ninety-one SNPs were identified as genetic instruments in our study. A mild causation was found by IVW (odds ratio [OR] = 0.998, 95% confidence interval [CI] = 0.996 to 1.000, p = 0.049]), which is commonly used in two-sample MR analysis, indicating that tea consumption has a statistically significant but medically weak effect on obesity control. However, the other four approaches did not show significance. Since there was no heterogeneity and pleiotropy in this study, the IVW approach has the priority of recommendation. Further studies are needed to clarify the effects of tea consumption on obesity-related health problems in detail. [ABSTRACT FROM AUTHOR]

Background: A former cohort study has raised concern regarding the unanticipated hazard of omeprazole in expediting osteoarthritis (OA) advancement. The precise nature of their causal evidence, however, remains undetermined. The present research endeavors to investigate the underlying causal link between omeprazole and OA through the application of mendelian randomization (MR) analysis., Methods: The study incorporated the ukb-a-106 and ukb-b-14,486 datasets. The investigation of causal effects employed methodologies such as MR-Egger, Weighted median, Inverse variance weighted (IVW) with multiplicative random effects, and IVW (fixed effects). The IVW approach was predominantly considered for result interpretation. Sensitivity analysis was conducted, encompassing assessments for heterogeneity, horizontal pleiotropy, and the Leave-one-out techniques., Results: The outcomes of the MR analysis indicated a causal relationship between omeprazole and OA, with omeprazole identified as a contributing risk factor for OA development (IVW model: OR = 1.2473, P < 0.01 in ukb-a-106; OR = 1.1288, P < 0.05 in ukb-b-14,486). The sensitivity analysis underscored the robustness and dependability of the above-mentioned analytical findings., Conclusion: This study, employing MR, reveals that omeprazole, as an exposure factor, elevates the risk of OA. Considering the drug's efficacy and associated adverse events, clinical practitioners should exercise caution regarding prolonged omeprazole use, particularly in populations with heightened OA risks. Further robust and high-quality research is warranted to validate our findings and guide clinical practice., (© 2024. The Author(s).)

Epidemiology studies suggested that low birthweight was associated with a higher risk of hypertension in later life. However, little is known about the causality of such associations. In our study, we evaluated the causal association of low birthweight with adulthood hypertension following a standard analytic protocol using the study-level data of 183,433 participants from 60 studies (CHARGE-BIG consortium), as well as that with blood pressure using publicly available summary-level genome-wide association data from EGG consortium of 153,781 participants, ICBP consortium and UK Biobank cohort together of 757,601 participants. We used seven SNPs as the instrumental variable in the study-level analysis and 47 SNPs in the summary-level analysis. In the study-level analyses, decreased birthweight was associated with a higher risk of hypertension in adults (the odds ratio per 1 standard deviation (SD) lower birthweight, 1.22; 95% CI 1.16 to 1.28), while no association was found between genetically instrumented birthweight and hypertension risk (instrumental odds ratio for causal effect per 1 SD lower birthweight, 0.97; 95% CI 0.68 to 1.41). Such results were consistent with that from the summary-level analyses, where the genetically determined low birthweight was not associated with blood pressure measurements either. One SD lower genetically determined birthweight was not associated with systolic blood pressure (β = − 0.76, 95% CI − 2.45 to 1.08 mmHg), 0.06 mmHg lower diastolic blood pressure (β = − 0.06, 95% CI − 0.93 to 0.87 mmHg), or pulse pressure (β = − 0.65, 95% CI − 1.38 to 0.69 mmHg, all p > 0.05). Our findings suggest that the inverse association of birthweight with hypertension risk from observational studies was not supported by large Mendelian randomization analyses. [ABSTRACT FROM AUTHOR]