Multiple Random Effects Research Articles

Genomic prediction using the lmekin function from the coxme R package

The increasing use of genomic selection (GS) in plant and animal breeding programs has led to the development of software that fits models based on unique scenarios. Accordingly, several R packages have been developed for GS. The lmekin function from the coxme R package was one of the first functions implemented in R to fit models with random family effects using the pedigree–based relationship matrix. The function allows the user to provide the covariance structures for the random effects; thus, the GBLUP model can be fitted. This fitting process consists of replacing, in the traditional BLUP model, the additive relationship matrix derived from a pedigree by the additive relationship matrix derived from markers. Thus, the objective of this study was to employ the lmekin function in the context of genomic prediction by comparing the results of this function with those obtained using five R packages for GS: rrBLUP, BGLR, sommer, lme4qtl, and lme4GS. The comparisons were performed considering the computational times and predicted values for a wheat dataset and simulated big data. In addition, we implemented a 5-fold cross-validation scheme through considering the values predicted by the lmekin function for the wheat dataset. The results indicated that the lmekin function was effective in predicting genomic breeding values considering multiple random effects and relatively small sample sizes. The rrBLUP package processed the fastest for the scenario with only one genetic random effect, and the high temporal efficiency of the sommer package was confirmed for the scenario with more than one genetic random effect. Differences in computational times occurred because of the different algorithms implemented in the packages to estimate the variance components.

Bidirectional causal relationship between hypercholesterolemia and ischemic heart disease: a Mendelian randomization study.

Ischemic Heart Disease (IHD) is a leading cause of morbidity and mortality worldwide. Hypercholesterolaemia, a metabolic syndrome distinguished by elevated cholesterol levels, is positively correlated with IHD, yet the precise causal relationship between these two health conditions remains to be clearly defined. We conducted a two-sample MR analysis using genetic variants associated with hypercholesterolemia and IHD. Various statistical techniques including MR-Egger, Weighted Median, Inverse Variance Weighted (IVW), Simple Mode, and Weighted Mode were employed. We also performed sensitivity analyses to assess pleiotropy, heterogeneity, and influence of individual SNPs. Furthermore, genetic co-localization analysis was performed to identify shared genes between hypercholesterolemia and IHD. Our MR study illuminated a bidirectional causal relationship between hypercholesterolaemia and ischaemic heart disease. Utilising the IVW with multiplicative random effects, upon considering IHD as the outcome, we identified an OR of 2.27 (95% CI: 1.91-2.70, p = 1.68 × 10-20). Conversely, when hypercholesterolaemia was viewed as the outcome, the OR detected was 1.80 (95% CI: 1.58-2.05, p = 2.79 × 10-19). These findings remained consistent across various MR methods and sensitivity analyses. Additionally, our research pinpointed four co-localised genes CELSR2, PCSK9, LPA, and APOE as integral candidates implicated in the pathogenesis of both conditions, thereby suggesting shared common genetic causal variants and offering potential targets for innovative therapeutic strategies. bidirectional MR studies reveal genetic evidence of a potential causal link between hypercholesterolaemia and IHD. Notably, these findings also lend credence to the less traditional hypothesis that IHD may instigate hypercholesterolaemia episodes. Moreover, co-localisation analyses intimate the presence of shared genetic causal variants, paving the way for the development of new therapeutic strategies.

Causal associations between liver traits and Colorectal cancer: a Mendelian randomization study

ObjectiveThis study aimed to investigate the causal associations between several liver traits (liver iron content, percent liver fat, alanine transaminase levels, and liver volume) and colorectal cancer (CRC) risk using a Mendelian randomization (MR) approach to improve our understanding of the disease and its management.MethodsGenetic variants were used as instrumental variables, extracted from genome-wide association studies (GWAS) datasets of liver traits and CRC. The Two-Sample MR package in R was used to conduct inverse variance weighted (IVW), MR Egger, Maximum likelihood, Weighted median, and Inverse variance weighted (multiplicative random effects) MR approaches to generate overall estimates of the effect. MR analysis was conducted with Benjamini-Hochberg method-corrected P values to account for multiple testing (P < 0.013). MR-PRESSO was used to identify and remove outlier genetic variants in Mendelian randomization (MR) analysis. The MR Steiger test was used to assess the validity of the assumption that exposure causes outcomes. Leave-one-out validation, pleiotropy, and heterogeneity testing were also conducted to ensure the reliability of the results. Multivariable MR was utilized for validation of our findings using the IVW method while also adjusting for potential confounding or pleiotropy bias.ResultsThe MR analysis suggested a causal effect between liver volume and a reduced risk of CRC (OR 0.60; 95% CI, 0.44–0.82; P = 0.0010) but did not provide evidence for causal effects of liver iron content, percent liver fat, or liver alanine transaminase levels. The MR-PRESSO method did not identify any outliers, and the MR Steiger test confirmed that the causal direction of the analysis results was correct in the Mendelian randomization analysis. MR results were consistent with heterogeneity and pleiotropy analyses, and leave-one-out analysis demonstrated the overall values obtained were consistent with estimates obtained when all available SNPs were included in the analysis. Multivariable MR was utilized for validation of our findings using the IVW method while also adjusting for potential confounding or pleiotropy bias.ConclusionThe study provides tentative evidence for a causal role of liver volume in CRC, while genetically predicted levels of liver iron content, percent liver fat, and liver alanine transaminase levels were not associated with CRC risk. The findings may inform the development of targeted therapeutic interventions for colorectal liver metastasis (CRLM) patients, and the study highlights the importance of MR as a powerful epidemiological tool for investigating causal associations between exposures and outcomes.

Mind, A methodology for multivariate small area estimation with multiple random effects

mind, A methodology for multivariate small area estimation with multiple random effects

Causal association of physical activity with low back pain, intervertebral disc degeneration and sciatica: a two-sample mendelian randomization analysis study.

Objective: Previous studies are insufficient to confirm a causal association between physical activity (PA) and low back pain (LBP), intervertebral disc degeneration (IDD), and sciatica. The present study used a two-sample Mendelian randomization (MR) analysis method to demonstrate whether or not there was a causal connection. Methods: First, four PA phenotypes were selected [accelerometer-based PA (average acceleration), accelerometer-based PA (acceleration fraction >425mg), self-reported moderate-to-vigorous PA, and self-reported vigorous PA], setting thresholds for single nucleotide polymorphisms (SNPs) significantly concerned with PA p < 5 × 10-8, linkage disequilibrium (LD) r 2 < 0.01, genetic distance >5,000kb, and F-value >10. SNPs associated with the outcome and confounding factors were then excluded using the PhenoScanncer database. Finally, after coordinating the genetic instruments from genome-wide association studies (GWAS) effect alleles for exposure and outcomes, multiplicative random effects inverse variance weighting (IVW), MR-Egger, weighted median method (WMM), and weighted mode method were used to assess exposure-outcome causality and perform sensitivity analysis on the estimated results. Results: The current study's IVW findings revealed proof of a causal connection between PA and LBP. While there was a positive causal tie between accelerometer-based PA (acceleration fraction >425mg) and LBP [OR: 1.818, 95% CI:1.129-2.926, p = 0.012], there was a negative causal link between accelerometer-based PA (average acceleration) and LBP [OR: 0.945, 95% CI: 0.909-0.984, p = 0.005]. However causal relationship between PA and IDD or sciatica was not found. Conclusion: Increasing average PA but needing to avoid high-intensity PA may be an effective means of preventing low back pain. Although PA is not directly causally related to disc degeneration and sciatica, it can act through indirect pathways.

Causal effects of genetically vitamins and sepsis risk: a two-sample Mendelian randomization study

BackgroundIn recent years, observational studies have been conducted to investigate the potential impact of vitamins on sepsis. However, many of these studies have produced inconsistent results. Our Mendelian randomization (MR) study aims to evaluate the causality between vitamins and sepsis from a genetic perspective.MethodsOur MR study was designed following the STROBE-MR guidelines. Genetic instrumental variables for vitamins including folate, vitamin B12, B6, A (Retinol), C, D, and K were obtained from previous genome-wide association studies (GWAS) and MR studies. Five different sepsis severity levels were included in the analysis. The genetic instrumental variables were screened for potential confounders using PhenoScanner V2. MR analysis was performed using MR-egger, inverse-variance weighted multiplicative random effects (IVW-RE), inverse-variance weighted multiplicative fixed-effects (IVW-FE), and wald ratio methods to assess the relationship between vitamins and sepsis. Sensitivity analysis was performed using the MR-egger_intercept method, and the MR-PRESSO package and Cochran’s Q test were used to evaluate the heterogeneity of the instrumental variables.ResultsOur MR study found no statistically significant association between vitamins and sepsis risk, regardless of the type of vitamin (P-value > 0.05). The odds ratios (ORs) for folate, vitamin B6, vitamin B12, vitamin A, vitamin D, vitamin K, and vitamin C were 1.164 (95% CI: 0.895–1.514), 0.987 (95% CI: 0.969–1.005), 0.975 (95% CI: 0.914–1.041), 0.993 (95% CI: 0.797–1.238), 0.861 (95% CI: 0.522–1.42), 0.955 (95% CI: 0.86–1.059), and 1.049 (95% CI: 0.911–1.208), respectively. Similar results were observed in subgroups of different sepsis severity levels.ConclusionsOur MR study found no evidence of a causal association between vitamins and sepsis risk from a genetic perspective. Further randomized controlled trials are necessary to confirm these results.

Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data.

Accurate disease risk prediction is an essential step in the modern quest for precision medicine. While high-dimensional multi-omics data have provided unprecedented data resources for prediction studies, their high-dimensionality and complex inter/intra-relationships have posed significant analytical challenges. We proposed a two-step Bayesian linear mixed model framework (TBLMM) for risk prediction analysis on multi-omics data. TBLMM models the predictive effects from multi-omics data using a hybrid of the sparsity regression and linear mixed model with multiple random effects. It can resemble the shape of the true effect size distributions and accounts for non-linear, including interaction effects, among multi-omics data via kernel fusion. It infers its parameters via a computationally efficient variational Bayes algorithm. Through extensive simulation studies and the prediction analyses on the positron emission tomography imaging outcomes using data obtained from the Alzheimer's Disease Neuroimaging Initiative, we have demonstrated that TBLMM can consistently outperform the existing method in predicting the risk of complex traits. The corresponding R package is available on GitHub (https://github.com/YaluWen/TBLMM).

The association between thyroid and breast cancers: a bidirectional mendelian randomization study.

Thyroid and breast cancers are the two most frequent types of endocrine-related tumors among women worldwide, and their incidence is still on the rise. Observational studies have shown a relationship between thyroid and breast cancers. Nevertheless, many confounders predispose the results to interference. Accordingly, we performed a two-sample Mendelian randomization (MR) study to investigate the causal association between thyroid and breast cancers. We acquired breast cancer data from the UK Biobank (13,879 breast cancer cases and 198,523 controls) and the Breast Cancer Association Consortium (BCAC; 122,977 breast cancer cases and 105,974 controls), and thyroid cancer data from FinnGen Biobank (989 thyroid cancer and 217,803 controls). Then, the multiplicative random effects inverse variance weighting (IVW), weight median (WM), and MR Egger methods were executed for MR analysis. Overall, IVW showed a causal effect of breast cancer on thyroid cancer using the BCAC dataset (odds ratio [OR] = 1.17; 95% confidence interval [CI] = 1.036-1.322; P = 0.011), and this relationship was also supported by the UK Biobank dataset (OR = 23.899; 95% CI = 2.331-245.003; P = 0.007), which showed that breast cancer patients were more likely to be diagnosed with thyroid cancer. On the whole, the reverse MR analysis did not show a causal effect of breast cancer on thyroid cancer. However, IVW showed a causal effect of thyroid cancer on estrogen receptor -negative breast cancer using the BCAC dataset (OR = 1.019; 95% CI = 1.001-1.038; P = 0.043), which suggested that people with thyroid cancer were more likely to develop breast cancer. Breast cancer represents a possible risk factor for thyroid cancer and thyroid cancer also represents a possible risk factor for ER-negative breast cancer. Future studies using powerful genetic tools to determine the causal relationship between breast and thyroid cancers are required.

Bayesian linear mixed model with multiple random effects for family-based genetic studies

Motivation: Family-based study design is one of the popular designs used in genetic research, and the whole-genome sequencing data obtained from family-based studies offer many unique features for risk prediction studies. They can not only provide a more comprehensive view of many complex diseases, but also utilize information in the design to further improve the prediction accuracy. While promising, existing analytical methods often ignore the information embedded in the study design and overlook the predictive effects of rare variants, leading to a prediction model with sub-optimal performance.Results: We proposed a Bayesian linear mixed model for the prediction analysis of sequencing data obtained from family-based studies. Our method can not only capture predictive effects from both common and rare variants, but also easily accommodate various disease model assumptions. It uses information embedded in the study design to form surrogates, where the predictive effects from unmeasured/unknown genetic and environmental risk factors can be modelled. Through extensive simulation studies and the analysis of sequencing data obtained from the Michigan State University Twin Registry study, we have demonstrated that the proposed method outperforms commonly adopted techniques.Availability: R package is available at https://github.com/yhai943/FBLMM.

Ethnic disparities in bidirectional causal effects between serum uric acid concentrations and kidney function: Trans-ethnic Mendelian randomization study

IntroductionResearchers have investigated the causal effect between serum uric acid (SUA) concentrations and kidney function for decades, but studies produced inconsistent results. This study aimed to clarify the bidirectional causal effects between SUA concentrations and kidney function and to explore the potential ethnic disparities by conducting a trans-ethnic Mendelian randomization study in European, African, and Asian ancestries. Materials and methodsThe summary-level data for this study were obtained from the Global Urate Genetics Consortium, CKDGen Consortium, UK Biobank, and Japan Biobank for different outcomes and exposures, respectively. The traits of kidney function were estimated glomerular filtration rate from serum creatinine (eGFRcr), estimated glomerular filtration rate from cystatin C (eGFRcys), and blood urea nitrogen (BUN). Using the multiplicative random-effects inverse variance weighting mode, our primary analysis produced robust results despite heterogeneity. Additionally, we performed the Mendelian randomization pleiotropy residual sum and outlier test to eliminate the horizontal pleiotropy and obtain accurate results. ResultsOur findings revealed that elevated SUA concentrations had causal effects on declined eGFRcys, BUN, and a diagnosis of chronic kidney disease in European ancestries and eGFRcr in Asian ancestries. Additionally, the causal effects of declined eGFRcr and elevated BUN concentrations on elevated SUA concentrations were observed in both European and Asian ancestries. However, no bidirectional causal effect was found between SUA concentrations and eGFRcr among African ancestries. ConclusionsThis trans-ethnic Mendelian randomization study confirmed the bidirectional causal effects between SUA concentrations and kidney function and highlighted the importance of considering ethnic disparities in clinical treatments.

Causal effect of atrial fibrillation on pulmonary embolism: a mendelian randomization study.

Atrial fibrillation (AF) can increase thrombosis, especially arterial thrombosis, and some studies show that AF patients have a higher risk of developing pulmonary embolism (PE). The objective of our study is to investigate whether there is a direct causal effect of AF on PE. A two-sample Mendelian randomization (MR) approach was utilized to determine whether there is a causal relationship between AF and PE. European population-based consortia provided statistical data on the associations between Single Nucleotide Polymorphisms (SNPs) and relevant traits. The AF dataset was obtained from genome-wide association studies (GWAS) comprising 60,620 cases and 970,216 controls, while a GWAS of 1846 cases and 461,164 controls identified genetic variations associated with PE. Estimation of the causal effect was mainly performed using the random effects inverse-variance weighted method (IVW). Additionally, other tests such as MR-Egger intercept, MR-PRESSO, Cochran's Q test, "Leave-one-out," and funnel plots were conducted to assess the extent of pleiotropy and heterogeneity. Using 70 SNPs, there was no evidence to suggest an association between genetically predicted AF and risk of PE with multiplicative random-effects IVW MR analysis (odds ratio = 1.0003, 95% confidence interval: 0.9998-1.0008, P = 0.20). A null association was also observed in other methods. MR-Egger regression and MR-PRESSO respectively showed no evidence of directional (intercept, - 2.25; P = 0.94) and horizontal(P-value in the global heterogeneity test = 0.99) pleiotropic effect across the genetic variants. No substantial evidence was found to support the causal role of AF in the development of PE.

Causal relationships of circulating amino acids with cardiovascular disease: a trans-ancestry Mendelian randomization analysis

BackgroundEpidemiological studies demonstrated that multiple amino acids (AAs) were associated with cardiovascular diseases (CVDs), but whether these associations were causal remains unclear. This study aims to investigate the causal relationships between circulating levels of 20 AAs and 10 CVDs in European and East Asian populations by Mendelian randomization (MR).MethodsThis MR study utilized single-nucleotide polymorphisms that were significantly associated with AAs as instrumental variables. Summary-level data for AAs and CVDs were obtained from public genome-wide association studies. The causal effects were primarily estimated by inverse variance weighting with multiplicative random effect method. Sensitivity analyses, including weighted median, weighted mode, and MR Egger regression, were used to test the robustness of our results.ResultsIn the European population, alanine and serine were inversely associated with angina pectoris (AP) and chronic heart failure, respectively. With each unit increase of leucine, the risk of ischemic stroke increased by 10%. Moreover, tyrosine was positively associated with AP and deep vein thrombosis. In the East Asian population, each unit increase in glycine was associated with 4.1% and 9.0% decreased risks of coronary artery disease (CAD) and myocardial infarction (MI), respectively. A unit increase in serine was associated with 13.1%, 12.6% and 15.5% decreased risks of AP, CAD and MI, respectively. Sensitivity analyses supported the robustness of our results.ConclusionsThis MR study demonstrated significant causal effects of circulating levels of AAs on CVDs, indicating the potential use of AAs as biomarkers or as therapeutic targets for CVD in clinical scenarios.Graphical

Primary sclerosing cholangitis causally affects kidney function decline: A Mendelian randomization study.

The causal linkage between primary sclerosing cholangitis (PSC) and kidney function is unexplored despite their potential for long-term detrimental effects on kidney function. Two-sample summary-level Mendelian randomization (MR) study was conducted to identify the association between PSC and kidney function. The genetic variants were extracted from the PSC-specific multi-trait analyzed genome-wide association study (GWAS) of European ancestry. Summary-level data for kidney function traits, including estimated glomerular filtration rate (eGFR), annual eGFR decline, and chronic kidney disease (CKD), were obtained from the CKDGen consortium. Multiplicative random-effects inverse-variance weighted (MR-IVW), and a series of pleiotropy-robust analyses were performed to investigate the causal effects and ascertain their robustness. Significant causal associations between genetically predicted PSC and kidney function traits were identified. Genetically predicted PSC was associated with decreased log-transformed eGFR (MR-IVW; beta=-0.41%; standard error [SE]=0.02%; P<0.001), increased rate of annual eGFR decline (MR-IVW; beta=2.43%; SE=0.18%; P<0.001), and higher risk of CKD (MR-IVW; odds ratio=1.07; 95% confidence interval = 1.06-1.08; P<0.001). The main findings were supported by pleiotropy-robust analysis, including MR-Egger with bootstrapped error and weighted median. Our study demonstrates that genetically predicted PSC is causally associated with kidney function impairment. Further studies are warranted to identify the underlying mechanisms.

Minimax rates for sparse signal detection under correlation

Abstract We fully characterize the nonasymptotic minimax separation rate for sparse signal detection in the Gaussian sequence model with $p$ equicorrelated observations, generalizing a result of Collier, Comminges and Tsybakov. As a consequence of the rate characterization, we find that strong correlation is a blessing, moderate correlation is a curse and weak correlation is irrelevant. Moreover, the threshold correlation level yielding a blessing exhibits phase transitions at the $\sqrt{p}$ and $p-\sqrt{p}$ sparsity levels. We also establish the emergence of new phase transitions in the minimax separation rate with a subtle dependence on the correlation level. Additionally, we study group structured correlations and derive the minimax separation rate in a model including multiple random effects. The group structure turns out to fundamentally change the detection problem from the equicorrelated case and different phenomena appear in the separation rate.

Statistical Modeling of the Effectiveness of Preventive Maintenance for Repairable Systems

Preventive maintenance (PM) is commonly adopted in practice to improve a system’s health condition and reduce the risk of unexpected failures. When a PM action is poorly performed, however, it is likely to have adverse effects on system reliability. We observe this phenomenon when evaluating the effectiveness of a PM program for a fleet of service vehicles based on their four-year operating data. This phenomenon is also commonly reported in the maintenance of vehicles and aircraft. Motivated by this observation, we propose a statistical model for repairable systems by taking potential PM adverse effects into account. In the formulation, the baseline failure process without PM effects is modeled by a nonhomogeneous Poisson process. When a PM action is performed, its effect on the failure process is modeled as a multiplicative random effect on the system rate of occurrence of failures. Statistical inference under the proposed model is discussed, and we further develop goodness-of-fit test procedures to validate the adequacy of this model. The above-mentioned service vehicle operating data are used to demonstrate the proposed methods.

A Reliability Evaluation Method for Gamma Processes with Multiple Random Effects

The multi-random-effects gamma process has a better characterization effect for degraded data with individual differences. In this paper, a reliability evaluation method for gamma stochastic processes with multiple random effects is studied. The mathematical model of multiple random effects gamma process was established. The model parameters estimation method was established based on the Bayesian approach. The prior distribution acquisition method was discussed, and the parameters of the multiple randomeffects gamma process were estimated by the MCMC-Gibbs method. The correctness of the model and method was verified by numerical simulation, the influence of algorithm parameters on the algorithm solving process was studied. In the fourth part, the reliability of aviation hydraulic rotary joints was studied by using multiple random effects gamma processes.

Causal relationship between endometriosis with infertility and ankylosing spondylitis

Retrospective studies have identified an increased risk of ankylosing spondylitis (AS) in endometriosis patients. The purpose of this study was to investigate the causal relationship between clinical phenotypes of endometriosis and AS using mendelian randomized analysis (MR). MR was performed using data from genome-wide association studies (GWASs). Heterogeneity, pleiotropy and sensitivity analyses were performed to evaluate the robustness of the results by MR Egger and inverse variance weighted (IVW), leave-one-out analysis. IVW, IVW-MRE (inverse variance weighted multiplicative random effects), weighted median and MR Egger were used to explore the relationship between endometriosis and AS. The IVW analysis showed a causal relationship between infertile endometriosis and AS (OR = 0.8334, P = 0.02191), and the same result was observed with IVW-MRE (OR = 0.8334, P = 0.0007933). However, further stratified analysis showed that no matter which statistical method was used, ovarian endometriosis (IVW: OR = 0.1662, P = 0.4986; IVW-MRE: OR = 0.1662, P = 0.4986; MR Egger: OR = − 0.9577, P = 0.2798; Weighted median: OR = 0.2628, P = 0.3452), pelvic peritoneum endometriosis (IVW: OR = 0.4363, P = 0.225; IVW-MRE: OR = 0.4363, P = 0.225, MR Egger: OR = 4.159, P = 0.1705; Weighted median: OR = 0.4112, P = 0.2714), rectovaginal endometriosis (IVW: OR = 0.1365, P = 0.805; IVW-MRE: OR = 0.1365, P = 0.805) there was no causal relationship between endometriosis and AS. This study suggested that patients with infertility endometriosis are at increased risk for AS. This study supports clinicians to pay more attention to the occurrence of AS in endometriosis patients with infertility.

Appraising the causal relationship between plasma caffeine levels and neuropsychiatric disorders through Mendelian randomization

BackgroundCaffeine exposure modifies the turnover of monoamine neurotransmitters, which play a role in several neuropsychiatric disorders. We conducted a Mendelian randomization study to investigate whether higher plasma caffeine levels are causally associated with the risk of anorexia nervosa, bipolar disorder, major depressive disorder (MDD), and schizophrenia.MethodsSummary-level data on the neuropsychiatric disorders were obtained from large-scale genome-wide association studies (GWASs) of European ancestry participants (n = 72,517 to 807,553) and meta-analyzed with the corresponding data from the FinnGen study (n = 356,077). Summary-level data on plasma caffeine were extracted from a GWAS meta-analysis of 9876 European ancestry individuals. The Mendelian randomization analyses estimated the Wald ratio for each genetic variant and meta-analyzed the variant-specific estimates using multiplicative random effects meta-analysis.ResultsAfter correcting for multiple testing, genetically predicted higher plasma caffeine levels were associated with higher odds of anorexia nervosa (odds ratio [OR] = 1.124; 95% confidence interval [CI] = 1.024–1.238, pFDR = 0.039) and a lower odds of bipolar disorder (OR = 0.905, 95% CI = 0.827–0.929, pFDR = 0.041) and MDD (OR = 0.965, 95% CI = 0.937–0.995, pFDR = 0.039). Instrumented plasma caffeine levels were not associated with schizophrenia (OR = 0.986, 95% CI = 0.929–1.047, pFDR = 0.646).ConclusionsThese Mendelian randomization findings indicate that long-term higher plasma caffeine levels may lower the risk of bipolar disorder and MDD but increase the risk of anorexia nervosa. These results warrant further research to explore whether caffeine consumption, supplementation, or abstinence could render clinically relevant therapeutic or preventative psychiatric effects.

Genetically predicted thyroid function and risk of colorectal cancer: a bidirectional Mendelian randomization study.

Observational studies have reported an association between thyroid function and colorectal cancer (CRC), with conflicting results. Elucidating the causal relationship between thyroid function and CRC facilitates the development of new preventive strategies to reduce CRC incidence. We applied a two-sample Mendelian randomization (MR) method to evaluate the causal relationship between five thyroid-related indexes, including hyperthyroidism, hypothyroidism, thyroid stimulating hormone (TSH), free thyroxine (FT4) and basal metabolic rate (BMR), and CRC. Genome-wide association study statistics for thyroid-related phenotypes were obtained from the ThyroidOmics consortium, and summary statistics for genetic associations with CRC were obtained from the FinnGen consortium. We set a series of criteria to screen single nucleotide polymorphisms (SNPs) as instrumental variables and then performed bidirectional MR analysis, stratified analysis and extensive sensitivity analysis. Multiplicative random-effects inverse variance weighted was the primary analysis method, supplemented by weighted median and MR-Egger. We identified 12 SNPs for hyperthyroidism, 10 SNPs for hypothyroidism, 41 SNPs for TSH, 18 SNPs for FT4, and 556 SNPs for BMR. Genetically predicted hyperthyroidism, hypothyroidism, TSH, and FT4 were not associated with CRC risk (all P > 0.05). Sensitivity analysis revealed no heterogeneity or pleiotropy. Genetically predicted BMR was significantly associated with increased CRC risk after removing outlier (OR = 1.30, P = 0.0029). Stratified analysis showed that BMR was significantly associated with colon cancer (OR = 1.33, P = 0.0074) but not rectal cancer. In the reverse analysis, there was no evidence of an effect of CRC on thyroid function (all P > 0.05). Our bidirectional MR analysis provides new insights into the relationship between thyroid function and CRC. CRC prevention may benefit from enhanced screening of high BMR populations.

Causal Effect of Chondroitin, Glucosamine, Vitamin, and Mineral Intake on Kidney Function: A Mendelian Randomization Study.

The causal effects of chondroitin, glucosamine, and vitamin/mineral supplement intake on kidney function remain unknown, despite being commonly used. We conducted a two-sample summary-level Mendelian randomization (MR) analysis to test for causal associations between regular dietary supplement intake and kidney function. Genetic instruments for chondroitin, glucosamine, and vitamin/mineral supplement intake were obtained from a genome-wide association study of European ancestry. Summary statistics for the log-transformed estimated glomerular filtration rate (log-eGFR) were provided by the CKDGen consortium. The multiplicative random-effects inverse-variance weighted method showed that genetically predicted chondroitin and glucosamine intake was causally associated with a lower eGFR (chondroitin, eGFR change beta = -0.113%, standard error (SE) = 0.03%, p-value = 2 × 10-4; glucosamine, eGFR change beta = -0.240%, SE = 0.035%, p-value = 6 × 10-12). However, a genetically predicted vitamin/mineral supplement intake was associated with a higher eGFR (eGFR change beta = 1.426%, SE = 0.136%, p-value = 1 × 10-25). Validation analyses and pleiotropy-robust MR results for chondroitin and vitamin/mineral supplement intake supported the main results. Our MR study suggests a potential causal effect of chondroitin and glucosamine intake on kidney function. Therefore, clinicians should carefully monitor their long-term effects.