Adjustment For Multiple Testing Research Articles

Attributing Racial Differences in Care to Health Plan Performance or Selection

There is increased interest in public reporting of, and linking financial incentives to, the performance of organizations on health equity metrics, but variation across organizations could reflect differences in performance or selection bias. To assess whether differences across health plans in sex- and age-adjusted racial disparities are associated with performance or selection bias. This cross-sectional study leveraged a natural experiment, wherein a southern US state randomly assigned much of its Medicaid population to 1 of 5 plans after shifting to managed care in 2012. Enrollee-level administrative claims and enrollment data from 2011 to 2015 were obtained for self-identified Black and White enrollees. The analyses were limited to Black and White Medicaid enrollees because they accounted for the largest percentages of the population and could be compared with greater statistical power than other groups. Data were analyzed from June 2021 to September 2024. Plan enrollment via self-selection (observational population) vs random assignment (randomized population). Annual counts of primary care visits, low-acuity emergency department visits, prescription drug fills, and total spending. For observational and randomized populations, models of each outcome were fit as a function of plan indicators, indicators for race, interactions between plan indicators and race, and age and sex. Models estimated the magnitude of racial differences within each plan and tested whether this magnitude varied across plans. Of 118 101 enrollees (mean [SD] age, 9.3 [7.5] years; 53.0% female; 61.4% non-Hispanic Black; and 38.6% non-Hispanic White), 70.2% were included in the randomized population, and 29.8% were included in the observational population. Within-plan differences in primary care visits, low-acuity emergency department visits, prescription drug use, and total spending between Black and White enrollees were large but did not vary substantially and were not statistically significantly different across plans in the randomized population, suggesting minimal effects of plans on racial differences in these measures. In contrast, in the observational population, racial differences varied substantially across plans (standard deviations 2-3 times greater than in the randomized population); this variation was statistically significant after adjustment for multiple testing, except for emergency department visits. Greater between-plan variation in racial differences in the observational population was only partially explained by sampling error. Stratifying by race did not bring observational estimates of plan effects meaningfully closer to randomized estimates. This cross-sectional study showed that selection bias may mischaracterize plans' relative performance on measures of health care disparities. It is critical to address disparities in Medicaid, but adjusting plan payments based on disparity measures may have unintended consequences.

Internet-based treatment for provoked vulvodynia: factors associated with treatment outcomes

Abstract Introduction Previous research has indicated that internet-based Acceptance and Commitment Therapy (ACT) can reduce pain during intercourse and increase pain acceptance in individuals with provoked vulvodynia, but the factors associated with treatment outcomes remain unknown. Aim This study aimed to investigate factors associated with changes in pain acceptance following an internet treatment based on ACT. Methods This exploratory study used data from the EMBLA study, a multicenter randomized controlled trial that investigated the effects of internet-based ACT for provoked vulvodynia. The examined factors included sociodemographics, medical history, pain and sexual behavior, and psychosocial characteristics. Linear regression analysis was employed to assess the association of these factors with pain acceptance, including interaction effects. Results were adjusted for multiple testing using Bonferroni correction. Outcomes The outcome measure was pain acceptance assessed on the scale Chronic Pain Questionnaire–Revised, which comprises two sub-scales: activity engagement and pain willingness. Results Before adjustment, greater improvement in overall pain acceptance and the subscale activity engagement was seen participants in the intervention group who had a history of physical violence or sexual assault. Increased time spent on the treatment platform per week was also associated with greater improvement in pain acceptance. Participants who reported gastrointestinal problems before the internet-based treatment showed better treatment outcomes in activity engagement. Previous contact with a psychologist or counselor was associated with less improvement in activity engagement. The intervention was less effective on the subscale pain willingness with increased age and for those reporting urinary problems. No associations remained statistically significant after adjustment for multiple testing. Clinical implications Previous exposure to violence and sexual assault, concomitant gastrointestinal or urinary problems, and adherence to treatment should be further investigated in larger studies on factors associated with treatment outcomes after internet treatment based on ACT, especially regarding pain. Strengths and limitations This was a novel and exploratory study and provides information for researchers in future investigations of how individual characteristics may influence treatment outcomes. A range of variables were explored in the models, underscoring the importance of future studies to strengthen the findings. One limitation concerns the sample size, which was fairly small considering the nature of the study. Conclusion After correcting for multiple testing, no factors were found to be statistically associated with changes in pain acceptance after the treatment.

Enhanced adaptive permutation test with negative binomial distribution in genome-wide omics datasets.

The permutation test has been widely used to provide the p-values of statistical tests when the standard test statistics do not follow parametric null distributions. However, the permutation test may require huge numbers of iterations, especially when the detection of very small p-values is required for multiple testing adjustments in the analysis of datasets with a large number of features. To overcome this computational burden, we suggest a novel enhanced adaptive permutation test that estimates p-values using the negative binomial (NB) distribution. By the method, the number of permutations are differently determined for individual features according to their potential significance. In detail, the permutation procedure stops, when test statistics from the permuted dataset exceed the observed statistics from the original dataset by a predefined number of times. We showed that this procedure reduced the number of permutations especially when there were many insignificant features. For significant features, we enhanced the reduction with Stouffer's method after splitting datasets. From the simulation study, we found that the enhanced adaptive permutation test dramatically reduced the number of permutations while keeping the precision of the permutation p-value within a small range, when compared to the ordinary permutation test. In real data analysis, we applied the enhanced adaptive permutation test to a genome-wide single nucleotide polymorphism (SNP) dataset of 327,872 features. We found the analysis with the enhanced adaptive permutation took a feasible time for genome-wide omics datasets, and successfully identified features of highly significant p-values with reasonable confidence intervals.

The added value of deep learning to submaximal exercise electrocardiogram for the 10-year prediction of major adverse cardiovascular and cerebrovascular events

Abstract Background The added value of exercise ECG data to traditional risk factors in predicting cardiovascular events remains unclear. Deep learning of ECG signals has demonstrated state-of-the-art performance in detecting subtle abnormalities indicative of cardiovascular disease. We investigated whether deep learning analysis of exercise ECGs could improve the prediction of major cardiovascular and cerebrovascular events (MACCE) with respect to established risk models in a large population-based cohort. Purpose To assess the added value of submaximal exercise ECG measurements to established cardiovascular risk prediction models. Methods We obtained ECG recordings from 41,076 UK Biobank participants without cardiovascular disease (CVD) who underwent a submaximal exercise ECG test. We obtained ECG recordings from 41,076 UK Biobank participants without cardiovascular disease (CVD) who underwent a submaximal exercise ECG test. We created two deep neural network ECG risk scores: one derived from conventional ECG parameters, measured at rest, peak exercise and late-stage recovery, and another based on the complete ECG (Q-ECG). We assessed the added value of conventional ECG parameters and Q-ECG risk scores to the UK's current prediction algorithm (QRISK3). We estimate the association between ECG parameters and MACCE, using Cox Proportional hazard models, following adjustment for traditional risk factors. All models were internally validated via 5-fold cross-validation and 1000 bootstrap iterations. Predictive performance was evaluated using Harrel's C-index, Net Reclassification Index (NRI) and net benefit. Findings: Incident MACCE was reported in 4,082 (9.9%) individuals in the study population and 3,463(9.7%) individuals with valid ECG parameters over a median follow-up period of 12.5 years. We found combined conventional ECG and Q-ECG scores were independently associated with MACCE, following adjustment for multiple testing: adjusted hazard ratio [HZ] = 1.76 (95% CI:1.63-1.91); HZ = 1.14 (95% CI:1.10-1.18), respectively, per standard deviation increase. Both conventional ECG parameters and Q-ECG were predictive of MACCE, independent of clinical risk factors, C-index = 0.64 (95%CI 0.63-0.65); net benefit = 0.09(95% CI 0.07 - 0.11) and C-index = 0.56 (95% CI 0.55-0.57); net benefit = 0.07(95% CI 0.05 - 0.09). ECG measurement's modestly improved model discrimination over the bassline QRISK3 risk score when combined with QRISK3 risk factors for conventional markers and Q-ECG score, respectively; ΔC-index 0.03 (95% CI: 0.02 – 0.04) and ΔC-index 0.03 (95% CI: 0.02 – 0.03); However, we observed no significant improvements in classification at the current recommended threshold of 10%. Conclusion In individuals without a history of prior cardiovascular disease, ECG measures independently predict the risk of MACCE. When combined with QRISK3, neural-network-derived ECG risk scores marginally improve cardiovascular risk prediction over QRISK3.

The relationship between anxiety and diet quality in adolescent populations: a cross-sectional analysis.

Globally, more than 13 % of adolescents have clinically significant mental health problems, with anxiety and depression comprising over 40 % of cases. Despite the high prevalence of anxiety disorders among youth, dietary research has been focused on youth with depression, resulting in a significant knowledge gap regarding the impact of anxiety on adolescent diet quality. Adolescents with diagnosed anxiety disorders and healthy controls were included in this study. Anxiety symptoms were measured using the Screen for Child Anxiety-Related Disorders. Diagnosis of anxiety disorder was determined using the Kiddie Schedule for Affective Disorders and Schizophrenia interview. Five diet quality indices were scored from FFQ. Diet quality indices associated with anxiety symptoms in the correlation matrix were interrogated using multiple linear regression modelling. All models were adjusted for depression. One hundred and twenty-eight adolescents (mean age 14·8 years (sd: 2·1); 66·4 % female) were included in this cross-sectional analysis. Although healthy controls and outpatient participants had similar unhealthy dietary index subscale scores, outpatient participants had lower healthy index scores. Higher anxiety symptoms were associated with lower healthy dietary indices in univariate analysis; after adjusting for comorbid depression; however, anxiety symptoms were no longer associated with dietary indices following adjustment for multiple testing (P = 0·038 to P = 0·077). The association between anxiety symptoms and a poor diet is attenuated by depression. The results of this study support the need for an integrated approach to the assessment of mental and physical well-being and further research aimed at understanding the unique contribution of depression to healthy dietary patterns.

Deciphering lipoprotein(a) atherogenicity with use of high-throughput proteomics

Abstract Background Accumulating observational and genetic evidence suggests that lipoprotein(a) [Lp(a)], is significantly more atherogenic than other apolipoprotein-B containing particles. Systemic inflammation is thought to mediate the atherogenic effects of Lp(a). Previous studies have predominantly focused on single inflammatory biomarkers, an approach that fails to capture the broader molecular response to elevated Lp(a) levels. Purpose The aim of this study was to derive a proteome-wide signature of plasma Lp(a) concentrations and subsequently identify the pathways through which Lp(a) influences the risk of coronary artery disease (CAD). Methods This study included 41,123 UK Biobank participants with baseline Lp(a) measurements who also underwent a proteomic assessment. Relative concentration of 2,920 proteins was quantified in EDTA plasma samples using a high-throughput proximity extension assay. Lp(a)-associated proteins were identified in linear regression models, adjusting for age, sex, body mass index, race, smoking status, estimated glomerular filtration rate, medication use, and apolipoprotein B. Proteins linked with Lp(a) concentration were then evaluated for their association with incident CAD risk using Cox proportional hazards models. Significant traits were further included in pathway enrichment analysis. Results Out of 2,920 analyzed proteins (Figure 1), 140 showed a positive association with Lp(a) concentration, while 336 exhibited a negative association after adjustment for multiple testing (False discovery rate < 0.05). The strongest positive associations were observed for apolipoprotein F, group 10 secretory phospholipase A, and C4b-binding protein beta chain. The strongest negative associations were observed for asporin, aminoacylase-1, and retinol-binding protein 5. Among 140 proteins positively associated with Lp(a), 49 were associated with an increased risk of CAD. Conversely, among the 336 proteins negatively associated with Lp(a), 10 were associated with a decreased risk of CAD. Pathway enrichment analysis (Figure 2) indicated that proteins linked with both Lp(a) levels and CAD risk predominantly upregulated pathways related to peptide-ligand receptor signalling, platelet degranulation, neutrophil degranulation, and cytosolic calcium response. Conversely, pathways related to HDL remodeling, PI3K/AKT, KIT, and nuclear receptor signalling were downregulated. Conclusions The greater atherogenic potential of Lp(a) may be attributable to the modulation of several molecular pathways, particularly those involving platelet and neutrophil responses, cell signalling, and lipoprotein metabolism.

Depression symptoms, wellbeing, health-related quality of life, and diabetes-related distress in novel subtypes of recent-onset diabetes in Germany: a 5-year observational follow-up study

The subjective experiences of individuals living with diabetes is commonly assessed with patient-reported outcomes (PROs; eg, depression symptoms, wellbeing, health-related quality of life [HRQOL], and diabetes-related distress). Cluster analyses have identified novel diabetes subtypes differing in phenotypic and metabolic characteristics. We aimed to investigate associations between these subtypes and PROs and whether subtype predicted PROs 5 years later. Baseline (<12 months after a diabetes diagnosis) and 5-year follow-up data were collected from German Diabetes Study (GDS) participants. Multiple regressions were applied to analyse associations between diabetes subtypes and depression symptoms (Center for Epidemiologic Studies Depression Scale), wellbeing (WHO-5), HRQOL (SF-36), and diabetes-related distress (Problem Areas in Diabetes Scale). Cluster analyses at baseline (n=1391) identified participants with severe autoimmune diabetes (SAID, 417 [30%]), severe insulin-deficient diabetes (SIDD, 33 [2%]), severe insulin-resistant diabetes (SIRD, 150 [11%]), mild obesity-related diabetes (MOD, 354 [25%]), and mild age-related diabetes (MARD, 437 [31%]). At baseline, multiple regression analyses showed that participants with SIRD had higher depression symptoms than participants with MARD and lower physical HRQOL than all other subtypes. Participants with SAID reported higher depression symptoms and lower mental HRQOL than participants with MARD, higher physical HRQOL than participants with MARD and MOD, and higher diabetes-related distress than most other subtypes. At the 5-year follow-up, clustering predicted no statistically significant changes in PROs after adjustment for multiple testing, whereas descriptive analyses demonstrated that individuals with SIRD were more likely to experience clinically relevant depression symptoms (16% vs 6%) and low wellbeing (31% vs 14%), respectively, than individuals with MARD. Diabetes subtypes already differ in PROs at diabetes diagnosis. Our analyses had limited predictive power during follow-up. However, our findings suggest that clustering could predict future changes in depression symptoms. The GDS was initiated and financed by the German Diabetes Center, which is funded by the German Federal Ministry of Health, the Ministry of Culture and Science of the state of North Rhine-Westphalia, and by the German Federal Ministry of Education and Research to the German Center for Diabetes Research. For the German translation of the abstract see Supplementary Materials section.

Plasma proteome and incident myocardial infarction: sex-specific differences.

Few population-based cohort studies, including both men and women, have explored circulating proteins associated with incident myocardial infarction (MI). This study investigated the relationships between circulating cardiometabolic-related proteins and MI risk using cohort-based and Mendelian randomization (MR) analyses and explored potential sex-specific differences. The discovery cohort included 11 751 Swedish adults (55-93 years). Data on 259 proteins assessed with Olink proximity extension assays, biochemical, and questionnaire-based information were used. Participants were followed up for incident MI and death over 8 years through linkage to Swedish registers. Replication analyses were conducted on the UK Biobank sample (n = 51 613). In MR analyses, index cis-genetic variants strongly related to the proteins were used as instrumental variables. Genetic association summary statistic data for MI were obtained from the CARDIoGRAMplusC4D consortium and FinnGen. Forty-five proteins were associated with incident MI in discovery and replication samples following adjustment for potential confounders and multiple testing. In the secondary analysis, 13 of the protein associations were sex-specific, with most associations identified among women. In MR analysis, genetically predicted higher levels of renin, follistatin, and retinoic acid receptor responder protein 2 were linked to an increased risk of MI. Tissue factor pathway inhibitor, tumor necrosis factor receptors 1 and 2, placenta growth factor had an inverse association with MI. This study identified both new and confirmed previously established associations between circulating proteins and incident MI and, for the first time, suggested sex-specific patterns in multiple protein-MI associations.

Food Insecurity and Women's Choice of Reversible Contraceptives: Differential Effects by Maternal Age.

We investigated the relationships between food insecurity (FI) and women's choice of reversible contraceptives, overall and according to the level of method effectiveness, among partnered women of reproductive age in Nigeria. This population-based cross-sectional analysis used nationally representative data from Round 6 of the UNICEF-supported Multiple Indicator Cluster Survey (MICS) conducted in Nigeria. The sample included married or in-union women aged 15-49 years who reported a live birth in the last 2 years preceding the survey (unweighted N = 8496). Survey-weighted multivariable binomial and multinomial logistic regression analyses were performed to generate estimates of the association between FI (none, moderate, and severe) and reversible contraceptive use (overall and by method effectiveness). A Bonferroni correction was used to account for multiple testing. We stratified the models by maternal age to describe the experiences of women aged 15-24 years, 25-34 years, and 35-49 years. Overall, 6438 (74.1%) of the women in the sample experienced food insecurity (moderate, n = 2559, 30.7%; severe, n = 3879, 43.4%). In the adjusted model, we observed no statistically significant association between experiencing MFI and SFI and the use of reversible contraceptives (overall and specific) after adjustment for multiple testing. The stratified analyses showed that among women aged 25-34 years in the sample, those experiencing SFI in the past 12 months, compared to their food-secure counterparts, had significantly lower odds of reporting the use of a least effective contraceptive method (OR, 0.53; 95% CI, 0.34-0.83; p = 0.0052). However, this failed to reach the significance threshold upon adjustment for multiple testing. We found no significant association between the FI levels and use of reversible contraceptives (overall and specific) among partnered women (15-49 years) in Nigeria who were 2 years postpartum following a live birth and who were at risk of pregnancy.

Elacestrant in ER+, HER2- Metastatic Breast Cancer with ESR1-Mutated Tumors: Subgroup Analyses from the Phase III EMERALD Trial by Prior Duration of Endocrine Therapy plus CDK4/6 Inhibitor and in Clinical Subgroups.

Elacestrant significantly prolonged progression-free survival (PFS) with manageable safety versus standard-of-care (SOC) endocrine therapy (ET) in patients with estrogen receptor-positive (ER+), HER2- metastatic breast cancer and tumors harboring estrogen receptor 1 (ESR1) mutation following ET plus a cyclin-dependent kinase 4/6 inhibitor (ET+CDK4/6i). In patients with ESR1-mutated tumors, we evaluated the efficacy and safety of elacestrant versus SOC based on prior ET+CDK4/6i duration and in clinical subgroups with prior ET+CDK4/6i ≥12 months. EMERALD, an open-label phase III trial, randomly assigned patients with ER+, HER2- metastatic breast cancer who had received 1-2 prior lines of ET, mandatory CDK4/6i, and ≤1 chemotherapy to elacestrant (345 mg daily) or SOC (aromatase inhibitor or fulvestrant). PFS was assessed across subgroups in post hoc exploratory analyses without adjustment for multiple testing. In patients with ESR1-mutated tumors and prior ET+CDK4/6i ≥12 months, the median PFS for elacestrant versus SOC was 8.6 versus 1.9 months (HR, 0.41; 95% confidence interval, 0.26-0.63). In this population, the median PFS (in months) for elacestrant versus SOC was 9.1 versus 1.9 (bone metastases), 7.3 versus 1.9 (liver and/or lung metastases), 9.0 versus 1.9 (<3 metastatic sites), 10.8 versus 1.8 (≥3 metastatic sites), 5.5 versus 1.9 (PIK3 catalytic subunit α mutation), 8.6 versus 1.9 (tumor protein p53 gene mutation), 9.0 versus 1.9 (HER2-low), 9.0 versus 1.9 (ESR1D538G-mutated tumors), and 9.0 versus 1.9 (ESR1Y537S/N-mutated tumors). Subgroup safety was consistent with the overall population. The duration of prior ET+CDK4/6i ≥12 months in metastatic breast cancer was associated with a clinically meaningful improvement in PFS for elacestrant compared with SOC and was consistent across all subgroups evaluated in patients with ER+, HER2-, ESR1-mutated tumors.

BLESS: bagged logistic regression for biomarker identification.

The traditional single nucleotide polymorphism (SNP)-wise approach in genome-wide association studies is focused on examining the marginal association between each SNP with the outcome separately and applying multiple testing adjustments to the resulting p-values to reduce false positives. However, the approach suffers a lack of power in identifying biomarkers. We design an ensemble machine learning approach to aggregate results from logistic regression models based on multiple subsamples, which helps to identify biomarkers from high-dimensional genomic data. We use different methods to analyze a genome-wide association study from the Alzheimer's Disease Neuroimaging Initiative. The SNP-wise approach does not identify any significant signal, while our novel approach provides a list of ranked SNPs associated with the cognitive functions of interests.

Association of biomarkers of endothelial function, coagulation activation and kidney injury with persistent albuminuria in sickle cell anaemia.

Persistent albuminuria (PA) is common in sickle cell anaemia (SCA). With the association of chronic kidney disease (CKD) with increased mortality, biomarkers that predict its development or progression are needed. We evaluated the association of select biomarkers with PA in adults with SCA using Kruskal-Wallis rank-sum test and logistic regression models, with adjustment for multiple testing. Of 280 subjects, 100 (35.7%) had PA. Median plasma levels of soluble vascular cell adhesion molecule-1 (VCAM-1) (1176.3 vs. 953.4 ng/mL, false discovery rate [FDR] q-value <0.003), thrombin-antithrombin complex (5.5 vs. 4.7 ng/mL, FDR q-value = 0.04), and urinary angiotensinogen (AGT) (12.2 vs. 5.3 ng/mg, FDR q-value <0.003), urinary nephrin (30.6 vs. 27.2 ng/mg, FDR q-value = 0.04), and urinary kidney injury molecule-1 (KIM-1) (0.8 vs. 0.5 ng/mg, FDR q-value <0.003), normalized to urine creatinine, were significantly higher in subjects with PA. In multivariable analysis, only urinary AGT (odds ratio = 1.058, FDR q-value <0.0001) remained a significant predictor of PA. In addition, soluble VCAM-1 (FDR q-value <0.0001), D-dimer (FDR q-value <0.0001), urinary AGT (FDR q-value <0.0001), KIM-1 (FDR q-value <0.0001), and nephrin (FDR q-value <0.0001) were significantly associated with urine albumin-creatinine ratio in multivariable analyses. Longitudinal studies to evaluate the predictive capacity of biomarkers for the development and progression of CKD in SCA are warranted.

IKNOW—Supporting the counseling of women with hereditary risk of breast and ovarian cancer with digital technologies: A randomized controlled trial

PurposeWe developed the online-based counseling tool iKNOW for women with a pathogenic germline variant in BRCA1/2 to improve risk understanding and quality of life. MethodsWith a randomized controlled trial, we investigated the efficacy of iKNOW with regard to risk understanding (primary endpoint), quality of life, risk perception, and anxiety (secondary endpoints). Self-report questionnaires were administered to N = 217 women with a pathogenic variant in BRCA1/2 before counseling (T0), immediately after (T1), 4 weeks after (T2), and 6 months after (T3). ResultDeviations between self-assessed and calculated cancer risks tended to be smaller in the intervention group than in the control group but no longer significantly after adjustment for multiple testing. In the intervention group, the proportion of women with a correct understanding of breast cancer risk at T3 was higher (30.7% vs 14.7%; P = .032). There were no differences in secondary endpoints. ConclusioniKNOW tends to positively influence the understanding of familial cancer risk. At the same time, it does not negatively influence any of the secondary endpoints. However, converging evidence suggests that iKNOW seems to affect the quality of counseling processes and can thus be used as a paradigm for reinventing the notion of efficient, digital care.

Elucidating the role of genetically determined metabolites in Diabetic Retinopathy: insights from a mendelian randomization analysis.

Diabetic retinopathy (DR) results from complex genetic and metabolic interactions. Unraveling the links between blood metabolites and DR can advance risk prediction and therapy. Leveraging Mendelian Randomization (MR) and Linkage Disequilibrium Score Regression (LDSC), we analyzed 10,413 DR cases and 308,633 controls. Data was sourced from the Metabolomics GWAS server and the FinnGen project. Our research conducted a comprehensive MR analysis across 486 serum metabolites to investigate their causal role in DR. After stringent selection and validation of instrumental variables, we focused on 480 metabolites for analysis. Our findings revealed 38 metabolites potentially causally associated with DR. Specifically, 4-androsten-3beta,17beta-diol disulfate 2 was identified as significantly associated with a reduced risk of DR (OR = 0.471, 95% CI = 0.324-0.684, p = 7.87 × 10- 5), even after rigorous adjustments for multiple testing. Sensitivity analyses further validated the robustness of this association, and linkage disequilibrium score regression analyses showed no significant genetic correlation between this metabolite and DR, suggesting a specific protective effect against DR. Our study identifies 4-androsten-3beta,17beta-diol disulfate 2, a metabolite of androgens, as a significant protective factor against diabetic retinopathy, suggesting androgens as potential therapeutic targets.

Gut microbiome in endometriosis: a cohort study on 1000 individuals

BackgroundEndometriosis, defined as the presence of endometrial-like tissue outside of the uterus, is one of the most prevalent gynecological disorders. Although different theories have been proposed, its pathogenesis is not clear. Novel studies indicate that the gut microbiome may be involved in the etiology of endometriosis; nevertheless, the connection between microbes, their dysbiosis, and the development of endometriosis is understudied. This case–control study analyzed the gut microbiome in women with and without endometriosis to identify microbial targets involved in the disease.MethodsA subsample of 1000 women from the Estonian Microbiome cohort, including 136 women with endometriosis and 864 control women, was analyzed. Microbial composition was determined by shotgun metagenomics and microbial functional pathways were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Partitioning Around Medoids (PAM) algorithm was performed to cluster the microbial profile of the Estonian population. The alpha- and beta-diversity and differential abundance analyses were performed to assess the gut microbiome (species and KEGG orthologies (KO)) in both groups. Metagenomic reads were mapped to estrobolome-related enzymes’ sequences to study potential microbiome-estrogen metabolism axis alterations in endometriosis.ResultsDiversity analyses did not detect significant differences between women with and without endometriosis (alpha-diversity: all p-values > 0.05; beta-diversity: PERMANOVA, both R2 < 0.0007, p-values > 0.05). No differential species or pathways were detected after multiple testing adjustment (all FDR p-values > 0.05). Sensitivity analysis excluding women at menopause (> 50 years) confirmed our results. Estrobolome-associated enzymes’ sequence reads were not significantly different between groups (all FDR p-values > 0.05).ConclusionsOur findings do not provide enough evidence to support the existence of a gut microbiome-dependent mechanism directly implicated in the pathogenesis of endometriosis. To the best of our knowledge, this is the largest metagenome study on endometriosis conducted to date.

Associations of bisphenol and phthalate exposure and anti-Müllerian hormone levels in women of reproductive age

In women, exposure to endocrine disrupting chemicals might accelerate the depletion of the ovarian reserve and might be associated with accelerative reproductive aging and fertility. We examined the longitudinal associations of exposure to bisphenols and phthalates with anti-Müllerian hormone concentrations. Pregnant women of 18 years or older that resided in Rotterdam between 2002 and 2006 were eligible for participation in this longitudinal prospective cohort study. We measured urinary bisphenol and phthalate concentration at three time-points in pregnancy among 1405 women, of whom 1322 women had serum Anti-Müllerian Hormone (AMH) measurements 6 and/or 9 years postpartum. We performed linear regression models to assess the association of urinary bisphenol and phthalate metabolites with AMH after 6 and 9 years, and linear mixed-effect model to assess the association with AMH over time. Models were adjusted for sociodemographic and lifestyle factors. In our multivariable linear regression models we observed associations of higher urinary pregnancy-averaged mono-isobutyl phthalate (mIBP), mono-(2-ethyl-5-oxohexyl) phthalate (mEOHP), and monobenzyl phthalate (mBzBP) with lower serum AMH after both 6 and 9 years. However, these associations did not remain after adjustment for multiple testing. No significant associations of bisphenol A with AMH were present in our study sample. In our linear mixed-effects models, higher mIBP, mono-(2-ethyl-5-hydroxyhexyl) phthalate (mEHHP), mEOHP, and mBzBP were associated with lower overall AMH levels (differences-0.07 (95% CI -0.13,-0.02),-0.09 (-0.15,-0.02),-0.08 (95% CI -0.14,-0.02), and-0.08 (-0.13,-0.03) μg/L per doubling in mIBP, mEHHP, mEOHP, and mBzBP respectively) (all False Discovery Rate adjusted p-values < 0.05). We identify decreases in indices of ovarian reserve in relationship to prenatal phthalate exposures. Studies are needed replicating our results among large multi-ethnic non-pregnant populations and assessing transgenerational effects of exposure on ovarian reserve. This study was supported by the Erasmus Medical Center and Erasmus University Rotterdam, the Netherlands Organisation for Health Research and Development, the European Research Council, the Dutch Heart Foundation, the Dutch Diabetes Foundation, the European Union's Horizon 2020 Research and Innovation Program, the National Institutes of Health, Ansh Labs Webster, and the Royal Netherlands Academy of Arts and Sciences.

Childhood-onset type 1 diabetes and subsequent adult psychiatric disorders: a nationwide cohort and genome-wide Mendelian randomization study

Childhood-onset type 1 diabetes (T1D) is associated with substantial psychiatric morbidity in later life, but it remains unknown whether these associations are due to common underlying biological mechanisms or the impacts of living with the condition and its treatment. Here, using Czech national register data, we identified children with T1D aged ≤14 years between 1994 and 2007 and estimated the risk of psychiatric disorders up to 24 years later. We found that children diagnosed with T1D had an elevated risk of developing substance use, mood, anxiety and personality disorders, and behavioral syndromes. Conversely, we found that children with T1D had a lower risk of developing psychotic disorders. In Mendelian randomization analysis, we found an association with schizophrenia, which, however, did not persist following multiple testing adjustment. The combined observational and Mendelian randomization evidence suggests that T1D diagnosis in childhood predisposes to far-reaching, extensive psychiatric morbidity, which is unlikely to be explicable by common underlying biological mechanisms. The findings of this study highlight that monitoring and addressing the mental health needs of children with T1D is imperative, whereas glucose dysregulation and/or inflammation implicated in schizophrenia pathogenesis warrants future research.

Overlap between COPD genetic association results and transcriptional quantitative trait loci.

Genome-wide association studies (GWAS) have identified multiple genetic loci associated with chronic obstructive pulmonary disease (COPD). When integrated with GWAS results, expression quantitative trait locus (eQTL) studies can provide insight into biological mechanisms involved in disease by identifying single nucleotide polymorphisms (SNPs) that contribute to whole gene expression. However, there are multiple genetically driven regulatory and isoform-specific effects which cannot be detected in traditional eQTL analyses. Here, we identify SNPs that are associated with alternative splicing (sQTL) in addition to eQTLs to identify novel functions for COPD associated genetic variants. We performed RNA sequencing on whole blood from 3743 subjects in the COPDGene Study. RNA sequencing data from lung tissue of 1241 subjects from the Lung Tissue Research Consortium (LTRC), and whole genome sequencing data on all subjects. Associations between all SNPs within 1000 kb of a gene (cis-) and splice and gene expression quantifications were tested using tensorQTL. In COPDGene a total of 11,869,333 SNPs were tested for association with 58,318 splice clusters, and 8,792,206 SNPs were tested for association with 70,094 splice clusters in LTRC. We assessed colocalization with COPD-associated SNPs from a published GWAS[1]. After adjustment for multiple statistical testing, we identified 28,110 splice-sites corresponding to 3,889 unique genes that were significantly associated with genotype in COPDGene whole blood, and 58,258 splice-sites corresponding to 10,307 unique genes associated with genotype in LTRC lung tissue. We found 7,576 sQTL splice-sites corresponding to 2,110 sQTL genes were shared between whole blood and lung, while 20,534 sQTL splice-sites in 3,518 genes were unique to blood and 50,682 splice-sites in 9,677 genes were unique to lung. To determine what proportion of COPD-associated SNPs were associated with transcriptional splicing, we performed colocalization analysis between COPD GWAS and sQTL data, and found that 38 genomic windows, corresponding to 38 COPD GWAS loci had evidence of colocalization between QTLs and COPD. The top five colocalizations between COPD and lung sQTLs include NPNT , FBXO38 , HHIP , NTN4 and BTC . A total of 38 COPD GWAS loci contain evidence of sQTLs, suggesting that analysis of sQTLs in whole blood and lung tissue can provide novel insights into disease mechanisms.

Unraveling the Environmental, Meteorological, and Lifestyle Determinants of Hypertension Mortality in Türkiye

Hypertension is a leading risk factor for cardiovascular diseases and a significant cause of mortality worldwide. This study investigates the association of environmental, meteorological, and lifestyle factors with hypertension mortality in Türkiye from 2010 to 2019. This study analyzed province-level hypertension mortality data for 81 provinces in Türkiye from 2010 to 2019. Predictors included air quality indicators (particulate matter 10, sulfur dioxide, carbon monoxide, nitrogen dioxide, ozone), meteorological variables (air pressure, humidity, temperature, wind speed), and lifestyle factors (smoking, alcohol consumption, exposure to second-hand smoke). An ordinal logistic regression approach was employed to model the likelihood of hypertension mortality, with adjustments for multiple testing using the Benjamini-Hochberg False Discovery Rate technique. In this study, it was found that significant associations are between hypertension mortality and several predictors. Air pressure, in terms of coefficient of variation and median levels, was significantly associated with hypertension mortality. Temperature and humidity showed strong associations, with median levels and variability impacting mortality rates. Lifestyle factors, notably smoking and alcohol consumption, were also significantly associated with increased hypertension mortality. Surprisingly, exposure to smoke at home showed a slight protective effect. The study highlights the complex interplay of environmental, meteorological, and lifestyle factors in terms of influencing hypertension mortality in Türkiye. These findings emphasize the need for multifaceted public health strategies that consider these diverse influences to manage and prevent hypertension-related mortality effectively.

Survey expectations and adjustments for multiple testing

Testing hypotheses regarding how individual survey respondents form their expectations is susceptible to the multiple testing problem. The probability of falsely rejecting the null hypothesis for one or more respondents will exceed the nominal single-hypothesis significance level. The Bonferroni correction and related approaches control the family-wise error rate, but are conservative and result in low power when the null hypotheses are false.We compare controlling the family-wise error rate with the effect of controlling the false discovery rate and the false discovery proportion, in terms of the conclusions we draw about forecaster behaviour.The effects of adjustments for multiple testing are investigated for tests of weak efficiency and the over-reaction hypothesis, for beliefs about the persistence of shocks to output growth, and for the accuracy of survey respondents’ perceptions of the uncertainty they face.