Investigating the Influence of Oral Contraceptive Pill use on Multiple Sclerosis Risk using UK Biobank data.

OBJECTIVE: To investigate the association between oral contraceptive (OC) pill use and the risk of developing Multiple Sclerosis (MS), attempting to address the limitations present in previous studies which produced conflicting results. DESIGN: Population-based cohort study using data from the UK Biobank. SUBJECTS: The study included 181,058 women of white ethnicity born in England between 1937 and 1970, among which 1131 had an MS diagnosis. EXPOSURE: OC use, considering the self-reported age of initiation and discontinuation. The exposures of interest include: i) ever use, ii) current use, iii) duration of current use in years, and iv) age and year at initiation. MAIN OUTCOME MEASURES: MS diagnosis (ICD-10: G35) was used as outcome of interest, and the associations with the exposures of interest were investigated using marginal structural models with a time-to-event approach. To adjust for confounding, we included in the models several variables including MS polygenic risk score (MS-PRS), education level, parity, smoking, fertility problems, obesity, and mononucleosis. We further aimed to evaluate the influence of parity using a mediation analysis. RESULTS: The association of both ever and current OC use did not result in a statistically significant MS hazard increase (ever vs never users, HR=1.30 [95%CI:0.93,1.82, p=0.12]; current vs never users, HR=1.35 [95%CI:0.81,2.25, p=0.25]). However, we highlighted parity as an effect modifier for this association. In nulliparous women, ever and current use resulted in a significant two-fold and three-fold MS hazard increase (HR=2.08 [95%CI:1.04,4.17, p=0.04] and HR=3.15 [95%CI:1.43,6.92, p=0.004]). These associations were supported by significant MS hazard increases for a higher duration of current use and for an earlier age at initiation. We further highlighted genetic MS susceptibility as another effect modifier, as a stronger OC-MS hazard association was found in women with a low MS-PRS. CONCLUSION: Our findings highlighted how the association between OC use and MS vary based on individual characteristics such as parity and genetic MS susceptibility. Importantly, current use in nulliparous women was found to be associated with a three-fold increase in MS hazard. We acknowledge the need for cautious causal interpretation and further research to validate these findings across diverse populations and OC types.

Assessing Additive Interactions between Protective Factors Using Relative Risk Reduction Due to Interaction.

Background and Objectives: In the context of disease prevention, interaction on an additive scale is commonly assessed to determine synergistic effects between exposures. While the "Relative Excess Risk due to Interaction" represents the main measure of additive interaction between risk factors, in this study we aimed to extend this approach to assess additive interaction between factors known to prevent the event's occurrence, such as medical interventions and drugs. Materials and Methods: We introduced and described the "Relative Risk Reduction due to Interaction" (RRRI) as a key measure to assess additive interactions between preventive factors, such as therapeutic interventions and drug combinations. For RRRI values closer to 1, the combination of exposures has a greater impact on reducing the event risk due to their interaction. As a purely illustrative example, we re-evaluated a previous investigation of the synergistic effect between statins and blood pressure-lowering drugs in preventing major adverse cardiovascular events (MACE). Moreover, simulation studies were used to empirically evaluate the performance of a robust Poisson regression model to estimate RRRI across different scenarios. Results: In our example, the drug combination revealed a positive additive interaction in further reducing MACE risk (RRRI > 0), even if not statistically significant. This result is more straightforward to interpret as compared to the original one based on the RERI. Additionally, our simulations highlighted the importance of large sample sizes for detecting significant interaction effects. Conclusion: We recommend RRRI as the main measure to be considered when exploring additive interaction effects between protective exposures, such as the investigation of synergistic effects between drug combinations or preventive treatments.

Disentangling Multiple Sclerosis heterogeneity in the French territory among genetic and environmental factors via Bayesian heritability analysis.

BACKGROUND: This study aimed to investigate the factors contributing to the variability of Multiple Sclerosis (MS) among individuals born and residing in France. Geographical variation in MS prevalence was observed in France, but the role of genetic and environmental factors in explaining this heterogeneity has not been yet elucidated. METHODS: We employed a heritability analysis on a cohort of 403 trios with an MS-affected proband in the French population. This sample was retrieved from REFGENSEP register of MS cases collected in 23 French hospital centers from 1992 to 2017. Our objective was to quantify the proportion of MS liability variability explained by genetic variability, sex, shared environment effects, region of birth and year of birth. We further considered gene x environment (GxE) interaction effects between genetic variability and region of birth. We have implemented a Bayesian liability threshold model to obtain posterior distributions for the parameters of interest adjusting for ascertainment bias. RESULTS: Our analysis revealed that GxE interaction effects between genetic variability and region of birth represent the primary significant explanatory factor for MS liability variability in French individuals (29 % [95 %CI: 5 %; 53 %]), suggesting that additive genetic effects are modified by environmental factors associated to the region of birth. The individual contributions of genetic variability and region of birth explained, respectively, ≈15 % and ≈16 % of MS variability, highlighting a significantly higher MS liability in individuals born in the Northern regions compared to the Southern region. Overall, the joint contribution of genetic variability, region of birth, and their interaction was then estimated to explain 65 % [95 %CI: 35 %; 92 %] of MS liability variability. The remaining proportion of MS variability is attributed to environmental exposures associated with the year of birth, shared within the same household, and specific to individuals. CONCLUSION: Overall, our analysis highlighted the interaction between genetic variability and environmental exposures linked to the region of birth as the main factor explaining MS variability within individuals born and residing in France. Among the environmental exposures prevalent in the Northern regions, and potentially interacting with genetic variability, lower vitamin D levels due to reduced sun exposure, higher obesity prevalence and higher pollution levels represent the main risk factors in influencing MS risk. These findings emphasize the importance of accounting for environmental factors linked to geographical location in the investigation of MS risk factors, as well as to further explore the influence of GxE interactions in modifying genetic risk.

Genetic and early life factors influence on time-to-multiple sclerosis diagnosis: A UK Biobank study.

BACKGROUND: Previous investigations into multiple sclerosis (MS) risk factors predominantly relied on retrospective studies, which do not consider different follow-up times and assume a constant risk effect throughout lifetime. OBJECTIVE: We aimed to evaluate the impact of genetic and early life factors on MS diagnosis by employing a time-to-event analysis in a prospective cohort. METHODS: We used the UK Biobank data, considering the observation period from birth up to 31 December 2022. We considered genetic risk, using a multiple sclerosis polygenic risk score (MS-PRS), and various early life factors. Tobacco smoking and infectious mononucleosis diagnosis were also considered as time-varying variables along the follow-up. Using a Cox proportional hazards model, we examined the associations between these factors and MS diagnosis instantaneous risk. RESULTS: We analyzed 345,027 participants, of which 1669 had an MS diagnosis. Our analysis revealed age-dependent effects for sex (females vs males) and higher MS-PRS, with greater hazard ratios observed in young adults. CONCLUSION: The age-dependent effects suggest that retrospective studies could have underestimated sex and genetic variants' risk roles during younger ages. Therefore, we emphasize the importance of a time-to-event approach using longitudinal data to better characterize age-dependent risk effects.

Contribution of Nutritional, Lifestyle, and Metabolic Risk Factors to Parkinson's Disease.

BACKGROUND: Modifiable risk factors for Parkinson's disease (PD) are poorly known. OBJECTIVES: The aim is to evaluate independent associations of different nutritional components, physical activity, and sedentary behavior and metabolic factors with the risk of PD. METHODS: In this population-based prospective cohort study using the data of the United Kingdom Biobank (from 2006-2010), 502,017 men and women who were free from PD (International Classification of Diseases 10th edition; "G20") at baseline were included. We implemented a Cox proportion hazard's model to evaluate the associations of different levels of physical activity, sitting time, sleep habits, diet quality, alcohol and coffee consumption, smoking, and body mass index with PD risk, adjusting for several confounding variables. RESULTS: During a median follow-up of 12.8 years, lifestyle factors including vigorous physical activity (hazard ration [HR] = 0.84; 95% confidence interval [CI], 0.75-0.94), low-to-moderate sitting time (HR = 0.89; 95% CI, 0.81-0.97), and high sleep quality (HR = 0.89; 95% CI, 0.80-0.99) were associated with a reduced risk of PD. Small amounts of coffee (HR = 0.88; 95% CI, 0.82-0.95), red meat (HR = 0.86; 95% CI, 0.76-0.97), and current smoking (HR = 0.65; 95% CI, 0.56-0.75) were also associated with a lower risk of PD, whereas alcohol intake (HR = 1.29; 95% CI, 1.06-1.56) with higher PD risk. Secondary analysis, including metabolic risk factors, confirmed these findings and highlighted the potential protective effect of plasma vitamin D and uric acid, but of low-density lipoprotein-cholesterol, triglycerides, and C-reactive protein as well. CONCLUSIONS: Vigorous physical activity, reduced sitting time, good sleep quality together with small coffee intake and vitamin D supplementation are potentially neuroprotective lifestyle interventions for the prevention of PD. © 2024 International Parkinson and Movement Disorder Society.

Bayesian Mendelian randomization with an interval causal null hypothesis: ternary decision rules and loss function calibration.

We enhance the Bayesian Mendelian Randomization (MR) framework of Berzuini et al. (Biostatistics 21(1):86-101, 2018) by allowing for interval null causal hypotheses, where values of the causal effect parameter that fall within a user-specified interval of "practical equivalence" (ROPE) (Kruschke, Adv Methods Pract Psychol Sci 1(2):270-80, 2018) are regarded as equivalent to "no effect". We motivate this move in the context of MR analysis. In this approach, the decision over the hypothesis test is taken on the basis of the Bayesian posterior odds for the causal effect parameter falling within the ROPE. We allow the causal effect parameter to have a mixture prior, with components corresponding to the null and the alternative hypothesis. Inference is performed via Markov chain Monte Carlo (MCMC) methods. We speed up the calculations by fitting to the data a simpler model than the intended, "true", one. We recover a set of samples from the "true" posterior distribution by weighted importance resampling of the MCMC-generated samples. From the final samples we obtain a simulation consistent estimate of the desired posterior odds, and ultimately of the Bayes factor for the interval-valued null hypothesis, [Formula: see text], vs [Formula: see text]. In those situations where the posterior odds is neither large nor small enough, we allow for an uncertain outcome of the test decision, thereby moving to a ternary decision logic. Finally, we present an approach to calibration of the proposed method via loss function. We illustrate the method with the aid of a study of the causal effect of obesity on risk of juvenile myocardial infarction based on a unique prospective dataset.