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.

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.

A systematic review of Mendelian randomization studies on multiple sclerosis.

Mendelian randomization (MR) is a powerful approach for assessing the causal effect of putative risk factors on an outcome, using genetic variants as instrumental variables. The methodology and application developed in the framework of MR have been dramatically improved, taking advantage of the many public genome-wide association study (GWAS) data. The availability of summary-level data allowed to perform numerous MR studies especially for complex diseases, pinpointing modifiable exposures causally related to increased or decreased disease risk. Multiple sclerosis (MS) is a complex multifactorial disease whose aetiology involves both genetic and non-genetic risk factors and their interplay. Previous observational studies have revealed associations between candidate modifiable exposures and MS risk; although being prone to confounding, and reverse causation, these studies were unable to draw causal conclusions. MR analysis addresses the limitations of observational studies and allows to establish reliable and accurate causal conclusions. Here, we systematically reviewed the studies evaluating the causal effect, through MR, of genetic and non-genetic exposures on MS risk. Among 107 papers found, only 42 were eligible for final evaluation and qualitative synthesis. We found that, above all, low vitamin D levels and high adult body mass index (BMI) appear to be uncontested risk factors for increased MS risk.

"I feel your fear": superior fear recognition in organised crime members.

Individuals who deviate from social norms by committing crimes may have reduced facial emotion recognition abilities. Nevertheless, a specific category of offenders - i.e. organised crime (OC) members - is characterised by hierarchically organised social networks and a tendency to manipulate others to reach their illicit goals. Since recognising emotions is crucial to building social networks, OC members may be more skilled in recognising the facial emotion expressions of others to use this information for their criminal purposes. Evidence of a difference between OC and non-organised crime (NOC) offenders in terms of facial emotion recognition is still lacking. To fill this gap in the literature, we tested 50 OC, 50 NOC offenders, and 50 non-offender controls for their ability to identify six basic emotions (happiness, sadness, fear, anger, disgust, and surprise). All participants underwent a cognitive and psychological evaluation to avoid alternative explanations. Results show that OC members were more able to detect the expression of fear in others as compared to NOC. We interpreted this finding in light of the social context and the behavioural criminal attitude of OC members.

Homozygosity Haplotype and Whole-Exome Sequencing Analysis to Identify Potentially Functional Rare Variants Involved in Multiple Sclerosis among Sardinian Families.

Multiple Sclerosis (MS) is a complex multifactorial autoimmune disease, whose sex- and age-adjusted prevalence in Sardinia (Italy) is among the highest worldwide. To date, 233 loci were associated with MS and almost 20% of risk heritability is attributable to common genetic variants, but many low-frequency and rare variants remain to be discovered. Here, we aimed to contribute to the understanding of the genetic basis of MS by investigating potentially functional rare variants. To this end, we analyzed thirteen multiplex Sardinian families with Immunochip genotyping data. For five families, Whole Exome Sequencing (WES) data were also available. Firstly, we performed a non-parametric Homozygosity Haplotype analysis for identifying the Region from Common Ancestor (RCA). Then, on these potential disease-linked RCA, we searched for the presence of rare variants shared by the affected individuals by analyzing WES data. We found: (i) a variant (43181034 T > G) in the splicing region on exon 27 of CUL9; (ii) a variant (50245517 A > C) in the splicing region on exon 16 of ATP9A; (iii) a non-synonymous variant (43223539 A > C), on exon 9 of TTBK1; (iv) a non-synonymous variant (42976917 A > C) on exon 9 of PPP2R5D; and v) a variant (109859349-109859354) in 3'UTR of MYO16.

A Bayesian approach to Mendelian randomization with multiple pleiotropic variants.

We propose a Bayesian approach to Mendelian randomization (MR), where instruments are allowed to exert pleiotropic (i.e. not mediated by the exposure) effects on the outcome. By having these effects represented in the model by unknown parameters, and by imposing a shrinkage prior distribution that assumes an unspecified subset of the effects to be zero, we obtain a proper posterior distribution for the causal effect of interest. This posterior can be sampled via Markov chain Monte Carlo methods of inference to obtain point and interval estimates. The model priors require a minimal input from the user. We explore the performance of our method by means of a simulation experiment. Our results show that the method is reasonably robust to the presence of directional pleiotropy and moderate correlation between the instruments. One section of the article elaborates the model to deal with two exposures, and illustrates the possibility of using MR to estimate direct and indirect effects in this situation. A main objective of the article is to create a basis for developments in MR that exploit the potential offered by a Bayesian approach to the problem, in relation with the possibility of incorporating external information in the prior, handling multiple sources of uncertainty, and flexibly elaborating the basic model.

Bergamot phytosome improved visceral fat and plasma lipid profiles in overweight and obese class I subject with mild hypercholesterolemia: A randomized placebo controlled trial.

Bergamot has been traditionally used for the relief of diseases related to oxidative stress. Our aim was to investigate the effect of bergamot phytosome on visceral adipose tissue (VAT) and on metabolic profile, in overweight and obese subjects with mild hypercholesterolemia. A total of 64 participants were randomized into two groups for 12 weeks: a supplemented group (33 individuals, BMI 27 ± 3 kg/m2 receiving 500 mg of bergamot phytosome, two daily tablets) and placebo group (31 subjects, BMI 28 ± 3 kg/m2 , two daily tablets). As to the within differences, the parameters of VAT, total and LDL-cholesterol were significantly decreased in the bergamot phytosome group, but not in the placebo group. As to between-group differences, a statistically significant interaction between time and group, that is, the change in score over time differs between the two groups was observed 30 days after supplementation for VAT (p-value = .005), total cholesterol (p-value <.0002), and LDL (p = .004) in respect to placebo. The other parameters (glucose, insulin, Homeostasis Model Assessment, high-density lipoprotein cholesterol, triglycerides, fat free mass, fat mass) were not significant. In conclusion, this clinical study gives evidence that bergamot phytosome provides beneficial effects, such as decrease of VAT and modulation of metabolic alterations, after just 30 days of supplementation, resulting a very promising protection of cardiovascular health.

Early-life factors, in-utero exposures and endometriosis risk: a meta-analysis.

This meta-analysis aimed to offer a general picture of the available data on the effects of early-life factors on the risk of developing endometriosis in adult life. An advanced, systematic search of the online medical databases PubMed, EMBASE and CINAHL was limited to full-length manuscripts published in English in peer-reviewed journals up to February 2019. Log of relative risk (RR) was employed to calculate the pooled effect sizes using both fixed and random effects modelling and I-squared tests to assess heterogeneity. Funnel plots were used to investigation publication bias. The meta-analysis was registered in PROSPERO (ID CRD42019138668). Six studies that included a total of 2360 women affected by endometriosis were analysed. The pooled results showed that the risk of developing endometriosis in adult life was significantly increased by being born prematurely (logRR 0.21, 95% CI -0.03 to 0.40), having a low birthweight (logRR 0.35, 95% CI -0.15 to 0.54), being formula-fed (logRR 0.65, 95% CI -0.35 to 0.95) and having been exposed to diethylstilbestrol (DES) in utero (logRR 0.65, 95% CI 0.26 to 1.04. Among intrauterine and early neonatal exposures, prematurity, birthweight, formula feeding and DES were risk factors for the development of endometriosis in adult life.

Epigenome Wide Association and Stochastic Epigenetic Mutation Analysis on Cord Blood of Preterm Birth.

Preterm birth (PTB) can be defined as the endpoint of a complex process that could be influenced by maternal and environmental factors. Epigenetics recently emerged as an interesting field of investigation since it represents an important mechanism of regulation. This study evaluates epigenetic impact of preterm birth on DNA methylation. Genome-wide DNAm was measured using the Illumina 450K array in cord blood samples obtained from 72 full term and 18 preterm newborns. Lymphocyte composition was calculated based on specific epigenetic markers that are present on the 450k array. Differential methylation analysis was performed both at site and region level; moreover, stochastic epigenetic mutations (SEMs) were also evaluated. The study showed significant differences in blood cell composition between the two groups. Moreover, after multiple testing correction, statistically significant differences in DNA methylation levels emerged between the two groups both at site and region levels. Results obtained were compared to those reported by previous EWAS, leading to a list of more consistent genes associated with PTB. Finally, the SEMs analysis revealed that the burden of SEMs resulted significantly higher in the preterm group. In conclusion, PTB resulted associated to specific epigenetic signatures that involve immune system. Moreover, SEMs analysis revealed an increased epigenetic drift at birth in the preterm group.

Acid sensing ion channel 2: A new potential player in the pathophysiology of multiple sclerosis.

Acid-sensing ion channels (ASICs) are proton-gated channels involved in multiple biological functions such as: pain modulation, mechanosensation, neurotransmission, and neurodegeneration. Earlier, we described the genetic association, within the Nuoro population, between Multiple Sclerosis (MS) and rs28936, located in ASIC2 3'UTR. Here we investigated the potential involvement of ASIC2 in MS inflammatory process. We induced experimental autoimmune encephalomyelitis (EAE) in wild-type (WT), knockout Asic1-/- and Asic2-/- mice and observed a significant reduction of clinical score in Asic1-/- mice and a significant reduction in the clinical score in Asic2-/- mice in a limited time window (i.e., at days 20-23 after immunization). Immunohistochemistry confirmed the reduction in adaptive immune cell infiltrates in the spinal cord of EAE Asic1-/- mice. Analysis of mechanical allodynia, showed a significant higher pain threshold in Asic2-/- mice under physiological conditions, before immunization, as compared to WT mice and Asic1-/- . A significant reduction in pain threshold was observed in all three strains of mice after immunization. More importantly, analysis of human autoptic brain tissue in MS and control samples showed an increase of ASIC2 mRNA in MS samples. Subsequently, in vitro luciferase reporter gene assays, showed that ASIC2 expression is under possible miRNA regulation, in a rs28936 allele-specific manner. Taken together, these findings suggest a potential role of ASIC2 in the pathophysiology of MS.

Mendelian randomisation analysis of clustered causal effects of body mass on cardiometabolic biomarkers.

BACKGROUND: Recent advances in data analysis methods based on principles of Mendelian Randomisation, such as Egger regression and the weighted median estimator, add to the researcher's ability to infer cause-effect links from observational data. Now is the time to gauge the potential of these methods within specific areas of biomedical research. In this paper, we choose a study in metabolomics as an illustrative testbed. We apply Mendelian Randomisation methods in the analysis of data from the DILGOM (Dietary, Lifestyle and Genetic determinants of Obesity and Metabolic syndrome) study, in the context of an effort to identify molecular pathways of cardiovascular disease. In particular, our illustrative analysis addresses the question whether body mass, as measured by body mass index (BMI), exerts a causal effect on the concentrations of a collection of 137 cardiometabolic markers with different degrees of atherogenic power, such as the (highly atherogenic) lipoprotein metabolites with very low density (VLDLs) and the (protective) high density lipoprotein metabolites. RESULTS: We found strongest evidence of a positive BMI effect (that is, evidence that an increase in BMI causes an increase in the metabolite concentration) on those metabolites known to represent strong risk factors for coronary artery disease, such as the VLDLs, and evidence of a negative effect on protective biomarkers. CONCLUSIONS: The methods discussed represent a useful scientific tool, although they assume the validity of conditions that are (at best) only partially verifiable. This paper provides a rigorous account of such conditions. The results of our analysis provide a proof-of-concept illustration of the potential usefulness of Mendelian Randomisation in genomic biobank studies aiming to dissect the molecular causes of disease, and to identify candidate pharmacological targets.

Investigating multiple sclerosis genetic susceptibility on the founder population of east-central Sardinia via association and linkage analysis of immune-related loci.

BACKGROUND: A wealth of single-nucleotide polymorphisms (SNPs) responsible for multiple sclerosis (MS) susceptibility have been identified; however, they explain only a fraction of MS heritability. OBJECTIVES: We contributed to discovery of new MS susceptibility SNPs by studying a founder population with high MS prevalence. METHODS: We analyzed ImmunoChip data from 15 multiplex families and 94 unrelated controls from the Nuoro Province, Sardinia, Italy. We tested each SNP for both association and linkage with MS, the linkage being explored in terms of identity-by-descent (IBD) sharing excess and using gene dropping to compute a corresponding empirical p-value. By targeting regions that are both associated and in linkage with MS, we increase chances of identifying interesting genomic regions. RESULTS: We identified 486 MS-associated (p < 1 × 10-4) and 18,426 MS-linked (p < 0.05) SNPs. A total of 111 loci were both linked and associated with MS, 18 of them pointing to 14 non-major histocompatibility complex (MHC) genes, and 93 of them located in the MHC region. CONCLUSION: We discovered new suggestive signals and confirmed some previously identified ones. We believe this to represent a significant step toward an understanding of the genetic basis of MS.

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.

Direct genetic effects and their estimation from matched case-control data.

In genetic association studies, a single marker is often associated with multiple, correlated phenotypes (e.g., obesity and cardiovascular disease, or nicotine dependence and lung cancer). A pervasive question is then whether that marker exerts independent effects on all phenotypes. In this paper, we address this question by assessing whether there is a genetic effect on one phenotype that is not mediated through the other ones, so called direct genetic effect. Answering such question may represent an important step in the elucidation of the underlying biological mechanism. Under rather restrictive conditions, such direct genetic effects are known to be estimable by standard regression methods. Under more lenient conditions, in a prospective or unmatched case-control study, these effects can be estimated by using a previously proposed G-estimation method (Vansteelandt [2009] Epidemiology 20, 851-860). The present paper extends this method to matched case-control studies, and investigates the conditions under which this extension is valid. We illustrate the method on data from a matched case-control study, which we use to elucidate the pathway implications of a detected association between myocardial infarction and a genetic locus in the chromosomal region of the FTO gene.

Multiple Sclerosis Heritability Estimation on Sardinian Ascertained Extended Families Using Bayesian Liability Threshold Model.

Heritability studies represent an important tool to investigate the main sources of variability for complex diseases, whose etiology involves both genetics and environmental factors. In this paper, we aimed to estimate multiple sclerosis (MS) narrow-sense heritability (h2), on a liability scale, using extended families ascertained from affected probands sampled in the Sardinian province of Nuoro, Italy. We also investigated the sources of MS liability variability among shared environment effects, sex, and categorized year of birth (<1946, ≥1946). The latter can be considered a proxy for different early environmental exposures. To this aim, we implemented a Bayesian liability threshold model to obtain posterior distributions for the parameters of interest adjusting for ascertainment bias. Our analysis highlighted categorized year of birth as the main explanatory factor, explaining ~70% of MS liability variability (median value = 0.69, 95% CI: 0.64, 0.73), while h2 resulted near to 0% (median value = 0.03, 95% CI: 0.00, 0.09). By performing a year of birth-stratified analysis, we found a high h2 only in individuals born on/after 1946 (median value = 0.82, 95% CI: 0.68, 0.93), meaning that the genetic variability acquired a high explanatory role only when focusing on this subpopulation. Overall, the results obtained highlighted early environmental exposures, in the Sardinian population, as a meaningful factor involved in MS to be further investigated.

Beneficial Effects of an Online Mindfulness-Based Intervention on Sleep Quality in Italian Poor Sleepers during the COVID-19 Pandemic: A Randomized Trial.

Sleep of inadequate quantity and quality is increasing in the present 24 h society, with a negative impact on physical and mental health. Mindfulness-based interventions (MBIs) generate a state of calm behavior that can reduce hyperactivity and improve sleep. We hypothesized that our specific MBI, administered online, may improve sleep quality and foster emotion regulation and mindfulness. The Pittsburgh Sleep Quality Index (PSQI), Sleep Condition Indicator (SCI), Arousal Predisposition Scale (APS), Ford Insomnia Response to Stress Test (FIRST), Sleep Hygiene Index (SHI) and Insomnia Severity Index (ISI) were used to measure sleep quality and stability. Emotion regulation and mindfulness were measured via the Emotion Regulation Questionnaire (ERQ) and Five Facet Mindfulness Questionnaire (FFMQ). Our MBI included 12 biweekly integral meditation (IM) classes, recorded IM training for individual practice, and dietary advice to promote sleep regulation. Fifty-six voluntary poor sleepers with a PSQI score of >5 were randomly allocated to treated (n = 28) and control (n = 28) groups. Linear mixed models were used to estimate the effectiveness of the intervention. Statistically significant results were observed in the FFMQ sub-domain non-reactivity to inner experience (ß = 0.29 [0.06; -0.52], p = 0.01), PSQI (ß = -1.93 [-3.43; -0.43], p = 0.01), SCI (ß = 3.39 [0.66; 6.13], p = 0.02) and ISI (ß = -3.50 [-5.86; -1.14], p = 0.004). These results confirm our hypothesis regarding the beneficial effects of our intervention on sleep quality.

Improving stress management, anxiety, and mental well-being in medical students through an online Mindfulness-Based Intervention: a randomized study.

Pressures and responsibilities of medical school put a strain on medical student's personal wellbeing, leading among all to high rates of anxiety, emotional discomfort and stress. In this work we evaluated the effectiveness of a comprehensive Mindfulness-Based Intervention (MBI) in reducing this load. The intervention comprised 10 twice-a-week Integral Meditation classes, dietary advice, and brief yoga sessions. We performed a randomized trial on two cohort of medical students from Italian universities: 239 in cohort 1 (106 treated and 133 controls), and 123 in cohort 2 (68 treated and 55 control) for a total sample of 362 students. Nine questionnaires for evaluating the effectiveness of our intervention on stress (PSS), state anxiety (STAIX-1), well-being (WEMWBS), mind-wandering (MW-S), overall distress (PANAS), emotion regulation (DERS), resilience (RS-14), and attentional control (ACS-C and ACS-D) were collected both pre and post intervention. Linear mixed effect models were run on the whole sample showing that, after multiple testing correction, our intervention was effective in reducing perceived stress (ß = - 2.57 [- 4.02; - 1.12], p = 0.004), improving mental well-being (ß = 2.82 [1.02; 4.63], p = 0.008) and emotional regulation (ß = - 8.24 [- 12.98; - 3.51], p = 0.004), resilience (ß = 3.79 [1.32; 6.26], p = 0.008), reducing the tendency to wander with the mind (ß = - 0.70 [- 0.99; - 0.39], p = 0.0001), ameliorating the ability to maintain attention (AC-S (ß = - 0.23 [- 0.44; - 0.02], p = 0.04) and AC-D (ß = - 0.19 [- 0.36; - 0.01], p = 0.04)), and the overall distress (ß = 1.84 [0.45; 3.23], p = 0.02).

The Association between Dysnatraemia during Hospitalisation and Post-COVID-19 Mental Fatigue.

COVID-19 may induce short- and long-term cognitive failures after recovery, but the underlying risk factors are still controversial. Here, we investigated whether (i) the odds of experiencing persistent cognitive failures differ based on the patients' disease course severity and sex at birth; and (ii) the patients' electrolytic profile in the acute stage represents a risk factor for persistent cognitive failures. We analysed data from 204 patients suffering from COVID-19 and hospitalised during the first pandemic wave. According to the 7-point WHO-OS scale, their disease course was classified as severe or mild. We investigated the presence of persistent cognitive failures collected after hospital discharge, while electrolyte profiles were collected during hospitalisation. The results showed that females who suffered from a mild course compared to a severe course of COVID-19 had a higher risk of presenting with persistent mental fatigue after recovery. Furthermore, in females who suffered from a mild course of COVID-19, persistent mental fatigue was related to electrolyte imbalance, in terms of both hypo- and hypernatremia, during hospitalisation in the acute phase. These findings have important implications for the clinical management of hospitalised COVID-19 patients. Attention should be paid to potential electrolyte imbalances, mainly in females suffering from mild COVID-19.

Hunting for protective drugs at the break of a pandemic: Causal inference from hospital data.

At the break of a pandemic, the protective efficacy of therapeutic interventions needs rapid evaluation. An experimental approach to the problem will not always be appropriate. An alternative route are observational studies, whether based on regional health service data or hospital records. In this paper, we discuss the use of methods of causal inference for the analysis of such data, with special reference to causal questions that may arise in a pandemic. We apply the methods by using the aid of a directed acyclic graph (DAG) representation of the problem, to encode our causal assumptions and to logically connect the scientific questions. We illustrate the usefulness of DAGs in the context of a controversy over the effects of renin aldosterone system inhibitors (RASIs) in hypertensive individuals at risk of (or affected by) severe acute respiratory syndrome coronavirus 2 disease. We consider questions concerning the existence and the directions of those effects, their underlying mechanisms, and the possible dependence of the effects on context variables. This paper describes the cognitive steps that led to a DAG representation of the problem, based on background knowledge and evidence from past studies, and the use of the DAG to analyze our hospital data and assess the interpretive limits of the results. Our study contributed to subverting early opinions about RASIs, by suggesting that these drugs may indeed protect the older hypertensive Covid-19 patients from the consequences of the disease. Mechanistic interaction methods revealed that the benefit may be greater (in a sense to be made clear) in the older stratum of the population.

A Pipeline for Phasing and Genotype Imputation on Mixed Human Data (Parents-Offspring Trios and Unrelated Subjects) by Reviewing Current Methods and Software.

Genotype imputation has become an essential prerequisite when performing association analysis. It is a computational technique that allows us to infer genetic markers that have not been directly genotyped, thereby increasing statistical power in subsequent association studies, which consequently has a crucial impact on the identification of causal variants. Many features need to be considered when choosing the proper algorithm for imputation, including the target sample on which it is performed, i.e., related individuals, unrelated individuals, or both. Problems could arise when dealing with a target sample made up of mixed data, composed of both related and unrelated individuals, especially since the scientific literature on this topic is not sufficiently clear. To shed light on this issue, we examined existing algorithms and software for performing phasing and imputation on mixed human data from SNP arrays, specifically when related subjects belong to trios. By discussing the advantages and limitations of the current algorithms, we identified LD-based methods as being the most suitable for reconstruction of haplotypes in this specific context, and we proposed a feasible pipeline that can be used for imputing genotypes in both phased and unphased human data.