Genome-wide association studies in pharmacogenomics: untapped potential for translation

Despite large public investments in genome-wide association studies of common human diseases, so far, few gene discoveries have led to applications for clinical medicine or public health. Genome-wide association studies in the context of clinical trials of drug safety and efficacy may be quicker to yield clinical applications. Certain methodological concerns, such as selection bias and confounding, may be mitigated when genome-wide association studies are conducted within clinical trials, in which randomization of exposure, prospective evaluation of outcome and careful definition of phenotype are incorporated by design

The chronification of post-COVID condition associated with neurocognitive symptoms, functional impairment and increased healthcare utilization.

Post-COVID condition is prevalent in 10-35% of cases in outpatient settings, however a stratification of the duration and severity of symptoms is still lacking, adding to the complexity and heterogeneity of the definition of post-COVID condition and its oucomes. In addition, the potential impacts of a longer duration of disease are not yet clear, along with which risk factors are associated with a chronification of symptoms beyond the initial 12 weeks. In this study, follow-up was conducted at 7 and 15 months after testing at the outpatient SARS-CoV-2 testing center of the Geneva University Hospitals. The chronification of symptoms was defined as the continuous presence of symptoms at each evaluation timepoint (7 and 15 months). Adjusted estimates of healthcare utilization, treatment, functional impairment and quality of life were calculated. Logistic regression models were used to evaluate the associations between the chronification of symptoms and predictors. Overall 1383 participants were included, with a mean age of 44.3 years, standard deviation (SD) 13.4 years, 61.4% were women and 54.5% did not have any comorbidities. Out of SARS-CoV-2 positive participants (n = 767), 37.0% still had symptoms 7 months after their test of which 47.9% had a resolution of symptoms at the second follow-up (15 months after the infection), and 52.1% had persistent symptoms and were considered to have a chronification of their post-COVID condition. Individuals with a chronification of symptoms had an increased utilization of healthcare resources, more recourse to treatment, more functional impairment, and a poorer quality of life. Having several symptoms at testing and difficulty concentrating at 7 months were associated with a chronification of symptoms. COVID-19 patients develop post-COVID condition to varying degrees and duration. Individuals with a chronification of symptoms experience a long-term impact on their health status, functional capacity and quality of life, requiring a special attention, more involved care and early on identification considering the associated predictors

Geomedicine: opportunities of using spatial information to move toward more precision in public health - spatial approaches and clusters: an introduction for clinicians

In this presentation we introduce basic knowledge about the use of located health data to detect clusters of disease prevalence. Most often, geographic maps are produced to *represent* health data. Medical information is transmitted through thematic choropleth (or not) maps. For instance administrative units – that can be surface or points - are colored according to the variable of interest. Today we will stress the importance of analysing health data by explicitly including geographic characteristics (distances, co-location) and also the potential and power of spatial statistics to detect specific patterns in the geographic distribution of disease occurrences (make visible the invisible). A classic example using clusters is the map produced by John Snow in 1854 showing the number of deaths caused by a cholera outbreak in London. Looking at a detail of Snow's original map, it is possible to realise how he graphically represented the number of deaths, with short bold lines representing death occurrences placed on the street at the addresses where it happened (this is what we name now georeferencing); and together the lines form histograms. The cluster of death people is an effect observed on the territory, and the existence of this cluster depends on an infected water pump located at the same place, and this is the cause. How can this spatial dependence be detected and measured? The main objective is to identify patterns in the geographic space. So we need to determine whether the variable of interest is randomly distributed or spatially dependent, and to check if the patterns observed are robust to random permutations. Finally we also need to explore the data to find out what is the range of influence of this spatial dependence. Here I will explain the functioning of one among several measures of spatial autocorrelation named Moran’s. Let us consider a cloud of points distributed in the geographic space and focus on a first point of the dataset around which we decide to use a neighborhood of 5km defining spatial weighting. The mean of the values of the variable of interest for all points located within this neighborhood will be compared with the value of the central point. Then the algorithm will move to the next point and do the same for all points in the dataset. We obtain two distributions of observed versus weighted values and then we process a linear regression between these 2 variables to obtain the coefficient of regression, which is equivalent to Moran’s I. After standardization, we obtain a Moran’s scattergram.The distribution of points among the quadrats of the scattergram defines 4 classes which correspond to the types of relationships between observed values and weighted values at all locations. E.g. High-high (red) = high observed value and high weighted value. Moran’s I translates the global relationship between points and their neighborhood, but the class membership provides a local information to be displayed on the map. Then we need to check if the Moran’s I obtained is statistically significant. The question is to know whether the spatial structure observed and quantified by the Moran’s I persists when BMI values are randomly distributed among all locations? (permutations are run by means of Monte-Carlo method). Moran’s I is calculated again after each run of random permutations and after each run feeds the histogram. A pseudo p-value is calculated on the basis of the number of random configurations that produce a Moran’s I higher or equal to the observed one. The white dots on the map thus correspond to a random situation showing a neutral space without spatial dependence. When using the local version of Moran’s I named LISA for Local Indicators of Spatial Association, the pseudo p-value obtained can be mapped to show the level of significance of the local spatial autocorrelation. This opportunity is interesting because it allows to introduce subtleties in the interpretation of the clusters obtained. Finally, it is important to keep in mind that spatial statistics like Moran’s I or Getis-Ord Gi are exploratory approaches and that it is always necessary to test several spatial lags to possibly identify different explanatory factors. To conclude we want to say that the measure of spatial dependence is key to detect and visualize spatial patterns in health data because spatial statistics can reveal signals that remain often hidden using thematic mapping. On the basis of the clusters highlighted by these exploratory methods, it is possible to formulate hypotheses about possible environmental or socio-economic causes and to test them with the help of confirmatory statistics «Ideas come from previous explorations» John Tukey said in a paper published in 1980 in The American Statistician, a paper entitled «We Need Both Exploratory and Confirmatory». First explore and then confirm was the reasoning applied by John Snow to detect deaths "hot spots" in London, which then allowed him to hypothesize that a particular water pump was infected, and finally to take public health steps to check the cholera epidemic

Physical activity and energy expenditure in rheumatoid arthritis patients and matched controls

Objectives. To compare daily energy expenditure between RA patients and matched controls, and to explore the relationship between daily energy expenditure or sedentariness and disease-related scores. Methods. One hundred and ten patients with RA and 440 age- and sex-matched controls were included in this study. Energy expenditure was assessed using the validated physical activity (PA) frequency questionnaire. Disease-related scores included disease activity (DAS-28), functional status (HAQ), pain visual analogue scale (VAS) and fatigue VAS. Total energy expenditure (TEE) and the amount of energy spent in low- (TEE-low), moderate- (TEE-mod) and high-intensity (TEE-high) PAs were calculated. Sedentariness was defined as expending <10% of TEE in TEE-mod or TEE-high activities. Between-group comparisons were computed using conditional logistic regression. The effect of disease-related scores on TEE was investigated using linear regression. Results. TEE was significantly lower for RA patients compared with controls [2392 kcal/day (95% CI 2295, 2490) and 2494 kcal/day (2446, 2543), respectively, P = 0.003]. A significant difference was found between groups in TEE-mod (P = 0.015), but not TEE-low (P = 0.242) and TEE-high (P = 0.146). All disease-related scores were significantly poorer in sedentary compared with active patients. TEE was inversely associated with age (P < 0.001), DAS-28 (P = 0.032) and fatigue VAS (P = 0.029), but not with HAQ and pain VAS. Conclusion. Daily energy expenditure is significantly lower in RA patients compared with matched controls, mainly due to less moderate-intensity PAs performed. Disease activity and fatigue are important contributing factors. These points need to be addressed if promoting PA in RA patients is a health goal. Trial registration. ClinicalTrials.gov, http://clinicaltrials.gov, NCT0122881

Spatial dependence of body mass index and exposure to night-time noise in the Geneva urban area

In this study, we calculated the night-noise mean (SonBase 2014, compatible with the EU Environmental Noise Directive) for the 5 classes obtained after computation of Local Indicators of Spatial Association (LISA; Anselin et al 1995) on the BMI of the participants in the Bus Santé study, a cohort managed by the Geneva University Hospitals (N=15’544; Guessous et al 2014). We expected the mean of dBs to be significantly higher in the group showing spatial dependence of high BMI values (high-high class). We ran an ANOVA and multiple T-tests to compare the dB means between LISA clusters. The approach was applied to the participants of the whole State Geneva cohort, and to a reduced set of individuals living in the urban environment of the municipality of Geneva only

Domains of importance to the quality of life of older people from two Swiss regions

Background: quality of life (QoL) is a subjective perception whose components may vary in importance between individuals. Little is known about which domains of QoL older people deem most important. Objective: this study investigated in community-dwelling older people the relationships between the importance given to domains defining their QoL and socioeconomic, demographic and health status. Methods: data were compiled from older people enrolled in the Lc65+ cohort study and two additional, population-based, stratified random samples (n = 5,300). Principal components analysis (PCA) was used to determine the underlying domains among 28 items that participants defined as important to their QoL. The components extracted were used as dependent variables in multiple linear regression models to explore their associations with socioeconomic, demographic and health status. Results: PCA identified seven domains that older persons considered important to their QoL. In order of importance (highest to lowest): feeling of safety, health and mobility, autonomy, close entourage, material resources, esteem and recognition, and social and cultural life. A total of six and five domains of importance were significantly associated with education and depressive symptoms, respectively. The importance of material resources was significantly associated with a good financial situation (β = 0.16, P = 0.011), as was close entourage with living with others (β = 0.20, P = 0.007) and as was health and mobility with age (β = −0.16, P = 0.014). Conclusion: the importance older people give to domains of their QoL appears strongly related to their actual resources and experienced losses. These findings may help clinicians, researchers and policy makers better adapt strategies to individuals' need

Screening primary-care patients forgoing health care for economic reasons

BACKGROUND: Growing social inequities have made it important for general practitioners to verify if patients can afford treatment and procedures. Incorporating social conditions into clinical decision-making allows general practitioners to address mismatches between patients' health-care needs and financial resources. OBJECTIVES: Identify a screening question to, indirectly, rule out patients' social risk of forgoing health care for economic reasons, and estimate prevalence of forgoing health care and the influence of physicians' attitudes toward deprivation. DESIGN: Multicenter cross-sectional survey. PARTICIPANTS: Forty-seven general practitioners working in the French-speaking part of Switzerland enrolled a random sample of patients attending their private practices. MAIN MEASURES: Patients who had forgone health care were defined as those reporting a household member (including themselves) having forgone treatment for economic reasons during the previous 12 months, through a self-administered questionnaire. Patients were also asked about education and income levels, self-perceived social position, and deprivation levels. KEY RESULTS: Overall, 2,026 patients were included in the analysis; 10.7% (CI95% 9.4-12.1) reported a member of their household to have forgone health care during the 12 previous months. The question "Did you have difficulties paying your household bills during the last 12 months" performed better in identifying patients at risk of forgoing health care than a combination of four objective measures of socio-economic status (gender, age, education level, and income) (R(2) = 0.184 vs. 0.083). This question effectively ruled out that patients had forgone health care, with a negative predictive value of 96%. Furthermore, for physicians who felt powerless in the face of deprivation, we observed an increase in the odds of patients forgoing health care of 1.5 times. CONCLUSION: General practitioners should systematically evaluate the socio-economic status of their patients. Asking patients whether they experience any difficulties in paying their bills is an effective means of identifying patients who might forgo health care

Spatial clusters of daytime sleepiness and association with nighttime noise levels in a Swiss general population (GeoHypnoLaus).

Daytime sleepiness is highly prevalent in the general adult population and has been linked to an increased risk of workplace and vehicle accidents, lower professional performance and poorer health. Despite the established relationship between noise and daytime sleepiness, little research has explored the individual-level spatial distribution of noise-related sleep disturbances. We assessed the spatial dependence of daytime sleepiness and tested whether clusters of individuals exhibiting higher daytime sleepiness were characterized by higher nocturnal noise levels than other clusters. Population-based cross-sectional study, in the city of Lausanne, Switzerland. Sleepiness was measured using the Epworth Sleepiness Scale (ESS) for 3697 georeferenced individuals from the CoLaus|PsyCoLaus cohort (period = 2009-2012). We used the sonBASE georeferenced database produced by the Swiss Federal Office for the Environment to characterize nighttime road traffic noise exposure throughout the city. We used the GeoDa software program to calculate the Getis-Ord G &lt;sub&gt;i&lt;/sub&gt; * statistics for unadjusted and adjusted ESS in order to detect spatial clusters of high and low ESS values. Modeled nighttime noise exposure from road and rail traffic was compared across ESS clusters. Daytime sleepiness was not randomly distributed and showed a significant spatial dependence. The median nighttime traffic noise exposure was significantly different across the three ESS Getis cluster classes (p &lt; 0.001). The mean nighttime noise exposure in the high ESS cluster class was 47.6, dB(A) 5.2 dB(A) higher than in low clusters (p &lt; 0.001) and 2.1 dB(A) higher than in the neutral class (p &lt; 0.001). These associations were independent of major potential confounders including body mass index and neighborhood income level. Clusters of higher daytime sleepiness in adults are associated with higher median nighttime noise levels. The identification of these clusters can guide tailored public health interventions