Obesity, a common multifactorial disorder, is a major risk factor for type 2 diabetes, hypertension and coronary heart disease (CHD). According to the definition of the World Health Organization (WHO), approximately 6-10% of the population in Westernized countries are considered obese. Epidemiological studies have shown that 30-70% of the variation in body weight may be attributable to genetic factors. To date, two genome-wide scans using different obesity-related quantitative traits have provided candidate regions for obesity. We have undertaken a genome-wide scan in affected sibpairs to identify chromosomal regions linked to obesity in a collection of French families. Model-free multipoint linkage analyses revealed evidence for linkage to a region on chromosome 10p (MLS=4.85). Two further loci on chromosomes 5cen-q and 2p showed suggestive evidence for linkage of serum leptin levels in a genome-wide context. The peak on chromosome 2 coincided with the region containing the gene (POMC) encoding pro-opiomelanocortin, a locus previously linked to leptin levels and fat mass in a Mexican-American population and shown to be mutated in obese humans. Our results suggest that there is a major gene on chromosome 10p implicated in the development of human obesity, and the existence of two further loci influencing leptin levels.

IntroductionOral health is an important component of people’s general health status. Many studies have shown that socioeconomic status is an important determinant of access to health services. In the present study, we explored the inequality and socioeconomic factors associated with people’s non-use of dental care across Europe.MethodsWe obtained data from the European Union Statistics on Income and Living Conditions survey conducted by Eurostat in 2007. These cross-sectional data were collected from people aged 16 years and older in 24 European countries, except those living in long-term care facilities. The variable of interest was the prevalence of non-use of dental care while needed. We used the direct method of standardisation by age and sex to eliminate confounders in the data. Socioeconomic inequalities in the non-use of dental care were measured through differences in prevalence, the relative concentration index (RCI), and the relative index of inequality (RII). We compared the results among countries and conducted standard and multilevel logistic regression analyses to examine the socioeconomic factors associated with the non-use of dental care while needed.ResultsThe results revealed significant socio-economic inequalities in the non-use of dental care across Europe, the magnitudes of which depended on the measure of inequality used. For example, inequalities in the prevalence of non-use among education levels according to the RCI ranged from 0.005 (in the United Kingdom) to −0.271 (Denmark) for men and from −0.009 (Poland) to 0.176 (Spain) for women, whereas the RII results ranged from 1.21 (Poland) to 11.50 (Slovakia) for men and from 1.62 (Poland) to 4.70 (Belgium) for women. Furthermore, the level-2 variance (random effects) was significantly different from zero, indicating the presence of heterogeneity in the probability of the non-use of needed dental care at the country level.ConclusionOverall, our study revealed considerable socioeconomic inequalities in the non-use of dental care at both the individual (intra-country) and collective (inter-country) levels. Therefore, to be most effective, policies to reduce this social inequality across Europe should address both levels.

BackgroundThe aim of this study was to assess the relationship between self-reported weight change, socio-economic status, and health-related quality of life (HRQOL) in patients with diabetes, 5 years after they underwent coronary angiography.MethodsBetween 2013 and 2014, 1873 of 4391 patients (319 with diabetes) who underwent coronary angiography between 2008 and 2009 participated in a follow-up study. Three out of four domains of the World Health Organization Quality of Life (WHOQOL)-BREF (physical health, psychological health and social relationships) were surveyed during the follow-up period. To assess the relationship between weight change and HRQOL, generalized linear models were constructed for every dimension of the WHOQOL-BREF, with educational level as a predictor and sex, age, marital status, smoking status, hypertension, cholesterol, ischemic heart disease, acute myocardial infarction, and stable angina pectoris as covariates.ResultsThe mean age of the patients was 70 years and almost three-quarters of the patients (72.7 %) were men. During the 12 months preceding the follow-up survey, 22.6 % of the patients reported weight loss, 20 % reported weight gain, and 57.4 % reported no weight change. There were significant differences in the HRQOL scores between patients who reported weight loss and those who reported either weight gain or unchanged weight. The most affected domains were physical and psychological health, with higher scores for patients who reported weight loss (54.7 and 67.2, respectively) than those who reported weight gain (46.3 and 58.5, respectively). The generalized linear model confirmed higher HRQOL scores among patients who reported weight loss and revealed an association between the HRQOL score and education level.ConclusionWeight change and education level were associated with HRQOL in patients with diabetes. Self-reported weight loss and no weight change were positively associated with HRQOL in patients with diabetes, while weight gain was negatively associated with HRQOL.

Background The 2019 coronavirus (COVID-19) epidemic began in Wuhan, China in December 2019 and quickly spread to the rest of the world. This study aimed to analyse the associations between the COVID-19 mortality rate in hospitals, the availability of health services, and socio-spatial and health risk factors at department level. Methods and findings This spatial cross-sectional study used cumulative mortality data due to the COVID-19 pandemic in hospitals until 30 November 2020 as a main outcome, across 96 departments of mainland France. Data concerning health services, health risk factors, and socio-spatial factors were used as independent variables. Independently, we performed negative binomial, spatial and geographically weighted regression models. Our results revealed substantial geographic disparities. The spatial exploratory analysis showed a global positive spatial autocorrelation in each wave indicating a spatial dependence of the COVID-19 deaths across departments. In first wave about 75% of COVID-19 deaths were concentrated in departments of five regions compared to a total of 13 regions. The COVID-19 mortality rate was associated with the physicians density, and not the number of resuscitation beds. Socio-spatial factors were only associated with the COVID-19 mortality rate in first wave compared to wave 2. For example, the COVID-19 mortality rate increased by 35.69% for departments densely populated. Health risk factors were associated with the COVID-19 mortality rate depending on each wave. This study had inherent limitations to the ecological analysis as ecological bias risks and lack of individual data. Conclusions Our results suggest that the COVID-19 pandemic has spread more rapidly and takes more severe forms in environments where there is already a high level of vulnerability due to social and health factors. This study showed a different dissemination pattern of COVID-19 mortality between the two waves: a spatial non-stationarity followed by a spatial stationarity in the relationships between the COVID-19 mortality rate and its potential drivers.