Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index.

Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and ∼ 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 × 10⁻⁸), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation.

Genetic studies of body mass index yield new insights for obesity biology

Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 × 10(-8)), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ∼2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.

The interrater reliability of the pulmonary embolism severity index

RésuméLe PESI (Pulmonary Embolism Severity Index) est un score clinique pronostique s'appliquant à des patients présentant un diagnostic d'embolie pulmonaire. Notre objectif était de démontrer la reproductibilité de ce score entre différents médecins chez des patients présentant une embolie pulmonaire. Nous avons donc identifié, de façon prospective, des patients présentant une embolie pulmonaire nouvellement diagnostiquée aux urgences d'un Hôpital Universitaire (CHUV, Lausanne). Pour tous ces patients, le médecin assistant en charge ainsi que le chef de clinique superviseur ont individuellement collecté les différentes variables permettant d'établir le score selon le PESI. Ils ont, ensuite, de façon indépendante, classifié les patients dans 5 classes de risque (1-V) ainsi qu'en deux groupes à bas risque versus haut risque, respectivement les classes i-ll et les classes III à V.Nous avons examiné la reproductibilité des données entre deux groupes de médecins (médecins assistants vs chefs de clinique), pour chacune des variables constituant le PESI, pour le score total en points, pour l'attribution aux 5 classes de risque ainsi que pour la classification en deux groupes à haut risque versus bas risque. Cette évaluation de la reproductibilité des résultats obtenus par les différents médecins s'est basée sur le calcul du Kappa (K) ainsi sur les Coefficients de Corrélation Intra-classe (ICC).Parmi les 48 patients présentant une Embolie Pulmonaire inclus dans notre étude, les coefficients de reproductibilité entre médecins assistants et chefs de clinique étaient supérieurs à 0.60 pour 10 des 11 variables du PESI. La reproductibilité entre les 2 groupes de médecins, pour le total des points, pour l'attribution à une classe de risque I à V, ainsi que pour la classification en bas versus haut risque était presque parfaite.Nos résultats démontrent la haute reproductibilité du PESI, et appuient donc l'intérêt de son utilisation pour la stratification du risque chez des patients présentant une embolie pulmonaire.

Development of an activity index for adult eosinophilic esophagitis: international experts propose dysphagia, withish exudates on endoscopy and peripheral eosinophilia as items with closest association to EoE activity

Background and Aims: Eosinophilic Esophagitis (EoE) is reported with increasing frequency over the last two decades. However, it is still unknown whether this reflects a true increase in incidence or just an increased awareness by gastroenterologists. Therefore, we evaluated the incidence and cumulative prevalence of EoE in Olten county over the last 20 years. Methods: Olten county is an area of approximately 91,000 inhabitants without pronounced demographic changes in the last two decades. EoE evaluation is based upon two gastroenterology centers and one pathology center. No public programs for increased EoE awareness were implemented in this region. All EoE patients diagnosed from 1989 to 2009 were entered prospectively into the Olten county database. Results: Fourty-six patients (76% males, mean age 41±16 yrs) were diagnosed with EoE from 1989 to 2009. Ninety-four percent presented with dysphagia. In 70% of the patients concomitant allergies were found. The number of upper endoscopies per year was stable during the entire observation period. An average annual incidence rate of 2/100,000 was found (range 0-8) with a marked increase in the period from 2001 to 2009. A current cumulative EoE prevalence of 43/100,000 inhabitants was calculated. The mean diagnostic delay (time from first symptoms to diagnosis) was 4.3 years from 1989 to 1998 and 4.8 years from 1999 to 2009. Conclusions: Over the last 20 years, a significant increase in EoE incidence was found in a stable indicator region of Switzerland. The constant rate of upper endoscopies, the constant diagnostic delay, as well as the lack of EoE awareness programs in Olten county indicate a true increase in EoE incidence.

Six new loci associated with body mass index highlight a neuronal influence on body weight regulation.

Common variants at only two loci, FTO and MC4R, have been reproducibly associated with body mass index (BMI) in humans. To identify additional loci, we conducted meta-analysis of 15 genome-wide association studies for BMI (n > 32,000) and followed up top signals in 14 additional cohorts (n > 59,000). We strongly confirm FTO and MC4R and identify six additional loci (P < 5 x 10(-8)): TMEM18, KCTD15, GNPDA2, SH2B1, MTCH2 and NEGR1 (where a 45-kb deletion polymorphism is a candidate causal variant). Several of the likely causal genes are highly expressed or known to act in the central nervous system (CNS), emphasizing, as in rare monogenic forms of obesity, the role of the CNS in predisposition to obesity.

Computer-aided system for morphometric mandibular index computation (Using dental panoramic radiographs)

Objective: We propose and validate a computer aided system to measure three different mandibular indexes: cortical width, panoramic mandibular index and, mandibular alveolar bone resorption index. Study Design: Repeatability and reproducibility of the measurements are analyzed and compared to the manual estimation of the same indexes. Results: The proposed computerized system exhibits superior repeatability and reproducibility rates compared to standard manual methods. Moreover, the time required to perform the measurements using the proposed method is negligible compared to perform the measurements manually. Conclusions: We have proposed a very user friendly computerized method to measure three different morphometric mandibular indexes. From the results we can conclude that the system provides a practical manner to perform these measurements. It does not require an expert examiner and does not take more than 16 seconds per analysis. Thus, it may be suitable to diagnose osteoporosis using dental panoramic radiographs.

Cross-Sectional and Longitudinal Associations between Body Mass Index and Cardiometabolic Risk Factors in Adolescents in a Country of the African Region.

We assessed the association between several cardiometabolic risk factors (CRFs) (blood pressure, LDL-cholesterol, HDL-cholesterol, triglycerides, uric acid, and glucose) in 390 young adults aged 19-20 years in Seychelles (Indian Ocean, Africa) and body mass index (BMI) measured either at the same time (cross-sectional analysis) or at the age of 12-15 years (longitudinal analysis). BMI tracked markedly between age of 12-15 and age of 19-20. BMI was strongly associated with all considered CRFs in both cross-sectional and longitudinal analyses, with some exceptions. Comparing overweight participants with those having a BMI below the age-specific median, the odds ratios for high blood pressure were 5.4/4.7 (male/female) cross-sectionally and 2.5/3.9 longitudinally (P < 0.05). Significant associations were also found for most other CRFs, with some exceptions. In linear regression analysis including both BMI at age of 12-15 and BMI at age of 19-20, only BMI at age of 19-20 remained significantly associated with most CRFs. We conclude that CRFs are predicted strongly by either current or past BMI levels in adolescents and young adults in this population. The observation that only current BMI remained associated with CRFs when including past and current levels together suggests that weight control at a later age may be effective in reducing CRFs in overweight children irrespective of past weight status.

Pulmonary embolism severity index and troponin testing for the selection of low-risk patients with acute symptomatic pulmonary embolism.

Summary Background: The combination of the Pulmonary Embolism Severity Index (PESI) and troponin testing could help physicians identify appropriate patients with acute pulmonary embolism (PE) for early hospital discharge. Methods: This prospective cohort study included a total of 567 patients from a single center registry with objectively confirmed acute symptomatic PE. On the basis of the PESI, each patient was classified into 1 of 5 classes (I to V). At the time of hospital admission, patients had troponin I (cTnI) levels measured. The endpoint of the study was all-cause mortality within 30 days after diagnosis. We calculated the mortality rates in 4 patient groups: group 1: PESI class I-II plus cTnI <0.1 ng mL(-1); group 2: PESI classes III-V plus cTnI <0.1 ng mL(-1); group 3: PESI classes I-II plus cTnI >/= 0.1 ng mL(-1); and group 4: PESI classes III-V plus cTnI >/= 0.1 ng mL(-1). Results: The study cohort had a 30-day mortality of 10% (95% confidence interval [CI], 7.6 to 12.5%). Mortality rates in the 4 groups were 1.3%, 14.2%, 0% and 15.4%, respectively. Compared to non-elevated cTnl, the low-risk PESI had a higher negative predictive value (NPV) (98.9% vs 90.8%) and negative likelihood ratio (NLR) (0.1 vs 0.9) for predicting mortality. The addition of non-elevated cTnI to low-risk PESI did not improve the NPV or the NLR compared to either test alone. Conclusions: Compared to cTnl testing, PESI classification more accurately identified patients with PE who are at low risk of all-cause death within 30-days of presentation.

FTO genetic variants, dietary intake and body mass index: insights from 177,330 individuals.

FTO is the strongest known genetic susceptibility locus for obesity. Experimental studies in animals suggest the potential roles of FTO in regulating food intake. The interactive relation among FTO variants, dietary intake and body mass index (BMI) is complex and results from previous often small-scale studies in humans are highly inconsistent. We performed large-scale analyses based on data from 177,330 adults (154 439 Whites, 5776 African Americans and 17 115 Asians) from 40 studies to examine: (i) the association between the FTO-rs9939609 variant (or a proxy single-nucleotide polymorphism) and total energy and macronutrient intake and (ii) the interaction between the FTO variant and dietary intake on BMI. The minor allele (A-allele) of the FTO-rs9939609 variant was associated with higher BMI in Whites (effect per allele = 0.34 [0.31, 0.37] kg/m(2), P = 1.9 × 10(-105)), and all participants (0.30 [0.30, 0.35] kg/m(2), P = 3.6 × 10(-107)). The BMI-increasing allele of the FTO variant showed a significant association with higher dietary protein intake (effect per allele = 0.08 [0.06, 0.10] %, P = 2.4 × 10(-16)), and relative weak associations with lower total energy intake (-6.4 [-10.1, -2.6] kcal/day, P = 0.001) and lower dietary carbohydrate intake (-0.07 [-0.11, -0.02] %, P = 0.004). The associations with protein (P = 7.5 × 10(-9)) and total energy (P = 0.002) were attenuated but remained significant after adjustment for BMI. We did not find significant interactions between the FTO variant and dietary intake of total energy, protein, carbohydrate or fat on BMI. Our findings suggest a positive association between the BMI-increasing allele of FTO variant and higher dietary protein intake and offer insight into potential link between FTO, dietary protein intake and adiposity.

Diagnostic accuracy of body mass index, body fat and waist in defining low aerobic fitness in adolescents

Objective: to assess the diagnostic accuracy of different anthropometric markers in defining low aerobic fitness among adolescents. Methods: cross-sectional study on 2,331 boys and 2,366 girls aged 10 - 18 years. Body mass index (BMI) was measured using standardized methods; body fat (BF) was assessed by bioelectrical impedance. Low aerobic fitness was assessed by the 20-meter shuttle run using the FITNESSGRAMR criteria. Waist was measured in a subsample of 1,933 boys and 1,897 girls. Overweight, obesity and excess fat were defined according to the International Obesity Task Force (IOTF) or FITNESSGRAMR criteria. Results: 38.5% of boys and 46.5% of girls were considered as unfit according to the FITNESSGRAMR criteria. In boys, the area under the ROC curve (AUC) and 95% confidence interval were 66.7 (64.1 - 69.3), 67.1 (64.5 - 69.6) and 64.6 (61.9 - 67.2) for BMI, BF and waist, respectively (P<0.02). In girls, the values were 68.3 (65.9 - 70.8), 63.8 (61.3 - 66.3) and 65.9 (63.4 - 68.4), respectively (P<0.001). In boys, the sensitivity and specificity to diagnose low fitness were 13% and 99% for obesity (IOTF); 38% and 86% for overweight + obesity (IOTF); 28% and 94% for obesity (FITNESSGRAMR) and 42% and 81% for excess fat (FITNESSGRAMR). For girls, the values were 9% and 99% for obesity (IOTF); 33% and 82% for overweight + obesity (IOTF); 22% and 94% for obesity (FITNESSGRAMR) and 26% and 90% for excess fat (FITNESSGRAMR). Conclusions: BMI, not body fat or waist, should be used to define low aerobic fitness. The IOTF BMI cut-points to define obesity have a very low screening capacity and should not be used.

A genetic risk score combining 32 SNPs is associated with body mass index and improves obesity prediction in people with major depressive disorder.

BACKGROUND: Obesity is strongly associated with major depressive disorder (MDD) and various other diseases. Genome-wide association studies have identified multiple risk loci robustly associated with body mass index (BMI). In this study, we aimed to investigate whether a genetic risk score (GRS) combining multiple BMI risk loci might have utility in prediction of obesity in patients with MDD. METHODS: Linear and logistic regression models were conducted to predict BMI and obesity, respectively, in three independent large case-control studies of major depression (Radiant, GSK-Munich, PsyCoLaus). The analyses were first performed in the whole sample and then separately in depressed cases and controls. An unweighted GRS was calculated by summation of the number of risk alleles. A weighted GRS was calculated as the sum of risk alleles at each locus multiplied by their effect sizes. Receiver operating characteristic (ROC) analysis was used to compare the discriminatory ability of predictors of obesity. RESULTS: In the discovery phase, a total of 2,521 participants (1,895 depressed patients and 626 controls) were included from the Radiant study. Both unweighted and weighted GRS were highly associated with BMI (P <0.001) but explained only a modest amount of variance. Adding 'traditional' risk factors to GRS significantly improved the predictive ability with the area under the curve (AUC) in the ROC analysis, increasing from 0.58 to 0.66 (95% CI, 0.62-0.68; χ(2) = 27.68; P <0.0001). Although there was no formal evidence of interaction between depression status and GRS, there was further improvement in AUC in the ROC analysis when depression status was added to the model (AUC = 0.71; 95% CI, 0.68-0.73; χ(2) = 28.64; P <0.0001). We further found that the GRS accounted for more variance of BMI in depressed patients than in healthy controls. Again, GRS discriminated obesity better in depressed patients compared to healthy controls. We later replicated these analyses in two independent samples (GSK-Munich and PsyCoLaus) and found similar results. CONCLUSIONS: A GRS proved to be a highly significant predictor of obesity in people with MDD but accounted for only modest amount of variance. Nevertheless, as more risk loci are identified, combining a GRS approach with information on non-genetic risk factors could become a useful strategy in identifying MDD patients at higher risk of developing obesity.