Sedentarism affects body fat mass index and fat-free mass index in adults aged 18 to 98 years.

OBJECTIVE: Body mass index does not discriminate body fat from fat-free mass or determine changes in these parameters with physical activity and aging. Body fat mass index (BFMI) and fat-free mass index (FFMI) permit comparisons of subjects with different heights. This study evaluated differences in body mass index, BFMI, and FFMI in physically active and sedentary subjects younger and older than 60 y and determined the association between physical activity, age, and body composition parameters in a healthy white population between ages 18 and 98 y. METHODS: Body fat and fat-free mass were determined in healthy white men (n = 3549) and women (n = 3184), between ages 18 and 98 y, by bioelectrical impedance analysis. BFMI and FFMI (kg/m2) were calculated. Physical activity was defined as at least 3 h/wk of endurance-type activity for at least 2 mo. RESULTS: Physically active as opposed to sedentary subjects were more likely to have a low BFMI (men: odds ratio [OR], 1.4; confidence interval [CI], 0.7-2.5; women: OR 1.9, CI 1.6-2.2) and less likely to have very high BFMI (men: OR, 0.2; CI, 0.1-0.2; women: OR, 0.1; CI, 0.02-0.2), low FFMI (men: OR, 0.5; CI, 0.3-0.9; women: OR, 0.7; CI, 0.6-0.9), or very high FFMI (men: OR, 0.6; CI, 0.4-0.8; women: OR, 0.7; CI, 0.5-1.0). Compared with subjects younger than 60 y, those older than 60 y were more like to have very high BFMI (men: OR, 6.5; CI, 4.5-9.3; women: OR, 14.0; CI, 9.6-20.5), and women 60 y and older were less likely to have a low BFMI (OR, 0.4; CI, 0.2-0.5). CONCLUSIONS: A clear association was found between low physical activity or age and height-normalized body composition parameters (BFMI and FFMI) derived from bioelectrical impedance analysis. Physically active subjects were more likely to have high or very high or low FFMI. Older subjects had higher body weights and BFMI.

Body mass index and risk of head and neck cancer in a pooled analysis of case-control studies in the International Head and Neck Cancer Epidemiology (INHANCE) Consortium.

BACKGROUND: Head and neck cancer (HNC) risk is elevated among lean people and reduced among overweight or obese people in some studies; however, it is unknown whether these associations differ for certain subgroups or are influenced by residual confounding from the effects of alcohol and tobacco use or by other sources of biases. METHODS: We pooled data from 17 case-control studies including 12 716 cases and the 17 438 controls. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated for associations between body mass index (BMI) at different ages and HNC risk, adjusted for age, sex, centre, race, education, tobacco smoking and alcohol consumption. RESULTS: Adjusted ORs (95% CIs) were elevated for people with BMI at reference (date of diagnosis for cases and date of selection for controls) 25.0-30.0 kg/m(2) (0.52, 0.44-0.60) and BMI >/=30 kg/m(2) (0.43, 0.33-0.57), compared with BMI >18.5-25.0 kg/m(2). These associations did not differ by age, sex, tumour site or control source. Although the increased risk among people with BMI 25 kg/m(2) was present only in smokers and drinkers. CONCLUSIONS: In our large pooled analysis, leanness was associated with increased HNC risk regardless of smoking and drinking status, although reverse causality cannot be excluded. The reduced risk among overweight or obese people may indicate body size is a modifier of the risk associated with smoking and drinking. Further clarification may be provided by analyses of prospective cohort and mechanistic studies.

Prepregnancy body mass index and resting metabolic rate during pregnancy.

AIM: The resting metabolic rate (RMR) varies among pregnant women. The factors responsible for this variability are unknown. This study aimed to assess the influence of the prepregnancy body mass index (BMI) on the RMR during late pregnancy. METHODS: RMR, height, weight, and total (TEE) and activity (AEE) energy expenditures were measured in 46 healthy women aged 31 ± 5 years (mean ± SD) with low (<19.8), normal (19.8-26.0), and high (>26.0) prepregnancy BMI at 38.2 ± 1.5 weeks of gestation (t(gest)) and 40 ± 7 weeks postpartum (t(post)) (n = 27). RESULTS: The mean t(gest) RMR for the low-, normal-, and high-BMI groups was 1,373, 1,807, and 2,191 kcal/day, respectively (p = 0.001). The overall mean t(gest) RMR was 316 ± 183 kcal/day (21%), higher than the overall mean t(post) value and this difference was correlated with gestational weight gain (r = 0.78, p < 0.001). The scaled metabolic rate by allometry (RMR/kilograms⁰·⁷³) was similar in the low-, normal-, and high-BMI groups, respectively (p = 0.45). Changes in t(gest) TEE closely paralleled changes in t(gest) RMR (r = 0.84, p < 0.001). AEE was similar among the BMI groups. CONCLUSION: The RMR is significantly increased in the third trimester of pregnancy. The absolute gestational RMR is higher in women with high prepregnancy BMI due to increased body weight. The scaled metabolic rate (RMR/kilograms⁰·⁷³) is similar among the BMI groups of pregnant women.

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.

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.