864 resultados para fat-free mass index
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The aim of this study was to describe the nonlinear association between body mass index (BMI) and breast cancer outcomes and to determine whether BMI improves prediction of outcomes. A cohort of906 breast cancer patients diagnosed at Henry Ford Health System, Detroit (1985-1990) were studied. The median follow-up was 10 years. Multivariate logistic regression was used to model breast cancer recurrence/progression and breast cancer-specific death. Restricted cubic splines were used to model nonlinear effects. Receiver operator characteristic areas under the curves (ROC AUC) were used to evaluate prediction. BMI was nonlinearly associated with recurrence/progression and death (p= 0.0230 and 0.0101). Probability of outcomes increased with increase or decrease ofBMI away from 25. BMI splines were suggestive of improved prediction of death. The ROC AUCs for nested models with and without BMI were 0.8424 and 0.8331 (p= 0.08). I f causally associated, modifying patients BMI towards 25 may improve outcomes.
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INTRODUCTION: Il existe peu d’évidences sur l’association entre le taux de chômage dans le milieu résidentiel (CR) et le risque de maladies cardiovasculaires parmi les résidents de milieux urbains. De plus, on ne sait pas si ce lien diffère entre les deux sexes. Cette thèse a pour objectif de déterminer la direction et la taille de l’association entre le CR et le risque de maladies cardiovasculaires, et d’examiner si cette association varie en fonction du sexe. MÉTHODES: Un sous-échantillon de 342 participants de l’Étude sur les habitudes de vie et la santé dans les quartiers montréalais a rapporté ses habitudes de vie et sa situation socio-économique. Des mesures biologiques et anthropométriques ont été recueillies par une infirmière. Le CR a été opérationnalisé en fonction d’une zone-tampon d’un rayon de 250 m centrée sur la résidence de chacun des participants à l’aide d’un Système d’Information Géographique (SIG). Des équations d’estimation généralisées ont été utilisées afin d’estimer l’association entre le CR et l’Indice de Masse Corporelle (IMC) et un score cumulatif de Risque Cardio-métabolique (RC) représentant la présence de valeurs élevées de cholestérol total, de triglycérides, de lipoprotéines de haute densité et d’hémoglobine glyquée. RÉSULTATS: Après ajustement pour l’âge, le sexe, le tabagisme, les comportements de santé et le statut socio-économique, le fait de vivre dans un endroit classé dans le 3e ou 4e quartile de CR était associé avec un IMC plus élevé (beta pour Q4 = 2.1 kg/m2, IC 95%: 1.02-3.20; beta pour Q3 = 1.5 kg/m2, IC 95%: 0.55-2.47) et un taux plus élevé de risque cardiovasculaires Risque Relatif [RR pour Q4 = 1.82 (IC 95 %: 1.35-2.44); RR pour Q3 = 1.66 (IC 95%: 1.33-2.06)] par rapport au 1er quartile. L'interaction entre le sexe et le CR révèle une différence absolue d’IMC de 1.99 kg/m2 (IC 95%: 0.00-4.01) et un risque supérieur (RR=1.39; IC 95%: 1.06-1.81) chez les femmes par rapport aux hommes. CONCLUSIONS: Le taux de chômage dans le milieux résidentiel est associé à un plus grand risque de maladies cardiovasculaires, mais cette association est plus prononcée chez les femmes.
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Trajectoires développementales de l’IMC durant l’enfance: Une étude longitudinale sur 8 ans. Introduction : L’obésité infantile, origine de nombreux problèmes de santé, représente un grand défi en santé publique. Récemment, l’importance d’étudier l’évolution du surpoids durant l’enfance ainsi que les facteurs de risques précoces pour l’obésité a été reconnue. Les trajectoires développementales d’indice de masse corporelle (IMC) chez les jeunes représentent une approche innovatrice qui nous permet de mieux comprendre cette problématique importante. Objectifs: 1) Identifier des trajectoires développementales distinctes de groupes d’enfants selon leur IMC durant l’enfance, et 2) Explorer les facteurs de risques précoces qui prédisent l’appartenance de l’enfant à la trajectoire d’IMC le plus élevé Hypothèses: 1) On s’attend à retrouver un groupe d’enfants qui suit une trajectoire d’IMC élevée durant l’enfance. 2) On s’attend à ce que certaines caractéristiques de la mère (ex : tabac pendant la grossesse et IMC élevé), soient associées à l’appartenance de l’enfant au groupe ayant la trajectoire «IMC élevé ». Méthodes: Estimation des trajectoires développementales d’IMC d’enfants, dans un échantillon populationnel (n=1957) au Québec (ELDEQ). Les IMC ont été calculés à partir de données fournies par les mères des enfants et recueillis chaque année sur une durée de 8 ans. Des données propres à l’enfant sa mère, ainsi que socioéconomiques, ont étés recueillies. Une régression logistique multinomiale a été utilisée pour distinguer les enfants avec un IMC élevé des autres enfants, selon les facteurs de risques précoces. Les programmes PROC TRAJ (extension de SAS), SPSS (version 16), et SAS (version 9.1.3) ont été utilisés pour ces analyses. Résultats: Trois trajectoires d’IMC ont étés identifiées : IMC « bas-stable » (54,5%), IMC « modéré » (41,0%) et IMC « élevé et en hausse » (4,5%). Le groupe « élevé et en hausse » incluait des enfants pour qui l’IMC à 8 ans dépassait la valeur limite pour l’obésité. Les analyses de régression logistique ont révélé que deux facteurs de risques maternels étaient significativement associés avec la trajectoire “en hausse” par rapport aux deux autres groupes : le tabac durant la grossesse et le surpoids maternel. Conclusions: Des risques d’obésité infantile peuvent êtres identifiés dès la grossesse. Des études d’intervention sont requises pour identifier la possibilité de réduire le risque d’obésité chez l’enfant en ciblant le tabac et le surpoids maternelle durant la grossesse. Mots clés: Indice de masse corporelle (IMC), obésité infantile, trajectoires développementales de groupe, facteurs de risque précoce, étude populationnelle, tabac pendant la grossesse, obésité maternelle.
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Objetivo: Recientemente, se han propuesto varios dispositivos de impedancia bioeléctrica (BIA) para la estimación rápida de la grasa corporal. Sin embargo, no han sido publicadas referencias de grasa corporal para niños y adolescentes en población Colombiana. El objetivo de este estudio fue establecer percentiles de grasa corporal por BIA en niños y adolescentes de Bogotá, Colombia de entre 9 y 17.9 años, pertenecientes al estudio FUPRECOL. Métodos: Estudio descriptivo y transversal, realizado en 2.526 niños y 3.324 adolescentes de entre 9 y 17.9 años de edad, pertenecientes a instituciones educativas oficiales de Bogotá, Colombia. El porcentaje de grasa corporal fue medido con Tanita® Analizador de Composición Corporal (Modelo BF-689), según edad y sexo. Se tomaron medidas de peso, talla, circunferencia de cintura, circunferencia de cadera y estado de maduración sexual por auto-reporte. Se calcularon los percentiles (P3, P10, P25, P50, P75, P90 y P97) y curvas centiles por el método LMS según sexo y edad y se realizó una comparación entre los valores de la CC observados con estándares internacionales. Resultados: Se presentan valores de porcentaje de grasa corporal y las curvas de percentiles. En la mayoría de los grupos etáreos la grasa corporal de las chicas fue mayor a la de los chicos. Sujetos cuyo porcentaje de grasa corporal estaba por encima del percentil 90 de la distribución estándar normal se consideró que tenían un elevado riesgo cardiovascular (chicos desde 23,4-28,3 y chicas desde 31,0-34,1). En general, nuestros porcentajes de grasa corporal fueron inferiores a los valores de Turquía, Alemania, Grecia, España y Reino Unido. Conclusiones: Se presentan percentiles del porcentaje de grasa por BIA según edad y sexo que podrán ser usados de referencia en la evaluación del estado nutricional y en la predicción del riesgo cardiovascular desde edades tempranas.
Body mass index as a standard of living measure: a different interpretation for the case of Colombia
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We analyze the Body Mass Index (BMI) in a distinct way of its traditional use and it lets us use it as a proxy of standard of living for the case of Colombia. Our approach is focused on studying how far the people are from the normal range and not on the score of each one and this lets us to treat equally extreme cases as severe thinness and obesity. We use a probabilistic model (Ordered Probit) that evaluates the probability of being within the normal range or another level. We found that socioeconomic variables have a significant effect on the dependent variable and that there are no linear effects. Besides, people with difficulties for walking and adults have less probability of having a normal BMI.
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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.
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Background Underweight and severe and morbid obesity are associated with highly elevated risks of adverse health outcomes. We estimated trends in mean body-mass index (BMI), which characterises its population distribution, and in the prevalences of a complete set of BMI categories for adults in all countries. Methods We analysed, with use of a consistent protocol, population-based studies that had measured height and weight in adults aged 18 years and older. We applied a Bayesian hierarchical model to these data to estimate trends from 1975 to 2014 in mean BMI and in the prevalences of BMI categories (<18·5 kg/m2 [underweight], 18·5 kg/m2 to <20 kg/m2, 20 kg/m2 to <25 kg/m2, 25 kg/m2 to <30 kg/m2, 30 kg/m2 to <35 kg/m2, 35 kg/m2 to <40 kg/m2, ≥40 kg/m2 [morbid obesity]), by sex in 200 countries and territories, organised in 21 regions. We calculated the posterior probability of meeting the target of halting by 2025 the rise in obesity at its 2010 levels, if post-2000 trends continue. Findings We used 1698 population-based data sources, with more than 19·2 million adult participants (9·9 million men and 9·3 million women) in 186 of 200 countries for which estimates were made. Global age-standardised mean BMI increased from 21·7 kg/m2 (95% credible interval 21·3–22·1) in 1975 to 24·2 kg/m2 (24·0–24·4) in 2014 in men, and from 22·1 kg/m2 (21·7–22·5) in 1975 to 24·4 kg/m2 (24·2–24·6) in 2014 in women. Regional mean BMIs in 2014 for men ranged from 21·4 kg/m2 in central Africa and south Asia to 29·2 kg/m2 (28·6–29·8) in Polynesia and Micronesia; for women the range was from 21·8 kg/m2 (21·4–22·3) in south Asia to 32·2 kg/m2 (31·5–32·8) in Polynesia and Micronesia. Over these four decades, age-standardised global prevalence of underweight decreased from 13·8% (10·5–17·4) to 8·8% (7·4–10·3) in men and from 14·6% (11·6–17·9) to 9·7% (8·3–11·1) in women. South Asia had the highest prevalence of underweight in 2014, 23·4% (17·8–29·2) in men and 24·0% (18·9–29·3) in women. Age-standardised prevalence of obesity increased from 3·2% (2·4–4·1) in 1975 to 10·8% (9·7–12·0) in 2014 in men, and from 6·4% (5·1–7·8) to 14·9% (13·6–16·1) in women. 2·3% (2·0–2·7) of the world's men and 5·0% (4·4–5·6) of women were severely obese (ie, have BMI ≥35 kg/m2). Globally, prevalence of morbid obesity was 0·64% (0·46–0·86) in men and 1·6% (1·3–1·9) in women. Interpretation If post-2000 trends continue, the probability of meeting the global obesity target is virtually zero. Rather, if these trends continue, by 2025, global obesity prevalence will reach 18% in men and surpass 21% in women; severe obesity will surpass 6% in men and 9% in women. Nonetheless, underweight remains prevalent in the world's poorest regions, especially in south Asia.
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Objective: The combination of twho anthropometric parameters has been more appropriate to assess body composition and proportions in children, with special attention to the Body Mass Index (BMI), as it relates weight and length. However the BMI values for the neonatal period have not been determined yet. This study shows the BMI for newborns at different gestational ages represented in a normal smoothed percentile curve. Methods: Retrospective study including 2,406 appropriate for gestational age newborns following the Alexander et al curve (1996) from 29 to 42 weeks of gestational age. Weight and lenght were measured following standard procedures. For the construction aof a normal smoothed percentile curve, the 3(rd) 5(th), 10(th), 25(th), 5(th), 75(th), 90(th) and 95(th) percentiles were determined and a statistical procedure based on the mathematical model ""sinosuoidal fit"" was applied to establish a curve that estimates biological growth parameters. Results: The Body Mass Index values for gestational age in all percentiles shows a steady increase up to 38 weeks, levels off up to the 40(th) week, followed by a slight decrease to the 42(nd) week in both genders. Conclusion: The results show a direct correlation between gestational age and Body Mass Index for both genders in the nine percentiles, and can provide a useful reference to assess intra-uterine proportional growth.
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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 upto 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 x 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 similar to 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 systemin 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.
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Little evidence-based guidance is available to aid clinicians in determining short-term prognoses in very severe COPD patients. Therefore, the present study was designed to provide a prospective assessment (1) of the mortality rates and (2) whether the baseline measurements may be determinants of 1-year mortality in hypoxemic COPD patients receiving long-term oxygen therapy (LTOT).Seventy-eight clinically stable patients with advanced COPD treated using LTOT were enrolled in a prospective cohort study. Outcome variable: first-year mortality. Baseline measurements: categorical variables: age (<60 or >= 60 years); gender; body mass index (<20 or >= 20 kg/m(2)); fat-free mass (FFM) index (<16 [men] and <15kg/m(2) [women]; baseline dyspnea index (BDI) (<= 3 or >3); and corticosteroid use. Continuous variables: smoking history; lung function; FFM; fat mass; hemoglobin; hematocrit; arterial blood gases; forearm muscle strength; St. George's Respiratory Questionnaire (SGRQ); and comorbidity score. By the end of 1-year of follow-up, 12 patients (15.4%) had died. Kaplan-Meier curves showed that BDI <= 3 was the only variable associated with higher mortality. Cox proportional hazards analysis revealed that tower PaO2 and SPO2, higher PaCO2 and SGRQ scores were associated with reduced survival. In the multivariate analysis, BDI remained predictive of mortality (hazard ratio [HR], 0.50; 95% confidence interval [CI], 0.31-0.81), as did PaO2 (HR, 0.49; 95% CI, 0.26-0.95). These data suggest that readily available parameters as dyspnea intensity and hypoxemia severity may be useful in predicting first-year survival rates in advanced COPD patients receiving LTOT (C) 2007 Elsevier Ltd. All rights reserved.
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In a cross-sectional study, we assessed beta-cell function and insulin sensitivity index (ISI) with hyperglycemic clamps (10 mmol/l) in 24 subjects with impaired fasting glycemia (IFG, fasting plasma glucose [FPG] between 6.1 and 7.0 mmol/l), 15 type 2 diabetic subjects (FPG >7.0 mmol/l), and 280 subjects with normal fasting glycemia (NFG, FPG <6.1 mmol/l). First-phase insulin release (0-10 min) was lower in IFG (geometric mean 541 pmol/l (.) 10 min; 95% confidence interval [CI] 416-702 pmol/l (.) 10 min) and in type 2 diabetes (geometric mean 376 pmol/l (.) 10 min; 95% CI 247-572 pmol/l (.) 10 min) than NFG (geometric mean 814 pmol/l (.) 10 min; 95% CI 759-873 pmol/l (.) 10 min) (P < 0.001). Second-phase insulin secretion (140-180 min) was also lower in IFG (geometric mean 251 pmol/l; 95% CI 198-318 pmol/l; P = 0.026) and type 2 diabetes (geometric mean 157 pmol/l; 95% CI 105-235 pmol/l; P < 0.001) than NFG (geometric mean 295 pmol/l; 95% CI 276-315 pmol/l): IFG and type 2 diabetic subjects had a lower ISI (0.15 +/- 0.02 and 0.16 +/- 0.02 mumol/kg fat-free mass [FFM]/min/ pmol/l, respectively) than NFG (0.24 +/- 0.01 mumol/kg FFM/min/pmol/l, P < 0.05). We found a stepwise decline in first-phase (and second-phase) secretion in NFG subjects with progressive decline in oral glucose tolerance (P < 0.05). IFG subjects with impaired glucose tolerance (IGT) had lower first-phase secretion than NFG subjects with IGT (P < 0.02), with comparable second-phase secretion and ISI. NFG and IFG subjects with a diabetic glucose tolerance (2-h glucose >11.1 mmol/l) had a lower ISI than their respective IGT counterparts (P < 0.05). We conclude that the early stages of glucose intolerance are associated with disturbances in beta-cell function, while insulin resistance is seen more markedly in later stages.
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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The aim of this study was to examine the agreement between the results of body fat (BF and BF%), fat-free mass (FFM) and FFM index (FFMI= FFM/height2) as estimated by skinfold anthropometry (ANT), bioelectrical impedance (BIA) and dual-energy X-ray absorptiometry (DXA) in two groups of men (> or = 50 y), one comprising healthy individuals (n=23) and the other, patients with chronic obstructive pulmonary disease (COPD) (n=24). Comparisons between body composition techniques were done by repeated measures ANOVA; the Bland & Altman procedure was used to analyse agreement. RESULTS AND CONCLUSIONS: 1) comparison between healthy and COPD groups showed significant differences between all studied variables; 2) in the healthy group, values for BF, BF%, FFM and FFMI were not significantly different when BIA or ANT was compared to DXA; however, in COPD, values for BF and BF% were significantly higher and for FFM and FFMI significantly lower when BIA was compared to DXA; in contrast, no differences were shown between values for these variables when ANT was compared with DXA; 3) Bland & Altman test, in both groups, showed no agreement between BIA and DXA and between ANT and DXA; it was also shown that body fat was overestimated and fat free mass underestimated by BIA in relation to DXA.
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Objective: The impact of obesity on ventricular remodeling after myocardial infarction (MI) is still poorly understood. Therefore, the aim of this study was to evaluate the role of waist circumference (WC) and body mass index as predictors of cardiac remodeling in patients after an anterior MI. Methods: Eighty-three consecutive patients with anterior MI were prospectively evaluated. Clinical characteristics and echocardiographic data were analyzed at admission and at a 6-mo follow-up. Ventricular remodeling was defined as a 10% increase in left ventricular end-systolic or end-diastolic diameter at the 6-mo follow-up. Results: In our study, 83 consecutive patients were evaluated (72% men). Ventricular remodeling was present in 31% of the patients (77% men). Patients with remodeling had higher creatine phosphokinase and creatine phosphokinase-MB peak values, a higher resting heart rate, a larger left atrial diameter, and a larger interventricular septum diastolic thickness. In addition, patients with remodeling had lower peak velocity of early ventricular filling deceleration time and ejection fraction. Patients with remodeling presented higher WC values (with remodeling, 99.2 ± 10.4 cm; without remodeling, 93.9 ± 10.8 cm, P = 0.04), but there were no differences in the body mass index values. In the logistic regression analysis, WC, adjusted by age, gender, ejection fraction, and creatine phosphokinase levels, was an independent predictor of left ventricular remodeling (odds ratio 1.067, 95% confidence interval 1.001-1.129, P = 0.02). Conclusion: Waist circumference, but not body mass index, is a predictor of ventricular remodeling after an anterior MI. Therefore, the WC of these patients should be measured in clinical practice. © 2013 Elsevier Inc.