393 research outputs found
Prévention du gain de poids chez les jeunes adultes
L'obésité atteint maintenant des proportions épidémiques à travers le monde. Le surplus de poids entraîne de nombreuses co-morbidités, en particulier les facteurs de risques cardiovasculaires tels que l'hypertension, la dyslipidémie, le syndrome métabolique et le diabète mellitus de type 2. L'obésité est associée à une augmentation des maladies cardiovasculaires et à une diminution de l'espérance de vie. Les comités de santé publique ont réaffirmé l'importance de la prévention dans la prise en charge de l'obésité et de ses complications. Plusieurs interventions de prévention du diabète chez les individus à haut risque se sont montrées efficaces. Peu d'études ont été conduites avec comme but une prévention primaire dans une population générale; aucune intervention n'a été démontrée efficace pour la prévention du gain de poids en communauté. Beaucoup d'études ont été entreprises pour la prévention de l'obésité chez les enfants et les adolescents par des interventions dans les écoles, peu se sont montrées efficaces. Il y a un manque flagrant d'études en prévention primaire dans la population étudiante post-secondaire. Le poids moyen d'un individu augmente graduellement après l'adolescence tout au long de la vie adulte, à partir du début de la vingtaine jusque dans la cinquantaine, pour se stabiliser dans la soixantaine. La période caractérisée par le gain de poids le plus important est la période jeune adulte. L'étude longitudinale de jeunes adultes CARDIA (18 à 30 ans au départ) a démontré un gain de poids constant d'environ 0,7kg par année dans une population nord-américaine caucasienne. Les études dans les populations universitaires démontrent une diminution importante de l'activité physique et une prise de poids au cours des premières années d'université. Les suivis de cohortes d'étudiants après leur graduation démontrent que le gain de poids à partir du début de l'université est prédicteur de plusieurs co-morbidités reliées au surplus de poids. Un haut niveau d'activité physique au cours des études post-secondaires semble protéger les individus contre plusieurs maladies et diminue la mortalité. L'objectif principal de mon projet de maîtrise était de démontrer qu'une intervention ayant pour but la prise et le maintien d'habitudes de vie saines pourrait prévenir le gain de poids habituellement observable lors des premières années d'université. Notre modèle expérimental était basé sur la population des étudiants de la Faculté de Faculté de médecine et des sciences de la santé et des Faculté des sciences de la Santé de l'Université de Sherbrooke. Nous avons mené une étude ouverte, randomisée, contrôlée auprès de 115 étudiants de première et deuxième année du premier cycle universitaire. Les participants furent randomisés selon leur indice de masse corporelle (IMC) et leur sexe en deux groupes: intervention par des ateliers en petits groupes ou sans intervention (témoin). Des mesures anthropométriques, un questionnaire d'activités physiques, un test de capacité physique, un journal alimentaire ainsi que des prises de sang ont été faits à des intervalles réguliers au cours des deux années de l'étude. Tel qu'attendu, le groupe témoin a pris du poids, surtout au cours de la première année, pendant que le poids du groupe intervention est resté stable et a même légèrement diminué au cours des deux ans. La différence de poids entre les deux groupes se situe à 1,3kg à 24mois (P =0,04). Le groupe intervention s'est aussi distingué statistiquement du groupe témoin pour la variation de l'indice de masse corporelle (IMC) (P =0,01), de sa consommation d'alcool (P =0,004) et des niveaux de triglycérides (P =0,04). Nous n'avons pas trouvé de différence significative pour les autres mesures anthropométriques (tour de taille, masse maigre), le niveau d'activité physique, la capacité physique ou l'apport calorique. Le gain de poids observé dans le groupe témoin est semblable au gain de poids observé dans d'autres études observationnelles. Les rares autres interventions de prévention du gain de poids dans une population universitaire n'ont pas réussi à démontrer une efficacité sauf dans une population ayant déjà un surplus de poids. Pour l'ensemble de la cohorte, le changement de poids était corrélé avec les variations de l'IMC, du tour de taille, de la masse maigre et non-maigre, de la capacité physique, ainsi que de la plupart des paramètres du bilan lipidique. Les corrélations nous montrent bien que même une petite variation de poids peut influencer sur les autres mesures anthropométriques. L'alimentation et l'activité physique ont une influence sur le gain de poids de notre cohorte mais les corrélations étaient faibles ou non significatives, probablement reliées au manque de précision des instruments de mesure (questionnaires, journaux alimentaires). Même un gain de poids modeste, tel qu'observé ici, a des impacts sur le profil lipidique
Validating metabolic syndrome through principal component analysis in a medically diverse, realistic cohort
Abstract: Background: The concept of metabolic syndrome has been subject to etiological and clinical controversies in recent years. Associations among the five risk factors (obesity, high blood pressure, high blood sugar, high triglyceride levels and low HDL cholesterol) may help establish the validity of the concept and its application, but most such studies have been conducted on targeted cohorts not representative of an actual population. Methods: We used principal component analysis (PCA) to analyze the structure of the physiological components of metabolic syndrome in 7213 patients contained in an administrative database for the CHUS hospital in Sherbrooke, Quebec, a realistic cohort with diverse medical histories. We validated the results by repeating the analysis on stratified and random subgroups of patients, and on different combinations of risk factors. The first axis of the PCA was used to predict coronary heart disease (CHD) and diabetes. Results: The two first axes explained 53% of the variance. The first axis (33%) was associated in the expected direction with all five predictor variables, consistent with its interpretation as metabolic syndrome. All validation analyses strongly confirmed this interpretation. The scores from the first axis were more predictive of subsequent CHD and diabetes than the formal definition of metabolic syndrome. Conclusions: These results suggest that the concept of metabolic syndrome accurately captures an existing underlying physiological process. A continuous indicator could be constructed to identify more accurately metabolic syndrome thus improving risk assessment for CHD and diabetes mellitus. Metabolic syndrome can be measured well even without all five predictors, though measurement is improved by PCA relative to dichotomized definitions. However, discrepancies with other studies suggest that our results may not be generalizable, perhaps because our cohort tends to be sicker
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Mediation Analysis Supports a Causal Relationship between Maternal Hyperglycemia and Placental DNA Methylation Variations at the Leptin Gene Locus and Cord Blood Leptin Levels.
Changes in fetal DNA methylation (DNAm) of the leptin (LEP) gene have been associated with exposure to maternal hyperglycemia, but their links with childhood obesity risk are still unclear. We investigated the association between maternal hyperglycemia, placental LEP DNAm (25 5-C-phosphate-G-3 (CpG) sites), neonatal leptinemia, and adiposity (i.e., BMI and skinfold thickness (ST) (subscapular (SS) + triceps (TR) skinfold measures, and the ratio of SS:TR) at 3-years-old, in 259 mother-child dyads, from Gen3G birth cohort. We conducted multivariate linear analyses adjusted for gestational age at birth, sex of the child, age at follow-up, and cellular heterogeneity. We assessed the causal role of DNAm in the association between maternal glycemia and childhood outcomes, using mediation analysis. We found three CpGs associated with neonatal leptinemia (p ≤ 0.002). Of these, cg05136031 and cg15758240 were also associated with BMI (β = -2.69, p = 0.05) and fat distribution (β = -0.581, p = 0.05) at 3-years-old, respectively. Maternal glycemia was associated with DNAm at cg15758240 (β = -0.01, p = 0.04) and neonatal leptinemia (β = 0.19, p = 0.004). DNAm levels at cg15758240 mediates 0.8% of the association between maternal glycemia and neonatal leptinemia (p < 0.001). Our results support that DNAm regulation of the leptin pathway in response to maternal glycemia might be involved in programming adiposity in childhood
Training health professionals to deliver healthy living medicine
The growing incidence and prevalence of unhealthy living behaviors leading to compromised health, along with unhealthy supportive environments, are the primary reasons for the current chronic disease crisis in almost all countries. Over the course of health professions training across disciplines, a large amount about information regarding various aspects of chronic disease is introduced, from pathophysiology to a broad array of approaches to examinations (focused on diagnosis and prognosis) and interventions. Currently, a late primary or secondary prevention focus is the primary educational approach in the health professions. In either scenario, the health professional is often trained to approach their discipline from a catch up approach, with little focus on how an individual's health condition, at the time of presentation, came to be. It is unfortunate that so little educational time and effort are devoted to train future health professionals on how to practice Healthy Living Medicine (HLM) and, deliver healthy living (HL) interventions. The primary goal should be to keep individuals healthy where they live, work and go to school and minimize initiating care in the hospital and outpatient clinical setting. The current review describes current trends in training health professionals in HLM and the delivery of HL interventions
Maternal lipid profile differs by gestational diabetes physiologic subtype
Aim
To characterize lipid profiles in women with different gestational diabetes mellitus (GDM) physiologic subtypes.
Methods
We measured seven lipid markers (total cholesterol, LDL, HDL, triglycerides, non-esterified fatty acids (NEFA), ApoA, ApoB) in fasting plasma collected in a prospective cohort of 805 pregnant women during second trimester. We estimated insulin sensitivity and secretion using oral glucose tolerance test-based validated indices. We categorized GDM physiologic subtypes by insulin sensitivity and secretion defects defined as values below the 25th percentile among women with normal glucose tolerance (NGT), as previously established. We compared lipid markers across NGT and GDM subtypes. We explored associations between lipid markers and newborn anthropometry in the overall group and stratified by glucose tolerance status.
Results
Among 805 women, 67 (8.3%) developed GDM. Women with GDM had higher body mass index (BMI; 29.3 vs. 26.6 kg/m2), while ethnicity (97.3% vs. 97.0% European ancestry) and age (28 vs. 29 years) were similar. In comparison to women with NGT, women with GDM characterized by a predominant insulin sensitivity defect had significantly higher triglycerides (2.20 vs. 1.82, P = 0.002), lower HDL (1.64 vs. 1.90, P = 0.01) and higher NEFA (0.34 vs. 0.24, P < 0.0001). GDM women with a predominant insulin secretion defect differed from women with NGT with respect to NEFA (0.32 vs. 0.24, P = 0.003) while other lipid markers were similar. These associations remained significant after adjusting for maternal age and BMI. Greater maternal levels of NEFA were associated with higher birth weight z-scores in women with an insulin secretion defect (BMI-adjusted r = 0.58, P = 0.01). We did not find significant associations between other lipid markers and newborn anthropometry in other groups.
Conclusion
Women with GDM have distinct lipid profiles based on their GDM physiologic subtype which may not be apparent when investigating GDM as a single group
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Associations of Prenatal Per- and Polyfluoroalkyl Substance (PFAS) Exposures with Offspring Adiposity and Body Composition at 16-20 Years of Age: Project Viva.
BackgroundFindings on the associations between prenatal PFAS exposures and offspring adiposity are inconsistent. Whether such associations may extend to adolescence is especially understudied.ObjectivesWe investigated associations of prenatal PFAS exposures with offspring adiposity and body composition at 16-20 years of age.MethodsWe studied 545 mother-child pairs in the prospective prebirth cohort Project Viva (Boston, Massachusetts). We measured six PFAS (PFOA, PFOS, PFNA, PFHxS, EtFOSAA, and MeFOSAA) in maternal early pregnancy (median age=9.6wk, range: 5.7-19.6 wk) plasma samples. At the late adolescence visit (median age=17.4 y, range: 15.9-20.0 y), we obtained anthropometric measures and assessed body composition using bioelectrical impedance analysis and dual-energy X-ray absorptiometry. We examined associations of individual PFAS with obesity [i.e., age- and sex-specific body mass index (BMI) ≥95th percentile] and adiposity and body composition using multivariable Poisson and linear regression models, respectively. We assessed PFAS mixture effects using Bayesian kernel machine regression (BKMR) and quantile g-computation. We used fractional-polynomial models to assess BMI trajectories (at 3-20 years of age) by prenatal PFAS levels.ResultsThirteen percent (n=73) of the children had obesity in late adolescence. After multivariable adjustment, higher prenatal PFAS concentrations were associated with higher obesity risk [e.g., 1.59 (95% CI: 1.19, 2.12), 1.24 (95% CI: 0.98, 1.57), and 1.49 (95% CI: 1.11, 1.99) times the obesity risk per doubling of PFOS, PFOA, and PFNA, respectively]. BKMR showed an interaction between PFOA and PFOS, where the positive association between PFOS and obesity was stronger when PFOA levels were lower. Each quartile increment of the PFAS mixture was associated with 1.52 (95% CI: 1.03, 2.25) times the obesity risk and 0.52 (95% CI: -0.02, 1.06) kg/m2 higher BMI. Children with higher prenatal PFOS, EtFOSAA, and MeFOSAA concentrations had higher rates of BMI increase starting from 9-11 years of age.DiscussionPrenatal PFAS exposures may have obesogenic effects into late adolescence. https://doi.org/10.1289/EHP12597
Branched Chain Amino Acids, Androgen Hormones, and Metabolic Risk Across Early Adolescence: A Prospective Study in Project Viva
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143686/1/oby22164.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143686/2/oby22164_am.pd
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