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International audience; Background: The associations between childhood adiposity and adult increased carotid intima-media thickness (cIMT) have been well established, which might be corroborated by the association between adiposity in children and inflammation in adults. However, longitudinal data regarding biological pathways associated with childhood adiposity are lacking.Methods: The current study included participants from the STANISLAS cohort who had adiposity measurements at age 5-18 years [ N = 519, mean (SD) age, 13.0 (2.9) years; 46.4% male], and who were measured with cIMT, vascular-related and metabolic-related proteins at a median follow-up of 19 ± 2 years. BMI, waist-to-height ratio and waist circumference were converted to age-specific and sex-specific z -scores.Results: A minority of children were overweight/obese (16.2% overweight-BMI z -score >1; 1.3% obesity- z -score >2). Higher BMI, waist-height ratio and waist circumference in children were significantly associated with greater adult cIMT in univariable analysis, although not after adjusting for C-reactive protein. These associations were more pronounced in those with consistently high adiposity status from childhood to middle adulthood. Participants with higher adiposity during childhood (BMI or waist-height ratio) had higher levels of insulin-like growth factor-binding protein-1, protein-2, matrix metalloproteinase-3, osteopontin, hemoglobin and C-reactive protein in adulthood. Network analysis showed that IL-6, insulin-like growth factor-1 and fibronectin were the key proteins associated with childhood adiposity.Conclusion: In a population-based cohort followed for 20 years, higher BMI or waist-to-height ratio in childhood was significantly associated with greater cIMT and enhanced levels of proteins reflective of inflammation, supporting the importance of inflammation as progressive atherosclerosis in childhood adiposity.

ispartof: Report status: published

ispartof: Report status: published

Additional file 1: Table S1. Comparison of included patients versus patients excluded for missing MAP values. Table S2. Multivariate analysis including mean MAP within the first 6h after ICU admission of factors associated with severe acute kidney injury occurrence within the first 48h after ICU admission. Table S3. Multivariate analysis including mean MAP within the first 12h after ICU admission of factors associated with severe acute kidney injury occurrence within the first 48h after ICU admission. Table S4. Multivariate analysis including ABT MAP < 65 mmHg within the first 6h after ICU admission of factors associated with severe acute kidney injury occurrence within the first 48h after ICU admission. Table S5. Multivariate analysis including ABT MAP < 75 mmHg within the first 6h after ICU admission of factors associated with severe acute kidney injury occurrence within the first 48h after ICU admission. Table S6. Multivariate analysis including ABT MAP < 85 mmHg within the first 6h after ICU admission of factors associated with severe acute kidney injury occurrence within the first 48h after ICU admission. Table S7. Multivariate analysis including ABT MAP < 65 mmHg within the first 12h after ICU admission of factors associated with severe acute kidney injury occurrence within the first 48h after ICU admission. Table S8. Multivariate analysis including ABT MAP < 75 mmHg within the first 12h after ICU admission of factors associated with severe acute kidney injury occurrence within the first 48h after ICU admission. Table S9. Multivariate analysis including ABT MAP < 85 mmHg within the first 12h after ICU admission of factors associated with severe acute kidney injury occurrence within the first 48h after ICU admission. Table S10. Multivariate analysis including the percentage time MAP < 65 mmHg within the first 6h after ICU admission of factors associated with severe acute kidney injury occurrence within the first 48h after ICU admission. Table S11. Multivariate analysis including the percentage time MAP < 75 mmHg within the first 6h after ICU admission of factors associated with severe acute kidney injury occurrence within the first 48h after ICU admission. Table S12. Multivariate analysis including the percentage time MAP < 85 mmHg within the first 6h after ICU admission of factors associated with severe acute kidney injury occurrence within the first 48h after ICU admission. Table S13. Multivariate analysis including the percentage time MAP < 65 mmHg within the first 12h after ICU admission of factors associated with severe acute kidney injury occurrence within the first 48h after ICU admission. Table S14. Multivariate analysis including the percentage time MAP < 75 mmHg within the first 12h after ICU admission of factors associated with severe acute kidney injury occurrence within the first 48h after ICU admission. Table S15. Multivariate analysis including the percentage time MAP < 85 mmHg within the first 12h after ICU admission of factors associated with severe acute kidney injury occurrence within the first 48h after ICU admission. Figure S1. Predictive ability of early mean MAP value for severe acute kidney injury occurrence within the first 48h after intensive care unit admission The predictive ability of the mean MAP value within the first 6 (A) and 12h (B) was studied by calculating ROC curves with their responding area under the curve (AUC).

Ce fascicule reprend les principaux résultats de l’étude menée par la Chaire de Dynamique Sociale de l’Université de Kinshasa (CDS-Unikin), en partenariat avec le HIVA-KU Leuven, sur l’économie informelle à Mbuji Mayi et à Kisangani. Le rapport contribue à l’élargissement et à la consolidation des données de l’observatoire national de ce secteur en République Démocratique du Congo. ispartof: Principaux résultats de l’observatoire sur l’économie informelle à Mbuji Mayi et à Kisangani status: Published online

Objectifs : les médecins généralistes sont amenés, de plus en plus, à accompagner des patients en fins de vie à domicile. Notre objectif principal était de comprendre le vécu émotionnel des médecins généralistes au cours de ces prises en charge. Le premier objectif secondaire était d’analyser les moyens mis en œuvre pour prendre du recul sur ce vécu. Le second objectif secondaire était de rechercher les moyens d’armer au mieux les internes de médecine générale à ce type de prise en charge. Méthodes : enquête qualitative exploratoire par entretiens semi-dirigés auprès de treize médecins généralistes de la Somme. Résultats : les médecins ressentent des émotions variées : tristesse, anxiété, culpabilité mais aussi soulagement, satisfaction et fierté. Ces émotions sont fortement influencées par le rapport à la mort du médecin et par la présence d’un lien affectif avec le patient. L’acceptation de la mort est un facteur protecteur des émotions négatives et favorise un vécu émotionnel serein. Les médecins interrogés favorisent la prise de recul par la mise à distance du patient et la recherche de soutien social. Les moyens proposés par les médecins pour aider les futurs médecins sont : la formation en soins palliatifs et l’évaluation psychologique des étudiants en vue d’un soutien éventuel. Conclusion : la psychologie cognitive nous permet de mieux comprendre le vécu émotionnel des médecins généralistes au cours des prises en charge de fins de vie. Cela ouvre des pistes d’exploration, notamment concernant le rôle de la personnalité du médecin dans son vécu émotionnel. Étudier le besoin d’une évaluation psychologique des étudiants en médecine, en vue d’un soutien psychologique éventuel, pourrait être intéressant.

International audience