References of "Samouda, Hanen"
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See detailCan health indicators and psychosocial characteristics predict attrition in youth with overweight and obesity seeking ambulatory treatment? Data from a retrospective longitudinal study in a paediatric clinic in Luxembourg.
Pit-Ten Cate, Ineke UL; Samouda, Hanen; Schierloh, Ulrike et al

in BMJ Open (2017), 7(9),

ABSTRACT Objectives: The current study aimed to identify factors that could predict attrition in youth starting ambulatory treatment to control or lose weight. Design: retrospective longitudinal study ... [more ▼]

ABSTRACT Objectives: The current study aimed to identify factors that could predict attrition in youth starting ambulatory treatment to control or lose weight. Design: retrospective longitudinal study Setting: paediatric clinic: ambulatory treatment program Patients and measures: A youth sample (N=191; 89 boys; age 7-17 years) completed measures of demographic characteristics, health and psychosocial traits before starting an ambulatory weight management program. Anthropometric and biological markers related to obesity were also obtained. Test of mean differences and regression analyses were used to investigate the relationship between these variables and attrition after one year. Results: Chi-square and t-test results showed both psychosocial and health indicators differentiated between participants who continued attending the treatment program and those that dropped out. More specifically, youth that dropped out of treatment were significantly older, had higher BMI-Z scores, higher levels of insulin, triglycerides and HOMA-IR, reported poorer health and more conduct problems, and were more dissatisfied with themselves and their bodies before starting treatment. Results of regression analyses revealed that weight status (anthropometric and biological markers), age and body dissatisfaction predict attrition (overall prediction success 73%; prediction success for continued attendance 90/91%; prediction success for dropout 42/44%). Conclusion: Attrition, but especially the continued attendance in treatment, can be successfully predicted by age, weight status and body dissatisfaction. For patients who present with one or more risk factors, careful consideration is needed to decide which (combination of) in- or outpatient program may facilitate prolonged engagement of the patient and hence may be most effective in establishing weight loss. [less ▲]

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See detailAdding anthropometric measures of regional adiposity to BMI improves prediction of cardiometabolic, inflammatory and adipokines profiles in youths: a cross-sectional study.
Samouda, Hanen; De Beaufort, Carine UL; Stranges, Saverio et al

in BMC pediatrics (2015), 15

BACKGROUND: Paediatric research analysing the relationship between the easy-to-use anthropometric measures for adiposity and cardiometabolic risk factors remains highly controversial in youth. Several ... [more ▼]

BACKGROUND: Paediatric research analysing the relationship between the easy-to-use anthropometric measures for adiposity and cardiometabolic risk factors remains highly controversial in youth. Several studies suggest that only body mass index (BMI), a measure of relative weight, constitutes an accurate predictor, whereas others highlight the potential role of waist-to-hip ratio (WHR), waist circumference (Waist C), and waist-to-height ratio (WHtR). In this study, we examined the effectiveness of adding anthropometric measures of body fat distribution (Waist C Z Score, WHR Z Score and/or WHtR) to BMI Z Score to predict cardiometabolic risk factors in overweight and obese youth. We also examined the consistency of these associations with the "total fat mass + trunk/legs fat mass" and/or the "total fat mass + trunk fat mass" combinations, as assessed by dual energy X-ray absorptiometry (DXA), the gold standard measurement of body composition. METHODS: Anthropometric and DXA measurements of total and regional adiposity, as well as a comprehensive assessment of cardiometabolic, inflammatory and adipokines profiles were performed in 203 overweight and obese 7-17 year-old youths from the Paediatrics Clinic, Centre Hospitalier de Luxembourg. RESULTS: Adding only one anthropometric surrogate of regional fat to BMI Z Score improved the prediction of insulin resistance (WHR Z Score, R(2): 45.9%. Waist C Z Score, R(2): 45.5%), HDL-cholesterol (WHR Z Score, R(2): 9.6%. Waist C Z Score, R(2): 10.8%. WHtR, R(2): 6.5%), triglycerides (WHR Z Score, R(2): 11.7%. Waist C Z Score, R(2): 12.2%), adiponectin (WHR Z Score, R(2): 14.3%. Waist C Z Score, R(2): 17.7%), CRP (WHR Z Score, R(2): 18.2%. WHtR, R(2): 23.3%), systolic (WHtR, R(2): 22.4%), diastolic blood pressure (WHtR, R(2): 20%) and fibrinogen (WHtR, R(2): 21.8%). Moreover, WHR Z Score, Waist C Z Score and/or WHtR showed an independent significant contribution according to these models. These results were in line with the DXA findings. CONCLUSIONS: Adding anthropometric measures of regional adiposity to BMI Z Score improves the prediction of cardiometabolic, inflammatory and adipokines profiles in youth. [less ▲]

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See detailCardiometabolic risk: leg fat is protective during childhood.
Samouda, Hanen; De Beaufort, Carine UL; Stranges, Saverio et al

in Pediatric diabetes (2015)

BACKGROUND: Childhood obesity is associated with early cardiometabolic risk (CMR), increased risk of adulthood obesity, and worse health outcomes. Leg fat mass (LFM) is protective beyond total fat mass ... [more ▼]

BACKGROUND: Childhood obesity is associated with early cardiometabolic risk (CMR), increased risk of adulthood obesity, and worse health outcomes. Leg fat mass (LFM) is protective beyond total fat mass (TFM) in adults. However, the limited evidence in children remains controversial. OBJECTIVE: We investigated the relationship between LFM and CMR factors in youth. SUBJECTS: A total of 203 overweight/obese children, 7-17-yr-old, followed in the Pediatric Clinic, Luxembourg. METHODS: TFM and LFM by dual energy x-ray absorptiometry and a detailed set of CMR markers were analyzed. RESULTS: After TFM, age, sex, body mass index (BMI) Z-score, sexual maturity status, and physical activity adjustments, negative significant partial correlations were shown between LFM and homeostasis model assessment of insulin resistance (HOMA) (variance explained: 6.05% by LFM*; 7.18% by TFM**), fasting insulin (variance explained: 5.71% by LFM*; 6.97% by TFM**), triglycerides (variance explained: 3.96% by LFM*; 2.76% by TFM*), systolic blood pressure (variance explained: 2.68% by LFM*; 4.33% by TFM*), C-reactive protein (variance explained: 2.31% by LFM*; 4.28% by TFM*), and resistin (variance explained: 2.16% by LFM*; 3.57% by TFM*). Significant positive partial correlations were observed between LFM and high-density lipoprotein (HDL) cholesterol (variance explained: 4.16% by LFM*) and adiponectin (variance explained: 3.09% by LFM*) (*p-value < 0.05 and **p-value < 0.001). In order to adjust for multiple testing, Benjamini-Hochberg method was applied and the adjusted significance level was determined for each analysis. LFM remained significant in the aforementioned models predicting HOMA, fasting insulin, triglycerides, and HDL cholesterol (Benjamini and Hochberg corrected p-value < 0.01). CONCLUSIONS: LFM is protective against CMR in children, at least in terms of insulin resistance and adverse blood lipid profiles. [less ▲]

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