What do parents want for their children who are overweight when visiting the paediatrician?

OBJECTIVE: The objective of this study was to determine whether parental preferences regarding primary care weight-management strategies differ by child age, gender, overweight severity, race/ethnicity or parental agreement that their child is overweight. METHODS: A survey was administered to parents of 2- to 18-year-old children who are overweight at an academic primary-care clinic regarding perception of child overweight, helpful/harmfulness of having the child present during weight discussions, and dietary-advice preferences. Multivariable analyses examined factors associated with preferred weight-management strategies, after adjustment for parent/child characteristics. RESULTS: Eighty-three per cent of parents agreed that a child's presence during weight discussions is helpful/very helpful, 74% that paediatricians should prescribe specific diets, and 55% preferred specific vs. general dietary advice only (N = 219). In multivariable analyses, characteristics associated with helpfulness of child presence included older child age (vs. 2-5 year olds, 6-11 year olds: odds ratio [OR], 4.6; 95% CI, 1.3-16; 12- to 18-year-olds: OR, 23; 95% CI, 4-136), male gender (OR, 5.0; 95% CI, 1.7-10) and obesity (vs. overweight: OR, 2.8; 95% CI, 1.7-12). Characteristics associated with preferring specific diets included Latino race/ethnicity (OR, 5.3; 95% CI, 3-12), older age (vs. 2-5 year olds, 6-11 year olds: OR, 2.8; 95% CI, 1.1-7; 12-18 year olds: OR, 3.7; 95% CI, 1.5-10) and agreement that the child is overweight (OR, 2.3; 95% CI, 1.1-5) and, for specific dietary advice, older age (vs. 2-5 year olds: OR, 2.3; 95% CI, 1.1-5) and agreement that the child is overweight (OR, 2.1; 95% CI, 1.2-4). CONCLUSIONS: Findings suggest that weight-management strategies tailored to child age, gender, over-weight severity, race/ethnicity and parental agreement that their child is overweight may prove useful in improving child weight status.

Branched-chain amino acid levels are associated with improvement in insulin resistance with weight loss.

AIMS/HYPOTHESIS: Insulin resistance (IR) improves with weight loss, but this response is heterogeneous. We hypothesised that metabolomic profiling would identify biomarkers predicting changes in IR with weight loss. METHODS: Targeted mass spectrometry-based profiling of 60 metabolites, plus biochemical assays of NEFA, ß-hydroxybutyrate, ketones, insulin and glucose were performed in baseline and 6 month plasma samples from 500 participants who had lost ≥4 kg during Phase I of the Weight Loss Maintenance (WLM) trial. Homeostatic model assessment of insulin resistance (HOMA-IR) and change in HOMA-IR with weight loss (∆HOMA-IR) were calculated. Principal components analysis (PCA) and mixed models adjusted for race, sex, baseline weight, and amount of weight loss were used; findings were validated in an independent cohort of patients (n = 22). RESULTS: Mean weight loss was 8.67 ± 4.28 kg; mean ∆HOMA-IR was -0.80 ± 1.73, range -28.9 to 4.82). Baseline PCA-derived factor 3 (branched chain amino acids [BCAAs] and associated catabolites) correlated with baseline HOMA-IR (r = 0.50, p < 0.0001) and independently associated with ∆HOMA-IR (p < 0.0001). ∆HOMA-IR increased in a linear fashion with increasing baseline factor 3 quartiles. Amount of weight loss was only modestly correlated with ∆HOMA-IR (r = 0.24). These findings were validated in the independent cohort, with a factor composed of BCAAs and related metabolites predicting ∆HOMA-IR (p = 0.007). CONCLUSIONS/INTERPRETATION: A cluster of metabolites comprising BCAAs and related analytes predicts improvement in HOMA-IR independent of the amount of weight lost. These results may help identify individuals most likely to benefit from moderate weight loss and elucidate novel mechanisms of IR in obesity.

Low HDL predicts differential blood pressure effects from two weight-loss approaches: a secondary analysis of blood pressure from a randomized, clinical weight-loss trial.

Examining predictors of blood-pressure (BP) response to weight-loss diets might provide insight into mechanisms and help guide clinical care. We examined whether certain baseline patient characteristics (e.g. diet, medical history and laboratory tests) predicted BP response to two weight-loss diet approaches that differ in macronutrient content. One hundred and forty-six overweight adult outpatients were randomized to either a low-carbohydrate diet (N = 72) or orlistat plus a low-fat diet (N = 74) for 48 weeks. Predictors of BP reduction were evaluated using a structured approach and random effects regression models. Participants were 56% African-American, 72% male and 53 (±10) years-old. Of the variables considered, low baseline high-density lipoprotein (HDL) predicted greater reduction in BP in those patients who received the low-carbohydrate diet (p = 0.03 for systolic BP; p = 0.03 for diastolic BP and p = 0.02 for mean arterial pressure). A low HDL level may identify patients who will have greater BP improvement on a low-carbohydrate diet.

Adipose tissue mass and location affect circulating adiponectin levels.

AIMS/HYPOTHESIS: Plasma levels of adiponectin are inversely associated with body mass. We hypothesised that adipose tissue distribution and body composition influences adiponectin levels. METHODS: We assessed plasma adiponectin concentrations and dual-energy X-ray absorptiometry (DEXA) measurements of body composition among 2,820 participants from the Dallas Heart Study. RESULTS: Among both women and men, adiponectin levels were higher in whites than in either Hispanics or African-Americans (for women: median 9.99 µg/ml [25th,75th percentile 7.11, 13.77] vs 7.56 µg/ml [5.05, 9.98] vs 6.39 µg/ml [4.37, 9.41], respectively, p < 0.0001; for men: 6.43 µg/ml [4.66, 9.19] vs 5.55 µg/ml [3.64, 7.50] vs 5.03 µg/ml [3.39, 7.28], p < 0.0001). In univariate analysis, each individual component of body mass was inversely associated with adiponectin. After multivariate analysis, adiponectin levels were found to be positively associated with lower extremity fat, whether expressed in absolute mass (for women: ß = 0.055, p < 0.0001; for men: ß = 0.061, p < 0.0001), or as a relative proportion (for women: ß = 0.035, p < 0.0001; for men: ß = 0.034, p < 0.0001). This association was consistent across ethnicities. Conversely, adiponectin was negatively correlated with truncal fat, both in absolute (for women: ß = -0.039, p < 0.0001; for men: ß = -0.044, p < 0.0001) and relative terms (for women: ß = -0.027, p < 0.0001; for men ß = -0.033, p < 0.0001). At the extreme of body mass, higher degrees of lower extremity and truncal adiposity were associated with higher levels of adiponectin. CONCLUSIONS/INTERPRETATION: These data suggest that the location of adipose depots differentially influences circulating adiponectin concentrations-a finding observed across ethnicity and sex. Gross measures of body mass alone do not adequately account for adiponectin levels. This supports a role of adiponectin as a mediator of the positive effects of lower extremity adiposity on improvements in insulin sensitivity.