Sex and puberty-related differences in metabolomic profiles associated with adiposity measures in youth with obesity.

BACKGROUND: Specific patterns of metabolomic profiles relating to cardiometabolic disease are associated with increased weight in adults. In youth with obesity, metabolomic data are sparse and associations with adiposity measures unknown. OBJECTIVES: Primary, to determine associations between adiposity measures and metabolomic profiles with increased cardiometabolic risks in youth with obesity. Secondary, to stratify associations by sex and puberty. METHODS: Participants were from COBRA (Childhood Overweight BioRepository of Australia; a paediatric cohort with obesity). Adiposity measures (BMI, BMI z-score, %truncal and %whole body fat, waist circumference and waist/height ratio), puberty staging and NMR metabolomic profiles from serum were assessed. Statistics included multivariate analysis (principal component analysis, PCA) and multiple linear regression models with false discovery rate adjustment. RESULTS: 214 participants had metabolomic profiles analyzed, mean age 11.9 years (SD ± 3.1), mean BMI z-score 2.49 (SD ± 0.24), 53% females. Unsupervised PCA identified no separable clusters of individuals. Positive associations included BMI z-score and phenylalanine, total body fat % and lipids in medium HDL, and waist circumference and tyrosine; negative associations included total body fat % and the ratio of docosahexaenoic acid/total fatty acids and histidine. Stratifying by sex and puberty, patterns of associations with BMI z-score in post-pubertal males included positive associations with lipid-, cholesterol- and triglyceride-content in VLDL lipoproteins; total fatty acids; total triglycerides; isoleucine, leucine and glycoprotein acetyls. CONCLUSION: In a paediatric cohort with obesity, increased adiposity measures, especially in post-pubertal males, were associated with distinct patterns in metabolomic profiles.

Psychosocial measures and weight change in a clinical paediatric population with obesity.

PURPOSE: Poor quality of life has been shown to occur in youth with obesity. This study aimed to assess associations between health-related quality of life, general mental health and general psychological distress measures, collectively termed psychosocial health questionnaires (PSH), with weight outcomes in a busy paediatric weight management service. METHODS: A cross-sectional longitudinal clinical cohort, 'Childhood Overweight BioRepository of Australia (COBRA)', was used (n = 250, median age 11, range 2-18 year, mean BMI z-score 2.5 ± 0.2). Clinical data were collected and HRQOL questionnaires; Pediatric Quality of Life 4.0 (PedsQL), 'Sizing Me/Them Up' (SMU/STU), and psychological well-being questionnaires; strengths and difficulties questionnaire (SDQ) and Kessler 10 (K10) were completed by the child and primary caregiver. PSH results were compared to age- and sex-adjusted BMI z-score at baseline and follow-up. Direct logistic regression modelling was performed to assess the impact of PSH factors on the likelihood of successful weight reduction over a period of ≥ 12 months. RESULTS: Mean self-report PSH scores were: 68.0 ± 15.28 (PedsQL, range 0-100), 64.8 ± 15.8, (SMU, range 0-100), 17.3 ± 4.4 (SDQ, range 0-40) and 20.0 ± 7.7 (K10, range 0-50). A significant negative correlation was observed between PSH scores and childhood obesity (baseline BMI z-scores (p < 0.01)). No correlations were observed between psychological well-being measures and BMI z-scores. Higher subscale scores of the PedsQL and SDQ, which measure impaired psychosocial health and more difficulties with hyperactivity and inattention, significantly predict weight loss in children with obesity after 12 months. CONCLUSION: PSH questionnaires may be useful in identifying individuals who require additional support to achieve weight loss goals in a tertiary weight management service.

Utility of the LIBRA Index in Relation to Cognitive Functioning in a Clinical Health Seeking Sample.

BACKGROUND: Dementia prevalence is expected to increase substantially over the next few decades. Since there is currently no cure for dementia available, there is an urgent need for the early identification of individuals at high risk for dementia, so that primary and secondary prevention strategies can be implemented. Recently, the LIfestyle for BRAin health (LIBRA) index was developed as a new dementia risk algorithm. It specifically focuses on modifiable risk and protective factors that can be targeted in midlife. OBJECTIVE: The objective of this study was to evaluate the LIBRA index in relation to markers of cognitive functioning in a clinical, health-seeking sample of community-based older adults. METHODS: 484 participants (mean age 62.7 years) were recruited from the Healthy Brain Ageing Clinic at the Brain and Mind Centre, Sydney. Participants underwent comprehensive clinical and neuropsychological assessment and completed a self-report survey pack. Participants were rated via consensus as having either subjective cognitive complaints (SCC) or meeting criteria for mild cognitive impairment (MCI). The LIBRA score was calculated based on 11 available risk and protective factors. RESULTS: 65.4% of the sample met criteria for MCI. People with MCI showed a significantly higher LIBRA score compared to people with SCC. Furthermore, multiple cognitive domains, in particular executive functioning, were associated with a higher LIBRA score, with stronger correlations in people with MCI. CONCLUSION: The LIBRA index might be a useful tool to determine lifestyle-attributable risk of cognitive decline in an older health-seeking population, including people with MCI.

Diagnostic Work-up and Follow-up in Children with Tall Stature: A Simplified Algorithm for Clinical Practice.

OBJECTIVE: No evidence-based guideline has been published about optimal referral criteria and diagnostic work-up for tall stature in children. The aim of our study was to describe auxological and clinical characteristics of a cohort of children referred for tall stature, to identify potential candidates for adult height reduction, and to use these observations for developing a simple algorithm for diagnostic work-up and follow-up in clinical practice. METHODS: Data regarding family and medical history, auxological measurements, bone age development, physical examination, additional diagnostic work-up, and final diagnosis were collected from all children referred for tall stature, irrespective of their actual height standard deviation score (HSDS). Predicted adult height (PAH) was calculated in children above 10 years. Characteristics of patients with an indication for adult height reduction were determined. RESULTS: Hundred thirty-two children (43 boys) with a mean ± SD age of 10.9±3.2 (range 0.5-16.9) years were included in the study. Fifty percent of the referred children had an HSDS ≤2.0 (n=66). Two pathological cases (1.5%) were found (HSDS 2.3 and 0.9). Tall children without pathology were diagnosed as idiopathic tall, further classified as familial tall stature (80%), constitutional advancement of growth (5%), or unexplained non-familial tall stature (15%). Of the 74 children in whom PAH was calculated, epiphysiodesis was considered in six (8%) and performed in four (5%) patients. CONCLUSION: The incidence of pathology was very low in children referred for tall stature, and few children were potential candidates for adult height reduction. We propose a simple diagnostic algorithm for clinical practice.