The purpose of this study was to establish a biorepository of clinical, metabolomic, and microbiome samples from adolescents with obesity as they undergo lifestyle modification. Methods: A total of 223 adolescents aged 10 to 18 years with BMI ≥95th percentile were enrolled, along with 71 healthy weight participants. Clinical data, fasting serum, and fecal samples were collected at repeated intervals over 6 months. Herein, the study design, data collection methods, and interim analysis-including targeted serum metabolite measurements and fecal 16S ribosomal RNA gene amplicon sequencing among adolescents with obesity (n = 27) and healthy weight controls (n = 27)-are presented. Results: Adolescents with obesity have higher serum alanine aminotransferase, C-reactive protein, and glycated hemoglobin, and they have lower high-density lipoprotein cholesterol when compared with healthy weight controls. Metabolomics revealed differences in branched-chain amino acid-related metabolites. Also observed was a differential abundance of specific microbial taxa and lower species diversity among adolescents with obesity when compared with the healthy weight group. Conclusions: The Pediatric Metabolism and Microbiome Study (POMMS) biorepository is available as a shared resource. Early findings suggest evidence of a metabolic signature of obesity unique to adolescents, along with confirmation of previously reported findings that describe metabolic and microbiome markers of obesity.

Objectives To validate the pediatric Patient Reported Outcomes Measurement Information System short forms (PROMIS®-SFs) in childhood-onset systemic lupus erythematosus (cSLE) in a clinical setting. Methods At three study visits, cSLE patients completed the PROMIS-SFs (Anger, Anxiety, Depressive Symptoms, Fatigue, Physical Function-Mobility, Physical Function-Upper Extremity, Pain Interference, Peer Relationships) using the PROMIS Assessment Center, and health-related quality of life (HRQoL) legacy measures (Pediatric Quality of Life Inventory™, Childhood Health Assessment Questionnaire, Simple Measure of Impact of Lupus Erythematosus in Youngsters [SMILEY], visual analog scales [VAS] of pain and well-being). Physicians rated cSLE activity on a VAS, and completed the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI). Physicians rated change of cSLE activity (GRC-MD1: better/same/worse) and change of patient overall health (GRC-MD2: better/same/worse) between study visits with a global rating scale of change. Questionnaire scores were compared in support of validity and responsiveness to change (external standards: GRC-MD1, GRC-MD2). Results In this population-based cohort (n=100) with a mean age of 15.8 (range: 10–20) years, the PROMIS-SFs were completed in less than five minutes in a clinical setting. The PROMIS-SF scores correlated at least moderately (Pearson’s r ≥0.5) with those of legacy HRQoL, except for the SMILEY. Measures of cSLE activity did not correlate with the PROMIS-SFs. Responsiveness to change of the PROMIS-SFs was supported by path, mixed model, and correlation analyses. Conclusions To assess HRQoL in cSLE, the PROMIS-SFs demonstrated feasibility, internal consistency, construct validity, and responsiveness to change in a clinical setting.

Background/Purpose: Childhood‐onset lupus (cSLE) is a chronic autoimmune disease and its effect on health‐related quality of life (HRQoL) has not been systematically established. The Patient Reported Outcomes Measurement Information System (PROMIS™, http://nihpromis.org) is a publicly available system to measure patient reported outcomes that features electronic data collection. Although several validated legacy QoL measures exist for cSLE, each is longer than the PROMIS™ Pediatric Short Forms (Short Forms). The objective of this study was to investigate the feasibility, construct and discriminant validity of the Short Forms in cSLE in a clinical setting. Methods: In this ongoing study at two sites, 98 of 100 projected patients completed the Pediatric PROMIS™ Short Forms (Anger, Anxiety, Depressive, Fatigue, Mobility, Upper Extremity, Pain Interference, Peer Relations) and legacy QoL measures (Pediatric Quality of Life Inventory™ Generic Core [GC] & Rheumatology Modules [RM], Simple Measure Of Impact Of Lupus Erythematosus In Youngsters [SMILEY], Childhood Health Assessment Questionnaire [CHAQ], Child Health Questionnaire [CHQ], pain and well‐being visual analog scales [VAS]). Questionnaire scores were compared and Spearman correlation analysis was performed in support of the construct validity of the Short Forms when used in cSLE. The discriminant validity was supported by analysis of PROMIS™ and legacy QoL score change at sequential visits and relationship of change was made with Spearman correlation analysis (to be shown). Results: Participants (78% female; 41% White, 44% Black, 6% Asian) had a mean age of 16 yrs (SD 3) and mean SLEDAI score of 6.07 (SD 5.99). There were no problems with completion of any of the PROMIS™ Short Forms (mean score = 50, clinically relevant difference = 10) which required 5–10 minutes in total (legacy QoL tools >15 min. each). On average, cSLE patients scored importantly worse in the Short Form assessing upper extremity function and mobility than the healthy children, while the other QoL domains were less affected (Table ). This is also supported by the scores of the CHQ–PHS. Concurrent validity of the Short Forms is supported by moderate correlations with the scores of various legacy measures (to be shown). Comparative scores for HRQoL in cSLE* PROMIS Short forms CHAQ PedsQL‐GC PedsQL‐RM SMILEY CHQ‐P50 Anger52.0 (4.3)Emotional74.1 (21.1)Worry70.0 (27.6)Effect63.3 (19.9)Behavior (BE)83.4 (15.9)Anxiety49.6 (3.8)Treatment80.8 (15.0)Burden60.1 (17.4)Mental Health (MH)77.5 (15.9)Depression51.9 (3.2)Self‐esteem (SE)81.8 (15.5)Fatigue53.5 (3.4)Role/Social Limits‐Physical (RP)87.5 (25.8)Mobility42.8 (3.5)Walk0.3 (0.6)Physical74.0 (20.3)Activity87.0 (17.7)Limitation68.2 (17.0)Physical Function (PF)78.2 (30.5)Arise0.4 (0.6)Hygiene0.3 (0.7)Play0.7 (0.9)Upper Extremity Function46.1 (3.9)Dress & Groom0.3 (0.7)Physical Summary (PHS)43.0 (13.2)Eat0.3 (0.7)Reach0.5 (0.8)Grip0.4 (0.7)Pain52.7 (3.1)Pain66.1 (28.2)Bodily Pain/Discomfort (BP)65.7 (29.7)Peer Relations46.7 (3.5)Social82.4 (20.0)Social...

Objective: To establish a biorepository of clinical, metabolomic, and microbiome samples from adolescents with obesity as they undergo lifestyle modification. Methods: We enrolled 223 adolescents aged 10-18 years with Body Mass Index ≥ 95th percentile, along with 71 healthy weight participants. We collected clinical data, fasting serum, and fecal samples at repeated intervals over 6 months. Here we present our study design, data collection methods, and an interim analysis, including targeted serum metabolite measurements and fecal 16S rRNA gene amplicon sequencing among adolescents with obesity (n=27) and healthy weight controls (n=27). Results: Adolescents with obesity have higher serum alanine aminotransferase, C-reactive protein, and glycated hemoglobin, and lower high-density lipoprotein cholesterol when compared with healthy weight controls. Metabolomics revealed differences in branched chain amino acid related metabolites. We also observed differential abundance of specific microbial taxa and lower species diversity among adolescents with obesity when compared with the healthy weight group. Conclusions: The Duke Pediatric Metabolism and Microbiome Study biorepository is available as a shared resource. Early findings suggest evidence of a metabolic signature of obesity unique to adolescents, along with confirmation of previously reported findings describing metabolic and microbiome markers of obesity.

Although nocturnal enuresis is common in children and young people, many families have difficulty understanding the condition and accessing appropriate advice and support. Healthcare professionals need to be able to demonstrate knowledge of assessment, causes and management of nocturnal enuresis to provide patients and their families with individualised care. This article discusses the importance of identifying the causes of bedwetting, thorough assessment of the patient and selection of appropriate management strategies.