Academic literature on the topic 'Non-syndromic obesity'

Abstract Introduction Early-onset obesity is a cardinal feature of rare genetic obesity disorders. According to the Endocrine Society guideline, genetic screening is indicated in selected cases with age of onset (AoO) of severe obesity (grade ≥2) <5 years. However, this cut-off is not validated. Aims To present the detailed BMI characteristics of children and adolescents with rare genetic obesity disorders; to evaluate whether the following growth chart characteristics can aid in assessing which children should be screened for genetic obesity disorders: AoO of obesity, AoO of severe obesity, BMI standard deviation scores (SDS) at yearly age intervals. Methods In this prospective observational study, children with non-syndromic and syndromic genetic obesity disorders treated at our tertiary obesity center were included. Growth measurements from birth onwards were collected. Children with obesity from a population-based cohort study with follow-up until age 10 years were included as an unselected reference cohort. Diagnostic performance (sensitivity [sens], specificity [spec], positive likelihood ratio [LR+], area-under-the-curve [AUC]) for AoO of obesity and severe obesity and for BMI-SDS at yearly age intervals was calculated. Results We included 64 children with genetic obesity disorders (32 non-syndromic, 32 syndromic) and 298 control children with obesity. At intake, median age of children with genetic obesity was 10.5 years (IQR 7.0–14.7) and mean BMI-SDS +3.1 ± 1.1. Median AoO of obesity was 1.2 years (IQR 0.6–3.7) in non-syndromic genetic obesity, 2.1 years (IQR 0.9–4.2) in syndromic genetic obesity, and 3.8 years (IQR 2.3–6.2) in the control population. For non-syndromic genetic obesity, optimal cut-off value for AoO of obesity was ≤1.5 years: sens 0.60, spec 0.88, LR+ 5.22, AUC 0.79 (p<0.001). For syndromic genetic obesity, optimal cut-off was ≤3.0 years: sens 0.68, spec 0.68, LR+ 2.06, AUC 0.67 (p=0.001). AoO of severe obesity showed worse diagnostic performance than AoO of obesity in both non-syndromic (AUC 0.58, p=0.20) and syndromic genetic obesity (AUC 0.58, p=0.21). Moreover, when the guideline cut-off <5 years was used, AoO of severe obesity showed a negative diagnostic performance (non-syndromic genetic obesity: sens 0.76, spec 0.13, LR+ 0.88; syndromic genetic obesity: sens 0.85, spec 0.14, LR+ 0.98). BMI-SDS showed good diagnostic performance for non-syndromic genetic obesity across the age intervals (AUCs 0.79-0.89, all P<0.001) but not for syndromic genetic obesity (AUCs 0.54-0.71, P-values ranging from <0.001-0.50). Conclusion This study shows that growth charts characteristics such as BMI-SDS and AoO of obesity (grade 1), but not AoO of severe obesity (grade ≥2), could be useful to distinguish between children with genetic obesity disorders and children with obesity from a population-based cohort study. However, all investigated growth charts characteristics showed misclassification, especially in syndromic genetic obesity, indicating that additional clinical features should be present to warrant genetic testing in these children. Presentation: Sunday, June 12, 2022 11:15 a.m. - 11:30 a.m.

Obesity is a metabolic state generated by the expansion of adipose tissue. Adipose tissue expansion depends on the interplay between hyperplasia and hypertrophy, and is mainly regulated by a complex interaction between genetics and excess energy intake. However, the genetic regulation of adipose tissue expansion is yet to be fully understood. Obesity can be divided into common multifactorial/polygenic obesity and monogenic obesity, non-syndromic and syndromic. Several genes related to obesity were found through studies of monogenic non-syndromic obesity models. However, syndromic obesity, characterized by additional features other than obesity, suggesting a more global role of the mutant genes related to the syndrome and, thus, an additional peripheral influence on the development of obesity, were hardly studied to date in this regard. This review summarizes present knowledge regarding the hyperplasia and hypertrophy of adipocytes in common obesity. Additionally, we highlight the scarce research on syndromic obesity as a model for studying adipocyte hyperplasia and hypertrophy, focusing on Bardet–Biedl syndrome (BBS). BBS obesity involves central and peripheral mechanisms, with molecular and mechanistic alternation in adipocyte hyperplasia and hypertrophy. Thus, we argue that using syndromic obesity models, such as BBS, can further advance our knowledge regarding peripheral adipocyte regulation in obesity.

Obesity is a complex multifactorial disorder with genetic and environmental factors. There is an increase in the worldwide prevalence of obesity in both developed and developing countries. The development of genome-wide association studies (GWAS) and next-generation sequencing (NGS) has increased the discovery of genetic associations and awareness of monogenic and polygenic causes of obesity. The genetics of obesity could be classified into syndromic and non-syndromic obesity. Prader–Willi, fragile X, Bardet–Biedl, Cohen, and Albright Hereditary Osteodystrophy (AHO) syndromes are examples of syndromic obesity, which are associated with developmental delay and early onset obesity. Non-syndromic obesity could be monogenic, polygenic, or chromosomal in origin. Monogenic obesity is caused by variants of single genes while polygenic obesity includes several genes with the involvement of members of gene families. New advances in genetic testing have led to the identification of obesity-related genes. Leptin (LEP), the leptin receptor (LEPR), proopiomelanocortin (POMC), prohormone convertase 1 (PCSK1), the melanocortin 4 receptor (MC4R), single-minded homolog 1 (SIM1), brain-derived neurotrophic factor (BDNF), and the neurotrophic tyrosine kinase receptor type 2 gene (NTRK2) have been reported as causative genes for obesity. NGS is now in use and emerging as a useful tool to search for candidate genes for obesity in clinical settings.

Abstract Background Pediatric obesity is a multifactorial disease characterized by a prolonged imbalance between energy intake and expenditure. In rare cases, it is caused by underlying medical disorders arising from disruptions in the leptin-melanocortin pathway which regulates satiety and energy expenditure. Aim To investigate and compare resting energy expenditure (REE) and body composition characteristics of children and adolescents with severe obesity with and without underlying medical causes. Methods This prospective observational study included pediatric patients who underwent an extensive diagnostic workup in our academic center in which endocrine, non-syndromic and syndromic genetic, hypothalamic, and medication-induced causes of obesity were evaluated. Patients in whom no underlying medical cause was identified were classified as multifactorial obesity. REE was assessed by indirect calorimetry; body composition by air displacement plethysmography. The ratio measured REE (mREE) vs predicted REE (Schofield equations) was expressed as REE%, with decreased mREE defined as REE% ≤90% and elevated mREE as ≥110%. Additionally, the ratio mREE vs fat-free-mass (FFM) was calculated. Results We included 292 patients, of which 218 (75%) patients had multifactorial obesity and 74 (25%) had an underlying medical cause: non-syndromic and syndromic genetic (n= 29 and 28, respectively), hypothalamic (n= 10), and medication-induced (n= 7) obesity. Mean age was 10.8 ± 4.3 years, 59% were female, mean BMI SDS was 3.8 ± 1.1, indicating severe obesity. Mean REE% was higher in children with non-syndromic genetic obesity (107.4% ± 12.7) and lower in children with hypothalamic obesity (87.6% ± 14.2) compared to multifactorial obesity (100.5% ± 12.6, both p<0.01). Measured REE was decreased in 60 (21%) patients (corresponding to an average overprediction of daily caloric needs of 341 kcal/day) and elevated in 69 (24%) patients. Only in hypothalamic obesity, a larger proportion of patients showed a decreased REE compared to multifactorial obesity (6/10 vs 41/218, p<0.01). FFM was higher in children with non-syndromic obesity compared to multifactorial obesity (+7.5kg, p<0.001), but lower in syndromic obesity (-5.2kg, p=0.03), hypothalamic obesity (-12.6kg, p<0.001), and similar in medication-induced obesity (+1.5kg FFM, p=0.80). Mean mREE/FFM was 46.5 ± 10.6 kcal/day/kg FFM and did not differ between patients with underlying medical causes compared to multifactorial obesity (all p>0.05). Conclusion In this cohort of children with severe obesity due to various etiologies, large inter-individual differences in mREE were found. Almost half of patients had decreased or elevated mREE. When relating mREE to FFM, no differences were found between children with underlying medical causes versus multifactorial obesity. Thus, our study underlines the importance of measuring REE and relating mREE to FFM in children with early-onset severe obesity with or without underlying medical causes. This knowledge is important for patient-tailored treatment, e.g. personalized dietary or physical activity interventions and consideration of pharmacotherapy affecting central energy expenditure regulation in children with decreased mREE. Presentation: Saturday, June 11, 2022 1:00 p.m. - 3:00 p.m., Saturday, June 11, 2022 1:18 p.m. - 1:23 p.m., Saturday, June 11, 2022 1:18 p.m. - 1:23 p.m.

Hyperostosis frontalis interna (HFI) is characterized by excess growth of the frontal bone. Although the exact cause of HFI is not known, it has been found to be associated with increased age, prolonged estrogen stimulation and elevated leptin. Literature shows its association with seizure, dementia, obesity, headache and endocrine abnormalities. HFI may be syndromic and non-syndromic. Timely identification of HFI can prevent unnecessary diagnostic tests. We report a case of non-syndromic HFI in an elderly female who presented with headache.

Prader–Willi syndrome (PWS) represents the most common genetic-derived obesity disorder caused by the loss of expression of genes located on the paternal chromosome 15q11.2-q13. The PWS phenotype shows peculiar physical, endocrine and metabolic characteristics compared to those observed in non-syndromic essential obesity. Since miRNAs have now a well-established role in many molecular pathways, including regulatory networks related to obesity, this pilot study was aimed to characterize the expression of circulating miRNAs in PWS compared to essential obesity. The circulating miRNome of 10 PWS and 10 obese subjects, adequately matched for age, BMI and sex, was profiled throughout Genechip miRNA 4.0 microarray analysis. We identified 362 out of 2578 mature miRNAs to be expressed in serum of the studied population. The circulating miRNA signature significantly characterising the two populations include 34 differently expressed RNAs. Among them, miR-24-3p, miR-122 and miR-23a-3p highly differ between the two groups with a FC >10 in obese compared to PWS. In the obese subjects, miR-7107-5p, miR-6880-3p, miR-6793-3p and miR-4258 were associated to the presence of steatosis. A different signature of miRNAs significantly distinguished PWS with steatosis from PWS without steatosis, involving miR-619-5p, miR-4507, miR-4656, miR-7847-3p and miR-6782-5p. The miRNA target GO enrichment analysis showed the different pathway involved in these two different forms of obesity. Although the rarity of PWS actually represents a limitation to the availability of large series, the present study provides novel hints on the molecular pathogenesis of syndromic and non-syndromic obesity.

Obesity, defined by an excess of body fat impacting on health, is a complex disease resulting from the interaction between many genetic/epigenetic factors and environmental triggers. For some clinical situations with severe obesity, it has been possible to classify these obesity forms according to the molecular alterations. These include: (i) syndromic obesity, which associates severe early-onset obesity with neurodevelopmental disorders and/or polymalformative syndrome and (ii) non-syndromic monogenic obesity, due to gene variants most often located in the leptin-melanocortin pathway. In addition to severe obesity, patients affected by these diseases display complex somatic conditions, eventually including obesity comorbidities, neuropsychological and psychiatric disorders. These conditions render the clinical management of these patients particularly challenging. Patients’ early diagnosis is critical to allow specialized and multidisciplinary care, with a necessary interaction between the health and social sectors. Up to now, the management of genetic obesity was only based, above all, on controlling the patient's environment, which involves limiting access to food, ensuring a reassuring daily eating environment that limits impulsiveness, and the practice of adapted, supported, and supervised physical activity. Bariatric surgery has also been undertaken in genetic obesity cases with uncertain outcomes. The context is rapidly changing, as new innovative therapies are currently being tested both for syndromic and monogenic forms of obesity. This review focuses on care management and new therapeutic opportunities in genetic obesity, including the use of the melanocortin 4 agonist, setmelanotide. The results from ongoing trials will hopefully pave the way to a future precision medicine approach for genetic obesity.

Diabetes that is neither type 1 nor type 2 in aetiology accounts for around 5% of cases, with a wide range of causes. The widest differential diagnosis is in the young adult group. Non-type 1/type 2 diabetes includes monogenic causes of both beta-cell dysfunction (MODY, neonatal diabetes, mitochondrial diabetes) and insulin resistance (lipodystrophy, insulin receptor defects and monogenic obesity), as well as pancreatic, endocrine, and syndromic causes. It is important to make the correct aetiological diagnosis as this is likely to affect treatment and other aspects of management as well as defining the risk of diabetes in family members. Apart from clinical features and basic biochemistry, there are a number of investigations which can help with determining aetiology. These include beta-cell antibodies, assessment of endogenous insulin secretion using C-peptide, specific biomarkers, and genetic testing. Combining some of these features has led to the development of an online probability model for MODY. It is important to understand the roles and limitations of these tests and tools in clinical practice.