Metabolic and biological changes in children with obesity and diabetes

Matsumoto, Shirou; Nakamura, Tomomi; Nagamatsu, Fusa; Kido, Jun; Sakamoto, Rieko; Nakamura, Kimotoshi

doi:10.13105/wjma.v9.i2.153

Cited by 2 publications

(2 citation statements)

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“…Finally, our results suggest that alterations in amino acid metabolism may play an important role in adiposity and dyslipidemia in children which may be relevant to the susceptibility of metabolic diseases later in life. Our findings are consistent with recent work which investigates the relationship between obesity in children and pathways (and their combinations) related with amino acid, lipid and glucose metabolism 71 …”

Section: Discussionsupporting

confidence: 93%

“…Our findings are consistent with recent work which investigates the relationship between obesity in children and pathways (and their combinations) related with amino acid, lipid and glucose metabolism. 71 The study of early mechanisms of obesity in the GUSTO cohort has required the development of appropriate methodological and computational tools. We propose a Bayesian semiparametric model enabling clustering of subjects based on both longitudinal trajectories and patterns of metabolic association.…”

Section: Discussionmentioning

confidence: 99%

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Joint modeling of association networks and longitudinal biomarkers: An application to childhood obesity

Cremaschi,

De Iorio,

Kothandaraman

et al. 2024

Statistics in Medicine

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The prevalence of chronic non‐communicable diseases such as obesity has noticeably increased in the last decade. The study of these diseases in early life is of paramount importance in determining their course in adult life and in supporting clinical interventions. Recently, attention has been drawn to approaches that study the alteration of metabolic pathways in obese children. In this work, we propose a novel joint modeling approach for the analysis of growth biomarkers and metabolite associations, to unveil metabolic pathways related to childhood obesity. Within a Bayesian framework, we flexibly model the temporal evolution of growth trajectories and metabolic associations through the specification of a joint nonparametric random effect distribution, with the main goal of clustering subjects, thus identifying risk sub‐groups. Growth profiles as well as patterns of metabolic associations determine the clustering structure. Inclusion of risk factors is straightforward through the specification of a regression term. We demonstrate the proposed approach on data from the Growing Up in Singapore Towards healthy Outcomes cohort study, based in Singapore. Posterior inference is obtained via a tailored MCMC algorithm, involving a nonparametric prior with mixed support. Our analysis has identified potential key pathways in obese children that allow for the exploration of possible molecular mechanisms associated with childhood obesity.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%