Eight comorbidity-based subtypes identified for pediatric obesity
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Categorizing children with obesity into one of eight subtypes could help providers better understand the condition and may lead to more specialized care, according to study findings published in PLOS Digital Health.
“Our latent class analysis modeling results suggest the existence of obesity subtypes,” Elizabeth A. Campbell, MPH, a PhD student in the department of information science at Drexel University College of Computing and Informatics in Philadelphia and a research assistant in the department of biomedical and health informatics at Children’s Hospital of Philadelphia, and colleagues wrote. “Obese patients with similar comorbidities at the time of obesity incidence may have similar future health trajectories. The clinical subtypes identified in our study can serve as hypothesis generation for such patient classes, whose future health outcomes can be explored. This would allow more specialized clinical care for pediatric patients with obesity and certain comorbidities.”
Researchers obtained electronic health record data from the Pediatric Big Data resource at Children’s Hospital of Philadelphia. Data from 49,594 children with obesity (55.3% boys; 47% white; 33.8% Black) were obtained from the health care visit in which the first BMI z score of 95th percentile or higher was recorded as well as the visit immediately before and after. A sequential pattern-mining algorithm was used to identify 163 conditions present in at least 1% of the study cohort. Of those conditions, 80 were identified as being more common among children with obesity when compared with a control cohort of children with normal BMI. Latent class analysis was used to identify potential obesity subtypes formed by the diagnoses in temporal condition patterns that were more common among children with obesity. Each participant was assigned to the subtype for which they had the highest probability of membership. The highest-prevalence diagnoses within each subtype were used to describe and name each subtype.
Researchers identified eight individual subtypes for pediatric obesity. The first subtype included children with a high prevalence of upper respiratory and sleep disorders (n = 2,336), the second included those with inflammatory skin conditions (n = 3,743), the third consisted of children with a high prevalence of seizures and other neurological disorders (n = 1,266) and the fourth included children with a high prevalence of asthma (n = 6,446). The fifth class of participants consisted of children without a clear morbidity pattern (n = 28,821). The remaining three subtypes included children with gastrointestinal and genitourinary symptoms (n = 2,131), those with neurodevelopmental disorders such as autism (n = 1,925) and children with a high prevalence of physical symptoms such as headaches, fever, nausea and vomiting (n = 2,925).
The mean probability of children belonging to their assigned group ranged from 70.21% in the physical symptoms subtype to 89.7% in the upper respiratory and sleep disorders subtype.
In a demographic analysis, girls made up the majority of those with gastrointestinal issues (51.9%), whereas boys made up more than three-quarters of those with neurodevelopment disorders (77.6%). More than half of those in the asthma subtype and more than 40% of those in inflammatory skin conditions and physical symptoms subtypes were Black children. The seizures subtype had the higher prevalence of Hispanic children (12.2%). Medicaid enrollment was highest among those in the seizures (51.3%), asthma (50.7%) and neurodevelopment disorders (57.1%) subtypes.
“Future research may utilize the identified subtypes as hypotheses for possible pediatric obesity subtypes to explore causality in the associations uncovered in this study,” the researchers wrote. “Understanding both the demographic characteristics and physical comorbidities that differentiate pediatric patients with obesity can help to better understand the etiology of the condition and appropriate treatment for the diverse groups of patients the condition affects.”
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