Stratifying cardiometabolic risk in urban Chinese children with obesity: Study protocol of the Shenzhen Children Cohort Study (SCCS) - Yuehao Xu; Xin Guo; Jinwen Liao; Xiaolin Sun; Zhangcong Liang; Mingguo Xu (PLoS ONE, 2026)
Background Cardiovascular risk factors often begin in early childhood, with obesity being a major contributor. However, not all children with obesity share the same risk profile. The Shenzhen Children Cohort Study (SCCS) is a prospective, multimodal cohort designed to follow overweight and obese children aged 5–11 years, aiming to identify subgroups with elevated long-term cardiometabolic risk. The study will longitudinally track the evolution of cardiometabolic risk by integrating anthropometric, biochemical, imaging, behavioral, and psychosocial data. In addition to profiling child-level risk trajectories, SCCS will examine how parental beliefs, health behaviors, and family environments shape the development and progression of obesity-related cardiovascular risks. By capturing interactions between family-level determinants and biological markers, the study aims to support individualized risk stratification and inform early-life prevention frameworks in urban China. Methods The Shenzhen Children Cohort Study (SCCS) will enroll 3,363 overweight and obese children aged 5–11 years from Longgang District, Shenzhen, through public recruitment campaigns. Participants will undergo annual follow-up visits over a five-year period. At each visit, standardized clinical examinations, laboratory tests, and caregiver-completed questionnaires will be conducted to assess cardiovascular, behavioral, and environmental risk factors. All data will be centrally managed and analyzed using longitudinal statistical models to characterize cardiometabolic risk trajectories and to evaluate how family-level factors interact with child health indicators over time. Discussion This study follows overweight and obese children aged 5–11 years over five years to document changes in physical measurements, biochemical indicators, imaging results, and questionnaire data on household structure, parental health history, and child routines. The aim is to build a multi-domain risk profile that moves beyond body mass index and simple metabolic categories. Prior studies often grouped children by BMI percentile or defined metabolically healthy and unhealthy types based on a few biomarkers. These methods overlook transitions, internal variation, and context. By applying repeated measurements and multiple modeling approaches, this study aim to identify subgroups based on shared risk trajectories, biomarker shifts, and family conditions. Risk is not treated as a single threshold but as a process shaped by exposures and responses. Although this cohort is located in one district, its structure may guide future work on pediatric risk classification under precision medicine frameworks.