ObjectiveSome studies have demonstrated a bidirectional association between obesity and depression, whereas others have not. This discordance might be due to the metabolic health status. We aimed to determine whether the relationship between obesity and depression is dependent on metabolic health status.MethodsIn total, 9,022,089 participants were enrolled and classified as one of four obesity phenotypes: metabolically healthy nonobesity (MHNO), metabolically unhealthy nonobesity (MUNO), metabolically healthy obesity (MHO), and metabolically unhealthy obesity (MUO). We then divided the population into eight phenotypes based on obesity and the number of metabolic risk factors. Furthermore, the associations of eight phenotypes, based on obesity and specific metabolic risk factors, with depression were assessed.ResultAmong all participants, a higher risk of depression was observed for MUNO, MHO and MUO than for MHNO. The risk was highest for MUO (OR = 1.442; 95% CI = 1.432, 1.451). However, the association between MHO and depression was different for men and women (OR = 0.941, men; OR = 1.132, women). The risk of depression increased as the number of metabolic risk factors increased. Dyslipidemia was the strongest metabolic risk factor. These relationships were consistent among patients ≥ 45 years of age.ConclusionsThe increased risk of obesity-related depression appears to partly depend on metabolic health status. The results highlight the importance of a favorable metabolic status, and even nonobese populations should be screened for metabolic disorders.

ImportanceAssessment tools are lacking for screening of esophageal squamous cell cancer (ESCC) in China, especially for the follow-up stage. Risk prediction to optimize the screening procedure is urgently needed.ObjectiveTo develop and validate ESCC prediction models for identifying people at high risk for follow-up decision-making.Design, Setting, and ParticipantsThis open, prospective multicenter diagnostic study has been performed since September 1, 2006, in Shandong Province, China. This study used baseline and follow-up data until December 31, 2021. The data were analyzed between April 6 and May 31, 2022. Eligibility criteria consisted of rural residents aged 40 to 69 years who had no contraindications for endoscopy. Among 161 212 eligible participants, those diagnosed with cancer or who had cancer at baseline, did not complete the questionnaire, were younger than 40 years or older than 69 years, or were detected with severe dysplasia or worse lesions were eliminated from the analysis.ExposuresRisk factors obtained by questionnaire and endoscopy.Main Outcomes and MeasuresPathological diagnosis of ESCC and confirmation by cancer registry data.ResultsIn this diagnostic study of 104 129 participants (56.39% women; mean [SD] age, 54.31 [7.64] years), 59 481 (mean [SD] age, 53.83 [7.64] years; 58.55% women) formed the derivation set while 44 648 (mean [SD] age, 54.95 [7.60] years; 53.51% women) formed the validation set. A total of 252 new cases of ESCC were diagnosed during 424 903.50 person-years of follow-up in the derivation cohort and 61 new cases from 177 094.10 person-years follow-up in the validation cohort. Model A included the covariates age, sex, and number of lesions; model B included age, sex, smoking status, alcohol use status, body mass index, annual household income, history of gastrointestinal tract diseases, consumption of pickled food, number of lesions, distinct lesions, and mild or moderate dysplasia. The Harrell C statistic of model A was 0.80 (95% CI, 0.77-0.83) in the derivation set and 0.90 (95% CI, 0.87-0.93) in the validation set; the Harrell C statistic of model B was 0.83 (95% CI, 0.81-0.86) and 0.91 (95% CI, 0.88-0.95), respectively. The models also had good calibration performance and clinical usefulness.Conclusions and RelevanceThe findings of this diagnostic study suggest that the models developed are suitable for selecting high-risk populations for follow-up decision-making and optimizing the cancer screening process.