Yang Xiaoping, Chen Miaomiao, Liu Xiaohui, Wang Lijun, Wang Yanyun, Zheng Yingjie, Ma Shailing
General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China.
School of Nursing, Ningxia Medical University, Yinchuan, Ningxia, China.
Front Psychol. 2025 Aug 18;16:1644701. doi: 10.3389/fpsyg.2025.1644701. eCollection 2025.
Overweight/obesity is associated with an increased risk of depression, which compromises the mental health of affected individuals. This study aimed to identify distinct depressive subtypes among overweight/obese individuals and examine associated multilevel factors based on the socio-ecological model (SEM), for guiding interventions enhancing mental health in this population.
Data were derived from the Psychology and Behavior Investigation of Chinese Residents in 2021 (PBICR 2021). Assessment instruments included a General Information Questionnaire, the Patient Health Questionnaire-9, the Eating Behavior Scale-Short Form, the Family Health Scale-Short Form, and the Perceived Social Support Scale. Latent profile analysis (LPA) was employed to identify depressive subtypes, and multinomial logistic regression was used to examine associated multilevel factors across the identified subtypes. Analyses were conducted using SPSS 24.0 and Mplus 8.3.
This study included 2,588 participants classified into low-level (52.3%), moderate-level (36.6%), and high-level depression (11.1%) groups. Compared to the low-level group, high-level depression was significantly associated with age (18-45 years), current medication count (≥3, excl. supplements), out-of-pocket medical expenditures, higher abnormal eating behavior scores, and lower family health and social support scores. Similarly, moderate-level depression showed significant associations with female gender, age (18-45 years), having chronic conditions, current medication count (≥3, excl. supplements), out-of-pocket medical expenditures, higher abnormal eating behavior scores, and lower family health and social support scores.
Depression demonstrates significant heterogeneity in overweight/obese individuals, with three distinct latent profiles identified. These findings highlight the need for future primary healthcare to prioritize personalized, depression subtype-specific interventions for overweight/obese individuals, guided by multidimensional factors identified through SEM, to improve mental health.
超重/肥胖与抑郁症风险增加相关，这会损害受影响个体的心理健康。本研究旨在识别超重/肥胖个体中不同的抑郁亚型，并基于社会生态模型（SEM）检查相关的多层次因素，以指导改善该人群心理健康的干预措施。
数据来源于2021年中国居民心理与行为调查（PBICR 2021）。评估工具包括一般信息问卷、患者健康问卷-9、饮食行为量表简版、家庭健康量表简版和感知社会支持量表。采用潜在类别分析（LPA）识别抑郁亚型，并使用多项逻辑回归分析确定的亚型中的相关多层次因素。使用SPSS 24.0和Mplus 8.3进行分析。
本研究纳入了2588名参与者，分为低水平（52.3%）、中等水平（36.6%）和高水平抑郁（11.1%）组。与低水平组相比，高水平抑郁与年龄（18 - 45岁）、当前用药数量（≥3，不包括补充剂）、自付医疗费用、较高的异常饮食行为得分以及较低的家庭健康和社会支持得分显著相关。同样，中等水平抑郁与女性、年龄（18 - 45岁）、患有慢性病、当前用药数量（≥3，不包括补充剂）、自付医疗费用、较高的异常饮食行为得分以及较低的家庭健康和社会支持得分显著相关。
超重/肥胖个体的抑郁症表现出显著的异质性，识别出了三种不同的潜在类别。这些发现强调，未来初级医疗保健需要根据通过SEM确定并受其指导的多维度因素，优先为超重/肥胖个体提供个性化的、针对抑郁亚型的干预措施，以改善心理健康。
