College of Physical Education, Huaqiao University, Quanzhou, China.
School of Public Health, Fujian Medical University, Fuzhou, China.
PLoS One. 2024 Jul 31;19(7):e0308073. doi: 10.1371/journal.pone.0308073. eCollection 2024.
Screening and treatment of dysglycemia (prediabetes and diabetes) represent significant challenges in advancing the Healthy China initiative. Identifying the crucial factors contributing to dysglycemia in urban-rural areas is essential for the implementation of targeted, precise interventions.
Data for 26,157 adults in Fujian Province, China, were collected using the Social Factors Special Survey Form through a multi-stage random sampling method, wherein 18 variables contributing to dysglycemia were analyzed with logistic regression and the random forest model.
Investigating urban-rural differences and critical factors in dysglycemia prevalence in Fujian, China, with the simultaneous development of separate predictive models for urban and rural areas.
The detection rate of dysglycemia among adults was 35.26%, with rates of 34.1% in urban areas and 35.8% in rural areas. Common factors influencing dysglycemia included education, age, BMI, hypertension, and dyslipidemia. For rural residents, higher income (OR = 0.80, 95% CI [0.74, 0.87]), average sleep quality (OR = 0.89, 95% CI [0.80, 0.99]), good sleep quality (OR = 0.89, 95% CI [0.80, 1.00]), and high physical activity (PA) (OR = 0.87, 95% CI [0.79, 0.96]) emerged as protective factors. Conversely, a daily sleep duration over 8 hours (OR = 1.46, 95% CI [1.03, 1.28]) and middle income (OR = 1.12, 95% CI [1.03, 1.22]) were specific risk factors. In urban areas, being male (OR = 1.14, 95% CI [1.02, 1.26]), cohabitation (OR = 1.18, 95% CI [1.02, 1.37]), and central obesity (OR = 1.35, 95% CI [1.19, 1.53]) were identified as unique risk factors. Using logistic regression outcomes, a random forest model was developed to predict dysglycemia, achieving accuracies of 75.35% (rural) and 76.95% (urban) with ROC areas of 0.77 (rural) and 0.75 (urban).
This study identifies key factors affecting dysglycemia in urban and rural Fujian residents, including common factors such as education, age, BMI, hypertension, and dyslipidemia. Notably, rural-specific protective factors are higher income and good sleep quality, while urban-specific risk factors include being male and central obesity. These findings support the development of targeted prevention and intervention strategies for dysglycemia, tailored to the unique characteristics of urban and rural populations.
在推进健康中国计划方面，筛查和治疗糖代谢异常（糖尿病前期和糖尿病）是一个重大挑战。确定城乡地区糖代谢异常的关键因素对于实施有针对性和精准的干预措施至关重要。
本研究采用多阶段随机抽样方法，使用社会因素专项调查表收集了中国福建省 26157 名成年人的数据，使用逻辑回归和随机森林模型分析了导致糖代谢异常的 18 个变量。
研究中国福建省城乡地区糖代谢异常的差异和关键因素，并为城乡地区分别开发预测模型。
成年人糖代谢异常的检出率为 35.26%，城市地区为 34.1%，农村地区为 35.8%。常见的影响糖代谢异常的因素包括教育、年龄、BMI、高血压和血脂异常。对于农村居民，较高的收入（OR=0.80，95%CI[0.74，0.87]）、平均睡眠质量（OR=0.89，95%CI[0.80，0.99]）、良好的睡眠质量（OR=0.89，95%CI[0.80，1.00]）和较高的体力活动（PA）（OR=0.87，95%CI[0.79，0.96]）是保护因素。相反，每天睡眠时间超过 8 小时（OR=1.46，95%CI[1.03，1.28]）和中等收入（OR=1.12，95%CI[1.03，1.22]）是特定的风险因素。在城市地区，男性（OR=1.14，95%CI[1.02，1.26]）、同居（OR=1.18，95%CI[1.02，1.37]）和中心性肥胖（OR=1.35，95%CI[1.19，1.53]）是独特的风险因素。使用逻辑回归结果，开发了一个随机森林模型来预测糖代谢异常，农村地区的准确率为 75.35%，城市地区的准确率为 76.95%，农村地区的 ROC 面积为 0.77，城市地区的 ROC 面积为 0.75。
本研究确定了影响福建城乡居民糖代谢异常的关键因素，包括教育、年龄、BMI、高血压和血脂异常等常见因素。值得注意的是，农村地区特有的保护因素是较高的收入和良好的睡眠质量，而城市地区特有的风险因素包括男性和中心性肥胖。这些发现支持针对城乡人群的糖代谢异常的有针对性的预防和干预策略的制定。
