Lv Tiansu, Tian Jie, Sun Yaohuan, Zhang Yujuan, Qi Fang, Xiang Liulan, Cao Yutian, Zhang Wenhui, Huai Jiaxuan, Dong Yinfeng, Zhou Xiqiao
Department of Endocrinology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, People's Republic of China.
The First Clinical Medical College of Nanjing University of Chinese Medicine, Nanjing, People's Republic of China.
Diabetes Metab Syndr Obes. 2024 Oct 10;17:3735-3752. doi: 10.2147/DMSO.S469677. eCollection 2024.
Metabolic Associated Fatty Liver Disease (MAFLD) poses a significant threat to human health, as it can result in hepatic fibrosis and potentially progress to cirrhosis, in addition to causing a range of extrahepatic complications. The early detection of MAFLD is crucial, particularly during the initial stages when the condition may be amenable to reversal and the body composition could be vital importance.
Data from participants at the Jiangsu Province Hospital of Traditional Chinese Medicine, covering the period from January 1 to December 31, 2022, were collected and subsequently randomized into training and validation cohorts. Independent risk factors for MAFLD were identified using statistical methodologies in conjunction with clinical relevance, and these factors were ultimately utilized to develop the nomogram.
In the training cohort, there were 356 cases of MAFLD out of a total of 513 patients, representing 71.2%, while in the validation cohort, 161 cases of MAFLD were identified out of 220 patients, accounting for 73.2%. In terms of statistical outcomes and clinical relevance, we identified a total of 12 closely related or significant variables. To enhance our understanding of the critical role of body composition parameters in predicting the incidence of MAFLD, we developed two distinct nomograms, one of which included body composition data. Notably, the nomogram that incorporated body composition demonstrated superior predictive performance, as evidenced by a well-fitted calibration curve and a C-index of 0.893 (with a range of 0.8625 to 0.9242). Furthermore, the decision curve analysis indicated that utilizing the nomogram that included body composition would yield greater benefits.
The nomogram serves as an effective tool for predicting MAFLD. Its utility in early risk identification of MAFLD is of significant importance, as it facilitates timely intervention and treatment for patients affected by this condition.
代谢相关脂肪性肝病(MAFLD)对人类健康构成重大威胁,因为它不仅会导致肝纤维化并可能进展为肝硬化,还会引发一系列肝外并发症。MAFLD的早期检测至关重要,尤其是在疾病初期,此时病情可能易于逆转,身体成分可能至关重要。
收集江苏省中医院2022年1月1日至12月31日期间参与者的数据,随后将其随机分为训练队列和验证队列。使用统计方法结合临床相关性确定MAFLD的独立危险因素,这些因素最终用于构建列线图。
在训练队列中,513例患者中有356例MAFLD,占71.2%;而在验证队列中,220例患者中有161例MAFLD,占73.2%。在统计结果和临床相关性方面,我们共确定了12个密切相关或显著的变量。为了更好地理解身体成分参数在预测MAFLD发病率中的关键作用,我们构建了两个不同的列线图,其中一个包含身体成分数据。值得注意的是,纳入身体成分的列线图显示出更好的预测性能,校准曲线拟合良好,C指数为0.893(范围为0.8625至0.9242)。此外,决策曲线分析表明,使用包含身体成分的列线图将带来更大的益处。
列线图是预测MAFLD的有效工具。它在MAFLD早期风险识别中的作用至关重要,因为它有助于对受该疾病影响的患者进行及时干预和治疗。