Callaghan Brian C, Gao LeiLi, Li Yufeng, Zhou Xianghai, Reynolds Evan, Banerjee Mousumi, Pop-Busui Rodica, Feldman Eva L, Ji Linong
Department of Neurology University of Michigan Ann Arbor MI.
Department of Endocrinology and Metabolism Peking University People's Hospital Beijing China.
Ann Clin Transl Neurol. 2018 Feb 14;5(4):397-405. doi: 10.1002/acn3.531. eCollection 2018 Apr.
To determine the associations between individual metabolic syndrome (MetS) components and peripheral neuropathy in a large population-based cohort from Pinggu, China.
A cross-sectional, randomly selected, population-based survey of participants from Pinggu, China was performed. Metabolic phenotyping and neuropathy outcomes were performed by trained personnel. Glycemic status was defined according to the American Diabetes Association criteria, and the MetS using modified consensus criteria (body mass index instead of waist circumference). The primary peripheral neuropathy outcome was the Michigan Neuropathy Screening Instrument (MNSI) examination. Secondary outcomes were the MNSI questionnaire and monofilament testing. Multivariable models were used to assess for associations between individual MetS components and peripheral neuropathy. Tree-based methods were used to construct a classifier for peripheral neuropathy using demographics and MetS components.
The mean (SD) age of the 4002 participants was 51.6 (11.8) and 51.0% were male; 37.2% of the population had normoglycemia, 44.0% prediabetes, and 18.9% diabetes. The prevalence of peripheral neuropathy increased with worsening glycemic status (3.25% in normoglycemia, 6.29% in prediabetes, and 15.12% in diabetes, < 0.0001). Diabetes (odds ratio [OR] 2.60, 95% CI 1.77-3.80) and weight (OR 1.09, 95% CI 1.02-1.18) were significantly associated with peripheral neuropathy. Age, diabetes, and weight were the primary splitters in the classification tree for peripheral neuropathy.
Similar to previous studies, diabetes and obesity are the main metabolic drivers of peripheral neuropathy. The consistency of these results reinforces the urgent need for effective interventions that target these metabolic factors to prevent and/or treat peripheral neuropathy.
在中国平谷地区一个基于人群的大型队列中,确定个体代谢综合征(MetS)各组分与周围神经病变之间的关联。
对来自中国平谷地区的参与者进行了一项横断面、随机选取、基于人群的调查。代谢表型分析和神经病变结局评估由经过培训的人员进行。血糖状态根据美国糖尿病协会标准定义,MetS采用改良的共识标准(用体重指数代替腰围)。主要的周围神经病变结局是密歇根神经病变筛查工具(MNSI)检查。次要结局是MNSI问卷和单丝检查。使用多变量模型评估MetS各组分与周围神经病变之间的关联。采用基于树的方法,利用人口统计学和MetS组分构建周围神经病变的分类器。
4002名参与者的平均(标准差)年龄为51.6(11.8)岁,男性占51.0%;37.2%的人群血糖正常,44.0%为糖尿病前期,18.9%为糖尿病。周围神经病变的患病率随血糖状态恶化而增加(血糖正常者为3.25%,糖尿病前期为6.29%,糖尿病患者为15.12%,<0.0001)。糖尿病(比值比[OR]2.60,95%可信区间1.77 - 3.80)和体重(OR 1.09,95%可信区间1.02 - 1.18)与周围神经病变显著相关。年龄、糖尿病和体重是周围神经病变分类树中的主要分割点。
与先前研究相似,糖尿病和肥胖是周围神经病变的主要代谢驱动因素。这些结果的一致性强化了迫切需要针对这些代谢因素进行有效干预以预防和/或治疗周围神经病变的需求。