Sun Mengmeng, Van Wijk Eduard, Koval Slavik, Van Wijk Roeland, He Min, Wang Mei, Hankemeier Thomas, van der Greef Jan
Analytical BioSciences, LACDR, Leiden University, P.O. Box 9502, 2300 RA, The Netherlands; Sino-Dutch Center for Preventive and Personalized Medicine, Leiden University, P.O. Box 9502, 2300 RA Leiden, The Netherlands; Changchun University of Chinese Medicine, No. 1035, Boshuo Rd, Jingyue Economic Development District, Changchun 130117, China.
Analytical BioSciences, LACDR, Leiden University, P.O. Box 9502, 2300 RA, The Netherlands; Sino-Dutch Center for Preventive and Personalized Medicine, Leiden University, P.O. Box 9502, 2300 RA Leiden, The Netherlands; Meluna Research, Koppelsedijk 1-a, 4191 LC Geldermalsen, The Netherlands; Changchun University of Chinese Medicine, No. 1035, Boshuo Rd, Jingyue Economic Development District, Changchun 130117, China.
J Photochem Photobiol B. 2017 Jan;166:86-93. doi: 10.1016/j.jphotobiol.2016.11.013. Epub 2016 Nov 17.
The global prevalence of type 2 diabetes is estimated to reach 4.4% by 2030, placing a significant burden on our healthcare system. Therefore, the ability to identify patients in early stages of the disease is essential for both prevention and effective management, and diagnostic methods based on traditional Chinese medicine (TCM) may be suitable for identifying patients with early-stage type 2 diabetes. Here, a panel of three physicians trained in TCM classified 44 pre-diabetic subjects into three syndrome subtypes using TCM-based diagnostics. In addition, ultra-weak photon emission (UPE) was measured at four anatomical sites in each subject. Ten properties encompassing 40 parameters were then extracted from the UPE time series. Statistical analyses, including multinomial logistic regression, were performed using the results of each parameter measured at the four sites. Sixteen UPE parameters were then selected and used to discriminate between the three subtypes of pre-diabetic subjects. Next, Spearman's correlation coefficient was used to quantify the correlation between the 16 UPE parameters and the TCM-based diagnoses. The resulting correlation networks accurately reflected the differences between the three syndrome subtypes. These results suggest that UPE is a suitable tool for detecting subtypes in early-stage type 2 diabetes. In addition, our results provide evidence that TCM may represent an important step toward personalized medicine.
据估计,到2030年,全球2型糖尿病患病率将达到4.4%,给我们的医疗系统带来沉重负担。因此,在疾病早期识别患者的能力对于预防和有效管理至关重要,基于中医(TCM)的诊断方法可能适用于识别早期2型糖尿病患者。在此,一组三名接受过中医培训的医生使用基于中医的诊断方法将44名糖尿病前期受试者分为三种证型。此外,在每个受试者的四个解剖部位测量了超微弱光子发射(UPE)。然后从UPE时间序列中提取了包含40个参数的十个属性。使用在四个部位测量的每个参数的结果进行了包括多项逻辑回归在内的统计分析。然后选择16个UPE参数用于区分糖尿病前期受试者的三种亚型。接下来,使用Spearman相关系数量化16个UPE参数与基于中医的诊断之间的相关性。所得的相关网络准确反映了三种证型之间的差异。这些结果表明,UPE是检测早期2型糖尿病亚型的合适工具。此外,我们的结果提供了证据,表明中医可能是迈向个性化医疗的重要一步。