1.Two-step cluster analysis and corresponding analysis in the syndrome type of knee osteoarthritis
Bin HU ; Xingwen XIE ; Ning LI ; Jin HUANG ; Linyuan QIN
Chinese Journal of Tissue Engineering Research 2014;(11):1799-1804
BACKGROUND:Both correspondence analysis and two-step cluster analysis are high-grade statistical analysis, the introduction of these analyses into the research on traditional Chinese medicine (TCM) syndrome type of knee osteoarthritis wil provide objective evidence for the standardization and normalization of TCM syndrome type, through the combination of mathematical statistical principle and TCM syndrome type.
OBJECTIVE:To explore distribution characteristics of knee osteoarthritis TCM syndrome type using correspondence analysis and two-step cluster analysis.
METHODS:The clinical symptoms of 200 patients with knees osteoarthritis were investigated through a knee osteoarthritis symptoms questionnaire. According to the criteria for three kinds of syndrome type issued in Diagnostic Criteria for TCM Syndrome, the characteristics of each syndrome were analyzed using two-step cluster analysis and corresponding analysis. Then knee osteoarthritis TCM syndrome type characteristics were defined.
RESULTS AND CONCLUSION:Cluster analysis is ineffective for the syndrome type, which is not present in the Diagnostic Criteria for TCM Syndrome. Corresponding analysis showed that, in addition to kidney marrow deficiency syndrome (50.5%), yang deficiency and congealing syndrome (13.5%), and blood stasis syndrome (23%), concurrent syndromes were also found, including kidney marrow deficiency combined yang deficiency and congealing syndrome (6.5%), yang deficiency and congealing combined blood stasis syndrome (3%), kidney marrow deficiency combined blood stasis syndrome (3.5%). Therefore we performed corresponding analysis. After analyzing the syndromes at 0.5, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5 radius, the most reasonable syndrome was those at 1.1 radius by corresponding analysis. Corresponding analysis is a scientific method for the classification of knee osteoarthritis syndrome.
2.Meta-analysis of insertion/deletion genetic variation of ACE gene and onset riskof type 2 diabetic nephropathy in Chinese population
Chunhua BEI ; Ying ZHANG ; Linyuan QIN ; Lin YANG ; Jieying DUAN ; Nian LIU ; Hongping YU ; Xiangyuan YU
Chongqing Medicine 2017;46(24):3362-3365
Objective To systematically assess the relation between angiotensin-I converting enzyme(ACE) gene insertion/deletion (I/D) variation and type 2 diabetic nephropathy (T2DN) onset risk among Chinese population.Methods The related literatures were retrieved from the China National Knowledge Infrastructure (CNKI) and Wanfang Data until June 1st,2016.The RevMan 5.0 was used to conduct the statistical analysis.The merge OR value and corresponding 95% confidence interval(95%CI) were used to assess ACE gene I/D polymorphism and T2DN onset risk.Results Totally 29 papers with 4 357 subjects were included according to the inclusion and exclusion standard,including 2 208 cases of DN and 2 149 cases of T2DM without DN.Meta analysis showed that compared with ACE gene I/D polymorphism I allele,D allele could significantly increase the risk of T2DM patients suffering from DN,the OR value and corresponding 95%CI were 1.44(1.25,1.66);the gene analysis showed that ACE gene I/D polymorphism loci were significantly correlated with DN onset risk in the Asian population.The corresponding relative onset risk OR and 95%CI were 1.42(1.15,1.76) and 1.75(1.46,2.10) in the dominant and recessive genetic model.The Begg′s test showed that the included data had no obvious publication bias existence.Conclusion ACE gene I/D polymorphism is closely correlated with the onset risk of T2DN,and D allele might be a risk genetic factor for DN occurrence in the patients with T2DM.
3.Ethnic differences in the association of hypertension duration with cardiovascular diseases risk in Chinese adults.
Leilei LIU ; Zixuan XU ; Linyuan ZHANG ; Xiao ZHANG ; Cailiang ZHANG ; Zixiu QIN ; Jing HUANG ; Qianyuan YANG ; Jun YANG ; Xuejie TANG ; Qiaorong WANG ; Feng HONG
Chinese Medical Journal 2023;136(15):1882-1884