Clinical data of 153 patients with multiple myeloma admitted from January 2005 to December 2015 were retrospectively reviewed,including 56 cases complicated with herpes zoster (case group) and 97 cases without herpes zoster (control group).The risk factors for herpes zoster in multiple myeloma patients were analyzed by univariate and multivariate logistic regression.Univariate analysis showed that herpes zoster significantly associated with albumin/globulin ratio (A/G) ＜ 0.5 (x2 =10.989,P ＜0.05),unremitted myeloma(x2 =12.310,P ＜ 0.05),neutropenia (x2 =8.100,P ＜ 0.05) and treatment with bortezomib(x2 =9.465,P ＜ 0.05).Multivariate logistic regression analysis showed that herpes zoster was positively correlated with A/G ＜ 0.5 (OR =6.344,95％ CI:1.511-26.645,P ＜ 0.05),neutropenia (OR =6.402,95％ CI:1.420-28.869,P ＜ 0.05),treatment with bortezomib (OR =7.335,95％ CI:1.587-33.911,P ＜ 0.05);and negatively correlated with remitted myeloma (OR =0.064,95％ CI:0.017-0.237,P ＜ 0.05).It is necessary to take corresponding countermeasures targeting the risk factors to prevent herpes zoster in multiple myeloma patients.

OBJECTIVETo evaluate the predictive power of risk model by combining traditional epidemiological factors and genetic factors.

METHODSOur previous GWAS data of lung cancer in Chinese were used in training set (Nanjing and Shanghai: 1473 cases vs. 1962 control) and testing set (Beijing and Wuhan: 858 cases vs. 1 115 control). All the single nucleotide polymorphisms (SNPs) associated with lung cancer risk were systematically selected and stepwise logistic regression analysis was used to select independent factors in the training set. The wGRS (weighted genetic score) was further used to calculate genetic risk score. To evaluate the contribution of the genetic factors, 3 risk models were established by using the training set, i.e. smoking model (based on smoking status) , genetic risk model (based on genetic risk score) and combined model (based on smoke and genetic risk score). The predictability of the models were evaluated by the areas under the receiver operating characteristic (ROC) curves, area under curve (AUC), net reclassification improvement (NRI) and integrated discrimination index (IDI). Besides, the results were further verified in the testing set.

RESULTSIn the training set, it was found that the AUC of the smoking, genetic risk and combined models were 0.65 (0.63-0.66), 0.60 (0.59-0.62) and 0.69 (0.67-0.71), respectively. Compared with combined model, the predictive power of other two models significantly declined, the difference was statistically significant (P<0.001). Furthermore, compared with the smoking model, the NRI of the combined model increased by 4.57% (2.23%-6.91%) and IDI increased by 3.11% (2.52%-3.69%) in the training set, the difference was statistically significant (P<0.001). Similarly, in the testing set NRI increased by 2.77%, the difference was not statistically significant (P=0.069) , and IDI increased by 3.16%, the difference was statistically significant (P<0.001).

CONCLUSIONThis study showed that combining 14 genetic variants with traditional epidemiological factors could improve the predictive power of risk model for lung cancer. The model could be used in the screening of high-risk population of lung cancer in Chinese and provide evidence for the early diagnosis and treatment of lung cancer.

Area Under Curve ; Asian Continental Ancestry Group ; Beijing ; Case-Control Studies ; China ; Genetic Predisposition to Disease ; Genetic Variation ; Genome-Wide Association Study ; Humans ; Lung Neoplasms ; epidemiology ; genetics ; Polymorphism, Single Nucleotide ; ROC Curve ; Risk Factors