Table of Content

  

10 June 2017
Volume 55 Issue 6

Healthcare big data-driven theory and methodology for health management

XUE Fuzhong

JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES). 2017, 55(6):  1-29.  doi:10.6040/j.issn.1671-7554.0.2017.430

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Todays Internet, cloud computing and networking technology is mature and developed, and the widespread adoption of medical/health information makes medical/health-related data grow at a staggering rate. At the same time, 山 东 大 学 学 报 (医 学 版)55卷6期 -薛付忠.健康医疗大数据驱动的健康管理学理论方法体系 \=-the popularization and application of omics technology(genome, radiomics, etc.)and the rapid development of wearable mobile medical care and mobile health technologies have promoted health care filed quickly into the “big data” era; This suggests that a new era for “health care big data-driven health/disease management” has come, that is, big data-driven health/disease management practices have become a reality. Based on this, we have developed a “health care big data-driven theory and methodology of health/disease management” that covers health/disease detection, risk assessment and intervention. The system includes the health/disease management theoretical framework and conceptual model by the guidance of life course epidemiology and exposome theory under the background of healthcare big data, health/disease detection index screening and evidence acquisition methods, health/disease risk assessment methods and the development of a theoretical approach to health/disease intervention strategies. It is of great theoretical and practical significance to guide the theory and practice development of big data-driven health/disease management, promote the transformation and upgrading of health/disease management and the industrialization of healthcare big data.

Shandong multi-center longitudinal cohort for health management: a brief introduction

LIU Yafei, XING Ping, XU Xiuqin, YANG Shufang, LIU Yanxun, YUAN Zhongshang, XUE Fuzhong

JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES). 2017, 55(6):  30-36.  doi:10.6040/j.issn.1671-7554.0.2017.376

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Objective To introduce the characteristics of diseases and main variables, and the objectives of Shandong Multi-center Longitudinal Cohort for Health Management. Methods Shandong Multi-center Longitudinal Cohort for Health Management is a prospective, dynamic and open cohort. Since 2004, we began to collect data, construct database, conduct follow-ups, and so on. So far the total number of cohort is about 100 million, the longest observation time is 12 years, and about 20% of data have merged with medical insurance disease outcome data and cause of death. The information collection methods include questionnaires, physical examinations and laboratory tests. Results A total of 76 368 people were enrolled in this study, including 43 818 males and 32 550 females. The cumulative risk of hypertension, diabetes, stroke, and coronary heart disease is 49.40%, 23.98%, 4.74% and 6.82%, respectively. Conclusion Shandong Multi-center Longitudinal Cohort for Health Management was established in order to explore the roles of various factors in the development of chronic diseases, to construct the risk assessment models, and to provide a scientific basis for interventions.

Sub-distribution hazard model and its applications in health risk assessment

WANG Jintao, SU Ping, YUAN Zhongshang, XUE Fuzhong

JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES). 2017, 55(6):  37-41.  doi:10.6040/j.issn.1671-7554.0.2017.367

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Objective To introduce the theory of sub-distribution hazard model and its applications in health risk assessment. Methods Given that competing risks are commonly encountered in health risk assessment, we have introduced the sub-distribution hazard model, and further evaluate itsefficiency and application based on the Shandong Multi-center cohort of hemorrhagic cerebral apoplexy. Results Under the framework of competing risk, the sub-distribution hazard model took the competing endpoint into the construction of the risk set, other than treating the competing endpoint simply as the censoring. Thus, it can have better performance than the traditional Cox model. Based on the Shandong Multi-center cohort of hemorrhagic cerebral apoplexy, it showed a good practicability in risk assessment of stroke death. Conclusion The sub-distribution hazard model can directly link the covariates with the cumulative incidence function, and can efficiently deal with the competing risk problem.

Cause-specific hazard model and its applications in health risk assessment

WANG Tingting, WANG Jintao, YUAN Zhongshang, SU Ping, XUE Fuzhong

JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES). 2017, 55(6):  42-46.  doi:10.6040/j.issn.1671-7554.0.2017.368

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Objective To introduce the theory of cause-specific hazard model and its applications in health risk assessment. Methods Given that competing risks are commonly encountered in health risk assessment, we have introduced the cause-specific hazard model from the perspective of modeling principle and parameter estimator, and further evaluate the efficiency and application based on the Shandong Multi-center cohort of hemorrhagic cerebral apoplexy. Results Under the framework of competing risk, the cause-specific hazard model constructed the cox-type survival model for each cause-specific endpoint. The parameter estimator can be obtained from the partial likelihood and thus have good properties, this can make sure that the absolute risk is accurate. Based on the Shandong Multi-center cohort of hemorrhagic cerebral apoplexy, it showed a good practicability in risk assessment of stroke death. Conclusion When competing risk can not be ignored, the cause-specific hazard model are preferred in health risk assessment.

A collecting and processing system for health care big data based on web crawler technology

BIAN Weiwei, WANG Yongchao, CUI Lizhen, GUO Wei, LI Hui, ZHOU Miao, XUE Fuzhong, LIU Jing

JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES). 2017, 55(6):  47-55.  doi:10.6040/j.issn.1671-7554.0.2017.365

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Objective To collect and process the medical data from public health service system rapidly and exactly, and to provide data base for establishing the population health risk assessment model. Methods The algorithm and program were based on focused web crawler. This study mainly improved the algorithm in three aspects: automatic recording and correcting URL anomaly, original data archiving and keeping login mode. Medical data of the authorized website were obtained by the advanced web crawler, and were parsed and sorted out via medical database system. Results Data from several public health service base were acquired to provide data analysis report for local government, and multiple health risk assessment models were constructed by means of the processed data. Conclusion Utilizing the data collecting and processing system based on web crawler,we can deal with the problem that acquiring and organizing the available data in real life. This technology can be applied in medicine and health field,which will make full use of the existing rich medical data resources and greatly improve the utilization efficiency.

Risk prediction model of cardiovascular disease based on health management cohort

LI Jiqing, ZHAO Huanzong, SONG Binghong, ZHANG Lichun, LI Xiangyi, CHEN Yafei, WANG Ping, XUE Fuzhong

JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES). 2017, 55(6):  56-60.  doi:10.6040/j.issn.1671-7554.0.2017.356

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Objective To establish a model to evaluate the risk of cardiovascular disease(CVD)among health management population. Methods The cohort consisted of 72 843 individuals who had physical check-up at Shandong Multi-center Longitudinal Cohort for Health Management. They were all free of CVD events. We randomly divided the cohort into the training group(70%)and testing group(30%). Cox proportional hazards regression model was applied to choose risk factors of CVD, competing risk prediction model was used to establish a prediction model for CVD, and ten-fold cross validation was used to test the stability of the model. Discriminatory ability was determined by the area under the receiver operating characteristic curve(AUC). Results There were 2 463 CVD cases during the study period and the incidence was 88.79/1 000 person-year, and 164 people died of other causes. The risk factors included age, 山 东 大 学 学 报 (医 学 版)55卷6期 -李吉庆,等.基于健康管理队列的心血管事件风险预测模型 \=-smoke, BMI, hypertension, diabetes, dyslipidemia, ST-T segment changes, T wave change, abnormal Q wave, arrhythmia and chronic kidney diseases. The estimated AUC of the model in the training group was 0.837(95%CI: 0.821-0.854)for males and 0.897(95%CI:0.880-0.913)for females. The estimated AUC of the model in the testing group was 0.838(95%CI:0.813-0.862)for males and 0.893(95%CI:0.872-0.914)for females. Conclusion The risk prediction model can be used to screen high-risk subjects of CVD in health management population.

A hypertension risk prediction model based on health management cohort

YU Tao, LIU Huanle, FENG Xin, XU Fuyin, CHEN Yafei, XUE Fuzhong, ZHANG Chengqi

JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES). 2017, 55(6):  61-65.  doi:10.6040/j.issn.1671-7554.0.2017.357

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Objective To establish a prediction model for the risk of hypertension based on health management cohort. Methods Based on Shandong Multi-center Longitudinal Cohort for Health Management and after exclusion of participants with baseline hypertension, cardiovascular and cerebrovascular diseases and other related diseases, a cohort with 22 177 subjects, including 12 044 males and 10 133 females, was established. Cox regression was used to construct prediction models for hypertension in males and females. The effect of model prediction was evaluated. Results A total of 4 571 new hypertention cases were observed, resulting in a cumulative incidence of 62.84/1 000 person years. The risk factors of the model for males included age, body mass index, systolic pressure, diastolic pressure, fasting blood-glucose and hematocrit. The estimated AUC of the model was 0.821(95%CI: 0.812-0.830). In the model for females, the risk factors included age, body mass index, systolic pressure, red blood cell and high density lipoprotein cholesterol. The estimated AUC of the model was 0.818(95%CI: 0.806-0.828). Ten-fold cross validation showed that the estimated AUC of the model for males was 0.819(95%CI: 0.810-0.828)and 0.814(95%CI: 0.803-0.825) 山 东 大 学 学 报 (医 学 版)55卷6期 -于涛,等.基于健康管理队列的高血压风险预测模型 \=-for females. Conclusion The model has good prediction ability and could be used to identify individuals with high risk of hypertension.

A prediction model for coronary heart disease risks based on health management cohort

WANG Chunxia, XU Yibo, YANG Ning, XIA Bing, WANG Ping, XUE Fuzhong

JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES). 2017, 55(6):  66-71.  doi:10.6040/j.issn.1671-7554.0.2017.358

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Objective To establish a risk predicting model for coronary heart disease in health management cohort in Shandong province. Methods The cohort consisted of the health management cohort in Shandong province. Cox proportional hazards regression model was applied to screen the variables based on other predictive models. We used competing risk prediction model to establish the prediction model, and ten-fold cross validation to test the stability of the model. Results There were 73 386 subjects in the cohort, including 41 968 males and 31 418 females. The median follow-up time was 3.10 years. Coronary heart disease occurred in 1 545 sbujects, including 958 males and 587 females. The incidence density was 5.95 per 1 000 person-years for males and 4.90 per 1 000 person-years for females. The AUC of the male model was 0.809(95CI: 0.804-0.815), and the O/E value was 0.98. The AUC of the female model was 0.869(95%CI: 0.863-0.874), and the O/E value was 1.02. In the ten-fold cross validation model, the AUC of the male model was 0.806(95%CI: 0.801-0.812), and the AUC of the female model was 0.866(95%CI: 0.860-0.872). Conclusion The predictive model for coronary heart disease has good predictive ability in the health management cohort.

A prediction model of hyperlipidemia risk based on the health management population

ZHANG Guang, WANG Guangyin, WU Hongyan, ZHANG Hongyu, WANG Tingting, LI Jiqing, LI Min, KANG Fengling, LIU Yanxun, XUE Fuzhong

JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES). 2017, 55(6):  72-76.  doi:10.6040/j.issn.1671-7554.0.2017.402

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Objective To construct a risk prediction model of hyperlipidemia for people aged 20 years and over. Methods A total of 30 056 people without hyperlipidemia at baseline were included based on the Shandong Multi-center Longitudinal Cohort for Health Management. The prediction model was built on Cox proportional hazards regression model. The predictability was evaluated with the area under the receiver operating characteristic(ROC)curve(AUC). The predictive effect and distinguishing ability were verified with ten-fold cross-validation. Results During the follow-up of 3.53±2.65 years, there were 5 063 new hyperlipidemia cases, and the incidence was 47.78‰. The risk factors of hyperlipidemia included age, sex, drinking, smoking, total cholesterol, triglyceride, total bilirubin, high-density lipoprotein cholesterol, diabetes and hypertension. The AUC was 0.741(95%CI:0.731-0.752). Ten-fold cross-validation verified that the AUC was 0.741. Conclusion The prediction model of hyperlipidemia has good prediction ability in the health management population.

The influencing factors of ischemic ECG abnormalities in a large health check-up population

LI Jiangbing, SONG Xinhong, LIN Haiyan, ZHANG Dongzhi, LI Xiangyi, XU Yibo, WANG Li, XUE Fuzhong

JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES). 2017, 55(6):  77-81.  doi:10.6040/j.issn.1671-7554.0.2017.381

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Objective To explore the influencing factors of ischemic electrocardiogram(ECG)abnormalities and the predictive value of non-ischemic ECG among the health check-up population. Methods Individuals who had taken at least 2 health check-ups during 2004 and 2014 were selected from the Shandong Multi-center Longitudinal Cohort for Health Management. Those who suffered coronary heart disease and presented with ischemic ECG were excluded. The baseline information of subjects who showed ischemic ECG during the follow-up was compared with who showed no ischemic ECG. The risk factors of ischemic ECG were screened and the Cox regression analysis model was established. Results The cohort included 45 546 subjects. During the follow-up of 1-7 years(mean 3.24 years), 7 656 individuals presented with ischemic ECG abnormalities. The incidence density was 77.57/1 000 person-year. The main influencing factors of ischemic ECG included old age, female, high systolic pressure and diastolic pressure, high fasting blood glucose(FBG), high white blood cell count, and non-ischemic abnormal high amplitude R waves(MC-3). Conclusion This study investigated the risk factors leading to ischemic ECG abnormalities and provided scientific information for the intervention of heart diseases.

Prediction models on the onset risks of type 2 diabetes among the health management population

SU Ping, YANG Yachao, YANG Yang, JI Jiadong, DAYIMU Alimu, LI Min, XUE Fuzhong, LIU Yanxun

JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES). 2017, 55(6):  82-86.  doi:10.6040/j.issn.1671-7554.0.2017.347

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Objective To construct prediction models to estimate the risks of developing type 2 diabetes mellitus(T2DM)in 3 years among the health management population in mainland China. Methods Non-diabetic people aged 20 to 75 years at the baseline were chosen from Shandong Multi-center Longitudinal Cohort for Health Management to compose our cohort. Coxs proportional hazards regression model was adopted to build T2DM prediction model. The area under the receiver operating characteristic(ROC)curve(AUC)was used to evaluate the predictability of the model. Ten-fold cross-validation was adopted to test the stability of the model. Results During the follow-up of 3.68±2.8 years, 1,624 cases of new-onset diabetes occurred. The incidence density of male and female was 15.00‰ and 10.83‰, respectively. The risk factors for the male model included age, body mass index(BMI), fasting plasma glucose(FPG), triglyceride, alanine aminotransferase(ALT), and white blood cell(WBC)count. The risk factors 山 东 大 学 学 报 (医 学 版)55卷6期 -苏萍,等.健康管理人群2型糖尿病发病风险预测模型 \=-for the female model included age, FPG, triglyceride, high density lipoprotein cholesterol(HDL-C), and ALT. The AUC of the male model and female model was 0.795(95% CI: 0.764-0.827)and 0.707(95%CI: 0.654-0.759), respectively. Conclusion The male and female prediction models we constructed have high predictability and reliability among the health management population.

A prediction model for metabolic syndrome risk: a study based on the health management cohort

SUN Yuanying, YANG Yachao, QU Mingling, CHEN Yanmin, LI Min, WANG Shukang, XUE Fuzhong, LIU Yunxia

JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES). 2017, 55(6):  87-92.  doi:10.6040/j.issn.1671-7554.0.2017.350

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Objective To construct a model to evaluate the risk of developing metabolic syndrome(MetS)within 5 years in Chinese mainland Han population based on a health management population. Methods A total of 15 872 people without MetS at baseline were included based on the Shandong Multi-center Longitudinal Cohort for Health Management. Cox proportional hazards regression model was used to build the prediction model and the discriminatory ability was evaluated with area under the receiver operating characteristic(ROC)curve(AUC)and observed/expected counts(OE ratio). Results A total of 1 591 new MetS cases(1 273 males and 318 females)were observed during the follow-up of 5 years, accounting for an incidence density of 38.57/1000 person-year. In the male model, the risk factors included age, body mass index(BMI), fasting blood-glucose(FBG), triglyceride(TG), high density lipoprotein cholesterol(HDL-C), uric acid(UA), total cholesterol and hypertension. In the female model, the risk factors included age, BMI, FBG, TC, UA and hypertension. The AUC was 0.751(95%CI: 0.742-0.759)and 0.745(95%CI: 山 东 大 学 学 报 (医 学 版)55卷6期 -孙苑潆,等.健康管理人群代谢综合征发病风险预测模型 \=- 0.734-0.756)in the male and female model, respectively. The OE ratio was 1.03 and 1.00 in the male and female model, respectively. Conclusion This study has constructed a 5-year risk model that could be informative for identifying individuals at a high risk of developing MetS in a health management population.

A stroke prediction model for the health management population

LI Min, WANG Chunxia, XIA Bing, ZHU Qian, SUN Yuanying, WANG Shukang, XUE Fuzhong, JIA Hongying

JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES). 2017, 55(6):  93-97.  doi:10.6040/j.issn.1671-7554.0.2017.349

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Objective To construct a stroke prediction model for the health management population aged above 20 years. Methods A total of 74,326 cohort members without stroke at baseline were included based on the Shandong Multi-center Longitudinal Cohort for Health Management. Fine-Gray model was used to construct a stroke risk prediction model for females and males respectively. Results During the average follow-up of 3.9 years, 1,299(male: 829, female: 470)new stroke occurred, and the incidence density was 4.51‰. The risk factors for males included age, hypertension, coronary heart disease, diabetes mellitus, smoking, body mass index, triglyceride, white blood cell count, platelet count, high-density lipoprotein, and total cholesterol. The risk factors for females included age, hypertension, coronary heart disease, red blood cell count, hemoglobin, and body mass index. The estimated area under the receiver-operating characteristic curve(AUC)for the male model and female model was 0.846(95%CI: 0.828-0.864), and 0.878(95%CI: 0.858-0.898). Conclusion The stroke risk prediction model we constructed is effective in identifying individuals at high risk of stroke in the health management population.

Risk prediction model of chronic kidney disease in health management population

ZHOU Miao, XIA Tongyao, SUN Ailing, LI Ming, SHEN Zhenwei, BIAN Weiwei, JIANG Zheng, KANG Fengling, LIU Xiaojuan, XUE Fuzhong, LIU Jing

JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES). 2017, 55(6):  98-103.  doi:10.6040/j.issn.1671-7554.0.2017.359

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Objective To establish a risk prediction model of chronic kidney disease(CKD). Methods The data were obtained from Shandong Multi-center Longitudinal Cohort for Health Management. A total of 17 654 subjects with age of 20 years or older were included who had no CKD at baseline and accepted health examination at least twice during the study period. The follow-up outcome was CKD. Cox proportional hazards regression was applied to establish the model and the predictive performance of the model was evaluated by AUC. Ten-fold cross validation was used to verify the stability of the model. Results A total of 770 cases were observed during the follow-up. The incidence density of CKD was 17.69 per thousand person-years. The predictive factors in the final model included age, sex, hypertension, diabetes, creatinine, blood urea nitrogen, uric acid and basophils percentage. The AUC of the model was 0.685(95%CI: 0.678-0.692). Conclusion We have constructed a risk model that could be useful for identifying individuals at high risk of CKD in health management population.

A prediction model to estimate risks of cataract based on health management cohort

YU Yuanyuan, WANG Chunxia, SU Ping, SUN Yuanying, XUE Fuzhong, LIU Yanxun

JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES). 2017, 55(6):  104-107.  doi:10.6040/j.issn.1671-7554.0.2017.401

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Objective To establish a prediction model to estimate risks of cataract among health management population aged above 50 years. Methods Based on the Shandong Multi-center Longitudinal Cohort for Health Management, a prediction model for cataract was constructed using Coxs proportional hazards regression model. The predictability was evaluated with the area under the receiver operating characteristic(ROC)curve(AUC). The stability was tested with ten-fold cross-validation. Results During the follow-up period, there were 1010 new cataract cases, and the incidence density was 24.76‰. The risk factors included in prediction model were age, sex, smoking habit, hyperviscosemia, tympanic diseases, ametropia, diabetes, total cholesterol and systolic blood pressure(SBP). The AUC of the prediction model was 0.712(95% CI: 0.693-0.732). The ten-fold cross-validation showed that the AUC was 0.714. Conclusion The prediction model of cataract has high predictability and reliability. It can provide scientific basis for identifying high-risk groups of cataract.

A prediction model of 5-year CVD risks for type 2 diabetic patients: a prospective cohort study among Chinese community population

ZHANG Zhentang, YANG Yang, HAN Fujun, CHEN Xianghua, JI Xiaokang, WANG Yongchao, WANG Shukang, SUN Yuanying, LI Min, CHEN Yafei, WANG Li, XUE Fuzhong, LIU Yanxun

JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES). 2017, 55(6):  108-113.  doi:10.6040/j.issn.1671-7554.0.2017.341

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Objective To construct a prediction model for risks of cardio-cerebrovascular disease(CVD)in 5 years for newly diagnosed type 2 diabetic patients in China. Methods We collected from an official chronic disease prevention and control project 2 899 participants newly diagnosed as diabetic who were free from CVD events. Cox proportional 山 东 大 学 学 报 (医 学 版)55卷6期 -张振堂,等.基于社区2型糖尿病患者的心脑血管事件5年风险预测模型 \=-hazards regression model was used to construct a 5-year CVD risk model. Calibration and goodness of fit test were applied. External validation based on Shandong Multi-center Health Management Large Database was adopted to assess the stability of model. Results a total of 228 first CVD events were recorded in the derivation cohort during an average follow-up of 4.7 years(16.86/1 000 person-year). The 6 variables included age, gender, systolic pressure, low-density lipoprotein, high-density lipoprotein and family history of CVD. In the derivation cohort, the area under the receiver operating characteristic curve(AUC)for the Cox model and scoring model was 0.678(95%CI: 0.660-0.695)and 0.663(95%CI: 0.648-0.680), respectively. In the external validation, the AUC for the Cox model and scoring model was 0.640(95%CI: 0.608-0.676)and 0.631(95% CI:0.600-0.661), respectively. Conclusion We have established a model to predict the 5-year risks of CVD in Chinese type 2 diabetic patients, which can be used for the early intervention of CVD among type 2 diabetic patients in residential communities.

Construction of a NAFLD screening tool based on health examination subjects

JIANG Zheng, SHEN Zhenwei, ZHANG Guang, LI Runzi, CAO Jin, WANG Li, XUE Fuzhong, LIU Yanxun

JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES). 2017, 55(6):  114-118.  doi:10.6040/j.issn.1671-7554.0.2016.1275

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Objective To devise and verify a screening model for nonalcoholic fatty liver disease(NAFLD). Methods From the “Shandong Multi-center Longitudinal Cohort for Health Management”, 8,993 health examination clients without excessive drinking habits were randomly selected and divided into 2 groups, internal group(80% subjects, for derivation and internal assessment), and external group(20% subjects, for external assessment). Multivariate stepwise logistic regression was used to build a screening model, and prediction power of the model was assessed. Results Multivariate analysis demonstrated that gender, body mass index(BMI), hypertension, dyslipidemia, aspartate transaminase to alanine aminotransferase ratio(AST/ALT)and fasting blood glucose(FBG)were involved in the model and fatty liver index(FLI)could be constructed. The discriminatory power of the model was tested with the area under the receiver-operating characteristic curve(AUC),(0.859, 95%CI, 0.851 2-0.867 in the internal group, and 0.853, 95%CI, 0.835-0.869 in the external group). When FLI≤1.25 was used to exclude the possibility of NAFLD, the negative predictive value was 93.1% and 93.3% respectively for the internal and external group. When FLI≥2.25 was used to detect NAFLD, the positive predictive value was 74.6% and 72.7% respectively. Conclusion FLI is an effective screening tool for the identification of high-risk subjects of NAFLD.

Association between neutrophil count and nonalcoholic fatty liver disease:a prospective cohort study

LIU Xiaojuan, JIANG Zheng, KANG Fengling, ZHOU Miao, LIN Weiqiang, XUE Fuzhong

JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES). 2017, 55(6):  119-123.  doi:10.6040/j.issn.1671-7554.0.2017.338

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Objective To explore the longitudinal association between neutrophil count and nonalcoholic fatty liver disease(NAFLD). Methods The cohort consisted of 15 463 non-NAFLD individuals at baseline who were followed for the incident of NAFLD. Cox model was applied to calculate the hazard ratio(HR)and its 95% confidence intervals(95%CI)of quantiles(Q1, Q2, Q3 and Q4)of neutrophil count predicting NAFLD. Results During the follow-up, 3 846 new cases of NAFLD occurred. With Q1 as reference group, after adjusting for age and gender, the HRs(95%CI)of Q2, Q3, and Q4 were 1.265(1.057,1.514), 1.446(1.214,1.724)and 1.605(1.350,1.907), respectively. On the basis of previous model, alanine aminotransferase and gamma glutamyltransferase were added into the model, and the HRs(95%CI)of Q2, Q3, and Q4 were 1.264(1.056,1.512), 1.434(1.202,1.710)and 1.582(1.330,1.882), respectively. After adding BMI, systolic pressure, diastolic pressure, fasting glucose and blood lipids into the model, the HRs(95%CI)were 1.181(0.986,1.415), 1.189(0.995,1.420)and 1.226(1.026,1.464), respectively. Conclusion Neutrophil count is an independent risk factor for the incidence of NAFLD.

The relationship between γ-glutamyltransferase and hyperuricemia: a cohort study

CAO Jin, JI Xiaokang, SUN Xiubin, JIANG Zheng, XUE Fuzhong

JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES). 2017, 55(6):  124-128.  doi:10.6040/j.issn.1671-7554.0.2016.1270

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Objective To explore the relationship between γ-glutamyltransferase(GGT)and hyperuricemia(HUA). Methods We selected 26,006 participants from the routine health check-up system in the “large sample longitudinal health management cohort data management system” as baseline to follow up and to construct the longitude cohort. After that, we divided GGT into four levels(Q1, Q2, Q3, Q4), and adopted Cox regression model to explore the relationship between GGT and HUA. Results The incidence density of HUA was 74.4/1 000 personyears. The Cox model adjusted age showed that relative hazards(RRs)of different levels(Q2, Q3, Q4)compared with the lowest level(Q1)were 1.64(95%CI: 1.46-1.84, P<0.001), 2.24(95%CI: 2.00-2.50, P<0.001), 2.68(95%CI: 2.41-2.99, P<0.001), respectively. After the age, BMI, systolic blood pressure, triglyceride, total cholesterol, high-density lipoprotein cholesterol and serum creatinine were adjusted, the RRs were 1.20(95%CI: 1.04-1.38, P<0.001), 1.43(95%CI:1.24-1.64, P<0.001), 1.51(95%CI: 1.31-1.74, P<0.001), respectively. After further adjusting for smoking and drinking, the RRs were 1.19(95%CI: 1.04-1.37, P<0.001), 1.41(95%CI:1.23-1.62, P<0.001), 1.49(95%CI: 1.29-1.72, P<0.001), respectively. Conclusion GGT is an independent risk factor of HUA. With the enhanced GGT level, the risk of developing HUA will increase.