Objective To compare the diagnostic criteria of metabolic syndrome(MS) developed by the International Diabetes Federation(IDF) in 2005,the 3th Report of National Cholesterol Education Program-Adult Treatment Panel Ⅲ(NCEP-ATP Ⅲ) in 2005,and the Chinese Diabetes Society(CDS) in 2004.Methors A total of 1039 adults aged 23 to 62 year-old were enrolled in this epidemiological investigation to assess the prevalence of MS by above three definitions.Results The MS prevalence rate was 14.8%,14.2% and 10.7% in ATPⅢ,IDF,and CDS,respectively.The diagnoses agreement of IDF with ATPⅢ was stronger(Kappa=0.912) than IDF with CDS(Kappa=0.466) and ATPⅢ with CDS (Kappa=0.504).CDS definition found 5.7% of non-MS individuals had risk factor accumulation.Those defined by ATPⅢ and IDF criteria were presented with central obesity + ypertriglyceridemia + abnormal blood pressure and central obesity + hypertriglyceridemia + low hish-density lipoprotein hyperlipidemia.However,those defined by CDS criterion were commonly presented with obesity + hypertriglyceridemia + abnormal blood pressure or obesity + hypertriglyceridemia + hyperglycemia.Conclusions The agreement of IDF and ATPⅢ definition was relatively stronger.For better screening sensitivity,those three criteria,or CDS and IDF criteria,or CDS and ATPⅢ criteria should be used together.

Objective To evaluate the use and effectiveness of Human-Computer Interaction (HC1) -based risk prediction of diabetes among Chinese adults.MethodsHCI-based risk prediction of diabetes was performed in 639 non-diabetics aged 23 to 61years old.Risk prediction results,main risk factors of diabetes and helpful suggestions were reported and used for self-management.After l-year follow-up,the participants received another assessment to find the changes of disease risk and risk factors.Non-parametric or Chi-square test was used for comparison of continuous or categorical variables,respectively.Receiver Operating Characteristic (ROC) curve was used to calculate the sensitivity and specificity of HCI.Results After1-year follow-up,the incidence of diabetes per year was1.4％,and all newly diagnosed diabetes was found in high-risk individuals.The proportion of high-risk individuals was 56.8％ and 57.9％before and after follow-up ( x2 =0.36,P ＞ 0.05 ).In comparison with baseline,average risk score of high-risk individuals was significantly declined ( 2.25 vs 2.91,Z =- 4.32,P ＜ 0.05 ).Oversized waist circumstance,higher total cholesterol (TC) and lower high-density lipoprotein cholesterol (HDL-C) was identified in 76.2％,36.2％ and 3.8％ of high risk individuals at1year,lower than those of baseline ( 87.3％,42.2％ and12.4％,respectively ; x2 values were 30.56,6.05 and 22.26,respectively; all P ＜0.05) ; although the prevalence of hypertension was higher (23.5％ vs18.1％,x2 =11.11,P＜0.05).Conclusions HCI and effective control of risk factors could prevent the development of diabetes in high risk individuals.

CD4 count is the standard method for determining eligibility for highly active antiretroviral therapy (HAART) and monitoring HIV/AIDS disease progression, but it is not widely available in resource-limited settings. This study examined the correlation between total lymphocyte count (TLC) and CD4 count of HIV-infected patients before and after HAART, and assessed the thresholds of TLC for making decisions about the initiation and for monitoring HAART. A retrospective study was performed, and 665 HIV-infected patients with TLC and CD4 count from four counties (Shangcai, Queshan, Shenqiu and Weishi) were included in the study. Pearson correlation and receiver operating characteristic (ROC) were used. TLC and CD4 count after HAART was significantly increased as compared with pre-HAART (P<0.01). An overall positive correlation was noted between TLC and CD4 count (pre-HAART, r=0.73, P=0.0001; follow-up HAART, r=0.56, P=0.0001). The ROC curve between TLC and CD4 count showed that TLC ≤ 1200 cells/mm(3) could predict CD4 < 200 cells/mm(3) with a sensitivity of 71.12%, specificity of 66.35% at pre-HAART. After 12-month HAART, the optimum prediction for CD4 count < 200 cells/mm3 was a TLC ≤ 1300 cells/mm(3), with a sensitivity of 63.27%, and a specificity of 74.84%. Further finding indicated that TLC change was positively correlated to CD4 change (r=0.77, P=0.0001) at the time point of 12-month treatment, and the best prediction point of TLC change for CD4 increasing was 135 cells/mm(3). TLC and its change can be used as a surrogate marker for CD4 count and its change of HIV-infected individuals for making decisions about the initiation and for monitoring HAART in resource-limited settings.

Objective To study the application of Internet of Things, wireless health monitor all-in-one machine, health management platform, energy consumption monitoring in employee health management. Methods Enrollment criteria were set based on employees' health examination data, 126 employees were enrolled in this study voluntarily, 97 were male, and 29 were female. The age was from 26 to 59 years, the average age was 43.7 ± 6.1 years. Using motion energy consumption monitor, wireless health monitor all-in-one machine and health management platform, employee's exercise, body weight, body mass index, fat and muscle mass, systolic blood pressure, diastolic blood pressure, cholesterol, triglyceride, low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), uric acid, fasting blood glucose etc. were monitored. Data were collected for before and after 3 months intensive intervention. Results After 3 month of intensive intervention, body weight ( (74.90 ± 9.95) kg, (71.77 ± 9.57) kg), body mass index ((25.94 ± 2.65) kg/m2, (24.96 ± 2.55) kg/m2), fat mass ((21.30 ± 4.31) kg, (18.89 ± 4.23) kg), muscle mass ((49.78 ± 7.12) kg, (49.07 ± 6.97) kg), systolic pressure ((129.72 ± 11.16) mmHg(1 mmHg=0.133 kPa), (118.32 ± 10.50) mmHg), diastolic blood pressure ((89.10 ± 8.28) mmHg, (76.94 ± 7.57) mmHg), cholesterol ((5.16±0.85) mmol/L, (4.96±0.90) mmol/L), triglyceride ((1.72±0.92) mmol/L, (1.43±0.64) mmol/L), uric acid ((353.00 ± 85.33) μmol/L, (345.00 ± 73.01) μmol/L) were decreased with statistical significance (t=10.92, 11.03, 6.75, 5.56, 4.23, 3.99, 4.26, 3.46, 1.98, P<0.05); and the value of HDL-C ((1.20 ± 0.24) mmol/L, (1.28 ± 0.25) mmol/L) increased significantly (t=-4.62, P<0.05); the value of LDL-C((2.54 ± 0.52) mmol/L, (2.66±0.58) mmol/L) increased and fast blood glucose ((5.05±0.73) mmol/L, (5.02±0.79) mmol/L) decreased, but there was no significant difference(t=-3.03, 0.14 respectively, P>0.05). Conclusion Health Internet of Things can help employees to develop scientific exercise habits , to correct unhealthy diet habits, and improve health. It will provide a new option for enterprise employee health management and can be recommended for health management programs by large enterprises with domestic and abroad projects.

CD4 count is the standard method for determining eligibility for highly active antiretroviral therapy (HAART) and monitoring HIV/AIDS disease progression,but it is not widely available in resource-limited settings.This study examined the correlation between total lymphocyte count (TLC) and CD4 count of HIV-infected patients before and after HAART,and assessed the thresholds of TLC for making decisions about the initiation and for monitoring HAART.A retrospective study was performed,and 665 HIV-infected patients with TLC and CD4 count from four counties (Shangcai,Queshan,Shenqiu and Weishi) were included in the study.Pearson correlation and receiver operating characteristic (ROC) were used.TLC and CD4 count after HAART was significantly increased as compared with pre-HAART (P＜0.01).An overall positive correlation was noted between TLC and CD4 count (pre-HAART,r=0.73,P=0.0001; follow-up HAART,r=0.56,P=0.0001).The ROC curve between TLC and CD4 count showed that TLC ≤ 1200 cells/mm3 could predict CD4 ＜ 200 cells/mm3 with a sensitivity of 71.12％,specificity of 66.35％at pre-HAART.After 12-month HAART,the optimum prediction for CD4 count ＜ 200 cells/mm3 was a TLC ≤ 1300 cells/mm3,with a sensitivity of 63.27％,and a specificity of 74.84％.Further finding indicated that TLC change was positively correlated to CD4 change (r=0.77,P=0.0001) at the time point of 12- month treatment,and the best prediction point of TLC change for CD4 increasing was 135 cells/mm3.TLC and its change can be used as a surrogate marker for CD4 count and its change of HIV-infected individuals for making decisions about the initiation and for monitoring HAART in resource-limited settings.