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AIM: To investigate the correlation between axial length and diabetic retinopathy(DR)in patients with type 2 diabetes mellitus.METHODS:This study is a cross-sectional study. A total of 53 cases(104 eyes)of type 2 diabetes patients who admitted to the ophthalmology department of the Jining No.1 People's Hospital between January and May 2023 were included. Among these cases, 51 patients had both eyes included, while 2 patients had only one eye included. The patients were divided into two groups based on the presence or absence of fundus lesions. The non-diabetic retinopathy(NDR)group consisted of 32 eyes, and the DR group consisted of 72 eyes. Within the DR group, the patients were further categorized based on the severity of fundus lesions. The non-proliferative diabetic retinopathy(NPDR)group comprised of 27 eyes, and the proliferative diabetic retinopathy(PDR)group consisted of 45 eyes. The axial lengths of eyes in the DR group were divided into four groups using quartiles: 19 eyes in the 20.00～22.09 mm group, 17 eyes in the 22.10～22.70 mm group, 18 eyes in the 22.71～23.12 mm group, and 18 eyes in the 23.13～24.48 mm group. Binary Logistic regression analysis was employed to investigate the factors influencing the occurrence of DR and PDR.RESULTS:Binary Logistic regression analysis showed that both axial length and age significantly influenced the development of DR and PDR.(Axial length: OR=0.296, 95%CI:0.130～0.672, P<0.05; OR=0.237, 95%CI:0.076～0.736, P<0.05; age: OR=0.949, 95%CI:0.907～0.994, P<0.05; OR=0.879, 95%CI: 0.820～0.942, P<0.05). The risk of PDR in the group with axial length of 23.13～24.48 mm was reduced compared to the group with axial length of 20.00～22.09 mm(OR=0.057; 95%CI: 0.006～0.515, P=0.011).CONCLUSION:The findings indicate that longer axial length in patients with type 2 diabetes are associated with a decreased risk of developing DR, as well as a reduced likelihood of DR progressing to PDR. Therefore, a long axial length can be considered a protective factor against DR.
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Objective@#To understand the incidence of stroke in the population of Jinchang Cohort and the relationship between metabolic diseases and stroke, and provide scientific evidence for the prevention and treatment of stroke in the population.@*Methods@#The epidemiological investigation data and physical examination data of the 33 042 follow-up participants in Jinchang Cohort were collected for a prospective cohort study. Restricted cubic splines functions was used to analyze the dose-response relationship between metabolic indexes and the risk of stroke incidence.@*Results@#1) The incidence rate of stroke in Jinchang Cohort was 1.59%, and the standardized incidence rate was 3.99%. 2) Hypertension (male HR=2.20, female HR=4.45) and dyslipidemia (male HR=1.49, female HR=1.79) were the risk factors of stroke incidence in the population and diabetes had influence on the incidence of stroke only in the males (HR=1.79), while obesity had influence only in the females (HR=1.64). The more kinds of metabolic diseases, the higher risk of stroke incidence was. 3) Systolic blood pressure had a non-linear dose-response correlation with the risk of stroke incidence, while diastolic blood pressure had a positive linear correlation with the risk of stroke incidence.@*Conclusions@#The incidence of stroke in Jinchang Cohort population was high compared with both domestic level and oversea level. The patients with metabolic diseases were the population at high-risk for stroke, and more attention should be paid to them in the prevention and treatment of stroke. Diastolic blood pressure might be more closely related to stroke.
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Postmortem interval (PMI) estimation is one of the most challenging problems in the field of forensic science. Vitreous humor is a hotspot which has been used for PMI estimation and postmortem chemical analysis in forensic pathology. In order to provide novel perspectives for the future research of PMI estimation using vitreous humor, the comparison between vitreous humor with other common body fluids, the effect of temperature on vitreous humor, vitreous humor detection method and data fitting method have been reviewed in this paper.
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Objective To test the changes of the potassium(K+)and magnesium(Mg2+)concentrations in vitreous humor of rabbits along with postmortem interval(PMI)under different temperatures, and explore the feasibility of PMI estimation using mixed-effect model. Methods After sacrifice, rabbit carcasses were preserved at 5℃, 15℃, 25℃ and 35℃, and 80-100μL of vitreous humor was collected by the double-eye alternating micro-sampling method at every 12 h. The concentrations of K+and Mg2+in vitreous humor were measured by a biochemical-immune analyser. The mixed-effect model was used to perform analy-sis and fitting, and established the equations for PMI estimation. The data detected from the samples that were stoned at 10℃, 20℃ and 30℃ with 20, 40 and 65 h were used to validate the equations of PMI estimation. Results The concentrations of K+and Mg2+[f(x,y)] in vitreous humor of rabbits under different temperature increased along with PMI(x). The relative equations of K+and Mg2+concentration with PMI and temperature under 5℃~35℃ were fK+(x,y)=3.413 0+0.309 2 x+0.337 6 y+0.010 83 xy-0.002 47 x2 (P<0.000 1), and fMg2+(x,y)=0.745 6+0.006 432 x+0.033 8 y(P<0.000 1), respectively. It was proved that the time of deviation for PMI estimation by K+and Mg2+was in 10 h when PMI was between 0 to 40 h, and the time of deviation was in 21 h when PMI was between 40 to 65 h. Conclusion In the ambient temperature range of 5℃-35℃, the mixed-effect model based on temperature and vitreous humor sub-stance concentrations can provide a new method for the practical application of vitreous humor chemi-cals for PMI estimation.
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Objective To explore the association between fatty liver and type 2 diabetes mellitus (T2DM) in the baseline-population of Jinchang cohort study.Methods Data from all the participants involved in the baseline-population of Jinchang cohort study was used,to compare the risks of T2DM in fatty liver and non fatty liver groups and to explore the interaction between family history or fatty liver of diabetes and the prevalence of T2DM.Results Among all the 46 861 participants,10 574 were diagnosed as having fatty liver (22.56％),with the standardized rate as 20.66％.Another 3 818 participants were diagnosed as having T2DM (8.15％) with standardized rate as 6.90％.The prevalence of T2DM increased in parallel with the increase of age (trendx2 =2 833.671,trend P＜ 0.001).The prevalence of T2DM in the fatty liver group was significantly higher than that in the non-fatty liver group,both in men or women and in the overall population.Compared with the group of non-fatty liver,the risks of T2DM in fatty liver group were seen 1.78 times higher in males,2.33 times in women and 2.10 times in the overall population,after adjustment for factors as age,levels of education,smoking,drinking,physical exercise,BMI,family history of diabetes and some metabolic indicators (pressure,TC,TG,uric acid,ALT,AST,gamma-glutamyl transferase).Date from the interaction model showed that fatty liver and family history of diabetes present a positive additive interaction on T2DM (RERI=1.18,95％CI:0.59-1.78;AP=0.24,95％CI:0.14-0.34;S=1.43,95％CI:1.21-1.69).Conclusions Fatty liver could significantly increase the risk of T2DM and a positive additive interaction was also observed between fatty liver and family history of diabetes on T2DM.It was important to strengthen the prevention program on T2DM,in order to effectively control the development of fatty liver.
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Objective To explore the association between fatty liver and type 2 diabetes mellitus (T2DM) in the baseline-population of Jinchang cohort study.Methods Data from all the participants involved in the baseline-population of Jinchang cohort study was used,to compare the risks of T2DM in fatty liver and non fatty liver groups and to explore the interaction between family history or fatty liver of diabetes and the prevalence of T2DM.Results Among all the 46 861 participants,10 574 were diagnosed as having fatty liver (22.56％),with the standardized rate as 20.66％.Another 3 818 participants were diagnosed as having T2DM (8.15％) with standardized rate as 6.90％.The prevalence of T2DM increased in parallel with the increase of age (trendx2 =2 833.671,trend P＜ 0.001).The prevalence of T2DM in the fatty liver group was significantly higher than that in the non-fatty liver group,both in men or women and in the overall population.Compared with the group of non-fatty liver,the risks of T2DM in fatty liver group were seen 1.78 times higher in males,2.33 times in women and 2.10 times in the overall population,after adjustment for factors as age,levels of education,smoking,drinking,physical exercise,BMI,family history of diabetes and some metabolic indicators (pressure,TC,TG,uric acid,ALT,AST,gamma-glutamyl transferase).Date from the interaction model showed that fatty liver and family history of diabetes present a positive additive interaction on T2DM (RERI=1.18,95％CI:0.59-1.78;AP=0.24,95％CI:0.14-0.34;S=1.43,95％CI:1.21-1.69).Conclusions Fatty liver could significantly increase the risk of T2DM and a positive additive interaction was also observed between fatty liver and family history of diabetes on T2DM.It was important to strengthen the prevention program on T2DM,in order to effectively control the development of fatty liver.
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Environmental exposure to heavy metals has been linked to a wide range of human health hazards. We detected the levels of 15 metals in urine samples from 500 representative sub-samples in an ongoing occupational cohort study (Jinchang Cohort) to directly evaluate metal exposure levels. Fifteen metals, namely As, Ba, Be, Cd, Cs, Cr, Co, Cu, Pb, Mn, Ni, Se, Tl, U, and Zn, were detected by inductively coupled plasma quadruple mass spectrometry. The results showed that median creatinine adjustment and geometric mean urinary metal levels were higher in the heavy metal-exposed group, except Se and Zn, than other reported general or occupational populations. Further studies should address the effects of heavy metals on human health.
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Humans , China , Cohort Studies , Environmental Pollutants , Blood , Metals, Heavy , Blood , Occupational ExposureABSTRACT
Objective To explore the incidence and risk factors of gout in Jinchang cohort and provide scientific evidence for the prevention and control of gout.Methods People without gout detected by baseline survey in Jinchang cohort were selected as study subjects.All the subjects were followed up through questionnaire interview,physical examination as well as laboratory test from January 24,2013 to November 24,2015.Cox regression model was used to analyze the risk factors for gout in Jinchang cohort.In addition,log-linear model was used to analyze the interaction between risk factors.Results A total of 33 153 subjects were followed up,and there were 277 newly diagnosed gout cases in the cohort.The overall incidence of gout was 0.8％.The incidence of gout in males was higher than that in the females,but the incidence of gout in males and females was similar after the age of 60 years.Cox regression analysis showed that age ＞40 years (at age 40 to 59 years:HR=2.982,95％CI:1.503-5.981;at age 60 to 91 years:HR=2.588,95％CI:1.107-6.049),alcohol abuse (HR=2.234,95％CI:1.128-4.427),obesity (HR=2.204,95％CI:1.216-3.997),diabetes (HR=2.725,95％CI:1.500-4.950) and high uric acid (HR=5.963,95％CI:3.577-9.943) were risk factors for gout,while weekly beans intake ≥0.25 kg (HR=0.528,95％CI:0.345-0.808) and regular physical exercise (HR=0.499,95％ CI:0.286-0.869) were protective factors for gout.The analysis with log-linear model showed that there were two order effects between the risk factors.Conclusions Age,beans intake,alcohol abuse,physical exercises,obesity,diabetes and high uric acid were important factors influencing the incidence of gout.It is important to have healthy lifestyle and dietary habits,receive regular health examination to prevent and control the incidence of gout in this cohort.
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Objective To explore the incidence and risk factors of gout in Jinchang cohort and provide scientific evidence for the prevention and control of gout.Methods People without gout detected by baseline survey in Jinchang cohort were selected as study subjects.All the subjects were followed up through questionnaire interview,physical examination as well as laboratory test from January 24,2013 to November 24,2015.Cox regression model was used to analyze the risk factors for gout in Jinchang cohort.In addition,log-linear model was used to analyze the interaction between risk factors.Results A total of 33 153 subjects were followed up,and there were 277 newly diagnosed gout cases in the cohort.The overall incidence of gout was 0.8％.The incidence of gout in males was higher than that in the females,but the incidence of gout in males and females was similar after the age of 60 years.Cox regression analysis showed that age ＞40 years (at age 40 to 59 years:HR=2.982,95％CI:1.503-5.981;at age 60 to 91 years:HR=2.588,95％CI:1.107-6.049),alcohol abuse (HR=2.234,95％CI:1.128-4.427),obesity (HR=2.204,95％CI:1.216-3.997),diabetes (HR=2.725,95％CI:1.500-4.950) and high uric acid (HR=5.963,95％CI:3.577-9.943) were risk factors for gout,while weekly beans intake ≥0.25 kg (HR=0.528,95％CI:0.345-0.808) and regular physical exercise (HR=0.499,95％ CI:0.286-0.869) were protective factors for gout.The analysis with log-linear model showed that there were two order effects between the risk factors.Conclusions Age,beans intake,alcohol abuse,physical exercises,obesity,diabetes and high uric acid were important factors influencing the incidence of gout.It is important to have healthy lifestyle and dietary habits,receive regular health examination to prevent and control the incidence of gout in this cohort.
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The prevalence of metabolic syndrome (MS) was investigated in Chinese nickel-exposed workers. A total of 35 104 subjects were enrolled in this study. The age-adjusted prevalence of MS, BMI⋝25, diabetes, hypertension, and abnormal lipid was 13.9%, 29.5%, 12.8%, 27.5%, and 47.1%, respectively. The prevalence of BMI⋝25, hyperglycemia, and hypertension increased with the age of males and females, and was higher in males than in females (37.1% vs 21.5%, 15.9% vs 12.1%, 35.0% vs 24.3%, 54.3% vs 40.4%).