Objective:To investigate the adjunctive diagnostic value of retinal imaging structural parameters combined with apolipoprotein E (ApoE) gene polymorphisms for Alzheimer′s disease (AD).Methods:It was a case-control study, 71 confirmed AD patients who attended the Department of Neurology in Sichuan Provincial People′s Hospital from May 2023 to June 2024 and 156 healthy medical checkups who participated in medical checkups in the Health Management Center were continuously with convenience sampling method; the subjects were included as the AD case group and healthy control group, respectively. Optical coherence tomography (OCT) was used to measure the structural parameters of retinal imaging such as the thickness of the retinal nerve fiber layer (RNFL) and the retinal nerve fiber layer-inner plexiform layer (RNFL-IPL) in the study subjects. Information on demographic characteristics and disease history of the study participants were collected through a questionnaire, and venous blood was collected to test for ApoE gene polymorphisms. The retinal imaging structural parameters, ApoE gene polymorphisms and other related indicators were included in a multifactorial logistic regression model to analyze the main factors affecting the risk of AD. Based on the results of the multifactorial analysis, the receiver operating characteristic (ROC) curves were plotted and the areas under the curve (AUC) were calculated to evaluate the efficacy of different models in the adjunctive diagnosis of AD.Results:Of the 227 study subjects included in the analysis, 153 were females and 74 were males; there were 71 cases in the AD case group with a mean age of (66.73±8.83) years, and there were 156 subjects in the healthy control group with an average age of (61.95±8.21) years. Educational attainment of elementary school and below ( OR=4.683, 95% CI: 2.133-10.282), living visual acuity<0.5 ( OR=2.716, 95% CI: 1.12-6.583), and carrying ≥1 ApoE ε4 genes ( OR=5.331, 95% CI: 2.309-11.891) were positively correlated with the risk of AD. RNFL thickening ( OR=0.923, 95% CI: 0.854-0.998) was negatively associated with the risk of AD (all P<0.05). The AD risk assessment model (Model 4), which included fundus imaging features and ApoE gene polymorphisms, had the highest predictive efficacy (AUC=0.857, P<0.001). Conclusion:Retinal imaging structural parameters differ significantly between AD patients and healthy examinees, and a risk assessment model combining retinal imaging structural parameters and ApoE gene polymorphisms has high predictive value and is expected to serve as an auxiliary diagnostic tool for AD.

Objective:To investigate the adjunctive diagnostic value of retinal imaging structural parameters combined with apolipoprotein E (ApoE) gene polymorphisms for Alzheimer′s disease (AD).Methods:It was a case-control study, 71 confirmed AD patients who attended the Department of Neurology in Sichuan Provincial People′s Hospital from May 2023 to June 2024 and 156 healthy medical checkups who participated in medical checkups in the Health Management Center were continuously with convenience sampling method; the subjects were included as the AD case group and healthy control group, respectively. Optical coherence tomography (OCT) was used to measure the structural parameters of retinal imaging such as the thickness of the retinal nerve fiber layer (RNFL) and the retinal nerve fiber layer-inner plexiform layer (RNFL-IPL) in the study subjects. Information on demographic characteristics and disease history of the study participants were collected through a questionnaire, and venous blood was collected to test for ApoE gene polymorphisms. The retinal imaging structural parameters, ApoE gene polymorphisms and other related indicators were included in a multifactorial logistic regression model to analyze the main factors affecting the risk of AD. Based on the results of the multifactorial analysis, the receiver operating characteristic (ROC) curves were plotted and the areas under the curve (AUC) were calculated to evaluate the efficacy of different models in the adjunctive diagnosis of AD.Results:Of the 227 study subjects included in the analysis, 153 were females and 74 were males; there were 71 cases in the AD case group with a mean age of (66.73±8.83) years, and there were 156 subjects in the healthy control group with an average age of (61.95±8.21) years. Educational attainment of elementary school and below ( OR=4.683, 95% CI: 2.133-10.282), living visual acuity<0.5 ( OR=2.716, 95% CI: 1.12-6.583), and carrying ≥1 ApoE ε4 genes ( OR=5.331, 95% CI: 2.309-11.891) were positively correlated with the risk of AD. RNFL thickening ( OR=0.923, 95% CI: 0.854-0.998) was negatively associated with the risk of AD (all P<0.05). The AD risk assessment model (Model 4), which included fundus imaging features and ApoE gene polymorphisms, had the highest predictive efficacy (AUC=0.857, P<0.001). Conclusion:Retinal imaging structural parameters differ significantly between AD patients and healthy examinees, and a risk assessment model combining retinal imaging structural parameters and ApoE gene polymorphisms has high predictive value and is expected to serve as an auxiliary diagnostic tool for AD.

Objective:To analyze the current prevalence of pre-diabetes (PDM) and its relationship with overweight and obesity in an adult health check-up population.Methods:This study was a cross-sectional and retrospective cohort study and was applied using whole-cluster random sampling method. A total of 491 379 adults who underwent health check-ups at the Health Management Centre of Sichuan Provincial People′s Hospital from January 2017 to July 2023 were selected to analyze the epidemiological characteristics of PDM and overweight-obesity, as well as the trend of change over time. A retrospective cohort study was conducted on 19 001 of the subjects who underwent≥3 health check-ups and did not have diabetes and PDM at baseline, and the relationships between body mass index, waist circumference and the risk for developing PDM were analyzed using Cox proportional risk regression models. And the dose-response relationship between body mass index, waist circumference and the risk for developing PDM was analyzed using restricted cubic spline regression (RCS).Results:Of the 491 379 cases included in the cross-sectional study, 275 084 were male and 216 295 were female, 163 158 cases were under 40 years old, and 328 221 cases were 40 years old and above; the total prevalence of PDM was 19.41% in 2017-2023, with an overall increasing trend. Of the 19 001 people included in the cohort study, a total of 2 487 (13.09%) new cases of PDM were identified at the end of follow-up. After adjusting for confounding factors, overweight ( HR=1.150, 95% CI: 1.047-1.263), obesity ( HR=1.335, 95% CI: 1.149-1.552) and abdominal obesity ( HR=1.218, 95% CI: 1.105-1.342) were risk factors for PDM. The risk of PDM rised with the increase of body mass index (>22.9 kg/m 2, Pnon-linear=0.973) and waist circumference (>80 cm, Pnon-linear=0.830), with a linear dose-response mode. In different gender and age groups, it was found the greater the body mass index (>24.1 kg/m 2 for men,>21.5 kg/m 2 for women;>23.3 kg/m 2 for age≥40 years,>24.1 kg/m 2 for age<40 years) and waist circumference (>85 cm for men, >73 cm for women; >82 cm for age ≥40 years, >85 cm for age <40 years), the higher the risk of PDM. Conclusions:The prevalence of PDM is on the rise in the adult health check-up population. To prevent PDM, it is necessary to control the body mass index and waist circumference to a lower level than the overweight and obesity standards.

OBJECTIVE To establish the mechanism for monitoring,standardizing and alarming of relative risk factors to reflect the tendency of hospital infection.METHODS Data of hospital infection during six years were surveyed and analyzed completely.The prevalence baseline of hospital infection was chosen as the value to assess the control rate of hospital infection.The alarm value was set on the baseline.RESULTS The hospital infection revalence baseline and alarm value were used to assess the quality of infection control in whole hospital and each department objectively and accurately.These values could be also used to assess the effect of control of hospital infection among departments.They also could be used to survey the tendency of hospital infection and determine prevalence and outbreak of hospital infection.CONCLUSIONS Prevalence baseline and alarm value for hospital infection are valuable for preventing hospital infection and its outbreak.