Development and validation of a potential risk area identification model for hand, foot, and mouth disease in metropolitan China.

Maximum Entropy model (MaxEnt), as a machine learning algorithm, is widely used to identify potential risk areas for emerging infectious diseases. However, MaxEnt usually overlooks the influence of the optimal selection of spatial grid scale and the optimal combination of factor information on identification accuracy. Furthermore, the internal level information of factors is closely related to the potential risk of disease occurrence but is rarely applied to enhance MaxEnt's accuracy. In this study, the Optimal Parameters-based Geographical Detectors-Information Value-MaxEnt (OPGD-IV-MaxEnt) was first proposed to identify the potential risk areas of hand, foot, and mouth disease (HFMD) in Shenzhen and compared its identification accuracy with that of OPGD-MaxEnt and MaxEnt. Firstly, the optimal grid scale and optimal combination of factor information were determined by OPGD. Secondly, the contributions of factors' internal level information to the potential risk of HFMD occurrence were quantified and incorporated by IV. Lastly, the spatial patterns of potential risk areas and their main driving factors were elucidated. Results showed that: (i) Area under the curve (AUC) of single MaxEnt were 0.638, 0.688, 0.763, 0.796, and 0.757 at 100 m, 250 m, 500 m, 750 m, and 1000 m scale, respectively, and 750 m were deemed the optimal scale. (ii) At the optimal scale, OPGD-IV-MaxEnt (AUC = 0.868) identified potential risk areas more accurately than MaxEnt (AUC = 0.796) and OPGD-MaxEnt (AUC = 0.827). (iii) Resident (r = 0.61, q = 0.39) and Market (r = 0.61, q = 0.36) were the primary factors affecting the identification of potential risk areas. (iv) Potential high-risk areas of HFMD were mainly distributed in northwestern, southwestern, and central Shenzhen, with dense resident and market distribution. Such insights are instrumental in devising targeted infection prevention and control measures for emerging infectious diseases and provide references for improving the identification accuracy of similar machine learning algorithms.

Expert consensus on One Health for establishing an enhanced and integrated surveillance system for key infectious diseases.

China has been continuously improving its monitoring methods and strategies to address key infectious diseases (KIDs). After the severe acute respiratory syndrome epidemic in 2003, China established a comprehensive reporting system for infectious diseases (IDs) and public health emergencies. The relatively lagging warning thresholds, limited warning information, and outdated warning technology are insufficient to meet the needs of comprehensive monitoring for modern KIDs. Strengthening early monitoring and warning capabilities to enhance the public health system has become a top priority, with increasing demand for early warning thresholds, information, and techniques, thanks to constant innovation and development in molecular biology, bioinformatics, artificial intelligence, and other identification and analysis technologies. A panel of 31 experts has recommended a fourth-generation comprehensive surveillance system targeting KIDs (41 notifiable diseases and emerging IDs). The aim of this surveillance system is to systematically monitor the epidemiology and causal pathogens of KIDs in hosts such as humans, animals, and vectors, along with associated environmental pathogens. By integrating factors influencing epidemic spread and risk assessment, the surveillance system can serve to detect, predict, and provide early warnings for the occurrence, development, variation, and spread of known or novel KIDs. Moreover, we recommend comprehensive ID monitoring based on the fourth-generation surveillance system, along with a data-integrated monitoring and early warning platform and a consortium pathogen detection technology system. This series of considerations is based on systematic and comprehensive monitoring across multiple sectors, dimensions, factors, and pathogens that is supported by data integration and connectivity. This expert consensus will provides an opportunity for collaboration in various fields and relies on interdisciplinary application to enhance comprehensive monitoring, prediction, and early warning capabilities for the next generation of ID surveillance. This expert consensus will serve as a reference for ID prevention and control as well as other related activities.

Inactivated SARS-CoV-2 Vaccine Booster Against Omicron Infection Among Quarantined Close Contacts.

Importance: Assessment of additional protection of a booster dose with an inactivated SARS-CoV-2 vaccine is key to developing vaccination strategies for billions of people worldwide who have received the primary 2-dose regimen. Objective: To estimate the relative effectiveness of a booster dose of an inactivated SARS-CoV-2 vaccine against Omicron infection. Design, Setting, and Participants: This cohort study was conducted among primary close contacts without previous SARS-CoV-2 infection identified in Shenzhen, China, between February and October 2022. Multiple strict nucleic acid testing and symptom surveillance for SARS-CoV-2 infection were regularly conducted during the 7-day centralized plus 7-day home-based quarantine. Exposure: A booster with an inactivated SARS-CoV-2 vaccine vs no booster after receipt of the primary 2-dose inactivated SARS-CoV-2 vaccine regimen. Main Outcomes and Measures: The primary outcomes were overall, symptomatic, and asymptomatic infections. Secondary outcomes were length of incubation and level of cycle threshold values. All the outcomes were assessed during the quarantine period. Results: Among 119 438 eligible participants (mean [SD] age, 37.6 [12.0] years; 66 201 men [55.4%]), 86 251 (72.2%) received a booster dose of an inactivated SARS-CoV-2 vaccine and 33 187 (27.8%) did not. A total of 671 cases infected with Omicron BA.2 were confirmed (464 symptomatic and 207 asymptomatic), and no severe infection or death events were observed. At a median (IQR) duration of 111 (75 to 134) days after booster vaccination, the relative effectiveness of a booster was 32.2% (95% CI, 11.3% to 48.2%) for overall infection, 23.8% (95% CI, -8.2% to 46.4%) for symptomatic infection, and 43.3% (95% CI, 12.3% to 63.3%) for asymptomatic infection. The effectiveness against overall infection changed nonlinearly over time following booster vaccination: 44.9% (95% CI, 4.9% to 68.1%) within 60 days, 50.4% (95% CI, 23.7% to 67.7%) at 61 to 120 days, 29.1% (95% CI, -4.8% to 52.1%) at 121 to 180 days, and 19.4% (95% CI, -14.4% to 43.2%) after 180 days (nonlinear P = .03). The effectiveness did not vary significantly according to the interval between booster vaccination and completion of primary vaccination. There was no association of booster vaccination with incubation or cycle threshold values. Conclusions and Relevance: In this cohort study, a booster dose of an inactivated SARS-CoV-2 vaccine provided additional moderate protection against mild infection for 120 days after receipt, but more research is needed to determine the optimal timing of a booster and its effectiveness in preventing severe infection for a longer duration.

Circulating Retinol-Binding Protein 4 Is Inversely Associated With Pancreatic ß-Cell Function Across the Spectrum of Glycemia.

OBJECTIVE: The aim of this study was to examine the association of circulating retinol-binding protein 4 (RBP4) levels with ß-cell function across the spectrum of glucose tolerance from normal to overt type 2 diabetes. RESEARCH DESIGN AND METHODS: A total of 291 subjects aged 35-60 years with normal glucose tolerance (NGT), newly diagnosed impaired fasting glucose or glucose tolerance (IFG/IGT), or type 2 diabetes were screened by a standard 2-h oral glucose tolerance test (OGTT) with the use of traditional measures to evaluate ß-cell function. From these participants, 74 subjects were recruited for an oral minimal model test, and ß-cell function was assessed with model-derived indices. Circulating RBP4 levels were measured by a commercially available ELISA kit. RESULTS: Circulating RBP4 levels were significantly and inversely correlated with ß-cell function indicated by the Stumvoll first-phase and second-phase insulin secretion indices, but not with HOMA of ß-cell function, calculated from the 2-h OGTT in 291 subjects across the spectrum of glycemia. The inverse association was also observed in subjects involved in the oral minimal model test with ß-cell function assessed by both direct measures and model-derived measures, after adjustment for potential confounders. Moreover, RBP4 emerged as an independent factor of the disposition index-total insulin secretion. CONCLUSIONS: Circulating RBP4 levels are inversely and independently correlated with ß-cell function across the spectrum of glycemia, providing another possible explanation of the linkage between RBP4 and the pathogenesis of type 2 diabetes.

Small dense LDL cholesterol is associated with metabolic syndrome traits independently of obesity and inflammation.

BACKGROUND: Small dense LDL cholesterol (sdLDL-c) has been established to be highly associated with metabolic disorder. However, the relationship between circulating sdLDL-c and the presence of metabolic syndrome (MetS) has not been fully established. METHODS: A total of 1065 Chinese males (45.07 ± 11.08 years old) without diabetes and general obesity was recruited into a population-based, cross-sectional study. The MetS was defined based on the updated National Cholesterol Education Program/ Adult Treatment Panel III criteria for Asian Americans. Serum sdLDL-c concentration was measured by a homogeneous assay method and its relationship with MetS and its traits was investigated. RESULTS: Serum sdLDL-c concentrations increased gradually with increasing numbers of MetS components (p < 0.001) and the proportion of patients with MetS increased gradually with increasing sdLDL-c levels (p for trend< 0.001). For the second, third, and fourth sdLDL-c quartiles versus the first, the OR (95% CI) for MetS were 4.47(2.41,8.28), 5.47(2.97,10.07) and 8.39(4.58,15.38) (p < 0.001 for trend) after multivariate adjustment. The stratified analysis conducted according to LDL-c levels showed that the OR between serum sdLDL-c levels and MetS was greater in those LDL-c levels lower than 3.3 mmol/L (OR = 22.97; 95% CI, 7.64-69.09) than in those LDL-c levels higher than 3.3 mmol/L (OR = 17.49; 95% CI, 4.43-68.98). Mediation analysis showed sdLDL-c mediated 38.6% of the association of waist circumference with triglycerides, while the association between sdLDL-c and MetS components did not mediate by hsCRP. CONCLUSIONS: This study found that high sdLDL-c concentrations were associated with the presence of MetS independently of central obesity and inflammation.

Association of Serum Retinol-Binding Protein 4 Levels and the Risk of Incident Type 2 Diabetes in Subjects With Prediabetes.

OBJECTIVE: To explore the association of serum retinol-binding protein 4 (RBP4) levels and risk for the development of type 2 diabetes in individuals with prediabetes. RESEARCH DESIGN AND METHODS: A population-based prospective study was conducted among 1,011 Chinese participants with prediabetes (average age 55.6 ± 7.2 years). Incident type 2 diabetes was diagnosed according to the American Diabetes Association 2010 criteria. Serum RBP4 levels were measured using a commercially available ELISA. We analyzed the association of serum RBP4 levels with the risk of incident type 2 diabetes using the Cox proportional hazards model. RESULTS: During a median follow-up period of 3.1 years, 153 participants developed incident type 2 diabetes. A U-shaped association was observed between serum RBP4 levels and the risk of incident type 2 diabetes, with the lowest risk in the RBP4 range of 31-55 µg/mL. Multivariate Cox regression model analysis showed that serum RBP4 levels <31 µg/mL and RBP4 levels >55 µg/mL were associated with an increased risk of incident type 2 diabetes. The adjusted hazard ratios (95% CI) were 2.01 (1.31-3.09) and 1.97 (1.32-2.93), respectively, after adjusting for age, sex, BMI, waist circumference, γ-glutamyltransferase, HOMA of insulin resistance index, fasting plasma glucose, 2-h plasma glucose, and glycated hemoglobin (HbA1c) levels. CONCLUSIONS: A U-shaped relationship exists between serum RBP4 levels and the risk of incident type 2 diabetes in subjects with prediabetes.