Dietary Inflammatory Index and diabetic retinopathy risk in US adults: findings from NHANES (2005-2008).

BACKGROUND: Inflammation is associated with the pathophysiology of diabetic retinopathy (DR). Within the framework of complete dietary patterns, the Dietary Inflammatory Index (DII) was formulated to evaluate the inflammatory properties inherent in a diet. The main purpose of the current study was to assess the relationship between DII and DR using National Health and Nutrition Examination Survey (NHANES). METHODS: The original sample size included 1,148 diabetes patients out of 2005-2008 NHANES surveys. Twenty-four-hour dietary consumptions were used to calculate the DII scores. Demographic characteristics and retina examinations were collected for the comparison between DR and non-DR groups in diabetes patients. The relationship between DII and DR was analyzed by a logistic regression model. RESULTS: 227 subjects (110 non-DR and 117 DR) were selected in the analyses by using undersampling method to balance the sample size. Compared with non-DR group, DR group had higher DII values (1.14 ± 0.29 vs. 1.49 ± 0.21, p = 0.32), higher levels of HbA1c (6.8 ± 1.1% vs. 7.7 ± 2.6%, p < 0.001), longer duration of diabetes (6.52 ± 12 years vs. 14 ± 11 years, p < 0.001). The odds rate (OR) of DII for DR from the logistic regression was 1.38 (95%CI 1.06-1.81, p < 0.001). HbA1c, diabetes duration and obesity were important influencing factors, and their ORs were 1.81 (95% CI:1.31-2.50), 1.12 (95%CI:1.04-1.20), 4.01 (95%CI:1.12-14.32), respectively. In addition, the most important dietary indices for DR were different across males and females. CONCLUSIONS: The current study demonstrates that a higher DII is associated with an increased risk of DR in US adults. Considering diet as a modifiable factor, limiting pro-inflammatory diets or encouraging an anti-inflammatory diet may be a promising and cost-effective method in the management of DR.

Surveillance of Plasmodium vivax transmission using serological models in the border areas of China-Myanmar.

BACKGROUND: To understand the Plasmodium vivax malaria transmission intensity and to assess the effectiveness of prevention and control measures taken along the China-Myanmar border, a catalytic model was used to calculate the seroconversion rate, an important indicator of malaria transmission intensity with high sensitivity, which is particularly useful in areas of low transmission. METHODS: Five counties in Yunnan Province bordering Myanmar were selected as survey sites, and subjects were obtained in each county by stratified random sampling in 2013-2014. Fingerstick blood was collected from each subject and tested for antibodies to P. vivax Merozoite Surface Protein 1-19 (PvMSP1-19) using indirect ELISA. A catalytic conversion model was used to assess the transmission intensity of P. vivax malaria based on the maximum likelihood of generating a community seroconversion rate. RESULTS: A total of 3064 valid blood samples were collected. Antibody levels were positively correlated with age. The seroconversion rate (SCR) values for each village were Luoping (0.0054), Jingqiao (0.0061), Longpen (0.0087), Eluo (0.0079), Banwang (0.0042) and Banbie (0.0046), respectively. CONCLUSION: Overall, the intensity of P. vivax malaria transmission in the border areas of Yunnan Province is low and not entirely consistent across counties. Consecutive serological surveys are needed to provide a sensitive evaluation of transmission dynamics and can help to confirm areas where infection is no longer present.

Alcohol Drinking and Amyotrophic Lateral Sclerosis: An Instrumental Variable Causal Inference.

Observational studies have shown alcohol drinking behaviors may be associated with the risk of amyotrophic lateral sclerosis (ALS), but contradictory findings have emerged, and whether such an association is causal is unclear. We here investigate the causal relationship between alcohol consumption and ALS. By leveraging instruments from large-scale genome-wide association studies, we performed a comprehensive Mendelian randomization analysis and found alcohol consumption was causally associated with ALS, leading to ∼1.5-fold (95% confidence interval = 1.4-3.4) higher risk of ALS for each ∼10g/day increase in alcohol intake. Our findings suggest accumulative alcohol consumption may serve as a crucial risk factor in the pathogenesis of ALS. ANN NEUROL 2020 ANN NEUROL 2020;88:195-198.

Phthalate metabolites and sex steroid hormones in relation to obesity in US adults: NHANES 2013-2016.

Background: Obesity and metabolic syndrome pose significant health challenges in the United States (US), with connections to disruptions in sex hormone regulation. The increasing prevalence of obesity and metabolic syndrome might be associated with exposure to phthalates (PAEs). Further exploration of the impact of PAEs on obesity is crucial, particularly from a sex hormone perspective. Methods: A total of 7780 adult participants in the National Health and Nutrition Examination Survey (NHANES) from 2013 to 2016 were included in the study. Principal component analysis (PCA) coupled with multinomial logistic regression was employed to elucidate the association between urinary PAEs metabolite concentrations and the likelihood of obesity. Weighted quartiles sum (WQS) regression was utilized to consolidate the impact of mixed PAEs exposure on sex hormone levels (total testosterone (TT), estradiol and sex hormone-binding globulin (SHBG)). We also delved into machine learning models to accurately discern obesity status and identify the key variables contributing most to these models. Results: Principal Component 1 (PC1), characterized by mono(2-ethyl-5-carboxypentyl) phthalate (MECPP), mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), and mono(2-ethyl-5-oxohexyl) phthalate (MEOHP) as major contributors, exhibited a negative association with obesity. Conversely, PC2, with monocarboxyononyl phthalate (MCNP), monocarboxyoctyl phthalate (MCOP), and mono(3-carboxypropyl) phthalate (MCPP) as major contributors, showed a positive association with obesity. Mixed exposure to PAEs was associated with decreased TT levels and increased estradiol and SHBG. During the exploration of the interrelations among obesity, sex hormones, and PAEs, models based on Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms demonstrated the best classification efficacy. In both models, sex hormones exhibited the highest variable importance, and certain phthalate metabolites made significant contributions to the model's performance. Conclusions: Individuals with obesity exhibit lower levels of TT and SHBG, accompanied by elevated estradiol levels. Exposure to PAEs disrupts sex hormone levels, contributing to an increased risk of obesity in US adults. In the exploration of the interrelationships among these three factors, the RF and XGBoost algorithm models demonstrated superior performance, with sex hormones displaying higher variable importance.

An interactive web application to identify early Parkinsonian non-tremor-dominant subtypes.

BACKGROUND: Parkinson's disease (PD) patients with tremor-dominant (TD) and non-tremor-dominant (NTD) subtypes exhibit heterogeneity. Rapid identification of different motor subtypes may help to develop personalized treatment plans. METHODS: The data were acquired from the Parkinson's Disease Progression Marker Initiative (PPMI). Following the identification of predictors utilizing recursive feature elimination (RFE), seven classical machine learning (ML) models, including logistic regression, support vector machine, decision tree, random forest, extreme gradient boosting, etc., were trained to predict patients' motor subtypes, evaluating the performance of models through the area under the receiver operating characteristic curve (AUC) and validating by the follow-up data. RESULTS: The feature subset engendered by RFE encompassed 20 features, comprising some clinical assessments and cerebrospinal fluid α-synuclein (CSF α-syn). ML models fitted in the RFE subset performed better in the test and validation sets. The best performing model was support vector machines with the polynomial kernel (P-SVM), achieving an AUC of 0.898. Five-fold repeated cross-validation showed the P-SVM model with CSF α-syn performed better than the model without CSF α-syn (P = 0.034). The Shapley additive explanation plot (SHAP) illustrated that how the levels of each feature affect the predicted probability as NTD subtypes. CONCLUSION: An interactive web application was developed based on the P-SVM model constructed from feature subset by RFE. It can identify the current motor subtypes of PD patients, making it easier to understand the status of patients and develop personalized treatment plans.

Prevalence and related factors of sleep quality among Chinese undergraduates in Jiangsu Province: multiple models' analysis.

Background and aims: In China, a significant number of undergraduates are experiencing poor sleep quality. This study was designed to investigate the prevalence of poor sleep quality and identify associated factors among undergraduates in Jiangsu Province, China. Methods: A total of 8,457 participants were collected in 2022 using whole-group convenience sampling. The factors studied included basic demographics, family and social support, personal lifestyles, physical and mental health, mobile phone addiction index (MPAI), and the Connor-Davidson resilience scale (CD-RISC). The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality. Four models, including weighted multiple linear regression, binary logistic regression, weighted linear mixed model, and logistic regression with random effects, were applied to identify associated factors for sleep quality. Results: Of the 8,457 participants analyzed, 26.64% (2,253) were classified into the poor sleep quality group with a PSQI score >7. No significant relationship was found between sleep quality and gender, native place, economic level of family, physical exercise, dormitory light, dormitory hygiene, and amativeness matter. Risk factors for sleep quality identified by the four models included lower CD-RISC, higher MPAI, fourth grade or above, smoking, drinking, greater academic pressure, greater employment pressure, roommate sleeping late, noisy dormitory, poorer physical health status, poorer mental health status, and psychological counseling. Conclusions: These findings provide valuable insights for university administrators, enabling them to better understand the risk factors associated with poor sleep quality in undergraduates. By identifying these factors, administrators can provide targeted intervention measures and counseling programs to improve students' sleep quality.

CPEB2 inhibit cell proliferation through upregulating p21 mRNA stability in glioma.

Glioma is the most common primary malignant brain tumor in adults and remains an incurable disease at present. Thus, there is an urgent need for progress in finding novel molecular mechanisms that control the progression of glioma which could be used as therapeutic targets for glioma patients. The RNA binding protein cytoplasmic polyadenylate element-binding protein 2 (CPEB2) is involved in the pathogenesis of several tumors. However, the role of CPEB2 in glioma progression is unknown. In this study, the functional characterization of the role and molecular mechanism of CPEB2 in glioma were examined using a series of biological and cellular approaches in vitro and in vivo. Our work shows CPEB2 is significantly downregulated in various glioma patient cohorts. Functional characterization of CPEB2 by overexpression and knockdown revealed that it inhibits glioma cell proliferation and promotes apoptosis. CPEB2 exerts an anti-tumor effect by increasing p21 mRNA stability and inducing G1 cell cycle arrest in glioma. Overall, this work stands as the first report of CPEB2 downregulation and involvement in glioma pathogenesis, and identifies CPEB2 as an important tumor suppressor gene through targeting p21 in glioma, which revealed that CPEB2 may become a promising predictive biomarker for prognosis in glioma patients.

Epidemiological and clinical characteristics of COVID-19 reinfection during the epidemic period in Yangzhou city, Jiangsu province.

Background: With the continuous progress of the epidemic of coronavirus disease 2019 (COVID-19) infection and the constant mutation of the virus strain, reinfection occurred in previously infected individuals and caused waves of the epidemic in many countries. Therefore, we aimed to explore the characteristics of COVID-19 reinfection during the epidemic period in Yangzhou and provide a scientific basis for assessing the COVID-19 situation and optimizing the allocation of medical resources. Methods: We chose previously infected individuals of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reported locally in Yangzhou city from January 2020 to November 30, 2022. A telephone follow-up of cases was conducted from February to March 2023 to collect the COVID-19 reinfection information. We conducted a face-to-face survey on that who met the definition of reinfection to collect information on clinical symptoms, vaccination status of COVID-19, and so on. Data were analyzed using SPSS 19.0. Results: Among the 999 eligible respondents (92.24% of all the participants), consisting of 42.28% males and 57.72% females, the reinfection incidence of females was significantly higher than that of male cases (χ2 = 5.197, P < 0.05); the ages of the respondents ranged from 1 to 91 years, with the mean age of 42.28 (standard deviation 22.73) years; the most of the sufferers were infected initially with Delta variant (56.88%), followed by the Omicron subvariants BA.1/BA.2 (39.52%). Among all the eligible respondents, 126 (12.61%) reported COVID-19 reinfection appearing during the epidemic period, and the intervals between infections were from 73 to 1,082 days. The earlier the initial infection occurred, the higher the reinfection incidence and the reinfection incidence was significantly increased when the interval was beyond 1 year (P < 0.01) .119 reinfection cases (94.4%) were symptomatic when the most common symptoms included fever (65.54%) and cough (61.34%); compared with the initial infection cases, the proportion of clinical symptoms in the reinfected cases was significantly higher (P < 0.01). The reinfection incidence of COVID-19 vaccination groups with different doses was statistically significant (P < 0.01). Fewer reinfections were observed among the respondents with three doses of COVID-19 vaccination compared to the respondents with two doses (χ2 = 14.595, P < 0.001) or without COVID-19 vaccination (χ2 =4.263, P = 0.039). Conclusion: After the epidemic period of COVID-19, the reinfection incidence varied with different types of SARS-CoV-2 strains. The reinfection incidence was influenced by various factors such as virus characteristics, vaccination, epidemic prevention policies, and individual variations. As the SARS-CoV-2 continues to mutate, vaccination and appropriate personal protection have practical significance in reducing the risk of reinfection.

Associations between serum copper, zinc, selenium level and sex hormones among 6-19 years old children and adolescents in NHANES 2013-2016.

Copper, zinc, and selenium are essential trace elements for human and have important effects on sex hormones. There are few studies on the relationships between the three trace elements and sex hormones. Therefore, our study aimed to investigate the relationships between serum copper, zinc, selenium and testosterone, estradiol, SHBG using data from the National Health and Nutrition Examination Survey (NHANES) 2013-2016 in participants 6-19 years. 1097 participants were enrolled and stratified into male/female children and adolescents. Weighted linear regression models combined regression diagnosis were used to estimate the relationships between trace elements and sex hormones according to the different stratifications. Our results showed that copper was inversely associated with testosterone and estradiol but positively correlated with SHBG. Zinc had positive relationships with testosterone in male adolescents and female children but an inverse relationship with testosterone in female adolescents. Furthermore, a negative association was observed between zinc and SHBG. With the rise of selenium level, testosterone and estradiol were increased but SHBG was decreased. In general, this study used more standardized statistical methods to investigate the relationships between copper, zinc, selenium and testosterone, estradiol, SHBG. Further study should pay attention to some details in statistical methods.

Estimation of Plasmodium falciparum transmission using multiepitope chimeric antigen in the postelimination phase in Yunnan, China.

BACKGROUND: The continuous monitoring of malaria transmission intensity is still required to maintain elimination status after reaching the malaria elimination stage. In this study, serological surveillance with multiepitope artificial antigen was used to assess the transmission of Plasmodium falciparum in Yunnan, China, where malaria elimination has just been achieved, to provide data to support malaria control in the postelimination period. METHODS: Samples were collected in three border counties and one inland county in Yunnan Province in 2016 using a stratified whole-group sampling method. Fingerstick blood was collected from all participants, and antibodies to Malaria Random Constructed Antigen-1 (M. RCAg-1) were detected by indirect ELISA. The transmission intensity of P. falciparum malaria was estimated using a catalytic conversion model based on the maximum likelihood of generating a community seroconversion rate (SCR). RESULTS: A total of 5566 samples were collected. There was no statistically significant difference in antibody level between the inland county and the nonendemic area, but the antibody level in border counties was significantly higher than those in the inland county and the nonendemic control area. No seropositive cases were found in Yanjin County, and the seropositivity rate increased with age in the three border counties. The highest intensity of P. falciparum malaria transmission was in Zhenkang County (SCR = 0.0030, CI: 0.0029, 0.0031), followed by Gengma County (SCR = 0.0013, CI: 0.0012, 0.0015) and Yingjiang County (SCR = 0.00088, CI: 0.00083, 0.00090). CONCLUSION: The transmission intensity of P. falciparum malaria in Yunnan Province has obviously decreased in recent years, but for the border areas where malaria has just been eliminated, the transmission intensity will not immediately drop to zero, and it still needs to be monitored for a period of time to maintain malaria elimination status.

Potential Genes Associated with the Survival of Lung Adenocarcinoma Were Identified by Methylation.

BACKGROUND: Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer. The purpose of this study is to search for genes related to the prognosis of LUAD through methylation based on a linear mixed model (LMM). METHODS: Gene expression, methylation, and survival data of LUAD patients were downloaded from the TCGA database. Based on the LMM model, the GEMMA algorithm was used to screen the predictive genes related to LUAD survival. The Cox model was used to further screen the predicted genes, and then, protein-protein interaction (PPI) network was constructed. Through the software plugin Cytoscape MCODE 3.8.0, the most closely related genes in the PPI network module were selected for in-depth biological function analysis to further explore the interaction and correlation between genes. RESULTS: We screened out 97 predictive genes from 18,834 genes and eliminated one gene associated with lung squamous cell carcinoma from previous studies, leaving 96 genes. The MCODE and the Kaplan-Meier curve analysis were used to finally identify two genes ASB16 and NEDD4 that are related to the prognosis of LUAD. CONCLUSIONS: The newly identified two genes associated with the prognosis of LUAD may provide a basis for the treatment of patients.

Multiple-Tissue Integrative Transcriptome-Wide Association Studies Discovered New Genes Associated With Amyotrophic Lateral Sclerosis.

Genome-wide association studies (GWAS) have identified multiple causal genes associated with amyotrophic lateral sclerosis (ALS); however, the genetic architecture of ALS remains completely unknown and a large number of causal genes have yet been discovered. To full such gap in part, we implemented an integrative analysis of transcriptome-wide association study (TWAS) for ALS to prioritize causal genes with summary statistics from 80,610 European individuals and employed 13 GTEx brain tissues as reference transcriptome panels. The summary-level TWAS analysis with single brain tissue was first undertaken and then a flexible p-value combination strategy, called summary data-based Cauchy Aggregation TWAS (SCAT), was proposed to pool association signals from single-tissue TWAS analysis while protecting against highly positive correlation among tests. Extensive simulations demonstrated SCAT can produce well-calibrated p-value for the control of type I error and was often much more powerful to identify association signals across various scenarios compared with single-tissue TWAS analysis. Using SCAT, we replicated three ALS-associated genes (i.e., ATXN3, SCFD1, and C9orf72) identified in previous GWASs and discovered additional five genes (i.e., SLC9A8, FAM66D, TRIP11, JUP, and RP11-529H20.6) which were not reported before. Furthermore, we discovered the five associations were largely driven by genes themselves and thus might be new genes which were likely related to the risk of ALS. However, further investigations are warranted to verify these results and untangle the pathophysiological function of the genes in developing ALS.

Jackknife Model Averaging Prediction Methods for Complex Phenotypes with Gene Expression Levels by Integrating External Pathway Information.

MOTIVATION: In the past few years many prediction approaches have been proposed and widely employed in high dimensional genetic data for disease risk evaluation. However, those approaches typically ignore in model fitting the important group structures that naturally exists in genetic data. METHODS: In the present study, we applied a novel model-averaging approach, called jackknife model averaging prediction (JMAP), for high dimensional genetic risk prediction while incorporating pathway information into the model specification. JMAP selects the optimal weights across candidate models by minimizing a cross validation criterion in a jackknife way. Compared with previous approaches, one of the primary features of JMAP is to allow model weights to vary from 0 to 1 but without the limitation that the summation of weights is equal to one. We evaluated the performance of JMAP using extensive simulation studies and compared it with existing methods. We finally applied JMAP to four real cancer datasets that are publicly available from TCGA. RESULTS: The simulations showed that compared with other existing approaches (e.g., gsslasso), JMAP performed best or is among the best methods across a range of scenarios. For example, among 14 out of 16 simulation settings with PVE = 0.3, JMAP has an average of 0.075 higher prediction accuracy compared with gsslasso. We further found that in the simulation, the model weights for the true candidate models have much smaller chances to be zero compared with those for the null candidate models and are substantially greater in magnitude. In the real data application, JMAP also behaves comparably or better compared with the other methods for continuous phenotypes. For example, for the COAD, CRC, and PAAD datasets, the average gains of predictive accuracy of JMAP are 0.019, 0.064, and 0.052 compared with gsslasso. CONCLUSION: The proposed method JMAP is a novel model-averaging approach for high dimensional genetic risk prediction while incorporating external useful group structures into the model specification.