Introduction: Diabetes mellitus (DM) and diabetic retinopathy (DR) increase the global burden. Since their pathogenesis is complex, it is necessary to use the biopsychosocial model to discover the most effective strategies. The study is aimed to investigate the psycho-behavioral factors of DR and confirm the discrepancies from previous studies.Research design and methods: The study comprised seven cycles of cross-sectional data of the National Health and Nutrition Examination Survey (NHANES) from 2005–2006 to 2017–2018. Samples of DM were selected from this complex multi-stage probability sample and divided into the non-DR and DR groups, where 4,426 samples represented 18,990,825 individuals after weighting. This study comprehensively explored the biological, social, and psychological risk factors of DR, among which the biological factors included blood pressure, blood routine, HbA1c%, blood glucose, the duration of DM, family history, comorbidities, and treatment methods. Social aspects include gender, education, income, insurance, smoking, drinking, sleep habits, and recreational activities. The Patient Health Questionnaire-9 (PHQ-9) was used to assess the psychological state. Taylor series regression was used to examine the connection between factors and DR.Results: Men accounted for 55.5% of the DR group (P = 0.0174). Lymphocyte count, insulin treatment, heart failure, stroke, liver condition, and renal failure showed significant differences in DR (P < 0.05). The incidence of depression in DR was 40.5%. Mild to moderate depression [odds ratio was associated with DR [(OR) = 1.37, 95% confidence interval (CI): 1.06–1.79], but there was no statistical difference in severe depression (OR = 1.34, 95% CI: 0.83–2.17). Although ≤ 6 h of sleep was associated with DR (OR = 1.38, 95% CI: 1.01–1.88), we found no statistical differences in alcohol consumption, recreational activities, or sedentary time between the two groups in our current study (P > 0.05).Conclusions: The biological risk factors of DR are significant. It showed that stroke is associated with DR, and retinal exams have the potential value as a screening tool for the brain. Besides, psycho-behavioral risk factors of DR should also be paid attention. Our study highlights that mild and moderate depression and ≤6 h of sleep are distinguishably associated with DM complicated with DR. It indicates that psycho-behavioral risk factors confer a vital influence on diabetic health care and DR.

Background: Extensive evidence suggests a link between alterations in serum metabolite composition and various autoimmune diseases (ADs). Nevertheless, the causal relationship underlying these correlations and their potential utility as dependable biomarkers for early AD detection remain uncertain.Objective: The objective of this study was to employ a two-sample Mendelian randomization (MR) approach to ascertain the causal relationship between serum metabolites and ADs. Additionally, a meta-analysis incorporating data from diverse samples was conducted to enhance the validation of this causal effect.Materials and methods: A two-sample MR analysis was performed to investigate the association between 486 human serum metabolites and six prevalent autoimmune diseases: systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), inflammatory bowel disease (IBD), dermatomyositis (DM), type 1 diabetes (T1D), and celiac disease (CeD). The inverse variance weighted (IVW) model was employed as the primary analytical technique for the two-sample MR analysis, aiming to identify blood metabolites linked with autoimmune diseases. Independent outcome samples were utilized for further validation of significant blood metabolites. Additional sensitivity analyses, including heterogeneity test, horizontal pleiotropy test, and retention rate analysis, were conducted. The results from these analyses were subsequently meta-integrated. Finally, metabolic pathway analysis was performed using the KEGG and Small Molecule Pathway Databases (SMPD).Results: Following the discovery and replication phases, eight metabolites were identified as causally associated with various autoimmune diseases, encompassing five lipid metabolism types: 1-oleoylglycerophosphoethanolamine, 1-arachidonoylglycerophosphoethanolamine, 1-myristoylglycerophosphocholine, arachidonate (20:4 n6), and glycerol. The meta-analysis indicated that three out of these eight metabolites exhibited a protective effect, while the remaining five were designated as pathogenic factors. The robustness of these associations was further confirmed through sensitivity analysis. Moreover, an investigation into metabolic pathways revealed a significant correlation between galactose metabolism and autoimmune diseases.Conclusion: This study revealed a causal relationship between lipid metabolites and ADs, providing novel insights into the mechanism of AD development mediated by serum metabolites and possible biomarkers for early diagnosis.

Biostatistics is a key but challenging subject in medical curricula that is usually delivered via a didactic approach in China. However, whether it is the best teaching approach to improve the learner’s competency, especially for medical postgraduates is yet to be proved. Therefore, a block curriculum design was initially developed to provide selective education to the postgraduates towards the professional career of their interest. A questionnaire was designed to assess the students’ perceptions toward biostatistics as these affective factors might impact the learning process. Thus, the present study aimed to detect whether the new block curriculum design could promote the students’ positive perceptions and further improve the course achievement. This cohort study investigated and assessed the perceptions toward biostatistics of the first-year postgraduates undergoing traditional teaching and block teaching, respectively. Structural equation modeling was applied to explore the association between perception and course achievement in the block teaching group. With a response rate of 97.84 and 96.67% from the two cohorts respectively, 499 block teaching postgraduates had more positive perceptions as compared to 465 traditionally teaching postgraduates with Likert 5-point agreement response mean of 3.50 vs. 3.31 for course value, 3.66 vs. 2.97 for course comment, and 4.29 vs. 4.10 for expectation. Moreover, block teaching students presented superior confidence about academic statistical knowledge, and therefore, 77.96% of them approved of the new teaching approach. Age, specialty, research experience, logical thinking capacity, mathematical basics, and computer basics might influence the postgraduates’ self-assessment ability (all P < 0.05). Structural equation modeling confirmed a positive correlation between perceptions and the course achievements with a reasonable fit. The block curriculum design in the biostatistics course improved the postgraduates’ positive perception and may have had a positive role in improving postgraduates’ achievement in learning biostatistics.

Despite the numerous advantages of mastering biostatistics, medical students generally perceive biostatistics as a difficult and challenging subject and even experience anxiety during the courses. Evidence for the correlation between students’ academic achievements and their attitudes, indicating that attitudes at the beginning of the biostatistics course may affect cognitive competence at the end of the course and subsequently influence student academic performance. However, there are current disagreements regarding the measurement and evaluation of attitudes related to statistics. Thus, there is a need for standard instruments to assess them. This study was conducted to develop a Chinese version of the Survey of Attitudes Toward Statistics (SATS-36) in order to acquire a valid instrument to measure medical students’ attitudes toward biostatistics under Chinese medical educational background. The Chinese version SATS-36 was developed through translation and back-translation of the original scale, with subsequent revisions based on expert advice to ensure the most appropriate item content. The local adaption was performed with a cohort of 1709 Chinese-speaking medical undergraduate and graduate students enrolled in biostatistics courses. And then, the reliability, validity and discrimination of the questionnaires were evaluated through correlation coefficient calculation, factor analysis, parallel analysis and other methods. The Chinese version SATS-36 consisted of 36 items and loaded a five-factor structure by factor analysis, which offered an alternative similar but not equal to that original six-factor structure. The cumulative variance contribution rate was 62.20%, the Cronbach’s α coefficient was 0.908, the Guttman split-half reliability coefficient was 0.905 and the test–retest reliability coefficient was 0.752. Discriminant analysis revealed small to large significant differences in the five attitude subscales. The Chinese version SATS-36 with good validity and reliability in this study can be used to evaluate the learning framework of Chinese medical students.

Although many methods have been proposed on the overall survival estimation in randomized trials permitting treatment switching after the progressive disease (PD), the cured subgroup of patients within these trials has not been fully considered. These cured patients would never experience PD and subsequent risk of treatment switching, yet they may suffer death hazard similar to those without the disease. Due to the mix of the cured subgroup, existing methods may yield biased effect estimation for the uncured patients between treatment groups. To address this limitation, we propose a multistate transition model that integrates multi-states of the cure, PD, treatment switching, and death during trials. In this model, the cure probability for all the patients and the death hazard of the cured subgroup are modeled separately. Meanwhile, the semi-competing risks model is used for the treatment effect evaluation on the uncured patients through transitional hazards between states of PD, treatment switching, and death. The particle swarm optimization algorithm is employed to estimate the model parameters. Extensive simulation studies have been conducted to assess the performance of the proposed multistate model in comparison with existing treatment switching adjustment methods. The results show that the treatment effect estimations of our proposed model are more accurate across all scenarios. Moreover, the illustration based on a simulated diffuse large B-cell lymphoma trial demonstrates the applicability and advantages of the proposed model. The robustness of the proposed multistate transition model enables it to accurately estimate the treatment effect in trials that involve a cured subgroup and the treatment switching after PD.

Background Despite the advantages of mastering biostatistics, medical students generally perceive biostatistics as a difficult and challenging subject and even experience anxiety during the courses. Evidence for the relationships between achievements and students' attitudes have been proven that attitudes implying cognitive competence at the beginning of the biostatistics course, could affect attitudes at the end of the course that, in turn, influenced student performance. However, there are disagreements in the results of measuring attitudes. Thus, we need acknowledged standard instruments to assess them. This study was conducted to develop a Chinese version of the Survey of Attitudes Toward Statistics (SATS-36) in order to acquire a valid instrument to measure attitudes toward biostatistics under Chinese medical educational background. Methods The Chinese version SATS-36 was developed by translation and back-translation of the original scale, followed by revision according to expert advice on the most appropriate item content. The local adaption was performed on a cohort of 1709 Chinese-speaking medical undergraduate and graduate students enrolled in biostatistics courses. And then, the reliability, validity and discrimination of the questionnaires were evaluated through correlation coefficient calculation, factor analysis, parallel analysis and other methods. Results The Chinese version SATS-36 consisted of 36 items and loaded a five-factor structure by factor analysis, which offered an alternative similar but not equal to that original six-factor structure. The cumulative variance contribution rate was 62.20%, the Cronbach's α coefficient was 0.908, the Guttman split-half reliability coefficient was 0.905 and the test–retest reliability coefficient was 0.752. Discriminant analysis revealed small to large significant differences in the five attitude subscales. Conclusions The Chinese version SATS-36 with good validity and reliability in this study can be used to evaluate the learning framework of Chinese medical students.