Gut microbial features may influence antiviral IgG levels after vaccination against viral respiratory infectious diseases: the evidence from two-sample bidirectional mendelian randomization.

BACKGROUND: Vaccination is effective in preventing viral respiratory infectious diseases through protective antibodies and the gut microbiome has been proven to regulate human immunity. This study explores the causal correlations between gut microbial features and serum-specific antiviral immunoglobulin G (IgG) levels. METHODS: We conduct a two-sample bidirectional Mendelian randomization (MR) analysis using genome-wide association study (GWAS) summary data to explore the causal relationships between 412 gut microbial features and four antiviral IgG (for influenza A, measles, rubella, and mumps) levels. To make the results more reliable, we used four robust methods and performed comprehensive sensitivity analyses. RESULTS: The MR analyses revealed 26, 13, 20, and 18 causal associations of the gut microbial features influencing four IgG levels separately. ​Interestingly, ten microbial features, like genus Collinsella, species Bifidobacterium longum, and the biosynthesis of L-alanine have shown the capacity to regulate multiple IgG levels with consistent direction (rise or fall). The ​reverse MR analysis suggested several potential causal associations of IgG levels affecting microbial features. CONCLUSIONS: The human immune response against viral respiratory infectious diseases could be modulated by changing the abundance of gut microbes, which provided new approaches for the intervention of viral respiratory infections.

Effect of multicomponent intervention on malnutrition in older adults: A multicenter randomized clinical trial.

BACKGROUND & AIMS: Malnutrition is a significant geriatric syndrome (GS) prevalent in older adults and seriously affects patient prognosis and quality of life. We assessed the impact of the multicomponent intervention of health education, dietary advice, and exercise with oral nutritional supplementation (ONS) on nutritional status, body composition, physical functions, and quality of life. METHODS: This multicenter randomized clinical trial (RCT) was performed from April 2021 to April 2022. The intervention lasted for 12 weeks, and 99 older adults with malnutrition or at risk of malnutrition were enrolled in six nursing homes. All participants were randomly assigned to the control (health education plus standard diet plus exercise) or research (health education plus standard diet plus exercise plus ONS) group. The research group consumed ONS (244 kcal, 9.8g protein, and 9.6g fat per time) twice a day between meals. The primary outcomes were changes in the nutritional status and body composition from baseline to 12 weeks. The secondary outcomes were changes in physical function, quality of life and nutritional associated other blood markers. RESULTS: For primary outcomes, after 12 weeks, body weight increased similarly in both treatment arms (time × treatment effect, P > 0.05). There were no between-group differences in body mass index (BMI) or mini nutritional assessment tool-short form (MNA-SF) scores (time × treatment effects, P > 0.05). The MNA-SF score from 11.0 (10.5, 12.0) to 13.0 (11.0, 13.0) in the research group and from 11.0 (10.0, 12.0) to 12.0 (11.0, 13.0) in the control group (both P < 0.05). There were no between-group differences in the skeletal muscle mass index (SMI), fat-free mass index (FFMI), appendicular skeletal muscle mass (ASMM), fat mass (FAT), or leg muscle mass (LMM) (time × treatment effects, P > 0.05). Both groups showed similar and highly significant increases in SMI, FFMI, and LMM after (P < 0.05). The research group showed an increase in fat-free mass (FFM) and ASMM and a decrease in the percent of body fat (PBF) and waist circumference (WC) (P < 0.05). For secondary outcomes, There were no between-group differences in grip strength, short physical performance battery (SPPB), 6-min walking distance (6MWD), activities of daily living (ADL), instrumental activities of daily living (IADL), frailty status (FRAIL), mini-mental state examination (MMSE), Tinetti, geriatric depression scale-15 (GDS-15), or 12-item short form survey (SF-12) (time × treatment effects, P > 0.05). Although there was no significant difference, the 6MWD changed differentially between the two treatment arms during the study period in favor of the research group. Although not significant, SF-12 scores improved after 12 weeks in both groups. No between-group differences were observed in prealbumin (PRE), c-reactive protein (CRP), vitamin D (VIT-D), insulin-like growth factor 1 (IGF-1), alanine transaminase (ALT), aspartate aminotransferase (AST), serum creatinine (Scr), interleukin-6 (IL-6), interleukin-10 (IL-10), tumor necrosis factor-α (TNF-α), insulin, and adiponectin levels (time × treatment effects, P > 0.05). Insulin and adiponectin levels were significantly higher in the control group (P < 0.05). CONCLUSION: The twelve-week multicomponent intervention improved the nutritional status of older people in China at risk of malnutrition. ONS may enhance the effects of exercise on muscle mass. This clinical trial was registered (https://www. CLINICALTRIALS: gov). The trial number is ChiCTR2000040343.

Leveraging baseline transcriptional features and information from single-cell data to power the prediction of influenza vaccine response.

Introduction: Vaccination is still the primary means for preventing influenza virus infection, but the protective effects vary greatly among individuals. Identifying individuals at risk of low response to influenza vaccination is important. This study aimed to explore improved strategies for constructing predictive models of influenza vaccine response using gene expression data. Methods: We first used gene expression and immune response data from the Immune Signatures Data Resource (IS2) to define influenza vaccine response-related transcriptional expression and alteration features at different time points across vaccination via differential expression analysis. Then, we mapped these features to single-cell resolution using additional published single-cell data to investigate the possible mechanism. Finally, we explored the potential of these identified transcriptional features in predicting influenza vaccine response. We used several modeling strategies and also attempted to leverage the information from single-cell RNA sequencing (scRNA-seq) data to optimize the predictive models. Results: The results showed that models based on genes showing differential expression (DEGs) or fold change (DFGs) at day 7 post-vaccination performed the best in internal validation, while models based on DFGs had a better performance in external validation than those based on DEGs. In addition, incorporating baseline predictors could improve the performance of models based on days 1-3, while the model based on the expression profile of plasma cells deconvoluted from the model that used DEGs at day 7 as predictors showed an improved performance in external validation. Conclusion: Our study emphasizes the value of using combination modeling strategy and leveraging information from single-cell levels in constructing influenza vaccine response predictive models.

Integrating single-cell RNA sequencing data to genome-wide association analysis data identifies significant cell types in influenza A virus infection and COVID-19.

With the global pandemic of COVID-19, the research on influenza virus has entered a new stage, but it is difficult to elucidate the pathogenesis of influenza disease. Genome-wide association studies (GWASs) have greatly shed light on the role of host genetic background in influenza pathogenesis and prognosis, whereas single-cell RNA sequencing (scRNA-seq) has enabled unprecedented resolution of cellular diversity and in vivo following influenza disease. Here, we performed a comprehensive analysis of influenza GWAS and scRNA-seq data to reveal cell types associated with influenza disease and provide clues to understanding pathogenesis. We downloaded two GWAS summary data, two scRNA-seq data on influenza disease. After defining cell types for each scRNA-seq data, we used RolyPoly and LDSC-cts to integrate GWAS and scRNA-seq. Furthermore, we analyzed scRNA-seq data from the peripheral blood mononuclear cells (PBMCs) of a healthy population to validate and compare our results. After processing the scRNA-seq data, we obtained approximately 70 000 cells and identified up to 13 cell types. For the European population analysis, we determined an association between neutrophils and influenza disease. For the East Asian population analysis, we identified an association between monocytes and influenza disease. In addition, we also identified monocytes as a significantly related cell type in a dataset of healthy human PBMCs. In this comprehensive analysis, we identified neutrophils and monocytes as influenza disease-associated cell types. More attention and validation should be given in future studies.

Association of frailty with clinical outcomes in chronic obstructive pulmonary disease: A retrospective longitudinal cohort study.

Background: Frailty is a clinical syndrome and common phenomenon in the elderly, particularly when it coexists with chronic obstructive pulmonary disease (COPD). However, the relationship between frailty and its prognosis in COPD patients has not been clearly elucidated. Methods: We collected electronic data of inpatients who were diagnosed with COPD in the First Affiliated Hospital with Nanjing Medical University (NJMU) from January 2018 to December 2020. In further, we divided them into different groups based on Frailty Index Common Laboratory Tests (FI-LAB). Binary logistic regression was performed to analyze the risk factors associated with COPD. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were applied to validate FI-LAB's value in prognosis. Primary clinical outcomes contained 30-day mortality and readmission. Moreover, we also compared the prognositic value of FI-LAB with Hospital Frailty Risk Score (HRS) by ROC curve, significance was set at P < 0·05. Findings: The final study included 826 COPD patients, among of them, 30-day mortality and readmission of frailty group was 11·2%, 25·9%, the robust group was 4·3%, 16·0%, and p value was 0·001, 0·004 respectively. Multivariate analysis revealed that smoking, CCI≥3, oral drug≥5, pneumonia, abnormal lymphocyte, abnormal haemoglobin were independent risk factors with frailty. As for the prediction of FI-LAB about frailty in 30-day mortality, the AUC was 0·832, and 30-day readmission was 0·661. As for the prognositic value, FI-LAB and HRS showed no difference in predicting clinical outcomes. Interpretation: COPD individuals have a higher rate of frailty and pre-frailty. There exists a strong correlation between frailty and 30-day mortality in COPD patients, and FI-LAB has good prognostic value in clinical outcomes of patients with COPD.

Prognostic significance of frailty in hospitalized elderly patients with community-acquired pneumonia: a retrospective cohort study.

BACKGROUND: Frailty is associated with poor prognosis in a wide range of illnesses. However, its prognostic implications for older patients with community-acquired pneumonia (CAP) are not adequately addressed. METHODS: In this study, patients were classified into 3 groups according to the frailty index based on standard laboratory tests (FI-Lab) score: robust (FI-Lab < 0.2), pre-frail (FI-Lab 0.2-0.35), and frail (FI-Lab ≥ 0.35). The relationships between frailty and all-cause mortality and short-term clinical outcomes (length of stay, duration of antibiotic therapy, in-hospital mortality) were examined. RESULTS: Finally, 1164 patients were included, the median age was 75 years (interquartile range: 69, 82), and 438 patients (37.6%) were women. According to FI-Lab, 261(22.4%), 395(33.9%), and 508(43.6%) were robust, pre-frail, and frail. After adjustment for confounding variables, frailty was independently associated with prolonged antibiotic treatment (p = 0.037); pre-frailty and frailty were independently associated with longer inpatient days (p < 0.05 for both). The risk of in-hospital mortality was independently increased in frail patients (HR = 5.01, 95% CI = 1.51-16.57, p = 0.008) but not pre-frail patients (HR = 2.87, 95% CI = 0.86-9.63, p = 0.088) compared to robust patients. During a median follow-up of 33.9 months (interquartile range: 32.8 to 35.1 months), 408 (35.1%) patients died, of whom 29 (7.1%) were robust, 112 (27.5%) were pre-frail, and 267 (65.9%) were frail. Compared to robust patients, frail and pre-frail were significantly associated with increased risk for all-cause death (HR = 4.29, 95%CI: 1.78-10.35 and HR = 2.42 95%CI: 1.01-5.82, respectively). CONCLUSIONS: Frailty is common among older patients with CAP and is strongly associated with increased mortality, longer length of stay, and duration of antibiotics. A routine frail assessment at the admission of elderly patients with CAP is necessary as the first step for appropriate multidisciplinary interventions.

Using multi-tissue transcriptome-wide association study to identify candidate susceptibility genes for respiratory infectious diseases.

Objective: We explore the candidate susceptibility genes for influenza A virus (IAV), measles, rubella, and mumps and their underlying biological mechanisms. Methods: We downloaded the genome-wide association study summary data of four virus-specific immunoglobulin G (IgG) level data sets (anti-IAV IgG, anti-measles IgG, anti-rubella IgG, and anti-mumps virus IgG levels) and integrated them with reference models of three potential tissues from the Genotype-Tissue Expression (GTEx) project, namely, whole blood, lung, and transformed fibroblast cells, to identify genes whose expression is predicted to be associated with IAV, measles, mumps, and rubella. Results: We identified 19 significant genes (ULK4, AC010132.11, SURF1, NIPAL2, TRAP1, TAF1C, AC000078.5, RP4-639F20.1, RMDN2, ATP1B3, SRSF12, RP11-477D19.2, TFB1M, XXyac-YX65C7_A.2, TAF1C, PCGF2, and BNIP1) associated with IAV at a Bonferroni-corrected threshold of p < 0.05; 14 significant genes (SOAT1, COLGALT2, AC021860.1, HCG11, METTL21B, MRPL10, GSTM4, PAQR6, RP11-617D20.1, SNX8, METTL21B, ANKRD27, CBWD2, and TSFM) associated with measles at a Bonferroni-corrected threshold of p < 0.05; 15 significant genes (MTOR, LAMC1, TRIM38, U91328.21, POLR2J, SCRN2, Smpd4, UBN1, CNTROB, SCRN2, HOXB-AS1, SLC14A1, AC007566.10, AC093668.2, and CPD) associated with mumps at a Bonferroni-corrected threshold of p < 0.05; and 13 significant genes (JAGN1, RRP12, RP11-452K12.7, CASP7, AP3S2, IL17RC, FAM86HP, AMACR, RRP12, PPP2R1B, C11orf1, DLAT, and TMEM117) associated with rubella at a Bonferroni-corrected threshold of p < 0.05. Conclusions: We have identified several candidate genes for IAV, measles, mumps, and rubella in multiple tissues. Our research may further our understanding of the pathogenesis of infectious respiratory diseases.

Associations of socioeconomic status with infectious diseases mediated by lifestyle, environmental pollution and chronic comorbidities: a comprehensive evaluation based on UK Biobank.

BACKGROUND: Socioeconomic status (SES) inequity was recognized as a driver of some certain infectious diseases. However, few studies evaluated the association between SES and the burden of overall infections, and even fewer identified preventable mediators. This study aimed to assess the association between SES and overall infectious diseases burden, and the potential roles of factors including lifestyle, environmental pollution, chronic disease history. METHODS: We included 401,009 participants from the UK Biobank (UKB) and defined the infection status for each participant according to their diagnosis records. Latent class analysis (LCA) was used to define SES for each participant. We further defined healthy lifestyle score, environment pollution score (EPS) and four types of chronic comorbidities. We used multivariate logistic regression to test the associations between the four above covariates and infectious diseases. Then, we performed the mediation and interaction analysis to explain the relationships between SES and other variables on infectious diseases. Finally, we employed seven types of sensitivity analyses, including considering the Townsend deprivation index as an area level SES variable, repeating our main analysis for some individual or composite factors and in some subgroups, as well as in an external data from the US National Health and Nutrition Examination Survey, to verify the main results. RESULTS: In UKB, 60,771 (15.2%) participants were diagnosed with infectious diseases during follow-up. Lower SES [odds ratio (OR) = 1.5570] were associated with higher risk of overall infections. Lifestyle score mediated 2.9% of effects from SES, which ranged from 2.9 to 4.0% in different infection subtypes, while cardiovascular disease (CVD) mediated a proportion of 6.2% with a range from 2.1 to 6.8%. In addition, SES showed significant negative interaction with lifestyle score (OR = 0.8650) and a history of cancer (OR = 0.9096), while a significant synergy interaction was observed between SES and EPS (OR = 1.0024). In subgroup analysis, we found that males and African (AFR) with lower SES showed much higher infection risk. Results from sensitivity and validation analyses showed relative consistent with the main analysis. CONCLUSIONS: Low SES is shown to be an important risk factor for infectious disease, part of which may be mediated by poor lifestyle and chronic comorbidities. Efforts to enhance health education and improve the quality of living environment may help reduce burden of infectious disease, especially for people with low SES.