Association of healthy lifestyle behaviours with incident irritable bowel syndrome: a large population-based prospective cohort study.

OBJECTIVES: To evaluate the association between healthy lifestyle behaviours and the incidence of irritable bowel syndrome (IBS). DESIGN: Population-based prospective cohort study. SETTING: The UK Biobank. PARTICIPANTS: 64 268 adults aged 37 to 73 years who had no IBS diagnosis at baseline were enrolled between 2006 and 2010 and followed up to 2022. MAIN EXPOSURE: The five healthy lifestyle behaviours studied were never smoking, optimal sleep, high level of vigorous physical activity, high dietary quality and moderate alcohol intake. MAIN OUTCOME MEASURE: The incidence of IBS. RESULTS: During a mean follow-up of 12.6 years, 961 (1.5%) incident IBS cases were recorded. Among the 64 268 participants (mean age 55.9 years, 35 342 (55.0%) female, 7604 (11.8%) reported none of the five healthy lifestyle behaviours, 20 662 (32.1%) reported 1 behaviour, 21 901 (34.1%) reported 2 behaviours and 14 101 (21.9%) reported 3 to 5 behaviours at baseline. The multivariable adjusted hazard ratios associated with having 1, 2 and 3 to 5 behaviours for IBS incidence were 0.79 (95% confidence intervals 0.65 to 0.96), 0.64 (0.53 to 0.78) and 0.58 (0.46 to 0.72), respectively (P for trend <0.001). Never smoking (0.86, 0.76 to 0.98, P=0.02), high level of vigorous physical activity (0.83, 0.73 to 0.95, P=0.006) and optimal sleep (0.73, 0.60 to 0.88, P=0.001) demonstrated significant independent inverse associations with IBS incidence. No significant interactions were observed between these associations and age, sex, employment status, geographic location, gastrointestinal infection, endometriosis, family history of IBS or lifestyle behaviours. CONCLUSIONS: Adhering to a higher number of healthy lifestyle behaviours is significantly associated with a lower incidence of IBS in the general population. Our findings suggest the potential of lifestyle modifications as a primary prevention strategy for IBS.

Differential Associations of GAD Antibodies (GADA) and C-Peptide With Insulin Initiation, Glycemic Responses, and Severe Hypoglycemia in Patients Diagnosed With Type 2 Diabetes.

OBJECTIVE: We examined the associations of GAD antibodies (GADA) and C-peptide (CP) with insulin initiation, glycemic responses, and severe hypoglycemia in type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: In 5,230 Chinese patients (47.6% men) with T2D (mean ± SD age: 56.5 ± 13.9 years; median diabetes duration: 6 [interquartile range 1, 12] years), enrolled consecutively in 1996-2012 and prospectively observed until 2019, we retrospectively measured fasting CP and GADA in stored serum and examined their associations with aforementioned outcomes. RESULTS: At baseline, 28.6% (n = 1,494) had low CP (<200 pmol/L) and 4.9% (n = 257) had positive GADA (GADA+). In the low-CP group, 8.0% had GADA+, and, in the GADA+ group, 46.3% had low CP. The GADA+ group had an adjusted hazard ratio (aHR) of 1.46 (95% CI 1.15-1.84, P = 0.002) for insulin initiation versus the GADA- group, while the low-CP group had an aHR of 0.88 (0.77-1.00, P = 0.051) versus the high-CP group. Following insulin initiation, the GADA+ plus low-CP group had the largest decrements in HbA1c (-1.9% at month 6; -1.5% at month 12 vs. -1% in the other three groups). The aHR of severe hypoglycemia was 1.29 (95% CI 1.10-1.52, P = 0.002) in the low-CP group and 1.38 (95% CI 1.04-1.83, P = 0.024) in the GADA+ group. CONCLUSIONS: There is considerable heterogeneity in autoimmunity and ß-cell dysfunction in T2D with GADA+ and high CP associated with early insulin initiation, while GADA+ and low CP, increased the risk of severe hypoglycemia. Extended phenotyping is warranted to increase the precision of classification and treatment in T2D.

The mechanism shaping the logistic growth of mutation proportion in epidemics at population scale.

Virus evolution is a common process of pathogen adaption to host population and environment. Frequently, a small but important fraction of virus mutations are reported to contribute to higher risks of host infection, which is one of the major determinants of infectious diseases outbreaks at population scale. The key mutations contributing to transmission advantage of a genetic variant often grow and reach fixation rapidly. Based on classic epidemiology theories of disease transmission, we proposed a mechanistic explanation of the process that between-host transmission advantage may shape the observed logistic curve of the mutation proportion in population. The logistic growth of mutation is further generalized by incorporating time-varying selective pressure to account for impacts of external factors on pathogen adaptiveness. The proposed model is implemented in real-world data of COVID-19 to capture the emerging trends and changing dynamics of the B.1.1.7 strains of SARS-CoV-2 in England. The model characterizes and establishes the underlying theoretical mechanism that shapes the logistic growth of mutation in population.

Suppression of influenza virus infection by rhinovirus interference - at the population, individual and cellular levels.

Background: Influenza virus (IV) and the rhinovirus (RV) are the two most common circulating respiratory viruses circulating. Natural viral interference has been suggested between them. The effect of such at the population level has been described in temperate region, while its effect at the individual and cellular levels warrants further validation. In this study, we described the respiratory virus epidemiology and the co-infection landscape in the hospitalized population and investigated the distinct molecular pathways involved in the inhibition of virus replication. Methods: Nasopharyngeal aspirates (NPAs) collected from patients during 2015 to 2019 were examined for the presence of respiratory viruses. The correlation of the monthly prevalence between all the tested respiratory viruses, the co-infection rate and the temporal interference of RV and IV were tested. The viral interference was validated in vitro by conducting sequential RV and IV infections in the well-differentiated primary human airway epithelial cells. The contributing molecular pathways were determined by transcriptome analysis. Findings: A total of 112,926 NPAs were evaluated, and the Enterovirus/RV was the most prevalent respiratory virus detected. The negative correlation between EV/RV and IVs prevalence was independent of age and meteorological factors. Compare with other viruses, EV/RV had a significantly lower incidence of co-infection with IVs. Prior exposure to RV inhibited the replication of IV species A, B and oseltamivir-resistance stain in vitro. RV uniquely downregulated genes related to processing of viral mRNA, ribosomal proteins, translation and influenza infection. Interpretation: Epidemiological surveillance and the sequential infection in vitro suggested viral interference between EV/RV and IV operates at the population, individual and cellular levels. Funding: This study was supported by the General Research Fund (Ref: 24107017 and 14103119 to RWYC) and the Chinese University Direct Grant for Research (Ref: 2019·073 to RWYC).

Characterizing superspreading potential of infectious disease: Decomposition of individual transmissibility.

In the context of infectious disease transmission, high heterogeneity in individual infectiousness indicates that a few index cases can generate large numbers of secondary cases, a phenomenon commonly known as superspreading. The potential of disease superspreading can be characterized by describing the distribution of secondary cases (of each seed case) as a negative binomial (NB) distribution with the dispersion parameter, k. Based on the feature of NB distribution, there must be a proportion of individuals with individual reproduction number of almost 0, which appears restricted and unrealistic. To overcome this limitation, we generalized the compound structure of a Poisson rate and included an additional parameter, and divided the reproduction number into independent and additive fixed and variable components. Then, the secondary cases followed a Delaporte distribution. We demonstrated that the Delaporte distribution was important for understanding the characteristics of disease transmission, which generated new insights distinct from the NB model. By using real-world dataset, the Delaporte distribution provides improvements in describing the distributions of COVID-19 and SARS cases compared to the NB distribution. The model selection yielded increasing statistical power with larger sample sizes as well as conservative type I error in detecting the improvement in fitting with the likelihood ratio (LR) test. Numerical simulation revealed that the control strategy-making process may benefit from monitoring the transmission characteristics under the Delaporte framework. Our findings highlighted that for the COVID-19 pandemic, population-wide interventions may control disease transmission on a general scale before recommending the high-risk-specific control strategies.

Modelling COVID-19 outbreak on the Diamond Princess ship using the public surveillance data.

The novel coronavirus disease 2019 (COVID-19) outbreak on the Diamond Princess (DP) ship has caused over 634 cases as of February 20, 2020. We model the transmission process on DP ship as a stochastic branching process, and estimate the reproduction number at the innitial phase of 2.9 (95%CrI: 1.7-7.7). The epidemic doubling time is 3.4 days, and thus timely actions on COVID-19 control were crucial. We estimate the COVID-19 transmissibility reduced 34% after the quarantine program on the DP ship which was implemented on February 5. According to the model simulation, relocating the population at risk may sustainably decrease the epidemic size, postpone the timing of epidemic peak, and thus relieve the tensive demands in the healthcare. The lesson learnt on the ship should be considered in other similar settings.

Poultry exposure and environmental protection against asthma in rural children.

BACKGROUND: Asthma is one of the most common chronic diseases in childhood, and the prevalence has been increasing over the past few decades. One of the most consistent epidemiological findings is that children living in a farming or rural environment are protected from development of asthma and allergies, but the protective factors in rural China are not clear. METHODS: A community-based, cross-sectional epidemiological study was performed in a total of 17,587 children aged 5-8 years, 3435 from Hong Kong (urban) and 14,152 from Conghua (rural county in southern China). Asthma and allergic symptoms as well as environmental exposures were ascertained by using a standardized and validated questionnaire. RESULTS: The prevalence of current wheeze was significantly lower in rural Conghua than that of urban Hong Kong (1.7% vs. 7.7%, p < 0.001). A lower rate of asthma ever was also reported in rural children compared with their urban counterparts (2.5% vs. 5.3%, p < 0.001). After adjusting for confounding factors, exposure to agricultural farming (adjusted odds ratio 0.74, 95% confidence interval: 0.56-0.97) and poultry (0.75, 0.59-0.96) were the most important factors associated with the asthma-protective effect in the rural area. Further propensity score-adjusted analysis indicated that such protection conferred by living in the rural environment was mainly attributable to poultry exposure. CONCLUSIONS: We confirmed that the prevalence of asthma and atopic disorders was significantly lower in rural children when compared with their urban peers. Exposure to poultry and agricultural farming are the most important factors associated with asthma protection in the rural area.

Mucosal Antibody Response to SARS-CoV-2 in Paediatric and Adult Patients: A Longitudinal Study.

BACKGROUND: SARS-CoV-2 enters the body through inhalation or self-inoculation to mucosal surfaces. The kinetics of the ocular and nasal mucosal-specific-immunoglobulin A(IgA) responses remain under-studied. METHODS: Conjunctival fluid (CF, n = 140) and nasal epithelial lining fluid (NELF, n = 424) obtained by paper strips and plasma (n = 153) were collected longitudinally from SARS-CoV-2 paediatric (n = 34) and adult (n = 47) patients. The SARS-CoV-2 spike protein 1(S1)-specific mucosal antibody levels in COVID-19 patients, from hospital admission to six months post-diagnosis, were assessed. RESULTS: The mucosal antibody was IgA-predominant. In the NELF of asymptomatic paediatric patients, S1-specific IgA was induced as early as the first four days post-diagnosis. Their plasma S1-specific IgG levels were higher than in symptomatic patients in the second week after diagnosis. The IgA and IgG levels correlated positively with the surrogate neutralization readout. The detectable NELF "receptor-blocking" S1-specific IgA in the first week after diagnosis correlated with a rapid decline in viral load. CONCLUSIONS: Early and intense nasal S1-specific IgA levels link to a rapid decrease in viral load. Our results provide insights into the role of mucosal immunity in SARS-CoV-2 exposure and protection. There may be a role of NELF IgA in the screening and diagnosis of SARS-CoV-2 infection.

Stress Hyperglycemia Is Associated With an Increased Risk of Subsequent Development of Diabetes Among Bacteremic and Nonbacteremic Patients.

OBJECTIVE: Stress hyperglycemia is associated with an increased risk of diabetes among survivors of critical illness. We investigated whether patients without diabetes hospitalized for bacteremia or nonbacteremic diseases with transient stress hyperglycemia would have a higher risk of subsequent diabetes development compared with those who remained normoglycemic. RESEARCH DESIGN AND METHODS: This retrospective observational study was conducted on 224,534 in-patients with blood culture records. Stress hyperglycemia was defined based on the highest random glucose level ≥7.8 mmol/L during the index admission period. Diagnosis of diabetes, as the primary end point of interest, was defined based on diagnostic codes, blood test results, or medication records. Differences in cumulative incidence and hazard ratios (HRs) of diabetes between groups were assessed using the Kaplan-Meier estimator and Cox regression. RESULTS: After exclusion of patients with preexisting or undiagnosed diabetes or indeterminate diabetes status and propensity score matching, bacteremic patients with stress hyperglycemia had a significantly higher cumulative incidence of diabetes (HR 1.7, 95% CI 1.2-2.4) compared with those who remained normoglycemic. Stress hyperglycemia was further confirmed to be a diabetes predictor independent of age, sex, comorbidity, and other serological markers. For the nonbacteremic patients, stress hyperglycemia was similarly associated with a higher cumulative incidence of diabetes (HR 1.4, 95% CI 1.2-1.7). CONCLUSIONS: Hospitalized patients with transient stress hyperglycemia had a higher risk of subsequent diabetes development compared with their normoglycemic counterparts. Recognition of an increased risk of diabetes in these patients can allow early detection and monitoring in their subsequent follow-ups.

The non-pharmaceutical interventions may affect the advantage in transmission of mutated variants during epidemics: A conceptual model for COVID-19.

As the COVID-19 pandemic continues, genetic mutations in SARS-CoV-2 emerge, and some of them are found more contagious than the previously identified strains, acting as the major mechanism for many large-scale epidemics. The transmission advantage of mutated variants is widely believed as an innate biological feature that is difficult to be altered by artificial factors. In this study, we explore how non-pharmaceutical interventions (NPI) may affect transmission advantage. A two-strain compartmental epidemic model is proposed and simulated to investigate the biological mechanism of the relationships among different NPIs, the changes in transmissibility of each strain and transmission advantage. Although the NPIs are effective in flattening the epidemic curve, we demonstrate that NPIs probably lead to a decline in transmission advantage, which is likely to occur if the NPIs become intensive. Our findings uncover the mechanistic relationship between NPIs and transmission advantage dynamically, and highlight the important role of NPIs not only in controlling the intensity of epidemics but also in slowing or even containing the growth of the proportion of variants.

Quantifying the effect of government interventions and virus mutations on transmission advantage during COVID-19 pandemic.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has become a major public health threat. This study aims to evaluate the effect of virus mutation activities and policy interventions on COVID-19 transmissibility in Hong Kong. METHODS: In this study, we integrated the genetic activities of multiple proteins, and quantified the effect of government interventions and mutation activities against the time-varying effective reproduction number Rt. FINDINGS: We found a significantly positive relationship between Rt and mutation activities and a significantly negative relationship between Rt and government interventions. The results showed that the mutations that contributed most to the increase of Rt were from the spike, nucleocapsid and ORF1b genes. Policy of prohibition on group gathering was estimated to have the largest impact on mitigating virus transmissibility. The model explained 63.2% of the Rt variability with the R2. CONCLUSION: Our study provided a convenient framework to estimate the effect of genetic contribution and government interventions on pathogen transmissibility. We showed that the S, N and ORF1b protein had significant contribution to the increase of transmissibility of SARS-CoV-2 in Hong Kong, while restrictions of public gathering and suspension of face-to-face class are the most effective government interventions strategies.

Temporal Patterns in the Evolutionary Genetic Distance of SARS-CoV-2 during the COVID-19 Pandemic.

During coronavirus disease 2019 (COVID-19) pandemic, the genetic mutations of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) occurred frequently. Some mutations in the spike protein are considered to promote transmissibility of the virus, while the mutation patterns in other proteins are less studied and may also be important in understanding the characteristics of SARS-CoV-2. We used the sequencing data of SARS-CoV-2 strains in California to investigate the time-varying patterns of the evolutionary genetic distance. The accumulative genetic distances were quantified across different time periods and in different viral proteins. The increasing trends of genetic distance were observed in spike protein (S protein), the RNA-dependent RNA polymerase (RdRp) region and nonstructural protein 3 (nsp3) of open reading frame 1 (ORF1), and nucleocapsid protein (N protein). The genetic distances in ORF3a, ORF8, and nsp2 of ORF1 started to diverge from their original variants after September 2020. By contrast, mutations in other proteins appeared transiently, and no evident increasing trend was observed in the genetic distance to the original variants. This study presents distinct patterns of the SARS-CoV-2 mutations across multiple proteins from the aspect of genetic distance. Future investigation shall be conducted to study the effects of accumulative mutations on epidemics characteristics.

Differences in the case fatality risks associated with SARS-CoV-2 Delta and non-Delta variants in relation to vaccine coverage: An early ecological study in the United Kingdom.

The circulation of SARS-CoV-2 Delta (i.e., B.1.617.2) variants challenges the pandemic control. Our analysis showed that in the United Kingdom (UK), the reported case fatality ratio (CFR) decreased from May to July 2021 for non-Delta variant, whereas the decreasing trends of the CFR of Delta variant appeared weak and insignificant. The association between vaccine coverage and CFR might be stratified by different circulating variants. Due to the limitation of ecological study design, the interpretation of our results should be treated with caution.

A tentative assessment of the changes in transmissibility and fatality risk associated with Beta SARS-CoV-2 variants in South Africa: an ecological study.

The circulation of SARS-CoV-2 Beta (B.1.351) variants challenged the control of COVID-19 pandemic. The numbers of COVID-19 cases and deaths and SARS-CoV-2 sequences in South Africa were collected. We reconstructed the variant-specified reproduction numbers (R t) and delay-adjusted case fatality ratio (CFR) to examine the changes in transmissibility and fatality risk of Beta over non-Beta variants. We estimated that Beta variants were 41% (95%CI: 16, 73) more transmissible and 53% (95%CI: 6, 108) more fatal than non-Beta variants. Higher risks of infection and fatality might lead to increasing volumes of infections and critical patients.

Impacts The circulation of SARS-CoV-2 Beta (B.1.351) variants, which were firstly reported in South Africa, challenged the control of COVID-19 pandemic.Using the national-wide COVID-19 cases and SARS-CoV-2 sequences data, Beta variants were estimated 41% more transmissible and 53% more fatal than non-Beta variants in South Africa.Higher risks of infection and fatality might lead to increasing volumes of infections and critical patients.

Real-time quantification of the transmission advantage associated with a single mutation in pathogen genomes: a case study on the D614G substitution of SARS-CoV-2.

BACKGROUND: The COVID-19 pandemic poses serious threats to global health, and the emerging mutation in SARS-CoV-2 genomes, e.g., the D614G substitution, is one of the major challenges of disease control. Characterizing the role of the mutation activities is of importance to understand how the evolution of pathogen shapes the epidemiological outcomes at population scale. METHODS: We developed a statistical framework to reconstruct variant-specific reproduction numbers and estimate transmission advantage associated with the mutation activities marked by single substitution empirically. Using likelihood-based approach, the model is exemplified with the COVID-19 surveillance data from January 1 to June 30, 2020 in California, USA. We explore the potential of this framework to generate early warning signals for detecting transmission advantage on a real-time basis. RESULTS: The modelling framework in this study links together the mutation activity at molecular scale and COVID-19 transmissibility at population scale. We find a significant transmission advantage of COVID-19 associated with the D614G substitution, which increases the infectivity by 54% (95%CI: 36, 72). For the early alarming potentials, the analytical framework is demonstrated to detect this transmission advantage, before the mutation reaches dominance, on a real-time basis. CONCLUSIONS: We reported an evidence of transmission advantage associated with D614G substitution, and highlighted the real-time estimating potentials of modelling framework.

A Bayesian method for synthesizing multiple diagnostic outcomes of COVID-19 tests.

The novel coronavirus disease 2019 (COVID-19) has spread worldwide and threatened human life. Diagnosis is crucial to contain the spread of SARS-CoV-2 infections and save lives. Diagnostic tests for COVID-19 have varying sensitivity and specificity, and the false-negative results would have substantial consequences to patient treatment and pandemic control. To detect all suspected infections, multiple testing is widely used. However, it may be challenging to build an assertion when the testing results are inconsistent. Considering the situation where there is more than one diagnostic outcome for each subject, we proposed a Bayesian probabilistic framework based on the sensitivity and specificity of each diagnostic method to synthesize a posterior probability of being infected by SARS-CoV-2. We demonstrated that the synthesized posterior outcome outperformed each individual testing outcome. A user-friendly web application was developed to implement our analytic framework with free access via http://www2.ccrb.cuhk.edu.hk/statgene/COVID_19/. The web application enables the real-time display of the integrated outcome incorporating two or more tests and calculated based on Bayesian posterior probability. A simulation-based assessment demonstrated higher accuracy and precision of the Bayesian probabilistic model compared with a single-test outcome. The online tool developed in this study can assist physicians in making clinical evaluations by effectively integrating multiple COVID-19 tests.

Shrinkage in serial intervals across transmission generations of COVID-19.

One of the key epidemiological characteristics that shape the transmission of coronavirus disease 2019 (COVID-19) is the serial interval (SI). Although SI is commonly considered following a probability distribution at a population scale, recent studies reported a slight shrinkage (or contraction) of the mean of effective SI across transmission generations or over time. Here, we develop a likelihood-based statistical inference framework with truncation to explore the change in SI across transmission generations after adjusting the impacts of case isolation. The COVID-19 contact tracing surveillance data in Hong Kong are used for exemplification. We find that for COVID-19, the mean of individual SI is likely to shrink with a factor at 0.72 per generation (95%CI: 0.54, 0.96) as the transmission generation increases, where a threshold may exist as the lower boundary of this shrinking process. We speculate that one of the probable explanations for the shrinkage in SI might be an outcome due to the competition among multiple candidate infectors within the same case cluster. Thus, the nonpharmaceutical interventive strategies are crucially important to block the transmission chains, and mitigate the COVID-19 epidemic.

Estimating the generation interval and inferring the latent period of COVID-19 from the contact tracing data.

The coronavirus disease 2019 (COVID-19) emerged by end of 2019, and became a serious public health threat globally in less than half a year. The generation interval and latent period, though both are of importance in understanding the features of COVID-19 transmission, are difficult to observe, and thus they can rarely be learnt from surveillance data empirically. In this study, we develop a likelihood framework to estimate the generation interval and incubation period simultaneously by using the contact tracing data of COVID-19 cases, and infer the pre-symptomatic transmission proportion and latent period thereafter. We estimate the mean of incubation period at 6.8 days (95 %CI: 6.2, 7.5) and SD at 4.1 days (95 %CI: 3.7, 4.8), and the mean of generation interval at 6.7 days (95 %CI: 5.4, 7.6) and SD at 1.8 days (95 %CI: 0.3, 3.8). The basic reproduction number is estimated ranging from 1.9 to 3.6, and there are 49.8 % (95 %CI: 33.3, 71.5) of the secondary COVID-19 infections likely due to pre-symptomatic transmission. Using the best estimates of model parameters, we further infer the mean latent period at 3.3 days (95 %CI: 0.2, 7.9). Our findings highlight the importance of both isolation for symptomatic cases, and for the pre-symptomatic and asymptomatic cases.