Differential analysis of the bacterial community in colostrum samples from women with gestational diabetes mellitus and obesity

Gestational Diabetes Mellitus (GDM) and obesity affect the functioning of multiple maternal systems and influence colonization of the newborn gastrointestinal through the breastmilk microbiota (BMM). It is currently unclear how GDM and obesity affect the human BMM composition. Here, we applied 16S-rRNA high-throughput sequencing to human colostrum milk to characterize BMM taxonomic changes in a cohort of 43 individuals classified in six subgroups according to mothers patho-physiological conditions (healthy control (n = 18), GDM (n = 13), or obesity (n = 12)) and newborn gender. Using various diversity indicators, including Shannon/Faith phylogenetic index and UniFrac/robust Aitchison distances, we evidenced that BMM composition was influenced by the infant gender in the obesity subgroup. In addition, the GDM group presented higher microbial diversity compared to the control group. Staphylococcus, Corynebacterium 1, Anaerococcus and Prevotella were overrepresented in colostrum from women with either obesity or GDM, compared to control samples. Finally, Rhodobacteraceae was distinct for GDM and 5 families (Bdellovibrionaceae, Halomonadaceae, Shewanellaceae, Saccharimonadales and Vibrionaceae) were distinct for obesity subgroups with an absolute effect size greater than 1 and a q-value ≤ 0.05. This study represents the first effort to describe the impact of maternal GDM and obesity on BMM.

Polygenic and socioeconomic risk for high body mass index: 69 years of follow-up across life

Background: Genetic influences on body mass index (BMI) appear to markedly differ across life, yet existing research is equivocal and limited by a paucity of life course data. Better understanding changes across life in the determinants of BMI may inform etiology, the timing of preventative efforts, and the interpretation of increasing number of studies utilizing genetically-informed designs to study BMI. We thus used a birth cohort study to investigate differences in association and explained variance in the polygenic prediction of BMI from infancy to old age (2-69 years) in a single sample. A secondary aim was to investigate how a key purported environmental influence on BMI (childhood socioeconomic position) differed across life, and whether it operated independently and/or multiplicatively of genetic influences. Methods: Data were from up to 2677 participants in the MRC National Survey of Health and Development, with measured weight and height from infancy to old age (12 timepoints from 2-69 years) and genetic data (obtained from blood samples at 53 years). We derived three polygenic indices derived from GWAS of a) adult BMI, b) recalled childhood body size, and c) childhood-adolescence BMI. We investigated associations of each polygenic index and BMI at each age and compared in terms of absolute effect size and explained variance (R2). We used linear and quantile regression models, and finally investigated the additive or multiplicative role of childhood socioeconomic position. Results: Mean BMI and its variance increased across adulthood. For polygenic liability to higher adult BMI (Khera et al), the trajectories of effect size (beta) and explained variance (R-squared) diverged: explained variance peaked in early adulthood and plateaued thereafter, while absolute effect sizes increased throughout adulthood. For polygenic liability to higher childhood BMI, explained variance was largest in adolescence and early adulthood; effect sizes were marginally smaller in absolute terms from adolescence to adulthood. All polygenic indices were related to higher variation in BMI; effect sizes were sizably larger at the upper end of the BMI distribution. Socioeconomic and polygenic risk for higher BMI across life appear to operate additively; we found little evidence of interaction. Conclusion: Our findings highlight the likely independent influences of polygenic and socioeconomic factors on BMI across life. Despite sizable associations, the BMI variance explained by each plateaued or declined across adulthood while BMI variance itself increased. This is suggestive of the increasing importance of chance (non-shared) environmental influences on BMI across life.

Early centralized isolation strategy for all confirmed cases of COVID-19 remains a core intervention to disrupt the pandemic spreading significantly

Background In response to the spread of the coronavirus disease 2019 (COVID-19), plenty of control measures were proposed. To assess the impact of current control measures on the number of new case indices 14 countries with the highest confirmed cases, highest mortality rate, and having a close relationship with the outbreak’s origin; were selected and analyzed. Methods In the study, we analyzed the impact of five control measures, including centralized isolation of all confirmed cases, closure of schools, closure of public areas, closure of cities, and closure of borders of the 14 targeted countries according to their timing; by comparing its absolute effect average, its absolute effect cumulative, and its relative effect average. Results Our analysis determined that early centralized isolation of all confirmed cases was represented as a core intervention in significantly disrupting the pandemic’s spread. This strategy helped in successfully controlling the early stage of the outbreak when the total number of cases were under 100, without the requirement of the closure of cities and public areas, which would impose a negative impact on the society and its economy. However, when the number of cases increased with the apparition of new clusters, coordination between centralized isolation and non-pharmaceutical interventions facilitated control of the crisis efficiently. Conclusion Early centralized isolation of all confirmed cases should be implemented at the time of the first detected infectious case.

Effects of nintedanib by inclusion criteria for progression of interstitial lung disease

The INBUILD trial investigated nintedanib versus placebo in patients with progressive fibrosing interstitial lung diseases (ILDs). We investigated decline in forced vital capacity (FVC) in subgroups based on the inclusion criteria for ILD progression.Subjects had a fibrosing ILD other than idiopathic pulmonary fibrosis and met these criteria for ILD progression within the 24 months before screening despite management deemed appropriate in clinical practice: Group A, relative decline in FVC ≥10% predicted; Group B, relative decline in FVC ≥5–<10% predicted with worsened respiratory symptoms and/or increased extent of fibrosis on HRCT; Group C, worsened respiratory symptoms and increased extent of fibrosis on HRCT only.In the placebo group, the rates of FVC decline over 52 weeks in Groups A, B and C, respectively, were −241.9, −133.1 and −115.3 mL·year−1 in the overall population (p=0.0002 for subgroup-by-time interaction) and −288.9, −156.2 and −100.1 mL·year−1 among subjects with a usual interstitial pneumonia [UIP]-like fibrotic pattern on HRCT (p=0.0005 for subgroup-by-time interaction). Nintedanib had a greater absolute effect on reducing the rate of FVC decline in Group A than Group B or C. However, the relative effect of nintedanib versus placebo was consistent across the subgroups (p>0.05 for heterogeneity).In conclusion, the inclusion criteria used in the INBUILD trial, based on FVC decline or worsening of symptoms and extent of fibrosis on HRCT, were effective at identifying patients with progressive fibrosing ILDs. Nintedanib reduced the rate of decline in FVC across the subgroups based on the inclusion criteria related to ILD progression.

When Brands (Don’t) Take My Stance: The Ambiguous Effectiveness of Political Brand Communication

Brands increasingly take a stance on political issues, whereas consumers increasingly choose to either support a brand by buying their products (“buycotting”), or turn away from a brand (“boycotting”) for political reasons. While buycotts can be understood as a rewarding and cooperative form of mostly individual behavior, boycotts are a conflict-oriented form of collective punishment. Even though research has acknowledged these conceptual differences, studies have failed to analyze the difference in the absolute effect of consumers’ disapproval and approval. Moreover, research to date has not identified boundary conditions that might explain variation in the difference between consumers’ willingness to boycott or buycott. This research investigates this different effectiveness by conducting two experiments with different sets of brands, issues, and countries. Our results suggest that boycotting outweighs buycotting, implying that political brand communication is a risky strategy. Furthermore, we identify consumers’ political interest and category involvement as moderators of this imbalance.

Modeling the effect of lockdown timing as a COVID-19 control measure in countries with differing social contacts

The application, timing, and duration of lockdown strategies during a pandemic remain poorly quantified with regards to expected public health outcomes. Previous projection models have reached conflicting conclusions about the effect of complete lockdowns on COVID-19 outcomes. We developed a stochastic continuous-time Markov chain (CTMC) model with eight states including the environment (SEAMHQRD-V), and derived a formula for the basic reproduction number, R0, for that model. Applying the $${R}_{0}$$ R 0 formula as a function in previously-published social contact matrices from 152 countries, we produced the distribution and four categories of possible $${R}_{0}$$ R 0 for the 152 countries and chose one country from each quarter as a representative for four social contact categories (Canada, China, Mexico, and Niger). The model was then used to predict the effects of lockdown timing in those four categories through the representative countries. The analysis for the effect of a lockdown was performed without the influence of the other control measures, like social distancing and mask wearing, to quantify its absolute effect. Hypothetical lockdown timing was shown to be the critical parameter in ameliorating pandemic peak incidence. More importantly, we found that well-timed lockdowns can split the peak of hospitalizations into two smaller distant peaks while extending the overall pandemic duration. The timing of lockdowns reveals that a “tunneling” effect on incidence can be achieved to bypass the peak and prevent pandemic caseloads from exceeding hospital capacity.

Prosthetic Valve in Chronic Dialysis: a Systematic Review and Meta-Analysis

Background: Many patients with end stage kidney disease (ESKD) have valvular heart disease requiring surgery. The optimal prosthetic valve is not established in this population. We performed a systematic review and meta-analysis assessing outcomes of patients with dialysis-dependent ESKD who received mechanical or bioprosthetic valves. Methods: We searched Cochrane CENTRAL, MEDLINE, and EMBASE from inception to January 2020. We performed screening, full-text assessment, risk of bias, and data-collection independently and in duplicate. We evaluated risk of bias using the ROBINS-I tool and certainty in evidence with GRADE. Data were pooled using a random-effects model. Results: We identified 28 observational studies (n=9857; 6680 mechanical and 3717 bioprosthetic) with a median follow-up of 3.45 years. Due to confounding, 22 studies were at “high” and one at “critical” risk of bias. Certainty in evidence for all outcomes, except for bleeding, was very-low. Mechanical valves were associated with reduced mortality at 30 days (RR0.79, 95%CI[0.65,0.97], I2=0, absolute effect 27 fewer deaths per 1000) and at ≥ 6 years (mean 9.7 years, RR0.83, 95%CI[0.72,0.96], I2=79%, absolute effect 145 fewer deaths per 1000), but increased bleeding (RR2.46, 95%CI[1.35,4.48], I2=69% absolute effect 113 more events per 1000) and stroke (RR1.53, 95%CI[1.13,2.07], I2=0%, absolute effect 21 more events per 1000). Conclusion: Mechanical valves are associated with reduced mortality, but increased risks of bleeding and stroke. Given very-low certainty for mortality and stroke, patients and clinicians may choose a prosthetic valve based on factors such as bleeding risk and valve longevity.

Using Additive and Relative Hazards to Quantify Colorectal Survival Inequalities for Patients with A Severe Psychiatric Illness

IntroductionDespite recommendations, most studies examining health inequalities fail to report both absolute and relative summary measures. We examine colorectal cancer (CRC) survival for patients with and without severe psychiatric illness (SPI) to demonstrate the use and importance of relative and absolute effects. Objectives and ApproachWe conducted a retrospective cohort study of CRC patients diagnosed between 01/04/2007 and 31/12/2012, using linked administrative databases. SPI was defined as diagnoses of major depression, bipolar disorder, schizophrenia, and other psychotic illnesses six months to five years preceding cancer diagnosis and categorized as inpatient, outpatient or none. Associations between SPI history and risk of death were examined using Cox Proportional Hazards regression to obtain hazard ratios and Aalen’s semi-parametric additive hazards regression to obtain absolute differences. Both models controlled for age, sex, primary tumour location, and rurality. ResultsThe final cohort included 24,507 CRC patients, 482 patients had an outpatient SPI history and 258 patients had an inpatient SPI history. 58.1% of patients with inpatient SPI history died, and 47.1% of patients with outpatient SPI history died. Patients with an outpatient SPI history had a 40% (HR 1.40, 95% CI: 1.22-1.59) increased risk of death and patients with an inpatient SPI history had a 91% increased risk of death (HR 1.91, 95% CI: 1.63-2.25), relative to no history of a mental illness. An outpatient SPI history was associated with an additional 33 deaths per 1000 person years, and an inpatient SPI was associated with an additional 82 deaths per 1000 person years after controlling for confounders. Conclusion / ImplicationsWe demonstrated that reporting of both relative and absolute effects is possible and calculating risk difference is relatively simple using Aalen models. We encourage future studies examining inequalities with time-to-event data to use this method and report both relative and absolute effect measures.

Modeling the Effect of Lockdown Timing as a COVID-19 Control Measure in Countries with Differing Social Contacts

The application, timing, and duration of lockdown strategies during a pandemic remain poorly quantified with regards to expected public health outcomes. Previous projection models have reached conflicting conclusions about the effect of complete lockdowns on COVID-19 outcomes. We developed a stochastic continuous-time Markov chain (CTMC) model with eight states including the environment (SEAMHQRD-V), and derived a formula for the basic reproduction number, R0, for that model. Applying the R0 formula as a function in previously-published social contact matrices from 152 countries, we produced the distribution and four categories of possible R0 for the 152 countries and chose one country from each quarter as a representative for four social contact categories (Canada, China, Mexico, and Niger). The model was then used to predict the effects of lockdown timing in those four categories through the representative countries. The analysis for the effect of a lockdown was performed without the influence of the other control measures, like social distancing and mask wearing, to quantify its absolute effect. Hypothetical lockdown timing was shown to be the critical parameter in ameliorating pandemic peak incidence. More importantly, we found that well-timed lockdowns can split the peak of hospitalizations into two smaller distant peaks while extending the overall pandemic duration. The timing of lockdowns reveals that a “tunneling” effect on incidence can be achieved to bypass the peak and prevent pandemic caseloads from exceeding hospital capacity.