Sorghum Association Panel Whole-Genome Sequencing Establishes Pivotal Resource for Dissecting Genomic Diversity

Association mapping panels represent foundational resources for understanding the genetic basis of phenotypic diversity and serve to advance plant breeding by exploring genetic variation across diverse accessions with distinct histories of evolutionary divergence and local adaptation. We report the whole-genome sequencing (WGS) of 400 sorghum [Sorghum bicolor (L.) Moench] accessions from the Sorghum Association Panel (SAP) at an average coverage of 38X (25X-72X), enabling the development of a high-density genomic-marker set of 43,983,694 variants including SNPs (~38 million), indels (~5 million), and CNVs (~170,000). We observe slightly more deletions among indels and a much higher prevalence of deletions among copy number variants compared to insertions. This new marker set enabled the identification of several putatively novel genomic associations for plant height and tannin content, which were not identified when using previous lower-density marker sets. WGS identified and scored variants in 5 kb bins where available genotyping-by-sequencing (GBS) data captured no variants, with half of all bins in the genome falling into this category. The predictive ability of genomic best unbiased linear predictor (GBLUP) models was increased by an average of 30% by using WGS markers rather than GBS markers. We identified 18 selection peaks across subpopulations that formed due to evolutionary divergence during domestication, and we found six Fst peaks resulting from comparisons between converted lines and breeding lines within the SAP that were distinct from the peaks associated with historic selection. This population has been and continues to serve as a significant public resource for sorghum research and demonstrates the value of improving upon existing genomic resources.

Retention, Fasting Patterns, and Weight Loss With an Intermittent Fasting App: A Large-scale, 52-week Observational Study (Preprint)

BACKGROUND Intermittent Fasting (IF) is an increasingly popular approach to dietary control that focuses on the timing of eating rather than the quantity and content of caloric intake. IF practitioners typically seek to improve their weight and other health factors. Millions of practitioners have turned to purpose-built mobile applications to help them track and adhere to their fasts and monitor changes in their weight and other biometrics. OBJECTIVE This study aimed to quantify user retention, fasting patterns, and weight loss by users of two commonly used IF mobile apps. We aimed to describe starting BMI, amount of fasting, frequency of weight tracking, and other demographics as correlates of retention and weight change. METHODS We assembled height, weight, fasting and demographic data for adult users (age 18-100yo) of the LIFE Fasting Tracker and LIFE Extend apps from 2018-2020. Retention up to 52 weeks was quantified based on recorded fasts and correlated with user demographics. Users who provided height and at least two weights and whose first fast and weight records were contemporaneous were included in the weight loss analysis. Fasting was quantified as Extended Fasting Hours (hours beyond 12 in a fast) averaged per Day (EFH/Day). RESULTS 792,692 users were followed for retention based on 26 million recorded fasts. 132,775 (16.7%) of users were retained at 13 weeks, 54,881 (6.9%) at 26 weeks, and 16,478 (2.1%) at 52 weeks, allowing 4 consecutive weeks of inactivity. Weight loss in the qualifying cohort (n=161,346) was strongly correlated with starting BMI and EFH/Day. Users with BMI ≥ 40 lost 11.3% of their starting weight by 52 weeks versus a slight weight gain on average for users with starting BMI <23. Additionally, EFH/Day was an approximately linear predictor of weight loss for a given time point and starting BMI. By week 26, users lost over 1% of their starting weight per EFH/Day on average. Furthermore, users who recorded their weight monthly lost considerably more weight than those who did not (eg, 8.5% vs 3.7% weight loss at week 13 for users with BMI ≥25). By 26 weeks, 69.2% (2985/4313) of users with starting BMI ≥ 25 who recorded monthly weights lost at least 5% of their starting weight, and 39.9% (1722/4313) lost at least 10% body weight. CONCLUSIONS Intermittent Fasting with the LIFE mobile apps appears to be a sustainable approach to weight reduction in the overweight and obese population. Healthy weight and underweight individuals do not lose much weight on average, even with extensive fasting. Obese users lose substantial weight over time, with more weight loss in those who fast more and who record their weight more frequently.

Impact of a Patient’s Baseline Risk on the Relative Benefit and Harm of a Preventive Treatment Strategy: Applying Trial Results in Clinical Decision Making

Background For translating an overall trial result into an individual patient’s expected absolute treatment effect, differences in relative treatment effect between patients need to be taken into account. The aim of this study was to evaluate whether relative treatment effects of medication in 2 large contemporary trials are influenced by multivariable baseline risk of an individual patient. Methods and Results In 9361 patients from SPRINT (Systolic Blood Pressure Intervention Trial), risk of major adverse cardiovascular events was assessed using a newly derived risk model. In 18 133 patients from the RE‐LY (Randomized Evaluation of Long‐Term Anticoagulant Therapy) trial, risk of stroke or systemic embolism and major bleeding was assessed using the Global Anticoagulant Registry in the Field–Atrial Fibrillation risk model. Heterogeneity of trial treatment effect was assessed using Cox models of trial allocation, model linear predictor, and their interaction. There was no significant interaction between baseline risk and relative treatment effect from intensive blood pressure lowering in SPRINT ( P =0.92) or from dabigatran compared with warfarin for stroke or systemic embolism in the RE‐LY trial ( P =0.71). There was significant interaction between baseline risk and treatment effect from dabigatran versus warfarin in the RE‐LY trial ( P <0.001) for major bleeding. Quartile‐specific hazard ratios for bleeding ranged from 0.40 (95% CI, 0.26–0.61) to 1.04 (95% CI, 0.83–1.03) for dabigatran, 110 mg, and from 0.61 (95% CI, 0.42–0.88) to 1.20 (95% CI, 0.97–1.50) for dabigatran, 150 mg, compared with warfarin. Conclusions Effect modification of relative treatment effect by individual baseline event risk should be assessed systematically in randomized clinical trials using multivariate risk prediction, not only in terms of treatment efficacy but also for important treatment harms, as a prespecified analysis. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT01206062.

Survival Analysis in Cognitively Normal Subjects and in Patients with Mild Cognitive Impairment Using a Proportional Hazards Model with Extreme Gradient Boosting Regression

Background: Evaluating the risk of Alzheimer’s disease (AD) in cognitively normal (CN) and patients with mild cognitive impairment (MCI) is extremely important. While MCI-to-AD progression risk has been studied extensively, few studies estimate CN-to-MCI conversion risk. The Cox proportional hazards (PH), a widely used survival analysis model, assumes a linear predictor-risk relationship. Generalizing the PH model to more complex predictor-risk relationships may increase risk estimation accuracy. Objective: The aim of this study was to develop a PH model using an Xgboost regressor, based on demographic, genetic, neuropsychiatric, and neuroimaging predictors to estimate risk of AD in patients with MCI, and the risk of MCI in CN subjects. Methods: We replaced the Cox PH linear model with an Xgboost regressor to capture complex interactions between predictors, and non-linear predictor-risk associations. We endeavored to limit model inputs to noninvasive and more widely available predictors in order to facilitate future applicability in a wider setting. Results: In MCI-to-AD (n = 882), the Xgboost model achieved a concordance index (C-index) of 84.5%. When the model was used for MCI risk prediction in CN (n = 100) individuals, the C-index was 73.3%. In both applications, the C-index was statistically significantly higher in the Xgboost in comparison to the Cox PH model. Conclusion: Using non-linear regressors such as Xgboost improves AD dementia risk assessment in CN and MCI. It is possible to achieve reasonable risk stratification using predictors that are relatively low-cost in terms of time, invasiveness, and availability. Future strategies for improving AD dementia risk estimation are discussed.

Is Hemoglobin Concentration a Linear Predictor of Mortality in Older Adults From Chinese Longevity Regions?

Introduction: The association patterns of hemoglobin (HB) concentrations with mortality among the longevity older adults are unclear. We aimed to evaluate the relationship among older adults form Chinese longevity regions.Methods: We included 1,785 older adults aged ≥65 years (mean age, 86.7 years; 1,002 women, 783 men) from the community-based Chinese Longitudinal Healthy Longevity Survey. We estimated the hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause mortality using multivariable Cox proportional hazards models and Cox models with restricted cubic spline.Results: In total, 999 deaths occurred during a median follow-up of 5.4 years from 2011 to 2017. Restricted cubic spline analysis found no non-linear association between HB concentrations and all-cause mortality after a full adjustment for covariates among the older adults form longevity regions (p &gt; 0.05 for non-linearity). The risk for all-cause mortality was significantly higher in the groups with HB concentration of &lt;11.0 g/dL (HR: 1.37, 95% CI: 1.10–1.70) and 11.0–12.0 g/dL (HR: 1.25, 95% CI: 1.01–1.54); the risk of all-cause mortality was significantly lower in the groups with HB concentration ≥14.0 g/dL (HR: 0.76, 95% CI: 0.60–0.97) compared with the reference group (13.0–13.9 g/dL).Conclusions: Among older adults form Chinese longevity regions, HB concentrations were found to be inversely and linearly associated with all-cause mortality. Further prospective intervention trials are needed to confirm whether higher HB concentrations had a lower risk of mortality in these older adults.

Using numerical methods to design simulations: revisiting the balancing intercept

We consider methods for generating draws of a binary random variable whose expectation conditional on covariates follows a logistic regression model with known covariate coefficients. We examine approximations for finding a “balancing intercept,” that is, a value for the intercept of the logistic model that leads to a desired marginal expectation for the binary random variable. We show that a recently proposed analytical approximation can produce inaccurate results, especially when targeting more extreme marginal expectations or when the linear predictor of the regression model has high variance. We describe and implement a numerical approximation based on Monte Carlo methods that appears to work well in practice. Our approach to the basic problem of the balancing intercept provides an example of a broadly applicable strategy for formulating and solving problems that arise in the design of simulation studies used to evaluate or teach epidemiologic methods.

Computational Analysis of Cortical Neuronal Excitotoxicity in a Large Animal Model of Neonatal Brain Injury

Background: Neonatal hypoxic brain injury is a major cause of intellectual and developmental disability. Hypoxia causes neuronal dysfunction and death in the developing cerebral cortex due to excitotoxic Ca2+-influx. In the translational piglet model of hypoxic encephalopathy, we have previously shown that hypoxia overactivates Ca2+/Calmodulin (CaM) signaling via Sarcoma (Src) kinase in cortical neurons, resulting in overexpression of proapoptotic genes. However, identifying the exact relationship between alterations in neuronal Ca2+-influx, molecular determinants of cell death, and the degree of hypoxia in a dynamic system represents a significant challenge. Methods: We used experimental and computational methods to identify molecular events critical to the onset of excitotoxicity-induced apoptosis in the cerebral cortex of newborn piglets. We used 2-3 day-old piglets (normoxic [Nx], hypoxic [Hx], and hypoxic + Src-inhibitor-treatment [Hx+PP2]groups) for biochemical analysis of ATP production, Ca2+-influx, and Ca2+/CaM-dependent protein kinase kinase 2(CaMKK2) expression. We then used SimBiology to build a computational model of the Ca2+/CaM-Src-kinase signaling cascade, simulating Nx, Hx, and Hx+PP2 conditions. To evaluate our model, we used Sobol variance decomposition, multiparametric global sensitivity analysis, and parameter scanning.Results: Our model captures important molecular trends caused by hypoxia in the piglet brain. Incorporating the action of Src kinase inhibitor PP2 further validated our model and enabled predictive analysis of the effect of hypoxia on CaMKK2. We determined the impact of a feedback loop related to Src phosphorylation of NMDA receptors and activation kinetics of CaMKII. We also identified distinct modes of signaling wherein Ca2+ level alterations following Src kinase inhibition may not be a linear predictor of changes in Bax expression. Importantly, our model indicates that while pharmacological pre-treatment significantly reduces the onset of abnormal Ca2+-influx, there exists a window of intervention after hypoxia during which targeted modulation of Src-NMDAR interaction kinetics in combination with PP2 administration can reduce Ca2+-influx and Bax expression to similar levels as pre-treatment. Conclusions: Our model identifies new dynamics of critical components in the Ca2+/CaM-Src signaling pathway leading to neuronal injury and provides an essential framework for drug efficacy studies in translational models of neonatal brain injury for the prevention of intellectual and developmental disabilities.

Interindividual differences in attentional vulnerability moderate cognitive performance during sleep restriction and subsequent recovery in healthy young men

We investigated whether interindividual attentional vulnerability moderates performance on domain-specific cognitive tasks during sleep restriction (SR) and subsequent recovery sleep. Fifteen healthy men (M ± SD, 22.3 ± 2.8 years) were exposed to three nights of baseline, five nights of 5-h time in bed SR, and two nights of recovery sleep. Participants completed tasks assessing working memory, visuospatial processing, and processing speed approximately every two hours during wake. Analyses examined performance across SR and recovery (linear predictor day or quadratic predictor day2) moderated by attentional vulnerability per participant (difference between mean psychomotor vigilance task lapses after the fifth SR night versus the last baseline night). For significant interactions between day/day2 and vulnerability, we investigated the effect of day/day2 at 1 SD below (less vulnerable level) and above (more vulnerable level) the mean of attentional vulnerability (N = 15 in all analyses). Working memory accuracy and speed on the Fractal 2-Back and visuospatial processing speed and efficiency on the Line Orientation Task improved across the entire study at the less vulnerable level (mean − 1SD) but not the more vulnerable level (mean + 1SD). Therefore, vulnerability to attentional lapses after SR is a marker of susceptibility to working memory and visuospatial processing impairment during SR and subsequent recovery.

Measuring the exposure of Black, Asian and other ethnic groups to COVID-infected neighbourhoods in English towns and cities

Drawing on the work of The Doreen Lawrence Review—a report on the disproportionate impact of COVID-19 on Black, Asian and minority ethnic communities in the UK—this paper develops an index of exposure, measuring which ethnic groups have been most exposed to COVID-19 infected residential neighbourhoods during the first and second waves of the pandemic in England. The index is based on a Bayesian Poisson model with a random intercept in the linear predictor, allowing for extra-Poisson variation at neighbourhood and town/city scales. This permits within-city differences to be decoupled from broader regional trends in the disease. The research finds that members of ethnic minority groups can be living in areas with higher infection rates but also that the risk of exposure is distributed unevenly across these groups. Initially, in the first wave, the disease disproportionately affected Black residents but, as the pandemic has progressed, especially the Pakistani but also the Bangladeshi and Indian groups have had the highest exposure. This higher exposure of the Pakistani group is not straightforwardly a function of neighbourhood deprivation because it is present across a range of average house prices. We find evidence to support the view, expressed in The Doreen Lawrence Review, that it is linked to occupational and environmental exposure, particularly residential density but, having allowed for these factors, differences between the towns and cities remain.