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1. Modeling the progression of Type 2 diabetes with underlying obesity

   https://doi.org/10.1371/journal.pcbi.1010914  

   Yang, Boya ; Li, Jiaxu ; Haller, Michael J. ; Schatz, Desmond A. ; Rong, Libin ( February 2023 , PLOS Computational Biology)

   Beard, Daniel A. (Ed.)

   Environmentally induced or epigenetic-related beta-cell dysfunction and insulin resistance play a critical role in the progression to diabetes. We developed a mathematical modeling framework capable of studying the progression to diabetes incorporating various diabetogenic factors. Considering the heightened risk of beta-cell defects induced by obesity, we focused on the obesity-diabetes model to further investigate the influence of obesity on beta-cell function and glucose regulation. The model characterizes individualized glucose and insulin dynamics over the span of a lifetime. We then fit the model to the longitudinal data of the Pima Indian population, which captures both the fluctuations and long-term trends of glucose levels. As predicted, controlling or eradicating the obesity-related factor can alleviate, postpone, or even reverse diabetes. Furthermore, our results reveal that distinct abnormalities of beta-cell function and levels of insulin resistance among individuals contribute to different risks of diabetes. This study may encourage precise interventions to prevent diabetes and facilitate individualized patient treatment. 

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2. Impact of mismatches in HbA 1c vs glucose values on the diagnostic classification of diabetes and prediabetes

   https://doi.org/10.1111/dme.14181  

   Gonzalez, A. ; Deng, Y. ; Lane, A. N. ; Benkeser, D. ; Cui, X. ; Staimez, L. R. ; Ford, C. N. ; Khan, F. N. ; Markley Webster, S. C. ; Leong, A. ; et al ( December 2019 , Diabetic Medicine)

   To determine whether HbA_1cmismatches (HbA_1clevels that are higher or lower than expected for the average glucose levels in different individuals) could lead to errors if diagnostic classification is based only on HbA_1clevels.

   In a cross‐sectional study, 3106 participants without known diabetes underwent a 75‐g oral glucose tolerance test (fasting glucose and 2‐h glucose) and a 50‐g glucose challenge test (1‐h glucose) on separate days. They were classified by oral glucose tolerance test results as having: normal glucose metabolism; prediabetes; or diabetes. Predicted HbA_1cwas determined from the linear regression modelling the relationship between observed HbA_1cand average glucose (mean of fasting glucose and 2‐h glucose from the oral glucose tolerance test, and 1‐h glucose from the glucose challenge test) within oral glucose tolerance test groups. The haemoglobin glycation index was calculated as [observed – predicted HbA_1c], and divided into low, intermediate and high haemoglobin glycation index mismatch tertiles.

   Those participants with higher mismatches were more likely to be black, to be men, to be older, and to have higherBMI(allP<0.001). Using oral glucose tolerance test criteria, the distribution of normal glucose metabolism, prediabetes and diabetes was similar across mismatch tertiles; however, using HbA_1ccriteria, the participants with low mismatches were classified as 97% normal glucose metabolism, 3% prediabetes and 0% diabetes, i.e. mostly normal, while those with high mismatches were classified as 13% normal glucose metabolism, 77% prediabetes and 10% diabetes, i.e. mostly abnormal (P<0.001).

   Measuring only HbA_1ccould lead to under‐diagnosis in people with low mismatches and over‐diagnosis in those with high mismatches. Additional oral glucose tolerance tests and/or fasting glucose testing to complement HbA_1cin diagnostic classification should be performed in most individuals.

    

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3. Identifying Key Instructional Practices in Rational Number Interventions: A Systematic Review and Meta-Analysis

   https://doi.org/10.1177/07319487241288823  

   Krowka, Sarah_K ; Jayanthi, Madhavi ; Schumacher, Robin_F ; Gersten, Russell ; Haymond, Kelly_S ; Newman-Gonchar, Rebecca_A ( October 2024 , Learning Disability Quarterly)

   Impacts of rational number interventions among U.S. students in Grades 3 through 9 with mathematics difficulties are examined using a systematic review and meta-analysis. The primary goal of the meta-analysis was to identify instructional practices that are key drivers of student impacts. From approximately 1,200 published and unpublished study records, we identified 28 studies that met our inclusion criteria and coded the interventions for their instructional practices, intervention characteristics, and study design features. The random-effects mean effect size across all 28 studies (90 effect sizes) was 0.68 ( SE = 0.08, p < .001, 95% confidence interval [CI]: [0.51, 0.85]). The 95% prediction interval was −0.36 to 1.8. Using meta-regression techniques, we found the teaching of mathematical language ( β = 0.50) and the use of the number line ( β = 0.47) during intervention to be significantly associated with positive impacts when adjusted for controls. We discuss implications for intervention practice and study limitations along with the challenges of examining complex, multifaceted interventions.

    

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4. A multiscale inflammatory map: linking individual stress to societal dysfunction

   https://doi.org/10.3389/fsci.2023.1239462  

   Vodovotz, Yoram ; Arciero, Julia ; Verschure, Paul_F_M J ; Katz, David L ( March 2024 , Frontiers in Science)

   As populations worldwide show increasing levels of stress, understanding emerging links among stress, inflammation, cognition, and behavior is vital to human and planetary health. We hypothesize that inflammation is a multiscale driver connecting stressors that affect individuals to large-scale societal dysfunction and, ultimately, to planetary-scale environmental impacts. We propose a “central inflammation map” hypothesis to explain how the brain regulates inflammation and how inflammation impairs cognition, emotion, and action. According to our hypothesis, these interdependent inflammatory and neural processes, and the inter-individual transmission of environmental, infectious, and behavioral stressors—amplified via high-throughput digital global communications—can culminate in a multiscale, runaway, feed-forward process that could detrimentally affect human decision-making and behavior at scale, ultimately impairing the ability to address these same stressors. This perspective could provide non-intuitive explanations for behaviors and relationships among cells, organisms, and communities of organisms, potentially including population-level responses to stressors as diverse as global climate change, conflicts, and the COVID-19 pandemic. To illustrate our hypothesis and elucidate its mechanistic underpinnings, we present a mathematical model applicable to the individual and societal levels to test the links among stress, inflammation, control, and healing, including the implications of transmission, intervention (e.g., via lifestyle modification or medication), and resilience. Future research is needed to validate the model’s assumptions and conclusions against empirical benchmarks and to expand the factors/variables employed. Our model illustrates the need for multilayered, multiscale stress mitigation interventions, including lifestyle measures, precision therapeutics, and human ecosystem design. Our analysis shows the need for a coordinated, interdisciplinary, international research effort to understand the multiscale nature of stress. Doing so would inform the creation of interventions that improve individuals’ lives; enhance communities’ resilience to stress; and mitigate the adverse effects of stress on the world.

    

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5. An Individualized, Data-Driven Digital Approach for Precision Behavior Change

   https://doi.org/10.1177/1559827619843489  

   Wongvibulsin, Shannon ; Martin, Seth_S ; Saria, Suchi ; Zeger, Scott_L ; Murphy, Susan_A ( April 2019 , American Journal of Lifestyle Medicine)

   Chronic disease now affects approximately half of the US population, causes 7 in 10 deaths, and accounts for roughly 80% of US health care expenditure. Because the root causes of chronic diseases are largely behavioral, effective therapies require frequent, individualized interventions that extend beyond the hospital and clinic to reach patients in their day-to-day lives. However, a mismatch currently exists between what the health care system is equipped to provide and the interventions necessary to effectively address the chronic disease burden. To remedy this health crisis, we present an individualized, data-driven digital approach for chronic disease management and prevention through precision behavior change. The rapid growth of information, biological, and communication technologies makes this an opportune time to develop digital tools that deliver precision interventions for health behavior change to address the chronic disease crisis. Building on this rapid growth, we propose a framework that includes the precise targeting of risk-producing behaviors using real-time sensing technology, machine learning data analysis to identify the most effective intervention, and delivery of that intervention with health-reinforcing feedback to provide real-time, individualized support to empower sustainable health behavior change.

    

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