DNA methylation clock DNAmFitAge shows regular exercise is associated with slower aging and systemic adaptation

DNAmPhenoAge, DNAmGrimAge, and the newly developed DNAmFitAge are DNA methylation (DNAm)-based biomarkers that reflect the individual aging process. Here, we examine the relationship between physical fitness and DNAm-based biomarkers in adults aged 33–88 with a wide range of physical fitness (including athletes with long-term training history). Higher levels of VO 2 max ( ρ = 0.2, p = 6.4E − 4, r = 0.19, p = 1.2E − 3), Jumpmax ( p = 0.11, p = 5.5E − 2, r = 0.13, p = 2.8E − 2), Gripmax ( ρ = 0.17, p = 3.5E − 3, r = 0.16, p = 5.6E − 3), and HDL levels ( ρ = 0.18, p = 1.95E − 3, r = 0.19, p = 1.1E − 3) are associated with better verbal short-term memory. In addition, verbal short-term memory is associated with decelerated aging assessed with the new DNAm biomarker FitAgeAcceleration ( ρ : − 0.18, p = 0.0017). DNAmFitAge can distinguish high-fitness individuals from low/medium-fitness individuals better than existing DNAm biomarkers and estimates a younger biological age in the high-fit males and females (1.5 and 2.0 years younger, respectively). Our research shows that regular physical exercise contributes to observable physiological and methylation differences which are beneficial to the aging process. DNAmFitAge has now emerged as a new biological marker of quality of life

Active Learning: Subtypes, Intra-Exam Comparison, and Student Survey in an Undergraduate Biology Course

Active learning improves undergraduate STEM course comprehension; however, student comprehension using different active learning methods and student perception of active learning have not been fully explored. We analyze ten semesters (six years) of an undergraduate biology course (honors and non-honors sections) to understand student comprehension and student satisfaction using a variety of active learning methods. First, we describe and introduce active learning subtypes. Second, we explore the efficacy of active learning subtypes. Third, we compare student comprehension between course material taught with active learning or lecturing within a course. Finally, we determine student satisfaction with active learning using a survey. We divide active learning into five subtypes based on established learning taxonomies and student engagement. We explore subtype comprehension efficacy (median % correct) compared to lecture learning (median 92% correct): Recognition (100%), Reflective (100%), Exchanging (94.1%), Constructive (93.8%), and Analytical (93.3%). A bivariate random intercept model adjusted by honors shows improved exam performance in subsequent exams and better course material comprehension when taught using active learning compared to lecture learning (2.2% versus 1.2%). The student survey reveals a positive trend over six years of teaching in the Perceived Individual Utility component of active learning (tau = 0.21, p = 0.014), but not for the other components (General Theoretical Utility, and Team Situation). We apply our findings to the COVID-19 pandemic and suggest active learning adaptations for newly modified online courses. Overall, our results suggest active learning subtypes may be useful for differentiating student comprehension, provide additional evidence that active learning is more beneficial to student comprehension, and show that student perceptions of active learning are positively changing

DNA methylation clock DNAmFitAge shows regular exercise is associated with slower aging and systemic adaptation

DNAmPhenoAge, DNAmGrimAge, and the newly developed DNAmFitAge are DNA methylation (DNAm)-based biomarkers that reflect the individual aging process. Here, we examine the relationship between physical fitness and DNAm-based biomarkers in adults aged 33–88 with a wide range of physical fitness (including athletes with long-term training history). Higher levels of VO 2 max ( ρ = 0.2, p = 6.4E − 4, r = 0.19, p = 1.2E − 3), Jumpmax ( p = 0.11, p = 5.5E − 2, r = 0.13, p = 2.8E − 2), Gripmax ( ρ = 0.17, p = 3.5E − 3, r = 0.16, p = 5.6E − 3), and HDL levels ( ρ = 0.18, p = 1.95E − 3, r = 0.19, p = 1.1E − 3) are associated with better verbal short-term memory. In addition, verbal short-term memory is associated with decelerated aging assessed with the new DNAm biomarker FitAgeAcceleration ( ρ : − 0.18, p = 0.0017). DNAmFitAge can distinguish high-fitness individuals from low/medium-fitness individuals better than existing DNAm biomarkers and estimates a younger biological age in the high-fit males and females (1.5 and 2.0 years younger, respectively). Our research shows that regular physical exercise contributes to observable physiological and methylation differences which are beneficial to the aging process. DNAmFitAge has now emerged as a new biological marker of quality of life