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Purpose

To examine the associations and substitutions of dietary sugars [extrinsic (free) or intrinsic (non-free)] as well as dietary starch and fiber intakes for indices of body fat and cardiometabolic health.

Methods

Dietary intake was assessed at multiple times using multi-day 24-hour recalls over 18-months for indices of body fat (body fat %, waist circumference, BMI, and weight change) (n = 1066) and at baseline and 12 months for cardiometabolic outcomes (LDL, HDL, HbA1c) (n = 736). Bayesian modeling was applied to analyze the probabilistic impact of dietary carbohydrate components using credible intervals for association and substitution analyses with repeated measures random effects modeling.

Results

A higher starch intake significantly associated with higher body fat %, BMI and waist circumference (WC) (all CrI > 0). Conversely, intrinsic sugar and fiber intakes were significantly linked to lower body fat indices, while free sugar showed no association. A 20 g substitution of free sugars with intrinsic sugars significantly associated with lower body fat (CrI: -4.2; -1.0%), BMI (CrI: -1.8; -0.4) and WC (CrI: -4.2; -1.0 cm), while substituting intrinsic sugars with starch resulted in significantly higher body fat, BMI, WC and weight change. Replacing starch with fiber associated with higher HDL-C (CrI: -0.0; 0.3) and lower LDL-C (CrI: -0.6; 0.1). Replacing free sugars with starch associated with a higher HbA1c level (CrI: 0.0;0.2).

Conclusion

These results underscore the importance of distinguishing between intrinsic versus extrinsic sugars and highlight the potential benefits of increasing intrinsic sugars and fiber while reducing starch for better body fat management and cardiometabolic health.

The CACHE challenges are a series of prospective benchmarking exercises to evaluate progress in the field of computational hit-finding. Here we report the results of the inaugural CACHE challenge in which 23 computational teams each selected up to 100 commercially available compounds that they predicted would bind to the WDR domain of the Parkinson’s disease target LRRK2, a domain with no known ligand and only an apo structure in the PDB. The lack of known binding data and presumably low druggability of the target is a challenge to computational hit finding methods. Of the 1955 molecules predicted by participants in Round 1 of the challenge, 73 were found to bind to LRRK2 in an SPR assay with a KD lower than 150 μM. These 73 molecules were advanced to the Round 2 hit expansion phase, where computational teams each selected up to 50 analogs. Binding was observed in two orthogonal assays for seven chemically diverse series, with affinities ranging from 18 to 140 μM. The seven successful computational workflows varied in their screening strategies and techniques. Three used molecular dynamics to produce a conformational ensemble of the targeted site, three included a fragment docking step, three implemented a generative design strategy and five used one or more deep learning steps. CACHE #1 reflects a highly exploratory phase in computational drug design where participants adopted strikingly diverging screening strategies. Machine learning-accelerated methods achieved similar results to brute force (e.g., exhaustive) docking. First-in-class, experimentally confirmed compounds were rare and weakly potent, indicating that recent advances are not sufficient to effectively address challenging targets.

Advances in artificial intelligence (AI) in the medical sector necessitate the development of AI literacy among future physicians. This article explores the pioneering efforts of the AI in Medicine Association (AIM) at Brigham Young University, which offers a framework for undergraduate pre-medical students to gain hands-on experience, receive principled education, explore ethical considerations, and learn appraisal of AI models. By supplementing formal, university-organized pre-medical education with a student-led, faculty-supported introduction to AI through an extracurricular academic association, AIM alleviates apprehensions regarding AI in medicine early and empowers students preparing for medical school to navigate the evolving landscape of AI in healthcare responsibly.