Biology subjects

Raubenheimer, D.

Publications and source records attributed to Raubenheimer, D..

3 recordsLinked to original sources

bioRxiv2024

High fat low carbohydrate diet is linked to protection against CNS autoimmunity

Multiple sclerosis (MS) is a common central nervous system (CNS) autoimmune disease, and diets and nutrients are emerging as critical contributing factors. However, a comprehensive understanding of their impacts and the underlying mechanisms involved is lacking. Harnessing state-of-the-art nutritional geometry analytical methods, we first revealed that globally, increased carbohydrate supply was associated with increased MS disease burden, while fat supply had an opposite effect. Furthermore, in a preclinical MS mouse model, experimental autoimmune encephalomyelitis (EAE), we found that an isocaloric diet high in carbohydrate aggravated EAE, while a diet enriched in fat was fully protective. This was reflected by reduced neuroinflammation and skewing towards anti-inflammatory phenotypes, which involved transcriptomic, epigenetic and immunometabolic changes. We showcased that manipulating diets is a potentially efficient and cost-effective approach to prevent and/or ameliorate EAE. This exhibits translational potentials for intervention/prevention of MS and possibly other autoimmune diseases.

immunology↗

bioRxiv2023

eNODAL: an experimentally guided nutriomics data clustering method to unravel complex drug-diet interactions

Unraveling the complex interplay between nutrients and drugs via their effects on omics features could revolutionize our fundamental understanding of nutritional physiology, personalized nutrition and ultimately human health-span. Experimental studies in nutrition are starting to use large-scale omics experiments to pick apart the effects of such interacting factors. However, the high dimensionality of the omics features, coupled with complex fully-factorial experimental designs together pose a challenge to the analysis. Current strategies for analyzing such types of data are based on between-feature correlations. However, these techniques risk overlooking important signals that arise from the experimental design and produce clusters that are hard to interpret. We present a novel approach for analyzing high-dimensional outcomes in nutriomics experiments, termed experiment-guided NutriOmics DatA cLustering (eNODAL). This three-step hybrid framework takes advantage of both ANOVA-type analyses and unsupervised learning methods to extract maximum information from experimental nutriomics studies. First, eNODAL categorizes the omics features into interpretable groups based on the significance of response to the different experimental variables using an ANOVA-like test. Such groups may include the main effects of a nutritional intervention, and drug exposure, or their interaction. Second, consensus clustering is performed within each interpretable group to further identify subclusters of features with similar response profiles to these experimental factors. Third, eNODAL annotates these subclusters based on their experimental responses and biological pathways enriched within the subcluster. We validate eNODAL using data from a mouse experiment to test for the interaction effects of macronutrient intake and drugs that target aging mechanisms in mice.

bioinformatics↗

bioRxiv2021

Engaging the complexity of diet and healthy aging in humans

Little is known about how normal variation in dietary patterns in humans affects the aging process, largely because both nutrition and the physiology of aging are highly complex and multidimensional. Here, we apply the nutritional geometry framework to data from 1560 older adults followed over four years to assess how nutrient intake patterns affect the aging process. Aging was quantified via blood biomarkers integrated to measure loss of homeostasis. Additionally, we extend nutritional geometry to 19 micronutrients. Salient results include benefits of intermediate protein and vitamin E intake. Broadly, we show that there are few simple answers of "good" or "bad" nutrients - optimal levels are generally intermediate, but dependent on other nutrients. Simpler linear/univariate analytical approaches are insufficient to capture such associations. We present an interactive tool to explore the results, and our approach presents a roadmap for future studies to explore the full complexity of the nutrition-aging landscape. Impact StatementMultidimensional nutritional analyses reveal how the association between diet and healthy aging is hard to untangle, as most nutrients have non-linear and interactive effects in humans.

bioinformatics↗