Alistair M. Senior’s research while affiliated with The University of Sydney and other places

Multiple sclerosis (MS) is an inflammatory and neurodegenerative disease of the central nervous system (CNS) believed to be driven by autoimmune mechanisms. Genetic and environmental factors are implicated in MS development, and among the latter, diets and nutrients are emerging as potential critical contributors. However, a comprehensive understanding of their impacts and the underlying mechanisms involved is lacking. Harnessing state‐of‐the‐art nutritional geometry analytical methods, it is first revealed that globally, increased carbohydrate supply is associated with increased MS disease burden, while fat supply has an opposite effect. Furthermore, in a MS mouse model, experimental autoimmune encephalomyelitis (EAE), it is found that an isocaloric diet high in carbohydrate aggravated EAE, while a diet enriched in fat (HF) is fully protective. This is reflected by reduced neuroinflammation and skewing toward anti‐inflammatory phenotypes. The protective effects from the HF diet are multifaceted. Metabolically, HF increased lipid storage in immune cells, correlating with their increased anti‐inflammatory IL‐10 production. Transcriptionally and epigenetically, HF feeding preprogrammed naïve T cells toward a less activated but more tolerogenic phenotype. It is showcased that manipulating diets is a potentially efficient and cost‐effective approach to prevent and/or ameliorate EAE. This exhibits translational potentials for prevention/intervention of MS and possibly other autoimmune diseases.

Dietary manipulations like ketogenic diets are established interventions for recalcitrant epilepsy. However, it remains unknown whether specific macronutrient exposure through dietary environments could possibly extend to primary preventive qualities, associated with changes in epilepsy disease burden (prevalence and incidence). Here, macronutrient supply, GDP, and idiopathic epilepsy disease burden data were collated from more than 150 countries from 1990 to 2018. Nutritional geometry generalized additive mixed models (GAMMs) modeling unraveled that dietary environments with high‐fat and low‐carbohydrate supplies were linked to lower epilepsy incidence and prevalence. Our analyses suggested a plausible primary preventive role of dietary manipulations for epilepsy.

Despite the importance of identifying predictable regularities for knowledge transfer across contexts, the generality of ecological and evolutionary findings is yet to be systematically quantified. We present the first large-scale evaluation of generality using new metrics. By focusing on biologically relevant study levels, we show that generalization is not uncommon. Overall, 20% of meta-analyses will produce a non-zero effect 95% of the time in future replication studies with a 70% probability of observing meaningful effects in study-level contexts. We argue that the misconception that generalization is exceedingly rare is due to conflating within-study and between-study variances in ecological and evolutionary meta-analyses, which results from focusing too much on total heterogeneity (the sum of within-study and between-study variances). We encourage using our proposed approach to elucidate general patterns underpinning ecological and evolutionary phenomena.

Despite the importance of identifying predictable regularities for knowledge transfer across contexts, the generality of ecological and evolutionary findings is yet to be systematically quantified. We present the first large-scale evaluation of generality using new metrics. By focusing on biologically relevant study levels, we show that generalization is not uncommon. Overall, 20% of meta-analyses will produce a non-zero effect 95% of the time in future replication studies with a 70% probability of observing meaningful effects in study-level contexts. We argue that the misconception that generalization is exceedingly rare is due to conflating within-study and between-study variances in ecological and evolutionary meta-analyses, which results from focusing too much on total heterogeneity (the sum of within-study and between-study variances). We encourage using our proposed approach to elucidate general patterns underpinning ecological and evolutionary phenomena.

Brown adipose tissue (BAT) dissipates energy as heat, not only under cold exposure but also in the dissipation of excess ingested energy. Therefore, enhancing BAT activity is a potential avenue to combat weight gain. Dietary macronutrient composition influences BAT size and has recently been shown to influence BAT size of daughters through the patriline of C57BL/6J mice. However, the effects of macronutrient composition and any paternal effects on BAT function have yet to be characterised. Using the Geometric Framework for Nutrition, we investigated the effects of macronutrient composition on the BAT proteome in male mice and intergenerational effects in their offspring. In fathers, >50% of the proteome was affected by macronutrient composition, with distinct clusters of proteins that responded in similar ways. We identified two clusters with inverse patterns that correlated with BAT mass. Notably, UCP1 was reduced on low fat diets that promoted increased BAT mass, while there were increased levels of proteins involved in protein turnover on those same diets. The same diets also led to a reduction in proteins involved in purine biosynthesis (often UCP1 inhibitors). We did not find any effects of paternal diet on the BAT proteome in sons, but paternal protein intake negatively affected basigin expression in daughters - a protein that regulates UCP1 transcription. Our results highlight that dietary macronutrient composition in males remodels the protein expression landscape of BAT, and pre-conceptionally reprograms BAT expression profiles of female offspring.

Purpose Dietary macronutrients significantly impact cardiometabolic health, yet research often focuses on individual macronutrient relationships. This study aimed to explore the associations between dietary macronutrient composition and cardiometabolic health. Methods This study included 33,681 US adults (49.7 ± 18.3 years; 52.5% female) from the National Health and Nutrition Examination Survey during 1999–2014. Dietary data was derived from 1 to 2 separate 24-hour recalls and cardiometabolic health included lipid profile, glycemic control, blood pressure, and adiposity collected in a mobile examination center. Associations between dietary macronutrient composition and cardiometabolic health were examined using generalized additive models adjusted for age, socio-demographics, lifestyle, and diet quality. Results In females, triglycerides (P < 0.01) and HDL cholesterol (P < 0.01) were the least optimal in diets containing lower fat (10%) and higher carbohydrate (75%). In males, HDL cholesterol was positively associated with fat (P < 0.01) and no association with triglycerides was detected. Total-C associations were male specific (P = 0.01) and highest in diets composed of 25% protein, 30% carbohydrate, and 45% fat. In both sexes, systolic blood pressure (P ≤ 0.02) was highest in diets containing lower fat (10%) coupled with moderate protein (25%). Diastolic blood pressure associations were female specific (P < 0.01) with higher values in those consuming the upper range of fat (55%). There were no associations of macronutrient composition with glycemic control or adiposity. Conclusion This study revealed sex-specific relationships between macronutrient composition and cardiometabolic health. Future research is needed to explore these relationships across age groups.

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, poses 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 Analysis of Variance (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.