Research using machine learning links threonine and other metabolites with potential lifespan effects, bypassing animal model limitations.
In a study that brings another string to the bow of longevity research, scientists at the Buck Institute for Research on Aging have used machine learning and systems biology to connect metabolomic data from both fruit flies and humans, identifying key metabolites that impact lifespan in both species.
The findings, recently published in Nature Communications, propose that threonine – a metabolite linked to diabetes resistance and other vital functions – could be further explored as a potential target for aging interventions [1].
Longevity.Technology: The integration of metabolomic data across species, as demonstrated in this research, holds significant potential to refine the models of longevity studies by reducing the reliance on animal testing, such as in mice or flies. If similar, replicable results can be achieved directly through data correlation, the time and resource investments required for multi-species testing could be substantially reduced, accelerating our understanding of longevity.
Buck professor Pankaj Kapahi, PhD, who led the study, describes the approach as transformative. “There is a lot of data sitting out there that is not being correlated between species,” he said.
“I think this approach could be a game-changer when it comes to identifying potential interventions to improve human health.”
The team’s strategy used extensive datasets from metabolomics, genomics and phenotypic studies to analyze responses to dietary restrictions, particularly examining how various genetic profiles responded to diet and impacted both lifespan and healthspan.
Threonine, identified as one of the promising metabolites in this study, has previously demonstrated benefits in animal models for conditions like diabetes, collagen and elastin production, blood clotting, fat metabolism and immune response. The research involved the not insignificant task of analyzing 120 metabolites across 160 strains of Drosophila (fruit flies) on both standard and calorie-restricted diets, and then cross-referencing this with human data from the UK Biobank [1].
Buck Institute postdoctoral fellow Vikram Narayan explained that using the human data allowed the team to focus on interesting metabolites such as those that are conserved in both species. It also allowed us to uncover the impact of those metabolites in humans.” Following these cross-species correlations, researchers validated the findings by reintroducing the identified metabolites into fly models.
The research revealed nuanced responses in flies, where threonine was found to extend lifespan in ways specific to both genetic strain and sex, suggesting the potential for personalized applications in humans [1]. “We’re not saying that threonine is going to work in all conditions,” says Kapahi. “Our research shows it works in subsets of both flies and people. I think most of us have stopped expecting to find a ‘magic-bullet’ intervention for aging. Our method provides another way to develop precision medicine for geroscience.”
Interestingly, orotate – a lesser-studied metabolite linked with fat metabolism – displayed a negative correlation with lifespan [1]. In flies, orotate countered the life-extending effects of dietary restriction across every strain tested and was similarly associated with shorter lifespan in humans; these findings emphasize the complexity of metabolic pathways and the likelihood that interventions targeting aging will always need individual variability into account.
Kapahi and his team hope this methodology will become more widely used in aging research, reducing reliance on multi-stage testing across animal models.
“So many times we find things that work in worms and flies and then we don’t have the resources to move the basic science forward,” he said. “This approach allows us to say with a lot more certainty that discoveries are going to be relevant in humans,” he explained, adding that it may lessen the need for studies involving mice, a result that he would welcome. (As would myriad other scientists!)
