Ancient climate adaptation links to current metabolic health
Human populations have evolved under diverse climates. To survive they had to modify the body’s ability to produce heat. Over millennia, these different environmental conditions seem to have acted as selective pressures to drive genetic and phenotypic variations. Traits such as energy use and temperature regulation have roots in these ancient adaptations. In particular, brown adipose tissue (BAT) and skeletal muscle play a relevant role in heat generation.
Diversity through climate adaptation
Undertaken with the support of the Marie Skłodowska-Curie Actions(opens in new window) (MSCA) programme, the ClimAHealth(opens in new window) project explored how genes related to thermogenesis – the body’s ability to produce heat – varied across populations. Researchers analysed genomes from 26 populations across four continents for genetic evidence of adaptation in thermogenic pathways over the last 30 000 years. “We found that genes involved in thermoregulation were repeatedly targeted by natural selection in different populations, possibly helping ancestral human populations to cope with contrasting climates,” explains Diego Salazar-Tortosa, principal investigator of ClimAHealth from the University of Granada(opens in new window) in Spain.
Decoding ancient genetic signals in modern genomes
Researchers discovered that thermogenic genes tended to associate with body mass. This is consistent with the connection between heat production, energy balance and lipid/glucose metabolism, suggesting that ancient events of adaptation to climate may still influence present-day variability in cardiometabolic traits. Moreover, these findings can help explain why some populations may have different genetic predispositions to obesity-related disorders. Importantly, they highlight how adaptive events caused by ancient climates may continue to shape human health today. “ClimAHealth deepens our understanding of how human biology has been shaped by the environment,” notes Salazar-Tortosa. “This foundational knowledge could inform future research in personalised medicine and public health.” The development of a novel machine learning framework(opens in new window) allowed researchers to model complex adaptation signals in the human genome over the past 30 000 years. Intriguingly, this framework provided robust evidence of frequent, widespread genetic adaptation related to thermogenesis.
Population diversity and genetic patterns
Analysing population diversity allowed ClimAHealth to uncover where genetic adaptation is strongest (or at least more visible). As indicated by co-principal investigator David Enard: “Yoruba populations in Africa, who experienced fewer historical population bottlenecks(opens in new window), showed especially strong adaptation signals.” By considering multiple populations across the world, researchers were able to identify genomic regions that may have been repeatedly targeted by natural selection in different environments. Understanding these patterns helps them reconstruct the evolutionary history of human populations and could provide insight into why certain health-related traits vary across populations today.
Clinical relevance and future directions
A major breakthrough from ClimAHealth was the creation of the BAT connectome(opens in new window), a curated set of genes associated with brown fat function. These genes were not only evolutionarily selected but also highly expressed in human BAT. As co-principal investigator Jonatan Ruiz explains: “This map highlights candidate genes that influence energy balance and metabolism through BAT.” Although clinical tools are not yet on the horizon, the BAT connectome offers a strong foundation for future polygenic risk scores accounting for multiple genes and focused on obesity-related traits. Overall, project findings contribute to a deeper understanding of the human genome and its evolutionary history. This knowledge is essential for understanding genetic contributions to disease risk across diverse populations and opens a door to future applications in evolutionary medicine and personalised health.
