Precision nutrition across climates: decoding diet, tradition, and genomic adaptations from South Asia to the Arctic

Abstract

Human populations have developed distinct genetic adaptations to diet in response to changes in lifestyle and environments in which they live. Particularly contrasting patterns of dietary adaptations are expected in populations living in tropical versus extreme cold environments. This article explores the genetic, dietary, and microbiome-related adaptations in populations of South Asia and the Arctic. We review adaptations related to high-carbohydrate, plant-based diets in South Asians and compare these against adaptations in Arctic populations who have evolved to rely on fat- and protein-rich diets from marine and animal sources. We discuss how these genetic adaptations interact with traditional diets and microbiomes, and the implications for chronic disease risks as modern, westernized diets disrupt ancestral gene-diet-microbiome interactions. By comparing these regions, we highlight the need for genome-based nutrition created strategies that account for genetic diversity, local dietary traditions, and environmental context to promote precision health and prevent diet-related chronic diseases. This analysis offers new insights into how nutrition, culture, and genetics intersect in shaping population-specific health outcomes.

Keywords: Arctic; South Asia; genetic variation; genome nutrition; natural selection; nutrigenetics; precision nutrition; traditional diet.

Figure 1

Precision Nutrition: key areas important for the understanding of inter-individual dietary differences and their impact on health covered in this review.

Figure 2

Different dietary, genetic, and environmental features that distinctly characterize the Arctic and South Asian populations. AMY1A, amylase alpha 1A gene; LCT, lactase gene; MCM6, minichromosome maintenance complex component 6; FADS1, fatty acid desaturase 1; FADS2, fatty acid desaturase 2; CPT1A, carnitine palmitoyl transferase 1A; TBC1D4, TBC1 domain family member 4; and UCP1, uncoupling protein 1.

Figure 3

AMY1A gene copy numbers in different populations across the world [data from (63)] created using Tableau (https://www.tableau.com). Each circle represents a population, and the color of the circle indicates a gradient of color from lowest – 2 (blue) to highest −15 (red) average copy number of AMY1A gene in the population, while the yellow indicates the mid-point of copy number distribution.

Figure 4

Frequency of main LCT/MCM6 allele −13,910C > T (rs4988235) in different populations across the world, data taken from UCL’s LP database – GLAD (https://www.ucl.ac.uk/biosciences/gee/molecular-and-cultural-evolution-lab/global-lactase-persistence-association-database-glad), and additional allele frequency estimation of Arctic groups from (162) data. Each circle represents a population, and the color of the circle indicates a gradient of color from low (blue) to high (red; i.e., 0–100%) occurrence of average allele frequencies. Global distribution map was generated using Tableau (https://www.tableau.com).

Figure 5

Global distribution of the so-called ‘vegetarian diet variant’ rs66698963 — frequent in South Asia, and the ‘marine diet variant’ rs174570—with the highest derived allele frequency in Arctic populations. (A) Genotype frequency distribution bar plot of rs66698963—22 bp indel polymorphism, insertion/insertion (I/I), insertion/deletion (I/D), deletion/deletion (D/D) based on the data retrieved from Kothapalli et al. (20) and Malyarchuk et al. (163). (B) Allele frequency distribution of rs174570 in global populations, including data from the 1000 Genomes Project populations retrieved from (21) and Arctic groups from (162). AFR-Africans, EUR-Europeans, EAS-East Asians, AMR-Americans, SAS-South Asians, ARC-Arctic regions.