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When the proclamation that the human genome had been fully sequenced was made around the turn of the century, there were pronouncements about the new era of big data genomics and its promise of informing personalized, precision medicine. However, as is often true in medicine, the translation to the bedside has been a bit slower to occur than originally thought. One of the issues in this translation process is lack of diversity in the backgrounds of the genomes that have been sequenced; the oft-used UK Biobank includes over half a million whole genome sequences, but around 85% are from people of white European descent. The NIH’s All of Us research program, which is collecting one million full genomes along with other health data from across the racial spectrum, is starting to provide data that will hopefully accelerate the application of polygenic risk scores (PRS, a measure of the risk of a specific condition based on the influence of many genetic variants including those of known and unknown function) to personalize treatments for a diverse spectrum of patients. The program also tracks patients’ activity levels via their personal fitness trackers.¹ Now, a new study using All of Us data suggests that obesity risk based on genetics can be modulated by changing the amount of physical activity one gets.² However, highlighting that the path of such progress isn’t always smooth, another recent study that attempted to link genetic markers with personalized dietary interventions did not find an effect of gene personalization on dietary effectiveness.³

In the All of Us study, researchers examined 3,051 US adults (73% women, median age 52.7) without obesity in the All of Us Research Program and found that incidence of obesity over a median follow-up of 5.4 years was 13% for those in the lowest quartile of BMI PRS but reached 43% for those in the highest quartile. Both higher genetic risk, measured by PRS, and lower daily step counts were each independently associated with an increased risk for obesity. Another way to look at the data is how much activity would someone with a higher genetic risk of obesity need to do to lower their risk? The researchers found that in order to have an obesity risk comparable to that of individuals in the 50^th percentile of PRS, those with a PRS in the 75th percentile would need to walk an average of 2,280 more steps per day. On the other hand, people in the 25th percentile of obesity risk could walk 3,660 fewer steps and have the same obesity risk as those in the 50^th percentile.²

This study highlights the concept that an individual’s genes are not their destiny, that someone with an increased risk of a condition based on their genes may attenuate that risk by changing their lifestyle; in this case, by increasing activity. Notably, this study did not examine nutrition or eating patterns at all, which would likely have a major impact on obesity risk as well, and it is also well known that microbiome composition also influences risk of obesity.

On the other side of the genomic personalized medicine coin, a smaller study looking at polygenic markers and diets did not find a relationship between genotype and dietary response. In a small, randomized controlled trial, researchers in the POINTS study found that among 145 overweight or obese participants, weight loss based on genetic markers did not result in significantly different amounts of weight loss over 12 weeks between those who followed a diet tailored to their genetics and those who followed a diet that was not tailored to their genetics.⁴ Participants were identified as fat-responders or carbohydrate-responders based on their combined genotypes at 10 genetic variants and randomized to a high-fat or high-carbohydrate diet yielding four groups, two of which were concordant between genetics and dietary plan and two discordant. The participants in all groups did seem to lose significant weight, although it does not seem that this was an outcome measure of the study, and the trend of a difference favored the concordant diets but was not significant.⁴

So why didn’t personalizing dietary plans in this way work? It might be related to the markers used, which appeared to be a somewhat novel polygenic measure for fat or carbohydrate response, or related to the small sample size, personal preferences of the subjects, or other effects not measured. But it might just be that 12 weeks wasn’t quite long enough to show a significant difference. Another recent randomized controlled trial that used dietary personalization based on genetics found that while personalization of dietary advice based on DNA did not result in fasting plasma glucose (FPG) changes within the first six weeks, it was associated with significant reduction of FPG and HbA1c at 26 weeks when compared to standard care.⁵ So perhaps it just takes a bit more time to show a difference, or it could be that factors such as an individual’s microbiome composition interacts with genes and diet to impact the effect.

There will certainly be more to come regarding personalizing lifestyle changes based on DNA, but even without access to a patient’s fully sequenced genome, functional medicine clinicians can use contextual factors to personalize treatment plans, including things like personal and family history, physical exam signs, and patient goals and preferences. And even if the change the patient makes isn’t necessarily the most efficient for their genotype, they can still improve their health and reduce risk of future disease and dysfunction. But this work moves us closer to the goal of personalizing lifestyle treatments based on genomic data, along with other relevant information specific to each patient, and the day when we can realize the promise of the genomic big data era.

References

1. Master H, Annis J, Huang S, et al. Association of step counts over time with the risk of chronic disease in the All of Us Research Program [published correction appears in Nat Med. 2023;29(12):3270]. Nat Med. 2022;28(11):2301-2308. doi:1038/s41591-022-02012-w
2. Brittain EL, Han L, Annis J, et al. Physical activity and incident obesity across the spectrum of genetic risk for obesity. JAMA Netw Open. 2024;7(3):e243821. doi:1001/jamanetworkopen.2024.3821
3. Geng J, Ni Q, Sun W, Li L, Feng X. The links between gut microbiota and obesity and obesity related diseases. Biomed Pharmacother. 2022;147:112678. doi:1016/j.biopha.2022.112678
4. Höchsmann C, Yang S, Ordovás JM, et al. The Personalized Nutrition Study (POINTS): evaluation of a genetically informed weight loss approach, a randomized clinical trial. Nat Commun. 2023;14(1):6321. doi:1038/s41467-023-41969-1
5. Karvela M, Golden CT, Bell N, et al. Assessment of the impact of a personalised nutrition intervention in impaired glucose regulation over 26 weeks: a randomised controlled trial. Sci Rep. 2024;14(1):5428. doi:1038/s41598-024-55105-6