Caution Urged in Using Polygenic Risk Scores to Predict Obesity
Key findings
- Using 25 years of data from the CARDIA study, Massachusetts General Hospital researchers compared polygenic risk score (PRS) performed versus other risk factors for obesity in predicting change in body mass index (BMI) from young adulthood to midlife
- An individual's BMI between ages 18 and 30 provided substantially more information about BMI in midlife than the PRS did (up to 5-fold increased variance explained)
- The PRS had a similar effect size as cardiorespiratory fitness and physical activity
- Serial assessment of BMI through young adulthood further augmented the ability to predict BMI by midlife
Because an appropriate body mass index (BMI) is important to general health, scientists are investigating ways to predict which young adults will develop obesity later in life to justify earlier intervention.
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Prior research has shown that a polygenic risk score (PRS) performance, developed from 2.1 million genomic variants in >500,000 individuals, explained 9% to 10% of the variation in BMI. Based on a statistically significant association between the PRS and BMI, it was concluded that the PRS can quantify inherited susceptibility to obesity.
However, having applied a different PRS to clinical data, Ravi V. Shah, MD, physician in the Cardiology Division at Massachusetts General Hospital, lead author Venkatesh L. Murthy, MD, of the University of Michigan, and colleagues urge caution about assuming that polygenic risk can be widely used to guide obesity prevention in adults. Their report appears in JAMA Cardiology.
Study Details
The team made use of the prospective Coronary Artery Risk Development in Young Adults (CARDIA) study, which involves white and black individuals from four U.S. communities who were 18 to 30 years old at baseline examination in March 1985 to June 1986. The researchers analyzed data collected between March 1985 and August 2011, the end of the year 25 follow-up examination period.
At study initiation, the participants had a normal BMI (average 23.6 kg/m2), although half reported that at least one parent was overweight. By year 25, average BMI had increased by approximately 5 kg/m2 in white participants and 7 kg/m2 in black participants.
As part of the CARDIA study, a PRS for obesity was derived in white individuals. In the current study, that PRS was applied to 1,608 white CARDIA participants as well as 909 black participants. The goal was to quantify the relative importance of the PRS, cardiorespiratory fitness, physical activity, parental history of overweight and BMI in young adulthood in predicting BMI trends over 25 years.
Predicting Midlife BMI
The researchers found there was insufficient precision for the PRS to predict midlife BMI. The PRS was significantly associated with BMI in young adulthood and midlife, but the association was modest (it explained 11%–14% of the variance in BMI once age, sex and parental history of overweight were considered).
In contrast, BMI in young adulthood explained 75% of the variation in BMI after five years (5.6-fold more than the PRS) and 52% of the variation in BMI in midlife (3.8-fold more than the PRS) in combination with age, sex and parental history of overweight.
Serial surveillance of BMI for deviation from growth curve profiles allowed the most precise predictions, accounting for approximately 80% of variation in BMI from young adulthood to midlife.
BMI in midlife was similarly associated with the PRS as fitness, activity and parental history of overweight were, and there was no evidence of interaction between fitness and the PRS. This suggests that higher levels of fitness and activity may limit obesity independent of polygenic risk.
Avoid Neglecting the Basic Factors
These results demonstrate that by young adulthood, BMI is more important than genetic susceptibility to obesity in understanding who may become obese in the future. Regardless of genetic risk, young adults should have their BMI closely followed and managed. Interventions that target change in activity patterns, diet, stress and other clinical contributors to obesity are more likely than genetic risk scores to alter BMI over time.
In general, clinicians should avoid rapid adoption of genetic risk assessments for complex traits, especially when under pressure from industry partners. Certainly, in the case of obesity, there is a risk of drawing attention away from more precise factors that are easily assessed and typically respond to lifestyle changes.
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