Genome-wide association studies continue to identify variants that affect the likelihood of developing one or more of several hundred common health conditions, but the risk associated with any one variant is generally quite small. To make this information clinically relevant, the cumulative effects of multiple variants need to be analyzed within the context of known environmental factors. As the cost of genotyping and sequencing falls, this problem is being addressed by including genetic variants in large epidemiologic trials.
One example is the recent study by Harvard investigators on the interaction between genetic predisposition to obesity and intake of sugar-sweetened colas, non-cola soft drinks, and fruit drinks (N. Engl. J. Med. 2012 [doi:10.1056/NEJMoa1203039).
The authors analyzed data from three prospective cohort studies: the Nurses’ Health Study (NHS) of female registered nurses, the Health Professionals Follow-Up Study (HPFS) of male health professionals, and the Women’s Genome Health Study (WGHS) of female health professionals. A total of 11,357 initially healthy NHS and HPFS participants had genotype data available and were used for primary analysis. From WGHS, 21,740 initially healthy women were used for a replication set.
Intake of sugar-sweetened beverages (SSBs) was measured by periodic questionnaire and divided into categories of less than 1 serving per month, 1-4 servings per month, 2-6 servings per week, and 1 or more serving per day. Height, weight, physical activity, and other dietary data also were obtained by periodic questionnaire.
All 32 single nucleotide polymorphisms (SNPs) currently known to be associated with obesity were included. A combined risk score was calculated for each participant, using a weighted value for each SNP according to its relative effect size. Since there are two copies of each SNP, the potential range of scores was 0-64; actual scores varied from 13-43, with a mean of 29. Effects on BMI were determined by 10-point increments in risk score.
As expected, there was a significant correlation between greater SSB intake and higher BMI, but this effect was much more pronounced among participants with higher genetic risk scores. In those with the lowest SSB intake, a 10-point increase in genetic risk corresponded with 1.00 kg/m2 increase in BMI in the pooled NHS/HPFS studies and a 1.46 kg/m2 increase in BMI in the WGHS.
At the other end of the spectrum, among those who consumed 1 or more SSBs daily, a 10-point increase in genetic risk corresponded to a 1.85 kg/m2 increase in BMI in the NHS/HPFS studies and a 2.43 kg/m2 increase in WGHS.
By contrast, intake of artificially sweetened beverages had no effect on the association between genetic risk score and BMI. In addition, while those with the greatest intake of SSB also had higher total calorie intake and lower physical activity, alcohol intake, and overall diet quality, statistical adjustment for these factors had no effect on the observed association between SSB intake and genetic predisposition to obesity. Furthermore, while the total genetic risk score showed strong statistical significance, very few of the individual SNPs demonstrated a significant effect by themselves, and exclusion of variation in the single strongest marker (in the FTO gene) did not change the overall findings.
The authors also looked at the incidence of obesity according to genetic risk score and SSB intake. Pooling data from the three prospective studies, the relative risk of new-onset obesity per increment of 10 genetic risk points was 1.35 for SSB intake less than 1 per month, 1.59 and 1.56 for the intermediate levels of SSB intake, and a striking 3.35 among participants consuming 1 or more SSB per day.
There are still plenty of limitations in genetic and obesity research. The genetic risk score calculated in this study accounted for different effect sizes of each variant, but we don’t yet know if there are interactions between individual variants that might require further adjustment. Most of the genetic variability in BMI is not accounted for by the currently known SNPs, so additional genetic factors will need to be considered in the future. SSB intake may or may not be the primary cause of the observed associations; other unmeasured lifestyle or environmental factors are likely also important. And understanding actual pathophysiologic mechanisms of interaction between genetic variants and dietary factors is still in its infancy. Nonetheless, this study provides compelling evidence that such interactions do exist and are clinically relevant.
For the majority of patients, these findings are unlikely to alter the general weight management advice to maximize exercise and limit total calories, especially sugars, simple starches and fats. The most immediate clinical utility may lie in counseling nonobese patients about their future risk. Family history remains an inexpensive test for overall genetic predisposition, and for those with a strong family history of obesity it may be especially important to limit SSB intake (and perhaps other sources of sugar and simple starch, too). Selective testing of a panel of obesity-linked genetic variants might be of value for a subset of patients who need extra motivation to make the appropriate lifestyle changes.