NEW YORK (GenomeWeb) – Genomic data may soon guide personalized weight management, according to a report from a National Institutes of Health working group.
Genome-wide association and other studies have linked some 150 genetic variants to BMI, waist circumference, or obesity risk. While some 40 percent to 70 percent of BMI is heritable, so is a portion of response to weight-loss interventions, suggesting that understanding the mechanisms influencing weight loss and weight gain may help tailor weight management approaches to individuals, as the working group led by the University of Texas's Molly Bray said in its Obesity paper.
"I think within five years, we'll see people start to use a combination of genetic, behavioral, and other sophisticated data to develop individualized weight management plans," Bray, a geneticist and professor of nutritional sciences, said in a statement.
Many of the genetic variants linked to BMI, waist circumference, or obesity risk have been traced to genes that are involved in central nervous system processing or neural regulation of feeding — such as BDNF, MC4R, and NEGR1 — or in fasting-related insulin secretion, RNA binding/processing, energy metabolism, lipid biology, or adipogenesis — like FTO, FOXO3, and MAPK3, among others.
The FTO gene in particular "has been consistently associated with obesity traits," Bray and her colleagues said in their review. It is located in an enhancer element that regulates the expression of the IRX3 and IXR5 genes and, through those, influences adipocyte development, thermogenesis, and lipid storage. FTO, though, only accounts for a small portion, 0.34 percent, of body size heritability.
Still, Bray and her colleagues noted that other variants have been associated with weight loss or weight gain. For instance, a variant in the MTIF3 gene was linked to weight loss in a study of 5,730 people randomly assigned to either a behavioral weight loss treatment or to a control condition. This allele had previously been linked to higher BMI and hip circumference, the researchers said, adding that people with the MTIF3 obesity-inducing allele seem to benefit more from intensive lifestyle intervention than others.
The microbiome and the epigenome also appear to wield influence over weight, the working group noted. Host gut bacteria have been shown to shift in response to weight loss and dietary changes. In particular, the Firmicutes to Bacteroidetes ratio appears to decrease with weight loss. Further, the archaeon Methanobrevibacter smithii can metabolize some foods better than other microbes, increasing host energy intake and leading to weight gain.
High-fat diets and physical activity, meanwhile, have been shown to influence DNA methylation patterns in skeletal muscle and adipose tissue, which are key to energy homeostasis. One study the researchers cited found that a six-month exercise program was linked to DNA methylation pattern changes involving a number of obesity-linked genes like FTO, GRB14, and TUB. Such epigenetic mechanisms, they added, could affect how a person responds to weight gain, loss, and maintenance by affecting genes that themselves regulate energy homeostasis.
At the same time, genes also affect what foods people eat, especially by influencing how they perceive bitter tastes and, to a degree, how physically active they are.
Bringing all of these different data together could tailor weight loss or weight management programs to an individual's particular needs, Bray and her colleagues said. Further, with the advent of cheaper sequencing approaches, activity-monitoring devices like FitBit, and other sensors, researchers and clinicians will have a wealth of data on which to draw.
But now, they said, the challenge is to make sense of it all. "Acquiring data is the easy part," the researchers wrote. "What is direly needed are innovative approaches for mining multiple levels of 'omics' and other data to discern patterns of data-disease relationships that may then be used for decision making in clinical treatment."