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. Author manuscript; available in PMC: 2019 Nov 1.
Published in final edited form as: Am Psychol. 2018 Nov;73(8):1045–1055. doi: 10.1037/amp0000253

Precision Behavioral Medicine: Implications of genetic and genomic discoveries for behavioral weight loss treatment

Jeanne McCaffery 1
PMCID: PMC6220707  NIHMSID: NIHMS945534  PMID: 30394782

Abstract

This article reviews the concept of precision behavioral medicine and the progress toward applying genetics and genomics as tools to optimize weight management intervention. We discuss genetic, epigenetic and genomic markers as well as interactions between genetics and the environment as they relate to obesity and behavioral weight loss to date. Recommendations for the conditions under which genetics and genomics could be incorporated to support clinical decision-making in behavioral weight loss are outlined and illustrative scenarios of how this approach could improve clinical outcomes are provided. It is concluded that there is not yet sufficient evidence to leverage genetics or genomics to aid the treatment of obesity but the foundations are being laid.

Keywords: Behavioral Medicine, Obesity, Weight Loss, Genetics, Genomics

Precision medicine reflects the goal of providing tailored treatment to individuals’ characteristics with the promise of improving diagnoses and the safety and efficacy of treatment modalities. Application of precision medicine has been ongoing for several years in the treatment of cancer. Individuals with a strong family history of breast cancer now routinely undergo genetic testing to determine if they carry high risk mutations, such as those in BRCA1 and BRCA2, guiding the course of screening, prevention and treatment. Women with breast cancer also receive genetic tests to determine if their tumor tissue expresses the HER2 gene, which can be targeted specifically by Herceptin (trastuzumab) to slow or block the growth of cancer tissue. More recently, it has been discovered that up to 50% of infants with neonatal diabetes have mutations within the KCNJ11 or ABCC8 genes (Carmody et al., 2014), resulting in faulty insulin secretion but not necessarily difficulty with insulin production. This deficit can be successfully treated with sulphonylureas, a class of diabetes medications specifically targeting insulin secretion, often dramatically improving survival and the ability to thrive.

Much of the emphasis in precision medicine to date has been placed on pharmacotherapy. The question arises, however, as to whether established predictors - be they genetic, genomic, biologic, environmental or behavioral - can aid in the targeting of evidence-based behavioral treatments. Several scenarios could emerge. For instance, it may be plausible to target the most intensive behavioral treatments to individuals who need them most. It may also be plausible to select individuals who are most likely to benefit from behavioral treatment and refer others less likely to benefit to alternative therapies. To the extent that predictors predate a disease state, it may also be plausible to provide feedback on risk as part of a prevention.

In this article, we focus on the potential of genetics and genomics to inform clinical decision-making in behavioral weight loss treatment and prevention. Although not explicitly addressed here, the role of behavioral and environmental variables as tools for precision medicine should not be underestimated. “Just-in-time” interventions, for example, leverage mobile technologies to track environmental antecedents of risk behavior and deliver interventions in a time of need. In behavioral weight loss, baseline measures, including weight history, co-morbid medical conditions, co-morbid eating pathology, taste preferences and resting metabolic rate, as well as process measures, such as treatment adherence, early weight loss, self-monitoring and physical activity, could all potentially be leveraged for the development of specific treatment components. We also do not focus on pharmacologic or surgical treatments although potential for precision medicine incorporating these treatment modalities certainly exists.

Genetics, epigenetics and genomics

Genomics is a broad term encompassing the structure and function of our DNA, inclusive of genetics, epigenetics and their functional products. The genetic code is housed within the chemical “ladder rungs” of the double-helix DNA molecule. Specific sequences of the genetic code combine to form a gene, which historically was thought to code for a single protein. But it is now known that the relationship of the genetic code to the production of protein is far more complex. Genes can code for multiple distinct proteins. Genetic sequences can also code for non-protein elements that can block the translation of a gene into protein.

The human genetic code is generally 99% the same across individuals. The 1% of variation gives rise to individual differences in physical characteristics, personality, health and disease. There are several known types of genetic variation - some of which impact function and others of which do not. For many, we do not yet fully understand what they might do. Polymorphism is a broad term encompassing different types of common genetic variation. Single nucleotide polymorphism (SNP) refers to variation in the building blocks (bases) of the DNA code. Four types of bases contribute to the DNA code, adenine (A), cytosine (C), guanine (G) and thymine (T). A SNP genotype refers to the combination of two bases across two chromosomes. So, for example, an AA genotype would indicate two copies of the A base at a given genetic locus, AT genotype would refer to one copy of an A base and one copy of a T base and a TT genotype would represent two copies of the T base. Allele refers to one copy of a genetic locus inherited from one or the other parent. An AA genotype, for instance, can also be referred to as two copies of the A allele. SNPs account for 90% of genetic variation.

Individual SNPs or a small number of SNPs have been examined in candidate gene studies where a specific gene has been selected for biologic function related to a given trait. This strategy gave rise to the discovery that a combination of two SNPs in the apolipoprotein (APOE) gene predicted Alzheimer’s Disease (Corder et al., 1993). SNPs also serve as the target for high-throughput genotyping techniques, which rapidly genotype millions of SNPs dispersed throughout the genetic code and permit genome-wide association studies (GWAS). GWAS has led to the discovery of thousands of genetic regions associated with human diseases or traits. Ninety-seven loci, for example, have now been discovered as associated with body mass index (BMI) (Locke et al., 2015) with an additional 49 loci related to waist-to-hip ratio (Shungin et al., 2015). More recently, whole genome sequencing has become available to genotype the entire DNA code with promise to identify more rare and unique variation as sample sizes increase.

Structural variation in the genetic code, including insertions, deletions and variable copy numbers of segments of DNA (copy number variants (CNVs)) are less understood but may be one of the most dynamic types of variation and reflect mechanisms of genomic evolution adapting to different environments (Zarrei, MacDonald, Merico, & Scherer, 2015). For example, Perry and colleagues (Perry et al., 2007) observed greater copy numbers of the salivary amylase gene (AMY1) in agrarian societies (high starch intake) as compared to cultures relying primarily on hunting and gathering (low starch intake). As salivary amylase initiates digestion of simple starches, higher copy numbers of the AMY1 CNV appear to have been selected over time with the development of farming and agriculture. Insertion/deletion polymorphisms have also been identified in the regulatory regions of the serotonin transporter gene (SLC6A4) and the monoamine oxidase gene (MAOA), which have been shown to interact with early life stress in predicting depression (Karg, Burmeister, Shedden, & Sen, 2011) and antisocial behavior (Byrd & Manuck, 2014), respectively.

Epigenetics, meaning “above genetics”, refers to chemical modifiers of the structure or function of the DNA molecule that do not constitute variation in the genetic code per se. Epigenetic mechanisms can render the DNA code more or less available for translation into gene products. One type of epigenetic modifier is DNA methylation, where methyl groups are added to the structure of the DNA molecule at specific sites in the DNA code - CpGs, or where a C base is located next to a G base. DNA methylation is critical in developmental biology when varying patterns of DNA methylation drive and maintain distinct cell types that lead to the development of the diverse tissues of the human body. Global changes in DNA methylation are a hallmark of cancer (Liang & Weisenberger, 2017). Increasing evidence further suggests that more subtle changes may occur, either as a consequence of aging or specific environmental exposures, changing patterns of the expression of genes and genomic function.

A prime example of environmental change to DNA methylation is cigarette smoking. A recent meta-analysis of the association of cigarette smoking with DNA methylation now identifies 62 CpGs discovered in 3 or more studies (Gao, Jia, Zhang, Breitling, & Brenner, 2015), several of which also predict all-cause mortality (Y. Zhang et al., 2017). A second, classic, example of environmental influence on DNA methylation comes from mouse models of maternal care (Weaver et al., 2004). Low attentive maternal care of pups (low maternal licking and grooming/arched-back nursing) predicts hypermethylation upstream of the glucocorticoid receptor gene (NR3C1 or GR) in offspring, inhibiting GR expression. Methylation upstream of GR has now been associated with early life stress in multiple research studies (Palma-Gudiel, Cordova-Palomera, Leza, & Fananas, 2015). A third example of epigenetic change comes from the aging literature where specific CpGs track so closely with chronologic age that DNA methylation has been proposed as a “biologic clock” (Hannum et al., 2013; Horvath, 2013).

Gene expression, quantified through RNA, is the functional product of a sequence of DNA, reflecting genetics and epigenetics as well as cellular transcription factors that promote translation of gene sequences into functional products. Recent data suggest that social adversity may alter the gene expression in peripheral immune cells toward a pro-inflammatory state, potentially with downstream effects on cancer biology, asthma and infectious disease risk (S. W. Cole et al., 2012; Kohrt et al., 2016).

Genetics and genomics of obesity

Obesity is at the intersection of genetics, genomics, behavior and environment, rendering excess weight a nexus for determining whether precision medicine employing genetics or genomics may be leveraged to optimize behavioral treatment. Over one third of the US population is estimated to be obese (body mass index ≥ 30 kg/m2) (Flegal, Kruszon-Moran, Carroll, Fryar, & Ogden, 2016; Ogden, Carroll, Fryar, & Flegal, 2015), representing a dramatic increase in prevalence over forty years (Flegal et al., 2016). Although some leveling of this trend has been observed recently, millions of Americans now suffer weight-related health complications, including cardiovascular disease, diabetes and certain cancers (Flegal, Panagiotou, & Graubard, 2015).

Body weight is well-known to be heritable with twin studies estimating that genetic factors account for between 40–70% of the variance in BMI (Maes, Neale, & Eaves, 1997). GWAS has led to discoveries of genetic loci reliably associated with body weight. The largest GWAS to date, including nearly 340,000 participants, identified 97 common genetic variants associated with BMI (Locke et al., 2015). Of particular interest, genes in close proximity to the BMI-related loci are overwhelming expressed in the central nervous system (Locke et al., 2015). Some genes play key roles in hypothalamic eating pathways (e.g., MC4R and BDNF) but even greater enrichment is seen for hippocampal and limbic regions, most commonly associated with learning, memory and emotion. Several of these loci are located in gene regions housing rare mutations causing monogenic forms of obesity (e.g., MC4R, BDNF (S. Farooqi & O’Rahilly, 2006)), providing convergent evidence for the specific genes involved. Thus, it appears quite plausible that behavior will play a key role in genetic pathways related to BMI.

Indeed, research now confirms that BMI-associated genetic variants relate to dietary intake. In replicated research, the FTO and BDNF obesity-risk regions predicted greater total caloric intake; the FTO region is further associated with percent fat intake (McCaffery et al., 2017; McCaffery et al., 2012). Nearly all rare, monogenic forms of obesity, including mutation within BDNF, also result in hyperphagia (S. Farooqi & O’Rahilly, 2006).

For waist-to-hip ratio, 49 additional genetic loci have been identified (Shungin et al., 2015). Genes in close proximity are largely expressed in peripheral tissues, such as adipose tissue, and relate to adipogenesis and insulin resistance.

It is important to note that the majority of cohorts included in adiposity-related GWAS have been of non-Hispanic Caucasian descent, leading to the question of how well these loci generalize to other racial and ethnic populations and whether additional loci would be identified with inclusion of a greater diversity in the research cohorts. Studies of African and East Asian participants do suggest that common loci contribute to obesity risk across Caucasian, African and East Asian participants but effect sizes can vary (Fall et al., 2017). Novel variants have also been discovered in African, East Asian and Samoan samples (Minister et al., 2016; Monda et al., 2013; Wen et al., 2014). Thus, common and distinct genetic variability will likely contribute to genetic risk for obesity across major population groups.

Moreover, while BMI and waist-to-hip ratio are convenient measures for large population studies, they do not directly quantify fat mass and may not fully capture genetic associations. Genome-wide study of percent body fat, for example, confirmed several adiposity loci, some with stronger effect sizes, but also identified novel genetic regions (Lu et al., 2016), highlighting the potential to discover more about the genetics of body weight with greater precision in measurement.

Epidemiologic gene x environment interaction in obesity

Given the ample evidence for genetics, environment and behavior in obesity development, it is not surprising that genetic risk for obesity is accentuated by environments and behaviors thought to increase risk for obesity and diminished by environments and behaviors thought to protect against obesity. This alteration of the magnitude of association of genetic risk by environment/behavior is tested by statistical interaction, and commonly referred to as “gene x environment interaction”. Yet, it is important to recognize that many of the “environmental” factors examined in gene x environment interaction studies of adiposity are in fact behaviors, such as dietary intake or physical activity. These behaviors have been shown to be heritable, thus, and do not strictly reflect “environment” in the sense of a “non-genetic” predictor (den Hoed et al., 2013; Faith, Rha, Neale, & Allison, 1999). Moreover, a “gene” per se is not typically assessed in these studies but instead genetic variation (e.g, SNPs) that may be within a gene or its regulatory region or may be in a gene desert as can be the case for GWAS results. With these caveats, we will use the term “gene x environment interaction” to be consistent with the prior literature.

Numerous studies now support interaction of genetic and behavioral factors in association with body weight. Early evidence for gene x environment interaction can be found in studies of the association of the FTO region and physical activity as predictors of obesity. In the initial report, the obesity risk SNP in the FTO region was associated with obesity but only among participants reporting little to no physical activity (Andreasen et al., 2008). This interaction between genetic risk and physical activity has now been confirmed for an expanded list of obesity-associated genetic markers in over 100,000 participants (Ahmad et al., 2013).

Most recently, in the UK Biobank including close to 130,000 participants, physical activity blunted the impact of the FTO region on BMI, whereas a poor diet, deviation from a normal sleep pattern and a socioeconomic deprivation score exaggerated genetic risk (Young, Wauthier, & Donnelly, 2016). Such findings substantiate the notion that environmental exposures/behaviors, from exercise frequency to food and beverage choice to sleep and socioeconomic factors, can lead to variation in the genetic effects on one’s health.

It has further been documented that elevated BMI can alter genetic influences on cardiometabolic risk. Lamina and colleagues identified a BMI x genetic risk score interaction for HDL cholesterol finding that elevated BMI heightened genetic risk for low HDL cholesterol (Lamina et al., 2012). Cole and colleagues replicated this interaction and extended the finding to triglycerides (C. B. Cole et al., 2014). Interestingly, genetic risk for type 2 diabetes is greater among individuals who are not obese, potentially suggesting that diabetes among non-obese individuals is more likely to result from genetic loading (Talmud et al., 2015).

Figure 1 summarizes the evidence for gene x environment interaction related to obesity. Taken together, this body of research indicates that, for most people, genetic risk for obesity is not destiny. Environmental and behavioral factors can augment or diminish the impact of genetics, and perhaps prevent obesity from occurring even among those with genetic risk. Moreover, obesity may heighten genetic risk for obesity-related co-morbidities, most notably dyslipidemia, highlighting the potential of weight gain prevention or weight loss in the context of genetic risk.

Figure 1.

Figure 1

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Overview of gene x environment interaction for body mass index.

Epigenetics, gene expression, microbiome and BMI

The discovery of epigenetic and gene expression changes related to obesity opens a new frontier in the potential for novel predictors of obesity treatment response. Hypermethylation of CpGs in HIF3A correlates with higher body weight in subcutaneous adipose tissue (AT) and blood (Agha et al., 2015; Huang et al., 2016) with potential implications for AT hypoxic response and dysfunction (Dick et al., 2014; Pfeiffer et al., 2016). Wahl and colleagues (Wahl et al., 2017) expanded the list to 187 CpGs from blood that covary with body weight. Many of the sites are located in close proximity to genes related to adipose tissue biology and insulin resistance, such as CpGs within ABCG1, a key regulator of lipid deposition in adipocytes also associated with type 2 diabetes in this sample.

The above studies identified DNA methylation markers in blood and found at least some correspondence with DNA methylation in AT. Yet studies originating in AT portray a more complex picture with DNA methylation and gene expression differing by location and sex. For example, lower-body fat depots that are commonly larger among women exhibit a pattern of DNA methylation and gene expression that efficiently stores lipid and protects against insulin resistance (Fried, Lee, & Karastergiou, 2015).

Sequencing of bacteria species within our gut, collectively labeled the gut microbiome, explains individual differences in the metabolism of consumed food with potential associations with body weight (Karlsson, Tremaroli, Nielsen, & Backhed, 2013). Gut permeability to bacteria is further associated with obesity and obesity-related inflammation (Teixeira et al., 2012). Over time, these mechanisms will more fully be integrated into the overarching models of obesity.

Genetics and genomics of behavioral weight loss

Obesity is amenable to behavioral intervention. Randomized, controlled trials of behavioral weight loss programs typically produce initial weight losses of 7% or more, resulting in clinically important health benefits (Knowler et al., 2002; Wing et al., 2010). In the Diabetes Prevention Program (DPP; Knowler et al., 2002), 3,324 participants with impaired glucose tolerance were randomized to a placebo control, 850 mg of metformin twice daily or lifestyle intervention focusing on weight loss through calorie restriction, a low-fat diet, increased physical activity and supporting behavioral strategies. Participants in the lifestyle intervention arm lost over 6 kgs at year 1, with little to no change in the placebo group, and reduced their incidence of diabetes by 58% (Knowler et al., 2002). In Look AHEAD (Wing et al., 2013), 5,145 overweight or obese individuals with type 2 diabetes were randomized to the Intensive Lifestyle Intervention (ILI) arm, based upon the DPP lifestyle intervention, or a Diabetes Support and Education (DSE) arm with no intensive weight loss intervention. Individuals randomized to ILI lost an average of 8.6±6.9% of their initial body weight at year 1 relative to losses of 0.7±4.8% among individuals assigned to DSE. ILI improved insulin sensitivity, and glucose and lipid control, reduced need of diabetes medications, and maintained physical mobility. However, the intervention did not prevent cardiovascular disease morbidty and mortality, the primary outcome of the study (Pi-Sunyer, 2014; Wing et al., 2013).

In addition to the overall weight loss trends, it is important to note that individual differed substantively in their response to weight loss treatment. In Look AHEAD, for example, ILI produced greater weight loss overall but a range of responses from successful to unsuccessful were observed in both treatment arms (Wadden et al., 2011; Wadden et al., 2009). Clearly, a greater understanding of predictors of individual differences in weight loss could have important health benefits. Behavioral predictors of weight loss have been identified, but primarily include process variables, such as adherence, participation in physical activity and initial weight loss (Unick et al., 2015; Wadden et al., 2009). Identifying genetic or genomic predictors of individual differences in weight loss could lead to a priori groups that differ in the likelihood of successful weight loss. With this knowledge, several opportunities for optimizing treatment could arise:

  1. Individuals who are most likely to succeed in behavioral programs could be referred for this approach, whereas alternative treatment methods, be they pharmacological or surgical, could be identified for those who are less likely to succeed.

  2. The intensity or mode of intervention could be varied. Behavioral weight loss interventions can be costly and time-intensive. It may be plausible to recommend less expensive and remote programs, such those using mobile technology or the internet, for individuals who are more likely to succeed and employ more intensive interventions for those who are likely to have more difficulty losing weight.

  3. The core components of the behavioral weight loss intervention could be tailored to genetic background. It is increasingly clear that weight loss programs differing in macronutrient content (e.g., low-fat, low-carbohydrate, high-protein) can produce weight loss as long as they result in calorie restriction. It may be that individuals who have difficulty losing weight with a low-fat approach perhaps due to genetically-driven taste preferences may succeed with a low-carbohydrate approach. Level of exercise intervention could also be tailored to individuals discovered to carry genetic variants related to low-resting energy expenditure.

Genetics of weight loss

A necessary condition for tailoring weight loss protocols to genetics or genomics is identifying reliable and meaningful genetic or genomic predictors. The heritability, or genetic variance, of weight loss first was documented in a careful laboratory study of identical twins. Bouchard and colleagues (C. Bouchard et al., 1994) induced weight loss in identical twin pairs through supervised exercise designed to produce of daily energy balance deficits of 500 kcals. Strong similarity between co-twins as compared to non-related individuals provided some of the first evidence of genetic involvement in magnitude of weight loss with intervention.

The largest study (Papandonatos et al., 2015) to date to test whether specific genetic variation predicts the magnitude of weight loss during lifestyle intervention combined analyses across Look AHEAD and DPP. The combined genetic sample included 5,730 participants randomly assigned to either behavioral weight loss treatment or a control condition. Analyses focused on 91/97 established obesity-predisposing loci derived from GWAS as of 2015 (Locke et al., 2015).

Results indicated that each copy of the minor G allele for the rs1885988 variant in the MTIF3 region was associated with a mean 0.83 kg, or 1.82 lb, greater weight loss in the lifestyle arm relative to the comparison arm over the course of four-year follow-up. No significant associations were observed in the control groups. The MTIF3 gene encodes a protein that is essential for energy balance in the mitochondria. The minor G allele has previously been associated with higher BMI. Thus, carriers of the MTIF3 obesity-inducing allele appear to benefit more from intensive lifestyle interventions than non-carriers.

Alternative weight loss interventions

The clinical guidelines for treatment of obesity state that diets of differing macronutrient content can be equally successful in producing weight loss as long as calories are restricted (“Executive summary: Guidelines (2013) for the management of overweight and obesity in adults,” 2014) and it is plausible that genetics or genomics may differentiate who will respond to a given diet strategy. The Pounds Lost trial, for example, found individuals carrying the A obesity risk allele at FTO rs1558902 exhibited potentiated weight loss on a high-protein diet relative to a low-protein suggesting that a high-protein diet may aid with weight loss particularly among those with specific genotypes in the FTO region (X. Zhang et al., 2012). Obesity risk variants in the FTO and MTIF3 locus have also been shown to be associated with magnitude of weight loss following bariatric surgery (Rasmussen-Torvik et al., 2015; Sarzynski et al., 2011). Further research is needed to confirm and extend these associations.

Does behavioral weight loss mitigate genetic risk for obesity-related comorbidities?

Behavioral weight loss may also differentially benefit those at high genetic risk for obesity-related co-morbidities, such as type 2 diabetes, cardiovascular disease, hypertension and certain cancers. One of the most promising results in this regard derives from the DPP, where one of the primary genetic markers associated with diabetes risk, rs7903146 in TCF7L2, differed in the prediction of progression to diabetes across treatment arms (Florez et al., 2006). Participants with the highest risk, TT, genotype demonstrated a marked increase in diabetes risk in the placebo arm. Yet no effect of the TT allele at rs7903146 was seen in the lifestyle intervention group, suggesting that behavioral lifestyle intervention may be able to counter the genetic risk for T2D. This research has now been confirmed incorporating a genetic risk score of 34 diabetes related variants in DPP (Hivert et al., 2011). The Look AHEAD trial has further found that lipid-related loci, including those within CETP, can blunt the beneficial changes in HDL cholesterol typically seen with weight loss intervention (Huggins et al., 2013). And, again in DPP, a genetic risk score comprised of 32 lipid-related SNPs also predicted resistance to LDL cholesterol change with weight loss intervention (Pollin et al., 2012). Thus, individuals carrying common risk variants for T2D should be encouraged to participate in behavioral weight loss interventions to mitigate this risk but the extent of improvement in lipids in response to weight loss may vary by genetic predisposition. Figure 2 summaries the evidence for genetic predictors of weight loss and of cardiometabolic change with behavioral weight loss intervention.

Figure 2.

Figure 2

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Overview of genetic predictors of weight loss and change in cardiometabolic risk with behavioral intervention.

Weight loss, DNA methylation, gene expression and the gut microbiome

The potential for epigenetic, gene expression or microbiome to change with weight loss or perhaps predict success with weight loss or the associated change in cardiometabolic health is, as yet, understudied. Small studies do suggest an impact of weight loss on DNA methylation (L. Bouchard et al., 2010) but a recent review concluded that there was little evidence for consistency or replication across studies (Aronica et al., 2017). Exercise intervention appears to change global DNA methylation and methylation within over 7,000 genes in AT, including ones related to obesity or diabetes (Ronn & Ling, 2013), but again replication is needed. With regard to gene expression, weight loss has been reported to progressively alter gene expression in subcutaneous AT, increasing expression of genes involved in cholesterol flux (such as ABCG1), and reducing expression in pathways related to lipid metabolism and oxidative stress among others (Magkos et al., 2016). Lastly, weight loss appears to restrict the diversity of bacteria in the gut with potential deleterious consequences (Seganfredo et al., 2017) but a beneficial impacts of weight loss have also been reported (Liu et al., 2017; Ott et al., 2017).

Implications for Precision Medicine in Obesity

Returning to the potential of applying precision medicine to behavioral weight loss treatment, at this stage, the genetic or genomic biomarkers do not yet sufficiently predict the course of weight loss to support clinical decision-making. But, foundations are being laid. Three criteria that must be met prior to use in clinical decisions include: reliability, generalizability and clinical significance.

Reliability

To be considered as a clinical test, a biomarker must show reliable association with treatment outcomes. MTIF3 rs1885988 is the first genetic variant consistently related to behavioral weight loss outcomes across two cohorts. It will be important to continue to validate this marker across additional trials and diverse cohorts.

Generalizability

Testing whether the genetic effect holds up across age, sex and in various population groups is critical. Almost no research addressed these questions in behavioral weight loss. Indeed, many GWAS studies, including those of body mass index, have focused primarily on homogeneous populations of European descent to discover new regions of association, although more recent research is beginning to address this gap (Shungin et al., 2015) (Locke et al., 2015). Continued research is needed to validate discoveries in groups differing in age, sex, race and ethnicity as well as to discover novel regions that may be specific to a given developmental period, sex or population group (Fried et al., 2015; Minster et al., 2016).

Clinical significance

To be useful in clinical decision-making, genetics and genomics must predict a sufficient amount of variance in treatment response to alter health outcomes. In behavioral weight loss, 5% weight loss (~5 kgs or 11 lbs for an average treatment seeking individual) has been considered a benchmark for clinically significant differences as the health benefits of weight loss begin to emerge (Magkos et al., 2016). With a change in weight loss of 0.70 kg, or 1.51 lbs, for one copy of the risk allele at MTIF3 rs1885988, and 1.40 kg, or 3.02 lbs, for two copies, the magnitude of the MTIF3 effect does not approach the level of clinical significance alone. However, with the addition of additional reliable genetic, epigenetic or genomic discoveries, predicting 5% weight loss may become a reality over time.

Next steps

To confirm whether genetics and genomics can predict clinically significant differences in magnitude of weight loss, continued research focused on the discovery of genetic associations with weight loss is necessary. Bray and colleagues (Bray et al., 2016) composed a white paper outlining priorities for continued research. As a GWAS has not been conducted in relation to weight loss, it is plausible that novel regions related to weight loss may be discovered. A high priority is to combine randomized, controlled trials of behavioral weight loss with DNA samples to amass larger sample sizes capable of detecting and replicating novel loci related to weight loss. It will also be critical to examine other types of genetic variation in relation to obesity and weight loss, such as CNVs.

Only a few studies have considered whether epigenetics, gene expression or the gut microbiome predicts ability to lose weight or may change with successful weight loss, although existing studies show promise. Continued study and replication of the interrelationship of genetics, epigenetics, gene expression and the gut microbiome in the context of weight loss will continue to complete the picture of how genetics and genomics contribute to weight loss.

Ultimately, it will be critical to determine the optimal approach for incorporating genetic or genomic data into clinical interventions. Should treatment providers solely evaluate genetic and genomic risk and recommend therapies to patients? Could patients also be consumers of genetic and genomic data to guide their treatment decision-making? If the latter, how is genetic or genomic risk optimally communicated to patients? Which treatment strategies would be best complement the presentation of genetic or genomic feedback? Lastly, the cost-effectiveness of the interventions guided by genetics or genomics relative to existing evidence-based interventions must be evaluated and cost and access to genetic tests must be considered in determining the reach of such interventions. Collaboration between psychologists, genetic counselors, medical providers and economists will be important to addressing these questions.

Summary

Precision medicine holds great promise to optimize treatments - including behavioral treatments - by tailoring therapies to genetic or genomic background. Research to date has begun to lay the building blocks for this endeavor in behavioral weight loss treatment. The relationship of obesity-related loci to magnitude of weight loss has been systematically characterized in large clinical trials and forays into the discovery of novel genetic loci as well as epigenetic and gene expression mechanisms have begun. With the goals of replication, generalization and clinical significance in mind, it will ultimately be determined whether genetics or genomics predict a clinically-significant response to behavioral weight loss treatment. The next challenge would be testing how such predictive information could best optimize obesity treatment in a cost-effective manner.

Table 1.

List of genes, gene names, biologic roles for genes referenced in this article.

Gene abbreviation Gene name Biologic function
ABCC8 ATP binding cassette subfamily C member 8 Codes for a subunit of the potassium channel controlling insulin secretion in the pancreas. Mutations in this gene occur in a subset of children with neonatal diabetes.
ABCG1 ATP binding cassette subfamily G member 1 Involved in macrophage and lipid transport, DNA hypermethyltion in this gene region is associated with obesity
AHRR Aryl-hydrocarbon receptor repressor Involved in innate immunity. De- methylated with cigarette smoking.
AMY1 Salivary amylase gene 1 Codes for salivary amylase, an enzyme involved in breaking down simple starches. Has been related to obesity.
APOE Apolipoprotein E Involved in packaging and transport of lipids. Associated with Alzheimer’s Disease among others
BDNF Brain derived neurotrophic factor Involved in the growth and maintenance of neurons as well as synapic plasticity. Associated with obesity and dietary intake.
BRCA1 Breast cancer 1 Involved in tumor suppression and repairing DNA. Mutations cause familial breast cancer.
BRCA2 Breast cancer 2 Involved in tumor suppression and repairing DNA. Mutations cause familial breast cancer.
CETP Cholesteryl ester transfer protein Regulator of lipids in HDL cholesterol and triglycerides; predicts change in HDL in response to weight loss, methylation pattern is changed with weight loss.
FADS2 Fatty acid desaturase 2 Desaturation of polyunsaturated and saturated fatty acids from the diet. Associated with change in HDL cholesterol during weight loss.
FTO Fat mass and obesity associated gene Strongest association with obesity from GWAS. Mechanism remains debated. Has been related to weight loss with high- protein diets and response to bariatric surgery.
HBB Hemoglobin subunit beta Codes for a component of hemoglobin, which carries oxygen to cells throughout the body. Mutations cause sickle cell disease.
HDAC4 Histone deacetylase 4 Epigenetic mechanism which alters chromosome structure and transcription of genes. Change in methylation during weight loss has been reported.
HER2 (ERBB2) Erb-b2 receptor tyrosine kinase 2 Overexpression of this gene has been reported in numerous cancers, including breast cancer
HIF3A Hypoxia inducible factor 3 alpha subunit Regulates response to hypoxia, DNA methylation in this gene relates to body mass index
KCNJ11 Potassium voltage-gated channel subfamily J member 11 Codes for a subunit of the potassium channel controlling insulin secretion in the pancreas. Mutations in this gene occur in a subset of children with neonatal diabetes.
LIPC Lipase C, hepatic type Codes hepatic triglyceride lipase; involved in lipid metabolism; related to change in HDL in response to weight loss
MAOA Monoanime oxydase A Catalyzes the oxidative deamination of dopamine, norepinephrine, and serotonin; has been related to antisocial behavior.
MC4R Melanocortin 4 receptor Expressed in hypothalamic eating pathways; predicts obesity
MTIF3 Mitochondrial translation initiation factor 3 Involved in mitochondrial protein synthesis; related to obesity and weight loss
NR3C1 Nuclear receptor subfamily 3 group C member 1 Encodes glucocorticoid receptor gene; involved in HPA axis regulation
SLC6A4 Solute carrier family 6 member 4 Codes the serotonin transporter gene,; may relate to depression particularly in interaction with early life stress.
TCF7L2 Transcription factor 7 like 2 Implicated in glucose homeostasis and type 2 diabetes. Weight loss blunts the impact of this variant on incident type 2 diabetes.
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Acknowledgments

The author would like to acknowledge grant support from the National Institutes of Health, DK109225, DK056992-11S, as well as Rena R. Wing, Ph.D., and Elissa Jelalian, Ph.D., for critical comments on this paper.

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