Phenotypes and Genotypes as Predictive Factors for Treatment Response in Chronic Weight Management
The differential variability of percentage weight loss seen in clinical practices and lack of statistically significant weight- loss results reported in clinical studies suggests that individualized therapy may improve patient outcomes. A meta-analysis conducted using data from studies of US Food and Drug Administration–approved weight-loss medications demonstrated similar weight loss among treatments. In addition, both the DIETFITS (Diet Intervention Examining The Factors Interacting with Treatment Success) and the POUNDS LOST (Preventing Overweight Using Novel Dietary Strategies) clinical studies reported no significant differences in weight change between low-fat and low-carbohydrate diets. Therefore, clinical studies and analyses are being conducted in an effort to provide a greater understanding of phenotypes and genotypes in obesity to individualize therapy for improved patient outcomes. Current studies are striving to identify novel obesity pathogenic mechanisms to provide possible targets for individualization of pharmacotherapy. Data from additional analyses of results from the POUNDS LOST study demonstrate that variants in genes, such as CRY2, MTNR1B, PCSK7, and CETP genotype, can affect obesity management and long-term weight loss. Further, studies have revealed associations between the hepatokine fibroblast growth factor 21 (FGF21) and obesity, with plasma FGF21 levels increasing acutely after oral sucrose ingestion and FGF21 decreasing after weight loss associated with diet and weight-loss surgery. Additional studies have demonstrated that genetic variants in the fat mass and obesity-associated (FTO) gene also increase obesity risk. Our featured experts in the field discuss using phenotypes and genotypes as possible predictive factors for treatment response in chronic weight management.
Q: Are there predictive factors for treatment response in patients with obesity?
Director, Nutrition and Weight Management
“In terms of precision medicine, obesity is a very complex disorder involving many different genes, making individualized treatment difficult. In addition to genetic predisposition, there are cultural, behavioral, and environmental factors that are involved.”
There are very few patients who have monogenetic syndromic obesity, such as those with extreme obesity and leptin deficiency, where there is a specific treatment, such as leptin therapy, that has been shown to be one of the only treatments that you can use for that kind of obesity. In terms of precision medicine, obesity is a very complex disorder involving many different genes, making individualized treatment difficult. In addition to genetic predisposition, there are cultural, behavioral, and environmental factors that are involved. There have been a few studies trying to address the idea of precision medicine for individualized treatment in patients with obesity. Recently, there have been studies looking at and comparing different macronutrient diets and weight loss and looking at certain genetic profiles, such as the DIETFITS study and the POUNDS LOST study, where the authors looked at 4 different kinds of diets varying in macronutrient content and weight loss. Initially, no difference was seen at the end of 1 year in terms of weight loss with varying carbohydrate and fat content in the diet, but the data were reanalyzed to look at the FGF21 genetic differences and researchers found that it appeared that if you had a certain FGF21 variant, you did better on a certain macronutrient content diet in terms of body composition changes. That is as far as we can get. The DIETFITS study by Gardner et al showed that there were no differences between a low-carbohydrate diet and a low-fat diet in 600 participants who were randomized, and, when they looked at certain genetic profiles, they really could not find any differences. Now, are these studies definitive? I do not think so. I think that when you are doing a behavioral study such as the DIETFITS study, where you are not actually monitoring and watching patients eat and not actually knowing exactly what they are eating, that, certainly, at the end of 6 months to a year, the low-fat versus low-carbohydrate patterns tend to merge with each other. In the end, the Gardner et al study, it looks as if the macronutrient distributions kind of regress to the means, and so, the fat content differences and the carbohydrate differences may not be that distinctive. It is very hard to do a study like this. Certainly, out to 1 year, it is very hard and it costs a lot of money if you want to actually measure exactly what people are eating.
Professor of Medicine, Institute of Human Nutrition
“With the available study results, it has been very difficult to hone in on an individual with obesity and say, 'You have got this problem, and it will be solved by somehow affecting this particular gene.'''
I agree that the single mutations, such as the one for leptin, are rare, and those, if identified, can be treated with a single protein that has been deleted or is ineffective for some reason, but these constitute a small number of persons compared with the overall group of individuals globally who are overweight or obese. There are other more minor mutations that may be important. One mutation that has been studied recently is the FTO gene, but if you look at its impact, the variance of body mass index (BMI) attributed to FTO is less than 1%. Therefore, the impact of individual mutation changes is very low. We are still way behind on establishing genotypes as being terribly important in personalized medicine for obesity, compared with other disorders. In phenotype, we have even less information. It is extremely difficult to determine the difference between a patient who has a BMI of 30 and a patient who has a BMI of 40. The impact of epigenetics is also being studied, but there are very little useful data available in terms of personalized therapy for individual patients. With the available study results, it has been very difficult to hone in on an individual with obesity and say, “You have got this problem, and it will be solved by somehow affecting this particular gene.”
Boyd Professor Emeritus, LSU,
“There are a few examples from the POUNDS LOST study that suggest the possibilities of ‘personalized’ weight-loss diets. If these can be replicated in the DIETFITS study, we may be on the way to putting new tools into the clinician’s weight-loss tool box.”
To expand a bit further on the outcomes of the POUNDS LOST trial, which began almost 15 years ago, it enrolled 811 individuals and is one of the largest trials comparing fat, protein, and carbohydrates on weight loss. The DIETFITS study enrolled 600 individuals. Both trials clearly showed that the macronutrient composition of the diet did not have a significant effect on weight loss. However, there was considerable variation in weight loss among individuals randomized to each of the diets in both of these trials. This provided an opportunity to examine factors, or phenotypes, that may predict weight loss and individual response to particular diets. Several demographic factors can be identified. Patients of older age and heavier weight tend to lose more weight. African American patients do not lose weight as well as white patients. Eating patterns also predicted differences in weight loss. Interestingly to me, higher values of baseline triiodothyronine concentration were associated with more weight loss. The amylase gene, which determines whether or not you have lactase, was also a predictor of weight loss. Individuals who adhered to their dietary program lost more weight than those who did not. In addition, more rapid early weight loss was a good predictor of achieving more weight loss. The change in plasma choline was also a strong predictor of weight loss; the greater the fall in choline—but not carnitine or trimethylamine N-oxide—the greater the weight loss. There were a number of additional factors that modify the way in which people respond to protein or carbohydrate levels in the diet. For example, the A allele of the FTO gene was associated with greater weight loss in individuals eating the higher-protein diet. Individuals who increased their protein intake the most lost the most weight. The FGF21 gene is a gene that affects weight loss on the carbohydrate diet. Individuals with the C allele lose more weight when eating a high-carbohydrate diet. There are a few examples from the POUNDS LOST study that suggest the possibilities of “personalized” weight-loss diets. If these can be replicated in the DIETFITS study, we may be on the way to putting new tools into the clinician’s weight-loss tool box.
Aslibekyan S, Demerath EW, Mendelson M, et al. Epigenome-wide study identifies novel methylation loci associated with body mass index and waist circumference. Obesity (Silver Spring). 2015;23(7):1493-1501.
Ávalos Y, Kerr B, Maliqueo M, Dorfman M. Cell and molecular mechanisms behind diet-induced hypothalamic inflammation and obesity. J Neuroendocrinol. 2018 Apr 12:e12598. doi: 10.1111/jne.12598. [Epub ahead of print]
Bray GA, Heisel WE, Afshin A, et al. The science of obesity management: an Endocrine Society scientific statement. Endocr Rev. 2018;39(2):79-132.
Gardner CD, Trepanowski JF, Del Gobbo LC, et al. Effect of low-fat vs low-carbohydrate diet on 12-month weight loss in overweight adults and the association with genotype pattern or insulin secretion: the DIETFITS randomized clinical trial [published corrections appear in JAMA. 2018;319(13):1386 and JAMA. 2018;319(16):1728]. JAMA. 2018;319(7):667-679.
Gómez-Ambrosi J, Gallego-Escuredo JM, Catalán V, et al. FGF19 and FGF21 serum concentrations in human obesity and type 2 diabetes behave differently after diet- or surgically-induced weight loss. Clin Nutr. 2017;36(3):861-868.
Huang T, Huang J, Qi Q, et al. PCSK7 genotype modifies effect of a weight-loss diet on 2-year changes of insulin resistance: the POUNDS LOST trial. Diabetes Care. 2015;38(3):439-444.
Khera R, Pandey A, Chandar AK, et al. Effects of weight-loss medications on cardiometabolic risk profiles: a systematic review and network meta-analysis. Gastroenterology. 2018;154(5):1309-1319.e7.
Melhorn SJ, Askren MK, Chung WK, et al. FTO genotype impacts food intake and corticolimbic activation. Am J Clin Nutr. 2018;107(2):145-154.
Mirzaei K, Xu M, Qi Q, et al. Variants in glucose- and circadian rhythm-related genes affect the response of energy expenditure to weight-loss diets: the POUNDS LOST trial. Am J Clin Nutr. 2014;99(2):392-399.
Qi Q, Durst R, Schwarzfuchs D, et al. CETP genotype and changes in lipid levels in response to weight-loss diet intervention in the POUNDS LOST and DIRECT randomized trials. J Lipid Res. 2015;56(3):713-721.
Sacks FM, Bray GA, Carey VJ, et al. Comparison of weight-loss diets with different compositions of fat, protein, and carbohydrates. N Engl J Med. 2009;360(9):859-873.
Søberg S, Sandholt CH, Jespersen NZ, et al. FGF21 is a sugar-induced hormone associated with sweet intake and preference in humans. Cell Metab. 2017;25(5):1045-1053.e6.
Wahl S, Drong A, Lehne B, et al. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature. 2017;541(7635):81-86.
Zhu T, Yong XLH, Xia D, Widaqdo J, Anqqono V. Ubiquitination regulates the proteasomal degradation and nuclear translocation of the fat mass and obesity-associated (FTO) protein. J Mol Biol. 2018;430(3):363-371.