Personalized nutrition for weight loss: should you diet according to your genes? Original paper

In this 12-week randomized controlled study, participants lost a similar amount of weight whether they were assigned to a diet that purportedly matched with their genotype or one that didn’t.

The effects of a personalized genotype-specific diet on weight loss in adults with overweight or obesity.

The primary outcome was weight loss over 12 weeks.

122 adults (average age of 54; 84% women, 16% men; average BMI of 35).

In this 12-week randomized controlled study, known as the Personalized Nutrition Study (POINTS), the participants were classified as being a “fat responder” or a “carbohydrate responder” based on their genotype. Six gene variants were considered fat responsive, and 8 gene variants were determined to be carbohydrate responsive.

Next, the participants were assigned to 1 of 4 different diet groups, 2 of which aligned participants with their genotype (concordant diet) and 2 of which did not align participants with their genotype (discordant diet):

• Fat responders + a high-fat diet (concordant diet, 44 participants)
• Fat responders + a high-carbohydrate diet (discordant diet, 41 participants)
• Carbohydrate responders on a high-carbohydrate diet (concordant diet, 16 participants)
• Carbohydrate responders on a high-fat diet (discordant diet, 21 participants)

All groups were prescribed a hypocaloric diet at −500 kcal/day.

In addition to weight loss, blood pressure, waist circumference and hip circumference, changes in body fat percentage, and changes in food cravings, appetite, and food preferences were assessed before and after the study.

Weight loss was similar for fat responders following a high-fat diet (−5.5 kg/12.1 lb), fat responders following a high-carb diet (−5.3 kg/11.7 lb), carbohydrate responders following a high-carb diet (−5.1 kg/11.2 lb), and carbohydrate responders following a high-fat diet (−4.1 kg/9.0 lb).

Weight loss according to genotype and diet

Body fat percentage, waist circumference, and hip circumference decreased (improved) in all groups. There were no differences in blood pressure, food cravings, appetite, or food preferences, with the exception of carbohydrate responders on the high-fat diet reporting a lower craving for carbohydrates/starches relative to participants on the high-carb diet.

Personalized nutrition, which also goes by names like “nutrigenomics” and “precision nutrition”, is the one-size-does-not-fit-all approach. Individual differences in biochemistry, metabolism, genetics, and the gut microbiome can contribute to the body’s response to food. In other words, you are what you eat … and how your genome responds to what you eat. Although a consensus is lacking on a formal definition of “personalized nutrition”, some have been proposed, including “a field that leverages human individuality to drive nutrition strategies that prevent, manage, and treat disease and optimize health.”^[1]

Personalized nutrition

Information gleaned from the Human Genome Project suggests humans share 99.9% of their genomes. The differences in the 0.01% — which arise from single nucleotide polymorphisms (SNPs) — are responsible for variations in weight, height, eye color, hair color, and a thousand other characteristics.^[2] There are also SNPs of genes that encode for processes related to metabolism, such as the ability to break down fat and carbohydrates from food and convert them to energy. What people eat may also affect the expression of certain genes, underscoring the bidirectionality of diet-gene interactions.

The idea behind personalized nutrition is that tailoring one’s diet to their genes should result in superior health outcomes when compared to general dietary advice, whether the diet is designed to help someone lose weight, gain muscle, or just feel and perform better.

To date, only a few personalized nutrition studies have been conducted.

The Diet Intervention Examining The Factors Interacting with Treatment Success (DIETFITS) study was a 12-month trial that investigated the effect of diet, genes, and insulin levels on weight loss outcomes in adults with overweight and obesity.^[3] The participants were randomized to a healthy low-fat or a healthy low-carbohydrate diet. Diet responsiveness was assessed with a panel of three SNPs, and the participants’ baseline insulin sensitivity was assessed to determine the association (if any) of these factors with weight loss.

At the end of the trial, weight loss was statistically similar between both groups: 5.4 kg/11.9 lb for the healthy low-fat diet and 6.0/13.2 lb kg for the healthy low-carb diet. There was no diet/genotype or diet/insulin interaction for weight loss, suggesting that the improvements in diet quality were the main driver of this outcome. The DIETFITS study investigated the effects of 3 genes: PPARG, ADRB2, and FABP2. The summarized study (POINTS) used a 10-gene panel to identify diet responsiveness — FGF2, TCF7L2, IRS1, APOA5, PLIN1, APOA2, FTO, PPARG, GIPR, and GYS2 — in hopes of improving the comprehensiveness of characterizing participants as fat responders or carbohydrate responders.

Another trial called the Food4Me study^[4] investigated the effects of a 6-month personalized nutrition program on weight loss and blood biomarkers in 1,269 European adults. The participants were assigned to receive conventional dietary advice or one of three levels of personalized nutrition advice: advice based on their baseline diet, advice based on their baseline diet and phenotype (e.g., body weight, body composition, blood biomarkers), or advice based on their baseline diet, phenotype, and genotype (diet-responsive gene variants).

Although participants receiving personalized nutrition improved their overall diet quality — eating less red meat and saturated fat while improving their Healthy Eating Index score — there was no evidence of an advantage to personalized nutrition for improving body weight, BMI, waist circumference, or blood biomarkers. The Food4Me study used SNPs at 5 genetic loci to determine the participants’ diet responsiveness: MTHFR, FTO, TCF7L2, ApoE, and FADS1.

Finally, a study published in 2018^[5] assigned participants to a hypocaloric lower-carbohydrate or moderate-carbohydrate diet for 24 weeks. The diets either aligned or didn’t align with the participants’ diet-responsive genotypes determined via the assessment of lipid-metabolism-related genes. Though the lower-carbohydrate group experienced greater improvements in weight, fat mass, and body fat percentage compared to the moderate-carbohydrate group, there were no differences in these outcomes between participants assigned to a diet that aligned or didn’t align with their genotype.

Combining findings from the above studies, the evidence on personalized nutrition is underwhelming. Diet quality, energy intake, and physical activity seem to be three pillars with the most influence on weight loss. Genetics, not so much.

Are these null findings a nail in the coffin for personalized nutrition? Or is more research still needed? The latter is most likely — there may just be insufficient information about the genetics of nutrition and weight loss. It’s unlikely that a set of 3, 5, or even 10 genes have a strong enough pull on metabolic responses to nutrition to affect weight loss in a meaningful way. Perhaps the theories on what causes people to gain and lose weight — ideas which inform many personalized nutrition approaches — are incomplete.

One such theory is called the carbohydrate-insulin model (CIM) of obesity. In brief, the CIM alleges that diets high in carbohydrates elevate insulin secretion, suppressing the breakdown and release of fatty acids from body fat and directing circulating fatty acids toward storage and away from oxidation (breakdown). The insulin-driven shift in partitioning of energy leads to a state of "internal starvation" and thus, reductions in energy expenditure and increases in hunger — the combined effects of which promote weight and fat gain independent of total energy intake. Despite its strong theoretical constructs, some recent studies have challenged key predictions of the CIM.^[6]

The POINTS study (and the DIETFITS study, for that matter) somewhat falsify the CIM model. In the POINTS study, there was no association between baseline insulin levels or baseline insulin resistance (HOMA-IR) and weight loss on either diet. In the DIETFITS study, insulin secretion at baseline was also not associated with weight loss on the healthy low-carb or healthy low-fat diet. According to the CIM, participants on the high(er) carb diets should lose less weight when compared to an isocaloric lowe(er) carbohydrate diet, and participants with greater insulin/insulin resistance and/or a carbohydrate-insensitive genotype should lose less weight when assigned to a diet high in carbs or that didn’t align with their genotype. None of these predictions held up in POINTS or DIETFITS.

Until nutrigenomics proves otherwise, reducing calories and engaging in physical activity are still the mainstays of weight loss.

There were substantially fewer carbohydrate responders (37 participants) than fat responders (85 participants) in this study, and it’s unknown whether this reflects a statistical/recruitment anomaly or a genuine difference in the prevalence of these genotypes in the population. Future studies should be designed to recruit equal numbers of participants in each genotype group to more comprehensively study the effects of a personalized diet in people characterized as carbohydrate responders.