Skip to main content
Download PDF

A comparison of a ketogenic diet with a LowGI/nutrigenetic diet over 6 months for weight loss and 18-month follow-up

BMC Nutrition volume 6, Article number: 53 (2020) Cite this article

Abstract

Background

Obesity and its related metabolic disturbances represent a huge health burden on society. Many different weight loss interventions have been trialled with mixed efficacy, as demonstrated by the large number of individuals who regain weight upon completion of such interventions. There is evidence that the provision of genetic information may enhance long-term weight loss, either by increasing dietary adherence or through underlying biological mechanisms.

Methods

The investigators followed 114 overweight and obese subjects from a weight loss clinic in a 2-stage process. 1) A 24-week dietary intervention. The subjects self-selected whether to follow a standardized ketogenic diet (n = 53), or a personalised low-glycemic index (GI) nutrigenetic diet utilising information from 28 single nucleotide polymorphisms (n = 61). 2) After the 24-week diet period, the subjects were monitored for an additional 18 months using standard guidelines for the Keto group vs standard guidelines modified by nutrigenetic advice for the low-Glycaemic Index nutrigenetic diet (lowGI/NG) group.

Results

After 24 weeks, the keto group lost more weight: − 26.2 ± 3.1 kg vs − 23.5 ± 6.4 kg (p = 0.0061). However, at 18-month follow up, the subjects in the low-GI nutrigenetic diet had lost significantly more weight (− 27.5 ± 8.9 kg) than those in the ketogenic diet who had regained some weight (− 19.4 ± 5.0 kg) (p < 0.0001). Additionally, after the 24-week diet and 18-month follow up the low-GI nutrigenetic diet group had significantly greater (p < 0.0001) improvements in total cholesterol (ketogenic − 35.4 ± 32.2 mg/dl; low-GI nutrigenetic − 52.5 ± 24.3 mg/dl), HDL cholesterol (ketogenic + 4.7 ± 4.5 mg/dl; low-GI nutrigenetic + 11.9 ± 4.1 mg/dl), and fasting glucose (ketogenic − 13.7 ± 8.4 mg/dl; low-GI nutrigenetic − 24.7 ± 7.4 mg/dl).

Conclusions

These findings demonstrate that the ketogenic group experienced enhanced weight loss during the 24-week dietary intervention. However, at 18-month follow up, the personalised nutrition group (lowGI/NG) lost significantly more weight and experienced significantly greater improvements in measures of cholesterol and blood glucose. This suggests that personalising nutrition has the potential to enhance long-term weight loss and changes in cardiometabolic parameters.

Trial registration

NCT04330209, Registered 01/04/2020, retrospectively registered.

Peer Review reports

Background

Obesity is characterised by excessive fat accumulation, and it is well established that the percentage of the population that is either obese or overweight is rising over time [1]. Obesity is also associated with several health issues, including the development of metabolic syndrome, hypertension, cardiovascular disease, arthritis, and various cancers [2]. The causes of obesity are not simply the consumption of a greater amount of energy than is utilised; instead, obesity is a complex disorder, with many biological, psychological, and sociological factors combining in its development [3].

A considerable number of health interventions have been trialled to reduce obesity [2]. In terms of dietary interventions, the efficacy of these trials is mixed, with a sizeable proportion of dieters regaining more weight than they initially lose [4]. Emerging research suggests that the ability to place obese subjects on a personalised nutrition regime using genetic information may increase both adherence to that diet and subsequent post-diet nutrition, enhancing health outcomes [5,6,7,8]. This has been demonstrated in recent years in studies on gene-diet interactions and the emergence of nutrigenetics, a goal of which is to add a level of personalization to standard nutrition by adjusting it according to genetic variation. Prior studies suggest that specific genetic variants may increase susceptibility to certain disease states, but that this increased risk can be reduced or completely mitigated with dietary modification [9,10,11,12]. As an example, variation in MTHFR C677T genotype leads to alterations in plasma folate status [13], which can increase the risk of hyperhomocysteinemia, potentially increasing the risk of cardiovascular disease [14]. However, in those with the risk (TT) genotype, intakes of greater than the recommended daily allowance (RDA) of folate are associated with a lowering of homocysteine to reference values [9]. This indicates that a “one-size fits all” approach to nutrition is perhaps insufficient, and that individualised nutrient guidelines may improve population health.

Whilst obesity itself increases all-cause mortality [15], it is also associated with other issues that may negatively affect health, including elevated total cholesterol (TC) [16], lower high-density lipoprotein cholesterol (HDL) [17] and raised fasting blood glucose (FBG) [18]. Management of these markers is important in optimising individual health and reducing mortality risk.

A key observation, made by many, is the unsurprising conclusion that hypocaloric dietary adherence, regardless of the diet macronutrient composition, is the most critical aspect of weight loss success [19,20,21]. Furthermore, ongoing adherence to healthy nutrition after the initial caloric restriction phase is equally critical to achieving long-term weight loss. A weight management study by Arkadianos et al. [5] utilised a nutrigenetic test which was not specifically targeted at weight loss. Participants were allocated into either a nutrigenetic or control group, similar in calories and macronutrient composition. Within the nutrigenetic group, diet was modified by the addition of certain micronutrients based on the genetic results, and small changes in macronutrients (e.g. a reduction in refined carbohydrate from a maximum of 10% calories to max 8% calories; reduction of glycaemic load (GL) (from a maximum of 100 to 70). Adherence was determined by clinic visits and questionnaires. Weight loss during the 24-week diet was similar in both groups, but during follow-up over a 1-year period, the nutrigenetic group maintained weight loss while the non-genetic group regained weight [5].

The aim of the present study was to observe weight loss 2-stage process:

  1. 1.

    A 24-week dietary intervention comparing two diets: The well-known, and generally the most effective in the short-term ketogenic diet [22] vs. A lowGI/Nutrigenetic diet.

  2. 2.

    A second phase, lasting 18 months, utilising standard guidelines for the ketogenic group vs standard guidelines modified by nutrigenetic advice for the lowGI/NG group.

The study protocol was similar to Arkadianos et al. [5] and used a nutrigenetic test which had been further developed in the European Union (EU) funded consortium project EUROGENE [23]. The addition of nutrigenetic advice was not designed nor proposed to patients as a weight loss diet, nor to predict either disease risk or obesity risk; the aim was to optimize the nutrient content of an individual’s daily food intake, based on current understanding of an individual’s genetic profile. Whilst an individual is achieving weight loss, food consumption is generally reduced and some nutrients in the diet may not be in adequate supply; nutrigenetics may be a tool to help achieve optimum nutrient content on an individual basis. Furthermore, the use of nutrigenetics in designing personalized diet and lifestyle programs has the potential to increase motivation and compliance with long-term lifestyle changes.

The primary objective was measurement of weight loss in the two groups after the 24-week diet and at 18-months of follow up. Secondary objectives were blood measurement of key risk factors, comprised of glucose, total cholesterol, and HDL cholesterol.

Methods

One hundred fourteen subjects (1 overweight; 113 obese) subjects (M = 55, F = 59, age 24-56y, all of Romanian heritage and similar socio-economic status), who were patients at a weight management clinic (Bucharest, Romania), gave written informed consent for their weight loss data to be prospectively analysed for this study. All patient data were handled according to the Romanian Code of Medical Deontology and in accordance with the Helsinki Agreement. Approval was given by the Ethics Committee of the University and Pharmacy, Cluj Napoca, Romania (registration number 444). Upon enrolment at the weight management clinic, the subjects self-selected either a ketogenic diet or a low-GI nutrigenetic diet. A ketogenic diet was utilised as the comparison diet due to its reported efficacy in the treatment of obesity [24]. Fifty-three subjects (25 female; age 43.0 ± 7.2y) selected the ketogenic diet plan, and 61 subjects (34 female; age 42.0 ± 6.7y) selected the low-GI nutrigenetic diet plan. Subjects in the low-GI nutrigenetic diet group underwent DNA testing (NutriGENE by Eurogenetica Ltd./DNAfit, UK) for 28 Single Nucleotide Polymorphisms (SNPs) in 22 genes with good evidence of gene-diet/lifestyle interactions (Table 1). Overall participation in both diet groups cost a similar amount, comprised of approximately €300 for the genetic test along with 1-month diet plan, initial evaluation, body composition, and medical history for the nutrigenetic group, and €280 for the ketogenic group, providing Ketostix and the same evaluations. Further visits through the 24-week program the overall cost per patient was approximately €800. After 24 weeks diet follow up visits had no further cost, After the diet period, the subjects were monitored for an additional 18 months.

Table 1 Gene and polymorphisms tested in the low-GI nutrigenetic patient group
Full size table

At the study onset, patients were not type 1 or type 2 diabetics, although many were hyperglycemic, a common issue in obese subjects. Any patients with records of any other disease were excluded prior to commencing the dietary intervention. The patients, apart from obesity, were otherwise “healthy”. According to the Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults [53], an obese person is considered healthy if they present with fewer than three of these conditions:

  • a waist measurement over 40 in. in men or over 35 in. in women

  • triglyceride levels in the blood of > 200 mg per deciliter (mg/dl)

  • levels of high-density lipoprotein below 35 mg/dl

  • fasting blood glucose > 120 mg/dl

  • blood pressure ≥ 14/90 mmHg

Diet overview

Both diets were followed for 24 weeks and contained approximately1600kcal per day. Both groups were provided with a meal plan and nutritional advice by the lead study author, a qualified nutritionist, a position requiring at least a Masters degree. After the 24-week study period, the subjects were monitored for an additional 18 months. Here, the ketogenic group followed population based nutrition and exercise guidelines, whilst the low-GI nutrigenetic group followed population based guidelines, slightly modified based on the genetic results of each patient. Other than the modifications to the standard diet and exercise program, the patients in both groups were treated in an identical manner.

Ketogenic diet

The ketogenic diet group were instructed to consume ≤35 g of carbohydrates per day, and ≤ 10% of total calories were from saturated fats. Daily protein intake was set at 1.2 g/kg bodyweight for females, and 1.5 g/kg bodyweight for males. A sample day’s menu is available in the supplementary materials (S1).

Low-GI Nutrigenetic diet

The low-GI nutrigenetic diet group had individualised dietary instructions based on their genetic results; examples of the advice given are found in Table 2. The gene variants were selected based on previous evidence of gene-diet interactions, in which a nutrition or exercise intervention was demonstrated to modify the effect of the variation, and which fulfilled the criteria described in [54]. The genetic results of each individual were then analysed for sensitivity to carbohydrates (utilising SNPs in ACE, PPARG, TCF7L2, ADRB2 and FABP2) and saturated fats (ADRB2, ADRB3, APOA2, FABP2, FTO, PPARG). A score for both carbohydrate and saturated fat sensitivity was determined by utilising a point system, with the aggregate result being utilised. The base diet for each person was similar and was modified by refined carbohydrate content (maximum 10, 8%, or 6% total calories) and saturated fat content (maximum 10, 8%, or 6% total calories) according to the relevant sensitivity scores. Sample menus can be found in the supplementary files S2-S4. All subjects were advised to focus on whole-grain complex carbohydrates, as well as fruits and vegetables, as their primary sources of carbohydrates. Subject diets were also modified for some micronutrients and other macronutrients where the evidence is sufficient to deviate from the standard guidelines [54]. For example, individuals with a deletion allele for either GSTM1 or GSTT1 were recommended to increase their cruciferous vegetable intake [39], whilst those with a C allele for CYP1A2 were recommended to limit their caffeine intake to < 200 mg/d [10].

Table 2 Examples of personalized recommendations given to the patients in Low-GI Nutrigenetic group in addition to base diet
Full size table

Exercise

Both groups were provided with general exercise advice and were asked to exercise for 30–45 min per day, 5 days per week. In both groups the exercise protocol for each person was carefully planned to avoid over-exertion in this overweight and generally sedentary cohort. The low-GI nutrigenetic group were given additional exercise advice, based on their results of six SNPs; ACE, ADRB2, ADRB3, FTO, PPARG and TCF7L2. As per the diet scoring, the results of each of these SNPs was combined, to give subjects guidance as to the volume of high, medium or low-intensity exercise recommended, total exercise duration was matched between the groups. Sample exercise plans are found in Supplementary Material file S5. Exercise adherence was based on questions at clinic visits.

Dietary adherence

Patients visited the clinic every 2 weeks during the first 24 weeks for body measurements, and detailed dietary diaries were presented. Detailed diaries were maintained throughout the 24-week period, including the weighing of foods. All subjects received a menu plan with recipes. In addition, in the ketogenetic group, patients were taught how to test ketosis using Ketostix® (Ascensia Diabetes Care Holdings AG, Basel, Switzerland), measuring ketone bodies in the urine, which they did daily. Ketostix strips determine the presence of AA (acetoacetate). The end of the strip is passed through the urine stream and the colour then compared to the colour chart provided with the product. The scale is negative, trace, small, moderate, and large. Ketosis starts from “small”. In the 18-month follow-up period, patients presented at the clinic every 6 months for further body measurements and blood measurements, along with dietary diary assessment and exercise assessment (via diary and step counter). Dietary macronutrient composition was also tested.

Subject testing

Cheek cell samples were taken in the clinic using two buccal swabs from the respective companies. The samples were sent by courier to the laboratory (Synlab Italia Srl, Monza, Italy). For DNA extraction swabs were added to 550 μl of sterile, nuclease-free H2O and DNA extracted with QIASYMPHONY DSP DNA Mini kit eluted in 100 μl liquid. The genotype analysis was done with MassARRAY system with iPLEX chemistry (ex-Sequenom now called Agena) on a 384 chip. Primers and PCR conditions were designed with Agena Assay Design Suite (ADS) software. Through this process, genetic information (Table 1) was determined.

Fasting venous blood samples were taken at baseline, 24-weeks and 104-weeks to determine total cholesterol (TC), high density lipoprotein (HDL) cholesterol, and fasting blood glucose (FBG). TC and HDL concentrations were measured using an enzymatic colorimetric method (CHOL-CHOD-PAP, HDL Homogenic Enzymatic reaction, Roche Diagnostic, Germany). FBG was determined using an enzymatic kit (Glucose GOD-PAP, Roche Diagnostic, Germany). Weight and height were also measured, and body mass index (BMI) was calculated by dividing each subject’s weight (kg) by the square of their height (m).

Statistical analysis

All genotype distributions were tested for deviation from the Hardy-Weinberg equilibrium by a χ2 test with 1 df (P > 0.05). Means, standard deviations and 95% confidence intervals were calculated for test scores at baseline, 6 weeks, 12 weeks, 24 weeks and 2 years (104 weeks). Percentage weight and BMI change for each individual participant was calculated for all post-baseline time points. Percentage changes for each individual participant in TC, HDL and FBG were calculated between baseline and 2 years. Normality was determined utilising Shapiro-Wilks. Data were analysed using ANCOVA. To reduce the chances of a type-I error, significance was set at p < 0.001for secondary objectives [55]. Percentage change from baseline was calculated by subtracting baseline data from the measurement at a given time point, dividing by the baseline measurement, and multiplying by 100. Data were analysed using Microsoft Excel 15.29 (Microsoft Corporation, Redmond, WA, USA) and IBM SPSS Statistics 23 (IMB Corporation, Armonk, NY, USA). All data are reported as mean (95% CI) unless otherwise specified.

Results

Genotype frequencies

SNP minor allele and genotype frequencies were calculated from all subjects who completed the study and for which DNA data were available (n = 61). Genotype distributions did not deviate from Hardy-Weinberg expectations. Minor allele frequencies in our subjects were in close agreement with those listed for populations of European ancestry on dbSNP [56].

Baseline phenotype

Table 3 illustrates the baseline characteristics of all subjects. There were no significant differences at baseline between the groups in terms of age, sex, body weight, or BMI. Except for one subject in the low-GI nutrigenetic group who was classified as overweight (BMI 25.1–30 kg/m2) all subjects were classified as obese (BMI > 30 kg/m2) according to BMI. There were no significant differences between the groups regarding mean total cholesterol or mean HDL at baseline. There were significant differences in terms of mean fasting blood glucose at baseline, with the low-GI nutrigenetic group having significantly lower (p < 0.0001) values.

Table 3 Baseline Characteristics of Subjects. All data are reported as mean (95% CI) unless otherwise specified
Full size table

Diet & nutrition adherence

All participants completed the 24-week study and 18-month follow up. In the first 4 weeks, all patients in the ketogenic group maintained ketosis. In the following weeks, 13 patients went out of a ketosis state; following dietary data analysis, it was determined that that 8 patients exceeded the amount of carbohydrates required to maintain ketosis, and 5 patients consumed higher protein than prescribed, which triggered gluconeogenesis. After re-adjusting the diet, these patients regained their ketosis state. Within the first year of follow up, 17 patients in the ketogenic group reported having deviated at least 3 times a month from the nutrition plan, consuming foods other than those prescribed. In the second year of follow-up, 24 patients were found to have diverged frequently from the nutrition plan due to special family events, social events, holidays, prolonged weekends and in some cases lack of motivation.

In the low-GI nutrigenetic group, during the 24-week diet phase, all patients successfully followed their individual diet plan. In follow up, 10 patients reported small deviations from the nutrigenetic nutrition plan, with these deviations relating to weddings, holidays, or anniversary events. The patients in the low-GI nutrigenetic group demonstrated greater adherence and consistency.

Change in body mass and weight loss

Body mass changed in both diet groups, with similar weight loss at 6 and 12 weeks while at 24 weeks the ketogenic group had lost more weight compared to the low-GI nutrigenetic group. The ketogenic diet was associated with a 17.2% loss in body mass at 18-month follow up, which represented a significant (p < 0.0001) reduction. The low-GI nutrigenetic group was associated with a significant (p < 0.0001) reduction in body mass, with a mean reduction on 25.3%. When examining for differences between the groups, clear differences emerge at the two-year time point, with the low-GI nutrigenetic group continued to lose weight (p < 0.0001) while the ketogenic group had regained some weight compared to the 24-week time point (Table 4). Figure 1 details the percentage change in weight loss within each group.

Table 4 Body mass (kg) changes between diet groups
Full size table
Fig. 1
figure 1

Percentage weight lost compared to baseline for each group (mean, 95% CI). At 104 weeks (2 years), the low-GI nutrigenetic group lost significantly more weight

Full size image

Health data

The results for metabolic health data are shown in Table 5 and Figs. 2 and 3. Here, we see that at 18-month follow up, the low-GI nutrigenetic group was associated with a significantly greater (p < 0.0001) decrease in both total cholesterol and fasting blood glucose when compared to the ketogenic diet group (Table 5). Similarly, the low-GI nutrigenetic group was associated with a positive HDL change (i.e. increase) to a significantly (p < 0.0001) greater extent than the ketogenic diet group (Table 5). There were significant differences in terms of mean fasting blood glucose at baseline, with the low-GI nutrigenetic group being associated with significantly lower values. However, there was considerable overlap at baseline between the two groups. This overlap became more marked at the 24-week point, with less difference in the mean values. The two groups diverged again at the two-year follow up point (Fig. 2). Figure 3 shows the individual participant metabolic health data. Finally, the low-GI nutrigenetic group was associated with a significantly (p < 0.0001) greater reduction in fasting glucose compared to the ketogenic diet group at the 18-month follow up point (Table 5).

Table 5 Differences in cardiometabolic parameters between both groups at different time points. All data are reported as mean (95% CI)
Full size table
Fig. 2
figure 2

Individual fasting glucose (mg/dl) between diet groups

Full size image
Fig. 3
figure 3

Percentage change from baseline for Total Cholesterol (TC), High Density Lipoprotein (HDL), and Fasting Blood Glucose (FBG) for both groups at two-year follow up

Full size image

Discussion

The main findings of this study are (1) after 6 months diet the ketogenic group lost more weight compared to the LowGI/nutrigenetic group (26.2 kg vs 23.5 kg; p = 0.0061) (2). After 18-month follow-up the ketogenic group had regained some of the weight lost but the LowGI/nutrigenetics group continued to lose weight, at a slower rate (19.4 kg vs 27.5 kg; p < 0.0001). The LowGI/nutrigenetic group, after 18-month follow-up, also reported better results for total cholesterol, HDL and glucose. Also, though a few individuals of the LowGI/nutrigenetic group had regained some weight at 18-month follow-up (Fig. 1) all 61 were below the pre-diabetes blood glucose level of 100 mg/dl while only 16 of 53 of the keto group were below that level (Fig. 2).

These results mirror previous those of previous studies, such as those by Arkadianos et al. [5]. In the current study, these differences became more apparent over time; during the 18-month post-diet timeframe, particularly regarding changes in body weight. Indeed, up to the 24-week time point, there were significant differences between the diet groups in terms of body weight change and biomarker improvement in favour of the ketogenic group.

Long term maintenance of weight loss requires permanent lifestyle changes in exercise and eating habits. These changes need to be significant but not necessarily radical or unachievable if planned over several years of gradual but sustainable weight loss. Nutrigenetic modifications, as part of personalized nutrition (PN) add a small part to the overall diet, with an obvious question being whether it improves adherence to healthy nutrition. Nutrigenetics includes advice dependent on genetic results and following the straight-forward gene-diet advice the aim is that the overall eating pattern will improve. Nutrigenetic PN does not create complex personalized diets, merely slight differences between diet types.

One possibility for this enhanced long-term weight loss, combined with improvements in markers of metabolic health, is that healthy eating compliance is greater when nutrigenetics is used to modify the standard nutrition guidelines at the end of the dieting phase. The dietary adherence data in this study agrees with this possibility, as does previous research in personalized nutrition. Nielsen and El-Sohemy [57] reported that participants tend to find genetically tailored nutritional advice useful. A subsequent study by the same authors [8] reported that personalized dietary advice based on a person’s genetic makeup improves eating habits compared to current ‘one-size-fits-all’ dietary recommendations. The authors reported that subjects who received DNA-based dietary advice started to show improvements to their diets after 3 months, with the changes becoming more apparent after 12 months. A recent randomised controlled trial of nutrigenomics-guided lifestyle intervention reported “Weight management interventions guided by nutrigenomics can motivate long-term improvements in dietary fat intake above and beyond gold-standard population-based interventions.” [6].

Some studies exploring the genetic risk of common diseases have found varying results, with some reporting that knowledge of genetic risk score has little impact on behaviour [58]. Key differences with personalized nutrigenetics studies, compared to genetic risk studies, include that (a) genetic information is linked only to nutrient/lifestyles requirements and is not explicitly linked to disease risk and (b) the information given to individuals includes precise advice on increasing or decreasing specific food groups. Thus, adherence to a dietary/lifestyle intervention appears to be more successful in nutrigenetics compared to a genetic disease risk score, which often do not come with personalised nutrition guidelines to mitigate any increased risk. As a result, there is increasing evidence that one of the main benefits with nutrition and genetics is that of behavioural change [59].

It is important to note some potential limitations to this current study. The mechanisms underpinning the enhanced weight loss and health improvements in the low-GI nutrigenetic group are unclear; it may be potentially due to dietary adherence, or specific biological mechanisms, which were not tested. It is also possible that the effects were placebo or expectancy mediated in nature. Additionally, the lead study author was not blinded to the results of each individual’s genotype results, which may have affected the study outcome.

Another limitation is that in this study the associations observed with the low GI/nutrigenetic diet were only in comparison to a ketogenic diet, it would have been ideal to have other groups including lowGI only, without nutrigenetics – but that wasn’t feasible with the resources available and we had showed previously that a lowGI/nutrigenetics diet gave better long-term results to lowGI only [5].

The choice of a ketogenic diet as the control diet has the potential to alter the results; a ketogenic diet can be difficult to adhere to for certain individuals [60]. A ketogenic diet may also disturb basal metabolic processes leading to adverse reactions when returning to standard nutrition. Additionally, fasting blood glucose was significantly lower at baseline in the low-GI nutrigenetic group when compared to the ketogenic diet. The low-GI nutrigenetic group lowered their fasting blood glucose to a greater extent in this study, and the baseline differences may have contributed to this variation between groups. It is not clear why these baseline differences were present, but they overlapped.

An additional limitation is that the population used in this study were almost exclusively obese; it is not clear whether such a lifestyle modification would be effective in non-obese, but overweight, individuals – although other studies demonstrated an improved healthy-eating index in such people [61]. However, despite these issues, the study addressed an important unmet need to generate real world data; a common issue with clinical trials is that participant behaviour may be altered simply by being part of a study [62]. It has been increasingly recognized that such data in real world settings is needed to improve health outcomes [63]. Thus, we believe that the present study does accurately represent the real-world, in which high-risk individuals were given a dietary intervention in order to improve health. Study subjects were European Caucasians; ethnicity is a known potential modifier of gene-diet interactions, so it’s not clear whether the findings of this study would hold true for other populations, although the majority of the variants utilised here are functional—i.e. they directly affect the protein—and so their effect should be the similar regardless of ethnicity. Further research in this area should examine the use of a low-GI nutrigenetic personalized nutrition to reduce the risk of developing obesity or metabolic syndromes in healthy, non-obese subjects, as well as replicating the results of this study in populations of non-European ethnicity. Finally, this study utilised a low-GI, nutrigenetic diet, and compared the outcomes of this diet to a ketogenic diet. As the ketogenic diet group did not undergo genetic testing, and have their nutritional intervention tailored to their genetic results, a next study should look at the addition of nutrigenetic advice after the 24-week ketogenic diet, to see if the benefits of the ketogenic diet are maintained in the long term, especially as after the 24-week diet and benefits were mostly greater in the ketogenic group.

Conclusions

The results of this study suggest that a 24-week, ketogenic diet was superior to a low-GI nutrigenetic diet at improving weight loss and health markers compared to baseline upon completion of the dietary intervention, but at 18-month follow up the low-GI nutrigenetic group fared better. These findings suggest that despite the better results in the ketogenic group following a 24-week dietary intervention, over longer periods the low-GI nutrigenetic diet may be useful in the treatment of both obesity and altered blood markers of metabolic health, and that these benefits appear to be maintained following the completion of a dietary intervention, an effective aid in long term lifestyle changes leading to sustained weight loss and health improvements.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

BMI:

Body mass index

EU:

European Union

FBG:

Fasting blood glucose

GL:

Glycaemic load

HDL:

high-density lipoprotein cholesterol

Low-GI:

Low-glycaemic index

LowGI/NG:

Low-Glycaemic Index nutrigenetic diet

PN:

Personalized nutrition

RDA:

Recommended daily allowance

SNP:

SINGLE Nucleotide Polymorphism

TC:

Total cholesterol

References

  1. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: A systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2014;384(9945):766–81.

    PubMed  PubMed Central  Google Scholar 

  2. Thom G, Lean M. Is there an optimal diet for weight management and metabolic health? Gastroenterology. 2017;152(7):1739–51.

    PubMed  Google Scholar 

  3. Palou A, Serra F, Bonet ML, Picó C. Obesity: Molecular bases of a multifactorial problem. Eur J Nutr. 2000;39:127–44.

    CAS  PubMed  Google Scholar 

  4. Soeliman FA, Azadbakht L. Weight loss maintenance: A review on dietary related strategies. J Res Med Sci. 2014;19:268–75.

    PubMed  PubMed Central  Google Scholar 

  5. Arkadianos I, Valdes AM, Marinos E, Florou A, Gill RD, Grimaldi KA. Improved weight management using genetic information to personalize a calorie controlled diet. Nutr J. 2007;6(29). https://doi.org/10.1186/1475-2891-6-29.

  6. Horne J, Gilliland J. Enhanced long-term dietary change and adherence in a nutrigenomics-guided lifestyle intervention compared to a population-based (GLB/DPP) lifestyle intervention for weight management: results from the NOW randomised controlled trial. BMJ Nutrition, Prevention & Health. 2020;3. https://doi.org/10.1136/bmjnph-2020-000073.

  7. Horne J, Gilliland J, Madill J. Assessing the effectiveness of actionable nutrigenomics and lifestyle genomics interventions for weight management in clinical practice: A critical, scoping review with directions for future research. Nutrition and Health, 2020;26(3):167–173. http://journals.sagepub.com/doi/10.1177/0260106020928667.

  8. Nielsen DE, El-Sohemy A. Disclosure of Genetic Information and Change in Dietary Intake: A Randomized Controlled Trial. DeAngelis MM, editor. PLoS One. 2014;9(11):e112665.

    PubMed  PubMed Central  Google Scholar 

  9. Ashfield-watt PAL, Pullin CH, Whiting JM, Clark ZE, Moat SJ, Newcombe RG, et al. Methylenetetrahydrofolate reductase 677C → T genotype modulates homocysteine responses to a folate-rich diet or a low-dose folic acid supplement : a randomized controlled trial. Am J Clin Nutr. 2002;76(1):180–6.

    CAS  PubMed  Google Scholar 

  10. Cornelis MC, El-Sohemy A, Kabagambe EK, Campos H. Coffee, CYP1A2 genotype, and risk of myocardial infarction. JAMA J Am Med Assoc. 2006;295(10):1135–41.

    CAS  Google Scholar 

  11. Hindy G, Sonestedt E, Ericson U, Jing X-J, Zhou Y, Hansson O, et al. Role of TCF7L2 risk variant and dietary fibre intake on incident type 2 diabetes. Diabetologia. 2012;55(10):2646–54.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Li H, Kantoff PW, Giovannucci E, Leitzmann MF, Gaziano JM, Stampfer MJ, et al. Manganese Superoxide Dismutase Polymorphism , Prediagnostic Antioxidant Status , and Risk of Clinical Significant Prostate Cancer. Cancer Res. 2005;6:2498–504.

    Google Scholar 

  13. Tsang BL, Devine OJ, Cordero AM, Marchetta CM, Mulinare J, Mersereau P, et al. Assessing the association between the methylenetetrahydrofolate reductase (MTHFR) 677C>T polymorphism and blood folate concentrations: a systematic review and meta-analysis of trials and observational studies. Am J Clin Nutr. 2015;101(6):1286–94.

    CAS  PubMed  Google Scholar 

  14. Wald DS, Law M, Morris JK. Homocysteine and cardiovascular disease: evidence on causality from a meta-analysis. BMJ. 2002;325:1202.

    PubMed  PubMed Central  Google Scholar 

  15. Flegal KM, Kit BK, Orpana H, Graubard BI. Association of all-cause mortality with overweight and obesity using standard body mass index categories a systematic review and meta-analysis. JAMA - Journal of the American Medical Association. 2013;309:71–82.

    CAS  PubMed  Google Scholar 

  16. Stamler J, Wentworth D, Neaton JD. Is Relationship Between Serum Cholesterol and Risk of Premature Death From Coronary Heart Disease Continuous and Graded?: Findings in 356 222 Primary Screenees of the Multiple Risk Factor Intervention Trial (MRFIT). JAMA J Am Med Assoc. 1986;256(20):2823–8.

    CAS  Google Scholar 

  17. Goldbourt U, Yaari S, Medalie JH. Isolated low HDL cholesterol as a risk factor for coronary heart disease mortality. A 21-year follow-up of 8000 men. Arterioscler Thromb Vasc Biol. 1997;17(1):107–13.

    CAS  PubMed  Google Scholar 

  18. Bonora E, Muggeo M. Postprandial blood glucose as a risk factor for cardiovascular disease in Type II diabetes: The epidemiological evidence. Diabetologia. 2001;44:2107–14.

    CAS  PubMed  Google Scholar 

  19. Gardner CD, Kiazand A, Alhassan S, Kim S, Stafford RS, Balise RR, et al. Comparison of the Atkins, Zone, Ornish, and LEARN diets for change in weight and related risk factors among overweight premenopausal women: The A to Z weight loss study: A randomized trial. J Am Med Assoc. 2007;297(9):969–77.

    CAS  Google Scholar 

  20. Gardner CD, Trepanowski JF, Gobbo LCD, Hauser ME, Rigdon J, Ioannidis JPA, 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. JAMA. 2018;319(7):667–79.

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Freire R. Scientific evidence of diets for weight loss: Different macronutrient composition, intermittent fasting, and popular diets. Nutrition. 2020;69:110549.

    PubMed  Google Scholar 

  22. Paoli A, Rubini A, Volek JS, Grimaldi KA. Beyond weight loss: A review of the therapeutic uses of very-low-carbohydrate (ketogenic) diets. Eur J Clin Nutr. 2013;67(8):789–96.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Eurogene - Home [Internet]. [cited 2020 Aug 21]. Available from: https://web.archive.org/web/20100715113132/http:/eurogene.biomed.ntua.gr:80/Default.htm.

  24. Bueno NB, De Melo ISV, De Oliveira SL, Da Rocha Ataide T. Very-low-carbohydrate ketogenic diet v. low-fat diet for long-term weight loss: A meta-analysis of Randomised controlled trials. Br J Nutr. 2013;110:1178–87.

    CAS  PubMed  Google Scholar 

  25. Bienertova-Vasku J, Bienert P, Sablikova L, Slovackova L, Forejt M, Piskackova Z, et al. Effect of ID ACE gene polymorphism on dietary composition and obesity-related anthropometric parameters in the Czech adult population. Genes Nutr. 2009;4(3):207–13.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Muthumala A, Gable DR, Palmen J, Cooper J a, Stephens JW, Miller GJ, et al. Is the influence of variation in the ACE gene on the prospective risk of Type 2 diabetes in middle-aged men modified by obesity? Clin Sci (Lond). 2007;113(12):467–72. https://doi.org/10.1042/CS20070158.

    CAS  Article  PubMed  Google Scholar 

  27. Prior SJ, Goldberg AP, Ryan AS. ADRB2 haplotype is associated with glucose tolerance and insulin sensitivity in obese postmenopausal women. Obesity. 2011;19(2):396–401.

    CAS  PubMed  Google Scholar 

  28. Martinez JA, Corbala MS, Sa A, Forga L, Marti A, Martı MA. Nutrient-Gene Interactions Obesity Risk Is Associated with Carbohydrate Intake in Women Carrying the Gln27Glu β2-Adrenoceptor Polymorphism. J Nutr. 2003;133(8):2549–54.

  29. Song Y, Zhu L, Richa M, Li P, Yang Y, Li S. Associations of the APOC3 rs5128 polymorphism with plasma APOC3 and lipid levels: a meta-analysis. Lipids Health Dis. 2015;14:32.

    PubMed  PubMed Central  Google Scholar 

  30. Corella D, Arnett DK, Tucker KL, Kabagambe EK, Tsai M, Parnell LD, et al. A High Intake of Saturated Fatty Acids Strengthens the Association between the Fat Mass and Obesity-Associated Gene and BMI. J Nutr. 2011;141:2219–25.

  31. Smith CE, Tucker KL, Arnett DK, Noel SE, Corella D, Borecki IB, et al. Apolipoprotein A2 Polymorphism Interacts with Intakes of Dairy Foods to Influence Body Weight in Two U.S. Populations. J Nutr. 2013;143(12):1865–71.

  32. Ahn J, Nowell S, McCann SE, Yu J, Carter L, Lang NP, et al. Associations between catalase phenotype and genotype: modification by epidemiologic factors. Cancer Epidemiol Biomarkers Prev A Publ Am Assoc Cancer Res Cosponsored by Am Soc Prev Oncol. 2006;15(6):1217–22.

    CAS  Google Scholar 

  33. Pavanello S, Simioli P, Mastrangelo G, Lupi S, Gabbani G, Gregorio P, et al. Role of metabolic polymorphisms NAT2 and CYP1A2 on urinary mutagenicity after a pan-fried hamburger meal. Food Chem Toxicol An Int J Publ Br Ind Biol Res Assoc. 2002;40(8):1139–44.

    CAS  Google Scholar 

  34. Catsburg C, Joshi AD, Corral R, Lewinger JP, Koo J, John EM, et al. Polymorphisms in carcinogen metabolism enzymes, fish intake, and risk of prostate cancer. Carcinogenesis. 2012;33(7):1352–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Gilsing AMJ, Berndt SI, Ruder EH, Graubard BI, Ferrucci LM, Burdett L, et al. Meat-related mutagen exposure, xenobiotic metabolizing gene polymorphisms and the risk of advanced colorectal adenoma and cancer. Carcinogenesis. 2012;33(7):1332–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Zhao T, Nzekebaloudou M, Lv J. Ala54Thr polymorphism of fatty acid-binding protein 2 gene and fasting blood lipids: a meta-analysis. Atherosclerosis. 2010;210(2):461–7.

    CAS  PubMed  Google Scholar 

  37. Sonestedt E, Roos C, Gullberg B, Ericson U, Wirfa E. Fat and carbohydrate intake modify the association between genetic variation in the FTO genotype and obesity. Am J Clin Nutr. 2009;90:1418–25.

    CAS  PubMed  Google Scholar 

  38. Jablonska E, Gromadzinska J, Reszka E, Wasowicz W, Sobala W, Szeszenia-Dabrowska N, et al. Association between GPx1 Pro198Leu polymorphism, GPx1 activity and plasma selenium concentration in humans. Eur J Nutr. 2009;48(6):383–6.

    CAS  PubMed  Google Scholar 

  39. Brennan P, Hsu CC, Moullan N, Szeszenia-Dabrowska N, Lissowska J, Zaridze D, et al. Effect of cruciferous vegetables on lung cancer in patients stratified by genetic status: a mendelian randomisation approach. Lancet. 2005;366(9496):1558–60.

  40. Palli D, Masala G, Peluso M, Gaspari L, Krogh V, Munnia A, et al. The effects of diet on DNA bulky adduct levels are strongly modified by GSTM1 genotype : a study on 634 subjects. Carcinogenesis. 2004;25(4):577–84.

    CAS  PubMed  Google Scholar 

  41. Monsuur AJ, De Bakker PIW, Zhernakova A, Pinto D, Verduijn W, Romanos J, et al. Effective Detection of Human Leukocyte Antigen Risk Alleles in Celiac Disease Using Tag Single Nucleotide Polymorphisms. PLOS ONE. 2008;4(5) https://doi.org/10.1371/journal.pone.0002270.

  42. Jones KG, Brull DJ, Brown LC, Sian M, Greenhalgh RM, Humphries SE, et al. Interleukin-6 (IL-6) and the prognosis of abdominal aortic aneurysms. Circulation. 2001;103(18):2260–5.

    CAS  PubMed  Google Scholar 

  43. Razquin C, Martinez JA, Martinez-Gonzalez MA, Fernández-Crehuet J, Santos JM, Marti A. A Mediterranean diet rich in virgin olive oil may reverse the effects of the -174G/C IL6 gene variant on 3-year body weight change. Mol Nutr Food Res. 2010;54(Suppl 1):S75–82.

    CAS  PubMed  Google Scholar 

  44. Järvelä I, Torniainen S, Kolho KL. Molecular genetics of human lactase deficiencies. Ann Med. 2009;41(8):568–75.

    PubMed  Google Scholar 

  45. Ayyappa KA, Shatwan I, Bodhini D, Bramwell LR, Ramya K, Sudha V, et al. High fat diet modifies the association of lipoprotein lipase gene polymorphism with high density lipoprotein cholesterol in an Asian Indian population. Nutr Metab. 2017;14(1):1–9.

    Google Scholar 

  46. Memisoglu A, Hu FB, Hankinson SE, Manson JAE, De Vivo I, Willett WC, et al. Interaction between a peroxisome proliferator-activated receptor γ gene polymorphism and dietary fat intake in relation to body mass. Hum Mol Genet. 2003;12(22):2923–9.

    CAS  PubMed  Google Scholar 

  47. Ruchat SM, Pérusse L, Rankinen T, Weisnagel SJ, Rice T, Rao DC, et al. Improvements in glucose homeostasis in response to regular exercise are influenced by the PPARG Pro12Ala variant: Results from the HERITAGE family study. Diabetologia. 2010;53(4):679–89.

    CAS  PubMed  Google Scholar 

  48. Cornelis MC, Qi L, Kraft P, Hu FB. TCF7L2, dietary carbohydrate, and risk of type 2 diabetes in US women. Am J Clin Nutr. 2009;89(4):1256–62.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Grimble RF, Howell WM, O’Reilly G, Turner SJ, Markovic O, Hirrell S, et al. The ability of fish oil to suppress tumor necrosis factor {alpha} production by peripheral blood mononuclear cells in healthy men is associated with polymorphisms in genes that influence tumor necrosis factor {alpha} production. Am J Clin Nutr. 2002;76(2):454–9.

    CAS  PubMed  Google Scholar 

  50. Fontaine-bisson B, Wolever TMS, Chiasson J, Rabasa-lhoret R, Maheux P, Josse RG, et al. Genetic polymorphisms of tumor necrosis factor-α modify the association between dietary polyunsaturated fatty acids and fasting. Am J Clin Nutr. 2007;1:768–74.

    Google Scholar 

  51. Rapuri PB, Gallagher JC, Kinyamu HK, Ryschon KL. Caffeine intake increases the rate of bone loss in elderly women and interacts with vitamin D receptor genotypes. Am J Clin Nutr. 2001;74(5):694–700.

    CAS  PubMed  Google Scholar 

  52. Stathopoulou MG, Grigoriou E, Dedoussis GVZ. Calcium and Vitamin D Intake Interactions with Genetic Variants on Bone Phenotype. Curr Nutr Rep. 2012;1:169–74.

    CAS  Google Scholar 

  53. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: executive summary. Expert Panel on the Identification, Evaluation, and Treatment of Overweight in Adults. Am J Clin Nutr. 1998;68(4):899–917.

    Google Scholar 

  54. Grimaldi KA, van Ommen B, Ordovas JM, Parnell LD, Mathers JC, Bendik I, et al. Proposed guidelines to evaluate scientific validity and evidence for genotype-based dietary advice. Genes Nutr. 2017;12(35). https://doi.org/10.1186/s12263-017-0584-0.

  55. Colquhoun D. An investigation of the false discovery rate and the misinterpretation of p-values. R Soc Open Sci. 2014;1(3):140216.

    PubMed  PubMed Central  Google Scholar 

  56. dbSNP Home Page. [cited 2020 Apr 25]. Available from: https://www.ncbi.nlm.nih.gov/projects/SNP/.

  57. Nielsen DE, El-Sohemy A. A randomized trial of genetic information for personalized nutrition. Genes Nutr. 2012;7(4):559–66.

    CAS  PubMed  PubMed Central  Google Scholar 

  58. McBride CM, Koehly LM, Sanderson SC, Kaphingst KA. The behavioral response to personalized genetic information: will genetic risk profiles motivate individuals and families to choose more healthful behaviors? Annu Rev Public Health. 2010:89–103.

  59. Horne J, Madill J, O’Connor C, Shelley J, Gilliland J. A Systematic Review of Genetic Testing and Lifestyle Behaviour Change: Are We Using High-Quality Genetic Interventions and Considering Behaviour Change Theory? Lifestyle Genomics. 2018;11(1):49–63.

    CAS  PubMed  Google Scholar 

  60. Amari A, Grace NC, Fisher WW. Achieving and maintaining compliance with the ketogenic diet. J Appl Behav Anal. 1995;28(3):341–2.

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Celis-Morales C, Livingstone KM, Marsaux CFM, Macready AL, Fallaize R, O’Donovan CB, et al. Effect of personalized nutrition on health-related behaviour change: Evidence from the Food4Me European randomized controlled trial. Int J Epidemiol. 2017;46(2):578–88.

    PubMed  Google Scholar 

  62. Gann PH. Randomized trials of antioxidant supplementation for cancer prevention: first bias, now chance--next, cause. JAMA. 2009;301(1):102–3.

    CAS  PubMed  Google Scholar 

  63. Frank L, Basch E, Selby JV. The PCORI perspective on patient-centered outcomes research. JAMA. 2014;312:1513–4.

Download references

Acknowledgements

Not applicable.

Funding

No funding was received in support of this project.

Author information

Authors and Affiliations

  1. Department of Toxicology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj Napoca, Romania

    Maria Vranceanu, Ioana Ecaterina Pralea & Daniela-Saveta Popa

  2. Institute of Coaching and Performance, School of Sport and Wellbeing, University of Central Lancashire, Preston, UK

    Craig Pickering

  3. Department of Bromatology and Hygiene, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj Napoca, Romania

    Lorena Filip

  4. Department of Nutrigenetics and Personalized Nutrition, Eurogenetica, Rome, Italy

    Keith A. Grimaldi

  5. Prenetics Ltd, Quarry Bay, Hong Kong SAR, China

    Senthil Sundaram

  6. Arab Gulf University, Manama, Bahrain

    Aseel Al-Saleh

  7. Prenetics DNAfit Research Centre, London, UK

    Keith A. Grimaldi

Authors
  1. Maria Vranceanu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Craig Pickering
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lorena Filip
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ioana Ecaterina Pralea
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Senthil Sundaram
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Aseel Al-Saleh
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Daniela-Saveta Popa
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Keith A. Grimaldi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MV recruited the subjects, designed the project, collected data, and authored the first draft. CP re-worked the first draft into the final, published version, and provided data analysis. LF assisted in the design and delivery of the nutritional intervention and had significant input on re-working the first draft. IEP, DSP, SS and AAS carried out data analysis and statistical testing and had significant input on re-working the first draft. KG assisted in project design, data analysis, and manuscript preparation. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Keith A. Grimaldi.

Ethics declarations

Ethics approval and consent to participate

This research complied with the guidelines for human studies and was conducted ethically in accordance with the World Medical Declaration of Helsinki. All participants gave their written informed consent to participate in this study, and the study protocol was approved by the Ethics Committee of the University and Pharmacy, Cluj Napoca, Romania (registration number 444).

Consent for publication

Not applicable.

Competing interests

CP is a former employee of DNAfit Life Sciences; he receives no financial benefits at present from either DNAfit or Prenetics. At the time of the study, and during the writing of the manuscript, he was a full-time employee of DNAfit Life Sciences. SS is an employee of Prenetics, a genetic testing company. KG is Chief Scientific Officer of DNAFit Life Sciences, and is the founder and director of Eurogenetica Ltd. MV uses Eurogenetica tests, among others, in the clinic. LF, AS, DSP, and IP have no competing interests to declare, and receive no financial benefits from either DNAfit or Prenetics.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Additional file 1.

S1. Sample Ketogenic Diet meal plan. S2. Sample Nutrigenetic (Low Carbohydrate) meal plan. S3. Sample Nutrigenetic (Mixed Diet) meal plan. S4. Sample Nutrigenetic (Low Fat) meal plan. S5. Sample exercise plans.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Vranceanu, M., Pickering, C., Filip, L. et al. A comparison of a ketogenic diet with a LowGI/nutrigenetic diet over 6 months for weight loss and 18-month follow-up. BMC Nutr 6, 53 (2020). https://doi.org/10.1186/s40795-020-00370-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40795-020-00370-7

Keywords

  • Glycaemic index
  • Genetic testing
  • Nutrigenetics
  • Weight loss
  • Ketogenic
  • BMI
  • Cholesterol

BMC Nutrition

ISSN: 2055-0928

Contact us