Medium term effects of a ketogenic diet and a Mediterranean diet on resting energy expenditure and respiratory ratio

none6noneAntonio Paoli;Keith Grimaldi;Antonino Bianco;Alessandra Lodi;Lorenzo Cenci;Andrea ParmagnaniPaoli, Antonio; Keith, Grimaldi; Antonino, Bianco; Lodi, Alessandra; Lorenzo, Cenci; Parmagnani, Andre

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

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

Precision nutrition and the microbiome part ii: Potential opportunities and pathways to commercialisation

Modulation of the human gut microbiota through probiotics, prebiotics and dietary fibre are recognised strategies to improve health and prevent disease. Yet we are only beginning to understand the impact of these interventions on the gut microbiota and the physiological consequences for the human host, thus forging the way towards evidence-based scientific validation. However, in many studies a percentage of participants can be defined as ‘non-responders’ and scientists are beginning to unravel what differentiates these from ‘responders;’ and it is now clear that an individual’s baseline microbiota can influence an individual’s response. Thus, microbiome composition can potentially serve as a biomarker to predict responsiveness to interventions, diets and dietary components enabling greater opportunities for its use towards disease prevention and health promotion. In Part I of this two-part review, we reviewed the current state of the science in terms of the gut microbiota and the role of diet and dietary components in shaping it and subsequent consequences for human health. In Part II, we examine the efficacy of gut-microbiota modulating therapies at different life stages and their potential to aid in the management of undernutrition and overnutrition. Given the significance of an individual’s gut microbiota, we investigate the feasibility of microbiome testing and we discuss guidelines for evaluating the scientific validity of evidence for providing personalised microbiome-based dietary advice. Overall, this review highlights the potential value of the microbiome to prevent disease and maintain or promote health and in doing so, paves the pathway towards commercialisation

The impact of MTHFR 677C → T risk knowledge on changes in folate intake: findings from the Food4Me study

Background It is hypothesised that individuals with knowledge of their genetic risk are more likely to make health-promoting dietary and lifestyle changes. The present study aims to test this hypothesis using data from the Food4Me study. This was a 6-month Internet-based randomised controlled trial conducted across seven centres in Europe where individuals received either general healthy eating advice or varying levels of personalised nutrition advice. Participants who received genotype-based personalised advice were informed whether they had the risk (CT/TT) (n = 178) or non-risk (CC) (n = 141) alleles of the methylenetetrahydrofolate reductase (MTHFR) gene in relation to cardiovascular health and the importance of a sufficient intake of folate. General linear model analysis was used to assess changes in folate intake between the MTHFR risk, MTHFR non-risk and control groups from baseline to month 6 of the intervention. Results There were no differences between the groups for age, gender or BMI. However, there was a significant difference in country distribution between the groups (p = 0.010). Baseline folate intakes were 412 ± 172, 391 ± 190 and 410 ± 186 μg per 10 MJ for the risk, non-risk and control groups, respectively. There were no significant differences between the three groups in terms of changes in folate intakes from baseline to month 6. Similarly, there were no changes in reported intake of food groups high in folate. Conclusions These results suggest that knowledge of MTHFR 677C → T genotype did not improve folate intake in participants with the risk variant compared with those with the non-risk variant

A multifactorial analysis of obesity as CVD risk factor: Use of neural network based methods in a nutrigenetics context

<p>Abstract</p> <p>Background</p> <p>Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm.</p> <p>Results</p> <p>PDM-ANN and GA-ANN were comparatively assessed in terms of their ability to identify the most important factors among the initial 63 variables describing genetic variations, nutrition and gender, able to classify a subject into one of the BMI related classes: normal and overweight. The methods were designed and evaluated using appropriate training and testing sets provided by 3-fold Cross Validation (3-CV) resampling. Classification accuracy, sensitivity, specificity and area under receiver operating characteristics curve were utilized to evaluate the resulted predictive ANN models. The most parsimonious set of factors was obtained by the GA-ANN method and included gender, six genetic variations and 18 nutrition-related variables. The corresponding predictive model was characterized by a mean accuracy equal of 61.46% in the 3-CV testing sets.</p> <p>Conclusions</p> <p>The ANN based methods revealed factors that interactively contribute to obesity trait and provided predictive models with a promising generalization ability. In general, results showed that ANNs and their hybrids can provide useful tools for the study of complex traits in the context of nutrigenetics.</p

Effect of personalized nutrition on health-related behaviour change: evidence from the Food4me European randomized controlled trial

Background: Optimal nutritional choices are linked with better health, but many current interventions to improve diet have limited effect. We tested the hypothesis that providing personalized nutrition (PN) advice based on information on individual diet and lifestyle, phenotype and/or genotype would promote larger, more appropriate, and sustained changes in dietary behaviour. Methods: Adults from seven European countries were recruited to an internet-delivered intervention (Food4Me) and randomized to: (i) conventional dietary advice (control) or to PN advice based on: (ii) individual baseline diet; (iii) individual baseline diet plus phenotype (anthropometry and blood biomarkers); or (iv) individual baseline diet plus phenotype plus genotype (five diet-responsive genetic variants). Outcomes were dietary intake, anthropometry and blood biomarkers measured at baseline and after 3 and 6 months’ intervention. Results: At baseline, mean age of participants was 39.8 years (range 18–79), 59% of participants were female and mean body mass index (BMI) was 25.5 kg/m2. From the enrolled participants, 1269 completed the study. Following a 6-month intervention, participants randomized to PN consumed less red meat [-5.48 g, (95% confidence interval:- 10.8,-0.09), P=0.046], salt [-0.65 g, (-1.1,-0.25), P=0.002] and saturated fat [-1.14 % of energy, (-1.6,-0.67), P<0.0001], increased folate [29.6 mg, (0.21,59.0), P=0.048] intake and had higher Healthy Eating Index scores [1.27, (0.30, 2.25), P=0.010) than those randomized to the control arm. There was no evidence that including phenotypic and phenotypic plus genotypic information enhanced the effectiveness of the PN advice. Conclusions: Among European adults, PN advice via internet-delivered intervention produced larger and more appropriate changes in dietary behaviour than a conventional approach

Proposed guidelines to evaluate scientific validity and evidence for genotype-based dietary advice

Nutrigenetic research examines the effects of inter-individual differences in genotype on responses to nutrients and other food components, in the context of health and of nutrient requirements. A practical application of nutrigenetics is the use of personal genetic information to guide recommendations for dietary choices that are more efficacious at the individual or genetic subgroup level relative to generic dietary advice. Nutrigenetics is unregulated, with no defined standards, beyond some commercially adopted codes of practice. Only a few official nutrition-related professional bodies have embraced the subject, and, consequently, there is a lack of educational resources or guidance for implementation of the outcomes of nutrigenetic research. To avoid misuse and to protect the public, personalised nutrigenetic advice and information should be based on clear evidence of validity grounded in a careful and defensible interpretation of outcomes from nutrigenetic research studies. Evidence requirements are clearly stated and assessed within the context of state-of-the-art ‘evidence-based nutrition’. We have developed and present here a draft framework that can be used to assess the strength of the evidence for scientific validity of nutrigenetic knowledge and whether ‘actionable’. In addition, we propose that this framework be used as the basis for developing transparent and scientifically sound advice to the public based on nutrigenetic tests. We feel that although this area is still in its infancy, minimal guidelines are required. Though these guidelines are based on semiquantitative data, they should stimulate debate on their utility. This framework will be revised biennially, as knowledge on the subject increases

Effect of ketogenic mediterranean diet with phytoextracts and low carbohydrates/high-protein meals on weight, cardiovascular risk factors, body composition and diet compliance in Italian council employees

<p>Abstract</p> <p>Background</p> <p>There has been increased interest in recent years in very low carbohydrate ketogenic diets (VLCKD) that, even though they are much discussed and often opposed, have undoubtedly been shown to be effective, at least in the short to medium term, as a tool to tackle obesity, hyperlipidemia and some cardiovascular risk factors. For this reason the ketogenic diet represents an interesting option but unfortunately suffers from a low compliance. The aim of this pilot study is to ascertain the safety and effects of a modified ketogenic diet that utilizes ingredients which are low in carbohydrates but are formulated to simulate its aspect and taste and also contain phytoextracts to add beneficial effects of important vegetable components.</p> <p>Methods</p> <p>The study group consisted of 106 Rome council employees with a body mass index of ≥ 25, age between 18 and 65 years (19 male and 87 female; mean age 48.49 ± 10.3). We investigated the effects of a modified ketogenic diet based on green vegetables, olive oil, fish and meat plus dishes composed of high quality protein and virtually zero carbohydrate but which mimic their taste, with the addition of some herbal extracts (KEMEPHY ketogenic Mediterranean with phytoextracts). Calories in the diet were unlimited. Measurements were taken before and after 6 weeks of diet.</p> <p>Results</p> <p>There were no significant changes in BUN, ALT, AST, GGT and blood creatinine. We detected a significant (p < 0.0001) reduction in BMI (31.45 Kg/m²to 29.01 Kg/m²), body weight (86.15 kg to 79.43 Kg), percentage of fat mass (41.24% to 34.99%), waist circumference (106.56 cm to 97.10 cm), total cholesterol (204 mg/dl to 181 mg/dl), LDLc (150 mg/dl to 136 mg/dl), triglycerides (119 mg/dl to 93 mg/dl) and blood glucose (96 mg/dl to 91 mg/dl). There was a significant (p < 0.0001) increase in HDLc (46 mg/dl to 52 mg/dl).</p> <p>Conclusions</p> <p>The KEMEPHY diet lead to weight reduction, improvements in cardiovascular risk markers, reduction in waist circumference and showed good compliance.</p

The effect of the apolipoprotein E genotype on response to personalized dietary advice intervention: findings from the Food4Me randomized controlled trial

Background: The apolipoprotein E (APOE) risk allele (ɛ4) is associated with higher total cholesterol (TC), amplified response to saturated fatty acid (SFA) reduction, and increased cardiovascular disease. Although knowledge of gene risk may enhance dietary change, it is unclear whether ɛ4 carriers would benefit from gene-based personalized nutrition (PN). Objectives: The aims of this study were to 1) investigate interactions between APOE genotype and habitual dietary fat intake and modulations of fat intake on metabolic outcomes; 2) determine whether gene-based PN results in greater dietary change than do standard dietary advice (level 0) and nongene-based PN (levels 1–2); and 3) assess the impact of knowledge of APOE risk (risk: E4+, nonrisk: E4−) on dietary change after gene-based PN (level 3). Design: Individuals (n = 1466) recruited into the Food4Me pan-European PN dietary intervention study were randomly assigned to 4 treatment arms and genotyped for APOE (rs429358 and rs7412). Diet and dried blood spot TC and ω-3 (n–3) index were determined at baseline and after a 6-mo intervention. Data were analyzed with the use of adjusted general linear models. Results: Significantly higher TC concentrations were observed in E4+ participants than in E4− (P < 0.05). Although there were no significant differences in APOE response to gene-based PN (E4+ compared with E4−), both groups had a greater reduction in SFA (percentage of total energy) intake than at level 0 (mean ± SD: E4+, −0.72% ± 0.35% compared with −1.95% ± 0.45%, P = 0.035; E4−, −0.31% ± 0.20% compared with −1.68% ± 0.35%, P = 0.029). Gene-based PN was associated with a smaller reduction in SFA intake than in nongene-based PN (level 2) for E4− participants (−1.68% ± 0.35% compared with −2.56% ± 0.27%, P = 0.025). Conclusions: The APOE ɛ4 allele was associated with higher TC. Although gene-based PN targeted to APOE was more effective in reducing SFA intake than standard dietary advice, there was no difference between APOE “risk” and “nonrisk” groups. Furthermore, disclosure of APOE nonrisk may have weakened dietary response to PN