The ALINFA Intervention Improves Diet Quality and Nutritional Status in Children 6 to 12 Years Old.

The study aimed to evaluate the efficacy of a new nutritional intervention, focused on improving the quality of the diet in children aged 6 to 12 years. A 2-month parallel, controlled randomized trial was conducted in the Spanish child population. The children were randomized to ALINFA nutritional intervention, which consisted of a normocaloric diet that incorporates products, ready-to-eat meals and healthy recipes specifically designed for the study, or a control group, which received the usual advice on healthy eating. The change in diet quality was assessed through the Kidmed index. The secondary outcomes were anthropometry, glucose and lipid profiles, inflammation markers, dietary intake and lifestyle. The participants in the intervention group showed an increase in the mean score of the Kidmed index (p < 0.001). Alongside that, these children decreased their intake of calories (p = 0.046), and total and saturated fat (p = 0.016//p = 0.011), and increased fiber intake (p < 0.001). Likewise, the children in the ALINFA group increased the intake of white fish (p = 0.001), pulses (p = 0.004), whole grains (p < 0.001) and nuts (p < 0.001), and decreased fatty meat (p = 0.014), refined grain (p = 0.008), pastry (p < 0.001), fast food (p < 0.001) and sugar (p = 0.001) intake. Moreover, these children had a significantly decreased BMI (p < 0.001), BMI z-score (p < 0.001), waist circumference (p = 0.016) and fat mass (p = 0.011), as well as leptin (p = 0.004). Participants in the control group did not report significant changes in diet quality. In conclusion, ALINFA nutritional intervention is possibly a useful strategy to increase the diet quality in children, which is associated to improvements in the nutritional status. These results highlight the importance of developing well-designed nutritional interventions.

A weight-loss model based on baseline microbiota and genetic scores for selection of dietary treatments in overweight and obese population.

BACKGROUND & AIMS: The response to weight loss depends on the interindividual variability of determinants such as gut microbiota and genetics. The aim of this investigation was to develop an integrative model using microbiota and genetic information to prescribe the most suitable diet for a successful weight loss in individuals with excess of body weight. METHODS: A total of 190 Spanish overweight and obese participants were randomly assigned to two hypocaloric diets for 4 months: 61 women and 29 men followed a moderately high protein (MHP) diet, and 72 women and 28 men followed a low fat (LF) diet. Baseline fecal DNA was sequenced and used for the construction of four microbiota subscores associated with the percentage of BMI loss for each diet (MHP and LF) and for each sex. Bootstrapping techniques and multiple linear regression models were used for the selection of families, genera and species included in the subscores. Finally, two total microbiota scores were generated for each sex. Two genetic subscores previously reported to weight loss were used to generate a total genetic score. In an attempt to personalize the weight loss prescription, several linear mixed models that included interaction with diet between microbiota scores and genetic scores for both, men and women, were studied. RESULTS: The microbiota subscore for the women who followed the MHP-diet included Coprococcus, Dorea, Flavonifractor, Ruminococcus albus and Clostridium bolteaea. For LF-diet women, Cytophagaceae, Catabacteriaceae, Flammeovirgaceae, Rhodobacteriaceae, Clostridium-x1vb, Bacteriodes nordiiay, Alistipes senegalensis, Blautia wexlerae and Psedoflavonifractor phocaeensis. For MHP-diet men, Cytophagaceae, Acidaminococcaceae, Marinilabiliaceae, Bacteroidaceae, Fusicatenibacter, Odoribacter and Ruminococcus faecis; and for LF-men, Porphyromanadaceae, Intestinimonas, Bacteroides finegoldii and Clostridium bartlettii. The mixed models with microbiota scores facilitated the selection of diet in 72% of women and in 84% of men. The model including genetic information allows to select the type of diet in 84% and 73%, respectively. CONCLUSIONS: Decision algorithm models can help to select the most adequate type of weight loss diet according to microbiota and genetic information. CLINICAL TRIAL REGISTRY NUMBER: This trial was registered at www. CLINICALTRIALS: gov as NCT02737267 (https://clinicaltrials.gov/ct2/show/NCT02737267?term=NCT02737267&cond=obekit&draw=2&rank=1).

Effectiveness of Nutritional Strategies on Improving the Quality of Diet of Children from 6 to 12 Years Old: A Systematic Review.

Dietary habits, that are formed during childhood and consolidated in adulthood, are known to influence the development of future chronic diseases such as metabolic syndrome or type 2 diabetes. The aim of this review was to evaluate the effectiveness of nutritional interventions carried out in recent years focused on improving the quality of the diet of the child population. A systematic search of the PubMed and Scopus databases was performed from January 2011 until September 2021. A total of 910 articles were identified and screened based on their title, abstract and full text. Finally, 12 articles were included in the current systematic review. Of those, in six studies the intervention was based on the provision of healthy meals and in the other six studies the intervention focused on modifying the school environment. Six of the studies selected included other components in their intervention such as nutritional education sessions, physical activity and/or families. A wide variety of methods were used for diet assessments, from direct method to questionnaires. The results suggest that interventions that modify the school environment or provide different meals or snacks may be effective in improving children's dietary patterns, both in the short and long term. Further research is necessary to evaluate the real effectiveness of strategies with multidisciplinary approach (nutritional sessions, physical activity and family's involvement).

Differentially methylated regions (DMRs) in PON3 gene between responders and non-responders to a weight loss dietary intervention: a new tool for precision management of obesity.

Differentially methylated regions (DMR) are genomic regions with different methylation status. The aim of this research was to identify DMRs in subjects with obesity that predict the response to a weight-loss dietary intervention and its association with metabolic variables. Based on the change in body mass index (BMI), 201 subjects with overweight and obesity were categorized in tertiles according to their response to a hypocaloric diet: Responders (R; n = 64) and Non-Responders (NR; n = 63). The R group lost 4.55 ± 0.91 BMI units (kg/m2) and the NR group lost 1.95 ± 0.73 kg/m2 (p < 0.001). DNA methylation was analysed in buffy coat through a methylation array at baseline. DMRs were analysed using a function of ChAMP (Chip Analysis Methylation Pipeline) in R software. Baseline DNA methylation analysis between R and NR exhibited a DMR located at paraoxonase 3 gene (PON3) consisting of 13 CpG sites, eleven of them significantly hypermethylated in R. To analyse the implication of these 11 CpGs on weight loss, a z-score was performed as a measure of DMR methylation. This analysis showed a correlation between PON3 DNA methylation and BMI loss. This z-score negatively correlated with PON3 protein serum levels. Total paraoxonase activity in serum was not different between groups, but PON enzymatic activity positively correlated with oxidized LDL levels. The present study identified a DMR within PON3 gene that is related to PON3 protein levels in serum, and that could be used as a potential biomarker to predict the response to weight-loss dietary interventions.

Effects of DHA-Rich n-3 Fatty Acid Supplementation and/or Resistance Training on Body Composition and Cardiometabolic Biomarkers in Overweight and Obese Post-Menopausal Women.

Resistance training (RT) and n-3 polyunsaturated fatty acids (n-3 PUFA) supplementation have emerged as strategies to improve muscle function in older adults. Overweight/obese postmenopausal women (55-70 years) were randomly allocated to one of four experimental groups, receiving placebo (olive oil) or docosahexaenoic acid (DHA)-rich n-3 PUFA supplementation alone or in combination with a supervised RT-program for 16 weeks. At baseline and at end of the trial, body composition, anthropometrical measures, blood pressure and serum glucose and lipid biomarkers were analyzed. Oral glucose tolerance tests (OGTT) and strength tests were also performed. All groups exhibit a similar moderate reduction in body weight and fat mass, but the RT-groups maintained bone mineral content, increased upper limbs lean mass, decreased lower limbs fat mass, and increased muscle strength and quality compared to untrained-groups. The RT-program also improved glucose tolerance (lowering the OGTT incremental area under the curve). The DHA-rich supplementation lowered diastolic blood pressure and circulating triglycerides and increased muscle quality in lower limbs. In conclusion, 16-week RT-program improved segmented body composition, bone mineral content, and glucose tolerance, while the DHA-rich supplement had beneficial effects on cardiovascular health markers in overweight/obese postmenopausal women. No synergistic effects were observed for DHA supplementation and RT-program combination.

Diet- and sex-related changes of gut microbiota composition and functional profiles after 4 months of weight loss intervention.

PURPOSE: Obesity has been related to intestinal dysbiosis and the modification of gut microbiota composition by dietary strategies becomes a promising strategy to help manage obesity. The aim of the current study was to evaluate the effect of two weight-loss diets on the composition and functional profile of gut microbiota. METHODS: 55 men and 124 women with BMI > 25 kg/m2 were randomly assigned to moderately high-protein (MHP) or low-fat (LF) diet. Differences in fecal bacteria abundance (based on 16 s rRNA sequencing) between before and after 4 months of calorie restriction was analyzed using EdgeR tool in MicrobiomeAnalyst platform. Bacterial functional profile was predicted using Tax4Fun and metagenomeSeq analysis. Significant KEGG Orthology (KO) terms were selected for the metabolomic study using chromatography. RESULTS: After the intervention, MHP-men showed a significant decrease in Negativicutes, Selenomonadales, Dielma and Dielma fastidiosa. LF-men showed a significant increase in Bacilli, Lactobacillales, Christensenellaceae, Peptococcaceae, and Streptococcaceae, Peptococcus, Streptococcus and Christensenella, Duncaniella dubosii_CP039396_93.49%, Roseburia sp_AB744234_98.96% and Alistipes inops_KJ572413_99.57%. MHP-women increased Pasteurellales, Phascolarctobacterium succinatutens, Ruthenibacterium lactatiformans_LR215981_99.55% and decreased in Phascolarctobacterium succinatutens_NR112902_99.56%. Finally, LF-women presented a significant decrease in Bacteroides clarus and Erysipelothrix inopinata_CP060715_84.4%. Surprisingly, no matching bacterial changes were found between these four groups. A total of 42 KO, 10 metabolic pathways and 107 related metabolites related were found implicated in these bacterial changes. Seven metabolites were confirmed in plasma. CONCLUSION: Weight-loss-related-changes in gut microbiome composition and the functional profile occur in a sex- and diet-related manner, showing that women and men could differentially benefit from the consumption of MHP and LF diets. TRIAL REGISTRATION: NCT02737267, 10th March 2016 retrospectively registered.

Unravelling gender-specific factors that link obesity to albuminuria.

BACKGROUND: Obesity is a major public health problem, which continues to be diagnosed and classified by BMI, excluding the most elemental concepts of the precision medicine approach. Obesity does not equally affect males and females, even with the same BMI. Microalbuminuria is a risk marker of cardiovascular disease closely related to obesity. The aim of this study was to evaluate the gender-dependent differences in the development of early obesity-related disease, focusing on pathologic microalbuminuria (PMA). MATERIAL AND METHODS: We developed a single-centre cross-sectional study including 1068 consecutive adults from May 2016 to January 2018, divided into two groups: one including the first 787 patients attended, evaluated as a description population; the second group included 281 subjects analysed as an external validation population. Collected data included medical history, anthropometric measures, abdominal bioimpedance and routine laboratory tests. RESULTS: First, we confirmed the lack of accuracy of classic obesity measures in predicting microalbuminuria. Second, we tested the utility of a tailored evaluation to predict PMA, with an area under the ROC curve of 0.78 for females and 0.82 for males. We also confirmed the different physiology of visceral adiposity for males when compared to females, in which small variations of fat mass entail major changes in the clinical repercussion. Third, we performed an external validation of our results, achieving a 77% accuracy rate. CONCLUSIONS: Our findings support that there is an individual threshold of fat amount necessary to develop obesity-dependent PMA and that gender plays a major role in the interplay between PMA and adiposity.

Modeling of an integrative prototype based on genetic, phenotypic, and environmental information for personalized prescription of energy-restricted diets in overweight/obese subjects.

BACKGROUND: Interindividual variability in weight loss and metabolic responses depends upon interactions between genetic, phenotypic, and environmental factors. OBJECTIVE: We aimed to model an integrative (nutri) prototype based on genetic, phenotypic, and environmental information for the personalized prescription of energy-restricted diets with different macronutrient distribution. METHODS: A 4-mo nutritional intervention was conducted in 305 overweight/obese volunteers involving 2 energy-restricted diets (30% restriction) with different macronutrient distribution: a moderately high-protein (MHP) diet (30% proteins, 30% lipids, and 40% carbohydrates) and a low-fat (LF) diet (22% lipids, 18% proteins, and 60% carbohydrates). A total of 201 subjects with good dietary adherence were genotyped for 95 single nucleotide polymorphisms (SNPs) related to energy homeostasis. Genotyping was performed by targeted next-generation sequencing. Two weighted genetic risk scores for the MHP (wGRS1) and LF (wGRS2) diets were computed using statistically relevant SNPs. Multiple linear regression models were performed to estimate percentage BMI decrease depending on the dietary macronutrient composition. RESULTS: After energy restriction, both the MHP and LF diets induced similar significant decreases in adiposity, body composition, and blood pressure, and improved the lipid profile. Furthermore, statistically relevant differences in anthropometric and biochemical markers depending on sex and age were found. BMI decrease in the MHP diet was best predicted at ∼28% (optimism-corrected adjusted R2 = 0.279) by wGRS1 and age, whereas wGRS2 and baseline energy intake explained ∼29% (optimism-corrected adjusted R2 = 0.287) of BMI decrease variability in the LF diet. The incorporation of these predictive models into a decision algorithm allowed the personalized prescription of the MHP and LF diets. CONCLUSIONS: Different genetic, phenotypic, and exogenous factors predict BMI decreases depending on the administration of a hypocaloric MHP diet or an LF diet. This holistic approach may help to personalize dietary advice for the management of excessive body weight using precision nutrition variables.This trial was registered at clinicaltrials.gov as NCT02737267.

Interplay of an Obesity-Based Genetic Risk Score with Dietary and Endocrine Factors on Insulin Resistance.

This study aimed to nutrigenetically screen gene-diet and gene-metabolic interactions influencing insulin resistance (IR) phenotypes. A total of 232 obese or overweight adults were categorized by IR status: non-IR (HOMA-IR (homeostatic model assessment - insulin resistance) index ≤ 2.5) and IR (HOMA-IR index > 2.5). A weighted genetic risk score (wGRS) was constructed using 95 single nucleotide polymorphisms related to energy homeostasis, which were genotyped by a next generation sequencing system. Body composition, the metabolic profile and lifestyle variables were evaluated, where individuals with IR showed worse metabolic outcomes. Overall, 16 obesity-predisposing genetic variants were associated with IR (p < 0.10 in the multivariate model). The wGRS strongly associated with the HOMA-IR index (adj. R squared = 0.2705, p < 0.0001). Moreover, the wGRS positively interacted with dietary intake of cholesterol (P int. = 0.002), and with serum concentrations of C-reactive protein (P int. = 0.008) regarding IR status, whereas a negative interaction was found regarding adiponectin blood levels (P int. = 0.006). In conclusion, this study suggests that interactions between an adiposity-based wGRS with nutritional and metabolic/endocrine features influence IR phenotypes, which could facilitate the prescription of personalized nutrition recommendations for precision prevention and management of IR and diabetes.

Genetic and nongenetic factors explaining metabolically healthy and unhealthy phenotypes in participants with excessive adiposity: relevance for personalized nutrition.

BACKGROUND: Different genetic and environmental factors can explain the heterogeneity of obesity-induced metabolic alterations between individuals. In this study, we aimed to screen factors that predict metabolically healthy (MHP) and unhealthy (MUP) phenotypes using genetic and lifestyle data in overweight/obese participants. METHODS: In this cross-sectional study we enrolled 298 overweight/obese Spanish adults. The Adult Treatment Panel III criteria for metabolic syndrome were used to categorize MHP (at most, one trait) and MUP (more than one feature). Blood lipid and inflammatory profiles were measured by standardized methods. Body composition was determined by dual-energy X-ray absorptiometry. A total of 95 obesity-predisposing single-nucleotide polymorphisms (SNPs) were genotyped by a predesigned next-generation sequencing system. SNPs associated with a MUP were used to compute a weighted genetic-risk score (wGRS). Information concerning lifestyle (dietary intake and physical activity level) was collected using validated questionnaires. RESULTS: The prevalence of MHP and MUP was 44.3% and 55.7%, respectively, in this sample. Overall, 12 obesity-related genetic variants were associated with the MUP. Multiple logistic regression analyses revealed that wGRS (OR = 4.133, p < 0.001), total dietary fat [odds ratio (OR) = 1.105, p = 0.002], age (OR = 1.064, p = 0.001), and BMI (OR = 1.408, p < 0.001) positively explained the MUP, whereas female sex (OR = 0.330, p = 0.009) produced a protective effect. The area under the receiver operating characteristic curve using the multivariable model was high (0.8820). Interestingly, the wGRS was the greatest contributor to the MUP (squared partial correlation = 0.3816, p < 0.001). CONCLUSIONS: The genetic background is an important factor explaining MHP and MUP related to obesity, in addition to lifestyle variables. This information could be useful to metabolically categorize individuals, as well as for the design/implementation of personalized nutrition interventions aimed at promoting metabolic health and nutritional wellbeing.

Models Integrating Genetic and Lifestyle Interactions on Two Adiposity Phenotypes for Personalized Prescription of Energy-Restricted Diets With Different Macronutrient Distribution.

Aim: To analyze the influence of genetics and interactions with environmental factors on adiposity outcomes [waist circumference reduction (WCR) and total body fat loss (TFATL)] in response to energy-restricted diets in subjects with excessive body weight. Materials and Methods: Two hypocaloric diets (30% energy restriction) were prescribed to overweight/obese subjects during 16 weeks, which had different targeted macronutrient distribution: a low-fat (LF) diet (22% energy from lipids) and a moderately high-protein (MHP) diet (30% energy from proteins). At the end of the trial, a total of 201 participants (LF diet = 105; MHP diet = 96) who presented good/regular dietary adherence were genotyped for 95 single nucleotide polymorphisms (SNPs) previously associated with weight loss through next-generation sequencing from oral samples. Four unweighted (uGRS) and four weighted (wGRS) genetic risk scores were computed using statistically relevant SNPs for each outcome by diet. Predictions of WCR and TFATL by diet were modeled through recognized multiple linear regression models including genetic (single SNPs, uGRS, and wGRS), phenotypic (age, sex, and WC, or TFAT at baseline), and environment variables (physical activity level and energy intake at baselines) as well as eventual interactions between genes and environmental factors. Results: Overall, 26 different SNPs were associated with differential adiposity outcomes, 9 with WCR and 17 with TFATL, most of which were specific for each dietary intervention. In addition to conventional predictors (age, sex, lifestyle, and adiposity status at baseline), the calculated uGRS/wGRS and interactions with environmental factors were major contributors of adiposity responses. Thus, variances in TFATL-LF diet, TFATL-MHP diet, WCR-LF diet, and WCR-MHP diet were predicted by approximately 38% (optimism-corrected adj. R 2 = 0.3792), 32% (optimism-corrected adj. R 2 = 0.3208), 22% (optimism-corrected adj. R 2 = 0.2208), and 21% (optimism-corrected adj. R 2 = 0.2081), respectively. Conclusions: Different genetic variants and interactions with environmental factors modulate the differential individual responses to MHP and LF dietary interventions. These insights and models may help to optimize personalized nutritional strategies for modeling the prevention and management of excessive adiposity through precision nutrition approaches taking into account not only genetic information but also the lifestyle/clinical factors that interplay in addition to age and sex.

Definition of nutritionally qualitative categorizing (proto)nutritypes and a pilot quantitative nutrimeter for mirroring nutritional well-being based on a quality of life health related questionnaire / Definición de (proto)nutritipos basados en la categorización cualitativa nutricional y un nutrimetro cuantitativo para reflejar el bienestar nutricional relacionado con la calidad de vida

Background: there are numerous approaches to assess nutritional status, which are putatively applied to nutritionally classify diseased people, but less information is available to study the role of environmental factors on nutritional well-being. A qualitative (nutritypes) and quantitative (nutrimeter) nutritional categorization based on dietary, lifestyle and disease criteria can be a useful nutritional approach to personalize health interventions and identify at risk individuals. Methods: cross-sectional study conducted on 102 patients (60 women), evaluating quality of life using the Short-Form 36 questionnaire (SF-36) and lifestyle factors with a general questionnaire, the Mediterranean Diet Adherence Screener (MEDAS) and the Global Physical Activity Questionnaire (GPAQ). A nutrimeter based on physical activity, fat mass, diet and diseases (hypertension, prediabetes, obesity and dyslipidemia) data was defined with an equation to quantitatively score the nutritive well-being of the participants, and classify them into two (proto)nutritypes. Results: participants were categorized into two groups (lower/higher global health) according to quality of life. Significant or marginal statistical differences in physical activity, fat mass, diet and disease were found (all p < 0.1). Two (proto)nutritypes were identified based on participant's age, sex, fat mass, physical activity, diet and diseases. Participants classified as high nutritional well-being nutritype showed higher values for physical, mental and global health dimensions. Age, fat mass, physical activity and diet, when categorized by the median, confirm that the designed nutritional well-being nutrimeter identified two (proto)nutritypes. Conclusions: the association between phenotypical (fat mass/diseases) and lifestyle factors (diet/physical activity) with quality of life allowed categorizing individuals with a nutritional quantitative score or nutrimeter according to their nutritional well-being and discriminate two qualitative (proto)nutritypes

Introducción: el estado nutricional puede clasificar metabólicamente a las personas enfermas, pero falta información sobre el papel de distintos factores ambientales relacionados con el bienestar nutricional. Una categorización nutricional cualitativa (nutritipo) y cuantitativa (nutrimetro) basada en la dieta, el estilo de vida y la enfermedad es una herramienta nutricional útil para personalizar las intervenciones de salud e identificar a aquellos individuos en riesgo. Métodos: estudio transversal en 102 pacientes, en el que se evalúa la calidad de vida mediante el cuestionario Short-Form 36 (SF-36) y los factores del estilo de vida con un cuestionario general, el Mediterranean Diet Adherence Screener (MEDAS) y el Global Physical Activity Questionnaire (GPAQ). Se diseñó una herramienta de evaluación (nutrimetro) de actividad física, masa grasa, dieta y enfermedades a través de una ecuación para calificar cuantitativamente el bienestar nutricional y clasificar a los participantes en (proto)nutritipos. Resultados: los participantes se clasificaron según la calidad de vida en dos grupos (menor/mayor salud global) y se encontraron diferencias estadísticas (p < 0,1) en la masa grasa, la actividad física, la dieta y las enfermedades. Se identificaron dos (proto)nutritipos en función de la edad, el sexo, la masa grasa, la actividad física, la dieta y las enfermedades. Los participantes clasificados en el nutritipo de alto bienestar nutricional mostraron valores significativamente más altos para las dimensiones físicas, mentales y de salud global. La edad, la masa grasa, la actividad física y la dieta confirman que el nutrimetro diseñado puede discriminar dos (proto)nutritipos. Conclusiones: factores fenotípicos (masa grasa/enfermedades) y del estilo de vida (dieta/actividad física) se han relacionado con la calidad de vida, permitiendo clasificar a individuos con una puntuación nutricional cuantitativa o nutrimetro según su bienestar nutricional y discriminar dos (proto)nutritipos

Definition of nutritionally qualitative categorizing (proto)nutritypes and a pilot quantitative nutrimeter for mirroring nutritional well-being based on a quality of life health related questionnaire.

Introduction: Background: there are numerous approaches to assess nutritional status, which are putatively applied to nutritionally classify diseased people, but less information is available to study the role of environmental factors on nutritional well-being. A qualitative (nutritypes) and quantitative (nutrimeter) nutritional categorization based on dietary, lifestyle and disease criteria can be a useful nutritional approach to personalize health interventions and identify at risk individuals. Methods: cross-sectional study conducted on 102 patients (60 women), evaluating quality of life using the Short-Form 36 questionnaire (SF-36) and lifestyle factors with a general questionnaire, the Mediterranean Diet Adherence Screener (MEDAS) and the Global Physical Activity Questionnaire (GPAQ). A nutrimeter based on physical activity, fat mass, diet and diseases (hypertension, prediabetes, obesity and dyslipidemia) data was defined with an equation to quantitatively score the nutritive well-being of the participants, and classify them into two (proto)nutritypes. Results: participants were categorized into two groups (lower/higher global health) according to quality of life. Significant or marginal statistical differences in physical activity, fat mass, diet and disease were found (all p < 0.1). Two (proto)nutritypes were identified based on participant's age, sex, fat mass, physical activity, diet and diseases. Participants classified as high nutritional well-being nutritype showed higher values for physical, mental and global health dimensions. Age, fat mass, physical activity and diet, when categorized by the median, confirm that the designed nutritional well-being nutrimeter identified two (proto)nutritypes. Conclusions: the association between phenotypical (fat mass/diseases) and lifestyle factors (diet/physical activity) with quality of life allowed categorizing individuals with a nutritional quantitative score or nutrimeter according to their nutritional well-being and discriminate two qualitative (proto)nutritypes.

Introducción: Introducción: el estado nutricional puede clasificar metabólicamente a las personas enfermas, pero falta información sobre el papel de distintos factores ambientales relacionados con el bienestar nutricional. Una categorización nutricional cualitativa (nutritipo) y cuantitativa (nutrimetro) basada en la dieta, el estilo de vida y la enfermedad es una herramienta nutricional útil para personalizar las intervenciones de salud e identificar a aquellos individuos en riesgo. Métodos: estudio transversal en 102 pacientes, en el que se evalúa la calidad de vida mediante el cuestionario Short-Form 36 (SF-36) y los factores del estilo de vida con un cuestionario general, el Mediterranean Diet Adherence Screener (MEDAS) y el Global Physical Activity Questionnaire (GPAQ). Se diseñó una herramienta de evaluación (nutrimetro) de actividad física, masa grasa, dieta y enfermedades a través de una ecuación para calificar cuantitativamente el bienestar nutricional y clasificar a los participantes en (proto)nutritipos. Resultados: los participantes se clasificaron según la calidad de vida en dos grupos (menor/mayor salud global) y se encontraron diferencias estadísticas (p < 0,1) en la masa grasa, la actividad física, la dieta y las enfermedades. Se identificaron dos (proto)nutritipos en función de la edad, el sexo, la masa grasa, la actividad física, la dieta y las enfermedades. Los participantes clasificados en el nutritipo de alto bienestar nutricional mostraron valores significativamente más altos para las dimensiones físicas, mentales y de salud global. La edad, la masa grasa, la actividad física y la dieta confirman que el nutrimetro diseñado puede discriminar dos (proto)nutritipos. Conclusiones: factores fenotípicos (masa grasa/enfermedades) y del estilo de vida (dieta/actividad física) se han relacionado con la calidad de vida, permitiendo clasificar a individuos con una puntuación nutricional cuantitativa o nutrimetro según su bienestar nutricional y discriminar dos (proto)nutritipos.

Changes in Anxiety and Depression Traits Induced by Energy Restriction: Predictive Value of the Baseline Status.

Current evidence proposes diet quality as a modifiable risk factor for mental or emotional impairments. However, additional studies are required to investigate the effect of dietary patterns and weight loss on improving psychological symptoms. The aim of this investigation was to evaluate the effect of energy-restriction, prescribed to overweight and obese participants, on anxiety and depression symptoms, as well as the potential predictive value of some baseline psychological features on weight loss. Overweight and obese participants (n = 305) were randomly assigned for 16 weeks to two hypocaloric diets with different macronutrient distribution: a moderately high-protein (MHP) diet and a low-fat (LF) diet. Anthropometrical, clinical, psychological, and lifestyle characteristics were assessed at baseline and at the end of the intervention. The nutritional intervention evidenced that weight loss has a beneficial effect on trait anxiety score in women (ß = 0.24, p = 0.03), depression score in all population (ß = 0.15, p = 0.02), particularly in women (ß = 0.22, p = 0.03) and in subjects who followed the LF diet (ß = 0.22, p = 0.04). Moreover, weight loss could be predicted by anxiety status at baseline, mainly in women and in those who were prescribed a LF diet. This trial suggests that weight loss triggers an improvement in psychological traits, and that anxiety symptoms could predict those volunteers that benefit most from a balanced calorie-restricted intervention, which will contribute to individualized precision nutrition.

Prediction of Blood Lipid Phenotypes Using Obesity-Related Genetic Polymorphisms and Lifestyle Data in Subjects with Excessive Body Weight.

BACKGROUND AND AIM: Individual lipid phenotypes including circulating total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c), high-density lipoprotein cholesterol (HDL-c), and triglycerides (TG) determinations are influenced by gene-environment interactions. The aim of this study was to predict blood lipid level (TC, LDL-c, HDL-c, and TG) variability using genetic and lifestyle data in subjects with excessive body weight-for-height. METHODS: This cross-sectional study enrolled 304 unrelated overweight/obese adults of self-reported European ancestry. A total of 95 single nucleotide polymorphisms (SNPs) related to obesity and weight loss were analyzed by a targeted next-generation sequencing system. Relevant genotypes of each SNP were coded as 0 (nonrisk) and 1 (risk). Four genetic risk scores (GRS) for each lipid phenotype were calculated by adding the risk genotypes. Information concerning lifestyle (diet, physical activity, alcohol drinking, and smoking) was obtained using validated questionnaires. Total body fat (TFAT) and visceral fat (VFAT) were determined by dual-energy X-ray absorptiometry. RESULTS: Overall, 45 obesity-related genetic variants were associated with some of the studied blood lipids. In addition to conventional factors (age, sex, dietary intakes, and alcohol consumption), the calculated GRS significantly contributed to explain their corresponding plasma lipid trait. Thus, HDL-c, TG, TC, and LDL-c serum concentrations were predicted by approximately 28% (optimism-corrected adj. R 2 = 0.28), 25% (optimism-corrected adj. R 2 = 0.25), 24% (optimism-corrected adj. R 2 = 0.24), and 21% (optimism-corrected adj. R 2=0.21), respectively. Interestingly, GRS were the greatest contributors to TC (squared partial correlation (PC2) = 0.18) and LDL-c (PC2 = 0.18) features. Likewise, VFAT and GRS had a higher impact on HDL-c (PC2 = 0.09 and PC2 = 0.06, respectively) and TG levels (PC2 = 0.20 and PC2 = 0.07, respectively) than the rest of variables. CONCLUSIONS: Besides known lifestyle influences, some obesity-related genetic variants could help to predict blood lipid phenotypes.

Interaction between an ADCY3 Genetic Variant and Two Weight-Lowering Diets Affecting Body Fatness and Body Composition Outcomes Depending on Macronutrient Distribution: A Randomized Trial.

The adenylate cyclase 3 (ADCY3) gene is involved in the regulation of several metabolic processes including the development and function of adipose tissue. The effects of the ADCY3 rs10182181 genetic variant on changes in body composition depending on the macronutrient distribution intake after 16 weeks of the dietary intervention were tested. The ADCY3 genetic variant was genotyped in 147 overweight or obese subjects, who were randomly assigned to one of the two diets varying in macronutrient content: a moderately-high-protein diet and a low-fat diet. Anthropometric and body composition measurements (DEXA scan) were recorded. Significant interactions between the ADCY3 genotype and dietary intervention on changes in weight, waist circumference, and body composition were found after adjustment for covariates. Thus, in the moderately-high-protein diet group, the G allele was associated with a lower decrease of fat mass, trunk and android fat, and a greater decrease in lean mass. Conversely, in the low-fat diet group carrying the G allele was associated with a greater decrease in trunk, android, gynoid, and visceral fat. Subjects carrying the G allele of the rs10182181 polymorphism may benefit more in terms of weight loss and improvement of body composition measurements when undertaking a hypocaloric low-fat diet as compared to a moderately-high-protein diet.

Phenotype and genotype predictors of BMI variability among European adults.

BACKGROUND/OBJECTIVE: Obesity is a complex and multifactorial disease resulting from the interactions among genetics, metabolic, behavioral, sociocultural and environmental factors. In this sense, the aim of the present study was to identify phenotype and genotype variables that could be relevant determinants of body mass index (BMI) variability. SUBJECTS/METHODS: In the present study, a total of 1050 subjects (798 females; 76%) were included. Least angle regression (LARS) analysis was used as regression model selection technique, where the dependent variable was BMI and the independent variables were age, sex, energy intake, physical activity level, and 16 polymorphisms previously related to obesity and lipid metabolism. RESULTS: The LARS analysis obtained the following formula for BMI explanation: (64.7 + 0.10 × age [years] + 0.42 × gender [0, men; 1, women] + -40.6 × physical activity [physical activity level] + 0.004 × energy intake [kcal] + 0.74 × rs9939609 [0 or 1-2 risk alleles] + -0.72 × rs1800206 [0 or 1-2 risk alleles] + -0.86 × rs1801282 [0 or 1-2 risk alleles] + 0.87 × rs429358 [0 or 1-2 risk alleles]. The multivariable regression model accounted for 21% of the phenotypic variance in BMI. The regression model was internally validated by the bootstrap method (r2 original data set = 0.208, mean r2 bootstrap data sets = 0.210). CONCLUSION: In conclusion, age, physical activity, energy intake and polymorphisms in FTO, APOE, PPARG and PPARA genes are significant predictors of the BMI trait.

Association of the Gly482Ser PPARGC1A gene variant with different cholesterol outcomes in response to two energy-restricted diets in subjects with excessive weight.

OBJECTIVES: The aim of this study was to investigate the influence of two PPARGC1A gene polymorphisms on metabolic outcomes in response to two energy-restricted diets. METHODS: A 4-mo nutritional intervention was conducted that involved two different hypo-energetic diets based on low-fat (LF) and moderately high-protein (MHP) dietary patterns. Unrelated subjects with excessive weight were genotyped for two PPARGC1A polymorphisms: Rs8192678 (Gly482Ser) and rs3755863 (G > A). Genotyping was performed by next-generation sequencing and haplotypes were screened. Anthropometric measurements and biochemical tests were assessed with standardized methods. RESULTS: Different cholesterol outcomes were observed by diet and Gly482Ser genotype. The Gly482 Gly homozygotes after an LF diet had lower reductions in total cholesterol (-9 mg/dL vs. -27 mg/dL; P = 0.017) and low-density lipoprotein cholesterol levels (-5 mg/dL vs. -18 mg/dL; P = 0.016) than the subjects who were carriers of 482 Ser allele. However, this finding was not recorded in the MHP group where Gly482 Gly homozygotes underwent similar cholesterol decreases as the 482 Ser allele carriers. Likewise, all genotype carriers had significant reductions in the frequencies of hypercholesterolemia (total cholesterol ≥200 mg/dL) except for Gly482 Gly homozygotes in the LF group. Meanwhile, the rs3755863 polymorphism and PPARGC1A haplotypes showed borderline effects with regard to cholesterol decreases. CONCLUSIONS: An energy-restricted MHP diet might be more beneficial than an LF diet to reduce serum cholesterol among subjects who are carriers of the PPARGC1A Gly482Gly genotype. The analysis of this genetic variant might be the basis for a precise, nutrigenetic management of hypercholesterolemia based on genetic makeup.

Macronutrient-specific effect of the MTNR1B genotype on lipid levels in response to 2 year weight-loss diets.

Compelling evidence indicates that lipid metabolism is in partial control of the circadian system. In this context, it has been reported that the melatonin receptor 1B (MTNR1B) genetic variant influences the dynamics of melatonin secretion, which is involved in the circadian system as a chronobiotic. The objective was to analyze whether the MTNR1B rs10830963 genetic variant was related to changes in lipid levels in response to dietary interventions with different macronutrient distribution in 722 overweight/obese subjects from the POUNDS Lost trial. We did not find a significant association between the MTNR1B genotype and changes in lipid metabolism. However, dietary fat intake significantly modified genetic effects on 2 year changes in total and LDL cholesterol (P interaction = 0.006 and 0.001, respectively). In the low-fat diet group, carriers of the sleep disruption G allele (minor allele) showed a greater reduction of total cholesterol (ß ± SE = -5.78 ± 2.88 mg/dl, P = 0.04) and LDL cholesterol (ß ± SE = -7.19 ± 2.37 mg/dl, P = 0.003). Conversely, in the high-fat diet group, subjects carrying the G allele evidenced a smaller decrease in total cholesterol (ß ± SE = 5.81 ± 2.65 mg/dl, P = 0.03) and LDL cholesterol (ß ± SE = 5.23 ± 2.21 mg/dl, P = 0.002). Subjects carrying the G allele of the circadian rhythm-related MTNR1B variant may present a bigger impact on total and LDL cholesterol when undertaking an energy-restricted low-fat diet.

Effect of the interaction between diet composition and the PPM1K genetic variant on insulin resistance and ß cell function markers during weight loss: results from the Nutrient Gene Interactions in Human Obesity: implications for dietary guidelines (NUGENOB) randomized trial.

Background: Circulating branched-chain amino acids (BCAAs) and aromatic amino acids (AAAs) have been shown to be associated with insulin resistance and diabetes risk. The common rs1440581 T allele in the protein phosphatase Mg2+/Mn2+ dependent 1K (PPM1K) gene has been related to elevated BCAA concentrations and risk of type 2 diabetes.Objective: In the present study, we tested whether dietary fat and carbohydrate intakes influenced the association between the rs1440581 PPM1K genetic variant and glucose-metabolism traits during weight loss.Design: The rs1440581 PPM1K genetic variant was genotyped in a total of 757 nondiabetic individuals who were randomly assigned to 1 of 2 energy-restricted diets that differed in macronutrient composition (low-fat diet: 20-25% fat, 15% protein, and 60-65% carbohydrate; high-fat diet: 40-45% fat, 15% protein, and 40-45% carbohydrate). The changes in fasting glucose, fasting insulin, insulin resistance (homeostasis model assessment of insulin resistance) and homeostasis model assessment of ß cell function (HOMA-B) were measured after a mean ± SD weight loss of 6.8 ± 3.4 kg over 10 wk and analyzed according to the presence of the T allele of rs1440581.Results: The rs1440581 T allele was associated with a smaller improvement in glucose concentrations after the 10-wk dietary intervention (ß ± SE: 0.05 ± 0.02 mg/dL; P = 0.03). In addition, significant gene-diet interactions were shown for the rs1440581 PPM1K genetic variant in relation to changes in insulin and HOMA-B (P-interaction = 0.006 and 0.002, respectively). In response to the high-fat diet, the T allele was associated with a higher reduction of insulin (ß ± SE: -0.77 ± 0.40 µU/mL; P = 0.04) and HOMA-B (ß ± SE: -13.2 ± 3.81; P = 0.003). An opposite effect was observed in the low-fat diet group, although in this group the T allele was marginally (P = 0.10) and not significantly (P = 0.24) associated with insulin and HOMA-B, respectively.Conclusion:PPM1K rs1440581 may affect changes in glucose metabolism during weight loss, and this effect is dependent on dietary fat and carbohydrate intakes. This trial was registered at controlled-trials.com as ISRCTN25867281.