Insulin receptor substrate 1 gene variation modifies insulin resistance response to weight-loss diets in a 2-year randomized trial: the Preventing Overweight Using Novel Dietary Strategies (POUNDS LOST) trial

DUSP9-mediated inhibition of IRS1/PI3K/AKT pathway contributes to insulin resistance and metabolic dysfunction in gestational diabetes mellitus

BACKGROUND

Gestational diabetes mellitus (GDM) is a common pregnancy complication associated with adverse maternal and fetal outcomes. Recent studies suggest a role for dual-specificity phosphatase 9 (DUSP9) in insulin resistance and metabolic dysregulation, though its specific contribution to GDM remains unclear. This study aims to investigate the function of DUSP9 in GDM pathophysiology and its underlying molecular mechanisms.

METHODS

We analyzed DUSP9 expression in umbilical cord blood and placental tissues from GDM patients (n = 16) and healthy controls (n = 14) using RT-qPCR and western blot assays. In vitro, functional assays were conducted on high glucose-induced HTR-8/SVneo trophoblast cells to evaluate the effects of DUSP9 knockdown on cell viability, apoptosis, and insulin signaling. In vivo, a GDM mouse model was constructed, and lentivirus-mediated shRNA was used to downregulate DUSP9 expression. Furthermore, metabolic parameters, including insulin resistance indices, lipid metabolism, and placental apoptosis were assessed, along with the phosphorylation status of key proteins in the IRS1/PI3K/AKT pathway.

RESULTS

We first observed that DUSP9 expression was significantly upregulated in the umbilical cord blood and placental tissues of GDM patients compared to healthy controls (p < 0.01). Using high glucose-induced HTR-8/SVneo trophoblast cells to mimic GDM conditions, we found that downregulation of DUSP9 increased cell viability and inhibited apoptosis (p < 0.01). Mechanistically, co-immunoprecipitation and pull-down assays demonstrated that DUSP9 directly interacts with insulin receptor substrate 1 (IRS1) and inhibits HG-mediated IRS1 phosphorylation at Tyr632, impairing downstream insulin signaling (p < 0.01). In vivo, a GDM mouse model revealed elevated DUSP9 expression, along with significant metabolic dysfunction, including insulin resistance and increased placental apoptosis (p < 0.01). Lentivirus-mediated knockdown of DUSP9 in these mice ameliorated insulin resistance, improved lipid metabolism, and reduced placental apoptosis by improving fasting glucose and insulin levels, lipid profiles, and decreased apoptotic markers (p < 0.01). Moreover, DUSP9 knockdown in these mice promoted activation of the IRS1/PI3K/AKT signaling pathway (p < 0.01).

CONCLUSIONS

DUSP9 contributes to GDM progression by inhibiting the IRS1/PI3K/AKT pathway, leading to insulin resistance and metabolic dysfunction. The knockdown of DUSP9 ameliorates key pathological features of GDM, including insulin resistance, impaired lipid metabolism, and placental apoptosis, suggesting that targeting DUSP9 may represent a potential therapeutic strategy for GDM.

Using Metabolic Testing to Personalize Behavioral Obesity Treatment

Background

There are large individual differences in weight loss and maintenance. Metabolic testing can provide phenotypical information that can be used to personalize treatment so that people remain in negative energy balance during weight loss and remain in energy balance during maintenance. Behavioral testing can assess the reinforcing value and change in the temporal window related to the personalized diet and exercise program to motivate people to maintain engagement in healthier eating and activity programs.

Objective

Provide an expository overview of how metabolic testing can be used to personalize weight control. Ideas about incorporating behavioral economic concepts are also included.

Methods

A broad overview of how resting metabolic rate, thermic effect of food and respiratory quotient can be used to improve weight control. Also discussed are behavioral economic principles that can maximize adherence to diet and activity protocols.

Results

Research suggests that measuring metabolic rate can be used to set calorie goals for weight loss and maintenance, thermic effect of food to increase energy expenditure, and respiratory quotient to guide macronutrient composition of the diet and maximize fat loss. Developing programs that foster a strong motivation to eat healthier and be active can maximize treatment success.

Conclusion

Incorporating metabolic measures can personalize behavioral weight loss programs, and the use of behavioral economic principles can increase the probability of adherence and long-term success in weight control.

Machine-learning approaches to predict individualized treatment effect using a randomized controlled trial

Recent advancements in machine learning (ML) for analyzing heterogeneous treatment effects (HTE) are gaining prominence within the medical and epidemiological communities, offering potential breakthroughs in the realm of precision medicine by enabling the prediction of individual responses to treatments. This paper introduces the methodological frameworks used to study HTEs, particularly based on a single randomized controlled trial (RCT). We focus on methods to estimate conditional average treatment effect (CATE) for multiple covariates, aiming to predict individualized treatment effects. We explore a range of methodologies from basic frameworks like the T-learner, S-learner, and Causal Forest, to more advanced ones such as the DR-learner and R-learner, as well as cross-validation for CATE estimation to enhance statistical efficiency by estimating CATE for all RCT participants. We also provide a practical application of these approaches using the Preventing Overweight Using Novel Dietary Strategies (POUNDS Lost) trial, which compared the effects of high versus low-fat diet interventions on 2-year weight changes. We compared different sets of covariates for CATE estimation, showing that the DR- and R-learners are useful for the estimation of CATE in high-dimensional settings. This paper aims to explain the theoretical underpinnings and methodological nuances of ML-based HTE analysis without relying on technical jargon, making these concepts more accessible to the clinical and epidemiological research communities.

Precision nutrition-based interventions for the management of obesity in children and adolescents up to the age of 19 years

BACKGROUND

Precision nutrition-based methods develop tailored interventions and/or recommendations accounting for determinants of intra- and inter-individual variation in response to the same diet, compared to current 'one-size-fits-all' population-level approaches. Determinants may include genetics, current dietary habits and eating patterns, circadian rhythms, health status, gut microbiome, socioeconomic and psychosocial characteristics, and physical activity. ​​​​In this systematic review, we examined the evidence base for the effect of interventions based on precision nutrition approaches on overweight and obesity in children and adolescents to help inform future research and global guidelines.

OBJECTIVES

To examine the impact of precision nutrition-based interventions for the management of obesity in children and adolescents in all their diversity.

SEARCH METHODS

We searched CENTRAL, MEDLINE, CINAHL, Web of Science Core Collection, BIOSIS Previews, Global Index Medicus (all regions), IBECS, SciELO, PAHO, PAHO IRIS, WHO IRIS, WHOLIS, Bibliomap, and TRoPHI, as well as the WHO ICTRP and ClinicalTrials.gov. We last searched the databases on 23 July 2024. We did not apply any language restrictions.

SELECTION CRITERIA

We included randomised or quasi-randomised controlled trials that evaluated precision nutrition-based interventions (accounting for 'omics' such as phenotyping, genotyping, gut microbiome; clinical data, baseline dietary intake, postprandial glucose response, etc., and/or including artificial intelligence such as machine learning methods) compared to general or one-size-fits-all interventions or no intervention in children and adolescents aged 0 to 9 years or 10 to 19 years with overweight or obesity.

DATA COLLECTION AND ANALYSIS

Two review authors independently conducted study screening, data extraction, and risk of bias and GRADE assessments. We used fixed-effect analyses. Our outcomes of interest were physical and mental well-being, physical activity, health-related quality of life, obesity-associated disability, and adverse events associated with the interventions as defined or measured by trialists, and weight change (reduction, stabilisation or maintenance).

MAIN RESULTS

Two studies (3 references, 105 participants) conducted in Ukraine and Greece met our eligibility criteria. One study reported nonprofit funding sources, whilst the other did not report funding, and the certainty of evidence ranged from very low to low across outcomes (all measured at endpoint). Only one trial (65 participants) contributed data on our primary outcomes of interest. Precision nutrition-based intervention versus one-size-fits-all intervention or standard of care In children 0 to 9 years of age, evidence is very uncertain about the effect of a precision nutrition-based intervention (a computerised Decision Support Tool (DST) that incorporates a variety of participant data and provides personalised diet recommendations based on decision-tree algorithms) on body mass index (BMI) (mean difference (MD) -1.40 kg/m2, 95% confidence interval (CI) -3.48 to 0.68; 1 study, 35 participants; very low-certainty evidence) and on weight (MD -2.60 kg, 95% CI -8.42 to 3.22; 1 study, 35 participants; very low-certainty evidence) compared with a one-size-fits-all control intervention. In children and adolescents 10 to 19 years of age, evidence is very uncertain about the effect of a precision nutrition-based intervention (computerised DST) on BMI (MD 3.00 kg/m2, 95% CI -0.26 to 6.26; 1 study, 30 participants; very low-certainty evidence) and on weight (MD 11.40 kg, 95% CI -0.47 to 23.27; 1 study, 30 participants; very low-certainty evidence) compared with a one-size-fits-all control intervention.

AUTHORS' CONCLUSIONS

Based on data from two small studies with a total of 105 participants, the evidence is very uncertain about the effect of precision nutrition-based interventions on body weight or BMI. This review was limited by the number of available randomised controlled trials in this relatively nascent field. Given these limitations, the two studies do not provide sufficient evidence to adequately inform practice. Future research should report participant outcome data, including outcomes related to mental, emotional, and functional well-being, in addition to biochemical and physical measures, stratified by World Health Organization-defined age groups (children (0 to 9 years), and children and adolescents (10 to 19 years)). Future studies should also report methods related to randomisation, blinding, and compliance, as well as include prespecified analysis plans.

Combined effects of genetic background and diet on mouse metabolism and gene expression

In humans, dietary patterns impact weight and metabolism differentially across individuals. To uncover genetic determinants for differential dietary effects, we subjected four genetically diverse mouse strains to humanized diets (American, Mediterranean, vegetarian, and vegan) with similar macronutrient composition, and performed body weight, metabolic parameter, and RNA-seq analysis. We observed pronounced diet- and strain-dependent effects on weight, and triglyceride and insulin levels. Differences in fat mass, adipose tissue, and skeletal muscle glucose uptake, and gene expression changes in most tissues were strain-dependent. In visceral adipose tissue, ∼400 genes responded to diet in a strain-dependent manner, many of them in metabolite transport and lipid metabolism pathways and several previously identified to modify diet effects in humans. Thus, genetic background profoundly impacts metabolism, though chosen dietary patterns modify the strong genetic effects. This study paves the way for future mechanistic investigations into strain-diet interactions in mice and translation to precision nutrition in humans.

Exploring the Interplay of Genetics and Nutrition in the Rising Epidemic of Obesity and Metabolic Diseases

Background: Obesity has become a significant global health issue. This multifaceted condition is influenced by genetic, environmental, and lifestyle factors, significantly influenced by nutrition. Aim: The study's objective is to elucidate the relationship between obesity-related genes, nutrient intake, and the development of obesity and the importance of other metabolic diseases. Methods: A comprehensive literature review spanning the past two decades was conducted to analyze the contributions of genetic variants-including FTO, MC4R, and LEPR-and their associations with dietary habits, highlighting how specific nutrients affect gene expression and obesity risk and how the coexistence of metabolic diseases such as type 2 diabetes and osteoporosis may modulate these factors. Moreover, the role of epigenetic factors, such as dietary patterns that encourage the development of obesity, was explored. Discussion and Conclusions: By understanding the intricate relationships among genetics, nutrients, and obesity development, this study highlights the importance of personalized dietary strategies in managing obesity. Overall, an integrated approach that considers genetic predispositions alongside environmental influences is essential for developing effective prevention and treatment methodologies, ultimately contributing to better health outcomes in diverse populations.

Integration of proteomics with prospective birth cohort to elucidate early life origins of cardiometabolic diseases: rationale, study design, lab assay, and quality control

There is growing evidence that the plasma proteome provides insights into personal health status at different stages of life. However, limited data are available on high-throughput proteomic studies in pediatric populations, especially, using prospective birth cohorts. We launched a proteomics study in 990 children from a US predominantly urban, low-income, multi-ethnic prospective Boston Birth Cohort (BBC, referred as "BBC proteomics study"), which aimed to leverage proteomics to investigate the biological pathways underlying the link between preterm birth and child long-term cardiometabolic health. The objective of this paper is to describe the rationale, study design, proteomic assay and quality control steps for the BBC proteomics study in a subset of children with available proteomic profiling. Using the OLINK® Explore 3072 platform, proteomic profiling was performed in cord plasma at birth and in postnatal plasma collected during early childhood. Quality control (QC) steps were performed, including calculation of coefficient of variation (CV), missingness rates per sample or per protein, principal component analyses to identify clustering and outliers, and correlation analyses among the duplicates to indicate reproducibility. A total of 2,941 proteins from eight OLINK panels were successfully measured at both time points. Almost 100% of samples passed lab-prespecified QC. Approximately 89% of proteins were detected in > 50% samples; 79.6% had intra-CV < 15% and 79.9% of had inter-CV < 30%. Four samples were identified as outliers due to high missingness rates. Our data also demonstrated that this assay had a good reproducibility with correlation coefficient (r) > 0.65 in most of the duplicates, although we also identified potential batch effects. In conclusion, our data suggests that this high-throughput proteomic profiling is feasible and reproducible in archived plasma samples, including cord blood. We anticipated that successful completion of this proteomics study will help identify novel predictive biomarkers and therapeutic targets so that high-risk newborns can be identified, and effective interventions can be initiated during the earliest developmental window when they may have the greatest life-long benefit.

Ethnicity-related differences in mitochondrial regulation by insulin stimulation in diabetes

Mitochondrial dysfunction has long been implicated in the development of insulin resistance, which is a hallmark of type 2 diabetes. However, recent studies reveal ethnicity-related differences in mitochondrial processes, underscoring the need for nuance in studying mitochondrial dysfunction and insulin sensitivity. Furthermore, the higher prevalence of type 2 diabetes among African Americans and individuals of African descent has brought attention to the role of ethnicity in disease susceptibility. In this review, which covers existing literature, genetic studies, and clinical data, we aim to elucidate the complex relationship between mitochondrial alterations and insulin stimulation by considering how mitochondrial dynamics, contact sites, pathways, and metabolomics may be differentially regulated across ethnicities, through mechanisms such as single nucleotide polymorphisms (SNPs). In addition to achieving a better understanding of insulin stimulation, future studies identifying novel regulators of mitochondrial structure and function could provide valuable insights into ethnicity-dependent insulin signaling and personalized care.

Is There an Ideal Diet? Some Insights from the POUNDS Lost Study

Diets for weight loss have a long history but an ideal one has not yet been clearly identified. To compare low-fat and lower carbohydrate diets, we designed The Preventing Overweight by Novel Dietary Strategies (POUNDS) Lost study. This is a 2 × 2 factorial study with diets of 20% or 40% fat and 15% or 25% protein with a graded carbohydrate intake of 35, 45, 55 and 65%. Weight loss, overall, was modest at nearly 6% with all four diets, and no significant dietary difference. The variability in weight loss in each diet group was significant, ranging from greater than 20% to a small weight gain. Studies of genetic variations in relation to weight loss showed that the diet that was selected could significantly affect weight loss, emphasizing that there is no ideal diet and more than one diet can be used to treat obesity. Weight loss was also influenced by the level of baseline triiodothyronine or thyroxine, and baseline carbohydrate and insulin resistance. Achieving a stable Health Eating Food Diversity Index, eating more protein, eating more fiber, engaging in more physical activity, sleeping better and eating less ultra-processed foods were beneficial strategies for weight loss in this trial. Although there is no "ideal diet", both the DASH diet and the Mediterranean diet have clinical trials showing their significant benefit for cardiovascular risk factors. Finally, the lesson of the "Last Chance Diet", which recommended a diet with protein from gelatin, proved that some diets could be hazardous.

Potential Mechanisms of Precision Nutrition-Based Interventions for Managing Obesity

Precision nutrition (PN) considers multiple individual-level and environmental characteristics or variables to better inform dietary strategies and interventions for optimizing health, including managing obesity and metabolic disorders. Here, we review the evidence on potential mechanisms-including ones to identify individuals most likely to respond-that can be leveraged in the development of PN interventions addressing obesity. We conducted a review of the literature and included laboratory, animal, and human studies evaluating biochemical and genetic data, completed and ongoing clinical trials, and public programs in this review. Our analysis describes the potential mechanisms related to 6 domains including genetic predisposition, circadian rhythms, physical activity and sedentary behavior, metabolomics, the gut microbiome, and behavioral and socioeconomic characteristics, i.e., the factors that can be leveraged to design PN-based interventions to prevent and treat obesity-related outcomes such as weight loss or metabolic health as laid out by the NIH 2030 Strategic Plan for Nutrition Research. For example, single nucleotide polymorphisms can modify responses to certain dietary interventions, and epigenetic modulation of obesity risk via physical activity patterns and macronutrient intake have also been demonstrated. Additionally, we identified limitations including questions of equitable implementation across a limited number of clinical trials. These include the limited ability of current PN interventions to address systemic influences such as supply chains and food distribution, healthcare systems, racial or cultural inequities, and economic disparities, particularly when designing and implementing PN interventions in low- and middle-income communities. PN has the potential to help manage obesity by addressing intra- and inter-individual variation as well as context, as opposed to "one-size fits all" approaches though there is limited clinical trial evidence to date.

Precision Nutrition Unveiled: Gene-Nutrient Interactions, Microbiota Dynamics, and Lifestyle Factors in Obesity Management

BACKGROUND

Obesity is a complex metabolic disorder that is associated with several diseases. Recently, precision nutrition (PN) has emerged as a tailored approach to provide individualised dietary recommendations.

AIM

This review discusses the major intrinsic and extrinsic components considered when applying PN during the management of obesity and common associated chronic conditions.

RESULTS

The review identified three main PN components: gene-nutrient interactions, intestinal microbiota, and lifestyle factors. Genetic makeup significantly contributes to inter-individual variations in dietary behaviours, with advanced genome sequencing and population genetics aiding in detecting gene variants associated with obesity. Additionally, PN-based host-microbiota evaluation emerges as an advanced therapeutic tool, impacting disease control and prevention. The gut microbiome's composition regulates diverse responses to nutritional recommendations. Several studies highlight PN's effectiveness in improving diet quality and enhancing adherence to physical activity among obese patients. PN is a key strategy for addressing obesity-related risk factors, encompassing dietary patterns, body weight, fat, blood lipids, glucose levels, and insulin resistance.

CONCLUSION

PN stands out as a feasible tool for effectively managing obesity, considering its ability to integrate genetic and lifestyle factors. The application of PN-based approaches not only improves current obesity conditions but also holds promise for preventing obesity and its associated complications in the long term.

Genotype-based precision nutrition strategies for the prediction and clinical management of type 2 diabetes mellitus

Globally, type 2 diabetes mellitus (T2DM) is one of the most common metabolic disorders. T2DM physiopathology is influenced by complex interrelationships between genetic, metabolic and lifestyle factors (including diet), which differ between populations and geographic regions. In fact, excessive consumptions of high fat/high sugar foods generally increase the risk of developing T2DM, whereas habitual intakes of plant-based healthy diets usually exert a protective effect. Moreover, genomic studies have allowed the characterization of sequence DNA variants across the human genome, some of which may affect gene expression and protein functions relevant for glucose homeostasis. This comprehensive literature review covers the impact of gene-diet interactions on T2DM susceptibility and disease progression, some of which have demonstrated a value as biomarkers of personal responses to certain nutritional interventions. Also, novel genotype-based dietary strategies have been developed for improving T2DM control in comparison to general lifestyle recommendations. Furthermore, progresses in other omics areas (epigenomics, metagenomics, proteomics, and metabolomics) are improving current understanding of genetic insights in T2DM clinical outcomes. Although more investigation is still needed, the analysis of the genetic make-up may help to decipher new paradigms in the pathophysiology of T2DM as well as offer further opportunities to personalize the screening, prevention, diagnosis, management, and prognosis of T2DM through precision nutrition.

Triangulating nutrigenomics, metabolomics and microbiomics toward personalized nutrition and healthy living

The unique physiological and genetic characteristics of individuals influence their reactions to different dietary constituents and nutrients. This notion is the foundation of personalized nutrition. The field of nutrigenetics has witnessed significant progress in understanding the impact of genetic variants on macronutrient and micronutrient levels and the individual's responsiveness to dietary intake. These variants hold significant value in facilitating the development of personalized nutritional interventions, thereby enabling the effective translation from conventional dietary guidelines to genome-guided nutrition. Nevertheless, certain obstacles could impede the extensive implementation of individualized nutrition, which is still in its infancy, such as the polygenic nature of nutrition-related pathologies. Consequently, many disorders are susceptible to the collective influence of multiple genes and environmental interplay, wherein each gene exerts a moderate to modest effect. Furthermore, it is widely accepted that diseases emerge because of the intricate interplay between genetic predisposition and external environmental influences. In the context of this specific paradigm, the utilization of advanced "omic" technologies, including epigenomics, transcriptomics, proteomics, metabolomics, and microbiome analysis, in conjunction with comprehensive phenotyping, has the potential to unveil hitherto undisclosed hereditary elements and interactions between genes and the environment. This review aims to provide up-to-date information regarding the fundamentals of personalized nutrition, specifically emphasizing the complex triangulation interplay among microbiota, dietary metabolites, and genes. Furthermore, it highlights the intestinal microbiota's unique makeup, its influence on nutrigenomics, and the tailoring of dietary suggestions. Finally, this article provides an overview of genotyping versus microbiomics, focusing on investigating the potential applications of this knowledge in the context of tailored dietary plans that aim to improve human well-being and overall health.

Influence of polymorphisms in IRS1, IRS2, MC3R, and MC4R on metabolic and inflammatory status and food intake in Brazilian adults: An exploratory pilot study

Polymorphisms in genes of leptin-melanocortin and insulin pathways have been associated with obesity and type 2 diabetes. We hypothesized that polymorphisms in IRS1, IRS2, MC3R, and MC4R influence metabolic and inflammatory markers and food intake composition in Brazilian subjects. This exploratory pilot study included 358 adult subjects. Clinical, anthropometric, and laboratory data were obtained through interview and access to medical records. The variants IRS1 rs2943634 A˃C, IRS2 rs1865434 C>T, MC3R rs3746619 C>A, and MC4R rs17782313 T>C were analyzed by real-time polymerase chain reaction. Food intake composition was assessed in a group of subjects with obesity (n = 84) before and after a short-term nutritional counseling program (9 weeks). MC4R rs17782313 was associated with increased risk of obesity (P = .034). Multivariate linear regression analysis adjusted by covariates indicated associations of IRS2 rs1865434 with reduced low-density lipoprotein cholesterol and resistin, MC3R rs3746619 with high glycated hemoglobin, and IRS1 rs2943634 and MC4R rs17782313 with increased high-sensitivity C-reactive protein (P < .05). Energy intake and carbohydrate and total fat intakes were reduced after the diet-oriented program (P < .05). Multivariate linear regression analysis showed associations of IRS2 rs1865434 with high basal fiber intake, IRS1 rs2943634 with low postprogram carbohydrate intake, and MC4R rs17782313 with low postprogram total fat and saturated fatty acid intakes (P < .05). Although significant associations did not survive correction for multiple comparisons using the Benjamini-Hochberg method in this exploratory study, polymorphisms in IRS1, IRS2, MC3R, and MC4R influence metabolic and inflammatory status in Brazilian adults. IRS1 and MC4R variants may influence carbohydrate, total fat, and saturated fatty acid intakes in response to a diet-oriented program in subjects with obesity.

The Personalized Nutrition Study (POINTS): evaluation of a genetically informed weight loss approach, a Randomized Clinical Trial

Weight loss (WL) differences between isocaloric high-carbohydrate and high-fat diets are generally small; however, individual WL varies within diet groups. Genotype patterns may modify diet effects, with carbohydrate-responsive genotypes losing more weight on high-carbohydrate diets (and vice versa for fat-responsive genotypes). We investigated whether 12-week WL (kg, primary outcome) differs between genotype-concordant and genotype-discordant diets. In this 12-week single-center WL trial, 145 participants with overweight/obesity were identified a priori as fat-responders or carbohydrate-responders based on their combined genotypes at ten genetic variants and randomized to a high-fat (n = 73) or high-carbohydrate diet (n = 72), yielding 4 groups: (1) fat-responders receiving high-fat diet, (2) fat-responders receiving high-carbohydrate diet, (3) carbohydrate-responders receiving high-fat diet, (4) carbohydrate-responders receiving high-carbohydrate diet. Dietitians delivered the WL intervention via 12 weekly diet-specific small group sessions. Outcome assessors were blind to diet assignment and genotype patterns. We included 122 participants (54.4 [SD:13.2] years, BMI 34.9 [SD:5.1] kg/m2, 84% women) in the analyses. Twelve-week WL did not differ between the genotype-concordant (-5.3 kg [SD:1.0]) and genotype-discordant diets (-4.8 kg [SD:1.1]; adjusted difference: -0.6 kg [95% CI: -2.1,0.9], p = 0.50). With the current ability to genotype participants as fat- or carbohydrate-responders, evidence does not support greater WL on genotype-concordant diets. ClinicalTrials identifier: NCT04145466.

Toward Precision Weight-Loss Dietary Interventions: Findings from the POUNDS Lost Trial

The POUNDS Lost trial is a 2-year clinical trial testing the effects of dietary interventions on weight loss. This study included 811 adults with overweight or obesity who were randomized to one of four diets that contained either 15% or 25% protein and 20% or 40% fat in a 2 × 2 factorial design. By 2 years, participants on average lost from 2.9 to 3.6 kg in body weight in the four intervention arms, while no significant difference was observed across the intervention arms. In POUNDS Lost, we performed a series of ancillary studies to detect intrinsic factors particular to genomic, epigenomic, and metabolomic markers that may modulate changes in weight and other cardiometabolic traits in response to the weight-loss dietary interventions. Genomic variants identified from genome-wide association studies (GWASs) on obesity, type 2 diabetes, glucose and lipid metabolisms, gut microbiome, and dietary intakes have been found to interact with dietary macronutrients (fat, protein, and carbohydrates) in relation to weight loss and changes of body composition and cardiometabolic traits. In addition, we recently investigated epigenomic modifications, particularly blood DNA methylation and circulating microRNAs (miRNAs). We reported DNA methylation levels at NFATC2IP, CPT1A, TXNIP, and LINC00319 were related to weight loss or changes of glucose, lipids, and blood pressure; we also reported thrifty miRNA expression as a significant epigenomic marker related to changes in insulin sensitivity and adiposity. Our studies have also highlighted the importance of temporal changes in novel metabolomic signatures for gut microbiota, bile acids, and amino acids as predictors for achievement of successful weight loss outcomes. Moreover, our studies indicate that biochemical, behavioral, and psychosocial factors such as physical activity, sleep disturbance, and appetite may also modulate metabolic changes during dietary interventions. This review summarized our major findings in the POUNDS Lost trial, which provided preliminary evidence supporting the development of precision diet interventions for obesity management.

Genetics: A Starting Point for the Prevention and the Treatment of Obesity

Obesity is a common, serious, and costly disease. More than 1 billion people worldwide are obese-650 million adults, 340 million adolescents, and 39 million children. The WHO estimates that, by 2025, approximately 167 million people-adults and children-will become less healthy because they are overweight or obese. Obesity-related conditions include heart disease, stroke, type 2 diabetes, and certain types of cancer. These are among the leading causes of preventable, premature death. The estimated annual medical cost of obesity in the United States was nearly $173 billion in 2019 dollars. Obesity is considered the result of a complex interaction between genes and the environment. Both genes and the environment change in different populations. In fact, the prevalence changes as the result of eating habits, lifestyle, and expression of genes coding for factors involved in the regulation of body weight, food intake, and satiety. Expression of these genes involves different epigenetic processes, such as DNA methylation, histone modification, or non-coding micro-RNA synthesis, as well as variations in the gene sequence, which results in functional alterations. Evolutionary and non-evolutionary (i.e., genetic drift, migration, and founder's effect) factors have shaped the genetic predisposition or protection from obesity in modern human populations. Understanding and knowing the pathogenesis of obesity will lead to prevention and treatment strategies not only for obesity, but also for other related diseases.

Genetically determined gut microbial abundance and 2-year changes in central adiposity and body composition: The POUNDS lost trial

BACKGROUND AND AIM

Growing evidence has linked gut microbiota with regulation of adiposity. We aimed to examine whether the genetically determined relative abundance of gut microbial taxa was associated with long-term changes in adiposity and body composition among individuals who were overweight or obese in weight-loss diet interventions.

METHODS

The study included 692 participants with overweight or obese from the POUNDS Lost trial. We created a genetic risk score (GRS) for the relevant abundance of gut microbial taxa using 20 single nucleotide polymorphisms identified from a recent genome-wide association study. Body composition was assessed using dual-energy X-ray absorptiometry.

RESULTS

Higher GRS for the relative abundance of gut microbial taxa was significantly associated with greater reductions in waist circumference, total fat mass (FM), whole-body total percentage of fat mass (FM%), and percentage of trunk fat (TF%) at 2 years (p = 0.022, 0.034, 0.023, 0.023, respectively). In addition, dietary protein significantly modified the association between GRS for gut microbial abundance and changes in total FM, FM%, and TF% (p-interactions = 0.04, 0.013, and 0.006, respectively) at 6-month, when the maximum weight loss was achieved, even though such interactions were attenuated at 2 years. In the average-protein diet group, a higher microbial abundance GRS was associated with greater reductions in total FM (p = 0.007), FM% (p = 0.002), and TF% (p < 0.001) at 6 months, while no associations were found in the high-protein diet group (p > 0.05).

CONCLUSION

Our results suggest that the higher genetically determined relative abundance of gut microbial taxa may be related to long-term improvement of whole-body and central fatness and body composition in response to low-calorie diet interventions.

Differences in weight loss outcomes for males and females on a low-carbohydrate diet: A systematic review

It has been widely demonstrated that there are a broad range of individual responses to all weight management regimens, often masked by reports of the mean. Identifying features of responders and non-responders to weight loss regimens enables a more tailored approach to the provision of weight management advice. Low-carbohydrate diets are currently popular, and anecdote suggests that males are more successful at losing weight using this approach. This is feasible given the physiological and socio-psychological differences between the genders. We analysed the extent and variation in weight change for males and females separately through a systematic search for all low-carbohydrate diet trials published since 1985. Very few studies compared weight loss outcomes by gender and, of those that did, most lacked supporting data. The majority of studies reported no gender difference but when a gender difference was found, males were more frequently reported as losing more weight than females on a low-carbohydrate diet. The lack of gender stratification in weight loss trials is concerning, as there are a range of gender-based factors that affect weight loss outcomes. This study highlights the importance of examining weight change for males and females separately, since as failure to do so may mask any potential differences, which, if detected, could assist with better weight loss outcomes.

Nutrition for precision health: The time is now

Precision nutrition has emerged as a boiling area of nutrition research, with a particular focus on revealing the individual variability in response to diets that is determined mainly by the complex interactions of dietary factors with the multi-tiered "omics" makeups. Reproducible findings from the observational studies and diet intervention trials have lent preliminary but consistent evidence to support the fundamental role of gene-diet interactions in determining the individual variability in health outcomes including obesity and weight loss. Recent investigations suggest that the abundance and diversity of the gut microbiome may also modify the dietary effects; however, considerable instability in the results from the microbiome research has been noted. In addition, growing studies suggest that a complicated multiomics algorithm would be developed by incorporating the genome, epigenome, metabolome, proteome, and microbiome in predicting the individual variability in response to diets. Moreover, precision nutrition would also scrutinize the role of biological (circadian) rhythm in determining the individual variability of dietary effects. The evidence gathered from precision nutrition research will be the basis for constructing precision health dietary recommendations, which hold great promise to help individuals and their health care providers create precise and effective diet plans for precision health in the future.

Insulin receptor substrate 1 gene variations and lipid profile characteristics in the type 2 diabetic patients with comorbid obesity and chronic pancreatitis

Objective. Type 2 diabetes mellitus (T2DM) is one of diseases that develops in a setting of polymorbid processes or more often promotes their development, forming in this spectrum the phenomenon of comorbidity. The aim of this study was to evaluate changes in the lipid panel data in T2DM patients with comorbid obesity and chronic pancreatitis (CP) taking into account the C/A polymorphism of the insulin receptor substrate 1 (IRS1) gene (rs2943640). Methods. The study involved 34 T2DM patients and 10 healthy individuals. The rs2943640 IRS1 gene polymorphism was genotyped using the TaqMan real-time polymerase chain reaction (PCR) method. Blood serum lipid panel data were determined with commercially available kits on a Cobas 6000 analyzer. Results. In patients with only T2DM and T2DM + comorbid obesity, an association between IRS1 gene polymorphism (rs2943640) and lipid profile abnormalities with maximum changes of the lipid characteristics recorded in C/C genotype carriers was found. Within the C/C genotype of the IRS1 gene (rs2943640) in type 2 diabetic patients with comorbid obesity and CP, significantly lower high-density lipoprotein cholesterol (HDL-C) levels and significantly higher levels of triglycerides (TG), non-HDL-C and remnant cholesterol (RC) in relation to type 2 diabetic patients with comorbid obesity were found. At the same time, within the C/A genotype of the IRS1 gene (rs2943640), significant changes of lipid panel data were found in type 2 diabetic patients with comorbid obesity relative to the control group (p<0.001). Conclusions. Our data indicate that the presence of the C allele of IRS1 gene (rs2943640) in both homozygous and heterozygous states may indicate increased risk of dyslipidemia in type 2 diabetic patients with comorbidities.

α-Lactalbumin peptide Asp-Gln-Trp alleviates hepatic insulin resistance and modulates gut microbiota dysbiosis in high-fat diet-induced NAFLD mice

α-Lactalbumin peptide Asp-Gln-Trp (DQW) alleviates hepatic insulin resistance via activating the IRS1/PI3K/AKT pathway and modulates gut microbiota dysbiosis in high-fat diet-induced NAFLD mice.

Nutrigenetic variants and response to diet/lifestyle intervention in obese subjects: a pilot study

AIMS

Nutritional and lifestyle interventions can contribute to prevent and treat obesity and its complications; however, genetic background may influence the success of a therapy. The aim of this pilot study is to evaluate the effects of the interaction between nutrigenetic variants and nutritional intervention, as well as the changes in clinical parameters and the adherence to Mediterranean diet (MedDiet) and to physical activity, of 18 overweight or obese subjects affected by T2D or dysglycemia included in a nutritional program.

METHODS

The subjects' clinical parameters as well as their PREDIMED score and physical activity levels were recorded and compared at baseline, at 6 months and at the end of the intervention. Rs9939609 in FTO, rs17782313 near MC4R, rs326 in LPL, rs16147 in NPY, rs2943641 near IRS-1 were genotyped.

RESULTS

The subjects carrying the A allele in FTO lost less weight (p = 0.022) and had a lower BMI decrease from baseline to 12 months (p-interaction = 0.047) than TT carriers. In addition, there was a significant PREDIMED score modification over time, according to genotypes for FTO rs9939609 (p = 0.025) and NPY rs16147 (p = 0.039), respectively.

CONCLUSIONS

These preliminary findings show a significant interaction between genetic variants and the PREDIMED score, suggesting that individuals carrying the FTO variant may lose less weight than non-carriers through diet/lifestyle intervention.

Insulin Receptor Substrate 1 Gene and Glucose Metabolism Characteristics in Type 2 Diabetes Mellitus with Comorbidities

BACKGROUND

Genetic variants that affect insulin signaling play an important role in insulin resistance (IR) in type 2 diabetes mellitus (T2DM). This study aimed to evaluate changes of the glycemic profile and IR in T2DM with comorbid obesity and chronic pancreatitis (CP) considering the allele status of the IRS1 gene (rs2943640).

METHODS

The study involved 33 type-2 diabetic patients and 10 healthy individuals. The IRS-1 gene rs2943640 C>A polymorphism was genotyped using the TaqMan real-time PCR method.

RESULTS

In type 2 diabetic patients regardless of the presence/absence of comorbid obesity and CP glycemic profile parameters significantly did not differ between carriers of allele C or allele A of the IRS1 gene (rs2943640). At the same time significantly higher HOMA-IR (by 2.25 times) was established in carriers of the C allele. In type 2 diabetic patients with both comorbidities (carriers of the C allele) the maximum HOMA-IR was established, which significantly differed from the data of patients with only T2DM and patients with comorbid obesity. In carriers of the A allele significantly higher level of HOMA-IR was found in patients with comorbid obesity and CP vs patients with only T2DM, and also in patients with comorbid obesity vs patients with only T2DM.

CONCLUSIONS

Presence of the C allele of the IRS1 gene (rs2943640) may indicate risk of high IR in type 2 diabetic patients regardless of the presence/absence of comorbid obesity and CP; here with CP is a more important factor in IR progression then obesity.

Nutrigenomics in Regulating the Expression of Genes Related to Type 2 Diabetes Mellitus

Nutrigenomics is the study of the gene-nutrient interaction and it indicates that some nutrients, called bioactive compounds, can mold the genetic expression or change the nucleotide chain. Polyphenols are secondary metabolites found in plants that are regularly consumed in functional foods and help prevent or delay the onset of type 2 diabetes mellitus (T2DM) and its complications. This article objected to review studies about the interaction of diet with polyphenols and Mediterranean diet in the expression of human genes related to T2DM. Resveratrol acts as an antioxidant, anti-inflammatory, and increases mitochondrial function. Regular consumption of quercetin resulted in improvement of hypertension and suppression of diabetes-induced vasoconstriction. Genistein also showed positive results in T2DM, such as increased cell mass and improved glucose tolerance and insulin levels. Catechins showed efficiency in inducing genes in triacylglycerol biosynthesis, inhibition of fatty acids and cholesterol, and resulting in their participation in mitigating complications of diabetes. Lastly, curcumin was demonstrated to be a protector of the pancreatic islets against streptozotocin-induced oxidative stress. Growing evidence suggest that bioactive compounds such as polyphenols have an important role in T2DM and the prevention and treatment of its complication, as they cause activation or inhibition of related genes.

Interaction of diabetes genetic risk and successful lifestyle modification in the Diabetes Prevention Programme

AIM

To test whether diabetes genetic risk modifies the association of successful lifestyle changes with incident diabetes.

MATERIALS AND METHODS

We studied 823 individuals randomized to the intensive lifestyle intervention (ILS) arm of the Diabetes Prevention Programme who were diabetes-free 1 year after enrolment. We tested additive and multiplicative interactions of a 67-variant diabetes genetic risk score (GRS) with achievement of three ILS goals at 1 year (≥7% weight loss, ≥150 min/wk of moderate leisure-time physical activity, and/or a goal for self-reported total fat intake) on the primary outcome of incident diabetes over 3 years of follow-up.

RESULTS

A lower GRS and achieving each or all three ILS goals were each associated with lower incidence of diabetes (all P < 0.05). Additive interactions were significant between the GRS and achievement of the weight loss goal (P < 0.001), physical activity goal (P = 0.02), and all three ILS goals (P < 0.001) for diabetes risk. Achievement of all three ILS goals was associated with 1.8 (95% CI 0.3, 3.4), 3.1 (95% CI 1.5, 4.7), and 3.9 (95% CI 1.6, 6.2) fewer diabetes cases/100-person-years in the first, second and third GRS tertiles (P < 0.001 for trend). Multiplicative interactions between the GRS and ILS goal achievement were significant for the diet goal (P < 0.001), but not for weight loss (P = 0.18) or physical activity (P = 0.62) goals.

CONCLUSIONS

Genetic risk may identify high-risk subgroups for whom successful lifestyle modification is associated with greater absolute reduction in the risk of incident diabetes.

Visceral Obesity, Metabolic Syndrome, and Esophageal Adenocarcinoma

Esophageal adenocarcinoma (EAC) represents an exemplar of obesity-associated carcinogenesis, with a progressive increase in EAC risk with increased body mass index. In this context, there is increased focus on visceral adipose tissue and associated metabolic dysfunction, including hypertension, diabetes mellitus and hyperlipidemia, or combinations of these in the metabolic syndrome. Visceral obesity (VO) may promote EAC via both directly impacting on gastro-esophageal reflux disease and Barrett's esophagus, as well as via reflux-independent effects, involving adipokines, growth factors, insulin resistance, and the microbiome. In this review these pathways are explored, including the impact of VO on the tumor microenvironment, and on cancer outcomes. The current evidence-based literature regarding the role of dietary, lifestyle, pharmacologic and surgical interventions to modulate the risk of EAC is explored.

Distinct genetic subtypes of adiposity and glycemic changes in response to weight-loss diet intervention: the POUNDS Lost trial

PURPOSE

Obesity is a heterogeneous condition and distinct adiposity subtypes may differentially affect type 2 diabetes risk. We assessed relations between genetically determined subtypes of adiposity and changes in glycemic traits in a dietary intervention trial.

METHODS

The four genetic subtypes of adiposity including waist-hip ratio-increase only (WHRonly+), body mass index-increase only (BMIonly+), WHR-increase and BMI-increase (BMI+WHR+), and WHR-decrease and BMI-increase (BMI+WHR-) were assessed by polygenetic scores (PGSs), calculated based on 159 single nucleotide polymorphisms related to BMI and/or WHR. We examined the associations between the four PGSs and changes in fasting glucose, insulin, β-cell function (HOMA-B) and insulin resistance (HOMA-IR) in 692 overweight participants (84% white Americans) who were randomly assigned to one of four weight-loss diets in a 2-year intervention trial.

RESULTS

Higher BMI+WHR-PGS was associated with a greater decrease in 2-year changes in waist circumference in white participants (P = 0.002). We also found significant interactions between WHRonly+PGS and dietary protein in 2-year changes in fasting glucose and HOMA-B (P = 0.0007 and < 0.0001, respectively). When consuming an average-protein diet, participants with higher WHRonly+PGS showed less increased fasting glucose (β = - 0.46, P = 0.006) and less reduction in HOMA-B (β = 0.02, P = 0.005) compared with lower WHRonly+PGS. Conversely, eating high-protein diet was associated with less decreased HOMA-B among individuals with lower than higher WHRonly+PGS (β = - 0.02, P = 0.006).

CONCLUSIONS

Distinct genetically determined adiposity subtypes may differentially modify the effects of weight-loss diets on improving glucose metabolism in white Americans. This trial was registered at clinicaltrials.gov as NCT00072995.

Genetically Guided Mediterranean Diet for the Personalized Nutritional Management of Type 2 Diabetes Mellitus

The current consensus for the prevention and management of type 2 diabetes mellitus (T2DM) is that high-quality diets and adherence to a healthy lifestyle provide significant health benefits. Remarkably, however, there is little agreement on the proportions of macronutrients in the diet that should be recommended to people suffering from pre-diabetes or T2DM. We herein discuss emerging evidence that underscores the importance of gene-diet interactions in the improvement of glycemic biomarkers in T2DM. We propose that we can achieve better glycemic control in T2DM patients by coupling Mediterranean diets to genetic information as a predictor for optimal diet macronutrient composition in a personalized manner. We provide evidence to support this concept by presenting a case study of a T2DM patient who achieved rapid glycemic control when adhered to a personalized, genetically-guided Mediterranean Diet.

Dietary interventions for obesity: clinical and mechanistic findings

Dietary modification is central to obesity treatment. Weight loss diets are available that include various permutations of energy restriction, macronutrients, foods, and dietary intake patterns. Caloric restriction is the common pathway for weight reduction, but different diets may induce weight loss by varied additional mechanisms, including by facilitating dietary adherence. This narrative Review of meta-analyses and select clinical trials found that lower-calorie diets, compared with higher-calorie regimens, reliably induced larger short-term (<6 months) weight losses, with deterioration of this benefit over the long term (>12 months). Few significant long-term differences in weight loss were observed for diets of varying macronutrient composition, although some regimens were found to have short-term advantages (e.g., low carbohydrate versus low fat). Progress in improving dietary adherence, which is critical to both short- and long-term weight loss, could result from greater efforts to identify behavioral and metabolic phenotypes among dieters.

Personalized Nutrition and -Omics

With change in global concern toward food quality over food quantity, consumer concern and choice of healthy food has become a matter of prime importance. It gave rise to concept of “personalized or precision nutrition”. The theory behind personalization of nutrition is supported by multiple factors including advances in food analytics, nutrition based diseases and public health programs, increasing use of information technology in nutrition science, concept of gene-diet interaction and growing consumer capacity or concern by better and healthy foods. The advances in “omics” tools and related analytical techniques have resulted into tremendous scope of their application in nutrition science. As a consequence, a better understanding of underlying interaction between diet and individual is expected with addressing of key challenges for successful implementation of this science. In this chapter, the above aspects are discussed to get an insight into driving factors for increasing concern in personalized nutrition.

Genetic variation in lean body mass, changes of appetite and weight loss in response to diet interventions: The POUNDS Lost trial

AIM

To investigate whether the genetic risk score (GRS) for lean body mass (LBM) modified the effects of weight-loss diets on changes in appetite and adiposity among overweight and obese individuals.

PARTICIPANTS AND METHODS

In the 2-year Preventing Overweight Using Novel Dietary Strategies (POUNDS Lost) trial, we included 692 adults who were randomly assigned to one of four diets varying in macronutrient intake. A GRS was calculated using five single nucleotide polymorphisms associated with LBM.

RESULTS

The LBM-GRS was not associated with the baseline LBM measured by dual-energy x-ray absorptiometry in a subgroup (50%) of the study population. We found that the LBM-GRS had significantly different associations with changes in appetite from baseline to 6 months according to low- or high-fat diet group (P-interaction < 0.001, 0.021, 0.005 and 0.024 for total appetite score, hunger, fullness and prospective consumption, respectively). Lower LBM-GRS (indicating a greater genetic predisposition to LBM) was associated with greater decreases in the total appetite score (P < 0.001), hunger (P = 0.01), fullness (P = 0.001) and prospective consumption (P = 0.019) in participants in the low-fat diet group, whereas no significant associations with these appetite measures were observed in the high-fat diet group. In addition, lower LBM-GRS was associated with greater reduction in body weight (P = 0.003) and waist circumference (P = 0.011) among participants in the low-fat diet group, while no associations were observed in the high-fat diet group. The interactions attenuated, along with weight regain, from 6 months to 2 years.

CONCLUSIONS

Our findings suggest that genetic variation in LBM may be differentially associated with appetite changes, and may subsequently be related to changes in body weight and waist circumference, according to dietary fat intake.

Genetic test for the prescription of diets in support of physical activity

Owing to the fields of nutrigenetics and nutrigenomics today we can think of devising approaches to optimize health, delay onset of diseases and reduce its severity according to our genetic blue print. However this requires a deep understanding of nutritional impact on expression of genes that may result in a specific phenotype. The extensive research and observational studies during last two decades reporting interactions between genes, diet and physical activity suggest a cross talk between various genetic and environmental factors and lifestyle interventions. Although considerable efforts have been made in unraveling the mechanisms of gene-diet interactions the scientific evidences behind developing commercial genetic tests for providing personalized nutrition recommendations are still scarce. In this scenario the current mini-review aims to provide useful insights into salient feature of nutrition based genetic research and its commercial application and the ethical issue and concerns related to its outcome.

The Role of Nutrition in the Prevention and Intervention of Type 2 Diabetes

Type 2 diabetes (T2D) is a rapidly growing epidemic, which leads to increased mortality rates and health care costs. Nutrients (namely, carbohydrates, fat, protein, mineral substances, and vitamin), sensing, and management are central to metabolic homeostasis, therefore presenting a leading factor contributing to T2D. Understanding the comprehensive effects and the underlying mechanisms of nutrition in regulating glucose metabolism and the interactions of diet with genetics, epigenetics, and gut microbiota is helpful for developing new strategies to prevent and treat T2D. In this review, we discuss different mechanistic pathways contributing to T2D and then summarize the current researches concerning associations between different nutrients intake and glucose homeostasis. We also explore the possible relationship between nutrients and genetic background, epigenetics, and metagenomics in terms of the susceptibility and treatment of T2D. For the specificity of individual, precision nutrition depends on the person’s genotype, and microbiota is vital to the prevention and intervention of T2D.

Associations between Genotype-Diet Interactions and Weight Loss-A Systematic Review

Studies on the interactions between single nucleotide polymorphisms (SNPs) and macronutrient consumption on weight loss are rare and heterogeneous. This review aimed to conduct a systematic literature search to investigate genotype-diet interactions on weight loss. Four databases were searched with keywords on genetics, nutrition, and weight loss (PROSPERO: CRD42019139571). Articles in languages other than English and trials investigating special groups (e.g., pregnant women, people with severe diseases) were excluded. In total, 20,542 articles were identified, and, after removal of duplicates and further screening steps, 27 articles were included. Eligible articles were based on eight trials with 91 SNPs in 63 genetic loci. All articles examined the interaction between genotype and macronutrients (carbohydrates, fat, protein) on the extent of weight loss. However, in most cases, the interaction results were not significant and represented single findings that lack replication. The publications most frequently analyzed genotype-fat intake interaction on weight loss. Since the majority of interactions were not significant and not replicated, a final evaluation of the genotype-diet interactions on weight loss was not possible. In conclusion, no evidence was found that genotype-diet interaction is a main determinant of obesity treatment success, but this needs to be addressed in future studies.

Reduction in saturated fat intake for cardiovascular disease

BACKGROUND

Reducing saturated fat reduces serum cholesterol, but effects on other intermediate outcomes may be less clear. Additionally, it is unclear whether the energy from saturated fats eliminated from the diet are more helpfully replaced by polyunsaturated fats, monounsaturated fats, carbohydrate or protein.

OBJECTIVES

To assess the effect of reducing saturated fat intake and replacing it with carbohydrate (CHO), polyunsaturated (PUFA), monounsaturated fat (MUFA) and/or protein on mortality and cardiovascular morbidity, using all available randomised clinical trials.

SEARCH METHODS

We updated our searches of the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE (Ovid) and Embase (Ovid) on 15 October 2019, and searched Clinicaltrials.gov and WHO International Clinical Trials Registry Platform (ICTRP) on 17 October 2019.

SELECTION CRITERIA

Included trials fulfilled the following criteria: 1) randomised; 2) intention to reduce saturated fat intake OR intention to alter dietary fats and achieving a reduction in saturated fat; 3) compared with higher saturated fat intake or usual diet; 4) not multifactorial; 5) in adult humans with or without cardiovascular disease (but not acutely ill, pregnant or breastfeeding); 6) intervention duration at least 24 months; 7) mortality or cardiovascular morbidity data available.

DATA COLLECTION AND ANALYSIS

Two review authors independently assessed inclusion, extracted study data and assessed risk of bias. We performed random-effects meta-analyses, meta-regression, subgrouping, sensitivity analyses, funnel plots and GRADE assessment.

MAIN RESULTS

We included 15 randomised controlled trials (RCTs) (16 comparisons, 56,675 participants), that used a variety of interventions from providing all food to advice on reducing saturated fat. The included long-term trials suggested that reducing dietary saturated fat reduced the risk of combined cardiovascular events by 17% (risk ratio (RR) 0.83; 95% confidence interval (CI) 0.70 to 0.98, 12 trials, 53,758 participants of whom 8% had a cardiovascular event, I² = 67%, GRADE moderate-quality evidence). Meta-regression suggested that greater reductions in saturated fat (reflected in greater reductions in serum cholesterol) resulted in greater reductions in risk of CVD events, explaining most heterogeneity between trials. The number needed to treat for an additional beneficial outcome (NNTB) was 56 in primary prevention trials, so 56 people need to reduce their saturated fat intake for ~four years for one person to avoid experiencing a CVD event. In secondary prevention trials, the NNTB was 53. Subgrouping did not suggest significant differences between replacement of saturated fat calories with polyunsaturated fat or carbohydrate, and data on replacement with monounsaturated fat and protein was very limited. We found little or no effect of reducing saturated fat on all-cause mortality (RR 0.96; 95% CI 0.90 to 1.03; 11 trials, 55,858 participants) or cardiovascular mortality (RR 0.95; 95% CI 0.80 to 1.12, 10 trials, 53,421 participants), both with GRADE moderate-quality evidence. There was little or no effect of reducing saturated fats on non-fatal myocardial infarction (RR 0.97, 95% CI 0.87 to 1.07) or CHD mortality (RR 0.97, 95% CI 0.82 to 1.16, both low-quality evidence), but effects on total (fatal or non-fatal) myocardial infarction, stroke and CHD events (fatal or non-fatal) were all unclear as the evidence was of very low quality. There was little or no effect on cancer mortality, cancer diagnoses, diabetes diagnosis, HDL cholesterol, serum triglycerides or blood pressure, and small reductions in weight, serum total cholesterol, LDL cholesterol and BMI. There was no evidence of harmful effects of reducing saturated fat intakes.

AUTHORS' CONCLUSIONS

The findings of this updated review suggest that reducing saturated fat intake for at least two years causes a potentially important reduction in combined cardiovascular events. Replacing the energy from saturated fat with polyunsaturated fat or carbohydrate appear to be useful strategies, while effects of replacement with monounsaturated fat are unclear. The reduction in combined cardiovascular events resulting from reducing saturated fat did not alter by study duration, sex or baseline level of cardiovascular risk, but greater reduction in saturated fat caused greater reductions in cardiovascular events.

Genetic susceptibility, lifestyle intervention and glycemic changes among women with prior gestational diabetes

AIMS

Women with prior gestational diabetes mellitus (GDM) or high genetic susceptibility are prone to development of type 2 diabetes. We examined whether a lifestyle intervention modified the genetic effect on changes in glycemic markers among women with prior GDM.

RESEARCH DESIGN AND METHODS

This study included 560 women with prior GDM from a randomized controlled trial, the Tianjin Gestational Diabetes Mellitus Prevention Program, who were assigned into an intervention arm (improved physical activity and healthy dietary intakes) or a control arm. We assessed associations of GDM related genetic variants in/near the CDKAL1 (rs7754840) and MTNR1B (rs10830962) genes with changes in fasting levels of glucose and insulin, β-cell function (HOMA-B) and insulin resistance (HOMA-IR) at 1 year and 2 years after the baseline.

RESULTS

We found significant interactions between CDKAL1 variant rs7754840 and lifestyle intervention on changes in fasting insulin and HOMA-IR at 1 year (P for interactions = 0.008 and 0.006, respectively). The GDM-increasing C allele was associated with a 0.07-unit greater increase in fasting insulin (P = 0.048) and HOMA-IR (P = 0.045) in the control group, while opposite-directional associations were observed in the intervention group; women with the C allele seemed to decrease more in these glycemic markers than the non-C-carriers (both P ≤ 0.06). The interactions between the CDKAL1 genetic variant and lifestyle intervention on changes in fasting insulin (P = 0.035) and HOMA-IR (P = 0.024) remained significant over the 2-year period, even though the effects of lifestyle intervention were attenuated at 2-year. The MTNR1B variant rs10830962 did not show interaction with lifestyle intervention on changes in the glycemic markers.

CONCLUSIONS

Healthy lifestyle intervention may be beneficial for women with the GDM predisposing CDKAL1 genetic variant in improvement of insulin resistance.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov NCT01554358. URL OF REGISTRATION: https://clinicaltrials.gov/ct2/show/NCT01554358.

Reduction in saturated fat intake for cardiovascular disease

BACKGROUND

Reducing saturated fat reduces serum cholesterol, but effects on other intermediate outcomes may be less clear. Additionally, it is unclear whether the energy from saturated fats eliminated from the diet are more helpfully replaced by polyunsaturated fats, monounsaturated fats, carbohydrate or protein.

OBJECTIVES

To assess the effect of reducing saturated fat intake and replacing it with carbohydrate (CHO), polyunsaturated (PUFA), monounsaturated fat (MUFA) and/or protein on mortality and cardiovascular morbidity, using all available randomised clinical trials.

SEARCH METHODS

We updated our searches of the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE (Ovid) and Embase (Ovid) on 15 October 2019, and searched Clinicaltrials.gov and WHO International Clinical Trials Registry Platform (ICTRP) on 17 October 2019.

SELECTION CRITERIA

Included trials fulfilled the following criteria: 1) randomised; 2) intention to reduce saturated fat intake OR intention to alter dietary fats and achieving a reduction in saturated fat; 3) compared with higher saturated fat intake or usual diet; 4) not multifactorial; 5) in adult humans with or without cardiovascular disease (but not acutely ill, pregnant or breastfeeding); 6) intervention duration at least 24 months; 7) mortality or cardiovascular morbidity data available.

DATA COLLECTION AND ANALYSIS

Two review authors independently assessed inclusion, extracted study data and assessed risk of bias. We performed random-effects meta-analyses, meta-regression, subgrouping, sensitivity analyses, funnel plots and GRADE assessment.

MAIN RESULTS

We included 15 randomised controlled trials (RCTs) (16 comparisons, ~59,000 participants), that used a variety of interventions from providing all food to advice on reducing saturated fat. The included long-term trials suggested that reducing dietary saturated fat reduced the risk of combined cardiovascular events by 21% (risk ratio (RR) 0.79; 95% confidence interval (CI) 0.66 to 0.93, 11 trials, 53,300 participants of whom 8% had a cardiovascular event, I² = 65%, GRADE moderate-quality evidence). Meta-regression suggested that greater reductions in saturated fat (reflected in greater reductions in serum cholesterol) resulted in greater reductions in risk of CVD events, explaining most heterogeneity between trials. The number needed to treat for an additional beneficial outcome (NNTB) was 56 in primary prevention trials, so 56 people need to reduce their saturated fat intake for ~four years for one person to avoid experiencing a CVD event. In secondary prevention trials, the NNTB was 32. Subgrouping did not suggest significant differences between replacement of saturated fat calories with polyunsaturated fat or carbohydrate, and data on replacement with monounsaturated fat and protein was very limited. We found little or no effect of reducing saturated fat on all-cause mortality (RR 0.96; 95% CI 0.90 to 1.03; 11 trials, 55,858 participants) or cardiovascular mortality (RR 0.95; 95% CI 0.80 to 1.12, 10 trials, 53,421 participants), both with GRADE moderate-quality evidence. There was little or no effect of reducing saturated fats on non-fatal myocardial infarction (RR 0.97, 95% CI 0.87 to 1.07) or CHD mortality (RR 0.97, 95% CI 0.82 to 1.16, both low-quality evidence), but effects on total (fatal or non-fatal) myocardial infarction, stroke and CHD events (fatal or non-fatal) were all unclear as the evidence was of very low quality. There was little or no effect on cancer mortality, cancer diagnoses, diabetes diagnosis, HDL cholesterol, serum triglycerides or blood pressure, and small reductions in weight, serum total cholesterol, LDL cholesterol and BMI. There was no evidence of harmful effects of reducing saturated fat intakes.

AUTHORS' CONCLUSIONS

The findings of this updated review suggest that reducing saturated fat intake for at least two years causes a potentially important reduction in combined cardiovascular events. Replacing the energy from saturated fat with polyunsaturated fat or carbohydrate appear to be useful strategies, while effects of replacement with monounsaturated fat are unclear. The reduction in combined cardiovascular events resulting from reducing saturated fat did not alter by study duration, sex or baseline level of cardiovascular risk, but greater reduction in saturated fat caused greater reductions in cardiovascular events.

Precision nutrition: hype or hope for public health interventions to reduce obesity?

High-income countries are experiencing an obesity epidemic that follows a socioeconomic gradient, affecting groups of lower socioeconomic status disproportionately. Recent clinical findings have suggested new perspectives for the prevention and treatment of obesity, using personalized dietary approaches. Precision nutrition (PN), also called personalized nutrition, has been developed to deliver more preventive and practical dietary advice than ‘one-size-fits-all’ guidelines. With interventions becoming increasingly plausible at a large scale thanks to artificial intelligence and smartphone applications, some have begun to view PN as a novel way to deliver the right dietary intervention to the right population. We argue that large-scale PN, if taken alone, might be of limited interest from a public health perspective. Building on Geoffrey Rose’s theory regarding the differences in individual and population causes of disease, we show that large-scale PN can only address some individual causes of obesity (causes of cases). This individual-centred approach is likely to have a small impact on the distribution of obesity at a population level because it ignores the population causes of obesity (causes of incidence). The latter are embedded in the populations’ social, cultural, economic and political contexts that make environments obesogenic. Additionally, the most socially privileged groups in the population are the most likely to respond to large-scale PN interventions. This could have the undesirable effect of widening social inequalities in obesity. We caution public health actors that interventions based only on large-scale PN are unlikely, despite current expectations, to improve dietary intake or reduce obesity at a population level.

The Combined Effect of Polygenic Risk from FTO and ADRB2 Gene Variants, Odds of Obesity, and Post-Hipcref Diet Differences

BACKGROUND

Computing polygenic risk scores (PRS) to predict the degree of risk for obesity may contribute to weight management programs strategically.

OBJECTIVES

To investigate the combined effect of FTO rs9930501, rs9930506, and rs9932754 and ADRB2 rs1042713 and rs1042714 using PRS on (1) the odds of obesity and (2) post-intervention differences in dietary, anthropometric, and cardiometabolic parameters in response to high-protein calorie-restricted, high-vitamin E, high-fiber (Hipcref) diet intervention in Malaysian adults.

METHODS

Both a cross-sectional study (n = 178) and a randomized controlled trial (RCT) (n = 128) were conducted to test the aforementioned objectives. PRS was computed as the weighted sum of the risk alleles possessed by each individual participant. Participants were stratified into first (PRS 0-0.64), second (PRS 0.65-3.59), and third (PRS 3.60-8.18) tertiles.

RESULTS

The third tertile of PRS was associated with significantly higher odds of obesity: 2.29 (95% CI = 1.11-4.72, adjusted p = 0.025) compared to the first tertile. Indians (3.9 ± 0.3) had significantly higher PRS compared to Chinese (2.1 ± 0.4) (p = 0.010). In the RCT, a greater reduction in high-sensitivity C-reactive protein (hsCRP) levels was found in second and third tertiles after Hipcref diet intervention compared to the control diet (p interaction = 0.048).

CONCLUSION

Higher PRS was significantly associated with increased odds of obesity. Individuals with higher PRS had a significantly greater reduction in hsCRP levels after Hipcref diet compared to the control diet.

Lessons Learned from the POUNDS Lost Study: Genetic, Metabolic, and Behavioral Factors Affecting Changes in Body Weight, Body Composition, and Cardiometabolic Risk

PURPOSE OF REVIEW

This paper reviews the genetic and non-genetic factors that provided predictions of, or were associated with, weight loss and other metabolic changes in the POUNDS Lost clinical trial of weight loss. This trial randomized 811 individuals who were overweight or obese to one of four diets that contained either 15% or 25% protein and 20% or 40% fat in a 2 × 2 factorial design. A standard behavioral weight loss program was available for all participants who were followed for 2 years with an 80% completion rate.

RECENT FINDINGS

Nineteen genes and five genetic risk scores were used along with demographic, behavioral, endocrine, and metabolic measurements. Genetic variations in most of the genes were associated with weight loss, but this association often varied with the dietary assignment. A number of demographic and behavioral factors, including attendance at behavioral sessions and food cravings were predictive of weight changes. A high baseline level of free triiodothyronine or free thyroxine predicted the magnitude of weight loss. Several perfluoroakyl compounds predicted more rapid weight regain. Genetic evidence from POUNDS Lost provides guidance toward selection of a personalized weight loss diet and improvement in metabolic profile. There is still room for additional research into the predictors of weight loss.

Lifestyle intervention modifies the effect of the MC4R genotype on changes in insulin resistance among women with prior gestational diabetes: Tianjin Gestational Diabetes Mellitus Prevention Program

BACKGROUND

A history of gestational diabetes mellitus (GDM) has been related to an elevated risk of type 2 diabetes. The melanocortin-4 receptor (MC4R) genotype has been related to glycemic changes in women with prior GDM.

OBJECTIVE

The objective of this study was to analyze whether lifestyle intervention modified the association between the MC4R genotype and changes in insulin sensitivity among women with prior GDM.

METHODS

We genotyped MC4R rs6567160 and measured glucose and insulin in fasting plasma samples at baseline and during the first 2 follow-up visits in 1128 women with prior GDM. They were randomly assigned to either a 4-y lifestyle intervention involving both diet and physical activity or a control group from a randomized clinical trial, the Tianjin Gestational Diabetes Mellitus Prevention Program. We analyzed the interaction between the MC4R genotype and lifestyle intervention on changes in insulin resistance.

RESULTS

From baseline to 1.28 y, the MC4R genotype was related to changes in fasting insulin, HOMA-IR, and homeostasis model assessment of β cell function (HOMA-B) in the intervention group. Each risk allele (C) of rs6567160 was associated with a 0.08-unit greater decrease in log(insulin), log(HOMA-IR), and log(HOMA-B) (P = 0.02, 0.04, and 0.04, respectively), whereas in the control group, each C allele tended to be associated with a greater increase in HOMA-IR (P = 0.09). We found significant interactions between the MC4R genotype and lifestyle intervention on 1.28-y changes in fasting insulin and HOMA-IR (P = 0.006 and 0.008, respectively), and such interaction remained significant when we analyzed the trajectory of changes in insulin and HOMA-IR from baseline to 2.55 y (both P = 0.03).

CONCLUSIONS

The exploratory results from the first 2 follow-up visits indicate that women with prior GDM carrying a diabetes-increasing MC4R genotype (CC or TC) may obtain better improvement than the TT genotype in insulin resistance through lifestyle intervention. This trial was registered at clinicaltrials.gov as NCT01554358.

The HealtheSteps™ lifestyle prescription program to improve physical activity and modifiable risk factors for chronic disease: a pragmatic randomized controlled trial

BACKGROUND

Our objective was to determine the influence of the HealtheSteps™ lifestyle prescription program on physical activity and modifiable risk factors for chronic disease in individuals at risk.

METHODS

One hundred eighteen participants were recruited from 5 sites in Southwestern Ontario, Canada and randomized to either the intervention (HealtheSteps™ program, n = 59) or a wait-list control group (n = 59). The study comprised three phases: an Active Phase (0 to 6 months) consisted of bi-monthly in-person lifestyle coaching with access to a suite of eHealth technology supports (Heathesteps app, telephone coaching and a private HealtheSteps™ social network) followed by a Minimally-Supported Phase I (6 to 12 months), in which in-person coaching was removed, but participants still had access to the full suite of eHealth technology supports. In the final stage, Minimally-Supported Phase II (12 to 18 months), access to the eHealth technology supports was restricted to the HealtheSteps™ app. Assessments were conducted at baseline, 6, 12 and 18 months. The study primary outcome was the 6-month change in average number of steps per day. Secondary outcomes included: self-reported physical activity and sedentary time; self-reported eating habits; weight and body composition measures; blood pressure and health-related quality of life. Data from all participants were analyzed using an intent-to-treat approach. We applied mixed effects models for repeated measurements and adjusted for age, sex, and site in the statistical analyses.

RESULTS

Participants in HealtheSteps™ increased step counts (between-group [95% confidence interval]: 3132 [1969 to 4294], p < 0.001), decreased their sitting time (- 0.08 [- 0.16 to - 0.006], p = 0.03), and improved their overall healthful eating (- 1.5 [- 2.42 to - 0.58], p = 0.002) to a greater extent compared to control at 6 months. Furthermore, exploratory results showed that these individuals maintained these outcomes 12 months later, after a minimally-supported phase; and retained improvements in sedentary time and improved healthful eating after 18 months. No differences in self-reported physical activity, health-related quality of life, weight, waist circumference or blood pressure were observed between groups at 6 months.

CONCLUSIONS

Our findings suggest that HealtheSteps™ is effective at increasing physical activity (i.e., step counts per day), decreasing weekday sitting time, and improving healthful eating in adults at increased risk for chronic disease after 6 months; however, we did not see change in other risk factors. Nonetheless, the maintenance of these behaviours with minimal support after 12 and even 18 months indicates the promise of HealtheSteps™ for long-term sustainability.

TRIAL REGISTRATION

The trial was registered on April 6, 2015 with ClinicalTrials.gov (identifier: NCT02413385 ).

A Scientific Perspective of Personalised Gene-Based Dietary Recommendations for Weight Management

Various studies showed that a "one size fits all" dietary recommendation for weight management is questionable. For this reason, the focus increasingly falls on personalised nutrition. Although there is no precise and uniform definition of personalised nutrition, the inclusion of genetic variants for personalised dietary recommendations is more and more favoured, whereas scientific evidence for gene-based dietary recommendations is rather limited. The purpose of this article is to provide a science-based viewpoint on gene-based personalised nutrition and weight management. Most of the studies showed no clinical evidence for gene-based personalised nutrition. The Food4Me study, e.g., investigated four different groups of personalised dietary recommendations based on dietary guidelines, and physiological, clinical, or genetic parameters, and resulted in no difference in weight loss between the levels of personalisation. Furthermore, genetic direct-to-consumer (DTC) tests are widely spread by companies. Scientific organisations clearly point out that, to date, genetic DTC tests are without scientific evidence. To date, gene-based personalised nutrition is not yet applicable for the treatment of obesity. Nevertheless, personalised dietary recommendations on the genetic landscape of a person are an innovative and promising approach for the prevention and treatment of obesity. In the future, human intervention studies are necessary to prove the clinical evidence of gene-based dietary recommendations.

Dietary Fatty Acids and the Metabolic Syndrome: A Personalized Nutrition Approach

Dietary fatty acids are present in a wide variety of foods and appear in different forms and lengths. The different fatty acids are known to have various effects on metabolic health. The metabolic syndrome (MetS) is a constellation of risk factors of chronic diseases. The etiology of the MetS is represented by a complex interplay of genetic and environmental factors. Dietary fatty acids can be important contributors of the evolution or in prevention of the MetS; however, great interindividual variability exists in the response to fatty acids. The identification of genetic variants interacting with fatty acids might explain this heterogeneity in metabolic responses. This chapter reviews the mechanisms underlying the interactions between the different components of the MetS, dietary fatty acids and genes. Challenges surrounding the implementation of personalized nutrition are also covered.