Genome-wide association study of osteoporosis identifies genetic risk and interactions with DASH diet and sugar sweetened beverages in a Hispanic cohort of older adults

BACKGROUND

Osteoporosis (OP) and low bone mass can be debilitating and costly conditions if not acted on quickly. This disease is also difficult to diagnose as symptoms develop unnoticed until fracture occurs. Therefore, gaining understanding of the genetic risk associated with these conditions could be beneficial for healthcare professionals in early detection and prevention.

METHODS

The Boston Puerto Rican Osteoporosis (BPROS) study, an ancillary study to the Boston Puerto Rican Health Study (BPRHS), collected information regarding bone and bone health. All bone measurements were taken during regular BPROS visits using dual-energy x-ray absorptiometry. Osteoporosis was defined as T-score ≤ -2.5 (2.5 SD or more below peak bone mass). Dietary variables were collected at the second wave of the BPRHS via food frequency questionnaire. We conducted genome-wide associations with bone outcomes including bone mineral density (BMD) and OP for 978 participants. We also examined interactions with dietary quality on the relationships between genotype and bone outcomes. We further tested if candidate genetic variants described in previous GWAS on OP and BMD contribute to OP risk in this population.

RESULTS

Four variants were associated with OP: rs114829316 (IQCJ), rs76603051, rs12214684 (MCHR2), and rs77303493 (RIN2), and two variants with BMD of lumbar spine (rs11855618, CGNL1) and hip (rs73480593, NTRK2), reaching the genome-wide significance threshold of P ≤ 5E-08. In a gene-diet interaction analysis, we found that one SNP showed a significant interaction with the overall DASH score, and 7 SNPs with sugar-sweeten beverages, a major contributor to the DASH score.

CONCLUSION

This study identifies new genetic markers related to osteoporosis and BMD in older Hispanic adults. Additionally, we uncovered unique genetic markers that interact with dietary quality, specifically sugar-sweetened beverages, in relation to bone health. These findings may be useful to guide early detection and preventative care.

Rate-Limiting Enzymes in Cardiometabolic Health and Aging in Humans

INTRODUCTION

Rate-limiting enzymes (RLEs) are innate slow points in metabolic pathways, and many function in bio-processes related to nutrient sensing. Many RLEs carry causal mutations relevant to inherited metabolic disorders. Because the activity of RLEs in cardiovascular health is poorly characterized, our objective was to assess their involvement in cardiometabolic health and disease and where altered biophysical and biochemical functions can promote disease.

METHODS

A dataset of 380 human RLEs was compared to protein and gene datasets for factors likely to contribute to cardiometabolic disease, including proteins showing significant age-related altered expression in blood and genetic loci with variants that associate with common cardiometabolic phenotypes. The biochemical reactions catalyzed by RLEs were evaluated for metabolites enriched in RLE subsets associating with various cardiometabolic phenotypes. Most significance tests were based on Z-score enrichment converted to p values with a normal distribution function.

RESULTS

Of 380 RLEs analyzed, 112 function in mitochondria, and 53 are assigned to inherited metabolic disorders. There was a depletion of RLE proteins known as aging biomarkers. At the gene level, RLEs were assessed for common genetic variants that associated with important cardiometabolic traits of LDL-cholesterol or any of the five outcomes pertinent to metabolic syndrome. This revealed several RLEs with links to cardiometabolic traits, from a minimum of 26 for HDL-cholesterol to a maximum of 45 for plasma glucose. Analysis of these GWAS-linked RLEs for enrichment of the molecular constituents of the catalyzed reactions disclosed a number of significant phenotype-metabolite links. These included blood pressure with acetate (p = 2.2 × 10-4) and NADP+ (p = 0.0091), plasma HDL-cholesterol and triglyceride with diacylglycerol (p = 2.6 × 10-5, 6.4 × 10-5, respectively) and diolein (p = 2.2 × 10-6, 5.9 × 10-6), and waist circumference with d-glucosamine-6-phosphate (p = 1.8 × 10-4).

CONCLUSION

In the context of cardiometabolic health, aging, and disease, these results highlight key diet-derived metabolites that are central to specific rate-limited processes that are linked to cardiometabolic health. These metabolites include acetate and diacylglycerol, pertinent to blood pressure and triglycerides, respectively, as well as diacylglycerol and HDL-cholesterol.

The impact of alcoholic drinks and dietary factors on epigenetic markers associated with triglyceride levels

Background: Many epigenetic loci have been associated with plasma triglyceride (TG) levels, but epigenetic connections between those loci and dietary exposures are largely unknown. This study aimed to characterize the epigenetic links between diet, lifestyle, and TG. Methods: We first conducted an epigenome-wide association study (EWAS) for TG in the Framingham Heart Study Offspring population (FHS, n = 2,264). We then examined relationships between dietary and lifestyle-related variables, collected four times in 13 years, and differential DNA methylation sites (DMSs) associated with the last TG measures. Third, we conducted a mediation analysis to evaluate the causal relationships between diet-related variables and TG. Finally, we replicated three steps to validate identified DMSs associated with alcohol and carbohydrate intake in the Genetics of Lipid-Lowering Drugs and Diet Network (GOLDN) study (n = 993). Results: In the FHS, the EWAS revealed 28 TG-associated DMSs at 19 gene regions. We identified 102 unique associations between these DMSs and one or more dietary and lifestyle-related variables. Alcohol and carbohydrate intake showed the most significant and consistent associations with 11 TG-associated DMSs. Mediation analyses demonstrated that alcohol and carbohydrate intake independently affect TG via DMSs as mediators. Higher alcohol intake was associated with lower methylation at seven DMSs and higher TG. In contrast, increased carbohydrate intake was associated with higher DNA methylation at two DMSs (CPT1A and SLC7A11) and lower TG. Validation in the GOLDN further supports the findings. Conclusion: Our findings imply that TG-associated DMSs reflect dietary intakes, particularly alcoholic drinks, which could affect the current cardiometabolic risk via epigenetic changes. This study illustrates a new method to map epigenetic signatures of environmental factors for disease risk. Identification of epigenetic markers of dietary intake can provide insight into an individual's risk of cardiovascular disease and support the application of precision nutrition. Clinical Trial Registration: www.ClinicalTrials.gov, the Framingham Heart Study (FHS), NCT00005121; the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN), NCT01023750.

Metabolomic profiling in dogs with dilated cardiomyopathy eating non-traditional or traditional diets and in healthy controls

Dilated cardiomyopathy (DCM), caused by genetic and environmental factors, usually progresses to heart failure, a major cause of death in elderly people. A diet-associated form of DCM was recently identified in pet dogs eating non-traditional (NT) diets. To identify potential dietary causes, we analyzed metabolomic signatures and gene set/pathway enrichment in (1) all dogs based on disease, diet, and their interactions and (2) dogs with DCM based on diet. Metabolomic analysis was performed in 38 dogs with DCM eating NT diets (DCM-NT), 8 dogs with DCM eating traditional diets, 12 healthy controls eating NT diets, and 17 healthy controls eating traditional diets. Overall, 153 and 63 metabolites differed significantly between dogs with DCM versus healthy controls and dogs eating NT versus traditional diets, respectively, with 12 metabolites overlapping both analyses. Protein-protein interaction networks and gene set enrichment analysis identified 105 significant pathways and gene sets including aging-related pathways (e.g., nuclear factor-kappa B, oxidative damage, inflammation). Seventeen metabolites differed significantly in dogs with DCM eating NT versus traditional diets (e.g., fatty acids, amino acids, legume biomarkers), suggesting different mechanisms for primary versus diet-associated DCM. Our multifaceted metabolomic assessment of DCM in dogs highlighted diet's role in some forms of DCM.

Using Machine Learning to Predict Obesity Based on Genome-Wide and Epigenome-Wide Gene-Gene and Gene-Diet Interactions

Obesity is associated with many chronic diseases that impair healthy aging and is governed by genetic, epigenetic, and environmental factors and their complex interactions. This study aimed to develop a model that predicts an individual's risk of obesity by better characterizing these complex relations and interactions focusing on dietary factors. For this purpose, we conducted a combined genome-wide and epigenome-wide scan for body mass index (BMI) and up to three-way interactions among 402,793 single nucleotide polymorphisms (SNPs), 415,202 DNA methylation sites (DMSs), and 397 dietary and lifestyle factors using the generalized multifactor dimensionality reduction (GMDR) method. The training set consisted of 1,573 participants in exam 8 of the Framingham Offspring Study (FOS) cohort. After identifying genetic, epigenetic, and dietary factors that passed statistical significance, we applied machine learning (ML) algorithms to predict participants' obesity status in the test set, taken as a subset of independent samples (n = 394) from the same cohort. The quality and accuracy of prediction models were evaluated using the area under the receiver operating characteristic curve (ROC-AUC). GMDR identified 213 SNPs, 530 DMSs, and 49 dietary and lifestyle factors as significant predictors of obesity. Comparing several ML algorithms, we found that the stochastic gradient boosting model provided the best prediction accuracy for obesity with an overall accuracy of 70%, with ROC-AUC of 0.72 in test set samples. Top predictors of the best-fit model were 21 SNPs, 230 DMSs in genes such as CPT1A, ABCG1, SLC7A11, RNF145, and SREBF1, and 26 dietary factors, including processed meat, diet soda, French fries, high-fat dairy, artificial sweeteners, alcohol intake, and specific nutrients and food components, such as calcium and flavonols. In conclusion, we developed an integrated approach with ML to predict obesity using omics and dietary data. This extends our knowledge of the drivers of obesity, which can inform precision nutrition strategies for the prevention and treatment of obesity. Clinical Trial Registration: [www.ClinicalTrials.gov], the Framingham Heart Study (FHS), [NCT00005121].

Metabolite patterns link diet, obesity, and type 2 diabetes in a Hispanic population

INTRODUCTION

Obesity is a precursor of type 2 diabetes (T2D).

OBJECTIVES

Our aim was to identify metabolic signatures of T2D and dietary factors unique to obesity.

METHODS

We examined a subsample of the Boston Puerto Rican Health Study (BPRHS) population with a high prevalence of obesity and T2D at baseline (n = 806) and participants (without T2D at baseline) at 5-year follow-up (n = 412). We determined differences in metabolite profiles between T2D and non-T2D participants of the whole sample and according to abdominal obesity status. Enrichment analysis was performed to identify metabolic pathways that were over-represented by metabolites that differed between T2D and non-T2D participants. T2D-associated metabolites unique to obesity were examined for correlation with dietary food groups to understand metabolic links between dietary intake and T2D risk. False Discovery Rate method was used to correct for multiple testing.

RESULTS

Of 526 targeted metabolites, 179 differed between T2D and non-T2D in the whole sample, 64 in non-obese participants and 120 unique to participants with abdominal obesity. Twenty-four of 120 metabolites were replicated and were associated with T2D incidence at 5-year follow-up. Enrichment analysis pointed to three metabolic pathways that were overrepresented in obesity-associated T2D: phosphatidylethanolamine (PE), long-chain fatty acids, and glutamate metabolism. Elevated intakes of three food groups, energy-dense takeout food, dairy intake and sugar-sweetened beverages, associated with 13 metabolites represented by the three pathways.

CONCLUSION

Metabolic signatures of lipid and glutamate metabolism link obesity to T2D, in parallel with increased intake of dairy and sugar-sweetened beverages, thereby providing insight into the relationship between dietary habits and T2D risk.

A Catalog of Natural Products Occurring in Watermelon-Citrullus lanatus

Sweet dessert watermelon (Citrullus lanatus) is one of the most important vegetable crops consumed throughout the world. The chemical composition of watermelon provides both high nutritional value and various health benefits. The present manuscript introduces a catalog of 1,679 small molecules occurring in the watermelon and their cheminformatics analysis for diverse features. In this catalog, the phytochemicals are associated with the literature describing their presence in the watermelon plant, and when possible, concentration values in various plant parts (flesh, seeds, leaves, roots, rind). Also cataloged are the chemical classes, molecular weight and formula, chemical structure, and certain physical and chemical properties for each phytochemical. In our view, knowing precisely what is in what we eat, as this catalog does for watermelon, supports both the rationale for certain controlled feeding studies in the field of precision nutrition, and plant breeding efforts for the development of new varieties with enhanced concentrations of specific phytochemicals. Additionally, improved and comprehensive collections of natural products accessible to the public will be especially useful to researchers in nutrition, cheminformatics, bioinformatics, and drug development, among other disciplines.

Investigation of diets associated with dilated cardiomyopathy in dogs using foodomics analysis

Dilated cardiomyopathy (DCM) is a disease of the heart muscle that affects both humans and dogs. Certain canine diets have been associated with DCM, but the diet-disease link is unexplained, and novel methods are needed to elucidate mechanisms. We conducted metabolomic profiling of 9 diets associated with canine DCM, containing ≥ 3 pulses, potatoes, or sweet potatoes as main ingredients, and in the top 16 dog diet brands most frequently associated with canine DCM cases reported to the FDA (3P/FDA diets), and 9 non-3P/FDA diets. We identified 88 named biochemical compounds that were higher in 3P/FDA diets and 23 named compounds that were lower in 3P/FDA diets. Amino acids, amino acid-derived compounds, and xenobiotics/plant compounds were the largest categories of biochemicals that were higher in 3P/FDA diets. Random forest analyses identified the top 30 compounds that distinguished the two diet groups with 100% predictive accuracy. Four diet ingredients distinguished the two diet groups (peas, lentils, chicken/turkey, and rice). Of these ingredients, peas showed the greatest association with higher concentrations of compounds in 3P/FDA diets. Moreover, the current foodomics analyses highlight relationships between diet and DCM in dogs that can identify possible etiologies for understanding diet-disease relationships in dogs and humans.

Associations of network-derived metabolite clusters with prevalent type 2 diabetes among adults of Puerto Rican descent

INTRODUCTION

We investigated whether network analysis revealed clusters of coregulated metabolites associated with prevalent type 2 diabetes (T2D) among Puerto Rican adults.

RESEARCH DESIGN AND METHODS

We used liquid chromatography-mass spectrometry to measure fasting plasma metabolites (>600) among participants aged 40-75 years in the Boston Puerto Rican Health Study (BPRHS; discovery) and San Juan Overweight Adult Longitudinal Study (SOALS; replication), with (n=357; n=77) and without (n=322; n=934) T2D, respectively. Among BPRHS participants, we used unsupervised partial correlation network-based methods to identify and calculate metabolite cluster scores. Logistic regression was used to assess cross-sectional associations between metabolite clusters and prevalent T2D at the baseline blood draw in the BPRHS, and significant associations were replicated in SOALS. Inverse-variance weighted random-effect meta-analysis was used to combine cohort-specific estimates.

RESULTS

Six metabolite clusters were significantly associated with prevalent T2D in the BPRHS and replicated in SOALS (false discovery rate (FDR) <0.05). In a meta-analysis of the two cohorts, the OR and 95% CI (per 1 SD increase in cluster score) for prevalent T2D were as follows for clusters characterized primarily by glucose transport (0.21 (0.16 to 0.30); FDR <0.0001), sphingolipids (0.40 (0.29 to 0.53); FDR <0.0001), acyl cholines (0.35 (0.22 to 0.56); FDR <0.0001), sugar metabolism (2.28 (1.68 to 3.09); FDR <0.0001), branched-chain and aromatic amino acids (2.22 (1.60 to 3.08); FDR <0.0001), and fatty acid biosynthesis (1.54 (1.29 to 1.85); FDR <0.0001). Three additional clusters characterized by amino acid metabolism, cell membrane components, and aromatic amino acid metabolism displayed significant associations with prevalent T2D in the BPRHS, but these associations were not replicated in SOALS.

CONCLUSIONS

Among Puerto Rican adults, we identified several known and novel metabolite clusters that associated with prevalent T2D.

Diet-derived fruit and vegetable metabolites show sex-specific inverse relationships to osteoporosis status

BACKGROUND

The impact of nutrition on the metabolic profile of osteoporosis (OS) is unknown.

OBJECTIVE

Identify biochemical factors driving the association of fruit and vegetable (FV) intakes with OS prevalence using an untargeted metabolomics approach.

DESIGN

Cross-sectional dietary, anthropometric and plasma metabolite data were examined from the Boston Puerto Rican Osteoporosis Study, n = 600 (46-79 yr).

METHODS

Bone mineral density was assessed by DXA. OS was defined by clinical standards. A culturally adapted FFQ assessed usual dietary intake. Principal components analysis (PCA) of 42 FV items created 6 factors. Metabolomic profiles derived from plasma samples were assessed on a commercial platform. Differences in levels of 525 plasma metabolites between disease groups (OS vs no-OS) were compared using logistic regression; and associations with FV intakes by multivariable linear regression, adjusted for covariates. Metabolites significantly associated with OS status or with total FV intake were analyzed for enrichment in various biological pathways using Mbrole 2.0, MetaboAnalyst, and Reactome, using FDR correction of P-values. Correlation coefficients were calculated as Spearman's rho rank correlations, followed by hierarchical clustering of the resulting correlation coefficients using PCA FV factors and sex-specific sets of OS-associated metabolites.

RESULTS

High FV intake was inversely related to OS prevalence (Odds Ratio = 0.73; 95% CI = 0.57, 0.94; P = 0.01). Several biological processes affiliated with the FV-associating metabolites, including caffeine metabolism, carnitines and fatty acids, and glycerophospholipids. Important processes identified with OS-associated metabolites were steroid hormone biosynthesis in women and branched-chain amino acid metabolism in men. Factors derived from PCA were correlated with the OS-associated metabolites, with high intake of dark leafy greens and berries/melons appearing protective in both sexes.

CONCLUSIONS

These data warrant investigation into whether increasing intakes of dark leafy greens, berries and melons causally affect bone turnover and BMD among middle-aged and older adults at risk for osteoporosis via sex-specific metabolic pathways, and how gene-diet interactions alter these sex-specific metabolomic-osteoporosis links. ClinicalTrials.gov Identifier: NCT01231958.

Carbohydrate and fat intake associated with risk of metabolic diseases through epigenetics of CPT1A

BACKGROUND

Epigenome-wide association studies identified the cg00574958 DNA methylation site at the carnitine palmitoyltransferase-1A (CPT1A) gene to be associated with reduced risk of metabolic diseases (hypertriglyceridemia, obesity, type 2 diabetes, hypertension, metabolic syndrome), but the mechanism underlying these associations is unknown.

OBJECTIVES

We aimed to elucidate whether carbohydrate and fat intakes modulate cg00574958 methylation and the risk of metabolic diseases.

METHODS

We examined associations between carbohydrate (CHO) and fat (FAT) intake, as percentages of total diet energy, and the CHO/FAT ratio with CPT1A-cg00574958, and the risk of metabolic diseases in 3 populations (Genetics of Lipid Lowering Drugs and Diet Network, n = 978; Framingham Heart Study, n = 2331; and REgistre GIroní del COR study, n = 645) while adjusting for confounding factors. To understand possible causal effects of dietary intake on the risk of metabolic diseases, we performed meta-analysis, CPT1A transcription analysis, and mediation analysis with CHO and FAT intakes as exposures and cg00574958 methylation as the mediator.

RESULTS

We confirmed strong associations of cg00574958 methylation with metabolic phenotypes (BMI, triglyceride, glucose) and diseases in all 3 populations. Our results showed that CHO intake and CHO/FAT ratio were positively associated with cg00574958 methylation, whereas FAT intake was negatively correlated with cg00574958 methylation. Meta-analysis further confirmed this strong correlation, with β = 58.4 ± 7.27, P = 8.98 x 10-16 for CHO intake; β = -36.4 ± 5.95, P = 9.96 x 10-10 for FAT intake; and β = 3.30 ± 0.49, P = 1.48 x 10-11 for the CHO/FAT ratio. Furthermore, CPT1A mRNA expression was negatively associated with CHO intake, and positively associated with FAT intake, and metabolic phenotypes. Mediation analysis supports the hypothesis that CHO intake induces CPT1A methylation, hence reducing the risk of metabolic diseases, whereas FAT intake inhibits CPT1A methylation, thereby increasing the risk of metabolic diseases.

CONCLUSIONS

Our results suggest that the proportion of total energy supplied by CHO and FAT can have a causal effect on the risk of metabolic diseases via the epigenetic status of CPT1A.Study registration at https://www.clinicaltrials.gov/: the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN)-NCT01023750; and the Framingham Heart Study (FHS)-NCT00005121.

Statin Use Associates With Risk of Type 2 Diabetes via Epigenetic Patterns at ABCG1

Statin is the medication most widely prescribed to reduce plasma cholesterol levels. Yet, how the medication contributes to diabetes risk and impaired glucose metabolism is not clear. This study aims to examine the epigenetic mechanisms of ABCG1 through which statin use associates with risk of type 2 diabetes. We determined the association between the statin use, DNA methylation at ABCG1 and type 2 diabetes/glycemic traits in the Framingham Heart Study Offspring (FHS, n = 2741), with validation in the Women’s Health Initiative Study (WHI, n = 2020). The causal effect of statin use on the risk of type 2 diabetes was examined using a two-step Mendelian randomization approach. Next, based on transcriptome analysis, we determined the links between the medication-associated epigenetic status of ABCG1 and biological pathways on the pathogenesis of type 2 diabetes. Our results showed that DNA methylation levels at cg06500161 of ABCG1 were positively associated with the use of statin, type 2 diabetes and related traits (fasting glucose and insulin) in FHS and WHI. Two-step Mendelian randomization suggested a causal effect of statin use on type 2 diabetes and related traits through epigenetic mechanisms, specifically, DNA methylation at cg06500161. Our results highlighted that gene expression of ABCG1, ABCA1 and ACSL3, involved in both cholesterol metabolism and glycemic pathways, was inversely associated with statin use, CpG methylation, and diabetic signatures. We concluded that DNA methylation site cg06500161 at ABCG1 is a mediator of the association between statins and risk of type 2 diabetes.

PhyteByte: identification of foods containing compounds with specific pharmacological properties

Background

Phytochemicals and other molecules in foods elicit positive health benefits, often by poorly established or unknown mechanisms. While there is a wealth of data on the biological and biophysical properties of drugs and therapeutic compounds, there is a notable lack of similar data for compounds commonly present in food. Computational methods for high-throughput identification of food compounds with specific biological effects, especially when accompanied by relevant food composition data, could enable more effective and more personalized dietary planning. We have created a machine learning-based tool (PhyteByte) to leverage existing pharmacological data to predict bioactivity across a comprehensive molecular database of foods and food compounds.

Results

PhyteByte uses a cheminformatic approach to structure-based activity prediction and applies it to uncover the putative bioactivity of food compounds. The tool takes an input protein target and develops a random forest classifier to predict the effect of an input molecule based on its molecular fingerprint, using structure and activity data available from the ChEMBL database. It then predicts the relevant bioactivity of a library of food compounds with known molecular structures from the FooDB database. The output is a list of food compounds with high confidence of eliciting relevant biological effects, along with their source foods and associated quantities in those foods, where available. Applying PhyteByte to the human PPARG gene, we identified irigenin, sesamin, fargesin, and delta-sanshool as putative agonists of PPARG, along with previously identified agonists of this important metabolic regulator.

Conclusions

PhyteByte identifies food-based compounds that are predicted to interact with specific protein targets. The identified relationships can be used to prioritize food compounds for experimental or epidemiological follow-up and can contribute to the rapid development of precision approaches to new nutraceuticals as well as personalized dietary planning.

Salivary AMY1 Copy Number Variation Modifies Age-Related Type 2 Diabetes Risk

BACKGROUND

Copy number variation (CNV) in the salivary amylase gene (AMY1) modulates salivary α-amylase levels and is associated with postprandial glycemic traits. Whether AMY1-CNV plays a role in age-mediated change in insulin resistance (IR) is uncertain.

METHODS

We measured AMY1-CNV using duplex quantitative real-time polymerase chain reaction in two studies, the Boston Puerto Rican Health Study (BPRHS, n = 749) and the Genetics of Lipid-Lowering Drug and Diet Network study (GOLDN, n = 980), and plasma metabolomic profiles in the BPRHS. We examined the interaction between AMY1-CNV and age by assessing the relationship between age with glycemic traits and type 2 diabetes (T2D) according to high or low copy numbers of the AMY1 gene. Furthermore, we investigated associations between metabolites and interacting effects of AMY1-CNV and age on T2D risk.

RESULTS

We found positive associations of IR with age among subjects with low AMY1-copy-numbers in both studies. T2D was marginally correlated with age in participants with low AMY1-copy-numbers but not with high AMY1-copy-numbers in the BPRHS. Metabolic pathway enrichment analysis identified the pentose metabolic pathway based on metabolites that were associated with both IR and the interactions between AMY1-CNV and age. Moreover, in older participants, high AMY1-copy-numbers tended to be associated with lower levels of ribonic acid, erythronic acid, and arabinonic acid, all of which were positively associated with IR.

CONCLUSIONS

We found evidence supporting a role of AMY1-CNV in modifying the relationship between age and IR. Individuals with low AMY1-copy-numbers tend to have increased IR with advancing age.

A gene-diet interaction-based score predicts response to dietary fat in the Women's Health Initiative

BACKGROUND

Although diet response prediction for cardiometabolic risk factors (CRFs) has been demonstrated using single genetic variants and main-effect genetic risk scores, little investigation has gone into the development of genome-wide diet response scores.

OBJECTIVE

We sought to leverage the multistudy setup of the Women's Health Initiative cohort to generate and test genetic scores for the response of 6 CRFs (BMI, systolic blood pressure, LDL cholesterol, HDL cholesterol, triglycerides, and fasting glucose) to dietary fat.

METHODS

A genome-wide interaction study was undertaken for each CRF in women (n ∼ 9000) not participating in the dietary modification (DM) trial, which focused on the reduction of dietary fat. Genetic scores based on these analyses were developed using a pruning-and-thresholding approach and tested for the prediction of 1-y CRF changes as well as long-term chronic disease development in DM trial participants (n ∼ 5000).

RESULTS

Only 1 of these genetic scores, for LDL cholesterol, predicted changes in the associated CRF. This 1760-variant score explained 3.7% (95% CI: 0.09, 11.9) of the variance in 1-y LDL cholesterol changes in the intervention arm but was unassociated with changes in the control arm. In contrast, a main-effect genetic risk score for LDL cholesterol was not useful for predicting dietary fat response. Further investigation of this score with respect to downstream disease outcomes revealed suggestive differential associations across DM trial arms, especially with respect to coronary heart disease and stroke subtypes.

CONCLUSIONS

These results lay the foundation for the combination of many genome-wide gene-diet interactions for diet response prediction while highlighting the need for further research and larger samples in order to achieve robust biomarkers for use in personalized nutrition.

Metabolomic Links between Sugar-Sweetened Beverage Intake and Obesity

BACKGROUND

Sugar-sweetened beverage (SSB) consumption is highly associated with obesity, but the metabolic mechanism underlying this correlation is not understood.

OBJECTIVE

Our objective was to examine metabolomic links between SSB intake and obesity to understand metabolic mechanisms.

DESIGN

We examined the association of plasma metabolomic profiles with SSB intake and obesity risk in 781 participants, aged 45-75 y, in the Boston Puerto Rican Health Study (BPRHS) using generalized linear models, controlling for potential confounding factors. Based on identified metabolites, we conducted pathway enrichment analysis to identify potential metabolic pathways that link SSB intake and obesity risk. Variants in genes encoding enzymes known to function in identified metabolic pathways were examined for their interactions with SSB intake on obesity.

RESULTS

SSB intake was correlated with BMI (β = 0.607, P=0.045). Among 526 measured metabolites, 86 showed a significant correlation with SSB intake and 148 with BMI (P ≤ 0.05); 28 were correlated with both SSB intake and BMI (P ≤ 0.05). Pathway enrichment analysis identified the phosphatidylcholine and lysophospholipid pathways as linking SSB intake to obesity, after correction for multiple testing. Furthermore, 8 of 10 genes functioning in these two pathways showed strong interaction with SSB intake on BMI. Our results further identified participants who may exhibit an increased risk of obesity when consuming SSB.

CONCLUSIONS

We identified two key metabolic pathways that link SSB intake to obesity, revealing the potential of phosphatidylcholine and lysophospholipid to modulate how SSB intake can increase obesity risk. The interaction between genetic variants related to these pathways and SSB intake on obesity further supports the mechanism.

Development of a Genetic Score to Predict an Increase in HDL Cholesterol Concentration After a Dietary Intervention in Adults with Metabolic Syndrome

BACKGROUND

Dietary intervention (DI) is a primary strategy to attenuate some of the metabolic abnormalities associated with metabolic syndrome (MetS), including low HDL cholesterol. There is no biomarker that can identify individuals who respond to DI by increasing HDL cholesterol.

OBJECTIVE

The aim of this study was to assess the predictive power of a genetic predisposition score (GPS) in Mexican adults with MetS to identify HDL cholesterol responders to DI.

METHODS

This study followed a prospective cohort design. Sixty-seven Mexican adults aged 20-60 y (21% men) with BMI ≥25 and ≤39.9 kg/m², who had at least 3 of 5 positive criteria for MetS, were included. Participants consumed a low saturated fat diet for 2.5 mo (<7% energy as saturated fat, <200 mg of cholesterol/d) and reduced their usual diet by ∼440 kcal/d, a reduction in total energy intake of about 25%. Anthropometry and serum biochemical markers, including HDL cholesterol, were measured before and after DI. A multilocus GPS was constructed using previously reported genetic variants associated with response to diet in subjects with MetS. GPS values, designed to predict the response of HDL cholesterol to the DI, were computed for each individual as the sum of the number of effect alleles across 14 SNPs.

RESULTS

Individuals were dichotomized as high and low GPS according to median GPS (-2.12) and we observed a difference in HDL cholesterol changes on DI of +3 mg/dL (6.3%) in subjects with low GPS, whereas those with high GPS had HDL cholesterol decreases of -3 mg/dL (-7.9%) (P = 0.04).

CONCLUSIONS

Individuals with low GPS showed greater increases in their HDL cholesterol than those with high GPS. Therefore, the GPS can be useful for predicting the HDL cholesterol response to diet.

Incremental Elevations in TNFα and IL6 in the Human Colon and Procancerous Changes in the Mucosal Transcriptome Accompany Adiposity

BACKGROUND

Obesity, a risk factor for colorectal cancer, raises systemic levels of proinflammatory mediators. Whether increased levels also reside in the colons of obese individuals and are accompanied by procancerous alterations in the mucosal transcriptome is unknown.

METHODS

Concentrations of TNFα, IL1β, and IL6 in blood and colonic mucosa of 16 lean and 26 obese individuals were examined. Differences in the mucosal transcriptome between the two groups were defined.

RESULTS

Plasma IL6 and TNFα were 1.4- to 3-fold elevated in obese subjects [body mass index (BMI) ≥ 34 kg/m²] compared with the lean controls (P < 0.01). Among individuals with BMI ≥ 34 kg/m² colonic concentrations of IL6 and TNFα were 2- to 3-fold greater than in lean subjects (P < 0.03). In a general linear model, adjusted for NSAID use, colonic IL6 (partial r = 0.41; P < 0.01) and TNFα (partial r = 0.41; P = 0.01) increased incrementally over the entire range of BMIs (18.1-45.7). Regular use of nonsteroidal anti-inflammatory drugs (NSAIDs) was associated with a reduction in colonic IL6 (β = -0.65, P < 0.02). RNA sequencing (NSAID users excluded) identified 182 genes expressed differentially between lean and obese subjects. The two gene networks most strongly linked to changes in expression included several differentially expressed genes known to regulate the procarcinogenic signaling pathways, NFκB and ERK 1/2, in a pattern consistent with upregulation of each in the obese subjects.

CONCLUSIONS

Incremental increases in two major proinflammatory colonic cytokines are associated with increasing BMI, and in the obese state are accompanied by procancerous changes in the transcriptome.

IMPACT

These observations delineate means by which an inflammatory milieu may contribute to obesity-promoted colon cancer.

Dietary epicatechin improves survival and delays skeletal muscle degeneration in aged mice

We recently reported that epicatechin, a bioactive compound that occurs naturally in various common foods, promoted general health and survival of obese diabetic mice. It remains to be determined whether epicatechin extends health span and delays the process of aging. In the present study, epicatechin or its analogue epigallocatechin gallate (EGCG) (0.25% w/v in drinking water) was administered to 20-mo-old male C57BL mice fed a standard chow. The goal was to determine the antiaging effect. The results showed that supplementation with epicatechin for 37 wk strikingly increased the survival rate from 39 to 69%, whereas EGCG had no significant effect. Consistently, epicatechin improved physical activity, delayed degeneration of skeletal muscle (quadriceps), and shifted the profiles of the serum metabolites and skeletal muscle general mRNA expressions in aging mice toward the profiles observed in young mice. In particular, we found that dietary epicatechin significantly reversed age-altered mRNA and protein expressions of extracellular matrix and peroxisome proliferator–activated receptor pathways in skeletal muscle, and reversed the age-induced declines of the nicotinate and nicotinamide pathway both in serum and skeletal muscle. The present study provides evidence that epicatechin supplementation can exert an antiaging effect, including an increase in survival, an attenuation of the aging-related deterioration of skeletal muscles, and a protection against the aging-related decline in nicotinate and nicotinamide metabolism.—Si, H., Wang, X., Zhang, L., Parnell, L. D., Ahmed, B., LeRoith, T., Ansah, T.-A., Zhang, L., Li, J., Ordovás, J. M., Si, H., Liu, D., Lai, C.-Q. Dietary epicatechin improves survival and delays skeletal muscle degeneration in aged mice.

Epigenomics and metabolomics reveal the mechanism of the APOA2-saturated fat intake interaction affecting obesity

Background

The putative functional variant -265T>C (rs5082) within the APOA2 promoter has shown consistent interactions with saturated fatty acid (SFA) intake to influence the risk of obesity.

Objective

The aim of this study was to implement an integrative approach to characterize the molecular basis of this interaction.

Design

We conducted an epigenome-wide scan on 80 participants carrying either the rs5082 CC or TT genotypes and consuming either a low-SFA (<22 g/d) or high-SFA diet (≥22 g/d), matched for age, sex, BMI, and diabetes status in the Boston Puerto Rican Health Study (BPRHS). We then validated the findings in selected participants in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) Study (n = 379) and the Framingham Heart Study (FHS) (n = 243). Transcription and metabolomics analyses were conducted to determine the relation between epigenetic status, APOA2 mRNA expression, and blood metabolites.

Results

In the BPRHS, we identified methylation site cg04436964 as exhibiting significant differences between CC and TT participants consuming a high-SFA diet, but not among those consuming low-SFA. Similar results were observed in the GOLDN Study and the FHS. Additionally, in the FHS, cg04436964 methylation was negatively correlated with APOA2 expression in the blood of participants consuming a high-SFA diet. Furthermore, when consuming a high-SFA diet, CC carriers had lower APOA2 expression than those with the TT genotype. Lastly, metabolomic analysis identified 4 pathways as overrepresented by metabolite differences between CC and TT genotypes with high-SFA intake, including tryptophan and branched-chain amino acid (BCAA) pathways. Interestingly, these pathways were linked to rs5082-specific cg04436964 methylation differences in high-SFA consumers.

Conclusions

The epigenetic status of the APOA2 regulatory region is associated with SFA intake and APOA2 -265T>C genotype, promoting an APOA2 expression difference between APOA2 genotypes on a high-SFA diet, and modulating BCAA and tryptophan metabolic pathways. These findings identify potential mechanisms by which this highly reproducible gene-diet interaction influences obesity risk, and contribute new insights to ongoing investigations of the relation between SFA and human health. This study was registered at clinicaltrials.gov as NCT03452787.

Effect of personalized nutrition on health-related behaviour change: evidence from the Food4Me European randomized controlled trial

Background

Optimal nutritional choices are linked with better health, but many current interventions to improve diet have limited effect. We tested the hypothesis that providing personalized nutrition (PN) advice based on information on individual diet and lifestyle, phenotype and/or genotype would promote larger, more appropriate, and sustained changes in dietary behaviour.

Methods

: Adults from seven European countries were recruited to an internet-delivered intervention (Food4Me) and randomized to: (i) conventional dietary advice (control) or to PN advice based on: (ii) individual baseline diet; (iii) individual baseline diet plus phenotype (anthropometry and blood biomarkers); or (iv) individual baseline diet plus phenotype plus genotype (five diet-responsive genetic variants). Outcomes were dietary intake, anthropometry and blood biomarkers measured at baseline and after 3 and 6 months' intervention.

Results

At baseline, mean age of participants was 39.8 years (range 18-79), 59% of participants were female and mean body mass index (BMI) was 25.5 kg/m 2 . From the enrolled participants, 1269 completed the study. Following a 6-month intervention, participants randomized to PN consumed less red meat [-5.48 g, (95% confidence interval:-10.8,-0.09), P  = 0.046], salt [-0.65 g, (-1.1,-0.25), P  = 0.002] and saturated fat [-1.14 % of energy, (-1.6,-0.67), P  < 0.0001], increased folate [29.6 µg, (0.21,59.0), P  = 0.048] intake and had higher Healthy Eating Index scores [1.27, (0.30, 2.25), P  = 0.010) than those randomized to the control arm. There was no evidence that including phenotypic and phenotypic plus genotypic information enhanced the effectiveness of the PN advice.

Conclusions

Among European adults, PN advice via internet-delivered intervention produced larger and more appropriate changes in dietary behaviour than a conventional approach.

From SNPs to pathways: Biological interpretation of type 2 diabetes (T2DM) genome wide association study (GWAS) results

Genome-wide association studies (GWAS) have become a common method for discovery of gene-disease relationships, in particular for complex diseases like Type 2 Diabetes Mellitus (T2DM). The experience with GWAS analysis has revealed that the genetic risk for complex diseases involves cumulative, small effects of many genes and only some genes with a moderate effect. In order to explore the complexity of the relationships between T2DM genes and their potential function at the process level as effected by polymorphism effects, a secondary analysis of a GWAS meta-analysis is presented. Network analysis, pathway information and integration of different types of biological information such as eQTLs and gene-environment interactions are used to elucidate the biological context of the genetic variants and to perform an analysis based on data visualization. We selected a T2DM dataset from a GWAS meta-analysis, and extracted 1,971 SNPs associated with T2DM. We mapped 580 SNPs to 360 genes, and then selected 460 pathways containing these genes from the curated collection of WikiPathways. We then created and analyzed SNP-gene and SNP-gene-pathway network modules in Cytoscape. A focus on genes with robust connections to pathways permitted identification of many T2DM pertinent pathways. However, numerous genes lack literature evidence of association with T2DM. We also speculate on the genes in specific network structures obtained in the SNP-gene network, such as gene-SNP-gene modules. Finally, we selected genes relevant to T2DM from our SNP-gene-pathway network, using different sources that reveal gene-environment interactions and eQTLs. We confirmed functions relevant to T2DM for many genes and have identified some-LPL and APOB-that require further validation to clarify their involvement in T2DM.

Genome-Wide Interactions with Dairy Intake for Body Mass Index in Adults of European Descent

SCOPE

Body weight responds variably to the intake of dairy foods. Genetic variation may contribute to inter-individual variability in associations between body weight and dairy consumption.

METHODS AND RESULTS

A genome-wide interaction study to discover genetic variants that account for variation in BMI in the context of low-fat, high-fat and total dairy intake in cross-sectional analysis was conducted. Data from nine discovery studies (up to 25 513 European descent individuals) were meta-analyzed. Twenty-six genetic variants reached the selected significance threshold (p-interaction <10-7) , and six independent variants (LINC01512-rs7751666, PALM2/AKAP2-rs914359, ACTA2-rs1388, PPP1R12A-rs7961195, LINC00333-rs9635058, AC098847.1-rs1791355) were evaluated meta-analytically for replication of interaction in up to 17 675 individuals. Variant rs9635058 (128 kb 3' of LINC00333) was replicated (p-interaction = 0.004). In the discovery cohorts, rs9635058 interacted with dairy (p-interaction = 7.36 × 10-8) such that each serving of low-fat dairy was associated with 0.225 kg m-2 lower BMI per each additional copy of the effect allele (A). A second genetic variant (ACTA2-rs1388) approached interaction replication significance for low-fat dairy exposure.

CONCLUSION

Body weight responses to dairy intake may be modified by genotype, in that greater dairy intake may protect a genetic subgroup from higher body weight.