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The age-related loss of the cognitive function is a growing concern for global populations. Many factors that determine cognitive resilience or dementia also have metabolic functions. However, this duality is not universally appreciated when the action of that factor occurs in tissues external to the brain. Thus, we examined a set of genes involved in dementia, i.e., those related to vascular dementia, Alzheimer’s disease, Parkinson’s disease, and the human metabolism for activity in 12 metabolically active tissues. Mining the Genotype-Tissue Expression (GTEx) data showed that most of these metabolism–dementia (MD) genes (62 of 93, 67%) exhibit a higher median expression in any of the metabolically active tissues than in the brain. After identifying that several MD genes served as blood-based biomarkers of longevity in other studies, we examined the impact of the intake of food, nutrients, and other dietary factors on the expression of MD genes in whole blood in the Framingham Offspring Study (n = 2134). We observed positive correlations between flavonoids and HMOX1, taurine and UQCRC1, broccoli and SLC10A2, and myricetin and SLC9A8 (p < 2.09 × 10−4). In contrast, dairy protein, palmitic acid, and pie were negatively correlated, respectively, with the expression of IGF1R, CSF1R, and SLC9A8, among others (p < 2.92 × 10−4). The results of this investigation underscore the potential contributions of metabolic enzyme activity in non-brain tissues to the risk of dementia. Specific epidemiological or intervention studies could be designed using specific foods and nutrients or even dietary patterns focused on these foods and nutrients that influence the expression of some MD genes to verify the findings presented here.

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.

IntroductionWe investigated whether network analysis revealed clusters of coregulated metabolites associated with prevalent type 2 diabetes (T2D) among Puerto Rican adults.Research design and methodsWe 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.ResultsSix metabolite clusters were significantly associated with prevalent T2D in the BPRHS and replicated in SOALS (false discovery rate (FDR) ConclusionsAmong Puerto Rican adults, we identified several known and novel metabolite clusters that associated with prevalent T2D.

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.

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.

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.

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. This work was partially supported by grant 201806105018 from the China Scholarship Council (CSC), and by the United States Department of Agriculture, under agreement no. 8050-51000-098-00D. Sí

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.

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., Admed, 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.

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 (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.

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.

The impact of nutrition on the metabolic profile of osteoporosis is incompletely characterized. The objective of this cross-sectional study was to detangle the association of fruit and vegetable (FV) intakes with osteoporosis prevalence. Dietary, anthropometric and blood plasma metabolite data were examined from the Boston Puerto Rican Osteoporosis Study, a cohort of 600 individuals (age 46-79yr). High FV intake was protective against osteoporosis prevalence. Associations of 525 plasma metabolites were assessed with fruit and vegetable intake, and separately with osteoporosis status. Several biological processes affiliated with the FV-associating metabolites, including caffeine metabolism, carnitines and fatty acids, and glycerophospholipids. For osteoporosis-associated metabolites, important processes were steroid hormone biosynthesis in women, and branched-chain amino acid metabolism in men. In all instances, the metabolite patterns differed greatly between sexes, arguing for a stratified nutrition approach in recommending FV intakes to improve bone health. Factors derived from principal components analysis of the FV intakes were correlated with the osteoporosis-associated metabolites, with high intake of dark leafy greens and berries/melons appearing protective in both sexes. These data warrant investigation into whether increasing intakes of dark leafy greens, berries and melons causally affect bone turnover and BMD among adults at risk for osteoporosis via sex-specific metabolic pathways, and how gene-diet interactions alter these sex-specific metabolomic-osteoporosis links.

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 .

Postprandial lipemia (PPL), the increased plasma TG concentration after consuming a high-fat meal, is an independent risk factor for CVD. Individual responses to a meal high in fat vary greatly, depending on genetic and lifestyle factors. However, only a few loci have been associated with TG-PPL response. Heritable epigenomic changes may be significant contributors to the unexplained inter-individual PPL variability. We conducted an epigenome-wide association study on 979 subjects with DNA methylation measured from CD4+ T cells, who were challenged with a high-fat meal as a part of the Genetics of Lipid Lowering Drugs and Diet Network study. Eight methylation sites encompassing five genes, LPP, CPT1A, APOA5, SREBF1, and ABCG1, were significantly associated with PPL response at an epigenome-wide level (P < 1.1 × 10−7), but no methylation site reached epigenome-wide significance after adjusting for baseline TG levels. Higher methylation at LPP, APOA5, SREBF1, and ABCG1, and lower methylation at CPT1A methylation were correlated with an increased TG-PPL response. These PPL-associated methylation sites, also correlated with fasting TG, account for a substantially greater amount of phenotypic variance (14.9%) in PPL and fasting TG (16.3%) when compared with the genetic contribution of loci identified by our previous genome-wide association study (4.5%). In summary, the epigenome is a large contributor to the variation in PPL, and this has the potential to be used to modulate PPL and reduce CVD.

BACKGROUND S100 calcium-binding protein A9 (S100A9) has previously been identified as a type 2 diabetes (T2D) gene. However, this finding requires independent validation and more in-depth analyses in other populations and ancestries. OBJECTIVES We aimed to replicate the associations between an S100A9 variant and insulin resistance and T2D and to initiate an investigation of potential interactions with the habitual diet in several independent populations. DESIGN We investigated the association of the S100A9 variant rs3014866 with insulin resistance and T2D risk and its interactions with diet in 3 diverse populations as follows: the CORDIOPREV (Coronary Diet Intervention with Olive Oil and Cardiovascular Prevention; n = 711), which consisted of Spanish white adults; the GOLDN (Genetics of Lipids Lowering Drugs and Diet Network; n = 818), which involved North American non-Hispanic white adults; and Hispanic adults who participated in the BPRHS (Boston Puerto Rican Health Study; n = 1155). RESULTS Meta-analysis indicated that T carriers presented a lower risk of T2D than CC carriers (pooled OR: 0.714; 95% CI: 0.584, 0.845; P = 0.002). In all 3 populations (CORDIOPREV, GOLDN, and BPRHS), we showed a significant interaction between the rs3014866 single nucleotide polymorphism and dietary SFA:carbohydrate ratio intake for the homeostasis model assessment of insulin resistance (HOMA-IR) (P = 0.028, P = 0.017, and P = 0.026, respectively). CC carriers had a significantly higher HOMA-IR only when SFA:carbohydrate intake was high (P = 0.045 for the CORDIOPREV, P = 0.033 for the GOLDN, and P = 0.046 for the BPRHS) but not when SFA:carbohydrate ratio intake was low. CONCLUSIONS The minor allele (T) of the S100A9 variant rs3014866 is associated with lower T2D risk in 3 populations of different ancestries. Note that individuals with the high-risk CC genotype may be more likely to benefit from a low SFA:carbohydrate ratio intake to improve insulin resistance as evaluated with the use of the HOMA-IR. These trials were registered at clinicaltrials.gov as NCT00924937 (CORDIOPREV), NCT00083369 (GOLDN), and NCT01231958 (BPRHS).

The major royal-jelly proteins (MRJPs) are the main constituents responsible for the specific physiological role of royal jelly (RJ) in honeybees. Male and female Drosophila flies were fed diets containing either no MRJPs (A) or casein (B) at 1.25% (w/w) of diet or MRJPs at 1.25% (C), 2.50% (D), or 5.00% (E). Diets B, C, D, and E increased mean lifespan by 4.3%, 9.0%, 12.4%, and 13.9% in males and by 5.8%, 9.7%, 20.0%, and 11.8% in females in comparison to results from diet A, respectively. The diet supplemented with 2.50% MRJPs seems to have the optimal dose to improve both physiological and biochemical measures related to aging in both sexes. Interestingly, lifespan extension by MRJPs in Drosophila was positively associated with feeding and fecundity and up-regulation of copper and zinc-superoxide dismutase (CuZn-SOD) and the Egfr-mediated signaling pathway. This study provides strong evidence that MRJPs are important components of RJ for prolonging lifespan in Drosophila.

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 (

This is the peer reviewed version of the following article: Carlos Celis-Morales, et al, ‘Physical activity attenuates the effect of the FTO genotype on obesity traits in European adults: The Food4Me study’, Obesity, Vol. 24 (4): 962-969, April 2016, which has been published in final form at DOI: https://doi.org/10.1002/oby.21422. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.

Background: Folate status has been positively associated with cognitive function in many studies; however, some studies have observed associations of poor cognitive outcomes with high folate. In search of an explanation, we hypothesized that the association of folate with cognition would be modified by the interaction of high-folate status with a common 19-bp deletion polymorphism in the dihydrofolate reductase (DHFR) gene. To our knowledge, the cognitive effects of this gene have not been studied previously. Objective: We examined the association between cognitive outcomes with the 19-bp deletion DHFR polymorphism, folate status, and their interaction with high or normal plasma folate. Design: This was a pooled cross-sectional study of the following 2 Boston-based cohorts of community living adults: the Boston Puerto Rican Health Study and the Nutrition, Aging, and Memory in Elders study. Individuals were genotyped for the DHFR 19-bp deletion genotype, and plasma folate status was determined. Cognitive outcomes included the Mini-Mental State Examination, Center for Epidemiologic Studies Depression Scale, and factor scores for the domains of memory, executive function, and attention from a set of cognitive tests. Results: The prevalence of the homozygous deletion (del/del) genotype was 23%. In a multivariable analysis, high folate status (>17.8 ng/mL) was associated with better memory scores than was normal-folate status (fourth–fifth quintiles compared with first–third quintiles: β ± SE = −0.22 ± 0.06, P < 0.01). Carriers of the DHFR del/del genotype had worse memory scores (β ± SE = −0.24 ± 0.10, P < 0.05) and worse executive scores (β = −0.19, P < 0.05) than did those with the del/ins and ins/ins genotypes. Finally, we observed an interaction such that carriers of the del/del genotype with high folate had significantly worse memory scores than those of both noncarriers with high-folate and del/del carriers with normal-folate (β-interaction = 0.26 ± 0.13, P < 0.05). Conclusions: This study identifies a putative gene-nutrient interaction that, if confirmed, would predict that a sizable minority carrying the del/del genotype might not benefit from high-folate status and could see a worsening of memory. An understanding of how genetic variation affects responses to high-folate exposure will help weigh risks and benefits of folate supplementation for individuals and public health.

The triglyceride (TG) response to a high-fat meal (postprandial lipemia, PPL) affects cardiovascular disease risk and is influenced by genes and environment. Genes involved in lipid metabolism have dominated genetic studies of PPL TG response. We sought to elucidate common genetic variants through a genome-wide association (GWA) study in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN).The GOLDN GWAS discovery sample consisted of 872 participants within families of European ancestry. Genotypes for 2,543,887 variants were measured or imputed from HapMap. Replication of our top results was performed in the Heredity and Phenotype Intervention (HAPI) Heart Study (n = 843). PPL TG response phenotypes were constructed from plasma TG measured at baseline (fasting, 0 hour), 3.5 and 6 hours after a high-fat meal, using a random coefficient regression model. Association analyses were adjusted for covariates and principal components, as necessary, in a linear mixed model using the kinship matrix; additional models further adjusted for fasting TG were also performed. Meta-analysis of the discovery and replication studies (n = 1715) was performed on the top SNPs from GOLDN.GOLDN revealed 111 suggestive (p1E-05) associations, with two SNPs meeting GWA significance level (p5E-08). Of the two significant SNPs, rs964184 demonstrated evidence of replication (p = 1.20E-03) in the HAPI Heart Study and in a joint analysis, was GWA significant (p = 1.26E-09). Rs964184 has been associated with fasting lipids (TG and HDL) and is near ZPR1 (formerly ZNF259), close to the APOA1/C3/A4/A5 cluster. This association was attenuated upon additional adjustment for fasting TG.This is the first report of a genome-wide significant association with replication for a novel phenotype, namely PPL TG response. Future investigation into response phenotypes is warranted using pathway analyses, or newer genetic technologies such as metabolomics.

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.

Nutrigenetic research examines the effects of inter-individual differences in genotype on responses to nutrients and other food components, in the context of health and of nutrient requirements. A practical application of nutrigenetics is the use of personal genetic information to guide recommendations for dietary choices that are more efficacious at the individual or genetic subgroup level relative to generic dietary advice. Nutrigenetics is unregulated, with no defined standards, beyond some commercially adopted codes of practice. Only a few official nutrition-related professional bodies have embraced the subject, and, consequently, there is a lack of educational resources or guidance for implementation of the outcomes of nutrigenetic research. To avoid misuse and to protect the public, personalised nutrigenetic advice and information should be based on clear evidence of validity grounded in a careful and defensible interpretation of outcomes from nutrigenetic research studies. Evidence requirements are clearly stated and assessed within the context of state-of-the-art 'evidence-based nutrition'. We have developed and present here a draft framework that can be used to assess the strength of the evidence for scientific validity of nutrigenetic knowledge and whether 'actionable'. In addition, we propose that this framework be used as the basis for developing transparent and scientifically sound advice to the public based on nutrigenetic tests. We feel that although this area is still in its infancy, minimal guidelines are required. Though these guidelines are based on semi-quantitative data, they should stimulate debate on their utility. This framework will be revised biennially, as knowledge on the subject increases. © 2017 The Author(s).

Purpose of review Postprandial lipemia (PPL), the prolonged increase in plasma triglyceride-rich lipoproteins following food consumption, is an independent risk factor for cardiovascular disease. Genetic variation, environment and the interplay between these direct an individual's postprandial lipid response. From such interplay, inducible and reversible epigenetic changes arise. Increasing evidence suggests epigenetic variation contributes to postprandial response in lipids and risk. Recent findings Diet and exercise are central agents affecting postprandial lipemia - triglyceride, but heterogeneity of the findings warrant more and larger studies. Several epigenetic loci identified from a human intervention study account for a substantial proportion of PPL phenotype variation, but the burden to conduct an intervention study of postprandial responses likely limits translation to personalized nutrition. Summary The impact of both DNA methylation patterns and environmental factors such as diet, exercise, sleep and medication on PPL is multifaceted. Discovery of interactions that modify the association between CpG (oligodeoxydinucleotide) methylation and postprandial phenotypes is unfolding.

Pathway analysis is a powerful method for data analysis in genomics, most often applied to gene expression analysis. It is also promising for single-nucleotide polymorphism (SNP) data analysis, such as genome-wide association study data, because it allows the interpretation of variants with respect to the biological processes in which the affected genes and proteins are involved. Such analyses support an interactive evaluation of the possible effects of variations on function, regulation or interaction of gene products. Current pathway analysis software often does not support data visualization of variants in pathways as an alternate method to interpret genetic association results, and specific statistical methods for pathway analysis of SNP data are not combined with these visualization features. In this review, we first describe the visualization options of the tools that were identified by a literature review, in order to provide insight for improvements in this developing field. Tool evaluation was performed using a computational epistatic dataset of gene-gene interactions for obesity risk. Next, we report the necessity to include in these tools statistical methods designed for the pathway-based analysis with SNP data, expressly aiming to define features for more comprehensive pathway-based analysis tools. We conclude by recognizing that pathway analysis of genetic variations data requires a sophisticated combination of the most useful and informative visual aspects of the various tools evaluated.

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.

Lipoprotein lipase (LPL) is a candidate gene for obesity based on its role in triglyceride hydrolysis and the partitioning of fatty acids towards storage or oxidation. Whether dietary fatty acids modify LPL associated obesity risk is unknown.We examined five single nucleotide polymorphisms (SNPs) (rs320, rs2083637, rs17411031, rs13702, rs2197089) for potential interaction with dietary fatty acids for obesity traits in 1171 participants (333 men and 838 women, aged 45-75 y) of the Boston Puerto Rican Health Study (BPRHS). In women, SNP rs320 interacted with dietary polyunsaturated fatty acids (PUFA) for body mass index (BMI) (P = 0.002) and waist circumference (WC) (P = 0.001) respectively. Higher intake of PUFA was associated with lower BMI and WC in homozygotes of the major allele (TT) (P = 0.01 and 0.005) but not in minor allele carriers (TG and GG). These interactions were replicated in an independent population, African American women of the Atherosclerosis Risk in Communities (ARIC) study (n = 1334).Dietary PUFA modulated the association of LPL rs320 with obesity traits in two independent populations. These interactions may be relevant to the dietary management of obesity, particularly in women.

In murine schistosomiasis, immunopathology and cytokine production in response to parasite eggs are uneven and strain dependent. CBA/J (CBA) mice develop severe hepatic granulomatous inflammation associated with prominent Th17 cell responses driven by dendritic cell (DC)-derived IL-1β and IL-23. Such Th17 cells fail to develop in low-pathology C57BL/6 (BL/6) mice, and the reasons for these strain-specific differences in APC reactivity to eggs remain unclear. We show by gene profiling that CBA DCs display an 18-fold higher expression of the C-type lectin receptor CD209a, a murine homolog of human DC-specific ICAM-3–grabbing nonintegrin, compared with BL/6 DCs. Higher CD209a expression was observed in CBA splenic and granuloma APC subpopulations, but only DCs induced Th17 cell differentiation in response to schistosome eggs. Gene silencing in CBA DCs and overexpression in BL/6 DCs demonstrated that CD209a is essential for egg-elicited IL-1β and IL-23 production and subsequent Th17 cell development, which is associated with SRC, RAF-1, and ERK1/2 activation. These findings reveal a novel mechanism controlling the development of Th17 cell–mediated severe immunopathology in helminthic disease.

OBJECTIVES: To identify genetic and dietary factors, and their interactions that contribute to type 2 diabetes (T2D) and predict an individual's risk to design more precise prevention and treatment strategies. METHODS: A genome-wide scan for up to three-way interactions between 717,275 single nucleotide polymorphisms (SNPs), and 139 dietary and lifestyle factors was conducted on 1380 participants of the Boston Puerto Rican Health Study using the Generalized Multifactor Dimensionality Reduction (GMDR) method. Based on identified genetic and dietary factors, we then used machine learning (ML) to predict T2D risk, and the accuracy of prediction was assessed using area under the Receiver Operating Characteristic curve (ROC-AUC). RESULTS: A genome-wide scan for main effects and up to three-way interactions between SNPs and dietary factors using GMDR identified a set of 818 SNPs and 12 dietary factors that were selected for the prediction of T2D incidence. Comparing several ML algorithms, we found that stochastic gradient boosting provided the best prediction accuracy of T2D incidence with ROC-AUC of 0.93 in the training set, and overall accuracy of 85% based on test set validation. This approach identified that 52 SNPs in 37 genes, three food groups of high sugar content, and age were key predictors of the best-fit model. CONCLUSIONS: This study illustrates a powerful methodology that can predict incidence of T2D based on gene-gene and gene-environment interactions in combination with machine learning. This genome-wide approach allows identification of those diet and lifestyle factors that interact with genotype and can inform personalized nutrition strategies for the prevention and treatment of T2D. FUNDING SOURCES: This work was funded by the US Department of Agriculture, under agreement no. 8050-51000-098-00D, and NIH grants P01 AG023394, P50 HL105185, and R01 AG027087.