Identifying Metabolomic Profiles of Insulinemic Dietary Patterns.
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ABSTRACT:
SUBMITTER: Tabung FK
PROVIDER: S-EPMC6630814 | biostudies-other | 2019 Jun
REPOSITORIES: biostudies-other
Publications
Identifying Metabolomic Profiles of Insulinemic Dietary Patterns.
Tabung Fred K FK Balasubramanian Raji R Liang Liming L Clinton Steven K SK Cespedes Feliciano Elizabeth M EM Manson JoAnn E JE Van Horn Linda L Wactawski-Wende Jean J Clish Clary B CB Giovannucci Edward L EL Rexrode Kathryn M KM
Metabolites 20190624 6
The food-based empirical dietary index for hyperinsulinemia (EDIH) score assesses the insulinemic potential of diet. This cross-sectional study evaluated associations between EDIH scores from food frequency questionnaires with c-peptide concentrations and with 448 metabolites, from fasting plasma samples, in multivariable linear regression analyses. Metabolites were measured with liquid chromatography tandem mass spectroscopy. Using a robust two-stage study design, discovery of metabolite associ ...[more]
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Project description:Background: Clinical nutrition research often lacks robust markers of compliance, complicating the interpretation of clinical trials and observational studies of free-living subjects.Objective: We aimed to examine metabolomics profiles in response to 3 diets that differed widely in macronutrient composition during a controlled feeding protocol.Design: Twenty-one adults with a high body mass index (in kg/m2; mean ± SD: 34.4 ± 4.9) were given hypocaloric diets to promote weight loss corresponding to 10-15% of initial body weight. They were then studied during weight stability while consuming 3 test diets, each for a 4-wk period according to a crossover design: low fat (60% carbohydrate, 20% fat, 20% protein), low glycemic index (40% carbohydrate, 40% fat, 20% protein), or very-low carbohydrate (10% carbohydrate, 60% fat, 30% protein). Plasma samples were obtained at baseline and at the end of each 4-wk period in the fasting state for metabolomics analysis by using liquid chromatography-tandem mass spectrometry. Statistical analyses included adjustment for multiple comparisons.Results: Of 333 metabolites, we identified 152 whose concentrations differed for ?1 diet compared with the others, including diacylglycerols and triacylglycerols, branched-chain amino acids, and markers reflecting metabolic status. Analysis of groups of related metabolites, with the use of either principal components or pathways, revealed coordinated metabolic changes affected by dietary composition, including pathways related to amino acid metabolism. We constructed a classifier using the metabolites that differed between diets and were able to correctly identify the test diet from metabolite profiles in 60 of 63 cases (>95% accuracy). Analyses also suggest differential effects by diet on numerous cardiometabolic disease risk factors.Conclusions: Metabolomic profiling may be used to assess compliance during clinical nutrition trials and the validity of dietary assessment in observational studies. In addition, this methodology may help elucidate mechanistic pathways linking diet to chronic disease risk. This trial was registered at clinicaltrials.gov as NCT00315354.
| S-EPMC5320413 | biostudies-literature
Project description:BackgroundWe previously showed that a food-based empirical dietary inflammatory pattern (EDIP) score is associated with circulating inflammatory biomarkers. Metabolomic profiling of inflammatory diets may therefore provide insights on mechanisms contributing to disease etiology and prognosis. We aimed to elucidate metabolites associated with inflammatory diets among postmenopausal women, utilizing a robust study design that incorporates independent discovery and validation datasets.MethodsThis baseline cross-sectional investigation evaluated associations between continuous EDIP scores calculated from food frequency questionnaires and 448 log-transformed plasma metabolites as outcomes in multivariable-adjusted linear regression analyses. Metabolites were measured with liquid chromatography tandem mass spectroscopy. Metabolite discovery was conducted among 1109 Women's Health Initiative (WHI) Hormone Therapy trial participants and results were replicated in an independent dataset of 810 WHI Observational Study participants. Secondary analyses were stratified by standard body mass index (BMI, kg/m2) categories. In discovery and replication datasets statistical significance was based on false-discovery rate adjusted P < 0.05.ResultsAfter adjusting for energy intake, BMI, physical activity, and other confounding variables, 23 metabolites were significantly associated with EDIP score in the discovery dataset. Of these, the following ten were replicated: trigonelline, caffeine, acethylamino-6-amino-3-methyluracil, 7-methylxanthine, 1,7-dimethyluric acid, 3-methylxanthine, C18:3CE, glycine, associated with lower dietary inflammatory potential; whereas C52:3 triacylglycerol and linoleate associated with higher dietary inflammatory potential. Four of the ten were associated [glycine (inversely), caffeine, 1,7-dimethyluric acid, C52:3 triacylglycerol, (positively)], with C-reactive protein levels. In secondary analyses, associations showed differences by BMI category. Four metabolites, related to coffee/caffeine metabolism were inversely associated among normal weight women, and 83 metabolites associated with EDIP among overweight/obese women, including 40 (48%) that were also associated with C-reactive protein.ConclusionMetabolites associated with coffee/caffeine and lipid metabolism may reflect the inflammatory potential of diet. Potential differences by BMI and the linkage to disease outcomes, require further study.
| S-EPMC6918009 | biostudies-literature
Project description:Background and objectivesMetabolomics is a relatively new field of "-omics" research, focusing on high-throughput identification of small molecular weight metabolites. Diet has both acute and chronic effects on metabolic profiles; however, alterations in response to dietary sodium restriction (DSR) are completely unknown. The goal of this study was to explore changes in urine metabolites in response to DSR, as well as their association with previously reported improvements in vascular function with DSR.Design, setting, participants, & measurementsUsing stored urine samples from a 10-week randomized placebo-controlled crossover study of DSR in 17 middle-aged/older adults (six men and 11 women; mean age 62±8 years) who had moderately elevated systolic BP (130-159 mmHg) and were otherwise healthy, a liquid chromatography/mass spectrometry-based analysis of 289 metabolites was performed. This study identified metabolites that were significantly altered between the typical (153±29 mmol/d) and low (70±29 mmol/d) sodium conditions, as well as their baseline (typical sodium) association with responsiveness to previously reported improvements in vascular endothelial function (brachial artery flow-mediated dilation) and large elastic artery stiffness (aortic pulse wave velocity).ResultsOf the 289 metabolites surveyed, 10 were significantly altered (nine were upregulated and one was downregulated) during the low sodium condition, and eight of these exceeded our prespecified clinically significant threshold of a >40% change. These metabolites were involved in biologic pathways broadly related to cardiovascular risk, nitric oxide production, oxidative stress, osmotic regulation, and metabolism. One metabolite, serine, was independently (positively) associated with previously reported improvements in the primary vascular outcome of brachial artery flow-mediated dilation.ConclusionsThis proof-of-concept study provides the first evidence that DSR is a stimulus that induces significant changes in urinary metabolomic profiles. Moreover, serine was independently associated with corresponding changes in vascular endothelial function after DSR. Larger follow-up studies will be required to confirm and further elucidate the metabolic pathways that are altered in response to DSR.
| S-EPMC4491302 | biostudies-literature
Project description:BACKGROUND:Healthy dietary patterns that conform to national dietary guidelines are related to lower chronic disease incidence and longer life span. However, the precise mechanisms involved are unclear. Identifying biomarkers of dietary patterns may provide tools to validate diet quality measurement and determine underlying metabolic pathways influenced by diet quality. OBJECTIVE:The objective of this study was to examine the correlation of 4 diet quality indexes [the Healthy Eating Index (HEI) 2010, the Alternate Mediterranean Diet Score (aMED), the WHO Healthy Diet Indicator (HDI), and the Baltic Sea Diet (BSD)] with serum metabolites. DESIGN:We evaluated dietary patterns and metabolites in male Finnish smokers (n = 1336) from 5 nested case-control studies within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study cohort. Participants completed a validated food-frequency questionnaire and provided a fasting serum sample before study randomization (1985-1988). Metabolites were measured with the use of mass spectrometry. We analyzed cross-sectional partial correlations of 1316 metabolites with 4 diet quality indexes, adjusting for age, body mass index, smoking, energy intake, education, and physical activity. We pooled estimates across studies with the use of fixed-effects meta-analysis with Bonferroni correction for multiple comparisons, and conducted metabolic pathway analyses. RESULTS:The HEI-2010, aMED, HDI, and BSD were associated with 23, 46, 23, and 33 metabolites, respectively (17, 21, 11, and 10 metabolites, respectively, were chemically identified; r-range: -0.30 to 0.20; P = 6 × 10-15 to 8 × 10-6). Food-based diet indexes (HEI-2010, aMED, and BSD) were associated with metabolites correlated with most components used to score adherence (e.g., fruit, vegetables, whole grains, fish, and unsaturated fat). HDI correlated with metabolites related to polyunsaturated fat and fiber components, but not other macro- or micronutrients (e.g., percentages of protein and cholesterol). The lysolipid and food and plant xenobiotic pathways were most strongly associated with diet quality. CONCLUSIONS:Diet quality, measured by healthy diet indexes, is associated with serum metabolites, with the specific metabolite profile of each diet index related to the diet components used to score adherence. This trial was registered at clinicaltrials.gov as NCT00342992.
| S-EPMC5267308 | biostudies-literature
Project description:BackgroundRecent studies, primarily in non-Hispanic whites, suggest that dietary patterns have distinct metabolomic signatures that may influence disease risk. However, evidence in South Asians, a group with unique dietary patterns and a high prevalence of cardiometabolic risk, is lacking.ObjectiveWe investigated the metabolomic profiles associated with 2 distinct dietary patterns among a sample of Asian Indians living in the United States. We also examined the cross-sectional associations between metabolomic profiles and cardiometabolic risk markers.MethodsWe used cross-sectional data from 145 Asian Indians, aged 45-79 y, in the Metabolic Syndrome and Atherosclerosis in South Asians Living in America (MASALA) pilot study. Metabolomic profiles were measured from fasting serum samples. Usual diet was assessed by using a validated food-frequency questionnaire. We used principal components analysis to derive dietary and metabolomic patterns. We used adjusted general linear regression models to examine associations between dietary patterns, individual food groups, metabolite patterns, and cardiometabolic risk markers.ResultsWe observed 2 major principal components or metabolite clusters, the first comprised primarily of medium- to long-chain acylcarnitines (metabolite pattern 1) and the second characterized by branched-chain amino acids, aromatic amino acids, and short-chain acylcarnitines (metabolite pattern 2). A "Western/nonvegetarian" pattern was significantly and positively associated with metabolite pattern 2 (all participants: ? ± SE = 0.180 ± 0.090, P = 0.05; participants without type 2 diabetes: ? ± SE = 0.323 ± 0.090, P = 0.0005). In all participants, higher scores on metabolite pattern 2 were adversely associated with measures of glycemia (fasting insulin: ? ± SE = 2.91 ± 1.29, P = 0.03; 2-h insulin: ? ± SE = 22.1 ± 10.3, P = 0.03; homeostasis model assessment of insulin resistance: ? ± SE = 0.94 ± 0.42, P = 0.03), total adiponectin (? ± SE = -1.46 ± 0.47, P = 0.002), lipids (total cholesterol: ? ± SE = 7.51 ± 3.45, P = 0.03; triglycerides: ? ± SE = 14.4 ± 6.67, P = 0.03), and a radiographic measure of hepatic fat (liver-to-spleen attenuation ratio: ? ± SE = -0.83 ± 0.42, P = 0.05).ConclusionsOur findings suggest that a "Western/nonvegetarian" dietary pattern is associated with a metabolomic profile that is related to an adverse cardiometabolic profile in Asian Indians. Public health efforts to reduce cardiometabolic disease burden in this high-risk group should focus on consuming a healthy plant-based diet.
| S-EPMC6251517 | biostudies-literature
Project description:Data on proteomic and metabolomic signatures of healthy dietary patterns are limited. We evaluated the cross-sectional association of serum proteomic and metabolomic markers with three dietary patterns: the Alternative Healthy Eating Index (AHEI), the Dietary Approaches to Stop Hypertension (DASH) diet; and a Mediterranean-style (MDS) diet. We examined participants from the Framingham Offspring Study (mean age; 55 years; 52% women) who had complete proteomic (n = 1713) and metabolomic (n = 2284) data; using food frequency questionnaires to derive dietary pattern indices. Proteins and metabolites were quantified using the SomaScan platform and liquid chromatography/tandem mass spectrometry; respectively. We used multivariable-adjusted linear regression models to relate each dietary pattern index (independent variables) to each proteomic and metabolomic marker (dependent variables). Of the 1373 proteins; 103 were associated with at least one dietary pattern (48 with AHEI; 83 with DASH; and 8 with MDS; all false discovery rate [FDR] ? 0.05). We identified unique associations between dietary patterns and proteins (17 with AHEI; 52 with DASH; and 3 with MDS; all FDR ? 0.05). Significant proteins enriched biological pathways involved in cellular metabolism/proliferation and immune response/inflammation. Of the 216 metabolites; 65 were associated with at least one dietary pattern (38 with AHEI; 43 with DASH; and 50 with MDS; all FDR ? 0.05). All three dietary patterns were associated with a common signature of 24 metabolites (63% lipids). Proteins and metabolites associated with dietary patterns may help characterize intermediate phenotypes that provide insights into the molecular mechanisms mediating diet-related disease. Our findings warrant replication in independent populations.
| S-EPMC7284467 | biostudies-literature
Project description:MotivationCollection of spatial signals in large numbers has become a routine task in multiple omicsfields, but parsing of these rich data sets still pose certain challenges. In whole or near-full transcriptome spatial techniques, spurious expression profiles are intermixed with those exhibiting an organized structure. To distinguish profiles with spatial patterns from the background noise, a metric that enables quantification of spatial structure is desirable. Current methods designed for similar purposes tend to be built around a framework of statistical hypothesis testing, hence we were compelled to explore a fundamentally different strategy.ResultsWe propose an unexplored approach to analyze spatial transcriptomics data, simulating diffusion of individual transcripts to extract genes with spatial patterns. The method performed as expected when presented with synthetic data. When applied to real data, it identified genes with distinct spatial profiles, involved in key biological processes or characteristic for certain cell types. Compared to existing methods, ours seemed to be less informed by the genes' expression levels and showed better time performance when run with multiple cores.AvailabilityOpen-source Python package with a command line interface (CLI), freely available at https://github.com/almaan/sepal under a MIT licence. A mirror of the GitHub repository can be found at Zenodo, doi: 10.5281/zenodo.4573237.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8428601 | biostudies-literature
Project description:OBJECTIVE:The present study aimed to identify dietary patterns and explore their associations with blood lipid profiles among Chinese women. DESIGN:In a cross-sectional study, we identified dietary patterns using principal component analysis of data from three consecutive 24 h dietary recalls. The China Health and Nutrition Survey (CHNS) collected blood samples in the morning after an overnight fast and measured total cholesterol (TC), HDL cholesterol (HDL-C), LDL cholesterol (LDL-C) and TAG. SETTING:Data were from the 2009 wave of the CHNS. SUBJECTS:We studied 2468 women aged 18-80 years from the CHNS. RESULTS:We identified three dietary patterns: traditional southern (high intakes of rice, pork and vegetables), snack (high intakes of fruits, eggs and cakes) and Western (high intakes of poultry, fast foods and milk). The traditional southern pattern was inversely associated with HDL-C (?=-0·68; 95 % CI -1·22, -0·14; P<0·05). The snack pattern was significantly associated with higher TAG (?=4·14; 95 % CI 0·44, 7·84; P<0·05). The Western pattern was positively associated with TC (?=2·52; 95 % CI 1·03, 4·02; P<0·01) and LDL-C (?=2·26; 95 % CI 0·86, 3·66; P<0·01). CONCLUSIONS:We identified three dietary patterns that are significantly associated with blood lipid profiles. This information is important for developing interventions and policies addressing dyslipidaemia prevention among Chinese women.
| S-EPMC6383153 | biostudies-literature
Project description:BackgroundIn individuals with chronic kidney disease (CKD), healthy dietary patterns are inversely associated with CKD progression. Metabolomics, an approach that measures many small molecules in biofluids, can identify biomarkers of healthy dietary patterns.ObjectivesWe aimed to identify known metabolites associated with greater adherence to 4 healthy dietary patterns in CKD patients.MethodsWe examined associations between 486 known plasma metabolites and Healthy Eating Index (HEI)-2015, Alternative Healthy Eating Index (AHEI)-2010, the Dietary Approaches to Stop Hypertension (DASH) diet, and alternate Mediterranean diet (aMED) in 1056 participants (aged 21-74 y at baseline) in the Chronic Renal Insufficiency Cohort (CRIC) Study. Usual dietary intake was assessed using a semiquantitative FFQ. We conducted multivariable linear regression models to study associations between healthy dietary patterns and individual plasma metabolites, adjusting for sociodemographic characteristics, health behaviors, and clinical factors. We used principal component analysis to identify groups of metabolites associated with individual food components within healthy dietary patterns.ResultsAfter Bonferroni correction, we identified 266 statistically significant diet-metabolite associations (HEI: n = 60; AHEI: n = 78; DASH: n = 77; aMED: n = 51); 78 metabolites were associated with >1 dietary pattern. Lipids with a longer acyl chain length and double bonds (unsaturated) were positively associated with all 4 dietary patterns. A metabolite pattern low in saturated diacylglycerols and triacylglycerols, and a pattern high in unsaturated triacylglycerols was positively associated with intake of healthy food components. Plasmalogens were negatively associated with the consumption of nuts and legumes and healthy fat, and positively associated with the intake of red and processed meat.ConclusionsWe identified many metabolites associated with healthy dietary patterns, indicative of food consumption. If replicated, these metabolites may be considered biomarkers of healthy dietary patterns in patients with CKD.
| S-EPMC8485904 | biostudies-literature
Project description:In a large cohort of older women, a mechanism-driven statistical technique for assessing dietary patterns that considers a potential nutrient pathway found two dietary patterns associated with lumbar spine and femoral neck bone mineral density. A "healthy" dietary pattern was observed to be beneficial for bone mineral density.IntroductionDietary patterns represent a broader, more realistic representation of how foods are consumed, compared to individual food or nutrient analyses. Partial least-squares (PLS) is a data-reduction technique for identifying dietary patterns that maximizes correlation between foods and nutrients hypothesized to be on the path to disease, is more hypothesis-driven than previous methods, and has not been applied to the study of dietary patterns in relation to bone health.MethodsWomen from the Aberdeen Prospective Osteoporosis Screening Study (2007-2011, n = 2129, age = 66 years (2.2)) provided dietary intake using a food frequency questionnaire; 37 food groups were created. We applied PLS to the 37 food groups and 9 chosen response variables (calcium, potassium, vitamin C, vitamin D, protein, alcohol, magnesium, phosphorus, zinc) to identify dietary patterns associated with bone mineral density (BMD) cross-sectionally. Multivariable regression was used to assess the relationship between the retained dietary patterns and BMD at the lumbar spine and femoral neck, adjusting for age, body mass index, physical activity level, smoking, and national deprivation category.ResultsFive dietary patterns were identified, explaining 25% of the variation in food groups and 77% in the response variables. Two dietary patterns were positively associated with lumbar spine (per unit increase in factor 2: 0.012 g/cm2 [95% CI: 0.006, 0.01]; factor 4: 0.007 g/cm2 [95% CI: 0.00001, 0.01]) and femoral neck (factor 2: 0.006 g/cm2 [95% CI: 0.002, 0.01]; factor 4: 0.008 g/cm2 [95% CI: 0.003, 0.01)]) BMD. Dietary pattern 2 was characterized by high intakes of milk, vegetables, fruit and vegetable juices, and wine, and low intakes of processed meats, cheese, biscuits, cakes, puddings, confectionary, sweetened fizzy drinks and spirits while dietary pattern 4 was characterized by high intakes of fruits, red and white meats, and wine, and low intakes of vegetables and sweet spreads.ConclusionOur findings using a robust statistical technique provided important support to initiatives focusing on what constitutes a healthy diet and its implications.
| S-EPMC5506508 | biostudies-literature