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Blood biomarkers of various dietary patterns correlated with metabolic indicators in Taiwanese type 2 diabetes.

ABSTRACT: Background:Metabolic alterations correlate with adverse outcomes in type 2 diabetes. Dietary modification serves as an integral part in its treatment. Objective:We examined the relationships among dietary patterns, dietary biomarkers, and metabolic indicators in type 2 diabetes (n = 871). Design:Diabetic patients (n = 871) who provided complete clinical and dietary data in both 2008 and 2009 were selected from a cohort participating in a diabetic control study in Taiwan. Dietary data were obtained using a short, semiquantitative food frequency questionnaires, and dietary pattern identified by factor analysis. Multiple linear regressions were used to analyze the association between dietary biomarkers (ferritin, folate, and erythrocyte n-3 polyunsaturated fatty acids [n-3 PUFAs]) and metabolic control upon adjusting for confounders. Results:Three dietary patterns (high-fat meat, traditional Chinese food-snack, and fish-vegetable) were identified. Ferritin correlated positively with high-fat meat factor scores (P for trend <0.001). Erythrocyte n-3 PUFAs (eicosapentaenoic acid [EPA] + docosahexaenoic acid [DHA], n-3/n-6 PUFA ratio) correlated positively with fish-vegetable factor scores (all P for trends <0.001). Multiple linear regressions revealed a positive relationship between ferritin concentrations and fasting plasma glucose (FPG), hemoglobin A1c (HbA1c), and triglycerides, but a negative relationship with high-density lipoprotein cholesterol (HDL-C). Erythrocyte n-3 PUFA, EPA+DHA, and n-3/n-6 PUFA ratio were negatively linked to FPG, HbA1c, and triglycerides (all P < 0.05) and positively with HDL-C (though n-3/n-6 ratio marginally correlated). Conclusions:Ferritin and n-3 PUFA can serve as valid biomarkers for high-fat meat and fish-vegetable dietary patterns. Unlike ferritin, erythrocyte n-3 PUFA status was related to better glycemic and blood lipid profiles. Our results suggest that habitual consumption of diet pattern rich in fish and vegetables may contribute in part to a healthier metabolic profile in type 2 diabetes.

SUBMITTER: Huang MC

PROVIDER: S-EPMC6878969 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundHealthy 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.ObjectiveThe 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.DesignWe 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.ResultsThe 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.ConclusionsDiet 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.

Project description:Gestational diabetes mellitus (GDM) “program” an elevated risk of metabolic syndrome in the offspring. Epigenetic alterations are a suspected mechanism. GDM has been associated with placental DNA methylation changes in some epigenome-wide association studies. It remains unclear which genes or pathways are affected, and whether any placental differential gene methylations are correlated to fetal growth or circulating metabolic health biomarkers. In an epigenome-wide association study using the Infinium MethylationEPIC Beadchip, we sought to identify genome-wide placental differentially methylated genes and enriched pathways in GDM, and to assess the correlations with fetal growth and metabolic health biomarkers in cord blood. The study samples were 30 pairs of term placentas in GDM vs. euglycemic pregnancies (controls) matched by infant sex and gestational age at delivery in the Shanghai Birth Cohort. Cord blood metabolic health biomarkers included insulin, C-peptide, proinsulin, IGF-I, IGF-II, leptin and adiponectin. Adjusting for maternal age, pre-pregnancy BMI, parity, mode of delivery and placental cell type heterogeneity, 256 differentially methylated positions (DMPs,130 hypermethylated and 126 hypomethylated) were detected between GDM and control groups accounting for multiple tests with false discovery rate <0.05 and beta-value difference >0.05. WSCD2 was identified as a differentially methylated gene in both site- and region-level analyses. We validated 7 hypermethylated (CYP1A2, GFRA1, HDAC4, LIMS2, NAV3, PAX6, UPK1B) and 10 hypomethylated (DPP10, CPLX1, CSMD2, GPR133, NRXN1, PCSK9, PENK, PRDM16, PTPRN2, TNXB) genes reported in previous epigenome-wide association studies. We did not find any enriched pathway accounting for multiple tests. DMPs in 11 genes (CYP2D7P1, PCDHB15, ERG, SIRPB1, DKK2, RAPGEF5, CACNA2D4, PCSK9, TSNARE1, CADM2, KCNAB2) were correlated with birth weight (z score) accounting for multiple tests. There were no significant correlations between placental gene methylations and cord blood biomarkers. In conclusions, GDM was associated with DNA methylation changes in a number of placental genes, but these placental gene methylations were uncorrelated to the observed metabolic health biomarkers (fetal growth factors, leptin and adiponectin) in cord blood. We validated 17 differentially methylated placental genes in GDM, and identified 11 differentially methylated genes relevant to fetal growth.

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Blood biomarkers of various dietary patterns correlated with metabolic indicators in Taiwanese type 2 diabetes.

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