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Metabolite Signatures of Metabolic Risk Factors and their Longitudinal Changes.

ABSTRACT: Metabolic dysregulation underlies key metabolic risk factors—obesity, dyslipidemia, and dysglycemia.To uncover mechanistic links between metabolomic dysregulation and metabolic risk by testing metabolite associations with risk factors cross-sectionally and with risk factor changes over time.Cross-sectional—discovery samples (n = 650; age, 36–69 years) from the Framingham Heart Study (FHS) and replication samples (n = 670; age, 61–76 years) from the BioImage Study, both following a factorial design sampled from high vs low strata of body mass index, lipids, and glucose. Longitudinal—FHS participants (n = 554) with 5–7 years of follow-up for risk factor changes.Observational studies.Cross-sectional samples with or without obesity, dysglycemia, and dyslipidemia, excluding prevalent cardiovascular disease and diabetes or dyslipidemia treatment. Age- and sex-matched by group.None.Gas chromatography-mass spectrometry detected 119 plasma metabolites. Cross-sectional associations with obesity, dyslipidemia, and dysglycemia were tested in discovery, with external replication of 37 metabolites. Single- and multi-metabolite markers were tested for association with longitudinal changes in risk factors.Cross-sectional metabolite associations were identified with obesity (n = 26), dyslipidemia (n = 21), and dysglycemia (n = 11) in discovery. Glutamic acid, lactic acid, and sitosterol associated with all three risk factors in meta-analysis (P < 4.5 × 10?4). Metabolites associated with longitudinal risk factor changes were enriched for bioactive lipids. Multi-metabolite panels explained 2.5–15.3% of longitudinal changes in metabolic traits.Cross-sectional results implicated dysregulated glutamate cycling and amino acid metabolism in metabolic risk. Certain bioactive lipids were associated with risk factors cross-sectionally and over time, suggesting their upstream role in risk factor progression. Functional studies are needed to validate findings and facilitate translation into treatments or preventive measures.

SUBMITTER: Yin X

PROVIDER: S-EPMC4880163 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Metabolite Signatures of Metabolic Risk Factors and their Longitudinal Changes.

Yin Xiaoyan X Subramanian Subha S Willinger Christine M CM Chen George G Juhasz Peter P Courchesne Paul P Chen Brian H BH Li Xiaohang X Hwang Shih-Jen SJ Fox Caroline S CS O'Donnell Christopher J CJ Muntendam Pieter P Fuster Valentin V Bobeldijk-Pastorova Ivana I Sookoian Silvia C SC Pirola Carlos J CJ Gordon Neal N Adourian Aram A Larson Martin G MG Levy Daniel D

The Journal of clinical endocrinology and metabolism 20160223 4

<h4>Context</h4>Metabolic dysregulation underlies key metabolic risk factors—obesity, dyslipidemia, and dysglycemia.<h4>Objective</h4>To uncover mechanistic links between metabolomic dysregulation and metabolic risk by testing metabolite associations with risk factors cross-sectionally and with risk factor changes over time.<h4>Design</h4>Cross-sectional—discovery samples (n = 650; age, 36–69 years) from the Framingham Heart Study (FHS) and replication samples (n = 670; age, 61–76 years) from th ...[more]

PMID: 26908103

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Project description:BackgroundIncidence of childhood high blood pressure (BP) is increasing worldwide. This study examined the longitudinal relationship between changes in body composition (i.e. body mass index (BMI), waist circumference (WC) and percentage body fat) and selected metabolic risk factors (abdominal obesity and BP) among adolescents from the Tlokwe municipality in the North West Province of South Africa.MethodOne hundred and eight-six adolescents (81 boys and 105 girls) aged 14 to 16 years participated in the study. Body composition was measured following the International Society of the Advancement of Kinanthropometry standard procedures. BMI, abdominal obesity using WC measurement, and resting BP were determined. Analysis of variance (ANOVA) for repeated measures was calculated to determine changes in anthropometric measures and body composition as well as changes in BP. Additionally, Univariate analysis of variance with repeated measures and participants as a random sample was applied. Diastolic BP (DBP) and systolic BP (SBP) were used as dependent variables and sex, age, BMI, WC, and waist-to-height ratio as independent variables.ResultsSignificant changes were found for stature, BMI, body mass, WC, SBP, and DBP. BMI for the total group was significant and positively related to abdominal obesity in 2012 (r = 0.55; p < 0.01) and in 2013 (r = 0.77; p < 0.01) and to SBP (r = 0.26; p < 0.05) in 2012 and (r = 0.17; p = 0.43) in 2013. BMI among the boys was significantly and positively related to abdominal obesity in 2012 (r = 0.83; p < 0.01) and 2013 (r = 0.91; p < 0.01). For the girls, BMI was significantly and positively related to abdominal obesity (r = 0.49; p < 0.01) and to SBP (r = 0.32; p = 0.05) in 2012. Boys with a higher WC in 2012 had significantly increased DBP (p < 0.05). Boys measured in 2012 with greater WC and BMI show a significant increase in SBP.ConclusionsBMI was positively related to BP and abdominal obesity over time. Relatively high BMI and abdominal obesity significantly increased the likelihood of elevated BP over time, especially in boys. BMI was a predictor of abdominal obesity in boys, while in girls, BMI was a predictor of both abdominal obesity and SBP. In view of the future health implications of both abdominal obesity and elevated BP, urgent strategic interventions programs aimed at increasing physical activity and advocating for well-balanced dietary practices as well as importance of keeping normal blood pressure among South African adolescents are needed.

Project description:Aims/hypothesisDespite recommendations to screen women with diabetes risk factors for hyperglycaemia in the first trimester, criteria for normal glucose values in early pregnancy have not been firmly established. We aimed to compare glucose levels in early pregnancy with those later in gestation and outside of pregnancy in women with diabetes risk factors.MethodsIn pregnant women (N = 123) followed longitudinally through the postpartum period, and a separate cohort of non-pregnant women (N = 65), we performed 75 g oral glucose tolerance tests. All participants had one or more risk factors for diabetes. Using linear regression, we tested for differences in glucose levels between non-pregnant and pregnant women at early (7-15 weeks) and mid-late (24-32 weeks) gestation as well as postpartum, with adjustment for maternal age, parity, marital status and BMI. In a longitudinal analysis using mixed-effects models, we tested for differences in glucose levels across early and mid-late pregnancy compared with postpartum. Differences are expressed as β (95% CI).ResultsFasting glucose was lower in pregnant compared with non-pregnant women by 0.34 (0.18, 0.51) mmol/l (p < 0.0001) in early pregnancy and by 0.45 (0.29, 0.61) mmol/l (p < 0.0001) in mid-late pregnancy. In longitudinal models, fasting glucose was lower by 0.13 (0.04, 0.21) mmol/l (p = 0.003) in early pregnancy and by 0.16 (0.08, 0.25) mmol/l (p = 0.0003) in mid-late pregnancy compared with the same women postpartum. Early pregnancy post-load glucose levels did not differ from those in non-pregnant women or the same women postpartum. In mid-late pregnancy, compared with non-pregnant women, elevations in 1 h post-load glucose level (0.60 [-0.12, 1.33] mmol/l, p = 0.10) and 2 h post-load glucose (0.49 [-0.21, 1.19] mmol/l, p = 0.17) were not statistically significant. However, in longitudinal analyses, 1 h and 2 h post-load glucose levels were higher in mid-late pregnancy (by 0.78 [0.35, 1.21] mmol/l, p = 0.0004, and 0.67 [0.30, 1.04] mmol/l, p = 0.0005, respectively) when compared with postpartum.Conclusions/interpretationIn women with diabetes risk factors, fasting glucose declines in the first trimester. Post-load glucose increases later in pregnancy. These findings may inform criteria for diagnosing hyperglycaemia early in pregnancy.

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Metabolite Signatures of Metabolic Risk Factors and their Longitudinal Changes.

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Metabolite Signatures of Metabolic Risk Factors and their Longitudinal Changes.

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