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1.
Sci Rep ; 9(1): 11623, 2019 Aug 12.
Artigo em Inglês | MEDLINE | ID: mdl-31406173

RESUMO

Telomere shortening has been associated with multiple age-related diseases such as cardiovascular disease, diabetes, and dementia. However, the biological mechanisms responsible for these associations remain largely unknown. In order to gain insight into the metabolic processes driving the association of leukocyte telomere length (LTL) with age-related diseases, we investigated the association between LTL and serum metabolite levels in 7,853 individuals from seven independent cohorts. LTL was determined by quantitative polymerase chain reaction and the levels of 131 serum metabolites were measured with mass spectrometry in biological samples from the same blood draw. With partial correlation analysis, we identified six metabolites that were significantly associated with LTL after adjustment for multiple testing: lysophosphatidylcholine acyl C17:0 (lysoPC a C17:0, p-value = 7.1 × 10-6), methionine (p-value = 9.2 × 10-5), tyrosine (p-value = 2.1 × 10-4), phosphatidylcholine diacyl C32:1 (PC aa C32:1, p-value = 2.4 × 10-4), hydroxypropionylcarnitine (C3-OH, p-value = 2.6 × 10-4), and phosphatidylcholine acyl-alkyl C38:4 (PC ae C38:4, p-value = 9.0 × 10-4). Pathway analysis showed that the three phosphatidylcholines and methionine are involved in homocysteine metabolism and we found supporting evidence for an association of lipid metabolism with LTL. In conclusion, we found longer LTL associated with higher levels of lysoPC a C17:0 and PC ae C38:4, and with lower levels of methionine, tyrosine, PC aa C32:1, and C3-OH. These metabolites have been implicated in inflammation, oxidative stress, homocysteine metabolism, and in cardiovascular disease and diabetes, two major drivers of morbidity and mortality.

2.
Sci Total Environ ; 653: 1025-1033, 2019 Feb 25.
Artigo em Inglês | MEDLINE | ID: mdl-30759543

RESUMO

Light is the strongest zeitgeber currently known for the synchronization of the human circadian timing system. Especially shift workers are exposed to altered daily light profiles. Our objective is the characterization of differences in blue-light exposures between day and night shift taking into consideration modifying factors such as chronotype. We describe 24-hour blue-light profiles as measured with ambient light data loggers (LightWatcher) during up to three consecutive days with either day or night shifts in 100 female hospital staff including 511 observations. Linear mixed models were applied to analyze light profiles and to select time-windows for the analysis of associations between shift work, individual factors, and log mean light exposures as well as the duration of darkness per day. Blue-light profiles reflected different daily activities and were mainly influenced by work time. Except for evening (7-9 p.m.), all time windows showed large differences in blue-light exposures between day and night shifts. Night work reduced the duration of darkness per day by almost 4 h (ß^ = -3:48 hh:mm, 95% CI (-4:27; -3.09)). Late chronotypes had higher light exposures in the morning and evening compared to women with intermediate chronotype (e.g. morning ß^ = 0.50 log(mW/m2/nm), 95% CI (0.08; 0.93)). Women with children had slightly higher light exposures in the afternoon than women without children (ß^ = 0.48, 95% CI (-0.10; 1,06)). Time windows for the description of light should be chosen carefully with regard to timing of shifts. Our results are helpful for future studies to capture relevant light exposure differences and potential collinearities with individual factors. Improvement of well-being of shift workers with altered light profiles may therefore require consideration of both - light at the workplace and outside working hours.


Assuntos
Recursos Humanos em Hospital , Exposição à Radiação/análise , Jornada de Trabalho em Turnos , Adulto , Ritmo Circadiano , Feminino , Alemanha , Humanos , Modelos Lineares , Pessoa de Meia-Idade
3.
Metabolites ; 8(3)2018 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-30134533

RESUMO

Night shift work can have a serious impact on health. Here, we assess whether and how night shift work influences the metabolite profiles, specifically with respect to different chronotype classes. We have recruited 100 women including 68 nurses working both, day shift and night shifts for up to 5 consecutive days and collected 3640 spontaneous urine samples. About 424 waking-up urine samples were measured using a targeted metabolomics approach. To account for urine dilution, we applied three methods to normalize the metabolite values: creatinine-, osmolality- and regression-based normalization. Based on linear mixed effect models, we found 31 metabolites significantly (false discovery rate <0.05) affected in nurses working in night shifts. One metabolite, acylcarnitine C10:2, was consistently identified with all three normalization methods. We further observed 11 and 4 metabolites significantly associated with night shift in early and late chronotype classes, respectively. Increased levels of medium- and long chain acylcarnitines indicate a strong impairment of the fatty acid oxidation. Our results show that night shift work influences acylcarnitines and BCAAs, particularly in nurses in the early chronotype class. Women with intermediate and late chronotypes appear to be less affected by night shift work.

4.
Anal Chim Acta ; 1032: 18-31, 2018 Nov 22.
Artigo em Inglês | MEDLINE | ID: mdl-30143216

RESUMO

Urinary analyte data has to be corrected for the sample specific dilution as the dilution varies intra- and interpersonally dramatically, leading to non-comparable concentration measures. Most methods of dilution correction utilized nowadays like probabilistic quotient normalization or total spectra normalization result in a division of the raw data by a dilution correction factor. Here, however, we show that the implicit assumption behind the application of division, log-linearity between the urinary flow rate and the raw urinary concentration, does not hold for analytes which are not in steady state in blood. We explicate the physiological reason for this short-coming in mathematical terms and demonstrate the empirical consequences via simulations and on multiple time-point metabolomic data, showing the insufficiency of division-based normalization procedures to account for the complex non-linear analyte specific dependencies on the urinary flow rate. By reformulating normalization as a regression problem, we propose an analyte specific way to remove the dilution variance via a flexible non-linear regression methodology which then was shown to be more effective in comparison to division-based normalization procedures. In the progress, we developed several, easily applicable methods of normalization diagnostics to decide on the method of dilution correction in a given sample. On the way, we identified furthermore the time-span since last urination as an important variance factor in urinary metabolome data which is until now completely neglected. In conclusion, we present strong theoretical and empirical evidence that normalization has to be analyte specific in dynamically influenced data. Accordingly, we developed a normalization methodology for removing the dilution variance in urinary data respecting the single analyte kinetics.


Assuntos
Urinálise , Feminino , Humanos , Técnicas de Diluição do Indicador , Cinética
5.
Atherosclerosis ; 276: 140-147, 2018 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-30059845

RESUMO

BACKGROUND AND AIMS: Preclinical experiments on animal models are essential to understand the mechanisms of cardiovascular disease (CVD). Metabolomics allows access to the metabolic perturbations associated with CVD in heart and vessels. Here we assessed which potential animal CVD model most closely mimics the serum metabolite signature of increased carotid intima-media thickness (cIMT) in humans, a clinical parameter widely accepted as a surrogate of CVD. METHODS: A targeted mass spectrometry assay was used to quantify and compare a series of blood metabolites between 1362 individuals (KORA F4 cohort) and 5 animal CVD models: ApoE-/-, Ldlr-/-, and klotho-hypomorphic mice (kl/kl) and SHRSP rats with or without salt feeding. The metabolite signatures were obtained using linear regressions adjusted for various co-variates. RESULTS: In human, increased cIMT [quartile Q4 vs. Q1] was associated with 26 metabolites (9 acylcarnitines, 2 lysophosphatidylcholines, 9 phosphatidylcholines and 6 sphingomyelins). Acylcarnitines correlated preferentially with serum glucose and creatinine. Phospholipids correlated preferentially with cholesterol (total and LDL). The human signature correlated positively and significantly with Ldlr-/- and ApoE-/- mice, while correlation with kl/kl mice and SHRP rats was either negative and non-significant. Human and Ldlr-/- mice shared 11 significant metabolites displaying the same direction of regulation: 5 phosphatidylcholines, 1 lysophosphatidylcholines, 5 sphingomyelins; ApoE-/- mice shared 10. CONCLUSIONS: The human cIMT signature was partially mimicked by Ldlr-/- and ApoE-/- mice. These animal models might help better understand the biochemical and molecular mechanisms involved in the vessel metabolic perturbations associated with, and contributing to metabolic disorders in CVD.

6.
Diabetologia ; 61(1): 117-129, 2018 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-28936587

RESUMO

AIMS/HYPOTHESIS: Circulating metabolites have been shown to reflect metabolic changes during the development of type 2 diabetes. In this study we examined the association of metabolite levels and pairwise metabolite ratios with insulin responses after glucose, glucagon-like peptide-1 (GLP-1) and arginine stimulation. We then investigated if the identified metabolite ratios were associated with measures of OGTT-derived beta cell function and with prevalent and incident type 2 diabetes. METHODS: We measured the levels of 188 metabolites in plasma samples from 130 healthy members of twin families (from the Netherlands Twin Register) at five time points during a modified 3 h hyperglycaemic clamp with glucose, GLP-1 and arginine stimulation. We validated our results in cohorts with OGTT data (n = 340) and epidemiological case-control studies of prevalent (n = 4925) and incident (n = 4277) diabetes. The data were analysed using regression models with adjustment for potential confounders. RESULTS: There were dynamic changes in metabolite levels in response to the different secretagogues. Furthermore, several fasting pairwise metabolite ratios were associated with one or multiple clamp-derived measures of insulin secretion (all p < 9.2 × 10-7). These associations were significantly stronger compared with the individual metabolite components. One of the ratios, valine to phosphatidylcholine acyl-alkyl C32:2 (PC ae C32:2), in addition showed a directionally consistent positive association with OGTT-derived measures of insulin secretion and resistance (p ≤ 5.4 × 10-3) and prevalent type 2 diabetes (ORVal_PC ae C32:2 2.64 [ß 0.97 ± 0.09], p = 1.0 × 10-27). Furthermore, Val_PC ae C32:2 predicted incident diabetes independent of established risk factors in two epidemiological cohort studies (HRVal_PC ae C32:2 1.57 [ß 0.45 ± 0.06]; p = 1.3 × 10-15), leading to modest improvements in the receiver operating characteristics when added to a model containing a set of established risk factors in both cohorts (increases from 0.780 to 0.801 and from 0.862 to 0.865 respectively, when added to the model containing traditional risk factors + glucose). CONCLUSIONS/INTERPRETATION: In this study we have shown that the Val_PC ae C32:2 metabolite ratio is associated with an increased risk of type 2 diabetes and measures of insulin secretion and resistance. The observed effects were stronger than that of the individual metabolites and independent of known risk factors.


Assuntos
Biomarcadores/sangue , Biomarcadores/metabolismo , Diabetes Mellitus Tipo 2/sangue , Diabetes Mellitus Tipo 2/metabolismo , Arginina/metabolismo , Glicemia/metabolismo , Feminino , Peptídeo 1 Semelhante ao Glucagon/metabolismo , Glucose/metabolismo , Teste de Tolerância a Glucose , Humanos , Insulina/metabolismo , Masculino , Fatores de Risco
7.
BMC Bioinformatics ; 18(1): 429, 2017 Sep 29.
Artigo em Inglês | MEDLINE | ID: mdl-28962546

RESUMO

BACKGROUND: Genome-wide association studies allow us to understand the genetics of complex diseases. Human metabolism provides information about the disease-causing mechanisms, so it is usual to investigate the associations between genetic variants and metabolite levels. However, only considering genetic variants and their effects on one trait ignores the possible interplay between different "omics" layers. Existing tools only consider single-nucleotide polymorphism (SNP)-SNP interactions, and no practical tool is available for large-scale investigations of the interactions between pairs of arbitrary quantitative variables. RESULTS: We developed an R package called pulver to compute p-values for the interaction term in a very large number of linear regression models. Comparisons based on simulated data showed that pulver is much faster than the existing tools. This is achieved by using the correlation coefficient to test the null-hypothesis, which avoids the costly computation of inversions. Additional tricks are a rearrangement of the order, when iterating through the different "omics" layers, and implementing this algorithm in the fast programming language C++. Furthermore, we applied our algorithm to data from the German KORA study to investigate a real-world problem involving the interplay among DNA methylation, genetic variants, and metabolite levels. CONCLUSIONS: The pulver package is a convenient and rapid tool for screening huge numbers of linear regression models for significant interaction terms in arbitrary pairs of quantitative variables. pulver is written in R and C++, and can be downloaded freely from CRAN at https://cran.r-project.org/web/packages/pulver/ .


Assuntos
Software , Algoritmos , Simulação por Computador , Ilhas de CpG/genética , Humanos , Modelos Lineares , Polimorfismo de Nucleotídeo Único/genética , Fatores de Tempo
9.
J Proteome Res ; 16(7): 2547-2559, 2017 07 07.
Artigo em Inglês | MEDLINE | ID: mdl-28517934

RESUMO

Blood is one of the most used biofluids in metabolomics studies, and the serum and plasma fractions are routinely used as a proxy for blood itself. Here we investigated the association networks of an array of 29 metabolites identified and quantified via NMR in the plasma and serum samples of two cohorts of ∼1000 healthy blood donors each. A second study of 377 individuals was used to extract plasma and serum samples from the same individual on which a set of 122 metabolites were detected and quantified using FIA-MS/MS. Four different inference algorithms (ARANCE, CLR, CORR, and PCLRC) were used to obtain consensus networks. The plasma and serum networks obtained from different studies showed different topological properties with the serum network being more connected than the plasma network. On a global level, metabolite association networks from plasma and serum fractions obtained from the same blood sample of healthy people show similar topologies, and at a local level, some differences arise like in the case of amino acids.


Assuntos
Aminoácidos/sangue , Ácidos Carboxílicos/sangue , Lipídeos/sangue , Plasma/química , Soro/química , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Feminino , Voluntários Saudáveis , Humanos , Espectroscopia de Ressonância Magnética/normas , Masculino , Metaboloma , Pessoa de Meia-Idade , Espectrometria de Massas em Tandem/normas
11.
Int J Endocrinol ; 2017: 7938216, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-28348587

RESUMO

Coronary artery disease (CAD) is a common complication of type 2 diabetes mellitus (T2D). This case-control study was done to identify metabolites with different concentrations between T2D patients with and without CAD and to characterise implicated metabolic mechanisms relating to CAD. Fasting serum samples of 57 T2D subjects, 26 with (cases) and 31 without CAD (controls), were targeted for metabolite profiling of 163 metabolites. To assess the association between metabolite levels and CAD, partial least squares (PLS) analysis and multivariate logistic regression analysis with adjustment for CAD risk factors and medications were performed. We observed a separation of cases and controls with two classes of metabolites being significantly associated with CAD, including phosphatidylcholines, and serine. Four metabolites being independent from the common CAD risk factors displaying best separation between cases and controls were further selected. Addition of the metabolite concentrations to risk factor analysis raised the area under the receiver-operating-characteristic curve from 0.72 to 0.88 (p = 0.020), providing improved sensitivity and specificity for CAD classification. Serum phospholipid and serine levels independently discriminate T2D patients with and without CAD. Oxidative stress and reduced antioxidative capacity lead to lower metabolite concentrations probably due to changes in membrane composition and accelerated phospholipid degradation.

12.
Heart ; 103(16): 1278-1285, 2017 08.
Artigo em Inglês | MEDLINE | ID: mdl-28255100

RESUMO

OBJECTIVE: The comprehensive assaying of low-molecular-weight compounds, for example, metabolomics, provides a unique tool to uncover novel biomarkers and understand pathways underlying myocardial infarction (MI). We used a targeted metabolomics approach to identify biomarkers for MI and evaluate their involvement in the pathogenesis of MI. METHODS AND RESULTS: Using three independent, prospective cohorts (KORA S4, KORA S2 and AGES-REFINE), totalling 2257 participants without a history of MI at baseline, we identified metabolites associated with incident MI (266 cases). We also investigated the association between the metabolites and high-sensitivity C reactive protein (hsCRP) to understand the relation between these metabolites and systemic inflammation. Out of 140 metabolites, 16 were nominally associated (p<0.05) with incident MI in KORA S4. Three metabolites, arginine and two lysophosphatidylcholines (LPC 17:0 and LPC 18:2), were selected as biomarkers via a backward stepwise selection procedure in the KORA S4 and were significant (p<0.0003) in a meta-analysis comprising all three studies including KORA S2 and AGES-REFINE. Furthermore, these three metabolites increased the predictive value of the Framingham risk score, increasing the area under the receiver operating characteristic score in KORA S4 (from 0.70 to 0.78, p=0.001) and AGES-REFINE study (from 0.70 to 0.76, p=0.02), but was not observed in KORA S2. The metabolite biomarkers attenuated the association between hsCRP and MI, indicating a potential link to systemic inflammatory processes. CONCLUSIONS: We identified three metabolite biomarkers, which in combination increase the predictive value of the Framingham risk score. The attenuation of the hsCRP-MI association by these three metabolites indicates a potential link to systemic inflammation.


Assuntos
Biomarcadores/metabolismo , Inflamação/metabolismo , Infarto do Miocárdio/metabolismo , Medição de Risco/métodos , Adulto , Idoso , Progressão da Doença , Feminino , Alemanha/epidemiologia , Humanos , Incidência , Masculino , Espectrometria de Massas , Pessoa de Meia-Idade , Infarto do Miocárdio/diagnóstico , Infarto do Miocárdio/epidemiologia , Valor Preditivo dos Testes , Estudos Prospectivos , Inquéritos e Questionários
13.
J Invest Dermatol ; 137(5): 1074-1081, 2017 05.
Artigo em Inglês | MEDLINE | ID: mdl-28011146

RESUMO

Epidemiological studies suggested an association between atopic dermatitis (AD) and cardiovascular disease. Therefore, we investigate associations and potential underlying pathways of AD and cardiovascular disease in large cohort studies: the AOK PLUS cohort (n = 1.2 Mio), the GINIplus/LISAplus birth cohorts (n = 2,286), and the Cooperative Health Research in the Region of Augsburg (KORA) F4 cohort (n = 2,990). In addition, metabolomics in KORA F4 and established cardiovascular risk loci in genome-wide data on 10,788 AD cases and 30,047 controls were analyzed. Longitudinal analysis of patients with AD in AOK PLUS showed slightly increased risk for incident angina pectoris (adjusted risk ratio 1.17 [95% confidence interval 1.12-1.23]), hypertension (1.04 [1.02-1.06]), and peripheral arterial disease (1.15 [1.11-1.19]) but not for myocardial infarction (1.05 [0.99-1.12]) and stroke (1.02 [0.98-1.07]). In KORA F4 and GINIplus/LISAplus, AD was not associated with cardiovascular risk factors and no differences in metabolite levels were detected. There was no robust evidence for shared genetic risk variants of AD and cardiovascular disease. This study indicates only a marginally increased risk for angina pectoris, hypertension, and peripheral arterial disease and no increased risk for myocardial infarction or stroke in patients with AD. Relevant associations of AD with cardiovascular risk factors reported in US populations could not be confirmed. Likewise, patients with AD did not have increased genetic risk factors for cardiovascular disease.


Assuntos
Doenças Cardiovasculares/epidemiologia , Dermatite Atópica/complicações , Predisposição Genética para Doença , Metabolômica/métodos , Adulto , Doenças Cardiovasculares/genética , Doenças Cardiovasculares/fisiopatologia , Estudos de Coortes , Dermatite Atópica/genética , Feminino , Estudo de Associação Genômica Ampla , Humanos , Estudos Longitudinais , Masculino , Fatores de Risco
14.
Metabolomics ; 13(1): 4, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-27980503

RESUMO

INTRODUCTION: Few studies have investigated the influence of storage conditions on urine samples and none of them used targeted mass spectrometry (MS). OBJECTIVES: We investigated the stability of metabolite profiles in urine samples under different storage conditions using targeted metabolomics. METHODS: Pooled, fasting urine samples were collected and stored at -80 °C (biobank standard), -20 °C (freezer), 4 °C (fridge), ~9 °C (cool pack), and ~20 °C (room temperature) for 0, 2, 8 and 24 h. Metabolite concentrations were quantified with MS using the AbsoluteIDQ™ p150 assay. We used the Welch-Satterthwaite-test to compare the concentrations of each metabolite. Mixed effects linear regression was used to assess the influence of the interaction of storage time and temperature. RESULTS: The concentrations of 63 investigated metabolites were stable at -20 and 4 °C for up to 24 h when compared to samples immediately stored at -80 °C. When stored at ~9 °C for 24 h, few amino acids (Arg, Val and Leu/Ile) significantly decreased by 40% in concentration (P < 7.9E-04); for an additional three metabolites (Ser, Met, Hexose H1) when stored at ~20 °C reduced up to 60% in concentrations. The concentrations of four more metabolites (Glu, Phe, Pro, and Thr) were found to be significantly influenced when considering the interaction between exposure time and temperature. CONCLUSION: Our findings indicate that 78% of quantified metabolites were stable for all examined storage conditions. Particularly, some amino acid concentrations were sensitive to changes after prolonged storage at room temperature. Shipping or storing urine samples on cool packs or at room temperature for more than 8 h and multiple numbers of freeze and thaw cycles should be avoided.

15.
Int J Epidemiol ; 45(5): 1528-1538, 2016 10.
Artigo em Inglês | MEDLINE | ID: mdl-27892410

RESUMO

BACKGROUND: Short-term exposure to air pollution is associated with morbidity and mortality. Metabolites are intermediaries in biochemical processes, and associations between air pollution and metabolites can yield unique mechanistic insights. METHODS: We used independent cross-sectional samples with targeted metabolomics (138 metabolites across five metabolite classes) from three cohort studies, each a part of the Cooperative Health Research in the Region of Augsburg (KORA). The KORA cohorts are numbered (1 to 4) according to which survey they belong to, and lettered S or F according to whether the survey was a baseline or follow-up survey. KORA F4 (N = 3044) served as our discovery cohort, with KORA S4 (N = 485) serving as the primary replication cohort. KORA F4 and KORA S4 were primarily fasting cohorts. We used the non-fasting KORA F3 (N = 377) cohort to evaluate replicated associations in non-fasting individuals, and we performed a random effects meta-analysis of all three cohorts. Associations between the 0-4-day lags and the 5-day average of particulate matter (PM)2.5, NO2 and ozone were modelled via generalized additive models. All air pollution exposures were scaled to the interquartile range, and effect estimates presented as percent changes relative to the geometric mean of the metabolite concentration (ΔGM). RESULTS: There were 10 discovery cohort associations, of which seven were lysophosphatidylcholines (LPCs); NO2 was the most ubiquitous exposure (5/10). The 5-day average NO2-LPC(28:0) association was associated at a Bonferroni corrected P-value threshold (P < 1.2x10-4) in KORA F4 [ΔGM = 11.5%; 95% confidence interval (CI) = 6.60, 16.3], and replicated (P < 0.05) in KORA S4 (ΔGM = 21.0%; CI = 4.56, 37.5). This association was not observed in the non-fasting KORA F3 cohort (ΔGM = -5.96%; CI = -26.3, 14.3), but remained in the random effects meta-analysis (ΔGM = 10.6%; CI = 0.16, 21). CONCLUSIONS: LPCs are associated with short-term exposure to air pollutants, in particular NO2 Further research is needed to understand the effect of nutritional/fasting status on these associations and the causal mechanisms linking air pollution exposure and metabolite profiles.


Assuntos
Poluição do Ar/efeitos adversos , Ácidos Graxos/sangue , Metaboloma , Dióxido de Nitrogênio/efeitos adversos , Adulto , Idoso , Consumo de Bebidas Alcoólicas/epidemiologia , Estudos Transversais , Diabetes Mellitus Tipo 2/epidemiologia , Feminino , Alemanha , Humanos , Modelos Lineares , Lisofosfatidilcolinas/sangue , Masculino , Metabolômica , Pessoa de Meia-Idade , Ozônio/análise , Material Particulado/análise , Estudos Prospectivos , Estações do Ano , Fatores Sexuais , Fumar/epidemiologia , Fatores de Tempo
16.
Sci Rep ; 6: 37005, 2016 11 11.
Artigo em Inglês | MEDLINE | ID: mdl-27833161

RESUMO

Metabolomic analyses in epidemiological studies have demonstrated a strong sexual dimorphism for most metabolites. Cross-sex hormone treatment (CSH) in transgender individuals enables the study of metabolites in a cross-gender setting. Targeted metabolomic profiling of serum of fasting transmen and transwomen at baseline and following 12 months of CSH (N = 20/group) was performed. Changes in 186 serum metabolites and metabolite ratios were determined by targeted metabolomics analysis based on ESI-LC-MS/MS. RandomForest (RF) analysis was applied to detect metabolites of highest interest for grouping of transwomen and transmen before and after initiation of CSH. Principal component analysis (PCA) was performed to check whether group differentiation was achievable according to these variables and to see if changes in metabolite levels could be explained by a priori gender differences. PCA predicted grouping of individuals-determined by the citrulline/arginine-ratio and the amino acids lysine, alanine and asymmetric dimethylarginine - in addition to the expected grouping due to changes in sex steroids and body composition. The fact that most of the investigated metabolites did, however, not change, indicates that the majority of sex dependent differences in metabolites reported in the literature before may primarily not be attributable to sex hormones but to other gender-differences.


Assuntos
Disforia de Gênero/metabolismo , Hormônios Esteroides Gonadais/farmacologia , Redes e Vias Metabólicas/efeitos dos fármacos , Metabolômica , Caracteres Sexuais , Procedimentos de Readequação Sexual , Adulto , Aminoácidos/metabolismo , Antropometria , Arginina/análogos & derivados , Arginina/metabolismo , Composição Corporal/efeitos dos fármacos , Metabolismo Energético/efeitos dos fármacos , Jejum/metabolismo , Feminino , Disforia de Gênero/tratamento farmacológico , Hormônios Esteroides Gonadais/sangue , Hormônios Esteroides Gonadais/uso terapêutico , Humanos , Metabolismo dos Lipídeos , Masculino , Pessoa de Meia-Idade , Análise de Componente Principal
17.
PLoS Genet ; 12(10): e1006379, 2016 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-27768686

RESUMO

Insulin resistance (IR) and impaired insulin secretion contribute to type 2 diabetes and cardiovascular disease. Both are associated with changes in the circulating metabolome, but causal directions have been difficult to disentangle. We combined untargeted plasma metabolomics by liquid chromatography/mass spectrometry in three non-diabetic cohorts with Mendelian Randomization (MR) analysis to obtain new insights into early metabolic alterations in IR and impaired insulin secretion. In up to 910 elderly men we found associations of 52 metabolites with hyperinsulinemic-euglycemic clamp-measured IR and/or ß-cell responsiveness (disposition index) during an oral glucose tolerance test. These implicated bile acid, glycerophospholipid and caffeine metabolism for IR and fatty acid biosynthesis for impaired insulin secretion. In MR analysis in two separate cohorts (n = 2,613) followed by replication in three independent studies profiled on different metabolomics platforms (n = 7,824 / 8,961 / 8,330), we discovered and replicated causal effects of IR on lower levels of palmitoleic acid and oleic acid. A trend for a causal effect of IR on higher levels of tyrosine reached significance only in meta-analysis. In one of the largest studies combining "gold standard" measures for insulin responsiveness with non-targeted metabolomics, we found distinct metabolic profiles related to IR or impaired insulin secretion. We speculate that the causal effects on monounsaturated fatty acid levels could explain parts of the raised cardiovascular disease risk in IR that is independent of diabetes development.


Assuntos
Diabetes Mellitus Tipo 2/genética , Ácidos Graxos Monoinsaturados/metabolismo , Resistência à Insulina/genética , Insulina/genética , Adulto , Idoso , Idoso de 80 Anos ou mais , Ácidos e Sais Biliares/metabolismo , Cafeína/metabolismo , Diabetes Mellitus Tipo 2/sangue , Diabetes Mellitus Tipo 2/patologia , Glucose/metabolismo , Teste de Tolerância a Glucose , Glicerofosfolipídeos/metabolismo , Humanos , Insulina/sangue , Insulina/metabolismo , Masculino , Redes e Vias Metabólicas/genética , Metabolômica , Pessoa de Meia-Idade , Tirosina/sangue
18.
Circ Cardiovasc Genet ; 9(5): 436-447, 2016 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-27651444

RESUMO

BACKGROUND: DNA methylation leaves a long-term signature of smoking exposure and is one potential mechanism by which tobacco exposure predisposes to adverse health outcomes, such as cancers, osteoporosis, lung, and cardiovascular disorders. METHODS AND RESULTS: To comprehensively determine the association between cigarette smoking and DNA methylation, we conducted a meta-analysis of genome-wide DNA methylation assessed using the Illumina BeadChip 450K array on 15 907 blood-derived DNA samples from participants in 16 cohorts (including 2433 current, 6518 former, and 6956 never smokers). Comparing current versus never smokers, 2623 cytosine-phosphate-guanine sites (CpGs), annotated to 1405 genes, were statistically significantly differentially methylated at Bonferroni threshold of P<1×10-7 (18 760 CpGs at false discovery rate <0.05). Genes annotated to these CpGs were enriched for associations with several smoking-related traits in genome-wide studies including pulmonary function, cancers, inflammatory diseases, and heart disease. Comparing former versus never smokers, 185 of the CpGs that differed between current and never smokers were significant P<1×10-7 (2623 CpGs at false discovery rate <0.05), indicating a pattern of persistent altered methylation, with attenuation, after smoking cessation. Transcriptomic integration identified effects on gene expression at many differentially methylated CpGs. CONCLUSIONS: Cigarette smoking has a broad impact on genome-wide methylation that, at many loci, persists many years after smoking cessation. Many of the differentially methylated genes were novel genes with respect to biological effects of smoking and might represent therapeutic targets for prevention or treatment of tobacco-related diseases. Methylation at these sites could also serve as sensitive and stable biomarkers of lifetime exposure to tobacco smoke.


Assuntos
Metilação de DNA , Epigênese Genética , Fumar/efeitos adversos , Fumar/genética , Transcriptoma , Idoso , Estudos de Casos e Controles , Ilhas de CpG , Feminino , Perfilação da Expressão Gênica/métodos , Marcadores Genéticos , Estudo de Associação Genômica Ampla , Genótipo , Humanos , Leucócitos/química , Masculino , Pessoa de Meia-Idade , Análise de Sequência com Séries de Oligonucleotídeos , Fenótipo , Fumar/etnologia , Abandono do Hábito de Fumar , Prevenção do Hábito de Fumar , Fatores de Tempo
19.
Int J Epidemiol ; 45(5): 1406-1420, 2016 10.
Artigo em Inglês | MEDLINE | ID: mdl-27591264

RESUMO

BACKGROUND: The application of metabolomics in prospective cohort studies is statistically challenging. Given the importance of appropriate statistical methods for selection of disease-associated metabolites in highly correlated complex data, we combined random survival forest (RSF) with an automated backward elimination procedure that addresses such issues. METHODS: Our RSF approach was illustrated with data from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study, with concentrations of 127 serum metabolites as exposure variables and time to development of type 2 diabetes mellitus (T2D) as outcome variable. Out of this data set, Cox regression with a stepwise selection method was recently published. Replication of methodical comparison (RSF and Cox regression) was conducted in two independent cohorts. Finally, the R-code for implementing the metabolite selection procedure into the RSF-syntax is provided. RESULTS: The application of the RSF approach in EPIC-Potsdam resulted in the identification of 16 incident T2D-associated metabolites which slightly improved prediction of T2D when used in addition to traditional T2D risk factors and also when used together with classical biomarkers. The identified metabolites partly agreed with previous findings using Cox regression, though RSF selected a higher number of highly correlated metabolites. CONCLUSIONS: The RSF method appeared to be a promising approach for identification of disease-associated variables in complex data with time to event as outcome. The demonstrated RSF approach provides comparable findings as the generally used Cox regression, but also addresses the problem of multicollinearity and is suitable for high-dimensional data.


Assuntos
Biomarcadores/sangue , Interpretação Estatística de Dados , Metabolômica/métodos , Modelos Estatísticos , Análise de Sobrevida , Adulto , Idoso , Algoritmos , Diabetes Mellitus Tipo 2/sangue , Diabetes Mellitus Tipo 2/mortalidade , Europa (Continente) , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Modelos de Riscos Proporcionais , Estudos Prospectivos , Fatores de Risco
20.
Diabetes ; 65(12): 3776-3785, 2016 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-27621107

RESUMO

Metformin is the first-line oral medication to increase insulin sensitivity in patients with type 2 diabetes (T2D). Our aim was to investigate the pleiotropic effect of metformin using a nontargeted metabolomics approach. We analyzed 353 metabolites in fasting serum samples of the population-based human KORA (Cooperative Health Research in the Region of Augsburg) follow-up survey 4 cohort. To compare T2D patients treated with metformin (mt-T2D, n = 74) and those without antidiabetes medication (ndt-T2D, n = 115), we used multivariable linear regression models in a cross-sectional study. We applied a generalized estimating equation to confirm the initial findings in longitudinal samples of 683 KORA participants. In a translational approach, we used murine plasma, liver, skeletal muscle, and epididymal adipose tissue samples from metformin-treated db/db mice to further corroborate our findings from the human study. We identified two metabolites significantly (P < 1.42E-04) associated with metformin treatment. Citrulline showed lower relative concentrations and an unknown metabolite X-21365 showed higher relative concentrations in human serum when comparing mt-T2D with ndt-T2D. Citrulline was confirmed to be significantly (P < 2.96E-04) decreased at 7-year follow-up in patients who started metformin treatment. In mice, we validated significantly (P < 4.52E-07) lower citrulline values in plasma, skeletal muscle, and adipose tissue of metformin-treated animals but not in their liver. The lowered values of citrulline we observed by using a nontargeted approach most likely resulted from the pleiotropic effect of metformin on the interlocked urea and nitric oxide cycle. The translational data derived from multiple murine tissues corroborated and complemented the findings from the human cohort.


Assuntos
Diabetes Mellitus Tipo 2/tratamento farmacológico , Diabetes Mellitus Tipo 2/metabolismo , Hipoglicemiantes/uso terapêutico , Metformina/uso terapêutico , Tecido Adiposo/efeitos dos fármacos , Tecido Adiposo/metabolismo , Animais , Citrulina/sangue , Diabetes Mellitus Tipo 2/sangue , Jejum/sangue , Humanos , Resistência à Insulina/fisiologia , Estudos Longitudinais , Masculino , Camundongos , Modelos Biológicos , Músculo Esquelético/efeitos dos fármacos , Músculo Esquelético/metabolismo
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