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Dietary approaches contribute to the prevention and treatment of type 2 diabetes. High protein diets were shown to exert beneficial as well as adverse effects on metabolism. However, it is unclear whether the protein origin plays a role in these effects. The LeguAN study investigated in detail the effects of two high protein diets, either from plant or animal origin, in type 2 diabetic patients. Both diets contained 30 EN% protein, 40 EN% carbohydrates, and 30 EN% fat. Fiber content, glycemic index, and composition of dietary fats were similar in both diets. In comparison to previous dietary habits, the fat content was exchanged for protein, while the carbohydrate intake was not modified. Overall, both high protein diets led to improvements of glycemic control, insulin sensitivity, liver fat, and cardiovascular risk markers without remarkable differences between the protein types.
Fasting glucose together with indices of insulin resistance were ameliorated by both interventions to varying extents but without significant differences between protein types. The decline of HbA1c was more pronounced in the plant protein group, whereby the improvement of insulin sensitivity in the animal protein group. The high protein intake had only slight influence on postprandial metabolism seen for free fatty acids and indices of insulin secretion, sensitivity and degradation. Except for GIP release, ingestion of animal and plant meals did not provoke differential metabolic and hormonal responses despite diverse circulating amino acid levels.
The animal protein diets led to a selective increase of fat-free mass and decrease of total fat mass, which was not significantly different from the plant protein diet. Moreover, the high protein diets potently decreased liver fat content by 42% on average which was linked to significantly diminished lipogenesis, free fatty acids flux and lipolysis in adipose tissue. Moderate decline of circulating liver enzymes was induced by both interventions. The liver fat reduction was associated with improved glucose homeostasis and insulin sensitivity which underlines the protective effect of the diets.
Blood lipid profile improved in all subjects and was probably related to the lower fat intake. Reductions in uric acid and markers of inflammation further argued for metabolic benefits of both high protein diets. Systolic and diastolic blood pressure declined only in the PP group pointing a possible role of arginine.
Kidney function was not altered by high protein consumption over 6 weeks. The rapid decrease of serum creatinine in the PP group was noteworthy and should be further investigated. Protein type did not seem to play a role but long-term studies are warranted to fully elucidate safety of high protein regimen.
Varying the source of dietary proteins did not affect the mTOR pathway in adipose tissue and blood cells under neither acute nor chronic settings. Enhancement of whole-body insulin sensitivity suggested also no alteration of mTOR and no impairment of insulin sensitivity in skeletal muscle.
A remarkable outcome was the extensive reduction of FGF21, critical regulator of metabolic processes, by approximately 50% independently of protein type. Whether hepatic ER-stress, ammonia flux or rather macronutrient preferences is behind this paradoxical finding remains to be investigated in detail.
Unlike initial expectations and previous reports plant protein based diet had no clear advantage over animal proteins. The pronounced beneficial effect of animal protein on insulin homeostasis despite high BCAA and methionine intake was certainly unexpected assuming more complex metabolic adaptations occurring upon prolonged consumption. In addition, the reduced fat intake may have also contributed to the overall improvements in both groups.
Taking into account the above observed study results, a short-term diet containing 30 EN% protein (either from plant or animal origin), 40 EN% carbohydrates, and 30 EN% fat with lower SFA amount leads to metabolic improvements in diabetic patients, regardless of protein source.
Metabolic derangement with poor glycemic control accompanying overweight and obesity is associated with chronic low-grade inflammation and hyperinsulinemia. Macrophages, which present a very heterogeneous population of cells, play a key role in the maintenance of normal tissue homeostasis, but functional alterations in the resident macrophage pool as well as newly recruited monocyte-derived macrophages are important drivers in the development of low-grade inflammation. While metabolic dysfunction, insulin resistance and tissue damage may trigger or advance pro-inflammatory responses in macrophages, the inflammation itself contributes to the development of insulin resistance and the resulting hyperinsulinemia. Macrophages express insulin receptors whose downstream signaling networks share a number of knots with the signaling pathways of pattern recognition and cytokine receptors, which shape macrophage polarity. The shared knots allow insulin to enhance or attenuate both pro-inflammatory and anti-inflammatory macrophage responses. This supposedly physiological function may be impaired by hyperinsulinemia or insulin resistance in macrophages. This review discusses the mutual ambiguous relationship of low-grade inflammation, insulin resistance, hyperinsulinemia and the insulin-dependent modulation of macrophage activity with a focus on adipose tissue and liver.
Background: Consumption of whole-grain, coffee, and red meat were consistently related to the risk of developing type 2 diabetes in prospective cohort studies, but potentially underlying biological mechanisms are not well understood. Metabolomics profiles were shown to be sensitive to these dietary exposures, and at the same time to be informative with respect to the risk of type 2 diabetes. Moreover, graphical network-models were demonstrated to reflect the biological processes underlying high-dimensional metabolomics profiles.
Aim: The aim of this study was to infer hypotheses on the biological mechanisms that link consumption of whole-grain bread, coffee, and red meat, respectively, to the risk of developing type 2 diabetes. More specifically, it was aimed to consider network models of amino acid and lipid profiles as potential mediators of these risk-relations.
Study population: Analyses were conducted in the prospective EPIC-Potsdam cohort (n = 27,548), applying a nested case-cohort design (n = 2731, including 692 incident diabetes cases). Habitual diet was assessed with validated semiquantitative food-frequency questionnaires. Concentrations of 126 metabolites (acylcarnitines, phosphatidylcholines, sphingomyelins, amino acids) were determined in baseline-serum samples. Incident type 2 diabetes cases were assed and validated in an active follow-up procedure. The median follow-up time was 6.6 years.
Analytical design: The methodological approach was conceptually based on counterfactual causal inference theory. Observations on the network-encoded conditional independence structure restricted the space of possible causal explanations of observed metabolomics-data patterns. Given basic directionality assumptions (diet affects metabolism; metabolism affects future diabetes incidence), adjustment for a subset of direct neighbours was sufficient to consistently estimate network-independent direct effects. Further model-specification, however, was limited due to missing directionality information on the links between metabolites. Therefore, a multi-model approach was applied to infer the bounds of possible direct effects. All metabolite-exposure links and metabolite-outcome links, respectively, were classified into one of three categories: direct effect, ambiguous (some models indicated an effect others not), and no-effect.
Cross-sectional and longitudinal relations were evaluated in multivariable-adjusted linear regression and Cox proportional hazard regression models, respectively. Models were comprehensively adjusted for age, sex, body mass index, prevalence of hypertension, dietary and lifestyle factors, and medication.
Results: Consumption of whole-grain bread was related to lower levels of several lipid metabolites with saturated and monounsaturated fatty acids. Coffee was related to lower aromatic and branched-chain amino acids, and had potential effects on the fatty acid profile within lipid classes. Red meat was linked to lower glycine levels and was related to higher circulating concentrations of branched-chain amino acids. In addition, potential marked effects of red meat consumption on the fatty acid composition within the investigated lipid classes were identified.
Moreover, potential beneficial and adverse direct effects of metabolites on type 2 diabetes risk were detected. Aromatic amino acids and lipid metabolites with even-chain saturated (C14-C18) and with specific polyunsaturated fatty acids had adverse effects on type 2 diabetes risk. Glycine, glutamine, and lipid metabolites with monounsaturated fatty acids and with other species of polyunsaturated fatty acids were classified as having direct beneficial effects on type 2 diabetes risk.
Potential mediators of the diet-diabetes links were identified by graphically overlaying this information in network models. Mediation analyses revealed that effects on lipid metabolites could potentially explain about one fourth of the whole-grain bread effect on type 2 diabetes risk; and that effects of coffee and red meat consumption on amino acid and lipid profiles could potentially explain about two thirds of the altered type 2 diabetes risk linked to these dietary exposures.
Conclusion: An algorithm was developed that is capable to integrate single external variables (continuous exposures, survival time) and high-dimensional metabolomics-data in a joint graphical model. Application to the EPIC-Potsdam cohort study revealed that the observed conditional independence patterns were consistent with the a priori mediation hypothesis: Early effects on lipid and amino acid metabolism had the potential to explain large parts of the link between three of the most widely discussed diabetes-related dietary exposures and the risk of developing type 2 diabetes.
Diabetes is hallmarked by high blood glucose levels, which cause progressive generalised vascular damage, leading to microvascular and macrovascular complications. Diabetes-related complications cause severe and prolonged morbidity and are a major cause of mortality among people with diabetes. Despite increasing attention to risk factors of type 2 diabetes, existing evidence is scarce or inconclusive regarding vascular complications and research investigating both micro- and macrovascular complications is lacking. This thesis aims to contribute to current knowledge by identifying risk factors – mainly related to lifestyle – of vascular complications, addressing methodological limitations of previous literature and providing comparative data between micro- and macrovascular complications.
To address this overall aim, three specific objectives were set. The first was to investigate the effects of diabetes complication burden and lifestyle-related risk factors on the incidence of (further) complications. Studies suggest that diabetes complications are interrelated. However, they have been studied mainly independently of individuals’ complication burden. A five-state time-to-event model was constructed to examine the longitudinal patterns of micro- (kidney disease, neuropathy and retinopathy) and macrovascular complications (myocardial infarction and stroke) and their association with the occurrence of subsequent complications. Applying the same model, the effect of modifiable lifestyle factors, assessed alone and in combination with complication load, on the incidence of diabetes complications was studied. The selected lifestyle factors were body mass index (BMI), waist circumference, smoking status, physical activity, and intake of coffee, red meat, whole grains, and alcohol. Analyses were conducted in a cohort of 1199 participants with incident type 2 diabetes from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam, who were free of vascular complications at diabetes diagnosis. During a median follow-up time of 11.6 years, 96 cases of macrovascular complications (myocardial infarction and stroke) and 383 microvascular complications (kidney disease, neuropathy and retinopathy) were identified. In multivariable-adjusted models, the occurrence of a microvascular complication was associated with a higher incidence of further micro- (Hazard ratio [HR] 1.90; 95% Confidence interval [CI] 0.90, 3.98) and macrovascular complications (HR 4.72; 95% CI 1.25, 17.68), compared with persons without a complication burden. In addition, participants who developed a macrovascular event had a twofold higher risk of future microvascular complications (HR 2.26; 95% CI 1.05, 4.86). The models were adjusted for age, sex, state duration, education, lifestyle, glucose-lowering medication, and pre-existing conditions of hypertension and dyslipidaemia. Smoking was positively associated with macrovascular disease, while an inverse association was observed with higher coffee intake. Whole grain and alcohol intake were inversely associated with microvascular complications, and a U-shaped association was observed for red meat intake. BMI and waist circumference were positively associated with microvascular events. The associations between lifestyle factors and incidence of complications were not modified by concurrent complication burden, except for red meat intake and smoking status, where the associations were attenuated among individuals with a previous complication.
The second objective was to perform an in-depth investigation of the association between BMI and BMI change and risk of micro- and macrovascular complications. There is an ongoing debate on the association between obesity and risk of macrovascular and microvascular outcomes in type 2 diabetes, with studies suggesting a protective effect among people with overweight or obesity. These findings, however, might be limited due to suboptimal control for smoking, pre-existing chronic disease, or short-follow-up. After additional exclusion of persons with cancer history at diabetes onset, the associations between pre-diagnosis BMI and relative annual change between pre- and post-diagnosis BMI and incidence of complications were evaluated in multivariable-adjusted Cox models. The analyses were adjusted for age, sex, education, smoking status and duration, physical activity, alcohol consumption, adherence to the Mediterranean diet, and family history of diabetes and cardiovascular disease (CVD). Among 1083 EPIC-Potsdam participants, 85 macrovascular and 347 microvascular complications were identified during a median follow-up period of 10.8 years. Higher pre-diagnosis BMI was associated with an increased risk of total microvascular complications (HR per 5 kg/m2 1.21; 95% CI 1.07, 1.36), kidney disease (HR 1.39; 95% CI 1.21, 1.60) and neuropathy (HR 1.12; 95% CI 0.96, 1.31); but no association was observed for macrovascular complications (HR 1.05; 95% CI 0.81, 1.36). Effect modification was not evident by sex, smoking status, or age groups. In analyses according to BMI change categories, BMI loss of more than 1% indicated a decreased risk of total microvascular complications (HR 0.62; 95% CI 0.47, 0.80), kidney disease (HR 0.57; 95% CI 0.40, 0.81) and neuropathy (HR 0.73; 95% CI 0.52, 1.03), compared with participants with a stable BMI. No clear association was observed for macrovascular complications (HR 1.04; 95% CI 0.62, 1.74). The impact of BMI gain on diabetes-related vascular disease was less evident. Associations were consistent across strata of age, sex, pre-diagnosis BMI, or medication but appeared stronger among never-smokers than current or former smokers.
The last objective was to evaluate whether individuals with a high-risk profile for diabetes and cardiovascular disease (CVD) also have a greater risk of complications. Within the EPIC-Potsdam study, two accurate prognostic tools were developed, the German Diabetes Risk Score (GDRS) and the CVD Risk Score (CVDRS), which predict the 5-year type 2 diabetes risk and 10-year CVD risk, respectively. Both scores provide a non-clinical and clinical version. Components of the risk scores include age, sex, waist circumference, prevalence of hypertension, family history of diabetes or CVD, lifestyle factors, and clinical factors (only in clinical versions). The association of the risk scores with diabetes complications and their discriminatory performance for complications were assessed. In crude Cox models, both versions of GDRS and CVDRS were positively associated with macrovascular complications and total microvascular complications, kidney disease and neuropathy. Higher GDRS was also associated with an elevated risk of retinopathy. The discrimination of the scores (clinical and non-clinical) was poor for all complications, with the C-index ranging from 0.58 to 0.66 for macrovascular complications and from 0.60 to 0.62 for microvascular complications.
In conclusion, this work illustrates that the risk of complication development among individuals with type 2 diabetes is related to the existing complication load, and attention should be given to regular monitoring for future complications. It underlines the importance of weight management and adherence to healthy lifestyle behaviours, including high intake of whole grains, moderation in red meat and alcohol consumption and avoidance of smoking to prevent major diabetes-associated complications, regardless of complication burden. Risk scores predictive for type 2 diabetes and CVD were related to elevated risks of complications. By optimising several lifestyle and clinical factors, the risk score can be improved and may assist in lowering complication risk.
Background: The role of fatty acid (FA) intake and metabolism in type 2 diabetes (T2D) incidence is controversial. Some FAs are not synthesised endogenously and, therefore, these circulating FAs reflect dietary intake, for example, the trans fatty acids (TFAs), saturated odd chain fatty acids (OCFAs), and linoleic acid, an n-6 polyunsaturated fatty acids (PUFA). It remains unclear if intake of TFA influence T2D risk and whether industrial TFAs (iTFAs) and ruminant TFAs (rTFAs) exert the same effect. Unlike even chain saturated FAs, the OCFAs have been inversely associated with T2D risk, but this association is poorly understood. Furthermore, the associations of n-6 PUFAs intake with T2D risk are still debated, while delta-5 desaturase (D5D), a key enzyme in the metabolism of PUFAs, has been consistently related to T2D risk. To better understand these relationships, the FA composition in circulating lipid fractions can be used as biomarkers of dietary intake and metabolism. The exploration of TFAs subtypes in plasma phospholipids and OCFAs and n-6 PUFAs within a wide range of lipid classes may give insights into the pathophysiology of T2D.
Aim: This thesis aimed mainly to analyse the association of TFAs, OCFAs and n-6 PUFAs with self-reported dietary intake and prospective T2D risk, using seven types of TFAs in plasma phospholipids and deep lipidomics profiling data from fifteen lipid classes.
Methods: A prospective case-cohort study was designed within the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study, including all the participants who developed T2D (median follow-up 6.5 years) and a random subsample of the full cohort (subcohort: n=1248; T2D cases: n=820). The main analyses included two lipid profiles. The first was an assessment of seven TFA in plasma phospholipids, with a modified method for analysis of FA with very low abundances. The second lipid profile was derived from a high-throughout lipid profiling technology, which identified 940 distinct molecular species and allowed to quantify OCFAs and PUFAs composition across 15 lipid classes. Delta-5 desaturase (D5D) activity was estimated as 20:4/20:3-ratio. Using multivariable Cox regression models, we examined the associations of TFA subtypes with incident T2D and class-specific associations of OCFA and n-6 PUFAs with T2D risk.
Results: 16:1n-7t, 18:1n-7t, and c9t11-CLA were positively correlated with the intake of fat-rich dairy foods. iTFA 18:1 isomers were positively correlated with margarine. After adjustment for confounders and other TFAs, higher plasma phospholipid concentrations of two rTFAs were associated with a lower incidence of T2D: 18:1n-7t and t10c12-CLA. In contrast, the rTFA c9t11-CLA was associated with a higher incidence of T2D. rTFA 16:1n-7t and iTFAs (18:1n-6t, 18:1n-9t, 18:2n-6,9t) were not statistically significantly associated with T2D risk.
We observed heterogeneous integration of OCFA in different lipid classes, and the contribution of 15:0 versus 17:0 to the total OCFA abundance differed across lipid classes. Consumption of fat-rich dairy and fiber-rich foods were positively and red meat inversely correlated to OCFA abundance in plasma phospholipid classes. In women only, higher abundances of 15:0 in phosphatidylcholines (PC) and diacylglycerols (DG), and 17:0 in PC, lysophosphatidylcholines (LPC), and cholesterol esters (CE) were inversely associated with T2D risk. In men and women, a higher abundance of 15:0 in monoacylglycerols (MG) was also inversely associated with T2D. Conversely, a higher 15:0 concentration in LPC and triacylglycerols (TG) was associated with higher T2D risk in men. Women with a higher concentration of 17:0 as free fatty acids (FFA) also had higher T2D incidence.
The integration of n-6 PUFAs in lipid classes was also heterogeneous. 18:2 was highly abundant in phospholipids (particularly PC), CE, and TG; 20:3 represented a small fraction of FA in most lipid classes, and 20:4 accounted for a large proportion of circulating phosphatidylinositol (PI) and phosphatidylethanolamines (PE). Higher concentrations of 18:2 were inversely associated with T2D risk, especially within DG, TG, and LPC. However, 18:2 as part of MG was positively associated with T2D risk. Higher concentrations of 20:3 in phospholipids (PC, PE, PI), FFA, CE, and MG were linked to higher T2D incidence. 20:4 was unrelated to risk in most lipid classes, except positive associations were observed for 20:4 enriched in FFA and PE. The estimated D5D activities in PC, PE, PI, LPC, and CE were inversely associated with T2D and explained variance of estimated D5D activity by genomic variation in the FADS locus was only substantial in those lipid classes.
Conclusion: The TFAs' conformation is essential in their relationship to diabetes risk, as indicated by plasma rTFA subtypes concentrations having opposite directions of associations with diabetes risk. Plasma OCFA concentration is linked to T2D risk in a lipid class and sex-specific manner. Plasma n-6 PUFA concentrations are associated differently with T2D incidence depending on the specific FA and the lipid class. Overall, these results highlight the complexity of circulating FAs and their heterogeneous association with T2D risk depending on the specific FA structure, lipid class, and sex. My results extend the evidence of the relationship between diet, lipid metabolism, and subsequent T2D risk. In addition, my work generated several potential new biomarkers of dietary intake and prospective T2D risk.
Effect of benzylglucosinolate on signaling pathways associated with type 2 diabetes prevention
(2014)
Type 2 diabetes (T2D) is a health problem throughout the world. In 2010, there were nearly 230 million individuals with diabetes worldwide and it is estimated that in the economically advanced countries the cases will increase about 50% in the next twenty years. Insulin resistance is one of major features in T2D, which is also a risk factor for metabolic and cardiovascular complications. Epidemiological and animal studies have shown that the consumption of vegetables and fruits can delay or prevent the development of the disease, although the underlying mechanisms of these effects are still unclear. Brassica species such as broccoli (Brassica oleracea var. italica) and nasturtium (Tropaeolum majus) possess high content of bioactive phytochemicals, e.g. nitrogen sulfur compounds (glucosinolates and isothiocyanates) and polyphenols largely associated with the prevention of cancer. Isothiocyanates (ITCs) display their anti-carcinogenic potential by inducing detoxicating phase II enzymes and increasing glutathione (GSH) levels in tissues. In T2D diabetes an increase in gluconeogenesis and triglyceride synthesis, and a reduction in fatty acid oxidation accompanied by the presence of reactive oxygen species (ROS) are observed; altogether is the result of an inappropriate response to insulin. Forkhead box O (FOXO) transcription factors play a crucial role in the regulation of insulin effects on gene expression and metabolism, and alterations in FOXO function could contribute to metabolic disorders in diabetes. In this study using stably transfected human osteosarcoma cells (U-2 OS) with constitutive expression of FOXO1 protein labeled with GFP (green fluorescent protein) and human hepatoma cells HepG2 cell cultures, the ability of benzylisothiocyanate (BITC) deriving from benzylglucosinolate, extracted from nasturtium to modulate, i) the insulin-signaling pathway, ii) the intracellular localization of FOXO1 and iii) the expression of proteins involved in glucose metabolism, ROS detoxification, cell cycle arrest and DNA repair was evaluated. BITC promoted oxidative stress and in response to that induced FOXO1 translocation from cytoplasm into the nucleus antagonizing the insulin effect. BITC stimulus was able to down-regulate gluconeogenic enzymes, which can be considered as an anti-diabetic effect; to promote antioxidant resistance expressed by the up-regulation in manganese superoxide dismutase (MnSOD) and detoxification enzymes; to modulate autophagy by induction of BECLIN1 and down-regulation of the mammalian target of rapamycin complex 1 (mTORC1) pathway; and to promote cell cycle arrest and DNA damage repair by up-regulation of the cyclin-dependent kinase inhibitor (p21CIP) and Growth Arrest / DNA Damage Repair (GADD45). Except for the nuclear factor (erythroid derived)-like2 (NRF2) and its influence in the detoxification enzymes gene expression, all the observed effects were independent from FOXO1, protein kinase B (AKT/PKB) and NAD-dependent deacetylase sirtuin-1 (SIRT1). The current study provides evidence that besides of the anticarcinogenic potential, isothiocyanates might have a role in T2D prevention. BITC stimulus mimics the fasting state, in which insulin signaling is not triggered and FOXO proteins remain in the nucleus modulating gene expression of their target genes, with the advantage of a down-regulation of gluconeogenesis instead of its increase. These effects suggest that BITC might be considered as a promising substance in the prevention or treatment of T2D, therefore the factors behind of its modulatory effects need further investigation.
Efficacy, Safety & Modification of Albuminuria in Type 2 Diabetes Subjects with Renal Disease with LINAgliptin (MARLINA-T2D), a multicentre, multinational, randomized, double-blind, placebo-controlled, parallel-group, phase 3b clinical trial, aims to further define the potential renal effects of dipeptidyl peptidase-4 inhibition beyond glycaemic control. A total of 350 eligible individuals with inadequately controlled type 2 diabetes and evidence of renal disease are planned to be randomized in a 1:1 ratio to receive either linagliptin 5mg or placebo in addition to their stable glucose-lowering background therapy for 24weeks. Two predefined main endpoints will be tested in a hierarchical manner: (1) change from baseline in glycated haemoglobin and (2) time-weighted average of percentage change from baseline in urinary albumin-to-creatinine ratio. Both endpoints are sufficiently powered to test for superiority versus placebo after 24weeks with =0.05. MARLINA-T2D is the first of its class to prospectively explore both the glucose- and albuminuria-lowering potential of a dipeptidyl peptidase-4 inhibitor in patients with type 2 diabetes and evidence of renal disease.
Background
High blood glucose and diabetes are amongst the conditions causing the greatest losses in years of healthy life worldwide. Therefore, numerous studies aim to identify reliable risk markers for development of impaired glucose metabolism and type 2 diabetes. However, the molecular basis of impaired glucose metabolism is so far insufficiently understood. The development of so called 'omics' approaches in the recent years promises to identify molecular markers and to further understand the molecular basis of impaired glucose metabolism and type 2 diabetes. Although univariate statistical approaches are often applied, we demonstrate here that the application of multivariate statistical approaches is highly recommended to fully capture the complexity of data gained using high-throughput methods.
Methods
We took blood plasma samples from 172 subjects who participated in the prospective Metabolic Syndrome Berlin Potsdam follow-up study (MESY-BEPO Follow-up). We analysed these samples using Gas Chromatography coupled with Mass Spectrometry (GC-MS), and measured 286 metabolites. Furthermore, fasting glucose levels were measured using standard methods at baseline, and after an average of six years. We did correlation analysis and built linear regression models as well as Random Forest regression models to identify metabolites that predict the development of fasting glucose in our cohort.
Results
We found a metabolic pattern consisting of nine metabolites that predicted fasting glucose development with an accuracy of 0.47 in tenfold cross-validation using Random Forest regression. We also showed that adding established risk markers did not improve the model accuracy. However, external validation is eventually desirable. Although not all metabolites belonging to the final pattern are identified yet, the pattern directs attention to amino acid metabolism, energy metabolism and redox homeostasis.
Conclusions
We demonstrate that metabolites identified using a high-throughput method (GC-MS) perform well in predicting the development of fasting plasma glucose over several years. Notably, not single, but a complex pattern of metabolites propels the prediction and therefore reflects the complexity of the underlying molecular mechanisms. This result could only be captured by application of multivariate statistical approaches. Therefore, we highly recommend the usage of statistical methods that seize the complexity of the information given by high-throughput methods.