Linking of metabolomic biomarkers with cardiometabolic health in Chinese population

Due to rapid nutrition transitions, the prevalence of cardiometabolic diseases, such as metabolic syndrome, type 2 diabetes, and cardiovascular diseases, has been increasing at an alarming rate in the Chinese population. Moreover, Asians, including Chinese, have been hypothesized to have a higher susceptibility to cardiometabolic diseases than Caucasians. Early prediction and prevention are key to controlling this epidemic trend; to this end, the identification of novel biomarkers is critical to reflect environmental exposure, as well as to reveal endogenous metabolic and pathophysiologic mechanisms. The emerging “omics” technologies, especially metabolomics, offer a unique opportunity to provide novel signatures or fingerprints to understand the effects of genetic and non‐genetic factors on cardiometabolic health. During the past two decades, metabolomic approaches have been increasingly used in various epidemiological studies, primarily in Western populations. Although the field is still in its early stages, some studies have tried to identify novel compounds or confirm their metabolites and associations with cardiometabolic diseases in Chinese populations, including amino acids, fatty acids, acylcarnitines and other metabolites. Despite major efforts to discover novel biomarkers for disease prediction or intervention, the limits in current study design, analytical platforms, and data processing approaches are challenges in metabolomic research worldwide. Therefore, future research with more advanced technologies, rigorous study designs, standardized detection and analytic approaches, and integrated data from multiomics approaches are essential to evaluate the feasibility of using metabolomics in clinical settings. Finally, the functional roles and underlying biological mechanisms of metabolomic biomarkers should be elucidated by future mechanistic research.     

Phenomics, lipodystrophy, and the metabolic syndrome

The metabolic syndrome (MetS) is a common multiplex cluster of phenotypes strongly related to cardiovascular disease that includes central obesity with hypertension, dyslipidemia, and type 2 diabetes. The core molecular defect of the MetS is insulin resistance; indeed, the terms "MetS" and "insulin resistance syndrome" often are used interchangeably. The successful translation to clinical medicine of molecular genetic research on other rare monogenic metabolic disorders has stimulated the evaluation of such rare monogenic forms of insulin resistance as partial lipodystrophy resulting from mutations in either LMNA or PPARG genes. Careful phenotypic evaluation of carriers of monogenic insulin resistance using a range of diagnostic methods--an approach sometimes called "phenomics"--may help to find early presymptomatic biomarkers of cardiovascular disease, which, in turn, may uncover new pathways and targets for interventions for the common MetS, diabetes, and atherosclerosis.     

Diabetes - the Role of Metabolomics in the Discovery of New Mechanisms and Novel Biomarkers

Diabetes is a noncommunicable disease associated with ineffective production or utilization of insulin, which causes hyperglycemia and both short- and long-term effects on health including diabetic complications (nephropathy, neuropathy, and retinopathy) and an increased risk of developing cardiovascular diseases. Globally, there were 346 million people diagnosed with diabetes in 2011. The majority of deaths due to diabetes are observed in the developing world and an estimated 12 % of all healthcare costs are attributed to its treatment. Diabetes is a metabolic disorder and in recent decades it has been shown that metabolites other than glucose play important roles in insulin resistance and development of diabetes. To this end the holistic study of metabolites (termed metabolomics) is an important research tool to apply in diabetes research. Here, the importance of metabolomics in discovering novel mechanisms and biomarkers is highlighted through studies published in the period January 2011 to August 2012. Particular focus is placed on the study of branched chain amino acid metabolism, insulin secretion from ß-cells, diabetes complications and interventions, as well as showing how studies in cell and animal models can complement or validate observations in the human population.     

Early identification of metabolic syndrome risk: A review of reviews and proposal for defining pre-metabolic syndrome status

Aimsa) To analyze the relationship of known and emerging biomarkers/indicators for early risk identification of cardiometabolic health risk; b) to identify early risk markers to be used in both clinical and nonclinical settings; and c) to propose a definition of early risk identification in terms of pre-metabolic syndrome (PreMetSyn).Data synthesisPubmed/Medline, Web of Science, Embase, and Cochrane were searched for Systematic Reviews and Meta-analysis. Selected studies were evaluated, and relevant data were extracted and synthesized.ConclusionsSerum uric acid is a good predictive biomarker of metabolic syndrome (MetSyn) and has been associated with non-alcoholic liver fat disease (NAFLD) and type 2 diabetes. NAFLD emerges as an early risk indicator of PreMetSyn by itself. Muscle strength should also be included as an early risk marker of cardiometabolic health. High serum triglycerides and waist circumference confirm their predictive value regarding MetSyn. Indicators related to an inflammatory/pro-inflammatory status usually linked to MetSyn showed limited evidence as robust biomarkers for PreMetSyn. Authors suggest defining PreMetSyn related to cardiometabolic risk. It is also necessary to determine how close people are to the cut-off point of MetSyn components, including emerging indicators proposed by our review. Some biomarkers could be used as indicators of PreMetSyn, before any of the MetSyn components appear, allowing early health interventions to prevent its development. Defining a PreMetSyn status might consider both emerging indicators and those variables already included in the definition of MetSyn. New indicators should be considered to create a new risk score specifically meant for PreMetSyn.     

?Metabolomics“ in der Diabetesforschung

Recently a new promising strategy has been introduced to the well-established approaches in diabetes research. Biomedical metabolomic analyses comprise the examination of metabolite patterns in different body fluids, tissues or samples from cell culture experiments with the objective to maximize the simultaneous detection of intermediate and end products of metabolism. Metabolomic analysis in diabetes research could provide new insights in the pathogenetic scenario of prediabetes, diabetes and its late complications as well as the discovery of novel diagnostic biomarkers. This review provides an overview of metabolomic analyses and a summary of current research results in metabolomics in diabetes research.     

Biomarkers of Diabetic Retinopathy

Diabetic retinopathy (DR), a leading cause of acquired vision loss, is a microvascular complication of diabetes. While traditional risk factors for diabetic retinopathy including longer duration of diabetes, poor blood glucose control, and dyslipidemia are helpful in stratifying patient’s risk for developing retinopathy, many patients without these traditional risk factors develop DR; furthermore, there are persons with long diabetes duration who do not develop DR. Thus, identifying biomarkers to predict DR or to determine therapeutic response is important. A biomarker can be defined as a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. Incorporation of biomarkers into risk stratification of persons with diabetes would likely aid in early diagnosis and guide treatment methods for those with DR or with worsening DR. Systemic biomarkers of DR include serum measures including genomic, proteomic, and metabolomics biomarkers. Ocular biomarkers including tears and vitreous and retinal vascular structural changes have also been studied extensively to prognosticate the risk of DR development. The current studies on biomarkers are limited by the need for larger sample sizes, cross-validation in different populations and ethnic groups, and time-efficient and cost-effective analytical techniques. Future research is important to explore novel DR biomarkers that are non-invasive, rapid, economical, and accurate to help reduce the incidence and progression of DR in people with diabetes.     

Update on Diabetic Cardiomyopathy: Inches Forward,Miles to Go

Diabetes causes cardiomyopathy, both directly and by potentiating the effect of its common comorbidities, coronary artery disease and hypertension, on its development. With the common and growing prevalence of diabetes worldwide, diabetic cardiomyopathy is a significant public health problem. Recent research identifies both mitochondrial dysfunction and epigenetic effects as newly recognized factors in the complex pathogenesis of diabetic cardiomyopathy. Diagnostically, specialized echocardiography techniques, cardiac magnetic resonance imaging, and serologic biomarkers all appear to have promise in detecting the early stages of diabetic cardiomyopathy. Research into treatments includes both traditional diabetes and heart failure therapies, but also explores the potential of newer metabolic and anti-inflammatory agents. These recent insights provide important additions to our knowledge about diabetic cardiomyopathy, but much remains unknown.     

Role of microRNAs in diabetes and its cardiovascular complications

Diabetes is the most common metabolic disorder and is recognized as one of the most important health threats of our time. MicroRNAs (miRNAs) are a novel group of non-coding small RNAs that have been implicated in a variety of physiological processes, including glucose homeostasis. Recent research has suggested that miRNAs play a critical role in the pathogenesis of diabetes and its related cardiovascular complications. This review focuses on the aberrant expression of miRNAs in diabetes and examines their role in the pathogenesis of endothelial dysfunction, cardiovascular disease, and diabetic retinopathy. Furthermore, we discuss the potential role of miRNAs as blood biomarkers and examine the potential of therapeutic interventions targeting miRNAs in diabetes.     

The DEXLIFE study methods: Identifying novel candidate biomarkers that predict progression to type 2 diabetes in high risk individuals

The incidence of type 2 diabetes (T2D) is rapidly increasing worldwide and T2D is likely to affect 592 million people in 2035 if the current rate of progression is continued. Today, patients are diagnosed with T2D based on elevated blood glucose, either directly or indirectly (HbA1c). However, the information on disease progression is limited.Therefore, there is a need to identify novel early markers of glucose intolerance that reflect the underlying biology and the overall physiological, metabolic and clinical characteristics of progression towards diabetes.In the DEXLIFE study, several clinical cohorts provide the basis for a series of clinical, physiological and mechanistic investigations in combination with a range of – omic technologies to construct a detailed metabolic profile of high-risk individuals across multiple cohorts.In addition, an exercise and dietary intervention study is conducted, that will assess the impact on both plasma biomarkers and specific functional tissue-based markers.The DEXLIFE study will provide novel diagnostic and predictive biomarkers which may not only effectively detect the progression towards diabetes in high risk individuals but also predict responsiveness to lifestyle interventions known to be effective in the prevention of diabetes.     

Proteomic and metabolic prediction of response to therapy in gastric cancer

Several new treatment options for gastric cancer have been introduced but the prognosis of patients diagnosed with gastric cancer is still poor. Disease prognosis could be improved for high-risk individuals by implementing earlier screenings. Because many patients are asymptomatic during the early stages of gastric cancer, the diagnosis is often delayed and patients present with unresectable locally advanced or metastatic disease. Cytotoxic treatment has been shown to prolong survival in general, but not all patients are responders. The application of targeted therapies and multimodal treatment has improved prognosis for those with advanced disease. However, these new therapeutic strategies do not uniformly benefit all patients. Predicting whether patients will respond to specific therapies would be of particular value and would allow for stratifying patients for personalized treatment strategies. Metabolic imaging by positron emission tomography was the first technique with the potential to predict the response of esophago-gastric cancer to neoadjuvant therapy. Exploring and validating tissue-based biomarkers are ongoing processes. In this review, we discuss the status of several targeted therapies for gastric cancer, as well as proteomic and metabolic methods for investigating biomarkers for therapy response prediction in gastric cancer.     

Combinations of biomarkers predictive of later life mortality

A wide range of biomarkers, reflecting activity in a number of biological systems (e.g., neuroendocrine, immune, cardiovascular, and metabolic), have been found to prospectively predict disability, morbidity, and mortality outcomes in older adult populations. Levels of these biomarkers, singly or in combination, may serve as an early warning system of risk for future adverse health outcomes. In the current investigation, 13 biomarkers were examined as predictors of mortality occurrence over a 12-year period in a sample of men and women (n = 1,189) 70-79 years of age at enrollment into the study. Biomarkers examined in analyses included markers of neuroendocrine functioning (epinephrine, norepinephrine, cortisol, and dehydroepiandrosterone), immune activity (C-reactive protein, fibrinogen, IL-6, and albumin), cardiovascular functioning (systolic and diastolic blood pressure), and metabolic activity [high-density lipoprotein (HDL) cholesterol, total to HDL cholesterol ratio, and glycosylated hemoglobin]. Recursive partitioning techniques were used to identify a set of pathways, composed of combinations of different biomarkers, that were associated with a high-risk of mortality over the 12-year period. Of the 13 biomarkers examined, almost all entered into one or more high-risk pathways although combinations of neuroendocrine and immune markers appeared frequently in high-risk male pathways, and systolic blood pressure was present in combination with other biomarkers in all high-risk female pathways. These findings illustrate the utility of recursive partitioning techniques in identifying biomarker combinations predictive of mortal outcomes in older adults, as well as the multiplicity of biological pathways to mortality in elderly populations.     

Ovarian aging and implications for fertility female health

Ovarian aging is a major detrimental factor of pregnancy achievement and it is related to other issues of women's health. The purpose of this review is to present an update on ovarian aging risk factors followed by contemporary methods of its assessment and an overview of its current management strategies in assisted reproductive technologies (ART). Ovarian aging is a multifactorial trait governed by several factors including medical, lifestyle, genetic, autoimmune and idiopathic. There are several established risk factors and many others that are still being revealed. Heritability has a major influence on ovarian aging. Different genetic strategies and approaches for ovarian aging evaluation have been rapidly expanding; however the mission is far from complete. Genome-wide association studies seems to be the most applicable to advance this research. Although anti-Müllerian hormone and antral follicle count (AFC) biomarkers seems to be the most reliable predictors of ovarian aging, none has demonstrated conclusive evidence to predict pregnancy achievement in an ART setting. The debate continues which of the two predictors is the most suitable in ART as well as non-ART settings. Although multivariate models have been shown to be equally predictive to AFC, latest data support the notion that chronological age and genetic markers inclusion may increase their reliability. Several strategies have been suggested to manage ovarian aging in ART settings. None of the stimulation protocols or ART interventions has been shown to be convincingly beneficial to ovarian aging women and individualization of treatment is still recommended. Ovarian priming by different androgen preparations has been shown to be promising but more randomized controlled studies are required to substantiate these findings. Except for oocyte donation other ART strategies have not shown a persuasive evidence for advanced ovarian aging infertility patients. The new development of oocyte vitrification may well introduce opportunities for fertility preservation to woman at risk. It is concluded that proper assessment and detection of ovarian aging, employing current or developing biomarkers of ovarian reserve, may enable health providers to recommend, at appropriate biological time, early pregnancy achievement or fertility preservation in women at risk.     

A systematic review of metabolite profiling in gestational diabetes mellitus

Aims/hypothesis

Gestational diabetes mellitus is associated with adverse maternal and fetal outcomes during, as well as subsequent to, pregnancy, including increased risk of type 2 diabetes and cardiovascular disease. Because of the importance of early risk stratification in preventing these complications, improved first-trimester biomarker determination for diagnosing gestational diabetes would enhance our ability to optimise both maternal and fetal health. Metabolomic profiling, the systematic study of small molecule products of biochemical pathways, has shown promise in the identification of key metabolites associated with the pathogenesis of several metabolic diseases, including gestational diabetes. This article provides a systematic review of the current state of research on biomarkers and gestational diabetes and discusses the clinical relevance of metabolomics in the prediction, diagnosis and management of gestational diabetes.

Methods

We conducted a systematic search of MEDLINE (PubMed) up to the end of February 2014 using the key term combinations of ‘metabolomics,’ ‘metabonomics,’ ‘nuclear magnetic spectroscopy,’ ‘mass spectrometry,’ ‘metabolic profiling’ and ‘amino acid profile’ combined (AND) with ‘gestational diabetes’. Additional articles were identified through searching the reference lists from included studies. Quality assessment of included articles was conducted through the use of QUADOMICS.

Results

This systematic review included 17 articles. The biomarkers most consistently associated with gestational diabetes were asymmetric dimethylarginine and NEFAs. After QUADOMICS analysis, 13 of the 17 included studies were classified as ‘high quality’.

Conclusions/interpretation

Existing metabolomic studies of gestational diabetes present inconsistent findings regarding metabolite profile characteristics. Further studies are needed in larger, more racially/ethnically diverse populations.     

Platelet extracellular vesicles as biomarkers for arterial thrombosis

Arterial thrombosis is a major and global cause of human death and disability. Considering the socioeconomic costs of arterial thrombosis, identification of biomarkers to predict and detect arterial thrombosis at an early stage is an important public health goal. Platelet extracellular vesicles (PEV) are a new candidate biomarker of arterial thrombosis. PEV can be measured in biorepositories, thereby offering the possibility to validate PEV in multicenter clinical trials. PEV analysis has been hitherto hampered by lack of standardized methodology, but substantial technological improvements of PEV detection techniques have been achieved recently. However, before PEV emerge from research tools to clinical applications, a number of issues should be clarified. To facilitate validation of PEV as biomarkers of thrombosis, we discuss (i) whether PEV are useful as biomarkers of thrombosis, (ii) why previous conclusions on PEV concentrations, composition and functions require re-evaluation, and (iii) which questions have to be answered before PEV become clinically useful.     

Differentially methylated circulating DNA: A novel biomarker to monitor beta cell death

Diabetes mellitus (DM) is a metabolic disorder of glucose homeostasis caused by insufficient secretion or inadequate action of insulin. Nowadays, the increased morbidity of DM is a worldwide issue. Pancreatic beta cell death plays a key role in the progress of DM, especially Type 1 diabetes (T1D). Traditional biomarkers, such as C-peptide and islet autoimmune antibodies are limited to reflect beta cell death and to identify high risk patients in the clinical practice. Recently, a novel biomarker, differentially methylated circulating DNA, has become a research hotspot. It has better sensitivity and specificity in the detection of beta cell death. Assays of beta cell-derived differentially methylated insulin DNA in serum are helpful to predict the possibility to develop T1D in the high risk population. They have also been applied to evaluate beta cell death in Type 2 diabetes (T2D), gestational diabetes mellitus (GDM), islet transplantation and islet specific immune therapy. Although more studies are needed to identify the best methylation target sites in the INS gene, differentially methylated circulating DNA may be a good method to evaluate the progression and prognosis of islet related diseases in the future.     

Role of atherosclerosis assessment and other novel markers in the metabolic syndrome

The metabolic syndrome is a constellation of cardiovascular disease risk factors predisposing to future cardiovascular disease events as well as the development of type 2 diabetes mellitus. This syndrome is closely linked to both subclinical atherosclerosis and vascular inflammation. The extent of vascular inflammation can be estimated by a number of biomarkers, such as high-sensitivity C-reactive protein, that are associated with the presence of the metabolic syndrome. Evaluating for the presence of subclinical atherosclerosis and inflammatory biomarkers may help to risk stratify patients with the metabolic syndrome.     

Mit Big Data zur personalisierten Diabetesprävention

Since 1980, the number of people with diabetes has quadrupled worldwide. In Germany alone, almost 7 million people suffer from this metabolic disease and every year, there are up to 500,000 new diagnoses. These numbers show the urgent need for new effective prevention measures and innovative forms of treatment. Digitalization makes it possible to explore the widespread disease of diabetes in a new dimension in order to identify subtypes of diabetes very early on and offer suitable personalized preventive measures. With the establishment of a Digital Diabetes Prevention Center, health and research data from a wide variety of sources could be brought together, analysed and evaluated using innovative information technology (IT) capabilities to identify different diabetes subtypes and offer specific prevention and therapy measures that can be used directly through close cooperation with the population.     

Lifestyle and precision diabetes medicine: will genomics help optimise the prediction,prevention and treatment of type 2 diabetes through lifestyle therapy?

Precision diabetes medicine, the optimisation of therapy using patient-level biomarker data, has stimulated enormous interest throughout society as it provides hope of more effective, less costly and safer ways of preventing, treating, and perhaps even curing the disease. While precision diabetes medicine is often framed in the context of pharmacotherapy, using biomarkers to personalise lifestyle recommendations, intended to lower type 2 diabetes risk or to slow progression, is also conceivable. There are at least four ways in which this might work: (1) by helping to predict a person’s susceptibility to adverse lifestyle exposures; (2) by facilitating the stratification of type 2 diabetes into subclasses, some of which may be prevented or treated optimally with specific lifestyle interventions; (3) by aiding the discovery of prognostic biomarkers that help guide timing and intensity of lifestyle interventions; (4) by predicting treatment response. In this review we overview the rationale for precision diabetes medicine, specifically as it relates to lifestyle; we also scrutinise existing evidence, discuss the barriers germane to research in this field and consider how this work is likely to proceed.