Metabolomics profiles associated with diabetic retinopathy in type 2 diabetes patients

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Abstract

Diabetic retinopathy (DR) is a common complication of diabetes, and it is the consequence of microvascular retinal changes due to high glucose levels over a long time. Metabolomics profiling is a rapidly evolving method used to identify the metabolites in biological fluids and investigate disease progression. In this study, we used a targeted metabolomics approach to quantify the serum metabolites in type 2 diabetes (T2D) patients. Diabetes patients were divided into three groups based on the status of their complications: non-DR (NDR, n = 143), non-proliferative DR (NPDR, n = 123), and proliferative DR (PDR, n = 51) groups. Multiple logistic regression analysis and multiple testing corrections were performed to identify the significant differences in the metabolomics profiles of the different analysis groups. The concentrations of 62 metabolites of the NDR versus DR group, 53 metabolites of the NDR versus NPDR group, and 30 metabolites of the NDR versus PDR group were found to be significantly different. Finally, sixteen metabolites were selected as specific metabolites common to NPDR and PDR. Among them, three metabolites including total DMA, tryptophan, and kynurenine were potential makers of DR progression in T2D patients. Additionally, several metabolites such as carnitines, several amino acids, and phosphatidylcholines also showed a marker potential. The metabolite signatures identified in this study will provide insight into the mechanisms underlying DR development and progression in T2D patients in future studies.

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Diabetic retinopathy.

David A Antonetti, Ronald Klein, Thomas Gardner (2012)

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Metabolomics Applied to Diabetes Research

James Bain, Robert D. Stevens, Brett Wenner … (2009)

Type 2 diabetes is caused by a complex set of interactions between genetic and environmental factors. Recent work has shown that human type 2 diabetes is a constellation of disorders associated with polymorphisms in a wide array of genes, with each individual gene accounting for 40 different genetic diseases of lipid and amino acid metabolism (21,22). In these applications, less emphasis is placed on absolute quantification of multiple analytes in a module, since the screen only requires detection of differences in a single or discrete cluster of analytes with respect to established laboratory norms. For example, a defect in HMG-CoA lyase results in large and specific increases in 3-hydroxy-isovaleryl-carnitine and 3-methylglutaryl-carnitine species detected by MS-MS, whereas defects in long-chain 3-hydroxyacyl CoA dehydrogenase (LCHAD) or mitochondrial trifunctional protein (MTP) are associated with increases in 3-hydroxy-palmitoyl and 3-hydroxy stearoyl-carnitine metabolites (22). In more recent years, many of the MS-based targeted metabolic profiling techniques developed for diagnosing inborn errors of metabolism have been adopted, refined, and supplemented for studies of mechanisms of disease pathogenesis. The approach taken in our laboratory has been to assemble multiple targeted MS-based assay modules that in aggregate report on several critical metabolic pathways (17 –20). Using a combination of GC-MS and MS-MS, we are currently able to perform quantitative analysis of ∼180 metabolites in seven groups, as summarized in Table 1. TABLE 1 Analyte modules in the Stedman Center laboratory and methods of analysis Free fatty acids of varying chain length and degrees of saturation (GC-MS) Total fatty acids (free + esterified) (GC-MS) Acylcarnitines of varying chain length and degrees of saturation (representing precursors and products of mitochondrial fatty acid, amino acid, and carbohydrate oxidation) (MS-MS) Acyl CoAs of varying chain length and degree of saturation (MS-MS) Organic acids (TCA cycle intermediates and related metabolites) (GC-MS) Amino acids, including urea cycle intermediates (MS-MS) Ceramides and sphingolipids (MS-MS) Although the total number of analytes measured with these tools is small relative to estimates of 6,500 total metabolites in the metabolome, they are nevertheless highly useful for understanding changes in metabolic function under different physiological and pathophysiological circumstances. Moreover, expansion of the platform to include a broader range of analytes of interest in disease pathogenesis is possible in the near term. A particular recent focus of the metabolomics community has been in the area of “lipidomics,” and methodologic advances are beginning to emerge for profiling of phospholipids, prostaglandins, and their metabolites (eicosanoids) and sphingolipids (9 –13). “Shotgun lipidomics,” or the broad survey of neutral lipids such as triacylglyerols and diacylglyerols, including the profiling of the acyl side chains of these molecules, is another emergent technology. Early methods focused on separation of neutral lipid species by thin-layer chromatography followed by capillary GC and detection of lipid species with a flame-ionization detector (23). More recent studies use a four-step procedure that includes organic phase extraction (Bligh and Dyer method), intrasource separation of lipid species based on propensity for ionization, separation of ionized species by tandem-MS, and processing of data to assign molecular species and determine relative abundances (24). It should be emphasized, however, that development of new targeted modules is not a trivial or inexpensive undertaking, since it requires acquisition or synthesis of stable isotope–labeled internal standards, development of extraction procedures that are efficient for multiple analytes in a class, tailoring of protocols specific to the various biological matrices, and demonstration of quantitative reproducibility of the methods. The advantage gained is that such tools can be applied to the understanding of metabolic regulatory mechanisms in isolated cells, animal models of disease, and human disease states. Nontargeted metabolite profiling. Nontargeted profiling involves use of NMR, MS, or complementary technologies for measurement of as many metabolites as possible in a biological specimen simultaneously, regardless of the chemical class of the metabolites. In contrast to targeted profiling, in which added internal standards allow quantification of specific metabolites in molar units, nontargeted metabolomics generally adopts a strategy of comparison of two biological states and reporting of those analytes that qualitatively differ in the two states based on statistical analysis (Fig. 1 B). When applied to nontargeted profiling, both NMR and MS have advantages and limitations, and neither technology can currently be used for surveying all of the metabolites in a sample in a quantitative fashion. NMR spectroscopy is theoretically an excellent tool for nontargeted metabolic profiling of all small molecule metabolites, since the method detects spectral features emanating from any molecules that contain carbon or hydrogen (5,7). Moreover, analyses can be conducted directly in bodily fluids, cells, and even in intact tissues without the need for chemical extraction or derivatization of the analytes. These advantages are offset by significant technical challenges, including poor sensitivity, effects of pH and ionic strength, and the difficulty of deconvolution and normalization of spectra of complex metabolite mixtures in biological matrices like plasma, urine, or tissue extracts. Thus, although NMR spectra are information rich, lack of sensitivity and data complexity limit quantitative profiling to ≤100 metabolites in most biological samples by current methods. In some applications, NMR datasets are analyzed by statistical tools such as principal components analysis to identify spectral features (often not identified as specific metabolites) that characterize different biological or disease states (7,25). MS has the immediate advantage of much higher sensitivity compared with NMR, and the most advanced MS platforms such as Fourier transform ion cyclotron (FT-ICR)-MS have the ability to detect metabolites in the femtomole range (11). Moreover, modern MS platforms such as those that incorporate time of flight (TOF), orbitrap, and FT-ICR mass analyzers offer very high mass resolution and mass accuracy. FT-ICR-MS, for example, is capable of achieving a mass resolution of >100,000 while providing mass accurate measurements of 1,100 individual metabolites in prostate tumor explants, blood, and urine from biopsy-positive cancer patients and biopsy-negative control subjects. Analytes were not measured in physical units (nmol or μmol) but rather in relative units in the cancer patients versus the control subjects. No meaningful differences were found in metabolite profiles in urine or blood of cancer subjects compared with control subjects. In contrast, statistically meaningful increments were found in a small subset of metabolites in tumor explants, particularly in metastatic tumors relative to benign prostate. Six metabolites were found to increase with progression from benign prostate to localized cancer to metastatic cancer, including sarcosine, a glycine metabolite. Importantly, the authors then developed a targeted stable isotope–dilution method for quantitative measurement of sarcosine and found it to be elevated by 10- to 20-fold in metastatic tumors compared with benign prostrate. They also showed that manipulation of enzymes of sarcosine metabolism influenced prostate cancer invasion. These results show that nontargeted MS methods are able to detect changes in metabolites within the tissue of origin of the metabolic variability. However, the changes in sarcosine may only have been by the semiquantitative nontargeted approach because the changes were very large in magnitude. Nevertheless, this study is one of the rare but welcome examples of translation of a metabolic profile associated with disease to actual mechanistic investigation. There has been limited application of nontargeted MS-based metabolomics to diabetes research to date. In one study comparing pre-diabetic insulin-resistant to healthy and insulin-sensitive individuals, a complex set of technologies including LC-MS and Fourier-transform ion cyclotron resonance (FTICR)-MS coupled with multivariate statistical analysis was used to identify a single metabolite, 3-hydoxyhippuric acid, as a biomarker of the insulin-resistant state (76). In another study, GC-MS coupled with multivariate statistical analysis was used to evaluate the metabolic impact of three diabetes drugs, rosiglitazone, metformin, and repaglinide, in newly diagnosed type 2 diabetic subjects (77). Abnormalities in several amino acids and fatty acids were reported in diabetic compared with healthy subjects, and rosiglitazone was shown to correct more of these abnormalities than the other two drugs. The significance of these profiles in terms of molecular mechanisms or disease progression remains to be addressed. Similar issues and other problems have emerged when using nontargeted 1H NMR for human studies. A recent study reported that NMR-based metabolic profiles can predict the presence and severity of coronary artery disease (78). Partial least-squares discriminant analysis was used to identify peaks in the major lipid regions of the spectra that appeared to provide separation between the groups. The specific lipid species involved were not identified by this analysis, although it was suggested that choline-containing metabolites were particularly diagnostic. However, a subsequent study using very similar techniques demonstrated that the predictive value of the NMR-based metabolic profiles was weak when other factors such as sex and use of medical interventions such as statins were taken into account (79). This second group of authors demonstrated that the 1H NMR technique could identify male versus female subjects with 100% accuracy but was much less able to identify statin users or subjects with CVD, despite expectations of substantial changes in lipid profile in the former group. Based on these findings, it seems clear that 1H NMR is currently not a substitute for the more invasive procedure of angiography in the diagnosis and staging of CVD. An intriguing and more promising recent application of 1H NMR–based metabolic analysis has been to study the influence of intestinal bacteria (microbiota) on development of obesity and metabolic diseases (80). Indeed, inoculation of germ-free mice with microbiota from the cecum of normal mice causes an increase in body fat content and appearance of insulin resistance within 14 days of transfer (81). 1H NMR–based metabolic profiling of plasma and urine samples from a mouse strain known to be susceptible to hepatic steatosis and insulin resistance (129S6) versus a strain with relative resistance (BALBc) revealed low circulating levels of plasma phosphatidylcholine and high levels of methylamines in urine in the 129S6 strain (80). The authors propose that the increased propensity of the 129S6 strain for metabolic disease could be due to increased metabolism of phosphatidylcholine to methylamines by intestinal bacteria, resulting in a reduced pool for the assembly of VLDL particles, leading to deposition of triglycerides in liver. This hypothesis remains to be tested. 1H NMR has also been applied to research on type 1 diabetes (82,83). One study involved 613 patients with type 1 diabetes and used a novel set of statistical methods to identify a set of metabolites that stratified subgroups in the population according to micro- and macrovascular complications and mortality (83). Another study used LC-MS for lipid profiling, and two-dimensional GC-MS for profiling of organic acids, amino acids, and other small molecule metabolites to implicate gut microbiota in development of type 1 diabetes (84). These findings were made in a prospective study of Finnish children who progressed to type 1 diabetes versus control subjects who remained nondiabetic and autoantibody negative. Children progressing to diabetes had reduced serum levels of phosphatidylcholine and succinic acid at birth, possibly suggestive of increased metabolism of choline by intestinal microbes in the mother or the child. Type 1 diabetic children also had very high levels of glutamate and branched-chain amino acids in blood appearing prior to emergence of autoantibodies, for example, against GAD and insulin. The source of these very interesting surges in amino acid levels and their potential mechanistic significance remain to be established. Conclusions and future directions. In the postgenomic era, biologists and translational investigators alike have gained a new appreciation for metabolic analysis as a critical tool for assessing the physiological and pathophysiological impact of genetic variation. The current surge in methods development in the field of metabolomics is built on the foundation of decades of analytical biochemistry and its use in detecting inborn errors of metabolism. The major difference between then and now is that the current emphasis is on methods that allow simultaneous measurement of multiple analytes in a biological sample, whereas earlier work was often focused on one or a small number of metabolites per assay. Despite significant advances, no single profiling method currently allows simultaneous analysis of all of the metabolites in the metabolome. Ultimate achievement of this goal will require continued intensive development of deeper libraries of chemical standards, instrument platforms with broad sensitivity range and high mass accuracy, and likely integration of MS and NMR methods to gain full analyte coverage. These advances must be coupled with continued development of computational methods for analysis of complex metabolomic datasets and their integration with equally complex genomic, transcriptomic, and proteomic profiles. Meanwhile, considerable progress can be made with the currently available “targeted” technologies that allow profiling of key intermediates of lipid, carbohydrate, purine, pyrimidine, and protein metabolism. The examples provided herein about scientific insights gained by application of current tools suggest a broad horizon and provide strong encouragement for further technology development in this area. However, it may be apparent to the reader that, to date, only a subset of the studies cited in this article have gone beyond the description of metabolic “signatures” that characterize different physiological, pathophysiological, or drug-treated states (information) to actual use of the signatures to pose and then test new hypotheses (knowledge). The paramount challenge of the next phase of metabolomics investigation is to better harvest the information from large datasets to create knowledge about metabolic regulatory mechanisms, perhaps leading to better understanding of perturbations in chronic diseases and conditions such as type 2 diabetes, obesity, CVD, and cancer.

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Global systems biology, personalized medicine and molecular epidemiology

Jeremy Nicholson (2006)

Systems biology in individuals and populations One of the great challenges for 21st century medicine is to deliver effective therapies that are tailored to the exact biology or biological state of an individual to enable so-called ‘personalized healthcare solutions'. Ideally, this would involve a system of patient evaluation that would tell clinicians the correct drug, dose or intervention for any individual before the start of therapy. A practical approach to this evaluation is the concept of patient stratification in which individuals are biologically subclassified (classically according to some genetic features) and biofeatures modelled in relation to outcome. In principle, such stratification for personalized therapy can be applied to drug safety and efficacy modelling and to more general healthcare paradigms involving optimized nutrition and lifestyle management. Of course, truly personalized treatments, even if they can be developed and applied widely, will lamentably always be a luxury of the worlds' richest citizens and nations. So in some respects, personalized healthcare might appear to be at the opposite end of the medical spectrum to the subject of epidemiology in which disease risk factors and disease incidence are studied in populations rich and poor alike. Systems biology provides us with a common language for both describing and modelling the integrated action of regulatory networks at many levels of biological organization from the subcellular through cell, tissue and organ right up to the whole organism. The relatively new science of molecular epidemiology concerns the measurement of the fundamental biochemical factors that underlie population disease demography and understanding ‘the health of nations' and this subject naturally lends it to systems biology approaches. Hence, systems biology is certain to have in future a major role in both the development of personalized medicine and in molecular epidemiological studies. Populations are, of course, made up of individuals and, in principle, there are important unifying features that can be considered from a systems perspective in which biological parameter variability in individuals and their statistical description in large populations can be used to interrogate the outcomes of therapeutic interventions and global patterns of disease distribution. Personalized healthcare and molecular epidemiology are thus effectively two sides of the same ‘systems biology coin'; the essential differences are with respect to the type of medical end points or outcomes that are to be modelled (Figure 1). Metabonomics (see Box 1 for definitions of terms) offers a practical approach to measuring the metabolic end points that link directly to whole system activity and metabolic profiles are determined by both host genetic and environmental factors (Nicholson et al, 2002). The majority of personalized approaches have so far been mainly based on measuring genotype variations relating to polymorphisms in drug-metabolizing enzymes such as cytochrome P450 isoenzymes and N-acetyl transferases (Meyer and Zanger, 1997; Eichelbaum and Burk, 2001; Srivastava, 2003). As there are many of examples of adverse drug reactions being linked to specific enzymatic deficiencies or mutations (Meyer and Zanger, 1997; Eichelbaum and Burk, 2001; Srivastava, 2003), it seems perfectly reasonable to pursue genetically based personalized medicine strategies. However, pharmacogenomic results have thus far proved to be surprisingly disappointing, partly because of the unexpected complexity of the human genome and the difficulties in accurately and unequivocally describing human genotypes and phenotypes (Nebert and Menon, 2001; Nebert et al, 2003; Nebert and Vessell, 2004). Moreover, when considering the wider aspects of human health, it is clear that most major diseases are subject to strong environmental influences, and the majority of people in the world die from what are, in the broadest sense, environmental causes. At the personal level external influences also affect drug metabolism and toxicity, and individual outcomes of a drug intervention are the result of conditional probabilistic interactions between complexes of drug-metabolizing enzyme genes, a range of metabolic regulatory genes and environmental factors such as diet (Nicholson et al, 2004). Even the basic concept of a ‘specified' human population is actually confusing and has often involved ill-defined notions of ethnicity, which are associated with historical culturally biased thinking rather than the genuine and usually small genetic differences between human population groups. The overall lack of genetic variation between populations is remarkable in itself and this is a consequence of humans having moved out of Africa only ca. 100 000 years ago. Thus, according to microsatellite studies, only about 5–10% of the total human genetic variance actually occurs between populations or ethnic groups (Cavalli-Sforza and Feldman, 2003). Of course phenotypically, population subgroups around the world vary widely, as do human disease distributions that are related to diet and environmental factors. There are also well-known differences in drug metabolism (and hence toxicity potential) associated with variations in human genotype and phenotype at both individual and population levels (Meyer and Zanger, 1997; Eichelbaum and Burk, 2001; Nebert and Menon, 2001; Nebert et al, 2003; Srivastava, 2003; Nebert and Vessell, 2004). Obviously, there are many connections between the health of general populations and that of the individuals that make them up, and so it is useful to consider this from a molecular systems biology viewpoint (Figure 1). However, measurement of parameters that relate system level activities to drug interventional outcomes is practically highly limited in applications involving large-scale human populations (Box 2). Population stratification (in the epidemiological rather than personal sense) according to age, gender, diet, ‘ethnicity' and socioeconomic factors is complicated by the fuzziness of some of the classes, and this complicated modelling of these features in relation to systems biology (omics) metrics. Thus, bridging the subjects of personal healthcare and population epidemiology via system biology will require a pragmatic and practical approach, which leads us to the concept of ‘top-down' systems biology and the derivation of metabolic parameters of ‘global' system function. ‘Top-down' systems biology and metabonomics We have been advocating the use of metabolic measurement at the system level utilizing metrics obtainable from biological fluids such as urine and plasma for many years (Nicholson and Wilson, 1989). A particular advantage of biological fluid monitoring or screening is that it is minimally invasive or non-invasive and can be applied on a large scale for human population phenotyping (Nicholson et al, 1999, 2002). The science of metabonomics deals with understanding metabolic changes of a complete system caused by interventions (Nebert et al, 2003; Dumas et al, 2006a, 2006b) and in particular we have noted that metabolic end points are the result of gene–environment interactions in their broadest sense, including extended genome and parasitic interactions (Wang et al, 2004; Dumas et al, 2006a, 2006b; Martin et al, 2006). We have previously outlined our ideas about conditional probabilistic (Bayesian) interactions between genes and environment with respect to adverse drug reactions in individuals and have suggested a hypothetical (Pachinko) model to help study and visualize these interactions (Nicholson and Wilson, 2003). In the Pachinko model, a popular Japanese pinball machine game is used as a metaphor to underscore the idea that metabolic fate results from a sequence of conditional probabilistic interactions between metabolites and components of the cellular biochemical network. In particular, drug molecules can be thought of as a tumbling shaped charge represented as a ball in the machine. Each ball (drug molecule) hits pins (representing drug-metabolizing enzymes—the exact position of which would analogously vary with SNP variations), which transforms the molecule sequentially and so alters its course through the machine (cell/body). Eventually, the drug is metabolized to a state that readily leaves the body and so the exits from the machine at hypothetical ‘excretion points'. The behaviour of each individual ball is difficult to predict, but the probabilistic path of the whole population can be modelled. Thus, the environmental interaction components, for example, gut microbial metabolites, chemicals or dietary compounds, can also be visualized as other balls or shaped objects tumbling through the Pachinko machine. These agents may then block or interfere with or even enhance the drug metabolism pathways. This equates to altering the probabilities of metabolic flow through the system, and the resulting changes in the pathway utilization may be modelled using Bayesian methods. These gene–environment interactions can result in many outcomes—some of which may generate metabolites that cause cellular damage or idiosyncratic (unpredictable) toxicity. Related to this is our concept of the ‘conditional metabolic phenotype' or CMP (Nicholson et al, 2005) in which both genetic factors and exogenous factors, such as diet, exposure to foreign chemicals and so on, interact to determine the possible outcomes of a drug or dietary intervention (Nicholson et al, 2004, 2005; Dumas et al, 2006a, 2006b). The most important feature of the CMP concept is that it represents a starting point of an individual in a multivariate metabolic space that is the result of the combination of many physical, chemical, genetic and environmental influences. We have hypothesized that it is the starting position irrespective of the relative contributions of the individual ‘vector' components that determines the outcome of an intervention (Nicholson et al, 2004) and this is exactly the basis of the personalized healthcare paradigm. So how do we start to apply these ideas to real systems? ‘Bottom-up' modelling approaches if viewed in the cold light of day can never really work in the world of gigantic human phenotypic variability. Indeed even in vitro to in vivo extrapolations of drug metabolism and toxicity within one species are notoriously unreliable, and ‘bottom-up' systems biology modelling poses a vastly more complex challenge because most of the quantitative features needed to make reasonable cellular models are simply not measurable in ‘intact' humans. So approaches appear to work very well in the systems biology of yeast or Escherichia coli cultures are not readily translatable into the modelling of either individual human or population biology. Pharmaco-metabonomics and prediction of drug intervention outcomes In the alternative ‘top-down' approach where metrics of the systemic homeostatic activity are obtained, we have now shown a ‘proof-of-concept' of a new ‘pharmaco-metabonomic' approach to understanding and predicting interventional outcome of drugs (such as toxicity and xenobiotic metabolism in animal model systems) based on mathematical models of a pre-dose metabolic profiles (Clayton et al, 2006). In these studies, we investigated the effects of three structurally diverse hepatotoxins in rats (galactosamine, allyl alcohol and paracetamol), which act via different mechanisms, and found that pre-dose urinary profiles carried information about the degree of post-dosing toxicity, and in the case of paracetamol information about variation of drug metabolism itself. Pharmaco-metabonomics is thus the prediction of the outcome of an intervention in individuals based on pre-dose metabolic state of that individual (Nicholson and Wilson, 2003; Clayton et al, 2006). In a preliminary study on galactosamine toxicity, we found that the responder/non-responder pattern of liver damage at 24 h post-dosing was reflected in the pre-dose metabolic profile of the urine. This was achieved using a simple principal components analysis (which is an unsupervised method that is blind to class in its construction). In a more complex study, a supervised approach, projection-on-latent structure method, was used working with animals given a threshold toxic dose of paracetamol that produced a wide range of liver toxicity between individuals (Figure 2). Here, we found once again that there was a significant association between pre-dose metabolic profile and post-dose outcome with respect to liver damage severity and indeed to drug metabolism (specifically the paracetamol to paracetamol glucuronide excretion ratio was strongly correlated with pre-dose urinary metabolite profiles). These studies imply that there may be future possibilities of applying this approach non-invasively to screening humans in populations. However, practically this is still far off, and we need to extend our knowledge on the relationships between endogenous metabolic status and drug metabolism outcomes for a much wider range of drugs. Of course, there are also significant ethical issues involved with such screening procedures in man. Furthermore, we should not forget that models obtained by the integration of various ‘omics' approaches (pharmacogenomics, pharmacoproteomics and pharmaco-metabonomics) may have improved predictive power, which might indeed be required to get personalized healthcare to work in the real world. Indeed, we have recently shown that proteomics and metabonomics can be statistically integrated to produce new trans-omic combination biomarkers to classify experimental disease states such as xenograft models of prostatic cancer (Rantalainen et al, 2006). However, with current technology, the scale-up of multi-omics strategies to man would be impractical and prohibitively expensive. The most likely near-term implementation of pharmaco-metabonomics would be in the pharmaceutical industry itself at the clinical trial or development stage when drugs are first going into man. Here pre-dose metabolic models could be built and then related to quantitative metabolic fates of compounds and any observed adverse reactions. This would then lead to knowledge about the possible contraindications of a particular drug used in certain phenotypic classes of individuals, which is effectively a type of patient stratification. In any case, both early and clinical safety studies would benefit from the improved metabolic descriptions of test subjects (animal or man) and their responses to novel therapeutic agents, good or bad. It must also be said that the pharmaco-metabonomic concept is not limited just to drug interventions. Effects of dietary modulation, pre-biotic and probiotic treatments and other lifestyle changes could also ultimately be evaluated in this way. This is important because ‘personalized healthcare' means different things for different people and, in general populations, it is lifestyle management not drug therapy that is most effective for disease prevention, which of course is better than having to find a cure. Populations and molecular epidemiology: getting systems biology into man Getting systems biology out of the laboratory into the more general human population both for screening purposes and in order to understand our own changing health patterns is a formidable challenge. Despite relentless advances in medical technology, many major indications of population morbidity and mortality such as heart disease, diabetes, obesity and cancer (all problems in which genetic and environmental factors are closely entwined) are rising all over the world. Interestingly, many of these diseases may be related to changes in the activities or composition of the gut microbiota (microbiome), which has probably been profoundly affected by our lifestyle changes (especially antibiotic use) over the last 50 or so years. In fact, the microbiome is the exact point where host genetics meets environment and can be considered to be our most integrated and influential ‘environmental' factor (Nicholson et al, 2005). Given that humans have slowly evolved with this ‘extended genome' of the microbiota, perturbation of this close association is potentially dangerous and, controversially, may be a root cause of many of our rapidly spreading ‘modern' diseases (Nicholson et al, 2005). Indeed recent studies by us and others have shown that gut microbiotal variations affect the development of diet-induced insulin resistance and type II diabetes mellitus (Dumas et al, 2006a, 2006b) and even the development of type I diabetes in experimental animals (Brugman et al, 2006), which until recently was thought of as being related to purely mammalian (human) genome problems. Thus, wherever we turn we see hypercomplexity in disease development and this must be taken into account in systems biology disease modelling if we are ever going to get effective treatments that actually work in man. In examining human populations for molecular epidemiological purposes, it will probably be important to measure metagenomic features of the gut microbiome, which strongly influences exact mammalian metabolic phenotypes of mice and men (Holmes and Nicholson, 2005; Gavaghan-McKee et al, 2006) and so, using the pharmaco-metabonomic argument, must also influence disease development and possibly optimized therapeutic interventions in individuals and populations. So as systems biology moves forward with the strong driver of personalized medicine, we will also be able to apply these strategies for looking at the changing demography of human disease around the world. Also the creation of personalized health science for the ‘rich nations' should hopefully also benefit the people of developing nations, perhaps especially those countries that are trying to Westernize their economies and lifestyles, and in so doing are now acquiring Western disease patterns at an alarming rate.

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Jun Ho Yun: Role: Data curationRole: Formal analysisRole: MethodologyRole: Writing – original draft

Jeong-Min Kim:

ORCID: https://orcid.org/0000-0002-4305-5162

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: Writing – original draft

Hyun Jeong Jeon: Role: MethodologyRole: Resources

Taekeun Oh: Role: MethodologyRole: Resources

Hyung Jin Choi: Role: ConceptualizationRole: MethodologyRole: ResourcesRole: Writing – review & editing

Bong-Jo Kim:

ORCID: https://orcid.org/0000-0003-3562-2654

Role: ConceptualizationRole: Project administrationRole: SupervisionRole: Writing – review & editing

Jing Chen: Role: Editor

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Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS One

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Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Electronic): 1932-6203

Publication date (Electronic): 29 October 2020

Publication date Collection: 2020

Volume: 15

Issue: 10

Electronic Location Identifier: e0241365

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[1 ] Division of Genome Research, Center for Genome Science, Korea National Institute of Health, Cheongju, Chungbuk, Republic of Korea

[2 ] College of Pharmacy, Chungbuk National University, Cheongju, Chungbuk, Republic of Korea

[3 ] Department of Internal Medicine, Chungbuk National University College of Medicine, Cheongju, Chungbuk, Republic of Korea

[4 ] Department of Biomedical Sciences & Department of Anatomy and Cell Biology, Wide River Institute of Immunology, Seoul National University College of Medicine, Seoul, Republic of Korea

Children's Hospital Boston, UNITED STATES

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Competing Interests: The authors have declared that no competing interests exist.

* E-mail: kbj6181@ 123456cdc.go.kr (BJK); hjchoi@ 123456snu.ac.kr (HJC)

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Jeong-Min Kim https://orcid.org/0000-0002-4305-5162

Bong-Jo Kim https://orcid.org/0000-0003-3562-2654

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Publisher ID: PONE-D-20-18512

DOI: 10.1371/journal.pone.0241365

PMC ID: 7595280

PubMed ID: 33119699

SO-VID: c413bee9-fa18-4dcd-a3a0-b216562cfba2

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Date received : 17 June 2020

Date accepted : 13 October 2020

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Figures: 2, Tables: 2, Pages: 12

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This study was supported by intramural grants from the Korea National Institute of Health (Nos. 2013-NG73001-00 & 2019-NG-053-00). Biospecimens and data were provided by the Korean Genome Analysis Project (4845-301), the Korean Genome and Epidemiology Study (4851-302), and the Korea Biobank Projects (4851-307), which were supported by the Korea Centers for Disease Control and Prevention, Republic of Korea.

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Data Availability All relevant data are within the manuscript and its Supporting Information file. Additional raw and processed data are uploaded to Open Science Framework ( https://osf.io/9baw8)

Metabolomics profiles associated with diabetic retinopathy in type 2 diabetes patients

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Diabetic retinopathy.

Metabolomics Applied to Diabetes Research

Global systems biology, personalized medicine and molecular epidemiology

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