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Home , Lipidomics, Metabolome, Metabolomics

CellR4 2020; 8: e2941 and contributions to type 1 research F. Cesare Marincola Department of Chemical and Geological Sciences, University of Cagliari, University Campus, Monserrato, Cagliari, Italy

Corresponding Author: F. Cesare Marincola, PhD; e-mail: [email protected]

Keywords: Lipidomics, Metabolomics, Type 1 diabetes, creatic b-cells with consequent insulin deficiency. T1D. Type 2 diabetes (T2D) is the most common form of diabetes (about 90% of cases) and generally occurs Abstract after 30-40 years of age5. T2D is characterized by a Metabolomics is a “omic” science with a focus on progressive loss of adequate b- insulin secretion the characterization of , that is the frequently on the background of insulin resistance. pool of low molecular weight (< 1.5 kDa) metab- Gestational diabetes is one of the most common olites involved in the of a biological pregnancy complications and usually disappears af- system. When the targets are , metabolomics ter giving birth6. This condition occurs in about 7% is commonly referred to as lipidomics. Since the of pregnancies. The definition is valid regardless of metabolome composition is influenced by several the type of treatment (diet, physical exercise or insu- factors such as environment, disease, , and lin) and the persistence of diabetes even after preg- genetics, metabolomics is extensively used in the nancy. Both World Health Organization (WHO) and biomedical research for the identification of met- American Diabetes Association (ADA) have identi- abolic signatures or novel biomarkers useful in fied an intermediate type of diabetes for individu- diagnosis, prediction, prognosis, and prevention als whose glucose levels do not meet the criteria for of disease. In recent years, both metabolomics diabetes but are too high to be considered normal. and lipidomics have been extensively employed This condition of very high risk of evolving towards in diabetes research to elucidate the molecular the diabetes, especially T2D, is referred to as im- mechanisms triggering this disease and to discov- paired glucose tolerance, also known as pre-diabe- er biomarkers for its prevention, diagnosis, and tes. Finally, there is a form of hybrid diabetes called treatment. In this review, some of the most signifi- “latent autoimmune diabetes in adults” (LADA) that cant findings relative to studies performed on type includes both autoimmune destruction of pancreatic 1 diabetes patients are summarized to provide an b-cells and some degrees of insulin resistance7. Pa- overview of the potential of these approaches. tients with LADA are treated for a short period after diagnosis without insulin injections. Epidemiological data indicate that the number of Introduction people affected by T1D and T2D is increasing rap- idly worldwide. According to the International Di- The term diabetes mellitus, commonly referred to abetes Federation8, in 2019 approximately 463 mil- as diabetes, is used to define a group of metabolic lion adults (20-79 years) were living with diabetes, disorders characterized by elevated blood glucose and by 2045 this number is estimated to reach 700 levels due to defects in insulin secretion and/or ac- million. More than 1.1 million children and adoles- tion1. Type 1 diabetes (T1D) is an autoimmune dis- cents are living with T1D with the highest incidence ease that represents 5‑10% of all cases of diabetes2. in Finland (64.2/100.000)9 followed by the island of It develops at any age but occurs most frequently in Sardinia, Italy (45/100.000)10. These data are further children and adolescents3,4. T1D is characterized by aggravated by the fact that the chronic hyperglyce- a progressive destruction of insulin-producing pan- mia of diabetes is associated with long-term com-

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1 2 F. Cesare Marincola plications and comorbidities such as cardiovascular factors such as lifestyle, diet, consumption, disease, , dyslipidemia, diabetic ne- and development of diseases, to name just a few15. phropathy, neuropathy, and retinopathy, becoming The main analytical platforms used in metabolom- one of the top 10 causes of death in adults11. ics are the following: nuclear magnetic resonance The exact etiology of diabetes is unknown. The (NMR) spectroscopy and (MS) genetic component has an important weight on the combined with gas (GC-MS), liq- predisposition of diabetes, but it does not have a uid chromatography (LC-MS), high performance direct cause-effect relationship with this disease. liquid chromatography (HPLC-MS), and capillary Indeed, the pathogenetic mechanisms underlying electrophoresis (CE-MS). Basically, NMR offers the clinical of diabetes are by nature a good reproducibility, is not destructive, and re- complex and arise from a variable interaction be- quires relatively simple and fast preparations of tween genetic and environmental (non-genetic) risk samples. Nevertheless, the low sensitivity of NMR factors. In case of T2D, one of the most significant confines its applications to concentration mea- factors influencing the development of disease is surements in the micromolar to millimolar range. lifestyle, commonly associated with urbanization. MS method is more sensitive than NMR, but it Different studies have established that lifestyle is a destructive technique and requires extensive modification together with physical activity and/or sample preparation, including the derivatization healthy diet can delay or prevent the onset of T2D12. of compounds in case of GC-MS. No one of these Differently, at present, T1D cannot be prevented techniques can provide alone a complete picture yet since the complex molecular network responsi- of metabolome; therefore, a complementary use of ble for the destruction of b-cells is still enigmatic, both technologies is particularly useful to gain a thus slowing down the identification of parameters broader perspective. A detailed description of these with the role of specific T1D indicators13. So, pre- techniques, their advantages and drawbacks are the diction of T1D is done based on the risk assessment subject of various reviews. The reader is referred to defined by a combined use of genetic, immunolog- the literature for further information16-19. ic, and metabolic parameters, while the diagnosis Different types of biofluids can be analyzed in is done when the symptoms are evident. To reduce metabolomics such as blood, urine, faecal extracts the pandemic of T1D and its disastrous social, eco- and saliva. No single biofluid is appropriate for all nomic, and health impacts worldwide, there is an studies; blood20 and urine21 samples can provide sig- urgent need to improve the understanding of the nificant insight into the dysregulation of metabolism molecular network responsible for the destruction at the level, while faecal22 and saliva23 metab- of b-cells to identify strategies useful to the pre- olome are specifically informative on microbiota. vention, diagnosis and treatment of this disease. Over the past decades, metabolomics has been successfully applied in medicine and molecular biol- ogy. It has provided useful contributions to the dis- Metabolomics and Lipidomics covery of new biochemical pathways in diseases, the understanding of their role in triggering pathologies, Metabolomics is a “ science” aimed at iden- and the discovery of new therapeutic targets and bio- tifying and quantifying the set of low molecular markers for early diagnosis or monitoring therapeu- weight (typically < 1500 Da), known tic activities24. More recently, lipidomics, a branch of as metabolome, present in a biological sample metabolomics which studies the complete set of lipids such as biofluids, cells, tissues, and organs14. The () produced in a given cell or , has metabolome consists of both endogenous and ex- garnered attention, particularly in relation to diseases ogenous components, including amino acids, pep- characterized by dysregulated metabolism. The tides, nucleic acids, organic acids, , and development of lipidomics has been largely driven by carbohydrates. Since the metabolome composition rapid advances in MS technologies25. can be viewed as a mirror that reflects the glob- Both metabolomics and lipidomics have emerged al assessment of a cellular state, interrogating the as promising approaches also in diabetes research. metabolome enables to investigate the metabolic According to a statistics obtained by searching status of an organism in normal or disruptive phys- “metabolomics, metabolome, lipidomics, or lip- iologic conditions due to altered environmental idome and diabetes” in the “article title, abstract Metabolomics and lipidomics contributions to type 1 diabetes research 3

Figure 1. Research outputs of metabolomics and lipidomics studies in diabetes research. Data were gathered by Scopus26 searching for the terms “metabolomics” or “metabolome” or “lipidomics” or “lipidome” and “diabetes” in “article title, abstract and keywords” field.

and keywords” in SCOPUS database26, 2976 con- Within these studies, important contributions to tributions (articles, reviews, books, book chapters, the understanding of T1D pathogenesis have been editorials, notes, and letters) have been published obtained by applying metabolomics and lipidomics since 2002 (Figure 1). Among these, 139 have been on subjects starting from birth until clinical diag- focused exclusively on T1D. Exhaustive reviews on nosis. In 2008, an integrated lipidomics and me- these topics have been published so far27-35. The aim tabolomics longitudinal study was performed on of the current overview is to illustrate the findings the serum specimens of 56 children of the DIPP of some of the most representative human studies cohort who progressed to T1D (progressors) with performed in different areas of T1D research, pro- age at time of diagnosis between 6 and 162 months, viding the reader an outline of the metabolomics and 73 healthy and autoantibody negative matched and lipidomics contributions to diabetes controls (non-progressors)41. Additionally, the cord discovery and the relative experimental designs. blood samples of 39 DIPP children, 15 of which progressed to diabetes before the age of 12 years, were analyzed. The lipidome of 515 samples col- T1D in children: pre- and post-diagnosis lected from progressors, among which 112 taken before seroconversion to autoantibody positivi- Different long-term studies worldwide have fol- ty, was investigated by ultra-performance liquid lowed up children from birth to investigate the chromatography coupled to mass spectrometry natural history of islet autoimmunity (IA) and set (UPLC-MS), while the metabolome of a subset of up strategies to predict, delay, and prevent the T1D 419 samples was characterized by two-dimension- onset. Among these, we remind the Finnish Type al coupled to time of flight 1 Diabetes Prediction and Prevention (DIPP)36, the mass spectrometry (GCxGC-TOF/MS). Compared Norwegian Environmental Triggers of Type 1 Dia- to non-progressors, the serum lipidome of pro- betes Study (MIDIA)37, the German BABYDIAB38 gressors exhibited decreased phosphatidylcholine and BABYDIET39, and the Colorado Diabetes (PC) at birth, a reduction of triglycerides (TGs) and Autoimmunity Study in the Young (DAISY)40. antioxidant ether phospholipids during the follow 4 F. Cesare Marincola up, and an increase of proinflammatory lysophos- be most pronounced in very young children, i.e. at phatidylcholine (LysoPC) in the period before se- 3 months of age. Consistent with the literature,41,42 roconversion to autoantibody positivity. Before IA sphingomyelins were found to be persistently emergence and T1D diagnosis, the metabolome downregulated in progressors when compared to of progressors showed lower levels of ketoleucine the other two groups, while triacylglycerols and and higher contents of branched chain amino acids PCs were mainly downregulated in progressors as (BCAA) and glutamic acid compared to controls. compared to children who developed at least a sin- The metabolic profile was partially normalized af- gle islet autoantibody but did not progress to T1D ter the seroconversion. at the age of 3 months. A possible role of phospholipids in cord blood as The evidence of differences between the autoim- predictors of development of autoimmunity was fur- munity characteristics in the neonatal and puberty ther supported by other two studies performed on a period has suggested age-dependent differences in larger DIPP cohort42,43 and one conducted in children the events that lead to IA or in the immune response of the Diabetes Prediction in Skåne (DiPiS) study to the event46. In light of this hypothesis, a possible group44. In the first two investigations, the popula- age effect differentiating also the lipidomic profiles tion of T1D children was divided into two subgroups: of early developers of islet autoantibodies in com- cases that developed T1D-associated autoantibodies parison with late developers has been investigated and progressed to T1D during the follow-up; cases by UPLC-MS47. In this study, serum samples were that developed antibodies (one, two or three/four) but collected from children of the BABYDIAB cohort: were clinically unaffected. The differences in cord 35 cases who developed T1D before 2 years of age lipidome of cases when compared to controls con- (n=13) or after 8 years of age (n=22) and matched firmed that T1D progressors have a characteristic lip- controls who remained islet autoantibody-nega- idomic profile already present at birth. In particular, tive (n=35). Amino acids (AA) and lipid profiles choline-containing phospholipids, mainly sphingo- were assessed in the first antibody-positive serum myelins and PC were present at lower concentration samples from children who seroconverted to islet in T1D progressors than in non-progressors and con- autoantibody-positive and in the serum samples trols. Since maternal choline intake during pregnan- of age-matched controls. A second serum sample cy can affect metabolic and physiologic function of was analyzed after 1 year of follow-up. Additional- child, a possible link of the abovementioned findings ly, only for 13 early autoantibody-positive children with the intrauterine environment and the pregnancy, and matched controls a pre-seroconversion sample such as the mother’s nutritional status, was proposed. was available and analyzed for the AA profile. In Corroborating the hypothesis of an association be- good agreement with the literature, seroconverters tween choline-containing phospholipids and progres- had higher levels of odd-chain TGs and polyunsat- sion to T1D, PCs and phosphatidylethanolamines in urated –containing phospholipids than cord blood have been found to be significantly low controls. Furthermore, compared with children also in Swedish children with a T1D diagnosis before who developed autoantibodies in late childhood 4 years of age, while TGs were low in children diag- or remained autoantibody-negative, those who de- nosed before 2 years of age44. veloped autoantibodies by the age of 2 years had Additional information on the dysregulation of two-fold lower concentration of methionine, a me- lipid metabolism prior to IA and T1D onset has tabolite involved in glucose metabolism, insulin been recently obtained by the longitudinal lipidome resistance, and β-cell dysfunction48. analysis of plasma samples from three groups of In Poland, the first metabolomics study on T1D children matched by diabetes risk associated to hu- paediatric patients was performed in 201349. The man leukocyte antigen (HLA), gender, and period study population included 30 T1D children and of birth: patients who progressed to T1D (n = 40); teenagers aged 4–19 years and 12 healthy controls patients who developed at least a single islet auto- aged 9 years. The case group was divided into two antibody but did not progress to T1D during the fol- subgroups: patients with low (L-T1D) and high low-up (n = 40); healthy non diabetic controls (n = (H-T1D) level of glycated hemoglobin (HbA1c). 40)45. Samples were taken at 3, 6, 12, 18, 24, and 36 Urine of children was analyzed by 1H NMR spec- months from birth. The distinct lipidomics profile troscopy. The findings were in agreement with associated with the progression to T1D appeared to those obtained by plasma metabolomics35, pointing Metabolomics and lipidomics contributions to type 1 diabetes research 5 out a metabolic shift induced by T1D development compared to 30 healthy controls (average age of 10.7 which altered the urinary levels of glucose, ± 2.8 years). In addition to a dysregulation of lipid bodies, and other metabolites such as amino acids metabolism pathways and changes in gut microbio- and organic acids, although with different trends ta, the findings of this study suggested an alteration for the two subgroups of cases. For instance, for of the steroid metabolism and a possible impairment L-T1D all measured AA were down regulated, of tryptophan catabolism in pediatric T1D patients, while in the case of H-T1D the glucogenic amino in good agreement with the literature57. acid levels were increased, especially glycine, al- In a study involving T1D infants from the Nor- anine, and valine. Besides a dysregulation at the wegian MIDIA project, the impact of age, sex, levels of the two endogenous pathways of glucose breastfeeding, and development of IA or T1D on the production in human organism (gluconeogenesis– plasma metabolic profile were explored58. The study lactate pathway and proteins catabolism), a possible population included 29 subjects with two or three role of intestinal microbiota was suggested by the autoantibodies or overt T1D and 29 controls neg- lower urinary levels of hippurate (a host-bacterial ative for islet autoantibodies matched for sex, date co-)50 in cases compared to controls. of birth, and area of residence with cases. GC×GC- Another notable example of metabolomics ap- TOFMS was employed for a longitudinal analysis of plication in T1D research is the study performed in samples collected at 3 month-intervals (up to 1 year a cohort of 49 Spanish children, ranging from 6 to of life) during infancy, prior to and close to the de- 11 years old (34 with T1D and 15 controls)51. Plasma velopment of IA. Although some differences were and urine samples were analyzed by LC-MS and observed between the metabolic profiles of cases CE-MS, respectively. The plasma specimens were and controls, overall a clear association between the taken from 26 cases and 14 controls, while urine metabolic profiles and later development of islet au- was provided by 16 cases and 15 controls. Urinary toimmunity was not observed. AA, their metabolites and derivatives were excreted Recently, in the DAISY study, an integrated anal- in higher amount in diabetic children than controls, ysis of genetic, immunologic, metabolomics, and suggesting alterations in glomerular filtration rate proteomic data has been used for modelling the de- and/or proteins and metabolism. The velopment of IA and progression to T1D in a cohort higher levels of nonesterified fatty acids, lysophos- of 42 children which developed IA (among which pholipids, and other derivatives of fatty acids in 20 progressed to diabetes) matched by sex and age- plasma of cases compared to controls further sug- matched with healthy subjects59. Biomarkers were gested an altered lipid metabolism. Furthermore, in assessed at four time points: earliest available sam- good agreement with other studies performed on ple, just prior to IA, just after IA, and just prior to animal52 and human53,54 models, alteration in bile diabetes onset. The analysis of serum metabolome acids and p-cresol sulfate, two well-known metab- was carried out by UPLC-MS/MS. The top select- olites linked to activity, supported ed feature among the best predictors of progression the hypothesis of a role for the gut microbiota in to IA was found to be ascorbic acid ( C). the development of b-cell autoimmunity and T1D. In particular, the levels of this antioxidant agent in Similar conclusions were also reached in a previous children who developed IA at the earliest time point urine-based metabolomics study performed on Sar- were lower than that of controls. This finding is in dinian children’s (29 T1D cases and 90 controls)55, line with the recent observations of an inverse asso- revealing a significant role of hippurate, p-cresol ciation between the plasma level of vitamin C and sulfate and phenylacetylglycine in classifying the the risk of IA in children with increased genetic risk groups of cases and controls. of T1D60, corroborating the hypothesis of a possible The potential of urine-based metabolomics to role of genetic variation in vitamin C in T1D devel- monitor metabolic dysregulation in T1D children has opment. In the same study, glucose was found to be been recently supported also by an Italian study56. the top metabolite for progression from IA to diabe- An excess of urinary glucocorticoids and mineralo- tes59. Other two promising metabolites for prediction corticoids, phenylalanine and tryptophan catabo- of IA outcome were a-ketoisovaleric acid, a valine lites, small , , fatty ac- degradation product61 as well as precursor of leucine ids, and gut bacterial products have been observed and valine synthesis62, and 4-hydroxyhippurate, a in 56 T1D children (average age of 11.4 ± 2.2 years) gut microbial fermentation product. 6 F. Cesare Marincola

An accurate characterization of gut metabolic spectra of I- were richer in lactate, acetate, allanto- composition in combination with the analysis of in, and , while MS spectra exhibited higher serum metabolome and gut microbiota has provid- amount of AA and AA metabolites. Furthermore, ed information on the dynamics and stability of the results of the correlation analysis among metab- developing in at-risk T1D infants63. olites measured in common by both techniques This prospective and longitudinal study was per- showed opposite trends for I+ and I-. For instance, formed on stool samples of infants from Finland (n ketone levels (acetoacetate, 3-hydroxybutyrate, ac- = 27) and Estonia (n = 6) recruited at birth based etone) were found to be positively correlated with on a positive cord blood testing for HLA: 7 patients lactate and several AA during insulin deprivation, were positive for at least two of the five autoanti- but negatively correlated with these metabolites bodies but they did not develop T1D; 4 subjects de- during insulin treatment. Also, citrate was neg- veloped T1D; 22 were controls matched for gender, atively correlated with glutamate during insulin HLA genotype, and country with seroconverters. deprivation but the relationship between these two Serum metabolomics (by GCxGC-TOFMS) and metabolites became positive with insulin treat- lipidomics (by UPLC-MS) measurements were ment. Taken together, the compositional changes in performed on samples at 0 (cord blood), 3, 6, 12, plasma of T1D subjects induced by short-term in- 18, 24, and 36 months from birth. Each sample was sulin deprivation provided evidence for known al- paired with the closest stool specimen before the tered physiological processes such as mitochondri- serum collection time that was analyzed by LC-MS al dysfunction, oxidative stress, protein synthesis, to measure polar metabolites and lipids. The anal- degradation, and oxidation, gluconeogenesis, and ysis of gut microbiota showed a significant shift of ketogenesis64. In an extension study, these findings inter- and intra-individual taxonomic composition were further confirmed by using UPLC-TOFMS, with age during the first 3 years of life before be- which allowed the detection and identification of coming more stable. Nevertheless, the stool metab- a larger pool of metabolites (i.e. 330) in all three olomics profile remained approximately unchanged study groups (I+, I-, controls)65. In addition to the over time and across individuals. Additionally, as abovementioned pathways, the findings pointed compared to non-converters and seroconverters, a toward a different regulation of other metabolites drop in α-diversity in progressors was observed in including prostaglandin, arachidonic acid, leukot- the temporal window between the seroconversion rienes, neurotransmitters, nucleotides, and anti-in- and the diagnosis of clinical disease. Correlation flammatory responses. Consistent changes were analysis between absolute abundances of metabo- also observed when the metabolite plasma levels of lites and microbial relative abundances highlighted I+ subjects were compared with those of controls, several metabolite-microbe clusters, among which evidencing that insulin treatment in T1D does not one of the most significant was between TGs, completely restore the metabolic alterations65. BCAA and a number of microbes63. The presence of a metabolic fingerprint associ- ated with good glycemic control in T1D subjects has been evaluated by comparing plasma profiles T1D in adults of 14 patients with poor glycemic control (T1D(‑), HbA1c ≥ 8.5 %), 14 patients with good glycemic The influence of insulin therapy and deficiency on control (T1D(+), HbA1c ≤ 6.5 %), and matched human metabolome has been investigated by ana- non-diabetic controls66. Compared to controls, lyzing plasma samples of seven T1D subjects (31.16 T1D(-) showed an elevation of carbohydrate me- ± 2.9 years) during insulin treatment (I+) and acute tabolites, various AA (such as BCAA, lysine, pro- insulin deprivation (I-) and matched controls (30.26 line, serine, N,N-dimethyl histidine, methionine), ± 3.4 years)64. AA and AA metabolites were pro- short-chain fatty acids, and lipid inflammatory filed by LC-MS/MS, while 1H NMR spectroscopy mediators, like eicosanoids, and metabolites asso- was used to assess the profile of the pool of wa- ciated with the vitamin D pathway, accompanied ter-soluble low-molecular weight metabolites. Mul- by lower levels of glycolytic metabolites and TCA tivariate statistical analysis of NMR data allowed (tricarboxylic acid) cycle metabolites, metabolites I- subjects to be clearly differentiated from I+ and in purine metabolism, alanine and homoarginine. controls. In particular, as compared to I+, the NMR Although many of these alterations were normal- Metabolomics and lipidomics contributions to type 1 diabetes research 7 ized by good glycemic control, several abnormal- nephropathy) is the one most studied by metabo- ities persisted even in presence of long-term good lomics and lipomics. DKD is the leading cause of glycemic control66. end-stage renal disease (ESRD)69. Moderately in- A combined use of 1H-NMR and GC-MS has creased albuminuria, known as microalbuminuria been applied in an exercise-based investigation of and defined as urine albumin excretion rate (AER) the metabolic changes induced by a short-term ses- ranging from 30 to 300 mg/day, is considered an sion of acute aerobic exercise in ten T1D patients early marker of renal dysfunction, and, especially (30 min exercise on a cycle ergometer at an inten- for T1D patients, it may be indicative of an ear- sity of 80% of maximal oxygen uptake, VO2m- ly clinical manifestation of diabetic nephropathy. ax)67. A serum sample was obtained by cases and Severely increased albuminuria, also known as matched healthy controls at rest and after a short macroalbuminuria (defined as urine AER ≥ 300 period of intense exercise. Findings revealed sim- mg/day) has long been regarded as the stage of ir- ilar metabolic events in the two groups of subjects reversible kidney damage70. The only therapeutic such as increased concentrations of gluconeogenic options available for ESRD are dialysis or kidney precursors and TCA cycle intermediates, although transplantation. Since in adult DKD contributes to the overall metabolic response after exercise was significant morbidity and mortality, an early detec- attenuated in T1D patients. In particular, compared tion of diabetic patients (especially with T1D) who to controls, a reduced activation of glycogenolysis are at risk of micro- or macroalbuminuria may rep- and , a less significant accumulation of resent a great opportunity to prevent or delay the TCA cycle intermediates, an attenuated lipolytic incidence of ESRD. action and a decrease in protein catabolism were Different NMR-based investigations have been observed in the T1D group after exercise67. performed on patients enrolled in the Finnish Di- In a German case-control study, the plasma abetic Nephropathy (FinnDiane) study, pointing metabolome profiles of T1D (n = 127) and T2D out clear distinct metabolic characteristics of T1D (n = 244) adults with disease duration less than 1 related to DKD71-74. In 2006, the 1H NMR profiles year have been compared to those of non-diabetic of serum samples from 182 T1D subjects (73 with controls (n = 129) to explore the metabolic alter- normoalbuminuria, 16 with microalbuminuria, and ations occurring in early onset diabetes68. Similar 93 with macroalbuminuria) were compared to those altered metabolite patterns in both T1D and T2D from 21 non-diabetic controls71. Samples were ana- were observed when compared with healthy sub- lyzed without any pretreatment and two types of 1H jects, suggesting common mechanisms respon- NMR experiments were performed on each spec- sible for the deregulation of metabolic control. In imen in order to explore separately two different particular, 28 and 49 metabolites were found to be molecular classes: lipids (LIPO) and significantly altered in T1D and T2D, respectively. low-molecular-weight metabolites (LMWM). The For both diabetes types, the levels of various phos- NMR spectrum of serum recorded in the LIPO phatidylcholine species were reduced compared to window was dominated by broad bands arising controls, while those of BAA, aromatic AA and from fatty acids in TGs, cholesterol compounds, short-chain fatty acids were higher. Additionally, phospholipids in various lipoprotein particles, and diabetes type-specific differences were observed albumin. The spectrum of serum recorded in the for free fatty acids, which were more abundant LMWM window exhibited peaks from BCAA, in T2D compared to controls and T1D, as a likely some organic acids (citric, acetic and lactic acids), consequence of a higher degree of systemic inflam- glucose, creatinine, choline, and N-acetyl protons mation in T2D68. of mobile N-acetylated carbohydrate side-chains of glycoproteins. Spectroscopic and biochemical data were analyzed at three different levels. First, a pair- Diabetic complications wise association between the two type of data was explored. Second, the quantitative nature of NMR Patients with diabetes are at increased risk of mi- data was investigated. Finally, models aiming to crovascular complications such as diabetic kid- separate non-diabetic controls from T1D patients ney disease (DKD), neuropathy, and retinopathy. were constructed. Overall, the good correlation be- Among these diseases, DKD (also called diabetic tween biochemical variables and NMR spectra (us- 8 F. Cesare Marincola ing Spearman correlation coefficient) and the high T1D Finnish patients were enrolled. In this study, predictive values of models built with NMR data the NMR analysis of serum was focused on three highlighted the diagnostic potential of the NMR molecular windows: lipoprotein lipids, low-molec- metabolomics for diabetic nephropathy71. ular-weight metabolites and lipid extracts. Correla- An extension of the abovementioned NMR-based tion-based network analysis evidenced connection study was later performed on a population of 613 of many of the clinical variables with each other T1D subjects, among which 251 with normoalbu- and to the biochemical measures, while no strong minuria, 137 with microalbuminuria, and 225 with connections to lipids or other metabolites were ob- macroalbuminuria72. To structure and categorize served, except via urinary albumin. Furthermore, the metabolic features within these NMR data, a the SOM analysis of data highlighted the presence self-organizing map (SOM)-based analysis was car- of different according to the phases ried out, pointing out complex interactions between of disease: the early sub-clinical phase was char- DKD, insulin resistance, diabetic retinal disease acterized by an increase of phospholipids, inter- (DRD), macrovascular diseases (MVDs), and met- mediate-density (IDL), low-density abolic syndrome (MetS). These pathophysiological lipoproteins (LDL), and unsaturated fatty acids; processes were found to share many features of the the accelerated progression exhibited metabolic di- same biochemical basis. For instance, plasma met- versity for saturated fatty acids, inflammation, and abolic profiles of patients with a detectable loss in HDL metabolism; a possible role of sphingolipid kidney function (i.e., elevated creatinine and urea, pathway was proposed for the ESRD and/or prema- decreased serum albumin) partially overlapped with ture death73. The latter hypothesis was further sup- those of subjects with insulin resistance and related ported by the results of another blood-based me- impairment in glucose metabolism (dyslipidemia, tabolomics study74. More recently, a sophisticated high insulin dose, high HbA1c, elevated lactate and serum metabolomics approach combining UPLC/ acetate and increased fasting glucose). Nevertheless, MS/MS, GC/MS and analysis, has as compared to patients with normoalbuminuria, pa- evidenced the presence of 111 metabolites signifi- tient with DKD exhibited higher contents of TGs, cantly different between progressors and non-pro- creatinine, and urea and lower levels of high-density gressors, among which erythritol, 3-phenylpropio- lipoproteins (HDL) cholesterol and albumin in cas- nate, and N-trimethyl-5-aminovalerate were found es, while the MetS profile was richer in TGs, lactate, to be the best set of variables to predict early-phase and acetate72. diabetic nephropathy76. A similar strategy has been used in a more recent Also, urine metabolomics has exhibited its po- study to assess the composition of serum from 637 tential to asses metabolic alteration linked to DKD. Danish T1D adults (297 with normoalbuminuria, LC- and GC-MS have been employed to character- 158 with microalbuminuria and 182 with macroal- ize the urine metabolome of 52 T1D patients clin- buminuria) by GC×GC-TOFMS75. Cross-sectional ically defined as having a normal AER77. Patients associations between single metabolites and lon- were followed for a median of 5.5 years. Half of gitudinal outcomes data at follow-up (median 5.5 this group progressed to microalbuminuria or DKD years) on renal events (including estimated glomer- (progressors); the other half did not show a pro- ular filtration rate and albuminuria slopes, ESRD gression in albumin excretion (non-progressors). and all-cause mortality) provided evidence for an By using logistic regression models, a distinction association between polyols, AA and hydroxybu- between progressors and non-progressors was ob- tyrates with renal endpoints, delineating a potential tained with an accuracy of 75% and a precision of link of diabetes with the pentose phosphate path- 73% from LC-MS data, and an accuracy of 65% way and gut dysbiosis75. and a precision of 64% from GC-MS data. Novel A comprehensive metabolomics approach has biomarkers including acylcarnitines, acylglycines been applied to associate the serum metabolite and metabolites related to tryptophan metabolism profiles to progression of albuminuria, taking into were found, supporting new biochemical events as- account three different phases, namely: i) from sociated with DKD77. normal AER to micro- or macroalbuminuria; ii) Metabolomic profiles of T1D patients who prog- from microalbuminuria to macroalbuminuria; iii) ress to ESRD have been analyzed by LC- and GC- from macroalbuminuria to ESRD73. In total, 325 MS68. One hundred fifty-eight subjects enrolled in Metabolomics and lipidomics contributions to type 1 diabetes research 9 the Joslin Proteinuria Cohort Study were included disease diagnosis and monitoring, for the study of in this study with a median follow-up of 11.5 years. the pathogenesis of several diseases, as well as for Ninety-nine patients developed ESRD. The global the investigation of oral host–microbiome interac- serum metabolomic profiling revealed 9 candidate tions. The salivary metabolome of 34 T1D children metabolite biomarkers as potential risk factors for under six years of age has been compared to that progression to ESRD in T1D: C-glycosyltryptophan, of 34 healthy controls82. The case group included pseudouridine, O-sulfotyrosine, N-acetylthreonine, 20% of uncontrolled diabetic subjects according to N-acetylserine, N6-carbamoylthreonyladenosine, the postprandial glucose level (> 200 mg/dL) at the N6-acetyllysine, N-acetylalanine, and phenol sul- time of saliva collection. Levels of different organ- fate. These metabolites correlated with one another ic acids were significantly different between cases and with the indices of tubular injury78. and controls. In particular, saliva of uncontrolled Diabetic retinopathy (DR) is the most common T1D children was characterized by high contents of microvascular complication of diabetes79. It is a lactate and acetate, suggesting their possible impli- multifaced disease with progression and clinical cation in the metabolic acidosis observed in patients manifestations varying between patients, and its with diabetic ketoacidosis. In addition, uncontrolled pathophysiological mechanisms are still unknown. T1D subjects exhibited low salivary succinate lev- Vitreous samples from 22 T1D patients (age: 46.1 ± els. Since this metabolite is an intermediate of the 9.2 years; diabetes duration: 14.2 ± 6.7 years) with Krebs cycle in mitochondria as well as an insulino- DR and 22 non-diabetic age-matched controls with tropic compound83, this result could be linked to an macular hole (MH) taken as control group (age: impaired metabolism and insulin secretion produced 45.3 ± 11.5 years) have been analyzed by 1H NMR. by hyperglycaemia and diabetes84. Multivariate statistical analysis of the NMR data- set showed that the main differences in the spectra of MH and DR samples were higher levels of lac- Conclusions tate and glucose, and lower levels of galactitol and ascorbic acid in DR compared to MH. Although this The multifactorial nature of T1D makes the patho- study has the limitation of the absence of healthy genesis of this disease in many respects still ob- non-diabetic controls (justified by the nature of the scure in the eyes of the scientific community. In- sample under investigation), the results provided ev- deed, although in the recent years the knowledge of idence for a metabolic signature of DR likely associ- the metabolic imbalances due to insulin deficiency ated with a metabolic shift due to anaerobic glycol- has increased significantly, the complex relation- ysis and/or inflammation (lactate), polyol pathway ship between the T1D-related metabolic disorders (galactitol), ketone body formation by b-oxidation and the interplay of trigger factors (genetic and en- (acetate), and oxidative stress (ascorbic acid)79. vironmental factors) is not fully explained yet. Very recently, a novel study has been performed Several metabolomics and lipidomics studies to profile the metabolic perturbations associated have been performed in this field, providing contri- with T1D and progression of cardiovascular auto- butions useful to generate and support hypotheses nomic neuropathy (CAN), a complication charac- on the molecular mechanisms of T1D and to iden- terized by impaired autonomic control of the car- tify potential biomarkers for early detection, moni- diovascular system80. Plasma samples were drawn toring, and prevention of these disease. In addition from 47 T1D subjects and 10 age- and sex-matched to the changes in carbohydrate metabolism, the healthy controls followed for 3 years. Three main analysis of metabolome and lipidome in case-con- pathways were found to be correlated with CAN trol investigations provided evidence for metabol- progression: gluconeogenesis, ornithine synthesis, ic dysregulations at the level of other metabolites and TCA cycle. including lipids, branched-chain amino acids, ar- Among the various disorders associated with omatic amino acids, markers of oxidative stress, diabetes mellitus, there are also different oral com- tryptophan, tricarboxylic acid cycle intermediates, plications such as gingivitis, periodontitis, dental and gut microbiota-related metabolites. Different caries, salivary gland dysfunction, oral infections, are the variables that hindered, in some cases, the and oral mucosal diseases81. Due to its role in the achievement of results with a high degree of agree- oral cavity, saliva is a biofluid that can be used for ment, such as the size of study population, age at 10 F. Cesare Marincola the time of disease onset, gender, width of the tem- 14. Shulaev V. Metabolomics technology and . poral window between seroconversion and T1D on- Brief Bioinform 2006; 7: 128-139. 15. Wishart DS. Quantitative metabolomics using NMR. set, environmental factors, to name just a few. Due Trends Anal Chem 2008; 27: 228-237. to the complexity of the molecular network behind 16. Lindon JC, Nicholson JK. Spectroscopic and Statistical this disease and in light of the rapid technological Techniques for Information Recovery in Metabonomics advances in the field of over the and Metabolomics. Annu Rev Anal Chem 2008; 1: 45-69. past decades, a multi-omic data integration applied 17. Lenz EM, Wilson ID. Analytical strategies in metabo- to larger cohort of patients is certainly the future nomics. J Res 2007; 6: 443-458. 18. Smolinska A, Blanchet L, Buydens LMC, Wijmenga SS. direction toward progresses in the identification NMR and pattern recognition methods in metabolomics: and validation of novel biomarkers for early detec- From data acquisition to : A review. tion, prevention, and treatment of T1D. Anal Chim Acta 2012; 750: 82-97. 19. Scalbert A, Brennan L, Fiehn O, Hankemeier T, Kristal BS, van Ommen B, Pujos-Guillot E, Verheij E, Wishart D, Wo- Funding: pereis S. Mass-spectrometry-based metabolomics: limitations No funding is declared for this article and recommendations for future progress with particular focus on nutrition research. Metabolomics 2009; 5: 435-458. 20. Denery JR, Nunes AAK, Dickerson TJ. Characterization Conflict of Interest: of Differences between Blood Sample Matrices. Anal The author declares that she has no conflicts of interest to dis- Chem 2011; 83: 1040-1047. close. 21. Zhang A, Sun H, Wu X, Wang X. Urine metabolomics. Clin Chim Acta 2012; 414: 65-69. 22. Karu N, Deng L, Slae M, Chi A, Sajed T, Huynh H, Wine eferences R E, Wishart DS. A review on human fecal metabolomics: 1. American Diabetes Association. Classification and diag- Methods, applications and the human fecal metabolome nosis of diabetes: Standards of medical care in diabetes. database. Anal Chim Acta 2018; 1030: 1-24. Diabetes Care 2019; 42: S13-S28 23. Gardner A, Carpenter G. Salivary Metabolomics : From 2. Atkinson MA, Eisenbarth GS, Michels AW. Type 1 dia- Diagnostic Biomarker Discovery to Investigating Biolog- betes. Lancet 2014; 383: 69-82. ical Function. Metabolites 2020; 10: 1-23. 3. Maahs DM, West NA, Lawrence JM, Mayer-Davis EJ. 24. Beger RD, Dunn W, Schmidt MA, Gross SS, Kirwan Epidemiology of type 1 diabetes. Endocrinol Metab Clin JA, Cascante M, Brennan L, Wishart DS, Fiehn O. Me- North Am 2010; 39: 481-497. tabolomics enables : "A White Paper, 4. Infante M, Ricordi C. Editorial - Moving forward on the Community Perspective". Metabolomics 2016; 12: 1-15. pathway of targeted immunotherapies for type 1 diabe- 25. Hu T, Zhang JL. Mass-spectrometry-based lipidomics. J tes: the importance of disease heterogeneity. Eur Rev Sep Sci 2018; 41: 351-372. Med Pharmacol Sci 2019; 23: 8702-8704. 26. www.scopus.com, accessed July 30, 2020. 5 Chatterjee S, Khunti K, Davies MJ. Type 2 diabetes. 27. Arneth B, Arneth R, Shams M. Metabolomics of Type 1 Lancet 2017; 389: 2239-2251. and Type 2 Diabetes. Int J Mol Sci 2019; 20: 1-14. 6 American Diabetes Association. Gestational diabetes 28. Pallares-Méndez R, Aguilar-Salinas CA, Cruz-Cautista mellitus. Diabetes Care 2004; 27: s88-s90. I, del Bosque-Plata L. Metabolomics in diabetes. Ann 7. Fourlanos S, Dotta F, Greenbaum CJ, Palmer JP, Ro- Med 2016; 48: 89-102. landsson O, Colman PG, Harrison LC. Latent autoim- 29. Lu J, Xie G, Jia W, Jia W. Metabolomics in human type mune diabetes in adults (LADA) should be less latent. 2 diabetes research. Front Med 2013; 7: 4-13. Diabetologia 2005; 48: 2206-2212. 30. Sas KM, Karnovsky A, Michailidis G, Pennathur S. Me- 8. www.idf.org, accessed July 30, 2020. tabolomics and Diabetes: Analytical and Computational 9 Harjutsalo V, Sund R, Knip M, Groop PH. Incidence of Approaches. Diabetes 2015; 64: 718-732. Type 1 Diabetes in Finland. JAMA 2013; 310: 427-428. 31. Zhang A, Qiu S, Xu H, Sun H, Wang X. Metabolomics in 10 Bruno G, Maule M, Biggeri A, Ledda A, Mannu C, Mer- diabetes. Clin Chim Acta 2014; 429: 106-110. letti F, Songini M. More Than 20 Years of Registration 32. Bain JR, Stevens RD, Wenner BR, Ilkayeva O, Muoio of Type 1 Diabetes in Sardinian Children Temporal Vari- DM, Newgard CB. Metabolomics Applied to Diabetes ations of Incidence With Age, Period of Diagnosis, and Research Moving From Information to Knowledge . Dia- Year of Birth. Diabetes 2013; 62: 3542-3546. betes 2009; 58: 2429-2443. 11. www.who.int, accessed July 30, 2020. 33. Klein MS, Shearer J. Metabolomics and Type 2 Diabetes: 12. Uusitupa M, Khan T, Viguiliouk E, Kahleova H. Preven- Translating Basic Research into Clinical Application. J tion of Type 2 Diabetes by Lifestyle Changes. Nutrients Diabetes Res 2016; 3898502: 1-10. 2019; 11: 1-22. 34. Oresic M. Metabolomics in the Studies of Islet Autoimmuni- 13. Skyler JS. Prediction and prevention of type 1 diabetes: ty and Type 1 Diabetes. Rev Diabet Stud 2012; 9: 236-247. Progress, problems, and prospects. Clin Pharmacol Ther 35. Frohnert BI, Rewers MJ. Metabolomics in childhood 2007; 81: 768-771. diabetes. Pediatr Diabetes 2016; 17: 3-14. Metabolomics and lipidomics contributions to type 1 diabetes research 11

36. Kupila A, Muona P, Simell T, Arvilommi P, Savolainen 48. Yin J, Ren W, Chen S, Li Y, Han H, Gao J, Liu G, Wu H, Hämäläinen AM, Korhonen S, Kimpimäki T, Sjöroos X, Li T, Kim SW, Yin Y. Metabolic Regulation of Methi- M, Ilonen J, Knip M, Simell O. Feasibility of genetic and onine Restriction in Diabetes. Mol Nutr Food Res 2018; immunological prediction of Type I diabetes in a popula- 62: 1-9. tion-based birth cohort. Diabetologia 2001; 44: 290-297. 49. Deja S, BargE, MłynarzP, Basiak A, Willak-Janc E. 37. Stene LC, Witsø E, Torjesen PA, Rasmussen T, Magnus 1H NMR-based metabolomics studies of urine reveal P, Cinek O, Wetlesen T, Rønningen KS. Islet autoanti- differences between type 1 diabetic patients with high body development during follow-up of high-risk chil- and low HbAc1 values. J Pharm Biomed Anal 2013; 83: dren from the general Norwegian population from three 43-48. months of age: design and early results from the MIDIA 50. Nicholls AW, Mortishire-Smith RJ, Nicholson JK. NMR study. J Autoimmun 2007; 29: 44-51. Spectroscopic-Based Metabonomic Studies of Urinary 38. Ziegler AG, Hummel M, Schenker M, Bonifacio E. Metabolite Variation in Acclimatizing Germ-Free Rats. Autoantibody appearance and risk for development of Chem Res Toxicol 2003; 16: 1395-1404. childhood diabetes in offspring of parents with type 1 di- 51. Balderas C, Rupérez F J, Ibañez E, Señorans J, Guerre- abetes: The 2-year analysis of the German BABYDIAB ro-Fernández J, Casado IG, Gracia-Bouthelier R, García Study. Diabetes 1999; 48: 460-468. A, Barbas C. Plasma and urine metabolic fingerprinting 39. Hummel S, Pflüger M, Hummel M, Bonifacio E, Ziegler of type 1 diabetic children. Electrophoresis 2013; 34: AG. Primary dietary intervention study to reduce the risk 2882-2890. of islet autoimmunity in children at increased risk for 52. Wen L, Ley RE, Volchkov PV, Stranges PB, Avanesyan type 1 diabetes. Diabetes Care 2011; 34: 1301-1305. L, Stonebraker AC, Hu C, Wong FS, Szot GL, Jeffrey 40. Rewers M, Bugawan TL, Norris JM, Blair A, Beaty B, Hoff- AB, Gordon JI, Chervonsky AV. Innate immunity and man M, McDuffie RS, Hamman RF, Klingensmith G, Eisen- intestinal microbiota in the development of Type 1 dia- barth GS, Elrich HA. Newborn screening for HLA markers betes. Nature 2009; 455: 1109-1113. associated with IDDM: Diabetes autoimmunity study in the 53. Goffau MC De, Luopajärvi K, Knip M, Ilonen J, Ruohtu- young (DAISY). Diabetologia 1996; 39: 807-812. la T, HärkönenT, Orivuori L, Hakala S, Welling GW, 41. Orešič M, Simell S, Sysi-Aho M, Näntö-Salonen K, Harmsen HJ. Fecal Microbiota Composition Differs Be- Seppänen-Laakso T, Parikka V, Katajamaa M, Hekkala tween Children With b -Cell Autoimmunity and Those A, Mattila I, Keskinen P, Yetukuri L , Reinikainen A, Lä Without. Diabetes 2013; 62: 1238-1244. hde J, Suortti T , Hakalax J, Simell T , Hyöty H , Veijola 54. Soyucen E, Gulcan A, Aktuglu-Zeybek A C, Onal H, R, Ilonen J , Lahesmaa R, Knip M, Simell O. Dysregu- Kiylim E, Aydin A. Differences in the gut microbiota of lation of lipid and amino acid metabolism precedes islet healthy children and those with type 1 diabetes. Pediatr autoimmunity in children who later progress to type 1 Int 2014; 56: 336-343. diabetes. J Exp Med 2008; 205: 2975-2984. 55. Culeddu N, Chessa M, Porcu M C, Fresu P, Tonolo 42. Orešič M, Gopalacharyulu P, Mykkänen J, Lietzen N, Mäki- G, Virgilio G, Migaleddu V. NMR-based metabolomic nen M, Nygren H, Simell S, Simell, V, Hyöty H, Veijola R, study of type 1 diabetes. Metabolomics 2012; 8: 1162- Ilonen J, Sysi-Aho M, Knip M, Hyötyläinen T, Simell O. 1169. Cord serum lipidome in prediction of islet autoimmunity and 56. Galderisi A, Pirillo P, Moret V, Stocchero M, Gucciardi type 1 diabetes. Diabetes 2013; 62: 3268-3274. A, Perilongo G, Moretti C, Monciotti C, Giordano G, 43. Lamichhane S, Ahonen L, Dyrlund TS, Dickens, AM, Baraldi E. Metabolomics reveals new metabolic pertur- Siljander H, Hyöty H, Ilonen J, Toppari J, Veijola R, bations in children with type 1 diabetes. Pediatr Diabetes Hyötyläinen T, Knip M, Orešič O. Cord-blood lipidome 2018; 19: 59-67. in progression to islet autoimmunity and type 1 diabetes. 57. Herrera R, Manjarrez G, Nishimura E, Hernandez J. Biomolecules 2019; 9: 1-9. Serotonin-related tryptophan in children with insulin-de- 44. La Torre D, Seppänen-Laakso T, Larsson HE, Hyötyläin- pendent diabetes. Pediatr Neurol 2003; 28: 20-23. en T, Ivarsson SA, Lernmark Å, Orešič M. Decreased 58. Jørgenrud B, Stene LC, Tapia G, Bøås H, Pepaj M, Berg cord-blood phospholipids in young age-at-onset type 1 J P, Thorsby PM, Orešič M, Hyötyläinen T, Rønningen diabetes. Diabetes 2013; 62: 3951-3956. KS. Longitudinal plasma metabolic profiles, infant feed- 45. Lamichhane S, Ahonen L, Dyrlund TS, Kemppainen ing, and islet autoimmunity in the MIDIA study. Pediatr E, Siljander H, Hyöty H, Ilonen J, Toppari J, Veijola R, Diabetes 2017; 18: 111-119. Hyötyläinen T, Knip M, Orešič O. Dynamics of Plasma 59. Frohnert BI, Webb-Robertson B J, Bramer LM, Reehl Lipidome in Progression to Islet Autoimmunity and Type SM, Waugh K, Steck AK, Norris JM, Rewers M. Predic- 1 Diabetes-Type 1 Diabetes Prediction and Prevention tive modeling of Type 1 diabetes stages using disparate Study (DIPP). Sci Rep 2018; 8: 1-12. data sources. Diabetes 2020; 69: 238-248. 46. Ziegler A, Nepom GT. Prediction and Pathogenesis in 60. Mattila M, Erlund I, Lee H, Niinisto S, Uusitalo U, Type 1. Immunity 2010; 332: 468-478. Andrén Aronsson C, Hummel S, Parikh H, Rich SS, 47. Pflueger M, Seppänen-Laakso T, Suortti T, Hyötyläinen Hagopian W, Toppari J, Lernmark A, Ziegler AG, Re- T, Achenbach P, Bonifacio E, Orešič M, Ziegler AG. wers M, Krischer JP, Norris JM, Virtanen SV. Plasma Age- and islet autoimmunity-associated differences in ascorbic acid and the risk of islet autoimmunity and amino acid and lipid metabolites in children at risk for type 1 diabetes: the TEDDY study. Diabetologia 2020; type 1 diabetes. Diabetes 2011; 60: 2740-2747. 63: 278-286. 12 F. Cesare Marincola

61. Gorissen SHM, Phillips SM. Branched-Chain Amino 73. Makinen VP, Tynkkynen T, Soininen P, Pelotla T, Kan- Acids (Leucine, Isoleucine, and Valine) and Skeletal gas A J, Forsblom C, Thorn LM, Kaski K, Laatikainen Muscle. Nutrition and Skeletal Muscle 2019; pp. 283-298. R. Metabolic Diversity of Progressive Kidney Disease in 62. Reitzer L. Amino Acid Synthesis. Encyclopedia of Mi- 325 Patients with Type 1 Diabetes (the FinnDiane Study). crobiology 2009; pp. 1-17. J Proteome Res 2012; 11: 1782-1790 63. Kostic AD, Gevers D, Siljander H, Vatanen T, Hyötyläin- 74. Mäkinen VP, Tynkkynen T, Soininen P, ForsblomC, Pelto- en T, Hämäläinen AM, Peet A, Tillmann V, Pöhö P, la T, Kangas AJ, Groop P H, Ala-Korpela M. Sphingomy- Mattila I, Lähdesmäki H, Franzosa EA, Vaarala O, de elin is associated with kidney disease in type 1 diabetes Goffau M, Harmsen H, Ilonen J, Virtanen SM, Clish CB, (The FinnDiane Study). Metabolomics 2012; 8: 369-375. Orešič M, Huttenhower C, Knip M; DIABIMMUNE 75. Tofte N, Suvitaival T, Trost K, Mattila IM, Theilade S, Study Group. The dynamics of the human infant gut mi- Winther SA, Ahluwalia TS, Frimodt-Møller M, Legi- crobiome in development and in progression toward type do-Quigley C, Rossing, P. Metabolomic Assessment Re- 1 diabetes. Cell Host Microbe 2015; 17: 260-273. veals Alteration in Polyols and Branched Chain Amino 64. Lanza IR, Zhang S, Ward LE, Karakelides H, Raftery D, Acids Associated With Present and Future Renal Impair- Nair KS. Quantitative metabolomics by H-NMR and LC- ment in a Discovery Cohort of 637 Persons With Type 1 MS/MS confirms altered metabolic pathways in diabetes. Diabetes. Front Endocrinol 2019; 10: e818. PLoS One 2010; 5: e10538. 76. Haukka JK, Sandholm N, Forsblom C, Cobb JE, Groop 65. Dutta T, Chai HS, Ward LE, Ghosh A, Persson X M T, Ford P, Ferrannini E. Metabolomic Profile Predicts Develop- GC, Kudva YC, Sun Z, Asmann YW, Kocher JPA, Nail KS. ment of Microalbuminuria in Individuals with Type 1 Concordance of changes in metabolic pathways based on Diabetes. Sci Rep 2018; 8: e13853. plasma metabolomics and skeletal muscle transcriptomics 77. van der Kloet FMr, Tempels FWA, Ismail N, van Der in type 1 diabetes. Diabetes 2012; 61: 1004-1016. Heijden R, Kasper PT, Doorn R Van, Spijksma G, Koek 66. Dutta T, Kudva YC, Persson XMT, Schenck LA, Ford M, Forsblom C, Holthoef, Groop PH, Reijemers TH, CG, Singh RJ, Carter R, Nair KS. Impact of long-term Hankemeier T. Discovery of early-stage biomarkers for poor and good glycemic control on metabolomics alter- diabetic kidney disease using ms-based metabolomics ations in type 1 diabetic people. J Clin Endocrinol Metab (FinnDiane study). Metabolomics 2012; 8: 109-119. 2016; 101: 1023-1033. 78. Niewczas MA, Mathew AV, Croall S, Byun J, Major 67. Brugnara L, Vinaixa M, Murillo S, Samino S, Rodriguez M, Sabisetti VS, Smiles A, Bonventre J V, Pennathur S. M A, Beltran A, Lerin C, Davison G, Correig X, Novials Circulating Modified Metabolites and a Risk of ESRD A. Metabolomics approach for analyzing the effects of in Patients With Type 1 Diabetes and Chronic Kidney exercise in subjects with type 1 diabetes mellitus. PLoS Disease. Diabetes Care 2017; 40: 383-390. One 2012; 7: 2-9. 79. Duh EJ, Sun JK, Stitt AW. Diabetic retinopathy: current 68. Knebel B, Strassburger K, Szendroedi J, Kotzka J, Scheer understanding, mechanisms, and treatment strategies. M, Nowotny B, Müssig K, Lehr S, Pacini G, Finner H, JCI Insight 2017; 2: e93751. Klüppelholz B, Giani G, Al-Hasani H, Roden M, for 80. Mathew AV, Jaiswal M, Ang L, Michailidis G, Pennathur the German Diabetes Study Group. Specific metabolic S, Pop-Busui R. Impaired amino acid and TCA metabo- profiles and their relationship to insulin resistance in re- lism and cardiovascular autonomic neuropathy progres- cent-onset type 1 and type 2 diabetes. J Clin Endocrinol sion in type 1 diabetes. Diabetes 2019; 6: 2035-2044. Metab 2016; 101: 2130-2140. 81. Lamster IB, Lalla E, Borgnakke WS, Taylor GW. The 69. Persson F, Rossing P. Diagnosis of diabetic kidney dis- relationship between oral health and diabetes mellitus. J ease: state of the art and future perspective. Kidney Int Am Dent Assoc 2008; 139 Suppl: 19S-24S. Suppl 2018; 8: 2-7. 82. De Oliveira LRP, Martins C, Fidalgo TKS, Freitas-Fer- 70. de Boer IH, Afkarian M, Rue TC, Cleary PA, Lachin nandes LB, De Oliveira Torres R, Soares AL, Almeida JM, Molitch ME, Steffes MW, Sun W, Zinman B. Renal FCL, Valente AP, De Souza IPR. Salivary metabolite fin- outcomes in patients with type 1 diabetes and macroal- gerprint of type 1 diabetes in young children. J Proteome buminuria. J Am Soc Nephrol 2014; 25: 2342-2350. Res 2016; 15: 2491-2499. 71. Mäkinen VP, Soininen P, Forsblom C, Parkkonen M, 83. Attali V, Parnes M, Ariav Y, Cerasi E, Kaiser N, Lei- Ingman P, Kaski K, Groop PH, Ala-Korpela M. Diagnos- bowitz G. Regulation of insulin secretion and proinsulin ing diabetic nephropathy by 1H NMR metabonomics of by succinate. Endocrinology 2006; 147: serum. Magn Reson Mater Physics, Biol Med 2006; 19: 5110-5118. 281-296. 84. Haythorne E, Rohm M, van de Bunt M, Brereton MF,. 72. Mäkinen VP, Soininen P, Forsblom C, Parkkonen M, Tarasov AI, Blacker TS, Sachse G, Silva Dos Santos M, Ingman P, Kaski K, Groop PH; FinnDiane Study Group, Terron Exposito R, Davis S, Baba O, Fischer R. Duchen Ala-Korpela M. 1H NMR metabonomics approach to the MR, Rorsman P. MacRae JI,. Ashcroft FM. Diabetes disease continuum of diabetic complications and prema- causes marked inhibition of mitochondrial metabolism in ture death. Mol Syst Biol 2008; 4: 167. pancreatic beta-cells. Nat Commun 2019; 10: 2474.