Metabolic profiling in Maturity-onset diabetes of the young (MODY) and young onset type 2 diabetes fails to detect robust urinary biomarkers

[Biochemical markers of autism]

Autism spectrum disorder (ASD) is becoming an increasingly common disorder of the development of the nervous system in the modern world. The diagnosis is made based on observation of the patient's behavior, which significantly complicates the diagnosis and treatment of the disorder. The subjectivity of behavioral diagnostics dictates the need for the study of biomarkers of ASD. Over the past two decades, researchers have focused on identifying specific biological abnormalities in ASD that will help in the diagnosis of the disease. This review discusses the state of research on various biomarkers currently being developed for ASD.

Elevated level of lysophosphatidic acid among patients with HNF1B mutations and its role in RCAD syndrome: a multiomic study

INTRODUCTION

Patients with hepatocyte nuclear factor-1 beta (HNF1B) mutations present a variable phenotype with two main symptoms: maturity onset diabetes of the young (MODY) and polycystic kidney disease (PKD).

OBJECTIVES

Identification of serum metabolites specific for HNF1Bmut and evaluation of their role in disease pathogenesis.

METHODS

We recruited patients with HNF1Bmut (N = 10), HNF1Amut (N = 10), PKD: non-dialyzed and dialyzed (N = 8 and N = 13); and healthy controls (N = 12). Serum fingerprinting was performed by LC-QTOF-MS. Selected metabolite was validated by ELISA (enzyme-linked immunosorbent assay) measurements and then biologically connected with HNF1B by in silico analysis. HepG2 were stimulated with lysophosphatidic acid (LPA) and HNF1B gene was knocked down (kd) by small interfering RNA. Transcriptomic analysis with microarrays and western blot measurements were performed.

RESULTS

Serum levels of six metabolites including: arachidonic acid, hydroxyeicosatetraenoic acid, linoleamide and three LPA (18:1, 18:2 and 20:4), had AUC (the area under the curve) > 0.9 (HNF1Bmut vs comparative groups). The increased level of LPA was confirmed by ELISA measurements. In HepG2^HNF1Bkd cells LPA stimulation lead to downregulation of many pathways associated with cell cycle, lipid metabolism, and upregulation of steroid hormone metabolism and Wnt signaling. Also, increased intracellular protein level of autotaxin was detected in the cells. GSK-3alpha/beta protein level and its phosphorylated ratio were differentially affected by LPA stimulation in HNF1Bkd and control cells.

CONCLUSIONS

LPA is elevated in sera of patients with HNF1Bmut. LPA contributes to the pathogenesis of HNF1B-MODY by affecting Wnt/GSK-3 signaling.

The Role of Lipidomics in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental syndrome commonly diagnosed in early childhood; it is usually characterized by impairment in reciprocal communication and speech, repetitive behaviors, and social withdrawal with loss in communication skills. Its development may be affected by a variety of environmental and genetic factors. Trained physicians diagnose and evaluate the severity of ASD based on clinical evaluations of observed behaviors. As such, this approach is inevitably dependent on the expertise and subjective assessment of those administering the clinical evaluations. There is a need to identify objective biological markers associated with diagnosis or clinical severity of the disorder. Several important issues and concerns exist regarding the diagnostic competence of the many abnormal plasma metabolites produced in the different biochemical pathways evaluated in individuals with ASD. The search for high-performing bio-analytes to diagnose and follow-up ASD development is still a major target in medicine. Dysregulation in the oxidative stress response and proinflammatory processes are major etiological causes of ASD pathogenesis. Furthermore, dicarboxylic acid metabolites, cholesterol-related metabolites, phospholipid-related metabolites, and lipid transporters and mediators are impaired in different pathological conditions that have a role in the ASD etiology. A mechanism may exist by which pro-oxidant environmental stressors and abnormal metabolites regulate clinical manifestations and development of ASD.

Proteomics and Metabolomics Approaches towards a Functional Insight onto AUTISM Spectrum Disorders: Phenotype Stratification and Biomarker Discovery

Autism spectrum disorders (ASDs) are neurodevelopmental disorders characterized by behavioral alterations and currently affect about 1% of children. Significant genetic factors and mechanisms underline the causation of ASD. Indeed, many affected individuals are diagnosed with chromosomal abnormalities, submicroscopic deletions or duplications, single-gene disorders or variants. However, a range of metabolic abnormalities has been highlighted in many patients, by identifying biofluid metabolome and proteome profiles potentially usable as ASD biomarkers. Indeed, next-generation sequencing and other omics platforms, including proteomics and metabolomics, have uncovered early age disease biomarkers which may lead to novel diagnostic tools and treatment targets that may vary from patient to patient depending on the specific genomic and other omics findings. The progressive identification of new proteins and metabolites acting as biomarker candidates, combined with patient genetic and clinical data and environmental factors, including microbiota, would bring us towards advanced clinical decision support systems (CDSSs) assisted by machine learning models for advanced ASD-personalized medicine. Herein, we will discuss novel computational solutions to evaluate new proteome and metabolome ASD biomarker candidates, in terms of their recurrence in the reviewed literature and laboratory medicine feasibility. Moreover, the way to exploit CDSS, performed by artificial intelligence, is presented as an effective tool to integrate omics data to electronic health/medical records (EHR/EMR), hopefully acting as added value in the near future for the clinical management of ASD.

Metabolic Profiling of Multiorgan Samples: Evaluation of MODY5/RCAD Mutant Mice

In the present study, we performed a metabolomics analysis to evaluate a MODY5/RCAD mouse mutant line as a potential model for HNF1B-associated diseases. Gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) of gut, kidney, liver, muscle, pancreas, and plasma samples uncovered the tissue specific metabolite distribution. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) was used to identify the differences between MODY5/RCAD and wild-type mice in each of the tissues. The differences included, for example, increased levels of amino acids in the kidneys and reduced levels of fatty acids in the muscles of the MODY5/RCAD mice. Interestingly, campesterol was found in higher concentrations in the MODY5/RCAD mice, with a four-fold and three-fold increase in kidneys and pancreas, respectively. As expected, the MODY5/RCAD mice displayed signs of impaired renal function in addition to disturbed liver lipid metabolism, with increased lipid and fatty acid accumulation in the liver. From a metabolomics perspective, the MODY5/RCAD model was proven to display a metabolic pattern similar to what would be suspected in HNF1B-associated diseases. These findings were in line with the presumed outcome of the mutation based on the different anatomy and function of the tissues as well as the effect of the mutation on development.

Prospective Associations of Systemic and Urinary Choline Metabolites with Incident Type 2 Diabetes

BACKGROUND

Several compounds in the choline oxidation pathway are associated with insulin resistance and prevalent diabetes; however, prospective data are scarce.

We explored the relationships between systemic and urinary choline-related metabolites and incident type 2 diabetes in an observational prospective study among Norwegian patients.

METHODS

We explored risk associations by logistic regression among 3621 nondiabetic individuals with suspected stable angina pectoris, of whom 3242 provided urine samples. Reclassification of patients was investigated according to continuous net reclassification improvement (NRI >0).

RESULTS

After median (25th to 75th percentile) follow-up of 7.5 (6.4–8.7) years, 233 patients (6.4%) were registered with incident type 2 diabetes. In models adjusted for age, sex, and fasting status, plasma betaine was inversely related to new-onset disease [odds ratio (OR) per 1 SD, 0.72; 95% CI, 0.62–0.83; P < 0.00001], whereas positive associations were observed for urine betaine (1.25; 1.09–1.43; P = 0.001), dimethylglycine (1.22; 1.06–1.40; P = 0.007), and sarcosine (1.30; 1.13–1.49; P < 0.001). The associations were maintained in a multivariable model adjusting for body mass index, hemoglobin A1c, urine albumin-to-creatinine ratio, estimated glomerular filtration rate, C-reactive protein, HDL cholesterol, and medications. Plasma betaine and urine sarcosine, the indices most strongly related to incident type 2 diabetes, improved reclassification [NRI >0 (95% CI) 0.33 (0.19–0.47) and 0.16 (0.01–0.31), respectively] and showed good within-person reproducibility.

CONCLUSIONS

Systemic and urinary concentrations of several choline metabolites were associated with risk of incident type 2 diabetes, and relevant biomarkers may improve risk prediction.

Potential serum biomarkers from a metabolomics study of autism

BACKGROUND

Early detection and diagnosis are very important for autism. Current diagnosis of autism relies mainly on some observational questionnaires and interview tools that may involve a great variability. We performed a metabolomics analysis of serum to identify potential biomarkers for the early diagnosis and clinical evaluation of autism.

METHODS

We analyzed a discovery cohort of patients with autism and participants without autism in the Chinese Han population using ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UPLC/Q-TOF MS/MS) to detect metabolic changes in serum associated with autism. The potential metabolite candidates for biomarkers were individually validated in an additional independent cohort of cases and controls. We built a multiple logistic regression model to evaluate the validated biomarkers.

RESULTS

We included 73 patients and 63 controls in the discovery cohort and 100 cases and 100 controls in the validation cohort. Metabolomic analysis of serum in the discovery stage identified 17 metabolites, 11 of which were validated in an independent cohort. A multiple logistic regression model built on the 11 validated metabolites fit well in both cohorts. The model consistently showed that autism was associated with 2 particular metabolites: sphingosine 1-phosphate and docosahexaenoic acid.

LIMITATIONS

While autism is diagnosed predominantly in boys, we were unable to perform the analysis by sex owing to difficulty recruiting enough female patients. Other limitations include the need to perform test-retest assessment within the same individual and the relatively small sample size.

CONCLUSION

Two metabolites have potential as biomarkers for the clinical diagnosis and evaluation of autism.

Assessment of Newly Proposed Clinical Criteria to Identify HNF1A MODY in Patients with an Initial Diagnosis of Type 1 or Type 2 Diabetes Mellitus

The most common form of maturity-onset diabetes of the young (MODY) is caused by mutations in the hepatocyte nuclear factor 1A (HNF1A) gene. However, most HNF1A mutation-carriers are initially misdiagnosed with type 1 (T1DM) or type 2 (T2DM) diabetes mellitus; hence, they often receive nonoptimal treatment. The aim of our study was to test newly proposed clinical criteria for the identification of HNF1A MODY in patients with a diagnosis of T1DM or T2DM. To achieve this, the following criteria to preselect patients for screening were used: for T1DM: TDIR (total daily insulin requirement) > 0.3 IU of insulin/kg and the percentage of basal insulin > 30% of TDIR; for T2DM: sulphonylurea- (SU-) based oral treatment (monotherapy or combined with Metformin) > 15 years and BMI < 30 kg/m(2). We reviewed the clinical data of 140 patients with T1DM and 524 clinically diagnosed with T2DM. On the basis of these criteria, we found a HNF1A mutation in 1 out of 2 individuals with a diagnosis of T1DM and 1 out of 11 selected individuals with a diagnosis of T2DM. We believe that the simplicity of the proposed criteria might prove useful in clinical practice, as an alternative to more time-consuming classical diagnostic techniques.

Clinical and non-targeted metabolomic profiling of homozygous carriers of Transcription Factor 7-like 2 variant rs7903146

An important role of the type 2 diabetes risk variant rs7903146 in TCF7L2 in metabolic actions of various tissues, in particular of the liver, has recently been demonstrated by functional animal studies. Accordingly, the TT diabetes risk allele may lead to currently unknown alterations in human. Our study revealed no differences in the kinetics of glucose, insulin, C-peptide and non-esterified fatty acids during an OGTT in homozygous participants from a German diabetes risk cohort (n = 1832) carrying either the rs7903146 CC (n = 15) or the TT (n = 15) genotype. However, beta-cell function was impaired for TT carriers. Covering more than 4000 metabolite ions the plasma metabolome did not reveal any differences between genotypes. Our study argues against a relevant impact of TCF7L2 rs7903146 on the systemic level in humans, but confirms the role in the pathogenesis of type 2 diabetes in humans as a mechanism impairing insulin secretion.

Assessment of urinary betaine as a marker of diabetes mellitus in cardiovascular patients

Abnormal urinary excretion of betaine has been demonstrated in patients with diabetes or metabolic syndrome. We aimed to identify the main predictors of excretion in cardiovascular patients and to make initial assessment of its feasibility as a risk marker of future diabetes development. We used data from 2396 patients participating in the Western Norway B-vitamin Intervention Trial, who delivered urine and blood samples at baseline, and in the majority at two visits during follow-up of median 39 months. Betaine in urine and plasma were measured by liquid-chromatography-tandem mass spectrometry. The strongest determinants of urinary betaine excretion by multiple regression were diabetes mellitus, age and estimated glomerular filtration rate; all p<0.001. Patients with diabetes mellitus (n = 264) had a median excretion more than three times higher than those without. We found a distinct non-linear association between urinary betaine excretion and glycated hemoglobin, with a break-point at 6.5%, and glycated hemoglobin was the strongest determinant of betaine excretion in patients with diabetes mellitus. The discriminatory power for diabetes mellitus corresponded to an area under the curve by receiver-operating characteristics of 0.82, and betaine excretion had a coefficient of reliability of 0.73. We also found a significant, independent log-linear relation between baseline betaine excretion and the risk of developing new diabetes during follow-up. The good discriminatory power for diabetes, high test-retest stability and independent association with future risk of new diabetes should motivate further investigation on the role of betaine excretion in risk assessment and long-term follow-up of diabetes mellitus.

RD Lawrence lecture 2012: assessing aetiology in diabetes: how C-peptide, CRP and fucosylation came to the party!

Assigning the correct aetiology in diabetes is important for treatment, understanding prognosis and for follow-up of family members. Despite these benefits, many are missing out on the opportunity to have testing for monogenic forms of diabetes. This review gives the clinical features of the commoner forms of monogenic diabetes and examines which clinical and biological markers can be used to identify those at highest risk of having Maturity onset diabetes of the young (MODY). MODY is characterised by young-onset, familial diabetes which is C-peptide positive, β-cell antibody negative and not associated with metabolic syndrome. Differentiating from type 1 and type 2 diabetes can be challenging due to the overlap of clinical features. In type 1 diabetes, insulin production ceases after the honeymoon period. Thus C-peptide can be used to detect those with persisting insulin secretion who might have a different cause for their diabetes. In type 2 diabetes, most have insulin resistance, so absence of metabolic syndrome could be used to identify those most likely to have MODY. Another approach is to look for non-pancreatic features associated with mutations in MODY genes. Following results from Genome-wide association studies, we have shown that those with HNF1A mutations (the commonest form of MODY) have decreased serum levels of highly-sensitive C-reactive protein (hsCRP) and altered patterns of plasma protein fucosylation. These features can differentiate HNF1A-MODY from common forms of diabetes with a high degree of discriminative accuracy. Using combinations of clinical features and new biomarkers in diagnostic pathways will help increase diagnosis rates of MODY.