Abbreviations:1 H NMR-Proton nuclear magnetic resonance; AD -Alzheimer's disease; AUC -area under the curve; AUROC -area under the receiver operating curve; BBB -blood brain barrier; BCAA -branched-chain amino acid; CNS -central nervous system; CSF -cerebral spinal fluid; DSS -Sodium 2,2-dimethyl-2-silapentane-5-sulfonate; FDR -false discovery rate; fMRI -functional magnetic resonance imaging; GC-Tof-MS -gas chromatography time of flight mass spectrometry; HD -Huntington's disease; IGF-1 -insulin like growth factor; MRImagnetic resonance imaging; MAS-NMR -magic angle spinning NMR; MRS -magnetic resonance spectroscopy; ROC -Receiver operating characteristic.

Objective To interrogate the pathogenesis of intrauterine growth restriction (IUGR) and apply Artificial Intelligence (AI) techniques to multi-platform i.e. nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) based metabolomic analysis for the prediction of IUGR. Materials and methods MS and NMR based metabolomic analysis were performed on cord blood serum from 40 IUGR (birth weight < 10 th percentile) cases and 40 controls. Three variable selection algorithms namely: Correlation-based feature selection (CFS), Partial least squares regression (PLS) and Learning Vector Quantization (LVQ) were tested for their diagnostic performance. For each selected set of metabolites and the panel consists of metabolites common in three selection algorithms so-called overlapping set (OL), support vector machine (SVM) models were developed for which parameter selection was performed busing 10-fold cross validations. Area under the receiver operating characteristics curve (AUC), sensitivity and specificity values were calculated for IUGR diagnosis. Metabolite set enrichment analysis (MSEA) was performed to identify which metabolic pathways were perturbed as a direct result of IUGR in cord blood serum. Results All selected metabolites and their overlapping set achieved statistically significant accuracies in the range of 0.78–0.82 for their optimized SVM models. The model utilizing all metabolites in the dataset had an AUC = 0.91 with a sensitivity of 0.83 and specificity equal to 0.80. CFS and OL (Creatinine, C2, C4, lysoPC.a.C16.1, lysoPC.a.C20.3, lysoPC.a.C28.1, PC.aa.C24.0) showed the highest performance with sensitivity (0.87) and specificity (0.87), respectively. MSEA revealed significantly altered metabolic pathways in IUGR cases. Dysregulated pathways include: beta oxidation of very long fatty acids, oxidation of branched chain fatty acids, phospholipid biosynthesis, lysine degradation, urea cycle and fatty acid metabolism. Conclusion A systematically selected panel of metabolites was shown to accurately detect IUGR in newborn cord blood serum. Significant disturbance of hepatic function and energy generating pathways were found in IUGR cases.

For people with Parkinson’s disease (PD), considered the most common neurodegenerative disease behind Alzheimer’s disease, accurate diagnosis is dependent on many factors; however, misdiagnosis is extremely common in the prodromal phases of the disease, when treatment is thought to be most effective. Currently, there are no robust biomarkers that aid in the early diagnosis of PD. Following previously reported work by our group, we accurately measured the concentrations of 18 bile acids in the serum of a prodromal mouse model of PD. We identified three bile acids at significantly different concentrations (p < 0.05) when mice representing a prodromal PD model were compared with controls. These include ω-murichoclic acid (MCAo), tauroursodeoxycholic acid (TUDCA) and ursodeoxycholic acid (UDCA). All were down-regulated in prodromal PD mice with TUDCA and UDCA at significantly lower levels (17-fold and 14-fold decrease, respectively). Using the concentration of three bile acids combined with logistic regression, we can discriminate between prodromal PD mice from control mice with high accuracy (AUC (95% CI) = 0.906 (0.777–1.000)) following cross validation. Our study highlights the need to investigate bile acids as potential biomarkers that predict PD and possibly reflect the progression of manifest PD.

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ACL rupture is a major risk factor for post-traumatic osteoarthritis (PTOA) development. Little information exists on acute systemic metabolic indicators of disease development. Thirty-six female Lewis rats were randomized to Control or noninvasive anterior cruciate ligament rupture (ACLR) and to three post-injury time points: 72 h, 4 weeks, 10 weeks (n = 6). Serum was collected and analyzed by H nuclear magnetic resonance (NMR) spectroscopy and combined direct injection and liquid chromatography (LC)-mass spectrometry (MS)/MS (DI-MS). Univariate and multivariate statistics were used to analyze metabolomic data, and predictive biomarker models were analyzed by receiver operating characteristic (ROC) analysis. Topological pathway analysis was used to identify perturbed pathways. Two hundred twenty-two metabolites were identified by H NMR and DI-MS. Differences in the serum metabolome between ACLR and Control were dominated by medium- and long-chain acylcarnitine species. Further, decreases in several tryptophan metabolites were either found to be significantly different in univariate analysis or to play important contributory roles to multivariate model separation. In addition to acylcarnitines and tryptophan metabolites, glycine, carnosine, and D-mannose were found to differentiate ACLR from Control. Glycine, 9-hexadecenoylcarnitine, trans-2-Dodecenoylcarnitine, linoelaidyl carnitine, hydroxypropionylcarnitine, and D-Mannose were identified as biomarkers with high area under ROC curve values and high predictive accuracies. Our analysis provides new information regarding the potential contribution of inflammatory processes and immune dysregulation to the onset and progression of PTOA following ACL injury. As these processes have most commonly been associated with inflammatory arthropathies, larger-scale studies elucidating their involvement in PTOA development and progression are necessary. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:1969-1979, 2018.