Feasible peripheral biomarker for Alzheimer's disease (AD) is lacking. Dysregulation of N-methyl-D-aspartate (NMDA) receptor is implicated in the pathogenesis of AD. D-amino acid oxidase (DAO) and amino acids can regulate the NMDA receptor function. This study aimed to examine whether peripheral DAO and amino acids levels are characteristic of age-related cognitive decline. We enrolled 397 individuals (including amnestic mild cognitive impairment (MCI), mild AD, moderate to severe AD, and healthy elderly). DAO levels in the serum were measured using ELISA. Amino acids levels in serum were measured by high performance liquid chromatography. Severity of the cognitive deficits in subjects was assessed using Clinical Dementia Rating Scale (CDR). The DAO levels increased with the severity of the cognitive deficits. DAO levels were significantly associated with D-glutamate and D-serine levels. The Receiver Operating Characteristics analysis of DAO levels for AD patients vs. healthy controls determined the optimal cutoff value, 30.10, with high sensitivity (0.842) and specificity (0.889) (area under curve = 0.928). This is the first study indicating that the peripheral DAO levels may increase with age-related cognitive decline. The finding supports the hypofunction of NMDA receptor hypothesis in AD. Whether DAO could serve as a potential surrogate biomarker needs further studies.

Current anti-dementia drugs cannot benefit mild cognitive impairment (MCI). Sodium benzoate (a D-amino acid oxidase [DAO] inhibitor) has been found to improve the cognitive function of patients with early-phase Alzheimer's disease (mild Alzheimer's disease or MCI). However, its effect on brain function remains unknown. This study aimed to evaluate the influence of benzoate on functional magnetic resonance imaging in patients with amnestic MCI.

pLG72, named as D-amino acid oxidase activator (although it is not an activator of D-amino acid oxidase demonstrated by later studies), in mitochondria has been regarded as an important modulator of D-amino acid oxidase that can regulate the N-methyl-D-aspartate receptor (NMDAR). Both oxidative stress in mitochondria and NMDAR neurotransmission play essential roles in the process of neurodegenerative dementia. The aim of the study was to investigate whether pLG72 levels changed with the severity of neurodegenerative dementia. We enrolled 376 individuals as the overall cohort, consisting of five groups: healthy elderly, amnestic mild cognitive impairment [MCI], mild Alzheimer's disease [AD], moderate AD, and severe AD. pLG72 levels in plasma were measured using Western blotting. The severity of cognitive deficit was principally evaluated by Clinical Dementia Rating Scale. A gender- and age- matched cohort was selected to elucidate the effects of gender and age. pLG72 levels increased in the MCI and mild AD groups when compared to the healthy group. However, pLG72 levels in the moderate and severe AD groups were lower than those in the mild AD group. D-serine level and D- to total serine ratio were significantly different among the five groups. L-serine levels were correlated with the pLG72 levels. The results in the gender- and age- matched cohort were similar to those of the overall cohort. The finding supports the hypothesis of NMDAR hypofunction in early-phase dementia and NMDAR hyperfunction in late-phase dementia. Further studies are warranted to test whether pLG72 could reflect the function of NMDAR.

The D-amino acid oxidase activator (DAOA, also known as G72) gene is a strong schizophrenia susceptibility gene. Higher G72 protein levels have been implicated in patients with schizophrenia. The current study aimed to differentiate patients with schizophrenia from healthy individuals using G72 single nucleotide polymorphisms (SNPs) and G72 protein levels by leveraging computational artificial intelligence and machine learning tools. A total of 149 subjects with 89 patients with schizophrenia and 60 healthy controls were recruited. Two G72 genotypes (including rs1421292 and rs2391191) and G72 protein levels were measured with the peripheral blood. We utilized three machine learning algorithms (including logistic regression, naive Bayes, and C4.5 decision tree) to build the optimal predictive model for distinguishing schizophrenia patients from healthy controls. The naive Bayes model using two factors, including G72 rs1421292 and G72 protein, appeared to be the best model for disease susceptibility (sensitivity = 0.7969, specificity = 0.9372, area under the receiver operating characteristic curve (AUC) = 0.9356). However, a model integrating G72 rs1421292 only slightly increased the discriminative power than a model with G72 protein alone (sensitivity = 0.7941, specificity = 0.9503, AUC = 0.9324). Among the three models with G72 protein alone, the naive Bayes with G72 protein alone had the best specificity (0.9503), while logistic regression with G72 protein alone was the most sensitive (0.8765). The findings remained similar after adjusting for age and gender. This study suggests that G72 protein alone, without incorporating the two G72 SNPs, may have been suitable enough to identify schizophrenia patients. We also recommend applying both naive Bayes and logistic regression models for the best specificity and sensitivity, respectively. Larger-scale studies are warranted to confirm the findings.