Artificial intelligence approaches for early detection of neurocognitive disorders among older adults.

Introduction: Dementia is one of the major global health issues among the aging population, characterized clinically by a progressive decline in higher cognitive functions. This paper aims to apply various artificial intelligence (AI) approaches to detect patients with mild cognitive impairment (MCI) or dementia accurately. Methods: Quantitative research was conducted to address the objective of this study using randomly selected 343 Saudi patients. The Chi-square test was conducted to determine the association of the patient's cognitive function with various features, including demographical and medical history. Two widely used AI algorithms, logistic regression and support vector machine (SVM), were used for detecting cognitive decline. This study also assessed patients' cognitive function based on gender and developed the predicting models for males and females separately. Results: Fifty four percent of patients have normal cognitive function, 34% have MCI, and 12% have dementia. The prediction accuracies for all the developed models are greater than 71%, indicating good prediction capability. However, the developed SVM models performed the best, with an accuracy of 93.3% for all patients, 94.4% for males only, and 95.5% for females only. The top 10 significant predictors based on the developed SVM model are education, bedtime, taking pills for chronic pain, diabetes, stroke, gender, chronic pains, coronary artery diseases, and wake-up time. Conclusion: The results of this study emphasize the higher accuracy and reliability of the proposed methods in cognitive decline prediction that health practitioners can use for the early detection of dementia. This research can also stipulate substantial direction and supportive intuitions for scholars to enhance their understanding of crucial research, emerging trends, and new developments in future cognitive decline studies.

Cerebrospinal Fluid Changes in the Renin-Angiotensin System in Alzheimer's Disease.

Observations in autopsied brain tissue indicate that overactivation of the classical renin-angiotensin system (cRAS) and underactivity within regulatory RAS pathways (rRAS) are associated with pathology in Alzheimer's disease (AD). The primary aim of this study was to investigate whether cerebrospinal fluid (CSF) markers of RAS are altered in AD in relation to established CSF markers of disease pathology (lower Aß42 and elevated tau) and CSF markers of capillary dysfunction. We studied 40 controls and 40 AD cases grouped according to a biomarker profile (i.e., AD cases t-tau>400 pg/mL, pTau >60 pg/mL, and Aß42 <550 pg/mL). Angiotensin-II converting enyme-1 (ACE1) and ACE2 enzyme activity was measured using fluorogenic peptide substrates; sPDGFRß and albumin level by sandwich ELISA; and angiotensin-I (Ang-I), Ang-II, and Ang-(1-7) by direct ELISA. CSF Aß42, total, and phosphorylated tau levels were previously measured by INNOTEST sandwich ELISA. CSF ACE1 activity was significantly elevated in AD (p = 0.008) and positively correlated with ACE2 in AD (r = 0.420, p = 0.007). CSF ACE1 weakly correlated with t-tau (r = 0.294, p = 0.066) and p-tau (r = 0.329, p = 0.038) but not with Aß42 in the controls but not in AD. ACE1 correlated positively with sPDGFRß (r = 0.426, p = 0.007), a marker of pericyte injury, and ACE2 correlated positively with albumin (r = 0.422, p = 0.008), a marker of blood-brain barrier integrity. CSF Ang-I, Ang-II, and Ang-(1-7) levels were unchanged in AD. This cross-sectional CSF study indicates RAS dysfunction in relation to capillary damage in AD.

Angiotensin-converting enzyme 2 is reduced in Alzheimer's disease in association with increasing amyloid-ß and tau pathology.

BACKGROUND: Hyperactivity of the classical axis of the renin-angiotensin system (RAS), mediated by angiotensin II (Ang II) activation of the angiotensin II type 1 receptor (AT1R), is implicated in the pathogenesis of Alzheimer's disease (AD). Angiotensin-converting enzyme-2 (ACE-2) degrades Ang II to angiotensin 1-7 (Ang (1-7)) and counter-regulates the classical axis of RAS. We have investigated the expression and distribution of ACE-2 in post-mortem human brain tissue in relation to AD pathology and classical RAS axis activity. METHODS: We measured ACE-2 activity by fluorogenic peptide substrate assay in mid-frontal cortex (Brodmann area 9) in a cohort of AD (n = 90) and age-matched non-demented controls (n = 59) for which we have previous data on ACE-1 activity, amyloid ß (Aß) level and tau pathology, as well as known ACE1 (rs1799752) indel polymorphism, apolipoprotein E (APOE) genotype, and cerebral amyloid angiopathy severity scores. RESULTS: ACE-2 activity was significantly reduced in AD compared with age-matched controls (P < 0.0001) and correlated inversely with levels of Aß (r = -0.267, P < 0.001) and phosphorylated tau (p-tau) pathology (r = -0.327, P < 0.01). ACE-2 was reduced in individuals possessing an APOE ε4 allele (P < 0.05) and was associated with ACE1 indel polymorphism (P < 0.05), with lower ACE-2 activity in individuals homozygous for the ACE1 insertion AD risk allele. ACE-2 activity correlated inversely with ACE-1 activity (r = -0.453, P < 0.0001), and the ratio of ACE-1 to ACE-2 was significantly elevated in AD (P < 0.0001). Finally, we show that the ratio of Ang II to Ang (1-7) (a proxy measure of ACE-2 activity indicating  conversion of Ang II to Ang (1-7)) is reduced in AD. CONCLUSIONS: Together, our findings indicate that ACE-2 activity is reduced in AD and is an important regulator of the central classical ACE-1/Ang II/AT1R axis of RAS, and also that dysregulation of this pathway likely plays a significant role in the pathogenesis of AD.