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Machine learning for comprehensive prediction of high risk for Alzheimer's disease based on chromatic pupilloperimetry.

ABSTRACT: Currently there are no reliable biomarkers for early detection of Alzheimer's disease (AD) at the preclinical stage. This study assessed the pupil light reflex (PLR) for focal red and blue light stimuli in central and peripheral retina in 125 cognitively normal middle age subjects (45-71 years old) at high risk for AD due to a family history of the disease (FH+), and 61 age-similar subjects with no family history of AD (FH-) using Chromatic Pupilloperimetry coupled with Machine Learning (ML). All subjects had normal ophthalmic assessment, and normal retinal and optic nerve thickness by optical coherence tomography. No significant differences were observed between groups in cognitive function and volumetric brain MRI. Chromatic pupilloperimetry-based ML models were highly discriminative in differentiating subjects with and without AD family history, using transient PLR for focal red (primarily cone-mediated), and dim blue (primarily rod-mediated) light stimuli. Features associated with transient pupil response latency (PRL) achieved Area Under the Curve Receiver Operating Characteristic (AUC-ROC) of 0.90 ± 0.051 (left-eye) and 0.87 ± 0.048 (right-eye). Parameters associated with the contraction arm of the rod and cone-mediated PLR were more discriminative compared to parameters associated with the relaxation arm and melanopsin-mediated PLR. Significantly shorter PRL for dim blue light was measured in the FH+ group in two test targets in the temporal visual field in right eye that had highest relative weight in the ML algorithm (mean ± standard error, SE 0.449 s ± 0.007 s vs. 0.478 s ± 0.010 s, p = 0.038). Taken together our study suggests that subtle focal changes in pupil contraction latency may be detected in subjects at high risk to develop AD, decades before the onset of AD clinical symptoms. The dendrites of melanopsin containing retinal ganglion cells may be affected very early at the preclinical stages of AD.

SUBMITTER: Lustig-Barzelay Y

PROVIDER: S-EPMC9200977 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundUsing interpretable machine learning, we sought to define the immune microenvironment subtypes and distinctive genes in AD.MethodsssGSEA, LASSO regression, and WGCNA algorithms were used to evaluate immune state in AD patients. To predict the fate of AD and identify distinctive genes, six machine learning algorithms were developed. The output of machine learning models was interpreted using the SHAP and LIME algorithms. For external validation, four separate GEO databases were used. We estimated the subgroups of the immunological microenvironment using unsupervised clustering. Further research was done on the variations in immunological microenvironment, enhanced functions and pathways, and therapeutic medicines between these subtypes. Finally, the expression of characteristic genes was verified using the AlzData and pan-cancer databases and RT-PCR analysis.ResultsIt was determined that AD is connected to changes in the immunological microenvironment. WGCNA revealed 31 potential immune genes, of which the greenyellow and blue modules were shown to be most associated with infiltrated immune cells. In the testing set, the XGBoost algorithm had the best performance with an AUC of 0.86 and a P-R value of 0.83. Following the screening of the testing set by machine learning algorithms and the verification of independent datasets, five genes (CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12) that were closely associated with AD pathological biomarkers and allowed for the accurate prediction of AD progression were found to be immune microenvironment-related genes. The feature gene-based nomogram may provide clinical advantages to patients. Two immune microenvironment subgroups for AD patients were identified, subtype2 was linked to a metabolic phenotype, subtype1 belonged to the immune-active kind. MK-866 and arachidonyltrifluoromethane were identified as the top treatment agents for subtypes 1 and 2, respectively. These five distinguishing genes were found to be intimately linked to the development of the disease, according to the Alzdata database, pan-cancer research, and RT-PCR analysis.ConclusionThe hub genes associated with the immune microenvironment that are most strongly associated with the progression of pathology in AD are CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12. The hypothesized molecular subgroups might offer novel perceptions for individualized AD treatment.

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Machine learning for comprehensive prediction of high risk for Alzheimer's disease based on chromatic pupilloperimetry.

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