Genetic Correlates of Presenile Dementia and Cognitive Decline in the Armenian Population Following COVID-19: A Case-Control Study
Bibliographic record
Abstract
The presence of cognitive lapses in the post-COVID-19 period, particularly among younger individuals, suggests a potential genetic predisposition. This case–control study aimed to assess the association between neurodegeneration-associated genes and cognitive declines in the post-COVID-19 Armenian population under the age of 65. In addition, we examined other contributing factors, including depressive symptoms, hypovitaminosis D, vitamin B12 and B9 deficiencies, and some viral infections, as potential confounders or effect modifiers. A total of 162 participants (ages 19–65, Med = 43), who were exposed to SARS-CoV-2 in Armenia between 2020 and 2022, participated in this study. Standardized assessments, including the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and the Montreal Cognitive Assessment (MoCA), were used to evaluate cognitive functions and mental status, while the Patient Health Questionnaire-9 (PHQ-9) was utilized to assess depressive symptoms. Clinical interview data, comprising yes/no self-reports regarding the presence of cognitive problems and depressive symptoms, were also included. Genetic analysis identified copy number variations (CNVs) in the APP, PSEN1, PSEN2, MAPT, and GRN genes, while viral infections (HSV-1, HSV-2, CMV, EBV, HIV, SARS-CoV-2, Hepatitis A, B, and C) and vitamin D, B12, and B9 deficiencies were measured. Lower cognitive performance was associated with CNVs in PSEN1 (exons 1, 9, 12), GRN (exons 1, 6, 12), and MAPT (exons 2, 8), along with viral infections (HSV-1, HSV-2, HAV-2). The findings indicate that post-COVID-19 cognitive problems are multifactorial and are linked to genetic mutations, viral infections, age, gender, and folic acid deficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".