[P1–140]: ALZHEIMER's GENETIC RISK SCORE LINKED TO MILD BEHAVIORAL IMPAIRMENT
Bibliographic record
Abstract
Mild behavioral impairment (MBI) defines a syndrome of neuropsychiatric symptoms (NPS) that, in the absence of cognitive impairment, confer an increased risk of developing dementia (Ismail et al. 2016). It is well established that NPS typically increase with progression of late onset Alzheimer's Disease (LOAD). Examining the associations of LOAD genetic risk variants with endophenotypes of LOAD, such as MBI, can further elucidate role of genetic variants in the development of LOAD. This study is the first to examine the role of LOAD risk variants, individually and as a composite risk score, with MBI. 1377 older adults with normal cognition and preclinical cognition (aged 72–79; 738 males) from the PATH Through Life project (MCI=133; ‘cognitively normal, but-at-risk’ = 397; cognitively healthy = 847). MBI was assessed in accordance with the ISTAART-AA diagnostic criteria for MBI using the Neuropsychiatric Inventory. Participants were genotyped for SNPs at 25 LOAD risk loci (APOE, ABCA7, BIN1, CD2AP, CD33, CLU, CR1, EPHA1, MS4A4A, MS4A4E, MS4A6A, PICALM, HLA-DRB5, PTK2B, SORL1, SLC24A4-RIN3, DSG2, INPP5D, MEF2C, NME8, ZCWPW1, CELF1, FERMT2 and CASS4). Using the genotyped SNPs, a weighted genetic risk score (GRS) was constructed. Logistic regression adjusting for age, gender, years of education, and cognitive status examined the association between LOAD GRS and MBI domains. A 1SD increase in the LOAD GRS and APOE e4 were associated with higher odds of affective dysregulation (OR=1.23 [1.07–1.41], p=0.003; OR=1.60 [1.17–2.19], p=0.003); MS4A4A-rs4938933 and ZCWPW1-rs1476679 were linked to reduced odds of affective dysregulation (OR=0.78 [0.64–0.94], p=0.01) and social inappropriateness (OR=0.64 [0.41–0.96], p=0.03) respectively; BIN1-rs744373 and EPHA1-rs11767557 were associated with higher odds of abnormal perception/thought control (OR=2.58 [1.36–4.94], p=0.04; OR=2.2 [1.09–4.32], p=0.02). This is the first study to link a weighted AD GRS and five individual risk loci (APOE, MS4A4A, ZCWPW1, BIN1 and EPHA1) to risk of MBI in a large subclinical population-based sample. These findings suggest a common genetic etiology between MBI and traditionally recognized memory problems observed in dementia/AD. Identifying genetic risk factors for MBI will improve understanding of pathophysiological features underlying MBI and help optimize early identification and treatment to reduce dementia risk.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".