Research on Influencing Factors and Risk Prediction of Cognitive Function in Community-dwelling Middle-aged and Elderly People
Bibliographic record
Abstract
Background The incidence of cognitive impairment is rising year by year among middle-aged and elderly individuals, yet its pathogenesis remains unclear and effective treatments are lacking. Integrating multidimensional factors to construct a predictive model can enhance the early identification and intervention of high-risk populations for cognitive impairment. Objective To explore and construct a risk prediction model for cognitive impairment in community-dwelling middle-aged and elderly adults based on a biomarkers-genetic-environment multidimensional perspective. Methods A total of 2 243 middle-aged and elderly people in the community who underwent health examinations at Songjiang District Sijing Community Health Center of Shanghai from April to September 2021 were included as the research subjects. Their sociodemographic data, lifestyle, personal disease history and physical examination indicators were collected. The homocysteine (Hcy) concentration was measured by fully automatic biochemical analyzer to determine whether it was hyperhomocysteinemia (HHcy), and single nucleotide polymorphism (SNP) gene sites rs429358 and rs7412 were detected by ligase detection reaction technology to determine the Apolipoprotein E (APOE) genotype. Cognitive function was assessed using Two-tiered Cognitive Self-Assessment (TCSA), and the subjects were divided into normal cognitive group and cognitive impairment risk group according to the assessment results. The general data and physical examination indicators of the two groups were compared. The multivariate logistic stepwise regression method was used to screen independent predictors, and a nomogram prediction model for the risk of cognitive impairment in middle-aged and elderly people was constructed. The Bootstrap self-sampling method was used for internal validation to determine the accuracy of the prediction model. Results The incidence rate of cognitive impairment risk in the community-dwelling middle-aged and elderly people was 16.72%. Multivariate Logistic regression analysis revealed that advanced age (OR=1.064, 95%CI=1.040-1.088, P<0.001), smoking (OR=1.746, 95%CI=1.277-2.386, P<0.001), hypertension (OR=2.584, 95%CI=1.761-3.793, P<0.001), stroke (OR=1.451, 95%CI=1.048-2.008, P=0.025), HHcy (OR=2.421, 95%CI=1.827-3.207, P<0.001) and E4 carrier (OR=2.034, 95%CI=1.473-2.808, P<0.001) were risk factors for cognitive impairment in middle-aged and elderly people in the community, while long years of education (OR=0.922, 95%CI=0.893-0.952, P<0.001) and appropriate sleep duration (OR=0.614, 95%CI=0.470-0.802, P<0.001) were protective factors for cognitive impairment. The nomogram prediction model was constructed based on the influencing factors in the multivariate Logistic regression analysis. The consistency index of the model was 0.743 (95%CI=0.712-0.771) . Conclusion Years of education, smoking, adequate sleep, history of hypertension and stroke, hyperhomocysteinemia (HHcy), and E4 carrier are influencing factors for cognitive impairment in middle-aged and elderly people. A risk prediction model based on multi-dimensional prediction of "biomarkers-genetics-environment" can provide guidance for screening the risk of cognitive impairment in community-dwelling middle-aged and elderly people.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".