A Diagnostic Model for Alzheimer's Disease Based on Blood Levels of Autophagy-Related Genes.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35645767", "kind": "science-record", "revision": 4, "content_hash": "sha256:ee19f67574db61ee5041fefb92763bb32220c44906e87cea591d8e183ebd2a3d", "created_at": "2026-11-01T18:14:33Z", "updated_at": "2026-11-01T22:06:38Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Diagnostic Model for Alzheimer's Disease Based on Blood Levels of Autophagy-Related Genes.", "doi": "10.3389/fnagi.2022.881890", "pmid": "35645767", "authors": ["Qiangqiang Qin", "Zhanfeng Gu", "Fei Li", "Yanbing Pan", "TianXiang Zhang", "Yang Fang", "Lesha Zhang"], "journal": "Frontiers in aging neuroscience", "year": 2022, "abstract": "Alzheimer's disease (AD) is a common neurodegenerative disease. The major problems that exist in the diagnosis of AD include the costly examinations and the high-invasive sampling tissue. Therefore, it would be advantageous to develop blood biomarkers. Because AD's pathological process is considered tightly related to autophagy; thus, a diagnostic model for AD based on ATGs may have more predictive accuracy than other models. We obtained GSE63060 dataset from the GEO database, ATGs from the HADb and screened 64 differentially expressed autophagy-related genes (DE-ATGs). We then applied them to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses as well as DisGeNET and PaGenBase enrichment analyses. By using the univariate analysis, least absolute shrinkage and selection operator (LASSO) regression method and the multivariable logistic regression, nine DE-ATGs were identified as biomarkers, which are ATG16L2, BAK1, CAPN10, CASP1, RAB24, RGS19, RPS6KB1, ULK2, and WDFY3. We combined them with sex and age to establish a nomogram model. To evaluate the model's distinguishability, consistency, and clinical applicability, we applied the receiver operating characteristic (ROC) curve, C-index, calibration curve, and on the validation datasets GSE63061, GSE54536, GSE22255, and GSE151371 from GEO database. The results show that our model demonstrates good prediction performance. This AD diagnosis model may benefit both clinical work and mechanistic research.", "claims": [{"id": "claim-method", "statement": "By using the univariate analysis, least absolute shrinkage and selection operator (LASSO) regression method and the multivariable logistic regression, nine DE-ATGs were identified as biomarkers, which are ATG16L2, BAK1, CAPN10, CASP1, RAB24, RGS19, RPS6KB1, ULK2, and WDFY3.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35645767", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "By using the univariate analysis, least absolute shrinkage and selection operator (LASSO) regression method and the multivariable logistic regression, nine DE-ATGs were identified as biomarkers, which are ATG16L2, BAK1, CAPN10, CASP1, RAB24, RGS19, RPS6KB1, ULK2, and WDFY3."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63060, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63061, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE54536, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE22255, and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE151371.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35645767", "locator": "sections[14] \"Data Availability Statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63060, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63061, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE54536, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE22255, and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE151371."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using LASSO and multivariable logistic regression on peripheral-blood microarray data, the study developed a nine-gene AD diagnostic nomogram that achieved an AUC of 0.836 in the training dataset and 0.731 in the validation dataset.", "kind": "quantitative", "target": {"value": 0.731, "unit": "auc", "tolerance": null, "source": "Subsequently, we employed this model to validate the GSE63061 dataset, and the results demonstrated that its AUC was 0.731, which confirmed that the predictive accuracy of this diagnostic model was worth approving (Figure 4B)."}, "evidence": [{"asset": "source:pmid:35645767", "locator": "sections[12] \"Evaluation and Validation of Autophagy-Related Predictive Features\" paragraph 2", "quote": "The AUC of the ROC curve of the training set GSE63060 was 0.836, demonstrating the model’s good predictive ability."}, {"asset": "source:pmid:35645767", "locator": "sections[12] \"Evaluation and Validation of Autophagy-Related Predictive Features\" paragraph 2", "quote": "Subsequently, we employed this model to validate the GSE63061 dataset, and the results demonstrated that its AUC was 0.731, which confirmed that the predictive accuracy of this diagnostic model was worth approving (Figure 4B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using LASSO and multivariable logistic regression on peripheral-blood microarray data, the study developed a nine-gene AD diagnostic nomogram that achieved an AUC of 0.836 in the training dataset and 0.731 in the validation dataset.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:35645767"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE151371"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE151371"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35645767", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fnagi.2022.881890", "sha256": "sha256:e336317f4a9947993d9e2407df0e398035806f89fb4b250ab5dde72fb01928e7", "bytes": 33851, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fnagi.2022.881890", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9133665", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE151371", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE151371", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE151371", "version": null, "note": "Publicly available datasets were analyzed in this study. 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"title": "Alzheimer, Mild Cognitive impairment and control samples from AddneuroMed Cohort (batch 2)", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE151371 not downloaded", "asset:data:geo:GSE151371 licence not read", "asset:data:geo:GSE22255 not downloaded", "asset:data:geo:GSE22255 licence not read", "asset:data:geo:GSE54536 not downloaded", "asset:data:geo:GSE54536 licence not read", "asset:data:geo:GSE63060 not downloaded", "asset:data:geo:GSE63060 licence not read", "asset:data:geo:GSE63061 not downloaded", "asset:data:geo:GSE63061 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "regression_modelling", "specific": "logistic", "label": "Regression modelling", "dsl_operations": ["glm_logit_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictors_and_outcome"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "By using the univariate analysis, least absolute shrinkage and selection operator (LASSO) regression method and the multivariable logistic regression, nine DE-ATGs were identified as biomarkers, which are ATG16L2, BAK1, CAPN10, CASP1, RAB24, RGS19, RPS6KB1, ULK2, and WDFY3."}}, {"id": "method-2", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.8, "band": "high", "quote": "limma"}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "differential_expression_fit", "expected_value": 0.731, "unit": "auc", "tolerance": null, "state": "complete", "note": "The published estimate as the 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No tolerance is published because the paper states none."}], "gaps": ["method.reader_differs_from_extractor"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["glm_logit_fit"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: logistic."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["glm_logit_fit"], "assets": ["source:pmid:35645767", "data:geo:GSE151371"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE151371"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T17:48:07Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 9865, "completion_tokens": 2425, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:35645767", "type": "paper", "label": "pmid:35645767"}, {"id": "source:pmid:35645767", "type": "manuscript", "label": "PMID 35645767 full text"}, {"id": "data:geo:GSE151371", "type": "dataset", "label": "GEO GSE151371"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:35645767", "type": "paper", "label": "pmid:35645767"}, {"id": "source:pmid:35645767", "type": "manuscript", "label": "PMID 35645767 full text"}, {"id": "data:geo:GSE151371", "type": "dataset", "label": "GEO GSE151371"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-35645767", "type": "extraction", "used": ["source:pmid:35645767"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE151371", "type": "accession_resolution", "used": ["data:geo:GSE151371"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:35645767", "parent_hash": "sha256:ba64dedeb8148cf8018779ccd5ada87f808b0191f2561603b468b88734301090", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE151371 licence not read", "asset:data:geo:GSE151371 not downloaded", "asset:data:geo:GSE22255 licence not read", "asset:data:geo:GSE22255 not downloaded", "asset:data:geo:GSE54536 licence not read", "asset:data:geo:GSE54536 not downloaded", "asset:data:geo:GSE63060 licence not read", "asset:data:geo:GSE63060 not downloaded", "asset:data:geo:GSE63061 licence not read", "asset:data:geo:GSE63061 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "regression_modelling", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["glm_logit_fit"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["By using the univariate analysis, least absolute shrinkage and selection operator (LASSO) regression method and the multivariable logistic regression, nine DE-ATGs were identified as biomarkers, which are ATG16L2, BAK1, CAPN10, CASP1, RAB24, RGS19, RPS6KB1, ULK2, and WDFY3."], "order_basis": "analysis_only", "id": "assay:1"}]}