Clustering Alzheimer's Disease Gene Expression Dataset Reveals Underlying Sexually Dimorphic and Disease Status Profiles.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34514337", "kind": "science-record", "revision": 7, "content_hash": "sha256:e9e0b19284c70ca910aba609dfb229ddaafe15901479c07f65064ebcae96e5bf", "created_at": "2026-11-01T15:41:51Z", "updated_at": "2026-11-01T22:47:29Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Clustering Alzheimer's Disease Gene Expression Dataset Reveals Underlying Sexually Dimorphic and Disease Status Profiles.", "doi": "10.3233/ADR-210014", "pmid": "34514337", "authors": ["Sigal Levy", "Nili Guttmann-Beck", "Dorit Shweiki"], "journal": "Journal of Alzheimer's disease reports", "year": 2021, "abstract": "BACKGROUND: The multiple appearance phenotypes in Alzheimer's disease (AD) are manifested in epidemiologic sexual dimorphism, variation in age of onset, progress, and severity of the disease.\nOBJECTIVE: In this study, we focused on sexual dimorphism, aiming to untie some of the complex interconnections in AD between sex, disease status, and gene expression profiles. Two strategic decisions guided our study: 1) to value transcriptomic multi-layered profiles over alterations in single genes expression; and 2) to embrace a sexual dimorphism centered approach, as we suspect that transcriptomic profiles may dramatically differ not only between healthy and sick individuals but between men and women as well.\nMETHODS: Microarray dataset GSE15222, fulfilling our strict criteria, was retrieved from the GEO repository. We performed cluster analysis for each sex separately, comparing the proportion of healthy and AD individuals in each cluster.\nRESULTS: We were able to identify a biased, female, AD-typified cluster. Furthermore, we showed that this female AD-typified cluster is highly similar to one of the male clusters. While the female cluster constitutes mostly sick individuals, the male cluster constitutes healthy and sick individuals in almost identical proportion.\nCONCLUSION: Our results clearly indicate that similar transcriptomic profiles in the two sexes are \"physiologically translated\" in to a very different, dramatic outcome. Thus, our results suggest the need for a sex-based and transcriptomic profile-based study, for a better understanding of the onset and progression of AD.", "claims": [{"id": "claim-method", "statement": "We used multiple logistic regression in order to test for the effects of gender and cluster assignment on disease status, and the Chi-square test as follow-up analysis in order to correlate cluster assignment with disease status separately for men and women and learn the nature of the interaction.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34514337", "locator": "sections[2] \"METHODS\" paragraph 8", "quote": "We used multiple logistic regression in order to test for the effects of gender and cluster assignment on disease status, and the Chi-square test as follow-up analysis in order to correlate cluster assignment with disease status separately for men and women and learn the nature of the interaction."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": null, "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:34514337"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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Table 1 presents the different interconnections between disease status and sexes in each cluster, by sex and by disease status. p-value of disease status and sex of individual’s distribution is presented", "structure": {"pmcid": "PMC8385427", "section_index": 3, "section_title": "RESULTS", "row_count": 6, "column_count": 5}, "table": {"label": "Table 1", "caption": "Uneven distribution of healthy and sick women between the two gene expression profiles clusters. Table 1 presents the different interconnections between disease status and sexes in each cluster, by sex and by disease status. p-value of disease status and sex of individual’s distribution is presented", "rows": [["Dataset ID", "GSE15222"], ["Sex", "Female (n = 177)", "", "Male (n = 190)"], ["Cluster ID [size]/ Disease state", "Cluster I", "Cluster II", "Cluster I", "Cluster II"], ["Healthy", "78", "7", "76", "26"], ["AD", "59", "33", "55", "33"], ["p", "< 0.001", "", "Not significant"]], "row_count": 6, "column_count": 5, "numeric_cells": 12, "header_rows": 3, "locator": "section[3]/table-wrap[1]#adr-5-adr210014-t001"}}, {"id": "asset:table:pmid:34514337:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:97063c7ac8b60c4f0cd48978f3373ca6c55b61143c2d6a44545e8f9d8abda96a", "bytes": 686, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[3]/table-wrap[2]#adr-5-adr210014-t002", "version": null, "note": "Square root and medians of the squared distances between cluster centers. Table 2 presents the square root of mean squared distance, and the median squared distance of the two sexes in each cluster and of the two clusters within each sex", "structure": {"pmcid": "PMC8385427", "section_index": 3, "section_title": "RESULTS", "row_count": 3, "column_count": 5}, "table": {"label": "Table 2", "caption": "Square root and medians of the squared distances between cluster centers. Table 2 presents the square root of mean squared distance, and the median squared distance of the two sexes in each cluster and of the two clusters within each sex", "rows": [["", "Cluster I, male versus female", "Cluster II, male versus female", "Male, cluster I versus cluster II", "Female, cluster I versus cluster II"], ["Square root of mean squared distance", "359.3", "1321.4", "2868.8", "3778.3"], ["Median squared distance", "8,715", "43,708", "137,058", "177,754"]], "row_count": 3, "column_count": 5, "numeric_cells": 8, "header_rows": 1, "locator": "section[3]/table-wrap[2]#adr-5-adr210014-t002"}}], "gaps": []}, "analysis": {"state": "complete", "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": "We used multiple logistic regression in order to test for the effects of gender and cluster assignment on disease status, and the Chi-square test as follow-up analysis in order to correlate cluster assignment with disease status separately for men and women and learn the nature of the interaction."}}, {"id": "method-2", "family": "clustering", "specific": "kmeans", "label": "Clustering", "dsl_operations": ["kmeans_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["feature_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "K-means"}}, {"id": "method-3", "family": "contingency_analysis", "specific": "chi_square", "label": "Categorical (contingency table) analysis", "dsl_operations": ["chi_square_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["categorical_cross_tabulation"], "evidence": {"score": 0.8, "band": "high", "quote": "Chi-square"}}], "targets": [], "gaps": []}, "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": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "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. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": [], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": ["glm_logit_fit"], "assets": ["source:pmid:34514337", "asset:table:pmid:34514337:1", "asset:table:pmid:34514337:2"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:None"]}, "gaps": ["data:accession_absent"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-10-31T19:48:22Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": null}, "calls": 3, "usage": {"prompt_tokens": 17214, "completion_tokens": 1000, "cached_tokens": 520}}, "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:34514337", "type": "paper", "label": "pmid:34514337"}, {"id": "source:pmid:34514337", "type": "manuscript", "label": "PMID 34514337 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:34514337", "type": "paper", "label": "pmid:34514337"}, {"id": "source:pmid:34514337", "type": "manuscript", "label": "PMID 34514337 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-34514337", "type": "extraction", "used": ["source:pmid:34514337"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-None", "type": "accession_resolution", "used": ["data:geo:None"], "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:34514337", "parent_hash": "sha256:fe37c9c9f97ea86f4a92332ae52bc3c66e7c10a32e3c32dfabc0bbbb7178b616", "fork_of": null, "generation": 6}, "gaps": ["execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"], "assays": [{"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["but between men and women as well. 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