{"id":"https://openalex.org/W7223448352","doi":"https://doi.org/10.51470/ai.2026.5.1.68","title":"Artificial Intelligence-Based Prediction of Cardiovascular Risk Using Lipid Profiles and Inflammatory Biomarkers: A Systematic Review","display_name":"Artificial Intelligence-Based Prediction of Cardiovascular Risk Using Lipid Profiles and Inflammatory Biomarkers: A Systematic Review","publication_year":2025,"publication_date":"2025-07-25","ids":{"openalex":"https://openalex.org/W7223448352","doi":"https://doi.org/10.51470/ai.2026.5.1.68"},"language":"en","primary_location":{"id":"doi:10.51470/ai.2026.5.1.68","doi":"https://doi.org/10.51470/ai.2026.5.1.68","is_oa":false,"landing_page_url":"https://doi.org/10.51470/ai.2026.5.1.68","pdf_url":null,"source":{"id":"https://openalex.org/S5407056050","display_name":"AI & Cyber Forum An International Journal","issn_l":"3117-6399","issn":["3117-6399"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"AI & Cyber Forum: An International Journal","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119072874","display_name":"Noelyn Nailon Fontanoza-Cedonio","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210102571","display_name":"Cebu Doctors' University","ror":"https://ror.org/011tc6n71","country_code":"PH","type":"education","lineage":["https://openalex.org/I4210102571"]}],"countries":["PH"],"is_corresponding":false,"raw_author_name":"Noelyn N Fontanoza- Cedonio","raw_affiliation_strings":["Department of Medical Technology, Cebu Doctors' University, Mandaue City, Philippines"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Medical Technology, Cebu Doctors' University, Mandaue City, Philippines","institution_ids":["https://openalex.org/I4210102571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127798560","display_name":"Anthony Vince P. Bongo","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210102571","display_name":"Cebu Doctors' University","ror":"https://ror.org/011tc6n71","country_code":"PH","type":"education","lineage":["https://openalex.org/I4210102571"]}],"countries":["PH"],"is_corresponding":false,"raw_author_name":"Anthony Vince P. Bongo","raw_affiliation_strings":["Department of Medical Technology, Cebu Doctors' University, Mandaue City, Philippines"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Medical Technology, Cebu Doctors' University, Mandaue City, Philippines","institution_ids":["https://openalex.org/I4210102571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129607747","display_name":"Jerome A. Tan","orcid":"https://orcid.org/0009-0001-8050-1766","observed_orcids":["https://orcid.org/0009-0001-8050-1766"]},"institutions":[{"id":"https://openalex.org/I4210102571","display_name":"Cebu Doctors' University","ror":"https://ror.org/011tc6n71","country_code":"PH","type":"education","lineage":["https://openalex.org/I4210102571"]}],"countries":["PH"],"is_corresponding":false,"raw_author_name":"Jerome A. Tan","raw_affiliation_strings":["Department of Medical Technology, Cebu Doctors' University, Mandaue City, Philippines"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Medical Technology, Cebu Doctors' University, Mandaue City, Philippines","institution_ids":["https://openalex.org/I4210102571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130599441","display_name":"Paul Peejay E. Celo","orcid":"https://orcid.org/0009-0008-9897-7325","observed_orcids":["https://orcid.org/0009-0008-9897-7325"]},"institutions":[{"id":"https://openalex.org/I4210102571","display_name":"Cebu Doctors' University","ror":"https://ror.org/011tc6n71","country_code":"PH","type":"education","lineage":["https://openalex.org/I4210102571"]}],"countries":["PH"],"is_corresponding":false,"raw_author_name":"Paul Peejay E. Celo","raw_affiliation_strings":["Department of Medical Technology, Cebu Doctors' University, Mandaue City, Philippines"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Medical Technology, Cebu Doctors' University, Mandaue City, Philippines","institution_ids":["https://openalex.org/I4210102571"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5138463577","display_name":"Chinwebudu Miller Melford","orcid":"https://orcid.org/0009-0005-0654-5479","observed_orcids":["https://orcid.org/0009-0005-0654-5479"]},"institutions":[{"id":"https://openalex.org/I4210102571","display_name":"Cebu Doctors' University","ror":"https://ror.org/011tc6n71","country_code":"PH","type":"education","lineage":["https://openalex.org/I4210102571"]}],"countries":["PH"],"is_corresponding":false,"raw_author_name":"Chinwebudu M. Melford","raw_affiliation_strings":["Department of Medical Technology, Cebu Doctors' University, Mandaue City, Philippines"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Medical Technology, Cebu Doctors' University, Mandaue City, Philippines","institution_ids":["https://openalex.org/I4210102571"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210102571"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"4","issue":"2","first_page":"68","last_page":"86"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.7221999764442444,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.7221999764442444,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10394","display_name":"Lipoproteins and Cardiovascular Health","score":0.21230000257492065,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13522","display_name":"Cardiovascular Health and Risk Factors","score":0.010999999940395355,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cardiovascular-risk-prediction","display_name":"cardiovascular risk prediction","score":0.9150000214576721},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"artificial intelligence","score":0.9419999718666077},{"id":"https://openalex.org/keywords/lipid-profile","display_name":"lipid profile","score":0.8410000205039978},{"id":"https://openalex.org/keywords/inflammatory-markers","display_name":"inflammatory markers","score":1.0},{"id":"https://openalex.org/keywords/machine-learning","display_name":"machine learning","score":0.6510000228881836},{"id":"https://openalex.org/keywords/ensemble-methods","display_name":"ensemble methods","score":0.503000020980835},{"id":"https://openalex.org/keywords/ldl-cholesterol","display_name":"LDL cholesterol","score":0.7039999961853027},{"id":"https://openalex.org/keywords/hdl-cholesterol","display_name":"HDL cholesterol","score":0.9089999794960022},{"id":"https://openalex.org/keywords/triglycerides","display_name":"triglycerides","score":0.9890000224113464},{"id":"https://openalex.org/keywords/apolipoproteins","display_name":"apolipoproteins","score":0.9350000023841858},{"id":"https://openalex.org/keywords/lipoprotein-a","display_name":"lipoprotein(a)","score":0.9980000257492065},{"id":"https://openalex.org/keywords/c-reactive-protein","display_name":"C-reactive protein","score":0.9900000095367432},{"id":"https://openalex.org/keywords/high-sensitivity-c-reactive-protein","display_name":"high-sensitivity C-reactive protein","score":0.8880000114440918},{"id":"https://openalex.org/keywords/systemic-inflammatory-index","display_name":"systemic inflammatory index","score":0.8349999785423279},{"id":"https://openalex.org/keywords/model-calibration","display_name":"model calibration","score":0.6349999904632568},{"id":"https://openalex.org/keywords/external-validation","display_name":"external validation","score":0.777999997138977},{"id":"https://openalex.org/keywords/clinical-implementation","display_name":"clinical implementation","score":0.6159999966621399},{"id":"https://openalex.org/keywords/risk-stratification","display_name":"risk stratification","score":0.6470000147819519}],"concepts":[{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.6186000108718872},{"id":"https://openalex.org/C2781197716","wikidata":"https://www.wikidata.org/wiki/Q864574","display_name":"Biomarker","level":2,"score":0.6123999953269958},{"id":"https://openalex.org/C189708586","wikidata":"https://www.wikidata.org/wiki/Q1504425","display_name":"Systematic review","level":3,"score":0.5751000046730042},{"id":"https://openalex.org/C188816634","wikidata":"https://www.wikidata.org/wiki/Q2113324","display_name":"Prospective cohort study","level":2,"score":0.5012000203132629},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.4814999997615814},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4366999864578247},{"id":"https://openalex.org/C2779473830","wikidata":"https://www.wikidata.org/wiki/Q1540899","display_name":"MEDLINE","level":2,"score":0.421999990940094},{"id":"https://openalex.org/C177713679","wikidata":"https://www.wikidata.org/wiki/Q679690","display_name":"Intensive care medicine","level":1,"score":0.39989998936653137},{"id":"https://openalex.org/C124535831","wikidata":"https://www.wikidata.org/wiki/Q4915074","display_name":"Biomarker discovery","level":4,"score":0.3869999945163727},{"id":"https://openalex.org/C95190672","wikidata":"https://www.wikidata.org/wiki/Q815382","display_name":"Meta-analysis","level":2,"score":0.37040001153945923},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.3598000109195709},{"id":"https://openalex.org/C60644358","wikidata":"https://www.wikidata.org/wiki/Q128570","display_name":"Bioinformatics","level":1,"score":0.3571999967098236},{"id":"https://openalex.org/C2778270857","wikidata":"https://www.wikidata.org/wiki/Q2723972","display_name":"Lipid profile","level":3,"score":0.3564000129699707},{"id":"https://openalex.org/C3020196126","wikidata":"https://www.wikidata.org/wiki/Q101991","display_name":"Inflammatory response","level":3,"score":0.33959999680519104},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.3382999897003174},{"id":"https://openalex.org/C535046627","wikidata":"https://www.wikidata.org/wiki/Q30612","display_name":"Clinical trial","level":2,"score":0.33640000224113464},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.32109999656677246},{"id":"https://openalex.org/C50440223","wikidata":"https://www.wikidata.org/wiki/Q1475848","display_name":"Risk factor","level":2,"score":0.31360000371932983},{"id":"https://openalex.org/C198433322","wikidata":"https://www.wikidata.org/wiki/Q3910099","display_name":"Predictive value of tests","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C2777275308","wikidata":"https://www.wikidata.org/wiki/Q234668","display_name":"Cart","level":2,"score":0.29840001463890076},{"id":"https://openalex.org/C3020132585","wikidata":"https://www.wikidata.org/wiki/Q2671652","display_name":"Diagnostic accuracy","level":2,"score":0.2865000069141388},{"id":"https://openalex.org/C163763905","wikidata":"https://www.wikidata.org/wiki/Q17075943","display_name":"Precision medicine","level":2,"score":0.28380000591278076},{"id":"https://openalex.org/C3019719930","wikidata":"https://www.wikidata.org/wiki/Q3910099","display_name":"Predictive value","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2721000015735626},{"id":"https://openalex.org/C3020001037","wikidata":"https://www.wikidata.org/wiki/Q836575","display_name":"Quality assessment","level":3,"score":0.2667999863624573},{"id":"https://openalex.org/C11783203","wikidata":"https://www.wikidata.org/wiki/Q5478027","display_name":"Framingham Risk Score","level":3,"score":0.25519999861717224}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.51470/ai.2026.5.1.68","doi":"https://doi.org/10.51470/ai.2026.5.1.68","is_oa":false,"landing_page_url":"https://doi.org/10.51470/ai.2026.5.1.68","pdf_url":null,"source":{"id":"https://openalex.org/S5407056050","display_name":"AI & Cyber Forum An International Journal","issn_l":"3117-6399","issn":["3117-6399"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"AI & Cyber Forum: An International Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":1.0,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"study_designs":[{"id":"https://openalex.org/study-designs/systematic-review","display_name":"Systematic Review"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7172426226","https://openalex.org/W7169653255","https://openalex.org/W4391531696","https://openalex.org/W7141579708","https://openalex.org/W6939881249","https://openalex.org/W4313435945","https://openalex.org/W7161064659","https://openalex.org/W3164598001","https://openalex.org/W7150025337","https://openalex.org/W7171792826"],"abstract_inverted_index":{"Background:":[0],"Artificial":[1],"intelligence":[2],"(AI)":[3],"and":[4,26,34,56,82,102,151,167,176,188,202,209,213,224,255,260],"machine-learning":[5],"approaches":[6,154],"are":[7],"increasingly":[8],"being":[9],"applied":[10],"to":[11,18,195],"cardiovascular":[12,51,89,230],"risk":[13,231],"prediction,":[14],"offering":[15],"the":[16,30,123],"potential":[17],"integrate":[19],"complex":[20],"relationships":[21],"among":[22],"routinely":[23],"measured":[24],"lipid":[25,54,223],"inflammatory":[27,57,84,174,181,225],"biomarkers.":[28,58],"However,":[29,197],"performance,":[31,98],"validation,":[32,99,248],"interpretability,":[33],"clinical":[35,103,211,240,253],"readiness":[36,215],"of":[37,127,222],"these":[38],"models":[39,184],"remain":[40],"heterogeneous.":[41],"Objective:":[42],"This":[43],"systematic":[44,118],"review":[45,119],"evaluated":[46],"global":[47],"evidence":[48,235],"on":[49],"AI-based":[50,220],"prediction":[52,90],"using":[53,112],"profiles":[55],"Methods:":[59],"Following":[60],"PRISMA":[61],"2020":[62],"guidelines,":[63],"relevant":[64],"studies":[65,75,140,243],"involving":[66],"adults":[67],"were":[68,105,141,155,179,216],"identified":[69],"across":[70,200],"major":[71],"scientific":[72],"databases.":[73],"Eligible":[74],"incorporated":[76],"at":[77],"least":[78],"one":[79,83],"lipid-related":[80,159],"parameter":[81],"biomarker":[85],"within":[86],"an":[87],"AI-assisted":[88],"framework.":[91],"Study":[92],"characteristics,":[93],"biomarkers,":[94],"AI":[95],"methods,":[96],"predictive":[97],"calibration,":[100,210,251],"explainability,":[101],"utility":[104],"narratively":[106],"synthesized,":[107],"with":[108,122],"methodological":[109],"quality":[110],"assessed":[111],"PROBAST+AI.":[113],"The":[114],"protocol":[115],"for":[116,229],"this":[117],"was":[120],"registered":[121],"International":[124],"Prospective":[125],"Register":[126],"Systematic":[128],"Reviews":[129],"(PROSPERO).":[130],"Registration":[131],"Number:":[132],"CRD420261532128.":[133],"Results:":[134],"Thirty-four":[135],"reports":[136],"representing":[137],"33":[138],"individual":[139],"included.":[142],"Random":[143],"forest,":[144],"XGBoost,":[145],"support":[146,238],"vector":[147],"machines,":[148],"gradient":[149],"boosting,":[150],"related":[152],"ensemble":[153],"frequently":[156,192],"applied.":[157],"Common":[158],"predictors":[160],"included":[161],"LDL-C,":[162],"HDL-C,":[163],"triglycerides,":[164],"apolipoproteins,":[165],"lipoprotein(a),":[166],"derived":[168],"indices,":[169,175],"while":[170],"CRP,":[171],"hs-CRP,":[172],"systemic":[173],"leukocyte-derived":[177],"ratios":[178],"prominent":[180],"markers.":[182],"Several":[183],"demonstrated":[185],"good":[186],"discrimination,":[187],"composite":[189],"lipid-inflammatory":[190],"indices":[191],"contributed":[193],"meaningfully":[194],"prediction.":[196],"performance":[198],"varied":[199],"populations":[201],"outcomes,":[203],"independent":[204,246],"external":[205,247],"validation":[206,256],"remained":[207],"limited,":[208],"utility,":[212],"implementation":[214],"inconsistently":[217],"reported.":[218],"Conclusion:":[219],"integration":[221],"biomarkers":[226],"shows":[227],"promise":[228],"stratification,":[232],"but":[233],"current":[234],"does":[236],"not":[237],"widespread":[239],"implementation.":[241],"Future":[242],"should":[244],"prioritize":[245],"standardized":[249],"reporting,":[250],"prospective":[252],"evaluation,":[254],"in":[257],"geographically":[258],"diverse":[259],"underrepresented":[261],"populations.":[262]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-10-25T00:00:00"}