{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T02:21:47Z","timestamp":1790734907859,"version":"4.1.0"},"reference-count":53,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"1","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2026,1,6]]},"abstract":"\n Background<\/jats:title>\n \n Chronic kidney disease (\n CKD<\/jats:styled-content>\n ) leads to premature mortality from cardiovascular events before kidney replacement therapy. Despite recognition of syndromes like cardiorenal anemia and cardiovascular\u2010kidney\u2010metabolic, predictive models for kidney and cardiovascular outcomes remain inadequate. This study aimed to develop a minimally invasive, risk model using circulating small extracellular vesicle\u2010derived mi\n RNAs<\/jats:styled-content>\n among patients with\n CKD<\/jats:styled-content>\n .\n <\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n A derivation cohort (n=36) underwent microarray\u2010based\n miRNA<\/jats:styled-content>\n profiling, and a least absolute shrinkage and selection operator\u2010penalized Cox proportional hazards model was constructed. Validation was performed using\n TaqMan quantitative polymerase chain reaction<\/jats:styled-content>\n in a cohort of 234 patients with\n CKD<\/jats:styled-content>\n without kidney replacement therapy. The primary outcome was a \u226530% reduction in\n estimated glomerular filtration rate<\/jats:styled-content>\n or progression to kidney replacement therapy. The secondary outcome included all\u2010cause mortality, kidney replacement therapy initiation, and major adverse cardiovascular events.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n In the derivation cohort, 36% of patients had hypertensive glomerulosclerosis as the underlying\n CKD<\/jats:styled-content>\n cause, increasing to 48% in the validation cohort. Twenty\u2010three\n miRNAs<\/jats:styled-content>\n were significantly downregulated in advanced\n CKD<\/jats:styled-content>\n , associated with cellular senescence,\n FOXO<\/jats:styled-content>\n (forkhead box, class O) signaling, and cell cycle pathways. From these, 3\n miRNAs<\/jats:styled-content>\n \u2014\n hsa\u2010let\u20107d\u20105p<\/jats:italic>\n ,\n \n hsa\u2010\n miR<\/jats:styled-content>\n \u201024\u20103p\n <\/jats:italic>\n , and\n \n hsa\u2010\n miR<\/jats:styled-content>\n \u2010126\u20103p\n <\/jats:italic>\n \u2014were selected and integrated into the final risk score with cystatin C and urinary protein levels, following optimization in the validation cohort. Lower\n miRNA<\/jats:styled-content>\n levels were linked to cardiovascular comorbidities and cardiorenal anemia syndrome. Over a median follow\u2010up of 39 and 59\u2009months, 108 kidney events and 70 composite outcomes occurred. The model effectively predicted adverse outcomes across\n CKD<\/jats:styled-content>\n causes, further stratifying risk within cardiovascular\u2010kidney\u2010metabolic stage classifications.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n \n Circulating small extracellular vesicle\u2010derived\n miRNA<\/jats:styled-content>\n profiles enable a noninvasive, longitudinally predictive model for adverse kidney and cardiovascular outcomes in\n CKD<\/jats:styled-content>\n . This approach may improve early risk identification and clinical decision\u2010making.\n <\/jats:p>\n <\/jats:sec>","DOI":"10.1161\/jaha.125.045148","type":"journal-article","created":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T13:26:03Z","timestamp":1765373163000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":4,"title":["Circulating Extracellular Vesicle MicroRNAs as Predictive Biomarkers for Kidney and Cardiovascular Events"],"prefix":"10.1161","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7901-1659","authenticated-orcid":false,"given":"Shunsuke","family":"Inaba","sequence":"first","affiliation":[{"name":"Institute of Science Tokyo","place":["Tokyo, Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7251-9950","authenticated-orcid":false,"given":"Takanori","family":"Hasegawa","sequence":"additional","affiliation":[{"name":"Institute of Science 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