[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100621693":3},{"organization":4,"outcomesModule":7,"designInfo":41,"detailedDescription":43,"studyPopulation":44,"armGroups":45,"interventions":55,"overallOfficials":68,"centralContacts":73,"locations":80,"responsibleParty":100,"collaborators":40,"id":102,"slug":40,"hasResults":103,"nctId":104,"briefTitle":105,"officialTitle":106,"acronym":40,"eligibilityCriteria":107,"healthyVolunteers":108,"sex":109,"minAge":110,"maxAge":111,"enrollmentInfo":112,"targetDuration":115,"studyType":116,"phases":40,"briefSummary":117,"conditions":118,"keywords":120,"overallStatus":83,"whyStopped":40,"lastUpdateSubmitDate":123,"lastUpdatePostDateStruct":124,"startDateStruct":127,"completionDateStruct":129,"leadSponsor":131,"locationsCount":132},{"fullName":5,"class":6},"Cardenal Herrera University","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":40,"otherOutcomes":40},[9,13,16,19,22,25,28,31,34,37],{"measure":10,"description":11,"timeFrame":12},"SCORE2","* For participants aged 50-69 years:\n\n  * Low-to-moderate risk: \\\u003C5%\n  * High risk: ≥5% to \\\u003C10%\n  * Very high risk: ≥10%\n* For participants aged 40-49 years:\n\n  * Low-to-moderate risk: \\\u003C2.5%\n  * High risk: ≥2.5% to \\\u003C7.5%\n  * Very high risk: ≥7.5%","baseline",{"measure":14,"description":15,"timeFrame":12},"SCORE2 - Diabetes","Low (\\\u003C5%), moderate (5-10%), high (10-20%), and very high (\\>20%)",{"measure":17,"description":18,"timeFrame":12},"Energy or angular second moment (ASM)","This measures the uniformity or regularity in the distribution of image values. Higher values indicate greater uniformity in the image",{"measure":20,"description":21,"timeFrame":12},"Homogeneity or inverse difference moment (IDM)","This reflects the homogeneity of image composition, associated with pixel pairs. Homogeneous images with minimal variations produce high IDM valueS",{"measure":23,"description":24,"timeFrame":12},"Contrast (CON)","Represents the degree of local variations in grey levels within the image. The greater the variation, the greater the contrast",{"measure":26,"description":27,"timeFrame":12},"Textural correlation (TCOR)","Expresses linear dependencies between grey levels in the image. Regions with similar grey levels tend to exhibit higher values",{"measure":29,"description":30,"timeFrame":12},"Entropy (ENT)","This indicates the level of disorder within the image. Homogeneous images result in lower entropy values",{"measure":32,"description":33,"timeFrame":12},"Carotid intima-media thickness (CIMT)","(mm)",{"measure":35,"description":36,"timeFrame":12},"Echointensity","The mean pixel intensity within an ultrasound region of interest (related to tissue brightness\u002Fecho)",{"measure":38,"description":39,"timeFrame":12},"Echovariation","The variability or dispersion of pixel intensity within the ultrasound region of interest, corresponding to a measure of tissue heterogeneity",null,{"allocation":40,"interventionModel":40,"interventionModelDescription":40,"primaryPurpose":40,"observationalModel":6,"timePerspective":42,"maskingInfo":40},"CROSS_SECTIONAL","Cardiovascular disease (CVD) is the leading cause of death worldwide and accounts for approximately 45% of all deaths in Europe. Beyond mortality, CVD has a substantial impact on patients' quality of life and represents a significant economic burden on healthcare systems. T2DM is a key cardiovascular risk factor and an important determinant of serious cardiovascular complications, as it is associated with a worse prognosis after cardiac events and almost doubles the risk of all-cause mortality.\n\nPrimary prevention of CVD is a cornerstone of nursing practice, especially in the management of chronic diseases such as T2DM, where lifestyle interventions and long-term follow-up are essential. Several tools are available for the early detection of CVD, including cardiovascular risk (CVR) prediction models and imaging techniques. SCORE2 and SCORE2-Diabetes are widely used algorithms for estimating the 10-year risk of major cardiovascular events in European adults. Imaging modalities, such as carotid ultrasound, are becoming increasingly relevant, not only as diagnostic tools but also as support resources in nurse-led clinical assessment, as they provide objective and visual biomarkers of vascular health.\n\nCarotid ultrasound allows for the assessment of established parameters related to CVR, such as CIMT, echogenicity, echovariation, and wall texture. Intima-media thickness (IMT) is a well-recognized marker of arterial injury and cardiovascular risk, especially in people with T2DM. While echogenicity and echovariation reflect tissue composition and structural heterogeneity, they may not detect early microstructural alterations. In contrast, texture features derived from gray-level co-occurrence matrix (GLCM) analyze spatial relationships between pixels, allowing the detection of subtle arterial changes associated with cardiovascular risk. Therefore, in nursing practice, layer-specific carotid texture analysis may offer a more accurate and personalized assessment of cardiovascular risk.","European population aged between 40 and 69 years without known cardiovascular disease.",[46,51],{"label":47,"type":40,"description":40,"interventionNames":48},"Type 2 diabetes mellitus",[49,50],"Diagnostic Test: SCORE2-Diabetes","Diagnostic Test: Ultrasound Assessment",{"label":52,"type":40,"description":40,"interventionNames":53},"Healthy Subjects",[54,50],"Diagnostic Test: SCORE2",[56,60,64],{"type":57,"name":10,"description":58,"armGroupLabels":59,"otherNames":40},"DIAGNOSTIC_TEST","Will classify individuals into four cardiovascular risk categories:\n\n* For participants aged 50-69 years:\n\n  * Low-to-moderate risk: \\\u003C5%\n  * High risk: ≥5% to \\\u003C10%\n  * Very high risk: ≥10%\n* For participants aged 40-49 years:\n\n  * Low-to-moderate risk: \\\u003C2.5%\n  * High risk: ≥2.5% to \\\u003C7.5%\n  * Very high risk: ≥7.5%",[52],{"type":57,"name":61,"description":62,"armGroupLabels":63,"otherNames":40},"SCORE2-Diabetes","Will also classify individuals into four risk categories: low (\\\u003C5%), moderate (5-10%), high (10-20%), and very high (\\>20%).",[47],{"type":57,"name":65,"description":66,"armGroupLabels":67,"otherNames":40},"Ultrasound Assessment","Three bilateral longitudinal scans of the common carotid artery will be obtained for carotid intima-media thickness (CIMT) measurement and stratification of carotid wall layers for subsequent texture analysis. Additionally, a bilateral video recording of the same imaging plane containing a minimum of five cardiac cycles will be acquired. One end-diastolic frame per video-corresponding to the relaxed arterial wall-will be selected to standardize image acquisition and CIMT measurement.",[52,47],[69],{"name":70,"affiliation":71,"role":72},"FRANCISCO JAVIER MOLINA PAYÁ, PhD","CEU CARDENAL HERRERA UNIVERSITY","PRINCIPAL_INVESTIGATOR",[74],{"name":75,"role":76,"phone":77,"phoneExt":78,"email":79},"SERGIO MONTERO NAVARRO, PhD","CONTACT","+34965426486","67506","sergio.montero@uchceu.es",[81],{"facility":82,"status":83,"city":84,"state":85,"zip":86,"country":87,"countryCode":88,"cosmosGeoPoint":89,"geoPoint":94,"contacts":95},"Francisco Javier Molina Payá","RECRUITING","Elche","Alicante","03204","Spain","ES",{"type":90,"coordinates":91},"Point",[92,93],-0.70107,38.26218,{"lat":93,"lon":92},[96],{"name":97,"role":76,"phone":98,"phoneExt":99,"email":79},"SERGIO MONTERO NAVARRO","+34 96 542 64 86","67509",{"type":72,"investigatorFullName":97,"investigatorTitle":101,"investigatorAffiliation":5,"oldNameTitle":40,"oldOrganization":40},"Clinical Professor","100621693",false,"NCT07373938","Carotid Wall Texture as a Cardiovascular Risk Biomarker in Type 2 Diabetes Mellitus","Carotid Wall Layer Texture as a Potential Biomarker for Cardiovascular Risk Assessment in Preventive Nursing: a Study in Type 2 Diabetes Mellitus","Inclusion Criteria\n\n* European adults.\n* Age between 40 and 69 years.\n* Confirmed diagnosis of type 2 diabetes mellitus.\n* No established cardiovascular disease.\n* Availability of the required clinical and metabolic variables: Age; Sex; Systolic blood pressure; Non-high-density lipoprotein (non-HDL) cholesterol; Smoking status; Duration of diabetes; Glycated hemoglobin (HbA1c); Presence or absence of diabetes-related target organ damage (e.g., albuminuria, retinopathy)\n\nExclusion Criteria\n\n* History of clinical cardiovascular disease (secondary prevention).\n* Type 1 diabetes mellitus.\n* Patients with established severe target organ damage or conditions that automatically classify them as very high cardiovascular risk according to ESC guidelines.\n* Advanced chronic kidney disease (estimated glomerular filtration rate \\\u003C30 mL\u002Fmin\u002F1.73 m²).",true,"ALL","40 Years","69 Years",{"count":113,"type":114},80,"ESTIMATED","1 Day","OBSERVATIONAL","This study aims to compare carotid intima-media thickness (CIMT) and layer-specific texture characteristics of the carotid wall between individuals with Type 2 diabetes mellitus (T2DM) and normoglycemic controls, to assess the impact of T2DM on these ultrasound variables and evaluate their ability to discriminate between low and high cardiovascular risk at 10 years.",[119],"Type 2 Diabetes Mellitus",[121,122,119],"ultrasound","textural analysis","2026-01-28",{"date":125,"type":126},"2026-01-29","ACTUAL",{"date":128,"type":126},"2026-01-25",{"date":130,"type":114},"2026-08-02",{"name":5,"class":6},1]