{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T21:28:09Z","timestamp":1780349289880,"version":"3.54.1"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T00:00:00Z","timestamp":1678406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003196","name":"Italian Ministry of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003196","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Aims<\/jats:title><jats:p>Cardiovascular risk factor control fluctuates, tends to change over time, and is potentially impacted by multifactorial interactions. Currently, the presence of risk factors, rather than their variability or interplay with one another, is taken into account to define the population at risk. The association between variability of risk factors and cardiovascular morbidity and mortality risk among patients with Type 2 diabetes mellitus (T2DM) remains debatable.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods and results<\/jats:title><jats:p>Using registry-derived data, we identified 29 471 people with T2DM, without cardiovascular disease (CVD) at baseline, and with at least five measurements of risk factors. Variability for each variable was expressed as quartiles of the standard deviation during 3 years (exposure). The incidence of myocardial infarction, stroke, and all-cause mortality was assessed during 4.80 (2.40\u20136.70) years following the exposure phase. The association between the measures of variability and the risk of developing the outcome was investigated through multivariable Cox proportional-hazards regression analysis with stepwise variable selection. Then, the recursive partitioning and amalgamation (RECPAM) algorithm was used to explore the interaction among the variability of risk factors associated with the outcome. An association between the variability of HbA1c, body weight, systolic blood pressure, and total cholesterol with the outcome considered was found. Among the six classes of risk identified by RECPAM, patients with a high variability of both body weight and blood pressure had the highest risk [Class 6, hazard ratio (HR) = 1.81; 95% confidence interval (CI) 1.61\u20132.05] compared with patients with low variability of both body weight and total cholesterol (Class 1, reference), despite a progressive reduction in the mean level of risk factors during successive visits. Individuals with high weight variability but low\u2013moderate systolic blood pressure variability (Class 5, HR = 1.57; 95% CI 1.28\u20131.68), patients with moderate\/high weight variability associated with high\/very high HbA1c variability (Class 4, HR = 1.33; 95% CI 1.20\u20131.49), subjects with moderate\/high weight variability and with low\/moderate HbA1c variability (Class 3, HR = 1.12; 95% CI 1.00\u20131.25), as well as those with low weight variability associated with high\/very high total cholesterol variability (Class 2, HR = 1.14; 95% CI 1.00\u20131.30) also showed a significant increase in the risk of an event.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Combined high variability of two risk factors, particularly body weight and blood pressure, is associated with cardiovascular risk among patients with T2DM. These findings highlight the importance of continuous balancing of multiple risk factors.<\/jats:p><\/jats:sec>","DOI":"10.1093\/eurjpc\/zwad072","type":"journal-article","created":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T14:26:17Z","timestamp":1678458377000},"page":"719-727","source":"Crossref","is-referenced-by-count":25,"title":["Risk factor variability and cardiovascular risk among patients with diabetes: a nationwide observational study"],"prefix":"10.1093","volume":"30","author":[{"given":"Antonio","family":"Ceriello","sequence":"first","affiliation":[{"name":"IRCCS MultiMedica , Via Gaudenzio Fantoli, 16\/15, Milan 20138 , Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giuseppe","family":"Lucisano","sequence":"additional","affiliation":[{"name":"CORESEARCH\u2014Center for Outcomes Research and Clinical Epidemiology , Corso Umberto I, 103, 65122, Pescara , 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