The identification of prediabetes condition with ARIC algorithm predicts long-term CV events in patients with erectile dysfunction.

INTRODUCTION: The Atherosclerosis Risk in Communities (ARIC) algorithm is one of the most efficient instruments for the prediction of incident type 2 diabetes. Recently, it has been shown to predict another relevant cardiovascular (CV) risk factor, such as chronic kidney disease. AIM: To verify whether, in patients with erectile dysfunction (ED), the use of ARIC diabetes risk score might improve the efficacy in predicting major CV events of other CV risk algorithms specifically developed for the assessment of CV risk. METHODS: A consecutive series of 2,437 men (mean age 52.5 ± 12.9 years) attending our outpatient clinic for sexual dysfunction was retrospectively studied. A subset of this sample (N = 1,687) was enrolled in a longitudinal study (mean follow-up of 4.3 ± 2.6 years). MAIN OUTCOME MEASURES: The assessment of metabolic risk was evaluated with the ARIC algorithm. The assessment of CV risk was evaluated using the Progetto Cuore risk engine. RESULTS: In the cross-sectional study, ARIC score was inversely related with testosterone levels, sexual functioning, and penile blood flow. When longitudinal sample was analyzed, higher baseline ARIC score significantly predicted major adverse cardiovascular event (MACE) even when subjects with diabetes mellitus at baseline were excluded from the analysis (hazard ratio = 1.522 [1.086-2.135]; P = 0.015 for trend). In addition, among subjects classified as "low risk" (CV risk <20% at 10 years corresponding to <9% at 4.3 years) by Progetto Cuore, a receiving operating curve (ROC) analysis for ARIC (vs. MACE) allowed the identification of a threshold of 0.22, which had a positive predictive value for 4.3-year MACE of 9%. Applying the ARIC score (with a threshold of 0.22) to Progetto Cuore "low-risk" subjects, we could classify as "at high risk" 89.8% of subjects with incident MACE vs. 79.6% with Progetto Cuore only. CONCLUSIONS: In patients with ED, identifying prediabetes, even with algorithms, predicts long-term CV events.