Magnetic resonance fingerprinting based comprehensive quantification of T1 and T2 values of the background prostatic peripheral zone: Correlation with clinical and demographic features.

PURPOSE: To quantify and assess the distribution of MR fingerprinting (MRF)-derived T1 and T2 values of the whole prostatic peripheral zone (PZ), and perform subgroup analyses according to clinical and demographic features. METHOD: One hundred and twenty-four patients with prostate MR exams and MRF-based T1 and T2 maps of the prostatic apex, mid gland, and base were identified from our database and included. Regions of interest encompassing the right and left lobes of the PZ were drawn for each axial slice on the T2 map and copied to the T1 map. Clinical data were obtained from medical records. Kruskal-Wallis test was used for assessing differences between subgroups and the Spearman coefficient was used for assessing any correlations. RESULTS: Mean T1 and T2 values were 1941 and 88 ms, respectively, for the whole-gland, 1884 and 83 ms for the apex, 1974 and 92 ms for the mid-gland, 1966 and 88 ms for the base. T1 values were weakly negatively correlated with PSA values, while T1 and T2 values were weakly positively correlated with prostate weight and moderately positively correlated with PZ width. Finally, patients with PI-RADS 1 scores had higher T1 and T2 values of the whole PZ, compared with those with scores 2-5. CONCLUSION: Mean T1 and T2 values of the background PZ of the whole gland were 1941 ± 313 and 88 ± 39 ms, respectively. Among clinical and demographic factors, there was a significant positive correlation between T1 and T2 values and PZ width.

Cardiometabolic-based chronic disease: adiposity and dysglycemia drivers of heart failure.

Heart failure (HF) is a complex clinical syndrome, associated with high rates of mortality, hospitalization, and impairment of quality of life. Obesity and type 2 diabetes are major cardiometabolic drivers, represented as distinct stages of adiposity- and dysglycemia-based chronic disease (ABCD, DBCD), respectively, and leading to cardiometabolic-based chronic disease (CMBCD). This review focuses on one aspect of the CMBCD model: how ABCD and DBCD influence genesis and progression of HF phenotypes. Specifically, the relationships of ABCD and DBCD stages with structural and functional heart disease, HF risk, and outcomes in overt HF are detailed. Also, evidence-based lifestyle, pharmacological, and procedural interventions that promote or reverse cardiac remodeling and outcomes in individuals at risk or with HF are discussed. In summary, driver-based chronic disease models for individuals at risk or with HF can expose prevention targets for more comprehensive interventions to improve clinical outcomes. Future randomized trials that investigate structured lifestyle, pharmacological, and procedural therapies specifically tailored for the CMBCD model are needed to develop personalized care plans to decrease HF susceptibility and improve outcomes.