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ObjectiveVitamin D and fibroblast growth factor-23 (FGF-23) are related with cardiovascular disorders. We have investigated the relationship of calcidiol (vitamin D metabolite) and FGF-23 plasma levels with the incidence of adverse outcomes in patients with coronary artery disease.MethodsProspective follow-up study of 704 outpatients, attending the departments of Cardiology of four hospitals in Spain, 6–12 months after an acute coronary event. Baseline calcidiol, FGF-23, parathormone, and phosphate plasma levels were assessed. The outcome was the development of acute ischemic events (any acute coronary syndrome, stroke, or transient ischemic attack), heart failure, or death. Cox regression adjusted for the main confounders was performed.ResultsCalcidiol levels showed a moderate-severe decrease in 57.3% of cases. Parathormone, FGF-23, and phosphate levels were increased in 30.0%, 11.5% and 0.9% of patients, respectively. Only 22.4% of patients had glomerular filtration rate<60 ml/min1.73 m2. After a mean follow-up was 2.15±0.99 years, 77 patients developed the outcome. Calcidiol (hazard ratio [HR] = 0.67; 95% confidence interval [CI] = 0.48–0.94; p = 0.021) and FGF-23 (HR = 1.13; 95% CI = 1.04–1.23; p = 0.005) plasma levels predicted independently the outcome. There was a significant interaction between calcidiol and FGF-23 levels (p = 0.025). When the population was divided according to FGF-23 levels, calcidiol still predicted the outcome independently in patients with FGF-23 levels higher than the median (HR = 0.50; 95% CI = 0.31–0.80; p = 0.003) but not in those with FGF-23 levels below this value (HR = 1.03; 95% CI = 0.62–1.71; p = 0.904).ConclusionsAbnormalities in mineral metabolism are frequent in patients with stable coronary artery disease. In this population, low calcidiol plasma levels predict an adverse prognosis in the presence of high FGF-23 levels.

Aims As evidenced by scintigraphy imaging, the prevalence of transthyretin (TTR) cardiac amyloidosis in heart failure patients with preserved ejection fraction (HFpEF) and left ventricular hypertrophy (LVH) ranges between 13% and 19%. The natural evolution of cardiac amyloidosis begins with the deposition of amyloid material in the myocardium, with LVH ensuing at later stages. With current imaging modalities, it is possible to detect TTR cardiac amyloidosis before the hypertrophic stage. The aim of this study was to determine the prevalence of TTR cardiac amyloidosis in HFpEF patients without LVH. Methods and resultsThe study prospectively enrolled patients admitted for HF with LV ejection fraction (LVEF) ≥ 50% and LV wall thickness <12 mm. TTR cardiac amyloidosis was diagnosed according to accepted criteria, which include positive cardiac 99-Tc-DPD scintigraphy in the absence of monoclonal protein expansion in blood. Transthyretin gene sequencing was performed in positive patients. From July 2017 to January 2020, 329 patients with HFpEF and LV thickness <12 mm were identified. After exclusions, 58 patients completed the study with cardiac scintigraphy (79 years, 54% men; median LVEF 60% and LV wall thickness 10.5 mm). Three patients (5.2%) were positive for TTR cardiac amyloidosis; genetic analysis excluded the presence of hereditary TTR amyloidosis. Positive patients baseline characteristics (84 years, 67% men, LVEF 60%, and LV wall thickness 11 mm) were similar to patients without TTR, except for troponin levels (0.05 vs. 0.02 ng/mL, P = 0.03) and glomerular filtration rate (82 vs. 60 mL/min, P = 0.032), which were higher in TTR patients. Conclusions In a cohort of patients with HFpEF without LVH, the prevalence of TTR cardiac amyloidosis was 5%. Early diagnosis of cardiac involvement in TTR amyloidosis (before manifest LVH) would seem recommendable because newly approved specific treatments can prevent additional deposition of amyloid material.

Objectives: This study aims to determine similarities and differences in clinical characteristics between the patients from two waves of severe acute respiratory syndrome coronavirus-2 infection at the time of hospital admission, as well as to identify risk biomarkers of coronavirus disease 2019 severity. Design: Retrospective observational study. Setting: A single tertiary-care center in Madrid. Patients: Coronavirus disease 2019 adult patients admitted to hospital from March 4, 2020, to March 25, 2020 (first infection wave), and during July 18, 2020, and August 20, 2020 (second infection wave). Interventions: Treatment with a hospital-approved drug cocktail during hospitalization. Measurements and Main Results: Demographic, clinical, and laboratory data were compared between the patients with moderate and critical/fatal illness across both infection waves. The median age of patients with critical/fatal coronavirus disease 2019 was 67.5 years (interquartile range, 56.75–78.25 yr; 64.5% male) in the first wave and 59.0 years (interquartile range, 48.25–80.50 yr; 70.8% male) in the second wave. Hypertension and dyslipidemia were major comorbidities in both waves. Body mass index over 25 and presence of bilateral pneumonia were common findings. Univariate logistic regression analyses revealed an association of a number of blood parameters with the subsequent illness progression and severity in both waves. However, some remarkable differences were detected between both waves that prevented an accurate extrapolation of prediction models from the first wave into the second wave. Interleukin-6 and d-dimer concentrations at the time of hospital admission were remarkably higher in patients who developed a critical/fatal condition only during the first wave (p < 0.001), although both parameters significantly increased with disease worsening in follow-up studies from both waves. Multivariate analyses from wave 1 rendered a predictive signature for critical/fatal illness upon hospital admission that comprised six blood biomarkers: neutrophil-to-lymphocyte ratio (≥ 5; odds ratio, 2.684 [95% CI, 1.143–6.308]), C-reactive protein (≥ 15.2 mg/dL; odds ratio, 2.412 [95% CI, 1.006–5.786]), lactate dehydrogenase (≥ 411.96 U/L; odds ratio, 2.875 [95% CI, 1.229–6.726]), interleukin-6 (≥ 78.8 pg/mL; odds ratio, 5.737 [95% CI, 2.432–13.535]), urea (≥ 40 mg/dL; odds ratio, 1.701 [95% CI, 0.737–3.928]), and d-dimer (≥ 713 ng/mL; odds ratio, 1.903 [95% CI, 0.832–4.356]). The predictive accuracy of the signature was 84% and the area under the receiver operating characteristic curve was 0.886. When the signature was validated with data from wave 2, the accuracy was 81% and the area under the receiver operating characteristic curve value was 0.874, albeit most biomarkers lost their independent significance. Follow-up studies reassured the importance of monitoring the biomarkers included in the signature, since dramatic increases in the levels of such biomarkers occurred in critical/fatal patients over disease progression. Conclusions: Most parameters analyzed behaved similarly in the two waves of coronavirus disease 2019. However, univariate logistic regression conducted in both waves revealed differences in some parameters associated with poor prognosis in wave 1 that were not found in wave 2, which may reflect a different disease stage of patients on arrival to hospital. The six-biomarker predictive signature reported here constitutes a helpful tool to classify patient’s prognosis on arrival to hospital.