No abstract

C-reactive protein (CRP) and pentraxin 3 (PTX3) are members of a highly conserved pentraxin superfamily. CRP is synthesized in the liver and may represent a systemic response to local inflammation. PTX3 is synthesized locally at the inflammatory sites and may represent a marker for local inflammation at sites of vessel injury. We compared plasma high-sensitivity CRP (hsCRP) and PTX3 concentrations after bare-metal stent (BMS) and drug-eluting stent (DES) implantation. Fifty-three patients with stable coronary artery disease who underwent percutaneous coronary intervention were divided into 2 groups: 1-24 patients (BMS group) and 2-29 patients (DES group). Patients were scheduled for an elective, 6-month clinical follow-up. Major adverse cardiovascular events (MACEs) (death, myocardial infarction, target vessel revascularization) were assessed. Baseline clinical characteristics were similar in both groups. Patients after BMS implantation had a higher median PTX3 concentration 1.02 ng/mL compared to patients after DES implantation 0.80 ng/mL, P=0.045. Median hsCRP concentrations were similar in both groups: 0.9 mg/L versus 0.89 mg/L, respectively. Six-month follow-up was available in 33 patients. Four out of 33 patients had MACEs during follow-up. The cut-off value to predict MACEs for PTX3 was >1.16 ng/mL (P=0.004) and for hsCRP was >0.95 mg/L (P<0.001). Patients after DES implantation showed significantly lower plasma PTX3 levels compared with patients after BMS implantation. hsCRP showed no difference between the study groups. PTX3 may be a more sensitive marker of local inflammatory response due to vessel injury by BMS than hsCRP. DES implantation may attenuate the early inflammatory response. Lower PTX3 levels may reflect potent anti-inflammatory properties of DES.

Few reports have analyzed the effect of pentraxin 3 (PTX3) on platelets and their activation. We explored the association between plasma PTX3 and platelet indices. Forty-nine patients with stable coronary artery disease (CAD) were enrolled. Based on median PTX3, the study population was divided into group 1 (n = 25; PTX3 ≤ 0.98 ng/mL) and group 2 (n = 24; PTX3 > 0.98 ng/mL). Platelet indices investigated included mean platelet volume (MPV), platelet distribution width (PDW), platelets and large cell ratio (P-LCR), MPV to platelet count ratio (MPV/PC), platelet to lymphocyte ratio (PLR), and MPV to lymphocyte ratio (MPVLR). Patients with lower PTX3 had a higher lymphocyte count. Platelet count was similar in both groups. Notwithstanding, patients with higher PTX3 concentrations had elevated MPV (8.3 vs 10.0 fL; P < .001) and PDW (9.4 vs 12.4 fL; P < .001). However, the MPV/PC ratio was similar in both groups. Thromboinflammatory biomarkers (PLR, MPVLR) were also elevated in group 2. Pentraxin 3showed a strong, positive correlation with MPV ( r = .75, P < .01) and PDW ( r = .80, P < .01), and weak to moderate correlation with MPVLR. In conclusion, PTX3 is associated with larger platelet size as assessed by platelet volume indices. There is a strong correlation between plasma PTX3 level and MPV and PDW.

Background. Generally, most vitamin D in the human body (90–95%) is produced in the skin during exposure to sunlight. The effectiveness of this process depends on several biological and physical factors, e.g., age or latitude. Skin synthesis of vitamin D among elderly people is reduced. The aim of the study was to assess serum 25-hydroxyvitamin D [25(OH)D] seasonal variations in elderly patients hospitalized at the geriatric department. Methods. The study was carried out on 242 patients aged 60 years or older hospitalized at the geriatric department. The study group was categorized by four seasons as well as month. Results. The median (interquartile range) 25(OH)D concentration among all patients (n = 242) was 33.95 (26.96–45.18) nmol/L. There was no statistical significance in the median serum 25(OH)D concentration with regard to each of the four seasons: in the spring 32.95 (25.96–43.68) nmol/L, in the summer 38.69 (27.46–50.67) nmol/L, in the autumn 33.45 (27.08–44.18) nmol/L, in the winter 34.57 (23.46–43.93) nmol/L, (p = 0.48). Conclusion. Vitamin D deficiency was observed in all geriatric patients, irrespective of the season. The results of the study indicate no significant differences in median vitamin D concentration among the hospitalized patients across all four seasons. Even in the summer months, in our climate, it is fairly difficult for an elderly person to produce an adequate amount of vitamin D through the skin. Therefore, proper vitamin D supplementation is recommended and should be implemented in the elderly irrespective of the season.

Background and Aims: Body-mass index (BMI) is a popular method implemented to define weight status. However, describing obesity by BMI may result in inaccurate assessment of adiposity. The Body Adiposity Index (BAI) is intended to be a directly validated method of estimating body fat percentage. We set out to compare body weight status assessment by BMI and BAI in a cohort of elderly patients with stable coronary artery disease (CAD). Methods: A total of 169 patients with stable CAD were enrolled in an out-patient cardiology clinic. The National Research Council (US) Committee on Diet and Health classification was used for individuals older than 65 years as underweight BMI < 24 kg/m2, normal weight BMI 24–29 kg/m2, overweight BMI 29–35 kg/m2, and obesity BMI > 35 kg/m2. In case of BAI, we used sex- and age-specific classification of weight status. In addition, body fat was estimated by bioelectrical impedance analysis (BImpA). Results: Only 72 out of 169 patients (42.6%) had concordant classification of weight status by both BMI and BAI. The majority of the patients had their weight status either underestimated or overestimated. There were strong positive correlations between BMI and BImpA (FAT%) (R = 0.78 p < 0.001); BAI and BImpA (FAT%) (R = 0.79 p < 0.001); and BMI and BAI (R = 0.67 p < 0.001). BMI tended to overestimate the rate of underweight, normal weight or overweight, meanwhile underestimating the rate of obesity. Third, BMI exhibited an average positive bias of 14.4% compared to the reference method (BImpA), whereas BAI exhibited an average negative bias of −8.3% compared to the reference method (BImpA). Multivariate logistic regression identified independent predictors of discordance in assessing weight status by BMI and BAI: BImpA (FAT%) odds ratio (OR) 1.29, total body water (%) OR 1.61, fat mass index OR 2.62, and Controlling Nutritional Status (CONUT) score OR 1.25. Conclusions: There is substantial rate of misclassification of weight status between BMI and BAI. These findings have significant implications for clinical practice as the boundary between health and disease in malnutrition is crucial to accurately define criteria for intervention. Perhaps BMI cut-offs for classifying weight status in the elderly should be revisited.