Background and Aims Eating habits may contribute to longevity. We characterized the eating habits and cardiovascular risk (CVR) biomarkers in Portuguese centenarians (CENT) compared to controls. Methods and Results Centenarians (n = 253), 100.26 ± 1.98 years, were compared with 268 controls (67.51 ± 3.25), low (LCR) and high (HCR) CVR (QRISK®2-2016). Anthropometric and body composition were evaluated by bioimpedance. Abdominal obesity, BMI, and fat mass (FM) cut-offs were according to the WHO. Sarcopenia was defined by muscle mass index cut-off ≤ 16.7 kg/m2. Daily red meat intake, adjusted for age and gender, was sarcopenia protective (OR = 0.25, 95% CI = 0.096–0.670, P = 0.006); however, it contributes for FM excess (OR = 4.946, 95% CI = 1.471–16.626, P = 0.01), overweight, and obesity (OR = 4.804, 95% CI = 1.666–13.851, P = 0.004). This centenarian eating habit (2%) contrasts to HCR (64.3%). The history of red meat (P < 0.0001) and canned/industrialized food intakes (P < 0.0001) was associated with HCR. Basal metabolism was lower in centenarians versus LCR/HCR (CENT = 1176.78 ± 201.98; LCR = 1356.54 ± 170.65; HCR = 1561.33 ± 267.85; P < 0.0001), BMI (CENT = 21.06 ± 3.68; LCR = 28.49 ± 4.69; HCR = 29.56 ± 5.26; P < 0.0001), waist circumference (CENT = 85.29 ± 10.83; LCR = 96.02 ± 11.71; HCR = 104.50 ± 11.84; P < 0.0001), and waist-hip ratio (CENT = 0.88 ± 0.07; LCR = 0.92 ± 0.08; HCR = 1.01 ± 0.08; P < 0.0001). CENT had lower total cholesterol, LDL cholesterol, non-HDL cholesterol, and cholesterol/HDL ratio than controls. Conclusions Frequent consumption of red meat, cholesterol, and heme iron rich may contribute to obesity and increased CVR. The low frequency of this consumption, observed in centenarians, although associated with sarcopenia, may be one of the keys to longevity.

Background: A single prolactin sampling is recommended for the diagnosis of hyperprolactinemia. We aimed to study the utility of the prolactin serial sampling and to determine the best cut-offs associated with persistent hyperprolactinemia. Methods: Retrospective study of hyperprolactinemic patients [referral prolactin (rPRL)] that underwent prolactin serial samplings. Prolactin at 0 minutes (PRL0′), 20 to 30, and 40 to 60 minutes. The lowest of these last 2 was defined as nadir prolactin (nPRL). Persistent hyperprolactinemia was defined as nPRL above normal. We excluded patients under dopamine receptor agonists. Receiver-operating characteristic (ROC) curves were used to determine the best rPRL and PRL0′ cut-offs predicting persistent hyperprolactinemia. Results: We studied 53 patients (3 males). Median rPRL 48.0 ng/mL (39.5–72.5), PRL0′ 34.3 ng/mL (18.0–50.8) and nPRL 29.5 ng/mL (11.4–44.4). PRL0′ was elevated in 35 (66.0%) patients and in 7 of them a normal nPRL was reached; therefore 28 (52.8%) had persistent hyperprolactinemia. The area under curve (AUC) for the association between rPRL and persistent hyperprolactinemia was 0.70 (95%CI: 0.56–0.84); best cut-off: 53.4 ng/mL [sensitivity 53.6%, specificity 80.0%, positive predictive value (PPV) 75.0%, and negative predictive value (NPV) 60.6%]. In the 35 patients with elevated PRL0′, the AUC was 0.92 (95%CI: 0.81–1.00); best cut-off: 35.2 ng/mL (sensitivity 85.7%, specificity 85.7%, PPV 60.0%, and NPV 96.0%). Conclusions: Approximately 1/3 of the patients reached a normal PRL0′. In an additional 20%, prolactin normalized after serial samplings. Patients with rPRL >53.4 ng/mL had 75% probability of having persistent hyperprolactinemia and those with PRL0′ <35.2 ng/mL had a 96% probability of not having persistent hyperprolactinemia.

No abstract

LDL-C can be measured directly by beta-quantification, the gold standard procedure based on ultracentrifugation and lipoprotein particle separation according to their densities [4]. However, despite being a more practical method and with a good approximation to the real value of LDL-C, this is a time-consuming, not totally accurate, and equally expensive assay to be widely available in clinical practice, especially when compared to the use of a formula [4,5]. Therefore, LDL-C determination is generally estimated by measuring the remaining lipoproteins and triglycerides (TG).The Friedewald formula is considered worldwide the gold standard for clinical estimation of LDL-C concentrations due to its great convenience, cost-effectiveness, and concordance with directly measured LDL-C (LDLd-C) in most patients [6].

No abstract