Estimation of Dietary Iron Bioavailability from Food Iron Intake and Iron Status

Currently there are no satisfactory methods for estimating dietary iron absorption (bioavailability) at a population level, but this is essential for deriving dietary reference values using the factorial approach. The aim of this work was to develop a novel approach for estimating dietary iron absorption using a population sample from a sub-section of the UK National Diet and Nutrition Survey (NDNS). Data were analyzed in 873 subjects from the 2000–2001 adult cohort of the NDNS, for whom both dietary intake data and hematological measures (hemoglobin and serum ferritin (SF) concentrations) were available. There were 495 men aged 19–64 y (mean age 42.7±12.1 y) and 378 pre-menopausal women (mean age 35.7±8.2 y). Individual dietary iron requirements were estimated using the Institute of Medicine calculations. A full probability approach was then applied to estimate the prevalence of dietary intakes that were insufficient to meet the needs of the men and women separately, based on their estimated daily iron intake and a series of absorption values ranging from 1–40%. The prevalence of SF concentrations below selected cut-off values (indicating that absorption was not high enough to maintain iron stores) was derived from individual SF concentrations. An estimate of dietary iron absorption required to maintain specified SF values was then calculated by matching the observed prevalence of insufficiency with the prevalence predicted for the series of absorption estimates. Mean daily dietary iron intakes were 13.5 mg for men and 9.8 mg for women. Mean calculated dietary absorption was 8% in men (50th percentile for SF 85 µg/L) and 17% in women (50th percentile for SF 38 µg/L). At a ferritin level of 45 µg/L estimated absorption was similar in men (14%) and women (13%). This new method can be used to calculate dietary iron absorption at a population level using data describing total iron intake and SF concentration

The placebo effect in thyroid cancer: a meta-analysis

ContextThe natural history of advanced thyroid malignancies is largely unknown. The outcome of patients included in the placebo arm of clinical trials could be reflective of their therapy-free evolution.ObjectiveTo analyze the response rate, symptoms and adverse effects of locally advanced or metastatic differentiated (DTC) and medullary thyroid cancer (MTC) in patients treated with placebo in clinical trials.DesignPubMed (MEDLINE) and SCOPUS databases were searched through September 2015 to identify high-quality randomized controlled clinical trials. We included studies that recruited patients with DTC or MTC with a placebo arm.MethodsWe conducted a meta-analysis for each category of response rate, adherence to treatment, and adverse events. An empirical Bayesian random-effect model was used.ResultsWe identified five clinical trials. DTC and MTC were independently analyzed. In the placebo arm, no complete response was observed; partial response occurred in 1.6% (0.6–3) and 6.4% (3.4–10.3) of DTC and MTC respectively; stable disease was described in 40.5% (34.6–46.9) and 53.9% (44.3–64.4) of DTC and MTC respectively. DTC reached a disease control rate of 42.3% (36.2–48.9) and MTC of 60.2 (50.1–71.4). Treatment discontinuation rate was 3.5% (1.9–5.5) in DTC and 5.7% (3.0–9.4) in MTC. Rate of dose reduction was 7.3% (4.8–10.5) in DTC and 6.2% (3.3–10.0) in MTC.ConclusionsThis meta-analysis provides extensive data on the response rate and adverse effects of locally advanced or metastatic DTC and MTC in patients treated with placebo. These results may be used for comparisons with results from clinical trials without a placebo arm.</jats:sec

Nodular Thyroid Disease and Thyroid Cancer in the Era of Precision Medicine

The management of thyroid nodules, one of the main clinical challenges in endocrine clinical practice, is usually straightforward. Although the most important concern is ruling out malignancy, there are grey areas where uncertainty is frequently present: the nodules labelled as indeterminate by cytology and the extent of therapy when thyroid cancer is diagnosed pathologically. There is evidence that the current available precision medicine tools (from all the "-omics" to molecular analysis, fine-tuning imaging or artificial intelligence) may help to fill present gaps in the future. We present here a commentary on some of the current challenges faced by endocrinologists in the field of thyroid nodules and cancer, and illustrate how precision medicine may improve their diagnostic and therapeutic capabilities in the future