Background Mild-to-moderate iodine deficiency, particularly in pregnancy, is prevalent; this is of concern because observational studies have shown negative associations with child neurodevelopment. Although neither the benefits nor the safety of iodine supplementation in pregnancy in areas of mild-to-moderate deficiency are well researched, such supplementation is increasingly being recommended by health authorities in a number of countries. Objectives By reviewing the most recent published data on the effects of iodine supplementation in mildly-to-moderately deficient pregnant women on maternal and infant thyroid function and child cognition, we aimed to determine whether the evidence was sufficient to support recommendations in these areas. Methods A systematic review of randomized controlled trials (RCTs), non-RCT interventions, and observational studies was conducted. To identify relevant articles, we searched the PubMed and Embase databases. We defined mild-to-moderate iodine deficiency as a baseline median urinary iodine concentration (UIC) of 50–149 µg/L. Eligible studies were included in meta-analyses. Results In total, 37 publications were included—10 RCTs, 4 non-RCT interventions, and 23 observational studies. Most studies showed no effect of iodine supplementation on maternal or infant thyroid-stimulating hormone and free thyroxine. Most RCTs found that supplementation reduced maternal thyroglobulin and in 3 RCTs, it prevented or diminished the increase in maternal thyroid volume during pregnancy. Three RCTs addressed child neurodevelopment; only 1 was adequately powered. Meta-analyses of 2 RCTs showed no effect on child cognitive [mean difference (MD): −0.18; 95% CI: −1.22, 0.87], language (MD: 1.28; 95% CI: −0.28, 2.83), or motor scores (MD: 0.28; 95% CI: −1.10, 1.66). Conclusions There is insufficient good-quality evidence to support current recommendations for iodine supplementation in pregnancy in areas of mild-to-moderate deficiency. Well-designed RCTs, with child cognitive outcomes, are needed in pregnant women who are moderately deficient (median UIC < 100 µg/L). Maternal intrathyroidal iodine stores should be considered in future trials by including appropriate measures of preconceptional iodine intake. This review was registered at www.crd.york.ac.uk/prospero as CRD42018100277.

The results of this feasibility study suggest that a definitive study can be done, but that minor changes are needed to the protocol to standardise the administration of clinically assisted hydration (which may reduce the incidence of certain adverse effects).

Lixisenatide reduced [13C]oleate concentration, derived from a single meal in CM-TAG, as well as glucose Rameal, through delayed gastric emptying. However day-long CM production, measured with repeated meal-feeding, was not reduced by lixisenatide and decreased CM-TAG concentration was due to increased CM-TAG clearance.

Survival prediction is integral to oncology and palliative care, yet robust prognostic models remain elusive. We assessed the feasibility of combining actigraphy, sleep diary data, and routine clinical parameters to prognosticate. Fifty adult outpatients with advanced cancer and estimated prognosis of <1 year were recruited. Patients were required to wear an Actiwatch® (wrist actigraph) for 8 days, and complete a sleep diary. Univariate and regularised multivariate regression methods were used to identify predictors from 66 variables and construct predictive models of survival. A total of 49 patients completed the study, and 34 patients died within 1 year. Forty-two patients had disrupted rest-activity rhythms (dichotomy index (I < O ≤ 97.5%) but I < O did not have prognostic value in univariate analyses. The Lasso regularised derived algorithm was optimal and able to differentiate participants with shorter/longer survival (log rank p < 0.0001). Predictors associated with increased survival time were: time of awakening sleep efficiency, subjective sleep quality, clinician’s estimate of survival and global health status score, and haemoglobin. A shorter survival time was associated with self-reported sleep disturbance, neutrophil count, serum urea, creatinine, and C-reactive protein. Applying machine learning to actigraphy and sleep data combined with routine clinical data is a promising approach for the development of prognostic tools.

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