14 research outputs found

    Maternal iodine status, intrauterine growth, birth outcomes and congenital anomalies in a UK birth cohort.

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    BACKGROUND: Severe iodine insufficiency in pregnancy has significant consequences, but there is inadequate evidence to indicate what constitutes mild or moderate insufficiency, in terms of observed detrimental effects on pregnancy or birth outcomes. A limited number of studies have examined iodine status and birth outcomes, finding inconsistent evidence for specific outcomes. METHODS: Maternal iodine status was estimated from spot urine samples collected at 26-28 weeks' gestation from 6971 mothers in the Born in Bradford birth cohort. Associations with outcomes were examined for both urinary iodine concentration (UIC) and iodine-to-creatinine ratio (I:Cr). Outcomes assessed included customised birthweight (primary outcome), birthweight, small for gestational age (SGA), low birthweight, head circumference and APGAR score. RESULTS: There was a small positive association between I:Cr and birthweight in adjusted analyses. For a typical participant, the predicted birthweight centile at the 25th percentile of I:Cr (59 μg/g) was 2.7 percentage points lower than that at the 75th percentile of I:Cr (121 μg/g) (99% confidence interval (CI) 0.8 to 4.6), birthweight was predicted to be 41 g lower (99% CI 13 to 69) and the predicted probability of SGA was 1.9 percentage points higher (99% CI 0.0 to 3.7). There was no evidence of associations using UIC or other birth outcomes, including stillbirth, preterm birth, ultrasound growth measures or congenital anomalies. CONCLUSION: Lower maternal iodine status was associated with lower birthweight and greater probability of SGA. Whilst small, the effect size for lower iodine on birthweight is comparable to environmental tobacco smoke exposure. Iodine insufficiency is avoidable, and strategies to avoid deficiency in women of reproductive age should be considered. TRIAL REGISTRATION: ClinicalTrials.gov NCT03552341. Registered on June 11, 2018

    Maternal iodine status in a multi-ethnic UK birth cohort: associations with autism spectrum disorder.

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    BACKGROUND: Maternal iodine requirements increase during pregnancy to supply thyroid hormones essential for fetal brain development. Maternal iodine deficiency can lead to hypothyroxinemia, a reduced fetal supply of thyroid hormones which, in the first trimester, has been linked to an increased risk of autism spectrum disorder (ASD) in the child. No study to date has explored the direct link between maternal iodine deficiency and diagnosis of ASD in offspring. METHODS: Urinary iodine concentrations (UIC) and iodine/creatinine ratios (I:Cr) were measured in 6955 mothers at 26-28 weeks gestation participating in the Born in Bradford (BiB) cohort. Maternal iodine status was examined in relation to the probability of a Read (CTV3) code for autism being present in a child's primary care records through a series of logistic regression models with restricted cubic splines. RESULTS: Median (inter-quartile range) UIC was 76 μg/L (46, 120) and I:Cr was 83 μg/g (59, 121) indicating a deficient population according to WHO guidelines. Ninety two children (1·3%) in our cohort had received a diagnosis of ASD by the census date. Overall, there was no evidence to support an association between I:Cr or UIC and ASD risk in children aged 8-12 years (p = 0·3). CONCLUSIONS: There was no evidence of an increased clinical ASD risk in children born to mothers with mild-to-moderate iodine deficiency at 26 weeks gestation. Alternative functional biomarkers of exposure and a wider range of conditions may provide further insight

    Cardiovascular disease risk and dietary fibre intake in the United Kingdom Women’s Cohort Study

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    Background: Dietary fibre has been associated with risk of cardiovascular disease (CVD) in some cohort studies around the world. Key health messages may be created by examining the types or sources of fibre in the diet and associated risk of different CVD events but few studies have explored this. Methods: I conducted a systematic literature review of published studies investigating dietary fibre intake and CVD. Associations were explored using dose-response meta-analysis in addition to potential non-linear associations. CVD event data for the UK Women’s Cohort Study were obtained from death records, hospital episode statistics (HES) and the Myocardial Ischaemia National Audit Project (MINAP). Capture-recapture methods were then applied to estimate the potential for missing cases. Survival analyses for coronary heart disease (CHD), stroke and total CVD risk in association with total fibre intake and fibre from key food sources were conducted using a cohort approach for food frequency data and case-cohort methods were used for analyses with food diary data. Results: Meta-analyses broadly supported inverse associations between CVD and fibre intake. Combined data from 9 studies indicate lower CVD risk per 7g/day greater intake in total fibre, relative risk 0.91 (95% confidence intervals (CI) 0.88, 0.94). After 14 years, 821 CHD and 388 stroke cases were observed. Total fibre, soluble, insoluble and fibre from cereals assessed using FFQs were associated with lower risk of stroke. With each 6g/day higher total fibre intake, hazard ratio (HR) 0.89 (95% CI: 0.81, 0.99). Higher fibre density was associated with lower risk of myocardial infarction, for every 2g/1000kcal/day higher intake, HR 0.89 (95% CI: 0.81, 0.98). Higher cereal fibre intake, calculated using food diaries, was associated with lower risk of acute coronary events HR 0.76 (95% CI: 0.58, 1.00). Conclusion: Fibre intake is inversely associated with CVD risk in a dose response relationship after accounting for other potentially confounding influences. Associations were stronger for stroke risk, when the source of fibre was cereals and in those without hypertension

    The Method Quality of Cross-Over Studies Involved in Cochrane Systematic Reviews

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    <div><p>Background</p><p>It is possible that cross-over studies included in current systematic reviews are being inadequately assessed, because the current risk of bias tools do not consider possible biases specific to cross-over design. We performed this study to evaluate whether this was being done in cross-over studies included in Cochrane Systematic Reviews (CSRs).</p><p>Methods</p><p>We searched the Cochrane Library (up to 2013 issue 5) for CSRs that included at least one cross-over trial. Two authors independently undertook the study selection and data extraction. A random sample of the CSRs was selected and we evaluated whether the cross-over trials in these CSRs were assessed according to criteria suggested by the Cochrane handbook. In addition we reassessed the risk of bias of these cross-over trials by a checklist developed form the Cochrane handbook.</p><p>Results</p><p>We identified 688 CSRs that included one or more cross-over studies. We chose a random sample of 60 CSRs and these included 139 cross-over studies. None of these CSRs undertook a risk of bias assessment specific for cross-over studies. In fact items specific for cross-over studies were seldom considered anywhere in quality assessment of these CSRs. When we reassessed the risk of bias, including the 3 items specific to cross-over trials, of these 139 studies, a low risk of bias was judged for appropriate cross-over design in 110(79%), carry-over effects in 48(34%) and for reporting data in all stages of the trial in 114(82%).Assessment of biases in cross-over trials could affect the GRADE assessment of a review’s findings.</p><p>Conclusion</p><p>The current Cochrane risk of bias tool is not adequate to assess cross-over studies. Items specific to cross-over trials leading to potential risk of bias are generally neglected in CSRs. A proposed check list for the evaluation of cross-over trials is provided.</p></div

    Summary of the quality assessment of the 139 cross-over trials

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    <p>1. Appropriate cross-over design; 2. Randomized order of receiving treatment; 3. Carry-over effects; 4. Unbiased data; 5. Allocation concealment; 6. Blinding; 7. Incomplete outcome data; 8. Selective outcome reporting; 9. Other bias</p
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