Objective: Continuous glucose monitoring (CGM) provides far greater detail about fetal exposure to maternal glucose across the 24 hour day. Our aim was to examine the role of temporal glucose variation on the development of large for gestational age infants (LGA) in women with treated gestational diabetes (GDM). Research Design and Methods:A prospective observational study of 162 pregnant women with GDM in specialist multidisciplinary antenatal diabetes clinics. Participants undertook a 7-day masked CGM at 30-32 weeks gestation. Standard summary indices and glycemic variability measures of CGM were calculated. Functional data analysis was applied to determine differences in temporal glucose profiles.LGA was defined as birth weight ≥90th percentile adjusted for infant sex, gestational age, maternal BMI, ethnicity and parity.Results -Mean glucose was significantly higher in women who delivered an LGA infant (6.2 vs 5.8 mmol/l P=0.025 or 111.6 mg/dl vs 104.4 mg/dl respectively). There were no significant differences in percentage time in, above or below the target glucose range, or in glucose variability measures (all P>0.05). Functional data analysis revealed that the higher mean glucose was driven by a significantly higher glucose for 6 hours overnight (00h30-06h30) in mothers of LGA infants (6.0 ± 1.0 mmol/l vs 5.5 ± 0.8 mmol/l p=0.005; 108.0 ± 18.0 mg/dl vs 99.0 ± 14.4 mg/dl respectively). Conclusions: Mothers ofLGA infants run significantly higher glucose overnight compared to mothers without LGA. Detecting and addressing nocturnal glucose control may help to further reduce rates of LGA in women with GDM.3
Sleep curtailment is common in the Westernised world and coincides with an increase in the prevalence of type 2 diabetes mellitus (T2DM). This review considers the recently published evidence for whether sleep duration is involved in the development of T2DM in human subjects and whether sleep has a role to play in glucose control in people who have diabetes. Data from large, prospective studies indicate a U-shaped relationship between sleep duration and the development of T2DM. Smaller, cross-sectional studies also support a relationship between short sleep duration and the development of both insulin resistance and T2DM. Intervention studies show that sleep restriction leads to insulin resistance, with recent sleep extension studies offering tantalising data showing a potential benefit of sleep extension on glucose control and insulin sensitivity. In people with established diabetes the published literature shows an association between poor glucose control and both short and long sleep durations. However, there are currently no studies that determine the causal direction of this relationship, nor whether sleep interventions are likely to offer benefit for people with diabetes to help them achieve tighter glucose control
Background This study explored the potential role that sleep, and sleep improvement, might play in the aetiology of obesity, using data from the first wave of the Understanding Society (USoc) cohort – a longitudinal household panel study representative of the UK population. Methods The first wave of USoc collected data on seven self-reported sleep-related variables comprising measures of: sleep duration; sleep quality; snoring and/or coughing while asleep; falling asleep within 30 minutes; use of sleep medication; night time waking; and staying awake during the day. Linear (for log-transformed BMI) and multinomial logistic (for BMI categorised as underweight through morbidly obese) regression analyses were conducted using STATA 12.0, before and after adjustment for age, sex, pregnancy, household income per person and household crowding index (bedrooms per person) as potential confounders. Models were specified using a Directed Acyclic Graph. Results Complete data were available on 20,956 (41.1%) of the 50,994 cohort participants. Regardless of the sleep-related variable and whether BMI was operationalised as a continuous or multinomial variable, there was a strong, consistent and statistically significant association between sleep and BMI. Shorter, worse quality and more frequently medicated, disturbed and inadequate sleep were all associated with elevated BMI or an increased risk of overweight and obesity before and after adjustment for confounding. The sleep-related variable most strongly associated with BMI was “cough or snore loudly” where participants who reported doing so “most nights” had an adjusted odds of morbid obesity of 11.94 (95% CI 8.35, 17.07). However, even the sleep-related variable with the weakest association with BMI (“medicine for sleep”) was associated with more than twice the odds of obesity and morbid obesity amongst those participants reporting “medicine for sleep” use three or more time per week. Indeed, the overall consistency of the association between sleep and BMI was evident not only across all seven of the different sleep measures but also in the trends observed for those measures with continuous or ordinal scales. Conclusion This is the first nationally representative study to demonstrate a powerful association between sleep duration, sleep quality and obesity in the UK. However, the study is limited by: the substantial number of participants with missing data; its cross-sectional design; and potential clustering of sleep and BMI within households. Further research is therefore required to explore whether naturally occurring changes in sleep duration and quality are associated with changes in BMI to strengthen the evidence for a causal link between sleep and obesity.
Background Ankle Brachial Pressure Index (ABPI) is the ratio of ankle to brachial systolic blood pressure. It is widely used as a non-invasive, relatively simple method for the diagnosis of peripheral arterial diseases (PAD) in symptomatic patients. It is also widely used to estimate PAD prevalence. Furthermore, low ABPI – as an indicator of atherosclerosis – was also used to predict cardiovascular morbidity and mortality in many epidemiological studies. Several researchers think that ABPI is overvalued and its use in epidemiological studies had attracted some statistical pitfalls. This paper aims to investigate these pitfalls, and to suggest possible advance statistical solution for some of them. Methods Critical review of literature Results The use of ABPI was proposed by studies comparing ABPI with peripheral angiography and Doppler ultrasound. However, most of these studies either had small sample size (issues with representativeness and generalisability of the result), compared asymptomatic legs with symptomatic legs using independent sample t-test (inappropriate test as legs of the same patients are not independent), or chosen cases from old age group and controls from young age group (age is a potential confounder which might had distorted their results). There were no attempts to test for the potential association between ABPI and other variables such as gender, obesity, cholesterol level and physical activity. Nevertheless, several researchers tested the correlation between ABPI and brachial systolic blood pressure and claimed that ABPI is usually lower in patients with high brachial systolic blood pressure even in the absence of PAD. Some of these researchers used correlation coefficients, however, using Pearson correlation test is inappropriate as brachial pressure is mathematically coupled to the ABPI, thus a null hypothesis of zero correlation is erroneous and the conclusion based on it should be rendered invalid. Ordinary Least Square Linear Regression model will be unsuitable as well for the same reason. Bland-altman plot or Ordinary Least Product Regression would be more appropriate. Furthermore, most of the studies that used ABPI as a predictor of cardiovascular morbidity and mortality had introduced some major statistical flaws in their regression analysis such as collinearity, mathematical coupling and reversal paradox. Such statistical issues would have distorted those studies’ associations. Conclusion Caution should be taken when using ABPI in epidemiological studies. Covariate selection and adjustment for confounder should be done carefully in regressing analysis. Directed Acyclic Graph (DAG) is a good method for addressing these issues.
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