Alexandra Hemmer

Growing evidence from large epidemiological and small lab-controlled studies links night shift work to impaired sleep, cognitive performance, and cardiometabolic health, but evidence from real-world occupational environments remains limited. Using multiple wearable devices and mobile health apps, alongside other objective and subjective assessment tools, we assess the nutritional intake and cardiometabolic parameters among shift workers during three consecutive night shifts and three consecutive day shifts. Here, we report data on 72 night shift workers, consisting of 96% healthcare workers, 82% female, with a median age of 31 years and a median body mass index 22.7 Kg/m2. While intake of calories, protein, and fat is reduced during night shifts, glycemic variability and postprandial glucose spikes are increased compared to day shifts. After three consecutive night shifts, the morning systolic blood pressure is elevated, plasma cortisol is reduced, and the 24-hour heart rate and heart rate variability are altered, indicating disrupted parasympathetic function. Exploratory analysis reveals higher protein intake correlates with lower glycemic variability, whereas nighttime eating is associated with increased glycemic variability. Future interventional studies could target meal composition and meal timing to improve glycemic variability in night shift workers.

Aims/hypothesis: Gestational diabetes mellitus (GDM) affects 14% of all pregnancies worldwide and is associated with cardiometabolic risk. We aimed to exploit high-resolution wearable device time-series data to create a fine-grained physiological characterisation of the postpartum GDM state in free-living conditions, including clinical variables, daily glucose dynamics, food and drink consumption, physical activity, sleep patterns and heart rate. Methods: In a prospective observational study, we employed continuous glucose monitors (CGMs), a smartphone food diary, triaxial accelerometers and heart rate and heart rate variability monitors over a 2 week period to compare women who had GDM in the previous pregnancy (GDM group) and women who had a pregnancy with normal glucose metabolism (non-GDM group) at 1–2 months after delivery (baseline) and 6 months later (follow-up). We integrated CGM data with ingestion events recorded with the smartphone app MyFoodRepo to quantify the rapidity of returning to preprandial glucose levels after meal consumption. We inferred the properties of the underlying 24 h rhythm in the baseline glucose. Aggregating the baseline and follow-up data in a linear mixed model, we quantified the relationships between glycaemic variables and wearable device-derived markers of circadian timing. Results: Compared with the non-GDM group (n=15), the GDM group (n=22, including five with prediabetes defined based on fasting plasma glucose [5.6–6.9 mmol/l (100–125 mg/dl)] and/or HbA_1c [39–47 mmol/mol (5.7–6.4%)]) had a higher BMI, HbA_1c and mean amplitude of glycaemic excursion at baseline (all p≤0.05). Integrating CGM data and ingestion events showed that the GDM group had a slower postprandial glucose decrease (p=0.01) despite having a lower proportion of carbohydrate intake, similar mean glucose levels and a reduced amplitude of the underlying glucose 24 h rhythm (p=0.005). Differences in CGM-derived variables persisted when the five women with prediabetes were removed from the comparison. Longitudinal analysis from baseline to follow-up showed a significant increase in fasting plasma glucose across both groups. The CGM-derived metrics showed no differences from baseline to follow-up. Late circadian timing (i.e. sleep midpoint, eating midpoint and peak time of heart rate) was correlated with higher fasting plasma glucose and reduced amplitudes of the underlying glucose 24 h rhythm (all p≤0.05). Conclusions/interpretation: We reveal GDM-related postpartum differences in glucose variability and 24 h rhythms, even among women clinically considered to be normoglycaemic. Our results provide a rationale for future interventions aimed at improving glucose variability and encouraging earlier daily behavioural patterns to mitigate the long-term cardiometabolic risk of GDM. Trial registration: ClinicalTrials.gov no. NCT04642534 Graphical