Nicholas Edward Phillips

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

Motivation: Wearable biosensors measure physiological variables with high temporal resolution over multiple days and are increasingly employed in clinical settings, such as continuous glucose monitoring in diabetes care. Such datasets bring new opportunities and challenges, and patients, clinicians and researchers are today faced with a common challenge: how to best capture and summarise relevant information from multimodal wearable time series? Here, we aim to provide insights into individual blood glucose dynamics and their relationships with food and drink ingestion, time of day, and coupling with other physiological states such as physical and heart activity. To this end, we generate and analyse multiple wearable device data through the lens of a parsimonious mathematical model with interpretable components and parameters. A key innovation of our method is that the models are learnt on a personalised level for each participant within a Bayesian framework, which enables the characterisation of inter-individual heterogeneity in features such as the glucose response time following meals or underlying circadian rhythms. This framework may prove useful in other populations at risk of cardiometabolic diseases. Summary: Wearable biosensors and smartphone applications can measure physiological variables over multiple days in free-living conditions. We measure food and drink ingestion, glucose dynamics, physical activity, heart rate (HR) and heart rate variability (HRV) in 25 healthy participants over 14 days. We develop a Bayesian framework to learn personal parameters that quantify circadian rhythms and physiological responses to external stressors. Modelling the effects of ingestion events on glucose levels reveals that slower glucose decay kinetics elicit larger postprandial glucose spikes, and we uncover a circadian baseline rhythm of glucose with high amplitudes in some individuals. Physical activity and circadian rhythms explain as much as 40-65% of the HR variance, whereas the variance explained for HRV is more heterogeneous across individuals (20-80%). A more complex model incorporating activity, HR and HRV explains up to 15% additional glucose variability, highlighting the relevance of integrating multiple biosensors to better predict glucose dynamics.

Weight loss is key to controlling the increasing prevalence of metabolic syndrome (MS) and its components, i.e., central obesity, hypertension, prediabetes and dyslipidaemia. The goals of our study were two-fold. First, we characterised the relationships between eating duration, unprocessed and processed food consumption and metabolic health. During 4 weeks of observation, 213 adults used a smartphone application to record food and drink consumption, which was annotated for food processing levels following the NOVA classification. Low consumption of unprocessed food and low physical activity showed significant associations with multiple MS components. Second, in a pragmatic randomised controlled trial, we compared the metabolic benefits of 12 h time-restricted eating (TRE) to standard dietary advice (SDA) in 54 adults with an eating duration > 14 h and at least one MS component. After 6 months, those randomised to TRE lost 1.6% of initial body weight (SD 2.9, p = 0.01), compared to the absence of weight loss with SDA (−1.1%, SD 3.5, p = 0.19). There was no significant difference in weight loss between TRE and SDA (between-group difference −0.88%, 95% confidence interval −3.1 to 1.3, p = 0.43). Our results show the potential of smartphone records to predict metabolic health and highlight that further research is needed to improve individual responses to TRE such as a shorter eating window or its actual clock time.

The circadian clock is an endogenous and self‐sustained oscillator that anticipates daily environmental cycles. While rhythmic gene expression of circadian genes is well‐described in populations of cells, the single‐cell mRNA dynamics of multiple core clock genes remain largely unknown. Here we use single‐molecule fluorescence in situ hybridisation (smFISH) at multiple time points to measure pairs of core clock transcripts, Rev‐erbα ( Nr1d1 ), Cry1 and Bmal1 , in mouse fibroblasts. The mean mRNA level oscillates over 24 h for all three genes, but mRNA numbers show considerable spread between cells. We develop a probabilistic model for multivariate mRNA counts using mixtures of negative binomials, which accounts for transcriptional bursting, circadian time and cell‐to‐cell heterogeneity, notably in cell size. Decomposing the mRNA variability into distinct noise sources shows that clock time contributes a small fraction of the total variability in mRNA number between cells. Thus, our results highlight the intrinsic biological challenges in estimating circadian phase from single‐cell mRNA counts and suggest that circadian phase in single cells is encoded post‐transcriptionally.

Weight loss is key to control the increasing prevalence of metabolic syndrome (MS) and its components, i.e. central obesity, hypertension, prediabetes, and dyslipidaemia. We characterised the relationships between eating duration, unprocessed and processed food consumption, and metabolic health. During 4 weeks of observation, 213 adults used a smartphone application to record food and drink consumption, which was annotated for food processing levels following the NOVA classification. Consumption of unprocessed food items showed the highest number of significant associations with MS components after age and sex. In a pragmatic randomised controlled trial, we compared the metabolic benefits of 12h time-restricted eating (TRE) to standard dietary advice (SDA) in 54 adults with an eating duration >14h and at least one MS component. After 6 months, those randomised to TRE lost 1.6% of initial body weight (SD 2.9, p=0.01), compared to the absence of weight loss with SDA (−1.1%, SD 3.5, p=0.19). There was no significant difference in weight loss between TRE and SDA (between-group difference −0.88%, 95% confidence interval −3.1 to 1.3, p=0.43). Our results show the potential of smartphone records to predict metabolic health and highlight that further research is needed to understand individual responses to TRE.

The circadian clock and feeding rhythms are both important regulators of rhythmic gene expression in the liver. To further dissect the respective contributions of feeding and the clock, we analyzed differential rhythmicity of liver tissue samples across several conditions. We developed a statistical method tailored to compare rhythmic liver messenger RNA (mRNA) expression in mouse knockout models of multiple clock genes, as well as PARbZip output transcription factors (Hlf/Dbp/Tef). Mice were exposed to ad libitum or night-restricted feeding under regular light–dark cycles. During ad libitum feeding, genetic ablation of the core clock attenuated rhythmic-feeding patterns, which could be restored by the night-restricted feeding regimen. High-amplitude mRNA expression rhythms in wild-type livers were driven by the circadian clock, but rhythmic feeding also contributed to rhythmic gene expression, albeit with significantly lower amplitudes. We observed that Bmal1 and Cry1/2 knockouts differed in their residual rhythmic gene expression. Differences in mean expression levels between wild types and knockouts correlated with rhythmic gene expression in wild type. Surprisingly, in PARbZip knockout mice, the mean expression levels of PARbZip targets were more strongly impacted than their rhythms, potentially due to the rhythmic activity of the D-box–repressor NFIL3. Genes that lost rhythmicity in PARbZip knockouts were identified to be indirect targets. Our findings provide insights into the diurnal transcriptome in mouse liver as we identified the differential contributions of several core clock regulators. In addition, we gained more insights on the specific effects of the feeding–fasting cycle. Significance The rotation of the Earth around its own axis creates daily changes in the environment for virtually all living organisms. To anticipate and adapt to those changes, mammals possess an evolutionarily conserved circadian clock that controls most aspects of physiology. Using a previously undescribed analysis tool, we studied the impact of the circadian clock and its underlying feeding rhythms on hepatic gene expression. Our analysis shows that the loss of feeding rhythms in clock-disrupted animals is an important component of their phenotype. Finally, we were able to decipher the specific role of feeding rhythms, the circadian clock, and its controlled output of PARbZip transcription factors in the regulation of liver rhythmic gene expression.

The circadian clock is an endogenous and self-sustained oscillator that anticipates daily environmental cycles and coordinates physiology accordingly. While rhythmic gene expression of circadian genes is well-described in populations of cells, the single-cell mRNA dynamics of multiple core-clock genes remain largely unknown. Here we use single molecule fluorescence in-situ hybridization (smFISH) at multiple time points to measure pairs of core-clock transcripts, Rev-erbα ( Nr1d1 ), Cry1 and Bmal1 , in mouse fibroblasts at single-molecule resolution. The mean mRNA level oscillates over 24 hours for all three genes, but mRNA numbers show considerable spread between cells. While transcript number scales with cell size for all genes, gene-to-gene correlations of mRNA number depends on the gene pair. To account for these features of the data, we develop a probabilistic model for multivariate smFISH mRNA counts that quantifies changes in transcriptional bursting across genes and over circadian time. We identify a mixture model of negative binomials as the preferred model of the mRNA count distributions, which accounts for cell-to-cell heterogeneity, notably in cell size. The paired count data and modelling allows the decomposition of mRNA variability into distinct noise sources, showing that circadian clock time contributes only a small fraction of the total variability in mRNA number between cells. Thus, our results highlight the intrinsic biological challenges in estimating circadian phase from single-cell mRNA counts and suggest that circadian phase in single cells is encoded post-transcriptionally.

Phenotypically identical mammalian cells often display considerable variability in transcript levels of individual genes. How transcriptional activity propagates in cell lineages, and how this varies across genes is poorly understood. Here we combine live-cell imaging of short-lived transcriptional reporters in mouse embryonic stem cells with mathematical modelling to quantify the propagation of transcriptional activity over time and across cell generations in phenotypically homogenous cells. In sister cells we find mean transcriptional activity to be strongly correlated and transcriptional dynamics tend to be synchronous; both features control how quickly transcriptional levels in sister cells diverge in a gene-specific manner. Moreover, mean transcriptional activity is transmitted from mother to daughter cells, leading to multi-generational transcriptional memory and causing inter-family heterogeneity in gene expression.