Table 1.
Patterns of habitual coffee consumption by baseline characteristics.
Fig 1.
(A) Schematic overview of the study, including data selection/filtration and main analysis steps. (B) Directed Acyclic Graph (DAG) that displays assumptions about the relationship between coffee consumption, sleep quality/ESS, and a selection of other variables. Potential confounders (i.e., factors that may influence both coffee consumption and sleep quality and therefore could bias the analysis) are indicated by a node with gray background color. (C) Overview of coffee consumption in the studied population. Bar plot showing the number of individuals (y-axis) for each level of coffee consumption (x-axis).
Fig 2.
Coffee consumption in the studied population and association with known genetic markers.
(A) Stacked bar plots representing the distribution of coffee consumption (y-axis) for each level (x-axis) of age, sex, BMI, tea consumption, stress, physical activity, and smoking status, respectively. BMI has been divided into categories according to guidelines from the World Health Organization (WHO). (B) Simplified schematic description of key steps used in the genome-wide association studies (GWAS) analysis. (C-D) Region plots showing zoomed-in view at chromosome 7 (C), and chromosome 15 (D). Blue points represent individual SNPs. A p-value threshold of 5e-08 was used for determining significance (red-colored horizontal dashed line). Top significantly associated SNPs have been annotated in the plots.
Fig 3.
Influence of habitual coffee consumption on sleep and day-time sleepiness.
(A) Histogram showing the distribution of calculated sleep scores (left panel) and ESS scores (right panel) across participants. Vertical dashed line represents the mean value. (B) Association of habitual coffee intake assessed by regression analysis. For each of the seven sleep factors, an ordinal logistic regression model was fitted, while for each of the sleep score and ESS score, a Quasi-Poisson regression model was fitted. All models were adjusted for basic demographics (age and sex), BMI, and lifestyle factors (habitual tea intake, stress, physical activity, and smoking), as identified by the DAG analysis. Dots show cumulative odds ratio (ordinal logistic regression) or rate ratio (Quasi-Poisson regression). Black lines on each side of the dots represent 95% confidence interval. Vertical dashed line shows Odds Ratio = 1. Significant results (p < 0.05) are highlighted as yellow dots. (C) Heatmaps showing differences in predicted outcomes. For categorical sleep factors (sleep duration, sleep quality, difficulty falling asleep, frequency waking up, frequency waking up too early, reflux, and snoring) partial proportional models were used to calculate predicted category probabilities. For sleep score and ESS score, quasi-Poisson models were used to calculate predicted counts. Results are presented as the difference in predicted probabilities/counts comparing coffee consumption level (high vs none, moderate vs none, and low vs none, respectively).