Association of shift work with incident dementia: a community-based cohort study

For this community-based cohort study, data were extracted from the public UK Biobank Resource [25]. The UK Biobank is a prospective cohort study with over 500,000 community-dwelling participants across the UK aged 37–73 years when recruited between 2006 and 2010 [26].

Participants who indicated they were in paid employment or self-employed at baseline were included in our study. We excluded those who (1) reported previous cognitive impairment or dementia, (2) lack of information about shift work or night shift work status, and (3) have no genetic data.

The definition of shift work in UK Biobank was “a schedule falling outside of 9 am to 5 pm; by definition, such schedules involved afternoon, evening, or night shifts or rotating through these shifts,” while night shift work was defined as “a work schedule that involves working through the normal sleeping hours, for instance, working through the hours from 12 to 6 am.”

The UK Biobank first asked participants employed at baseline to report whether their current main job involved shift schedule; if so, participants were further asked if night shifts were involved. For both questions, response options were never/rarely, sometimes, usually, or always. We derived individual current shift work status according to responses to the two questions, and categorized them as “non-shift workers” or “shift workers,” with “non-shift workers” defined as working between hours 9 am to 5 pm; among shift workers, participants were categorized as “night shift workers” or “shift but non-night shift workers”, with “non-night shift workers” defined working between hours 5 pm to 12 am; among night shift workers, participants were further categorized as “some night shift workers” or “usual/permanent night shift workers.”

The primary outcome was all-cause dementia in a time-to-event analysis, and the secondary outcomes included AD, VD, and other types of dementia. The electronic health records (EHRs), a data linkage to hospital inpatient admissions and death registries, include primary or secondary events in England, Scotland, and Wales. A previous comparison between EHRs and expert clinical adjudicators in the UK Biobank showed that the overall positive predictive value for dementia diagnosis is 82.5% [27], suggesting that the EHRs were effective to assess the association between risk factors and dementia. We used the algorithms provided by UK Biobank to identify dementia cases, which were generated based on EHRs, using ICD-9 and ICD-10 codes (Additional file 1: Table S1). In the time-to-event analysis, the date of incident dementia during follow-up was set as the earliest date of dementia codes recorded regardless of the source used. At the time of analysis, as hospital admission data were available until 30 June 2021, we, therefore, censored the disease-specific outcome analysis at this date or the date of the first disease incidence or death, whichever occurred first. Mortality data were available for participants until 31 May 2021.

We developed a polygenetic risk score (PRS) for quantifying the genetic predisposition to dementia using single-nucleotide polymorphisms (SNPs) associated with dementia based on previous genome-wide association studies that did not include UK Biobank participants [28]. Information on the 23 selected SNPs is listed in Additional file 1: Table S2. Individual SNPs were coded as 0, 1, and 2 according to the number of risk alleles. The PRS was formulated as the sum of the number of risk alleles at each locus multiplied by the respective regression coefficient, divided by the number of SNPs, using PRSice-2 [29, 30]. The PRS was then divided into quartiles and categorized as low (quartiles 1 to 2), intermediate (quartile 3), and high (quartile 4) genetic predisposition to dementia (Additional file 1: Table S3).

Possible confounding variables include: age; sex; ethnicity (white/not white); education, categorized as higher (college/university degree or other professional qualification), upper secondary (second/final stage of secondary education), lower secondary (first stage of secondary education), vocational (work-related practical qualifications), or other; socioeconomic status, categories derived from Townsend deprivation index quartiles 1 (low), 2 to 3 (intermediate), and 4 (high); diabetes mellitus (DM); hypertension (HTN); stroke; coronary heart disease (CHD); cholesterol-lowering medication; antihypertensives; aspirin; body mass index (BMI); systolic blood pressure (SBP); total cholesterol (TC); triglycerides (TG); high-density lipoprotein (HDL); low-density lipoprotein (LDL); glycated hemoglobin (HbA1c); smoking status (current or no current smoking); alcohol consumption; healthy diet, based on consumption of at least 4 of 7 commonly eaten food groups following recommendations on dietary priorities [31]; regular physical activity, defined as meeting the 2017 UK Physical activity guidelines of 150 min of moderate activity per week or 75 min of vigorous activity; years of work; sleep duration, categorized as ≤ 6, 7–8, and ≥ 9 h/day; chronotype preference (definitely a “morning” person, more a “morning” than “evening” person, more an “evening” than a “morning” person, and definitely an “evening” person).

For baseline characteristics, continuous variables conforming to normal distribution were described by their means and standard deviations, while those not conforming to normal distribution were described by medians and interquartile ranges. Categorical variables were described by counting numbers and calculating percentages. Univariate comparisons between groups were performed using Student’s t, Mann–Whitney, or χ² tests according to the type and distribution of variables.

In the primary analysis, time-to-event analysis for all-cause dementia was performed using the Cox proportional hazard regression model, and we constructed several models that included different covariates to estimate hazard ratios (HR) and their 95% confidence intervals (95% CI). Model 1 was adjusted for age at baseline and sex. Model 2 was adjusted for terms in model 1, ethnicity, education, and socioeconomic status. Model 2 was chosen as the primary model.

We conducted several sensitivity analyses. First, we further adjusted some covariate. Model 3 was further adjusted for terms in model 2, DM, HTN, stroke, CHD, cholesterol-lowering medication, antihypertensives, aspirin, BMI, SBP, TC, TG, HDL, LDL, HbA1c, smoking status, alcohol consumption, healthy diet, and regular physical activity. Model 4 was adjusted for terms in model 3, genetic predisposition to dementia by PRS category. Model 5 was adjusted for terms in model 4, years of work. Model 6 was adjusted for terms in model 5, sleep duration. Model 7 was adjusted for terms in model 6, chronotype preference. Second, we analyzed the impact of shift work on dementia using Fine-Gray methods accounting for death as a competing risk, to assess the robustness of our findings [32]. Third, we also excluded subjects with follow-up time < 1 year or incident dementia < 1 year from baseline to perform the analysis. Forth, we perform the same analysis in the dataset containing 278,270 participants using multiple imputations by chained equations with 5 imputations to impute missing values.

All P values were reported as two-sided tests with significance defined as P < 0.05. Statistical analyses were performed in the R software (Version 4.0.3, R Core Team, https://​www.​r-project.​org).

Figure 1 has illustrated the participants’ selection. Of 170,722 eligible participants, 27,450 (16.1%) had reported shift work status and 143,272 (83.9%) had not (non-shift workers). Baseline characteristics of eligible participants were displayed in Tables 1 and 2. Participants who had reported shift work status (vs. non-shift workers) were younger, more likely to be men, had a lower education level and higher Townsend deprivation index, and had a higher prevalence of DM and HTN. Shift workers also tended to take more cholesterol-lowering medication, antihypertensives, and aspirin, and to be not current smokers, had lower alcohol consumption, less healthy diet, but more physically active and had a shorter sleep duration. (Table 1).

The incidence of the primary and secondary outcomes was shown in Additional file 1: Table S4. We observed 716 dementia cases during a median follow-up period of 12.4 years, of whom 134 (18.7%) and 582 (81.3%) were in the shift workers group and the non-shift workers group, respectively. Shift workers had a higher incidence of all-cause dementia compared with non-shift workers (unadjusted-HR, 1.21; 95% CI, 1.00 to 1.46; P = 0.04; Fig. 2). After adjusting for confounders, the risk of all-cause dementia among shift workers remained significantly higher than non-shift workers (adjusted-HR, 1.30; 95% CI, 1.08 to 1.58; P = 0.006; Table 3). Among shift workers, we did not observe a significant association between night shift work and the risk of dementia after multivariable adjustment in the Cox model (adjusted-HR, 1.04; 95% CI, 0.73 to 1.47; P = 0.83; Table 3), and the sensitivity analysis yielded similar results (Additional file 1: Table S5-8).

For the secondary outcomes of dementia subtypes, we found no significance of the association between shift work and the risks of AD (adjusted-HR, 1.23; 95% CI, 0.90 to 1.69; P = 0.20) and VD (adjusted-HR, 1.46; 95% CI, 0.94 to 2.27; P = 0.09) (Table 4).

As shown in Fig. 3, the impact of shift work on dementia did not differ among participants who were in the low-, intermediate-, or high-PRS subgroups (P for interaction = 0.77). Similarly, no significant interaction was observed in the subgroups of age at baseline, ethnicity, sex, socioeconomic status, and sleep duration.

In order to assess the robustness of our findings, we conducted several sensitivity analyses, including the models further adjusted for genetic predisposition to dementia by PRS, years of work, sleep duration, and chronotype category, the Fine-Gray methods under consideration of the competing risk of death, the models excluding subjects with follow-up time < 1 year or incident dementia < 1 year from baseline and the models of the imputed dataset. The results showed no substantial change of the impact of shift work on dementia (Additional file 1: Table S5-8).

Our study has several major strengths. Firstly, the large sample size and the wealth of information on lifestyle, and other covariates of UK Biobank participants, enabled this study of comprehensive sensitivity analyses and subgroup analyses. Secondly, to our best knowledge, it was the first study to examine the interaction between shift work and genetic predisposition to dementia. There were also several limitations in our study. Firstly, the study was a retrospective analysis of data from the UK Biobank, thus confounders that were included in the multivariable Cox model were based on available variables in the database and there might be some unknown or unmeasured biases confounding the association between shift work and dementia. Besides, some covariates had too much missing data, resulting in a loss of sample size. Secondly, although we believe that the UK Biobank has sufficient capacity to identify risk factors, its low response rate and healthy volunteer bias may still contribute to an underestimation of the impact of shift work on dementia, which needs to be further assessed in future studies. Thirdly, dementia might be misdiagnosed or underdiagnosed, and participants with cognitive impairment usually are more likely to be lost to follow-up, hence some dementia cases might not be captured by EHRs. Furthermore, the work schedule information was assessed only at the baseline. Participants’ work status might change over time during the follow-up while people tend to stop doing shift or night shift work at an older age, which might bias our results toward the null hypothesis, resulting in an underestimation of the effect size [53]. Future prospective studies measuring the longitudinal change of employment status would be needed to assess the association of lifetime exposure to shift work with the risk of dementia. Finally, participants recruited by UK Biobank were mostly white British, which may limit the extrapolation of our findings to other ethnicities, such as Asians and Africans.