Abstract
Background
Sleep patterns and depression are increasingly linked to endocrine health, yet their associations with thyroid diseases remain underexplored. This study aimed to investigate the prospective relationships between sleep patterns, depressive symptoms, and incident thyroid diseases in a large cohort.
Methods
Utilizing data from 371,627 United Kingdom Biobank participants, sleep patterns were categorized as healthy, moderate, or poor based on five behaviors: duration, chronotype, insomnia, snoring, and napping. Depressive symptoms were assessed using the PHQ-2 questionnaire. Thyroid diseases (hyperthyroidism, hypothyroidism, goiter, thyroiditis) were identified via ICD codes. Cox proportional hazards models evaluated associations, adjusting for demographics, lifestyle, and clinical covariates. Additive and multiplicative interactions between sleep patterns and depression were examined.
Results
Over a median 13.7-year follow-up, 15,609 participants developed thyroid diseases. Compared to the poor sleep patterns, moderate (HR = 0.84, 95% CI: 0.78–0.90) and healthy sleep patterns (HR = 0.77, 95% CI: 0.72–0.83) were associated with a significantly lower hazard of thyroid diseases. Healthier sleep patterns were linked to a reduced hazard of hypothyroidism and goiter, while associations with hyperthyroidism and thyroiditis were nonsignificant. Participants with depressive symptoms showed a higher hazard of thyroid diseases (HR = 0.74 for non-depressed, 95%CI: 0.69–0.78). Joint analysis revealed the lowest hazard in non-depressed participants with healthy sleep (HR = 0.59, 95%CI: 0.50–0.70). Sensitivity analyses confirmed robustness across subgroups.
Conclusion
Healthier sleep patterns are independently associated with reduced thyroid diseases incidence, particularly hypothyroidism and goiter. Depression exacerbates risk and has a joint effect with sleep pattern on total thyroid diseases. Future longitudinal or interventional studies are needed to determine whether a causal link exits and explore potential preventive strategies.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-026-26435-z.
Keywords: Sleep patterns, Depression, UK biobank, Thyroid diseases
Introduction
Sleep is very important for human health, affecting a wide range of key physiological functions including development, energy conservation, brain waste clearance, modulation of immune responses, cognition, performance, disease, vigilance, and psychological conditions [1]. By the recommendations of the US National Sleep Foundation, the appropriate sleep duration for adults lies between 7 and 9 h per night [2]. However, interrupted sleep and decreased sleep duration are very common in modern society, and the adverse health consequences have caused widespread attention and debate. The endocrine system is closely related to circadian rhythms and sleep/wakefulness states, both of which affect hormone secretion [3]. Among these hormones, thyroid stimulating hormone (TSH) is one of the most well-known for being primarily influenced by the circadian rhythm [4, 5].
Thyroid diseases constitute a significant global public health burden due to their high prevalence and associated morbidity. In China, the condition affects over 50% of the older adult population, with subclinical and clinical hypothyroidism being particularly common [6]. In the United Kingdom, Hashimoto’s thyroiditis and Graves’ disease are the predominant causes of widespread thyroid dysfunction, affecting an estimated 2–5% of the general population [7]. Longitudinal data, notably from the seminal Whickham Survey and its 20-year follow-up, demonstrated an annual incidence of spontaneous hypothyroidism and hyperthyroidism at 3.5 and 0.8 per 1000 women, respectively [8]. Population-based study in Scotland indicated a significant increase in both the prevalence and incidence of thyroid diseases between 1993 and 2001 [9]. And the prevalence of autoimmune diseases during pregnancy climbed from 3.5% in 2000 to 4.7% in 2021 [10]. This escalating and unevenly distributed disease burden underscores the critical need for research to inform effective public health and clinical strategies.
Of note, sleep has a bidirectional regulatory relationship with the hypothalamic pituitary thyroid (HPT) axis, and several studies have shown the impact of sleep deprivation [11], sleep quality [12] or sleep duration [13] on TSH secretion. In our preliminary research, sleep quality scores may assist in the differential diagnosis of benign and malignant thyroid nodules, and sleep disorders may be associated with an increased hazard of thyroid cancer [14]. Autoimmune thyroiditis was also supposed to be related to sleep due to the proven evidence of sleep on certain autoimmune diseases [15–17].
Thyroid dysfunction and depression are both common in clinical practice, but their relationship is still controversial. Many studies indicated that disorders of the HPT axis may be associated with the pathophysiology of depression [18]. Trzepacz et al. studied psychiatric and neuropsychological features with untreated Grave’s disease, identifying increased rates of anxiety, depression, and sleep disturbances in hyperthyroid patients [19]. Furthermore, patients with chronic autoimmune thyroiditis and normal thyroid function may have an increased risk of developing depression [20], the positive TPOAb was a vulnerability marker for depressed [21]. However, large-scale epidemiological evidence prospectively linking composite sleep patterns and depression to incident thyroid diseases remains scarce.
Given the substantial individual and societal burden imposed by thyroid diseases, sleep disorders, and depression, identifying modifiable risk factors is a public health priority. Elucidating the independent and joint roles of sleep and depression in thyroid pathogenesis could inform integrated prevention strategies. Moreover, there is no evidence indicating whether depression plays a combined or mediating role in the relationship between the above two. To fill these knowledge gaps, in this study we utilize the prospective UK Biobank data to examine the association of sleep patterns and depression with thyroid outcomes and explore the potential interaction effects.
Materials and methods
Study population
The UK Biobank (https://www.ukbiobank.ac.uk/) is a large-scale prospective cohort study that recruited approximately 500,000 volunteers aged 40–70 years from across the United Kingdom between 2006 and 2010 through 22 assessment centers. At recruitment, all participants provided detailed information on sociodemographic characteristics, lifestyle factors, health status, and medical history. It was approved by the North West Multicentre Research Ethics Committee (MREC) and the National Health Service (NHS) on June 17, 2011, and extended on May 10, 2016. All participants have written the informed consent before data collection.
We used the UK Biobank resource to analyze the association of different sleep patterns with the incidence of thyroid diseases. Baseline measurements, including sleep behaviors and PHQ-2 questionnaire, were collected at recruitment. In this study, 371,627 individuals were ultimately included after excluding those with baseline sleep information loss, baseline self-reported history or medical records of thyroid diseases, and baseline missing depressive symptoms.
Sleep patterns
We used an algorithm based on self-reported sleep quality information, which was introduced to the UK Biobank cohort in 2020 [22]. In order to optimize the sleep quality assessment, sleep patterns were evaluated through five sleep behaviors in the UK Biobank, including sleep duration, morning chronotype, insomnia, snoring, and napping, to generate sleep scores [23, 24].
Sleep duration was self-reported with a question: “About how many hours sleep do you get in every 24 hours (include naps)?” as an integer (hours per day). If the answer was less than 3 hours or more than 12 hours, participant would be asked again to confirm. It was classified into four categories: ≤5 h/day, 5-<7 h/day, 7-8 h/day, and ≥9 h/day. The US National Sleep Foundation has suggested that optimal sleep duration for adults was 7-8 h/day [2]. Thus, we use 7-8 h/day as the reference group. Morning chronotype was with a question: “Do you consider yourself to be a defining ‘morning’ person?” Insomnia was with a question: “Do you have trouble falling asleep at night or do you wake up in the middle of the night?” Snoring was with a question: “Does your partner or a close relative or friend complain about your snoring?” Napping was with a question: “How are you likely to doze off or fall asleep during the daytime when you don’t mean to?” These behaviors were divided into two groups: yes or no.
The definition of low-risk sleep factors was as follows: sleep duration of 7–8 h/d, early chronotype (“morning” or “morning than evening”), reported never or rarely insomnia, no self-reported snoring [25], and never or rarely napping [26]. If participants were considered as low-risk for one certain behavior, they would receive 1 point, otherwise, 0 point. Add up all behavior scores to obtain a healthy sleep score ranging from 0 to 5, with higher scores indicating healthier sleep patterns. Thus, sleep patterns were classified into healthy sleep pattern (≥ 4 points), moderate sleep pattern (2–4 points) and poor sleep pattern (< 2 points), which has been previously used in UK biobank cohort-based studies [22, 27].
Assessment of depressive symptoms
The baseline assessment of depressive symptoms was conducted using the PHQ-2 questionnaire [28], which consisted of two questions: the frequency of depressive emotions and loss of interest in the past two weeks. The answer options included “completely none”, “how many days”, “more than half of the days”, and “almost every day”, which were scored as 0, 1, 2, and 3 points. The total score range of PHQ-2 was 0 to 6 points, with a score ≥ 3 indicating the presence of depressive disorder, which was widely adopted in previous studies [29–31].
The outcomes
The UK Biobank collected disease information from hospital inpatient visits and coded them according to the International Classification of Diseases versions 9 and 10 (ICD-9、ICD-10). In our study, the outcome events were thyroid diseases, including hyperthyroidism, hypothyroidism, goiter and thyroiditis (ICD-9: 240–246; ICD-10: E01-E07; self-reported medical history). All individuals were followed up from baseline to the date of disease occurrence, date of death (ascertained through linkage to the National Death Register), or the last date of follow up, whichever came first. The end of follow-up was October 31, 2022, for participants in England, August 31, 2022, for those in Scotland, and May 31, 2022, for those in Wales.
Covariates
Demographic characteristics, lifestyle factors, clinical indicators and socioeconomic status were considered as the relevant covariates. The demographic characteristics included age (< 60 years old, ≥ 60 years old), sex (male, female), race (white, non-white). Lifestyle factors included smoking status (never, previous, and current), drinking status (never, previous, and current), and physical activity. Clinical indicators include body mass index (BMI), blood glucose, systolic blood pressure and diastolic blood pressure. BMI was calculated as the body weight (kg) divided by the square of height (m): normal and underweight (< 25 kg/m2), overweight (25–30 kg/m2), and obesity (≥ 30 kg/m2). Physical activity was classified into two categories: inactive, active, according to the World Health Organization 2020 guidelines on physical activity [32]. Socioeconomic status was assessed by the Townsend Deprivation Index (TDI), where a lower TDI value corresponds to a higher socioeconomic level [33]. The comprehensive information of all variables included in the study was accessible on the UK Biobank website (www.ukbiobank.ac.uk).
Statistical analysis
We described baseline continuous variables using mean ± standard deviation and categorical variables using frequency (%). Variables were compared using the Kruskal-Wallis test and chi-square test. We performed multiple imputation by chained equations (MICE) on missing values of covariates, including ethnicity (953 missing, 0.2%), smoking status (942 missing, 0.2%), drinking status (190 missing, 0.05%), BMI (1702 missing, 0.5%), physical activity (70204 missing, 18.9%), socioeconomic status (453 missing, 0.1%), blood glucose (50991 missing, 13.7%), diastolic blood pressure (31768 missing, 8.5%) and systolic blood pressure (31777 missing, 8.6%). The imputation model included all variables used in the main analysis (including the outcome and covariates). Random forest was used as the imputation method, and 20 imputations were generated. Considering death as a competing risk, we plotted cumulative incidence curves of thyroid diseases across different sleep patterns and compared differences in cumulative incidence using the Fine-Gray test. Furthermore, Cox proportional hazards models were employed to assess associations between sleep patterns/individual sleep behaviors and thyroid disease incidence. The proportional hazards assumption was verified using Schoenfeld residual plots, with no violations detected. When analyzing associations between individual sleep behaviors and thyroid diseases, other sleep-related factors were adjusted as covariates. Testing linear trends using the healthy sleep score as a continuous variable derived the P for trend. Additionally, we evaluated the effects of sleep patterns on thyroid disease subtypes. Finally, we explored interactive and joint effects of sleep patterns and depression on thyroid diseases. The multiplicative interaction was evaluated by incorporating the product term of sleep patterns and depression into the Cox proportional hazards model. Additive interactions were evaluated using Relative Excess Risk due to Interaction (RERI), Attributable Proportion of Interaction (AP), and Synergy Index (S) [34]. If RERI and AP both equaled 0 with S = 1, no additive interaction was considered; if RERI and AP were both > 0 (95% CI excluding 0) with S > 1 (95% CI excluding 1), a synergistic effect was considered; if RERI and AP were both < 0 (95% CI excluding 0) with S < 1 (95% CI excluding 1), an antagonistic effect was considered.
In addition, we conducted several sensitivity analyses to test the stability of the results. First, we performed subgroup analysis to investigate whether the relationship between sleep patterns and thyroid disease would change with age, gender, and smoking status. Second, we excluded participants diagnosed with thyroid disease within 1, 2 and 3 years of follow-up (1 year: N = 606; 2 years: N = 1468; 3 years: N = 2355) to minimize the possibility of reverse causality. Third, we reprocessed the missing values of covariates. For categorical variables, a separate missing category was created to represent missing values. For continuous variables, gender-stratified mean imputation was performed. Fourth, we reanalyzed the association between sleep patterns and thyroid diseases after excluding observations with missing covariates. Finally, due to the high mortality rate of cancer patients, we excluded participants with baseline cancer (N = 17324). All analyses were accomplished by using SAS version 9.4 and R4.4.3. P < 0.05 was considered statistically significant in all tests.
Results
Characteristics of the study population
A total of 371,627 participants were selected in the current study, of which 16,028 (4.3%) with poor sleep pattern, 214,627 (57.8%) with moderate sleep pattern, and 140,972 (37.9%) with healthy sleep pattern. Among them, 20,380 (5.5%) were diagnosed with depression. The proportions of depression in poor, moderate and healthy sleep patterns were 14.3%, 6.4%, and 3.2%, respectively. The median follow-up time was 13.7 years, and finally 15,609 (4.2%) participants were newly diagnosed with thyroid diseases. The flow chart for recruitment of the study was shown in Fig. 1.
Fig. 1.
The flowchart of the study
The baseline characteristics of participants with different sleep patterns were shown in Table 1. It can be observed that among all participants, 46.5% were male and 42.5% were older adults. Compared to people with healthy sleep patterns, those with the poor sleep patterns were more likely to be men, middle-aged people, smokers, and those with low socioeconomic status.
Table 1.
Baseline characteristics of the study participants by different sleep patterns
| Characteristics | Sleep pattern | P value | ||
|---|---|---|---|---|
| Poor | Moderate | Healthy | ||
| N (%) | 16,028 (4.3%) | 214,627 (57.8%) | 140,972 (37.9%) | |
| Gender | < 0.001 | |||
| Male | 8835 (55.1%) | 102,471 (47.7%) | 61,330 (43.5%) | |
| Female | 7193 (44.9%) | 112,156 (52.3%) | 79,642 (56.5%) | |
| Age | < 0.001 | |||
| < 60 years old | 9544 (59.6%) | 121,616 (56.7%) | 82,399 (58.5%) | |
| ≥ 60 years old | 6484 (40.4%) | 93,011 (43.3%) | 58,573 (41.5%) | |
| Ethnicity (953 missing) | < 0.001 | |||
| White | 14,351 (89.5%) | 198,366 (92.4%) | 131,031 (93.0%) | |
| Non-white | 1610 (10.0%) | 15,724 (7.3%) | 9592 (6.8%) | |
| BMI (1702 missing) | < 0.001 | |||
| Normal | 3061 (19.1%) | 63,926 (29.8%) | 55,708 (39.5%) | |
| Underweight | 43 (0.3%) | 1002 (0.5%) | 837 (0.6%) | |
| Overweight | 6411 (40.0%) | 93,037 (43.4%) | 58,857 (41.8%) | |
| Obesity | 6388 (39.9%) | 55,650 (25.9%) | 25,005 (17.7%) | |
| Smoking status (942 missing) | < 0.001 | |||
| Never | 6924 (43.2%) | 111,355 (51.9%) | 84,292 (59.8%) | |
| Previous | 6269 (39.1%) | 77,919 (36.3%) | 45,682 (32.4%) | |
| Current | 2792 (17.4%) | 24,772 (11.5%) | 10,680 (7.6%) | |
| Drinking status (190 missing) | < 0.001 | |||
| Never | 599 (3.7%) | 7859 (3.7%) | 6014 (4.3%) | |
| Previous | 713 (4.5%) | 7120 (3.3%) | 4482 (3.2%) | |
| Current | 14,705 (91.8%) | 199,531 (93.0%) | 130,414 (92.5%) | |
| Economics (453 missing) | < 0.001 | |||
| High level | 4639 (28.9%) | 73,268 (34.1%) | 51,510 (36.5%) | |
| Moderate level | 5010 (31.3%) | 72,018 (33.6%) | 48,587 (34.5%) | |
| Low level | 6351 (40.0%) | 69,087 (32.2%) | 40,704 (28.9%) | |
| Physical activity (70204 missing) | < 0.001 | |||
| Low level | 3473 (21.7%) | 33,612 (15.7%) | 17,390 (12.3%) | |
| Moderate level | 4847 (30.2%) | 70,509 (32.9%) | 47,457 (33.7%) | |
| High level | 4157 (25.9%) | 68,033 (31.7%) | 51,945 (36.9%) | |
| Depression | < 0.001 | |||
| Yes | 2286 (14.3%) | 13,648 (6.4%) | 4446 (3.2%) | |
| No | 13,742 (85.7%) | 200,979 (93.6%) | 136,526 (96.8%) | |
| Diastolic blood pressure (31768 missing, mmHg) | 83.6 ± 10.2 | 82.6 ± 10.1 | 81.5 ± 10.0 | < 0.001 |
| Systolic blood pressure (31777 missing, mmHg) | 138.8 ± 18.1 | 138.2 ± 18.5 | 136.8 ± 18.7 | < 0.001 |
| Blood glucose (50991 missing, mmol/L) | 5.3 ± 1.6 | 5.1 ± 1.2 | 5.1 ± 1.1 | < 0.001 |
| Sleep duration (N, %) | < 0.001 | |||
| 7–8 h/day | 715 (4.5%) | 121,252 (56.5%) | 133,478 (94.7%) | |
| <7 h/day or >8 h/day | 15,313 (95.5%) | 93,375 (43.5%) | 7494 (5.3%) | |
| Morning chronotype (N, %) | < 0.001 | |||
| No | 14,907 (93.0%) | 107,489 (50.1%) | 15,808 (11.2%) | |
| Yes | 1121 (7.0%) | 107,138 (49.9%) | 125,164 (88.8%) | |
| Insomnia (N, %) | < 0.001 | |||
| Yes | 15,852 (98.9%) | 189,143 (88.1%) | 73,068 (51.8%) | |
| No | 176 (1.1%) | 25,484 (11.9%) | 67,904 (48.2%) | |
| Snore (N, %) | < 0.001 | |||
| Yes | 15,208 (94.9%) | 106,083 (49.4%) | 16,949 (12.0%) | |
| No | 820 (5.1%) | 108,544 (50.6%) | 124,023 (88.0%) | |
| Frequent napping (N, %) | < 0.001 | |||
| Yes | 3674 (22.9%) | 5719 (2.7%) | 317 (0.2%) | |
| No | 12,354 (77.1%) | 208,908 (97.3%) | 140,655 (99.8%) | |
Associations of sleep patterns and behaviors with thyroid diseases
After considering the risk of death competition, there were differences in the cumulative incidence rates of thyroid diseases in different sleep patterns. The population with the poor sleep pattern had the highest incidence rate, followed by the moderate sleep pattern, and healthy sleep pattern was the lowest, p < 0.05 (Fig. 2). After a median follow-up period of 13.7 years, the annual incidence rates of thyroid diseases per thousand people in poor, moderate and healthy sleep pattern were 3.87, 3.22 and 2.89, respectively. Compared with the poor sleep pattern, moderate sleep pattern (HR = 0.84, 95% CI: 0.78–0.90) and healthy sleep pattern (HR = 0.77, 95% CI: 0.72–0.83) were both associated with a lower hazard of thyroid diseases. Furthermore, the hazard of incident thyroid diseases tended to be lower with increasing healthy sleep score (HR = 0.93, 95% CI: 0.91–0.94, P for trend < 0.05). In addition, each healthy sleep behavior (including 7–8 h per day, no insomnia, no snoring, infrequent napping, morning chronotype) was associated with a lower hazard of thyroid diseases. Specifically, infrequent napping exhibited a 22% reduced risk of thyroid diseases (HR = 0.78, 95% CI: 0.71–0.84) (Fig. 3).
Fig. 2.
The cumulative incidences of thyroid diseases according to sleep patterns
Fig. 3.
The association of sleep pattern and sleep behaviors with incident thyroid diseases. Adjusted model adjusts for sex, age, BMI, economic level, physical activity, smoking and drinking status, ethnic, depression, diastolic blood pressure, systolic blood pressure and blood glucose
Adjusted model adjusts for sex, age, BMI, economic level, physical activity, smoking and drinking status, ethnic, depression, diastolic blood pressure, systolic blood pressure and blood glucose.
Associations of sleep patterns with specific thyroid diseases
For specific thyroid disorders, a significantly decreased hazard of hypothyroidism was observed among individuals with moderate sleep pattern (HR = 0.85, 95%CI: 0.78–0.92) and healthy sleep pattern (HR: 0.78, 95% CI: 0.72–0.85), compared to those with the poor sleep pattern. Moreover, moderate (HR = 0.72, 95% CI: 0.60–0.88) and healthy sleep patterns (HR = 0.66, 95% CI: 0.54–0.82) were related to a lower hazard of goiter. However, the association of sleep patterns with hyperthyroidism and thyroiditis was not significant, which may be due to insufficient statistical power caused by too few incidence cases (Fig. 4).
Fig. 4.
The association between sleep patterns and incident specific thyroid diseases. Adjusted model adjusts for sex, age, BMI, economic level, physical activity, smoking and drinking status, ethnic, depression, diastolic blood pressure, systolic blood pressure and blood glucose
Adjusted model adjusts for sex, age, BMI, economic level, physical activity, smoking and drinking status, ethnic, depression, diastolic blood pressure, systolic blood pressure and blood glucose.
Interaction effect between sleep patterns and depression on outcomes
Cox regression analysis showed that compared to the depressed, non-depressed individuals had a lower hazard of developing thyroid diseases (HR = 0.74, 95% CI: 0.69–0.78). Table 2 shows the joint effect of sleep patterns and depressive symptoms on total thyroid diseases. There was no statistically significant association between moderate sleep pattern and thyroid diseases in individuals with depression, while a healthy sleep pattern was associated with a 23% lower hazard (moderate: HR = 0.86, 95% CI: 0.72–1.02; healthy: HR = 0.77, 95% CI: 0.62–0.94). Compared with depressive participants with the poor sleep pattern, those with healthy sleep pattern and non-depressive symptoms had the lowest hazard of thyroid diseases (HR = 0.59, 95% CI: 0.50–0.70), followed by participants with moderate sleep pattern and non-depressive symptoms (HR = 0.64, 95% CI: 0.55–0.75). However, the multiplicative interaction between sleep patterns and depressive symptoms was not statistically significant (moderate: HR = 0.98, 95% CI: 0.81–1.18; healthy: HR = 1.01, 95% CI: 0.82–1.26), neither was additive interaction effect (moderate: RERI = 0.02, 95% CI: -0.16-0.20; AP = 0.03, 95% CI: -0.03-0.09; S = 0.95, 95% CI: 0.60–1.50; healthy: RERI = 0.06, 95% CI: -0.13-0.25; AP = 0.11, 95% CI: 0.04–0.18; S = 0.87, 95% CI: 0.60–1.26), seen in Table 3.
Table 2.
The joint effects of sleep pattern and depression on incident thyroid diseases
| Sleep pattern | Depression | Events | Rate per 1000 person years |
Crude HR (95%CI) |
Adjusted HR (95%CI) |
|---|---|---|---|---|---|
| Poor | Yes | 153 | 5.04 | Ref. | |
| Poor | No | 678 | 3.67 | 0.73 (0.61,0.87) | 0.76 (0.64,0.91) |
| Moderate | Yes | 800 | 4.40 | 0.87 (0.73,1.03) | 0.86 (0.72,1.02) |
| Moderate | No | 8501 | 3.14 | 0.62 (0.53,0.73) | 0.64 (0.55,0.75) |
| Healthy | Yes | 232 | 3.90 | 0.77 (0.63,0.95) | 0.77 (0.62,0.94) |
| Healthy | No | 5245 | 2.84 | 0.56 (0.48,0.66) | 0.59 (0.50,0.70) |
Adjusted model adjusts for sex, age, BMI, economic level, physical activity, smoking and drinking status, ethnic, diastolic blood pressure, systolic blood pressure and blood glucose
Table 3.
The interaction effects of sleep pattern and depression on incident thyroid diseases
| Interaction measures | Crude model HR | Adjusted model |
|---|---|---|
| Moderate sleep duration & non-depression | ||
| Additive interaction | ||
| RERI (95%CI) | 0.02 (-0.15,0.20) | 0.02 (-0.16,0.20) |
| AP (95%CI) | 0.04 (-0.02,0.09) | 0.03 (-0.03,0.09) |
| S (95%CI) | 0.94 (0.62,1.44) | 0.95 (0.60,1.50) |
| Multiplicative interaction | ||
| HR (95%CI) | 0.98 (0.81,1.19) | 0.98 (0.81,1.18) |
| Healthy sleep duration & non-depression | ||
| Addictive interaction | ||
| RERI (95%CI) | 0.06 (-0.12,0.25) | 0.06 (-0.13,0.25) |
| AP (95%CI) | 0.11 (0.05,0.18) | 0.11 (0.04,0.18) |
| S (95%CI) | 0.87 (0.61,1.24) | 0.87 (0.60,1.26) |
| Multiplicative interaction | ||
| HR (95%CI) | 1.00 (0.81,1.25) | 1.01 (0.82,1.26) |
Setting the group with poor sleep pattern and the depressed group as a common reference group.Crude model is an unadjusted model.Adjusted model adjusts for sex, age, BMI, economic level, physical activity, smoking and drinking status, ethnic, diastolic blood pressure, systolic blood pressure and blood glucose. RERI Relative Excess Risk due to Interaction, AP Attributable Proportion of Interaction, S Synergy Index
Adjusted model adjusts for sex, age, BMI, economic level, physical activity, smoking and drinking status, ethnic, diastolic blood pressure, systolic blood pressure and blood glucose.
Setting the group with poor sleep pattern and the depressed group as a common reference group.
Crude model is an unadjusted model.Adjusted model adjusts for sex, age, BMI, economic level, physical activity, smoking and drinking status, ethnic, diastolic blood pressure, systolic blood pressure and blood glucose. RERI: Relative Excess Risk due to Interaction; AP: Attributable Proportion of Interaction; S: Synergy Index.
Subgroup and sensitivity analyses
In the sensitivity analysis, we found that the associations between sleep patterns and the incidence rates of thyroid diseases still existed regardless of participants’ gender and age (Table S1). After excluding individuals diagnosed with thyroid disease within 1, 2 and 3 years of follow-up, the relationship between sleep patterns and thyroid disease was similar to the main results (Table S2). In addition, alternative approaches to handling missing covariates and exclusion of participants with baseline cancer did not alter the association between sleep patterns and thyroid disease (Table S3-5).
Discussion
In some previous studies, researchers have proposed the relationship between sleep disorders and thyroid diseases, including hyperthyroidism, hypothyroidism, nonthyroidal illness syndrome, autoimmune thyroiditis, thyroid nodules and thyroid cancer [35–39]. However, most of the studies are cross-sectional, lack of large-scale epidemiological data support. We conducted a study using the latest UK Biobank resource, which included a cohort of 371,627 participants. Over a median follow-up period of 13.7 years, 15,609 incident cases of thyroid disease were identified. We found that compared with the poor sleep pattern, moderate sleep patterns and healthy sleep patterns were both associated with a lower hazard of thyroid disease, especially hypothyroidism and goiter, and the hazard of incident thyroid diseases tended to be lower with increasing healthy sleep scores. Furthermore, each healthy sleep behavior was also associated with a lower hazard of thyroid diseases.
Our findings can also be interpreted within the context of the UK’s healthcare landscape. The National Health Service (NHS) has long recognized the burden of thyroid diseases and has integrated thyroid function testing into routine clinical assessments for specific high-risk groups [40]. However, the rising incidence of thyroid disorders, particularly among women and older adults, suggests that current preventive strategies may be insufficient. The UK’s National Institute for Health and Care Excellence (NICE) guidelines emphasize the importance of managing modifiable risk factors for chronic diseases, including sleep and mental health [41]. Our results align with this approach by demonstrating that healthier sleep patterns and the absence of depressive symptoms are associated with a lower hazard of thyroid diseases. This supports the potential for integrating sleep and mental health screening into primary care pathways for thyroid disease prevention, particularly in regions with high disease burden and limited specialist resources.
Although sleep disorders are known to affect many bodily systems, the exact relationship between sleep patterns and thyroid diseases has yet to be elucidated, and a direct underlying pathophysiology has not been pinpointed. The thyroid produces two main hormones, thyroxine (T4) and triiodothyronine (T3), which play critical roles in the differentiation, growth, metabolism, and physiological function of virtually all tissues [42]. Most current evidence suggests that thyroid dysfunction clearly affects people’s ability to achieve healthy and restful sleep [35]. On one hand, hyperthyroidism mediated increases in appetite, bowel movements, and anxiety are significantly associated with prolonged sleep latency [43]. On the other hand, lower thyroid hormone levels or even subclinical hypothyroidism typically have longer sleep latency, shorter sleep duration, and lower satisfaction with sleep quality [44].
Meanwhile, researchers suggested that compared to the optimal sleep time, long sleep time and short sleep time are risk factors for TSH elevation and descent respectively, based on data from KNHANES in South Korea [3]. The potential reason is speculated that the rhythm of TSH secretion may be related to the main hormone melatonin secreted by pineal gland stromal cells. Changes in light exposure time can affect the effect of melatonin on the anterior pituitary gland. TSH originates from the HPT axis and is regulated by the circadian rhythm [45], which is influenced by sleep quality and duration. The HPT axis and the sleep-wake cycle interact to regulate energy and metabolic homeostasis, such that a disruption in one may destabilize the other. Study shows that isolated elevated TSH levels often normalize with improved sleep status [46]. Short-term sleep restriction has been shown to significantly attenuate the amplitude of nocturnal TSH secretion, and may modulate thyroid hormone through an increased sympathetic activity [46]. Consistent with this, scholars emphasize that sleep patterns and the HPT axis exhibit a bidirectional relationship, and both processes are interdependent in maintaining physiological stability [47].
It is well known that thyroid function is closely related to depression, and disturbances in the HPT axis may be of etiologic significance in depressive states [48]. Depression is significantly negatively correlated with T4 and T3 levels in both younger adults and older adults [18]. Even individuals with low-normal TSH levels exhibit more concurrent depressive symptoms and have a significantly increased risk of developing depression in the subsequent years [49, 50]. Another study found that among over 200 patients with depression, 26.2% had thyroid dysfunction [51]. In some patients with depression, the nighttime surge of TSH is often absent [52], and the response of TSH to thyroid stimulating hormone-releasing hormone (TRH) is weakened [53]. Moreover, alterations in hypothalamic-pituitary-adrenal/thyroid (HPA/HPT) axes are associated with the clinical manifestations of depression [54], both of which are related to sleep. Studies have shown that in depressed, the hippocampus was impaired in the negative feedback regulation of glucocorticoids through the HPA axis, resulting in increased cortisol, glucocorticoids activated TRH neurons, resulting in increased TRH secretion, thereby down-regulating TRH receptors on thyrotrophs [55, 56].
The Patient Health Questionnaire-2 (PHQ-2) is the most important scale for screening depression in clinical and population-based studies [11], and international guidelines identify PHQ-2 as the most reliable screening tool [13]. In this study, we used PHQ-2-based depression symptoms with sleep patterns to analyze the joint effect on incident thyroid diseases. The results revealed that compared to depressed individuals with the poor sleep pattern, those non-depressed with healthy or moderate sleep patterns had the lowest hazard of incident thyroid diseases. Although the additive and multiplicative interaction between sleep patterns and depressive symptoms was not statistically significant. Therefore, more research is required to explore the relationship between sleep patterns, depression, and thyroid diseases, as well as the role of depression in these two states.
There are several limitations in our study that need to be acknowledged. Firstly, the diagnosis of thyroid diseases relied solely on ICD codes based on participants’ self-reported medical history, which may be susceptible to recall bias or social desirability bias. Secondly, all measures including sleep patterns, were obtained only once at baseline and not repeatedly, which restricts our ability to capture dynamic changes over time. Thirdly, the lack of objective measures to assess sleep, such as actigraphy or polysomnography. Additionally, this study involves mostly middle-aged volunteers that are healthier of a mostly homogenous ethnic group, which might limit the generalizability of the results. Thus, replication in other, more diverse ethnic cohorts is warranted. Finally, our findings demonstrate observational associations and do not permit causal inference. It is also important to note that thyroid dysfunction may itself influence sleep patterns, suggesting potential bidirectional relationships. Future longitudinal studies incorporating repeated and objective measures of both thyroid function and sleep are warranted to clarify their temporal associations.
Conclusions
We conduct this prospective cohort study using the UK Biobank database and arrive at the following conclusions: both moderate and healthy sleep patterns are associated with a lower hazard of thyroid diseases, and each healthy sleep behavior is also correlated with a decreased disease risk. Sleep patterns and depressive symptoms have a joint effect on total thyroid diseases. Participants with healthy sleep patterns and no depressive symptoms have the lowest hazard of thyroid diseases, but the interaction effect is not statistically significant. The results of our study suggest that improving sleep patterns and alleviating depressive symptoms may hold potential implications for the prevention and management of thyroid diseases. However, this notion remains speculative based on the current evidence. Future longitudinal or interventional studies are needed to determine whether improving sleep can meaningfully offset adverse effects on thyroid hormones or thyroid diseases.
Supplementary Information
Acknowledgements
This research has been conducted using the UK Biobank Resource under Application Number (98410). We thank our departments for the platform support and thank all the authors for their contributions.
Authors’ contributions
Xudan Lou and Yuxin Huang wrote the original draft, reviewed and edited the manuscript. Bingxin Jiang conducted statistical analysis. Jieyuzhen Qiu and Cuiping Jiang collected data and interpretation. Yongfu Yu was responsible for database maintenance and guidance. Bin Lu designed and funded the study. Xudan Lou, Bingxin Jiang and Yuxin Huang contributed equally to this work and shared first authorship.
Funding
This work was supported by the National Natural Science Foundation of China (grant number 82370831). Talent Plan of Shanghai Municipal Health Commission (grant number 2022XD020). Shanghai Municipal Health Commission Health Industry Clinical Research Special Project (grant number 202240258). Shanghai Oriental Talent Program (BJWS2025043).
Data availability
Data are available from the UK Biobank upon application (www.ukbiobank.ac.uk). Additional analytical details may be requested from the corresponding authors.
Declarations
Ethics approval and consent to participate
Ethical approval was obtained from the Northwest Multi-centre Research Ethics Committee (MREC) and the National Health Service (NHS) National Research Ethics Service on 17th June 2011 (Ref 11/NW/0382) and extended on the 10th May 2016 (Ref 16/NW/0274). All participants have written the informed consent before data collection.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Yongfu Yu, Email: yu@fudan.edu.cn.
Bin Lu, Email: binlu@fudan.edu.cn.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data are available from the UK Biobank upon application (www.ukbiobank.ac.uk). Additional analytical details may be requested from the corresponding authors.