Abstract
Common sleep problems reduce quality of life and increase chronic disease risk. The relationship between sleep-related disorders and thyroid function is unclear. This study examines the relationship between sleep-related disorders and thyroid disease using the National Health and Nutrition Examination Survey (NHANES) data and Mendelian randomization (MR) analysis. Data from NHANES 2007 to 2012 were used to assess the relationship between sleep-related disorders and thyroid function using weighted multivariable-adjusted logistic regression. A two-sample MR study was conducted using genome-wide association study summary statistics, and methods such as inverse variance weighted were used to explore the causal relationship between sleep-related disorders and thyroid disease. Sensitivity analysis ensured robustness. The study included 6919 NHANES participants. Logistic regression analysis revealed higher thyroid-stimulating hormone levels in the long sleep group (P < .0001, β = 0.85, 95% confidence interval [CI]: 0.54, 1.15). Lower free triiodothyronine levels were found in the normal sleep group (P = .0030, β = −0.06, 95% CI: −0.06, 0.00). Total T4 levels were lower in those with sleep disorders (P = .0157, β = −0.11, 95% CI: −0.20, −0.02). Long sleep was positively associated with thyroglobulin antibodies (TGAb) positivity (P = .0288, odds ratio [OR] = 1.81, 95% CI: 1.06, 3.07), while sleep disorders were negatively associated with TGAb positivity (P = .0176, OR = 0.72, 95% CI: 0.56, 0.95). MR analysis indicated a positive association between long sleep and Graves disease risk (P = .0240, OR = 99.98, 95% CI: 1.83, 5453.63), and a negative association between sleep duration and Hashimoto’s thyroiditis risk (P = .0294, OR = 0.72, 95% CI: 0.54, 0.97). Sleep duration is associated with thyroid hormone levels and autoimmunity. Long sleep is linked to higher thyroid-stimulating hormone levels, lower free triiodothyronine levels, and increased TGAb positivity risk, while sleep disorders are linked to lower total T4 levels and decreased TGAb positivity risk. MR studies suggest long sleep may increase Graves disease risk, while shorter sleep may decrease Hashimoto’s thyroiditis risk.
Keywords: causality, cross-sectional study, Mendelian randomization, National Health and Nutrition Examination Survey, sleep-related disorders
1. Introduction
The thyroid gland is the largest endocrine gland in the body; its main function is to secrete thyroid hormones. The regulation of the availability of thyroid hormones, including triiodothyronine (T3) and thyroxine (T4), is a complex and diverse process. This regulation is not only at the central level, that is, the hypothalamic-pituitary-thyroid (HPT) axis, but also involves fine-tuning at the local level. At the local level, thyroid hormone availability is influenced by the differential expression of a variety of factors such as thyroid hormone transporter proteins, deiodinases, and thyroid nuclear hormone receptors.[1] In addition, transcriptional co-repressors and co-activators are involved in this regulatory process, which together ensure the homeostasis and efficient utilization of thyroid hormone in the body.[1,2] Active free thyroid hormones in the body include free triiodothyronine (FT3) and free thyroxine (FT4), which are converted to total T3 and total T4 (TT4) when FT3 and FT4 are depleted.[3] Under physiologic conditions, circulating concentrations of thyroid-stimulating hormone (TSH) and FT4 are regulated by negative feedback through the HPT axis. In thyroid dysfunction, central regulation may fail, leading to abnormal changes in thyroid hormone levels and causing the corresponding symptoms of hypothyroidism or hyperthyroidism. Hashimoto’s thyroiditis (HT) is characterized by the presence of thyroid peroxidase antibodies (TPOAb) and thyroglobulin antibodies (TGAb), as well as diffuse lymphocytic infiltration of the thyroid tissue.[4] In contrast, Graves disease (GD) is associated with autoantibodies against the TSH receptor.[5] HT and GD are the result of an attack by the immune system on the thyroid tissue, which ultimately manifests as changes in thyroid hormone levels, namely hyperthyroidism and hypothyroidism. Various lifestyle habits have been shown to cause changes in thyroid hormone levels, such as smoking, alcohol consumption, diet, and exercise.[6] In addition, almost all hormones are produced in a cyclical rhythm over 24 hours, and sleep has different effects on the regulation of this rhythm.[7]
Sleep plays a vital role in maintaining overall physical and mental health. In the United States, approximately one-third of adults do not get the recommended 7 to 9 hours of sleep per night.[8] More worryingly, millions of Americans self-report sleep disorders each year.[9] It is worth noting that, based on observational data from the past few decades, some Western countries are also gradually showing a trend of sleep-related disorders and shortened sleep time.[10] Common sleep problems, such as insufficient sleep, insomnia, and difficulty falling asleep, not only reduce the quality of life but also cause economic burdens and are closely related to physical and mental health and an increased risk of various chronic diseases (such as inflammation, metabolic syndrome, obesity, stroke, diabetes, and cancer).[11–16] Hyperthyroidism and hypothyroidism are known to be common causes of sleep disorders. One study found that short sleepers have a higher risk of subclinical hyperthyroidism (elevated TSH).[17] Another study showed that people with low thyroid hormone levels or even subclinical hypothyroidism (reduced TSH) typically have longer sleep latency and shorter sleep duration compared with people with normal thyroid function.[18] In contrast, the study by Kessler et al evaluated the effects of prolonged, moderate sleep deprivation on the circadian rhythm of TSH secretion. While acute, extreme deprivation increases TSH secretion and release, chronic, moderate sleep deprivation suppresses TSH secretion.[19] However, the aforementioned studies were observational and had small sample sizes. Therefore, the present study was designed to explore the correlation between sleep-related disorders (sleep duration and sleep disorders) and the thyroid using cross-sectional data from the National Health and Nutrition Examination Survey (NHANES). In addition, quantifying the causal effects of traditional observational studies is challenging, as observational studies do not completely eliminate potential bias from confounders. Therefore, this study will also incorporate Mendelian randomization (MR) analyses to validate the findings of cross-sectional studies from the perspective of genetic variation and further assess the causal relationship between sleep characteristics (sleep duration, long sleep, short sleep, and insomnia) and thyroid disorders (hyperthyroidism, hypothyroidism, HT, and GD).
2. Materials and methods
2.1. Study design
This study combined an observational epidemiologic study with MR analysis. Clinical associations between sleep-related disorders (sleep duration and sleep disorders) and thyroid function were first explored by epidemiologic analysis of NHANES. Next, a two-sample MR analysis using genome-wide association study (GWAS) summary statistics was performed to validate the causal association between sleep-related disorders (sleep duration, long sleep, short sleep, and insomnia) and thyroid disorders (hyperthyroidism, hypothyroidism, HT, and GD). MR relies on 3 basic assumptions: the association hypothesis, which states that genetic variation is associated with exposure; the independence hypothesis, which states that genetic variation is independent of confounding factors between exposure and outcome; and the exclusivity hypothesis, which states that genetic variation affects the outcome only through exposure. The flowchart of the MR study design is shown in Figure 1.
Figure 1.
The flowchart of the MR study design. MR = Mendelian randomization.
2.2. Epidemiological observation study
2.2.1. Data source
The NHANES project, part of the National Center for Health Statistics, is a cross-sectional survey that uses a complex multistage probability sampling design to generate a nationally representative sample of the noninstitutionalized civilian population of the United States. The National Center for Health Statistics Research Ethics Review Board approved the NHANES study, and all NHANES participants provided informed consent. This study used data from the 2007 to 2012 NHANES cycles as a secondary analysis of a public data source, so no additional ethics approval or informed consent was required.
Data on 30,442 participants from 2007 to 2012 were publicly available from the NHANES database (https://wwwn.cdc.gov/nchs/nhanes/Default.aspx). Among all subjects, we excluded the following individuals: individuals with missing data on thyroid function; individuals with missing information on sleep duration and sleep disorder (for participants who answered “don’t know” or “refused,” their data were considered missing values); individuals with previous thyroid disease; pregnant women; and individuals with incomplete data on education, body mass index (BMI), and poverty-to-income ratio (PIR). Finally, 6919 individuals were included. The flowchart of participant screening is shown in Figure 2.
Figure 2.
The flowchart of patient screening. NHANES = National Health and Nutrition Examination Survey.
2.2.2. Measurements and definitions
The sleep-related disorders addressed in this study included sleep duration and sleep disorders. Sleep duration was assessed based on a self-report questionnaire (SLD010H), in which participants were asked to answer the question, “How much sleep do you usually get at night on weekdays or workdays?” If they reported 12 hours or more, it was recorded as 12 hours. According to the National Sleep Foundation’s recommendations for sleep duration,[20] sleep duration was categorized into 3 groups in this study: short sleep (<7 hours/day), normal (7–9 hours/day), and long sleep (>9 hours/day). The assessment of sleep disorders was based on a self-report questionnaire (SLD050H), and participants were asked to answer the question, “Have you ever told a doctor or other health professional that you have trouble sleeping?” Sleep disorders were categorized into 2 groups based on the responses: non-sleep disorders and sleep disorders.
The serum samples involved in the outcome of this study for thyroid function indices included TSH, FT3, FT4, total T3, TT4, thyroglobulin, TPOAb, and TGAb. The NHANES Laboratory/Healthcare Technician Procedures Manual for specimen collection and processing provides a comprehensive and detailed description of the methodology used in the collection and processing of serum specimens. TPOAb >9.0 IU/mL and TGAb >4.0 IU/mL were classified as positive, respectively.
2.2.3. Covariates
Covariates in this study included race (Mexican American, Other Hispanic, non-Hispanic White, non-Hispanic Black, and Other races), age, sex (male and female), education (less than high school, high school or equivalent, and high school above), marital status (married/living with partner, widowed/divorced/separated, and never married), BMI (less than normal, normal, overweight, and obesity), PIR (low, medium, and high), smoking status (no and yes), diabetes (no and yes), and hypertension (no and yes). Socioeconomic status was assessed using PIR values, which were divided into 3 groups: low (<1.5), medium (1.5–3.5), and high (>3.5). Weight was divided into 4 groups: below normal (≤18.5 kg/m2), normal (>18.5 and ≤25.0 kg/m2), overweight (>25.0 and ≤30.0 kg/m2), and obese (>30 kg/m2).
2.3. Mendelian randomization analysis
2.3.1. Data source
The exposures included in this study were sleep-related disorders, categorized as long sleep, short sleep, sleep duration, and insomnia. The outcomes included thyroid-related disorders, specifically hyperthyroidism, hypothyroidism, HT, and GD. Ethical approval or informed consent was not required as the data were obtained from secondary analyses of publicly available data sources. Specific information on the GWAS data in this study is detailed in Table S1, Supplemental Digital Content 3.
Dashti et al[21] obtained genetic association data on sleep duration using GWAS data published by the UK Biobank for 446,118 adults of European ancestry. In this study, the mean self-reported habitual sleep duration was 7.2 hours with a standard deviation of 1.1 hours. Subsequent analysis converted the continuous variable of sleep duration into a categorical variable. There were 106,192 cases of short sleep (<7 hours) and 34,184 cases of long sleep (≥9 hours).
Lane et al[22] analyzed self-reported insomnia symptoms in 453,379 European participants in the UK Biobank. Participants were asked to answer the question, “Do you have difficulty falling asleep or waking up in the middle of the night?” The analysis showed that 29% reported frequent insomnia symptoms (“usually”), and the prevalence was higher in women (32% vs 24%) and older participants, shift workers, and those who self-reported shorter sleep duration.
The GWAS data for hyperthyroidism and hypothyroidism were obtained from IEU OpenGWAS. The genetic association data for hyperthyroidism included 3545 cases and 459,388 controls. The genetic association data for hypothyroidism included 22,687 cases and 440,246 controls.
The GWAS data for HT and GD were compiled and published in 2021 by Sakaue et al.[23] The pooled data for GD included 395,640 European participants (15,654 cases and 379,986 controls). The pooled GWAS data for HT included 458,620 European participants (1678 cases and 456,942 controls).
2.3.2. Selection of instrument variables
The selection criteria for instrumental variables (IVs) were as follows: single-nucleotide polymorphism (SNP) loci with genome-wide significance were screened from the exposure database; the PLINK clustering algorithm was performed with a 10,000-kb linkage disequilibrium window to ensure independence (R2 < 0.0001); SNPs with an F statistic <10 were excluded, as F > 10 indicates strongly associated IVs; and the exposure and outcome datasets were harmonized, and palindromic sequences were excluded to ensure that the effects of SNPs on exposure and outcome were from the same allele. SNPs used as IVs in this study are listed in Tables S2 to S5, Supplemental Digital Content 4 to 7.
2.4. Statistical analyses
2.4.1. Cross-sectional study
All statistical analyses were performed according to the recommendations of the Centers for Disease Control and Prevention, using appropriate NHANES sampling weights. Continuous variables are expressed as mean ± standard deviation (x ± s), whereas categorical variables are expressed as n (%). Between-group differences were assessed by weighted linear regression for continuous variables or the weighted chi-square test for categorical variables. The association between sleep behavior and thyroid was studied by multiple linear regression using 2 different models. The first model did not adjust for covariates. The second model adjusted for age, sex, race, education, marital status, PIR, BMI, smoking, diabetes, and hypertension.
All analyses were performed using EmpowerStats (4.0) (http://www.empowerstats.com) and R software (http://www.r-project.org) using MEC weights. P values < .05 were considered statistically significant.
2.4.2. Two-sample Mendelian randomization
All MR analyses were performed using the TwoSampleMR package in R (4.2.1) software. In this study, the inverse variance weighted (IVW) method was used to pool the Wald estimates of individual SNPs for weighted linear regression to summarize the total effect value. IVW, as the main analysis method, can provide unbiased causal effect estimates in the absence of horizontal pleiotropy. At the same time, to improve the robustness of the results, MR-Egger regression and the weighted median estimator were used as supplementary methods of IVW because these methods can provide more reliable effect estimates under more relaxed conditions. In addition, we also used Cochran’s Q test calculated by the IVW method to estimate heterogeneity. At the same time, the MR-Egger intercept test was used to evaluate and correct for potential pleiotropy. Finally, the robustness of the results was further verified by observing the funnel plot and leave-one-out plot.
3. Results
3.1. Baseline characteristics
The baseline characteristics of the participants are summarized in Table 1. After strict screening, a total of 6919 subjects were included in this study. In the sleep duration group, 3930 (56.80%) were female, 5345 (77.25%) were non-Hispanic White, 2567 (37.10%) had normal BMI, 4362 (63.04%) had a medium PIR level, 4442 (64.20%) had an education level higher than high school, and 4476 (64.69%) were married or living with a partner. In addition, there were 2032 cases of hypertension, 883 cases of diabetes, and 2888 cases of smoking, of which 265 were TPOAb positive and 169 were TGAb positive. In the sleep disorder group, 3802 (54.95%) were female, 5340 (77.18%) were non-Hispanic White, 2713 (39.21%) had normal BMI, 4618 (66.74%) had a medium family income, 4616 (66.71%) had an education level higher than high school, and 4598 (66.45%) were married or living with a partner. In addition, 1914 had hypertension, 968 had diabetes, and 2659 smoked; 183 were TPOAb positive and 158 were TGAb positive.
Table 1.
Thyroid function and thyroid autoimmunity of the NHANES (2007–2012) study population in sleep duration groups and sleep disorder groups.
| Characteristics | Sleep duration | P-value | Sleep disorder | P-value | |||||
|---|---|---|---|---|---|---|---|---|---|
| Total | Short sleep | Normal | Long sleep | Total | Non-sleep disorder | Sleep disorder |
|||
| N† | 6919 (100) | 2816 (40.7) | 3934 (56.86) | 169 (2.44) | 6919 (100) | 5346 (77.27) | 1573 (22.73) | ||
| Sex† | <.0001 | <.0001 | |||||||
| Male | 2989 (43.20) | 770 (27.35) | 2150 (54.66) | 69 (40.79) | 3117 (45.05) | 2747 (51.39) | 370 (23.53) | ||
| Female | 3930 (56.80) | 2046 (72.65) | 1784 (45.34) | 100 (59.21) | 3802 (54.95) | 2599 (48.61) | 1203 (76.47) | ||
| Age (yr)* | 60.07 ± 18.89 | 49.66 ± 17.22 | 51.02 ± 21.47 | <.0001 | 52.18 ± 19.36 | 55.69 ± 13.06 | <.0001 | ||
| Race† | <.0001 | <.0001 | |||||||
| Mexican American | 232 (3.35) | 100 (3.53) | 119 (3.03) | 13 (7.92) | 219 (3.16) | 184 (3.44) | 35 (2.22) | ||
| Other Hispanic | 162 (2.35) | 83 (2.96) | 63 (1.60) | 16 (9.79) | 143 (2.07) | 111 (2.08) | 32 (2.02) | ||
| Non-Hispanic White | 5345 (77.25) | 2324 (82.53) | 2912 (74.01) | 109 (64.43) | 5340 (77.18) | 3942 (73.74) | 1398 (88.86) | ||
| Non-Hispanic Black | 986 (14.25) | 209 (7.42) | 756 (19.23) | 21 (12.15) | 1040 (15.03) | 961 (17.98) | 79 (5.03) | ||
| Other race | 194 (2.80) | 100 (3.56) | 84 (2.13) | 10 (5.71) | 177 (2.56) | 148 (2.76) | 29 (1.87) | ||
| BMI (kg/m2)† | <.0001 | <.0001 | |||||||
| Less than normal | 47 (0.68) | 16 (0.55) | 26 (0.65) | 5 (3.18) | 43 (0.62) | 36 (0.67) | 7 (0.47) | ||
| Normal | 2567 (37.10) | 939 (33.34) | 1570 (39.92) | 58 (33.96) | 2713 (39.21) | 1720 (32.19) | 993 (63.13) | ||
| Overweight | 2515 (36.35) | 1343 (47.70) | 1116 (28.37) | 56 (33.22) | 2306 (33.33) | 2050 (38.34) | 256 (16.27) | ||
| Obesity | 1790 (25.87) | 518 (18.40) | 1222 (31.06) | 50 (29.64) | 1857 (26.84) | 1540 (28.80) | 317 (20.12) | ||
| Smoke† | <.0001 | <.0001 | |||||||
| No | 4031 (58.26) | 1253 (44.50) | 2688 (68.33) | 90 (53.00) | 4260 (61.57) | 3125 (58.46) | 1135 (72.14) | ||
| Yes | 2888 (41.74) | 1563 (55.50) | 1246 (31.67) | 79 (47.00) | 2659 (38.43) | 2221 (41.54) | 438 (27.86) | ||
| Poverty-to-income ratio† | <.0001 | <.0001 | |||||||
| Low | 1355 (19.59) | 970 (34.45) | 311 (7.91) | 74 (44.09) | 1114 (16.10) | 896 (16.77) | 218 (13.89) | ||
| Medium | 4362 (63.04) | 1284 (45.60) | 3015 (76.63) | 63 (37.19) | 4618 (66.74) | 3618 (67.67) | 1000 (63.58) | ||
| High | 1202 (17.37) | 562 (19.94) | 608 (15.46) | 32 (18.72) | 1187 (17.16) | 832 (15.56) | 355 (22.54) | ||
| Education level† | <.0001 | <.0001 | |||||||
| Less than high school | 1775 (25.65) | 822 (29.19) | 898 (22.83) | 55 (32.36) | 1661 (24.01) | 1515 (28.34) | 146 (9.29) | ||
| High school or equivalent | 702 (10.15) | 355 (12.61) | 290 (7.37) | 57 (33.79) | 642 (9.28) | 460 (8.61) | 182 (11.57) | ||
| High school above | 4442 (64.20) | 1639 (58.20) | 2746 (69.79) | 57 (33.85) | 4616 (66.71) | 3371 (63.05) | 1245 (79.14) | ||
| Marital status† | <.0001 | <.0001 | |||||||
| Married or living with partner | 4476 (64.69) | 1710 (60.72) | 2679 (68.09) | 87 (51.51) | 4598 (66.45) | 3352 (62.69) | 1246 (79.21) | ||
| Widowed, divorced, or separated | 1078 (15.58) | 826 (29.32) | 209 (5.31) | 43 (25.63) | 881 (12.74) | 684 (12.80) | 197 (12.54) | ||
| Never married | 1365 (19.73) | 280 (9.96) | 1046 (26.60) | 39 (22.86) | 1440 (20.81) | 1310 (24.51) | 130 (8.26) | ||
| Hypertension† | <.0001 | <.0001 | |||||||
| No | 4887 (70.63) | 1829 (64.94) | 2955 (75.12) | 103 (61.18) | 5005 (72.34) | 3754 (70.22) | 1251 (79.52) | ||
| Yes | 2032 (29.37) | 987 (35.06) | 979 (24.88) | 66 (38.82) | 1914 (27.66) | 1592 (29.78) | 322 (20.48) | ||
| Diabetes† | <.0001 | <.0001 | |||||||
| No | 6036 (87.24) | 2701 (95.91) | 3185 (80.95) | 150 (88.49) | 5951 (86.01) | 4469 (83.60) | 1482 (94.19) | ||
| Yes | 883 (12.76) | 115 (4.09) | 749 (19.05) | 19 (11.51) | 968 (13.99) | 877 (16.40) | 91 (5.81) | ||
| TSH (mIU/L)* | 1.60 ± 1.20 | 1.91 ± 1.42 | 2.32 ± 4.11 | <.0001 | 1.69 ± 1.44 | 2.40 ± 1.06 | <.0001 | ||
| FT3 (pg/mL)* | 3.12 ± 0.43 | 3.16 ± 0.29 | 3.11 ± 0.41 | .0002 | 3.15 ± 0.35 | 3.12 ± 0.26 | .0167 | ||
| FT4 (ng/dL)* | 0.86 ± 0.23 | 0.74 ± 0.10 | 0.81 ± 0.18 | <.0001 | 0.78 ± 0.17 | 0.79 ± 0.10 | .0037 | ||
| TT3 (ng/dL)* | 107.33 ± 18.54 | 113.73 ± 16.53 | 111.61 ± 22.99 | <.0001 | 109.51 ± 16.26 | 121.80 ± 19.03 | <.0001 | ||
| TT4 (µg/dL)* | 8.07 ± 1.45 | 6.79 ± 1.66 | 8.03 ± 1.64 | <.0001 | 6.89 ± 1.64 | 8.51 ± 1.35 | <.0001 | ||
| TG (ng/mL)* | 17.62 ± 19.82 | 11.09 ± 18.73 | 18.54 ± 25.06 | <.0001 | 12.12 ± 19.42 | 17.61 ± 18.42 | <.0001 | ||
| TPOAb† | .0008 | .1501 | |||||||
| Negative | 6654 (96.17) | 2687 (95.41) | 3813 (96.93) | 154 (90.85) | 6669 (96.39) | 5163 (96.58) | 1506 (95.75) | ||
| Positive | 265 (3.83) | 129 (4.59) | 121 (3.07) | 15 (9.15) | 250 (3.61) | 183 (3.42) | 67 (4.25) | ||
| TGAb† | .0029 | .3568 | |||||||
| Negative | 6750 (97.56) | 2739 (97.26) | 3856 (98.01) | 155 (91.87) | 6761 (97.72) | 5229 (97.82) | 1532 (97.39) | ||
| Positive | 169 (2.44) | 77 (2.74) | 78 (1.99) | 14 (8.13) | 158 (2.28) | 117 (2.18) | 41 (2.61) | ||
BMI = body mass index, FT3 = free triiodothyronine, FT4 = free thyroxine, NHANES = National Health and Nutrition Examination Survey, SD = standard deviation, TG = thyroglobulin, TGAb = thyroglobulin antibodies, TPOAb = thyroid peroxidase antibodies, TSH = thyroid-stimulating hormone, TT3 = total T3, TT4 = total T4.
Continuous variables: weighted mean (SD).
Categorical variables: actual frequency (weighted percentage).
3.2. Relationship between sleep-related disorders and thyroid
The results of the multivariate regression analyses have been presented in Tables 2 and 3. In Model 2, adjusted for covariates, we found significantly higher TSH levels in the long sleep group compared with those in the short sleep group (P < .0001, β = 0.85, 95% confidence interval [CI]: 0.54, 1.15). FT3 levels were lower in the normal group compared with those in the short sleep group (P = .0030, β = −0.06, 95% CI: −0.06, 0.00). Lower TT4 levels were also found in people with sleep disorders compared with those with non-sleep disorders (P = .0157, β = −0.11, 95% CI: −0.20, −0.02).
Table 2.
Effects of sleep duration and sleep disorders on thyroid function.
| Model 1† |
P-value |
Model 2* | P-value | |
|---|---|---|---|---|
| β (95% CI) | β (95% CI) | |||
| TSH (mIU/L) | ||||
| Sleep duration | ||||
| Short sleep | Reference | Reference | ||
| Normal | 0.15 (0.06, 0.25) | .0018 | 0.09 (−0.01, 0.18) | .0767 |
| Long sleep | 0.97 (0.66, 1.28) | <.0001 | 0.85 (0.54, 1.15) | <.0001 |
| Sleep disorder | ||||
| Non-sleep disorder | Reference | Reference | ||
| Sleep disorder | 0.01 (−0.10, 0.12) | .8354 | −0.06 (−0.17, 0.05) | .3043 |
| FT3 (pg/mL) | ||||
| Sleep duration | ||||
| Short sleep | Reference | Reference | ||
| Normal | −0.04 (−0.07, −0.01) | .0036 | −0.03 (−0.06, 0.00) | .0030 |
| Long sleep | −0.12 (−0.21, −0.04) | .0053 | −0.06 (−0.14, 0.02) | .1517 |
| Sleep disorder | ||||
| Non-sleep disorder | Reference | Reference | ||
| Sleep disorder | −0.08 (−0.11, −0.05) | <.0001 | −0.02 (−0.06, 0.01) | .1149 |
| FT4 (ng/dL) | ||||
| Sleep duration | ||||
| Short sleep | Reference | Reference | ||
| Normal | 0.01 (0.00, 0.01) | .0875 | 0.01 (0.00, 0.01) | .1088 |
| Long sleep | 0.02 (0.00, 0.04) | .1099 | 0.01 (−0.01, 0.04) | .2219 |
| Sleep disorder | ||||
| Non-sleep disorder | Reference | Reference | ||
| Sleep disorder | −0.01 (−0.02, 0.00) | .0670 | −0.01 (−0.02, 0.00) | .0614 |
| TT3 (ng/dL) | ||||
| Sleep duration | ||||
| Short sleep | Reference | Reference | ||
| Normal | −1.08 (−2.26, 0.10) | .0731 | −0.47 (−1.62, 0.67) | .4180 |
| Long sleep | −5.06 (−8.85, −1.28) | .0088 | −2.83 (−6.46, 0.81) | .1275 |
| Sleep disorder | ||||
| Non-sleep disorder | Reference | Reference | ||
| Sleep disorder | −1.83 (−3.20, −0.46) | .0089 | −0.11 (−1.47, 1.24) | .8690 |
| TT4 (µg/dL) | ||||
| Sleep duration | ||||
| Short sleep | Reference | Reference | ||
| Normal | 0.00 (−0.08, 0.08) | .9727 | 0.04 (−0.04, 0.11) | .3589 |
| Long sleep | 0.25 (−0.01, 0.50) | .0455 | 0.21 (−0.04, 0.45) | .0961 |
| Sleep disorder | ||||
| Non-sleep disorder | Reference | Reference | ||
| Sleep disorder | −0.04 (−0.13, 0.05) | .3646 | −0.11 (−0.20, −0.02) | .0157 |
CI = confidence interval, FT3 = free triiodothyronine, FT4 = free thyroxine, TSH = thyroid-stimulating hormone, TT3 = total T3, TT4 = total T4.
Model 1: no covariates were adjusted.
Model 2: age, sex, race, poverty-to-income ratio, marital status, body mass index, smoking, education level, hypertension, and diabetes were adjusted.
Table 3.
Effects of sleep duration and sleep disorders on thyroid autoimmunity.
| Model 1* | P-value | Model 2* | P-value | |
|---|---|---|---|---|
| OR (95% CI) | OR (95% CI) | |||
| TPOAb† | ||||
| Sleep duration | ||||
| Short sleep | Reference | Reference | ||
| Normal | 1.14 (0.96, 1.36) | .1309 | 1.03 (0.86, 1.23) | .7563 |
| Long sleep | 1.64 (1.02, 2.65) | .0416 | 1.39 (0.85, 2.27) | .1857 |
| Sleep disorder | ||||
| Non-sleep disorder | Reference | Reference | ||
| Sleep disorder | 0.90 (0.73, 1.10) | .2993 | 0.83 (0.67, 1.02) | .0803 |
| TGAb† | ||||
| Sleep duration | ||||
| Short sleep | Reference | Reference | ||
| Normal | 1.21 (0.98, 1.50) | .0737 | 1.10 (0.88, 1.36) | .4096 |
| Long sleep | 2.23 (1.33, 3.74) | .0024 | 1.81 (1.06, 3.07) | .0288 |
| Sleep disorder | ||||
| Non-sleep disorder | Reference | Reference | ||
| Sleep disorder | 0.77 (0.60, 1.00) | .0478 | 0.72 (0.56, 0.95) | .0176 |
CI = confidence interval, OR = odds ratio, TGAb = thyroglobulin antibodies, TPOAb = thyroid peroxidase antibodies.
Model 1: no covariates were adjusted.
Model 2: age, sex, race, poverty-to-income ratio, marital status, body mass index, smoking, education level, hypertension, and diabetes were adjusted.
In terms of thyroid autoimmunity, from Model 2, it was found that long sleep was positively associated with the risk of TGAb positivity (P = .0288, odds ratio [OR] = 1.81, 95% CI: 1.06, 3.07), whereas sleep disorders were negatively associated with TGAb positivity (P = .0176, OR = 0.72, 95% CI: 0.56, 0.95).
3.3. Mendelian randomization analysis
All MR analysis results have been presented in Figures 3 to 5. IVW results indicate that there may be a positive association between long sleep and the risk of GD (P = .0240, OR = 99.98, 95% CI: 1.83, 5453.63), while there may be a negative association between sleep duration and the risk of HT (P = .0294, OR = 0.72, 95% CI: 0.54, 0.97). In this study, there was heterogeneity between sleep-related disorders and hypothyroidism, heterogeneity between sleep duration and hyperthyroidism, heterogeneity between short sleep and insomnia and GD, and the remaining results showed no heterogeneity or pleiotropic effects (P > .05; Table S6, Supplemental Digital Content 8). We also observed the funnel plot, which was roughly symmetrical, indicating that the risk of bias was relatively low and the results were highly reliable (Fig. S1, Supplemental Digital Content 1). The leave-one-out method was used to eliminate SNPs one by one, and no specific SNP was found to cause significant changes in the results (Fig. S2, Supplemental Digital Content 2).
Figure 3.
MR analysis results. CI = confidence interval, MR = Mendelian randomization, OR = odds ratio.
Figure 5.
MR analysis results. CI = confidence interval, MR = Mendelian randomization, OR = odds ratio.
Figure 4.
MR analysis results. CI = confidence interval, MR = Mendelian randomization, OR = odds ratio.
4. Discussion
This study is the first to combine large-scale observational study data and MR analysis to explore the association between sleep-related disorders and the thyroid. In terms of thyroid function, cross-sectional studies have shown that, compared with short sleep, TSH levels were significantly higher in the long sleep group, and FT3 levels were lower in the normal sleep group. It was also found that people with sleep disorders had lower TT4 levels than those with non-sleep disorders. In terms of thyroid autoimmunity, the study found that long sleep was positively correlated with the risk of TGAb positivity, while sleep disorders were negatively correlated with the risk of TGAb positivity. In addition, a further two-sample MR analysis verified this conclusion, indicating that there may be a positive correlation between long sleep and GD risk and there may be a negative correlation between sleep duration and HT risk.
The HPT axis is controlled by the circadian rhythm system, which plays a key role in the sleep-wake cycle, and disruption of one axis leads to dysregulation of the other.[24] Previous studies have assessed, to varying degrees, the effects of sleep on thyroid function. There is evidence that sleep affects thyroid hormone secretion, leading to a decrease in the circadian amplitude of TSH, which in turn feeds back to suppress thyroid hormones through the HPT axis.[25] Previous observational studies have observed circadian rhythm changes in TSH secretion to varying degrees.[26–29] Epidemiologic findings suggest that there are long-term effects of chronic circadian rhythm disruption on human health, and indeed, chronic sleep deprivation disrupts rhythmic TSH secretion.[24,30] Although acute, extreme deprivation increases TSH secretion and increases the surge in TSH release, chronic, moderate sleep deprivation suppresses the effects of circadian TSH secretion.[19] The results of a recent study showed an increase in TSH levels and a decrease in FT3 levels with increasing sleep duration, which is broadly similar to our study.[31] A similar circadian rhythmicity of FT3 was found in another study by observing that FT3 peaked 90 minutes after TSH secretion in 86% to 100% of participants.[29] In contrast, FT4 did not show a clear circadian rhythm.[29] Regardless of the effects of sleep, TSH is regulated by circadian rhythms. Starting in the evening, concentrations increase dramatically, peak at 2 to 3 am, and do not decrease until the afternoon. Thus, although NHANES self-reported the same sleep duration, the effect of sleep duration on TSH secretion may not be attributable only to duration but may also vary depending on the individual’s sleep onset and termination times. For example, Participant A slept from 9 pm to 3 am (when TSH secretion was in the ascending phase), whereas Participant B slept from 2 to 9 am (when TSH secretion was in the descending phase), and both individuals slept for the same duration of 7 hours; however, the effect of sleep on TSH secretion would differ depending on the period.
Previous studies have demonstrated that HPT axis function is regulated by the circadian rhythm system, with TSH secretion exhibiting a nocturnal peak and being closely associated with deep sleep stages.[26,29] The finding observed in this study – that longer sleep duration correlates with higher TSH levels – aligns with this theory and further supports the potential influence of sleep duration on HPT axis rhythms. However, as a cross-sectional study, this research cannot directly elucidate the specific molecular mechanisms through which sleep influences the HPT axis (e.g., clock gene expression and neurotransmitter regulation). Future validation via animal experiments or intervention studies is warranted. Existing literature suggests that sleep disorders may affect thyroid function via inflammatory mediators (e.g., interleukin-6 and tumor necrosis factor alpha) or immune cell regulation.[32,33] While this study found that “sleep disturbances correlate with reduced TT4 levels,” it did not measure inflammatory or immune cell markers, precluding clarification of specific pathways. Furthermore, the finding that “sleep disturbances negatively correlate with TGAb positivity” differs from some previous studies,[17] potentially influenced by population characteristics and confounding factors. Further mechanistic research (e.g., immune tolerance, neuroendocrine regulation) is needed to explain this discrepancy.
Previous observational studies have shown a correlation between hypothyroidism and sleep. A study based on a Chinese population showed that subclinical hypothyroidism was a risk factor for poor sleep quality.[18] In addition, the results of another single-center retrospective study showed that patients with hypothyroidism were more likely to have an early chronotype.[34] A large Korean cohort study showed that either too much or too little sleep was associated with an increased risk of subclinical thyroid dysfunction.[17] However, some studies have taken the opposite view, suggesting that there is no significant difference in subjective sleep measures between individuals with hyperthyroidism and normal thyroid function.[35] Similarly, a study by Akatsu et al did not find a relationship between subclinical hypothyroidism and sleep quality.[36] The current evidence on the association between sleep and thyroid disorders is controversial, and the results of our MR study found no correlation between sleep-related disorders (sleep duration, long sleep, short sleep, and insomnia) and thyroid disorders (hyperthyroidism and hypothyroidism).
The association between the HPT axis and sleep can affect thyroid function, and the mutual interference between autoimmunity and sleep duration may further exacerbate the potential impact of sleep on thyroid function.[37,38] Although previously understudied in the context of thyroid autoimmune diseases, sleep disorders have been reported to increase the risk of multiple autoimmune diseases, such as systemic lupus erythematosus, rheumatoid arthritis, and ankylosing spondylitis.[33,39] Some clinical studies have also observed that autoimmune disease activity is associated with sleep.[40,41] In addition, sleep is bidirectionally related to the immune system, and sleep can also regulate the immune system.[37] Therefore, sleep may play a role in the immune mechanism of autoimmune thyroid disease. Previous studies have shown that HT has no effect on lymph node metastasis in patients with thyroid cancer, and HT may inhibit the occurrence of PTC, making it a protective factor.[42,43] Our cross-sectional study found that long sleep was positively associated with the risk of TGAb positivity, while sleep disorders were negatively associated with TGAb positivity. This was verified in further MR study results, indicating that long sleep may be positively associated with the risk of GD, while sleep duration may be negatively associated with the risk of HT. Regarding the result that “sleep disorders are negatively correlated with positive TGAb,” we propose the following hypotheses: Alteration of immune tolerance: Sleep disorders may affect the tolerance of the immune system, causing a weakened immune response to thyroid antigens and thus reducing the production of TGAb. This change may involve abnormal functions of regulatory T cells (Tregs), which play a crucial role in maintaining immune tolerance. Neuroendocrine regulation: Sleep disorders may indirectly regulate the autoimmune state of the thyroid by affecting the neuroendocrine system (such as the activation of the sympathetic nervous system). This regulation may lead to changes in the thyroid microenvironment, resulting in a reduction in antibody production. Behavioral compensation mechanism: Another possibility is that individuals with sleep disorders may compensate for the negative effects of sleep deprivation through other behaviors (such as healthier eating habits and increased physical activity), which may help reduce the risk of autoimmune reactions. To further verify these hypotheses, future research can design more detailed experiments, including detecting levels of inflammatory factors, the distribution of immune cell subsets, and changes in neuroendocrine factors, to reveal the detailed mechanism by which sleep affects thyroid function.
The main strength of this study is that it is the first to combine the NHANES observational study with the MR method to explore the association between sleep-related disorders and thyroid. The strong sample size and thorough adjustment for multiple confounders allowed us to increase confidence in the study results in the multivariate regression model. Although the cross-sectional study design limits causal inference, the MR analysis essentially produces a natural randomized controlled trial and provides high-power (P < 5 × 10−8) and strongly associated (F statistic > 10) SNPs to evaluate the causal association between sleep-related disorders and thyroid disease. However, we still need to consider the study’s limitations. First, although we tried to control for various confounders, the data on sleep-related disorders were self-reported, which may be subject to recall bias. Second, the sleep duration measurement only included sleep time on weekdays or workdays and did not include additional information related to sleep (such as shift work, working conditions, and sleep onset and end times), which limited further analysis of the study results. Finally, our NHANES study focused primarily on the US population (which is predominantly White), whereas the MR study focused on individuals of European descent. There has been a lack of validation in non-European populations (Asians, Africans), which limits the generalizability of the findings. It will be necessary to validate the current findings in the same or other ethnic populations in the future.
5. Conclusion
Our cross-sectional study found that sleep duration was associated with TSH and FT3 levels, and sleep disorders were associated with TT4 levels. In terms of thyroid autoimmunity, long sleep was positively associated with TGAb positivity, while sleep disorders were negatively associated with TGAb positivity. Further, through MR analysis, we speculate that long sleep may be positively associated with the risk of GD, and sleep duration may be negatively associated with the risk of HT. Future research should also investigate clinicians’ awareness and practices regarding the role of sleep in thyroid health. As suggested by Isik et al, there is a gap between guideline recommendations and real-world practice in thyroid nodule management.[44] Similarly, whether sleep-related factors are routinely considered in thyroid disease assessment remains unknown and warrants further investigation.
Acknowledgments
We sincerely thank all the projects (NHANES, UK Biobank, and IEU OpenGWAS) that participated in this study.
Author contributions
Conceptualization: Rongliang Qiu, Xiaoquan Hong, Suqiong Lin, Jinbo Fu.
Data curation: Rongliang Qiu, Xiaoquan Hong, Suqiong Lin, Jinbo Fu.
Formal analysis: Rongliang Qiu, Xiaoquan Hong, Suqiong Lin, Jinbo Fu.
Investigation: Rongliang Qiu, Xiaoquan Hong, Suqiong Lin, Jinbo Fu.
Methodology: Rongliang Qiu, Xiaoquan Hong, Suqiong Lin, Jinbo Fu.
Project administration: Rongliang Qiu, Xiaoquan Hong, Suqiong Lin, Jinbo Fu.
Resources: Suqiong Lin, Jinbo Fu.
Software: Rongliang Qiu, Xiaoquan Hong, Suqiong Lin.
Supervision: Xiaoquan Hong, Jinbo Fu.
Validation: Rongliang Qiu, Xiaoquan Hong, Suqiong Lin.
Visualization: Rongliang Qiu, Xiaoquan Hong.
Writing – original draft: Rongliang Qiu, Xiaoquan Hong, Suqiong Lin, Jinbo Fu.
Writing – review & editing: Jinbo Fu.
Abbreviations:
- BMI
- body mass index
- FT3
- free triiodothyronine
- FT4
- free thyroxine
- GD
- Graves disease
- GWAS
- genome-wide association study
- HPT
- hypothalamic-pituitary-thyroid
- HT
- Hashimoto’s thyroiditis
- IVW
- inverse variance weighted
- MR
- Mendelian randomization
- NHANES
- National Health and Nutrition Examination Survey
- PIR
- poverty-to-income ratio
- T3
- triiodothyronine
- T4
- thyroxine
- TGAb
- thyroglobulin antibodies
- TPOAb
- thyroid peroxidase antibodies
- TSH
- thyroid-stimulating hormone
- TT3
- total T3
- TT4
- total T4
The authors have no funding and conflicts of interest to declare.
All data generated or analyzed during this study are included in this published article (and its supplementary information files).
Supplemental Digital Content is available in the online version of this article (http://dx.doi.org/10.1097/MD.0000000000050791).
How to cite this article: Hong X, Qiu R, Lin S, Fu J. Sleep-related disorders and thyroid gland: Results from the National Health and Nutrition Examination Survey 2007 to 2012 and Mendelian randomization analyses. Medicine 2026;105:39(e50791).
Contributor Information
Xiaoquan Hong, Email: 15280233579@163.com.
Rongliang Qiu, Email: qrldoctor@163.com.
Suqiong Lin, Email: 709318340@qq.com.
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