Manganese and Sleep Outcomes in United States Adults: Results from the 2017–2020 National Health and Nutrition Examination Survey (NHANES)

Abstract

Background

Manganese (Mn) is an essential micronutrient, but inadequate or excess Mn intake can have a detrimental impact on human health. Despite the essentiality, little is known about the relationship between Mn and sleep.

Objective

This study aimed to examine the relationship between blood Mn concentrations and sleep outcomes in US adults.

Methods

This cross-sectional study used data on blood Mn and sleep from the 2017–2020 National Health and Nutrition Examination Survey (NHANES) (n = 8356, age ≥18 y). Multivariable logistic regression was used to examine associations between quintiles of blood Mn concentrations and subjective sleep outcomes (short sleep duration, late sleep midpoint, trouble sleeping, and obstructive sleep apnea [OSA] symptoms), adjusting for age, gender, body mass index, race/ethnicity, income, smoking, inflammation-adjusted serum ferritin concentration (iron status), caffeine, and alcohol intake. Gender-stratified models were used due to interactions with gender.

Results

The mean (SE) blood Mn concentration was 9.7 (0.1) μg/L in US adults. In males, a nonlinear association was noted in the relationship between blood Mn levels and short sleep duration on weekdays and weekends. The third Mn quintile (Q3) group had lower odds of short sleep duration (<7 h) on weekdays (odds ratio [OR]=0.6, 95% confidence interval [CI]: 0.4, 0.9) than the lowest Mn quintile (Q1, reference) after adjusting for covariates in males. The second Mn quintile (Q2) group had lower odds of late sleep midpoint on weekdays than Q1 (OR=0.6, 95% CI: 0.4, 0.8). In females, Q2 group had lower odds of OSA symptoms than Q1 (OR: 0.6, 95% CI: 0.4, 0.9). No relationship was noted between Mn and trouble sleeping.

Conclusions

Gender differences exist in the association between Mn and sleep in adults. Q1 group had the poorest sleep outcomes, including higher odds of short sleep duration (in males), late sleep midpoint (in males), and OSA symptoms (in females).

Introduction

Sleep is essential for human health, as short sleep duration, late sleep timing (a proxy of circadian misalignment), and sleep disorders have been associated with an increased risk of chronic diseases and obesity [[1], [2], [3]]. In the United States, short sleep duration and sleep disorders are prevalent, with 9% of adults sleeping < 6 h on weekdays [4], and 30% of them reported having sleep disorders [5]. Late sleep midpoint (median of bedtime and wake time) is also common and is emerging as an independent risk factor for adverse cardiometabolic health [6]. Recent studies have identified risk factors for these poor sleep outcomes and have recognized diet as a modifiable factor for promoting better sleep [7]. The role of macronutrients in sleep has been noted; for example, diets high in complex carbohydrates (including dietary fiber) and healthier fats (including unsaturated fat) were related to better sleep quality [8]. Evidence also points to the relationship between some micronutrients and sleep, with longer sleep duration associated with higher iron, zinc, and magnesium concentrations and lower copper, potassium, and vitamin B₁₂ concentrations [9]. However, the relationship between manganese (Mn) and sleep is not fully understood.

Mn is an essential micronutrient that is important for normal growth and physiological processes, such as immune function, regulation of blood sugar and cellular energy levels, blood coagulation and hemostasis, and defense against reactive oxygen species (ROS) [10]. Mn contributes to these processes by acting as a cofactor for numerous enzymes, such as Mn superoxide dismutase (MnSOD), which protects against free radicals, and glutamine synthetase, which converts glutamate to glutamine in the brain [11]. Mn is obtained mainly through diet and is abundant in plant-based foods, such as whole grains, legumes, rice, nuts, and vegetables [12]. In the United States, the intake considered adequate for Mn is 2.3 mg/d for males and 1.8 mg/d for females [13].

Mn is crucial for brain development and function, as the brain is one of the most metabolically active organs in the body. Mn deficiency can be a risk factor for epilepsy in humans and rats [14,15]. Recent genetic studies have shown that patients with congenital Mn deficiency can display psychomotor disabilities, cerebral and cerebellar atrophy, seizures, and hearing and vision impairment [16]. In addition, exposure to high levels of Mn can lead to Mn accumulation in the brain and cause a parkinsonian-like disorder called manganism [17,18]. Historically, Mn toxicity has been extensively documented in occupational settings where miners, welders, and dry manufacturers are exposed to chronic inhalation of high concentrations of respirable airborne Mn [17,18].

The critical roles of Mn in the brain raise the possibility of a relationship between Mn and sleep. Mn accumulation in the brain, particularly in the basal ganglia, can affect neuronal activity and disrupt the sleep and wake cycle [19]. An animal study reported that Mn toxicity increased deep sleep and decreased rapid eye movement (REM) sleep, suggesting that Mn toxicity can change the duration of different sleep stages [20]. Additionally, 1 case-control study found higher serum Mn concentrations in patients with obstructive sleep apnea (OSA) than in a control group [21]. Prior studies reported gender differences in sleep patterns [22]. However, little is known about the relationship between Mn and sleep outcomes in humans and if this relationship differs among genders.

In this study, we examined the associations between blood Mn concentrations and sleep characteristics, integrating 4 aspects of sleep outcomes (duration, midpoint of sleep, OSA symptoms, and trouble sleeping) among males and females. Given the narrow range between essential and toxic doses of Mn [13,16,17], we hypothesized that both low and high concentrations of Mn would be associated with poor sleep outcomes.

Methods

Study population

The National Health and Nutrition Examination Survey (NHANES) is an ongoing nationally representative health survey of the civilian noninstitutionalized population in the United States [23]. Data from NHANES are released in 2-y waves by the National Center for Health Statistics (NCHS) and Centers for Disease Control and Prevention. The NHANES sample was selected based on a complex, multistage probability sampling design. Participants complete in-person household interviews that collect demographic information, health history, and lifestyle behaviors (including sleep) and undergo standardized health examinations in the Mobile Examination Centers (MEC), where they provide blood samples.

For this analysis (cross-sectional study), the participants included 8356 adults (≥18 y) with both blood Mn concentrations and sleep parameters from the 2017 to 2020 cycles. Due to the coronavirus disease 2019 (COVID-19), the data collection in the 2019–2020 cycle was suspended in March 2020. The 2019–March 2020 prepandemic data were combined with the 2017–2018 survey cycle to form nationally representative data. The NHANES survey protocol was approved by the NCHS Research Ethics Review Board. All participants provided written informed consent.

Assessment of blood Mn concentrations

Blood Mn was chosen as a measure of Mn exposure because it is considered more reliable than urine Mn [24]. Whole blood samples were obtained from participants through trained phlebotomists at the MEC and then stored under appropriate conditions (–30°C) and shipped to the National Center for Environmental Health (NCEH), Centers for Disease Control and Prevention, for analysis. Whole blood samples were prepared and analyzed for Mn concentrations. Mn was measured by Inductively Coupled Plasma Mass Spectrometry with Dynamic Reaction Cell Technology (ICP-DRC-MS) in the 2017–2018 survey cycle and by inductively coupled plasma triple quadrupole mass spectrometer (ICP-QQQ-MS) in the 2019–2020 March survey. The 2 survey cycles have different lower detection limits due to differences in measurement. Therefore, the higher detection limit of Mn (0.99 μg/L) was used for the combined survey cycles to make the dataset compatible. Based on blood Mn concentrations, the participants were divided into quintiles (μg/L) for analysis: Q1 (<7.0), n = 1680; Q2 (7.0–8.4), n = 1666; Q3 (8.5–10.0), n = 1668; Q4 (10.1–12.2), n = 1675; and Q5 (>12.2), n = 1667.

Assessment of outcomes

The outcomes of interest included sleep duration, sleep midpoint, trouble sleeping, and OSA symptoms.

Sleep duration on both weekdays and weekends was determined by the question, “What time (do you/does the study participant) usually fall (asleep/wake up) on (weekdays or workdays/weekends or non-workdays)?” A sleep duration of <7 h was considered a short sleep duration [25]. The sleep midpoint was determined by the same question as sleep duration by calculating the median time point of falling asleep and waking up. A sleep midpoint > 5 AM was considered a late sleep midpoint [26]. A late sleep midpoint is a proxy marker of circadian misalignment, which occurs when sleep/wake behaviors are out of sync with the body’s underlying circadian rhythm and/or an inconsistency between central and peripheral rhythms [27]. Binary responses of short sleep duration and late sleep midpoint were generated. For example, if a participant reported a sleep time of 01:00 and a wake time of 07:00, then their sleep duration would be 6 h, and their sleep midpoint would be 04:00. This participant was considered to have a short sleep duration but not a late sleep midpoint. Trouble sleeping was determined by the question, “(Have you/Has the study participant) ever told a doctor or other health professional that (you have/s/he has) trouble sleeping?” The responses to this question were yes/no. Symptoms of OSA were defined based on 3 questions. These included 1)snoring 3 or more nights a week, 2) experiencing snorting, gasping, or stopping breathing 3 or more nights a week; and 3) feeling excessively sleepy during the day 16 to 30 times a month, even though ≥7 h of sleep were attained on weekdays or worknights. Participants were determined to have symptoms of OSA if they had any of these symptoms [28].

Covariates

The sociodemographic characteristics included in the analysis were age, gender (males; females), body mass index (BMI), race/ethnicity (Mexican American; Other Hispanic; NonHispanic White; NonHispanic Black; NonHispanic Asian; other race/ multiethnic), educational level (for adults 20+ y old; less than high school; high school; some college; bachelors’ degree or higher), marital/cohabitation status (Married/Living with a partner; Widowed/ Divorced/ Separated/ Never married), poverty-income ratio (PIR) (≤ 100%, >100–200%, >200–300%, >300–400%, and >400%), alcohol consumption (ever consume a drink of alcohol), caffeine intake (a continuous variable from the 24-h dietary recall), and smoking status. Height and weight were measured to calculate BMI (kg/m²), which was categorized into underweight (<18.5), normal weight (18.5–24.9), overweight (25.0–29.9), and obesity (3 categories: 30–34.9, 35–39.9, and ≥40) [29]. Caffeine consumption <400 mg/d for adults was considered within the recommended amount [30]. Smoking status was defined as never smoker (have smoked <100 cigarettes in life), former smoker (have smoked ≥ 100 cigarettes in life and do not smoke now), and current smoker (have smoked ≥ 100 cigarettes and smoke now).

Serum ferritin, representing iron status, was also included as a covariate since it is known to be highly related to Mn concentrations and sleep disorders [12,31]. Serum ferritin was measured with a Roche Cobas e601 analyzer and an electrochemiluminescence immunoassay (ECLIA) method. Serum specimens were also processed under the appropriate conditions and shipped to NCES for testing. The lower limit of detection for ferritin was 0.5 μg/L. It was recommended by prior literature to adjust serum ferritin for inflammation, including α-1-acid glycoprotein (AGP) and C-reactive protein (CRP) (32). Due to only CRP being available in the current dataset, the Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia method was used to adjust serum ferritin for CRP [32]. Inflammation-adjusted serum ferritin concentration <15 μg/L was considered iron deficiency [33].

Assessment of menopausal status and dietary supplements

Menopausal status was determined using the Reproductive Health Questionnaire. Participants who had not had a period in the past 12 mo due to menopause or hysterectomy were considered to be in postmenopausal status. Participants aged ≥55 were also categorized as postmenopausal. Participants who had a period in the past 12 mo, were pregnant, lactating, or cited other reasons for not having a period were considered to be in premenopausal status [34].

Information about dietary supplements was from the 24-h Dietary Supplements Data Files. Consumption of dietary supplements was determined if participants consumed any dietary supplements in the past 24 h in any of the 2 dietary recorded days.

Statistical analysis

The survey procedures accounting for the complex NHANES design (sampling weights, primary sampling unit, strata) were incorporated in all analyses. The mean blood Mn concentration among participants with different demographic characteristics was assessed using the t-test and one-way ANOVA. One-way ANOVA (continuous sleep duration and midpoint) and Rao-Scott Chi-Square tests (sleep disorders) were used to examine the relationships between Mn quintile concentrations and sleep parameters. Interaction analyses, including interaction terms for Mn and gender, were performed to determine possible interactions between Mn and gender on sleep parameters using logistic regressions. P values of interactions were tested with Type III Wald tests. Several statistically significant interactions were observed between Mn and gender on sleep outcomes; therefore, gender-stratified analyses were conducted. Multivariable logistic regression was used to examine the odds of binary sleep outcomes (short sleep duration, late sleep midpoint, trouble sleeping, and OSA symptoms) among participants with different Mn quintile concentrations. Type III P value was used to assess the significance of the overall association. The lowest Mn quintile concentration (Q1) was assigned as the reference group because Mn deficiency has been related to adverse neurological effects [16]. Covariates were selected based on known and observed relationships with blood Mn and sleep outcomes [31,35], including age (continuous), gender, BMI (continuous), race/ethnicity, PIR, smoking, inflammation-adjusted serum ferritin concentrations (continuous), alcohol consumption, and caffeine intake (continuous). The Variance Inflation Factor (VIF) was measured to assess multicollinearity for covariates. The VIFs were lower than 1.3 for all covariates. Since pregnancy could change sleep behaviors and Mn concentrations, a sensitivity analysis that excluded pregnant females was conducted. Pre- and postmenopausal status in females could possibly influence the relationship between Mn and sleep outcomes. Further sensitivity analysis was conducted to compare relationships with Mn concentrations and sleep in pre- and postmenopausal females using multivariable logistic regressions. All analyses were performed using SAS 9.4 software (SAS Institute Inc., Cary, NC, USA). Statistical significance was considered at P < 0.05, 2-sided.

Results

Participants’ characteristics

The characteristics of the participants and the mean blood Mn concentrations by characteristics are reported in Table 1. The mean blood Mn concentration (SE) was 9.7 (0.1) μg/L (95% confidence interval [CI]: 9.6, 9.8 μg/L) among the overall US adult population and was higher among participants who were younger, females, with obesity, identified as non-Hispanic Asian, had lower incomes, were nonsmokers, never drank alcohol, were iron deficient, and consumed caffeine within recommended range (Ps < 0.05). Education and marital status were not associated with Mn concentrations (Ps > 0.05).

TABLE 1.

Demographic characteristics of US adults aged ≥18 y by blood manganese levels (NHANES 2017−2020, n = 8356)

Characteristics	Weighted Percentage	n	Mean blood manganese concentrations (μg/L)	SE	P1
Age, y					<0.0001
18−29	20.4	1560	10.1	0.1
30−39	17.2	1227	9.8	0.1
40−49	15.9	1261	9.9	0.1
50−59	17.5	1376	9.7	0.2
60−69	15.3	1542	9.2	0.2
70 or more	13.8	1390	9.1	0.1
Gender					<0.0001
Males	48.2	4056	9.1	0.1
Females	51.8	4300	10.3	0.1
BMI (kg/m2)2					<0.0001
Underweight (<18.5)	1.5	127	9.6	0.3
Normal weight (18.5–24.9)	25.0	2032	9.5	0.1
Overweight (25–29.9)	32.1	2615	9.5	0.1
Obesity, class I (30–34.9)	21.8	1764	9.7	0.1
Class II (35–39.9)	11.0	928	10.3	0.1
Class III (≥40)	8.6	754	10.4	0.2
Race/Ethnicity					<0.0001
Mexican American	8.8	1023	10.8	0.2
Other Hispanic	7.7	877	9.8	0.2
NonHispanic White	62.8	2929	9.4	0.1
NonHispanic Black	10.8	2116	8.7	0.1
NonHispanic Asian	5.8	1005	12.2	0.2
Other race/ multiethnic	4.1	406	9.7	0.3
Educational level2					0.10
Less than high school	11.0	1493	9.9	0.2
High school	26.8	1907	9.9	0.1
Some college	30.5	2608	9.5	0.1
Bachelor’s degree or higher	31.7	1958	9.5	0.1
Marital/cohabitation status2					0.36
Married/ living with partner	62.3	4628	9.7	0.1
Widowed/ Divorced/ Separated/ Never married	37.7	3342	9.6	0.1
Poverty-income ratio (PIR)2					<0.0001
≤ 100%	13.1	1450	10.3	0.1
>100−200%	19.0	1916	10	0.1
>200−300%	14.9	1133	9.6	0.2
>300−400%	13.6	811	9.8	0.1
>400%	39.4	1909	9.3	0.1
Smoking2					<0.0001
Never smoker	58.4	4990	9.9	0.1
Former smoker	25.2	1913	9.4	0.1
Current smoker	16.3	1449	9.4	0.1
Alcohol consumption2					<0.0001
Ever drink alcohol	92.8	7082	9.6	0.1
Never drink alcohol	7.2	780	10.7	0.2
Iron status (Inflammation-adjusted serum ferritin (μg/L)) 2					<0.0001
Iron deficiency (<15 μg/L)	8.7	762	13.5	0.2
Normal ferritin concentration (≥15 μg/L)	91.3	7466	9.3	0.1
Caffeine					0.005
Within the recommended range (<400 mg/d)	94.2	8039	9.7	0.1
Exceed recommended range (≥400 mg/d)	5.8	317	9.1	0.2

¹The t-test (2 groups) and ANOVA (more than 2 groups) corrected for sampling design and weight were used for analysis.

²Missing data: BMI (n = 136); education level (n = 390); marital status (n = 386); poverty-income ratio (n = 1137); smoking (n = 4); alcohol consumption (n = 494); iron status (n = 128).

Associations between Mn and sleep duration

The mean (SE) sleep duration on weekdays and weekends was 7.6 (0.03) and 8.3 (0.04) h (Supplemental Table 1). A total of 25% of adults had short sleep duration (<7 h) on weekdays, and 13% of adults had short sleep duration on weekends. Among all adults (unstratified models), the Q3 group had a lower odds (odds ratio [OR]: 0.7, 95% CI: 0.6, 0.9) of short sleep duration on weekdays than the reference group (Q1) (Table 2). However, the gender-stratified analysis revealed associations among males but not females (weekends: P for interaction = 0.046; weekdays: P for interaction = 0.50). In males, there was a nonlinear association between blood Mn and short sleep duration: the odds of short sleep duration (<7 h) on weekdays were lower in Q3 group (OR=0.6, 95% CI: 0.4–0.9) compared with the reference group (Q1) (Table 2 and Figure 1A). A similar trend was found for the short sleep duration on weekends (Table 2 and Figure 1B).

TABLE 2.

The associations between Mn and sleep duration in US adults aged ≥18 y (NHANES 2017−2020)

	Short sleep duration on weekdays1			Short sleep duration on weekends1
Blood Mn concentrations (μg/L)	Weighted prevalence	Unadjusted OR (95% CI)	Adjusted OR (95% CI)2	Weighted prevalence	Unadjusted OR (95% CI)	Adjusted OR (95% CI)2
Unstratified3		n = 8356	n = 5541		n = 8303	n = 5510
Q1 (<7.0)	28.2	Ref	Ref	14.6	Ref	Ref
Q2 (7.0−8.4)	24.5	0.8 (0.6 to 1.1)	0.8 (0.5 to 1.1)	13.3	0.9 (0.7 to 1.2)	1.1 (0.8 to 1.5)
Q3 (8.5−10.0)	23.5	0.8 (0.7 to 0.9)∗	0.7 (0.6 to 0.9)∗	11.9	0.8 (0.6 to 1.1)	0.9 (0.6 to 1.3)
Q4 (10.1−12.2)	24.7	0.8 (0.7 to 1.0)	0.9 (0.7 to 1.1)	12.1	0.8 (0.6 to 1.1)	0.9 (0.7 to 1.3)
Q5 (>12.2)	24.8	0.8 (0.7 to 1.1)	0.9 (0.6 to 1.2)	13.9	0.9 (0.6 to 1.4)	1.2 (0.8 to 1.8)
P value		0.07	0.11		0.41	0.32
Males		n = 4056	n = 2657		n = 4033	n = 2642
Q1 (<7.0)	32.8	Ref	Ref	17.5	Ref	Ref
Q2 (7.0−8.4)	27.5	0.8 (0.6 to 1.1)	0.7 (0.5 to 1)	16.5	0.9 (0.6 to 1.4)	1.1 (0.6 to 1.9)
Q3 (8.5−10.0)	25.7	0.7 (0.5 to 1)∗	0.6 (0.4 to 0.9)∗	13.6	0.7 (0.5 to 1.1)	0.7 (0.4 to 1.2)
Q4 (10.1−12.2)	30.1	0.9 (0.7 to 1.1)	1.0 (0.6 to 1.4)	11	0.6 (0.4 to 0.8)∗	0.7 (0.4 to 1.1)
Q5 (>12.2)	27.7	0.8 (0.5 to 1.1)	0.7 (0.5 to 1.1)	16	0.9 (0.5 to 1.5)	1 (0.6 to 1.6)
P value		0.14	0.07		0.01	0.0458
Females		n = 4300	n = 2884		n = 4270	n = 2868
Q1 (<7.0)	21.4	Ref	Ref	10.3	Ref	Ref
Q2 (7.0−8.4)	20.9	1.0 (0.6 to 1.5)	0.9 (0.6 to 1.5)	9.4	0.9 (0.5 to 1.6)	1.1 (0.6 to 2.2)
Q3 (8.5−10.0)	21.5	1.0 (0.7 to 1.4)	1.0 (0.7 to 1.4)	10.4	1.0 (0.7 to 1.5)	1.2 (0.7 to 2.0)
Q4 (10.1−12.2)	20.4	0.9 (0.7 to 1.3)	0.8 (0.6 to 1.3)	13	1.3 (0.7 to 2.3)	1.5 (0.8 to 2.8)
Q5 (>12.2)	23.4	1.1 (0.8 to 1.6)	1.1 (0.8 to 1.7)	12.9	1.3 (0.7 to 2.3)	1.7 (0.8 to 3.4)
P value		0.69	0.33		0.34	0.46

Mn: manganese.

¹Short sleep duration was defined as less than 7 h. Multivariable logistic regressions were used for the analyses.

²Adjusted model: adjusted for age, gender, BMI, race, income, smoking, inflammation-adjusted serum ferritin concentration, caffeine intake, and alcohol consumption. Age, BMI, inflammation-adjusted serum ferritin concentration, and caffeine intake were treated as continuous variables.

³Participant numbers were 1680 in Q1, 1666 in Q2, 1668 in Q3, 1675 in Q4, and 1667 in Q5 groups.

^∗The 95% CI does not encompass 1.

FIGURE 1.

Data visualization for the significant results from TABLE 2, TABLE 3, TABLE 4.

Odds ratios were adjusted for age, gender, BMI, race, income, smoking, inflammation-adjusted serum ferritin concentration, caffeine intake, and alcohol consumption. Age, inflammation-adjusted serum ferritin concentration, BMI, and caffeine intake were treated as continuous variables.

Multivariable logistic regressions were used for the analyses.

Abbreviations: Mn, manganese; OSA, obstructive sleep apnea.

Associations between Mn and sleep midpoint

The mean (SE) sleep midpoint on weekdays and weekends was 02:30 (SE: 3 min) and 03:30 (SE: 4 min), respectively (Supplemental Table 1). A late sleep midpoint (≥ 5 AM) was reported in 6% and 13% of adults on weekdays and weekends, respectively. In the overall sample (unstratified), the Q2 group had lower odds of late sleep midpoint on weekdays than Q1 (OR=0.6, 95% CI: 0.4, 0.8) in the adjusted models (Table 3). In the stratified analysis, a significant relationship was detected between Mn and the sleep midpoint on weekdays in males but not in females (P for interaction = 0.007). The lowest odds of late sleep midpoint were among the Q2 group in the unadjusted (OR: 0.6, 95 CI%: 0.4, 0.9 comparing Q2 to Q1) and adjusted models (OR: 0.5, 95 CI%: 0.3, 0.8) in males (Table 3 and Figure 1C).

TABLE 3.

The associations between Mn and sleep midpoint in US adults aged ≥18 y (NHANES 2017−2020)

	Late sleep midpoint on weekdays1			Late sleep midpoint on weekends1
Blood Mn concentrations (μg/L)	Weighted prevalence	Unadjusted OR (95% CI)	Adjusted OR (95% CI)2	Weighted prevalence	Unadjusted OR (95% CI)	Adjusted OR (95% CI)2
Unstratified3		n = 8356	n = 5541		n = 8303	n = 5510
Q1 (<7.0)	7.5	Ref	Ref	13.1	Ref	Ref
Q2 (7.0−8.4)	5.9	0.8 (0.6 to 1.0)	0.6 (0.4 to 0.8)∗	13.3	1.0 (0.8 to 1.3)	0.8 (0.6 to 1.1)
Q3 (8.5−10.0)	6.2	0.8 (0.5 to 1.2)	0.9 (0.6 to 1.6)	13.3	1.0 (0.8 to 1.3)	1.1 (0.8 to 1.4)
Q4 (10.1−12.2)	5.3	0.7 (0.5 to 0.9)∗	0.8 (0.5 to 1.2)	13.1	1.0 (0.8 to 1.3)	1.0 (0.7 to 1.2)
Q5 (>12.2)	6.2	0.8 (0.6 to 1.1)	0.9 (0.5 to 1.4)	14.9	1.2 (0.9 to 1.4)	1.0 (0.8 to 1.2)
P value		0.15	0.03		0.71	0.57
Males		n = 4056	n = 2657		n = 4033	n = 2642
Q1 (<7.0)	10.1	Ref	Ref	15.3	Ref	Ref
Q2 (7.0−8.4)	6.2	0.6 (0.4 to 0.9)∗	0.5 (0.3 to 0.8)∗	14.4	0.9 (0.7 to 1.3)	0.8 (0.6 to 1.2)
Q3 (8.5−10.0)	6.4	0.6 (0.4 to 1.0)∗	0.7 (0.4 to 1.3)	14	0.9 (0.7 to 1.2)	1.0 (0.6 to 1.6)
Q4 (10.1−12.2)	6.1	0.6 (0.4 to 0.8)	0.7 (0.4 to 1.2)	15.1	1.0 (0.7 to 1.5)	1.2 (0.8 to 1.8)
Q5 (>12.2)	6.8	0.6 (0.3 to 1.2)	0.7 (0.3 to 1.6)	14	0.9 (0.6 to 1.4)	1.0 (0.5 to 1.8)
P value		0.02	0.057		0.93	0.67
Females		n = 4300	n = 2884		n = 4270	n = 2868
Q1 (<7.0)	3.5	Ref	Ref	9.7	Ref	Ref
Q2 (7.0−8.4)	5.5	1.6 (0.9 to 2.7)	1.0 (0.5 to 2)	11.9	1.2 (0.8 to 1.9)	0.8 (0.5 to 1.3)
Q3 (8.5−10.0)	6.1	1.8 (1.0 to 3.1)	1.7 (0.8 to 3.8)	12.7	1.4 (0.8 to 2.2)	1.1 (0.7 to 1.7)
Q4 (10.1−12.2)	4.6	1.3 (0.7 to 2.3)	1.2 (0.6 to 2.3)	11.5	1.2 (0.9 to 1.7)	0.7 (0.4 to 1.3)
Q5 (>12.2)	6	1.7 (1.1 to 2.7)	1.3 (0.6 to 2.8)	15.3	1.7 (1.1 to 2.5)	1.0 (0.6 to 1.7)
P value		0.12	0.66		0.11	0.53

¹Late sleep midpoint was defined as later than 5 AM. Multivariable logistic regressions were used for the analyses.

²Adjusted model: adjusted for age, gender, BMI, race, income, smoking, inflammation-adjusted serum ferritin concentration, caffeine intake, and alcohol consumption. Age, BMI, inflammation-adjusted serum ferritin concentration, and caffeine intake were treated as continuous variables.

³Participant numbers were 1680 in Q1, 1666 in Q2, 1668 in Q3, 1675 in Q4, and 1667 in Q5 groups.

^∗The 95% CI does not encompass 1.

Associations between Mn and sleep disorders

Trouble sleeping was reported in 30% of adults. Supplemental Figure 2 (A, C, E) shows the percentage of participants with trouble sleeping among different Mn concentrations. No statistically significant relationship was noted between trouble sleeping and Mn concentrations in the overall sample and the gender-stratified analysis in either the unadjusted or adjusted models (Table 4).

TABLE 4.

The associations between Mn and sleep disorders in US adults aged ≥18 y (NHANES 2017−2020)

	Trouble sleeping		OSA symptoms
Blood Mn concentrations (μg/L)	Unadjusted OR (95% CI)	Adjusted OR (95% CI)1	Unadjusted OR (95% CI)	Adjusted OR (95% CI)1
Unstratified2	n = 8350	n = 5540	n = 7535	n = 5051
Q1 (<7.0)	Ref	Ref	Ref	Ref
Q2 (7.0−8.4)	1.1 (0.9 to 1.4)	1.2 (0.9 to 1.5)	0.7 (0.6 to 0.9)	0.7 (0.5 to 0.9)∗
Q3 (8.5−10.0)	1.0 (0.8 to 1.3)	1.1 (0.8 to 1.5)	0.9 (0.7 to 1.1)	0.8 (0.6 to 1.1)
Q4 (10.1−12.2)	1.0 (0.8 to 1.3)	1.1 (0.9 to 1.5)	0.9 (0.7 to 1.1)	1.0 (0.8 to 1.3)
Q5 (>12.2)	1.0 (0.8 to 1.3)	1.2 (0.9 to 1.6)	0.8 (0.7 to 1.0)∗	0.8 (0.7 to 1.0)
P value	0.46	0.57	0.08	0.01
Males	n = 4054	n = 2657	n = 3679	n = 2433
Q1 (<7.0)	Ref	Ref	Ref	Ref
Q2 (7.0−8.4)	1.3 (0.9 to 1.7)	1.2 (0.8 to 1.7)	0.9 (0.6 to 1.3)	0.8 (0.5 to 1.1)
Q3 (8.5−10.0)	1.0 (0.7 to 1.3)	1.0 (0.7 to 1.4)	0.9 (0.6 to 1.2)	0.7 (0.5 to 1.0)
Q4 (10.1−12.2)	1.1 (0.8 to 1.5)	1.2 (0.9 to 1.8)	1.2 (0.9 to 1.6)	0.9 (0.7 to 1.2)
Q5 (>12.2)	1.2 (0.9 to 1.6)	1.4 (1.0 to 2)	1.0 (0.7 to 1.4)	0.7 (0.5 to 1.0)
P value	0.37	0.24	0.23	0.18
Females	n = 4296	n = 2883	n = 3856	n = 2618
Q1 (<7.0)	Ref	Ref	Ref	Ref
Q2 (7.0−8.4)	1.0 (0.7 to 1.4)	1.2 (0.7 to 1.8)	0.6 (0.4 to 0.9)∗	0.6 (0.4 to 0.9)∗
Q3 (8.5−10.0)	1.0 (0.7 to 1.4)	1.2 (0.8 to 1.9)	0.9 (0.7 to 1.2)	0.9 (0.7 to 1.3)
Q4 (10.1−12.2)	0.8 (0.6 to 1.2)	1.0 (0.7 to 1.6)	0.8 (0.6 to 1.0)	1.0 (0.7 to 1.4)
Q5 (>12.2)	0.8 (0.6 to 1.1)	1.2 (0.8 to 1.7)	0.8 (0.6 to 1.1)	0.9 (0.7 to 1.2)
P value	0.33	0.89	0.04	0.04

Multivariable logistic regressions were used for the analyses.

Mn: manganese. OSA: obstructive sleep apnea.

¹Adjusted model: adjusted for age, gender, BMI, race, income, smoking, inflammation-adjusted serum ferritin concentration, caffeine intake, and alcohol consumption. Age, BMI, inflammation-adjusted serum ferritin concentration, and caffeine intake were treated as continuous variables.

²Participant numbers were 1680 in Q1, 1666 in Q2, 1668 in Q3, 1675 in Q4, and 1667 in Q5 groups.

^∗The 95% CI does not encompass 1.

Symptoms of OSA were observed in 49% of the participants. Supplemental Figure 2 (B, D, F) shows the percentage of participants with OSA symptoms among different Mn concentrations. The Q2 group had lower odds of OSA symptoms than Q1 in the overall sample in the unadjusted model (OR: 0.7, 95% CI: 0.6, 0.9) and remained significant in the adjusted model (OR: 0.7, 95% CI: 0.5, 0.9) (Table 4). In the gender-stratified models, the relationship was not significant in males. In females, lower odds of OSA symptoms were noted in females in Q2 group in the unadjusted (OR: 0.6, 95 % CI: 0.4, 0.9) and in the adjusted models (OR: 0.6, 95 % CI: 0.4, 0.9) compared with the reference (Table 4 and Figure 1D).

Sensitivity analysis

We conducted sensitivity analysis where pregnant females were excluded from the analysis. The relationships between Mn and all sleep outcomes remained unchanged. When comparing pre- and postmenopausal females regarding the relationship between Mn and sleep outcomes, no significant differences in the odds ratio of sleep outcomes were noted (Supplemental Table 2). However, there was a borderline significant association, indicating lower odds of OSA symptoms in the Q2 group compared with Q1 group among postmenopausal females, which was not observed in premenopausal females.

Regarding dietary supplements, a total of 52.6% of the overall participants took at least one dietary supplement on the recorded days. In males, 45.7% of them consumed dietary supplements, whereas in females, 58.9% of them consumed dietary supplements. The mean blood Mn concentrations were 9.5 ± 0.1 and 9.8 ± 0.1 μg/L among overall participants with and without consumption of dietary supplements, respectively.

Discussion

This study examined the relationship between Mn and sleep outcomes in a representative sample of adults from the US population using a cross-sectional approach. The results revealed gender differences in the association between Mn concentrations and sleep. In males, after adjusting for covariates, a nonlinear association was observed between blood Mn concentrations and the odds of short sleep duration during weekdays. After adjusting for covariates, the lowest Mn quintile group had higher odds of short sleep duration than the Q3 group in males, had higher odds of late sleep midpoint than the Q2 group in males, and had higher odds of OSA symptoms than the Q2 group in females. Overall, the lowest Mn quintile group had the poorest sleep outcomes in both males and females.

The mean Mn concentrations reported in the present study (9.7 [0.1] μg/L) are comparable with those reported in other studies for Mn concentrations in US adults around the same time frame (Supplemental Table 3). For example, a study of 9732 US adults reported a mean of 9.6–10.4 μg/L [36]. In our study, we used quintiles to evaluate the possibility of nonlinear relationships between Mn and sleep, and comparing the Mn concentrations with other studies can help to contextualize the values. The lowest quintile in our study included values <7 μg/L. According to one source, a normal range of blood Mn concentrations was considered between 7 and 12 μg/L [37]. Thus, our lowest quintile of Mn was lower than this range and is even lower than other studies that have categorized the lowest Mn concentrations (e.g., 7.5 μg/L in US adults) [36,38,39]. The main source of Mn in the general population is the diet (12). Thus, the lowest Mn group, which was associated with poorer sleep outcomes in this study, likely has Mn deficiency or inadequacy from the diet. Conversely, the highest quintile of Mn (>12.2 μg/L) in the present analysis was higher than that considered a normal range in previous literature [37], but the value is lower than at least 1 prior study that reported high environmental Mn exposure (19.1–26.9 μg/L) in workers of a Mn refining factory [40]. Overall, the lowest Mn concentrations may have resulted from low Mn intake from diet, whereas the highest Mn concentrations were higher than the normal range but lower than toxic concentrations due to environmental exposure.

The present study conducted an analysis to understand the demographic predictors of Mn concentrations and observed that factors associated with higher Mn concentrations included younger age, female gender, higher BMI, identifying as non-Hispanic Asian, lower income, never smoking, never drank alcohol, iron deficient, and caffeine consumption within the recommended range. This is consistent with a previous analysis of the 2011–2012 NHANES survey, which reported that younger age, female, and non-Hispanic Asian participants had the highest Mn concentrations compared with other groups [41]. Factors such as dietary patterns, occupational exposure to Mn, and differences in metabolism might contribute to the differences in blood Mn among these groups (41). The primary source of Mn for the general population is dietary intake, with an estimated 2 to 6 mg of Mn consumed daily at a 1 to 5% absorption rate [42]. Although the relationship between dietary intake and blood concentrations of Mn has not been investigated in the NHANES, a reasonable assumption is that the consumption of Mn-rich plant-based foods, such as legumes, nut and seed products, rice, whole grains, and vegetables [12], may contribute to higher concentrations of blood Mn. For example, the present study found that the Asian population had higher Mn concentrations than the other groups, which could be attributed to their higher daily intake of rice and fish [43]. Therefore, certain populations may consume Mn-rich foods in higher amounts or frequencies that contribute to their blood Mn concentrations. Dietary supplements could contain minerals, including Mn. In the present study, over half of the participants took at least 1 dietary supplement, with females having a 13.2% higher consumption rate than males. However, whether participants consumed dietary supplements that contained Mn is not available based on the current dataset. Yet, blood Mn concentration was only 0.3 μg/L higher in participants without dietary supplement intake than in those who took dietary supplements. This suggests that dietary Mn was more likely to contribute to blood Mn than dietary supplements in this group. The information from the Office of Dietary Supplements of the National Institutes of Health suggests that not all multivitamin or mineral supplements include Mn; however, those that do usually contain 1.0−4.5 mg of Mn [44], which is 1 to 2 fold of the recommended amount for Mn (2.3 mg/d for males and 1.8 mg/d for females) (13).

Another source of Mn is the inhalation of Mn dust in occupational settings, as reported in workers in Mn dioxide mines, smelters, steel manufacturing plants, and dry-cell battery factories [10]. Thus far, no evidence of Mn toxicity caused by dietary sources has been found in humans [45] because whole-body Mn homeostasis is tightly controlled by intestinal absorption and excretion of the nutrient [46]. Unlike dietary sources of Mn, airborne Mn bypasses the first-pass clearance mechanisms, allowing it to cross the air-brain barrier and potentially accumulate in the brain [10]. This makes the inhalation of Mn dust a significant source of Mn exposure and Mn toxicity. Overall, both dietary and inhaled Mn could have contributed to blood Mn concentrations in the present study’s participants, but the health outcomes from these 2 sources of Mn could be vastly different.

The present study found a nonlinear association between Mn concentrations and poor sleep outcomes in males, although the precise mechanisms remain incompletely understood. One possible mechanism involves alterations in serotonin levels. One animal study in rats showed that exposure to Mn poisoning decreased serotonin levels in the brain [47]. Serotonin, a precursor of melatonin, serves as a neurotransmitter involved in the sleep-wake cycle and promotes sleep [48]. Furthermore, the pineal gland, which is a brain region that contains high concentrations of Mn in humans [49], plays a crucial role in regulating sleep by releasing melatonin, which follows circadian rhythms and facilitates sleep onset [48]. Thus, high Mn concentrations could decrease serotonin concentrations in the brain or directly influence melatonin, thereby leading to changes in sleep patterns. Another possible explanation could be related to thyroid hormones or function. A study conducted in the US population observed that higher serum Mn concentrations were related to increased concentrations of serum total triiodothyronine (T3), total thyroxine (T4), and free T3, indicating a potential association between Mn concentrations and subclinical changes in thyroid function [50]. Increased levels of thyroid hormones have been linked to poor sleep behaviors [51], including prolonged sleep onset associated with alterations in appetite, bowel movements, and mood [52]. Patients experiencing tremors due to increased thyroid hormones also reported greater difficulty in sustaining sleep [51]. In addition, Mn deficiency can reduce the activity of glutamine synthetase, leading to the accumulation of neurotoxic extracellular glutamine (Gln), which might influence sleep patterns [10]. Thus, further experimental studies are warranted to explore the mechanisms underlying the relationship between low Mn concentrations and poor sleep outcomes in males.

In the present study, the prevalence of symptoms of OSA was lower in the Q2 group than in the Q1 group in females. Low Mn concentrations may be due to a poor diet lacking in Mn-rich vegetables and nuts, which could increase risk of obesity [53] and OSA symptoms [54]. OSA is characterized by recurring obstruction of the upper airway, resulting in reduced blood oxygen saturation levels [21]. Intermittent hypoxia and reoxygenation from apnea can lead to increased oxidative stress [55], which is related to Mn concentrations. However, the present analysis did not include OSA diagnosis, nor did it use a validated OSA screening questionnaire (such as the STOP-Bang) [56]; therefore, drawing any conclusion is difficult based only on the survey tools we used and the results that we found. Further research with validated measures is needed to understand the association and mechanisms behind the relationship between Mn and sleep disorders in females.

Previous literature provides ample evidence of gender differences in sleep patterns and disorders. Females generally have longer sleep durations than males, but they also experience more nighttime awakenings [22]. Males in their young adulthood and middle age tend to have a later sleep onset time (time falling asleep) than is observed in females [22]. The prevalence and severity of OSA are higher among males than females [57], whereas females report more sleep complaints, have a higher prevalence of insomnia, and are more likely to be prescribed hypnotics than males (58). Lifetime events, such as pregnancy and menopause, could alter females’s risk of sleep disorders due to hormonal and physical changes [58]. The present study also noted borderline significance in the relationship between Mn and sleep: low blood Mn concentration increased odds of OSA symptoms in postmenopausal females but not in premenopausal females. This could be supported by the fact that estrogen and progesterone have sleep-promoting effects [59]. Therefore, premenopausal females might have protection effects from hormones. The present study also revealed gender differences in the relationship between Mn and sleep, as sleep duration and midpoint were associated with Mn concentrations in males, whereas OSA symptoms were related to Mn concentrations in females, but the underlying mechanisms are not fully understood. Further studies are needed to explore the possible pathways by which Mn effects and sleep differ by gender.

Our study adds to the limited literature on the relationship between Mn and sleep in the US population. We utilized the most recent NHANES dataset, which is nationally representative, making the findings applicable to US adults. Important covariates, including iron status, were considered in the analysis. Blood Mn was chosen as the exposure measure as it is more reliable than urine Mn when measuring Mn exposure [24]. However, our study has some limitations. First, the unknown nature of Mn exposure from dietary, occupational, and environmental sources suggests that the relationship between blood Mn and sleep should be interpreted with caution. Second, the cross-sectional design precludes determining the temporal relationships between Mn and sleep. Third, although we controlled for multiple confounders, the coexposure of Mn and other neurotoxic agents, such as arsenic [43], may have affected the results. Future studies could include multiple metals in the analysis. Next, self-reported sleep timing and sleep problems may have measurement errors. Although the 2 survey cycles (2017-2018 and 2019-2020) had different lower detection limits for Mn, our analysis separated participants into quintiles. The lowest Mn quartile was < 7 μg/L, so the combination of 2 cycles with different detection limits for Mn would not have biased the outcomes. Finally, it is unknown if participants live in an environment with Mn contamination or not, as there is currently no available data to confirm this. Despite these limitations, the present study adds to the evidence supporting an association between blood Mn concentrations and sleep outcomes in the US population.

In summary, blood Mn concentrations vary with different backgrounds and socioeconomic statuses. Gender differences were noted in the relationship between Mn and sleep in adults, with short sleep duration and late sleep timing related to low blood Mn concentrations in males, whereas the blood Mn concentrations were associated with OSA symptoms in females. Future studies are warranted to investigate the underlying mechanisms of these gender-specific associations as well as the use of OSA diagnosis in human studies to confirm the association. Overall, this study provides valuable insights into the relationship between Mn and sleep outcomes in the US adult population, highlighting the need for further research in this area.

Author contribution

The authors’ responsibilities were as follows—CLY, ECJ, YAS: designed the study; CLY: performed the statistical analyses; CWL, JF: provided statistical support; CLY, YAS wrote the manuscript; CWL, JF, SKP, ECJ, YAS: revised the manuscript; ECJ, YAS: had the primary responsibility for the final content. All authors have read and approved the final manuscript.

Funding

This work was supported by National Institutes of Health R01DK123022 and R21NS112974 to YAS, K01HL151673 to ECJ, and R01AG070897 and P30ES017885 to SKP.

Data availability

Data described in the manuscript, code book, and analytic code will be made available upon request pending application and approval.

Conflict of interest

The authors report no conflicts of interest.

Appendix A. Supplementary data

The following is the Supplementary data to this article: