Magnesium depletion score and circadian syndrome: Mediated by body mass index

Abstract

Objective

Circadian syndrome (CircS) is a recently defined condition encompassing metabolic, sleep, and mental health disturbances. Magnesium plays a vital role in metabolic and neurological processes, and magnesium depletion score (MDS) has emerged as a practical biomarker for magnesium deficiency. This cross-sectional retrospective study aimed to investigate the association between MDS and CircS in a nationally representative U.S. population.

Methods

Data were obtained from the National Health and Nutrition Examination Survey (NHANES) 2005-2018. MDS was calculated based on diuretic or proton pump inhibitor use, estimated glomerular filtration rate, and alcohol consumption. CircS was diagnosed using a combination of metabolic syndrome components, depressive symptoms, and short sleep duration. Weighted logistic regression models, restricted cubic spline analysis, subgroup analyses, receiver operating characteristic (ROC) curves, and mediation analysis were applied.

Results

A total of 12,328 participants were included. Higher MDS was significantly associated with an increased risk of CircS (OR = 1.193, 95% CI: 1.099-1.295, P < 0.001). Sensitivity analyses excluding participants with cardiovascular disease confirmed robustness of the findings. Mediation analysis showed that body mass index (BMI) explained 21% (95% CI: 12.98-34%) of the association between MDS and CircS. ROC analysis indicated modest discriminative ability of MDS for CircS (AUC = 0.616).

Conclusion

This study demonstrates that MDS is positively associated with CircS, and BMI partly mediates this association. These findings suggest that maintaining adequate magnesium levels may help support circadian health. Further longitudinal and mechanistic studies are warranted to validate the clinical applicability of MDS in CircS risk assessment.

Keywords

magnesium depletion score magnesium circadian syndrome mediation analysis NHANES

1. Introduction

Given the widespread adoption of Western dietary patterns and sedentary behaviors, the global prevalence of metabolic syndrome (MetS) is rising.¹ MetS is a cluster of modifiable risk factors for cardiovascular disease (CVD) and type 2 diabetes (T2DM). Based on this, Circadian Syndrome (CircS) has been introduced to describe the connection between disturbances in the circadian system and the components of MetS, along with their comorbidities.² In China, the prevalence of CircS among middle-aged to older adults was reported to be 31.5%, with contributing factors including social isolation, irritability, fear responses, and chronic diseases.³ The prevalence of CircS in middle-aged and elderly women was 51.4% in China, 20.19% in US, and 36.83% in UK.⁴

CircS includes sleep disorders and depression status. The circadian rhythms are 24-hour biological cycles involved in T2DM, obesity, coronary heart disease, hypertension, and MetS.⁵ Compared with MetS, CircS provides a detailed framework of metabolic, sleep, and mental health. CircS was a stronger and better predictor of CVD than MetS in Chinese adults.⁶ In cognitive health, CircS alone had a stronger association with cognitive impairment than those with both MetS and CircS.⁷ Moreover, CircS predicted better discriminatory ability for lower urinary tract symptoms suggestive of benign prostatic hyperplasia in men.⁸ These findings reveal that CircS could be used as a predictor of systemic health.

Recently, minerals have attracted interest regarding their effects on human health. Magnesium, playing an instrumental role in enzymatic reactions such as energy metabolism and protein synthesis, is involved in the physiological processes of the brain, heart, and skeletal muscles.⁹ An inverse relationship exists between magnesium intake and the risk of MetS, its components, and fasting insulin levels.¹⁰ A meta-analysis also confirmed this inverse association between magnesium intake and serum magnesium, but the association based on serum magnesium showed higher heterogeneity and sensitivity.¹¹ Dietary magnesium intake is also inversely associated with MetS prevalence.¹² A pooled analysis of randomized studies revealed that magnesium supplementation in individuals with hypomagnesemia may improve MetS outcomes.¹³ Notably, observational studies have revealed an association between magnesium status and sleep quality.¹⁴ Magnesium supplementation may also be beneficial in treating mental disorders.¹⁵ All these studies suggest a protective role of magnesium intake in reducing the risk of MetS, sleep, and mental disorders.

Studies have provided convincing evidence that magnesium deficiency contributes to chronic low-grade inflammation, thereby linking it to CVD, diabetes, and hypertension.¹⁶ Serum magnesium levels do not accurately reflect widespread magnesium deficiency.¹⁷ Magnesium depletion score (MDS) has been developed and emerged as a biomarker for magnesium deficiency. In individuals with magnesium intake below the estimated average requirement, MDS is correlated with increased systemic inflammatory biomarkers, as measured by C-reactive protein (CRP), and CVD mortality.¹⁸ Previous evidence has reported its predictive value in congestive heart failure, depression, sleep quality, and MetS.^19–22 Given the prevalence, economic, and health burden of MetS, the role of CircS should not be overlooked. The hypothesis is that MDS could be used in the diagnosis and management of CircS.

To date, no research has explored the relationship between MDS and the risk of CircS, a newly defined condition of metabolic, mental, and sleep health. This study aims to investigate the association between MDS and CircS using data from the National Health and Nutrition Examination Survey (NHANES), providing new insights into the diagnosis and risk evaluation of magnesium status in CircS.

2. Methods

2.1. Data origin

This cross-sectional retrospective study was conducted in accordance with the Helsinki Declaration of 1975 as revised in 2024, and followed the STROBE guidelines.²³ Data from the National Health and Nutrition Examination Survey (NHANES) cycles conducted between 2005 and 2018 were used. NHANES is conducted by the National Center for Health Statistics (NCHS), a division of the Centers for Disease Control and Prevention (CDC), and aims to provide a representative assessment of the health and nutritional status of the U.S. population. The Ethics Review Committee of the NCHS reviewed and approved the study, ensuring that all participants provided written informed consent before data collection. No personal information was used or presented in this study, and all patient details were de-identified. The participants were selected consecutively from those who met the criteria for MDS and CircS, as outlined in Figure 1. A total of 70,190 participants were included in the NHANES 2005-2018 dataset. Of these, 57,167 had available data on MDS, 42,328 had data on CircS, and 12,328, with complete covariates, were included in the final analysis.

Figure 1.

Flowchart of participants included from NHANES 1999–2006.

2.2. The calculation of magnesium depletion score (MDS)

MDS was used as an exposure in this study to assess systemic magnesium status, determined by the sum of the scores for the following four specific items: (1) current use of diuretics (1 point); (2) current use of proton pump inhibitors (PPIs) (1 point); (3) estimated glomerular filtration rate (eGFR) between 60 mL/min/1.73 m² and 90 mL/min/1.73 m² (1 point), and eGFR < 60 mL/min/1.73 m² (2 points); (4) heavy alcohol consumption (>1 drink/day for women and >2 drinks/day for men) (1 point). MDS ranges from 0 to 5 and is categorized into the following groups according to previous studies: 0-1 (low), 2(median), and 3-5 (high).^19,24

2.3. The definition of Circadian Syndrome (CircS)

MetS was diagnosed based on three components from the following five conditions: (1) Elevated waist circumference (WC) (≥88 cm for females, ≥102 cm for males); (2) elevated triglycerides (TG) (≥150 mg/dL) or the use of lipid-lowering drugs; (3) low high-density lipoprotein cholesterol (HDL-C) (<40 mg/dL for males and <50 mg/dL for females) or medication for low HDL-C; (4) elevated blood pressure (systolic BP ≥130 mmHg or diastolic BP ≥85 mmHg, or both) or the use of antihypertensive drugs; (5) elevated fasting plasma glucose (FPG) (≥100 mg/dL) or the use of anti-diabetic drugs. Additionally, the following conditions were assessed: (6) self-reported short sleep duration (<6 h/day); (7) depressive status (a Patient Health Questionnaire (PHQ-9) score ≥5). Sleep patterns, depressive status, and medication use were obtained through questionnaires; HDL, FPG, and TG were measured in laboratory tests; and WC and blood pressure were assessed through physical examinations. CircS was based on the seven components mentioned above, and individuals with four or more components were defined as having CircS.

2.4. The inclusion of covariates

Demographic, physical, and chronic disease-related covariates were included in this study, consistent with previous studies.^25,26 This study included demographic factors such as sex (male or female), race (Mexican American, non-Hispanic White, non-Hispanic Black, and other), education level (high school, below high school, or above high school), marital status (married, living with a partner, widowed, divorced, separated, or never married), and poverty level as indicated by the poverty income ratio (PIR). Lifestyle factors included smoking and alcohol consumption, while clinical chronic diseases included diabetes mellitus (DM), hypertension, and CVD. Physical and laboratory variables included obesity status, measured by body mass index (BMI), systemic immune-inflammation index (SII) and healthy eating index (HEI). Dietary magnesium intake and physical activity were also included. Detailed definition of confounder is presented in Table S1.

2.5. Statistical analyses

The analyses were conducted according to the complex multistage sampling design. The basic characteristics of the survey included participants were stratified into two groups based on the presence or absence of CircS. Continuous variables were expressed as the mean ± standard deviation (SD), and categorical variables as counts (n) and percentages (%). Baseline characteristics were compared using weighted unpaired Student’s t-tests and Chi-square tests. Univariate and multivariate logistic regression models were used to calculate the odds ratio (OR) and confidence interval (CI) to assess the relationship between MDS and CircS. Three models were established: Model I (no adjustment), Model II (adjustment for age, sex, race, poverty, marital status, and education level), and Model III (adjustment for age, sex, race, poverty, marital status, education level, BMI, smoking, alcohol consumption, DM, hypertension, CVD, SII, HEI-score, dietary magnesium intake and physical activity). Sensitivity analysis was performed to assess the associations between MDS and CircS by excluding those with CVD. Restricted cubic spline (RCS) analysis was conducted to detect any non-linear relationships. Subgroup analyses were conducted to explore stability and potential interactions. The diagnostic performance of MDS for CircS was evaluated using receiver operating characteristic (ROC) curve analysis, with the area under the curve (AUC) quantifying its discriminative ability. Mediation analysis was performed using the “Mediation” package to explore the role of BMI. All analyses accounted for the survey design and weighting using R software (version 4.3.3), with a P-value < 0.05 considered statistically significant.

3. Results

3.1. Weighted baseline characteristics of included participants

Figure 1 presents the flowchart illustrating the screening process for participants included in the study. A total of 12,328 participants were included, of whom 2,965 were diagnosed with CircS, resulting in a weighted prevalence of 21.3%. Baseline characteristics of participants are presented in Table 1, categorized by the presence of CircS, and baseline characteristics by MDS subgroups are presented in Table S2. According to Table 1, the MDS scores for individuals without and with CircS were 0.72 and 1.16, respectively, revealing a significant difference. Participants with CircS tended to be older, female, more highly educated, with moderate income, obese, and non-smokers. They also had lower dietary magnesium intake, lower HEI score, and higher systemic inflammation level.

Table 1.

Baseline characteristics of participants by the presence of circadian syndrome (CircS).

Variable	Total	Without CircS	With CircS	P-value
Age (year)	46.995(0.279)	45.055(0.305)	54.161(0.352)	<.001
Magnesium intake (mg/d)	305.024(2.435)	311.013(2.596)	282.905(4.257)	<.001
HEI-score	50.206(0.240)	50.723(0.264)	48.297(0.370)	<.001
SII	525.052(4.104)	511.820(4.413)	573.925(8.441)	<.001
MDS	0.810(0.013)	0.715(0.013)	1.160(0.027)	<.001
Gender				<.001
Female	6135(50.288)	4455(48.819)	1680(55.714)
Male	6193(49.712)	4908(51.181)	1285(44.286)
Race				0.139
Mexican American	1909(7.942)	1423(7.859)	486(8.249)
Non-Hispanic Black	2409(10.460)	1843(10.362)	566(10.821)
Non-Hispanic White	5638(69.869)	4216(69.692)	1422(70.522)
Other Hispanic	1152(5.000)	854(5.019)	298(4.931)
Other Race	1220(6.729)	1027(7.068)	193(5.477)
Education level				<.001
Below high school	2783(14.834)	1906(13.298)	877(20.508)
High school	2831(23.121)	2077(21.968)	754(27.377)
Above high school	6714(62.045)	5380(64.734)	1334(52.115)
Poverty				<.001
Low income	3648(19.875)	2575(18.538)	1073(24.812)
Moderate income	4744(36.441)	3580(35.649)	1164(39.365)
High income	3936(43.684)	3208(45.812)	728(35.823)
Marital status				<.001
Married/Living with Partner	7526(64.579)	5769(64.907)	1757(63.368)
Widowed/Divorced/Separated	2631(17.604)	1742(15.383)	889(25.805)
Never married	2171(17.817)	1852(19.710)	319(10.827)
BMI (kg/m²)				<.001
Normal	3547(30.189)	3373(36.897)	174(5.414)
Overweight	4105(32.942)	3288(34.788)	817(26.123)
Obese	4676(36.868)	2702(28.315)	1974(68.463)
Smoke				<.001
Never	6740(54.605)	5332(56.841)	1408(46.343)
Former	3089(25.414)	2215(23.994)	874(30.660)
Now	2499(19.981)	1816(19.165)	683(22.997)
Alcohol consumption				<.001
Never	1589(10.143)	1163(9.820)	426(11.338)
Former	1992(13.011)	1295(10.953)	697(20.614)
Mild	4338(37.890)	3405(38.572)	933(35.372)
Moderate	1898(17.644)	1523(18.402)	375(14.844)
Heavy	2511(21.311)	1977(22.253)	534(17.832)
Physical activity				<.001
Inactive	3018(20.406)	2014(17.843)	1004(29.872)
Insufficient	2289(18.930)	1705(18.744)	584(19.621)
Sufficient	7021(60.664)	5644(63.413)	1377(50.508)
CVD				<.001
No	11020(91.455)	8694(94.321)	2326(80.867)
Yes	1308(8.545)	669(5.679)	639(19.133)
Elevated FPG				<.001
No	6008(52.933)	5604(63.521)	404(13.826)
Yes	6320(47.067)	3759(36.479)	2561(86.174)
Low HDL				<.001
No	8923(73.390)	7852(83.705)	1071(35.288)
Yes	3405(26.610)	1511(16.295)	1894(64.712)
Elevated TG				<.001
No	9225(75.011)	8102(86.244)	1123(33.521)
Yes	3103(24.989)	1261(13.756)	1842(66.479)
Obesity				<.001
No	5319(43.807)	5082(53.900)	237(6.531)
Yes	7009(56.193)	4281(46.100)	2728(93.469)
Hypertension				<.001
No	7665(66.914)	6859(77.151)	806(29.105)
Yes	4663(33.086)	2504(22.849)	2159(70.895)
Short sleep				<.001
No	10589(88.191)	8479(92.037)	2110(73.988)
Yes	1739(11.809)	884(7.963)	855(26.012)
Depression				<.001
No	9479(78.653)	7937(85.178)	1542(54.552)
Yes	2849(21.347)	1426(14.822)	1423(45.448)

Abbreviations: BMI (Body mass index), CircS (circadian syndrome), CVD (cardiovascular disease), FPG (fasting plasma glucose), HEI (healthy eating index), HDL (high-density lipoprotein), MDS (magnesium depletion score), SII (systemic immune-inflammation), TG (triglyceride).

3.2. Associations between MDS with CircS

The relationship between MDS and CircS is shown in Table 2. Multivariate logistic regression analyses, using both continuous and categorical variables, consistently revealed a significant positive association between MDS and the risk of CircS. Each unit increase in MDS was associated with 19.3% higher odds of CircS development (OR=1.193, CI 1.099-1295, P<0.001) in the fully adjusted model. Compared with the lowest quartile of MDS (MDS = 0-1), participants with a score of 3-5 had a 0.522-fold higher risk of CircS (OR = 1.522, 95% CI: 1.165-1.987, P = 0.003). Trend analysis showed a significant association between higher MDS scores and an increased risk of CircS (P for trend < 0.05).

Table 2.

Association between MDS and CircS.

Variables	Model I		Model II		Model III
MDS	OR[CI]	P	OR[CI]	P	OR[CI]	P
Continuous	1.635(1.544,1.732)	<.001	1.348(1.253, 1.450)	<.001	1.193(1.099, 1.295)	<.001
Categorical
0-1	Reference
2	2.355(2.027,2.737)	<.001	1.584(1.347, 1.863)	<.001	1.293(1.094, 1.527)	0.003
3-5	4.206(3.483,5.080)	<.001	2.328(1.834, 2.955)	<.001	1.522(1.165, 1.987)	0.003
P for trend		<.001		<.001		0.001

Abbreviations: CircS (circadian syndrome), MDS (magnesium depletion score).

Note: Bold values indicate statistically significant results, defined as p < 0.05.

In the sensitivity analysis, after excluding participants with a history of CVD (Table 3), the association remained significant for both continuous and categorical MDS, confirming the results mentioned above.

Table 3.

Association between MDS and CircS by those with CVD excluded in the sensitivity analysis.

Variables	Model I		Model II		Model III
MDS	OR[CI]	P	OR[CI]	P	OR[CI]	P
Continuous	1.554(1.456,1.659)	<.001	1.317(1.217, 1.426)	<.001	1.190(1.092, 1.296)	<.001
Categorical
0-1	Reference
2	2.181(1.852,2.569)	<.001	1.481(1.246, 1.761)	<.001	1.226(1.025, 1.467)	0.027
3-5	3.720(2.959,4.678)	<.001	2.239(1.679, 2.984)	<.001	1.565(1.146, 2.138)	0.005
P for trend		<.001		<.001		<.001

Abbreviations: CircS (circadian syndrome), CVD (cardiovascular disease), MDS (magnesium depletion score).

Note: Bold values indicate statistically significant results, defined as p < 0.05.

3.3. Restricted cubic spline (RCS), stratified and receiver operating characteristic (ROC) analyses

RCS analysis was used to examine the non-linear relationship. RCS analysis shown in Figure 2 revealed a linear relationship between MDS and CircS (P for nonlinear = 0.2615). In stratified analyses by age, gender, education, BMI, smoking, alcohol use, CVD, and dietary magnesium intake (Figure 3), the relationship between MDS and CircS remained generally consistent, except for an interaction with magnesium intake: the association was stronger in individuals with insufficient daily magnesium intake. ROC analysis was conducted, and the AUC was calculated to assess the discriminative ability of MDS for CircS (Figure 4), showing that MDS had modest diagnostic performance with an AUC of 0.616.

Figure 2.

Restricted cubic spline (RCS) of the association between MDS and CircS.

Figure 3.

Subgroup analyses for relationship between MDS and CircS.

Figure 4.

Receiver operating characteristic (ROC) curve of the MDS for CircS.

3.4. Mediation analysis

Mediation analysis was conducted, as shown in Figure 5, demonstrating that MDS was positively and significantly associated with BMI, which, in turn, was positively associated with CircS. BMI mediated 21% (95% CI: 12.98%-34%) of the association between MDS and CircS.

Figure 5.

Mediation body mass index under the association between MDS and CircS.

4. Discussion

This study, for the first time, identified a positive relationship between MDS and an increased risk of CircS. Our findings suggest that maintaining optimal magnesium status through dietary supplementation and treatment of related diseases is associated with a reduced incidence of CircS. This study further provides evidence that maintaining magnesium health, as measured by the user-friendly tool MDS, is beneficial for circadian health.

Routinely measured serum magnesium levels do not always reflect total body magnesium status. Hence, the accurate surveillance of whole-body magnesium status is needed. MDS was developed and demonstrated with higher AUC than urinary magnesium levels.¹⁸ The results of this study are consistent with previous studies. Based on NHANES data from 2003 to 2018, higher urinary magnesium, as measured by MDS, confirmed that higher MDS was associated with the risk of MetS in a linear dose-response relationship. Each unit increase in MDS was associated with a 30% higher risk of MetS development.²² MDS has also been reported to be associated with sleep disturbances, sleep disorders, sleep apnea in adults, and excessive sleep in the elderly.²¹ A positive association between MDS and depression has also been observed.²⁰ Meanwhile, mediation analysis revealed that BMI partially explained this relationship, which is consistent with existing studies. In the Mexican population, dietary magnesium intake was inversely associated with lower BMI, WC, and serum glucose.²⁷ Serum magnesium was also negatively correlated with fat mass in Qatar.²⁸ Given that CircS is a synthesis of metabolic, mental, and sleep conditions, the positive association between MDS and CircS is both confirmed and acceptable.

The mechanism underlying the association between MDS and CircS may be explained by nutritional role, systemic inflammation and magnesium deficiency-related physiological changes. Magnesium deficiency may induce aldosteronism, which in turn induces oxidative stress and subsequent insulin resistance, leading to an inflammatory phenotype.²⁹ Additionally, magnesium deficiency elicits phagocyte activation, activates endothelial cells, and increases the secretion of cytokines.³⁰

Dietary magnesium intake was associated with a decreased risk of MetS and its components. This relationship was significant when magnesium intake was less than 280 mg/day.³¹ In individuals with MetS and hypomagnesemia, magnesium supplementation could improve MetS, specifically by reducing blood pressure, hyperglycemia, and hypertriglyceridemia.³² In MetS patients with normomagnesemia, magnesium citrate supplementation reduced HbA1c and blood pressure.³³ A magnesium deficiency diet (MGD) led to alterations in gut microbiota in mice, which were associated with depressive behaviors.³⁴ In the relationship between magnesium supplementation and depressive status, daily 500 mg magnesium oxide tablets resulted in improvements in depressed individuals.³⁵ Additionally, 500 mg of magnesium oxide intake for 8 weeks also improved Beck’s test scores in depressed patients.³⁶ Similarly, magnesium chloride supplementation for 6 weeks resulted in clinically significant improvements of depressive symptoms.³⁷ Regarding the role of magnesium in sleep, magnesium intake was associated with sleep quality and short sleep duration.³⁸ In Jiangsu, China, dietary magnesium intake was beneficial in reducing the risk of daytime sleepiness in women.³⁹ In individuals with obstructive sleep apnea (OSA), the severity of OSA negatively impacted serum magnesium levels, with improved magnesium levels corresponding to improvements in OSA.⁴⁰ Dietary magnesium supplementation improved subjective measurements of insomnia in the elderly.⁴¹ Then, the underlying mechanisms of magnesium on sleep health may specifically involve the glutamatergic and GABAergic systems, the inhibition or activation of Ca²⁺ and K⁺ ion channels, melatonin and serotonin production or synthesis, biological clocks and circadian rhythms, as well as sleep structure and electroencephalogram (EEG) activity.⁴²

In postmenopausal women, higher magnesium intake was associated with lower concentrations of systemic inflammation and endothelial dysfunction.⁴³ Lower intake of fiber and magnesium was correlated with elevated levels of high-sensitivity CRP (hs-CRP) in normal individuals.⁴⁴ In individuals with MetS, supplementation with zinc, magnesium, and chromium may reduce CRP levels, but does not lead to improvements in the individual components of this syndrome.⁴⁵ Magnesium supplementation in overweight subjects led to favorable alterations in metabolic pathways.⁴⁶ Supplementation with dietary magnesium in a rat model of concurrent MetS and chronic kidney disease (CKD) resulted in a decrease in biomarkers of inflammation, oxidative stress, and endothelial dysfunction.⁴⁷ A study revealed that low magnesium status contributed to chronic inflammatory stress and was associated with poor sleep quality.⁴⁸ Estrogen decline during menopause, accompanied by decreased intracellular Mg²⁺ in neurons, leads to neuroinflammation, and subsequent chronic pain, memory loss, and emotional deficits.⁴⁹ Women with obesity and mild to moderate depressive symptoms, combined or single supplementation of vitamin D and magnesium benefited mood and inflammation (TNF-α, IL-6).⁵⁰ Under these conditions, the mechanisms underlying MDS and CircS may be linked through shared consequences, including inflammation, oxidative stress, and endothelial damage.

Magnesium supplementation also benefits endocrine disorders. In women with polycystic ovary syndrome (PCOS), co-supplementation of magnesium and melatonin was beneficial for sleep quality and total testosterone levels, as well as for improving serum LDL, HDL, and insulin levels.⁵¹ This supplementation regimen also had a favorable effect on reducing tumor necrosis factor-α (TNF-α) and increasing total antioxidant capacity (TAC) in women with PCOS.⁵² These studies suggest that magnesium supplementation may be beneficial for improving metabolism, sleep, and inflammation.

This study has several advantages. It uses a nationally representative sample from the U.S., making its findings generalizable to the U.S. non-institutionalized population. Additionally, sensitivity analyses excluding individuals with CVD further confirmed the robustness of this relationship. Lastly, the utilization of CircS emphasizes the importance of a comprehensive approach to the health of individuals with MetS, sleep and depression disorders. Those with metabolic abnormalities, sleep symptoms and mental health conditions should be mindful of their magnesium intake and reabsorption. However, several limitations should be acknowledged. First, the study design prevents causal inference between MDS and CircS. Second, despite including relevant covariates, some unidentified confounders may exist and cause bias. Third, it should be noted that bioavailable magnesium status fluctuates between day and night under circadian light conditions, and the longitudinal relationship across diverse populations and groups requires further prospective research.⁵³ Lastly, the role of other trace elements, in addition to or in combination with magnesium, requires further investigation.⁵⁴ Therefore, future long-term prospective studies, including research across different regions and populations, as well as investigations into the mechanisms underlying various magnesium statuses-such as dietary intake, serum and urinary levels, and ionic forms-in relation to CircS and the circadian clock, are warranted to confirm these findings.

5. Conclusion

This study demonstrated an association between MDS and an increased risk of CircS. It underscored the potential importance of assessing magnesium status in relation to circadian well-being. However, these findings need to be validated before they can be applied in clinical practice.

Supplemental material

Supplemental material - Magnesium depletion score and circadian syndrome: Mediated by body mass index

Supplemental material for Magnesium depletion score and circadian syndrome: Mediated by body mass index by Ying Wang, Jiaqi Wu, Yunan Zhang, Peiyang Yu, Gongwei Wen, Kuncai Li, Xuan Zhang, Ronghui Xiang and Ziyang Zheng in Science Progress.

Footnotes

Acknowledgements

All authors appreciate the NHANES open policy and the provided data as well as all participants and staffs in this study.

ORCID iD

Ziyang Zheng

Ethical considerations

The studies involving human participants were approved by the Ethics Review Board of the National Center for Health Statistics. They were conducted in accordance with local regulations and institutional guidelines.

Consent to participate

All participants provided their written informed consent.

Author Contribution

Ying Wang: Conceptualization, Methodology, Data curation, Formal analysis. Jiaqi Wu: Conceptualization, Investigation, Software. Yunan Zhang: Software, Formal analysis, Writing-original draft. Peiyang Yu: Validation, Software, Writing-original draft. Gongwei Wen: Data curation, Formal analysis, Writing-original draft; Kuncai Li: Validation, Formal analysis, Writing-original draft. Xuan Zhang: Conceptualization, Writing-original draft; Ronghui Xiang: Methodology, Writing-original draft; Ziyang Zheng: Conceptualization, Methodology, Writing-review & editing.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The findings of this study are supported by data from the NHANES (National Health and Nutrition Examination Survey). The dataset is publicly available and can be accessed directly at the following link: .

Supplemental material

Supplemental material for this article is available online.

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