Abstract
Background
Circadian Syndrome (CircS) expands on metabolic syndrome (MetS) by including circadian rhythm disturbances and depression. It has been shown to be a better predictor of cardiovascular disease (CVD) than MetS. While magnesium has been linked to circadian rhythm disturbances, its association with CircS remains unclear. This study aimed to investigate the association between dietary magnesium intake and CircS using data from the National Health and Nutrition Examination Survey (NHANES).
Methods
Data from 10,486 adults aged 20 years and above who attended the 2005–2016 NHANES were analyzed in this cross-sectional study. Magnesium intake was assessed using two 24-h dietary recalls. CircS was defined based on components of MetS plus short sleep and depression, with a cut-off of ≥ 4 components. Multivariable logistic regression was used to assess the association between magnesium intake and CircS.
Results
The mean participant age was 50.3 (SD 17.6) years. The prevalence of CircS was 41.3%, decreasing from 47.3% in the lowest quartile of magnesium intake to 35.2% in the highest. After adjusting for age, gender, ethnicity, energy intake, education, and lifestyle factors, higher magnesium intake was linked to lower CircS prevalence. The adjusted odds ratios (ORs) (95% CI) across magnesium intake quartiles were: 1.00, 0.80 (0.68–0.95), 0.75 (0.64–0.87), and 0.61 (0.49–0.76) (p for trend < 0.001). The association remained significant after additional adjustment for healthy eating index. No significant interaction was found between magnesium intake and race, gender, smoking, alcohol use, or physical activity.
Conclusions
Higher magnesium intake was associated with lower CircS prevalence in U.S. adults, suggesting a potential role for magnesium in circadian health.
Keywords: Magnesium intake, Circadian syndrome, NHANES, Adults
Background
A group of cardiovascular risk factors, including central obesity, low HDL, elevated triglycerides, elevated glucose, and high blood pressure, are collectively known as metabolic syndrome (MetS) [1]. MetS affects 34.7% of adults in the United States, representing a significant public health burden [2]. Although several factors have been implicated in the development of MetS, there is no consensus on its exact etiology [3].
In recent years, circadian syndrome (CircS) has gained attention for its association with cardiovascular diseases [4], cognitive impairment [5], stroke [6], and chronic kidney disease [7]. CircS extends the definition of MetS by incorporating sleep disturbances, depression, and nonalcoholic fatty liver disease (NAFLD). Whereas MetS is defined by central obesity, dyslipidemia, hypertension, and impaired glucose metabolism, CircS integrates these metabolic abnormalities with circadian-related factors, highlighting the role of disrupted biological rhythms in disease risk [8]. Unlike MetS, which lacks a clear unifying mechanism, CircS identifies circadian disruption as a central driver of metabolic dysfunction [8]. Modern lifestyles characterized by poor dietary habits, shift work, irregular schedules, and chronic sleep deprivation have exacerbated circadian misalignment [9], fueling metabolic disturbances and accelerating chronic illnesses [10]. Thus, addressing CircS is crucial for shaping public health strategies and mitigating the impact of circadian-related diseases.
As metabolic dysfunction persists, researchers have turned to micronutrients like magnesium (Mg), for their potential role in mitigating these health challenges [11–13]. Magnesium, an essential cofactor for key enzymes in triglyceride metabolism and glycolysis, supports multiple pathways critical for energy balance and lipid metabolism [14, 15]. Notably, emerging evidence also highlights Mg’s role in regulating circadian rhythms across organisms through multiple mechanisms [16]. First, magnesium supports melatonin synthesis by enhancing the activity of N-acetyltransferase (NAT), with deficiency shown to lower melatonin levels in animal studies [17, 18]. Secondly, higher magnesium intake was linked to better sleep quality and reduced short sleep [19]. Furthermore, magnesium is essential for glucose metabolism and insulin signaling, a process closely linked to circadian clocks, providing a plausible pathway to improved metabolic health [20]. It also influences clock protein cycles in cyanobacteria [21], and affects circadian timing in plants [22]. Together, these roles position magnesium as a critical micronutrient for maintaining metabolic health and biological timekeeping.[23] Subclinical magnesium deficiency remains widespread, affecting 10–30% of individuals in developed countries [23]. In the United States, nearly half (48%) of the population consumes less than the recommended daily intake [24, 25], largely due to the high consumption of refined grains and the insufficient intake of magnesium-rich foods like whole grains, nuts, and leafy greens [26]. [26, 27] Magnesium deficiency has been linked to an increased risk of MetS [27], T2DM [28], hypertension [28], and sleep disorders [29]. Several epidemiological studies further suggested that higher magnesium levels are associated with reduced risk of MetS and improved cardiometabolic profiles [12, 30, 31]. Additionally, magnesium contributes to sleep and circadian regulation, with evidence showing beneficial effects on sleep duration and quality [32, 33]. However, despite these findings, the association between magnesium intake and CircS, which incorporates metabolic, circadian, and depressive components, remains unexplored. Therefore, this study aims to address this gap by examining the relationship between dietary magnesium intake and CircS using data from NHANES 2005–2016.
Methods
Study design and sample
NHANES is an ongoing program of cross-sectional surveys conducted by the Centers for Disease Control and Prevention. It employs a nationally representative, complex, multistage probability sampling design. Continuous NHANES, initiated in 1999, is conducted conducted in two-year cycles to evaluate the health and nutritional status of the U.S. population. Data collection methods include face-to-face and telephone interviews, comprehensive questionnaires, laboratory analyses, physical examinations, and medical, dental, and biochemical assessments. More information regarding NHANES survey data and methodologies is available at https://www.cdc.gov/nchs/nhanes/about_nhanes.htm.
In this study, data from six cycles of NHANES spanning 2005 and 2016 were used in the analysis. A total sample of 10,486 adults ≥ 20 years old participated in the mobile examination center (MEC) examination and were included (Fig. 1). Participants were excluded if they were under 20 years old , had incomplete two-day dietary intake data and/or any of the CircS components, reported implausible energy intakes (defined as less than 500 kcal or ≥ 6000 kcal for men and less than 500 kcal or ≥ 5000 kcal for women), or were pregnant.
Fig. 1.
Sample selection flowchart for study participants from NHANES 2005–2016
Outcome variable: circadian syndrome
CircS was defined based on the criteria determined by Shi et al. [4], as the presence of 4 or more of the following components (Table 1): Elevated waist circumference (≥ 88 cm for women and ≥ 102 cm for men). Elevated fasting glucose (≥ 100 mg/dL), or patients being on medications for it. Elevated triglyceride (≥ 150 mg/dL) or patients being on a medication for it. Reduced HDL-Cholesterol (< 40 mg/dL in men and < 50 mg/dL in women), or patients being on a medication for it. Elevated blood pressure (systolic blood pressure (SBP) ≥ 130 mmHg or a diastolic blood pressure (DBP) ≥ 85 mmHg), or patients being on antihypertensive medication. Short sleep was defined by the patients reporting < 6 hours of sleep per day. Depression symptoms were based on a score of ≥ 5 on the Patient Health Questionnaire (PHQ-9). The scores were categorized as: no/minimal depression (score of 0–4) or depression (score of ≥ 5). The cutoffs for the first five components were defined according to the criteria for MetS proposed by the International Diabetes Federation Task Force on Epidemiology and Prevention, the National Heart, Lung, and Blood Institute, the American Heart Association, the World Heart Federation, the International Atherosclerosis Society, and the International Association for the Study of Obesity [34]. All anthropometric and laboratory measurements were taken by trained examiners in the mobile examination centers (MEC) and during home examinations.
Table 1.
Definition of circadian syndrome
| Component | Definition | Assessment Method |
|---|---|---|
| Abdominal obesity | Waist circumference ≥ 102 cm (men) or ≥ 88 cm (women) [34] | Measured during MEC examination |
| Elevated fasting glucose | Fasting glucose ≥ 100 mg/dL or use of antidiabetic medication [34] | Laboratory measurement; NHANES question: “Now taking diabetic pills to lower your blood sugar?” |
| Elevated triglycerides | Serum TG ≥ 150 mg/dL or use of lipid-lowering medication [34] | Laboratory measurement; NHANES question: “Because of your high blood cholesterol, have you ever been told by a doctor or other health professional to take prescribed medicine?” |
|
Reduced HDL-C |
< 40 mg/dL (men) or < 50 mg/dL (women), or use of lipid-lowering medication [34] | Laboratory measurement; NHANES question: “To lower blood cholesterol, ever been told by a doctor or other health professional to take prescribed medicine?” |
| Elevated blood pressure | SBP ≥ 130 mmHg or DBP ≥ 85 mmHg, or use of antihypertensive medication [34] | Blood pressure measurement; NHANES question: “Are you now taking prescribed medicine for HBP?” |
| Short sleep duration | < 6 h/day [4, 60] | NHANES sleep questionnaire: “How much sleep do you usually get at night on weekdays or workdays?” |
| Depression | PHQ-9 score ≥ 5 [4] | NHANES Questionnaire PHQ-9; It is a 9-item screening tool designed to assess the presence of depressive symptoms over the past two weeks. Scores are classified as no or minimal depression (0–4) and depression (≥ 5) [61] |
Abbreviations: DBP Diastolic blood pressure, HDL-C High-density lipoprotein cholesterol, MEC Mobile examination center, PHQ-9 Patient Health Questionnaire, SBP Systolic blood pressure
Exposure variable: magnesium intake
The method of assessing dietary intake in the MEC of NHANES has been previously described. Magnesium intake was assessed using two 24-h dietary recall interviews. The first recall was assessed through interviews conducted in person at the MEC. The second recall was carried out through telephone calls by trained interviewers 3–10 days following the face-to-face interview. In the interviews, participants were asked to recall all food consumed in the previous 24 h. This included information on the type and amount of food as well as details of food descriptions. Afterward, the food items were linked to the U.S. Department of Agriculture’s (USDA) Food and Nutrient Database for Dietary Studies (FNDDS) database to be converted into nutrient values [35]. Information on dietary supplement intake was collected as part of the household interview. Beginning in 2007, supplement use data were collected through the Dietary Supplement Questionnaire using two 24-hour recalls, which were averaged to estimate the intake. This information included the type, amount, frequency, and duration for each dietary supplement used over the past 30 days. The daily average magnesium intake from dietary supplements was calculated based on the number of supplements taken per day, number of days taken and serving size on the product label [35].
Covariates
Several socioeconomic and lifestyle factors were considered as covariates and were adjusted for in the analysis because these factors may influence both magnesium intake and the risk of CircS, potentially confounding the association. These included age, gender, race (non-Hispanic White, non-Hispanic Black, Mexican–American/Hispanic or others), energy intake, physical activity (determined by Metabolic Equivalent of Task (METs) minutes/week categorized into < 600, 600–1200, and ≥ 1200 MET minutes/week) education (less than 11th grade, high school, some college and higher than college), smoking status (never, former and current smoker), alcohol consumption and Healthy Eating Index (HEI) quartiles.
The HEI was developed by the USDA and the National Cancer Institute (NCI) and was used to assess the participants’ adherence to the Dietary Guidelines for Americans. The HEI gives each participant a score from 0–100, with higher scores indicating better alignment to the dietary guidelines. The HEI is composed of 13 components which are further divided into two categories of adequacy and moderation. Higher scores in the adequacy components, which include foods such as fruits, vegetables and whole grains, reflect higher intake of these food items. Higher scores in the moderation components which are the food items to limit, such as saturated fats, added sugars and refined grains, reflect lower intake of them [36]. The HEI was also calculated using data from the 24-hour recall interviews.
Statistical analysis
The sample characteristics by magnesium intake quartiles were presented as means (SD) for continuous variables and counts (%) for categorical variables. Magnesium intake was divided into quartiles. Quartile 1 (Q1) served as the reference. Three multivariable logistic regression models were constructed adjusting for different sets of covariates and were used to assess the association between magnesium intake and CircS: Model 1 adjusted for age, gender, race, and total energy intake; Model 2 added physical activity, education, smoking status, and alcohol consumption; Model 3 further adjusted for dietary quality (HEI quartiles). The covariates selected were sociodemographic and lifestyle factors known to affect both magnesium intake and components of CircS. To test for linear trend, the medium magnesium intake in each quartile was modeled as a continuous variable in the logistic regression model. A sensitivity analysis included magnesium supplement use in Model 2. Appropriate sampling weights were applied to account for the survey design of the NHANES data. Subgroup analyses evaluated effect modification by age, sex, race, income-to-poverty ratio, education, physical activity, smoking, and HEI quartiles by including interaction terms in the regression models. Analyses were conducted using STATA version 18 (Stata Corporation, College Station, TX, USA).
Results
Sample description
Table 2 summarizes the sample characteristics by quartiles of magnesium intake. Overall, the mean magnesium intake in the total sample was 290.6 mg/day (SD 124.6), and the mean age was 50.3 years (SD 17.6). Participants with higher magnesium intake tended to be slightly younger, have a higher income-to-poverty ratio, greater energy intake, lower BMI, and report less short sleep. Circadian Syndrome (CircS) was present in 41.3% of the study population. A notable decline in the prevalence of CircS was observed across the quartiles of magnesium intake, with the highest prevalence found in the lowest quartile of magnesium intake (Q1: 160.3 ± 33.5 mg/day; 47.3%) and the lowest prevalence observed in the highest quartile of magnesium intake (Q4: 457.9 ± 109.5 mg/day; 35.2%). Similarly, the prevalence of MetS decreased from 54.4% in Q1 to 42.8% in Q4.
Table 2.
Sample characteristics by quartiles of dietary magnesium intake among NHANES 2005–2016 (N = 10,486 Participants)
| Total | Q1 | Q2 | Q3 | Q4 | p-value | |
|---|---|---|---|---|---|---|
| N = 10,486 | N = 2,634 | N = 2,624 | N = 2,614 | N = 2,614 | ||
| Dietary magnesium intake (mg/day) | 290.6 (124.6) | 160.3 (33.5) | 237.4 (18.3) | 308.0 (24.2) | 457.9 (109.5) | < 0.001 |
| Dietary magnesium intake range (mg/day) | 36.0–1721.0 | 36.0–205.0 | 205.5–269.0 | 269.5–353.5 | 354.0–1721.0 | |
| Magnesium supplement intake (mg/day) | 28.6 (106.0) | 19.0 (72.1) | 24.6 (76.8) | 35.0 (157.8) | 35.7 (94.8) | < 0.001 |
| Energy intake (kcal/day) | 2023.8 (781.8) | 1372.6 (439.4) | 1814.6 (490.3) | 2154.6 (571.0) | 2758.9 (813.3) | < 0.001 |
| Protein intake (g/day) | 80.2 (34.0) | 51.9 (17.7) | 71.5 (21.1) | 85.2 (24.3) | 112.5 (36.6) | < 0.001 |
| Fat intake (g/day) | 76.6 (36.3) | 51.5 (21.1) | 69.2 (25.9) | 80.8 (30.3) | 105.4 (41.2) | < 0.001 |
| Carbohydrate intake (g/day) | 246.5 (100.5) | 173.5 (67.3) | 220.7 (71.6) | 263.6 (78.6) | 329.0 (107.1) | < 0.001 |
| Healthy Eating Index | 46.3 (12.5) | 40.4 (11.0) | 44.6 (11.4) | 48.2 (12.0) | 52.1 (12.7) | < 0.001 |
| Age (years) (mean, SD) | 50.3 (17.6) | 51.8 (18.9) | 50.7 (18.0) | 50.3 (17.4) | 48.4 (15.9) | < 0.001 |
| Gender | < 0.001 | |||||
| Men | 5,147 (49.1%) | 911 (34.6%) | 1,108 (42.2%) | 1,354 (51.8%) | 1,774 (67.9%) | |
| Women | 5,339 (50.9%) | 1,723 (65.4%) | 1,516 (57.8%) | 1,260 (48.2%) | 840 (32.1%) | |
| Race | < 0.001 | |||||
| Non-Hispanic White | 4,973 (47.4%) | 1,183 (44.9%) | 1,174 (44.7%) | 1,294 (49.5%) | 1,322 (50.6%) | |
| Non-Hispanic Black | 2,002 (19.1%) | 696 (26.4%) | 554 (21.1%) | 438 (16.8%) | 314 (12.0%) | |
| Mex American/Hispanic | 1,587 (15.1%) | 315 (12.0%) | 393 (15.0%) | 412 (15.8%) | 467 (17.9%) | |
| Others | 1,924 (18.3%) | 440 (16.7%) | 503 (19.2%) | 470 (18.0%) | 511 (19.5%) | |
| Education | < 0.001 | |||||
| < 11 grade | 2,494 (23.8%) | 818 (31.1%) | 638 (24.3%) | 561 (21.5%) | 477 (18.3%) | |
| High school | 2,400 (22.9%) | 746 (28.4%) | 623 (23.7%) | 552 (21.1%) | 479 (18.3%) | |
| Some college | 3,043 (29.0%) | 724 (27.6%) | 801 (30.5%) | 746 (28.5%) | 772 (29.5%) | |
| Higher than college | 2,541 (24.3%) | 339 (12.9%) | 562 (21.4%) | 755 (28.9%) | 885 (33.9%) | |
| Smoking | < 0.001 | |||||
| Never | 5,694 (54.3%) | 1,370 (52.0%) | 1,463 (55.8%) | 1,462 (56.0%) | 1,399 (53.5%) | |
| Former | 2,698 (25.7%) | 604 (22.9%) | 638 (24.3%) | 677 (25.9%) | 779 (29.8%) | |
| Current smoker | 2,090 (19.9%) | 659 (25.0%) | 521 (19.9%) | 474 (18.1%) | 436 (16.7%) | |
| Alcohol drinking | < 0.001 | |||||
| No | 1,938 (18.5%) | 633 (24.0%) | 451 (17.2%) | 450 (17.2%) | 404 (15.5%) | |
| Yes | 7,143 (68.1%) | 1,520 (57.7%) | 1,812 (69.1%) | 1,832 (70.1%) | 1,979 (75.7%) | |
| Missing | 1,405 (13.4%) | 481 (18.3%) | 361 (13.8%) | 332 (12.7%) | 231 (8.8%) | |
| BMI (kg/m2) (mean, SD) | 29.1 (6.7) | 29.6 (7.1) | 29.6 (7.1) | 28.7 (6.3) | 28.4 (6.3) | < 0.001 |
| Physical activity (METs minutes/week) | < 0.001 | |||||
| < 600 | 4,153 (39.6%) | 1,325 (50.3%) | 1,118 (42.6%) | 984 (37.6%) | 726 (27.8%) | |
| 600–1200 | 1,218 (11.6%) | 285 (10.8%) | 312 (11.9%) | 305 (11.7%) | 316 (12.1%) | |
| ≥ 1200 | 5,114 (48.8%) | 1,024 (38.9%) | 1,194 (45.5%) | 1,325 (50.7%) | 1,571 (60.1%) | |
| Income to poverty ratio | < 0.001 | |||||
| < 1.30 | 2,904 (29.9%) | 945 (38.9%) | 742 (30.6%) | 614 (25.3%) | 603 (24.8%) | |
| 1.3–3.5 | 3,717 (38.3%) | 1,016 (41.8%) | 967 (39.9%) | 921 (38.0%) | 813 (33.4%) | |
| ≥ 3.5 | 3,084 (31.8%) | 467 (19.2%) | 713 (29.4%) | 888 (36.6%) | 1,016 (41.8%) | |
| Hypertension | 3,871 (37.0%) | 1,115 (42.4%) | 1,039 (39.7%) | 903 (34.6%) | 814 (31.1%) | < 0.001 |
| Central obesity | 6,056 (57.8%) | 1,697 (64.4%) | 1,610 (61.4%) | 1,471 (56.3%) | 1,278 (48.9%) | < 0.001 |
| Elevated glucose | 5,567 (53.1%) | 1,438 (54.6%) | 1,409 (53.7%) | 1,343 (51.4%) | 1,377 (52.7%) | 0.11 |
| Elevated triglycerides | 4,495 (42.9%) | 1,175 (44.6%) | 1,143 (43.6%) | 1,104 (42.2%) | 1,073 (41.0%) | 0.052 |
| Reduced HDL-C | 4,724 (45.1%) | 1,357 (51.5%) | 1,192 (45.4%) | 1,129 (43.2%) | 1,046 (40.0%) | < 0.001 |
| Elevated blood pressure | 5,137 (49.0%) | 1,428 (54.2%) | 1,342 (51.1%) | 1,236 (47.3%) | 1,131 (43.3%) | < 0.001 |
| Depression symptom | 2,421 (23.1%) | 739 (28.1%) | 636 (24.2%) | 509 (19.5%) | 537 (20.5%) | < 0.001 |
| Short sleep | 3,657 (34.9%) | 1,009 (38.3%) | 931 (35.5%) | 861 (32.9%) | 856 (32.7%) | < 0.001 |
| Metabolic syndrome | 5,124 (48.9%) | 1,433 (54.4%) | 1,328 (50.6%) | 1,245 (47.6%) | 1,118 (42.8%) | < 0.001 |
| Circadian syndrome | 4,331 (41.3%) | 1,245 (47.3%) | 1,141 (43.5%) | 1,026 (39.3%) | 919 (35.2%) | < 0.001 |
Data are presented as mean (SD) for continuous measures, and n (%) for categorical measures
Abbreviations: Mg Magnesium, kcal Kilocalories, g Grams, SD Standard deviation, BMI Body mass index, kg/m2, kilograms per square meter, METs Metabolic equivalents of task, HDL-C High-density lipoprotein cholesterol, Mets Metabolic syndrome, CircS Circadian syndrome
* p-values were 2 sided and generated from analysis of variance for continuous variables and chi-square for categorical variables
Association between magnesium intake and circadian syndrome
As shown in Table 3, magnesium intake was inversely associated with CircS across all three multivariable models. After adjusting for sociodemographic and lifestyle factors (model 3), across the ascending quartiles of magnesium intake, the odds ratios (OR, 95% CI) for CircS were 1.00, 0.85 (0.72–1.01), 0.83 (0.69–0.98), and 0.72 (0.57–0.92), respectively (p = 0.015). After further adjusting for dietary supplement use, the association was attenuated but remained statistically significant (Table 3). Effectively, there was an inverse association between total magnesium intake (dietary intake and supplement intake) and CircS (p = 0.022), but the effect size was smaller than that of the dietary magnesium intake alone. At Q4 (457.9 ± 109.5 mg/day) of intake, the OR for CircS was 0.77 (0.61–0.97) for total magnesium intake and 0.61 (0.49–0.76) for dietary magnesium intake.
Table 3.
Odds ratios (95%CI) for circadian syndrome according to quartiles of magnesium intake among participants attending NHANES 2005–2016 (N = 10,486)
| Quartiles of magnesium intake | |||||
|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | p-value for trend | |
| Unadjusted | 1.00 | 0.81 (0.70–0.93) | 0.74 (0.65–0.84) | 0.62 (0.54–0.72) | < 0.001 |
| Model 1 | 1.00 | 0.69 (0.59–0.81) | 0.58 (0.50–0.67) | 0.43 (0.36–0.53) | < 0.001 |
| Model 2 | 1.00 | 0.80 (0.68–0.95) | 0.75 (0.64–0.87) | 0.61 (0.49–0.76) | < 0.001 |
| Model 3 | 1.00 | 0.85 (0.72–1.01) | 0.83 (0.69–0.98) | 0.72 (0.57–0.92) | 0.015 |
| Model 2 + supplement use | 1.00 | 0.76 (0.63–0.92) | 0.71 (0.60–0.84) | 0.57 (0.45–0.73) | < 0.001 |
| Sensitivity analysis | 1.00 | 0.93 (0.77–1.12) | 0.80 (0.63–1.01) | 0.77 (0.61–0.97) | 0.022 |
Model 1 adjusted for age, gender, race, energy intake
Model 2 further adjusted for physical activity, education, smoking, and alcohol drinking
Model 3 further adjusted for Healthy Eating Index (quartiles)
Sensitivity analysis is for the association between quartiles of total magnesium intake (dietary intake plus magnesium supplement intake) and adjusted for the same covariates as model 2
p-value for trend was calculated using medium magnesium intake in each quartile as a continuous variable in the multivariable logistic regression models
Table 4 shows that magnesium intake was inversely associated with all components of CircS, whereby the OR varied from 0.64 (0.52–0.79, p < 0.001) for elevated blood pressure to 0.76 (0.62–0.92, p = 0.002) for elevated glucose in the highest quartile (Q4: 457.9 ± 109.5 mg/day) of magnesium intake compared to the lowest (Q1: 160.3 ± 33.5 mg/day).
Table 4.
Odds ratios (95%CI) for components of circadian syndrome by quartiles of magnesium intake among adults attending NHANES 2005–2016
| Quartiles of magnesium intake | |||||
|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | p-value | |
| Central obesity | 1.00 | 0.90 (0.77–1.05) | 0.84 (0.71–0.99) | 0.68 (0.56–0.82) | < 0.001 |
| Elevated glucose | 1.00 | 0.86 (0.73–1.02) | 0.72 (0.60–0.85) | 0.76 (0.62–0.92) | 0.002 |
| Elevated triglyceride | 1.00 | 0.89 (0.76–1.03) | 0.86 (0.73–1.00) | 0.76 (0.64–0.92) | 0.007 |
| Low HDL-C | 1.00 | 0.80 (0.68–0.93) | 0.70 (0.60–0.83) | 0.65 (0.53–0.80) | < 0.001 |
| Elevated blood pressure | 1.00 | 0.92 (0.77–1.09) | 0.74 (0.62–0.89) | 0.64 (0.52–0.79) | < 0.001 |
| Depressive symptom | 1.00 | 0.79 (0.66–0.95) | 0.66 (0.53–0.83) | 0.76 (0.58–0.99) | 0.018 |
| Short sleep | 1.00 | 0.78 (0.67–0.91) | 0.72 (0.61–0.83) | 0.68 (0.53–0.86) | < 0.001 |
Models adjusted for age, gender, ethnicity, energy intake, leisure time physical activity, education, smoking and alcohol drinking. Abbreviations: HDL-C, high-density lipoprotein cholesterol
p-value for trend was calculated using medium magnesium intake in each quartile as a continuous variable in the multivariable logistic regression models
Subgroup analysis
As presented in Table 5, there were no significant interactions between magnesium intake and race, sex, age, income-to-poverty-ratio, education, physical activity, smoking, and healthy eating index in relation to CircS (p for interaction > 0.05 for all). The association between magnesium intake and CircS was observed in both men (OR 0.54 95% CI 0.41–0.72 at Q4 compared to Q1) and women (OR 0.75 95% CI 0.57–1.00 at Q4 compared to Q1). By race, the association was only significant in non-Hispanic Whites (at Q4, OR 0.54, 95% CI 0.41–0.72), but not in other racial groups. Furthermore, the association was stronger in those with high education (higher than college) (OR 0.37, 95% CI 0.24–0.57) and income (OR 0.62, 95% CI 0.43–0.90).
Table 5.
Subgroup analyses of the association between quartiles of magnesium and circadian syndrome among adults attending NHANES 2005–2016
| Quartiles of magnesium intake | ||||||
|---|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | p for trend | p for interaction | |
| Age | 0.157 | |||||
| 20–39 | 1.00 | 0.90 (0.66–1.23) | 0.77 (0.54–1.09) | 0.75 (0.46–1.22) | 0.189 | |
| 40–59 | 1.00 | 0.72 (0.54–0.96) | 0.72 (0.57–0.91) | 0.53 (0.38–0.72) | < 0.001 | |
| 60 + | 1.00 | 0.78 (0.58–1.03) | 0.67 (0.50–0.91) | 0.55 (0.37–0.81) | 0.003 | |
| Sex | 0.348 | |||||
| Men | 1.00 | 0.73 (0.56–0.95) | 0.77 (0.59–1.01) | 0.53 (0.38–0.75) | < 0.001 | |
| Women | 1.00 | 0.89 (0.73–1.10) | 0.76 (0.59–0.97) | 0.75 (0.57–1.00) | 0.020 | |
| Race | 0.736 | |||||
| Non-Hispanic White | 1.00 | 0.71 (0.57–0.88) | 0.70 (0.57–0.86) | 0.54 (0.41–0.72) | < 0.001 | |
| Non-Hispanic Black | 1.00 | 1.15 (0.83–1.60) | 1.01 (0.71–1.45) | 1.07 (0.70–1.65) | 0.907 | |
| Mex American/Hispanic | 1.00 | 0.98 (0.65–1.47) | 0.96 (0.62–1.48) | 0.78 (0.43–1.40) | 0.408 | |
| Others | 1.00 | 1.02 (0.71–1.48) | 0.74 (0.48–1.13) | 0.73 (0.41–1.29) | 0.159 | |
| Education | 0.209 | |||||
| < 11 grade | 1.00 | 1.00 (0.74–1.36) | 0.87 (0.63–1.22) | 0.84 (0.53–1.35) | 0.372 | |
| High school | 1.00 | 0.87 (0.64–1.18) | 0.95 (0.66–1.36) | 0.90 (0.55–1.45) | 0.740 | |
| Some college | 1.00 | 0.78 (0.60–1.00) | 0.71 (0.54–0.93) | 0.67 (0.48–0.93) | 0.022 | |
| Higher than college | 1.00 | 0.65 (0.44–0.98) | 0.58 (0.40–0.85) | 0.37 (0.24–0.57) | < 0.001 | |
| Smoking status | 0.500 | |||||
| Never | 1.00 | 0.84 (0.68–1.05) | 0.76 (0.61–0.95) | 0.61 (0.45–0.82) | 0.002 | |
| Former | 1.00 | 0.76 (0.55–1.06) | 0.68 (0.48–0.97) | 0.60 (0.40–0.91) | 0.016 | |
| Current smoker | 1.00 | 0.78 (0.59–1.03) | 0.84 (0.58–1.23) | 0.62 (0.38–1.02) | 0.108 | |
| Alcohol drinking (past 12 months) | 0.910 | |||||
| No | 1.00 | 0.66 (0.46–0.97) | 0.68 (0.47–1.00) | 0.63 (0.38–1.04) | 0.079 | |
| Yes | 1.00 | 0.84 (0.69–1.02) | 0.76 (0.63–0.91) | 0.60 (0.46–0.79) | < 0.001 | |
| Missing | 1.00 | 0.86 (0.53–1.40) | 0.82 (0.45–1.48) | 0.75 (0.37–1.52) | 0.423 | |
| Physical activity (METs minutes/week) | 0.179 | |||||
| < 600 | 1.00 | 0.83 (0.62–1.10) | 0.66 (0.51–0.85) | 0.66 (0.46–0.94) | 0.004 | |
| 600–1200 | 1.00 | 0.77 (0.47–1.27) | 1.02 (0.60–1.72) | 1.24 (0.65–2.36) | 0.318 | |
| ≥ 1200 | 1.00 | 0.78 (0.60–1.02) | 0.75 (0.58–0.96) | 0.52 (0.37–0.72) | < 0.001 | |
| Income to poverty ratio | 0.249 | |||||
| < 1.30 | 1.00 | 0.92 (0.68–1.24) | 0.92 (0.67–1.27) | 0.94 (0.62–1.44) | 0.733 | |
| 1.3–3.5 | 1.00 | 0.75 (0.59–0.95) | 0.77 (0.59–1.00) | 0.54 (0.37–0.80) | 0.007 | |
| ≥ 3.5 | 1.00 | 0.81 (0.58–1.12) | 0.72 (0.53–0.98) | 0.62 (0.43–0.90) | 0.019 | |
| Healthy eating intake quartiles | 0.195 | |||||
| Q1 (low) | 1.00 | 0.89 (0.66–1.21) | 0.88 (0.60–1.28) | 0.67 (0.38–1.19) | 0.245 | |
| Q2 | 1.00 | 0.72 (0.48–1.08) | 0.85 (0.56–1.28) | 0.86 (0.51–1.46) | 0.676 | |
| Q3 | 1.00 | 1.09 (0.74–1.60) | 0.90 (0.61–1.33) | 0.73 (0.44–1.20) | 0.121 | |
| Q4 (high) | 1.00 | 0.64 (0.40–1.04) | 0.59 (0.36–0.96) | 0.59 (0.33–1.04) | 0.155 | |
Model adjusted for age, gender, ethnicity, energy intake, leisure time physical activity, education, smoking and alcohol drinking. Abbreviations: METs, metabolic equivalents of task
p-value for trend was calculated using medium magnesium intake in each quartile as a continuous variable in the multivariable logistic regression models
p for interaction was calculated by including product terms in the model
Discussion
In this nationally representative study, we found that CircS was highly prevalent, affecting 4 in 10 adults in the U.S. An inverse association was found between magnesium intake and CircS, and remained significant after controlling for supplement use. The association was consistently observed across all components of CircS. Notably, the mean magnesium intake in our population was 290 mg/day, which falls below the Estimated Average Requirement (EAR) for men (350 mg/day). This indicated a generally suboptimal magnesium status and may have affected the observed association with CircS.
Subgroup analyses revealed no significant interaction effect between sociodemographic and lifestyle factors and magnesium- CircS association. However, stratified analyses showed that the inverse association between magnesium and CircS was significant among both men and women, individuals with higher education and income levels, and never smokers. The results indicate that the positive effects of magnesium are probably consistent among various population groups, even if the differences observed between these groups were not statistically significant. This pattern supports the biological plausibility of magnesium’s role in regulating circadian physiology and metabolic health, regardless of subgroup characteristics [16]. Nevertheless, disparities in magnesium intake across different populations continue to be an issue [37]. Individuals from lower socioeconomic backgrounds typically have lower magnesium consumption because they have less access to foods rich in nutrients [38]. This could increase their risk for circadian rhythm disruptions and related cardiometabolic problems, even if there is no statistically significant interaction effect.
Given that CircS remains a novel concept, research on its determinants is still limited. To our knowledge, this is the first study to examine the link between magnesium intake and CircS, which is characterized by incorporating short sleep and depressive symptoms into MetS. However, previous research has consistently shown a comparable relationship between magnesium intake and cardiometabolic risk factors. A recently published meta-analysis of observational studies reported that magnesium intake and blood magnesium levels were inversely related to the risk of MetS [39].
In addition to cardiometabolic risk factors, similar links between magnesium intake and other CircS components, such as depression [40–42] and short sleep [43–45] have also been observed. A meta-analysis, including three cohort and ten cross-sectional studies, found that compared to individuals with the lowest magnesium intake(~ 170 mg/day), those with the highest magnesium intake (~ 370 mg/day) had a 34% reduced risk of depression [42]. Additionally, the results showed that each 100 mg/day increase in magnesium consumption was associated with a 7% reduction in depression risk [42]. Furthermore, a cross-sectional study based on NHANES found that low magnesium intake was significantly associated with depression, particularly in individuals under 65 years old [41]. [42 [42 [41]. Conversely, a cohort study did not find any significant link between magnesium intake and depression [40]. The variation in findings could be due to the different criteria used for detecting depression, and/or to the differences in study populations, including differences in their intake levels. In line with our findings, a longitudinal study by Zhang et al. [44] showed that higher magnesium intake was linked to improved sleep quality and a lower likelihood of short sleep duration (less than 7 h). Participants in the highest quartile of magnesium intake were less likely to experience short sleep compared to those in the lowest quartile [44]. The variation in findings could be due to the different criteria used for detecting depression, and/or to the differences in study populations, including differences in their intake levels. In line with our findings, a longitudinal study by Zhang et al. [44] showed that higher magnesium intake was linked to improved sleep quality and a lower likelihood of short sleep duration (less than 7 h). Participants in the highest quartile of magnesium intake were less likely to experience short sleep compared to those in the lowest quartile [44].
Interestingly, the study findings showed that dietary magnesium intake was more effective in lowering the odds of CircS compared to total magnesium intake (diet + supplements). This could be due to synergistic effects of nutrients present in whole foods [46]. Magnesium from food sources is present with other beneficial nutrients and bioactive compounds that enhance its absorption and overall effectiveness [47]. Studies have shown that a complex blend of micronutrients, such as those in a diet abundant in fruits and vegetables, could be more effective in lowering disease risk than higher amounts of a single micronutrient supplement [48]. [46]. [47, 48]. A meta-analysis of cohort studies [49] showed that higher intake of magnesium from diet alone was significantly linked to improved cardiometabolic health. This suggests that obtaining magnesium from whole foods such as leafy greens, nuts, and seeds may offer better cardiometabolic health benefits compared to relying solely on supplements.
Magnesium has been reported to play a role in regulating inflammatory pathways, circadian gene expression, and sleep physiology. There are several possible mechanisms that could explain the relationship between magnesium intake and CircS. Firstly, it has been shown that magnesium may affect sleep duration by regulating the circadian rhythm [19, 21, 22, 50–52] as well as the production of melatonin, a crucial hormone in regulating the sleep–wake cycle [50, 53]. Studies in rats have demonstrated that magnesium deficiency led to a reduction in plasma melatonin levels [18]. Additionally, magnesium plays a crucial role in ion channel conductivity, acting as a natural inhibitor of the NMDA (N-Methyl-D-aspartate) receptor and regulating the outward flow of potassium ions through potassium channels, thereby contributing to better sleep [16]. Moreover, magnesium intake is inversely associated with obesity [54] and inflammation [55], both of which are risk factors for the majority of CircS components [56]. A higher magnesium intake is linked to reduced levels of inflammatory markers like C-reactive protein (CRP) [57]. In turn, CRP, along with its precursors interleukin (IL)−6 and IL-1, has a positive association with both the occurrence and severity of depression [57], both of which are risk factors for the majority of CircS components [56]. [57, 57]. Taken together, this study supports the use of CircS as a practical tool for assessing circadian disruption in clinical practice. Lately, CircS has emerged as a significant health concern, shedding light on the disturbances in circadian rhythms that are intricately linked to cardiometabolic conditions frequently seen in MetS and its related comorbidities [3]. Factors like shift work, social jet lag (differences in sleep patterns between weekdays and weekends), artificial light exposure, and late-night eating contribute to these disturbances [3]. While the effects of circadian disruption are well known, the challenge of accurately diagnosing these disturbances remains a pressing concern that must be addressed. The prevalence of CircS has been studied in both Chinese and U.S. populations. A longitudinal study in China reported that 39% of the population had CircS at baseline [58], while a similar proportion of approximately 41% was observed among U.S. adults [59], aligning with findings from the current study. Growing research suggests that CircS significantly impacts health, largely due to modern lifestyle habits such as nighttime exposure to artificial light, excessive use of electronic devices, and prolonged eating hours. These can interfere with the natural alignment of the body's internal clock and the external environment, resulting in disturbances to the circadian rhythm [3]. The rising prevalence of CircS and its health consequences emphasize the urgent need for interventions to counteract circadian rhythm disturbances.
A previous study found that a prudent dietary pattern rich in whole grains, seeds, nuts and vegetables was inversely associated with CircS [11], while our study showed that high magnesium intake was linked to a lower risk of CircS. From a clinical and public health perspective, our findings suggest that increasing magnesium-rich foods as part of a balanced, prudent diet, may reduce the risk of CircS. Incorporating magnesium-rich foods into dietary guidelines and public health initiatives could further support population-level prevention. In clinical settings, screening for magnesium levels and providing nutritional counseling for individuals experiencing poor sleep, depressive symptoms, or metabolic abnormalities may help identify and support those at increased risk for CircS. More broadly, these findings underscore the importance of considering circadian health when developing dietary recommendations and chronic disease prevention strategies.
Although our study is the first of its kind to examine the association between magnesium intake and CircS using a large, representative U.S. sample, which enhances the generalizability of the findings, several limitations should be considered. Firstly, magnesium intake was assessed using a 24-h recall method, which may not accurately reflect habitual intake, and is subject to recall-bias and underreporting of energy intake. This method can lead to non-differential misclassification of intake, potentially affecting observed associations. To address this, the analysis included two separate dietary recalls conducted on different days. Nonetheless, this approach may not account for day-to-day variability or seasonal differences. There may also be misclassification of magnesium intake from supplements because of underreporting or lack of brand specificity. Additionally, sleep duration was self-reported rather than objectively measured, which may introduce bias. Moreover, the PHQ-9 threshold for depression symptoms was set at 5 (rather than 10), capturing only mild symptoms. While this may limit the scope, the PHQ-9 is valued for its ease of use and efficiency. Furthermore, although NAFLD was initially considered a part of CircS, it was excluded from the study’s definition, following the approach of previous studies for consistency. Despite the use of appropriate statistical analyses, residual confounding remains possible. Some measures such as meal timing, sleep apnea, chronotype, or stress levels that could affect magnesium intake and CircS were not included in the analyses. Lastly, cross-sectional study design limits the ability to establish causal inference. Future research should aim to assess the applicability of these findings to other populations.
In conclusion, analysis of NHANES 2005–2016 data indicates an inverse relationship between magnesium consumption and CircS. CircS has been associated with various chronic diseases, hence exploring the significance of magnesium intake as part of a healthy diet in CircS might provide insights for targeted treatments, potentially transforming treatment approaches. Nonetheless, additional research, including longitudinal studies and randomized controlled trials is necessary to confirm these findings across diverse populations and to clarify causality and underlying mechanisms.
Acknowledgements
None.
Author’s contributions
MB, JL and ZS contributed to the design and conduct of the study and the interpretation of the data. ZS contributed to the analysis and interpretation of data. RA, HA and SL wrote the first draft of the manuscript and all authors contributed to subsequent drafts and approved the final version for submission. ZS has full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Funding
None.
Data availability
Data is publicly available on the NHANES website.
Declarations
Ethics approval and consent to participate
The study used publicly available data. Ethical approval is exempted.
Consent for publication
The study used publicly available data. Ethical approval is exempted.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Alberti KGMM, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome. Circulation. 2009;120(16):1640–5. [DOI] [PubMed] [Google Scholar]
- 2.Hirode G, Wong RJ. Trends in the prevalence of metabolic syndrome in the United States, 2011–2016. JAMA. 2020;323(24):2526–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Zimmet P, Alberti K, Stern N, Bilu C, El-Osta A, Einat H, et al. The Circadian Syndrome: is the Metabolic Syndrome and much more! J Intern Med. 2019;286(2):181–91. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Shi Z, Tuomilehto J, Kronfeld-Schor N, Alberti GK, Stern N, El-Osta A, et al. The circadian syndrome predicts cardiovascular disease better than metabolic syndrome in Chinese adults. J Intern Med. 2021;289(6):851–60. [DOI] [PubMed] [Google Scholar]
- 5.Liu Y, Zang B, Shao J, Ning N, He L, Ma Y. Predictor of cognitive impairment: metabolic syndrome or circadian syndrome. BMC Geriatr. 2023;23(1):408. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Wang Y, Yang L, Zhang Y, Liu J. Relationship between circadian syndrome and stroke: A cross-sectional study of the national health and nutrition examination survey. Frontiers in Neurology. 2022;13:946172. 10.3389/fneur.2022.946172. [DOI] [PMC free article] [PubMed]
- 7.Xiong Y, Zhong Q, Zhang Y, Liu Z, Wang X. The association between circadian syndrome and chronic kidney disease in an aging population: a 4-year follow-up study. Front Endocrinol. 2024;15:1338110. 10.3389/fendo.2024.1338110. [DOI] [PMC free article] [PubMed]
- 8.Zimmet P, Alberti KGMM, Stern N, Bilu C, El-Osta A, Einat H, et al. The circadian syndrome: is the metabolic syndrome and much more! J Intern Med. 2019;286(2):181–91. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Sato T, Sato S. Circadian regulation of metabolism: commitment to health and diseases. Endocrinology. 2023;164(7): bqad086. 10.1210/endocr/bqad086. [DOI] [PubMed]
- 10.Fatima N, Rana S. Metabolic implications of circadian disruption. Pflügers Arch Eur J Physiol. 2020;472(5):513–26. [DOI] [PubMed] [Google Scholar]
- 11.Akbar Z, Shi Z. Dietary patterns and circadian syndrome among adults attending NHANES 2005–2016. Nutrients. 2023;15(15):3396. 10.3390/nu15153396. [DOI] [PMC free article] [PubMed]
- 12.Alsheikh R, Aldulaimi H, Hinawi R, Al-Sadi F, Al-Baker A, Alkuwari A, et al. Association of serum magnesium and calcium with metabolic syndrome: a cross-sectional study from the Qatar-biobank. Nutr Metab. 2025;22(1):8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Arabi A, Nasrallah D, Mohsen S, Abugharbieh L, Al-Hashimi D, AlMass S, et al. Association between serum vitamin D status and circadian syndrome: a cross-sectional study. Nutrients. 2024;16(13): 2111. 10.3390/nu16132111. [DOI] [PMC free article] [PubMed]
- 14.Dasgupta A, Sarma D, Saikia UK. Hypomagnesemia in type 2 diabetes mellitus. Indian J Endocrinol Metab. 2012;16(6):1000–3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Găman M-A, Dobrică E-C, Cozma M-A, Antonie N-I, Stănescu AMA, Găman AM, et al. Crosstalk of magnesium and serum lipids in dyslipidemia and associated disorders: a systematic review. Nutrients. 2021;13(5):1411. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Arab A, Rafie N, Amani R, Shirani F. The role of Magnesium in sleep health: a systematic review of available literature. Biol Trace Elem Res. 2023;201(1):121–8. [DOI] [PubMed] [Google Scholar]
- 17.Morton DJ, James MF. Effect of magnesium ions on rat pineal N-acetyltransferase (EC 2.3.1.5) activity. J Pineal Res. 1985;2(4):387–91. [DOI] [PubMed] [Google Scholar]
- 18.Billyard AJ, Eggett DL, Franz KB. Dietary magnesium deficiency decreases plasma melatonin in rats. Magnes Res. 2006;19(3):157–61. [PubMed] [Google Scholar]
- 19.Zhang Y, Chen C, Lu L, Knutson KL, Carnethon MR, Fly AD, et al. Association of magnesium intake with sleep duration and sleep quality: findings from the CARDIA study. Sleep. 2022;45(4):zsab276. 10.1093/sleep/zsab276. [DOI] [PMC free article] [PubMed]
- 20.Zhao B, Deng H, Li B, Chen L, Zou F, Hu L, et al. Association of magnesium consumption with type 2 diabetes and glucose metabolism: a systematic review and pooled study with trial sequential analysis. Diabetes Metab Res Rev. 2020;36(3):e3243. [DOI] [PubMed] [Google Scholar]
- 21.Jeong YM, Dias C, Diekman C, Brochon H, Kim P, Kaur M, et al. Magnesium regulates the circadian oscillator in cyanobacteria. J Biol Rhythms. 2019;34(4):380–90. [DOI] [PubMed] [Google Scholar]
- 22.de Melo JRF, Gutsch A, Caluwe T, Leloup JC, Gonze D, Hermans C, et al. Magnesium maintains the length of the circadian period in Arabidopsis. Plant Physiol. 2021;185(2):519–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.DiNicolantonio JJ, O’Keefe JH, Wilson W. Subclinical magnesium deficiency: a principal driver of cardiovascular disease and a public health crisis. Open Heart. 2018;5(1):e000668. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Cowan AE, Jun S, Tooze JA, Eicher-Miller HA, Dodd KW, Gahche JJ, et al. Total usual micronutrient intakes compared to the dietary reference intakes among U.S. adults by food security status. Nutrients. 2019;12(1):38. 10.3390/nu12010038. [DOI] [PMC free article] [PubMed]
- 25.Rosanoff A, Weaver CM, Rude RK. Suboptimal magnesium status in the United States: are the health consequences underestimated? Nutr Rev. 2012;70(3):153–64. [DOI] [PubMed] [Google Scholar]
- 26.James JD, James HOK, William W. Subclinical magnesium deficiency: a principal driver of cardiovascular disease and a public health crisis. Open Heart. 2018;5(1):e000668. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.He K, Liu K, Daviglus ML, Morris SJ, Loria CM, Van Horn L, et al. Magnesium intake and incidence of metabolic syndrome among young adults. Circulation. 2006;113(13):1675–82. [DOI] [PubMed] [Google Scholar]
- 28.Shi Z, Abou-Samra AB. Association of low serum magnesium with diabetes and hypertension: findings from Qatar Biobank study. Diabetes Res Clin Pract. 2019;158:107903. [DOI] [PubMed] [Google Scholar]
- 29.Nielsen FH. Chapter 31 - Relation between Magnesium Deficiency and Sleep Disorders and Associated Pathological Changes. In: Watson RR, editor. Modulation of Sleep by Obesity, Diabetes, Age, and Diet. San Diego: Academic Press; 2015. p. 291–6. [Google Scholar]
- 30.Evangelopoulos AA, Vallianou NG, Panagiotakos DB, Georgiou A, Zacharias GA, Alevra AN, et al. An inverse relationship between cumulating components of the metabolic syndrome and serum magnesium levels. Nutr Res. 2008;28(10):659–63. [DOI] [PubMed] [Google Scholar]
- 31.Guerrero-Romero F, Rodríguez-Morán M. Low serum magnesium levels and metabolic syndrome. Acta Diabetol. 2002;39(4):209–13. [DOI] [PubMed] [Google Scholar]
- 32.Luo X, Tang M, Wei X, Peng Y. Association between magnesium deficiency score and sleep quality in adults: a population-based cross-sectional study. J Affect Disord. 2024;358:105–12. [DOI] [PubMed] [Google Scholar]
- 33.Zhao S, Hu J, Yue C, Tian J, Zhou S, Zhu Q. Dietary magnesium intake is associated with self-reported short sleep duration but not self-reported sleep disorder. Brain Behav. 2025;15(2):e70251. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120(16):1640–5. [DOI] [PubMed] [Google Scholar]
- 35.Ahluwalia N, Dwyer J, Terry A, Moshfegh A, Johnson C. Update on NHANES dietary data: focus on collection, release, analytical considerations, and uses to inform public policy. Adv Nutr. 2016;7(1):121–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Krebs-Smith SM, Pannucci TE, Subar AF, Kirkpatrick SI, Lerman JL, Tooze JA, et al. Update of the Healthy Eating Index: HEI-2015. J Acad Nutr Diet. 2018;118(9):1591–602. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Jackson SE, Smith L, Grabovac I, Haider S, Demurtas J, Lopez-Sanchez GF, et al. Ethnic differences in magnesium intake in U.S. older adults: findings from NHANES 2005(-)2016. Nutrients. 2018;10(12):1901. 10.3390/nu10121901. [DOI] [PMC free article] [PubMed]
- 38.Wang DD, Leung CW, Li Y, Ding EL, Chiuve SE, Hu FB, et al. Trends in dietary quality among adults in the United States, 1999 through 2010. JAMA Intern Med. 2014;174(10):1587–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Kim Y, Je Y. Intake or blood levels of magnesium and risk of metabolic syndrome: a meta-analysis of observational studies. Nutrients. 2025;17(10):1667. 10.3390/nu17101667. [DOI] [PMC free article] [PubMed]
- 40.Martinez-Gonzalez MA, Sanchez-Villegas A. Magnesium intake and depression: the SUN cohort. Magnes Res. 2016;29(3):102–11. [DOI] [PubMed] [Google Scholar]
- 41.Tarleton EK, Littenberg B. Magnesium intake and depression in adults. J Am Board Fam Med. 2015;28(2):249. [DOI] [PubMed] [Google Scholar]
- 42.Hajhashemy Z, Shirani F, Askari G. Dietary magnesium intake in relation to depression in adults: a GRADE-assessed systematic review and dose-response meta-analysis of epidemiologic studies. Nutr Rev. 2025;83(2):217–29. [DOI] [PubMed] [Google Scholar]
- 43.Grandner MA, Jackson N, Gerstner JR, Knutson KL. Dietary nutrients associated with short and long sleep duration. Data from a nationally representative sample. Appetite. 2013;64:71–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Zhang Y, Chen C, Lu L, Knutson KL, Carnethon MR, Fly AD, et al. Association of magnesium intake with sleep duration and sleep quality: findings from the CARDIA study. Sleep. 2022;45(4):zsab276. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Ikonte CJ, Mun JG, Reider CA, Grant RW, Mitmesser SH. Micronutrient inadequacy in short sleep: analysis of the NHANES 2005–2016. Nutrients. 2019;11(10):2335. 10.3390/nu11102335. [DOI] [PMC free article] [PubMed]
- 46.Townsend JR, Kirby TO, Sapp PA, Gonzalez AM, Marshall TM, Esposito R. Nutrient synergy: definition, evidence, and future directions. Front Nutr. 2023;10:1279925. 10.3389/fnut.2023.1279925. [DOI] [PMC free article] [PubMed]
- 47.Natarajan TD, Ramasamy JR, Palanisamy K. Nutraceutical potentials of synergic foods: a systematic review. J Ethn Foods. 2019;6(1):27. [Google Scholar]
- 48.Woodside JV, McCall D, McGartland C, Young IS. Micronutrients: dietary intake v. supplement use. Proc Nutr Soc. 2005;64(4):543–53. [DOI] [PubMed] [Google Scholar]
- 49.Fang X, Wang K, Han D, He X, Wei J, Zhao L, et al. Dietary magnesium intake and the risk of cardiovascular disease, type 2 diabetes, and all-cause mortality: a dose-response meta-analysis of prospective cohort studies. BMC Med. 2016;14(1):210. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Durlach J, Pages N, Bac P, Bara M, Guiet-Bara A. Biorhythms and possible central regulation of magnesium status, phototherapy, darkness therapy and chronopathological forms of magnesium depletion. Magnes Res. 2002;15(1–2):49–66. [PubMed] [Google Scholar]
- 51.Feeney KA, Hansen LL, Putker M, Olivares-Yanez C, Day J, Eades LJ, et al. Daily magnesium fluxes regulate cellular timekeeping and energy balance. Nature. 2016;532(7599):375–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Maguire D, Neytchev O, Talwar D, McMillan D, Shiels PG. Telomere homeostasis: interplay with magnesium. Int J Mol Sci. 2018;19(1):157. 10.3390/ijms19010157. [DOI] [PMC free article] [PubMed]
- 53.Khalid S, Bashir S, Mehboob R, Anwar T, Ali M, Hashim M, et al. Effects of magnesium and potassium supplementation on insomnia and sleep hormones in patients with diabetes mellitus. Front Endocrinol (Lausanne). 2024;15:1370733. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Piuri G, Zocchi M, Della Porta M, Ficara V, Manoni M, Zuccotti GV, et al. Magnesium in obesity, metabolic syndrome, and type 2 diabetes. Nutrients. 2021;13(2):320. 10.3390/nu13020320. [DOI] [PMC free article] [PubMed]
- 55.Alateeq K, Walsh EI, Ambikairajah A, Cherbuin N. Association between dietary magnesium intake, inflammation, and neurodegeneration. Eur J Nutr. 2024;63(5):1807–18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Alberti KGMM, Zimmet P, Shaw J. The metabolic syndrome—a new worldwide definition. Lancet. 2005;366(9491):1059–62. [DOI] [PubMed] [Google Scholar]
- 57.Howren MB, Lamkin DM, Suls J. Associations of depression with C-reactive protein, IL-1, and IL-6: a meta-analysis. Psychosom Med. 2009;71(2):171–86. [DOI] [PubMed] [Google Scholar]
- 58.Shi Z, Tuomilehto J, Kronfeld-Schor N, Alberti GK, Stern N, El-Osta A, et al. The circadian syndrome predicts cardiovascular disease better than metabolic syndrome in Chinese adults. J Intern Med. 2021;289(6):851–60. [DOI] [PubMed] [Google Scholar]
- 59.Shi Z, Tuomilehto J, Kronfeld-Schor N, Alberti G, Stern N, El-Osta A, et al. The circadian syndrome is a significant and stronger predictor for cardiovascular disease than the metabolic syndrome-the NHANES survey during 2005–2016. Nutrients. 2022;14(24):5317. 10.3390/nu14245317. [DOI] [PMC free article] [PubMed]
- 60.Itani O, Jike M, Watanabe N, Kaneita Y. Short sleep duration and health outcomes: a systematic review, meta-analysis, and meta-regression. Sleep Med. 2017;32:246–56. [DOI] [PubMed] [Google Scholar]
- 61.Zuithoff NP, Vergouwe Y, King M, Nazareth I, van Wezep MJ, Moons KG, et al. The patient health questionnaire-9 for detection of major depressive disorder in primary care: consequences of current thresholds in a crosssectional study. BMC Fam Pract. 2010;11:98. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data is publicly available on the NHANES website.