The association between dietary magnesium intake and hemoglobin glycation index

DOI: https://doi.org/10.21203/rs.3.rs-2102579/v1

Abstract

Objective

The data for the effect of dietary magnesium (Mg) on hemoglobin glycation index (HGI) is limited. Thus, this study aimed to examine the relationship between dietary Mg and HGI in the general population.

Methods

Our research used the data from the National Health and Nutrition Examination Survey from 2001 to 2002. The dietary intake of Mg was assessed by two 24-h dietary recalls. The predicted HbA1c was calculated based on fasting plasma glucose. Logistic regression and restricted cubic spline models were applied to assess the relationship between dietary Mg intake and HGI.

Results

We found a significant inverse association between dietary Mg intake and HGI (β = -0.00016, 95%CI: -0.0003, -0.00003, P = 0.019). Dose-response analyses revealed that HGI decreased with increasing intakes of Mg when reached the point above 412 mg/d. There was a linear dose-response relationship between dietary Mg intake and HGI in diabetic subjects, and there was an L-shape dose-response relationship in non-diabetic individuals.

Conclusion

Increasing the intake of Mg might protect from HGI. Further prospective studies are requested before dietary recommendations.

1. Introduction

Magnesium (Mg) is an essential element in the human body, which acts as a cofactor in more than 600 enzymatic reactions1,2 and regulates the metabolism of muscles, bones, nervous system, and cardiovascular system3. A meta-analysis study showed that dietary magnesium intake exhibited an inversely linear dose-response relationship with the incidence of type 2 diabetes. After adjusting for age and body mass index (BMI), each 100 mg/day increase in dietary magnesium intake was associated with an 8%-13% lower risk of developing type 2 diabetes4. In a randomized clinical trial, administration of 250 mg of elemental Mg daily showed a significant reduction (8.32 to 7.96%) in the plasma level of glycated hemoglobin A1c (HbA1c) after a three-month intervention 5.

Glycated hemoglobin (HbA1c) is the gold standard to measure blood glucose control, and also an important means to diagnose and manage diabetes6. However, there are inter-individual differences in the accuracy of HbA1c detection. For example, factors that impact the red cell life span or glucose gradient across the red cell membrane can affect HbA1c 7,8. In these situations, the blunt use of HbA1c may lead to errors in the doctor's assessment of the patient's condition.

Recently, the hemoglobin glycation index (HGI) has been proposed as a marker to assess inter-individual variation in HbA1c 9,10. A clinical study showed a positive relationship between HGI and risk of diabetes-related complications11. A recent meta-analysis showed that high HGI was associated with an increased risk of cardiovascular diseases and mortality in type 2 diabetic patients 12.

Epidemiological studies have focused on the potential role of dietary Mg intake in modifying HbA1c, but no study on HGI. We hypothesized that dietary Mg intake may relate to the inter-individual variation in hemoglobin glycosylation. Thus, the current cross-sectional study aimed to investigate the association between dietary Mg intake and HGI in the representative general population.

2. Materials And Methods

2.1 Study population

NHANES is conducted by the National Center for Health Statistics to assess the health and nutritional status of the US general population. Participants receive a in-home interview and health examination. The protocol was approved by the NCHS Research Ethics Review Board 13.

A total of 11,039 individuals participated in NHANES during 2001–2002. Among them, 5,993 individuals were 18 years of age and older. Considering the sub-sample was random, the representative design of the study was maintained. We excluded participants without data on HbA1c (n = 622), fasting plasma glucose (FPG) (n = 80) and incomplete or unreliable 24-h recall data on dietary interview (n = 326). We excluded pregnant women (n = 291) and cancer patients (n = 385) because obvious changes often occur in their dietary intake and body weight. Additionally, to reduce misclassification bias caused by self-reported information in the 24-h dietary recall, we excluded individuals whose energy intake exceeds ± 3 SDs of the mean value of the log-transformed total energy intake (n = 40), leaving 4249 subjects for the following analyses (Fig. 1).

2.2. Assessment of dietary intakes

Two-day 24-hour dietary recall surveys were conducted by trained interviewers. Daily aggregates of food energy and Mg from all foods and beverages were calculated using the U.S. Department of Agriculture Food and Nutrient Database.

2.3. Definition of HGI

HGI was calculated by subtracting the predicted HbA1c from the observed HbA1c 11. The predicted HbA1c was obtained by inserting the corresponding FPG values into the simple linear regression equation (HbA1c (%) = 0.0245796 × FPG (mg/dL) + 3.203204, R-squared = 0.6414, P < 0.001). The simple linear regression equations were performed based on all enrolled participants (n = 4294). HbA1c was detected by the Primus Automated HPLC system. FPG was measured by a hexokinase enzymatic method using a Cobas Mira Chemistry System.

2.4. Assessment of other variables

NHANES provided information about age, gender, ethnicity, BMI, educational level, family income, smoking status, alcohol drinking status, hypertension, hypercholesterolemia, diabetes, cardiovascular disease, hemoglobin (Hb), and serum creatinine (CR). The race was categorized into Mexican American, other Hispanic, non-Hispanic white, non-Hispanic black, and other races. Educational level was also classified into three categories: < high school, high school, and > high school. Smoker was defined as individuals who smoked at least 100 cigarettes in life. Alcohol drinker was defined as those who consumed alcohol at least 12 times per year. Hypertension was defined as blood pressure ≥ 140/90 mmHg, self-reported history of hypertension or currently taking blood pressure lowering medication 14. Hypercholesterolemia was defined as either a self-reported physician diagnosis or a serum total cholesterol concentration exceeding 240 mg/dl 15. Diabetes was defined as FPG ≥ 126 mg/dL, HbA1c ≥ 6.5%, a self-reported physician diagnosis or current taking insulin treatment 6. Cardiovascular disease (CVD) was defined as self-reported physician diagnosis of either congestive heart failure (CHF), coronary heart disease (CHD), angina pectoris (AP), heart attack, and stroke 16.

2.5. Statistical analysis

Continuous variables were described by medians and interquartile range (IQR). Qualitative variables were described by frequency distributions. General characteristics were evaluated using the Mann-Whitney test and the chi-square test. Linear regression models were fitted to assess the relationship between dietary Mg intake and HGI. No adjustment was made in the first model. The second model adjusted for age, gender, and race. The third model was the same as the second model with additional adjustments for education level, family income, BMI, smoking, alcohol drinking, Hb, CR, hypertension, hypercholesterolemia, diabetes, cardiovascular disease, and daily energy intake. Potential interactions between dietary Mg intake and other covariates were tested by fitting regression models containing the main effect and their cross-product interaction terms. Dose-response relationships were assessed by restricted cubic spline with three knots based on the 10th, 50th and, 90th percentiles of dietary Mg intake in the fully adjusted model. SURVEY procedures were performed to account for the weighting, clusters, and strata present using Stata version 15.1 (Stata Corporation, College Station, TX, USA). All reported p-values were two-sided.

3. Results

Table 1 provided a description of the basic characteristics of the study participants by sex. There were 4,249 participants in the final analysis sample. The average age of the participants was 45.42±19.49 years. The median (IQR) magnesium intake was 248 (176, 365) mg. Compare to female, the male was more likely to be younger, smokers and alcohol drinkers, have higher dietary Mg and energy intakes, have higher Hb and serum CR values, have lower serum CRP and HGI values, have lower BMI and blood pressure (all P < 0.01).

Table 2 showed the association between dietary Mg intake and HGI using multiple linear regression analyses. In the crude model, Mg was negatively associated with HGI (β = -0.00029, 95%CI: -0.0004, -0.00018, P < 0.001). The association between dietary Mg intake and HGI was still observed after adjustment for age, sex, and race (P = 0.029). In the fully adjusted model, every 1 mg increase in dietary Mg intake was associated with -0.00016 (95%CI: -0.0003, -0.00003, P = 0.019) lower HGI. 

The restricted cubic spline model was applied to explore the shape of the dose-response relationship between dietary Mg intake and HGI. Figure 2 showed the association of dietary Mg intake with HGI was linear (P for non-linearity = 0.245). HGI decreased with increasing intakes of Mg when reached the point above 412 mg/d. In stratified analysis by diabetes, for diabetic individuals, we found a linear dose-response relationship (P for non-linearity = 0.156) when the dietary Mg intake was higher than 427 mg/d (Figure 3 A). However, for non-diabetic individuals, there was the L-shape dose-response relationship between (P for non-linearity = 0.565), and it reached a plateau when the dietary Mg intake was higher than 495 mg/d (Figure 3 B).

We explored the role of covariables in the association between dietary Mg intake and HGI. As shown in Figure 4, none of the variables, including age, gender, BMI, smoker, alcohol drinker, hypertension, hypercholesterolemia, diabetes, and cardiovascular diseases, showed significant effect modification on the association between dietary Mg and HGI (all P for interaction > 0.05). In sensitivity analysis, after adjusting observed HbA1c, dietary Mg intake was negatively correlated with higher HGI (β = -0.0002, 95%CI: -0.00033, -0.00006, P = 0.008).

4. Discussion

In the present study, we investigated the association of dietary Mg intake with HGI in a large population-based health and nutrition survey using the NHANES data. We found a negative correlation between dietary Mg intake and HGI. After adjusting for various potential confounding factors, including age, sex, race, education level, family income, BMI, smoking, alcohol drinking, Hb, CR, hypertension, hypercholesterolemia, diabetes, cardiovascular disease, and daily energy intake, the correlation was still significant. We found a linear dose-response relationship when dietary Mg intake exceeded 412 mg/d, which implied that there is a threshold effect on the inverse association of dietary Mg intake with HGI. According to the 2020–2025 American dietary guidelines17, the adequate intakes of Mg were 360 mg/d (aged 18 year), 310 mg/d (aged 19–30 years) and 320 mg/d (aged 31 + years) for females; 410 mg/d (aged 18 year), 400 mg/d (aged 19–30 years) and 420 mg/d (aged 31 + years) for males. Of the 4249 participants, only 869 (20.45%) met the criteria. In stratified analysis by diabetes, we found that dietary Mg intake was negatively linear correlated with HGI in both diabetic and non-diabetic individuals. However, for non-diabetic individuals, we found an L-shaped dose-response relationship, which means that when the plateau is reached, even if the dietary intake of Mg is increased, the protective effects on HGI will not be increased.

The prevalence of hypomagnesemia ranges between 14 and 48% in diabetic patients, and these patients with hypomagnesemia show a more rapid disease progression and have an increased risk for diabetes complications 18,19. Epidemiological studies have shown that dietary Mg intake is inversely associated with the risk of diabetes in a dose-response manner 20. Moreover, clinical studies have demonstrated that oral Mg supplementation reduces HbA1c among subjects with diabetes and diabetic foot ulcers 21,22. Serum Mg levels are tightly controlled between 0.7 and 1.05 mmol/L in healthy humans. Mg deficiency in diabetic patients is mainly caused by a low intake and impaired renal function. In our study, to exclude the potential effect of renal impairment on Mg wasting, serum CR is adjusted and a significant negative correlation still exists.

Although current strategies in the management of glycemia mainly rely on HbA1c. However, a discrepancy between HbA1c and other assessments of glycemia is well reported 10,23−25. Thus, Hempe et al. proposed the HGI as the difference between the measured HbA1c and the predicted HbA1c derived from blood glucose estimations 26. Trial conducted by AleCardio has shown a positive relationship between HGI and mortality after additional adjustment for HbA1c 27. Similarly, after adjusting for observed HbA1c, our study also showed a significant relationship between dietary Mg intake and HGI, which indicated that HGI is independent of HbA1c in these scenarios. On one hand, the mechanisms of Mg on glucose metabolism could explain the negative association between dietary Mg intake and HGI. For example, Mg-ATP complex is key regulators of glucokinase, PI3K/Akt kinase, and ATP-sensitive K+ channels; thus, Mg deficiency results in decreased Mg-ATP complex level, which induces hyperinsulinemia and decreased insulin sensitivity 28. Mg may also affect glucose metabolism by the modulation of inflammatory responses, such as interleukin-6, interleukin-10, and tumor necrosis factor-alpha in diabetic, prediabetic, and non-diabetic subjects 29–31. On the other hand, factors that impact red blood cell lifespan and the glucose gradient across the red cell membrane are known to affect HbA1c independently of glycemia. An animal study showed that new erythrocytes rapidly develop biochemical and morphologic abnormalities with aging in a magnesium-deficient plasma environment 31. Paolisso et a., find that the impairment of insulin-induced erythrocyte magnesium accumulation is correlated to impaired insulin-mediated glucose disposal in type 2 diabetic patients 32.

Our study has several strengths. Firstly, this study included nationally representative individuals, allowing us to generalize our findings to a broader population and avoiding selection bias. Secondly, the dose-response relationship between dietary Mg intake and HGI was explored, which allows nutritionists to provide dietary recommendations. Thirdly, we adjusted many confounding factors and made a sensitivity analysis. Nevertheless, several limitations should be addressed. Firstly, this was a cross-sectional observational study, thus the directional causality cannot be ascertained. Secondly, the potential effects from unmeasured or residual confounding cannot be ruled out. Thirdly, the data obtained through 24-h dietary recall might have recall bias.

In conclusion, in the representative sample of the US adult population, we found that a higher intake of dietary Mg was associated with a decreased HGI, independent of traditional risk factors, even HbA1c. These findings required confirmation in prospective studies before dietary recommendation.

Declarations

ACKNOWLEDGMENTS 

This study was supported by the National Natural Science Foundation of China Grant 

Award (82000743). Thanks to all individuals who collected data and making the NHANES datasets available on the website. 

 

CONFLICTS OF INTEREST 

The authors declare that there are no conflicts of interest.

 

DATA AVAILABILITY STATEMENT

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.cdc.gov/nchs/nhanes/index.htm.

 

ETHICS STATEMENT

The studies involving human participants were reviewed and approved by the Ethics Review Board of the National Center for Health Statistics. The participants provided their written informed consent to participate in this study.

 

AUTHOR CONTRIBUTIONS

JC, XW, and PG contributed to the study conception and design. Material preparation, data collection, and analysis were performed by SL and XW. The first draft of the manuscript was written by JC and SL, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

 

Biographies

Juan Chen is Associate Chief Professor at the Departments of Endocrinology at the Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, China.

Song Lin is Associate Professor at the Departments of Clinical Nutrition at the Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, China.

Xingzhou Wang is Associate Chief Professor at the Departments of Endocrinology at the Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, China.

Pengxia Gao is Chief Professor at the Departments of Endocrinology at the Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, China.

Xiwei Wang is a data analyst at the Department of Mathmatics at the University of Liverpool, UK.

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Tables

Table 1 Baseline characteristics of the study participants.

Variables

Total (n = 4249)

Male (n = 2180)

Female (n = 2069)

P

Age (years)

45.42±19.49

44.61±19.02

46.27±19.94

0.005

Hemoglobin (g/dL)

14.39±1.54

15.26±1.23

13.47±1.28

<0.001

Body mass index (kg/m2)

27.81±6.25

27.55±5.59

28.09±6.87

0.005

Creatinine (mg/dL)

0.91±0.43

1.02±0.39

0.8±0.44

<0.001

Magnesium intake (mg/d)

248(176-345)

284(200-389.5)

212(156-292)

<0.001

Energy intake (kcal/d)

2013(1461-2707)

2368(1739-3124.5)

1727(1287-2234)

<0.001

Hemoglobin glycation index

-0.06(-0.27-0.18)

-0.07(-0.29-0.16)

-0.04(-0.24-0.18)

0.006

Ethnicity, n(%)

 

 

 

0.944

    Mexican American

1002(23.58)

526(24.13)

476(23.01)

 

    Other Hispanic

182(4.28)

93(4.27)

89(4.30)

 

    Non-Hispanic White

2079(48.93)

1058(48.53)

1021(49.35)

 

    Non-Hispanic Black

844(19.86)

430(19.72)

414(20.01)

 

    Other Race

142(3.34)

73(3.35)

69(3.33)

 

Education, n(%)

 

 

 

0.835

    <High school

1145(26.95)

590(30.67)

555(29.79)

 

     High school

896(21.09)

454(23.60)

442(23.73)

 

    >High school

1746(41.09)

880(45.74)

866(46.48)

 

Famlily income, n (%)

 

 

 

0.080

    <55,000$

2813(66.20)

1421(66.71)

1392(69.25)

 

    ≥$55,000$

1327(31.23)

709(33.29)

618(30.75)

 

Smoker, n (%)

1853(43.61)

1131(58.78)

722(38.78)

<0.001

Alcohol drinker, n(%)

2523(59.38)

1551(83.03)

972(55.10)

<0.001

Hypertension,  n(%)

1244(29.28)

597(28.69)

647(32.68)

0.006

Hypercholesterolemia, n(%)

926(21.79)

479(21.97)

447(21.60)

0.772

Diabetes, n(%)

490(11.53)

267(12.25)

223(10.78)

0.134

Cardiovascular diseases, n(%)

363(8.54)

202(9.27)

161(7.78)

0.084

Data was presented as mean±standard deviation, median (25th percentile, 75th percentile) or percentages as appropriate.

 

Table 2 Association of dietary magnesium intake with hemoglobin glycation index.

Models

β (95% CI)

P

Crude model

-0.00029 (-0.0004, -0.00018)

<0.001

Model 1

-0.00018 (-0.00033, -0.00002)

0.029

Model 2

-0.00016 (-0.0003, -0.00003)

0.019

Model 1 adjusted for age, gender and race.

Model 2 adjusted for age, gender, race (non-Hispanic White; non-Hispanic Black; Mexican American; other Hispanic; other race), educational level (< high school; high school; > high school), body mass index (kg/m2), smoker (yes, no), alcohol drinker (yes, no), family income (< 55,000$, ≥ 55,000$), hypertension (yes, no), hypercholesterolemia (yes, no), diabetes (yes, no), cardiovascular diseases (yes, no), hemoglobin (g/dL), serum creatinine (mg/dL), and total daily energy intake (kcal/d).