The association between the level of vitamin D3 and insulin resistance in the general adult population of the United States: A cross-sectional study based on the NHANES database

doi:10.21203/rs.3.rs-1410497/v1

Background

Nowadays, the number of patients with type 2 diabetes is increasing yearly, the leading cause of which is insulin resistance. Therefore, it is imperative to investigate the causes of insulin resistance in the population. Understanding the mechanism of insulin resistance can effectively prevent and control the occurrence of type 2 diabetes. Few studies been conducted from the perspective of the general population previously, now, we used the NHANES public database to analyze the relationship between vitamin D3 and insulin resistance in the general population of the United States (≥18 years old), to provide clinical evidence and try to find a appropriate level of vitamin D3 in the general population.

Methods

The Health Center conducted a survey from 2008 to 2018, covering five cycles with 49,696 participants who were eligible for the study. Multivariate logistic regression was used to analyze the relationship between vitamin D3 and insulin resistance, excluding other potential confounders, and a smooth curve fitting was drawn.

Result

This study found a significant association between vitamin D3 and insulin resistance, with an odds ratio and 95%CI of 0.89 (0.83, 0.95). The association was most significant at BMI 24-28, with odds ratio and 95%CI of 0.80(0.69, 0.93).

Conclusion

This study suggests that keeping vitamin D3 levels as high as possible in the general US population can effectively reduce the incidence of insulin resistance.

Insulin resistance

Vitamin D3

NHANES

BMI

Type 2 Diabetes

Vitamin D3(VitD3) is an essential fat-soluble vitamin that plays an important role in maintaining the bone function, regulating the immune system, and reducing the risk of metabolic disorder [1, 2]. According to recent epidemiological studies, VitD3 deficiency occurs among general population worldwide in different age groups, regions, and ethnicities [3, 4]. There are two Main sources of VitD3 in the body. One is 7-dehydrocholesterol, which is formed through sunlight or ultraviolet (UVB) radiation into the skin, with approximately 70% of VitD3 is made this way. VitD3 can also be supplemented through food, usually in both dietary intake and supplement forms. Common sources of dietary intake are oily fish, milk, juice, grains, etc. About 30% of VitD3 is derived in this way [5, 6]. In the past, people used to exercise outdoors, but now people choose to spend more time indoors using electronic devices. This may result in reduced exposure to sunlight and therefore a correspondingly reduction in UV exposure [6, 7]. Moreover, studies have reported that the increase of free fatty acids in the blood inhibits the absorption and activity of VitD3 [8], which may partly elucidate the general deficiency of VitD3 in the modern life.

Insulin resistance is a phenomenon that occurs when islet-sensitive tissues become insensitive to insulin and is prevalent in type 2 diabetes [9, 10]. It has been reported that timely intervention can lead to remission or even disappearance of insulin resistance, which is significant for the control and prevention of type 2 diabetes [11, 12]. In previous studies, VitD3 appears to be a protective factor for diabetes [13]. In diabetic patients, high levels of VitD3 can reduce the probability of diabetic complications and alleviate insulin resistance symptoms [14–16]. However, little research has been conducted on the relationship between VitD3 and insulin resistance in the general population. Therefore, this study analyzed the relationship between VitD3 and insulin resistance in the general U.S. adult population (excluding underlying conditions such as hypertension, hyperuricemia, hyperlipidemias and diabetes) using publicly available data conducted by the National Center for Health Statistics to understand whether VitD3 is a risk factor for the development of insulin resistance, which is critical for the prevention and control of type 2 diabetes.

2.1 Study design

A cross-sectional study was adopted in this study. The evaluated VitD3 content was the target-independent variable. The presence or absence of IR was the dependent variable. Participants were divided into two groups based on whether they had IR or not, according to the American IR criteria [17]. One group was IR positive, the other was IR negative. The HOMA-IR formula is used to determine the definition of IR (Fasting insulin (µU/mL)×fasting glucose (mmoL/L) /22.5). We followed the methods of Shashaj B et al, which the normal American adult HOMA-IR ≥ 2.6 is IR positive, whereas HOMA-IR < 2.6 is considered IR negative[18][41].

2.2 Participating population

The National Health and Nutrition Examination Survey (NHANES) provided all the data. NHANES is a U.S. cross-sectional population-based survey that gathers data on household health and nutrition. These packets include adults and children across the United States, composed of demographic statistics, diet data, examination data, laboratory data, and questionnaire data. This study used data from five NHANES cycles from 2009 to 2018, as previous data had not been tested in laboratory tests of insulin or VitD3. We have established strict standards for admission and exclusion. The following criteria were used to select participants: (1) at least 18 years of age; (2) data of Insulin resistance index and VitD3 were also available. Exclusion criteria included: (1) patients with type 1 diabetes, gestational diabetes or type 2 diabetes; (2) acute complications (diabetic ketoacidosis, diabetic hyperosmolar coma and lactic acidosis); (3) patients with combined liver, biliary and renal diseases and/or diseases affecting calcium and phosphorus metabolism; (4) patients taking any medicine known to affect VitD3 metabolism and abnormal thyroid and parathyroid hormone secretion; (5) patients with infections, immune diseases and malignancies; (6) patients with history of osteoporosis or other abnormal bone metabolism diseases; (8) severe patients with a recent history of surgery, trauma, or a history of stress such as surgery, trauma, severe infection, etc.; (9) patients with mental illness. (10) VitD3 or vitamin D pills are taken by the participants. Finally, a total of 2746 participants were included in this study (Fig. 1).

2.3 Disease determination

In this study, we developed diagnostic criteria for severe disease based on international standards. The insulin resistance was defined HOMA-IR as described in study design. We followed the methods of Shashaj B et al to measure insulin resistance in the American population [18]. The definition of diabetes was based on the 2015 American Diabetes Association criteria. In current study, based on questionnaire surveys and laboratory tests, we defined diabetes as a group of participants who reported diabetes and took medicine for hemoglobin ≥ 6.5 and fasting glucose ≥ 7.0 [19]. We took an average of three blood pressure measurements taken while the participants were calm and used the average to assess whether the participants had high blood pressure. We defined hypertension as systolic blood pressure ≥ 130mmHg or diastolic blood pressure ≥ 80 mmHg or those who reported hypertension or were taking antihypertensive drugs [20, 21], which is consistent with the 2017 American Heart Association blood pressure guidelines. We followed the methods of Karr S et al. defined participants with criteria total cholesterol ≥ 5.72 mmol/L and triglyceride ≥ 1.70 mmol/L as hyperlipidemic participants by reviewing the literature [22]. Following the methods of White, G.E et al. participants were divided into two groups according to smoking status [20]. (1) Existing smokers: have smoked more than one cigarette per day in the past 30 days; (2) non-smokers: smoking less than 100 cigarettes in lifetime or never smoked. We investigated the classification of alcohol consumption in previous studies and classified alcohol consumption into two levels. Drinkers: Those who consume more than 12 drinks per year. Non-drinkers: People who has no more than 12 drinks per year [20, 23]. In this study, we defined those earning less than $20,000 as low income; Middle-income people are defined as those earning between $20,000 and $54,999 a year; High earners are defined as those earning $55,000 or more. We use the NHANES education level classification method to classify education level into five categories: below 9th grade, 9-11th grade (includes 12th grade without diploma), high school graduate/GED or equivalent, partial college or AA degree in addition to college graduate or above. BMI was calculated as follows: BMI = weight (kg)/ height (m²). The cutoff points for BMI were Low body weight (< 18.5 kg/m²), normal (18.5–24.9 kg/m²), overweight (25-29.9 kg/m²), and obesity (BMI ≥ 30.0 kg/m²) [23].

2.4 Data Collection

All data was gathered by uniformly trained investigators. The data were collected through questionnaires that included demographic information (gender, age, race/ethnicity, educational level, etc.), health-related behaviors (smoking and alcohol intake), diabetes, etc. Standard methodologies were used to measure the subject's height, weight, and waist circumference. Blood pressures were measured 3 times after 5 minutes of rest and if the blood pressure measurement is interrupted or incomplete, take a fourth time. Income, smoking status and alcohol consumption were collected through questionnaires. Quantitative determination of 25-hydroxyvitamin D3 (25OHD3), epi-25-hydroxyvitamin D3 (epi-25OHD3) in human serum were performed by ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Fasting glucose and insulin data were collected by morning venipuncture after fasting for 9 hours. The lipid data were provided by the University of Minnesota laboratory, and NHANES collected lipid data only for participants aged 6 years and older.

2.5 Statistical analysis

The NHANES database selects 5,000 individuals per year from fifteen different location frames across all counties in the United States. Therefore, the data are universal and extensive. The unique multi-stage probability sampling technique used by NHANES allows us to better represent the prevalence of insulin resistance in the U.S. population over the past few years. To make our study more convincing, we collected samples from five consecutive cycles of the NHANES database. We selected data with our requirements through a rigorous and comprehensive screening process to obtain a more representative sample. Subjects were divided into IR-positive and IR-negative groups. Statistical analysis was performed using R language version 4.0.2, and bilateral P value < 0.05 was considered statistically significant. In order to explain the missing variables, multivariate attribution was used in this study. With the purpose of increasing statistical power and avoiding bias, missing data covariates were removed from data analysis. A sensitivity study was also conducted to assess whether the generated complete data differed considerably from the estimated data. According to analysis, generated complete data were similar with raw data. Therefore, the outcomes of our following multivariable analyses were derived from the Rubin's guidelines based estimation datasets. The statistical study consisted of three main stages to assess whether there was an independent association between VitD3 and IR Step 1: We used weighted univariate and multivariate logistic regression model. We set up four different models: model 1, without adjusting covariates; model 2, only adjusted for gender, age and race; model 3, model 2 + ALT, AST, TG, TC, GGT, ALB, HDL; model 4, Model 3 + smoking, drinking, income, education. Step 2: Smooth curve fitting was used to observe the correlation between VitD3 and IR. Step 3: Weighted stratified logistic regression models were conducted in subgroup analysis to confirm whether the results were stable. After converting continuous variables to categorical variables based on clinical cut points or trilliers, an interaction trial was conducted. In the effect modification testing, interaction terms between subgroup indicators were used, followed by the likelihood ration test.

3.1 Baseline characteristics of selecting participants

A total of 49,694 participants from five consecutive cycles of NHANES were included in this study. After rigorous inclusion and exclusion criteria, 2,746 participants were screened for the following investigation (Fig. 1). Table 1 provides the baseline characteristics of the participants, dividing them into IR positive and IR negative groups, using HOMA-IR 2.6 as the comparative value. The average age of the 2,746 participants was 35.4 ± 14.6 years, of which 31.4% (862) were males. In addition to age, sex, smoking, alcohol consumption and AST, other factors in the two groups were statistically significant (P < 0.05). BMI, WC, UA, GGT and ALP in IR positive group were higher than those in IR negative group. Compared with IR negative group, the differences in education level, race, ALB, and VitD3 were significantly.

Table 1

Baseline characteristics of the study participants
Variables	Total (n = 2746)	Non-Insulin Resistance (n = 1884)	Insulin Resistance (n = 862)	P-value
sex, n (%)				0.943
male	1203 (43.8)	824 (43.7)	379 (44)
female	1543 (56.2)	1060 (56.3)	483 (56)
age, Mean ± SD	35.4 ± 14.6	35.5 ± 14.6	35.3 ± 14.8	0.772
race, n (%)				< 0.001
Mexican American	489 (17.8)	278 (14.8)	211 (24.5)
Other Hispanics	301 (11.0)	183 (9.7)	118 (13.7)
Non-Hispanic white	1080 (39.3)	807 (42.8)	273 (31.7)
Non-Hispanic black	507 (18.5)	316 (16.8)	191 (22.2)
Other Race	369 (13.4)	300 (15.9)	69 (8)
EDU, n (%)				< 0.001
non Received higher education	1754 (72.4)	1147 (68.8)	607 (80.1)
Received higher education	670 (27.6)	519 (31.2)	151 (19.9)
Income, n (%)				< 0.001
No more than $19.999	503 (19.4)	333 (18.7)	170 (20.9)
between $20,000 and $54.999	1007 (38.8)	646 (36.3)	361 (44.5)
more than $ 55,000	1083 (41.8)	802 (45)	281 (34.6)
SMOK, n (%)				0.316
NO	1835 (66.8)	1247 (66.2)	588 (68.2)
YES	911 (33.2)	637 (33.8)	274 (31.8)
Alcohol consumption, n (%)				0.002
NO	1704 (73.0)	1212 (74.9)	492 (68.7)
YES	630 (27.0)	406 (25.1)	224 (31.3)
BMI, Mean ± SD	25.3 (22.1, 29.3)	23.8 (21.3, 26.9)	29.8 (26.0, 33.8)	< 0.001
Waist, Mean ± SD	88.3 (79.8, 98.7)	84.3 (77.7, 92.7)	99.5 (90.4, 110.2)	< 0.001
ALB, Median (IQR)	43.0 (41.0, 45.0)	43.0 (41.0, 45.0)	42.0 (40.0, 44.0)	< 0.001
ALT, Median (IQR)	19.0 (15.0, 25.0)	18.0 (14.0, 23.0)	21.0 (16.0, 29.0)	< 0.001
AST, Median (IQR)	21.0 (19.0, 25.0)	21.0 (18.0, 25.0)	22.0 (19.0, 27.0)	< 0.001
ALP, Median (IQR)	61.0 (50.0, 74.0)	58.0 (48.0, 71.0)	66.0 (55.0, 80.0)	< 0.001
BUN, Median (IQR)	3.9 (3.2, 4.6)	3.9 (3.2, 5.0)	3.9 (2.9, 4.6)	0.031
GGT, Median (IQR)	15.5 (12.0, 23.0)	14.0 (11.0, 20.0)	18.0 (14.0, 28.0)	< 0.001
UA, Median (IQR)	285.5 (237.9, 333.1)	279.6 (237.9, 327.1)	297.4 (255.8, 345.0)	< 0.001
Vit D3, Median (IQR)	56.2 (40.8, 71.4)	58.5 (42.5, 74.5)	50.9 (37.5, 64.8)	< 0.001
Abbreviation: IR, Insulin resistance; EDU, education; SMOK, smoking; BMI, Body Mass Index; ALT, Alanine aminotransferase; ALB, albumin; AST, Aspartate aminotransferase; ALP, alkaline phosphatase; BUN, Urea nitrogen; GGT, γ-glutamyl transpeptidase; UA, Uric acid ;

3.2 Univariate analyses

Table 2 shows the correlation of age, sex, BMI, alcohol consumption, smoking, and several biochemical indicators with insulin resistance. We observed that higher education and income above $55,000 were negative factors for IR, with effect values OR and 95% CI of 0.68 (0.54 ~ 0.85). Compared with MexicAn-Americans, whites had a lower IR risk of 0.45(0.36 to 0.56) and other races 0.3(0.22 to 0.42). In contrast, IR was positively correlated with BMI, waist circumference, UA, AST, GGT, and alcohol intake based on a univariate analysis (Table 2).

Table 2

Univariate analysis for insulin resistance.
Variables	Insulin Resistance
Variables	OR (95%CI)	P-value
Age	1 (0.99 ~ 1)	0.772
gender
male	1
female	0.99 (0.84 ~ 1.17)	0.91
Race
Mexican American	1
Other Hispanics	0.85 (0.63 ~ 1.14)	0.275
Non-Hispanic white	0.45 (0.36 ~ 0.56)	< 0.001
Non-Hispanic black	0.8 (0.62 ~ 1.03)	0.078
Other Race	0.3 (0.22 ~ 0.42)	< 0.001
edu	0.55 (0.45 ~ 0.67)	< 0.001
Income, n (%)
No more than $19.999	1
between $20,000 and $54.999	1.11 (0.89 ~ 1.38)	0.356
more than $ 55,000	0.68 (0.54 ~ 0.85)	0.001
Smoking
NO	1
YES	0.91 (0.77 ~ 1.08)	0.296
Drink
NO	1
YES	1.33 (1.12 ~ 1.59)	0.001
BMI	1.23 (1.2 ~ 1.25)	< 0.001
Waist	1.09 (1.08 ~ 1.1)	< 0.001
ALB	0.9 (0.88 ~ 0.93)	< 0.001
ALT	1.03 (1.02 ~ 1.03)	< 0.001
AST	1.01 (1.01 ~ 1.02)	< 0.001
ALP	1.02 (1.02 ~ 1.02)	< 0.001
BUN	0.93 (0.88 ~ 0.99)	0.026
GGT	1.02 (1.01 ~ 1.02)	< 0.001
Vit D3	0.99 (0.98 ~ 0.99)	< 0.001
Abbreviation: IR, Insulin resistance; EDU, education; SMOK, smoking; BMI, Body Mass Index; ALT, Alanine aminotransferase; ALB, albumin; AST, Aspartate aminotransferase; ALP, alkaline phosphatase; BUN, Urea nitrogen; GGT, γ-glutamyl transpeptidase; UA, Uric acid ;

3.3 Multiple regression analysis

Table 3 shows four models of the relationship between VitD3 and IR after adjusting for correlation covariates through multivariate logistic regression analysis (univariate and multivariate logistic regression). The association between VitD3 and IR were presented in Table 3 as effect sizes OR and 95% confidence intervals. The model-based effect value can be understood as the risk of acquiring IR lowers with each unit of VitD3. For example, in the unadjusted model, when the IR effect size was 0.86, the risk of IR decreased by 14% while VitD3 increased by one unit (OR: 0.86(0.81, 0.91)). In model II, the risk of IR decreased by 13% (0.87(0.82, 0.93) for each unit increase in VitD3. In model III, the risk of IR decreased by 11% (0.89(0.83, 0.95) for each unit increase in VitD3. In model IV, the risk of IR decreased by 11% (0.89(0.83, 0.95) for each unit increase in VitD3.

Table 3

The association between Vit D3 and insulin resistance in a multiple regression model
outcome	β (95%CI)	P-value
Non-adjusted Model 1	0.86(0.81,0.91)	< 0.001
Model 2	0.87(0.82,0.93)	< 0.001
Model 3	0.89(0.83,0.95)	< 0.001
Model 4	0.89(0.83,0.95)	< 0.001
Model 1: Non-adjusted; Model 2: gender, age, race; Model 3: Mode 2 + ALT, AST, TG, TC, GGT, ALB, HDL. Model 4: Model 3 + smoking, drinking, income, education

3.4 Subgroup analysis

We additionally divided the data by age, BMI, RACE, and gender. The relationship between VitD3 and insulin resistance was studied separately. The relationship remained steady for age, sex, and race groups in the fully adjusted model (P for interaction > 0.05). The relationship, on the other hand, changed among BMI groups (P for interaction = 0.048 < 0.05). With an impact size of 0.8 (0.69, 0.93) and a 95% confidence interval of 0.8 (0.69, 0.93), the linkage between VitD3 and IR was strongest in the BMI 24–28 group, and weakened in the groups of BMI less than 24 or higher than 28. The effect values and 95% confidence intervals were 0.98 (0.87, 1.11) and 0.97 (0.86, 1.09), respectively (Fig. 5).

3.5 The results of linearity of VitD3 and IR

In this study, we analyzed the linear relationship between VitD3 level and IR (Fig. 2). After controlling factors such as age, race, education, income, smoking, alcohol, hypertension, BMI, WC, TC, ALT, and AST, the smooth curve and result of logistical regression revealed that the connection between VitD3 and IR was linear. In addition, we analyzed the linear association between VitD3 and IR at different BMI levels. (Fig. 3, 4) Figs. 3 and 4showed the relationship between VitD3 and IR at different BMI levels in summary and in each subgroup, respectively. The relationship of VitD3 and IR are not linear at different BMI levels, as shown in Figs. 3 and 4, which possibly due to the limited sample size. The general tendency showed that VitD3 is a negative factor for IR at all BMI levels.

VitD3 deficiency appears to be a widespread phenomenon worldwide currently. Numerous studies have shown that VitD3 deficiency leads to considerable metabolic changes [24–26]. Total cholesterol (TC), low-density lipoprotein (LDL), and high-density lipoprotein (HDL) changed, and HOMA-IR (an indicator of insulin resistance) elevated as the level of VitD3 decreased [27–29]. A recent study mentioned levels of VitD3 and its metabolites decreased, with a corresponding decrease in the activity of enzymes in pancreatic beta cells and insulin-responsive cells. This suggests that VitD3 may be a potential regulator of insulin secretion and a maintenance factor of pancreatic β-cell activity.

Previous studies have analyzed the relationship between VitD3 and insulin resistance in patients with pre-existing diabetes, which is flawed because the onset of type 2 diabetes is almost irreversible. In previous studies, insulin resistance has been confirmed as the pathogenesis of type 2 diabetes [11, 30, 31]. Therefore, in order to prevent and control the increase of type 2 diabetes, it is necessary to investigate the relationship between insulin resistance and VitD3 in the general population. Our findings suggest that VitD3 and insulin resistance are negatively associated after adjusting for potential confounders. The risk of insulin resistance decreased with the increase of VitD3 across the whole VitD3 level.

In general population, education, income, race, BMI, and waist circumference were all associated with the development of insulin resistance. For example, whites and other races were less likely to develop insulin resistance than Mexican Americans. The odds ratios for the insulin resistance were 0.42 (0.3, 0.58) in whites and 0.29 (0.18, 0.45) for other races compared with Mexicans. Waist circumference, BMI, and alcohol consumption were important factors in the development of insulin resistance, with each unit increase in waist circumference and BMI increasing the incidence of insulin resistance by 9% and 22%, respectively, in the unadjusted model. Compared with non-drinkers, drinkers were 26 percent more likely to develop insulin resistance. A previous animal study found that VitD3 inhibited the secretion of inflammatory factors that contribute to insulin resistance, such as interleukin-6 and tumor necrosis factor-α, that contribute to insulin resistance by activating the Jun n-terminal kinase 1 (JNK1) and IKK-β/NF-κB pathways to phosphorylate insulin receptor-1 and attenuate insulin signaling, and that VitD3 supplementation can effectively inhibit the physiological functions of interleukin-6 and tumor necrosis factor-α, thus preventing the development of insulin resistance[32, 33]. In this study, we observed consistent result in the general U.S. population, with a greater negative association between VitD3 and insulin resistance, particularly in the general U.S. population with a BMI of 24–28. According to data analysis, each 10-unit increase in VitD3 in this population was associated with a 20% reduction in the risk of insulin resistance, with an odds ratio and 95% CI of 0.8 (0.69, 0.93).

In previous studies, BMI, waist circumference, HDL, and TG were important insulin resistance factors [34, 35]. Subgroup studies have shown a stable stratified relationship between serum VitD3 and IR, and there was an interaction between VitD3 and insulin resistance at different BMI levels. The association was highest at the range of BMI 24–28. The exact mechanism needs to be elucidated by further studies.

Previous studies have investigated possible factors in the development of IR. For example, the relationship between age and IR development has been well documented [35]. In previous studies, smoking may be a risk factor for developing IR and inhibiting insulin sensitivity [36–40]. However, smoking and insulin resistance were not statistically significant in this study, which may have to do with our population composition. We found that the non-smoking population was too sparse to reflect populational differences.

There are several limitations to this study. Firstly, the composition of the previously mentioned population, because our data came from publicly available database and were not self-selected, excluded other populations that were not eligible for inclusion in the study. For example, there were not enough non-smoking samples, which made it hard to analyze whether smoking is a risk factor for IR. Secondly, this study excluded specific populations, including unhealthy populations such as hyperlipidemia, hyperglycemia, and hyperuricemia. Hence, our study is not representative for all the populations. Third, we did not investigate the risk of IR occurrence into a genetic level. Furthermore, the use of HOMA-IR to define insulin resistance is somewhat inaccurate and difficult to replicate.

Despite the limitations of the current study, the advantages of this study remain to be highlighted. Firstly, our study is different from previous studies. Previous studies mainly focused on the insulin resistance in diabetes patients, while in this study, IR was investigated in general population with more suitability. Secondly, this study explored the five-cycle database of NHANES. NHANES is based on complex design sampling. Finally, we discovered for the first time that BMI interacts with VitD3 in the occurrence of IR which has not been mentioned previously.

Briefly, our study shows that the development of insulin resistance is negatively associated with VitD3 levels, a discovery that further strengthens the understanding of insulin resistance in the context of widespread VitD3 deficiency. It is inferred the significance of VitD3 supplementation to reduce the insulin resistance and prevent the development of diabetes in the general US population. Additionally, the relationship between VitD3 and insulin resistance in different countries deserves further study.

IR, Insulin resistance; EDU, education; SMOK, smoking; BMI, Body Mass Index; ALT, Alanine aminotransferase; ALB, albumin; AST, Aspartate aminotransferase; ALP, alkaline phosphatase; BUN, Urea nitrogen; GGT, γ-glutamyl transpeptidase; UA, Uric acid; TG, triglyceride; TC, total cholesterol; VitD3, vitamin D3

Ethics approval and consent to participate

The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). All information from the NHANES program is available and free for public, so the agreement of the medical ethics committee board was not necessary.

Consent for publication

Not Applicable.

Availability of data and materials

Availability of data and materials Data can be downloaded from ‘NHANES’ database (https://www.cdc.gov/nchs/nhanes/index.htm).

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by Grants from National Natural Science Foundation of China (81860370), General Project of Natural Science Foundation of Qinghai Province (2018-ZJ-721), CAS “Light of West China” Program(2019) and the Open Project of State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University (2017-ZZ-15).

Authors' contributions

Rongpeng Gong, Mingxiang Wang, Lei Yang and Xiaoxing Wei conceived the idea; Rongpeng Gong, and Mingxiang Wang wrote the manuscript; Rongpeng Gong, Lei Yang and Mingxiang Wang collected and read the literature and revised the article; Xiaoxing Wei and read through and corrected the manuscript. All authors read and approved the final manuscript. Mingxiang Wang is the first author, Rongpeng Gong and Lei Yang is co-first author, Rongpeng Gong and Xiaoxing Wei is the corresponding author of this paper.

Acknowledgments

We wish to thank the timely help given by Xiaofang Shen and Zixin Xu in analyzing the large number of samples.

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No competing interests reported.

The association between the level of vitamin D3 and insulin resistance in the general adult population of the United States: A cross-sectional study based on the NHANES database

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Abstract

Figures

1. Background

2. Methods