Abstract
Background:
Vitamin D deficiency is associated with incidence of type 2 diabetes (T2DM) as well as poor glycemic control among T2DM patients, yet comparative studies of its association among ethnic minority populations are scarce.
Methods:
Using baseline data from a behavioral intervention study of Korean Americans (KAs) with T2DM (N = 250 KAs) and the NHANES data set, we explored differential roles of vitamin D on HbA1C level or T2DM control in several racial groups.
Results:
Significantly more KAs (55.2%) were vitamin D-deficient (U.S. average, 37.8%). Both common and unique correlates of vitamin D deficiency in minority populations were identified, including significant associations between Vitamin D and HbA1C in both non-diabetic and diabetic populations.
Discussion:
Future studies are warranted to explain the causal mechanism of the effect of vitamin D and glycemic control as well as to examine contextual factors associated with vitamin D deficiency in certain minority groups.
Keywords: Vitamin D, Type 2 Diabetes, Self-Help, Korean Americans, NHANES
BACKGROUND
Although accumulating evidence suggests that vitamin D may be beneficial for preventing and managing type 2 diabetes (T2DM) [1, 2, 3], there is no consensus about the underlying physiological mechanism. Vitamin D level has been thought to be both a modifier of T2DM because it is linked to insulin secretion, insulin resistance, and β-cell dysfunction [4, 5] and a mediator of DM control through the renin-angiotensin system and inflammation [6, 7, 8, 9]. An inverse relationship has been found between vitamin D deficiency and incidence of T2DM in many observational studies [10, 11, 12, 13], but with other factors introduced and controlled or with experimental trials of Vitamin D supplements, this causal relationship has become unclear [13] or has failed to demonstrate statistical significance for certain subpopulations [11]. For example, a national sample from the 2003–2006 National Health and Nutrition Examination Survey (NHANES) showed an inverse relationship between vitamin D and HbA1C levels in the 34-to 74-year-old age group, but not in others [14].
Although vitamin D supplements have been routinely recommended in regimens for T2DM management and control [1, 15, 16], the precise role of vitamin D in T2DM management and control is not yet fully understood, and research on their causative relationship remains inconclusive [17]. There is no consensus on a clinically meaningful threshold value for vitamin D deficiency or sufficiency with reference to T2DM [18]. Studies comparing Vitamin D’s effects on T2DM management among different racial/ethnic groups are scarce. Moreover, very little behavioral research is available on the sources of Vitamin D intake in different cultural groups. This critical gap in science and practice acts as a barrier to establishing a meaningful practice guideline for vitamin D supplementation based on precision health principles.
In this article, we present insights that we have gained from a clinical trial of an understudied ethnic minority sample, Korean Americans (KAs) with T2DM. Utilizing clinical and behavioral data from our clinical trial of the Self-Help Intervention Program for Diabetes Management (SHIP-DM) for KAs with T2DM and a comparison data set from the NHANES, we examine the vitamin D status of KAs with T2DM in comparison with other ethnic groups and explore vitamin D’s role in T2DM management.
METHODS
This study was part of an open-label, randomized controlled trial to assess the effectiveness of the SHIP-DM. Details of recruitment, enrollment, and retention are reported elsewhere [19, 20].
Participants
Participants were self-identified KAs with uncontrolled T2DM (HbA1C 7.0%, 53 mmol/mol, or higher), 35–80 years old, able to speak and read Korean. A total of 250 participants were randomized into an intervention (n = 120) or a control (n = 130) group; 209 (83.6%) completed the 12-month program.
For a comparable national sample, the NHANES 2007–2008 and 2009–2010 cycles were selected because the liquid chromatography-mass spectrometry (LCMS/MS) method was used in those cycles to measure blood serum 25-hydroxyvitamin D, or 25(OH)D [21]; data for dietary intake of vitamin D were also available from those cycles. Sources of vitamin D were provided by NHANES interview questions about prescribed medications and over-the-counter supplements. The data also included C-reactive protein (CRP) as a biological marker of acute and chronic inflammation. For analysis, CRP was categorized as (a) “low” if <1.0 mg/L, or (b) “high” if ≥1.0 mg/L [22]. For the national sample, we included individuals 35–80 years old but excluded those likely to have type 1 diabetes (diagnosed with DM before age 25; years of having DM equal to years on insulin).
Data Collection
Data collection protocols for the SHIP-DM and the NHANES were parallel; the SHIP-DM was a smaller study with five types of data for DM management: (1) demographic, (2) examination, (3) dietary, (4) laboratory, and (5) questionnaire data. The data were collected at baseline and 3, 6, 9, and 12 months, with the exception that dietary and questionnaire data were not collected at 9 months.
Measures
Demographic data comprised sex, age, education attainment, length of U.S. residency, housing type, living arrangement, and monthly income. Examination data encompassed blood pressure (BP) and height and weight to calculate body mass index (BMI). BMI was reclassified as (a) “normal” (<25), (b) “overweight” (25–29.9), or (c) “obese” (≥30).
Dietary data concerning eating behavior and food intake were obtained via 24-hour recall. Our sample was interviewed once for 4-hour recall; the national sample, twice. In both samples, vitamin D consumed was calculated as the sum of D2 (ergocalciferol) and D3 (cholecalciferol) in foods, measured in micrograms; it was not directly comparable to the vitamin D level in blood serum, measured in ng/ml. Therefore, without valid reference to the absorption rate of these pre-vitamin D compounds in the digestive system or their conversion rate to 25(OH)D, we assumed that orally taken pre-vitamin D was fully absorbed and converted to 25(OH)D, circulating in the bloodstream. We also assumed a blood volume (BV) of 75 ml/kg for males and 65 ml/kg for females. Dietary vitamin D intake was converted to an equivalent serum blood vitamin D level as follows: Vitamin Dng/ml = (Vitamin Dmcg × 1,000)/(body weightkg × BVml/kg).
Laboratory data comprised HbA1C (%), vitamin D level (ng/ml), and lipid profile (total cholesterol, HDL, LDL, and triglyceride). HbA1C, the clinical biomarker for DM control or DM diagnosis, was “normal” if <5.7%, “prediabetes” if 5.7%–6.4%, or “diabetes” if ≥6.5%. DM control was the response variable for logistic regression analysis to determine contributions of vitamin D in comparison with other confounding factors for the HbA1C level.
Vitamin D level was measured as 25(OH)D in the blood (ng/ml). In our sample, vitamin D data were available for 250 participants at baseline, 24 at 3 months, 85 at 6 months, 0 at 9 months, and 21 at 12 months. Vitamin D was classified as (a) “deficient” (<12 ng/ml), (b) “suboptimal” (12–19 ng/ml), (c) “adequate” (20–29 ng/ml), or (d) “sufficient” (≥30 ng/ml). In some analyses, these subcategories were combined as (a) “deficient,” <20 ng/ml, or (b) “adequate,” ≥20 ng/ml [23, 24].
Questionnaire data included health-related psychobehavioral indicators such as self-efficacy in DM care, quality of life, history of other chronic conditions, years since DM diagnosis, medications for all conditions (including DM), having a primary care doctor, depression experience in the last 2 weeks (measured by the Patient Health Questionnaire) [25], and exercise and physical activity.
Analysis
Descriptive statistics were used for vitamin D status among KAs, and a series of logistic regression analyses assessed vitamin D’s contribution to DM control. We used STATA (version 14; Stata Corp, College Station, TX) for the analysis, with significance set atp < .05.
RESULTS
Sample Characteristics
Participants were 142 males (56.8%) and 108 females (43.2%). Their average age was 58.9 years (SE 0.5; range, 35–80). The men had significantly more years of education (14.2) than did the women (12.2). The majority (89.6%) were married, living with spouse alone (37.6%) or with children (56.4%). Over half (59.6%) were currently working full- or half-time. One third (35.2%) reported financial stability; one third (33.2%), manageable finances; and one third (31.6%), unstable or very unstable finances. All were first-generation immigrants, in the U.S. for an average of 23.7 years (SE 0.70; range, <1–53). Slightly fewer than half (47.6%) lacked health insurance; 28.8% lacked a primary care physician. On average, they were aware of having had DM for 8.1 years (SE 0.5; range, 0–35). The KAs were similar to their national counterparts in age (35–80 years, HbA1C ≥7.0%) and in the proportion of males, but the KAs had higher education, weaker financial status, and less health insurance coverage.
Vitamin D Characteristics
Comparison with National Samples
The mean (SE) vitamin D level in our sample was 19.7 (0.6) ng/ml (range, 5–72 ng/ml). Using the two-category division, 138 (55.2%) participants were vitamin D-deficient (<20 ng/ml), with a mean of 13.1 (0.34) ng/ml, and 112 (44.8%) were vitamin D-adequate (≥20 ng/ml), with a mean of 27.8 (0.76) ng/ml. The vitamin deficiency among KAs was higher than the U.S. average (37.8%) and higher than that for whites (29.8%) and Mexican Americans (39.7%), but not blacks (63.5%). Vitamin D levels increased as people got older.
Sources of Vitamin D
Three participants in our sample reported taking over-the-counter vitamin D supplements or multivitamins. Analysis of dietary intake indicated that our participants obtained an average of 3.3 mcg (SE 0.4) per day of vitamin D from their diet (range, 0–49.2 mcg), equivalent to 0.7 ng/ml (SE 0.1). Dietary intake accounted for 3.6% of the vitamin D level; sunlight exposure was a plausible source of the rest. The same group in the national sample obtained 3.2% of its vitamin D from foods and slightly more from dietary supplements (3.5%). (See Table 1.)
Table 1.
Demographic Characteristics and Vitamin D Level and Status among Korean Americans and Racial Subpopulations 35–80 Years Old with Uncontrolled Type 2 Diabetes (HbA1C ≥7.0%)
| SHIP-DM (n = 250) |
NHANES sample1 (n =728; N = 9,178,622) | ||||
|---|---|---|---|---|---|
| Indicators | All | White | Black | Mexican American |
|
| Demographic information | |||||
| Age, years (SE) | 58.9 (0.5) | 59.8 (0.8) | 61.4 (1.0) | 58.2 (0.9) | 56.0 (0.9) |
| Male, % | 56.8% | 54.7% | 58.6% | 44.3% | 51.7% |
| Education level | |||||
| 0–11th grade | 13.6% | 32.3% | 22.8% | 36.5% | 70.4% |
| 12th grade | 36.0% | 22.8% | 24.7% | 25.7% | 11.6% |
| Some college | 15.6% | 29.6% | 32.9% | 30.7% | 14.4% |
| College or above | 34.8% | 15.3% | 19.6% | 7.0% | 3.6% |
| Federal Poverty level | |||||
| ≤138% | 38.0% | 28.0% | 18.8% | 31.3% | 52.8% |
| ≤250% | 29.1% | 23.4% | 20.6% | 29.9% | 27.7% |
| ≤400% | 17.4% | 18.4% | 20.9% | 21.2% | 8.8% |
| >400% | 15.5% | 30.1% | 39.7% | 17.6% | 10.6% |
| Years with DM | 8.1 (0.5) | 13.9 (0.7) | 14.9 (1.1) | 13.0 (1.0) | 12.9 (1.2) |
| No health insurance | 47.6% | 15.9% | 7.9% | 16.7% | 45.9% |
| BMI | 25.4 (0.2) | 33.5 (0.4) | 34.2 (0.5) | 34.7 (0.8) | 32.4 (0.6) |
| Blood pressure (<130/80) | 37.2% | 49.9% | 50.5% | 37.9% | 51.2% |
| % taking D supplement | -- | 31.9% | 34.2% | 31.6% | 26.7% |
| Vitamin D status and level | |||||
| Mean (SE), ng/ml | 19.7 (0.6) | 23.7 (0.6) | 25.9 (0.8) | 18.7 (0.8) | 21.6 (0.5) |
| With supplement | - | 29.6 (0.8) | 32.0 (1.0) | 23.2 (1.5) | 26.1 (0.8) |
| No supplement | - | 20.9 (0.8) | 22.7 (0.9) | 16.6 (0.8) | 20.0 (0.6) |
| Deficient (<12.0), % | 20.4% | 11.7% | 8.0% | 27.2% | 6.6% |
| Sub-optimal (12–19 ng/ml), % | 34.8% | 26.1% | 21.8% | 36.3% | 33.1% |
| Adequate (20–29 ng/ml), % | 31.2% | 39.0% | 38.2% | 24.9% | 50.6% |
| Sufficient (≥30 ng/ml) | 13.6% | 23.2% | 31.8% | 11.6% | 9.7% |
| Sources of vitamin D (ng/ml) | |||||
| Foods, mean (SE) | 0.7 (0.1) | 0.8 (0.0) | 0.8 (0.1) | 0.6 (0.1) | 0.9 (0.1) |
| % from foods | 3.6% | 3.2% | 3.0% | 3.0% | 4.0% |
| Suppl., mean (SE) | - | 0.9 (0.1) | 1.0 (0.2) | 0.6 (0.1) | 0.6 (0.1) |
| % total | - | 3.5% | 3.8% | 3.2% | 2.7% |
National Health and Nutritional Examination Survey (NHANES), 2007-2008 and 2009-2010; other Americans (including Asian Americans) were excluded from analysis because of too small sample size (n = 39).
Comparison of the Vitamin D-deficient and Vitamin D-adequate Groups in KAs
In our sample, the vitamin D-deficient group was younger and financially less stable than the vitamin D-adequate (≤20 ng/ml) group, had resided in the U.S. for fewer years, and had a more recent DM diagnosis; furthermore, fewer in the deficient group had health insurance.
Regarding physiological indicators, the KA vitamin D-deficient group had a statistically significantly higher level of TC, triglycerides, and LDL, and a lower level of potassium, than did the vitamin D-adequate group. The vitamin D-deficient group also had a significantly higher level of depressive symptoms and reported less daily exercise. A total of 59 vitamin D-deficient participants (48.2%) reported “not exercising at all,” a level significantly higher than that for vitamin D-adequate participants (25.0%). On average, in both groups, the vitamin D level was highest in the summer, followed closely by spring and fall, and lowest in the winter, but seasonal variations were not statistically significant.
Vitamin D level and DM control were related: HbA1C was higher in the vitamin D-deficient group than in the vitamin D-adequate group at baseline (diff = 0.4%, p = .087) and during the project period (diff = 0.5%, p = .002). (See Table 2.)
Table 2.
Comparison of Vitamin D Adequate and Deficient Groups in Korean Americans
| Indicators | Adequate Group (OG: ≥20 ng/ml) (n= 112) |
Deficient Group (DG: <20 ng/ml) (n = 138) |
Difference (OG-NG) |
p-value |
|---|---|---|---|---|
| Vitamin D, mean (SE) | 27.8 (0.8) | 13.1 (0.3) | 14.7 (0.8) | <0.001 |
| Age, years, mean (SE) | 61.0 (0.8) | 57.1 (0.7) | 3.9 (1.1) | <0.001 |
| Working part/full time, n (%) | 59 (39.6) | 90 (60.4) | 20.8% | 0.041 |
| Own housing, n (%) | 80 (71.4) | 80 (56.0) | 13.4% | 0.027 |
| Financially stable, n (%) | 54 (48.2) | 34 (24.6) | 23.6% | <0.001 |
| Years in US, mean (SE) | 25.3 (1.07) | 22.9 (0.91) | 2.8 (1.4) | 0.050 |
| Years of having DM, mean (SE) | 9.5 (0.75) | 6.9 (0.56) | 2.6 (0.93) | 0.006 |
| No health insurance, n (%) | 44 (39.3) | 75 (54.4) | −15.1% | 0.018 |
| Triglyceride (mg/dL), mean (SE) | 153.1 (9.1) | 222.4 (21.5) | −69.3 (23.4) | 0.003 |
| Cholesterol (mg/dL), mean (SE) | 176.7 (3.4) | 206.4 (4.2) | −29.7 (5.4) | <0.001 |
| LDL (mg/dL), mean (SE) | 98.3 (3.1) | 117.4 (3.3) | −19.1 (4.6) | <0.001 |
| Potassium (mEq/L), mean (SE) | 4.3 (0.04) | 4.2 (0.03) | −0.13 (0.06) | 0.020 |
| PHQ-9K (0-27), mean (SE) | 4.3 (0.4) | 5.8 (0.5) | −1.5 (0.6) | 0.016 |
| Daily exercise (0-3), mean (SE) | 1.5 (0.1) | 1.1 (0.1) | −0.4 (0.1) | 0.011 |
| Daily exercise time, n (%) | 0.024 | |||
| No exercise, n (%) | 28 (25.0) | 59 (42.8) | ||
| <30 min. | 25 (22.3) | 27 (19.6) | ||
| 30–60 minutes | 40 (35.7) | 32 (23.2) | ||
| >60 minutes | 19 (17.0) | 20 (14.5) | ||
| Seasonal Vitamin D level, ng/ml (SE) | <0.001 | |||
| Spring | 28.0 (1.0) | 13.3 (0.9) | ||
| Summer | 28.6 (11.2) | 14.0 (1.1) | ||
| Fall | 27.8 (0.8) | 13.1 (0.8) | ||
| Winter | 27.2 (0.2) | 12.6 (0.7) | ||
| Hemoglobin A1C (%), mean (SE) | ||||
| At baseline (n = 250) | 8.6 (0.1) | 9.0 (0.2) | −0.4 | 0.087 |
| During program (n = 380) | 8.3 (0.1) | 8.8 (0.1) | −0.5 | 0.002 |
In a series of multivariate logistic regression analyses, we explored relative contributions of vitamin D status to normal HbA1C or DM control in the national sample, using HbA1C level (0 = normal, 1= prediabetes, 2 = T2DM) as the response variable.
Overall, vitamin D showed some positive influence on lowering the HbA1c level. The positive influence was statistically significant in whites (OR = 0.61; 95% CI 0.4, 0.9) and in the national total sample (OR = 0.49; 95% CI 0.4, 0.6) when the vitamin D-sufficient group (≥30 ng/ml) was compared with the deficient group (<12 ng/ml). Similar patterns were observed in the national sample (OR = 0.67; 95% CI 0.5, 0.9) when the vitamin D-adequate group (20–29 ng/ml) was compared with the deficient group (<12 ng/ml). However, vitamin D had no detectable influence on achieving normal HbA1C in either black or Mexican Americans. The effects of cholesterol level, intensive BP control, and daily exercise were similar to those of vitamin D (i.e., manifesting themselves in the white American and national samples), except for the positive association of intensive BP control in Mexican Americans. However, the positive association of age, weight control, and inflammation status on achieving normal HbA1C manifested across all subpopulations (Table 3).
Table 3.
Predictors of HbA1c Level Using Ordered Logistic Regression Analysis of in Racial Subpopulations Age 35–80
| Response variable 0 = Normal (A1C <5.7%), 1 = Prediabetes (A1C:5.7%–6.4%), 2 = Diabetes (A1C ≥6.5%) | ||||
|---|---|---|---|---|
| Model Summary | Total (n = 7,375) |
White (n = 3,796) |
Black (n = 1,250) |
Mexican Am. (n = 1,255) |
| N LR/F Sig. |
132,184,128 F(16, 17) = 53.16 < 0.001 |
97,656,233 F(16, 16) = 39.76 < 0.001 |
12,339,008 F(16, 12) = 8.92 < 0.001 |
N = 9,318,789 F(16, 14) = 43.63 < 0.001 |
| Predictors | ||||
| Vitamin D status (vs. Deficient: <12 ng/ml) | ||||
| Sub-optimal (12–19 ng/ml) | 0.78 (0.6, 1.0) | 0.72 (0.5, 1.1) | 1.02 (0.7, 1.5) | 0.70 (0.4, 1.4) |
| Adequate (20–29 ng/ml) | 0.67 (0.5, 0.9) | 0.73 (0.5, 1.0) | 1.18 (0.8, 1.6) | 0.87 (0.6, 1.4) |
| Sufficient (≥30 ng/ml) | 0.49 (0.4, 0.6) | 0.61 (0.4, 0.9) | 0.98 (0.6, 1.6) | 0.72 (0.4, 1.2) |
| Age (vs. 35–49 years) | ||||
| 50–59 years | 2.41 (2.0, 3.0) | 2.53 (1.9, 3.3) | 2.25 (1.5, 3.4) | 2.94 (2.2, 3.9) |
| 60–69 years | 3.88 (3.2, 4.7) | 4.26 (3.3, 5.5) | 3.63 (2.3, 5.7) | 3.82 (2.9, 5.1) |
| ≥70 years | 4.41 (3.7, 5.2) | 5.00 (4.0, 6.2) | 3.90 (2.6, 5.9) | 4.44 (3.3, 6.0) |
| Body Mass Index (vs. <25 kg/m^2) | ||||
| Overweight (25 – <30) | 1.40 (1.2, 1.6) | 1.35 (1.1, 1.7) | 1.71 (1.3, 2.3) | 1.59 (1.2, 2.2) |
| Obese (≥30) | 2.93 (2.6-3.3) | 3.03 (2.5, 3.6) | 3.11 (2.2, 4.3) | 3.19 (2.3, 4.4) |
| Cholesterol (vs. normal, <200 mg/dl) | ||||
| Borderline (200–239 mg/dl) | 0.74 (0.6, 0.8) | 0.65 (0.6, 0.8) | 1.17 (0.9, 1.6) | 1.07 (0.9, 1.3) |
| High (≥240 mg/dl) | 0.90 (0.8, 1.1) | 0.85 (0.7, 1.0) | 1.12 (0.8, 1.5) | 1.32 (0.8, 2.1) |
| Blood Pressure (vs. systolic/diastolic <130/80) | ||||
| 130/80 – <140/90 | 1.06 (0.9, 1.3) | 1.04 (0.8, 1.3) | 1.11 (0.8, 1.5) | 1.19 (0.9, 1.6) |
| ≥140/90 | 1.19 (1.1, 1.3) | 1.09 (0.9, 1.3) | 1.35 (0.9, 1.8) | 1.55 (1.2, 2.0) |
| Daily exercise (vs. None) | ||||
| <30 min. | 0.75 (0.7, 0.8) | 0.71 (0.6, 0.8) | 0.96 (0.7, 1.3) | 0.81 (0.6, 1.1) |
| 30–60 min. | 0.64 (0.5, 0.8) | 0.56 (0.5, 0.7) | 0.92 (0.5, 1.6) | 0.82 (0.6, 1.1) |
| >60 min. | 0.78 (0.6, 0.9) | 0.69 (0.5, 0.9) | 1.04 (0.8, 1.4) | 0.90 (0.6, 1.3) |
| C-Reactive Protein (vs. low <1.0 mg/L) | ||||
| ≥1.0 mg/L | 1.45 (1.2, 1.7) | 1.43 (1.1, 1.9) | 1.51 (1.0, 2.3) | 1.88 (1.3, 2.7) |
Note: Bold-faced statistics are significant at p < 0.5.
To elucidate the complex relationship between vitamin D and HbA1C, logistic regression analyses were performed on a dichotomous response variable: the normal versus prediabetes groups and the prediabetes versus T2DM groups. Comparison of the subsets of analysis showed that benefits of vitamin D were most prominent in the transition between prediabetes and T2DM. However, these associations were significant only in the white subpopulation (Table 4).
Table 4.
Ordered Logistic Regression Analysis between Vitamin D and HbA1C in Racial Subpopulations Age 35–80
| Response Variable: 0 = Normal (A1C <5.7%) vs. 1 = Prediabetes (A1C 5.7%–6.4%) | ||||
|---|---|---|---|---|
| Predictors | Total | White | Black | Mexican American |
| Vitamin D status (vs. Deficient: <12 ng/ml) | ||||
| Sub-optimal (12–19 ng/ml) | 0.80 (0.6, 1.1) | 0.86 (0.5, 1.5) | 1.01 (0.7, 1.4) | 0.74 (0.4, 1.5) |
| Adequate (20–29 ng/ml) | 0.78 (0.6, 1.1) | 1.11 (0.7, 1.8) | 1.30 (0.9, 2.0) | 0.91 (0.5, 1.6) |
| Sufficient (≥30 ng.ml) | 0.62 (0.5, 0.8) | 0.99 (0.6, 1.6) | 0.81 (0.5, 1.4) | 0.93 (0.5, 1.7) |
| Age | 1.62 (1.5, 1.7) | 1.70 (1.6, 1.8) | 1.49 (1.3, 1.8) | 1.75 (1.5, 2.1) |
| Weight status | 1.51 (1.4, 1.7) | 1.54 (1.4, 1.7) | 1.53 (1.2, 1.9) | 1.71 (1.4, 2.1) |
| Cholesterol status | 0.99 (0.9, 1.1) | 0.98 (0.9, 1.1) | 1.10 (0.9, 1.3) | 1.06 (0.9, 1.3) |
| BP status | 1.02 (0.9, 1.1) | 0.97 (0.9, 1.1) | 1.11 (0.9, 1.3) | 1.17 (0.9, 1.4) |
| Exercise daily | 0.90 (0.9, 0.9) | 0.87 (0.8, 0.9) | 1.01 (0.9, 1.2) | 0.99 (0.9, 1.1) |
| C-Reactive protein | 1.29 (1.1, 1.6) | 1.34 (1.0, 1.7) | 1.09 (0.7, 1.6) | 1.47 (0.9, 2.6) |
| Response Variable: 0 = Prediabetes (A1C 5.7%–6.4%) vs. 1 = Diabetes (A1C ≥6.5%) | ||||
| Vitamin D status (vs. Deficient: <12 ng/ml) | ||||
| Sub-optimal (12–19 ng/ml) | 0.93 (0.7, 1.3) | 0.90 (0.5, 1.7) | 0.98 (0.6, 1.5) | 0.80 (0.5, 1.3) |
| Adequate (20–29 ng/ml) | 0.67 (0.5, 0.9) | 0.48 (0.3, 0.9) | 0.85 (0.5, 1.4) | 0.88 (0.5, 1.4) |
| Sufficient (≥30 ng.ml) | 0.47 (0.3, 0.7) | 0.40 (0.2, 0.8) | 1.32 (0.7, 2.4) | 0.60 (0.3, 1.1) |
| Age | 1.30 (1.1, 1.4) | 1.26 (1.1, 1.4) | 1.52 (1.3, 1.8) | 1.33 (1.1, 1.7) |
| Weight status | 1.93 (1.7, 2.3) | 2.15 (1.8, 2.6) | 1.79 (1.4, 2.2) | 1.51 (1.1, 2.2) |
| Cholesterol status | 0.79 (0.7, 0.9) | 0.65 (0.5, 0.8) | 1.07 (0.9, 1.3) | 1.13 (0.9, 1.5) |
| BP status | 1.19 (1.1, 1.4) | 1.23 (1.0, 1.4) | 1.14 (0.9, 1.4) | 1.13 (0.9, 1.5) |
| Exercise | 0.94 (0.9, 1.0) | 0.90 (0.8, 1.1) | 0.98 (0.9, 1.2) | 0.89 (0.8, 1.0) |
| CRP | 1.39 (1.1, 1.8) | 1.25 (0.8, 1.9) | 1.87 (1.2, 3.0) | 1.68 (1.0, 2.8) |
Note: Bold-faced statistics are significant at p < 0.5.
Age: 0 = 35–49 years; 1 = 50–59 years; 2 = 60–69 years; 3 = 70 years or old
Weight status: 0 = Normal (BMI <25 kg/m2); 1 = Overweight (25–29); 2 = Obese(≥30)
Cholesterol status: 0 = Normal (<200 mg/dl); 1 = Borderline (200–239 mg/dl); 2 = High (≥240 mg/dl)
BP status: 0 = Normal (SBP/DBP <130/80); 1 = Borderline (130/80–139/89); 2 = High (≥140/90)
Exercise: 0 = No exercise; 1 = less than 30 min; 2 = 30 – 59 min; 3 = 60 min or more
CRP: 0 = Low (<1.0 mg/dl); 1 = High (≥1.0 mg/dl)
DISCUSSION
The prevalence of vitamin D deficiency in our KA sample (55.2%) was higher than the U.S. average (37.8%), but lower than that for blacks (63.5%). Such disparities may be explained in part by contextual factors. Today’s KAs are predominantly engaged in family-owned retail businesses that require lengthy indoor work (10–16 hours per day). Among KAs working full- or part-time, 60.4% were vitamin D-deficient; of those not working, 39.6%. The majority of the latter were retired, financially stable, with more time and resources for leisure activities and more access to healthcare. BP control (i.e., systolic/diastolic BP <130/80) among KAs was lower (37.2%; national average, 49.9%) than in other racial groups. The average BMI of KAs (25.4) was lower than national BMI data (33.5). However, experts caution that that direct comparison of BMI in Asian and non-Asian groups is discouraged, as Asian populations are at higher risk of developing chronic diseases at a BMI level much below the current overweight BMI cut-off criterion of 25 kg/m2. [26] In addition, both KAs and Koreans in Korea tend to avoid the sun, owing to a cultural preference for light skin color. Many Koreans use sunscreen and clothe themselves to avoid darkening their skin, inadvertently inhibiting the absorption of sunlight and thereby affecting the body’s production of vitamin D.
Comparisons between the vitamin D-deficient and vitamin D-adequate groups identified several significant correlates of vitamin D deficiency: sociodemographic factors (age, working status, home ownership, financial stability, and years of residency in the U.S.), psychobehavioral factors (years of having DM, having health insurance, depression, and less daily exercise), physiobiological factors (lipids such as triglycerides, TC, LDL), nutrients (potassium), and the level of HbA1C. The wide range of significant correlates suggests that behavioral and environmental factors are more relevant to vitamin D status than biochemical factors [5, 27, 28].
The national sample showed a statistically significant positive association of vitamin D with the level of HbA1C; compared with the vitamin D-deficient group, the vitamin D-adequate or -sufficient group had a 33% or 51% lower chance of progressing from normal to prediabetes or from prediabetes to DM, respectively, after controlling for other factors. The vitamin D-sufficient group had a 38% lower chance of developing prediabetes than did the vitamin-deficient group. It is encouraging that the improvement in vitamin D status was proportional to the adequate glycemic level, although some relationships were not statistically significant. Similar patterns were found for both white and Mexican Americans.
Nevertheless, the positive influence of vitamin D on HbA1C revealed differences among racial subpopulations. The association of vitamin D was significant only among whites, not blacks or Mexican Americans, whereas the effects of other factors (e.g., age, weight control status, and inflammation) were consistently significant in all subpopulations. Several interpretations for these differences are possible: for example, the significant association of vitamin D with HbA1C in the white subpopulation might be due to large-sample bias (narrower confidence intervals). The odds ratios and confidence intervals for vitamin D status showed similar patterns in the national data and in the data for white and Mexican Americans, and statistical significance appeared to be proportional to respective sample sizes (Table 3).
To verify these relationships, the same logistic regression model was simulated on 500 subsamples from the national sample. The sampling rate, 20%, resulted in 1,448–1,498 observations. The coefficients were significant 378 times (75.6%) for the vitamin D-sufficient group, 142 times (28.4%) for the vitamin D-adequate group, and 63 times (12.6%) for the vitamin D-suboptimal group. The significant relationships were incremental from the suboptimal group, through the adequate group, to the sufficient group. When the same model was simulated 500 times with the 20% sampling rate and collapsed independent variables, the proportions of the significant coefficients were 100% for both age and weight control, 65.0% for vitamin D, 44.4% for inflammation, 41.78% for exercise, and 13.4% for both cholesterol and BP control.
Another possible interpretation is that the significant bivariate relationship between vitamin D and HbA1C was mediated through other factors, such as aging or weight control [29, 30, 31]. This could explain the unique relationship in blacks, in whom odds ratios for the vitamin D-suboptimal (OR = 1.02; 95% CI 0.7, 1.5), adequate (OR = 1.18; 95% CI 0.8, 1.6), and sufficient (OR = 0.98; 95% CI 0.6, 1.6) groups, although not significant, differed from those for white or Mexican Americans. The blacks were younger on average (52.9 years) than the whites (55.9 years), but weight, BP, daily exercise, cholesterol (in people with DM), and inflammation status were significantly poorer among blacks than among their white counterparts. These findings echo recent reports that the positive influence of vitamin D on DM control may have been interpreted unidimensionally, without considering complex biobehavioral mechanisms influenced by multiple factors such as ethnic cultural differences [17, 32, 33].
In general, combined dietary and supplement intake of vitamin D constituted less than 7% of the 25(OH)D; this distribution was similar across racial subgroups. However, the actual contribution of diet or vitamin supplement to the vitamin D level may be substantial, because pre-vitamin D could accumulate longer in a variety of forms of substrates. For example, those in the national sample who took vitamin D supplements had a higher vitamin D level (29.2 ng/ml) than did those who did not (20.9 ng/ml). The spread of 8.9 ng/ml is equivalent to approximately 10 days’ accumulation from supplements and 27.8% of the serum level of vitamin D, if the assumption of complete (100%) absorption and conversion of vitamin D supplement to 25(OH)D holds.
There are several limitations to generalizing our findings: (a) because of limited follow-up on vitamin D data beyond baseline, we could not conduct proper longitudinal analysis to shed new light on causality between vitamin D levels and DM control; (b) participants were limited to KAs with high-risk T2DM; (c) the study was administered at a single center, to a single ethnic/linguistic minority group.
Nevertheless, our findings from multiple data sources have clinical implications for achieving an adequate vitamin D level and better HbA1C level or DM control. Diet, exercise, or even safe sun exposure can provide simple, effective, low-cost public health strategies for reducing the risk of vitamin D deficiency. Daily exercise, for even a very small amount of time, outweighs seasonal variations in vitamin D. Vitamin D levels among daily exercisers (<30 minutes) in the winter (mean = 19.9, SD = 8.5) were higher than those for non-exercisers in the summer (mean = 16.9, SD = 5.9). This has strong implications for understanding health issues in minority populations that experience a higher prevalence of cardiovascular disease, hypertension, and metabolic syndrome, as well as vitamin D deficiency.
NEW CONTRIBUTIONS TO THE LITERATURE.
The prevalence of vitamin deficiency of KAs was much higher (55.2%) than the U.S. average (37.8%) and that for whites (29.8%) or Mexican Americans (39.7%), but lower than that for black Americans (64.3%).
Several contextual lifestyle-related factors exhibited an influence on average vitamin D levels in different racial/ethnic groups.
Although the positive influence of vitamin D on HbA1C or DM control across different racial groups is substantiated by our analyses, the importance of vitamin D in DM control varied significantly for different racial groups.
Acknowledgement
The study was supported by a grant from the National Institute of Diabetes and Digestive and Kidney Diseases (R18DK083936), with material support from LifeScan, including devices (OneTouch glucometers, OneTouch UltraSoft® test strips, and OneTouch UltraSoft lancets) for study participants. In addition, the Johns Hopkins ICTR supported the cost of blood serum lab tests. The authors are grateful for substantial editorial assistance provided by Dr. Deborah McClellan. Editorial support with manuscript development was also provided by the Cain Center for Nursing Research and the Center for Transdisciplinary Collaborative Research in Self-management Science (P30, NR015335) at The University of Texas at Austin School of Nursing. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or other supporters.
Footnotes
Conflict of Interest
All authors declare that they have no conflict of interest.
Ethical Approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed Consent
Informed consent was obtained from all individual participants included in the study.
Clinical Trials Registry: Identifier NCT01264796
Human Participant Protection
The research protocol was approved by the Johns Hopkins Medical Institutions Institutional Review Board, and written consent was obtained from all study participants.
Contributor Information
Miyong To Kim, The University of Texas at Austin, School of Nursing, Austin, TX.
Kim Byeng Kim, Korean Resource Center, Ellicott City, MD
Jisook Ko, University of Texas at Austin, School of Nursing, Austin, TX
Nicole Murry, University of Texas at Austin, School of Nursing, Austin, TX.
David Levine, Johns Hopkins University, School of Medicine, Baltimore, MD.
Ju-Young Lee, College of Nursing, The Catholic University of Korea, Seoul, South Korea.
REFERENCES
- 1.Pittas AG, Lau J, Hu FB, Dawson-Hughes B. The role of vitamin D and calcium in type 2 diabetes: a systematic review and meta-analysis. J Clin Endocrinol Metab. 2007;92(6):2017–29. 10.1210/jc.2007-0298 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Mitri J, Muraru MD, Pittas AG. Vitamin D and type 2 diabetes: a systematic review. Eur J Clin Nutr. 2011;65(9): 1005–15. 10.1038/ejcn.2011.118 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Mathieu C. Vitamin D and diabetes: where do we stand? Diabetes Res Clin Prac. 2015;108(2):201–9. https://doi.Org/10.1016/j.diabres.2015.01.036 [DOI] [PubMed] [Google Scholar]
- 4.Karnchanasorn R, Ou HY, Chiu KC. Plasma 25-hydroxyvitamin D levels are favorably associated with β-cell function. Pancreas. 2012;41(6):863–8. 10.1097/MPA.0b013e31823c947c [DOI] [PubMed] [Google Scholar]
- 5.Ozfirat Z, Chowdhury TA. Vitamin D deficiency and type 2 diabetes. Postgrad Med J. 2010;86(1011): 18–25. 10.1136/pgmj.2009.078626 [DOI] [PubMed] [Google Scholar]
- 6.Guadarrama-López AL, Valdés-Ramos R, Martínez-Carrillo BE. Type 2 diabetes, PUFAs, and vitamin D: their relation to inflammation. J Immunol Res. 2014;2014:860703 10.1155/2014/860703 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Quan W, Jo EK, Lee MS. Role of pancreatic β-cell death and inflammation in diabetes. Diabetes Obes Metab. 2013;15(suppl 3): 141–51. 10.1111/dom.12153 [DOI] [PubMed] [Google Scholar]
- 8.Hewison M Vitamin D and immune function: autocrine, paracrine or endocrine? Scand J Clin Lab Invest. 2012;72(suppl 243):92–102. [DOI] [PubMed] [Google Scholar]
- 9.Calton EK, Keane KN, Newsholme P, Soares MJ. The impact of vitamin D levels on inflammatory status: a systematic review of immune cell studies. PLoS One. 2015;10(11):e0141770 10.1371/journal.pone.0141770 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Mattila C, Knekt P, Männistö S, et al. Serum 25-hydroxyvitamin D concentration and subsequent risk of type 2 diabetes. Diabetes Care. 2007;30(10):2569–70. 10.2337/dc07-0292 [DOI] [PubMed] [Google Scholar]
- 11.Knekt P, Laaksonen M, Mattila C, et al. Serum vitamin D and subsequent occurrence of type 2 diabetes. Epidemiology. 2008;19(5):666–71. 10.1097/EDE.0b013e318176b8ad [DOI] [PubMed] [Google Scholar]
- 12.Liu E, Meigs JB, Pittas AG, et al. Predicted 25-hydroxyvitamin D score and incident type 2 diabetes in the Framingham Offspring Study. Am J Clin Nutr. 2010;91(6):1627–33. 10.3945/ajcn.2009.28441 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Pittas AG, Dawson-Hughes B, Li T, et al. Vitamin D and calcium intake in relation to type 2 diabetes in women. Diabetes Care. 2006;29(3):650–6. 10.2337/diacare.29.03.06.dc05-1961 [DOI] [PubMed] [Google Scholar]
- 14.Kositsawat J, Freeman VL, Gerber BS, Geraci S. Association of A1C levels with vitamin D status in U.S. adults: data from the National Health and Nutrition Examination Survey. Diabetes Care. 2010;33(6):1236–8. 10.2337/dc09-2150 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Kabadi SM, Lee BK, Liu L. Joint effects of obesity and vitamin D insufficiency on insulin resistance and type 2 diabetes: results from the NHANES 2001–2006. Diabetes Care. 2012;35(10):2048–54. 10.2337/dc12-0235 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Mangin M, Sinha R, Fincher K. Inflammation and vitamin D: the infection connection. Inflamm Res. 2014;63(10):803–19. 10.1007/s00011-014-0755-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Ye Z, Sharp SJ, Burgess S, et al. Association between circulating 25-hydroxyvitamin D and incident type 2 diabetes: a Mendelian randomisation study. Lancet Diabetes Endocrinol. 2015;3(1):35–42. 10.1016/S2213-8587(14)70184-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Brouwer-Brolsma EM, Bischoff-Ferrari HA, Bouillon R, et al. Vitamin D: do we get enough? A discussion between vitamin D experts in order to make a step towards the harmonisation of dietary reference intakes for vitamin D across Europe. Osteoporos Int. 2013;24(5):1567–77. 10.1007/s00198-012-2231-3 [DOI] [PubMed] [Google Scholar]
- 19.Kim MT, Han HR, Song HJ, et al. A community-based, culturally tailored behavioral intervention for Korean Americans with type 2 diabetes. Diabetes Educ. 2009;35(6):986–94. 10.1177/0145721709345774 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Kim MT, Kim KB, Huh B, et al. The effect of a community-based self-help intervention: Korean Americans with type 2 diabetes. Am J Prev Med. 2015;49(5):726–37. 10.1016/j.amepre.2015.04.033 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Centers for Disease Control and Prevention, National Center for Health Statistics. Analytical note for 25-hydroxyvitamin D data analysis using NHANES III (1988–1994), NHANES 2001–2006, and NHANES 2007–2010 (October 2015). https://wwwn.cdc.gov/nchs/nhanes/vitamind/analyticalnote.aspx
- 22.Tutuncu Y, Satman I, Celik S, et al. A comparison of hs-CRP levels in new diabetes groups diagnosed based on FPG, 2-hPG, or HbA1c criteria. J Diabetes Res. 2016;2016:5827041 10.1155/2016/5827041 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Institute of Medicine (US) Committee to Review Dietary Reference Intakes for Vitamin D and Calcium. Dietary Reference Intakes for Calcium and Vitamin D. Washington, DC: National Academies Press; 2011. [PubMed] [Google Scholar]
- 24.Holick MF, Binkley NC, Bischoff-Ferrari HA, et al. Guidelines for preventing and treating vitamin D deficiency and insufficiency revisited. J Clin Endocrinol Metab. 2012;97(4):1153–8. 10.1210/jc.2011-2601 [DOI] [PubMed] [Google Scholar]
- 25.Shin J, Park SY, Cho S, et al. Validation of Patient Health Questionnaire-9 Korean Version (PHQ-9K) scale for screening depression among Korean Americans in community settings. J Theory Constr Test. 2010;14(2):45–50. 10.1177/1043659607305191 [DOI] [Google Scholar]
- 26.Barba C, Cavalli-Sforza T, Cutter J, et al. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. The Lancet. 2004;363:157–163. 10.1016/S0140-6736(03)15268-3 [DOI] [PubMed] [Google Scholar]
- 27.Ford ES, Ajani UA, McBuire LC, Liu S. Concentrations of serum vitamin D and the metabolic syndrome among U.S. adults. Diabetes Care. 2005;28(5):1228–30. 10.2337/diacare.28.5.1228 [DOI] [PubMed] [Google Scholar]
- 28.Schroeder SA. We can do better—improving the health of the American people. N Engl J Med. 2007;357(12):1221–8. 10.1056/NEJMsa073350 [DOI] [PubMed] [Google Scholar]
- 29.Kahn SE, Hull RL, Utzschneider KM. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature. 2006;444(7121):840–6. 10.1038/nature05482 [DOI] [PubMed] [Google Scholar]
- 30.Sung KC, Jeong WS, Wild SH, Byrne CD. Combined influence of insulin resistance, overweight/obesity, and fatty liver as risk factors for type 2 diabetes. Diabetes Care. 2012;35(4):717–22. 10.2337/dc11-1853 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Lee DH, Jung KY, Park KS, et al. Characterization of patients with type 2 diabetes according to body mass index: Korea National Health and Nutrition Examination Survey from 2007 to 2011. Endocrinol Metab. 2015;30(4):514–21. https://doi.Org/10.3803/EnM.2015.30.4.514 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Mitri J, Pittas AG. Vitamin D and diabetes. Endocrin Metabol Clin North Am. 2014;43(1):205–32. https://doi.Org/10.1016/j.ecl.2013.09.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Buijsse B Vitamin D for prevention of type 2 diabetes: a clouded forecast for the sunshine vitamin. Lancet Diabetes Endocrinol. 2015;3(1):5–6. 10.1016/S2213-8587(14)70202-5 [DOI] [PubMed] [Google Scholar]