Association between dietary phylloquinone intake and peripheral metabolic risk markers related to insulin resistance and diabetes in elderly subjects at high cardiovascular risk

  • Martí Juanola-Falgarona1, 2, 3,

    Affiliated with

    • Jordi Salas-Salvadó1, 2, 3Email author,

      Affiliated with

      • Ramon Estruch2, 3, 4,

        Affiliated with

        • Maria P Portillo3, 5,

          Affiliated with

          • Rosa Casas2, 3, 4,

            Affiliated with

            • Jonatan Miranda3, 5,

              Affiliated with

              • Miguel A Martínez-González3, 6 and

                Affiliated with

                • Mònica Bulló1, 2, 3Email author

                  Affiliated with

                  Cardiovascular Diabetology201312:7

                  DOI: 10.1186/1475-2840-12-7

                  Received: 21 November 2012

                  Accepted: 5 January 2013

                  Published: 8 January 2013

                  Abstract

                  Background

                  Vitamin K has been related to glucose metabolism, insulin sensitivity and diabetes. Because inflammation underlies all these metabolic conditions, it is plausible that the potential role of vitamin K in glucose metabolism occurs through the modulation of cytokines and related molecules. The purpose of the study was to assess the associations between dietary intake of vitamin K and peripheral adipokines and other metabolic risk markers related to insulin resistance and type 2 diabetes mellitus.

                  Methods

                  Cross-sectional and longitudinal assessments of these associations in 510 elderly participants recruited in the PREDIMED centers of Reus and Barcelona (Spain). We determined 1-year changes in dietary phylloquinone intake estimated by food frequency questionnaires, serum inflammatory cytokines and other metabolic risk markers.

                  Results

                  In the cross-sectional analysis at baseline no significant associations were found between dietary phylloquinone intake and the rest of metabolic risk markers evaluated, with exception of a negative association with plasminogen activator inhibitor-1. After 1-year of follow-up, subjects in the upper tertile of changes in dietary phylloquinone intake showed a greater reduction in ghrelin (−15.0%), glucose-dependent insulinotropic peptide (−12.9%), glucagon-like peptide-1 (−17.6%), IL-6 (−27.9%), leptin (−10.3%), TNF (−26.9%) and visfatin (−24.9%) plasma concentrations than those in the lowest tertile (all p<0.05).

                  Conclusion

                  These results show that dietary phylloquinone intake is associated with an improvement of cytokines and other markers related to insulin resistance and diabetes, thus extending the potential protection by dietary phylloquinone on chronic inflammatory diseases.

                  Trial registration

                  http://​www.​controlled-trials.​com as ISRCTN35739639

                  Keywords

                  Vitamin K Inflammation Insulin resistance Diabetes

                  Introduction

                  Vitamin K (K1 or phylloquinone and K2 or menaquinones) is recognized as an essential element in the synthesis of carboxylate clotting factors involved in prothrombotic disorders and cardiovascular disease. More recently, it has been reported that vitamin K also participates in the gamma-carboxylation reactions of other proteins such as osteocalcin, and may also exert a protective role against age-related bone loss [1, 2]. However, additional roles of vitamin K, independent of these effects have been described [3]. Thus, there is evidence that both osteocalcin and vitamin K may have a potential beneficial role in glucose metabolism, insulin sensitivity and type 2 diabetes (T2DMs) [47]. Since inflammation underlies all these chronic metabolic conditions, it is plausible that the potential role of vitamin K in glucose metabolism partly occurs through the modulation of cytokines and other metabolic risk markers related to insulin resistance and diabetes.

                  In-vitro studies have shown an anti-inflammatory effect of vitamin K. Human macrophage THP-1 cells incubated with vitamin K reduced the interleukin-6 (IL-6) expression compared to non-incubated cells. Likewise, rats fed with a vitamin K-deficient diet showed an enhanced expression of genes involved in the acute inflammatory response [8]. In a subsample of 1,321 subjects from the Framingham Offspring Study, both plasma phylloquinone and dietary phylloquinone intake were inversely associated with peripheral concentrations of some inflammatory markers [9]. However, in a 3-year randomized clinical trial designed to assess the effect of vitamin K supplementation on bone loss, no differences were found in the plasma IL-6, C-reactive protein or osteoprotegerin concentrations of participants receiving or not a phylloquinone supplement [10].

                  The purposes of the present study were to assess the cross-sectional associations between dietary intake of vitamin K1 and selected adipokines or other metabolic risk markers related to inflammation, insulin resistance and diabetes; and to longitudinally analyse the associations between changes in dietary phylloquinone intake and changes in these risk markers after one-year of follow-up in a cohort of elderly subjects at high cardiovascular risk.

                  Methods

                  Study population

                  In the present study we conducted a cross-sectional and a longitudinal assessment of 568 consecutively recruited participants for the PREDIMED trial centers of Reus and Barcelona (Spain). The PREDIMED study is a large, parallel group, multicenter, controlled, randomized, clinical trial designed to evaluate the effect of the Mediterranean diet on the primary prevention of cardiovascular disease in elderly. Participants were community-dwelling men and women aged 55–80 and 60–80 years, respectively. At baseline they were free of cardiovascular disease and were either diabetic or met at least three or more coro-nary heart disease risk factors including smoking, hypertension (blood pressure ≥ 140/90 mmHg or treatment with antihypertensive drugs), dyslipidemia [low-density lipoprotein cholesterol level ≥ 160 mg/dL or treatment with hypolipidemic drugs], high-density lipoprotein cholesterol level of 40 mg/dL or lower, overweight [Body mass index ≥ 25 kg/m2 or family history of premature cardiovascular disease. Exclusion criteria included any severe chronic illness, drug or alcohol addiction, history of allergy or intolerance to olive oil or nuts, or a low predicted likelihood of changing dietary habits according to Prochaska and DiClemente’s stages-of-change model. The participants included in the PREDIMED study were randomly assigned to 3 intervention groups: a Mediterranean Diet with virgin olive oil, a Mediterranean Diet with mixed nuts and a control group where a low-fat diet is recommended according to the American Heart Association guidelines. Full details of the PREDIMED study protocol have been published elsewhere [11, 12]. The study protocol was approved by the institutional review boards of Hospital Clínic and Hospital Universitari Sant Joan de Reus, and all subjects agreed to participate in the study and gave their written informed consent. The trail was registered in http://​www.​controlled-trials.​com as ISRCTN35739639.

                  Dietary assessment

                  Two individual motivational interviews every 3 months to negotiate nutrition goals, and group educational sessions on a quarterly basis, focused to adapt the customary diet to a traditional Mediterranean diet, were compared with a control group, which received verbal instructions and a leaflet recommending the National Cholesterol Education Program Adult Treatment Panel III dietary guidelines (http://​www.​predimed.​org). At baseline and after one-year of follow-up participants were assessed by trained dieticians who administered a previously validated 137-item food frequency questionnaire (FFQ) [13]. Additionally, a validated brief 14-item Mediterranean Diet Adherence Screener was used to assess adherence to the traditional Mediterranean Diet (MedDiet) where subjects were asked for their consumption of the most common Mediterranean foods [14]. Subjects with a higher consumption of healthier foods such as olive oil, vegetables, legumes, fruit, nuts, fish and seafood, white meat instead of red meat, sofrito and red wine scored higher in this questionnaire. Energy and nutrient intakes were calculated from Spanish food composition tables [15, 16]. Dietary phylloquinone intake was calculated using the database of the US Department of Agriculture, Human Nutrition Research Center on Aging at Tufts University (http://​www.​nal.​usda.​gov/​fnic/​foodcomp/​search) and the reproducibility and relative validity of a self-administered FFQ used in the study was validated for dietary phylloquinone intake. Reproducibility for dietary phylloquinone intake explored by the Pearson correlation coefficient (r) ranged was 0.755, and the intraclass correlation coefficient (ICC) was 0.860, p<0.001.

                  Other measurements

                  Additional information was collected on subjects’ medical record, including the use of medication. Trained personnel measured baseline weight, height and waist circumference as previously reported [11, 12], as well as blood pressure in triplicate with a validated semiautomatic oscillometer (Omron HEM-705CP, Hoofddorp, the Netherlands). Leisure-time physical activity was evaluated using the validated Spanish version of the Minnesota leisure-time physical activity questionnaire. Centralized laboratory biochemical analyses were performed on blood samples obtained in fasting conditions. Plasma glucose, serum cholesterol, high-density lipoprotein cholesterol and triglyceride concentrations were determined using standard enzymatic automated methods. In patients whose triglyceride levels were less than 400 mg/dL, low-density lipoprotein cholesterol concentrations were estimated using the Friedewald formula. Inflammatory and metabolic markers (adiponectin, adipsin, C-peptide, ghrelin, glucagon-like peptide-1 (GLP-1), glucose-dependent insulinotropic polypeptide, IL-6, leptin, plasminogen activator inhibitor-1 (PAI-1), resistin, tumor necrosis factor (TNF) and visfatin were determined in plasma using the Bio-Plex cytokine assay (Bio-Rad Laboratories Inc., Hercules, CA, USA) according to manufacturer’s instructions.

                  Statistical analysis

                  Mean (SD) or percentages (%) were used to describe the participant’s baseline characteristics. Inflammatory and metabolic risk markers of insulin resistance and diabetes were logarithmically transformed to achieve a normal distribution, and the geometric mean and 95% confidence interval were used to describe these variables. For cross-sectional associations, we used multivariable linear regression models to assess the associations between metabolic risk markers (dependent variables in each model) and dietary vitamin K intake (independent variable in all models) adjusted for potential confounding variables [age, sex, body mass index, smoking (never, current, past), physical activity (kcal/d), type 2 diabetes mellitus (T2DM), total energy (kcal/d) and fibre intake (g/d), dietary polyunsaturated fatty acids (PUFA) intake (g/d) and adherence to MedDiet (14-item score, quantitative)]. The selection of potential confounders was done using clinical plausible and bibliographical criteria.

                  Interaction tests for sex and T2DM (sex*vitamin K intake, T2DM* vitamin K intake) were not statistically significant. No interaction was observed for intervention group and changes in dietary phylloquinone intake in any outcome (inclusion of intervention group*one-year changes in dietary phylloquinone intake in the regression models). In the longitudinal analyses, subjects were categorized according to tertiles of changes in dietary vitamin k intake from baseline to 1-year follow-up. A multivariable linear regression model was fitted to evaluate the relationship between metabolic risk markers at 1-year of follow-up (dependent variables in each model) and tertiles of change in vitamin k intake (independent variable in all models) adjusting by age, sex, smoking (never, current, past), physical activity (kcal/d), T2DM, intervention group, baseline values of each metabolic marker and changes in BMI, total energy intake (kcal/d), fiber intake (g/d), dietary PUFA intake (g/d) and adherence to MedDiet (the relative change in the 14-item score was expressed as a percentage). All statistical tests were two-tailed, and the significance level was p<0.05. Statistical analysis was performed using SPSS 17.0 for Windows (SPSS Inc, Chicago, IL).

                  Results

                  Of the 568 subjects consecutively recruited, 57 were excluded because they were using anti-inflammatory medication at baseline and 1 because he hd not completed the FFQ at follow-up. Table 1 summarizes the baseline characteristics of the study participants. Study subjects were 67.2±6.0 years old and 44.4% of them were male. Most of them were overweight or obese (92.2%), had hypertension (91.8%), were hypercholesterolemic (62.9%), and 55% had T2DM. Table 2 presents the baseline and 1-year dietary changes by tertiles of change in dietary phylloquinone intake. Subjects in the highest tertile of change consumed less amounts of vitamin K1 at baseline. This change after intervention was due to a higher consumption of total vegetables and, especially, leafy green vegetables, the primary dietary source of vitamin K1. In cross-sectional analyses at baseline, a negative significant association was found between dietary phylloquinone intake and PAI-1 plasma concentrations, even after adjusting for potential confounders (Table 3), but not for the rest of the metabolic risk markers. However, after 1-year of follow-up, those subjects in the upper tertile of changes in dietary phylloquinone intake showed a significant greater reduction in ghrelin (15.0%), GIP (12.9%), GLP-1 (17.6%), IL-6 (27.9%), leptin (10.3%), TNF (26.9%) and visfatin (24.9%) than those subjects in the lowest tertile (Table 4). No significant associations were found between changes in dietary phylloquinone intake and other metabolic markers of inflammation, insulin resistance and diabetes.
                  Table 1

                  Baseline characteristics of study subjects by tertiles of change in dietary phylloquinone intake (μg/day)

                  Characteristics

                  All subjects (n=510)

                  P

                  Tertile 1

                  Tertile 2

                  Tertile 3

                  (−718.2 to −69.9)

                  (−26.8 to 69.4)

                  (70.5 to 767.5)

                  Clinical characteristics

                      

                  Men/women, n

                  80/92

                  68/107

                  78/85

                  0.194

                  Age, years

                  66.8 ± 6.1

                  68.1 ± 6.2

                  66.6 ± 5.7

                  0.030

                  BMI, kg/m 2

                  29.39 ± 3.17

                  29.21 ± 3.18

                  29.17 ± 3.05

                  0.608

                  Waist circumference, cm

                  100.6 ± 8.6

                  100.5 ± 9.2

                  100.9 ± 8.9

                  0.940

                  Current smoker, n (%)

                  26 (15.1)

                  29 (16.6)

                  16 (9.8)

                  0.561

                  Type 2 diabetes, n (%),

                  95 (55.2)

                  100 (57.1)

                  85 (52.1)

                  0.650

                  Overweight/Obesity, n (%)

                  159 (92.4)

                  162 (92.6)

                  149 (91.4)

                  0.911

                  Hypertension, n (%)

                  156 (90.7)

                  164 (93.7)

                  148 (90.8)

                  0.121

                  Dyslipydemia, n (%)

                  103 (61.7)

                  116 (68.6)

                  92 (57.9)

                  0.511

                  Intervention group MD+VOO/MD+nuts/CD, n

                  44/54/74

                  64/57/54

                  76/45/42

                  0.001

                  Leisure-time physical activity, METS-min/day

                  277.8 ± 261.9

                  263.0 ± 247.8

                  294.2 ± 279.4

                  0.313

                  Metabolic risk markers

                      

                  C-Peptide (ng/mL)

                  1.41 (1.34 to 1.49)

                  1.33 (1.24 to 1.44)

                  1.34 (1.25 to 1.42)

                  0.374

                  Ghrelin (pg/mL)

                  12.88 (11.95 to 3.89)

                  13.38 (12.31 to 14.54)

                  12.77 (11.75 to 13.87)

                  0.685

                  GIP (pg/mL)

                  92.55 (84.76 to 101.05)

                  98.40 (89.70 to 107.95)

                  85.72 (78.25 to 93.91)

                  0.107

                  GLP-1 (ng/mL)

                  1.22 (1.09 to 1.36)

                  1.31 (1.17 to 1.48)

                  1.16 (1.03 to 1.30)

                  0.312

                  IL-6 (pg/ml)

                  9.96 (8.80 to 11.27)

                  10.88 (9.63 to 12.29)

                  8.99 (7.93 to 10.19)

                  0.102

                  Leptin (ng/mL)

                  2.87 (2.60 to 3.17)

                  3.20 (2.88 to 3.57)

                  2.91 (2.63 to 3.22)

                  0.262

                  PAI-1 (ng/mL)

                  3.20 (3.05 to 3.37)

                  3.28 (3.11 to 3.46)

                  3.37 (3.21 to 3.54)

                  0.378

                  Resistin (ng/mL)

                  0.98 (0.92 to 1.05)

                  1.05 (0.98 to 1.12)

                  1.01 (0.94 to 1.08)

                  0.413

                  TNF (pg/mL)

                  13.36 (11.57 to 15.44)

                  13.76 (11.89 to 15.93)

                  11.35 (9.80 to 13.14)

                  0.145

                  Visfatin (ng/mL)

                  4.13 (3.53 to 4.84)

                  4.54 (3.85 to 5.37)

                  3.70 (3.13 to 4.37)

                  0.225

                  Adiponectin (μg/mL)

                  48.87 (42.67 to 55.98)

                  49.51 (42.68 to 57.43)

                  42.37 (35.45 to 50.64)

                  0.302

                  Adipsin (μg/mL)

                  1.12 (1.01 to 1.24)

                  1.14 (1.03 to 1.28)

                  0.91 (0.77 to 1.08)

                  0.029

                  Data are given as mean (SD) or number (%) unless otherwise indicated. Metabolic risk markers are expressed as geometric means (IC95%). P values of the difference between tertils of change in dietary phylloquinone intake (ANOVA for the continuous variables and a χ 2 test for categorical variables). MD+VOO Mediterranean diet + Virgin Olive Oil, MD+nuts Mediterranean diet + Nuts, CD Control diet, BMI body mass index, GLP-1 glucagon-like peptide 1, GIP glucose-dependent insulinotropic polypeptide; IL-6 interleukin-6, PAI-1 plasminogen activator inhibitor-1, TNF-α tumor necrosis factor-α.

                  Table 2

                  Baseline and 1-year change of dietary characteristics by tertiles of change in dietary phylloquinone intake (μg/day)

                   

                  Tertile 1

                  Tertile 2

                  Tertile 3

                  P

                  (n=172)

                  (n=175)

                  (n=163)

                  Total energy intake, (kcal/d)

                      

                   Baseline

                  2526 ± 557

                  2359 ± 547

                  2270 ± 514

                  <0.001

                   Change

                  −85 ± 561

                  86 ± 30

                  198 ± 521

                  <0.001

                  Energy from total protein, (% kcal)

                      

                   Baseline

                  17 ± 3

                  17 ± 3

                  17 ± 3

                  0.386

                   Change

                  0 ± 3

                  −1 ± 3

                  0 ± 3

                  0.134

                  Energy from total carbohidrates, (% kcal)

                      

                   Baseline

                  42 ± 6

                  42 ± 7

                  42 ± 7

                  0.982

                   Change

                  −2 ± 6

                  −2 ± 7

                  −2 ± 8

                  0.958

                  Fiber intake, g/1,000 kcal

                      

                   Baseline

                  12 ± 4

                  12 ± 3

                  11 ± 3

                  0.023

                   Change

                  −1 ± 3

                  0 ± 2

                  2 ± 3

                  <0.001

                  Energy from total fat, (% kcal)

                      

                   Baseline

                  39 ± 6

                  39 ± 6

                  39 ± 6

                  0.393

                   Change

                  2 ± 6

                  3 ± 7

                  2 ± 8

                  0.240

                    Saturated fatty acids, (%)

                      

                     Baseline

                  27 ± 4

                  26 ± 5

                  26 ± 4

                  0.829

                     Change

                  −2 ± 5

                  −3 ± 5

                  −2 ± 5

                  0.271

                    MUFA, (%)

                      

                     Baseline

                  49 ± 5

                  48 ± 5

                  49 ± 5

                  0.242

                     Change

                  1 ± 6

                  2 ± 5

                  1 ± 6

                  0.140

                    PUFA, (%)

                      

                     Baseline

                  17 ± 4

                  17 ± 4

                  16 ± 4

                  0.456

                     Change

                  0 ± 5

                  1 ± 5

                  1 ± 5

                  0.221

                  Phylloquinone intake, (μg/d)

                      

                   Baseline

                  490 ± 229

                  401 ± 209

                  297 ± 163

                  <0.001

                   Change

                  −176 ± 150

                  16 ± 25

                  266 ± 164

                  <0.001

                  Vegetable consumption, (g/d)

                      

                   Baseline

                  413 ± 167

                  380 ± 182

                  335 ± 186

                  <0.001

                   Change

                  −42 ± 180

                  17 ± 131

                  141 ± 175

                  <0.001

                  Leafy green vegetables, (g/d)

                      

                   Baseline

                  112 ± 47

                  95 ± 47

                  75 ± 44

                  <0.001

                   Change

                  −30 ± 38

                  1 ± 20

                  55 ± 44

                  <0.001

                  Other vegetables, (g/d)

                      

                   Baseline

                  276 ± 138

                  265 ± 142

                  239 ± 147

                  0.053

                   Change

                  −13 ± 149

                  7 ± 120

                  79 ± 153

                  <0.001

                  Fruit consumption, (g/d)

                      

                   Baseline

                  479 ± 258

                  451 ± 240

                  432 ± 251

                  0.221

                   Change

                  −12 ± 223

                  36 ± 211

                  67 ± 238

                  0.005

                  Legume consumption, (g/d)

                      

                   Baseline

                  20 ± 11

                  19 ± 12

                  17 ± 9

                  0.046

                   Change

                  1 ± 12

                  4 ± 12

                  7 ± 13

                  <0.001

                  Cereal consumption, (g/d)

                      

                   Baseline

                  270 ± 103

                  256 ± 102

                  249 ± 103

                  0.150

                   Change

                  −27 ± 100

                  −10 ± 119

                  −1 ± 113

                  0.091

                  Dairy product consumption, (g/d)

                      

                   Baseline

                  357 ± 227

                  371 ± 252

                  349 ± 195

                  0.656

                   Change

                  −8 ± 212

                  −8 ± 190

                  2 ± 181

                  0.854

                  Meat consumption, (g/d)

                      

                   Baseline

                  154 ± 61

                  148 ± 61

                  143 ± 56

                  0.243

                   Change

                  −14 ± 58

                  −12 ± 62

                  −2 ± 61

                  0.137

                  Fish consumption, (g/d)

                      

                   Baseline

                  115 ± 46

                  106 ± 46

                  106 ± 42

                  0.099

                   Change

                  −1 ± 52

                  9 ± 46

                  15 ± 43

                  0.008

                  Alcohol intake, (g/d)

                      

                   Baseline

                  13 ± 19

                  10 ± 19

                  7 ± 12

                  0.013

                   Change

                  0 ± 13

                  −1 ± 17

                  1 ± 9

                  0.173

                  Olive oil consumption, (g/d)

                      

                   Baseline

                  39 ± 15

                  36 ± 14

                  38 ± 15

                  0.248

                   Change

                  5 ± 19

                  11 ± 19

                  9 ± 20

                  0.021

                  Nut consumption, (g/d)

                      

                   Baseline

                  16 ± 17

                  14 ± 15

                  11 ± 13

                  0.029

                   Change

                  7 ± 25

                  10 ± 25

                  11 ± 24

                  0.185

                  14-item PREDIMED MedDiet Score

                      

                   Baseline

                  9 ± 2

                  9 ± 2

                  8 ± 2

                  0.443

                   Change

                  1 ± 2

                  1 ± 2

                  2 ± 2

                  0.031

                  Data expressed as mean ± standard deviation. ANOVA was used for analysis of the difference between tertils of change in vitamin K intake. Abbreviations: MUFA monounsaturated fatty acids, PUFA polyunsaturated fatty acids, MedDiet Mediterranean diet.

                  Table 3

                  Cross-sectional associations between intake of 100 μg of dietary phylloquinone and inflammatory or metabolic markers at baseline

                   

                  Change in inflammatory marker for 100 additional μg of dietary phylloquinone intake (95% confidence intervals)

                  P

                  C-Peptide (ng/mL)

                  −0.73 (−2.68 to 1.27)

                  0.472

                  Ghrelin (pg/mL)

                  −1.25 (−3.76 to 1.33)

                  0.339

                  GIP (pg/mL)

                  −0.13 (−2.98 to 2.80)

                  0.928

                  GLP-1 (ng/mL)

                  −1.81 (−5.40 to 1.91)

                  0.335

                  IL-6 (pg/ml)

                  −1.68 (−5.46 to 2.25)

                  0.396

                  Leptin (ng/mL)

                  −1.48 (−3.80 to 0.90)

                  0.221

                  PAI-1 (ng/mL)

                  −1.64 (−3.23 to −0.0)2

                  0.047

                  Resistin (ng/mL)

                  −1.42 (−3.61 to 0.81)

                  0.210

                  TNF (pg/mL)

                  −1.24 (−5.73 to 3.46)

                  0.598

                  Visfatin (ng/mL)

                  −1.42 (−6.54 to 3.98)

                  0.599

                  Adiponectin (μg/mL)

                  2.45 (−2.23 to 7.35)

                  0.310

                  Adipsin (μg/mL)

                  1.21 (−2.84 to 5.43)

                  0.563

                  Multivariable linear regression models were used for analysis. Data are given in % of change (95% IC). Models are adjusted for sex, age, body-mass-index, smoking (never, current, past), total energy intake (kcal/d), dietary fibre intake (g/d), dietary polyunsaturated fatty acids intake (g/d), physical activity (kcal/d), Mediterranean Diet Score (14-item PREDIMED score, quantitative), and T2DM. GIP Gastric inhibitory polypeptide, GLP-1 Glucagon-like peptide-1, IL-6 Interleukin-6, PAI-1 Plasminogen activator inhibitor-1, TNF Tumor Necrosis Factor α.

                  Table 4

                  Longitudinal associations between one-year changes in inflammatory or metabolic markers and tertiles of change in dietary phylloquinone intake (μg/day)

                   

                  Tertile 1

                  Tertile 2

                  p

                  Tertile 3

                  p

                  p for trend

                  C-Peptide (ng/mL)

                  Ref.

                  −2.19 (−8.35 to 4.40)

                  0.505

                  −6.10 (−12.61 to 0.91)

                  0.086

                  0.083

                  Ghrelin (pg/mL)

                  Ref.

                  1.69 (−8.58 to 13.11)

                  0.758

                  −15.00 (−24.49 to −4.32)

                  0.007

                  0.003

                  GIP (pg/mL)

                  Ref.

                  0.08 (−10.17 to 11.49)

                  0.989

                  −12.94 (−22.75 to −1.89)

                  0.023

                  0.014

                  GLP-1 (ng/mL)

                  Ref.

                  0.21 (−12.99 to 15.42)

                  0.977

                  −17.64 (−29.67 to −3.56)

                  0.016

                  0.009

                  IL-6 (pg/ml)

                  Ref.

                  −4.50 (−18.17 to 11.46)

                  0.559

                  −27.89 (−39.20 to −14.48)

                  <0.001

                  <0.001

                  Leptin (ng/mL)

                  Ref.

                  −7.79 (−15.61 to 0.76)

                  0.073

                  −10.26 (−18.62 to −1.03)

                  0.030

                  0.044

                  PAI-1 (ng/mL)

                  Ref.

                  −3.06 (−10.65 to 5.17)

                  0.454

                  −3.24 (−11.61 to 5.93)

                  0.476

                  0.529

                  Resistin (ng/mL)

                  Ref.

                  −2.43 (−11.03 to 7.01)

                  0.601

                  −6.66 (−15.71 to 3.35)

                  0.184

                  0.180

                  TNF (pg/mL)

                  Ref.

                  −3.96 (−20.47 to 15.98)

                  0.674

                  −26.89 (−40.65 to −9.94)

                  0.003

                  0.002

                  Visfatin (ng/mL)

                  Ref.

                  −8.08 (−24.73 to 12.24)

                  0.408

                  −24.90 (−39.86 to −6.22)

                  0.012

                  0.010

                  Adiponectin (μg/mL)

                  Ref.

                  −3.27 (−19.20 to 15.79)

                  0.716

                  −7.34 (−23.98 to 12.93)

                  0.449

                  0.452

                  Adipsin (μg/mL)

                  Ref.

                  −2.45 (−15.37 to 12.45)

                  0.732

                  −7.39 (−20.90 to 8.44)

                  0.340

                  0.332

                  Multivariable linear regression models were used for analysis. Data are given in % of change (95% IC). Models are adjusted for sex, age, change of body-mass-index, smoking (never, current, past), change of total energy intake (kcal/d), change of fibre intake (g/d), change of dietary polyunsaturated fatty acids intake (g/d), physical activity (kcal/d), intervention group, change of Mediterranean Diet Score (%), T2DM and baseline values of each metabolic marker. GIP Gastric inhibitory polypeptide, GLP-1 Glucagon-like peptide-1, IL-6 Interleukin-6, PAI-1 Plasminogen activator inhibitor-1, TNF, Tumor Necrosis Factor α.

                  Discussion

                  The results of this study show, for the first time, that an increased dietary intake of phylloquinone is associated with an improvement in inflammatory and other metabolic risk markers related to insulin resistance and diabetes, thus supporting a protective role of vitamin K on low-grade chronic inflammatory diseases.

                  In recent years, vitamin K has been attributed a putative role in glucose metabolism, insulin resistance and T2DM [17]. Recently, in a prospective study our group has shown that a higher dietary phylloquinone intake was associated to a lower risk of developing T2DM. However, the exact mechanisms underlying this relationship remain still unknown. One potential explanatory mechanism could be related to the role of vitamin K as a cofactor in the carboxylation of vitamin K-dependent proteins, such as osteocalcin [18], GAS6 or Protein S [19] and through their role on NF-kB [2022]. Additionally, some authors have suggested that inflammation may be modulated by a possible antioxidant effect of vitamin K [23].

                  A cross-sectional study conducted in a subsample from the Framingham Offspring Study, showed an inverse association between 2-fold changes in usual dietary phylloquinone intake or plasma phylloquinone concentrations and seven of the fourteen peripheral inflammatory markers measured [9]. However, the same authors, in a cross-sectional analysis conducted on 400 healthy elderly men and women, did not find any significant association between plasma phylloquinone concentrations and C-reactive protein or IL-6 after adjusting for potential confounders [10]. Similar to the results from Shea and co-workers, we also failed to find any significant association between dietary phylloquinone intake and most of the metabolic risk markers analysed. However, our longitudinal findings are generally consistent with in-vitro studies or those of a previous epidemiological study. In-vitro studies found that the production of the proinflammatory cytokines IL-6 or TNF by human gingival fibroblast or mouse macrophage cells, respectively, decreased when cells were incubated with different vitamin K family compounds [21, 24]. Other studies have found that vitamin K suppresses inflammation by lowering the expression of genes for some proinflammatory cytokines, such as IL-6, IL-1β and TNF [8, 22]. However, only a single 3-year, double-blind, randomized controlled trial has been conducted to evaluate the effect of vitamin K supplementation on peripheral inflammatory marker concentrations. In that study, conducted with 379 healthy men and women, no significant relationship between plasma phylloquinone levels and inflammation markers was shown [10]. In our study we observed a significant improvement in the inflammatory status (leptin, IL-6, TNF) and a decrease in other metabolic risk markers related to insulin resistance and diabetes such as visfatin, ghrelin, GIP and GLP-1 among subjects who increased their dietary intake of phylloquinone after 1-year follow-up, thus contributing to extend the knowledge on the role of vitamin K in humans and to support our previous results on dietary phylloquinone intake and T2DM incidence [7]. The discrepancies between our results and those previously published could be partly explained by the differences between the populations. Our participants were elderly subjects at high cardiovascular risk, whereas, subjects in the study of Shea [9] were healthy and generally free of chronic disease. This may contribute to explain why inflammatory cytokines remained unchanged in that study. Moreover, in our study we assessed the association of inflammation with dietary phylloquinone intake instead of plasma phylloquinone as was done in the previous study. Whether circulating levels of phylloquinone are correlated to dietary phylloquinone intake remains to be elucidated.

                  It must be noted that leptin, IL6 and TNF are pro-inflammatory cytokines with a recognised role in the development and progression of insulin resistance, T2DM and cardiovascular disease [25, 26]. Also visfatin and ghrelin appears as important mediators of inflammation in addition to glucose-lowering and insulin-mimicking/sensitizing effects or a suppressive role of ghrelin in the release of insulin from the pancreatic islets [2730]. A strong inverse correlation between plasma ghrelin concentrations and insulin resistance has been observed in several studies [3032] and lower concentrations of ghrelin have been observed in T2DM subjects [33]. The negative relationship between changes in dietary phylloquinone intake and plasma incretin concentrations observed in our study could be explained because a higher intake of phylloquinone may promote better glycemic control thus leading less necessary the glucose and insulin regulation mediated by incretins. However, the pleiotropic role recently attributed to incretins could also contribute to explain our results. Although there is growing evidence that incretin hormones (GIP and GLP-1) simulate glucose-dependent insulin secretion and stimulate pancreatic synthesis of insulin, a novel link between inflammation and incretin hormones has been proposed. First, IL-6 increase GLP-1 production in intestinal L cells and alpha pancreatic cells improving insulin secretion and glycemia [34]. Therefore, the reduction of IL-6 levels observed in the subjects allocated in the highest tertile of change in phylloquinone intake could partly explain the reduction in GLP-1 concentrations in this group. Nie et al., have also demonstrated the capacity of GIP to activate inflammatory response and promote secretion of pro-inflammatory cytokines and chemokines in cell culture adipocytes [35]. Additionally, a potential role of GIP on adipose tissue insulin resistance mediated by osteopontin regulation has also been suggested [36]. The results from our study could be related to the potential role of incretins on adipose tissue in a fasting situation rather than to their established role on pancreatic cells in a post-prandial state. Whether or not GIP and GLP-1 need to be considered as new adipokynes or related pro-inflammatory markers could not be elucitaded from the results of our study. Specific studies are needed to get a deeper understanding of the exact role of incretins on adipose tissue and their interaction with the rest of adipokynes.

                  Our study has several limitations. It should be kept in mind that the subjects in our study were randomly allocated to a healthy Mediterranean diet that could partially account for the reduction in peripheral metabolic risk markers in some of them although no significant interaction between intervention group and dietary phylloquinone intake was observed for any of the outcomes. In order to minimize the potential effect of a healthy dietary pattern on inflammatory response we have adjusted the regression models for the adherence to a MedDiet. Subjects in the PREDIMED study reported a higher dietary intake of phylloquinone than in other epidemiological studies, probably because this study was conducted in a Mediterranean country where the consumption of fruit and vegetables is high. In populations with a lower consumption of phylloquinone or poor nutrition an increase in dietary vitamin K1 would probably be much more beneficial. The cohort studied was elderly and at high risk of cardiovascular disease, so our findings cannot be generalized to younger or healthier individuals. Because there is no perfect correlation between dietary phylloquinone intake and its absorption it would be interesting in the future to evaluate the associations showed in our study not only with dietary phylloquinone intake but also with a circulating marker of vitamin K status. Finally, we cannot discount a slight overestimation of dietary phylloquinone intake due to the use of FFQ and the USDA Food Database Composition. Although the FFQ used in our study was not specifically validated for phylloquinone intake, the intraclass correlation coefficient of vegetables (the main source of dietary vitamin K1) was 0.81, one of the highest coefficients obtained during the validation of the FFQ in the PREDIMED cohort [13]. Balanced against these limitations, the main strength of our study is its longitudinal design, which enables us to suggest a cause-effect relationship between changes in dietary phylloquinone intake and changes in inflammatory and related metabolic risk markers. In addition, our study was conducted in a large sample of individuals, and measured a panel of adipokines and related molecules involved in inflammation, glucose metabolism and cardiovascular risk.

                  In summary, our results support that an increase in dietary phylloquinone intake can lead to an improvement in inflammation and inflammatory-related molecules and also support the contention that high vitamin K1 intake has a beneficial effect on cardiovascular disease and other inflammation-related disorders.

                  Abbreviations

                  FFQ: 

                  Food Frequency Questionnaire

                  GLP-1: 

                  Glucagon-like peptide 1

                  IL: 

                  Interleukin

                  MedDiet: 

                  Mediterranean Diet

                  PAI-1: 

                  Plasminogen activator inhibitor-1

                  T2DM: 

                  Type 2 diabetes mellitus

                  TNF: 

                  Tumor necrosis factor alpha.

                  Declarations

                  Acknowledgments

                  We thank all the participants of the PREDIMED study for their enthusiastic collaboration, the PREDIMED personnel for excellent assistance and the personnel of all affiliated primary care centers. CIBERobn and RTIC RD 06/0045 are initiatives of ISCIII, Spain. We also acknowledge the grants from Centro Nacional de Investigaciones Cardiovasculares CNIC 06/2007, Fondo de Investigación Sanitaria PI 07/0473, Ministerio de Ciencia e Innovación (AGL-2009-13906-C02, AGL2010-22319-C03), and Fundación Mapfre 2010, Government of the Basque Country (IT386-10),University of the Basque Country (UFI 11/32).

                  Authors’ Affiliations

                  (1)
                  Human Nutrition Unit, Faculty of Medicine and Health Sciences, IISPV, Universitat Rovira i Virgili
                  (2)
                  CIBERobn Physiopathology of Obesity and Nutrition, Institute of Health Carlos III (ISCIII)
                  (3)
                  PREDIMED Network (RD 06/0045), ISCIII
                  (4)
                  Department of Internal Medicine, Institut d’Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clínic
                  (5)
                  Department of Nutrition and Food Science, University of Pais Vasco
                  (6)
                  Department of Preventive Medicine and Public Health, University of Navarra

                  References

                  1. Iwamoto J, Sato Y, Takeda T, Matsumoto H: Bone quality and vitamin K2 in type 2 diabetes: review of preclinical and clinical studies. Nutr Rev. 2011, 69 (3): 162-167. 10.1111/j.1753-4887.2011.00380.x.View ArticlePubMed
                  2. Shearer MJ: The roles of vitamins D and K in bone health and osteoporosis prevention. Proc Nutr Soc. 1997, 56 (3): 915-937. 10.1079/PNS19970099.View ArticlePubMed
                  3. Booth SL: Roles for vitamin K beyond coagulation. Annu Rev Nutr. 2009, 29: 89-110. 10.1146/annurev-nutr-080508-141217.View ArticlePubMed
                  4. Yoshida M, Booth SL, Meigs JB, Saltzman E, Jacques PF: Phylloquinone intake, insulin sensitivity, and glycemic status in men and women. Am J Clin Nutr. 2008, 88 (1): 210-215.PubMed CentralPubMed
                  5. Kumar R, Binkley N, Vella A: Effect of phylloquinone supplementation on glucose homeostasis in humans. Am J Clin Nutr. 2010, 92 (6): 1528-1532. 10.3945/ajcn.2010.30108.PubMed CentralView ArticlePubMed
                  6. Beulens JW, van Der ADL, Grobbee DE, Sluijs I, Spijkerman AM, van der Schouw YT: Dietary phylloquinone and menaquinones intakes and risk of type 2 diabetes. Diabetes Care. 2010, 33 (8): 1699-1705. 10.2337/dc09-2302.PubMed CentralView ArticlePubMed
                  7. Ibarrola-Jurado N, Salas-Salvado J, Martinez-Gonzalez MA, Bullo M: Dietary phylloquinone intake and risk of type 2 diabetes in elderly subjects at high risk of cardiovascular disease. Am J Clin Nutr. 2012, 96 (5): 1113-1118. 10.3945/ajcn.111.033498.View ArticlePubMed
                  8. Ohsaki Y, Shirakawa H, Hiwatashi K, Furukawa Y, Mizutani T, Komai M: Vitamin K suppresses lipopolysaccharide-induced inflammation in the rat. Biosci Biotechnol Biochem. 2006, 70 (4): 926-932. 10.1271/bbb.70.926.View ArticlePubMed
                  9. Shea MK, Booth SL, Massaro JM, Jacques PF, D’Agostino RBS, Dawson-Hughes B, Ordovas JM, O’Donnell CJ, Kathiresan S, Keaney JF, Vasan RS, Benjamin EJ: Vitamin K and vitamin D status: associations with inflammatory markers in the Framingham Offspring Study. Am J Epidemiol. 2008, 167 (3): 313-320.PubMed CentralView ArticlePubMed
                  10. Shea MK, Dallal GE, Dawson-Hughes B, Ordovas JM, O’Donnell CJ, Gundberg CM, Peterson JW, Booth SL: Vitamin K, circulating cytokines, and bone mineral density in older men and women. Am J Clin Nutr. 2008, 88 (2): 356-363.PubMed CentralPubMed
                  11. Estruch R, Martinez-Gonzalez MA, Corella D, Salas-Salvado J, Ruiz-Gutierrez V, Covas MI, Fiol M, Gomez-Gracia E, Lopez-Sabater MC, Vinyoles E, Aros F, Conde M, Lahoz C, Lapetra J, Saez G, Ros E, PREDIMED Study Investigators: Effects of a Mediterranean-style diet on cardiovascular risk factors: a randomized trial. Ann Intern Med. 2006, 145 (1): 1-11.View ArticlePubMed
                  12. Martinez-Gonzalez MA, Corella D, Salas-Salvado J, Ros E, Covas MI, Fiol M, Warnberg J, Aros F, Ruiz-Gutierrez V, Lamuela-Raventos RM, Lapetra J, Munoz MA, Martinez JA, Saez G, Serra-Majem L, Pinto X, Mitjavila MT, Tur JA, Portillo MD, Estruch R, for the PREDIMED Study Investigators: Cohort profile: design and methods of the PREDIMED study. Int J Epidemiol. 2012, 41 (2): 377-385. 10.1093/ije/dyq250.View ArticlePubMed
                  13. Fernandez-Ballart JD, Pinol JL, Zazpe I, Corella D, Carrasco P, Toledo E, Perez-Bauer M, Martinez-Gonzalez MA, Salas-Salvado J, Martin-Moreno JM: Relative validity of a semi-quantitative food-frequency questionnaire in an elderly Mediterranean population of Spain. Br J Nutr. 2010, 103 (12): 1808-1816. 10.1017/S0007114509993837.View ArticlePubMed
                  14. Schroder H, Fito M, Estruch R, Martinez-Gonzalez MA, Corella D, Salas-Salvado J, Lamuela-Raventos R, Ros E, Salaverria I, Fiol M, Lapetra J, Vinyoles E, Gomez-Gracia E, Lahoz C, Serra-Majem L, Pinto X, Ruiz-Gutierrez V, Covas MI: A short screener is valid for assessing Mediterranean diet adherence among older Spanish men and women. J Nutr. 2011, 141 (6): 1140-1145. 10.3945/jn.110.135566.View ArticlePubMed
                  15. Moreiras O, Carbajal A, Cabrera L, Cuadrado C: Tablas de composición de los alimentos. (Food Composition Tables). 2005, Madrid: Pirámide, 9th
                  16. Mataix Verdú J: Tabla de composicion de alimentos [Food Composition Tables]. 2003, Granada (Spain): Universidad de Granada, 4th
                  17. Pan Y, Jackson RT: Dietary phylloquinone intakes and metabolic syndrome in US young adults. J Am Coll Nutr. 2009, 28 (4): 369-379.View ArticlePubMed
                  18. Bullo M, Moreno-Navarrete JM, Fernandez-Real JM, Salas-Salvado J: Total and undercarboxylated osteocalcin predict changes in insulin sensitivity and beta cell function in elderly men at high cardiovascular risk. Am J Clin Nutr. 2012, 95 (1): 249-255. 10.3945/ajcn.111.016642.View ArticlePubMed
                  19. Fernandez-Fernandez L, Bellido-Martin L, Garcia de Frutos P: Growth arrest-specific gene 6 (GAS6). An outline of its role in haemostasis and inflammation. Thromb Haemost. 2008, 100 (4): 604-610.PubMed
                  20. Ozaki I, Zhang H, Mizuta T, Ide Y, Eguchi Y, Yasutake T, Sakamaki T, Pestell RG, Yamamoto K: Menatetrenone, a vitamin K2 analogue, inhibits hepatocellular carcinoma cell growth by suppressing cyclin D1 expression through inhibition of nuclear factor kappaB activation. Clin Cancer Res. 2007, 13 (7): 2236-2245. 10.1158/1078-0432.CCR-06-2308.View ArticlePubMed
                  21. Tanaka S, Nishiumi S, Nishida M, Mizushina Y, Kobayashi K, Masuda A, Fujita T, Morita Y, Mizuno S, Kutsumi H, Azuma T, Yoshida M: Vitamin K3 attenuates lipopolysaccharide-induced acute lung injury through inhibition of nuclear factor-kappaB activation. Clin Exp Immunol. 2010, 160 (2): 283-292.PubMed CentralView ArticlePubMed
                  22. Ohsaki Y, Shirakawa H, Miura A, Giriwono PE, Sato S, Ohashi A, Iribe M, Goto T, Komai M: Vitamin K suppresses the lipopolysaccharide-induced expression of inflammatory cytokines in cultured macrophage-like cells via the inhibition of the activation of nuclear factor kappaB through the repression of IKKalpha/beta phosphorylation. J Nutr Biochem. 2010, 21 (11): 1120-1126. 10.1016/j.jnutbio.2009.09.011.View ArticlePubMed
                  23. Li J, Lin JC, Wang H, Peterson JW, Furie BC, Furie B, Booth SL, Volpe JJ, Rosenberg PA: Novel role of vitamin k in preventing oxidative injury to developing oligodendrocytes and neurons. J Neurosci. 2003, 23 (13): 5816-5826.PubMed
                  24. Reddi K, Henderson B, Meghji S, Wilson M, Poole S, Hopper C, Harris M, Hodges SJ: Interleukin 6 production by lipopolysaccharide-stimulated human fibroblasts is potently inhibited by naphthoquinone (vitamin K) compounds. Cytokine. 1995, 7 (3): 287-290. 10.1006/cyto.1995.0034.View ArticlePubMed
                  25. Shoelson SE, Lee J, Goldfine AB: Inflammation and insulin resistance. J Clin Invest. 2006, 116 (7): 1793-1801. 10.1172/JCI29069.PubMed CentralView ArticlePubMed
                  26. Goldberg RB: Cytokine and cytokine-like inflammation markers, endothelial dysfunction, and imbalanced coagulation in development of diabetes and its complications. J Clin Endocrinol Metab. 2009, 94 (9): 3171-3182. 10.1210/jc.2008-2534.View ArticlePubMed
                  27. Kim SR, Bae YH, Bae SK, Choi KS, Yoon KH, Koo TH, Jang HO, Yun I, Kim KW, Kwon YG, Yoo MA, Bae MK: Visfatin enhances ICAM-1 and VCAM-1 expression through ROS-dependent NF-kappaB activation in endothelial cells. Biochim Biophys Acta. 2008, 1783 (5): 886-895. 10.1016/j.bbamcr.2008.01.004.View ArticlePubMed
                  28. Lee WJ, Wu CS, Lin H, Lee IT, Wu CM, Tseng JJ, Chou MM, Sheu WH: Visfatin-induced expression of inflammatory mediators in human endothelial cells through the NF-kappaB pathway. Int J Obes (Lond). 2009, 33 (4): 465-472. 10.1038/ijo.2009.24.View Article
                  29. Tong J, Prigeon RL, Davis HW, Bidlingmaier M, Kahn SE, Cummings DE, Tschop MH, D’Alessio D: Ghrelin suppresses glucose-stimulated insulin secretion and deteriorates glucose tolerance in healthy humans. Diabetes. 2010, 59 (9): 2145-2151. 10.2337/db10-0504.PubMed CentralView ArticlePubMed
                  30. Schofl C, Horn R, Schill T, Schlosser HW, Muller MJ, Brabant G: Circulating ghrelin levels in patients with polycystic ovary syndrome. J Clin Endocrinol Metab. 2002, 87 (10): 4607-4610. 10.1210/jc.2002-020505.View ArticlePubMed
                  31. Poykko SM, Kellokoski E, Horkko S, Kauma H, Kesaniemi YA, Ukkola O: Low plasma ghrelin is associated with insulin resistance, hypertension, and the prevalence of type 2 diabetes. Diabetes. 2003, 52 (10): 2546-2553. 10.2337/diabetes.52.10.2546.View ArticlePubMed
                  32. McLaughlin T, Abbasi F, Lamendola C, Frayo RS, Cummings DE: Plasma ghrelin concentrations are decreased in insulin-resistant obese adults relative to equally obese insulin-sensitive controls. J Clin Endocrinol Metab. 2004, 89 (4): 1630-1635. 10.1210/jc.2003-031572.View ArticlePubMed
                  33. Ostergard T, Hansen TK, Nyholm B, Gravholt CH, Djurhuus CB, Hosoda H, Kangawa K, Schmitz O: Circulating ghrelin concentrations are reduced in healthy offspring of Type 2 diabetic subjects, and are increased in women independent of a family history of Type 2 diabetes. Diabetologia. 2003, 46 (1): 134-136.PubMed
                  34. Ellingsgaard H, Hauselmann I, Schuler B, Habib AM, Baggio LL, Meier DT, Eppler E, Bouzakri K, Wueest S, Muller YD, Hansen AM, Reinecke M, Konrad D, Gassmann M, Reimann F, Halban PA, Gromada J, Drucker DJ, Gribble FM, Ehses JA, Donath MY: Interleukin-6 enhances insulin secretion by increasing glucagon-like peptide-1 secretion from L cells and alpha cells. Nat Med. 2011, 17 (11): 1481-1489. 10.1038/nm.2513.PubMed CentralView ArticlePubMed
                  35. Nie Y, Ma RC, Chan JC, Xu H, Xu G: Glucose-dependent insulinotropic peptide impairs insulin signaling via inducing adipocyte inflammation in glucose-dependent insulinotropic peptide receptor-overexpressing adipocytes. FASEB J. 2012, 26 (6): 2383-2393. 10.1096/fj.11-196782.View ArticlePubMed
                  36. Omar B, Banke E, Guiguis E, Kesson L, Manganiello V, Lyssenko V, Groop L, Gomez MF, Degerman E: Regulation of the pro-inflammatory cytokine osteopontin by GIP in adipocytes - A role for the transcription factor NFAT and phosphodiesterase 3B. Biochem Biophys Res Commun. 2012, 425 (4): 812-819. 10.1016/j.bbrc.2012.07.157.PubMed CentralView ArticlePubMed

                  Copyright

                  © Juanola-Falgarona et al.; licensee BioMed Central Ltd. 2013

                  This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                  Advertisement