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Article

Influence of the Mediterranean Diet on 25-Hydroxyvitamin D Levels in Adults

by
Luigi Barrea
1,2,*,†,
Giovanna Muscogiuri
1,2,†,
Daniela Laudisio
1,2,
Gabriella Pugliese
1,2,
Giulia de Alteriis
1,
Annamaria Colao
1,2,3 and
Silvia Savastano
1,2
1
Dipartimento di Medicina Clinica e Chirurgia, Unit of Endocrinology, Federico II University Medical School of Naples, Via Sergio Pansini 5, 80131 Naples, Italy
2
Centro Italiano per la cura e il Benessere del Paziente con Obesità (C.I.B.O), Department of Clinical Medicine and Surgery, Endocrinology Unit, University Medical School of Naples, Via Sergio Pansini 5, 80131 Naples, Italy
3
Cattedra Unesco “Educazione alla Salute e allo Sviluppo Sostenibile”, Federico II University Medical School of Naples, Via Sergio Pansini 5, 80131 Naples, Italy
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Nutrients 2020, 12(5), 1439; https://0-doi-org.brum.beds.ac.uk/10.3390/nu12051439
Submission received: 14 March 2020 / Revised: 26 April 2020 / Accepted: 14 May 2020 / Published: 16 May 2020
(This article belongs to the Special Issue The Role of Vitamin D in Chronic Diseases)
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Abstract

:
The Mediterranean diet (MD) is a dietary pattern effective in terms of prevention of obesity-related diseases, and represents the gold standard in preventive medicine, due to the synergistic action of many nutrients with antioxidant and anti-inflammatory properties. In addition, excess body weight significantly increases the risk of hypovitaminosis D, a well-recognized common feature of individuals with obesity. It is well-known that there is a clear gender difference in the adherence to the MD. The aim of this study was to investigate the association between adherence to the MD and 25-hydroxyvitamin D (25OHD) levels in adults, according to gender. Study population consisted of 617 participants; 296 were males and 321 were females, matched by age and body mass index (BMI). A validated 14-item questionnaire PREDIMED (Prevención con dieta Mediterránea) was used for the assessment of adherence to the MD. The 25OHD levels were determined by a direct competitive chemiluminescence immunoassay. Females have a higher PREDIMED score than males (7.4 ± 2.8 vs. 6.7 ± 3.1 score, p = 0.001), and according to PREDIMED categories, a greater percentage of males had low adherence to the MD compared to their female counterparts (40.2% vs. 37.1%; χ2 = 8.94, p = 0.003). The 25OHD levels were higher in males than in females (18.3 ± 7.3 vs. 16.8 ± 7.8 ng/mL, p = 0.01), and a higher percentage of males had sufficient 25OHD levels (>30 ng/mL) than their female counterparts (10.5% vs. 3.4%, χ2 = 10.96, p < 0.001). Stratifying the sample population according to 25OHD categories, BMI decreased and PREDIMED score increased significantly along with the increased 25OHD levels, in both males and females, respectively (p < 0.001). Looking at the bivariate correlations, PREDIMED score was positively correlated with 25OHD levels after adjusting for age and BMI, in both males (r = 0.21, p < 0.001) and females (r = 0.30, p < 0.001). At the bivariate proportional odds ratio (OR) model, 25OHD levels presented the highest OR values in the category low adherence vs. high adherence to the MD, in both genders (OR 1.21 and OR 1.31, in males and females, respectively). Receiver operator characteristic (ROC) analysis was performed to determine the cut-off values of PREDIMED scores predictive of 25OHD levels: PREDIMED score >5 in males (p < 0.001) and >7 in females (p < 0.001) could serve as thresholds for 25OHD levels above the median. The results of our study highlighted a novel positive association between adherence to the MD and 25OHD levels in both genders. Although 25OHD levels were higher in males than females, 69.7% were deficient. To the best of our knowledge, this is the first study to show that high adherence to the MD is associated with low BMI and high 25OHD levels in both genders, probably through the anti-inflammatory and anti-oxidant effects that are synergistically exerted by either MD or vitamin D on body weight.

1. Introduction

Hypovitaminosis D and obesity have concomitantly reached epidemic levels worldwide [1,2]. Clinical evidence reported that hypovitaminosis D represents an increasingly widespread phenomenon in patients with obesity [3,4] and it is considered a clinical syndrome due to low serum 25-hydroxyvitamin D (25OHD) levels. [1,2]. It is well-established that 25OHD levels are about 20% lower in subjects with obesity than people of normal weight [5], and the prevalence of 25OHD deficiency is greater in populations with obesity [6].
Several mechanisms have been hypothesized to explain the hypovitaminosis D in obesity, such as lower dietary vitamin D intake, lesser skin exposure to sunlight (due to reduced outdoor physical activity), reduced intestinal absorption, and accumulation of vitamin D in adipose tissue [7,8]. Different studies also indicate a strong negative association between hypovitaminosis D and several obesity-related diseases, including type 2 diabetes, cardiovascular disease, sleep disturbance related to obstructive sleep apnoea syndrome, and neurological diseases [9,10,11,12,13], suggesting that the 25OHD deficiency may play a crucial role in the pathogenesis of these diseases. In the context of weight excess, on the one hand, recent studies showed a causal role of obesity in determining 25OHD deficiency [14]. On the other hand, evidence has reported that obesity is associated with hypovitaminosis D [14,15] and suggested that low vitamin D status could affect health risks in obesity [9].
Interestingly, diet play a key role in obesity, and different dietary strategies, including MD, can reduce weight [16,17]. In addition, excess body weight significantly increases the risk of hypovitaminosis D, a well-recognized common feature of individuals with obesity [1,7,8]. The Mediterranean Diet (MD) is characterized by plant-based foods, including legumes, green-leafy vegetables, fruits, and nuts; high consumption of fish and low of red meat; moderate consumption of dairy products; and low consumption of red wine with meals (if already consuming alcohol). In addition, it includes the use of extra virgin olive oil as the main source of dietary fat. Indeed, it is the synergistic consumption of these Mediterranean foods which exemplifies the antioxidant and anti-inflammatory nature of this diet pattern [18].
It has been well reported that the MD prevents weight gain and obesity [16,19,20], and this has been attributed to its antioxidant and anti-inflammatory properties [16,21,22]. In addition, Castro-Quezada I et al. has recently reported that a higher adherence to the MD was associated with a higher prevalence of subjects showing adequate intake of nutrients, including vitamin D [23]. Other previous studies demonstrated that subjects with higher adherence to the MD also had higher dietary vitamin D intake [24,25,26].
In particular, Marventano S et al. reported the difference of nutrient intake according to quartiles of adherence to the MD and showed that dietary vitamin D intake was significantly different between the lowest and the highest quartile (4.4 vs. 6.4 µg, p < 0.001; respectively) [25]. Of interest, in Italy, the recommendation in both males and females is 15 µg/day (18–54 age) [27], an amount that is usually not achieved either from Western diets [28] or from a high adherence to the MD [25].
Only the HELENA study of 2330 European adolescents reported that adherence to the MD evaluated by the adapted MD quality index (KIDMED) was associated not only with better nutrient intakes, but also with higher levels of some nutritional biomarkers, including 25OHD [29]. Otherwise, to the best of our knowledge, no studies evaluated the association between the adherence to the MD and 25OHD levels in adults.
Of interest, it is well known that non-modifiable factors, including gender, influence food preferences and consumption amounts [30]. In particular, scientific evidence reported that females have a dietary profile characterized by a higher intake of fruits and vegetables than males, who consume more meat, eggs, and dairy products [31,32]. Beyond individual food choices, recent studies have reported that females showed an overall higher adherence to the MD compared with their male counterparts [33,34,35]. In addition, results of a large epidemiological study, The Nurses’ Health Study by Heidemann C et al., clearly indicated that greater adherence to a “prudent pattern,” characterized by a high intake of vegetables, fruit, legumes, fish, poultry, and whole grains, was related to a lower risk of cardiovascular and total mortality in healthy women [36]. Very recently, we have reported that 25OHD levels were lower in females than in males across all body mass index (BMI) categories [37]. As far as we know, evidence on a gender difference in the association between a healthy eating pattern, such as MD, and 25OHD levels, is still lacking. Considering the influence of the adherence to the MD on the weight excess and 25OHD levels, and the potential effects of gender on these variables, we designed the present cross sectional, single centre study to investigate the possible gender-specific association between adherence to the MD and 25OHD levels in a sample of adult population across BMI categories.

2. Material and Methods

2.1. Design and Setting

This cross-sectional, observational, single center study was performed at the Unit of Endocrinology, Department of Clinical Medicine and Surgery, Federico II University Naples (Italy), from October 2016 to January 2020. The study ws carried out taking into account the Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans. The protocol was formal approved by the Ethical Committee of the Federico II University of Naples, Medical School (number 173/16). Every enrolled subject provided informed consent after a thorough explanation of the protocol.

2.2. Population Study

Recruitment strategies included 617 adult Caucasians subjects (18–57 years) of both genders consecutively enrolled among patients in Endocrinology Unit, hospital volunteers, and employees. All participants were enrolled only in the autumn or winter seasons (October to March) and all were from the same geographical area around the Naples metropolitan area, Italy (latitude 40°49′ N; elevation 17 m). Therefore, all subjects had similar sun exposure potential. In addition, none of the participants reported sun exposure at the time of enrollment and were engaged in leisure time physical activity. All female subjects were non-pregnant and non-lactating, and were evaluated in the follicular phase of the menstrual cycle. A full medical history, including drug use, was collected. In order to increase the homogeneity of the subject samples, we included only adults of both genders passing the following criteria of exclusion:
  • Specific nutritional regimens or hypocaloric diet in the last three months, including vegan or vegetarian diets.
  • Vitamin/mineral or antioxidant supplementation.
  • Weight-loss medications.
  • Current therapy with calcium, vitamin D supplementation, or osteoporosis therapies; medications that may affect vitamin D absorption or metabolism, including anti-inflammatory drugs, sex hormone therapy, statins, and other hypolipidemic agents.
  • Underweight patients with BMI <18.5 kg/m2.
  • Altered thyroid hormone function tests or thyroid hormone treatment.
  • Presence of type 2 diabetes mellitus (defined by criteria of the American Diabetes Association as follows: basal plasma glucose level ≥126 mg/dL on two occasions, or glycated haemoglobin ≥6.5% (≥48 mmol/moL) on two occasions, or both at the same time. Participants on antidiabetic medication were considered to have type 2 diabetes mellitus.
  • Chronic diseases that could interfere fluid homeostasis, such as liver or renal chronic diseases, cancer, and acute or chronic inflammatory diseases.
  • Altered levels of serum creatinine, serum calcium, or albumin.

2.3. Power Size Justification

The power of the sample was calculated by the differences of means ± standard deviations (SDs) of the Prevención con dieta Mediterránea (PREDIMED) score and 25OHD levels in males and females (18.3 ± 7.3 vs. 16.8 ± 7.8 ng/mL and 6.7 ± 3.1 vs. 7.4 ± 2.8 scores; respectively). When considering the number of cases required in each group of 249 was set at 296 for males and 321 for females, a type I (alpha) error of 0.05 (95%), and a type II (beta) of 0.05, the calculated power size was 95%. The calculation of sample size and power were performed while using a sample size calculator Clinical Calc [38], as previously reported [39,40,41].

2.4. Anthropometric Measurements

Measurements were performed in the morning, between 8 am and 10 am, after an overnight fast, by a single nutritionist. The anthropometric measurements were performed following standard criteria by the same nutritionist according to the International Society for the Advancement of Kinanthropometry (ISAK 2006). The subjects were recommended to dress light clothes and to remove shoes during the assessment, as previously reported [42,43,44,45,46,47].
BMI (weight (kg) divided by height squared (m2), kg/m2) was calculated after measuring weight and height. A wall-mounted stadiometer (Seca 711; Seca, Hamburg, Germany) was used to measure height while a calibrated balance beam scale (Seca 711; Seca, Hamburg, Germany) was used to assess weight. The degree of obesity was established according to World Health Organization WHO’s criteria: BMI: 18.5–24.9 kg/m2, normal-weight; BMI: 25.0–29.9 kg/m2, overweight; BMI: 30.0–34.9 kg/m2, grade I obesity; BMI: 35.0–39.9 kg/m2, grade II obesity; BMI ≥40.0 kg/m2, grade III obesity [48].

2.5. Assay Methods

Samples were collected in the morning between 8 am and 10 am, after an overnight fast of at least 8 h, and stored at −80 °C until being processed. The 25OHD levels were quantified by a direct competitive chemiluminescence immunoassay (CLIA) (Liaison®, DiaSorin, Saluggia, Italy), with a specificity of 100% for 25OHD. The analytical measurement range of detection is 4–150 ng/mL, whereas the intra-assay coefficients of variation (CVs) were 5.4%, 2.8%, and 4.7%, and the inter-assay CVs were 10.1%, 4.8%, and 7.9% for low, medium, and high points of the standard curve, respectively; as previously reported [49,50]. A 25OHD deficiency was defined as 25OHD levels <20 ng/mL (50 nmol/L); insufficiency was between 21 and 29 ng/mL (from 52.5 to 72.5 nmol/L); and normal levels were ≥30 ng/mL (75 nmol/L) [51].

2.6. Adherence to the MD

The adherence to the MD was evaluated using the previously validated 14-item questionnaire for the assessment of PREDIMED [52]. A qualified nutritionist administered the questionnaire during a face-to-face interview with all the enrolled subjects. Briefly, each item was assigned a score 1 or 0; PREDIMED score was calculated as follows: 0–5, lowest adherence to the MD; score 6–9, average adherence to the MD; score ≥ 10, highest adherence to the MD [52], as extensively reported [34,53,54,55,56].

2.7. Statistical Analysis

The data distribution was evaluated by Kolmogorov–Smirnov test; only age had a non-normal distribution, and it was normalized by logarithm. Skewed age variable was back-transformed for presentation in tables. Results were expressed as means ± SDs and categorical variables (BMI categories: normal-weight, overweight, grade I obesity, grade II obesity, and grade III obesity; PREDIMED categories: low adherence to the MD, average adherence to the MD, and high adherence to the MD; and 25OHD categories: deficit, insufficiency, and sufficiency) were expressed as percentages.
Differences between males and females were analyzed by Student’s independent t-test; the chi square (χ2) test was used to determine the significance of differences in frequency distribution of BMI categories, PREDIMED categories, and 25OHD categories. Differences in PREDIMED score and 25OHD levels in the BMI categories, PREDIMED categories, and 25OHD categories were analyzed by ANOVA test, with the Bonferroni test as a post-hoc test. The correlations among PREDIMED score, 25OHD levels, age, and BMI were assessed with the Pearson r correlation coefficients. Two partial correlation analyses were used to determine the relationships between PREDIMED score and 25OHD levels controlling for age and BMI, in males and females, respectively. Bivariate proportional odds ratio (OR) OR models, 95% interval confidence (IC), R2, Wald test, standard error, p-heterogeneity, and p-trend [57], were checked to assess the association between 25OHD levels with PREDIMED categories, as extensively reported [41,58,59]. In addition, a multiple linear regression analysis model (stepwise method), expressed as R2, beta (β), and t, with 25OHD levels as dependent variables was used to estimate the predictive value of PREDIMED score, BMI, sex, and age. Receiver operator characteristic (ROC) curve analysis was carried out in order to identify sensitivity and specificity, area under the curve (AUC), IC, and cut-off values of PREDIMED score in detecting the highest 25OHD levels. Test AUC for ROC analysis was also calculated, and we entered 0.76 for AUC ROC and 0.5 for null hypothesis values. An Alfa α level of 0.05 (type 1 error) and a β level of 0.2 (type II error) were used as the cut-off values for statistical significance. Variables with a variance inflation factor (VIF) >10 were excluded in order to avoid multicollinearity. Values ≤5% were considered statistically significant. Data were collected and analyzed using the MedCalc® package (Version 12.3.0 1993–2012—Mariakerke, Belgium).

3. Results

Study population consisted of 617 participants; 296 were males and 321 females, matched by age and BMI. Age, anthropometric characteristics, adherence to the MD, and 25OHD levels in the whole study population and stratified by gender, are given in Table 1. Females have a higher PREDIMED score than males (p = 0.001), and according to PREDIMED categories, a greater percentage of males were low adherent to the MD compared to their female counterparts (p = 0.003). The 25OHD levels were higher in males than in females (p = 0.01), and a higher percentage of males had sufficient 25OHD levels (>30 ng/mL) than their female counterparts (p < 0.001).
The PREDIMED score and 25OHD levels in the population study across BMI categories are shown in Table 2. As reported, when stratifying the sample population according to the BMI categories, there were significant differences in both PREDIMED score and 25OHD levels; in particular, both PREDIMED score and 25OHD levels decreased significantly along with the increase of BMI (p < 0.001).
When analysing the response frequency of dietary components included in the PREDIMED questionnaire in detail, we found that the males consumed more extra-virgin olive oil (p = 0.025) and red/processed meats (p < 0.001), as compared with the females (Table 3).
Figure 1 and Figure 2 report BMIs (Figure 1a and Figure 2a) and PREDIMED scores (Figure 1b and Figure 2b), in males and females, respectively, across 25OHD categories. The same figures report BMI (Figure 1c and Figure 2c) and 25OHD levels (Figure 1d and Figure 2d), in males and females, respectively, across PREDIMED categories. In detail, stratifying the sample population according to 25OHD categories, BMI decreased and PREDIMED score increased significantly along with the increase in 25OHD levels (p < 0.001), in both males and females, respectively (Figure 1a and Figure 2a). Likewise, dividing the sample population according to PREDIMED categories, BMI decreased and 25OHD levels increased significantly, along with the increase in the adherence to the MD (p < 0.001), in both males and females, respectively (Figure 1b and Figure 2b).
Table 4 reported the descriptive data of PREDIMED score and 25OHD categories according to PREDIMED categories and stratified by gender. PREDIMED scores were higher in females than in males in the low and high PREDIMED categories (p < 0.001 and p = 0.03, respectively).

Correlation Analysis

Both PREDIMED score and 25OHD levels were negatively correlated with BMI (r = −0.62 and r = −0.67, p < 0.001; respectively), but 25OHD levels only showed a negative correlation with age (r = −0.09, p = 0.02). The PREDIMED score was positively correlated with 25OHD levels (p < 0.001). This association was maintained after adjusting for age and BMI, in both males (r = 0.21, p < 0.001) and females (r = 0.30, p < 0.001).
The results of the bivariate proportional OR model used to assess the association of 25OHD levels with PREDIMED categories are reported in Table 5. The 25OHD levels presented the highest OR values in the category low adherence vs. high adherence to the MD, in both genders (OR 1.21 and OR 1.31, in males and females, respectively).
To compare the relative predictive power of adherence to the MD associated with the 25OHD levels, we performed a multiple linear regression analysis using a model that included PREDIMED score, sex, age, and BMI. Using this model, BMI entered at the first step (p < 0.001), followed by PREDIMED score, sex, and age. Results are reported in Table 6.
ROC analysis was then performed to determine the cut-off value of PREDIMED score predictive of 25OHD levels. In particular, PREDIMED score >5 in males (p < 0.001, AUC 0.76, standard error 0.03, 95% CI 0.71 to 0.81; Figure 3), and >7 in females (p < 0.001, AUC 0.85, standard error 0.02, 95% CI 0.81 to 0.89; Figure 4) could serve as thresholds for 25OHD levels above the median (16.2 ng/mL and 16.9 ng/mL, in males and females, respectively).

4. Discussion

In this cross sectional, single center study, we reported a novel positive association between the adherence to the MD and the 25OHD levels, independent of BMI, in an adult population stratified according to gender across BMI categories. This stratification allowed us to confirm the presence of a gender difference either in the 25OHD levels or the adherence to the MD; although 25OHD levels were higher in males than females, 69.7% were deficient. In particular, compared to males, females exhibited higher adherence to the MD, despite lower 25OHD levels, with a lower percentage of 25OHD sufficiency as well. As expected, PREDIMED scores and 25OHD levels were the lowest in study participants with the highest grades of obesity (grade III). Nevertheless, the joint evaluation of PREDIMED score and 25OHD levels across different BMI categories in both genders had not previously been reported. As the highest values of PREDIMED score were reported in the subjects with the highest 25OHD levels and the lowest BMIs, these results suggested the key role of the adherence to the MD in both 25OHD levels and BMI. Based on the ROC curve analysis, the most sensitive and specific cut-off values for the PREDIMED score to predict the highest 25OHD levels were >5 and >7, in males and females, respectively. To date, to the best of our knowledge, this is the first study investigating gender differences in the relationship between adherence to the MD and 25OHD levels in a sample of an adult population across BMI categories.
Robust scientific evidence showed that 25OHD levels are lower in subjects with obesity than in normal-weight subjects [60,61,62]. The underlying mechanism of low 25OHD levels in subjects with obesity may be due to a number of factors, including low dietary vitamin D intake, little sun exposure, and low outdoor activity. In addition, accumulation of vitamin D in fat mass has been proposed as a potential mechanism [63]. Another hypothesis considers the result of volumetric dilution of vitamin D in the large adipose stores [64], as reported in women with obesity compared to normal-weight controls [65]. A recent meta-analysis of randomized controlled trials showed an overall inverse relationship between 25OHD levels and BMI in studies of non-diabetic subjects (r = −0.152, 95% = −0.187 to −0.116, p < 0.001), concluding that the vitamin D deficiency was associated with an increase in BMI [4].
Vitamin D receptors and vitamin D metabolizing enzymes are expressed in both adipocytes and inflammatory cells infiltrating adipose tissue depots. Beyond an anti-adipogenic effect, their presence also suggests an important role for vitamin D in regulating inflammatory pathways [66,67,68]. In this context, several piece of evidence showed that low vitamin D status was associated via low grade chronic inflammation, with consequent increased risk of metabolic and cardiovascular diseases [69,70]. Beyond its anti-inflammatory effects, the link between low 25OHD levels and risks of several diseases could be also partially explained by the antioxidant effects of vitamin D [71,72,73]. Of interest, in obese children, Codoñer-Franch P. et al. evidenced that a low vitamin D status was associated with high levels of oxidative stress biomarkers, including malondialdehyde, myeloperoxidase, 3-nitrotyrosine, interleukin (IL)-6, and soluble vascular cell adhesion molecule-(sVCAM)-1 [74]. According to these results, other studies confirmed the links among obesity, low vitamin D status, and increased oxidative stress [2,75].
The anti-inflammatory and antioxidant effects of the MD are equally well-known [76,77]. The MD pattern is the traditional diet of some of the countries of the Mediterranean basin that gathers the following characteristics: high consumption of whole grain, legumes, fruits, vegetables, nuts, and seeds; moderate intake of fish and shellfish, white meat, and eggs; and dairy products, red meat, processed meats, and foods rich in sugars in relatively small amounts. Moderate intake of wine, especially red wine with meals and extra virgin olive oil as the main source of fat, are also recommended [78,79,80]. From a nutrient point of view, MD is characterized by a relatively high fat consumption (40%–50% of total daily calories), by which between 15% and 25% of calories are represented by monounsaturated fatty acids, and ≤8% of calories are comprised of saturated fatty acids [81,82]. The favorable effects on health status of the MD are to be attributed also to high consumption of dietary fiber, food with low glycemic index, and glycemic load [83].
Among favorable effects of vitamin D, several studies reported that subjects with higher adherence to the MD also have higher dietary vitamin D intake [24,25,26], but only one study in adolescents reported that the adherence to the MD was positively associated with 25OHD levels [29]. However, in their study, the authors underlined a need to increase the knowledge on the nutritional adequacy of vitamins and the MD [23]. In addition, it is well known that gender is a well-known non-modifiable factor largely influencing food preferences and that women are more inclined to a Mediterranean dietary profile [31,32]. In our study, we confirmed that adult females had significantly lower 25OHD levels than adult males. However, it is evident from the gender difference in the association between the adherence to the MD and 25OHD levels in males, that despite a lower adherence to the MD compared to females, males have higher 25OHD levels. Although 25OHD levels were higher in males than females, 69.7% were deficient. As these results were independent of BMI, we hypothesized that the well-known sex difference in body fat and body fat distribution could likely account for this gender difference in the association between the two variables. Indeed, very recently we have demonstrated that females had significantly lower 25OHD concentrations than males among the different classes of BMI and that this sex difference was mainly explained by the higher fat mass percentage in women than men [37]. However, the different intakes of the foods of the MD between females and males could provide a further explanation. In our study the extra virgin olive oil consumption was different in males than in females, and it is known that dietary fats may change vitamin D absorption [84]. In particular, it has been reported that an increased consumption of monounsaturated fatty acids-rich oils, such as olive oil, may improve the bioavailability of vitamin D [85].
Of interest, although there is some disagreement [19], both a meta-analysis by Esposito et al. including 3436 participants [20], and a more recent systematic review of five randomized clinical trials indicated that a MD, in combination with physical activity, MD is effective at inducing weight loss, especially when it was energy restricted, and lasted longer than six months [16]. In particular, the high content of polyphenols in the MD could exert favorable its effects on weight loss through different mechanisms, including activation of β-oxidation, prebiotic effects for gut microbiota, induction of satiety, and modulation of both brown and white adipose tissue [19].
Taking into account the independent roles of adherence to the MD and vitamin D in obesity, the translational potential of this study suggests that the high adherence to the MD, likely due its anti-inflammatory and antioxidant properties, might contribute to offerring a unique advantage in the management of subjects with low vitamin D status and obesity. In addition, gender-specific cut-off values for the PREDIMED score might help to predict the risk of the lowest levels of 25OHD levels in adults.
We are aware that there are some limitations in the current study.
First, the cross-sectional design of this study did not allow for any statements on a causal relationship between adherence to the MD and 25OHD levels. Moreover, the suggested cut-offs values of PREDIMED score should be viewed with caution until results of studies in larger patient populations have become available to perform an appropriate cross-validation.
Second, the possible underlying inflammatory and antioxidant status linking PREDIMED score, 25OHD levels, and obesity, should be better investigated by measuring both serum inflammatory and antioxidant biomarkers, such as IL-6, c-reactive protein levels, superoxide dismutase, glutathione peroxidase, catalase, and malondialdehyde; thus, our hypothesis of a synergistic effect of adherence to the MD and 25OHD levels on obesity remains largely speculative.
Third, we did not include the measurements of the body composition in this study.
Fourth, the adherence to the MD was assessed with the 14-items questionnaire of the PREDIMED study [52]. This questionnaire is less expensive, less time-demanding, and requires less collaboration from participants than the usual full-length food frequency questionnaire or other more comprehensive methods [52], with which it was previously validated [86]. In addition, the PREDIMED questionnaire allows one to provide feedback to the participant immediately after the interview is completed. However, the design of this questionnaire of 14-items does not allow for an evaluation of total energy and dietary intake, and particularly, dietary vitamin D intake. This evaluation must be done with the seven-day food records, as it is the gold standard and allows for accurate measurements of the real dietary and macronutrient intakes compared to those that were obtained by retrospective food frequency questionnaires [87]. Therefore, we could only speculate that the observed lower adherence to the MD in males compared to females, despite their higher 25OHD levels, could be the epiphenomenon of the well-known sex difference in body fat and body fat distribution.
Fifth, sunlight exposure time have not been sufficiently evaluated. Sun is the major causal factor for melanoma, and melanoma incidence rates in Italian men (40.4 per 100,000) are much higher than in Italian women (11.0 per 100,000) [88]. This would therefore indicate that sun exposure in Italian males is higher than in Italian females. Sun exposure is also the main predictor of circulating 25OHD levels [59,89,90]; only a small proportion of circulating 25OHD levels are attributable to diet (excluding supplementation) [7]. In this study, we did not evaluate the sun-exposure habits. Thus, the role of sun exposure in influencing the difference in 25OHD levels between males and females can not be excluded.
Nevertheless, a major strength of this study includes the very large sample of subjects stratified by gender—making possible the comparisons between the study variables across different categories of BMI, PREDIMED, and 25OHD independently of gender. In addition, our study was based on a single clinical center, with a possibility of selection bias in the results.
However, the single-center study allowed us to increase the homogeneity of the sample as we included subjects evaluated in the same season and living in the same geographical area around Naples metropolitan area, Italy (latitude 40° 49′ N; elevation 17 m), so with the same effect of latitude on vitamin D levels, and likely, with similar nutrient availability. Finally, the PREDIMED questionnaire was face-to-face administered and not self-reported in order to minimize any bias related to the filling of the questionnaire, and to avoid the inter-operator variability, only one expert Nutritionist evaluated the anthropometric measures and administered the PREDIMED questionnaire.

5. Conclusions

In summary, the results of our study reported a novel positive association between adherence to the MD and 25OHD levels in an adult population both gender across BMI categories, with a gender-specific difference in this association. As possible translational applications, these findings: (i) suggest the advantage of possible synergistic anti-inflammatory and antioxidant effects of MD and vitamin D; (ii) provide gender-specific cut-off values for the PREDIMED score to predict the risk of the lowest 25OHD levels in adults; (iii) recommend the dietary assessment, and in particular, the evaluation of adherence to the MD, as good clinical practice in the management of patients with low vitamin D status and obesity. Further clinical trials on large series populations will be of paramount importance to elucidate the potential translational applications of the results of this study and the beneficial effects of MD as a medical prescription for low vitamin D status and obesity.

Author Contributions

Conceptualization, L.B. and S.S.; data curation, G.M.; formal analysis, L.B., G.M., and S.S.; funding acquisition, L.B. and D.L..; investigation, L.B., G.P.; D.L.; G.d.A., and S.S.; methodology, L.B. and G.M.; project administration, L.B. and G.M.; resources, L.B.; software, L.B.; supervision, L.B.; validation, L.B.; visualization, L.B.; writing—original draft, L.B. and S.S.; writing—review and editing, L.B., G.M., A.C., and S.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Acknowledgments

We are thankful to Panta Rei Impresa Sociale srl (https://www.panta-rei.eu/pantarei/) and Antonio Pasquale Riccio, for their scientific support.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

25OHD25-hydroxyvitamin D
MDMediterranean diet
BMIbody mass index
PREDIMEDPrevención con dieta Mediterránea
CVscoefficients of variation
ORProportional odds ratio
ICinterval confidence
ROCreceiver operator characteristic
AUCarea under the curve
ILinterleukin
sVCAMsoluble vascular cell adhesion molecule

References

  1. Migliaccio, S.; Di Nisio, A.; Mele, C.; Scappaticcio, L.; Savastano, S.; Colao, A.; Obesity Programs of Nutrition, Education, Research and Assessment (OPERA) Group. Obesity and hypovitaminosis D: Causality or casualty? Int. J. Obes. Suppl. 2019, 9, 20–31. [Google Scholar] [CrossRef] [PubMed]
  2. Pereira-Santos, M.; Costa, P.R.; Assis, A.M.; Santos, C.A.; Santos, D.B. Obesity and vitamin D deficiency: A systematic review and meta-analysis. Obes. Rev. 2015, 16, 341–349. [Google Scholar] [CrossRef] [PubMed]
  3. De Oliveira, L.F.; de Azevedo, L.G.; da Mota Santana, J.; de Sales, L.P.C.; Pereira-Santos, M. Obesity and overweight decreases the effect of vitamin D supplementation in adults: Systematic review and meta-analysis of randomized controlled trials. Rev. Endocr. Metab. Disord. 2019. [Google Scholar] [CrossRef] [PubMed]
  4. Rafiq, S.; Jeppesen, P.B. Body Mass Index, Vitamin D, and Type 2 Diabetes: A Systematic Review and Meta-Analysis. Nutrients 2018, 10, 1182. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Walsh, J.S.; Evans, A.L.; Bowles, S.; Naylor, K.E.; Jones, K.S.; Schoenmakers, I.; Jacques, R.M.; Eastell, R. Free 25-hydroxyvitamin D is low in obesity, but there are no adverse associations with bone health. Am. J. Clin. Nutr. 2016, 103, 1465–1471. [Google Scholar] [CrossRef] [Green Version]
  6. Samuel, L.; Borrell, L.N. The effect of body mass index on optimal vitamin D status in U.S. adults: The National Health and Nutrition Examination Survey 2001–2006. Ann. Epidemiol. 2013, 23, 409–414. [Google Scholar] [CrossRef]
  7. Savastano, S.; Barrea, L.; Savanelli, M.C.; Nappi, F.; Di Somma, C.; Orio, F.; Colao, A. Low vitamin D status and obesity: Role of nutritionist. Rev. Endocr. Metab. Disord. 2017, 18, 215–225. [Google Scholar] [CrossRef]
  8. Barrea, L.; Savastano, S.; Di Somma, C.; Savanelli, M.C.; Nappi, F.; Albanese, L.; Orio, F.; Colao, A. Low serum vitamin D-status, air pollution and obesity: A dangerous liaison. Rev. Endocr. Metab. Disord. 2017, 18, 207–214. [Google Scholar] [CrossRef] [Green Version]
  9. Hypponen, E.; Boucher, B.J. Adiposity, vitamin D requirements, and clinical implications for obesity-related metabolic abnormalities. Nutr. Rev. 2018, 76, 678–692. [Google Scholar] [CrossRef]
  10. Pramono, A.; Jocken, J.W.E.; Blaak, E.E. Vitamin D deficiency in the aetiology of obesity-related insulin resistance. Diabetes Metab. Res. Rev. 2019, 35, e3146. [Google Scholar] [CrossRef]
  11. Muscogiuri, G.; Barrea, L.; Scannapieco, M.; Di Somma, C.; Scacchi, M.; Aimaretti, G.; Savastano, S.; Colao, A.; Marzullo, P. The lullaby of the sun: The role of vitamin D in sleep disturbance. Sleep Med. 2019, 54, 262–265. [Google Scholar] [CrossRef] [PubMed]
  12. Di Somma, C.; Scarano, E.; Barrea, L.; Zhukouskaya, V.V.; Savastano, S.; Mele, C.; Scacchi, M.; Aimaretti, G.; Colao, A.; Marzullo, P. Vitamin D and Neurological Diseases: An Endocrine View. Int. J. Mol. Sci. 2017, 18, 2482. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Morishige, M.; Muramatsu, K.; Tominaga, Y.; Hashimoto, T.; Taguchi, T. Surgical treatment of metastatic femoral fractures: Achieving an improved quality of life for cancer patients. Anticancer Res. 2015, 35, 427–432. [Google Scholar]
  14. Vimaleswaran, K.S.; Berry, D.J.; Lu, C.; Tikkanen, E.; Pilz, S.; Hiraki, L.T.; Cooper, J.D.; Dastani, Z.; Li, R.; Houston, D.K.; et al. Causal relationship between obesity and vitamin D status: Bi-directional Mendelian randomization analysis of multiple cohorts. PLoS Med. 2013, 10, e1001383. [Google Scholar] [CrossRef] [PubMed]
  15. Wortsman, J.; Matsuoka, L.Y.; Chen, T.C.; Lu, Z.; Holick, M.F. Decreased bioavailability of vitamin D in obesity. Am. J. Clin. Nutr. 2000, 72, 690–693. [Google Scholar] [CrossRef]
  16. Mancini, J.G.; Filion, K.B.; Atallah, R.; Eisenberg, M.J. Systematic Review of the Mediterranean Diet for Long-Term Weight Loss. Am. J. Med. 2016, 129, 407–415. [Google Scholar] [CrossRef] [Green Version]
  17. D’Innocenzo, S.; Biagi, C.; Lanari, M. Obesity and the Mediterranean Diet: A Review of Evidence of the Role and Sustainability of the Mediterranean Diet. Nutrients 2019, 11, 1306. [Google Scholar] [CrossRef] [Green Version]
  18. Tapsell, L.C.; Neale, E.P.; Satija, A.; Hu, F.B. Foods, Nutrients, and Dietary Patterns: Interconnections and Implications for Dietary Guidelines. Adv. Nutr. 2016, 7, 445–454. [Google Scholar] [CrossRef]
  19. Castro-Barquero, S.; Lamuela-Raventos, R.M.; Domenech, M.; Estruch, R. Relationship between Mediterranean Dietary Polyphenol Intake and Obesity. Nutrients 2018, 10, 1523. [Google Scholar] [CrossRef] [Green Version]
  20. Esposito, K.; Kastorini, C.M.; Panagiotakos, D.B.; Giugliano, D. Mediterranean diet and weight loss: Meta-analysis of randomized controlled trials. Metab. Syndr. Relat. Disord. 2011, 9, 1–12. [Google Scholar] [CrossRef] [Green Version]
  21. Agnoli, C.; Sieri, S.; Ricceri, F.; Giraudo, M.T.; Masala, G.; Assedi, M.; Panico, S.; Mattiello, A.; Tumino, R.; Giurdanella, M.C.; et al. Adherence to a Mediterranean diet and long-term changes in weight and waist circumference in the EPIC-Italy cohort. Nutr. Diabetes 2018, 8, 22. [Google Scholar] [CrossRef] [PubMed]
  22. Bendall, C.L.; Mayr, H.L.; Opie, R.S.; Bes-Rastrollo, M.; Itsiopoulos, C.; Thomas, C.J. Central obesity and the Mediterranean diet: A systematic review of intervention trials. Crit. Rev. Food Sci. Nutr. 2018, 58, 3070–3084. [Google Scholar] [CrossRef] [PubMed]
  23. Castro-Quezada, I.; Roman-Vinas, B.; Serra-Majem, L. The Mediterranean diet and nutritional adequacy: A review. Nutrients 2014, 6, 231–248. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Grosso, G.; Marventano, S.; Giorgianni, G.; Raciti, T.; Galvano, F.; Mistretta, A. Mediterranean diet adherence rates in Sicily, southern Italy. Public Health Nutr. 2014, 17, 2001–2009. [Google Scholar] [CrossRef] [PubMed]
  25. Marventano, S.; Godos, J.; Platania, A.; Galvano, F.; Mistretta, A.; Grosso, G. Mediterranean diet adherence in the Mediterranean healthy eating, aging and lifestyle (MEAL) study cohort. Int. J. Food Sci. Nutr. 2018, 69, 100–107. [Google Scholar] [CrossRef]
  26. Grosso, G.; Marventano, S.; Buscemi, S.; Scuderi, A.; Matalone, M.; Platania, A.; Giorgianni, G.; Rametta, S.; Nolfo, F.; Galvano, F.; et al. Factors associated with adherence to the Mediterranean diet among adolescents living in Sicily, Southern Italy. Nutrients 2013, 5, 4908–4923. [Google Scholar] [CrossRef] [Green Version]
  27. VITAMINE—Assunzione Raccomandata per la Popolazione (PRI) e Assunzione Adeguata (AI). Available online: https://sinu.it/2019/07/09/assunzione-raccomandata-per-la-popolazione-pri-e-assunzione-adeguata-ai/ (accessed on 12 April 2020).
  28. Troesch, B.; Hoeft, B.; McBurney, M.; Eggersdorfer, M.; Weber, P. Dietary surveys indicate vitamin intakes below recommendations are common in representative Western countries. Br. J. Nutr. 2012, 108, 692–698. [Google Scholar] [CrossRef]
  29. Aparicio-Ugarriza, R.; Cuenca-Garcia, M.; Gonzalez-Gross, M.; Julian, C.; Bel-Serrat, S.; Moreno, L.A.; Breidenassel, C.; Kersting, M.; Arouca, A.B.; Michels, N.; et al. Relative validation of the adapted Mediterranean Diet Score for Adolescents by comparison with nutritional biomarkers and nutrient and food intakes: The Healthy Lifestyle in Europe by Nutrition in Adolescence (HELENA) study. Public Health Nutr. 2019, 22, 2381–2397. [Google Scholar] [CrossRef]
  30. Lee, H.; Kang, M.; Song, W.O.; Shim, J.E.; Paik, H.Y. Gender analysis in the development and validation of FFQ: A systematic review. Br. J. Nutr. 2016, 115, 666–671. [Google Scholar] [CrossRef] [Green Version]
  31. Westenhoefer, J. Age and gender dependent profile of food choice. Forum Nutr. 2005, 57, 44–51. [Google Scholar] [CrossRef]
  32. Wardle, J.; Haase, A.M.; Steptoe, A.; Nillapun, M.; Jonwutiwes, K.; Bellisle, F. Gender differences in food choice: The contribution of health beliefs and dieting. Ann. Behav. Med. 2004, 27, 107–116. [Google Scholar] [CrossRef]
  33. Barrea, L.; Muscogiuri, G.; Macchia, P.E.; Di Somma, C.; Falco, A.; Savanelli, M.C.; Colao, A.; Savastano, S. Mediterranean Diet and Phase Angle in a Sample of Adult Population: Results of a Pilot Study. Nutrients 2017, 9, 151. [Google Scholar] [CrossRef]
  34. Barrea, L.; Annunziata, G.; Muscogiuri, G.; Laudisio, D.; Di Somma, C.; Maisto, M.; Tenore, G.C.; Colao, A.; Savastano, S. Trimethylamine N-oxide, Mediterranean diet, and nutrition in healthy, normal-weight adults: Also a matter of sex? Nutrition 2019, 62, 7–17. [Google Scholar] [CrossRef] [PubMed]
  35. Bedard, A.; Lamarche, B.; Corneau, L.; Dodin, S.; Lemieux, S. Sex differences in the impact of the Mediterranean diet on systemic inflammation. Nutr. J. 2015, 14, 46. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Heidemann, C.; Schulze, M.B.; Franco, O.H.; van Dam, R.M.; Mantzoros, C.S.; Hu, F.B. Dietary patterns and risk of mortality from cardiovascular disease, cancer, and all causes in a prospective cohort of women. Circulation 2008, 118, 230–237. [Google Scholar] [CrossRef] [PubMed]
  37. Muscogiuri, G.; Barrea, L.; Somma, C.D.; Laudisio, D.; Salzano, C.; Pugliese, G.; de Alteriis, G.; Colao, A.; Savastano, S. Sex Differences of Vitamin D Status across BMI Classes: An Observational Prospective Cohort Study. Nutrients 2019, 11, 34. [Google Scholar] [CrossRef] [Green Version]
  38. Sample Size Calculator Determines the Minimum Number of Subjects for Adequate Study Power. Available online: https://clincalc.com/stats/samplesize.aspx (accessed on 21 February 2020).
  39. Barrea, L.; Arnone, A.; Annunziata, G.; Muscogiuri, G.; Laudisio, D.; Salzano, C.; Pugliese, G.; Colao, A.; Savastano, S. Adherence to the Mediterranean Diet, Dietary Patterns and Body Composition in Women with Polycystic Ovary Syndrome (PCOS). Nutrients 2019, 11, 2278. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Barrea, L.; Muscogiuri, G.; Laudisio, D.; Di Somma, C.; Salzano, C.; Pugliese, G.; de Alteriis, G.; Colao, A.; Savastano, S. Phase Angle: A Possible Biomarker to Quantify Inflammation in Subjects with Obesity and 25(OH)D Deficiency. Nutrients 2019, 11, 1747. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  41. Barrea, L.; Fabbrocini, G.; Annunziata, G.; Muscogiuri, G.; Donnarumma, M.; Marasca, C.; Colao, A.; Savastano, S. Role of Nutrition and Adherence to the Mediterranean Diet in the Multidisciplinary Approach of Hidradenitis Suppurativa: Evaluation of Nutritional Status and Its Association with Severity of Disease. Nutrients 2018, 11, 57. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Barrea, L.; Annunziata, G.; Muscogiuri, G.; Di Somma, C.; Laudisio, D.; Maisto, M.; de Alteriis, G.; Tenore, G.C.; Colao, A.; Savastano, S. Trimethylamine-N-oxide (TMAO) as Novel Potential Biomarker of Early Predictors of Metabolic Syndrome. Nutrients 2018, 10, 1971. [Google Scholar] [CrossRef] [Green Version]
  43. Barrea, L.; Altieri, B.; Muscogiuri, G.; Laudisio, D.; Annunziata, G.; Colao, A.; Faggiano, A.; Savastano, S. Impact of Nutritional Status on Gastroenteropancreatic Neuroendocrine Tumors (GEP-NET) Aggressiveness. Nutrients 2018, 10, 1854. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Barrea, L.; Macchia, P.E.; Di Somma, C.; Napolitano, M.; Balato, A.; Falco, A.; Savanelli, M.C.; Balato, N.; Colao, A.; Savastano, S. Bioelectrical phase angle and psoriasis: A novel association with psoriasis severity, quality of life and metabolic syndrome. J. Transl. Med. 2016, 14, 130. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Savastano, S.; Di Somma, C.; Colao, A.; Barrea, L.; Orio, F.; Finelli, C.; Pasanisi, F.; Contaldo, F.; Tarantino, G. Preliminary data on the relationship between circulating levels of Sirtuin 4, anthropometric and metabolic parameters in obese subjects according to growth hormone/insulin-like growth factor-1 status. Growth Horm. IGF Res. 2015, 25, 28–33. [Google Scholar] [CrossRef] [PubMed]
  46. Barrea, L.; Di Somma, C.; Macchia, P.E.; Falco, A.; Savanelli, M.C.; Orio, F.; Colao, A.; Savastano, S. Influence of nutrition on somatotropic axis: Milk consumption in adult individuals with moderate-severe obesity. Clin. Nutr. 2017, 36, 293–301. [Google Scholar] [CrossRef] [Green Version]
  47. Barrea, L.; Muscogiuri, G.; Di Somma, C.; Annunziata, G.; Megna, M.; Falco, A.; Balato, A.; Colao, A.; Savastano, S. Coffee consumption, metabolic syndrome and clinical severity of psoriasis: Good or bad stuff? Arch. Toxicol. 2018, 92, 1831–1845. [Google Scholar] [CrossRef]
  48. Body Mass Index—BMI. Available online: http://www.euro.who.int/en/health-topics/disease-prevention/nutrition/a-healthy-lifestyle/body-mass-index-bmi (accessed on 12 April 2020).
  49. Savanelli, M.C.; Scarano, E.; Muscogiuri, G.; Barrea, L.; Vuolo, L.; Rubino, M.; Savastano, S.; Colao, A.; Di Somma, C. Cardiovascular risk in adult hypopituitaric patients with growth hormone deficiency: Is there a role for vitamin D? Endocrine 2016, 52, 111–119. [Google Scholar] [CrossRef]
  50. Barrea, L.; Muscogiuri, G.; Annunziata, G.; Laudisio, D.; de Alteriis, G.; Tenore, G.C.; Colao, A.; Savastano, S. A New Light on Vitamin D in Obesity: A Novel Association with Trimethylamine-N-Oxide (TMAO). Nutrients 2019, 11, 1310. [Google Scholar] [CrossRef] [Green Version]
  51. Holick, M.F.; Binkley, N.C.; Bischoff-Ferrari, H.A.; Gordon, C.M.; Hanley, D.A.; Heaney, R.P.; Murad, M.H.; Weaver, C.M.; Endocrine, S. Evaluation, treatment, and prevention of vitamin D deficiency: An Endocrine Society clinical practice guideline. J. Clin. Endocrinol. Metab. 2011, 96, 1911–1930. [Google Scholar] [CrossRef] [Green Version]
  52. Martinez-Gonzalez, M.A.; Garcia-Arellano, A.; Toledo, E.; Salas-Salvado, J.; Buil-Cosiales, P.; Corella, D.; Covas, M.I.; Schroder, H.; Aros, F.; Gomez-Gracia, E.; et al. A 14-item Mediterranean diet assessment tool and obesity indexes among high-risk subjects: The PREDIMED trial. PLoS ONE 2012, 7, e43134. [Google Scholar] [CrossRef] [Green Version]
  53. Muscogiuri, G.; Barrea, L.; Laudisio, D.; Di Somma, C.; Pugliese, G.; Salzano, C.; Colao, A.; Savastano, S. Somatotropic Axis and Obesity: Is There Any Role for the Mediterranean Diet? Nutrients 2019, 11, 2228. [Google Scholar] [CrossRef] [Green Version]
  54. Muscogiuri, G.; Barrea, L.; Di Somma, C.; Altieri, B.; Vecchiarini, M.; Orio, F.; Spinosa, T.; Colao, A.; Savastano, S. Patient empowerment and the Mediterranean diet as a possible tool to tackle prediabetes associated with overweight or obesity: A pilot study. Hormones (Athens) 2019, 18, 75–84. [Google Scholar] [CrossRef] [PubMed]
  55. Barrea, L.; Muscogiuri, G.; Di Somma, C.; Tramontano, G.; De Luca, V.; Illario, M.; Colao, A.; Savastano, S. Association between Mediterranean diet and hand grip strength in older adult women. Clin. Nutr. 2019, 38, 721–729. [Google Scholar] [CrossRef] [PubMed]
  56. Barrea, L.; Tarantino, G.; Somma, C.D.; Muscogiuri, G.; Macchia, P.E.; Falco, A.; Colao, A.; Savastano, S. Adherence to the Mediterranean Diet and Circulating Levels of Sirtuin 4 in Obese Patients: A Novel Association. Oxid. Med. Cell. Longev. 2017, 2017, 6101254. [Google Scholar] [CrossRef] [PubMed]
  57. Preisser, J.S.; Das, K.; Benecha, H.; Stamm, J.W. Logistic regression for dichotomized counts. Stat. Methods Med. Res. 2016, 25, 3038–3056. [Google Scholar] [CrossRef]
  58. Savanelli, M.C.; Barrea, L.; Macchia, P.E.; Savastano, S.; Falco, A.; Renzullo, A.; Scarano, E.; Nettore, I.C.; Colao, A.; Di Somma, C. Preliminary results demonstrating the impact of Mediterranean diet on bone health. J. Transl. Med. 2017, 15, 81. [Google Scholar] [CrossRef] [Green Version]
  59. Lerche, C.M.; Philipsen, P.A.; Wulf, H.C. UVR: Sun, lamps, pigmentation and vitamin D. Photochem. Photobiol. Sci. 2017, 16, 291–301. [Google Scholar] [CrossRef] [Green Version]
  60. Karlsson, T.; Andersson, L.; Hussain, A.; Bosaeus, M.; Jansson, N.; Osmancevic, A.; Hulthen, L.; Holmang, A.; Larsson, I. Lower vitamin D status in obese compared with normal-weight women despite higher vitamin D intake in early pregnancy. Clin. Nutr. 2015, 34, 892–898. [Google Scholar] [CrossRef]
  61. Bellone, S.; Esposito, S.; Giglione, E.; Genoni, G.; Fiorito, C.; Petri, A.; Bona, G.; Prodam, F. Vitamin D levels in a paediatric population of normal weight and obese subjects. J. Endocrinol. Investig. 2014, 37, 805–809. [Google Scholar] [CrossRef]
  62. Roosta, S.; Kharadmand, M.; Teymoori, F.; Birjandi, M.; Adine, A.; Falahi, E. Effect of vitamin D supplementation on anthropometric indices among overweight and obese women: A double blind randomized controlled clinical trial. Diabetes Metab. Syndr. 2018, 12, 537–541. [Google Scholar] [CrossRef]
  63. Golzarand, M.; Shab-Bidar, S.; Koochakpoor, G.; Speakman, J.R.; Djafarian, K. Effect of vitamin D3 supplementation on blood pressure in adults: An updated meta-analysis. Nutr. Metab. Cardiovasc. Dis. 2016, 26, 663–673. [Google Scholar] [CrossRef] [Green Version]
  64. Drincic, A.T.; Armas, L.A.; Van Diest, E.E.; Heaney, R.P. Volumetric dilution, rather than sequestration best explains the low vitamin D status of obesity. Obesity (Silver Spring) 2012, 20, 1444–1448. [Google Scholar] [CrossRef] [PubMed]
  65. Carrelli, A.; Bucovsky, M.; Horst, R.; Cremers, S.; Zhang, C.; Bessler, M.; Schrope, B.; Evanko, J.; Blanco, J.; Silverberg, S.J.; et al. Vitamin D Storage in Adipose Tissue of Obese and Normal Weight Women. J. Bone Miner. Res. 2017, 32, 237–242. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  66. Sun, J.; Kong, J.; Duan, Y.; Szeto, F.L.; Liao, A.; Madara, J.L.; Li, Y.C. Increased NF-kappaB activity in fibroblasts lacking the vitamin D receptor. Am. J. Physiol. Endocrinol. Metab. 2006, 291, E315–E322. [Google Scholar] [CrossRef] [PubMed]
  67. Abbas, M.A. Physiological functions of Vitamin D in adipose tissue. J. Steroid Biochem. Mol. Biol. 2017, 165, 369–381. [Google Scholar] [CrossRef] [PubMed]
  68. Ding, C.; Gao, D.; Wilding, J.; Trayhurn, P.; Bing, C. Vitamin D signalling in adipose tissue. Br. J. Nutr. 2012, 108, 1915–1923. [Google Scholar] [CrossRef] [Green Version]
  69. Petersen, K.S.; Smith, C. Ageing-Associated Oxidative Stress and Inflammation Are Alleviated by Products from Grapes. Oxid. Med. Cell. Longev. 2016, 2016, 6236309. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  70. Cooke, A.A.; Connaughton, R.M.; Lyons, C.L.; McMorrow, A.M.; Roche, H.M. Fatty acids and chronic low grade inflammation associated with obesity and the metabolic syndrome. Eur. J. Pharmacol. 2016, 785, 207–214. [Google Scholar] [CrossRef]
  71. Wang, E.W.; Siu, P.M.; Pang, M.Y.; Woo, J.; Collins, A.R.; Benzie, I.F.F. Vitamin D deficiency, oxidative stress and antioxidant status: Only weak association seen in the absence of advanced age, obesity or pre-existing disease. Br. J. Nutr. 2017, 118, 11–16. [Google Scholar] [CrossRef] [Green Version]
  72. Christakos, S.; Dhawan, P.; Verstuyf, A.; Verlinden, L.; Carmeliet, G. Vitamin D: Metabolism, Molecular Mechanism of Action, and Pleiotropic Effects. Physiol. Rev. 2016, 96, 365–408. [Google Scholar] [CrossRef]
  73. Nikooyeh, B.; Neyestani, T.R. Oxidative stress, type 2 diabetes and vitamin D: Past, present and future. Diabetes Metab. Res. Rev. 2016, 32, 260–267. [Google Scholar] [CrossRef]
  74. Codoner-Franch, P.; Tavarez-Alonso, S.; Simo-Jorda, R.; Laporta-Martin, P.; Carratala-Calvo, A.; Alonso-Iglesias, E. Vitamin D status is linked to biomarkers of oxidative stress, inflammation, and endothelial activation in obese children. J. Pediatr. 2012, 161, 848–854. [Google Scholar] [CrossRef] [PubMed]
  75. Rani, V.; Deep, G.; Singh, R.K.; Palle, K.; Yadav, U.C. Oxidative stress and metabolic disorders: Pathogenesis and therapeutic strategies. Life Sci. 2016, 148, 183–193. [Google Scholar] [CrossRef] [PubMed]
  76. Casas, R.; Sacanella, E.; Estruch, R. The immune protective effect of the Mediterranean diet against chronic low-grade inflammatory diseases. Endocr. Metab. Immune Disord. Drug Targets 2014, 14, 245–254. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  77. Pitsavos, C.; Panagiotakos, D.B.; Tzima, N.; Chrysohoou, C.; Economou, M.; Zampelas, A.; Stefanadis, C. Adherence to the Mediterranean diet is associated with total antioxidant capacity in healthy adults: The ATTICA study. Am. J. Clin. Nutr. 2005, 82, 694–699. [Google Scholar] [CrossRef] [PubMed]
  78. Tobore, T.O. Towards a comprehensive theory of obesity and a healthy diet: The causal role of oxidative stress in food addiction and obesity. Behav. Brain Res. 2020, 384, 112560. [Google Scholar] [CrossRef] [PubMed]
  79. Huedo-Medina, T.B.; Garcia, M.; Bihuniak, J.D.; Kenny, A.; Kerstetter, J. Methodologic quality of meta-analyses and systematic reviews on the Mediterranean diet and cardiovascular disease outcomes: A review. Am. J. Clin. Nutr. 2016, 103, 841–850. [Google Scholar] [CrossRef] [Green Version]
  80. Barrea, L.; Di Somma, C.; Muscogiuri, G.; Tarantino, G.; Tenore, G.C.; Orio, F.; Colao, A.; Savastano, S. Nutrition, inflammation and liver-spleen axis. Crit. Rev. Food Sci. Nutr. 2018, 58, 3141–3158. [Google Scholar] [CrossRef] [Green Version]
  81. Bergouignan, A.; Momken, I.; Schoeller, D.A.; Simon, C.; Blanc, S. Metabolic fate of saturated and monounsaturated dietary fats: The Mediterranean diet revisited from epidemiological evidence to cellular mechanisms. Prog. Lipid Res. 2009, 48, 128–147. [Google Scholar] [CrossRef]
  82. Beulen, Y.; Martinez-Gonzalez, M.A.; van de Rest, O.; Salas-Salvado, J.; Sorli, J.V.; Gomez-Gracia, E.; Fiol, M.; Estruch, R.; Santos-Lozano, J.M.; Schroder, H.; et al. Quality of Dietary Fat Intake and Body Weight and Obesity in a Mediterranean Population: Secondary Analyses within the PREDIMED Trial. Nutrients 2018, 10, 2011. [Google Scholar] [CrossRef] [Green Version]
  83. Augustin, L.S.; Kendall, C.W.; Jenkins, D.J.; Willett, W.C.; Astrup, A.; Barclay, A.W.; Bjorck, I.; Brand-Miller, J.C.; Brighenti, F.; Buyken, A.E.; et al. Glycemic index, glycemic load and glycemic response: An International Scientific Consensus Summit from the International Carbohydrate Quality Consortium (ICQC). Nutr. Metab. Cardiovasc. Dis. 2015, 25, 795–815. [Google Scholar] [CrossRef] [Green Version]
  84. Dawson-Hughes, B.; Harris, S.S.; Lichtenstein, A.H.; Dolnikowski, G.; Palermo, N.J.; Rasmussen, H. Dietary fat increases vitamin D-3 absorption. J. Acad. Nutr. Diet. 2015, 115, 225–230. [Google Scholar] [CrossRef] [PubMed]
  85. Niramitmahapanya, S.; Harris, S.S.; Dawson-Hughes, B. Type of dietary fat is associated with the 25-hydroxyvitamin D3 increment in response to vitamin D supplementation. J. Clin. Endocrinol. Metab. 2011, 96, 3170–3174. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  86. Schroder, H.; Fito, M.; Estruch, R.; Martinez-Gonzalez, M.A.; Corella, D.; Salas-Salvado, J.; Lamuela-Raventos, R.; Ros, E.; Salaverria, I.; Fiol, M.; et al. A short screener is valid for assessing Mediterranean diet adherence among older Spanish men and women. J. Nutr. 2011, 141, 1140–1145. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  87. Hoidrup, S.; Andreasen, A.H.; Osler, M.; Pedersen, A.N.; Jorgensen, L.M.; Jorgensen, T.; Schroll, M.; Heitmann, B.L. Assessment of habitual energy and macronutrient intake in adults: Comparison of a seven day food record with a dietary history interview. Eur. J. Clin. Nutr. 2002, 56, 105–113. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  88. International World Cancer Research Fund. Skin Cancer Statistics. Melanoma of the Skin is the 19th Most Common Cancer Worldwide. Available online: https://www.wcrf.org/dietandcancer/cancer-trends/skin-cancer-statistics (accessed on 22 April 2020).
  89. Hoseinzadeh, E.; Taha, P.; Wei, C.; Godini, H.; Ashraf, G.M.; Taghavi, M.; Miri, M. The impact of air pollutants, UV exposure and geographic location on vitamin D deficiency. Food Chem. Toxicol. 2018, 113, 241–254. [Google Scholar] [CrossRef] [PubMed]
  90. Holick, M.F. Biological Effects of Sunlight, Ultraviolet Radiation, Visible Light, Infrared Radiation and Vitamin D for Health. Anticancer Res. 2016, 36, 1345–1356. [Google Scholar]
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Figure 1. BMIs (a) and PREDIMED scores (b) across 25OHD categories, and BMIs (c) and 25OHD levels (d) across PREDIMED categories, in males. A * p value denotes a significant difference (p < 0.05).
Figure 1. BMIs (a) and PREDIMED scores (b) across 25OHD categories, and BMIs (c) and 25OHD levels (d) across PREDIMED categories, in males. A * p value denotes a significant difference (p < 0.05).
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Figure 2. BMIs (a) and PREDIMED scores (b) across 25OHD categories, and BMIs (c) and 25OHD levels (d) across PREDIMED categories, in females. A * p value denotes a significant difference (p < 0.05).
Figure 2. BMIs (a) and PREDIMED scores (b) across 25OHD categories, and BMIs (c) and 25OHD levels (d) across PREDIMED categories, in females. A * p value denotes a significant difference (p < 0.05).
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Figure 3. ROC for value of PREDIMED score predictive of 25OHD levels above the median, in males. * A p-value in bold type denotes a significant difference (p < 0.05).
Figure 3. ROC for value of PREDIMED score predictive of 25OHD levels above the median, in males. * A p-value in bold type denotes a significant difference (p < 0.05).
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Figure 4. ROC for value of PREDIMED score predictive of 25OHD levels above the median, in females. * A p-value in bold type denotes a significant difference (p < 0.05).
Figure 4. ROC for value of PREDIMED score predictive of 25OHD levels above the median, in females. * A p-value in bold type denotes a significant difference (p < 0.05).
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Table
Table 1. Age, anthropometric characteristics, adherence to the MD, and 25OHD levels in the whole study population and stratified by gender.
Table 1. Age, anthropometric characteristics, adherence to the MD, and 25OHD levels in the whole study population and stratified by gender.
Parameters.All Participants
Mean ± SD
or Number (%)
n = 617		Males
Mean ± SD
or Number (%)
n = 296, 48.0%		Females
Mean ± SD
or Number (%)
n = 321, 52.0%		* p-Value
Age (years)36.5 ± 11.036.8 ± 11.236.1 ± 10.90.48
BMI (kg/m2)34.3 ± 8.034.3 ± 8.234.4 ± 7.90.95
Normal-weight96, 15.6%47, 15.9%49, 15.3%χ2 = 0.01, p = 0.92
Overweight109, 17.7%52, 17.6%57, 17.8%χ2 = 0.01, p = 0.96
Grade I obesity121, 19.6%57, 19.3%64, 19.9%χ2 = 0.01, p = 0.91
χ2 = 2.92, p = 0.08
Grade II obesity127, 20.6%70, 23.6%57, 17.8%
Grade III obesity164, 26.6%70, 23.6%94, 29.3%χ2 = 2.22, p = 0.14
PREDIMED (score)7.0 ± 3.06.7 ± 3.17.4 ± 2.80.001
Low adherence to the MD238, 38.6%119, 40.2%119, 37.1%χ2 = 8.94, p = 0.003
Average adherence to the MD245, 39.7%121, 40.9%124, 38.6%χ2 = 0.62, p = 0.06
High adherence to the MD134, 21.7%56, 18.9%78, 24.3%χ2 = 2.30, p = 0.12
25OHD levels17.5 ± 7.618.3 ± 7.316.8 ± 7.80.01
Deficit413, 66.9%206, 69.6%207, 64.5%χ2 = 1.59, p = 0.21
Insufficiency162, 26.3%59, 19.9%103, 32.1%χ2 = 11.13, p < 0.001
Sufficiency42, 6.8%31, 10.5%11, 3.4%χ2 = 10.96, p < 0.001
* A p value in bold type denotes a significant difference (p < 0.05) between males and females.
Table
Table 2. The PREDIMED score and 25OHD levels in the population study across BMI categories.
Table 2. The PREDIMED score and 25OHD levels in the population study across BMI categories.
BMI Categories.PREDIMED (Score)25OHD Levels (ng/mL)
Normal-weight9.8 ± 3.124.8 ± 6.5
Overweight8.6 ± 2.722.6 ± 6.5
Grade I obesity7.4 ± 2.518.5 ± 6.3
Grade II obesity6.3 ± 2.214.3 ± 5.1
Grade III obesity4.8 ± 1.911.6 ± 4.8
* p-value<0.001<0.001
* A p value denotes a significant difference (p < 0.05).
Table
Table 3. Response frequencies of dietary components included in the PREvención con DIetaMEDiterránea (PREDIMED) questionnaire in the study population according to gender.
Table 3. Response frequencies of dietary components included in the PREvención con DIetaMEDiterránea (PREDIMED) questionnaire in the study population according to gender.
Questions of PREDIMED QuestionnaireMalesFemales
n%n%χ* p-Value
Use of extra virgin olive oil as main culinary lipid23178.022469.85.000.03
Extra virgin olive oil >4 tablespoons14950.313843.03.050.08
Vegetables ≥2 servings/day13947.018056.14.070.04
Fruits ≥3 servings/day16254.720563.94.960.03
Red/processed meats <1/day9632.419159.544.27<0.001
Butter, cream, margarine <1/day17157.817755.10.330.56
Soda drinks <1/day13144.318256.79.050.003
Wine glasses ≥7/week17559.110766.740.23<0.001
Legumes ≥3/week15953.721165.78.770.003
Fish/seafood ≥3/week14850.017153.30.540.46
Commercial sweets and confectionery ≤2/week11438.516150.27.980.005
Tree nuts ≥3/week5719.36419.90.0120.91
Poultry more than red meats12843.218958.914.45<0.001
Use of sofrito sauce ≥2/week11137.517854.818.53<0.001
* A p value in bold type denotes a significant difference (p < 0.05) between males and females.
Table
Table 4. Descriptive data of the 25OHD categories and PREDIMED scores according to PREDIMED categories and stratified by gender.
Table 4. Descriptive data of the 25OHD categories and PREDIMED scores according to PREDIMED categories and stratified by gender.
ParametersMales
Number (%)
or Mean ± SD
n = 296, 48.0%		Females
Number (%)
or Mean ± SD
n = 321, 52.0%		* p-Value
Low adherence to the MD category χ2 = 1.01, p = 0.31
25OH deficiency102, 85.7%108, 90.8%
25OH insufficiency/normal levels17, 14.3%11, 9.2%
PREDIMED score3.5 ± 1.34.4 ± 0.8<0.001
Average adherence to the MD category χ2 = 0.69, p = 0.41
25OH deficiency86, 71.1%81, 65.3%
25OH insufficiency/normal levels35, 29.8%43, 34.7%
PREDIMED score7.7 ± 1.27.9 ± 1.10.48
High adherence to the MD category χ2 = 0.94, p = 0.33
25OH deficiency18, 32.1%18, 23.1%
25OH insufficiency/normal levels38, 67.9%60, 76.9%
PREDIMED score11.1 ± 1.111.5 ± 0.90.03
* A p value in bold type denotes a significant difference (p < 0.05) between males and females.
Table
Table 5. Bivariate proportional odds ratio (OR) model to assess the association between 25OHD levels and PREDIMED categories.
Table 5. Bivariate proportional odds ratio (OR) model to assess the association between 25OHD levels and PREDIMED categories.
Categories.ORWald TestStandard Errorp-Heterogeneity and p-Trend95% ICR2
Males, n = 296, 48.0%
Low adherence to the MD category
vs.
Average adherence to the MD category		1.1323.240.026<0.0011.08–1.190.120
Low adherence to the MD category
vs.
high adherence to the MD category		1.2137.950.031<0.0011.14–1.290.284
Average adherence to the MD category
vs.
high adherence to the MD category		1.1015.370.024<0.0011.04–1.490.089
Females, n = 321, 52.0%
Low adherence to the MD category
vs.
Average adherence to the MD category		1.1641.620.024<0.0011.11–1.220.203
Low adherence to the MD category
vs.
high adherence to the MD category		1.3160.500.035<0.0011.22–1.400.462
Average adherence to the MD category
vs.
high adherence to the MD category		1.1729.580.028<0.0011.10–1.230.174
Table
Table 6. Multiple regression analysis models (stepwise method) with the 25OHD level as the dependent variable to estimate the predictive value of PREDIMED score, sex, age, and BMI.
Table 6. Multiple regression analysis models (stepwise method) with the 25OHD level as the dependent variable to estimate the predictive value of PREDIMED score, sex, age, and BMI.
ParametersMultiple Regression Analysis
R2βt* p Value
BMI (kg/m2)0.44–0.67–22.1<0.001
PREDIMED score0.470.215.6<0.001
Sex (Males/Females)0.49–0.13–4.4<0.001
Age (years)0.49–0.09–2.90.003
* A p value in bold type denotes a significant difference (p < 0.05).

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Barrea, L.; Muscogiuri, G.; Laudisio, D.; Pugliese, G.; de Alteriis, G.; Colao, A.; Savastano, S. Influence of the Mediterranean Diet on 25-Hydroxyvitamin D Levels in Adults. Nutrients 2020, 12, 1439. https://0-doi-org.brum.beds.ac.uk/10.3390/nu12051439

AMA Style

Barrea L, Muscogiuri G, Laudisio D, Pugliese G, de Alteriis G, Colao A, Savastano S. Influence of the Mediterranean Diet on 25-Hydroxyvitamin D Levels in Adults. Nutrients. 2020; 12(5):1439. https://0-doi-org.brum.beds.ac.uk/10.3390/nu12051439

Chicago/Turabian Style

Barrea, Luigi, Giovanna Muscogiuri, Daniela Laudisio, Gabriella Pugliese, Giulia de Alteriis, Annamaria Colao, and Silvia Savastano. 2020. "Influence of the Mediterranean Diet on 25-Hydroxyvitamin D Levels in Adults" Nutrients 12, no. 5: 1439. https://0-doi-org.brum.beds.ac.uk/10.3390/nu12051439

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