**Association of Milk Consumption and Vitamin D Status in the US Population by Ethnicity: NHANES 2001–2010 Analysis**

#### **Moises Torres-Gonzalez 1,\*, Christopher J. Cifelli 1, Sanjiv Agarwal 2 and Victor L Fulgoni III 3**

Received: 17 November 2020; Accepted: 1 December 2020; Published: 2 December 2020

**Abstract:** Vitamin D has been identified as a nutrient of public health concern, and higher intake of natural or fortified food sources of vitamin D, such as milk, are encouraged by the 2015–2020 Dietary Guidelines for Americans. We, therefore, examined the association of milk consumption and vitamin D status in the United States (US) population. Twenty-four-hour dietary recall data and serum 25(OH)D concentrations were obtained from the National Health and Nutrition Examination Survey 2001–2010 and were analyzed by linear and logistic regression after adjusting for anthropometric and demographic variables. Significance was set at *p* < 0.05. Approximately 57–80% children and 42–60% adults were milk consumers. Milk intake (especially reduced-fat, low fat and no-fat milk) was positively associated (*p* linear trend < 0.05) with serum vitamin D status and with a 31–42% higher probability of meeting recommended serum vitamin D (>50 nmol/L) levels among all age groups. Serum vitamin D status was also associated with both type and amount of milk intake depending upon the age and ethnicity. In conclusion, the results indicate that milk consumers consistently have higher serum vitamin D levels and higher probability of meeting recommended levels. Therefore, increasing milk intake may be an effective strategy to improve the vitamin D status of the US population.

**Keywords:** 25-hydroxyvitamin D (25(OH)D); Mexican–American; Other Hispanic; non-Hispanic White; non-Hispanic Black

#### **1. Introduction**

Vitamin D (calciferol) is a fat-soluble vitamin, photosynthesized in the skin by the action of solar ultraviolet (UV) B radiation. It is naturally found in only a few foods, such as fish-liver oils, fatty fishes, mushrooms, egg yolks, and liver [1]. Vitamin D is known to regulate calcium and phosphorus absorption and, therefore, it has been traditionally associated with skeletal health, and its deficiency increases the risk of rickets in children, and osteoporosis, fractures and falls in adults [1–5]. Emerging evidence suggests that vitamin D deficiency may also be linked to the development of cardiovascular disease, hypertension, diabetes, metabolic syndrome, cancer, multiple sclerosis, rheumatoid arthritis, and sarcopenia [6–13].

The United States (US) Centers for Disease Control and Prevention (CDC) Second National Report on Biochemical Indicators of Diet and Nutrition in the US population reported that, in 2003–2006, approximately 8% of the population aged 1 year and older were at risk for vitamin D deficiency (VDD), which varied by age, gender, or race/ethnicity, and was as high as 31% in non-Hispanic blacks [14]. More recent analysis of NHANES 2011–2014 data, which oversampled Asian, non-Hispanic black, and Hispanic individuals to obtain reliable estimates for these population subgroups, indicated that 18.3% Americans aged 1+ years were at risk of vitamin D inadequacy (VDI) based on serum levels [15]. While there were no significant gender differences, there was a quadratic trend for risk of VDI by age and was higher for adults 20–39 years than for children 1–5 years and for seniors ≥ 60 years [15]. VDI also varied by ethnicity and was lowest among non-Hispanic White, followed by Hispanics and Asians, and was highest among non-Hispanic Blacks [15]. Vitamin D levels in humans are assessed by the determining serum 25-hydroxyvitamin D (25(OH)D) concentrations. Serum 25(OH)D levels of less than 30 nmol/L (12 ng/mL) are considered at risk for deficiency, serum levels between 30 and less than 50 nmol/L (12 to less than 20 ng/mL) are considered at risk for inadequacy; serum levels between 50–75 nmol/L (20–30 ng/mL) are considered sufficient; serum levels greater than 125 nmol/L (50 ng/mL) may be of potential concern [1].

Although, vitamin D is produced endogenously by exposure to sunlight, seasonal variations, cultural practices, and physiologic factors can impair sunlight-induced synthesis of vitamin D. The current usual dietary intakes of vitamin D among US adults aged 19+ years are 5.3 μg/d for males and 4.1 μg/d for females, and 92% males and over 97% females are below the Estimated Average Requirement (EAR) [16]. Similarly, vitamin D intakes of 90 to 93% in male and 95 to over 97% in female children aged 4–18 years are also below the EAR [16]. Accordingly, the 2015–2020 Dietary Guidelines for Americans (DGA) identified vitamin D as a "nutrient of public health concern" as it is under-consumed to an extent that may lead to adverse health outcomes [17]. The Scientific Report of the 2020 Dietary Guidelines Advisory Committee reaffirmed vitamin D is under-consumed and is of public health concern [18]. The EAR of vitamin D is 400 IU (10 μg) for ages 1+ years and the Recommended Dietary Allowance (RDA) is 600 IU (15 μg) for ages 1–70 years and 800 IU (20 μg) for ages 70+ years [1]. Vitamin D can be acquired from fortified foods and dietary supplements [1,17]. In the American diet, fortified foods are a main source of the vitamin D [1,19,20]. Varieties of foods fortified with vitamin D in the US include dairy products (mostly milk), cereals and fruit juices. Milk is voluntarily fortified with 400 IU per quart (or 385 IU/L) of vitamin D [19] and almost all fluid milks are fortified with vitamin D in the US market [21]. Indeed, DGA encourages a higher intake of food sources of vitamin D, such as milk, to meet the requirements [17].

The objective of the present investigation was to determine the association of milk consumption and vitamin D status in the US population, and to examine if milk consumers have better vitamin D status as compared to non-consumers. We hypothesized that higher milk consumption is associated with better vitamin D status and that milk consumers, regardless of the type of milk consumed, have better vitamin D status compared to non-milk consumers across all age groups.

#### **2. Materials and Methods**

#### *2.1. Database & Subjects*

Data from five separate cycles of What We Eat In America (WWEIA), the dietary intake component of the National Health and Nutrition Examination Survey (NHANES), a continuous survey conducted by the National Center for Health Statistics (NCHS), were used (2001–2010). NHANES data are collected using a complex stratified multistage cluster sampling probability design. A detailed description of the subject recruitment, survey design, and data collection procedures are available online [22] and all data obtained for this study are publicly available at: http://www.cdc.gov/nchs/nhanes/. Dietary intake data with reliable 24-h recall dietary interviews (day 1 data only) from 33,672 participants (17,132 male and 16,540 female; 4061 aged 2–8 years, 8700 aged 9–18 years, 17,457 aged 19–70 years and 3454 aged 71–99 years; 7827 Mexican American, 2094 Other Hispanic, 14,525 non-Hispanic White, 7739 non-Hispanic Black, 1487 of other ethnicity) were used after with exclusions for unreliable data (*n* = 5690), aged < 2 years (*n* = 3079), pregnant or lactating females (*n* = 1272), missing serum vitamin D data (*n* = 5733), missing Poverty Index Ratio (PIR; *n* = 2808), missing Body Mass Index (BMI; *n* = 836) or zero calorie intake (*n* = 2). All participants or proxies (i.e., parents or guardians) provided written informed consent and the Research Ethics Review Board at the NCHS approved the survey protocol. This study was a secondary data analysis which lacked personal identifiers and, therefore, did not require Institutional Review Board review.

#### *2.2. Estimation of Dietary Intake*

Intake of milk was assessed using the sum of Food Patterns Equivalents Database (FPED) variable "D\_Milk" as cup equivalents/day from associated WWEIA categories:

Non-consumers were defined as subjects not consuming any specific type of milk during the 24-h recall. Consumers of a specific type of milk were defined as subjects consuming that type of milk and no other milk during the 24-h recall. Subjects consuming more than one type of milk during the 24-h recall were designated as "Mixed Milk-Consumers". Consumer intake tertiles were calculated within each age/gender group (Table 1).

**Table 1.** Milk intake tertiles (cup eq/day) by milk type in different age groups, NHANES 2001–2010.

Mean usual intake ± standard error (SE). Intake of milk was assessed using the sum of Food Patterns Equivalents Database (FPED) variable "D\_Milk" as cup equivalents/day from associated What We Eat In America (WWEIA) categories: Whole Milk—1002 (Milk, Whole), 1202 (Flavored milk, whole); Reduced-fat Milk—1004 (Milk, reduced-fat), 1204 (Flavored milk, reduced-fat); Low-fat Milk—1006 (Milk, low-fat), 1206 (Flavored milk, low-fat); Non-fat Milk—1008 (Milk, nonfat), 1208 (Flavored milk, non-fat); All Milk—sum of whole, reduced-fat, low-fat and non-fat milks.

#### *2.3. Serum Vitamin D*

Serum 25(OH)D concentrations were obtained from NHANES laboratory files [22]. Briefly, NHANES measured serum 25(OH)D, using a standardized liquid chromatography–tandem mass (LC-MS/MS) method for 2007–2010 cycles and using a DiaSorin RIA kit for 2001–2006 cycles. RIA measurements of 25(OH)D concentration were later converted to LC-MS/MS method equivalent measurements adjusting for assay drifts, due to concerns about imprecision and bias in the method [23].

#### *2.4. Statistics*

All analyses were performed using SAS 9.2, 9.4 and SUDAAN 11. Day 1 weights were used all analyses and the data were adjusted for the complex sampling design of NHANES, using appropriate survey weights, strata, and primary sampling units. Separate analyses were conducted for the ages 2–8, 9–18, 19–70, and 71+ years. Least Square Means (LSM) were generated from models for each age/gender/ethnic group using linear regression across tertiles of milk intake and different types of milk intake after adjusting for age, gender, ethnicity, poverty income ratio and BMI or BMI Z-score when the population being analyzed was <19 years. Significance was set at *p* < 0.05. Logistic regression analysis was used to assess odds ratios (OR) and 95% confidence limits (Lower confidence limit (LCL); Upper confidence limit (UCL)) of meeting recommended levels of serum vitamin D (>50 nmol/L) associated with milk intake with non-consumers as reference group after adjusting for age, gender, ethnicity, poverty income ratio and BMI or BMI Z-score when the population being analyzed was <19 years old. Additionally, vitamin D from dietary supplements, seafood, and other non-milk vitamin D sources were also subsequently added to models to assess whether the intake of these variables impacted the association of milk with serum vitamin D (serum 25(OH)D) levels.

#### **3.  All milk (sum of all milk types) consumption was higher in child consumers (2–18 years), compared to adult consumers (19+ years). Adult consumers aged 71+ years consumed about 37% less milk than children consumers aged 9–18 years (1.12 cup eq/d vs. 1.77 cup eq/d, respectively) (Table 2). Consumption of whole milk compared to non-fat milk was about 21% greater among children consumers aged 2–8 years but was 19% less among adult consumers aged 71+ years. Consumption of whole milk was similar to that of non-fat milk for children consumers aged 9–18 years and for adult consumers aged 19–70 years (Table 2). Depending upon ethnicity, milk contributed about 61–71% among those aged 2–8 years, 49–62% among those aged 9–18 years, 24–42% among those aged 19–70 years, and 28–48% among those aged 71+ years of total vitamin D intake (Table 3).

Serum vitamin D (serum 25(OH)D) was positively associated with milk intake in children aged 9–18 years and adults aged 19+ years (*p* linear trend < 0.05). The association was also significant for both males and females of age 9+ years (*p* linear trend < 0.05), except for 9–18-year-old males and for 71+ yearold females (*p* linear trend > 0.05) (Table 4). In children aged 2–8 years, consumers of whole milk, reduced fat milk, and low-fat milk had significantly higher (*p* < 0.05) serum vitamin D (serum 25(OH)D) levels than non-consumers. Similarly, in children aged 9–18 years and adults aged 19+ years, consumers of reduced-fat milk, low-fat milk, and non-fat milk had higher (*p* < 0.05) serum vitamin D (serum 25(OH)D) levels than non-consumers of same age group. Children (aged 9–18 years) and adult (aged 19–70 years) consumers of mixed milk also had higher (*p* < 0.05) serum vitamin D (serum 25(OH)D) than respective non-consumers (Table 5). Adjusting for data in Tables 2 and 3 for dietary supplements, seafood, and other non-milk vitamin D sources did not change these results (Tables 4 and 5).

milk, reduced-fat); reduced-fat, low-fat and non-fat milks.

 Low-fat Milk—1006 (Milk, low-fat), 1206 (Flavored milk, low-fat); Non-fat Milk—1008 (Milk, nonfat), 1208 (Flavored milk, non-fat); All Milk—sum of whole,

Mean We Eat Incategories:reduced-fat); Low-fat Milk—1006 (Milk, low-fat), 1206 (Flavored milk, low-fat); Non-fat Milk—1008 (Milk, nonfat), 1208 (Flavored milk, non-fat); All Milk—sum of whole, reduced-fat, low-fat and non-fat milks. *N* = 42,154.

data for age, gender, ethnicity, poverty income ratio and BMI or BMI Z-score when the population being analyzed was < 19 years. Significant difference from non-consumer at *p* < 0.05. # Significant difference from non-consumer at *p* < 0.05 after additionally adjusting data for vitamin D intake from dietary supplements, sea food, and other non-milk dietary sources. a *p*lineartrendafteradditionallyadjustingdataforvitaminDintakefromnon-milksources,supplementsandseafood.*N*=4061aged2–8years;8700aged9–18years;17,457aged

#### *Nutrients* **2020** , *12*, 3720

231

19–70 years; 3454 aged 71–99 years.

**Table5.**SerumvitaminD(serum25(OH)D)status(nmol/L)bydifferenttypesofmilkintakeindifferentagegroups,NHANES2001–2010.

Mean serum vitamin D concentration ± standard error (SE). Least Square Means (LSM) were modeled for each age group using linear regression across different types of milk intake afteradjusting gender combined data for age, gender, ethnicity, poverty income ratio and BMI or BMI Z-score when the population being analyzed was < 19 years. \* Significant differencefrom non-consumer at *p* < 0.05. # Significant difference from non-consumer at *p* < 0.05 after additionally adjusting data for vitamin D intake from dietary supplements, sea food, andother non-milk dietary sources.

Consumption of all milk (for all ages—i.e., 2+ years), whole milk (for ages 19–70 years), reduced-fat milk (for ages 9–70 years), low-fat milk (for those aged 19+ years), and non-fat milk (for those aged 9+ years) was associated with significantly higher probability of meeting serum vitamin D recommendations when the analysis was conducted by milk amount (Table 6). Consumers of whole milk (of aged 2–8 years), reduced-fat milk (of all ages), low-fat milk (of all ages), non-fat milk (of aged 19+ years) and mixed milk (of aged 2–70 years) had a higher probability of meeting serum vitamin D recommendations in the analysis by milk type (Table 6).

Table 7 shows the serum vitamin D (serum 25(OH)D) status by tertiles of all milk intake among different ethnic populations. Serum vitamin D (serum 25(OH)D) was positively associated with milk intake among children aged 9–18 years and adults aged 19+ years of Mexican American and of "other" ethnicity (*p* linear trend < 0.05). The increase in serum vitamin D (serum 25(OH)D) status with increasing milk intake was also significant (*p* linear trend < 0.05) among non-Hispanic Whites aged 19+ years, and non-Hispanic Blacks aged 2–18 years. Additional adjustment for dietary supplements, seafood, and other non-milk vitamin D sources did not change the results (Table 7).

Table 8 presents the data on vitamin D status by milk type across different ethnic groups. Significantly (*p* < 0.05) higher levels of serum vitamin D (serum 25(OH)D) levels were observed for consumers of whole milk, reduced fat milk, low-fat milk, non-fat milk and mixed milk among the different age and ethnic groups examined (Table 6). Adjusting the data for dietary supplements, seafood, and other non-milk vitamin D sources did not modify the results (Table 8).

**Table 6.** Odds ratios (OR), 95% confidence limits (Lower confidence limit (LCL)/Upper confidence limit (UCL)) for meeting the recommended serum vitamin D (>50 nmol/L), NHANES 2001–2010.

Gender combined data. OR were estimated using logistic regressions to model meeting recommended serum vitamin d (>50 nmol/L) on milk intake (OR Amount) or on 6 types of milk consumers (OR Type). Non-consumers were the reference group in both estimations. *N* = 4061 aged 2–8 years; 8700 aged 9–18 years; 17,457 aged 19–70 years; 3454 aged 71–99 years.

from non-consumer at *p* < 0.05. # Significant difference from non-consumer at *p* < 0.05 after additionally adjusting data for vitamin D intake from non-milk sources, supplementssea food. a *p* linear trend after additionally adjusting data for vitamin D intake from non-milk sources, supplements and sea food. N = 7827 Mexican American; 2094 Other Hispanic;14,525 non-Hispanic White; 7739 non-Hispanic Black; 1487 Other.

American; 2094 Other Hispanic; 14,525 non-Hispanic

 White; 7739 non-Hispanic

 Black; 1487 Other.

#### **4. Discussion**

The current cross-sectional analysis of data from the NHANES 2001–2010 demonstrated a significant association between milk consumption and serum vitamin D (serum 25(OH)D) status. Additionally, the results showed that the probability of meeting the vitamin D recommendations was greater in milk consumers vs. non-consumers. To the best of our knowledge, this is first analysis of nationally representative, non-institutionalized population of US children and adults examining the association of milk intake with vitamin D levels.

Poor vitamin D status (low serum 25(OH)D levels) is a global public health concern as over 50% of population has less than adequate serum vitamin D status [24]. In the US, about 18% of the US population aged 1+ years had insufficient serum vitamin D levels and were at risk of inadequacy (less than 50 nmol/L) according a recent analysis of NHANES 2011–2014 [15]. Liu et al. [25] estimated the prevalence of inadequate serum vitamin D levels in US adults to be 28.9% for VDD and 41.4% for VDI, from analysis of NHANES 2001–2010 and using the criteria recommended by the Endocrinology Society to define VDD as 25(OH)D <50 nmol/L and VDI as 25(OH)D <75 nmol/L [26]. The present analysis showed that the average serum vitamin D levels ranged from 64 to 75 nmol/L depending on age and gender in representative population of US children and adults aged 2+ years. These average serum vitamin D levels are well with in the 50–75 nmol/L range and are considered sufficient by IOM definition [1]. Vitamin D is a "nutrient of public health concern" as it is under-consumed to an extent that may lead to adverse health outcomes and higher intake of food sources are encouraged by DGA [17].

In the present analysis, serum Vitamin D levels were significantly associated with the intake of milk, depending on the type. Milk consumers, especially those of low fat and reduced fat milk, had higher probability of meeting >50 nmol/L serum vitamin D level benchmark set by IOM [1] than non-consumers; however, the mean serum vitamin D levels were always higher than 50 nmol/L. Although milk contains a low amount of naturally occurring vitamin D, almost all milk in the US is fortified with 100 IU/cup vitamin D irrespective of the type of milk [1,19,21]. Effectiveness of milk and other fortified foods in improving serum vitamin D status has been demonstrated in both clinical and observational studies (see [27,28] for reviews). A recent systematic review and meta-analysis of randomized controlled trials showed that vitamin D fortified foods (mostly milk and dairy products) increased serum vitamin D levels by 1.2 nmol/L for each 1 μg/d increased intake of vitamin D [27]. A cup of milk/d provides ∼2.5 μg of vitamin D. A review of observational studies also concluded that the intake of vitamin D fortified milk products was positively associated with vitamin D intake and serum vitamin D status and the association was stronger in countries with a national vitamin D fortification policy [28]. However, this review included only five studies from US which had small sample sizes and included only certain population groups. In our present analysis, the intake of certain milk types (especially of whole milk) was not associated with an increase in serum vitamin D levels in all population sub-groups, which is not immediately understood.

Age and ethnicity have been shown to affect serum vitamin D status [14,15,29,30]. Vitamin D serum levels generally decrease with age, and non-Hispanic Blacks have the lowest vitamin D levels or highest prevalence of VDD, followed by Hispanics and Asians [14,15,29,30]. In contrast, non-Hispanic Whites have the highest vitamin D levels or lowest prevalence of VDD [14,15,29,30]. Lower intake of milk with age, which was also observed in our study, could potentially explain the inverse association of age with vitamin D. In regard to ethnicity and vitamin D status, differences in milk and overall vitamin D intake as well as skin pigmentation and other factors are potentially responsible for the ethnic differences in vitamin D status [31]. For instance, studies have shown lower milk intake among non-Hispanic Blacks compared to non-Hispanic Whites [32]. In the present analysis, serum vitamin D status was associated with both type and amount of milk intake depending upon the age and ethnicity. However, the effect of gender on serum vitamin D status has been reported to be insignificant or inconsistent [14,15,30], but the associations between amount of milk intake and serum vitamin D status were mostly significant for both males and females in the present analysis. Therefore, continuing to

encourage an increase in milk intake, especially among populations with VDD or VDI, could be an effective strategy to improve vitamin D status.

In addition to providing vitamin D, milk and dairy products, make significant nutrient contributions including nutrients under-consumed by most Americans—calcium and potassium—as well as magnesium, phosphorus, zinc, vitamin A, vitamin B12, riboflavin (B2), choline, high-quality protein and saturated fat; as such, the inclusion of dairy foods into healthy dietary patterns is associated with improving diet quality and reducing risk of obesity and chronic diseases [17,18,33–36].

A major limitation of our study is the inability to determine a cause–effect relationship due to the cross-sectional design of NHANES. Additionally, as with any study based on self-reported data, underor over-reporting cannot be ruled out. Additionally, the results from this study may not specifically reflect the effect of milk consumption on vitamin D status, although we used vitamin D from dietary supplements, seafood, and other non-milk sources as covariates to adjust some of our results. Strengths of this study included the use of a large nationally representative sample achieved through combining several sets of NHANES data releases and adjusting for numerous covariates, but even with these covariates, some residual confounding may still exist.

#### **5. Conclusions**

In conclusion, the results of this study indicate that milk consumers consistently have higher serum vitamin D levels and higher probability of meeting the recommended levels. Vitamin D has been identified as a "nutrient of public health concern in the US" and, therefore, increasing the intake of milk (especially low-fat and reduced-fat) should be encouraged. Other sources of vitamin D may also help in improving vitamin D status.

**Author Contributions:** The authors' responsibilities were as follows: M.T.-G. and C.J.C.: conceived the project, designed research, developed the overall research plan, and participated in revising the manuscript; S.A.: participated in interpretation of the data, prepared the first draft of the manuscript, and participated in revising the manuscript; V.L.F.III: designed research, developed overall research plan, analyzed data and performed statistical analysis, and participated in the interpretation of the data and revising the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** The study and the writing of the manuscript were supported by National Dairy Council, Rosemont, IL.

**Conflicts of Interest:** M.T.-G. and C.J.C. are employees of National Dairy Council, Rosemont, IL; S.A. as Principal of NutriScience, L.L.C. performs consulting and database analyses for various food and beverage companies and related entities; V.L.F.III is Senior Vice President of Nutrition Impact and received a research grant from National Dairy Council to conduct these analyses.

#### **References**

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*Article*


**6. References**

164 MATLAB – A Fundamental Tool for Scientific Computing and Engineering Applications – Volume 1

**Figure 28.** Buck-Boost Converter Synchronous and Asynchronous Efficiency

This chapter gave a general overview of AC and DC converters with special emphases on DC-DC converters and their applications. The different topologies were discussed and the importance of designing for efficiency and energy saving was emphasized. Design and analysis steps were illustrated using Matlab and Simulink as an engineering tool. The effectiveness of the SimPower toolbox was demonstrated via typical examples which lead the way for further investigation. The tools available for simulation were exploited to

10 50 100 500 1000 0.5

10kHz Asynchronous 10kHz Synchronous 50kHz Asynchronous 50kHz Synchronous 100kHz Asynchronous 100kHz Synchronous

Load Current (mA)

**5. Conclusion**

0.55

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**Author details**

Sven Fagerstrom

Nagy Bengiamin

facilitate additional related studies.

*Pacific Gas and Electric (PG&E), Fresno, CA, USA*

*California State University Fresno, Fresno, CA, USA*

	- Wang, T.G., Zhou, X., & Lee, F.C., (1997), *A Low Voltage High Efficiency and High Power Density DC/DC Converter*, 28th Annual IEEE Power Electronics Specialists Conference, Vol. 1, pp. 240-245.
	- Xiong, Y., Sun, S., Jia, H., Shea, P., & Shen, Z.J., (2009), *New Physical Insights on Power MOSFET Switching Losses*, IEEE Transactions on Power Electronics, Vol. 24, No. 2
	- Zhou, S., (2003). *Fully Integrated Power-Saving Solutions for DC-DC Converters Targeted for the Mobile, Battery-Powered Applications*, Georgia Tech Analog Consortium Industry Research Review
	- Zhou, S. & Rincon-Mora, G.A., (2006),*A high Efficiency, Soft Switching DC-DC Converter with Adaptive Current-Ripple Control for Portable Applications*, IEEE Transactions on Circuits and Systems, Vol.53, No. 4


**Abstract**

Drug repurposing or repositioning refers to "studying of clinically approved drugs in one disease to see if they have therapeutic value and do not trigger side effects in other diseases." Nowadays, it is a vital drug discovery approach to explore new therapeutic benefits of existing drugs or drug candidates in various human diseases including neurological disorders. This approach overcomes the shortage faced during traditional drug development in grounds of financial support and timeline. It is especially hopeful in some refractory diseases including neurological diseases. The feature that structure complexity of the nervous system and influence of blood–brain barrier permeability often becomes more difficult to develop new drugs in neuropathological conditions than diseases in other organs; therefore, drug repurposing is particularly of utmost importance. In this chapter, we discuss the role of drug repurposing in neurological diseases and make a summarization of repurposing candidates currently in clinical trials for neurological diseases and potential mechanisms as well as preliminary results. Subsequently we also outline drug repurposing approaches and limitations and challenges in the future investigations.

**Keywords:** drug repurposing, brain injury, neurological diseases, therapeutics


*2.1. Plant Material and Experimental Design*

Tomato (*Solanum lycopersicum*) seedlings cv. 'Trovanzo' were grafted onto 'Emperator' (TE) or onto 'Kaiser' (TK) in a double-stemmed (twin-head) system. 'Emperator' has been observed to promote vegetative growth (or a vigorous rootstock) whereas 'Kaiser' has been shown to promote generative growth. In this way, we could observe the homeostatic balance between two different rootstock types. Twin-head transplants (5 weeks old) raised by a commercial propagator were placed into rockwool slabs on top of a raised growing trough (30 cm high) in a large glass greenhouse (200 m2 growing area) at the Harrow Research and Development Centre, Agriculture and Agri-Food Canada, Harrow, Ontario, Canada (42.03◦ N, 82.9◦ W) on 11 November 2018 at a plant density of 2 plants m−2 (4 stems m−2 ). The plants were planted into 6 rows, with the 2 outside rows serving as guard rows. The two stems/heads of each plant in the row were trained upward along the vertical strings into a 'V' system as in commercial production. The strings were hung onto the top wires (3.5 m high). Once the plant head reached the overhead wires, a few bottom leaves were removed and the plants were lowered twice every week. The plants were drip-irrigated using a complete nutrient solution [22]. The electrical conductivity and pH were set at 2.8 dS m−1 and 5.8, respectively. Plants were grown at an enriched CO2 concentration of 800 µL L−1 when the greenhouse was not ventilated. The average daytime temperature was held between 21 and 24 ◦C during the months of November, December, and January. Average daytime temperatures during the months of February and March were between 21 and 25 ◦C. Average daytime temperatures during the months of April and May were between 22 and 27 ◦C depending on the ambient solar radiation. Nighttime temperature was maintained at 20 ± 1 ◦C throughout the production period. Relative humidity of 70 ± 10% was maintained during both daytime and nighttime periods.

The 4 middle rows were divided into 16 plots via white curtains, which were impenetrable to light. There were 24 twin-head plants (48 stems) in each plot; 12 plants for each of the 2 rootstocks were planted. Four supplemental overhead lighting treatments were applied to the 16 plots in a Latin square design with 4 replications (one lighting treatment in each row or column of the 16 plots): 100% red from 23:00 to 16:00 (Red 17 h), 100% red from 17:00 to 16:00 (Red 23 h), mixed light from 23:00 to 16:00 (Mix 17 h), and mixed light from 17:00 to 16:00 (Red 23 h; Figure 1). The mixed light had 75% red (600–700 nm), 20% blue (400–499 nm), and 5% green light (500–599 nm), with green light being introduced via white diodes. The four lighting treatments provided similar DLIs (Table 1). All the lighting treatments were applied using Pro 325e smart LED fixtures from LumiGrow (Emeryville, California, USA). A 23 h monochromatic red photoperiod was utilized because red light tends to preferentially support vegetative growth. Furthermore, red diodes are highly efficient, and thus a pure red spectrum is able to achieve the highest photosynthetic photon efficacy (PPE) [23]. The mixed light spectrum was chosen as a control, which provides close to the recommended amounts of blue light [24] and some green light, which has also been shown to improve tomato growth and yield [25].

Equation (1) [26].

**Supplemental Light Intensity (µmol m-2 s-1)**

**Lighting Treatment**

**Table 1.** Photosynthetic photon flux density of supplemental lighting treatments (400–700 nm; 80 cm below the LED fixtures) as determined by using a Li-190R quantum line sensor at night. Red to far-red ratios (R:Fr) were estimated by dividing the total photons of red (600–700 nm) by the total photons of far-red (700–780 nm) during both sunny and cloudy days when the supplementary lighting fixtures were on. Phytochrome photostationary state (PSS) was calculated using

Red 17 h 176 ± 7 3.78 25.56 0.82 0.88 10.79 ± 0.41 Red 23 h 127 ± 4 2.81 17.77 0.80 0.87 10.54 ± 0.36 Mix 17 h 169 ± 4 2.55 15.29 0.79 0.86 10.32 ± 0.27 Mix 23 h 134 ± 5 2.30 12.01 0.78 0.86 11.09 ± 0.42

**R:Fr— Cloudy Day**

**R:Fr— Sunny Day**

were determined on both sunny and cloudy days (February 11 and February 14, 2019, respectively) by dividing the total photons of red by the total photons of far-red (700–780 nm). Further to this, the phytochrome photostationary state (PSS) was determined using

are the photochemical cross-section of phytochrome in the red absorbing state and far-red

ଷ଼ ሻ

ଷ଼ + ∑ ி ଼

Spectral readings for these calculations were taken between 12:00 and 14:00 using a Li-COR Li-180 spectrometer on both sunny and cloudy days under their respective lighting treatments. The sunny day was one with no observable clouds in the sky and the cloudy day was fully overcast. Throughout the experiment, supplemental lighting remained on regardless of ambient light levels to ensure that all treatments received similar total DLIs (Figure 2). The curtains were closed during cloudy days to prevent light contamination between treatments. During sunny days, the curtains were opened to prevent shading of natural light. On days which were partly cloudy/sunny, the forecast was used to determine the majority (i.e., sun or cloudy) and then curtains were opened or closed as appropriate. Because there was sunlight between 16:00 and 17:00 (i.e., during natural sunset), the actual photoperiods (including sunlight) were 18 h and 24 h (CL) for the 17 h and 23 h lighting, respectively. In commercial greenhouses, bumble bees are used as pollinators for fruit setting. The stopping of supplemental lighting between 16:00 and 17:00 was to facilitate the return of bees to their hives under natural dusk/sunset conditions; otherwise, the bees may get lost and significantly increase the number of bees (and associated cost) needed for pollination [27]. Dusk/sunset varied throughout the course of the experiment from 16:52 to 20:22, which was sufficient to allow for bees to return to their hives.

> **PSS— Sunny Day**

**PSS— Cloudy Day**  **Supplemental DLI (mol m-2 d-1)**

*r* and *Fr*

ଷ଼ ሻ (1)

Equation (1) from Sager et al. [26], where *N* is the photon flux (mol m-2 s-1) and

= ሺ∑ ଼

ሺ∑ ଼

absorbing state, respectively.

**Figure 1.** Normalized photon flux density (380–780 nm) from red and mixed LED lighting treatments during both sunny and cloudy days. The spectra were measured using a Li-180 Spectrometer (Li-COR Inc., Lincoln, NE, USA) at a distance of 80 cm from the light fixtures. Measurements for sunny days were done on 11 February 2019 and cloudy days on 14 February 2019 between 12:00 and 14:00. Panel (**A**) represents spectra from both red light treatments during sunny and cloudy days. Panel (**B**) represents spectra from both mixed light treatments during sunny and cloudy days.

**Table 1.** Photosynthetic photon flux density of supplemental lighting treatments (400–700 nm; 80 cm below the LED fixtures) as determined by using a Li-190R quantum line sensor at night. Red to far-red ratios (R:Fr) were estimated by dividing the total photons of red (600–700 nm) by the total photons of far-red (700–780 nm) during both sunny and cloudy days when the supplementary lighting fixtures were on. Phytochrome photostationary state (PSS) was calculated using Equation (1) [26].

Application of the supplemental lighting treatments began on 16 November 2018. Supplemental light intensities as shown in Table 1 were determined at three different positions within a treatment at 80 cm from the light fixtures (just above the heads of the plants) using a Li-COR 190R (Li-COR Biosciences Inc., Lincoln, NE, USA) quantum line sensor during the nighttime period to exclude natural solar radiation. Red:far-red (R:Fr) were determined on both sunny and cloudy days (11 February and 14 February 2019, respectively) by dividing the total photons of red by the total photons of far-red (700–780 nm). Further to this, the phytochrome photostationary state (PSS) was determined using Equation (1) from Sager et al. [26], where *N* is the photon flux (mol m−2 s −1 ) and *σr* and *σFr* are the photochemical cross-section of phytochrome in the red absorbing state and far-red absorbing state, respectively.

$$PSS = \frac{\left(\sum\_{380}^{780} N \sigma\_r\right)}{\left(\sum\_{380}^{780} N \sigma\_r + \sum\_{380}^{780} N \sigma\_{Fr}\right)} \tag{1}$$

Spectral readings for these calculations were taken between 12:00 and 14:00 using a Li-COR Li-180 spectrometer on both sunny and cloudy days under their respective lighting treatments. The sunny day was one with no observable clouds in the sky and the cloudy day was fully overcast. Throughout the experiment, supplemental lighting remained on regardless of ambient light levels to ensure that all treatments received similar total DLIs

(Figure 2). The curtains were closed during cloudy days to prevent light contamination between treatments. During sunny days, the curtains were opened to prevent shading of natural light. On days which were partly cloudy/sunny, the forecast was used to determine the majority (i.e., sun or cloudy) and then curtains were opened or closed as appropriate. Because there was sunlight between 16:00 and 17:00 (i.e., during natural sunset), the actual photoperiods (including sunlight) were 18 h and 24 h (CL) for the 17 h and 23 h lighting, respectively. In commercial greenhouses, bumble bees are used as pollinators for fruit setting. The stopping of supplemental lighting between 16:00 and 17:00 was to facilitate the return of bees to their hives under natural dusk/sunset conditions; otherwise, the bees may get lost and significantly increase the number of bees (and associated cost) needed for pollination [27]. Dusk/sunset varied throughout the course of the experiment from 16:52 to 20:22, which was sufficient to allow for bees to return to their hives. *Plants* **2021**, *10*, x FOR PEER REVIEW 5 of 23 **Figure 1.** Normalized photon flux density (380–780 nm) from red and mixed LED lighting treatments during both sunny and cloudy days. The spectra were measured using a Li-180 Spectrometer (Li-COR Inc., Lincoln, NE, USA) at a distance of 80 cm from the light fixtures. Measurements for sunny days were done on February 11, 2019 and cloudy days on February 14, 2019 between 12:00 and 14:00. Panel (**A**) represents spectra from both red light treatments during sunny and cloudy days. Panel (**B**) represents spectra from both mixed light treatments during sunny and cloudy days.

**Figure 2.** Daily average solar radiation as measured from November 16, 2018 to May 22, 2019 using a Li-COR LI-200R pyranometer converted from W m-2 to µmol m-2 s-1 using the conversion value of 2.1. Readings were taken above the greenhouse and then corrected for an approximate 50% transmissivity to account for shading from the greenhouse structure, lighting fixtures, and shade curtains. Measurements were taken every 2 h, beginning at 08:00 and concluding at 16:00, between the wavelengths of 400 and 1100 nm. Measurements during this period were averaged to provide an average solar radiation for each day (line plot). The bar graph indicates the average daily solar radiation throughout the month. Breaks in the line plot indicate periods of time which were not documented due to a technical malfunction. **Figure 2.** Daily average solar radiation as measured from 16 November 2018 to 22 May 2019 using a Li-COR LI-200R pyranometer converted from W m−2 to µmol m−2 s −1 using the conversion value of 2.1. Readings were taken above the greenhouse and then corrected for an approximate 50% transmissivity to account for shading from the greenhouse structure, lighting fixtures, and shade curtains. Measurements were taken every 2 h, beginning at 08:00 and concluding at 16:00, between the wavelengths of 400 and 1100 nm. Measurements during this period were averaged to provide an average solar radiation for each day (line plot). The bar graph indicates the average daily solar radiation throughout the month. Breaks in the line plot indicate periods of time which were not documented due to a technical malfunction.

#### *2.2. Growth Measurements 2.2. Growth Measurements*

from [28].

Growth measurements were performed on 6 randomly selected plants from TE and TK at 31, 60, and 139 days into the treatment (DIT), corresponding with December 16th, 2018, January 14, 2019, and April 3, 2019, respectively. Growth measurements included leaf length, leaf width, and chlorophyll content of the 5th, 10th, and 15th leaf when applicable. Leaf chlorophyll was measured using a SPAD meter (model 502, Konica Minota, Osaka, Japan) and values were converted to chlorophyll content using correction equations generated by spectrophotometric pigment analysis. Chlorophyll correction curves were generated by extracting leaf punches in 95% ethanol at 78 °C for approximately 3 h until the tissue was cleared. Samples were then analyzed at 664.2 nm, 648.6 nm, and 470 nm wavelengths using a spectrophotometer (Beckman DU-640 UV–Vis, Indianapolis, IN, USA). Concentrations of *chlorophyll a*, *b*, and carotenoids were determined via equations Growth measurements were performed on 6 randomly selected plants from TE and TK at 31, 60, and 139 days into the treatment (DIT), corresponding with 16 December 2018, 14 January 2019, and 3 April 2019, respectively. Growth measurements included leaf length, leaf width, and chlorophyll content of the 5th, 10th, and 15th leaf when applicable. Leaf chlorophyll was measured using a SPAD meter (model 502, Konica Minota, Osaka, Japan) and values were converted to chlorophyll content using correction equations generated by spectrophotometric pigment analysis. Chlorophyll correction curves were generated by extracting leaf punches in 95% ethanol at 78 ◦C for approximately 3 h until the tissue was cleared. Samples were then analyzed at 664.2 nm, 648.6 nm, and 470 nm wavelengths using a spectrophotometer (Beckman DU-640 UV–Vis, Indianapolis, IN, USA). Concentrations of *chlorophyll a*, *b*, and carotenoids were determined via equations from [28].

The 5th leaves from TE plants were placed in the chamber of a Li-COR 6400 (Li-COR

temperature was set to 24 °C, with a relative humidity of 55–65% and a CO2 level held at 800 µL L−1. Three leaves from separate plants under each treatment were used at 21 DIT (December 6, 2018) and 55 DIT (January 9, 2019) for both daytime and nighttime measurements. Measurements were taken during the day on cloudy days to maximize the effect

*2.3. Leaf Gas Exchange: Day and Night Measurements*


**4.** *In vivo* **models**

The MS *in vivo* experimental models can be separated into three classes:

1.Auto-immune or inflammatory patterns, such as examples of experimental allergic encephalitis (EAE) and viral patterns.


**Abstract**

Plants contain a considerable reservoir of secondary metabolites (flavonoids, tannins, and essential oils). These molecules exhibit variations in chemical structure as well as a very wide range of biological activities. Essential oils (EOs) are secondary metabolites produced by aromatic plants. EOs contain bioactive molecules, mainly represented by monoterpene hydrocarbons, oxygenated monoterpenes and sesquiterpenes, and sesquiterpene hydrocarbons. The organoleptic properties and biological activities of EOs are distinguished by their respective compositions. They have long been recognized for their medicinal properties such as antibacterial, antifungal, bioherbicide, antioxidant, anti-inflammatory, antidiabetic, and hepato-nephroprotective activities. These organic compounds also exert beneficial effects on the nutrition of ruminants, by modulating digestibility and reducing the emission of methane, a greenhouse gas. This chapter is devoted to the study of chemical composition, medicinal uses, and beneficial nutritional effects of essential oils.

**Keywords:** essential oils, extraction method, phytochemistry, therapeutic potential, nutritional effects