Percentage macronutrient intake in the United States by sex and age [19].
\r\n\t
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He graduated and obtained his Ph.D. in Applied Life Sciences from Tokyo University of Agriculture and Technology (Japan) in 2011. He was awarded Japanese government scholarship and he visited University of California at Davis (UCD) as an exchange student in 2010. After his graduation, he became a research fellow at the German Cancer Research Center (Deutsches Krebsforschungszentrum, DKFZ) in Heidelberg (Germany). Dr. Ying acts as a reviewer of many scientific journals and has authored or co-authored over 25 scientific publications. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"65381",title:"Recent Advances in Complex Fluids Modeling",doi:"10.5772/intechopen.82689",slug:"recent-advances-in-complex-fluids-modeling",body:'\nSince viscoelastic materials are abundant in nature and present in our daily lives (examples are paints, blood, polymers, biomaterials, etc.), it is important to study and understand viscoelastic behavior. Therefore, in this chapter, we further develop the modeling of viscoelasticity making use of fractional calculus tools.
\nWe start this section with some basic concepts that are needed to derive and understand classical and fractional viscoelastic models. These are trivial concepts such as force, stress, viscosity, Hooke’s law of elasticity and also Newton’s law of viscosity. Later, we evolve to more complex concepts of viscoelasticity that involve the knowledge of fractional calculus, integral and differential models.
\nIt is well known that a force is any interaction that when unopposed will change the motion of an object/body. Stress is an internal resistance provided by the body itself whenever it is under deformation. Stress is defined as the intensity of internal forces developed in the material. The intensity of any quantity is defined as the ratio of the quantity to the area on which it is acting, leading to: Average Stress = Force/Area. If we want to know the stress in one material point, then we must take the limit of the area to zero. A good example on how stress works is given by imagining a person lying on top of thin layer of ice. When the person is lying down on the ice, the force (weight) divided by the area of the surface of the person in contact with the ice is smaller, when compared to the case when someone is standing up (the weight is the same, but the area in contact with the ice is smaller). Therefore, eventually, the ice will break due to the high internal stresses when the person is standing. Finally, we refer to elasticity as the ability of a body to resist a distorting influence and to return to its original size and shape when that influence or force is removed. See for example Figure 1 where three springs are stretched. If we remove the weights attached to the springs, the spring would ideally return to its initial/natural position.
\nSchematic of an experiment to obtain the relationship between force and deformation: (a) Experimental setup where three springs are stretched with the use of weights; (b) Graph showing the experimental results obtained from stretching three springs (the force is proportional to the deformation).
Figure 1(b) also shows an experiment where we observe that the force (mass times gravity) applied to the spring (increasing weight) is proportional to the displacement. This is known as Hooke’s law (the force \n
We will now explore the concept of viscosity in fluids. The viscosity of a fluid is a measure of the internal resistance to the rate of deformation:
\nAs an example, imagine that we have a thin film of fluid in between two parallel plates, as shown in Figure 2. The fluid is at rest, and suddenly the upper plate starts moving with constant velocity U. This velocity will be felt at the bottom layer due to diffusion of momentum, and to keep the bottom wall fixed, we must exert a restraining force, that is measured with a force gage or dynamometer attached to that wall. Note that if we take the view of this portion of fluid as infinitesimally thin layers, we observe that each layer will drag the underlying layer due to the action of viscosity (internal resistance). The higher the viscosity, the more force will be required to deform the fluid at a given speed U.
\nSchematic of an experiment to verify Newton’s law of viscosity: (a) Liquid at rest between parallel plates; (b) The top wall is pulled with velocity U and a force meter is used to measure the force exerted on the bottom wall; (c) Experimental results.
Since the velocity of the thin layer adjacent to the top wall is U and the velocity of the bottom layer is 0, the velocity of each layer (for a Newtonian fluid) is given by u(y) = Uy/h, with y the coordinate shown in Figure 2(a). Figure 2(b) shows the experimental forces measured for different ratios of U/h. We observe that the force is proportional to U/h and U/h = du(y)/dy; therefore, we conclude the following (Newton’s law of viscosity):
\nwith \n
A good example of something we may see every day and something that verifies Newton’s law of viscosity is a dashpot. It is used for example as a door closer to prevent it from slamming shut.
\nThe simplest model that considers both viscous and elastic behavior is the linear Maxwell model [1] and can be obtained from a combination in series of a dashpot, \n
Maxwell model.
The total deformation \n
The three-dimensional version of this model can be easily obtained by considering appropriate tensors instead of the scalar properties of stress and deformation, leading to the following model:
\nwith \n
where \n
The Maxwell model is not observer independent (frame invariant) and, therefore, the results obtained with this model may not be correct if large deformations are considered (e.g., we may obtain a viscosity that depends directly on the velocity rather than the velocity gradient, which is not correct, and is unphysical). To solve this problem, new models were proposed in the literature that can deal with this non-invariance problem.
\nTwo well-known examples of frame invariant models are the upper-convected Maxwell (UCM) model given by \n
where \n
Step strain of a Maxwell model. The step strain is given by \n\nγ\n=\n\nγ\n0\n\nH\n\n\nt\n−\n\nt\n0\n\n\n\n\n with \n\nH\n\nt\n\n\n the Heaviside function, and the stress relaxation is the solution of \n\nσ\n+\nλ\n\ndσ\n/\ndt\n=\nηd\n\nγ\n0\n\nH\n\n\nt\n−\n\nt\n0\n\n\n\n/\ndt\n\n with \n\nσ\n\n\nt\n0\n\n\n=\n\nσ\n0\n\n\n, given by \n\nσ\n=\n\nσ\n0\n\n\n\ne\n\n−\n\n\nt\n−\n\nt\n0\n\n\n\n/\nλ\n\n\n\n (\n\n\nσ\n0\n\n=\nG\n\nγ\n0\n\n\n).
Other well-known example of a frame-invariant but now nonlinear viscoelastic model is the variation of the K-BKZ [2] model proposed by Wagner, Raible and Meissner [3, 4],
\nwhere \n
where \n
Different differential models were proposed in the literature along the years, with the aim of improving the modeling of complex viscoelastic materials, and with the aim of achieving the same modeling quality of integral models (by only using differential operators). Note that integral models are non-local (in time) operators that take into account all the past deformation of the fluid while differential models ones describe the material response in terms of the rate of change of stress to the local deformation, thus influencing the fitting quality of the model and the computational effort to numerically solve them (when performing numerical simulations).
\nMore recently, new models have been proposed in the literature that basically take advantage of the generalization of the exponential function appearing in Eqs. (4), (5), and (7), thus allowing a more broad and accurate description of the relaxation of complex fluids (while the commonly used continuum approach describes the fluid as a whole, with only one relaxation, unless a Prony series is considered, that is, considering a series of the form \n
To understand the need and the concept of a fractional derivative and its importance in the context of modeling physical processes, let us start with a simple example (Figure 5).
\nMaterial formed by two regions where the same physical process occurs at different rates.
Imagine a portion of material that is principally formed of two different regions. In these regions, two similar physical processes \n
Although we have not defined yet what a fractional derivative is, the fact of having the possibility of non-integer derivatives seems quite attractive, allowing the creation of a continuous path between integer-order derivatives that may lead to a better description of the different rates of a certain physical process occurring in the same material. This means that fractional derivatives can transport more and more precise local information from the microscopic world to the continuum description.
\nNow, to understand a fractional derivative, we start by acknowledging that the n-fold integral of a generic function \n
A generalization to non-integer values of \n
where we have used \n
This last fractional derivative is the one chosen to deal with physical processes due to the ease in handling initial and boundary conditions [7].
\nNext, we present two models that rely on the Mittag-Leffler function (a function closely related to fractional calculus) to improve their modeling and fitting capabilities when describing the behavior of viscoelastic materials. These are the fractional K-BKZ (integral) and the generalized Phan-Thien and Tanner (differential) models.
\nWe first note that the Maxwell-Debye relaxation of stress (exponential decay—see Eqs. (4) and (5)) is quite common, but there are many real materials showing different types of fading memory, such as a power law decay \n
By recognizing that the Caputo fractional derivative of a general function \n
we obtain a generalized viscoelastic model [10, 11], that can be written in the simple compact form:
\nThis model provides a generalized viscoelastic response, in the sense that when \n
We can define the fractional Maxwell model (FMM) as a combination of two linear fractional elements (spring-pots) in series. In a series configuration, the stress felt by each spring-pot is the same, that is, \n
This model allows a much better fit of rheological data, as shown in [12] but it is not frame invariant. However, following the same procedure employed with the Maxwell and K-BKZ model, that is, using the derivative of the relaxation function obtained for the Maxwell model as the memory function of the K-BKZ model, one can also use the derivative of the relaxation function of the FMM and insert it in the K-BKZ model, thus, obtaining a frame-invariant constitutive model, that retains all the good fitting properties of the FMM.
\nThe relaxation function of the FMM can be obtained by solving the fractional differential Eq. (14) considering a constant deformation \n
where \n
and a characteristic measure of the relaxation spectrum described by the two spring-pots in series is \n
This leads to the fractional K-BKZ model proposed by Jaishankar and Mckinley [12, 13], with \n
Note that here the relaxation modulus \n
The fractional K-BKZ model is therefore given by:
\nand we need to ensure that the integral converges (see the Foundations of Linear Viscoelasticity by Coleman and Noll [15]). The main problem seems to be the term \n
It can be easily shown [1] that a Taylor series expansion of \n
with \n
In Refs. [11, 12, 17], the beneficial fitting qualities of this constitutive model framework are discussed in detail. Here, we are interested in determining to what extent the properties of the Mittag-Leffler function can be used to improve the fitting quality of differential models, and this will be discussed in the next subsection.
\nThe previous integral model given by Eq. (18) allows a good fit to experimental rheological data, in flows with defined kinematics where \n
The original exponential PTT model [19, 20] is given by.
\nwith \n
The model is then given by
\nwhere the factor Γ(β) is used to ensure that \n
This new model can further improve the accuracy of the description of real data obtained with the original exponential function of the trace of the stress tensor, as shown in [18].
\nWe will now present a detailed parametric study on the influence of the new parameters \n
As shown in [18], the steady shear viscosity is given by \n
and \n
Here \n
Since we consider a simple plane shear flow aligned with the x-axis, we have that \n
Eqs. (22) and (23) can readily be solved using the Newton-Raphson method (solving first Eq. (23) and then substituting the numerical values obtained for \n
Figure 6 shows the dimensionless steady shear viscosity obtained for the different parameters of the Mittag-Leffler function, \n
Dimensionless shear viscosity obtained for the different parameters of the Mittag-Leffler function; (a) varying \n\nα\n\n holding the other five parameters constant and (b) varying \n\nβ\n\n.
Figure 7(a) shows the dimensionless steady shear viscosity, now obtained for different values of \n
Note that (see Figure 7(b)) small variations of the parameter \n
Dimensionless shear viscosity obtained for the different parameters of the Mittag-Leffler function varying: (a) Constant \n\n\nε\n\n; (b) Varying \n\n\nε\n\n.
Figure 7 shows that by setting different combinations of \n
The steady unidirectional extensional viscosity is defined as \n
with \n
Figure 8 shows the dimensionless steady elongational viscosity obtained for different parameters of the Mittag-Leffler function. In Figure 8(a), we show the influence of \n
Dimensionless elongational viscosity obtained for different parameters of the Mittag-Leffler function: (a) Varying α; (b) Varying β.
Note that when we increase \n
We may conclude that by varying \n
Figure 9 shows the effect of the parameters used in Figure 7, for the case of elongational viscosity. The results are qualitatively similar to the ones obtained in Figure 7, that is, in terms of changes to the asymptotic slopes at high deformation rates and premature/delayed thinning. It can be observed that the elongational viscosity is more sensitive to changes in the parameters \n
Dimensionless elongational viscosity obtained for the different parameters of the Mittag-Leffler function: (a) Constant \n\n\nε\n\n; (b) Varying \n\n\nε\n\n.
Until now, we have explored generally the influence of the different model parameters on the behavior of the GPTT model for steady flows, but, a more quantitative side-by-side comparison between the shear and elongational flow curves was not performed, and the limited flexibility of the classical exponential PTT model for fitting experimental data (when compared to the GPTT) was not explored. In Figure 10, we try to illustrate the advantages of using the Mittag-Leffler function instead of the classical exponential one. To this end, we present the viscometric predictions obtained for both shear and elongational flows for both models (GPTT and exponential PTT).
\nComparison of the dimensionless elongational and shear viscosity obtained for different parameters of the Mittag-Leffler function, varying \n\n\nε\n\n, and the classical exponential PTT model (α = 1, β = 0).
Figure 10 illustrates the additional flexibility of using the Mittag-Leffler function, by showing that we can manipulate the magnitude of the increase in the elongational viscosity and at the same time only slightly change the shear viscosity. This allows better fits to rheological data when using the Mittag-Leffler function [18]. Note that in the exponential PTT model, when we increase the \n
In this chapter, we have presented a brief introduction to the world of viscoelastic models capable of describing the rheology of complex fluids, and we have summarized some of the well-known classical differential and integral models.
\nWith incorporation of ideas from fractional calculus, most of these models can be further improved, either by changing classical local operators for improved (non-local) fractional versions, or, either using new analytic functions that arise in the realm of fractional differential equations, such as the Mittag-Leffler function.
\nAs an example, we present the fractional K-BKZ model and the recently proposed generalized PTT model. The fractional K-BKZ model allows a better description of fluid flow behavior (when compared to the generalized PTT model), but, increases the need for high computational power. Therefore, the novelty of the present work is our detailed study on the influence of the Mittag-Leffler function in shear and elongational flows of a generalized PTT model.
\nL.L. Ferrás and A.M. Afonso acknowledge the Project PTDC/EMS-ENE/3362/2014-POCI-010145-FEDER-016665—funded by FEDER funds through COMPETE2020—Programa Operacional Competitividade e Internacionalização (POCI) and by national funds through FCT—Fundação para a Ciência e a Tecnologia; I.P. L.L. Ferrás would also like to thank the funding by FCT through the scholarship SFRH/BPD/100353/2014. M.L. Morgado would like to thank the funding by FCT through Project UID/MULTI/04621/2013 and M. Rebelo would also like to thank the funding by FCT through Project UID/MAT/00297/2013 (Centro de Matemática e Aplicações).
\nProteins are chains of amino acids which are involved in nearly every process in the body. Proteins function as enzymes, transcription factors, binding proteins, transmembrane transporters and channels, hormones, receptors, structural proteins, and signaling proteins [1]. However, the primary role of protein in the diet is to provide amino acids required for the synthesis of new proteins. We especially rely on dietary protein to provide the nine essential amino acids, which cannot be synthesized in the body. Protein intake greater than the dietary recommendations may prevent sarcopenia [2], help maintain energy balance [3], improve bone health [4, 5, 6, 7] and cardiovascular function [8, 9, 10], and aid in wound healing [11]. This chapter focuses on the role of dietary protein, and the associated health benefits, throughout the life cycle.
\nThe current dietary recommendations for protein intake include the estimated average requirement (EAR) [12] and the recommended dietary allowance [12]. For daily protein intake, the EAR for dietary protein is 0.66 g kg−1 day−1, and the RDA is 0.8 g kg−1 day−1 for all adults over 18 years of age. This can become confusing when trying to make recommendations for individuals at different stages of life. Even the Food and Nutrition Board recognizes a difference between what is recommended in the RDA and the level of protein intake needed for optimal health [12]. Therefore, there is a third recommendation for protein called the acceptable daily macronutrient range (ADMR) [13, 14]. The ADMR includes a recommendation for protein intakes ranging from 10 to 35% of daily energy (e.g., calorie intake), which makes the ADMR easier to use when developing dietary recommendations for protein [12].
\nA majority of the adult population in the United States exceeds the minimum recommendations for protein intake [15]. The current dietary protein intake in the United States is approximately 82 g d−1 for men and 67 g d−1 for women [16]. \nTable 1\n details the current protein intake as percent of energy intake in the United States based on sex and age. A majority of dietary protein comes from animal protein (46%), followed by plant protein (30%), dairy (16%), and mixed foods (8%) [16]. There is increasing evidence indicating that consuming dietary protein at levels above the current RDA (0.8 g dietary protein kg body weight−1 day−1) may be beneficial for children, adults, older adults, and physically active individuals [17]. For example, protein intake above the RDA may help reduce the risk of chronic diseases such as obesity, cardiovascular disease, type 2 diabetes, osteoporosis, and sarcopenia [13, 17]. However, high protein intake without a subsequent decrease in carbohydrates attenuates the beneficial effects of dietary protein [18].
\nAge | \nTotal | \nMen | \nWomen | \n
---|---|---|---|
Protein | \n|||
20–44 years | \n15.7 | \n16.1 | \n15.3 | \n
45–64 years | \n15.8 | \n16.0 | \n15.7 | \n
65–74 years | \n16.3 | \n16.6 | \n16.1 | \n
75 years and older | \n15.7 | \n16.1 | \n15.3 | \n
Percentage macronutrient intake in the United States by sex and age [19].
Adequate dietary protein intake is essential to support cellular integrity, growth, and physical function. Although protein malnutrition is not prevalent in the United States, there is little research on optimal protein requirements for health benefits in children. Current EARs are based on the factorial method and the nitrogen balance technique. The factorial method incorporates the estimated nitrogen (protein) requirement plus the rate of protein deposition and an estimate of the efficiency of protein utilization [20] which is derived from adult dietary protein needs [12]. By using the indicator amino acid oxidation method in a group of healthy children 6–11 years old, it was found that the mean and population-safe (upper 95% CI) protein requirements were 1.3 and 1.55 g kg−1 day−1, respectively. This is higher than the 2005 DRI for protein (0.76 and 0.95 g kg−1 day−1, respectively) [12]. A similar study using the nitrogen balance technique also found that protein requirements in children in this age range are above current recommendations at 1.2 g kg−1 day−1 [21]. These higher estimated protein requirements in children seem to be in line with current protein consumption patterns in different pediatric age groups. For instance, children 2–3 years old are currently daily consuming ~3.6 g/kg of ideal body weight, children 4–8 years old are currently consuming ~2.6 g kg−1 ideal body weight−1, and children 9–13 years old are consuming ~1.6 g kg−1 ideal body weight−1 [15]; however, the optimal protein intake for children is still under debate [22]. There are racial/ethnic differences in protein consumption in children (2–18 years old). For example, non-Hispanic black children eat about 5% below, non-Hispanic white children eat about 3% below, Hispanic children eat about 2% below, and Asian children eat less than 1% below the EAR for protein [15].
\nAlthough the currently established recommendations for protein intake in children may be lower than the requirements, the effect of diets higher in protein (e.g., 30% of total energy intake) in children is unclear [22]. Several studies have alluded to the potential benefit of higher protein intake dietary practices. For instance, diets higher in protein with a low glycemic index can be protective against obesity in children aged 5–18 years [23], and diets higher in protein can lead to smaller waist circumference, blood pressure, insulin, and serum cholesterol than lower-protein diets in children from the same age group. A recent cohort analysis found that protein intake in 8-year-olds is associated with higher fat-free mass [24], and an additional cohort analysis found that at ages 11, 15, and 22 years, protein intake is inversely associated with early adulthood BMI. However, protein intake at 2 years was positively associated with BMI and lean mass at age 22 [25], suggesting there are conflicting results regarding the benefits of increased dietary protein in children.
\nPregnancy is a period of rapid tissue growth during a short period of time. Maternal tissues, including breast, uterine, and adipose tissues, blood volume, and extracellular fluids, account for the largest amount of protein accretion during pregnancy at 60%. The remaining 40% of protein accretion occurs within the amniotic fluid, fetus, and placenta [26, 27]. In fact, protein needs to increase soon after conception to support tissue growth and development, maintenance of maternal homeostasis, and lactation preparation [27, 28, 29]. These alterations occur in an exponential way and only in response to adequate total energy intake. This means that protein deposition does not significantly change in the first trimester compared to pre-pregnancy, but increases during the second trimester and significantly increases to the highest levels of protein deposition in the third trimester. This variable period of growth makes it difficult to define recommendations regarding protein requirements. Thus, although current recommendations suggest constant protein intake throughout the duration of pregnancy, pregnancy may actually require an increase in protein intake throughout gestation to support adequate growth, although further research is needed. There are several benefits of protein intake during pregnancy including adequate maternal weight gain within recommendations, lower early pregnancy BMI, and decreased postpartum weight [30].
\nAlthough the benefits of increased protein intake during pregnancy are apparent as stated above, protein requirements during pregnancy are difficult to measure. This is due to the involved nature of some of the techniques used to measure protein requirements. Therefore, the current dietary protein recommendations during pregnancy are based on factorial estimates of recommendations for healthy, nonpregnant populations. Pregnancy protein needs have been derived from the EAR and RDA for healthy, nonpregnant populations and are set to 0.88 g kg−1 day−1 (EAR) and 1.1 g kg−1 day−1 (RDA) [12]. However, newer studies found protein needs to be 1.2 g kg−1 day−1 at 11–20 weeks, increasing to 1.52 g kg−1 day−1 at 30–38 weeks [31]. Both nonpregnant women of childbearing age (20–44 years) and pregnant women consume at or above the current recommendations of protein intake [32, 33]. One study [31] found that pregnant women consume the same amount of protein in early pregnancy (1.44 ± 0.30 g kg−1 day−1) as they do in late pregnancy (1.47 ± 0.53 g kg−1 day−1), not taking fluid retention and changes in body composition into account. These findings support others that have noted little overall change in dietary protein patterns from early to late pregnancy [33]. Collectively, these findings demonstrate that pregnant women meet the recommendations for dietary protein intake. Improvements may potentially be made to increase dietary protein requirements as pregnancy progresses.
\nAn important factor to consider when incorporating protein into the diet is how the source of dietary protein (e.g., protein derived from animal or plant sources) affects nutrient intake, nutrient adequacy, and diet quality [13, 34, 35]. Proteins with differing amino acid profiles exhibit varied digestion and absorption rates [36, 37, 38], and amino acid profiles depend directly on the quality and quantity of the dietary protein [37]. For example, the digestion and absorption rates of fast- (e.g., whey) versus slow (e.g., casein)-digesting proteins need to be taken into consideration when developing protein recommendations. One study provided young, healthy subjects with either a whey protein meal (30 g) or a casein meal (43 g) (both contained the same amount of leucine [one of the BCAAs]) and measured whole-body protein synthesis. Researchers determined that the subjects consuming the whey (fast) protein meal had a high, rapid increase in plasma amino acids, while subjects consuming the casein (slow) protein meal had a prolonged plateau of EAA [39]. In addition, the chemical structure and the presence of anti-nutritional compounds such as phytic acid within the protein source can influence digestion and amino acid availability [40]. Compared to animal sources, plant proteins are shown to have a lower anabolic impact on muscle; however, the reduced ability to elicit anabolic effects can be overcome by increasing protein intake and increasing the content of leucine [41].
\nWhether or not the amino acid source is derived from the whole protein or a mixture of free amino acids can also influence the rate of muscle protein synthesis [42]. For example, when older subjects were given either an EAA mixture (15 g) or a whey protein supplement (13.6 g) after an overnight fast, subjects consuming the EAA mixture had higher mixed muscle fractional synthetic rate [42], which is often associated with increases in muscle mass. The differing response could be due to the differing leucine content between the supplements (EAA, 2.8 g leucine, and whey, 1.8 g leucine) or because the EAA supplement was composed of individual amino acids while the whey protein supplement was intact protein. These subtle differences could influence the rate of appearance of the amino acids into blood circulation and thus the protein synthetic response.
\nAnother potential confounder of the protein synthetic response of various proteins is the form or texture of the protein itself, such as ground beef versus a beef steak [43]. When, older men consumed 135 g of protein as either ground beef or as a beef steak, the amino acids from the ground beef appeared more rapidly in the circulation than the amino acids from the beef steak. Whole-body protein balance was higher after consumption of the ground beef versus the beef steak. However, 6 h after the beef meals, muscle protein synthesis was not different [43]. Nonetheless, these data support that the form of the protein that is being consumed impacts digestion, absorption, and the rate of appearance of amino acids into circulation [35].
\nThe timing of dietary protein intake has received ample attention in the past several decades. Adults typically consume the majority of their protein intake at dinner (38 g) versus breakfast (13 g) [44]. However, recent research suggests that ingestion of more than 30 g of protein in a test meal does not further stimulate the effect of dietary protein on muscle protein synthesis [45]. This had led to discussion related to optimal timing of protein intake. For example, distributing protein intake throughout the day, timing of protein around nighttime eating, and protein eating at breakfast are all areas of increased interest. In general, research covering these topics is performed in young, healthy populations, or aging populations, and very few, if any, studies have been conducted in children and pregnant women.
\nBreakfast is often recognized as the most important meal of the day [46, 47, 48]. However, there is debate as to what defines the ideal breakfast meal [47], in addition to a lack of strong evidence to define which nutrients should be represented at breakfast [47]. A recent commentary published by the American Academy of Nutrition and Dietetics suggests that protein-containing foods (e.g., eggs, lean meat, and low-fat dairy products) should be included in breakfast meals [47]. Literature supports diets higher in protein aid in the treatment of chronic, metabolic diseases such as obesity, type 2 diabetes, and heart disease and have been shown to increase EE, improve satiety, regulate glycemic control, and improve body composition (reviewed in [13, 14, 34, 49]).
\nEating protein at night and immediately before bedtime has received substantial attention in the past decade. Although past common knowledge would claim that eating before bed precipitates negative effects on health and body composition, more recent studies show that there may be many metabolic, health, and body composition-related benefits [50]. Much of the previous research claiming the negative effects of nighttime eating was performed in shift workers [51], populations with night eating syndrome, who consume ≥50% of daily calories after dinner [52], and epidemiological data [53]. Although some of the negative effects of nighttime eating in these populations may include high BMI and abdominal obesity [54]; increased triglyceride concentration, dyslipidemia, and impaired glucose tolerance [55]; impaired kidney function [56]; and increased carbohydrate oxidation and decreased fat oxidation [57], many other factors need to be taken into consideration. For example, these populations are awake during abnormal hours and report sleep disturbances [58, 59]. In fact, the duration of sleep is inversely related to BMI [60, 61]. These populations also consume significantly more carbohydrate, protein, and fat throughout the day. Nonetheless, it is clear that eating large amounts of energy in the evening hours, in particular when the energy is carbohydrate- and fat-laden, may not be beneficial for health and body composition outcomes.
\nHowever, much more evidence has shown that eating a small protein snack (~200 kcal) before bed may elicit significant benefits. Improved muscle protein recovery, muscle mass, and strength gains mediated by enhanced overnight and next-morning muscle protein synthesis have been shown to be enhanced with 40 g of casein protein supplementation in elderly [62] and recreationally active men [63]. These effects are particularly enhanced when this dietary practice is added to the practice of resistance exercise [63]. In addition, reported hunger is lower and satiety is higher, and resting energy expenditure is higher the following morning after a small protein snack compared to a noncaloric placebo [50, 62]. Chronically (4 weeks) there are also reports of decreased blood pressure, decreased arterial stiffness [64], and a greater decrease in body fat in overweight and obese women when consuming nighttime protein [65, 66]. Importantly, these benefits are accompanied by no significant alterations in overnight or next-morning lipolysis, fat oxidation, substrate utilization, or any blood markers in obese men or resistance-trained young women [67].
\nCurrent research demonstrates that even distribution of protein intake throughout the day is more effective at stimulating a 24-h protein synthesis compared to an uneven distribution [68, 69]. This is supported by data from a longitudinal study on nutrition and aging, which found that even distribution of daily protein intake across meals is independently associated with greater muscle strength and higher muscle mass in older adult, but is not associated with loss in muscle mass [70] or mobility [71] over 2–3 years. However, there are some studies that fail to confirm the importance of spreading protein intake out over the course of the day [71, 72]. Additional studies have compared pulse feeding (72% of daily protein at lunch) versus protein being evenly distributed over four daily meals in hospitalized older patients for 6 weeks [73, 74]. These studies found that pulse feeding of protein increased postprandial amino acid bioavailability [75] and increased lean mass [74] compared to spreading protein intake throughout the day. Taken together, the optimal timing and distribution of protein intake still need to be determined.
\nObesity is a major public health concern [76] and is associated with the development of metabolic diseases such as cardiovascular disease, nonalcoholic fatty liver disease, and type 2 diabetes mellitus in both children and adults [77, 78]. Obesity is defined as having a body mass index (BMI) (weight in kilograms divided by height in centimeters squared) greater than or equal to 30.0. In 2015–2016, the prevalence of obesity (\nTable 2\n) in the United States was 39.6 for adults and 18.4% for youth [76]. Obesity also impacts racial and ethnic groups differently. For example, non-Hispanic black and Hispanic adults and youth have higher rates of obesity compared to non-Hispanic white and Asian populations [79].
\nAge group (years) | \nTotal (percent) | \nBoys or men (percent) | \nGirls or women (percent) | \n
---|---|---|---|
Youth, 2–19 | \n18.5 | \n19.1 | \n17.8 | \n
Young children, 2–5 | \n13.9 | \n14.3 | \n13.5 | \n
Youth, 6–11 | \n18.4\n$\n\n | \n20.4\n$\n\n | \n16.3 | \n
Adolescents, 12–19 | \n20.6\n$\n\n | \n20.2 | \n20.9\n$\n\n | \n
Adults, 20+ | \n39.6 | \n37.9 | \n41.1 | \n
Young adults, 20–39 | \n35.7 | \n34.8 | \n36.5 | \n
Middle-aged adults, 40–59 | \n42.8\n*\n\n | \n40.8\n*\n\n | \n44.7\n*\n\n | \n
Older adults, 60+ | \n41.0 | \n38.5 | \n43.1 | \n
Prevalence of obesity in the United States by age group and sex [76].
Significantly different from young children.
Significantly different from young adults.
A primary factor in controlling and preventing obesity and associated chronic diseases is through diet, for example, diets higher in protein [13, 14, 80, 81]. Diets higher in protein (>30% of energy intake) have been shown to improve body composition [82], improve glycemic response [81, 83, 84, 85], increase satiety [85, 86, 87], and increase postprandial energy metabolism [88, 89], which are all mediating factors of weight loss.
\nSarcopenia is the term for age-associated loss of muscle mass and function [35]. The loss of muscle function associated with sarcopenia is often referred to as dynapenia [90]. A loss or reduction in skeletal muscle function often leads to increased morbidity and mortality either directly, or indirectly, via the development of secondary diseases such as diabetes, obesity, and cardiovascular disease [91]. The causes of sarcopenia include poor nutrition, diminished responsiveness to anabolic hormones and/or nutrients, and a sedentary lifestyle.
\nThe loss in muscle mass observed with aging is often accompanied by an increase in fat mass [92], which can happen even in the absence of changes in BMI [35]. The loss in muscle mass results in a decrease in basal metabolic rate (BMR) or the amount of caloric energy we use while at rest [93]. The loss of muscle mass induces a 2–3% decrease in BMR per decade after the age of 20 and a 4% decline in BMR per decade after the age of 50 [93, 94]. Muscle loss and subsequent reduction in metabolic rate contribute to obesity that accompanies the aging process.
\nSeveral studies identify protein as a key nutrient for aging adults [2, 95]. Low protein intake is linked to a decrease in physical ability in aging adults [96]. However, protein intake greater than the dietary guidelines may prevent sarcopenia [96], help maintain BMR [3], improve bone health [4, 5, 6, 7], and improve cardiovascular function [8, 9, 10]. These benefits of increasing protein in the diet may improve function and quality of life in healthy older adults, as well as improve the ability of older patients to recover from disease and trauma [91].
\nCurrently, the dietary recommendations for protein intake are the same for all healthy adults above the age of 19. However, experts in the field of protein and aging recommend a protein intake between 1.2 and 2.0 g kg−1 day−1 or higher for elderly adults [91, 95, 97]. The RDA of 0.8 g kg−1 day−1 is well below these recommendations and reflects a value at the lowest end of the AMDR. It is estimated that 38% of adult men and 41% of adult women have dietary protein intakes below the RDA [16, 44].
\nBoth protein amount and source are important to consider when recommending protein intake to older adults [34, 35]. There are three important aspects to take into consideration when recommending a protein source: (1) the characteristics of the specific protein, such as the amount of essential amino acids (EAA); (2) the food matrix in which the protein is consumed, for example, as part of a beverage or a complete meal; and (3) the characteristics of the individuals consuming the food, including health status, physiological status, and energy balance [34]. In addition, the difference in digestibility and bioavailability of a protein can impact the quantity of protein that needs to be ingested to meet metabolic needs; this is especially important in older adults since gastric motility and nutrient absorption decrease with age. The speed of protein digestion and absorption of amino acids from the gut can influence whole-body protein building [36]. Proteins with differing amino acid profiles exhibit different digestion and absorption rates [36, 38, 98]. Amino acid availability depends directly on both the quality and quantity of the dietary protein [98].
\nOver the past 15 years, the gut microbiome has received increased attention regarding its role in impacting overall health [99]. Interestingly, it has been shown to influence diseases associated with metabolic health [100]. The intestinal mucosa houses nearly a trillion microorganisms, and the plasticity of this environment is highly reactive to changes in diet [101]. For instance, the gut becomes an active site for protein and amino acid metabolism prior to absorption. Following enzymatic denaturation by intestinal proteases, amino acids can become fermented into various metabolites which include short-chain fatty acids and ammonia [102]. The acute microbial response and long-term adaptation associated with dietary habits have become an important area of research.
\nAs gut assay methodologies improve, researchers have identified associations between microbial populations and their metabolite concentrations in response to dietary patterns. For instance, in vitro and human models demonstrate a potential negative link between animal protein intake and protein fermentation end products such as ammonia and trimethylamine-N-oxide [103, 104]. However, favorable outcomes associated with animal- and plant-based protein sources have been observed. For example, ingestion of both whey [105] and pea protein [106] has been shown to increase favorable gut bacterial species such as Bifidobacterium. In addition, supplementation with pea protein intake has been shown to increase the production of short-chain fatty acids, an important energy substrate utilized by enterocytes [106].
\nThere is sufficient evidence that protein intake higher than the current dietary recommendations is beneficial for most healthy individuals throughout the life cycle. However, benefits of dietary protein depend on the quality, the quantity, and the timing of protein intake. Although health benefits of dietary protein have been well-established for older adults, more research is needed to determine the health benefits of increased dietary protein intake through each state of life.
\nThis work was supported by a grant to J.I.B. and E.B. from the Arkansas Biosciences Institute.
\nThe authors have no conflicts of interest to declare.
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