The combination of all the tissues that make up the body such as bones, muscles, organs and body fat

Body Composition and Organ Function Affect Pharmacokinetics

In neonates, developmental changes in body composition, renal, and hepatic function, as well as pathophysiology with clinical disease, can affect PK characteristics of drugs, specifically Vd and CL.1,14,20 Much of the variation in Vd across varying gestational ages and postnatal ages can be explained by differences in body composition. Neonates have higher proportions of extracellular and total body water compared to older infants and children. Premature infants have the highest amount of total body water. Therefore, hydrophilic drugs that distribute in water have larger Vd in newborns and even larger Vd in premature newborns. Infants with larger Vd will have lower peak drug concentrations unless they receive a higher dose. Infants with excess extracellular fluid, such as those with ascites or hydrops, can also have large Vd for hydrophilic drugs.

For lipophilic drugs, the body composition of fat can affect drug distribution into adipose tissue. If more drug is sequestered in fat, then plasma drug concentrations will be lower, Vd higher, and even the drug elimination characteristics (CL) can be affected since drug sequestered in fat is less available for elimination. Very preterm infants with minimal body fat will have smaller Vd and less storage of lipophilic drugs (e.g., benzodiazepines, fentanyl) in peripheral fat; whereas large-for-gestational-age infants born to diabetic mothers can sequester lipophilic drug in their peripheral tissue and, therefore, exhibit larger Vd and lower plasma drug concentration.

Infants with low protein stores will have greater proportion of free drug for a given total plasma drug concentration. The free drug component is the pharmacologically active moiety and may be more likely to distribute into peripheral tissues (larger Vd). The increased proportion of free or unbound drug can also contribute to faster elimination and shorter half-life since free unbound drug is more available for drug excretion mechanisms. However, the higher proportion of active, unbound drug relative to total drug concentration may explain toxicity at a similar total drug concentration. Neonates can also have a higher proportion of free drug compared to older infants due to endogenous competitors of protein binding, such as bilirubin or free fatty acids.

Age-dependent differences in drug binding in tissues can also affect distribution. Drugs can have extraordinarily large Vd if they are bound to proteins in the peripheral tissues. The disposition of these drugs through the body typically requires a two- or three-compartment model to account for the transfer of drug in and out of peripheral tissues (seeFig. 45.5). For example, digoxin has low plasma concentration and very large Vd; the drug is bound to proteins in skeletal and cardiac muscle and thus sequestered in the peripheral tissue.11 Infants and children have increased myocardial uptake of digoxin partially explaining the higher Vd in infants.

Conclusion

The measurement of body composition allows for the estimation of body tissues, organs, and their distributions in living persons without inflicting harm. It is important to recognize that there is no single measurement method in existence that allows for the measurement of all tissues and organs and no method is error free. Furthermore, bias can be introduced if a measurement method makes assumptions related to body composition proportions and characteristics that are inaccurate across different populations. The clinical significance of the body compartment to be measured should first be determined before a measurement method is selected because the more advanced techniques are less accessible and more costly.

Body Composition

Just as endocrine changes bring about remarkable changes in secondary sexual development and growth, body composition is dramatically affected. GH and gonadal steroids play major roles in this process.208 LBM, skeletal mass, and body fat are equal in prepubertal boys and girls, but by maturity, men have 1.5 times the LBM and almost 1.5 times the skeletal mass of women, whereas women have twice as much body fat (25%) as men (13%), producing a gynecoid (woman-like) or android (man-like) appearance.209 LBM increases by the age of 6 years in girls and 9.5 years in boys. Boys acquire fat-free mass more quickly and for a longer period than girls during puberty; stability is attained by 15 to 16 years in girls and 2 to 3 years later in boys.150 Fat mass increases in girls at an average rate of 1.14 kg per year, but the fat mass does not change in boys during the pubertal years, leading to the greater fat value in girls than boys with age.150

The generalized distribution of fat in males (central fat, apple shaped, android), which is different from that in females (lower body fat predominance, pear shaped, gynecoid), develops largely during puberty as males become more android than they were in prepuberty, although girls start and remain gynecoid. There are ethnic differences in the pattern of change, and Asians have the most significant changes.210

A strength spurt occurs during puberty after the pubertal growth spurt. Muscle mass is 54% of body weight in adolescent boys and 42% of body weight in adolescent girls, with the difference partly due to the presence of more muscle cells and larger muscle cells in men. There is little gender difference before 8 years of age, but by 14 years, boys usually have developed greater lean leg mass and greater power than girls.211

Methods and Models Used for BCA (Table 1)

Methodological aspects of BCA have been extensively described (Heymsfield et al., 2005; Preedy, 2012). Anthropometric methods are non-invasive, they are still used in population studies to assess for example, skinfold thickness (as an estimate of subcutaneous FM) and midarm or thigh circumference (which can be used as a measures of skeletal muscle mass after correction for subcutaneous fat). Strictly spoken, these are local or at least regional assessments of body composition resulting in a “2C-model,” because they have been validated against so-called “2C-reference methods” (see below). Age-and sex-specific estimates of whole body FM are based on algorithms generated from the statistical associations between anthropometric measurements and the data obtained by the reference or gold standard method.

Table 1. Characteristics of individual methods used for body composition analysis

MDC, minimal detectable change (fat mass, kg); prec’, precision (Fat mass, %); MRI, magnetic resonance imaging; CT, computer tomography; DXA, dual X-ray absorptiometry; ADP, air displacement plethysmography; QMR, quantitative magnetic resonance; BIA, bioelectrical impedance analysis; TBW, total body water; AT, adipose tissue; SAT, subcutaneous adipose tissue; VAT, visceral adipose tissue; BAT, brown adipose tissue; MM, muscle mass; OM, organ mass; FM, fat mass; FFM, fat free mass.

There are a number of non-invasive and accurate methods for BCA. Different reference methods are used which differ in techniques, concepts and outcomes. At the whole body level reliable and valid measurements of body volume and thus body density (calculated as the ratio of body mass and body volume) by either underwater weighing or air displacement plethysmography (ADP) aim to assess FM and FFM (Forbes, 1987). Dilution techniques measure hydration status, that is, D2O-dilution to assess TBW and NaBr-dilution to measure ECW. ICW is then calculated from the difference between TBW and ECW (Heymsfield et al., 2015). Dual X-ray-absorptiometry (DXA) assesses bone mineral content and density (BMD), LST, and FM. LST of leg and arms (or LST of limbs) can be taken as a measure of skeletal muscle mass (so-called appendicular skeletal muscle mass; Heymsfield et al., 1990; Gallagher et al., 1997; Kim et al., 2002). Major body elements (e.g., total body K, N, Ca, etc.) are quantified by whole body counting (e.g., in a total body K counter as total body potassium, TBK, or by neutron activation analysis, NAA, of different elements, e.g., total body nitrogen, TBN) (Heymsfield et al., 2005). Today the latter two methods are of very limited use because of specialized equipment, requirements of high technical skills, high costs and a very limited availability. Quantitative magnetic resonance (QMR) technology is non-imaging and based on nuclear magnetic resonance (NMR) (Bosy-Westphal and Müller, 2015). QMR requires a low magnetic field of 67 G (or 0.0067 T). QMR measures FM, lean mass (with the exclusion of solid components mainly in the bone) and free water. Today, QMR is the most precise method for BCA (see Table 1). QMR estimates FM independently of FFM hydration.

Models used in BCA rely on certain assumptions, which are considered as fixed (e.g., 73.2% water content of FFM or measurements at a body temperature of 36°C or 37°C). In addition, it is usually assumed that an individual body component has a homogenous composition. These assumptions may be questioned in daily practice, for example, tissue hydration differs between children and the elderly and also between obese and normal weight patients. In addition, FFM hydration changes with weight loss and throughout the course of a clinical condition, for example, with inflammation and sepsis and in patients with liver cirrhosis. To minimize the shortcomings of individual methods, the results of different methods are combined, that is, DXA + ADP + D2O-dilution resulting in a so-called “4-compartment-” or “4C-model” (Fuller et al., 1992; Withers et al., 1999; Shen et al., 2005; Heymsfield et al., 2015). Thus the “4C-model” avoids assumptions of a fixed composition of FFM. It is considered as a gold standard for BCA.

Detailed body composition can be assessed by imaging technologies, that is, whole-body magnetic resonance imaging (MRI) or computer tomography (CT) (Müller et al., 2002; Heymsfield et al., 2005; Prado and Heymsfield, 2014). MRI allows reconstruction of all organs and tissues of the body. Transversal images are taken at different distances (e.g., a slice thickness of 7–10 mm for abdominal organs). Cross-sectional organ areas are segmented by hand or automatically using a validated software. Calculation of organ and tissue volumes is based on the sum of areas multiplied by slice thickness and the distance between the scans. The accuracy of volume determination is improved by using contiguously obtained images. Organ and tissue volumes are then converted into masses by taking into account their specific densities (i.e., 0.916 for adipose tissue, 1.0414 for skeletal muscle, 1.0298 for heart, 1.05 for liver, kidneys and spleen, 1.030 for brain, 1.99 for bone corticalis adding up to a whole body density of 1.07 for males and 1.04 for females). Using CT, attenuation intervals (Hounsfield units; HU) can be used for detailed BCA at the organ/tissue level. The interval between − 1001 and 191 HU covers air, gas and lung, − 190 and − 30 HU reflects adipose tissue and yellow bone mass, − 29 and 151 HU covers soft tissue whereas cortical bone and spongiosa are defined by an interval of + 151 and 2001 HU. To assess whole body subcutaneous and visceral adipose tissue (SAT, VAT) and skeletal muscle mass (SMM) a whole body-protocol can be reduced to measurements at a single slice at lumbar vertebra 3 (L3; Schweitzer et al., 2015, 2016). This site has been validated for healthy adults and the elderly. However as for longitudinal observations (e.g., during weight loss or weight gain) the variance in changes of regional fat depots limits the value of a single slice estimate (Schweitzer et al., 2015).

Besides the 4C-model, MRI and CT are also considered as Gold standard methods of BCA (Müller et al., 2002; Prado and Heymsfield, 2014). Comparing 4C-model data with MRI- or CT-derived estimates of body composition some differences become evident, that is, chemically defined fat mass as assessed by the 4C-model does not closely resemble the volume of adipose tissue as measured by MRI. There is a considerable inter-individual variance in these data with a fat content of adipose tissue volume varying between 60% and 90%. Thus, strictly spoken the results obtained by imaging technologies cannot be directly compared with the results obtained by the use of either a single reference methods or the 4C-model.

Magnetic resonance spectroscopy (MRS) can be used to assess fat infiltrations in liver, pancreas and skeletal muscle. Liver fat can also be measured by MRI using the 2-point Dixon method that calculates “fat-only” and “water-only” images from “in-phase” and “opposed-phase” images (Ma, 2008).

When compared to reference methods and the gold standard methods, bioelectrical impedance analysis (BIA) has become a widely applied field method for BCA (Lukaski, 2013). Individual BIA devices have been validated against the different reference methods, the “4C-model” and whole body MRI too (Bosy-Westphal et al., 2013b, 2017). These validations are specific for the individual devices, the reference populations and the individual reference or gold standard methods. In a standard approach impedance is measured with a current of 100 mA at a single frequency of 50 kHz. Using multifrequency BIA or bioelectrical impedance spectroscopy (BIS) frequencies between 1 and 1000 kHz body composition is calculated from the impedance to the flow of an electric current through total body fluid. The conductive volume (V, which represents TBW or FFM) is proportional to the square length of the conductor (Ht2) and inversely correlated to resistance (R) of the cross-sectional area (V = ρ × Ht2/R, where ρ is the specific resistance of the conductor). TBW can be further differentiated into ICW and ECW. It distinguishes excess fluid from the hydration of major body tissues (Chamney et al., 2007).

Whole-body impedance is mainly based on the impedance of the distal parts of the limbs near the electrodes. Algorithms used to calculate body composition from BIA measurements are based on statistical relationships between impedance and either TBW or FFM or muscle mass. FM is then calculated from the difference between body weight and FFM. Population specificity, the reference method used to generate the BIA algorithm and the BIA device add to a nearly endless list of varying BIA algorithms published so far. Thus, using a BIA device in a clinical setting, population specificity and validation and the device used for generation of a specific BIA algorithm has to be scrutinized.

Alternatively, the use of BIA raw data has gained popularity in body composition research (Bosy-Westphal et al., 2005, 2006). Resistance (R) and reactance (Xc) are standardized by body height in a bioelectrical impedance vector analysis (BIVA) to characterize hydration status and body cell mass (BCM). In a clinical setting, BIVA can be used to follow changes in hydration and BCM and thus malnutrition (e.g., in tumor patients undergoing treatment) (Norman et al., 2015). In addition, the phase angle (PA) can be directly calculated from R and Xc as arc-tangent (Xc/R) 180°/π. PA is associated with body cell mass (BCM), changes in cellular membrane integrity and alterations in fluid balance. A low PA is used for the diagnosis of malnutrition and clinical prognosis. For device-specific BIVA and PA, reference values from different populations stratified according to ethnic, age, and body mass index (BMI) groups are available. Modern BIA techniques are valid tools to estimate body composition in healthy and euvolemic adults. In the clinical setting the use of BIA raw data has value. By contrast, using standard BIA algorithms generated in healthy subjects for BCA in patients has obvious limitations.

The accuracy and outcomes of different methods used for BCA are presented in Table 1.

Body Fluid Composition in Fetuses and Newborns

Total body water (TBW) encompasses extracellular (interstitial and plasma) and intracellular water. Early in fetal development, TBW is almost 95% of the total body weight. As the fetus grows, there is a decrease in the proportion of body weight represented by water (Fig. 92.1).20 At birth, TBW represents approximately 75% of body weight in a full-term infant. The progressive decrease in TBW is caused primarily by decreases in the extracellular water compartment. The TBW percentage in premature infants, therefore, is higher than that of term infants and is proportional to gestational age.24 For instance, at 32 weeks, the infant's TBW is approximately 85% of the body weight, whereas that of a 23-week infant approaches 90%.43

During the first week of life, all healthy neonates experience a reduction in body weight. The major cause of this physiologic weight loss is a reduction in extracellular water.56 In the first 24-48 hours after birth, infants have decreased urine output, followed by a diuresis phase, with urinary losses of water and sodium in the first week of life, resulting in weight loss.34 Physiologic weight loss in the first few days of life in term and premature infants represents isotonic contraction of body fluids and seems to be part of a normal transitional physiologic process thought to be mediated through release of atrial natriuretic peptide.40 In term infants, this weight loss can be up to 10%. In very premature infants, weight loss can be up to 15%.58 Perturbations in this normal transitional physiology can lead to imbalances in sodium and water homeostasis. In ill term infants and premature infants, various factors (discussed subsequently) can lead to increased or decreased urinary or insensible water losses. Similarly, increased or decreased administration of intravenous fluids, with variable amounts of water and sodium, can have a significant impact on overall fluid balance.

Cellular

The cellular level of body composition consists of body cells (body cell mass) and their surrounding extracellular water, plus the skeleton and connective tissue. Although there is some lipid in the form of cell membranes, this compartment is largely fat-free and these components are sometimes termed the fat-free mass (FFM) or in older terminology the lean body mass (LBM). The body cell mass is responsible for almost all of the basal energy expenditure of the body, since that is where cellular metabolic and respiration processes take place. Together with the adipose tissue compartment (which consists mostly of fat), this level is often referred to as a two-compartment model, i.e., FFM and fat mass (FM). In the healthy individual, the FFM has a relatively constant composition, with a water content of 72–74%, an average density of 1.1 g cm−3 at 37 °C, a potassium content of 60–70 mmol kg−1 in men and 50–60 mmol kg−1 in women, and a protein content of 20%.

Anthropometric Characteristics.

Body composition should be measured in the deconditioned patient because this can affect his or her tolerance of certain types of exercise programs. A patient who is obese has an increased risk for developing overuse injuries during an exercise program.41Body mass index (BMI) is the most commonly used general indicator of body composition (Fig. 23-1). BMI is equal to a person's weight in kilograms (kg) divided by their height in meters (m) squared (kg/m2).42 Although BMI is often substituted for information from specific tests of body composition, one should keep in mind that it does not differentiate musculoskeletal weight from fat.43 BMI may be used to screen for risk factors associated with obesity.44 Patients with BMI greater than 30 should be referred for nutrition counseling, and patients with BMI greater than 35 need regular medical follow-up.45

More accurate but more cumbersome measures of body composition and body fat percentage are achieved through underwater weighing, skinfold measurements, or through bioelectrical impedance analysis (BIA).42 Underwater weighing is the gold standard indirect method for assessing body composition and involves comparing the patient's weight in air to their weight when fully submerged in water after a complete exhalation. The time and equipment needed to perform this procedure precludes its use in most clinical settings. Skinfold measurements can also be used to estimate body fat. With an accurate caliper and an experienced tester, this approach produces reliable and valid results in most patients. BIA estimates the percentage of body fat by using a low level current to measure body impedance. This is an easier, less invasive, and less technically-demanding method than skinfold measurements, but measurements in patients with excessive water retention or tissue edema may be inaccurate and yield inaccurate estimations of body composition. Using body fat percentage, obesity is generally regarded as greater than 30% for adult men and greater than 40% for adult women. Minimal or essential body fat percentages are 5% for men and 10% for women.42

Body Composition in Health and Disease

Body composition can be viewed from five perspectives: atomic, molecular, cellular, tissue, and whole body levels.1 At the atomic level, six elements form 98% of the body mass: 61% oxygen, 23% carbon, 10% hydrogen, 2.6% nitrogen, and 1.4% calcium; the remaining 2% of the mass consists of 44 other elements.

More than 100,000 distinct molecules constitute the molecular composition, ranging from simple molecules such as water to highly complex ones such as lipids and proteins. Water, which accounts for about 60% of a 70-kg “reference male” and about 50% of a “reference female,” is the major chemical component of the body and essential for the interior milieu. The total body water (TBW) is distributed between two major compartments, the intracellular volume (ICV) and the extracellular volume (ECV); the latter can be divided into the interstitial compartment, which constitutes the extracellular environment of the cells, and the vascular space. Body fat depends heavily on nutrition and training status, ranging from less than 10% to more than 50%. Protein and minerals account for 15% and 5% of body composition, respectively. The 1018 cells forming the cellular body composition domain can be divided into connective tissue cells (fat cells, blood cells, and bone cells), epithelial cells, neural cells, and muscle cells. In terms of tissue composition, bone, adipose tissue, and muscle make up 75% of body weight. The lean body mass is the mass of the body minus the fat mass (storage lipid).

In healthy adults, body composition is maintained over the short term within narrow limits. Gender, age, race, nutrition, physical activity, and hormonal status are the main determinants of body composition. Illness may have a significant effect on body composition; malnutrition is a major complication. Malnutrition, which develops when nutritional intake falls short of nutritional requirements, leads to organ dysfunction, reduced body cell mass, abnormal blood chemistry, and worsened clinical outcomes.2 Critically ill patients in particular are prone to malnutrition and consecutive unfavorable alterations in body composition. Malnutrition is observed frequently in patients regardless of type of illness.3 An increased intake of energy and protein in critically ill patients is associated with improved outcomes at different body mass index (BMI) in a non-linear fashion; better outcomes are shown in patients with BMI less than 25 or more than 35.4 In critically ill patients, hypermetabolism is caused by an activation of the sympathetic nervous system and the pituitary-adrenal axis, resulting in high plasma levels of catecholamines, adrenocorticotropic hormone, growth hormone, and cortisol. These metabolic adaptations contribute to protein-calorie malnutrition (defined as a negative balance of 100 g nitrogen and 10,000 kcal within a few days). Assessment of nutritional status and body composition in the critically ill patient is of major importance and guides adequate and sometimes aggressive nutritional support.

Fluid overload is very common in the intensive care unit (ICU). Impaired fluid balance is related with poor outcomes, such as an increased mortality risk.5 In a retrospective analysis a positive fluid balance of more than 4 L was present after 12 hours of ICU admission in septic shock patients and increased further up to +11 L after 4 days.6 A linear correlation has been described between cumulative fluid balance and risk of mortality.7

Body Composition

Body composition is emerging as a primary determinant of health and a predictor of morbidity and mortality in children. Preservation and accrual of lean body mass during illness are important predictors of clinical outcomes in patients with sepsis, cystic fibrosis, and malnutrition.8,9 Body composition is measured by a variety of techniques including body densitometry by underwater weighing, neutron activation analysis, total-body potassium determination, bioelectrical impedance assessment (BIA), and dual-energy x-ray absorptiometry (DXA). Most of these methods are not practical for application in the clinical management of a critically ill child. DXA is a radiographic technique that can determine the composition and density of different body compartments (fat, lean tissue, fat-free mass, and bone mineral content) and their distribution in the body. DXA has been used extensively in pediatric practice for determining fat-free mass, fat mass, and lean mass, and it is recognized as a reference method for body composition research.10 Its results correlate well with direct chemical analyses, and there is good agreement between percentage body fat estimated by hydrodensitometry and DXA.11 However, DXA is not practical for application in the PICU. BIA, in contrast, is a bedside technique that can be applied to pediatric patients without exposure to radiation and with ease.12,13 Electrical current is conducted by body water and is impeded by other body components. BIA estimates the volumes of body compartments, including extracellular water and total body water (TBW). TBW measures can be used to estimate lean body mass by applying age-appropriate hydration factors. BIA has not been validated in critically ill populations; hence, its use outside clinical studies is not recommended in the PICU. The ideal bedside body composition measurement technique in critically ill patients remains elusive.

Body composition is associated with the risk for cognitive decline and dementia. On the other hand, brain dysfunction itself exerts a remarkable influence on human body weight and body composition. Even before the manifestation of the symptoms of a dementia syndrome, most individuals lose body weight unintentionally. This weight loss accelerates after the manifestation of the disease and is associated with a more severe course of the disease. The mechanisms of the early preclinical weight loss are not known, and the causes for the later disease accompanying weight loss are not completely understood. It has been demonstrated in cross-sectional studies that this weight loss predominantly comprises fat mass, which would match with a simple long-lasting energy imbalance. Accordingly, interventional studies have demonstrated that weight loss in dementia syndromes is not unavoidable and may be prevented by several interventions such as additional calories with oral supplements. However, the effect of such interventions on the course of the disease still has to be investigated.