Skip to main content Accessibility help
×
Home

Information:

  • Access
  • Cited by 16

Actions:

      • Send article to Kindle

        To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

        Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

        Find out more about the Kindle Personal Document Service.

        Drug disposition in obesity and protein–energy malnutrition
        Available formats
        ×

        Send article to Dropbox

        To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

        Drug disposition in obesity and protein–energy malnutrition
        Available formats
        ×

        Send article to Google Drive

        To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

        Drug disposition in obesity and protein–energy malnutrition
        Available formats
        ×
Export citation

Abstract

Clinical response to medication can differ between patients. Among the known sources of variability is an individual's nutrition status. This review defines some pharmacokinetic terms, provides relevant body size metrics and describes the physiologic influences of protein–energy malnutrition and obesity on drug disposition. Weight-based drug dosing, which presumes a healthy BMI, can be problematic in the protein–energy malnourished or obese patient. The use of total body weight, lean body weight, or an adjusted body weight depends on the drug and how it is differently handled in malnutrition or obesity. Most of the recognized influences are seen in drug distribution and drug elimination as a result of altered body composition and function. Distribution characteristics of each drug are determined by several drug-related factors (e.g. tissue affinity) in combination with body-related factors (e.g. composition). Drug elimination occurs through metabolic and excretory pathways that can also vary with body composition. The current data are limited to select drugs that have been reported in small studies or case reports. In the meantime, a rational approach to evaluate the potential influences of malnutrition and obesity can be used clinically based on available information. Antimicrobials are discussed as a useful example of this approach. Further advancement in this field would require collaboration between experts in body composition and those in drug disposition. Until more data are available, routine monitoring by the clinician of the protein–energy malnourished or obese patient receiving weight-based drug regimens is necessary.

Abbreviations:
Adj-BW

adjusted body weight

BWt

total body weight

CYP

cytochrome-P450

FM

fat mass

LBW

lean body weight

PCM

protein–energy malnutrition

Vd

volume of distribution

The clinical variability that exists among patients is well-recognized by most clinicians. This inter-individual variability includes the response to a drug which can be accounted for by a number of factors. Appropriate patient care requires an appreciation of the factors that influence the disposition and effect of medication. As used here, the term disposition refers to the absorption, distribution and elimination of a drug; while the term effect reflects the physiologic action of the drug at the target tissue or cell. The sources of variability that influence medication are numerous and include age, life stage, sex, genotype, disease state and nutrition status(1). The latter is the focus of this review and encompasses primary protein–energy malnutrition (PCM) and obesity.

The influence that nutrition status can have on drug disposition and effect is included as one of the five broad categories in the classification of drug–nutrient interactions(2, 3). As a precipitating factor to the interaction, nutrition status may result in drug treatment failure or drug toxicity related to the degree of malnutrition(4, 5). Related to the other areas of drug–nutrient interactions, the data available to clinicians are limited for the influence of obesity on drug disposition and even less for PCM(6, 7). Drug evaluations in special patient populations (e.g. renal or hepatic impairment) take place routinely, but far less frequent is the influence of PCM or obesity evaluated. For example, despite longstanding descriptions of the potential influences on drug metabolism, the nutrition status of subjects enrolled in clinical drug trials is rarely described(5, 8, 9). In the meantime, a rational and conceptual approach that uses available information can be helpful in making clinical decisions.

Definitions

The introduction of a few additional terms would be useful to the remaining sections of this review. Pharmacokinetics can be simply referred to as the actions of the body on a medication; for systemic effect, a drug first needs to be absorbed by the body, it is then distributed within and excreted from the body with many drugs being metabolized in the process. These four pharmacokinetic processes: absorption, distribution, metabolism and excretion, are described by a number of parameters with mathematical derivations and relationships(1). Apart from direct intravenous injection, after a drug is administered to a patient, it requires absorption by the body to reach the systemic circulation. From the vascular space, the drug undergoes distribution to the presumed site of drug action as well as to other tissues that include those responsible for the metabolism and excretion of the drug. These latter two terms (metabolism and excretion) are often collectively referred to as drug elimination. Most of the known influences of nutrition status are seen in drug distribution and drug elimination. The pharmacokinetic parameters that reflect these processes are volume of distribution (Vd) and clearance, respectively.

The distribution or movement of an active drug from the bloodstream to the site(s) of effect are determined by several factors related to the drug (e.g. lipophilicity, plasma protein binding, degree of ionization and tissue affinity) and to the body (e.g. body composition, blood flow, tissue size and permeability)(1, 10, 11). As a result, the distribution of a given drug is not the same to all tissues of the body. The Vd is an expression that uniquely reflects the distribution of each drug and is described in litres per kilogram of body weight (litres/kg). Higher values indicate extensive distribution and tissue binding. This is not an anatomical volume, but a theoretical body volume that the drug would have to distribute into if the drug concentration everywhere in the body were the same as is found in the blood following the administration of a known dose. In other words, the Vd relates the serum drug concentration to the amount of drug in the body. Drug elimination can be viewed in two parts; metabolism and excretion(1). Although some drugs are excreted from the body unchanged, most undergo some form of metabolism first which transforms the drug's chemical structure through an enzymatic reaction. These reactions may generally be described as either phase I or phase II depending on whether they involve, respectively, addition/subtraction of a functional group or conjugation with an endogenous substance. These metabolic reactions take place not only in the liver, but also in other tissue sites (e.g. intestinal mucosa) to some degree. The removal of the drug and any metabolite(s) from the body occurs predominantly through renal and biliary routes of excretion. The expression clearance is used to define the elimination of a drug from the body and is described in litres per hour (litres/h). Physiologically, this reflects the volume of blood that is cleared of a drug per unit of time as observed from sequential serum drug concentrations following an administered dose. The term clearance refers to the sum of multiple routes of elimination when more than one route exists for a drug. The size of the initial or loading dose of a drug depends on its Vd, while maintenance doses are based on drug clearance. Many drugs rely on weight-based (mg/kg) dosing.

Malnutrition and body weight

PCM and obesity occur as a result of nutrient and metabolic needs being inadequately met over time; reflecting chronic deficits or imbalances in macronutrients, but both including micronutrient deficits as well(12, 13). PCM results in growth failure in children and unintended weight loss in adults and is associated with increased morbidity(12, 14). Overweight and obesity are also implicated in higher morbidity and mortality(1518). Consequently, PCM and obesity are disproportionately found in healthcare settings(19, 20). As a part of usual clinical practice, the height and weight are obtained to calculate the BMI. These patient parameters should actually be measured, as visual estimates are often inaccurate(21). The BMI is frequently used in adults to define PCM (e.g. <18·5 kg/m2) and varying degrees of obesity (e.g. ≥30 kg/m2). The BMI is not perfect, but remains the most valid indicator of body habitus and morbidity/mortality risk(22, 23). There are still some using percentage of ‘ideal’ body weight as an expression of PCM (e.g. ⩽70–80%) and obesity (e.g. ≥130%). In clinical drug trials, obesity has also often been defined arbitrarily as ≥120% or 130% of ‘ideal’ body weight. The use of ‘ideal’ body weight in practice or in drug studies is problematic(7, 23, 24). The equations often used to establish ‘ideal’ body weight are empirically derived and only take height and sex into account rather than being based on actual body composition data(2527). As a result, the ‘ideal’ body weight is not a true reference weight and cannot be used as a surrogate for a patient's lean body weight (LBW). A body metric that goes beyond just height and weight to include body composition would be more useful clinically. BMI is not easily applied in determining drug dosing, in particular, because at a given value it does not distinguish between excess adipose tissue and muscle mass. Weight-based drug dosing presumes a healthy BMI that is assumed to represent ‘normal’ body composition. In clinical practice, drug doses are often based on the total body weight (BWt) with the value arbitrarily scaled up or down in PCM and obesity.

BWt represents many compartments as can be appreciated in images from CT scans and MRI. Reflecting all body compartments, BWt cannot by itself represent body composition for weight-based dosing in PCM or obesity. An appreciation of the simple two-compartment view of body composition allows further discussion. Body composition can be conceptually divided into fat mass (FM) and fat-free mass, which resemble the anatomic adipose tissue and lean body mass, respectively, allowing recognized distinctions(28). The clinical term LBW is used to reflect fat-free mass. As expected, and documented by MRI, BMI is strongly correlated with FM, while LBW includes body water and is correlated with body cell mass(29). So if ‘ideal’ body weight is not appropriate, and BWt fails to differentiate between FM and LBW, then what dosing weight should be used for the patient with obesity or PCM?

The answer to this question depends on the drug being dosed and how it is handled differently, if at all, in the patient with obesity or PCM. In other words, the ‘dosing weight’ should be based on the characteristic behaviour of the drug rather than in relation to a standard weight-for-height metric. The body size metric should account for age and ethnicity, as well as height, weight, sex and remain robust at the extremes of BMI(30). For practical purposes, the BWt is used as the dosing weight most often except for the volume overloaded patient and for the patient with obesity. LBW may be used as the dosing weight for some drugs in PCM and obesity. In other cases, an adjusted body weight (Adj-BW) that lies between BWt and LBW is often used clinically to empirically adjust the altered body composition expected in obesity. The Adj-BW is calculated as

where the correction factor (cf) represents the fraction of the FM that normalizes the drug's Vd in obese patients to that of the non-obese. As each drug has a characteristic distribution between FM and LBW, the correction factor is expected to differ by drug. Unfortunately, this data is not readily available for many drugs. Some of the variability in distribution to FM reflects the various adipose tissue compartments (i.e. subcutaneous (superficial and deep), internal (visceral, including intraperitoneal; non-visceral)) each with their own blood supply and metabolic activity(10, 28). In the obese patient, the focus on drug dosing should go beyond excess FM, to appreciate that the totality of change in body composition and function influence the tissue distribution of a drug, its clearance and ultimately its clinical effects. Arbitrary dose reductions or dose capping in obesity may be counterproductive by adversely influencing the clinical outcome(31, 32).

The best approach would be to use a body size metric that closely reflects body composition. When body composition has been taken into account as part of a clinical pharmacokinetics trial, significant influence on drug distribution and clearance can be identified(32). Given the impracticality of obtaining body composition data on each patient, the incorporation of accurate height and weight data into validated equations for LBW derived from individuals across the BMI spectrum is recommended. Such equations do exist:

The first pair of equations was based on a report that included data from three studies(9, 33). There were a total of 133 subjects in which less than 10% had BMI >30 kg/m2(3436). Although this pair of equations works well at typical BMI values, they may not be appropriate at more severe BMI levels. The latter pair of equations has been suggested as the best currently available equation for determining LBW(37). These were developed from a semi-mechanistic model in which body composition was taken into account, and uses the same easily obtained patient parameters (height, weight and sex). The model was developed from a group of 303 subjects, 18–82 years of age, with BMI between 17 and 70 kg/m2, 70% of whom were overweight and obese(38). This pair of equations have good precision and no bias in predicting LBW, performing as well as the first pair of equations at modest BMI, but remaining predictive at higher BMI(38). The application of these equations performed much better clinically in critical drug dosing in obesity than previously used equations(39).

The distribution of a drug in the body can involve both compartments, while drug clearance is correlated with LBW. This correlation between systemic drug clearance and LBW is recognized(40). Although BWt may correlate with liver volume, LBW was the only variable to correlate with hepatic drug clearance following stepwise multi-regression analysis(41). The renal function is also closely related to LBW(42).

Both PCM and obesity induce changes in body composition and function. These physiologic changes seen in PCM and obesity in turn can influence drug disposition. The degree of difference in body composition and function ultimately influences drug distribution and clearance. Assuming a similar structure and function in PCM and obesity does not account for the influence of body composition on drug disposition and effect following administration. Having a sense of the patient's LBW (e.g. as calculated by an appropriate equation) is valuable(38, 39).

Physiologic changes influence pharmacokinetics in protein–energy malnutrition

Individuals with PCM have reductions in both adipose tissue and lean tissue (skeletal muscle and organ mass). The degree of loss depends on the severity of PCM but may include a FM of only 5% of body weight compared with normal of 20–25%(43, 44). Additionally, extracellular fluid volume may expand relatively from 20 to 40% of body mass(43, 44). PCM is also characterized by reductions in cardiac output with reduced hepatic blood flow and glomerular filtration rate(44, 45). Hepatic protein synthetic rates are also reduced in PCM(44). The altered body composition along with reduced transport proteins and regulatory hormones can influence drug distribution, while the reduced cardiac output, glomerular filtration rate, organ mass and function influence drug elimination.

In PCM, the human pharmacokinetic data are limited. A drug's Vd may be increased or decreased in PCM(4649). Renal drug clearance may be reduced or unchanged in severe PCM(5052). Severe PCM can decrease oxidative metabolism and possibly conjugation reactions(51, 53). Mild PCM may increase or have no effect on oxidative drug clearance by the cytochrome-P450 (CYP) isoenzymes. When examined, it appears that CYP3A is not influenced by PCM, but CYP1A2, CYP2E1, and others are reduced in PCM(54). Data from an animal model of PCM suggest supplementation with the S-containing amino acid cysteine can improve drug metabolizing enzyme activity(55).

Physiologic changes influence pharmacokinetics in obesity

Obese individuals have absolute and relative increases in adipose tissue, with absolute increases in both lean tissue and body water based on MRI and 2H data(29, 56). Approximately 20–40% of the excess body weight in obesity is made up of lean mass across BMI of 29–47 kg/m2(57). All tissue compartments (e.g. visceral organs, muscle mass, adipose tissue and body water), with the possible exception of bone, have a higher mass at higher BMI values(29, 56). Functionally, these individuals have a larger cardiac output with tissue blood flow and the glomerular filtration rate. There is a low proportion of blood flow to adipose tissue(58). Altered body composition and carrier proteins (e.g. increased lipoprotein and α1-acid glycoprotein) could influence the distribution, while an increased cardiac output, filtration rate and inflammation can influence drug clearance. Again the degree of altered physiology and function varies in patients, realizing that the body composition of every person with a BMI of 45 kg/m2 is not identical(59, 60). This makes predictions difficult. The degree of difference in body composition and function ultimately influences drug distribution and clearance. Although drug clearance is known to increase with BWt, the relationship is not linear as it is with LBW(61).

A few dozen drugs have been investigated for their influence by obesity(62). Generally, these studies use equations (e.g. ‘ideal’ body weight) without the benefit of actual body composition data. A better understanding of the quantitative relationship between body composition and function with drug distribution and elimination is required. Lipophilic drugs may have a larger Vd in obesity, but not necessarily(63, 64). Differences in Vd of hydrophilic drugs are just as unpredictable in obesity. This should not be too surprising as the oil–water partition coefficient (i.e. degree of lipophilicity) is only one of the characteristics of a drug that determines its distribution, and does not necessarily overcome the other factors (e.g. blood flow and tissue binding). Hepatic drug clearance may be increased, decreased, or unchanged in obesity(65, 66). It appears that there is no influence of obesity on CYP1A2, but reductions may exist in CYP3A, while increases in CYP2E1 and uridine diphosphate-glucuronyl-transferase activity have been suggested(6770). Renal drug clearance is likely to be increased in obesity(71). Individualizing drug dosing in obese individuals can reduce adverse effects without sacrificing therapeutic efficacy(31).

It should be clear, based on the available data so far, that generalizations cannot be made about the influence of PCM or obesity on drug disposition. Drug-specific data needs to be determined or closely evaluated when available.

Antimicrobial examples

Both PCM and obesity may predispose patients to infection(72). Of the 11 studies representing 3159 hospitalized patients which were included in a systematic review, 64% describe worse outcome from infection in obese and PCM patients compared with other patient groups(72). The role of inappropriate antimicrobial dosing is a factor to be seriously considered. Given the existing morbidity/mortality risk from infection, any altered drug disposition in PCM or obesity should be taken into account to assure optimal drug dosing. A recent study of 839 hospital admissions identified that obese patients are at a significantly greater risk for errors with weight-based drug dosing(73). Of the antimicrobial orders, under dosing was reported in 11% and overdosing in 5%, significantly greater than in the control group, although clinical outcomes were not described. In PCM, the Vd of aminoglycosides (e.g. tobramycin) is increased, while the clearance is reduced(46, 48, 50). This lends itself to a higher (mg/kg) dose with longer intervals between doses. Recall that maintenance doses are based on drug clearance, while the initial or loading doses are based on Vd. It has been suggested that loading doses of aminoglycosides be increased by a factor of 1·1 in underweight patients(74). Drug clearance is also reduced in PCM for chloramphenicol, penicillin, quinine and isoniazid(51, 53, 7578). The additive influence of acute infection on drug disposition may also play a distinct role(79, 80).

In obesity. the weight-corrected Vd of the aminoglycosides is unchanged or reduced, while the clearance is increased. The mg/kg dosing of these drugs should then be based on an Adj-BW using a correction factor of 0·4 to represent the proportion of the excess body weight into which the aminoglycosides distribute(74, 81). There is very little data on the antifungal agents, but dosing based on BWt has been suggested for amphotericin as both Vd and clearance may be increased in obesity(8284). Although the clearance of the β-lactam antimicrobials (e.g. penicillins and cephalosporins) is increased in obesity requiring more frequent dosing to maintain adequate drug concentrations, the available data on Vd appear to differ by drug(8587). For example, the Vd for ampicillin and piperacillin is increased, while Vd for ertapenem is reduced in obesity(85, 87). The Vd for vancomycin is not appreciably different in obesity, but the clearance of the drug is increased(8890). Dosing of this antibiotic should be based on BWt with a dosing interval frequent enough so that the serum trough concentration remains at a therapeutic level.

Much of the data are derived from single case reports or case series rather than well-designed comparative trials. Therefore, it may be instructive to review a couple of examples.

A 43-year-old man hospitalized with bacterial cellulitis received a course of linezolid at the usual dose of 600 mg twice daily after 7 d of cefazolin(91). This patient weighed 286 kg (BMI 86 kg/m2) and had a measured creatinine clearance of 75 ml/min. The measured linezolid Vd in this patient was 135·7 litres (0·47 litre/kg) as compared with the normal Vd of approximately 40–50 litres (about 0·64 litre/kg). Given the information from this case, that the weight-corrected Vd is lower in obesity compared with normal (0·47 litre/kg divided by 0·64 litre/kg=0·73), an Adj-BW should be used for dosing this drug. This further indicates that a correction factor of 0·27 (1−0·73) can be used in estimating the Adj-BW to account for the difference in Vd. This would need to be prospectively studied to document that such a dosing regimen would adequately and safely maintain drug concentrations.

A 60-year-old man admitted with a presumptive diagnosis of herpes encephalitis was treated with acyclovir 1000 mg intravenously every 8 h(92). This patient weighed 108·9 kg (BMI 38 kg/m2) with blood urea nitrogen and creatinine serum values of 9 mg/dl (3·2 mmol/l) and 0·7 mg/dl (61·9 μmol/l). By the third hospital day, he developed acyclovir-induced nephrotoxicity with peak blood urea nitrogen and creatinine serum values of 57 mg/dl (20·1 mmol/l) and 5·5 mg/dl (486 μmol/l). The dose of acyclovir appeared to be within normal limits at 9·2 mg/kg BWt. However, because this drug has a known reduction in Vd (litres/kg) in obesity, it should have been dosed based on the patient's estimated LBW. Given that this patient had a LBW of approximately 60 kg, the dosing of acyclovir was actually 17 mg/kg of dosing weight and would be much more likely to cause the documented toxicity.

Recommendations

Antimicrobial dosing should be individualized to patient BMI/body composition based on the available data for each agent in order to maximize effectiveness and safety(72). Dose individualization in PCM and obese individuals will be advanced by further exploration of the influence that body composition has on drug disposition(62). Weight-based drug dosing needs to take into account the relative distribution into various tissue compartments, as well as drug competition for transport proteins, lean tissue and adipose tissue binding characteristics. From an academic perspective, pharmacokinetic and pharmacodynamic evaluations in obesity should include a measurement of body composition; at least a two-compartment assessment. This requires a close collaboration between those with expertise in the evaluation of drug disposition and those with expertise in body composition. Body composition analysis methods associated with the least biologic variability or technical error (e.g. 2H dilution and dual-energy X-ray absorptiometry) should be directly applied to the study of drug disposition in PCM and obesity. If these studies are able to evaluate data across a wide spectrum of BMI and allow stratification by body composition, this would very helpful. Moving forward, it would be beneficial if all new drugs were required to perform these evaluations to ensure safety in posology. Dosing adjustments for PCM and obesity should be incorporated into the process of new drug development and regulatory review(7, 93). Covariates to explain variability allow better pharmacokinetic and pharmacodynamic modelling, whether descriptive or predictive. Mechanistic covariates should be physiologically appropriate (e.g. eye colour is less useful than renal function).

Given the limited data on the impact of PCM on most drugs, the use of caution in managing these patients is recommended especially when using narrow therapeutic index agents. When available, drug-specific data should be integrated with patient-specific data to develop an appropriate dosing and monitoring regimen in PCM. In obesity, the use of drug-specific data and close patient monitoring is also important for designing optimal dosing strategies. The general approach in obesity would include evaluating the influence on Vd when the loading dose is being determined, and the influence on drug clearance when maintenance doses are being evaluated (Table 1)(7). An empirical clinical approach integrates available data (in the absence of prospective trial results) to determine which dosing weight should be used for weight-based drug dosing. If a case suggests that the Vd (litres/kg) in obesity is equal to or greater than the expected Vd in the non-obese individual, then BWt can be used for the loading dose. If that comparison reveals a Vd (litres/kg) in obesity that is less than the expected value in the non-obese individual, then the LBW or possibly an Adj-BW should be used. The LBW estimates should be based on an appropriate equation(38). For maintenance dosing, the total clearance (litres/h) in obesity is compared with the expected clearance in the non-obese individual. If clearance is equal to or greater in obesity then BWt can be used, but if clearance is less than the expected value in the non-obese the LBW should be used. This is most important to evaluate weight-based dosing of narrow therapeutic index drugs or those drugs that possess concentration- or time-dependent action (e.g. antimicrobials). Of course these empirical recommendations necessitate close patient monitoring.

Table 1. Empirical guide to weight-based dosing in obesity

Vd, volume of distribution in litres per kilogram of total body weight; LBW, lean body weight; Adj-BW, adjusted body weight; BWt, total body weight; Cl, clearance in litres per hour.

For clinicians, as part of routine patient assessments, any unexpected clinical presentation may be an indication to rule out the influence of PCM or obesity on drug disposition and drug effect. In this way, the clinician may be able to better explain (or better yet to predict) altered drug effects in the patient with PCM or obesity. Clinicians are encouraged to publish drug-specific findings from their patients with obesity (or PCM) providing pharmacokinetic parameters as well as body composition data when available(90, 91). The dosing weight suggestions listed in Table 2 are based on limited data mostly from case reports. Prospective evaluations with body composition data will hopefully allow stronger recommendations to be made in the future.

Table 2. Suggested dosing weight for antimicrobial examples in obesity

LBW, lean body weight; Adj-BW, adjusted body weight; BWt, total body weight.

* Suggested correction factor: a0·4; b0·5; c0·3.

Acknowledgements

The author declares no conflicts of interest. The work presented received no specific grant from any funding agency in the public, commercial or non-profit sectors.

References

1.Rowland, M & Tozer, TN (editors) (2010) Clinical Pharmacokinetics and Pharmacodynamics: Concepts and Applications, 4th ed. New York, NY: Lippincott, Williams & Wilkins.
2.Santos, CA & Boullata, JI (2005) An approach to evaluating drug–nutrient interactions. Pharmacotherapy 25, 17891800.
3.Boullata, JI (2010) An introduction to drug–nutrient interactions. In Handbook of Drug-Nutrient Interactions, 2nd ed., pp. 326 [Boullata, JI and Armenti, VT, editors]. New York, NY: Humana Press.
4.Mehta, S (1990) Malnutrition and drugs: clinical implications. Dev Pharmacol Ther 15, 159165.
5.Walter-Sack, I & Klotz, U (1996) Influence of diet and nutritional status on drug metabolism. Clin Pharmacokinet 31, 4764.
6.Compher, CW & Boullata, JI (2010) Influence of protein-calorie malnutrition on medication. In Handbook of Drug–Nutrient Interactions, 2nd ed., pp. 137165 [Boullata, JI and Armenti, VT, editors]. New York, NY: Humana Press.
7.Boullata, JI (2010) Influence of overweight and obesity on medication. In Handbook of Drug–Nutrient Interactions, 2nd ed., pp. 167205 [Boullata, JI and Armenti, VT, editors]. New York, NY: Humana Press.
8.Conney, AH & Burns, JJ (1962) Factors influencing drug metabolism. Adv Pharmacol 1, 3158.
9.Cheymol, G (2000) Effects of obesity on pharmacokinetics: implications for drug therapy. Clin Pharmacokinet 39, 215231.
10.Summers, LK, Samra, JS, Humphreys, SM et al. . (1996) Subcutaneous adipose tissue blood flow: variation within and between subjects and relationship to obesity. Clin Sci 91, 679683.
11.Wada, DR, Bjorkman, S, Ebling, WF et al. . (1997) Computer simulation of the effects of alterations in blood flows and body composition on thiopental pharmacokinetics in humans. Anesthesiology 87, 884899.
12.Torún, B (2006) Protein–energy malnutrition. In Modern Nutrition in Health and Disease, 10th ed., pp. 881908 [Shils, ME, Shike, M, Ross, AC et al. , editors]. Philadelphia, PA: Lippincott, Williams & Wilkins.
13.Rolfes, SR, Pinna, K & Whitney, E (2006) Understanding Normal and Clinical Nutrition, 7th ed. Wadsworth, OH: West Wadsworth.
14.Gross, R & Webb, P (2006) Wasting time for wasted children: severe child undernutrition must be resolved in non-emergency settings. Lancet 367, 12091211.
15.Mokdad, AH, Ford, ES, Bowman, BA et al. . (2003) Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA 289, 7679.
16.Mensah, GA, Mokdad, AH, Ford, E et al. . (2004) Obesity, metabolic syndrome, and type 2 diabetes: emerging epidemics and their cardiovascular implications. Cardiol Clin 22, 485504.
17.Allison, DB, Fontaine, KR, Manson, JE et al. . (1999) Annual deaths attributable to obesity in the United States. JAMA 282, 15301538.
18.Centers for Disease Control and Prevention. Overweight and Obesity. Updated May 2010. http://www.cdc.gov/nccdphp/dnpa/obesity/index.htm (accessed 31 May 2010).
19.Stratton, RJ & Elia, M (2006) Deprivation linked to malnutrition risk and mortality in hospital. Br J Nutr 96, 870876.
20.Santry, HP, Gillen, DL & Lauderdale, DS (2005) Trends in bariatric surgical procedures. JAMA 294, 19091917.
21.Goutelle, S, Bourguignon, L, Bertrand-Passeron, N et al. (2009) Visual estimation of patients' body weight in hospital: the more observers, the better? Pharm World Sci 31, 422425.
22.Keys, A, Fidanza, F, Karvonen, MJ et al. . (1972) Indices of relative weight and obesity. J Chronic Dis 25, 329343.
23.National Institutes of Health Consensus Development Panel on the Health Implications of Obesity (1985) Health implications of obesity: National Institutes of Health consensus development conference statement. Ann Intern Med 103, 10731077.
24.Knapp, TR (1983) A methodological critique of the ‘ideal weight’ concept. JAMA 250, 506510.
25.Robinson, JD, Lupkiewicz, SM, Palenik, L et al. . (1983) Determination of ideal body weight for drug dosage calculations. Am J Hosp Pharm 40, 10161019.
26.Hamwi, GJ (1964) Therapy: changing dietary concepts. In Diabetes Mellitus: Diagnosis and Treatment, pp. 7378. [Danowski, TS]. New York, NY: American Diabetes Association.
27.Devine, BJ (1974) Gentamicin therapy. Drug Intell Clin Pharm 8, 650655.
28.Shen, W, Wang, ZM, Punyanita, M et al. . (2003) Adipose tissue quantification by imaging methods: a proposed classification. Obes Res 11, 516.
29.Sarkar, SR, Kuhlmann, MK, Kotanko, P et al. . (2006) Metabolic consequences of body size and body composition in hemodialysis patients. Kidney Int 70, 18321839.
30.Green, B & Duffull, S (2002) Caution when lean body weight is used as a size descriptor for obese subjects. Clin Pharmacol Ther 72, 743744.
31.Barras, MA, Duffull, SB, Atherton, JJ et al. . (2008) Individualized compared with conventional dosing of enoxaparin. Clin Pharmacol Ther 83, 882888.
32.Thompson, PA, Rosner, GL, Matthay, KK et al. . (2009) Impact of body composition on pharmacokinetics of doxorubicin in children: a Glaser Pediatric Research Network study. Cancer Chemother Pharmacol 64, 243251.
33.James, WP (1976) Research on Obesity. London, UK: Her Majesty's Stationary Office.
34.Hume, R & Weyers, E (1971) Relationship between total body water and surface area in normal and obese subjects. J Clin Pathol 24, 234238.
35.Boddy, K, King, PC, Hume, R et al. . (1972) The relation of total body potassium to height, weight, and age in normal adults. J Clin Pathol 25, 512517.
36.Womersley, J, Boddy, K, King, PC et al. . (1972) A comparison of the fat-free mass of young adults estimated by anthropometry, body density and total body potassium content. Clin Sci 43, 469475.
37.Green, B & Duffull, SB (2004) What is the best size descriptor to use for pharmacokinetic studies in the obese? Br J Clin Pharmacol 58, 119133.
38.Janmahasatian, S, Duffull, SB, Ash, S et al. . (2005) Quantification of lean bodyweight. Clin Pharmacokinet 44, 10511065.
39.La Colla, L, Albertin, A, La Colla, G et al. . (2010) Predictive performance of the ‘Minto’ remifentanil pharmacokinetic parameter set in morbidly obese patients ensuing from a new method for calculating lean body mass. Clin Pharmacokinet 49, 131139.
40.Morgan, DJ & Bray, KM (1994) Lean body mass as a predictor of drug dosage: implications for drug therapy. Clin Pharmacokinet 26, 292307.
41.Nawaratne, S, Brien, JE, Seeman, E et al. . (1998) Relationships among liver and kidney volumes, lean body mass and drug clearance. Br J Clin Pharmacol 46, 447452.
42.Janmahasatian, S, Duffull, SB, Chagnac, A et al. . (2008) Lean body mass normalizes the effect of obesity on renal function [letter]. Br J Clin Pharmacol 65, 964965.
43.Detsky, AS, McLaughlin, JR & Baker, JP (1987) What is subjective global assessment of nutritional status? J Parenter Enteral Nutr 11, 813.
44.Hoffer, JT (1999) Metabolic consequences of starvation. In Modern Nutrition in Health and Disease, 9th ed., pp. 645666 [Shils, ME, Olson, JA, Shike, M et al. , editors]. Philadelphia, PA: Lippincott, Williams & Wilkins.
45.Owen, OE, Felig, P, Morgan, AP et al. . (1969) Liver and kidney metabolism during prolonged starvation. J Clin Invest 48, 574583.
46.Bravo, ME, Arancibia, A, Jarpa, S et al. . (1982) Pharmacokinetics of gentamicin in malnourished infants. Eur J Clin Pharmacol 21, 499504.
47.Samotra, K, Gupte, S & Raina, RK (1985) Pharmacokinetics of gentamicin in protein–energy malnutrition. Eur J Clin Pharmacol 29, 255256.
48.Ronchera-Oms, CL, Tormo, C, Ordovas, JP et al. . (1995) Expanded gentamicin volume of distribution in critically ill adult patients receiving total parenteral nutrition. J Clin Pharm Ther 20, 253258.
49.Lares-Asseff, I, Zaltzman, S, Guillé, MGP et al. . (1997) Pharmacokinetics of cyclosporine as a function of energy-protein deficiency in children with chronic renal failure. J Clin Pharmacol 37, 179185.
50.Buchanan, N, Davis, MD & Eyberg, C (1979) Gentamicin pharmacokinetics in kwashiorkor. Br J Clin Pharmacol 8, 451453.
51.Mehta, S, Nain, CK, Sharma, B et al. . (1982) Disposition of four drugs in malnourished children. Drug-Nutrient Interact 1, 205211.
52.Berlinger, WG, Park, GD & Spector, R (1985) The effect of dietary protein on the clearance of allopurinol and oxypurinol. N Engl J Med 313, 771776.
53.Ashton, R, Boime, P, Alemayehu, E et al. . (1993) Decreased chloramphenicol clearance in malnourished Ethiopian children. Clin Pharmacol 45, 181186.
54.Lee, JH, Suh, OK & Lee, MG (2004) Pharmacokinetic changes in drugs during protein-calorie malnutrition: correlation between drug metabolism and hepatic microsomal cytochrome P450 isozymes. Arch Pharm Res 27, 693712.
55.Cho, MK, Kim, YG, Lee, MG et al. . (1999) Suppression of rat hepatic cytochrome P450s by protein-calorie malnutrition: complete or partial restoration by cysteine or methionine supplementation. Arch Biochem Biophys 372, 150158.
56.Sartorio, A, Malavolti, M, Agosti, F et al. . (2005) Body water distribution in severe obesity and its assessment from eight-polar bioelectrical impedance analysis. Eur J Clin Nutr 59, 155160.
57.Forbes, GB & Welle, SL (1983) Lean body mass in obesity. Int J Obes 7, 99107.
58.Rosell, S & Belfrage, E (1979) Blood circulation in adipose tissue. Physiol Rev 59, 10781104.
59.Gallagher, D, Visser, M & Sepulveda, D (1993) How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups? Am J Epidemiol 22, 228239.
60.Fernández, JR, Heo, M, Heymsfield, SB et al. (2003) Is percentage body fat differentially related to body mass index in Hispanic Americans, African Americans, and European Americans? Am J Clin Nutr 77, 7175.
61.McLeay, SC, Morrish, GA, Kirkpatrick, CM et al. . (2009) Encouraging the move towards predictive population models for the obese using propofol as a motivating example. Pharm Res 26, 16261634.
62.Han, PY, Duffull, SB, Kirkpatrick, CMJ et al. . (2007) Dosing in obesity: a simple solution to a big problem. Clin Pharmacol Ther 82, 505508.
63.Greenblatt, DJ, Abernathy, DR, Locniskar, A et al. . (1984) Effect of age, gender, and obesity on midazolam kinetics. Anesthesiology 61, 2735.
64.Flechner, SM, Kilbeinsson, ME, Tam, J et al. . (1989) The impact of body weight on cyclosporine pharmacokinetics in renal transplant recipients. Transplantation 47, 806810.
65.Milsap, RL, Plaisance, KI & Jusko, WJ (1984) Prednisolone disposition in obese men. Clin Pharmacol Ther 36, 824831.
66.Abernathy, DR & Greenblatt, DJ (1986) Drug disposition in obese humans: an update. Clin Pharmacokinet 11, 199213.
67.Caraco, Y, Zylber-Katz, E, Berry, EM et al. . (1995) Caffeine pharmacokinetics in obesity and following significant weight reduction. Int J Obes 19, 234239.
68.Hunt, CM, Watkins, PB & Saenger, P et al. . (1992) Heterogeneity of CYP3A isoforms metabolizing erythromycin and cortisol. Clin Pharmacol Ther 51, 1823.
69.O'Shea, D, Davis, SN, Kim, RB et al. . (1994) Effect of fasting and obesity in humans on the 6-hydroxylation of chlorzoxazone: a putative probe of CYP2E1 activity. Clin Pharmacol Ther 56, 359367.
70.Kotlyar, M & Carson, SW (1999) Effects of obesity on the cytochrome P450 enzyme system. Int J Clin Pharmacol Ther 37, 819.
71.Reiss, AR, Hass, CE, Karki, SD et al. . (1994) Lithium pharmacokinetics in the obese. Clin Pharmacol Ther 56, 392398.
72.Falagas, ME, Athanasoulia, AP, Peppas, G et al. . (2009) Effect of body mass index on the outcome of infections: a systematic review. Obesity 10, 280289.
73.Miller, JL, Johnson, PN, Harrison, DL et al. . (2010) Evaluation of inpatient admissions and potential antimicrobial and analgesic dosing errors in overweight children. Ann Pharmacother 44, 3542.
74.Traynor, AM, Nafziger, AN & Bertino, JS (1995) Aminoglycoside dosing weight correction factors for patients of various body sizes. Antimicrob Agents Chemother 39, 545548.
75.Bolme, P, Eriksson, M, Paazlow, L et al. . (1995) Malnutrition and pharmacokinetics of penicillin in Ethiopian children. Pharmacol Toxicol 76, 259262.
76.Buchanan, N, Robinson, R, Koornhof, HJ et al. . (1979) Penicillin pharmacokinetics in kwashiorkor. Am J Clin Nutr 32, 22332236.
77.Pussard, E, Barennes, H, Daouda, H et al. . (1999) Quinine disposition in globally malnourished children with cerebral malaria. Clin Pharmacol Ther 65, 500510.
78.Eriksson, M, Bolme, P, Habte, D et al. . (1988) INH and streptomycin in Ethiopian children with tuberculosis and different nutritional status. Acta Paediatr Scand 77, 890894.
79.Renton, KW (2001) Alteration of drug biotransformation and elimination during infection and inflammation. Pharmacol Ther 92, 147163.
80.Boucher, BA, Wood, GC & Swanson, JM (2006) Pharmacokinetic changes in critical illness. Crit Care Clin 22, 255271.
81.Leader, WG, Tsubaki, T & Chandler, MHH (1994) Creatinine-clearance estimates for predicting gentamicin pharmacokinetic values in obese patients. Am J Hosp Pharm 51, 2155–2130.
82.Gillum, JG, Johnson, M, Lavoie, S et al. . (1995) Flucytosine dosing in an obese patient with extrameningeal cryptococcal infection. Pharmacotherapy 15, 251253.
83.Pittrow, L & Penk, A (1999) Special pharmacokinetics of fluconazole in septic, obese and burn patients. Mycoses 42, Suppl. 2, 8790.
84.Cohen, LG, DiBiasio, A, Lisco, SJ et al. . (1997) Fluconazole serum concentrations and pharmacokinetics in an obese patient. Pharmacotherapy 17, 10231026.
85.Kampmann, JP, Klein, H, Lumholtz, B et al. . (1984) Ampicillin and propylthiouracil pharmacokinetics in intestinal bypass patients followed up to one year after operation. Clin Pharmacokinet 9, 168176.
86.Yuk, J, Nightengale, CH, Sweeney, K et al. (1988). Pharmacokinetics of nafcillin in obesity. J Infect Dis 157, 10881089.
87.Newman, D, Scheetz, MH, Adeyemi, OA et al. . (2007) Serum piperacillin/tazobactam pharmacokinetics in a morbidly obese individual. Ann Pharmacother 41, 17341739.
88.Blouin, RA, Bauer, LA, Miller, DD et al. . (1982) Vancomycin pharmacokinetics in normal and morbidly obese subjects. Antimicrob Agents Chemother 21, 575580.
89.Vance-Bryan, K, Guay, DR, Gilliland, SS et al. . (1993) Effect of obesity on vancomycin pharmacokinetic parameters as determined by using a Bayesian forecasting technique. Antimicrob Agents Chemother 37, 436440.
90.Penzak, SR, Gubbins, PO, Rodvold, KA et al. . (1998) Therapeutic drug monitoring of vancomycin in a morbidly obese patient. Ther Drug Monit 20, 261265.
91.Mersfelder, TL & Smith, CL (2005) Linezolid pharmacokinetics in an obese patient [letter]. Am J Health-Syst Pharm 62, 464, 467.
92.Hernandez, JO, Norstrom, J & Wysock, G (2009) Acyclovir-induced renal failure in an obese patient. Am J Health-Syst Pharm 66, 12881291.
93.Falagas, ME & Karageorgopoulos, DE (2010) Adjustment of dosing of antimicrobial agents for bodyweight in adults. Lancet 375, 248251.