Levofloxacin Dosing Regimen in Severely Morbidly Obese Patients (BMI ≥40 kg/m2) Should Be Guided by Creatinine Clearance Estimates Based on Ideal Body Weight and Optimized by Therapeutic Drug Monitoring

A formula has been developed to predict creatinine clearance (C cr ) from serum creatinine (S cr ) in adult males: Ccr = (140 – age) (wt kg)/72 × S cr (mg/100ml) (15% less in females). Derivation included the relationship found between age and 24-hour creatinine excretion/kg in 249 patients aged 18–92. Values for C cr were predicted by this formula and four other methods and the results compared with the means of two 24-hour C cr’s measured in 236 patients. The above formula gave a correlation coefficient between predicted and mean measured Ccr·s of 0.83; on average, the difference between predicted and mean measured values was no greater than that between paired clearances. Factors for age and body weight must be included for reasonable prediction.

Background Not all obese subjects have an adverse metabolic profile predisposing them to developing type 2 diabetes or cardiovascular disease. The BioSHaRE-EU Healthy Obese Project aims to gain insights into the consequences of (healthy) obesity using data on risk factors and phenotypes across several large-scale cohort studies. Aim of this study was to describe the prevalence of obesity, metabolic syndrome (MetS) and metabolically healthy obesity (MHO) in ten participating studies. Methods Ten different cohorts in seven countries were combined, using data transformed into a harmonized format. All participants were of European origin, with age 18–80 years. They had participated in a clinical examination for anthropometric and blood pressure measurements. Blood samples had been drawn for analysis of lipids and glucose. Presence of MetS was assessed in those with obesity (BMI ≥ 30 kg/m2) based on the 2001 NCEP ATP III criteria, as well as an adapted set of less strict criteria. MHO was defined as obesity, having none of the MetS components, and no previous diagnosis of cardiovascular disease. Results Data for 163,517 individuals were available; 17% were obese (11,465 men and 16,612 women). The prevalence of obesity varied from 11.6% in the Italian CHRIS cohort to 26.3% in the German KORA cohort. The age-standardized percentage of obese subjects with MetS ranged in women from 24% in CHRIS to 65% in the Finnish Health2000 cohort, and in men from 43% in CHRIS to 78% in the Finnish DILGOM cohort, with elevated blood pressure the most frequently occurring factor contributing to the prevalence of the metabolic syndrome. The age-standardized prevalence of MHO varied in women from 7% in Health2000 to 28% in NCDS, and in men from 2% in DILGOM to 19% in CHRIS. MHO was more prevalent in women than in men, and decreased with age in both sexes. Conclusions Through a rigorous harmonization process, the BioSHaRE-EU consortium was able to compare key characteristics defining the metabolically healthy obese phenotype across ten cohort studies. There is considerable variability in the prevalence of healthy obesity across the different European populations studied, even when unified criteria were used to classify this phenotype.

The prevalence of obesity has dramatically increased in recent years and now includes a significant proportion of the world's children, adolescents and adults. Obesity is linked to a number of co-morbidities, the most prominent being type 2 diabetes mellitus. While many agents are available to treat these conditions, the current knowledge regarding their disposition in the obese remains limited. Over the years, both direct and indirect methodologies have been utilized to assess body composition. Commonly used direct measures include underwater weighing, skinfold measurement, bioelectrical impedance analysis and dual-energy x-ray absorptiometry. Unfortunately, these methods are not readily available to the majority of clinicians. As a result, a number of indirect measures to assess body composition have been developed. Indirect measures rely on patient attributes such as height, bodyweight and sex. These size metrics are often utilized clinically and include body mass index (BMI), body surface area (BSA), ideal bodyweight (IBW), percent IBW, adjusted bodyweight, lean bodyweight (LBW) and predicted normal weight (PNWT). An understanding of how the volume of distribution (V(d)) of a drug changes in the obese is critical, as this parameter determines loading-dose selection. The V(d) of a drug is dependent upon its physiochemical properties, the degree of plasma protein binding and tissue blood flow. Obesity does not appear to have an impact on drug binding to albumin; however, data regarding alpha(1)-acid glycoprotein binding have been contradictory. A reduction in tissue blood flow and alterations in cardiac structure and function have been noted in obese individuals. At the present time, a universal size descriptor to describe the V(d) of all drugs in obese and lean individuals does not exist. Drug clearance (CL) is the primary determinant to consider when designing a maintenance dose regimen. CL is largely controlled by hepatic and renal physiology. In the obese, increases in cytochrome P450 2E1 activity and phase II conjugation activity have been observed. The effects of obesity on renal tubular secretion, tubular reabsorption, and glomerular filtration have not been fully elucidated. As with the V(d), a single, well validated size metric to characterize drug CL in the obese does not currently exist. Therefore, clinicians should apply a weight-normalized maintenance dose, using a size descriptor that corrects for differences in absolute CL between obese and non-obese individuals. The elimination half-life (t((1/2))) of a drug depends on both the V(d) and CL. Since the V(d) and CL are biologically independent entities, changes in the t((1/2)) of a drug in obese individuals can reflect changes in the V(d), the CL, or both. This review also examines recent publications that investigated the disposition of several classes of drugs in the obese--antibacterials, anticoagulants, antidiabetics, anticancer agents and neuromuscular blockers. In conclusion, pharmacokinetic data in obese patients do not exist for the majority of drugs. In situations where such information is available, clinicians should design treatment regimens that account for any significant differences in the CL and V(d) in the obese.