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Register Now for the June 2024 ACCP Virtual Journal Club Webinar: Estimation of Absolute and Relative Body Fat Content Using Noninvasive Surrogates: Can DXA Be Bypassed? https://lnkd.in/ecg9uEZB #ACCPVirtualJournalClub #Pharmacokinetics Why is this article important to your practice? This study explores innovative methods to predict body fat content using easily measurable anthropometric variables like age, height, weight and waist circumference instead of relying on the expensive and less accessible dual-energy x-ray absorptiometry (DXA) scanning. This research is crucial as it provides an improved and validated algorithm for predicting absolute body fat, which can enhance clinical practices related to obesity management. Understanding these new predictive methods can directly impact practice, especially considering the critical role that body composition plays in pharmacokinetics and pharmacodynamics. Efficient and accurate body fat estimation methods are essential for optimizing drug dosing and therapeutic strategies in obese patients, thereby improving treatment outcomes and patient care. Learners that complete this activity will be provided an evidence-based, validated and predictive algorithm as an alternative to DXA scanning to accurately estimate absolute body fat. Target Audience: Interprofessional team of Physicians, Pharmacists, PhDs, Nurse Practitioners and other health care professionals who use the assessment of body fat content in clinical trials and clinical practice. Learning Objectives After completing this activity, the learner will be able to: Describe at least one limitation to the use of DXA scanning for obesity clinical trials and/or drug development; Identify at least one DXA-determined measurement that was selected as a dependent (outcome) variable in this study; List at least one surrogate variable that was predictive of DXA-determined absolute body fat; Identify which surrogate variable had the greatest impact (i.e., magnitude of change) on DXA-determined total body fat in men and women utilizing standardized regression coefficient data.

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