Defining Overweight and Obesity by Percent Body Fat instead of Body Mass Index.

OBJECTIVE: Thresholds for overweight and obesity are currently defined by body mass index (BMI), a poor surrogate marker of actual adiposity (percent body fat, %BF). Practical modern technologies provide estimates of %BF but medical providers need outcome-based %BF thresholds to guide patients. This analysis determines %BF thresholds based on key obesity-related comorbidities, exhibited as metabolic syndrome (MetSyn). These limits were compared to existing BMI thresholds of overweight and obesity. DESIGN: Correlational analysis of data from cross sectional sampling of 16,918 adults (8,734 men and 8,184 women) from the US population, accessed by the National Health and Nutrition Examination Survey (NHANES) public use datasets. RESULTS: Individuals measured by BMI as overweight (BMI>25 kg/m2) and with obesity (BMI>30 kg/m2) included 5% and 35% of individuals with MetSyn, respectively. For men, there were no cases of MetSyn below 18%BF, %BF equivalence to "overweight" (i.e., 5% of MetSyn individuals) occurred at 25%BF, and "obesity" (i.e., 35% of MetSyn individuals) corresponded to 30%BF. For women, there were no cases of MetSyn below 30%BF, "overweight" occurred at 36%BF, and "obesity" corresponded to 42%BF. Comparison of BMI to %BF illustrates the wide range of variability in BMI prediction of %BF, highlighting the potential importance of using more direct measures of adiposity to manage obesity-related disease. CONCLUSIONS: Practical methods of body composition estimation can now replace the indirect BMI assessment for obesity management, using threshold values provided from this study. Clinically relevant "overweight" can be defined as 25 and 36% BF for men and women, respectively, and "obesity" is defined as 30 and 42% BF for men and women.

Evaluation of Pharmacogenomics Testing of Cytochrome P450 Enzymes in the Military Health System From 2015 to 2020.

Pharmacogenomics (PGx) plays a fundamental role in personalized medicine, providing an evidence-based treatment approach centered on the relationship between genomic variations and their effect on drug metabolism. Cytochrome P450 (CYP450) enzymes are responsible for the metabolism of most clinically prescribed drugs and a major source of variability in drug pharmacokinetics and pharmacodynamics. To assess the prevalence of PGx testing within the Military Health System (MHS), testing of specific CYP450 enzymes was evaluated. Data were retrospectively obtained from the Military Health System Management Analysis and Reporting Tool (M2) database. Patient demographics were identified for each test, along with TRICARE status, military treatment facility, clinic, and National Provider Identifier. A total of 929 patients received 1,833 PGx tests, predominantly composed of active duty/guard service members (N = 460; 49.5%), with highest testing rates in the army (51.5%). An even distribution in testing was observed among gender, with the highest rates in Caucasians (41.7%). Of the CYP enzymes assessed, CYP2C19 and CYP2D6 accounted for 87.8% of all PGx CYP testing. The majority of patients were tested in psychiatry clinics (N = 496; 53.4%) and primary care clinics (N = 233; 25.1%), accounting for 56.4% and 24.8% of all tests, respectively. Testing was found to be provider driven, suggesting a lack of a standardized approach to PGx and its application in patient care within the MHS. We initially recommend targeted education and revising testing labels to be more uniform and informative. Long-term recommendations include establishing pharmacy-driven protocols and point-of-care PGx testing to optimize patient outcomes.

Prescription Patterns and Relationship to Pharmacogenomics Testing in the Military Health System.

INTRODUCTION: Clinical utilization of pharmacogenomics (PGx) testing is highly institutionally dependent, and little information is known about provider practices of PGx testing in the Military Health System (MHS). In this study, we aimed to characterize Clinical Pharmacogenetics Implementation Consortium (CPIC) actionable prescription (Rx) patterns and their temporal relationship with PGx testing in the MHS. METHODS: Using data from the Military Health System Management Analysis and Reporting Tool (M2) database, this retrospective cohort study included all patients receiving at least one PGx test and at least one CPIC actionable Rx from January 2015 to August 2020 (845 patients, 1,471 PGx, 7,725 index CPIC actionable Rxs). Rx patterns and temporal relationships with PGx testing were characterized via descriptive statistics. Binomial regression was used to determine which patient and provider characteristics were associated with a patient receiving a PGx test within 30 days of an index Rx. RESULTS: Patients had a median of 9 index CPIC actionable Rx's (range 1-26). Pain medications were most commonly prescribed (N = 794, 94% patients with at least 1 Rx). However, pain medication had the lowest Rx-PGx match rate (40%) compared to an average of 62% Rx-PGx match rate for all CPIC drugs. Antidepressants were also commonly prescribed (N = 668, 79.1% patients with at least 1 Rx), and antidepressants had the highest Rx-PGx match rate of 86.7%. A minority of providers (20%, N = 249) ordered the majority of PGx tests (86.1%, N = 1,266) and only 8.3% of PGx tests (N = 398) matched to a CPIC actionable drug within 30 days of the test (defined by Rxs ordered within 30 days before or after the PGx test). However, approximately 39.8% of patients (N = 317) had at least one drug match to a PGx test within 30 days. The largest predictor of whether a patient received a PGx test within 30 days of any index Rx was whether or not a specific psychiatry provider ordered the PGx test (odds ratio; OR 3.7, 95% CI 2.13-6.54, P < 0.001). Neither the CPIC level of evidence nor FDA PGx actionable or informative labels had a significant effect on PGx test timing. CONCLUSIONS: PGx testing was generally limited to high Rx-drug users and was found to be an under-utilized resource. PGx testing did not typically follow CPIC guidelines. Implementing PGx testing protocols, simplifying PGx test-ordering by incorporating at minimum CYP2D6, CYP2C19, and CYP2C9 into PGx-testing panels, and unifying providers' PGx knowledgebase in the MHS are feasible and would improve the clinical utilization of PGx tests in the MHS.

Semimechanistic Modeling of the Effects of Blast Overpressure Exposure on Cefazolin Pharmacokinetics in Mice.

Cefazolin is a first-line antibiotic to treat infection related to deployment-associated blast injuries. Prior literature demonstrated a 331% increase cefazolin liver area under the curve (AUC) in mice exposed to a survivable blast compared with controls. We repeated the experiment, validated the findings, and established a semimechanistic two-compartment pharmacokinetic (PK) model with effect compartments representing the liver and skin. We found that blast statistically significantly increased the pseudo-partition coefficient to the liver by 326% (95% confidence interval: 76-737%), which corresponds to the observed 331% increase in cefazolin liver AUC described previously. To a lesser extent, plasma AUC in blasted mice increased 14-45% compared with controls. Nevertheless, the effects of blast on cefazolin PK were transient, normalizing by 10 hours after the dose. It is unclear as to how this blast effect t emporally translates to humans; however, given the short-lived effect on PK, there is insufficient evidence to recommend cefazolin dosing changes based on blast overpressure injury alone. Clinicians should be aware that cefazolin may cause drug-induced liver injury with a single dose and the risk may be higher in patients with blast overpressure injury based on our findings. SIGNIFICANCE STATEMENT: Blast exposure significantly, but transiently, alters cefazolin pharmacokinetics in mice. The questions of whether other medications or potential long-term consequences in humans need further exploration.