Evidence examining specific effects of a multidisciplinary team (MDT) on cardiometabolic risk factors (CMRFs) among multi-ethnic patients in real-world clinical settings is lacking. This one-year retrospective chart review (2018) analyzed 598 adults (African American 59%, Hispanic 35%, and Caucasian 6%) with mean age of 43.8 ± 14.0 years. Qualifying patients with primary inclusion criteria of having body mass indices and blood pressure (BP) measurements in the first and last quarter of the study period were treated under an MDT protocol and compared to those qualifying for MDT but treated solely by a primary care provider (PCP). MDT included endocrinologist-directed visits, lifestyle counseling, and shared medical appointments. MDT patients experienced a greater reduction (β; 95% CI) in weight (−4.29 kg; −7.62, −0.97), BMI (−1.43 kg/m²; −2.68, −0.18), systolic BP (−2.18 mmHg; −4.09, −0.26), and diastolic BP (−1.97 mmHg; −3.34, −0.60). Additionally, MDT patients had 77%, 83%, and 59% higher odds of reducing ≥5% of initial weight, 1 BMI point, and ≥2 mmHg DBP, respectively. Improvements in hemoglobin A1C measurements were observed in the MDT group (insufficient data to compare with the PCP group). Compared to PCP only, MDT co-management improves CMRF related to adiposity and hypertension in a multiethnic adult cohort in real-world clinical settings. Patient access to best practices in cardiometabolic care is a priority, including the incorporation of culturally adapted evidence-based recommendations translated within a multi-disciplinary infrastructure, where competing co-morbidities are better managed, and associated research and education programs can promote operational sustainability.

As adiposity increases in youth, so does the prevalence of cardiometabolic risk factors (CMRFs). The etiology of adiposity-based chronic disease and CMRFs includes ethnoracial disparities that are rarely considered in current treatment approaches. Precision interventions require further characterization of these disparities among high-risk youth. The objective of this study was to characterize differences in CMRF among African American (AA) and Hispanic (H) adolescents with varying levels of adiposity. A cross-sectional analysis of 2284 adolescents aged 12–17 was conducted using 3-year clinical data from Lifedoc Health. CMRF prevalence were compared using χ², with logistic regression models (LRM) applied to explore the relationships between exposures (age, sex, ethnoracial group, adiposity) and CMRF outcomes. Prevalence of CMRF rose with increasing adiposity, which was the strongest determinant of risk overall. However, individual risk profiles differed between the two groups, with H having higher prevalence of metabolic syndrome (MetS), higher triglycerides and liver enzymes, and low high-density lipoprotein cholesterol (HDL-c). Meanwhile, AA had higher prevalence of elevated blood pressure (BP) in the overweight category, prediabetes in overweight to severe obesity, and type 2 diabetes in obesity. LRM showed 3.0-fold greater chance of impaired glucose metabolism in AA than H, who were 1.7, 5.9, and 8.3 times more likely to have low HDL-c, high liver enzymes, and high triglycerides, respectively. Overweight/obesity prevalence was very high among AA and H adolescents. Excess adiposity was associated with an increased prevalence of CMRF, with individual risk factors differing between groups as adiposity increased. Research within routine clinical settings is required to better characterize these discrepancies and ameliorate their adverse impact on health in the transition to adulthood.

Obesity is defined as excess adipose tissue; however, commonly used methods may under-detect adiposity in adolescents. This study compared the performance of body mass index percentile (BMI%) and relative body mass index (RBMI) in identifying excess body fat percentage (BF%) and estimated RBMI cut points to better stratify severity of adiposity. In 567 adolescents ages 11–19 year, BF% measured by DXA was used to compare BMI% and RBMI performance at different degrees of adiposity. RBMI cut points for adiposity detection were derived via ROC curve analysis. BF% was strongly correlated with BMI% (r = 0.889, p < 0.001) and RBMI (r = 0.901, p < 0.001). However, RBMI exhibited less dispersion and better discriminated the relationship with BF% independent of age, race, and gender. Both BMI% and RBMI performed similarly for detecting high BF% (≥25 BF% in males; ≥30 BF% in females). Nonetheless, the relationship of BMI% with BF% was diminished among leaner adolescents. RBMI detected overweight in 21.3% more females and 14.2% more males. RBMI improved the detection of excess adiposity in individuals otherwise classified as having normal weight or overweight by BMI%. RBMI is a valuable and accessible tool for earlier detection, intervention, and effective follow-up of excess adiposity in youth at higher risk for complications.

Objectives: To derive sex-specific Relative Body Mass Index (rBMI) cut-offs for classifying adolescents into four adiposity categories defined by dual-energy X-ray absorptiometry (DXA)-based body fat percentage (BF%), quantify their stability, and evaluate their classification performance against current BMI%-based classification and previously published ROC-derived rBMI cut-offs. Materials and Methods: Data from 567 observations in adolescents aged 11–19 years were analyzed. Adiposity categories (normal, mildly elevated, moderately elevated, severely elevated) were defined by sex-specific BF% thresholds. Classification and Regression Tree models with inverse class-frequency weighting were fitted in two formulations: sex-stratified, and a pooled-threshold model with shared lower boundaries and sex-specific upper boundaries. Threshold stability was assessed by bootstrap resampling. Performance was estimated by repeated nested cross-validation, with the full derivation pipeline repeated within each training fold; the previously published ROC-derived thresholds were re-derived within each fold so that both approaches carried the same correction for optimism. Results: Sex-stratified thresholds were 103, 118, and 162 in males and 100, 118, and 176 in females; three of six agreed with the previously published ROC-derived values to within 0.5 units and a fourth to within 3.2 units. Five of the six published values fell within the corresponding bootstrap intervals, the exception being the female moderately/severely elevated boundary (160.0; 95% CI: 161.4–218.7). The male boundary separating normal from mildly elevated adiposity was identified in 76.0% of bootstrap resamples; estimating the lower boundaries from the pooled sample raised this to 99.9% and improved ordinal agreement among males (quadratic-weighted kappa 0.812 vs. 0.759). Out-of-fold accuracy was 0.697, 0.694, and 0.694 for the sex-stratified, pooled-threshold, and ROC-derived approaches, respectively, with all paired differences including zero in the overall sample. All three exceeded BMI% classification (accuracy 0.608), which identified only 15.5% of adolescents with mildly elevated adiposity and produced more than twice the proportion of errors spanning two or more categories (6.0% vs. 1.8–2.1%). Conclusions: rBMI cut-offs aligned more closely with DXA-defined adiposity than BMI% classification, particularly in the intermediate categories. Tree-derived and ROC-derived thresholds converged and performed equivalently, indicating that threshold location does not depend on the derivation procedure. A pooled-threshold formulation provided a simpler and more stable rule. These are derivation-stage estimates requiring external validation before clinical adoption.