Ricardo Rosero-Revelo

Obesity remains a global health crisis characterized by substantial heterogeneity in diagnosis, management, and policy implementation. Although numerous national and international clinical practice guidelines have been published over the past decade, their translation into evidence‐based and equitable care remains inconsistent. This narrative review critically evaluates 47 obesity guidelines published between 2014 and 2026 across North America, Europe, Latin America, the Asia‐Pacific region, and the World Health Organization to identify conceptual, structural, and implementation gaps that continue to fragment obesity care. Despite broad recognition of obesity as a chronic, relapsing disease, most guidelines continue to rely primarily on body mass index (BMI), with limited incorporation of accessible anthropometric measures, body composition assessment, and metabolic phenotyping. Lifestyle recommendations for nutrition, physical activity, and behavioral interventions remain largely descriptive, while access to pharmacotherapy and metabolic surgery is strongly influenced by regulatory and socioeconomic factors. Emerging advances, including GLP‐1 receptor agonists, dual incretin therapies, body composition–guided assessment, and anti‐stigma approaches, have been only partially incorporated into routine recommendations. Rather than redefining obesity, this review proposes a pragmatic, implementation‐oriented framework that integrates scalable strategies including anthropometric risk assessment, healthy dietary patterns, physical activity, behavioral support, non‐stigmatizing care, and equitable access to effective therapies with selective use of precision medicine and body composition assessment. Greater harmonization of clinical, behavioral, compositional, and policy domains is needed to achieve feasible, equitable, and sustainable improvements in metabolic and functional health outcomes.

Objective: To characterize Bioelectrical Vector Analysis (BIVA) vector patterns in adults with excess adiposity defined by fat mass index (FMI) and to examine the implications of obesity-related vector displacement for the clinical interpretation of BIVA. Methods: We conducted a retrospective cross-sectional analysis of adults attending a tertiary obesity clinic. Excess adiposity was defined using sex-specific FMI thresholds based on NHANES criteria. Resistance and reactance were normalized for height (R/H, Xc/H), and BIVA patterns were described overall and stratified by sex and ethnicity. Sex- and ethnicity-specific tolerance ellipses (50th, 75th, 95th percentiles) were derived from bivariate normal models and visually compared with Piccoli/NHANES reference ellipses for elevated adiposity. Correlation structure and ellipse geometry were evaluated within each sex–ethnicity stratum. Results: In 3072 adults with FMI-defined excess adiposity (723 men, 2349 women), impedance vectors showed a predominantly downward displacement characterized by lower Xc/H and lower phase angle, with variable R/H behavior across sex and ethnicity strata. This directional pattern was observed across the spectrum of FMI severity and remained stable across sex and major ethnic groups. Sex and ethnicity modulated mean vector position and ellipse dispersion but did not alter the overarching adiposity-related displacement. The correlation between R/H and Xc/H (r ≈ 0.63–0.75) was preserved in all strata, indicating intact underlying biophysical relationships. Conclusions: In adults with obesity, displacement of BIVA relative to classical reference ellipses primarily reflects the electrophysiological signature of excess adiposity rather than overt fluid overload or intrinsic cellular failure. These findings support the development of obesity-adjusted BIVA reference frameworks, further refined by sex and ethnicity, to improve the specificity and physiological interpretability of raw impedance measurements in obesity care.

Background: Large population‐based studies have demonstrated age‐ and sex‐related differences in phase angle (PhA). However, multifrequency bioelectrical impedance analysis (BIA) systems remain highly device‐specific due to proprietary algorithms and prediction equations. This limits comparability across platforms, making descriptive distributions from single‐device cohorts essential for routine clinical interpretation. Objective: To describe sex‐ and age‐specific distributions of PhA and skeletal muscle mass using a single multifrequency BIA platform in an outpatient cohort, and to explore relationships with other device‐derived body composition estimates. Methods: A retrospective cross‐sectional analysis was conducted on adults in an outpatient program using the SECA mBCA 514 platform. PhA and skeletal muscle mass data were available for 1849 and 1870 participants, respectively. Sex‐ and age‐stratified percentiles were generated. Fat‐to‐muscle ratio (FMR) was calculated, and its associations with PhA and device‐estimated visceral adiposity were evaluated using Spearman correlation. Results: Men demonstrated higher PhA and skeletal muscle mass values than women; both measures declined across age strata in both sexes. Women exhibited higher FMR values. Higher FMR was moderately associated with lower PhA (rho = −0.467, p < 0.001), while its association with device‐estimated visceral adiposity was weak (rho = 0.104, p < 0.001). Since all measurements originated from the same platform, these associations are descriptive rather than mechanistic. Conclusions: Device‐specific, stratified distributions derived from routine multifrequency BIA assessments assist the clinical contextual interpretation of outpatient body composition measurements. The observed relationships between FMR and PhA are hypothesis‐generating and require further validation using independent gold‐standard reference methods such as MRI, DXA, or CT.

Background: Body mass index (BMI) remains the primary tool for obesity classification despite its limitations in assessing body composition. This study aimed to evaluate the relationship between different body composition parameters and compare World Health Organization (WHO) and National Health and Nutrition Examination Survey (NHANES) classification systems. Methods: In this cross-sectional study of 3,255 patients (74.3% women) from a Colombian obesity and metabolism center, we analyzed body composition using bioelectrical impedance analysis. We assessed the concordance between the World Health Organization and the National Health and Nutrition Examination Survey classifications, and examined correlations between body composition parameters, with particular focus on phase angle (PhA) as a predictor of muscle mass. Results: While body mass index showed a strong correlation with body fat mass (? = 0.929, p < 0.001), it poorly predicted muscle mass. The World Health Organization and the National Health and Nutrition Examination Survey classifications showed fair overall agreement (? = 0.39), with better concordance in women (? = 0.43) than men (? = 0.28). Multiple regression analyses revealed PhA as a strong predictor of muscle mass (? = 1.032, p < 0.0001, R² = 0.332) but not fat mass (p = 0.525, R² = 0.055). Conclusions: While body mass index adequately predicts adiposity, it falls short in assessing muscle mass. Phase angle emerges as a promising predictor of muscle mass, independent of age and sex, suggesting its potential utility in clinical assessment of body composition.

Background: BMI has limitations in assessing metabolic health, especially in diverse populations. Fat mass index (FMI) and skeletal muscle mass (SMM) are more accurate predictors of obesity‐related risks. Redefining obesity as a fat accumulation disease highlights the need for body composition‐focused assessments in clinical practice. Objectives: This study assessed body composition changes in a Latino population over 6 months, evaluating the utility of fat mass index (FMI) and skeletal muscle mass (SMM) in detecting significant variations and exploring associations with baseline characteristics. Methods: In this retrospective observational study, 1571 Latino patients were categorized into hypocaloric‐high protein (HHP) and eucaloric‐high protein (EHP) groups based on baseline BMI. Body composition was measured using bioelectrical impedance analysis at baseline and after 6 months. Chi‐square tests evaluated categorical shifts in classifications, while multinomial logistic and linear regression models identified factors associated with fat and muscle mass changes, adjusting for sex, age, and baseline composition metrics. Results: Over 6 months, participants experienced significant reductions in body fat mass (31.24–27.25 kg, p < 0.001) and increases in skeletal muscle mass (24.31–25.85 kg, p < 0.001). In the HHP group, baseline FMI and age were significantly associated with fat mass changes, with younger individuals and those with lower baseline FMI being more prone to fat accumulation ( p < 0.001). In both groups, higher baseline SMM was associated with greater likelihood of muscle loss and lower probability of muscle gain, suggesting a physiological ceiling effect ( p < 0.001). Conclusions: FMI and SMM enhance clinical assessments by providing more individualized metrics for obesity management in Latino populations. These findings support the integration of body composition analysis into clinical practice for more precise evaluation of metabolic health beyond traditional BMI classifications.

Traditional reliance on Body Mass Index (BMI) as a diagnostic tool for obesity is increasingly challenged due to its inability to differentiate between fat and lean mass and to capture fat distribution. Emerging evidence—including findings from our longitudinal study in Latino patients with obesity and insights from the 2025 Lancet Commission on Obesity—suggests that a comprehensive evaluation of body composition is essential for accurate risk stratification. This review synthesizes historical perspectives and recent developments in obesity phenotyping, detailing how the field has evolved from simple BMI-based assessments to multifaceted approaches incorporating bioelectrical impedance analysis (BIA) and supplementary anthropometric measures such as waist circumference and waist-to-hip ratio. We also examine the metabolic, genetic, and hormonal mechanisms underlying phenotypic variability, which help explain why individuals with similar BMIs may exhibit markedly different health risks. By integrating our original data with an extensive review of current literature, we demonstrate that refined obesity phenotyping can serve as an early indicator of progression from preclinical to clinical obesity. Such nuanced classifications offer the potential for more personalized therapeutic interventions aimed at optimizing weight loss outcomes and reducing cardiometabolic risk. Overall, our findings advocate for a multidimensional approach to obesity assessment that promises to improve clinical outcomes through tailored, phenotype-based strategies.

Contexto: las enfermedades cardiovasculares (ECV) y la diabetes mellitus representan problemas importantes de salud pública en Colombia, que contribuyen significativamente a la morbilidad y mortalidad, por lo que la identificación temprana de individuos con riesgo cardiometabólico es crucial para desarrollar intervenciones preventivas. Objetivo: analizar, en una gran muestra de la población colombiana, la relación existente entre dos escalas, la primera estima el riesgo de desarrollar diabetes tipo 2: Findrisk (Finnish Diabetes Risk Score) y la segunda valora el riesgo cardiovascular y es de la Organización Panamericana de Salud (OPS). Metodología: se realizó un estudio observacional y descriptivo desde julio de 2019 hasta junio de 2022, con la participación de 126?113 individuos mayores de 18 años de Medellín, Colombia. En esta población se utilizaron las escalas Findrisk (para estimar el riesgo de padecer diabetes tipo 2) y de la OPS (para evaluar el riesgo cardiovascular en las categorías de bajo, moderado y alto); se tomaron medidas antropométricas, como el índice de masa corporal (IMC) y la circunferencia de cintura (CC); se aplicaron métodos estadísticos descriptivos y la prueba de Chi-cuadrado para examinar la asociación entre las categorías de riesgo y, además, se empleó un modelo de regresión logística ordinal, con el fin de conocer la asociación entre la escala de Findrisk y los niveles de riesgo cardiovascular de la OPS. Resultados: la mediana de edad fue de 43 años (RIC: 29-58), la del IMC fue de 26,08 kg/m2 y la mediana de la CC fue de 87 cm. La mayoría de los sujetos (62,56?%) presentaron un riesgo bajo de desarrollar diabetes (según Findrisk), mientras que el 8,86?% fue clasificado de riesgo cardiovascular alto (según OPS). El análisis de chi-cuadrado reveló asociaciones significativas entre las categorías de riesgo de las escalas de Findrisk y la OPS (p <0,001), con una correlación muy significativa en ambos extremos del riesgo. El modelo de regresión ordinal mostró una convergencia adecuada y una capacidad predictiva moderada (pseudo R-cuadrado?=?0,1195). La escala Findrisk se asoció positivamente con la estimación del riesgo según la OPS, incrementando en un 22,9?% las probabilidades de pertenecer a la categoría de “riesgo moderado” y en un 20,5?% para el “riesgo alto”, por cada unidad adicional (OR: 1,229 y 1,205, respectivamente). Las intersecciones fueron significativas y capturaron bien las diferencias entre los grupos de riesgo. Conclusiones: la combinación de las escalas de riesgo de Findrisk y de la OPS permite una evaluación más completa del riesgo cardiometabólico, mejorando la identificación de individuos con riesgo elevado, tanto de diabetes tipo 2 como de eventos cardiovasculares.

Introducción: El Índice de Masa Corporal (IMC) es insuficiente para diagnosticar obesidad. Este documento presenta al Índice de Masa Grasa (IMG) como indicador más preciso para el diagnóstico de esta enfermedad dado su nivel alto de correlación respecto a las variables propias del tejido graso. Objetivo: Identificar las variables cineantropométricas más relevantes para determinar el IMG como herramienta más adecuada para el diagnóstico de obesidad en la población que asistió a consulta ambulatoria. Metodología: El estudio retrospectivo, fue desarrollado con una población de 899 entre los 6 y los 81 años de edad. Los datos fueron obtenidos por bioimpedanciometría (Inbody® 770) durante un periodo de dos años (2017-2019). La información fue depurada mediante la metodología de minería de datos CRISP-DM y analizada estadísticamente mediante el programa estadístico SPSS. Finalmente se relacionan las variables antropométricas asociadas al tejido graso con el IMG y el IMC mediante el coeficiente de determinación (R2). Resultados: Se determinó que las variables con mayor relación con la masa grasa son: circunferencia medida cadera, circunferencia medida cuello, circunferencia medida abdomen y peso. Sin embargo, la importancia de esta relación depende del género. Conclusión: La relevancia de este estudio es el cálculo de masa grasa a partir de medidas antropométricas, para lograr así obtener el IMG y al ser este último específico para el diagnóstico de sobrepeso/obesidad, la implementación de cineantropometría en la consulta de paciente con obesidad es crucial para un adecuado abordaje del paciente, al no contar con la tecnología para determinar composición corporal.