Cagua Ordoñez, Jaen
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Cagua Ordoñez, Jaen
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Ambato

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Dietary Diversity, Dietary Patterns, and Cardiometabolic Health in University Students: A Cross-Sectional Study

2026 , Diana Fonseca-Pérez , Ludwig Álvarez-Córdova , Cecilia Arteaga-Pazmiño , Víctor Sierra-Nieto , Cagua Ordoñez, Jaen , Evelyn Frias-Toral , Giovanna Muscogiuri , Reytor González, Claudia , Simancas Racines, Daniel

Cardiometabolic risk is increasingly observed in young adults, particularly during university years, and is not limited to individuals with elevated body mass index. Emerging evidence highlights the presence of normal weight obesity—characterized by excess adiposity and unfavorable body composition despite normal BMI—which may confer early metabolic vulnerability. Dietary diversity is often promoted as a marker of dietary adequacy; however, its relationship with adiposity, body composition, and muscular health remains inconsistent, particularly in Latin American populations. Moreover, few studies have directly contrasted dietary diversity indicators with empirically derived dietary patterns in relation to cardiometabolic and functional outcomes. Objective: To examine the associations between dietary diversity, dietary patterns, and indicators of adiposity, muscular strength, and relative muscle mass in Ecuadorian university students. Methods: A cross-sectional study was conducted among 349 undergraduate students aged 18–26 years enrolled in health sciences programs in Ecuador. Dietary intake was assessed using a validated food frequency questionnaire. Dietary diversity was quantified using the Food and Agriculture Organization’s Individual Dietary Diversity Score, while dietary patterns were identified through principal component analysis followed by k-means clustering. Outcomes included excess body weight, relative muscle mass assessed by bioelectrical impedance analysis, and handgrip strength. Multivariable Poisson and linear regression models were fitted, adjusting for age, sex, academic program, physical activity level, and pre-existing conditions. Results: Despite their young age and low prevalence of diagnosed disease, approximately one-third of the participants exhibited markers of early cardiometabolic risk, including excess body weight and central adiposity. Higher dietary diversity was independently associated with a higher prevalence of excess body weight (adjusted prevalence ratio per one-unit increase in IDDS: 1.17; 95% CI: 1.06–1.30) and with greater relative muscle mass (adjusted β = 0.13; 95% CI: 0.05–0.22), whereas no association was observed with handgrip strength. In contrast, dietary patterns derived from multivariate analysis showed no significant associations with adiposity, muscular strength, or relative muscle mass after adjustment. Conclusions: In this young adult population, dietary diversity captured aspects of overall dietary exposure associated with both increased adiposity and greater lean mass, but not with muscular strength. Empirically derived dietary patterns demonstrated limited discriminatory capacity, likely reflecting dietary homogeneity within the cohort. These findings indicate that dietary diversity alone does not necessarily reflect diet quality and underscore the importance of interpreting diversity metrics alongside indicators of food quality, energy density, and body composition when evaluating early cardiometabolic risk in contemporary food environments.

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Phase-Related Resting Energy Expenditure in Critically Ill Adults: Metabolic Phenotypes and Determinants of Weight-Normalized Indices—A Retrospective Study

2026 , Sebastián Chapela , Cagua Ordoñez, Jaen , Angamarca Iguago, Jaime , Daniel Tettamanti , Claudia Kecskes , Jesica Asparch , Facundo Javier Gutierrez , Natalia Llobera , Mariana Rella , Martha Montalván , María Jimena Reberendo , Mario Omar Pozo , Ludwig Álvarez-Córdova , Daniel Simancas-Racines

Precise measurement of resting energy expenditure (REE) is essential in the intensive care unit (ICU), where metabolic requirements evolve throughout the course of critical illness. Predictive equations frequently fail to capture this variability, and limited data describe phase-dependent changes in REE using indirect calorimetry (IC). This study aimed to evaluate phase-related variation in REE and metabolic phenotypes in mechanically ventilated adults and to identify clinical and physiological correlates of both absolute REE and REE normalized by ideal body weight (REE/IBW). Methods: We conducted an observational, retrospective cross-sectional study in two ICUs at different hospitals. A total of 149 mechanically ventilated adults with a valid IC measurement were included and classified by illness phase: acute (0–3 days), intermediate (4–14 days), or chronic (>14 days). Differences in metabolic and gas-exchange variables were assessed using ANOVA or Kruskal–Wallis tests. Two multivariable linear regression models were fitted, one using absolute REE and a second using REE/IBW, incorporating metabolic phenotype categories to account for body-size heterogeneity. Results: Metabolic profiles differed across illness phases. Median REE increased from the acute (1664 kcal/day) to the intermediate (1869 kcal/day) and chronic (2074 kcal/day; p = 0.024) phases. Hypometabolic profiles were more frequent in the acute phase (64%), whereas hypermetabolic profiles were more prevalent in later phases (48%). RQ values were higher in the chronic phase compared with the acute phase (median 0.99 vs. 0.80; p < 0.001). In multivariable analyses, illness severity scores showed weak or inconsistent associations with REE after adjustment for gas-exchange variables. VCO2 was independently associated with absolute REE (adjusted R2 = 0.83). In the REE/IBW model, VCO2, RQ, BMI, and metabolic phenotype were associated with normalized energy expenditure, with higher adjusted R2 (0.87) and lower prediction error metrics. Conclusions: Resting energy expenditure and metabolic phenotypes vary across phases of critical illness. Gas-exchange variables, particularly VCO2, were more closely associated with measured energy expenditure than severity scores. Normalization of REE by ideal body weight reduced variability and improved model performance, highlighting the analytical value of indirect calorimetry for characterizing phase-dependent metabolic patterns in critically ill adults.

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Interpreting Resting Energy Expenditure in Critically Ill Patients with Obesity: Clinical Impact of Weight Adjustment

2026 , Sebastián Chapela , Cagua Ordoñez, Jaen , Parise Vasco, Juan Marcos , Daniel Tettamanti Miranda , Claudia Kecskes , Natalia Llobera , Jesica Asparch , Mariana Rella , María Victoria Peroni , Martha Montalvan , María Jimena Reberendo , Facundo Gutierrez , Mario O. Pozo , Ludwig Álvarez-Córdova , Simancas Racines, Daniel

Accurately estimating resting energy expenditure (REE) in critically ill obese patients remains a significant clinical challenge, as predictive equations are consistently inadequate. Metabolic heterogeneity across obesity classes and the role of substrate utilization are insufficiently characterized. Objective: To evaluate the impact of different weight-normalization methods on the interpretation of REE and to identify independent metabolic determinants of weight-adjusted energy expenditure in critically ill patients with obesity. Methods: Bicentric cross-sectional study of 148 critically ill adults with obesity undergoing indirect calorimetry. REE normalized by actual body weight (REE/kg), ideal body weight (REE/IBW), and adjusted body weight (REE/AdjBW) was calculated. Multivariable models with robust standard errors (HC3), stratified analyses by obesity class (I–III) with a Chow test, and internal validation were performed using 10-fold cross-validation and bootstrap resampling (1000 iterations). Results: Absolute REE did not differ significantly between BMI categories (p = 0.679), while REE/kg progressively decreased from normal weight (27.8 kcal/kg/day) to class III obesity (16.9 kcal/kg/day; p < 0.001). The respiratory quotient (RQ) emerged as the most robust independent correlate of adjusted REE (β = −13 to −15 kcal·kg−1·day−1; p < 0.001), whereas clinical severity scores (SOFA, APACHE II) and comorbidity (Charlson) did not show significant associations. Stratified analyses revealed significant structural heterogeneity between obesity classes (F = 4.545, p = 0.0001), with no significant predictors identified in class III obesity, likely reflecting limited statistical power in this subgroup. Conclusions: Normalizing REE using different weight indices fundamentally alters its metabolic interpretation. RQ surpasses traditional clinical scores as a correlate of adjusted REE, consistent with a phenotype of metabolic inflexibility. The heterogeneity between obesity classes underscores the need for individualized indirect calorimetry rather than reliance on predictive equations.

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