Camila Niclis

Introduction. Understanding Body Mass Index (BMI) trends over time is essential for addressing the global overweight and obesity pandemic and its associated health risks. However, evidence from low- and middle-income countries remains limited. This study analyzed long-term BMI trends in Argentina (2005-2018), examining variations by age, sex, and socioeconomic status (SES), and comparing patterns according to the presence of obesity-related chronic diseases in adults. Method. Data were obtained from repeated cross-sectional, population-based Argentine surveys: the National Risk Factors Survey (2005, 2009, 2013 and 2018; n = 126,590). Age specific BMI trends were estimated using three-age moving medians. Polynomial curves were fitted to estimate the age at maximum BMI (maxBMI) and its corresponding value in the general population according to sex, SES, and chronic conditions (hypertension and type 2 diabetes mellitus). Results. A steady increase in mean BMI was observed across survey waves. Overall, maxBMI was reached between approximately 58–61 years of age, and its value was similar for men and women; however, women reached maxBMI at slightly older ages. The low SES group showed both a higher maxBMI and an earlier age at maxBMI than the high SES group. Additionally, individuals with hypertension or type 2 diabetes exhibited a higher maxBMI, which occurred at earlier ages than in participants without these conditions. Conclusions. BMI increased steadily in Argentina during the study period, with differences according to sex, SES, and health status. Unlike conventional approaches based on average BMI values, age-specific BMI curves allow for a life-course perspective on obesity dynamics. This approach highlights the importance of considering these factors when monitoring BMI as population health indicator, especially among socially disadvantaged groups.

Background/Objectives: The association between plasma metabolites derived from dietary substrates and inflammatory processes remains underexplored, despite its potential relevance in the prevention of non-communicable diseases. This systematic review aimed to examine the relationship between blood metabolites and the modulation of inflammatory biomarkers. Methods: A total of 25 randomized controlled trials, published between 2019 and 2024, were included from an initial pool of 111 records. These studies investigated the effects of dietary patterns, specific food groups, or nutritional supplements on the human metabolome and their potential links to inflammation. Results: Metabolomic analyses were predominantly performed using mass spectrometry (MS)-based platforms (17 out of 25), with liquid chromatography–mass spectrometry as the most frequently employed method. Both targeted (n = 14) and untargeted (n = 11) approaches were represented, and samples were drawn from plasma, urine, and feces. Across the interventions, 64 metabolites were modulated, including fatty acyls, glycerolipids, benzenoids, and organic acids, reflecting potential changes in pathways related to oxidative stress, lipid and carbohydrate metabolism, and inflammatory signaling. Several studies also assessed classical inflammatory biomarkers such as C-reactive protein (CRP), tumor necrosis factor alpha (TNFα), interleukin-6 (IL-6), and monocyte chemoattractant protein-1 (MCP-1). Interventions involving healthy traditional dietary patterns, improvements in dietary fat quality, or the use of specific probiotic strains were often associated with favorable immunometabolic outcomes. In contrast, some interventions, such as Mohana Choorna, elicited upregulation of immune-related gene expression in adipose tissue without improvements in glucose or lipid metabolism. Conclusions: While metabolomic responses varied across studies, the evidence highlights the value of dietary interventions in modulating systemic metabolism and inflammation. These findings support the integration of metabolomics into clinical nutrition to define more personalized and effective dietary strategies for inflammation-related chronic disease prevention.

We developed a comprehensive Diet and Lifestyle Quality Index (DLQI) and examined its associations with health status and sociodemographic characteristics in Argentinian adults. The DLQI scores range from 0 to 100 points and comprises 49 indicators across 7 dimensions: diet and eating habits, physical activity, rest and sleeping habits, exposure to sunlight and nature, religiosity, abstinence from tobacco and alcohol, and emotional well-being and relationships. Cross-sectional data from 4215 adults were analyzed to explore links with sociodemographic characteristics and chronic disease prevalence. Participants’ mean age was 35.5±14.6 years and 69% were women. The mean DLQI score was 51.5±10.6. Higher scores were associated with older age, being married, lower financial concern, rural residence, and receipt of social welfare. Elevated DLQI and dimension-specific scores were inversely related to self-reported obesity, diabetes, fatty liver, hypertension, kidney disease, and cancer, while lower scores were positively associated with overweight and obesity. Score distribution and odds ratios supported cut-off points for classifying lifestyle quality. Our evaluation indicates the DLQI is a practical tool for assessing diverse diet and lifestyle factors, with scores reflecting health status in line with existing evidence. Further evaluation in other populations is recommended to confirm its clinical and public health applicability.

The global burden of obesity has risen due to various factors, including sociocultural aspects. Social representations (SRs) of obesity could help to understand the problem. Nowadays, social networks activate new social interaction processes and enable the construction of SRs. Tweets can identify mind-sets as cultural reflections of the times. This study aimed to identify widely shared obesity topics on Twitter-Argentina using Natural Language Processing. First, 134,766 Spanish tweets about obesity were collected from August 2021 to July 2022. Next, a geolocation filter removed non-Argentinian messages, leaving 48,149 tweets. The body text was cleaned and prepared for analysis. K-means clustering model was applied and 21 clusters were identified after reaching theoretical saturation. Then, 10 tweets from each cluster were randomly selected to analyse word usage and identify cluster themes. Thus, the main themes were “Gender-related”, “Obesity and family”, “Hate speech and fatphobia”, “Body image, perceptions and feelings”, “Public health actions” and “Obesity as a health-disease process”. These aggregations allowed us to understand obesity as a sociocultural phenomenon, the Argentinian culture-specific discourses surrounding it, and their connections to health. We firmly believe that this valuable information will enlighten the planning of future social-political actions to address this health-disease process.

La prevalencia de obesidad depende de factores biopsicosociales y ambientales, y es un factor de riesgo para enfermedades no transmisibles y transmisibles como COVID-19. Objetivo: Determinar la asociación entre características sociodemográficas y del estilo de vida y el desarrollo de obesidad y sus comorbilidades metabólicas (CM). Estudio observacional-poblacional-transversal en 306 individuos >18 años de San Luis Argentina. Se indagaron edad, sexo, nivel de instrucción y socioeconómico (NI y NSE: bajo/medio/alto), nivel de actividad física (METs/día), ingesta energética (Cal/día), obesidad (IMC>30kg/m2) y auto-reporte de CM (diabetes, hipertensión arterial [HTA], dislipemias y cardiopatías). Se ajustaron modelos de regresión logística múltiple incluyendo presencia de obesidad y CM como utcome, y características sociodemográficas y del estilo de vida como covariables. El 26,8% presentó al menos una enfermedad (17,3% obesidad, 3% diabetes, 11% HTA, 3,3% dislipidemia, 13% cardiopatía). Tener NI medio aumentó la chance de presentar cardiopatía, respecto al alto (OR 5,3;p=0,03). Un bajo NI se asoció con obesidad (OR 3,58;p=0,04), y sus CM (p<0,05). La chance de presentar diabetes aumentó un 17% al incrementarse el IMC (p=0,01). Las mujeres presentaron más chances de tener HTA (p=0,04) y cardiopatías (p=0,03). El NI medio-bajo, la edad y ser mujer se asocian al desarrollo de CM.

Background The complex nature of obesity increasingly requires a comprehensive approach that includes the role of environmental factors. For understanding contextual determinants, the resources provided by technological advances could become a key factor in obesogenic environment research. This study aims to identify different sources of non-traditional data and their applications, considering the domains of obesogenic environments: physical, sociocultural, political and economic. Methods We conducted a systematic search in PubMed, Scopus and LILACS databases by two independent groups of reviewers, from September to December 2021. We included those studies oriented to adult obesity research using non-traditional data sources, published in the last 5 years in English, Spanish or Portuguese. The overall reporting followed the PRISMA guidelines. Results The initial search yielded 1583 articles, 94 articles were kept for full-text screening, and 53 studies met the eligibility criteria and were included. We extracted information about countries of origin, study design, observation units, obesity-related outcomes, environment variables, and non-traditional data sources used. Our results revealed that most of the studies originated from high-income countries (86.54%) and used geospatial data within a GIS (76.67%), social networks (16.67%), and digital devices (11.66%) as data sources. Geospatial data were the most utilised data source and mainly contributed to the study of the physical domains of obesogenic environments, followed by social networks providing data to the analysis of the sociocultural domain. A gap in the literature exploring the political domain of environments was also evident. Conclusion The disparities between countries are noticeable. Geospatial and social network data sources contributed to studying the physical and sociocultural environments, which could be a valuable complement to those traditionally used in obesity research. We propose the use of information available on the Internet, addressed by artificial intelligence-based tools, to increase the knowledge on political and economic dimensions of the obesogenic environment.