Difference in prevalence using only body mass index compared to new parameters: A Secondary Analysis of Latin American Datasets.

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Edgar Antonio Ramos Gutierrez, Mario Ulises Pérez Zepeda, Natalia Sánchez Garrido, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8309007/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The present study aims to determine the prevalence of obesity in older adults in the Latin American population, making a comparison using the two definitions: the classic one based on the body mass index (BMI) and the new definition of the Lancet Consensus on obesity of 2025. Methods: Anthropometric data were collected from population studies in the aforementioned countries, determining the grouped prevalence, taking into account the 2 definitions of obesity that exist, performing a meta-analysis, which included 5 data sets using the population censuses of Puerto Rico, Costa Rica, Brazil, Ecuador and Mexico, extracting anthropometric data from men and women aged 65 years or older. Results: A total of 14,028 adults over 60 years of age were included, with an average age of 69 years without distinction by sex, showing a greater difference in the prevalence of obesity in Mexico (men 28.5 versus 18.5 / women 39.7 versus 35.5) and Brazil (men 24.7 versus 22.22 / women 35.6 versus 29.9), with a distribution of the general prevalence by sex of 28% in women and 17% in men. The difference in the prevalence of obesity decreased when using various anthropometric measures in older adults compared to BMI, having a greater impact in populations in Mexico and Brazil, showing a better categorization of obesity. Conclusions: Obesity prevalence decreases in older adults because BMI miss variations in muscle mass and adipose tissue, therefore the new definition could improve characterization of this clinical condition and properly assess risk and treatment. Obesity Geriatric Epidemiology Body Composition Figures Figure 1 Figure 2 Key summary points Aim The study aims to compare the prevalence of obesity in older adults, using the definition by body mass index and the new definition proposed by the Lancet consensus, which uses anthropometric measurements (taking into account adiposity measures), in Latin American populations. Findings It was found that there is a difference when analyzing the prevalence between both definitions of obesity, decreasing the prevalence when using body adiposity measures associated with height and weight, with the regions of Mexico and Brazil having the greatest impact when using these anthropometric measures. Message Body mass index is not an appropriate way to define obesity in older adults, as it does not take into account other anthropometric measurements, leading to an overestimation of obesity in older adults, which may have a negative impact when making decisions for obesity treatment. 1 Introduction Obesity is understood as a complex medical category with significant behavioral and social implications, influenced by environmental, economic and psychological factors. In addition, its associated with low physical activity and an excess in Calor intake, that lead to an increased risk of chronic diseases [ 1 ]. As currently defined and measured, obesity does not have the same meaning in all affected individuals. In this context, the question of whether obesity is a disease is ill-conceived because it presus an implausible all-or-nothing scenario, where obesity is either always a disease or never a disease [ 2 ]. Beyond its direct medical consequences, the biomedical classification of obesity also shapes healthcare delivery. This framing influences clinical practices and should promote access to adequate care, prevention of clinical complications, and healthier habits for all people living with obesity. The obesity epidemic has affected the lives of millions of people worldwide. In 2021 an estimated 3.71 million deaths and 129 million years of life lost were attributable to overweight and obesity [ 3 ]. According to the Global Burden of Disease Study (2021), 133 countries had obesity prevalence rates above 50%, with the highest rates observed in Oceania, North Africa, and the Middle East [ 4 ]. In regions with pronounced social disparities, such as Latin America, obesity is highly prevalent and disproportionately affects disadvantaged groups, including women and older adults. In this region, obesity is more common among women than men [ 4 ]. Obesity has been linked to an increased risk of infections, which may lead to greater health-care utilization and higher costs, ultimately affecting individuals’ quality of life and their psychological, physical, and social well-being [ 5 ]. For example, the COVID-19 pandemic highlighted the disproportionate risks experienced by people living with obesity in these countries [ 6 – 8 ]. The body mass index (BMI) –result from dividing weight by squared height– was proposed by Lambert Adolphe Francois Quetelet in the 1830’s [ 9 ] and was used more recently to define obesity as a BMI above 30kg/m 2 , staging as obesity grade I of 30–34.9 kg/m2, grade II 35–39.9 kg/m2 and grade III > 40 kg/m2, [ 10 ]. However, a recent report proposed a new definition of obesity, emphasizing excess adiposity (e.g., hip-waist ratio and skinfold thickness measurement) and its clinical consequences in addition to BMI [ 2 , 11 , 12 ]. The new classification includes the following categories: normal (BMI < 30 kg/m² with normal adiposity), preclinical obesity (BMI ≥ 30 kg/m² with high adiposity and no clinical manifestations), and clinical obesity (preclinical obesity with clinical manifestations). Within this medical framework, people who meet diagnostic criteria are offered targeted treatment strategies that address both obesity and related health conditions, while those in intermediate categories are generally recommended to begin lifestyle-oriented interventions. Although advances in healthcare have improved life expectancy, the health profile of older people still presents challenges, including high rates of obesity and multiple comorbidities. Older adults should not receive the same treatments as younger adults, given substantial differences such as sarcopenic obesity, which behaves differently from isolated obesity [ 11 , 12 ]. They also commonly present several comorbidities, which modify the clinical manifestations of chronic degenerative diseases and interact with age-related changes, leading to distinct clinical presentations in this population [ 13 , 14 ]. Moreover, Latin America is considered one of the fastest-aging regions [ 15 ] and with a high prevalence of obesity, when compared to other regions in the world [ 16 ]. Accordingly, our objective was to identify the differences between obesity prevalence measured by BMI and preclinical obesity prevalence according to the new criteria in older adults from five Latin American countries 2 Methods 2.1 Design and sampling This is a secondary analysis of five data sets from Latin American countries: Mexico- Mexican Health and Aging Study (MHAS) [ 17 ], Ecuador- Salud, Bienestar y Envejecimiento Ecuador (SABE E) [ 18 ], Puerto Rico- Puerto Rican Elder: Health Conditions (PREHCO) [ 19 ], Costa Rica- Costa Rica Estudio de Longevidad y Envejecimiento Saludable (CRELES) [ 20 ], and Brazil- Estudo Longitudinal da Saúde dos Idosos (ELSI) [ 21 ]. These datasets include anthropometric measurements and are based on nationally representative samples from different Latin American subregions (North, Central, South, and the Caribbean). Details on these data sets are shown in supplementary material. Apart from SABE E (2009), the rest of the data sets have follow-ups at different time points. Chosen waves were those including anthropometric measurements: MHAS-2012, PREHCO-2006, SABE E-2009, CRELES-2009 and ELSI-2015. To homogenize all data sets, age range was limited from 60 to 100 years. A brief description of each study is provided in the supplementary material. 2.2 Definition of Obesity Obesity was operationalized using a composite anthropometric parameter that integrates BMI with the waist–to-hip ratio (WHR), in line with the most recent diagnostic recommendations. BMI was calculated as weight in kilograms divided by the square of height in meters (kg/m²). Waist and hip circumferences were measured with a flexible, non-stretchable tape. Waist circumference was taken at the midpoint between the lower margin of the last palpable rib and the top of the iliac crest, and hip circumference at the level of the maximum gluteal protuberance. WHR was then calculated as waist circumference divided by hip circumference. Following the Commission's guidelines, excess adiposity was only confirmed when both criteria were met: BMI values ​​equal to or greater than the threshold established for obesity (≥ 30.0 ​​kg/m²) and WHR values ​​greater than the sex-specific limits (≥ 0.90 for men, ≥ 0.85 for women). Individuals who met both criteria were classified as obese; the waist-hip ratio is clinically relevant for the current definition, as it is associated with an increased risk of cardiovascular disease and metabolic disorders [ 22 ]. This combined definition addresses the limitations of BMI alone by accounting for central adiposity distribution, thus providing a more robust anthropometric phenotype for identifying excess adiposity within the framework of preclinical and clinical obesity. 2.3 Statistical Analysis To estimate the pooled prevalence of differences between these paradigms of obesity, we performed a meta-analysis including the five data sets. We analyzed the descriptive characteristics of the study, applying sample weighting to better represent the overall older adult population in Latin America, using a 95% confidence interval (CI) as a dispersion parameter instead of standard deviations. Measurements from the general population were summarized, and prevalence of obesity in older adults in Latin America was calculated using both the classic and newly proposed definitions. Results were presented graphically. Due to heterogeneity among studies, pooled prevalence estimates were calculated by year of population census using a random-effects model. Heterogeneity was assessed using I² test and the p-value from Cochran´s Q test. High heterogeneity was defined as I 2 values above 50% or p-values < 0.10 [ 23 ]. All point estimates with their 95% confidence intervals are displayed in the forest plot. Finally, to evaluate the impact of each dataset, a meta-analysis excluding each set at a time was conducted to determine whether excluding a specific dataset reduced heterogeneity. 2.4 Ethical Considerations All studies were approved by their respective institutional review boards (IRBs). Participants provided written informed consent, and the principles of the Declaration of Helsinki were followed in all studies. Further information can be found in the methodological documents of the studies. 3 Results 3.1 Study Selection A total sample of 14,028 individuals was obtained, comprising 8,754 women and 6,255 men (see flowchart in supplementary material). The participants were distributed as follows: 1,015 from MHAS, 1,629 from CRELES, 811 from PREHCO, 2,250 from SABE Ecuador, and 8,323 from ELSI Brazil. 3.2 Participant characteristics Table 1 shows the anthropometric characteristics stratified by sex in the analyzed populations. For age, women from Puerto Rico had the highest mean age, 84.0 years (95% CI: 84.0–85.2), while women from Mexico were the youngest, with a mean of 61.8 years (95% CI: 60.6–63.0). Among men, Puerto Rican participants also had the highest average age, 83.6 years (95% CI: 82.8–84.3), whereas those from Brazil had the lowest. Table 1 General characteristics of the population stratified by sex. MHAS SABE E PREHCO CRELES ELSI Total (N = 1,142) Men (n = 512) Women (n = 630) Total (N = 2,367) Men (n = 1,063) Women (n = 1,304) Total (N = 2,756) Men (n = 1,068) Women (n = 1,688) Total (N = 1,820) Men (n = 813) Women (n = 1,007) Total (N = 6,924) Men (n = 2,799) Women (n = 4,125) Age, mean (95% CI) 70.2 (69.4–71) 70.4 (69.2–71.6) 70.1 (68.9–71.2) 71.9 (70.8–73) 71.8 (70.5–73.1) 71.9 (67.5–76.3) 73.2 (72.8–73.6) 72.9 (72.4–73.5) 73.4 (72.8–73.9) 73.5 (73.1–73.9) 73.1 (72.6–73.7) 73.8 (73.3–74.3) 70.3 (70.1–70.5) 70.2 (69.8–70.6) 70.3 (70-70.6) Weight, mean (95% CI) 66.8 (65.4–68.3) 70.7 (68.4–72.9) 63.5 (61.8–65.1) 61.2 (60.3–62.1) 63.8 (63.5–64) 59.1 (56.5–61.8) 68.9 (68.1–69.7) 73.8 (72.6–75) 65.1 (64.1–66.1) 66.2 (65.3–66.9) 70.5 (69.2–71.7) 62.2 (61.2–63.1) 69.8 (69.4–70.3) 73.5 (72.8–74.2) 66.8 (66.3–67.5) Height, mean (95% CI) 1.54 (1.53–1.55) 1.61 (1.6–1.62) 1.48 (1.47–1.49) 1.52 (1.47–1.56) 1.6 (1.58–1.61) 1.46 (1.45–1.48) 1.59 (1.58–1.6) 1.66 (1.65–1.67) 1.52 (1.51–1.53) 1.57 (1.56–1.58) 1.64 (1.63–1.65) 1.5 (1.49–1.51) 1.59 (1.58–1.6) 1.65 (1.64–1.66) 1.54 (1.53–1.54) WC, mean (95% CI) 96.8 (95.6–98.1) 97.9 (95.9–99.9) 95.9 (94.5–97.3) 93.7 (91.7–95.7) 93.4 (92.8–93.9) 93.9 (90-97.8) 97.6 (96.9–98.2) 98.9 (98-99.8) 96.5 (95.5–97.5) 96.7 (96-97.5) 95.3 (94.3–96.4) 98 (97.1–99) 95.9 (95.4–96.3) 97.1 (96.4–97.8) 94.9 (94.3–95.5) HC, mean (95% CI) 101.5 (100.5-102.4) 98.5 (97.1–99.8) 104.1 (102.7-105.5) 99.8 (95.8-103.8) 96.1 (95.6–96.6) 102.7 (100-105.3) 103.9 (103.3-104.6) 102.2 (101.4–103) 105.3 (104.4-106.2) 97.7 (97.1–98.2) 95.9 (95.2–96.6) 99.3 (98.5-100.1) 101.4 (101.1-101.8) 99.1 (98.7–99.6) 103.3 (102.8-103.8) WHR, mean (95% CI) 0.95 (0.94–0.96) 0.99 (0.98-1) 0.92 (0.91–0.93) 0.94 (0.92–0.96) 0.97 (0.96–0.98) 0.92 (0.91–0.93) 0.94 (0.93–0.95) 0.96 (0.95–0.97) 0.91 (0.9–0.92) 0.99 (0.98-1) 0.99 (0.98-1) 0.99 (0.98-1) 0.95 (0.94–0.96) 0.97 (0.96–0.98) 0.92 (0.91–0.93) BMI, mean (95% CI) 27.9 (27.5–28.4) 27 (26.3–27.7) 28.8 (28.2–29.4) 26.5 (25.2–27.8) 25.3 (25.1–25.4) 27.4 (26.5–28.4) 27.3 (27-27.6) 26.6 (26.2–26.9) 27.9 (27.4–28.3) 26.9 (26.6–27.2) 26.2 (25.8–26.6) 27.6 (27.2–27.9) 27.6 (27.4–27.8) 26.8 (26.5–26.9) 28.3 (28-28.5) Obesity 1, % (95% CI) 31.6 (26.8–36.7) 24.5 (17.3–33.5) 38.1 (32-44.5) 19.9 (11.4–32.4) 10.7 (9.1–12.6) 27.2 (20.6–35.1) 26 (23.7–28.5) 19.9 (16.6–23.7) 30.7 (27.5–33.9) 22.1 (19.7–24.7) 16.3 (13-20.1) 27.5 (24.2–31.2) 28.6 (27.2–30.1) 20.9 (18.9–23.2) 34.8 (32.9–36.8) Obesity 2, % (95% CI) 26.7 (22.3–31.5) 17.1 (10.9–25.6) 35.4 (29.5–41.8) 17.7 (8.9–32.2) 10.4 (9.2–11.8) 23.4 (12.1–40.4) 24 (21.5–26.1) 19.9 (16.6–23.7) 26.7 (23.8–29.8) 21.7 (19.3–24.3) 16.3 (13-20.1) 26.7 (23.5–30.3) 25.1 (23.7–26.5) 19.1 (17.1–21.2) 29.9 (28.1–31.8) Difference, % -4.9 -7.4 -2.7 -2.2 -0.3 -3.8 -2 0 -4 -0.4 0 -0.8 -3.5 -1.8 -4.9 MHAS = Mexican Health and Aging Study, SABE-E, PREHCO, CRELES, ELSI, WC, HC, WHR, BMI Table 1. Describes the general and sex-specific anthropometric characteristics of each of the analyzed population censuses. In terms of height, women’s values were similar across countries, with the tallest observed in Brazil (1.55, 95% CI 1.54–1.55) and the shortest in Ecuador (1.46, 95% CI 1.44–1.48). Regarding weight, Brazilian women had the highest average (68.8, 95% CI 68.2–69.3), followed by Mexico (64.9, 95% CI 63.3–66.5, whereas Ecuador had the lowest (59.1, 95% CI 56.4–61.8). Among men, the highest mean weight was in Brazil (75.7, 95% CI 75.0-76.4), and the lowest in Ecuador (63.8, 95% CI 63.5–64.1). For the waist-to-hip ratio, women from Costa Rica had the highest values (0.98, 95% CI 0.98–0.99; waist 98.0, 95% CI 97.0-98.9; hip 99.2, 95% CI 95.1–96.6), while the lowest WHR was observed in Brazil (0.79, 95% CI 0.77–0.80). Among men, Costa Rica also showed the highest WHR (0.99, 95% CI 0.98–0.99; ​​​​waist 95.3 cm, 95% CI 94.2–96.3; hip 99.2, 95% CI 98.4–100.0). Regarding BMI, Mexican women had the highest mean (29.0, 95% CI 28.3–29.6), while Puerto Rican women had the lowest (26.8, 95% CI 26.1–27.6). Among men, Mexico also showed the highest BMI (27.5, 95% CI 27.0–28.0), like Brazil (27.1, 95% CI 26.9–27.3), whereas Puerto Rico had the lowest (26.6, 95% CI 25.8-27.49). Obesity prevalence was lower when applying the new definition proposed by Rubino compared with the traditional one. Among women, the highest prevalence under the new definition was in Mexico (32.5%, 95% CI: 30.6–40.6), and the lowest in Ecuador (23.5%, 95% CI: 11.9–41). The largest decrease between definitions was observed in Brazilian women (-5.7%), while Costa Rica showed the smallest change (-0.8%). In men, Mexico showed the greatest decrease (-6.9%), whereas Costa Rica had no difference between the two definitions. (See full results in Fig. 1 ). 3.3 Meta-analysis In the meta-analysis of obesity among older adults in Latin America, using the classical definition and stratified by sex, heterogeneity of 91% in women, with an overall prevalence of 31% (95% CI 27–35%) (Fig. 2 ). Among men, heterogeneity was 95%, with an overall obesity prevalence of 19% (95% CI 14–24%) (Fig. 4). Using the new definition, heterogeneity decreased slightly to 89% in women; with an overall prevalence of 28% (95% CI 25–32%) (Fig. 3), and to 93% in men, with an overall prevalence of 17% (95% CI 13–22%). When examining the general prevalence of obesity in older adults across the region, a reduction was observed with the new definition, about 3% in women and 2% in men. When analyzing the forest plot for obesity prevalence in older adults, adjusting based on the year the population study was conducted, It was observed that when dividing by year of implementation, we obtain a greater homogeneity of the samples, with a cut-off point in 2010, having greater significance in the Mexican and Brazilian population, with the prevalence of obesity being lower by the new definition in all cases. 3.3.1 Heterogeneity analysis and data analysis. The heterogeneity of the sample studied was studied through a cohort study, initially observing a high heterogeneity with an H2 value of 80–90%, so it is proposed to make an adjustment by year, the cut-off point being 2010, with PREHCO, SABE E, CRELES before 2010, and MHAS ELSI after the cut-off point; observing a more homogeneous distribution between the populations, increasing the I2 value to 95 to 96% in the male population and from 92 to 93% in women. 4 Discussion Globally, a recent meta-analysis showed a 25% prevalence of obesity in older adults when defined by only BMI, based on 44 original studies [ 24 ]. According to our results, applying the new definition of obesity substantially changes the estimated prevalence of older adults in Latin American living with clinical obesity, compared with the traditional BMI-based definition. To our knowledge, this is the first study to report such data in Latin America, including countries with some of the highest obesity rates worldwide (e.g., Mexico). Obesity is a major clinical entity linked to multiple metabolic and non-metabolic conditions [ 3 , 5 , 7 , 8 , 25 – 27 ]. Noteworthy, the clinical manifestations of obesity initially present cellular or tissue changes, with adequate preservation of the functional state, however, as the disease develops, it causes an organic alteration or dysfunction at different levels of the body, which lead to anatomical and pathophysiological changes, causing a limitation in functionality, excessive fatigue, decreased strength, which fails the presence of complications or worse outcomes, a striking similarity to clinical manifestations of frailty in older adults [ 3 , 5 , 7 , 8 , 25 – 27 ]. Its prevalence continues to increase globally, affecting all age group, including older adults. This rise is partly driven by social determinants of health, such as sedentary lifestyles and unhealthy diets [ 28 , 29 ]. In older populations, obesity has profound implications, contributing to cognitive decline, disability, frailty, and sarcopenia [ 26 , 30 ]. Using the definition proposed by The Lancet Commission , which incorporates anthropometric measures such as weight, height, and waist-to-hip ratio, we observed lower prevalence rates of obesity compared with the Quetelet definition [ 31 ]. This difference was more pronounced in countries with higher baseline obesity rates, such as Brazil and Mexico, while little variation was observed in Costa Rica and Ecuador. Currently, there is limited research on obesity prevalence in older adults in Latin America underscores the relevance of these findings. Most available studies focus on genetic factors or ethnicity for this region. For instance, Vinueza et al. reported differences in obesity prevalence between Ecuadorians of mixed European-Indigenous ancestry (81%) and indigenous populations (6.6%), largely attributed to differences in physical activity [ 32 ]. BMI, however, is increasingly considered an inadequate measure in this context. It does not account for age-related changes in fat distribution, muscle mass, and metabolic changes. Other indices, including waist circumferences and waist-to-height ratio, have been proposed [ 33 ], yet evidence in older populations remains limited. The Lancet Commission’s approach, which combines WHR with imaging to evaluate fat and muscle distribution, may offer more precise diagnosis. Our results highlight that overdiagnosis occurs when obesity is defined exclusively by BMI. In Brazil and Mexico, differences between definitions exceeded 5%, with women showing the greatest discrepancy. These findings underscore the importance of considering adiposopathy (defined as the pathological dysfunction of adipose tissue), proposed by Lorenzo et al, caused by a dysregulation between caloric intake and genetic and environmental predispositions[ 34 ].This is particularly relevant in older adults, in whom fat redistribution parallels age-related metabolic changes. Correct identification of clinical obesity in older adults can help propose individualized medical management, with a focus on lifestyle changes or increased physical activity, since studies for pharmacological treatment of obesity have not studied the impact on older adults, because they do not specify what type of mass is lost in the loss of BMI, so these treatments do not seem to provide a clinical benefit in older adults when using BMI as a measure. Ultimately, redefining obesity in older adults is critical for advancing clinical care. Incorporating distinctions such as preclinical versus clinical obesity could help target therapies appropriately. For instance, GLP-1 agonists may benefit some subgroups but inappropriate prescribing in older adults could increase risks rather than reduce complications [ 35 ] Finally, our study emphasizes the importance of culturally and anatomically tailored criteria for Latin American populations, where body proportions differ from those in other regions. Supporting The Lancet Commission’s recommendations may help reduce stigma, refine diagnosis, and improve person-centered strategies that promote healthy aging, independence, and quality of life in older adults. It is important to consider the anatomical characteristics of older Latin American populations, since a greater difference is observed in the waist-hip ratios, impacting the decrease in the prevalence of obesity when considered in association with height and weight, making us reflect on the role that adiposopathy may play, as proposed by De Lorenzo et al (33). The performance of the various BMI indices; waist circumference, waist-hip ratio, waist-height ratio, are high to assess the index of body adiposity, however, it is observed that it depends on multiple factors such as sex, ethnicity, comorbidities in older adults [ 34 ]. We recognize the importance of The Lancet Expert Consensus, which marks an important step toward redefining the perception of obesity as clinical entity. This new framework has already shown a significant impact in populations such as Brazil and Mexico and provides an opportunity to further study the benefits of a more precise classification system, supported by specific anthropometric measures of fat and muscle tissue. Moreover, this redefinition can help reduce the stigma that societies often place on obesity, prevent related complications, and improve understanding of the aging-related changes experienced by older adults, ultimately promoting successful aging, better quality of life, and greater independence. Declarations Acknowledgements We thank the National Institute of Geriatrics of Mexico for the support and funding that made this work possible. Author Contributions E.R. wrote the complete original draft, M. P. and N. S. collected the information and developed the database in STATA 19, M. A. and M. G. performed the data and results analysis, E. R. and M. P. and J. P. wrote the discussion and conclusions. All authors have read and agreed to the published version of the manuscript. Conflict of Interest Statement Authors declare not having any conflict of interest. Sponsor´s Role This work was made possible thanks to the generous support of the National Institute of Geriatrics of Mexico. Data sharing MHAS has an open policy of sharing their data just by a simple registration in mhas.web.org. SABE E has an open data-sharing policy, available at https://www.ecuadorencifras.gob.ec/encuesta-de-salud-bienestar-del-adulto-mayor/ PRHECO has an open data-sharing policy, available at https://prehco.rcm.upr.edu/. CRELES has an open data-sharing policy, available at https://populationsciences.berkeley.edu/creles/ , upon request. ELSI has an open policy of sharing your data simply by registering at https://elsi.cpqrr.fiocruz.br/ Our working group can share our code and syntax upon request. Conflict of Interest Statement Authors declare they do not have any conflict of interest. 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Geriatr Gerontol Int 24(12):1257–1268. 10.1111/ggi.14979 Supplementary Files STROBEchecklistv4combinedPlosMedicineobesity.docx renamedb6c13.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8309007","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":558694007,"identity":"ee65b3eb-199d-4fd3-bd64-64430178c962","order_by":0,"name":"Edgar Antonio Ramos 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16:57:06","extension":"html","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":107368,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8309007/v1/d3cc7be802c3e1636d3f95aa.html"},{"id":98334749,"identity":"0a5c53dd-d034-4674-b974-b3f2b6b39e5a","added_by":"auto","created_at":"2025-12-16 16:05:47","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":101113,"visible":true,"origin":"","legend":"\u003cp\u003eThe difference in the prevalence of obesity is shown when comparing both definitions, by sex in each of the population censuses analyzed. MHAS=Mexican Health and Aging Study, SABE-E, PREHCO, CRELES, ELSI\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8309007/v1/6daf94adbfa6bab23628de50.jpg"},{"id":98334750,"identity":"d6b4369b-860c-44a3-b241-773c60167a30","added_by":"auto","created_at":"2025-12-16 16:05:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":89383,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots for the prevalence for each definition stratified by sex and including all the datasets. A. Definition based only on body mass index for men. B. Definition based on body mass index and waist-hip ratio for men. C. Definition based only on body mass index for women. D. Definition based on body mass index and waist-hip ratio for women.\u003c/p\u003e\n\u003cp\u003eMHAS=Mexican Health and Aging Study, SABE-E, PREHCO, CRELES, ELSI\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8309007/v1/6c21eef697963732b3ca20bd.jpg"},{"id":99796345,"identity":"c67adb49-8392-476f-99d6-e9059aab2e09","added_by":"auto","created_at":"2026-01-08 13:41:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":961036,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8309007/v1/a5b69984-eba1-4c54-8961-41335862dce3.pdf"},{"id":98334760,"identity":"9b49395c-9aa9-491f-bb30-208fbab09e22","added_by":"auto","created_at":"2025-12-16 16:05:47","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":35727,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEchecklistv4combinedPlosMedicineobesity.docx","url":"https://assets-eu.researchsquare.com/files/rs-8309007/v1/4d4b7b81375e48d1613c76fb.docx"},{"id":98334754,"identity":"8f0d8276-441a-44c9-aa7b-fd1c84cec485","added_by":"auto","created_at":"2025-12-16 16:05:47","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":16604,"visible":true,"origin":"","legend":"","description":"","filename":"renamedb6c13.docx","url":"https://assets-eu.researchsquare.com/files/rs-8309007/v1/8b8dbd9ca1716d46a2b86513.docx"}],"financialInterests":"","formattedTitle":"Difference in prevalence using only body mass index compared to new parameters: A Secondary Analysis of Latin American Datasets.","fulltext":[{"header":"Key summary points","content":"\u003cp\u003e\u003cstrong\u003eAim\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study aims to compare the prevalence of obesity in older adults, using the definition by body mass index and the new definition proposed by the Lancet consensus, which uses anthropometric measurements (taking into account adiposity measures), in Latin American populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt was found that there is a difference when analyzing the prevalence between both definitions of obesity, decreasing the prevalence when using body adiposity measures associated with height and weight, with the regions of Mexico and Brazil having the greatest impact when using these anthropometric measures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMessage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBody mass index is not an appropriate way to define obesity in older adults, as it does not take into account other anthropometric measurements, leading to an overestimation of obesity in older adults, which may have a negative impact when making decisions for obesity treatment.\u003c/p\u003e"},{"header":"1 Introduction","content":"\u003cp\u003eObesity is understood as a complex medical category with significant behavioral and social implications, influenced by environmental, economic and psychological factors. In addition, its associated with low physical activity and an excess in Calor intake, that lead to an increased risk of chronic diseases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As currently defined and measured, obesity does not have the same meaning in all affected individuals. In this context, the question of whether obesity is a disease is ill-conceived because it presus an implausible all-or-nothing scenario, where obesity is either always a disease or never a disease [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Beyond its direct medical consequences, the biomedical classification of obesity also shapes healthcare delivery. This framing influences clinical practices and should promote access to adequate care, prevention of clinical complications, and healthier habits for all people living with obesity.\u003c/p\u003e \u003cp\u003eThe obesity epidemic has affected the lives of millions of people worldwide. In 2021 an estimated 3.71\u0026nbsp;million deaths and 129\u0026nbsp;million years of life lost were attributable to overweight and obesity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. According to the Global Burden of Disease Study (2021), 133 countries had obesity prevalence rates above 50%, with the highest rates observed in Oceania, North Africa, and the Middle East [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn regions with pronounced social disparities, such as Latin America, obesity is highly prevalent and disproportionately affects disadvantaged groups, including women and older adults. In this region, obesity is more common among women than men [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Obesity has been linked to an increased risk of infections, which may lead to greater health-care utilization and higher costs, ultimately affecting individuals\u0026rsquo; quality of life and their psychological, physical, and social well-being [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. For example, the COVID-19 pandemic highlighted the disproportionate risks experienced by people living with obesity in these countries [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe body mass index (BMI) \u0026ndash;result from dividing weight by squared height\u0026ndash; was proposed by Lambert Adolphe Francois Quetelet in the 1830\u0026rsquo;s [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and was used more recently to define obesity as a BMI above 30kg/m\u003csup\u003e2\u003c/sup\u003e, staging as obesity grade I of 30\u0026ndash;34.9 kg/m2, grade II 35\u0026ndash;39.9 kg/m2 and grade III\u0026thinsp;\u0026gt;\u0026thinsp;40 kg/m2, [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, a recent report proposed a new definition of obesity, emphasizing excess adiposity (e.g., hip-waist ratio and skinfold thickness measurement) and its clinical consequences in addition to BMI [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The new classification includes the following categories: normal (BMI\u0026thinsp;\u0026lt;\u0026thinsp;30 kg/m\u0026sup2; with normal adiposity), preclinical obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u0026sup2; with high adiposity and no clinical manifestations), and clinical obesity (preclinical obesity with clinical manifestations). Within this medical framework, people who meet diagnostic criteria are offered targeted treatment strategies that address both obesity and related health conditions, while those in intermediate categories are generally recommended to begin lifestyle-oriented interventions.\u003c/p\u003e \u003cp\u003eAlthough advances in healthcare have improved life expectancy, the health profile of older people still presents challenges, including high rates of obesity and multiple comorbidities. Older adults should not receive the same treatments as younger adults, given substantial differences such as sarcopenic obesity, which behaves differently from isolated obesity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. They also commonly present several comorbidities, which modify the clinical manifestations of chronic degenerative diseases and interact with age-related changes, leading to distinct clinical presentations in this population [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, Latin America is considered one of the fastest-aging regions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and with a high prevalence of obesity, when compared to other regions in the world [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Accordingly, our objective was to identify the differences between obesity prevalence measured by BMI and preclinical obesity prevalence according to the new criteria in older adults from five Latin American countries\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Design and sampling\u003c/h2\u003e \u003cp\u003eThis is a secondary analysis of five data sets from Latin American countries: Mexico-\u003cem\u003eMexican Health and Aging Study\u003c/em\u003e (MHAS) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], Ecuador-\u003cem\u003eSalud, Bienestar y Envejecimiento Ecuador\u003c/em\u003e (SABE E) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], Puerto Rico-\u003cem\u003ePuerto Rican Elder: Health Conditions\u003c/em\u003e (PREHCO) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], Costa Rica-\u003cem\u003eCosta Rica Estudio de Longevidad y Envejecimiento Saludable\u003c/em\u003e (CRELES) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and Brazil-\u003cem\u003eEstudo Longitudinal da Sa\u0026uacute;de dos Idosos\u003c/em\u003e (ELSI) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These datasets include anthropometric measurements and are based on nationally representative samples from different Latin American subregions (North, Central, South, and the Caribbean). Details on these data sets are shown in supplementary material. Apart from SABE E (2009), the rest of the data sets have follow-ups at different time points. Chosen waves were those including anthropometric measurements: MHAS-2012, PREHCO-2006, SABE E-2009, CRELES-2009 and ELSI-2015. To homogenize all data sets, age range was limited from 60 to 100 years. A brief description of each study is provided in the supplementary material.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Definition of Obesity\u003c/h2\u003e \u003cp\u003eObesity was operationalized using a composite anthropometric parameter that integrates BMI with the waist\u0026ndash;to-hip ratio (WHR), in line with the most recent diagnostic recommendations. BMI was calculated as weight in kilograms divided by the square of height in meters (kg/m\u0026sup2;). Waist and hip circumferences were measured with a flexible, non-stretchable tape. Waist circumference was taken at the midpoint between the lower margin of the last palpable rib and the top of the iliac crest, and hip circumference at the level of the maximum gluteal protuberance. WHR was then calculated as waist circumference divided by hip circumference.\u003c/p\u003e \u003cp\u003e Following the Commission's guidelines, excess adiposity was only confirmed when both criteria were met: BMI values ​​equal to or greater than the threshold established for obesity (\u0026ge;\u0026thinsp;30.0 ​​kg/m\u0026sup2;) and WHR values ​​greater than the sex-specific limits (\u0026ge;\u0026thinsp;0.90 for men, \u0026ge;\u0026thinsp;0.85 for women). Individuals who met both criteria were classified as obese; the waist-hip ratio is clinically relevant for the current definition, as it is associated with an increased risk of cardiovascular disease and metabolic disorders [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis combined definition addresses the limitations of BMI alone by accounting for central adiposity distribution, thus providing a more robust anthropometric phenotype for identifying excess adiposity within the framework of preclinical and clinical obesity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e \u003cp\u003eTo estimate the pooled prevalence of differences between these paradigms of obesity, we performed a meta-analysis including the five data sets. We analyzed the descriptive characteristics of the study, applying sample weighting to better represent the overall older adult population in Latin America, using a 95% confidence interval (CI) as a dispersion parameter instead of standard deviations.\u003c/p\u003e \u003cp\u003eMeasurements from the general population were summarized, and prevalence of obesity in older adults in Latin America was calculated using both the classic and newly proposed definitions. Results were presented graphically. Due to heterogeneity among studies, pooled prevalence estimates were calculated by year of population census using a random-effects model. Heterogeneity was assessed using I\u0026sup2; test and the p-value from Cochran\u0026acute;s Q test. High heterogeneity was defined as I\u003csup\u003e2\u003c/sup\u003e values above 50% or p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.10 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. All point estimates with their 95% confidence intervals are displayed in the forest plot. Finally, to evaluate the impact of each dataset, a meta-analysis excluding each set at a time was conducted to determine whether excluding a specific dataset reduced heterogeneity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Ethical Considerations\u003c/h2\u003e \u003cp\u003e All studies were approved by their respective institutional review boards (IRBs). Participants provided written informed consent, and the principles of the Declaration of Helsinki were followed in all studies. Further information can be found in the methodological documents of the studies.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Study Selection\u003c/h2\u003e \u003cp\u003eA total sample of 14,028 individuals was obtained, comprising 8,754 women and 6,255 men (see flowchart in supplementary material). The participants were distributed as follows: 1,015 from MHAS, 1,629 from CRELES, 811 from PREHCO, 2,250 from SABE Ecuador, and 8,323 from ELSI Brazil.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Participant characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the anthropometric characteristics stratified by sex in the analyzed populations. For age, women from Puerto Rico had the highest mean age, 84.0 years (95% CI: 84.0\u0026ndash;85.2), while women from Mexico were the youngest, with a mean of 61.8 years (95% CI: 60.6\u0026ndash;63.0). Among men, Puerto Rican participants also had the highest average age, 83.6 years (95% CI: 82.8\u0026ndash;84.3), whereas those from Brazil had the lowest.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral characteristics of the population stratified by sex.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"16\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMHAS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eSABE E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003ePREHCO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eCRELES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003eELSI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (N\u0026thinsp;=\u0026thinsp;1,142)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMen (n\u0026thinsp;=\u0026thinsp;512)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWomen (n\u0026thinsp;=\u0026thinsp;630)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal (N\u0026thinsp;=\u0026thinsp;2,367)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMen (n\u0026thinsp;=\u0026thinsp;1,063)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWomen (n\u0026thinsp;=\u0026thinsp;1,304)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal (N\u0026thinsp;=\u0026thinsp;2,756)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMen (n\u0026thinsp;=\u0026thinsp;1,068)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eWomen (n\u0026thinsp;=\u0026thinsp;1,688)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTotal (N\u0026thinsp;=\u0026thinsp;1,820)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eMen (n\u0026thinsp;=\u0026thinsp;813)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eWomen (n\u0026thinsp;=\u0026thinsp;1,007)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eTotal (N\u0026thinsp;=\u0026thinsp;6,924)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eMen (n\u0026thinsp;=\u0026thinsp;2,799)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eWomen (n\u0026thinsp;=\u0026thinsp;4,125)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70.2 (69.4\u0026ndash;71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.4 (69.2\u0026ndash;71.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70.1 (68.9\u0026ndash;71.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71.9 (70.8\u0026ndash;73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e71.8 (70.5\u0026ndash;73.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e71.9 (67.5\u0026ndash;76.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e73.2 (72.8\u0026ndash;73.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72.9 (72.4\u0026ndash;73.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e73.4 (72.8\u0026ndash;73.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e73.5 (73.1\u0026ndash;73.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e73.1 (72.6\u0026ndash;73.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e73.8 (73.3\u0026ndash;74.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e70.3 (70.1\u0026ndash;70.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e70.2 (69.8\u0026ndash;70.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e70.3 (70-70.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight, mean (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.8 (65.4\u0026ndash;68.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.7 (68.4\u0026ndash;72.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.5 (61.8\u0026ndash;65.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.2 (60.3\u0026ndash;62.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63.8 (63.5\u0026ndash;64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e59.1 (56.5\u0026ndash;61.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e68.9 (68.1\u0026ndash;69.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e73.8 (72.6\u0026ndash;75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e65.1 (64.1\u0026ndash;66.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e66.2 (65.3\u0026ndash;66.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e70.5 (69.2\u0026ndash;71.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e62.2 (61.2\u0026ndash;63.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e69.8 (69.4\u0026ndash;70.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e73.5 (72.8\u0026ndash;74.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e66.8 (66.3\u0026ndash;67.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight, mean (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.54 (1.53\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.61 (1.6\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.48 (1.47\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.52 (1.47\u0026ndash;1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.6 (1.58\u0026ndash;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.46 (1.45\u0026ndash;1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.59 (1.58\u0026ndash;1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.66 (1.65\u0026ndash;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.52 (1.51\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.57 (1.56\u0026ndash;1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.64 (1.63\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.5 (1.49\u0026ndash;1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.59 (1.58\u0026ndash;1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e1.65 (1.64\u0026ndash;1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1.54 (1.53\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC, mean (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96.8 (95.6\u0026ndash;98.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97.9 (95.9\u0026ndash;99.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.9 (94.5\u0026ndash;97.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.7 (91.7\u0026ndash;95.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e93.4 (92.8\u0026ndash;93.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e93.9 (90-97.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97.6 (96.9\u0026ndash;98.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e98.9 (98-99.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e96.5 (95.5\u0026ndash;97.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e96.7 (96-97.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e95.3 (94.3\u0026ndash;96.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e98 (97.1\u0026ndash;99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e95.9 (95.4\u0026ndash;96.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e97.1 (96.4\u0026ndash;97.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e94.9 (94.3\u0026ndash;95.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHC, mean (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e101.5 (100.5-102.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98.5 (97.1\u0026ndash;99.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e104.1 (102.7-105.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e99.8 (95.8-103.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.1 (95.6\u0026ndash;96.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e102.7 (100-105.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e103.9 (103.3-104.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e102.2 (101.4\u0026ndash;103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e105.3 (104.4-106.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e97.7 (97.1\u0026ndash;98.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e95.9 (95.2\u0026ndash;96.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e99.3 (98.5-100.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e101.4 (101.1-101.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e99.1 (98.7\u0026ndash;99.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e103.3 (102.8-103.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHR, mean (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.94\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99 (0.98-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.92 (0.91\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.94 (0.92\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.97 (0.96\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.92 (0.91\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.94 (0.93\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.96 (0.95\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.91 (0.9\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.99 (0.98-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.99 (0.98-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.99 (0.98-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.95 (0.94\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.97 (0.96\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.92 (0.91\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, mean (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.9 (27.5\u0026ndash;28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (26.3\u0026ndash;27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.8 (28.2\u0026ndash;29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.5 (25.2\u0026ndash;27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.3 (25.1\u0026ndash;25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27.4 (26.5\u0026ndash;28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.3 (27-27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26.6 (26.2\u0026ndash;26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27.9 (27.4\u0026ndash;28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e26.9 (26.6\u0026ndash;27.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e26.2 (25.8\u0026ndash;26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e27.6 (27.2\u0026ndash;27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e27.6 (27.4\u0026ndash;27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e26.8 (26.5\u0026ndash;26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e28.3 (28-28.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity 1, % (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.6 (26.8\u0026ndash;36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.5 (17.3\u0026ndash;33.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.1 (32-44.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.9 (11.4\u0026ndash;32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.7 (9.1\u0026ndash;12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27.2 (20.6\u0026ndash;35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26 (23.7\u0026ndash;28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.9 (16.6\u0026ndash;23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e30.7 (27.5\u0026ndash;33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e22.1 (19.7\u0026ndash;24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e16.3 (13-20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e27.5 (24.2\u0026ndash;31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e28.6 (27.2\u0026ndash;30.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e20.9 (18.9\u0026ndash;23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e34.8 (32.9\u0026ndash;36.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity 2, % (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.7 (22.3\u0026ndash;31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.1 (10.9\u0026ndash;25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.4 (29.5\u0026ndash;41.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.7 (8.9\u0026ndash;32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.4 (9.2\u0026ndash;11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.4 (12.1\u0026ndash;40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24 (21.5\u0026ndash;26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.9 (16.6\u0026ndash;23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26.7 (23.8\u0026ndash;29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e21.7 (19.3\u0026ndash;24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e16.3 (13-20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e26.7 (23.5\u0026ndash;30.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e25.1 (23.7\u0026ndash;26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e19.1 (17.1\u0026ndash;21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e29.9 (28.1\u0026ndash;31.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifference, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e-1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-4.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"16\"\u003eMHAS\u0026thinsp;=\u0026thinsp;Mexican Health and Aging Study, SABE-E, PREHCO, CRELES, ELSI, WC, HC, WHR, BMI\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable 1. Describes the general and sex-specific anthropometric characteristics of each of the analyzed population censuses.\u003c/p\u003e\u003cp\u003eIn terms of height, women\u0026rsquo;s values were similar across countries, with the tallest observed in Brazil (1.55, 95% CI 1.54\u0026ndash;1.55) and the shortest in Ecuador (1.46, 95% CI 1.44\u0026ndash;1.48). Regarding weight, Brazilian women had the highest average (68.8, 95% CI 68.2\u0026ndash;69.3), followed by Mexico (64.9, 95% CI 63.3\u0026ndash;66.5, whereas Ecuador had the lowest (59.1, 95% CI 56.4\u0026ndash;61.8). Among men, the highest mean weight was in Brazil (75.7, 95% CI 75.0-76.4), and the lowest in Ecuador (63.8, 95% CI 63.5\u0026ndash;64.1).\u003c/p\u003e \u003cp\u003eFor the waist-to-hip ratio, women from Costa Rica had the highest values (0.98, 95% CI 0.98\u0026ndash;0.99; waist 98.0, 95% CI 97.0-98.9; hip 99.2, 95% CI 95.1\u0026ndash;96.6), while the lowest WHR was observed in Brazil (0.79, 95% CI 0.77\u0026ndash;0.80). Among men, Costa Rica also showed the highest WHR (0.99, 95% CI 0.98\u0026ndash;0.99; ​​​​waist 95.3 cm, 95% CI 94.2\u0026ndash;96.3; hip 99.2, 95% CI 98.4\u0026ndash;100.0).\u003c/p\u003e \u003cp\u003eRegarding BMI, Mexican women had the highest mean (29.0, 95% CI 28.3\u0026ndash;29.6), while Puerto Rican women had the lowest (26.8, 95% CI 26.1\u0026ndash;27.6). Among men, Mexico also showed the highest BMI (27.5, 95% CI 27.0\u0026ndash;28.0), like Brazil (27.1, 95% CI 26.9\u0026ndash;27.3), whereas Puerto Rico had the lowest (26.6, 95% CI 25.8-27.49).\u003c/p\u003e \u003cp\u003eObesity prevalence was lower when applying the new definition proposed by Rubino compared with the traditional one. Among women, the highest prevalence under the new definition was in Mexico (32.5%, 95% CI: 30.6\u0026ndash;40.6), and the lowest in Ecuador (23.5%, 95% CI: 11.9\u0026ndash;41). The largest decrease between definitions was observed in Brazilian women (-5.7%), while Costa Rica showed the smallest change (-0.8%). In men, Mexico showed the greatest decrease (-6.9%), whereas Costa Rica had no difference between the two definitions. (See full results in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Meta-analysis\u003c/h2\u003e \u003cp\u003eIn the meta-analysis of obesity among older adults in Latin America, using the classical definition and stratified by sex, heterogeneity of 91% in women, with an overall prevalence of 31% (95% CI 27\u0026ndash;35%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among men, heterogeneity was 95%, with an overall obesity prevalence of 19% (95% CI 14\u0026ndash;24%) (Fig.\u0026nbsp;4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing the new definition, heterogeneity decreased slightly to 89% in women; with an overall prevalence of 28% (95% CI 25\u0026ndash;32%) (Fig.\u0026nbsp;3), and to 93% in men, with an overall prevalence of 17% (95% CI 13\u0026ndash;22%).\u003c/p\u003e \u003cp\u003eWhen examining the general prevalence of obesity in older adults across the region, a reduction was observed with the new definition, about 3% in women and 2% in men.\u003c/p\u003e \u003cp\u003eWhen analyzing the forest plot for obesity prevalence in older adults, adjusting based on the year the population study was conducted, It was observed that when dividing by year of implementation, we obtain a greater homogeneity of the samples, with a cut-off point in 2010, having greater significance in the Mexican and Brazilian population, with the prevalence of obesity being lower by the new definition in all cases.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e3.3.1 Heterogeneity analysis and data analysis.\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe heterogeneity of the sample studied was studied through a cohort study, initially observing a high heterogeneity with an H2 value of 80\u0026ndash;90%, so it is proposed to make an adjustment by year, the cut-off point being 2010, with PREHCO, SABE E, CRELES before 2010, and MHAS ELSI after the cut-off point; observing a more homogeneous distribution between the populations, increasing the I2 value to 95 to 96% in the male population and from 92 to 93% in women.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eGlobally, a recent meta-analysis showed a 25% prevalence of obesity in older adults when defined by only BMI, based on 44 original studies [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. According to our results, applying the new definition of obesity substantially changes the estimated prevalence of older adults in Latin American living with clinical obesity, compared with the traditional BMI-based definition. To our knowledge, this is the first study to report such data in Latin America, including countries with some of the highest obesity rates worldwide (e.g., Mexico).\u003c/p\u003e \u003cp\u003eObesity is a major clinical entity linked to multiple metabolic and non-metabolic conditions [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Noteworthy, the clinical manifestations of obesity initially present cellular or tissue changes, with adequate preservation of the functional state, however, as the disease develops, it causes an organic alteration or dysfunction at different levels of the body, which lead to anatomical and pathophysiological changes, causing a limitation in functionality, excessive fatigue, decreased strength, which fails the presence of complications or worse outcomes, a striking similarity to clinical manifestations of frailty in older adults [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Its prevalence continues to increase globally, affecting all age group, including older adults. This rise is partly driven by social determinants of health, such as sedentary lifestyles and unhealthy diets [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In older populations, obesity has profound implications, contributing to cognitive decline, disability, frailty, and sarcopenia [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUsing the definition proposed by \u003cem\u003eThe Lancet Commission\u003c/em\u003e, which incorporates anthropometric measures such as weight, height, and waist-to-hip ratio, we observed lower prevalence rates of obesity compared with the Quetelet definition [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This difference was more pronounced in countries with higher baseline obesity rates, such as Brazil and Mexico, while little variation was observed in Costa Rica and Ecuador.\u003c/p\u003e \u003cp\u003eCurrently, there is limited research on obesity prevalence in older adults in Latin America underscores the relevance of these findings. Most available studies focus on genetic factors or ethnicity for this region. For instance, Vinueza et al. reported differences in obesity prevalence between Ecuadorians of mixed European-Indigenous ancestry (81%) and indigenous populations (6.6%), largely attributed to differences in physical activity [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBMI, however, is increasingly considered an inadequate measure in this context. It does not account for age-related changes in fat distribution, muscle mass, and metabolic changes. Other indices, including waist circumferences and waist-to-height ratio, have been proposed [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], yet evidence in older populations remains limited. \u003cem\u003eThe Lancet Commission\u0026rsquo;s\u003c/em\u003e approach, which combines WHR with imaging to evaluate fat and muscle distribution, may offer more precise diagnosis.\u003c/p\u003e \u003cp\u003eOur results highlight that overdiagnosis occurs when obesity is defined exclusively by BMI. In Brazil and Mexico, differences between definitions exceeded 5%, with women showing the greatest discrepancy. These findings underscore the importance of considering adiposopathy (defined as the pathological dysfunction of adipose tissue), proposed by Lorenzo et al, caused by a dysregulation between caloric intake and genetic and environmental predispositions[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].This is particularly relevant in older adults, in whom fat redistribution parallels age-related metabolic changes.\u003c/p\u003e \u003cp\u003eCorrect identification of clinical obesity in older adults can help propose individualized medical management, with a focus on lifestyle changes or increased physical activity, since studies for pharmacological treatment of obesity have not studied the impact on older adults, because they do not specify what type of mass is lost in the loss of BMI, so these treatments do not seem to provide a clinical benefit in older adults when using BMI as a measure.\u003c/p\u003e \u003cp\u003eUltimately, redefining obesity in older adults is critical for advancing clinical care. Incorporating distinctions such as preclinical versus clinical obesity could help target therapies appropriately. For instance, GLP-1 agonists may benefit some subgroups but inappropriate prescribing in older adults could increase risks rather than reduce complications [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eFinally, our study emphasizes the importance of culturally and anatomically tailored criteria for Latin American populations, where body proportions differ from those in other regions. Supporting \u003cem\u003eThe Lancet Commission\u0026rsquo;s\u003c/em\u003e recommendations may help reduce stigma, refine diagnosis, and improve person-centered strategies that promote healthy aging, independence, and quality of life in older adults.\u003c/p\u003e \u003cp\u003eIt is important to consider the anatomical characteristics of older Latin American populations, since a greater difference is observed in the waist-hip ratios, impacting the decrease in the prevalence of obesity when considered in association with height and weight, making us reflect on the role that adiposopathy may play, as proposed by De Lorenzo et al (33). The performance of the various BMI indices; waist circumference, waist-hip ratio, waist-height ratio, are high to assess the index of body adiposity, however, it is observed that it depends on multiple factors such as sex, ethnicity, comorbidities in older adults [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe recognize the importance of \u003cem\u003eThe Lancet\u003c/em\u003e Expert Consensus, which marks an important step toward redefining the perception of obesity as clinical entity. This new framework has already shown a significant impact in populations such as Brazil and Mexico and provides an opportunity to further study the benefits of a more precise classification system, supported by specific anthropometric measures of fat and muscle tissue. Moreover, this redefinition can help reduce the stigma that societies often place on obesity, prevent related complications, and improve understanding of the aging-related changes experienced by older adults, ultimately promoting successful aging, better quality of life, and greater independence.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the National Institute of Geriatrics of Mexico for the support and funding that made this work possible.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eE.R. wrote the complete original draft, M. P. and N. S. collected the information and developed the database in STATA 19, M. A. and M. G. performed the data and results analysis, E. R. and M. P. and J. P. wrote the discussion and conclusions. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare not having any conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSponsor\u0026acute;s Role\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis work was made possible thanks to the generous support of the National Institute of Geriatrics of Mexico.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sharing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMHAS has an open policy of sharing their data just by a simple registration in mhas.web.org.\u003c/p\u003e\n\u003cp\u003eSABE E has an open data-sharing policy, available at https://www.ecuadorencifras.gob.ec/encuesta-de-salud-bienestar-del-adulto-mayor/\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePRHECO has an open data-sharing policy, available at https://prehco.rcm.upr.edu/.\u003c/p\u003e\n\u003cp\u003eCRELES has an open data-sharing policy, available at https://populationsciences.berkeley.edu/creles/ , upon request.\u003c/p\u003e\n\u003cp\u003eELSI has an open policy of sharing your data simply by registering at https://elsi.cpqrr.fiocruz.br/\u003c/p\u003e\n\u003cp\u003eOur working group can share our code and syntax upon request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare they do not have any conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eWhy does this paper matter?\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis new definition will have substantial changes in the management of obesity but appropriate data on how this will work on older adults is still missing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSponsor\u0026acute;s Role\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was made possible thanks to the generous support of the National Institute of Geriatrics of Mexico.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlfaris N et al (2023) Global Impact of Obesity. Gastroenterol Clin North Am 52(2):277\u0026ndash;293\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRubino F et al (2025) Definition and diagnostic criteria of clinical obesity. Lancet Diabetes Endocrinol 13(3):221\u0026ndash;262\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollaborators GBDAB (2025) Global, regional, and national prevalence of adult overweight and obesity, 1990\u0026ndash;2021, with forecasts to 2050: a forecasting study for the Global Burden of Disease Study 2021. Lancet 405(10481):813\u0026ndash;838\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollaborators GBDAB (2025) Global, regional, and national prevalence of child and adolescent overweight and obesity, 1990\u0026ndash;2021, with forecasts to 2050: a forecasting study for the Global Burden of Disease Study 2021. 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BMJ 376:e067516\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAraki A (2024) Individualized treatment of diabetes mellitus in older adults. Geriatr Gerontol Int 24(12):1257\u0026ndash;1268. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/ggi.14979\u003c/span\u003e\u003cspan address=\"10.1111/ggi.14979\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Obesity, Geriatric Epidemiology, Body Composition","lastPublishedDoi":"10.21203/rs.3.rs-8309007/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8309007/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe present study aims to determine the prevalence of obesity in older adults in the Latin American population, making a comparison using the two definitions: the classic one based on the body mass index (BMI) and the new definition of the Lancet Consensus on obesity of 2025.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Anthropometric data were collected from population studies in the aforementioned countries, determining the grouped prevalence, taking into account the 2 definitions of obesity that exist, performing a meta-analysis, which included 5 data sets using the population censuses of Puerto Rico, Costa Rica, Brazil, Ecuador and Mexico, extracting anthropometric data from men and women aged 65 years or older.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 14,028 adults over 60 years of age were included, with an average age of 69 years without distinction by sex, showing a greater difference in the prevalence of obesity in Mexico (men 28.5 versus 18.5 / women 39.7 versus 35.5) and Brazil (men 24.7 versus 22.22 / women 35.6 versus 29.9), with a distribution of the general prevalence by sex of 28% in women and 17% in men. The difference in the prevalence of obesity decreased when using various anthropometric measures in older adults compared to BMI, having a greater impact in populations in Mexico and Brazil, showing a better categorization of obesity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Obesity prevalence decreases in older adults because BMI miss variations in muscle mass and adipose tissue, therefore the new definition could improve characterization of this clinical condition and properly assess risk and treatment.\u003c/p\u003e","manuscriptTitle":"Difference in prevalence using only body mass index compared to new parameters: A Secondary Analysis of Latin American Datasets.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-16 16:05:42","doi":"10.21203/rs.3.rs-8309007/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f474ebaf-3811-42ad-86b7-f77ecd623960","owner":[],"postedDate":"December 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-07T10:14:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-16 16:05:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8309007","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8309007","identity":"rs-8309007","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}