Obesity prevalence: comparison of traditional and new classification approaches in a Swiss population-based study(2005-2024)

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In 2025, new guidelines recommended adding anthropometric measures. This study analyzed obesity prevalence comparing definitions, and their associations with cardiovascular and metabolic diseases. Methods: Using a population-based study in Geneva, Switzerland (2005-2024), we measured the prevalence of obesity based on the traditional and new definitions. Reclassification patterns were examined, and associations with diabetes, hypertension, and dyslipidemia were assessed via logistic regression and receiver operating characteristic analyses. Results: Among 14,658 individuals (mean age 48.2±13.7; 51.4% women), obesity prevalence ranged from 10.8% to 39.9% using new classifications, compared to 13.1% with BMI alone (p<0.001). Reclassifications differed among men and women. New classifications demonstrated superior discriminative performance for the detection of cardiovascular and metabolic outcomes compared to BMI alone. BMI + waist to hip ratio showed the strongest associations with diabetes (aOR 4.61; 3.87-5.47), and hypertension (aOR 3.61; 3.18-4.09), while waist to hip and waist to height ratio showed the strongest association with dyslipidemia (aOR 1.95; 1.75-2.16). Conclusion: Adding anthropometric measures to BMI substantially improves obesity detection, better identifies those at risk and could be a useful tool in primary care settings. Health sciences/Medical research/Epidemiology Health sciences/Diseases/Endocrine system and metabolic diseases/Obesity obesity reclassification guidelines chronic disease risk factors socioeconomic determinants Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Obesity is a central public health concern, officially recognized as a chronic complex disease by the World Health Organization (WHO) in 1948 1 . Obesity is rising globally and in 2024, nearly 40% of the adult population is classified as overweight and 15% with obesity 2 . Obesity is a major reason for consultation in primary care settings and is strongly associated with increased risks of numerous medical conditions such as cardiovascular diseases (CVDs) hypertension, type II diabetes, dyslipidemia, and several types of cancers 3 . The definition of obesity between the 1990s and 2025 was based on body mass index (BMI); however, this metric alone failed to fully capture the important factors such as fat distribution, the metabolic health of an individual, or other clinical variables that can contribute to an adiposity increase 4,5 . Recently, the Lancet Diabetes & Endocrinology Commission emphasized the need to consider obesity on a spectrum from pre-clinical to clinical obesity, looking at BMI and additional measures, known as anthropometric criteria (e.g., waist-circumference, waist-to-hip ratio, waist-to-height ratio) 3 . Under these new guidelines, obesity is first diagnosed by confirming excess adiposity, either through a combination of BMI and anthropometric criteria, direct fat measurement, or presumed BMI > 40 kg/m 2 . A second step distinguishes between pre-clinical obesity, defined as excess body fat without current organ dysfunction or limitations in daily activities, and clinical obesity characterized by the presence of obesity-related disease or functional impairment 3 . The traditional classification of underweight, healthy, overweight and obesity was replaced by underweight, healthy and obesity (pre-clinical, clinical). A key novelty of this framework is its ability to both declassify and newly classify individuals with obesity. Adding elements such as waist-circumference, waist-to-hip or waist-to-height ratios to traditional BMI-based assessments enhances the identification of individuals with excess visceral or abdominal fat, a key factor in metabolic risk. These additional anthropometric indicators increase the sensitivity of identifying individuals in the preclinical stage of obesity, those with excess fat but no organ damage or functional impairment, thereby supporting earlier lifestyle or therapeutic interventions. At the same time, they improve the specificity of identifying clinical obesity, by distinguishing individuals whose elevated BMI does not correspond with increased visceral adiposity or associated health risks (e.g., athletes), thus reducing the likelihood of misclassifying healthy individuals with clinical obesity. Additionally, these measurements are easily performed in a primary care setting. Of note, direct excess fat measurement has also been introduced as one of the measures in the new definition of obesity, however is less easily performed and requires more resources than anthropometric measures 3 . Waist-circumference (WC) and waist-to-hip ratio (WHR) are among the most widely cited indicators for assessing body fat distribution and are considered stronger predictors of obesity-related health risks when used in combination with BMI 6 . While BMI reflects general body size, WC and WHR provide additional insight by capturing abdominal fat 7 , which has been consistently associated with increased risk of mortality 8,9 . Notably, individuals with elevated WC levels face a higher likelihood of adverse outcomes even within the same BMI category 6 . In this context, waist-to-height ratio (WHtR) has also been proposed as a simple and universal metric for assessing abdominal obesity, offering the advantage of a single, non-sex specific cut-off point 10,11 . Although obesity has been widely studied, data on its prevalence using the newly proposed definition (2025) are still lacking in population-based studies worldwide, as most epidemiological surveillance continues to rely on BMI alone. Recent studies compared the prevalence of obesity based on the new guidelines and the traditional definition, in specific populations 12 or using electronic medical records 13 . In a research letter published in May 2025, results showed no difference the prevalence of obesity with or without confirmation of excess adiposity among 2225 adults using data from the 2017-2018 National Health and Nutrition Examination Survey (NHANES) 14 . These data were also used in a separate study to estimate the prevalence of clinical obesity between 2017-2020 using the new definition 15 . While these are important preliminary results, they are based on a single time point and a relatively small sample of individuals. In Geneva, Switzerland, our study group has previously investigated various aspects of obesity, such as understanding the spatial 16,17 and socioeconomic patterns of obesity and related behaviors 18 . As reclassifications can translate into substantial variations in clinical and epidemiological practices, it is important to investigate the prevalence of obesity based on the new guidelines compared to BMI alone, as well as potential associations with socioeconomic determinants and health outcomes. This population-based study aims to evaluate the prevalence of obesity based on the previous classification using BMI alone, compared to the new definition considering central adiposity measures (WC, WHR, and WHtR), along with potential associations with socioeconomic determinants and health outcomes using data between 2005 and 2024. Methods Study design and population This research draws upon data from the Bus Santé study, an ongoing, population-based, cross-sectional survey conducted annually in Geneva, Switzerland, since 1993 19 . Each year, around 1,000 residents are selected through an age- and sex-stratified random sampling process. Participants are initially contacted by mail, with follow-up attempts made via up to seven phone calls and two written reminders if needed. When individuals cannot be reached after these efforts, they are systematically replaced using the same stratified sampling method, based on annually updated records from local authorities. For the present analysis, participants included were enrolled between 2005 and 2024. Due to the COVID-19 pandemic, data collection was temporarily suspended between 2020 and 2023 and resumed in 2023. Initially, the study targeted individuals aged 35 to 74 years, but this was expanded to 20-74 years in 2011. All participants within this range were retained for the current analysis. Participation rate was 61%. All participants provided written informed consent, and the study protocol was approved by the Ethics Committee of Geneva (IRB00003116, PB_2016-00363; CCER 2022-01544). Data collection Participants completed self-administered questionnaires covering socioeconomic status, lifestyle factors, and general health status. A clinical examination was conducted by trained research nurses during scheduled visits. Measurements included height, weight, waist and hip circumference, and blood pressure. Blood pressure was assessed three times in seated position after a 5-minute rest, with 30-second intervals between readings; the average of the three measurements was used for analysis. Similarly, for waist, and hip measurements, three readings were taken, and the mean of the measurements was used, unless the first two measurements were identical, in which case a third measurement was not taken. Additionally, fasting blood samples were collected to measure glycemic and lipid profiles, including fasting glucose, total cholesterol, high-density lipoprotein (HDL) and low-density lipoprotein (LDL) cholesterol, and triglycerides, using standardized enzymatic methods (Bayer Technicon Diagnostics, CV 1.4%, 1.2%, and 1.5% for glucose, cholesterol and triglyceride respectively). Measures The primary outcome was the prevalence of obesity according to different definitions. Obesity was assessed using four standard indicators: Body Mass Index (BMI), Waist Circumference (WC), Waist-to-Hip Ratio (WHR), and Waist-to-Height Ratio (WHtR). BMI-based classification followed WHO guidelines, with obesity defined as BMI ≥ 30 kg/m 2 20 . WC classification used sex-specific cut-offs for abdominal obesity: WC > 88 cm for women and > 102 cm for men, in line with WHO recommendations 21 . WHR classification also relied on sex-specific thresholds: WHR ≥ 0.85 for women and ≥ 0.90 for men 21 . WHtR classification applied a single threshold of ≥ 0.5 for both men and women 10 . Obesity status was assessed using the previous definition of BMI only and the new definitions based on the recent guidelines 3 considering: (1) BMI combined with at least one central adiposity indicator (e.g., BMI+WC, BMI+WHR, BMI+WHtR), or (2) combinations of two central adiposity indicators excluding BMI (e.g., WC+WHR, WC+WHtR, WHR+WHtR). Individuals in the new obesity classifications (BMI+WC, BMI+WHR, WC+WHR, WC+WHtR, and WHR+WHtR) were categorized into four groups: stable obesity (obesity is present in both BMI only and the new classification), new obesity (reclassified with obesity under the new classification), declassified (obesity is not present under the new classification), and healthy (obesity is not present in both classification). Socioeconomic variables included education (primary, secondary, tertiary), civil status (single, living with someone, or living alone), nationality (Swiss, Non-Swiss), and household income. Household income was categorized into gross monthly household which were reported into four groups (1CHF ~ 1.2 USD): low ( 9,500 CHF). Diabetes was defined as fasting glucose ≥ 7 mmol/L or self-reported diabetes or use of antidiabetic treatment. Hypertension was defined as mean systolic and/or diastolic blood pressure ≥ 140/90 mmHg or self-reported hypertension or use of antihypertensive treatment 22 . Dyslipidemia was defined as a total cholesterol ≥ 6.2 mmol/L or self-reported hypercholesterolemia (dyslipidemia) or use of lipid-lowering medication. Covariates Covariates included the following demographic characteristics: age at the time of participation, sex, income group, educational level, civil status, nationality, and survey year. Additionally, the presence or absence of a chronic disease, including diabetes, hypertension, and dyslipidemia, was also considered as other potential covariates. Statistical analysis Descriptive statistics were computed for participant characteristics across study years. Continuous variables were expressed as means ± standard deviations (SD) or medians and interquartile ranges (IQR), as appropriate. Categorical variables were presented as frequencies and percentages. For comparisons of continuous variables between groups, Student’s t-tests were used for normally distributed data. For non-normally distributed continuous variables, the Kruskal-Wallis’s rank sum test was used when comparing more than two groups. The Wilcoxon rank-sum test was used for pairwise comparisons between two groups, when appropriate. Pearson’s chi-square test was used for comparisons of categorical variables, and Fisher’s exact test was used for count data with small sample sizes (e.g., when examining individuals transitioning between obesity classifications). A two-sided p-value < 0.05 was considered statistically significant. The discriminatory performance of each obesity definition for diabetes, hypertension and dyslipidemia was assessed using Receiver Operating Characteristic (ROC) curves. The Area Under the Curve (AUC) was calculated to quantify the ability of each classification to discriminate between participants with and without the outcome, with corresponding 95% confidence interval calculated using DeLong’s method. ROC curves with 95% confidence bands were plotted, and corresponding Youden indices, sensitivity, and specificity were reported. Associations between socioeconomic determinants and obesity based on the different classifications (BMI only, BMI+WC, BMI+WHR, or BMI+WHtR) were assessed using logistic regression models, adjusting for age, sex, household income, education, civil status, nationality, and survey year. Associations between obesity based on the different classifications and health outcomes (diabetes, hypertension, and dyslipidemia) were assessed using logistic regression models adjusting for age, sex, household income, education, civil status, nationality, and survey year. Forest plots were used to illustrate the effect sizes and 95% confidence intervals of these results. All analyses were conducted using R 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria). Results For this study between 2005 and 2024, n = 14,658 participants were included, with a mean age of 48.2 years old [standard deviation (SD) 13.7 years], 51.4% being women. Overall, 49.2% had a tertiary education, 40.0% a high income, and 8.2% had diabetes, 28.7% hypertension or 49.1% dyslipidemia. Table 1 shows the characteristics of participants. The prevalence of obesity was 13.1% overall when considering the classification with BMI only, 11.5% when considering BMI+WC, 10.8% when considering BMI+WHR, and 13.0% when considering BMI+WHtR. When obesity was defined using combined anthropometric indicators excluding BMI, the estimated prevalence varied depending on the classification used: 18.1% with WC+WHR, 21.2% with WC+WHtR, and as high as 39.9% with WHR+WHtR (Figure 1). Of note, many overweight individuals were reclassified with obesity especially when using WHR+WHtR. Obesity prevalence differed significantly between BMI only and all alternative measures: BMI+WC (χ² = 12,712, df = 1, p < 0.001), BMI+WHR (χ² = 11,768, df = 1, p < 0.001), BMI+WHtR (χ² = 14,588, df = 1, p < 0.001), WC+WHR (χ² = 4,829, df = 1, p < 0.001), WC+WHtR (χ² = 5,949, df = 1, p < 0.001), and WHR+WHtR (χ² = 1,666, df = 1, p < 0.001). All pairwise comparisons remained statistically significant after correction for multiple testing (adjusted α = 0.0083), indicating non-homogeneous distributions across classifications despite similar global obesity prevalence rates. Figure 1 shows the distribution of obesity based on the different classifications. Figure 2 shows the reclassification of individuals across categories (stable obesity, new obesity, declassified, healthy) according to the different obesity definitions. Reclassification analyses quantified the number of individuals with obesity by BMI only that were no longer considered with obesity when additional measures were applied: n=224 individuals with BMI+WC, n=336 with BMI+WHR, and n=7 when with BMI+WHtR. For the BMI+WHtR no further analysis was performed as the sample size was too small. Conversely, individuals were newly classified with obesity: n=1,211 with WC+WHR, n=1,411 with WC+WHtR, and n=4,264 with WHR+WHtR. Several of these individuals were overweight according to the BMI only definition. Mean BMI among individuals with new obesity was 27.4 (SD 1.8) for WC+WHR, 27.5 (1.8) for WC+WHtR, and 26.2 (SD 2.1) for WHR+WHtR. Individuals who were declassified had high BMI levels: 31.2 (SD 2.8) under BMI+WC and 33.4 (SD 3.7) under BMI+WHR, 36.0 (SD 12.1) under BMI+WHtR, but normal anthropometric measures. These individuals were mostly 35-49 years old, whereas the prevalence of individuals with new obesity increased with age. Sex-specific patterns were observed as women were more likely to be declassified when waist to hip ratio was added to BMI and were more likely to be reclassified with new obesity when waist to hip ratio was paired with waist circumference. On the other hand, men were more likely to be declassified under WC+WHtR, and more likely to be reclassified with new obesity under WHR+WHtR. Education disparities were consistent, with individuals with new obesity accounting for 12.4% to 38.7% among those with primary education, versus 6.6% to 25.3% among those with tertiary education. Income showed a similar gradient, with moderate-income groups more often reclassified with new obesity than high-income participants. Marital status further differentiated groups, with higher proportions of new obesity among those living with a partner (8.4%–32%) than those living alone (8%–23.4%). There were no differences in reclassification based on nationality. Tables S1.1 and S1.2 show the distribution of the prevalence of obesity according to the different classifications among groups among different sociodemographic groups. Figure 3 presents the ROC curve analyses comparing the different obesity definitions for diabetes, hypertension, and dyslipidemia. For diabetes, BMI-only yielded an AUC of 0.632 (95% CI: 0.616–0.647), with a Youden index of 0.264, sensitivity of 0.354, and specificity of 0.909. The combined anthropometric definitions demonstrated higher discriminative ability: WC+WHR (AUC 0.679) and WC+WHtR (AUC 0.681) compared to BMI only, while WHR+WHtR showed the highest AUC of 0.711, with a marked increase in sensitivity (0.736) but a lower specificity (0.685) compared to 0.920 (BMI only). For hypertension, BMI-only had a lower predictive ability (AUC 0.587), whereas WC+WHR (AUC 0.627), WC+WHtR (AUC 0.630), and WHR+WHtR (AUC 0.679) had higher discriminative ability. Sensitivity increased substantially for WHR+WHtR (0.653) compared with BMI-only (0.255), although specificity decreased (0.705). Similarly, for dyslipidemia, BMI-only showed lower predictive performance (AUC 0.540), compared to combined anthropometric classifications, especially WHR+WHtR (AUC 0.658), which provided better discrimination. Full results are presented in Table S3. Obesity was associated with diabetes, hypertension and dyslipidemia across all definitions when compared to healthy individuals. Obesity according to the different classifications showed an adjusted odds ratio (aOR 4.61; 3.87-5.47) for the association between BMI+WHR and diabetes, (aOR 3.61; 3.18-4.09) for the association between BMI+WHR and hypertension, while WHR+WHtR showed the highest association with dyslipidemia (aOR 1.95; 1.75-2.16). The full results of the associations are shown in Figure 4. Discussion In this population-based study, we examined obesity prevalence using traditional and new 2025 classifications 3 —including BMI alone, BMI combined with anthropometric measurements (WC, WHR, and WHtR), as well as combinations of two anthropometric criteria excluding BMI (WC+WHR, WC+WHtR, and WHR+WHtR). Our analysis of the adult population in Geneva between 2005 and 2024 (n=14,658) revealed significant variability in obesity prevalence across classifications, ranging from 13% when defined by BMI alone to 40% under WHR+WHtR, illustrating how different classifications highlight distinct dimensions of disease burden. Reclassification showed age and sex-specific patterns. For instance, adding waist circumference to BMI or other anthropometric measures showed the highest prevalence of obesity among women. In contrast, WHtR more frequently reclassified men with new obesity. BMI+WC often declassified men, likely due to higher lean mass or non-abdominal fat distribution that BMI alone could not discern. These sex-specific reclassification patterns support previous findings that reported that WHtR achieved the highest sensitivity in men (81.6%), while WC was more sensitive in women (86.5%) for detecting obesity-related health risks 23 . Additionally, socioeconomic disparities were identified with individuals with a primary education more likely to have new obesity when using an anthropometric measure. This highlights the need to measure and include anthropometric indicators in the detection of obesity especially for high-risk individuals. Of note, the new definition reclassified several overweight individuals with new obesity, as the definition became a spectrum of obesity 3 compared to the traditional classification of underweight, healthy, overweight and obesity. From a clinical perspective, our findings highlight some challenges with the recent definition of obesity, notably in primary care settings. Those declassified based on the addition of an anthropometric measure to BMI had lower risks of diabetes and hypertension than participants with consistent obesity, yet they remained at higher risk when compared to healthy individuals, demonstrating that reclassification should not be interpreted as a return to full metabolic health. Conversely, individuals with normal BMI but central obesity, reclassified with new obesity under WC+WHR, WC+WHtR, or WHR+WHtR, were more likely to have dyslipidemia, revealing the hidden burden of central adiposity even in the absence of high BMI 6 . Associations showed similar findings with the highest association between BMI+WHR and diabetes, and BMI+WHR and hypertension, while WHR+WHtR showed the highest association with dyslipidemia. Studies have highlighted the limitations of BMI alone in detecting visceral fat, which accumulates around internal organs in the abdominal area. Visceral fat is associated with increased risks of inflammation 24 , insulin resistance 25,26 , diabetes 27 , cancers, non-alcoholic fatty liver disease (NAFLD), cardiovascular diseases 28 , cognitive decline 29 , and mortality 30 , emphasizing the importance of fat distribution. Adding an anthropometric measure to BMI to detect obesity is a good approach to capture both abdominal fat and overall obesity. Within the new classification system, combining BMI with anthropometric indicators tended to improve sensitivity when compared to BMI alone and specificity when compared to anthropometric measures excluding BMI. A recent study using the new classifications of obesity showed that pre-clinical obesity using anthropometric measures in addition to BMI was associated with an increased cancer risk, highlighting the importance of early risk detection 31 , while another recent study showed that the prevalence of obesity by BMI only was similar to the obesity prevalence after confirmation of excess adiposity using the National Health and Nutrition Examination Survey (NHANES) data between 2017-2018. While this study showed different results to ours, it was limited by a smaller sample size (n=2225) and data over a smaller period of time 14 . Therefore, choice of classification should be context-dependent, guided by the health outcome of interest and whether the goal is to maximize detection and to prioritize individuals at greatest risk, using easily accessible measures in a primary care setting such as BMI and anthropometric measures. Although screening for obesity is recommended for all adults, it is not consistently performed in primary care settings 32 . Yet, detection and counseling delivered by healthcare professionals have been shown to be associated with almost 4 times increased odds of wanting to lose weight and 3.5 times increased odds of attempting to lose weight 33 . Effective counseling strategies include using people-first language such as patients with obesity instead of “obese patients”, and more motivating terminology 34 , as well as starting with small steps for behavioral change such as simply starting a dialogue by asking the patient’s permission to discuss weight. This is part of the 5A’s framework: ask, assess, advise, agree and assist 35,36 . This study has several limitations. First, it excluded younger individuals (children and adolescents under 20 years old), a population of increasing concern given the rising prevalence obesity, particularly central obesity, at early ages. Ethnic differences were not considered in the interpretation of anthropometric measures (WC, WHR, and WHtR), as 66.7% of the participants were Swiss, despite evidence that body fat distribution and corresponding health risks may vary significantly among populations 6,37 . This could limit generalizability to and interpretability in other populations. Conclusion Overall, this study shows that obesity should be considered with multidimensional approaches in its assessment and management, integrating anthropometric parameters in addition to BMI. Primary care physicians could benefit from this approach, increasing the sensitivity and detection of obesity, especially in high-risk populations using measures that can be easily performed and do not require special tools or interventions. Awareness of how the criteria may differentially impact men and women is also important in the choice and interpretation of the measures. Primary care physicians are in a unique position to counsel and follow patients with pre-clinical and clinical obesity and applying these new guidelines reinforces their role in detecting and confirming obesity early on, while avoiding misclassification based on BMI alone. Declarations Statement : I confirm that all the research meets the journal’s ethical guidelines, including adherence to the legal requirements of Geneva, Switzerland. We further confirm that this work has been conducted with the ethical approval of the Cantonal Research Ethics Commission of Geneva, Switzerland, and that the approvals are acknowledged within the manuscript. There are no potential conflicts of interest related to this work. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. CRediT authorship contribution statement: Conceptualization: I.G. and M.N.; Data curation: C.M., C.C.; Formal analysis: C.M., R.D., C.C. Methodology: C.M., R.D., C.C. and M.N.; Project administration: I.G. and M.N.; Supervision: I.G.; Writing – original draft: C.M. and M.N.; Writing - review & editing: M.N., C.M., C.C., R.D., S.S., N.FL, I.G Data availability: Data is available upon reasonable request made to the corresponding author. Funding : This study was funded by the cantonal office of health in Geneva. References WHO. Obesity and overweight [Internet]. 2024 [cited 2025 Mar 4];Available from: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight Kibret KT, Strugnell C, Backholer K, Peeters A, Tegegne TK, Nichols M. Life-course trajectories of body mass index and cardiovascular disease risks and health outcomes in adulthood: Systematic review and meta-analysis. Obes Rev 2024;25(4):e13695. Rubino F, Cummings DE, Eckel RH, et al. 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The impact of a health professional recommendation on weight loss attempts in overweight and obese British adults: a cross-sectional analysis. BMJ Open. 2013 Nov 4;3(11):e003693. doi: 10.1136/bmjopen-2013-003693. PMID: 24189083; PMCID: PMC3822310. Kahan SI. Practical Strategies for Engaging Individuals With Obesity in Primary Care. Mayo Clin Proc. 2018 Mar;93(3):351-359. doi: 10.1016/j.mayocp.2018.01.006. PMID: 29502565. Vallis M, Piccinini-Vallis H, Sharma AM, Freedhoff Y. Clinical review: modified 5 As: minimal intervention for obesity counseling in primary care. Can Fam Physician. 2013 Jan;59(1):27-31. PMID: 23341653; PMCID: PMC3555649. Rueda-Clausen CF, Benterud E, Bond T, Olszowka R, Vallis MT, Sharma AM. Effect of implementing the 5As of obesity management framework on provider-patient interactions in primary care. Clin Obes. 2014 Feb;4(1):39-44. doi: 10.1111/cob.12038. Epub 2013 Oct 29. PMID: 25425131. Ashwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev 2012;13(3):275–86. Table Table 1 is available in the Supplementary Files section. Additional Declarations There is NO conflict of interest to disclose Supplementary Files SupplTables.docx Table1Baselinecharacteristicsofallparticipantsbetween2005.docx Cite Share Download PDF Status: Published Journal Publication published 01 Apr, 2026 Read the published version in International Journal of Obesity → Version 1 posted Editorial decision: revise 19 Jan, 2026 Review # 2 received at journal 18 Dec, 2025 Reviewer # 2 agreed at journal 08 Dec, 2025 Review # 1 received at journal 10 Nov, 2025 Reviewer # 1 agreed at journal 30 Oct, 2025 Reviewers invited by journal 27 Oct, 2025 Submission checks completed at journal 02 Oct, 2025 First submitted to journal 01 Oct, 2025 Unknown event 01 Oct, 2025 Editor assigned by journal 30 Sep, 2025 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. 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1","display":"","copyAsset":false,"role":"figure","size":191030,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of obesity according to different classification methods\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7752385/v1/bf2fcee6d43c3afa616f7d94.png"},{"id":95329510,"identity":"cec9bacc-2696-48f2-bb2a-5944c90044b5","added_by":"auto","created_at":"2025-11-06 18:57:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":316001,"visible":true,"origin":"","legend":"\u003cp\u003eAlluvial plot showing transitions of moving individuals between BMI only classification and other obesity classifications\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7752385/v1/e18cb78f6083e4183363ecb6.png"},{"id":95329506,"identity":"816edc6f-ebe8-4cc9-b0cc-195c50743147","added_by":"auto","created_at":"2025-11-06 18:57:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":276703,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic (ROC) curves for three health outcomes (diabetes, hypertension, and dyslipidemia) according to different obesity definitions.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7752385/v1/fb7bca5897e1dd95b5d36bf5.png"},{"id":95523819,"identity":"1415caa0-e646-4969-ba36-19177f763509","added_by":"auto","created_at":"2025-11-10 10:01:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":124620,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of adjusted odds ratios (aOR) by obesity classification\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7752385/v1/f5cfc1e4dd7df245c300cfdf.png"},{"id":105984377,"identity":"21981bb2-590e-4765-8320-d7fee212d3b4","added_by":"auto","created_at":"2026-04-02 07:13:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1279735,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7752385/v1/c536b363-fd82-43a1-8f59-a31295f6087f.pdf"},{"id":95329503,"identity":"1fb809f2-c755-46e0-a5ae-db9996cbb0fe","added_by":"auto","created_at":"2025-11-06 18:57:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":66152,"visible":true,"origin":"","legend":"","description":"","filename":"SupplTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7752385/v1/579445ed3974233c7c63a2be.docx"},{"id":95523792,"identity":"ffb17c72-6708-4c8d-bd53-92752b845d0f","added_by":"auto","created_at":"2025-11-10 10:00:50","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18004,"visible":true,"origin":"","legend":"","description":"","filename":"Table1Baselinecharacteristicsofallparticipantsbetween2005.docx","url":"https://assets-eu.researchsquare.com/files/rs-7752385/v1/23b92421681292853db937e8.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Obesity prevalence: comparison of traditional and new classification approaches in a Swiss population-based study(2005-2024)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObesity is a central public health concern, officially recognized as a chronic complex disease by the World Health Organization (WHO) in 1948\u003csup\u003e1\u003c/sup\u003e. Obesity is rising globally and in 2024, nearly 40% of the adult population is classified as overweight and 15% with obesity\u003csup\u003e2\u003c/sup\u003e. Obesity is a major reason for consultation in primary care settings and is strongly associated with increased risks of numerous medical conditions such as cardiovascular diseases (CVDs) hypertension, type II diabetes, dyslipidemia, and several types of cancers\u003csup\u003e3\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe definition of obesity between the 1990s and 2025 was based on body mass index (BMI); however, this metric alone failed to fully capture the important factors such as fat distribution, the metabolic health of an individual, or other clinical variables that can contribute to an adiposity increase\u003csup\u003e4,5\u003c/sup\u003e. Recently, the Lancet Diabetes \u0026amp; Endocrinology Commission emphasized the need to consider obesity on a spectrum from pre-clinical to clinical obesity, looking at BMI and additional measures, known as anthropometric criteria (e.g., waist-circumference, waist-to-hip ratio, waist-to-height ratio)\u003csup\u003e3\u003c/sup\u003e. Under these new guidelines, obesity is first diagnosed by confirming excess adiposity, either through a combination of BMI and anthropometric criteria, direct fat measurement, or presumed BMI \u0026gt; 40 kg/m\u003csup\u003e2\u003c/sup\u003e. A second step distinguishes between pre-clinical obesity, defined as excess body fat without current organ dysfunction or limitations in daily activities, and clinical obesity characterized by the presence of obesity-related disease or functional impairment\u003csup\u003e3\u003c/sup\u003e. The traditional classification of underweight, healthy, overweight and obesity was replaced by underweight, healthy and obesity (pre-clinical, clinical).\u003c/p\u003e\n\u003cp\u003eA key novelty of this framework is its ability to both declassify and newly classify individuals with obesity. Adding elements such as waist-circumference, waist-to-hip or waist-to-height ratios to traditional BMI-based assessments enhances the identification of individuals with excess visceral or abdominal fat, a key factor in metabolic risk. These additional anthropometric indicators increase the sensitivity of identifying individuals in the preclinical stage of obesity, those with excess fat but no organ damage or functional impairment, thereby supporting earlier lifestyle or therapeutic interventions. At the same time, they improve the specificity of identifying clinical obesity, by distinguishing individuals whose elevated BMI does not correspond with increased visceral adiposity or associated health risks (e.g., athletes), thus reducing the likelihood of misclassifying healthy individuals with clinical obesity. Additionally, these measurements are easily performed in a primary care setting. Of note, direct excess fat measurement has also been introduced as one of the measures in the new definition of obesity, however is less easily performed and requires more resources than anthropometric measures\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWaist-circumference (WC) and waist-to-hip ratio (WHR) are among the most widely cited indicators for assessing body fat distribution and are considered stronger predictors of obesity-related health risks when used in combination with BMI\u003csup\u003e6\u003c/sup\u003e. While BMI reflects general body size, WC and WHR provide additional insight by capturing abdominal fat\u003csup\u003e7\u003c/sup\u003e, which has been consistently associated with increased risk of mortality\u003csup\u003e8,9\u003c/sup\u003e. Notably, individuals with elevated WC levels face a higher likelihood of adverse outcomes even within the same BMI category\u003csup\u003e6\u003c/sup\u003e. In this context, waist-to-height ratio (WHtR) has also been proposed as a simple and universal metric for assessing abdominal obesity, offering the advantage of a single, non-sex specific cut-off point\u0026nbsp;\u003csup\u003e10,11\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough obesity has been widely studied, data on its prevalence using the newly proposed definition (2025) are still lacking in population-based studies worldwide, as most epidemiological surveillance continues to rely on BMI alone. Recent studies compared the prevalence of obesity based on the new guidelines and the traditional definition, in specific populations\u003csup\u003e12\u003c/sup\u003e or using electronic medical records\u003csup\u003e13\u003c/sup\u003e. In a research letter published in May 2025, results showed no difference the prevalence of obesity with or without confirmation of excess adiposity among 2225 adults using data from the 2017-2018 National Health and Nutrition Examination Survey (NHANES)\u003csup\u003e14\u003c/sup\u003e. These data were also used in a separate study to estimate the prevalence of clinical obesity between 2017-2020 using the new definition\u003csup\u003e15\u003c/sup\u003e. While these are important preliminary results, they are based on a single time point and a relatively small sample of individuals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn Geneva, Switzerland, our study group has previously investigated various aspects of obesity, such as understanding the spatial\u003csup\u003e16,17\u003c/sup\u003e and socioeconomic patterns of obesity and related behaviors\u003csup\u003e18\u003c/sup\u003e. As reclassifications can translate into substantial variations in clinical and epidemiological practices, it is important to investigate the prevalence of obesity based on the new guidelines compared to BMI alone, as well as potential associations with socioeconomic determinants and health outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis population-based study aims to evaluate the prevalence of obesity based on the previous classification using BMI alone, compared to the new definition considering central adiposity measures (WC, WHR, and WHtR), along with potential associations with socioeconomic determinants and health outcomes using data between 2005 and 2024.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and population\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research draws upon data from the Bus Santé study, an ongoing, population-based, cross-sectional survey conducted annually in Geneva, Switzerland, since 1993\u003csup\u003e19\u003c/sup\u003e. Each year, around 1,000 residents are selected through an age- and sex-stratified random sampling process. Participants are initially contacted by mail, with follow-up attempts made via up to seven phone calls and two written reminders if needed. When individuals cannot be reached after these efforts, they are systematically replaced using the same stratified sampling method, based on annually updated records from local authorities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the present analysis, participants included were enrolled between 2005 and 2024. Due to the COVID-19 pandemic, data collection was temporarily suspended between 2020 and 2023 and resumed in 2023. Initially, the study targeted individuals aged 35 to 74 years, but this was expanded to 20-74 years in 2011. All participants within this range were retained for the current analysis. Participation rate was 61%. All participants provided written informed consent, and the study protocol was approved by the Ethics Committee of Geneva (IRB00003116, PB_2016-00363; CCER 2022-01544).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants completed self-administered questionnaires covering socioeconomic status, lifestyle factors, and general health status. A clinical examination was conducted by trained research nurses during scheduled visits. Measurements included height, weight, waist and hip circumference, and blood pressure. Blood pressure was assessed three times in seated position after a 5-minute rest, with 30-second intervals between readings; the average of the three measurements was used for analysis. Similarly, for waist, and hip measurements, three readings were taken, and the mean of the measurements was used, unless the first two measurements were identical, in which case a third measurement was not taken. Additionally, fasting blood samples were collected to measure glycemic and lipid profiles, including fasting glucose, total cholesterol, high-density lipoprotein (HDL) and low-density lipoprotein (LDL) cholesterol, and triglycerides, using standardized enzymatic methods (Bayer Technicon Diagnostics, CV 1.4%, 1.2%, and 1.5% for glucose, cholesterol and triglyceride respectively).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary outcome was the prevalence of obesity according to different definitions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eObesity was assessed using four standard indicators: Body Mass Index (BMI), Waist Circumference (WC), Waist-to-Hip Ratio (WHR), and Waist-to-Height Ratio (WHtR).\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eBMI-based classification followed WHO guidelines, with obesity defined as BMI ≥ 30 kg/m\u003csup\u003e2\u003c/sup\u003e \u003csup\u003e20\u003c/sup\u003e.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWC classification used sex-specific cut-offs for abdominal obesity: WC \u0026gt; 88 cm for women and \u0026gt; 102 cm for men, in line with WHO recommendations\u003csup\u003e21\u003c/sup\u003e.\u003c/li\u003e\n \u003cli\u003eWHR classification also relied on sex-specific thresholds: WHR ≥ 0.85 for women and ≥ 0.90 for men\u003csup\u003e21\u003c/sup\u003e.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWHtR classification applied a single threshold of ≥ 0.5 for both men and women\u003csup\u003e10\u003c/sup\u003e.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eObesity status was assessed using the previous definition of BMI only and the new definitions \u0026nbsp;based on the recent guidelines\u003csup\u003e3\u003c/sup\u003e considering: (1) BMI combined with at least one central adiposity indicator (e.g., BMI+WC, BMI+WHR, BMI+WHtR), or (2) combinations of two central adiposity indicators excluding BMI (e.g., WC+WHR, WC+WHtR, WHR+WHtR). Individuals in the new obesity classifications (BMI+WC, BMI+WHR, WC+WHR, WC+WHtR, and WHR+WHtR) were categorized into four groups: stable obesity (obesity is present in both BMI only and the new classification), new obesity (reclassified with obesity under the new classification), declassified (obesity is not present under the new classification), and healthy (obesity is not present in both classification).\u003c/p\u003e\n\u003cp\u003eSocioeconomic variables included education (primary, secondary, tertiary), civil status (single, living with someone, or living alone), nationality (Swiss, Non-Swiss), and household income. Household income was categorized into gross monthly household which were reported into four groups (1CHF ~ 1.2 USD): low (\u0026lt; 5,000 CHF), moderate-low (5,000-6,999 CHF), moderate-high (7,000-9,499 CHF), and high (\u0026gt; 9,500 CHF).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDiabetes was defined as fasting glucose ≥ 7 mmol/L or self-reported diabetes or use of antidiabetic treatment. Hypertension was defined as mean systolic and/or diastolic blood pressure ≥ 140/90 mmHg or self-reported hypertension or use of antihypertensive treatment\u0026nbsp;\u003csup\u003e22\u003c/sup\u003e. Dyslipidemia was defined as a total cholesterol ≥ 6.2 mmol/L or self-reported hypercholesterolemia (dyslipidemia) or use of lipid-lowering medication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCovariates included the following demographic characteristics: age at the time of participation, sex, income group, educational level, civil status, nationality, and survey year. Additionally, the presence or absence of a chronic disease, including diabetes, hypertension, and dyslipidemia, was also considered as other potential covariates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were computed for participant characteristics across study years. Continuous variables were expressed as means ± standard deviations (SD) or medians and interquartile ranges (IQR), as appropriate. Categorical variables were presented as frequencies and percentages.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor comparisons of continuous variables between groups, Student’s t-tests were used for normally distributed data. For non-normally distributed continuous variables, the Kruskal-Wallis’s rank sum test was used when comparing more than two groups. The Wilcoxon rank-sum test was used for pairwise comparisons between two groups, when appropriate. Pearson’s chi-square test was used for comparisons of categorical variables, and Fisher’s exact test was used for count data with small sample sizes (e.g., when examining individuals transitioning between obesity classifications). A two-sided p-value \u0026lt; 0.05 was considered statistically significant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe discriminatory performance of each obesity definition for diabetes, hypertension and dyslipidemia was assessed using Receiver Operating Characteristic (ROC) curves. The Area Under the Curve (AUC) was calculated to quantify the ability of each classification to discriminate between participants with and without the outcome, with corresponding 95% confidence interval calculated using DeLong’s method. ROC curves with 95% confidence bands were plotted, and corresponding Youden indices, sensitivity, and specificity were reported.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAssociations between socioeconomic determinants and obesity based on the different classifications (BMI only, BMI+WC, BMI+WHR, or BMI+WHtR) were assessed using logistic regression models, adjusting for age, sex, household income, education, civil status, nationality, and survey year. Associations between obesity based on the different classifications and health outcomes (diabetes, hypertension, and dyslipidemia) were assessed using logistic regression models adjusting for age, sex, household income, education, civil status, nationality, and survey year. Forest plots were used to illustrate the effect sizes and 95% confidence intervals of these results.\u003c/p\u003e\n\u003cp\u003eAll analyses were conducted using R 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFor this study between 2005 and 2024, \u003cem\u003en\u003c/em\u003e = 14,658 participants were included, with a mean age of 48.2 years old [standard deviation (SD) 13.7 years], 51.4% being women. Overall, 49.2% had a tertiary education, 40.0% a high income, and 8.2% had diabetes, 28.7% hypertension or 49.1% dyslipidemia. Table 1 shows the characteristics of participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe prevalence of obesity was 13.1% overall when considering the classification with BMI only, 11.5% when considering BMI+WC, 10.8% when considering BMI+WHR, and 13.0% when considering BMI+WHtR. When obesity was defined using combined anthropometric indicators excluding BMI, the estimated prevalence varied depending on the classification used: 18.1% with WC+WHR, 21.2% with WC+WHtR, and as high as 39.9% with WHR+WHtR (Figure 1). Of note, many overweight individuals were reclassified with obesity especially when using WHR+WHtR. Obesity prevalence differed significantly between BMI only and all alternative measures: BMI+WC (χ² = 12,712, df = 1, p \u0026lt; 0.001), BMI+WHR (χ² = 11,768, df = 1, p \u0026lt; 0.001), BMI+WHtR (χ² = 14,588, df = 1, p \u0026lt; 0.001), WC+WHR (χ² = 4,829, df = 1, p \u0026lt; 0.001), WC+WHtR (χ² = 5,949, df = 1, p \u0026lt; 0.001), and WHR+WHtR (χ² = 1,666, df = 1, p \u0026lt; 0.001). All pairwise comparisons remained statistically significant after correction for multiple testing (adjusted α = 0.0083), indicating non-homogeneous distributions across classifications despite similar global obesity prevalence rates. Figure 1 shows the distribution of obesity based on the different classifications. Figure 2 shows the reclassification of individuals across categories (stable obesity, new obesity, declassified, healthy) according to the different obesity definitions.\u003c/p\u003e\n\u003cp\u003eReclassification analyses quantified the number of individuals with obesity by BMI only that were no longer considered with obesity when additional measures were applied: n=224 individuals with BMI+WC, n=336 with BMI+WHR, and n=7 when with BMI+WHtR. For the BMI+WHtR no further analysis was performed as the sample size was too small. Conversely, individuals were newly classified with obesity: n=1,211 with WC+WHR, n=1,411 with WC+WHtR, and n=4,264 with WHR+WHtR. Several of these individuals were overweight according to the BMI only definition. Mean BMI among individuals with new obesity was 27.4 (SD 1.8) for WC+WHR, 27.5 (1.8) for WC+WHtR, and 26.2 (SD 2.1) for WHR+WHtR. Individuals who were declassified had high BMI levels: 31.2 (SD 2.8) under BMI+WC and 33.4 (SD 3.7) under BMI+WHR, 36.0 (SD 12.1) under BMI+WHtR, but normal anthropometric measures. These individuals were mostly 35-49 years old, whereas the prevalence of individuals with new obesity increased with age. Sex-specific patterns were observed as women were more likely to be declassified when waist to hip ratio was added to BMI and were more likely to be reclassified with new obesity when waist to hip ratio was paired with waist circumference. On the other hand, men were more likely to be declassified under WC+WHtR, and more likely to be reclassified with new obesity under WHR+WHtR.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEducation disparities were consistent, with individuals with new obesity accounting for 12.4% to 38.7% among those with primary education, versus 6.6% to 25.3% among those with tertiary education. Income showed a similar gradient, with moderate-income groups more often reclassified with new obesity than high-income participants. Marital status further differentiated groups, with higher proportions of new obesity among those living with a partner (8.4%–32%) than those living alone (8%–23.4%). There were no differences in reclassification based on nationality. Tables S1.1 and S1.2 show the distribution of the prevalence of obesity according to the different classifications among groups among different sociodemographic groups.\u003c/p\u003e\n\u003cp\u003eFigure 3 presents the ROC curve analyses comparing the different obesity definitions for diabetes, hypertension, and dyslipidemia. For diabetes, BMI-only yielded an AUC of 0.632 (95% CI: 0.616–0.647), with a Youden index of 0.264, sensitivity of 0.354, and specificity of 0.909. The combined anthropometric definitions demonstrated higher discriminative ability: WC+WHR (AUC 0.679) and WC+WHtR (AUC 0.681) compared to BMI only, while WHR+WHtR showed the highest AUC of 0.711, with a marked increase in sensitivity (0.736) but a lower specificity (0.685) compared to 0.920 (BMI only).\u003c/p\u003e\n\u003cp\u003eFor hypertension, BMI-only had a lower predictive ability (AUC 0.587), whereas WC+WHR (AUC 0.627), WC+WHtR (AUC 0.630), and WHR+WHtR (AUC 0.679) had higher discriminative ability. Sensitivity increased substantially for WHR+WHtR (0.653) compared with BMI-only (0.255), although specificity decreased (0.705).\u003c/p\u003e\n\u003cp\u003eSimilarly, for dyslipidemia, BMI-only showed lower predictive performance (AUC 0.540), compared to combined anthropometric classifications, especially WHR+WHtR (AUC 0.658), which provided better discrimination. Full results are presented in Table S3.\u003c/p\u003e\n\u003cp\u003eObesity was associated with diabetes, hypertension and dyslipidemia across all definitions when compared to healthy individuals. Obesity according to the different classifications showed an adjusted odds ratio (aOR 4.61; 3.87-5.47) for the association between BMI+WHR and diabetes, (aOR 3.61; 3.18-4.09) for the association between BMI+WHR and hypertension, while WHR+WHtR showed the highest association with dyslipidemia (aOR 1.95; 1.75-2.16). The full results of the associations are shown in Figure 4.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this population-based study, we examined obesity prevalence using traditional and new 2025 classifications\u003csup\u003e3\u003c/sup\u003e—including BMI alone, BMI combined with anthropometric measurements (WC, WHR, and WHtR), as well as combinations of two anthropometric criteria excluding BMI (WC+WHR, WC+WHtR, and WHR+WHtR). Our analysis of the adult population in Geneva between 2005 and 2024 (n=14,658) revealed significant variability in obesity prevalence across classifications, ranging from 13% when defined by BMI alone to 40% under WHR+WHtR, illustrating how different classifications highlight distinct dimensions of disease burden.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eReclassification showed age and sex-specific patterns. For instance, adding waist circumference to BMI or other anthropometric measures showed the highest prevalence of obesity among women. In contrast, WHtR more frequently reclassified men with new obesity. BMI+WC often declassified men, likely due to higher lean mass or non-abdominal fat distribution that BMI alone could not discern. These sex-specific reclassification patterns support previous findings that reported that WHtR achieved the highest sensitivity in men (81.6%), while WC was more sensitive in women (86.5%) for detecting obesity-related health risks\u003csup\u003e23\u003c/sup\u003e. Additionally, socioeconomic disparities were identified with individuals with a primary education more likely to have new obesity when using an anthropometric measure. This highlights the need to measure and include anthropometric indicators in the detection of obesity especially for high-risk individuals. Of note, the new definition reclassified several overweight individuals with new obesity, as the definition became a spectrum of obesity\u003csup\u003e3\u003c/sup\u003e compared to the traditional classification of underweight, healthy, overweight and obesity.\u003c/p\u003e\n\u003cp\u003eFrom a clinical perspective, our findings highlight some challenges with the recent definition of obesity, notably in primary care settings. Those declassified based on the addition of an anthropometric measure to BMI had lower risks of diabetes and hypertension than participants with consistent obesity, yet they remained at higher risk when compared to healthy individuals, demonstrating that reclassification should not be interpreted as a return to full metabolic health. Conversely, individuals with normal BMI but central obesity, reclassified with new obesity under WC+WHR, WC+WHtR, or WHR+WHtR, were more likely to have dyslipidemia, revealing the hidden burden of central adiposity even in the absence of high BMI\u003csup\u003e6\u003c/sup\u003e. Associations showed similar findings with the highest association between BMI+WHR and diabetes, and BMI+WHR and hypertension, while WHR+WHtR showed the highest association with dyslipidemia. Studies have highlighted the limitations of BMI alone in detecting visceral fat, which accumulates around internal organs in the abdominal area.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVisceral fat is associated with increased risks of inflammation\u003csup\u003e24\u003c/sup\u003e, insulin resistance\u003csup\u003e25,26\u003c/sup\u003e, diabetes\u003csup\u003e27\u003c/sup\u003e, cancers, non-alcoholic fatty liver disease (NAFLD), cardiovascular diseases\u003csup\u003e28\u003c/sup\u003e, cognitive decline\u003csup\u003e29\u003c/sup\u003e, and mortality\u003csup\u003e30\u003c/sup\u003e, emphasizing the importance of fat distribution. Adding an anthropometric measure to BMI to detect obesity is a good approach to capture both abdominal fat and overall obesity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWithin the new classification system, combining BMI with anthropometric indicators tended to improve sensitivity when compared to BMI alone and specificity when compared to anthropometric measures excluding BMI. A recent study using the new classifications of obesity showed that pre-clinical obesity using anthropometric measures in addition to BMI was associated with an increased cancer risk, highlighting the importance of early risk detection\u003csup\u003e31\u003c/sup\u003e, while another recent study showed that the prevalence of obesity by BMI only was similar to the obesity prevalence after confirmation of excess adiposity using the National Health and Nutrition Examination Survey (NHANES) data between 2017-2018. While this study showed different results to ours, it was limited by a smaller sample size (n=2225) and data over a smaller period of time\u003csup\u003e14\u003c/sup\u003e. Therefore, choice of classification should be context-dependent, guided by the health outcome of interest and whether the goal is to maximize detection and to prioritize individuals at greatest risk, using easily accessible measures in a primary care setting such as BMI and anthropometric measures. Although screening for obesity is recommended for all adults, it is not consistently performed in primary care settings\u003csup\u003e32\u003c/sup\u003e. Yet, detection and counseling delivered by healthcare professionals have been shown to be associated with almost 4 times increased odds of wanting to lose weight and 3.5 times increased odds of attempting to lose weight\u003csup\u003e33\u003c/sup\u003e. Effective counseling strategies include using people-first language such as patients with obesity instead of “obese patients”, and more motivating terminology\u003csup\u003e34\u003c/sup\u003e, as well as starting with small steps for behavioral change such as simply starting a dialogue by asking the patient’s permission to discuss weight. This is part of the 5A’s framework: ask, assess, advise, agree and assist\u003csup\u003e35,36\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, it excluded younger individuals (children and adolescents under 20 years old), a population of increasing concern given the rising prevalence obesity, particularly central obesity, at early ages. Ethnic differences were not considered in the interpretation of anthropometric measures (WC, WHR, and WHtR), as 66.7% of the participants were Swiss, despite evidence that body fat distribution and corresponding health risks may vary significantly among populations\u0026nbsp;\u003csup\u003e6,37\u003c/sup\u003e. This could limit generalizability to and interpretability in other populations.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall, this study shows that obesity should be considered with multidimensional approaches in its assessment and management, integrating anthropometric parameters in addition to BMI. Primary care physicians could benefit from this approach, increasing the sensitivity and detection of obesity, especially in high-risk populations using measures that can be easily performed and do not require special tools or interventions. Awareness of how the criteria may differentially impact men and women is also important in the choice and interpretation of the measures. Primary care physicians are in a unique position to counsel and follow patients with pre-clinical and clinical obesity and applying these new guidelines reinforces their role in detecting and confirming obesity early on, while avoiding misclassification based on BMI alone.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eStatement\u003c/strong\u003e: I confirm that all the research meets the journal’s ethical guidelines, including adherence to the legal requirements of Geneva, Switzerland. We further confirm that this work has been conducted with the ethical approval of the Cantonal Research Ethics Commission of Geneva, Switzerland, and that the approvals are acknowledged within the manuscript.\u003c/p\u003e\n\u003cp\u003eThere are no potential conflicts of interest related to this work. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: I.G. and M.N.; Data curation: C.M., C.C.; Formal analysis: C.M., R.D., C.C. Methodology: C.M., R.D., C.C. and M.N.; Project administration: I.G. and M.N.; Supervision: I.G.; Writing – original draft: C.M. and M.N.; Writing - review \u0026amp; editing: M.N., C.M., C.C., R.D., S.S., N.FL, I.G\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e Data is available upon reasonable request made to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This study was funded by the cantonal office of health in Geneva.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO. Obesity and overweight [Internet]. 2024 [cited 2025 Mar 4];Available from: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight\u003c/li\u003e\n\u003cli\u003eKibret KT, Strugnell C, Backholer K, Peeters A, Tegegne TK, Nichols M. Life-course trajectories of body mass index and cardiovascular disease risks and health outcomes in adulthood: Systematic review and meta-analysis. Obes Rev 2024;25(4):e13695. \u003c/li\u003e\n\u003cli\u003eRubino F, Cummings DE, Eckel RH, et al. Definition and diagnostic criteria of clinical obesity. Lancet Diabetes Endocrinol 2025;13(3):221\u0026ndash;62. \u003c/li\u003e\n\u003cli\u003eBohmann P, Stein MJ, Amadou A, et al. WHO guidelines on waist circumference and physical activity and their joint association with cancer risk. Br J Sports Med 2025;59(6):360\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eWang H, Qin ,Yaxin, Niu ,Jinzhu, et al. Evolving perspectives on evaluating obesity: from traditional methods to cutting-edge techniques. Ann Med 2025;57(1):2472856. \u003c/li\u003e\n\u003cli\u003eRoss R, Neeland IJ, Yamashita S, et al. Waist circumference as a vital sign in clinical practice: a Consensus Statement from the IAS and ICCR Working Group on Visceral Obesity. Nat Rev Endocrinol 2020;16(3):177\u0026ndash;89. \u003c/li\u003e\n\u003cli\u003eGarnett SP, Baur LA, Cowell CT. 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Clinical review: modified 5 As: minimal intervention for obesity counseling in primary care. Can Fam Physician. 2013 Jan;59(1):27-31. PMID: 23341653; PMCID: PMC3555649. \u003c/li\u003e\n\u003cli\u003eRueda-Clausen CF, Benterud E, Bond T, Olszowka R, Vallis MT, Sharma AM. Effect of implementing the 5As of obesity management framework on provider-patient interactions in primary care. Clin Obes. 2014 Feb;4(1):39-44. doi: 10.1111/cob.12038. Epub 2013 Oct 29. PMID: 25425131. \u003c/li\u003e\n\u003cli\u003eAshwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev 2012;13(3):275\u0026ndash;86. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-obesity","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ijo","sideBox":"Learn more about [International Journal of Obesity](http://www.nature.com/ijo/)","snPcode":"41366","submissionUrl":"https://mts-ijo.nature.com/cgi-bin/main.plex","title":"International Journal of Obesity","twitterHandle":"@intjobesity","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"obesity, reclassification, guidelines, chronic disease, risk factors, socioeconomic determinants","lastPublishedDoi":"10.21203/rs.3.rs-7752385/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7752385/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground/Objective:\u003c/strong\u003e Obesity has traditionally been defined using body mass index (BMI), but this may overlook central adiposity and related metabolic risks. In 2025, new guidelines recommended adding anthropometric measures. This study analyzed obesity prevalence comparing definitions, and their associations with cardiovascular and metabolic diseases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Using a population-based study in Geneva, Switzerland (2005-2024), we measured the prevalence of obesity based on the traditional and new definitions. Reclassification patterns were examined, and associations with diabetes, hypertension, and dyslipidemia were assessed via logistic regression and receiver operating characteristic analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAmong 14,658 individuals (mean age 48.2±13.7; 51.4% women), obesity prevalence ranged from 10.8% to 39.9% using new classifications, compared to 13.1% with BMI alone (p\u0026lt;0.001). Reclassifications differed among men and women. New classifications demonstrated superior discriminative performance for the detection of cardiovascular and metabolic outcomes compared to BMI alone. BMI + waist to hip ratio showed the strongest associations with diabetes (aOR 4.61; 3.87-5.47), and hypertension (aOR 3.61; 3.18-4.09), while waist to hip and waist to height ratio showed the strongest association with dyslipidemia (aOR 1.95; 1.75-2.16).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eAdding anthropometric measures to BMI substantially improves obesity detection, better identifies those at risk and could be a useful tool in primary care settings.\u003c/p\u003e","manuscriptTitle":"Obesity prevalence: comparison of traditional and new classification approaches in a Swiss population-based study(2005-2024)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 18:57:33","doi":"10.21203/rs.3.rs-7752385/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2026-01-19T18:58:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-12-18T13:00:41+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-12-08T07:48:34+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-11-10T17:53:58+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-10-30T07:27:47+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-10-27T20:08:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-02T13:51:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Obesity","date":"2025-10-01T10:56:08+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2025-10-01T10:50:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-30T14:05:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-obesity","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ijo","sideBox":"Learn more about [International Journal of Obesity](http://www.nature.com/ijo/)","snPcode":"41366","submissionUrl":"https://mts-ijo.nature.com/cgi-bin/main.plex","title":"International Journal of Obesity","twitterHandle":"@intjobesity","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0a159b1b-2a8b-44f8-85af-bb21b1422f5e","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":55676000,"name":"Health sciences/Medical research/Epidemiology"},{"id":55676001,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases/Obesity"}],"tags":[],"updatedAt":"2026-04-02T07:13:21+00:00","versionOfRecord":{"articleIdentity":"rs-7752385","link":"https://doi.org/10.1038/s41366-026-02076-5","journal":{"identity":"international-journal-of-obesity","isVorOnly":false,"title":"International Journal of Obesity"},"publishedOn":"2026-04-01 04:00:00","publishedOnDateReadable":"April 1st, 2026"},"versionCreatedAt":"2025-11-06 18:57:33","video":"","vorDoi":"10.1038/s41366-026-02076-5","vorDoiUrl":"https://doi.org/10.1038/s41366-026-02076-5","workflowStages":[]},"version":"v1","identity":"rs-7752385","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7752385","identity":"rs-7752385","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}