Proposing Waist-Adjusted Mass Index Considering Recent Obesity Diagnostic Reforms

The conceptualization of excess adiposity represents a significant advancement in the definition and diagnostic criteria of clinical obesity, as detailed in the 2025 Lancet Diabetes and clinical obesity, which is identified as a chronic systemic condition wherein excess adiposity leads to functional limitations or direct organ dysfunction. The report suggests that while body mass index (BMI) is an adequate tool for initial risk screening and population-level studies, it is most effective when integrated into a broader clinical assessment rather than used as a standalone diagnostic for individual patients. Verification of excess adiposity should be conducted through direct body composition methods, such as bioimpedance or dual-energy x-ray absorptiometry (DEXA), when available. Alternatively, at least one additional anthropometric measure should be employed, such as waist circumference, waist-to-hip ratio, or waist-to-height ratio. This approach emphasizes the assessment of visceral fat, which is documented to correlate with a twofold increase in the risk of heart disease, a threefold increase in the risk of dementia later in life, and an elevated risk of type 2 diabetes mellitus and hypertension.3 These updates are supported by a growing body of evidence accumulated over the past 20 years, including findings from the INTERHEART study,4 which has demonstrated that the waist-to-hip ratio serves as a more accurate predictor of myocardial infarction risk than BMI. While BMI offers practical advantages such as affordability, ease of calculation, and the ability to assess body mass across large populations, it frequently underestimates or overestimates actual adiposity.5 In this context, we propose the “waist-adjusted mass index (WAMI)” as a refined metric: WAMI = waist circumference (cm) × . This specific formulation was suggested for three primary reasons. First, waist circumference is prioritized as the key metric, as visceral fat distribution has proven to be the strongest predictor of cardiometabolic risk, consistently surpassing total body mass in extensive studies and recent international guidelines. Second, the square-root adjustment of weight relative to height provides a gentle scaling for overall body size and stature, mitigating the quadratic over-penalization of muscular or taller individuals that limits the efficacy of BMI, while still maintaining meaningful context regarding mass. Third, the equation was intentionally designed to be minimal, requiring only three universally accessible measurements and a single square-root operation, ensuring immediate usability in clinical, epidemiological, and field settings without the need for specialized equipment or complex computations. The square-root scaling of weight relative to height in the WAMI formula serves as an allometric adjustment for total body mass. This mathematical approach is designed to provide a nuanced scaling of overall body size and stature, effectively reducing the quadratic over-penalization of muscular or taller individuals often seen in BMI calculations. By employing this adjustment, WAMI preserves critical mass context while ensuring that waist circumference remains the primary predictor of central adiposity. Implementation requires only a tape measure and a scale. To evaluate the efficacy of the WAMI, we conducted a simulation using the National Health and Nutrition Examination Survey (NHANES).6 We initially obtained anthropometric examination data from all available cycles of the NHANES spanning 1999 to 2023 (N = 105,626). Data were imported directly into R statistical software version 4.5.3 (Reassured Reassurer) using the “nhanesdata” R package (version 0.2.1), which provides harmonized, cloud-hosted files for seamless multi-cycle analysis.7 Before analyses, we performed data cleaning. We first removed all participants with missing values (NA) in any of the four key variables of interest: weight (kg), height (cm), waist circumference (cm), or hip circumference (cm) (N = 12,752). We then deleted unusual or what we assumed implausible weight values 200 kg (N = 12,722). The final study sample consisted of 12,722 participants with a mean age of 46.2 years (standard deviation = 21.0, range 12–80). The cohort was 52.6% female and 47.4% male. Ethnic distribution was predominantly non-Hispanic white (45.7%), followed by non-Hispanic black (17.4%), Mexican American (11.1%), other Hispanic (10.0%), non-Hispanic Asian (9.5%), and individuals of other or multiple races (6.3%). Regarding educational attainment, 30.6% were college graduates and 31.3% had completed some college or an associate degree. The primary variables of interest for the analysis included body weight, height, and waist and hip circumferences, which were used to derive several indices including: BMI; waist-to-hip ratio (WHR); waist-to-height ratio (WHtR); body roundness index (BRI); a body shape index (ABSI); weight-adjusted waist index (WWI); and WAMI. Correlation analyses (Pearson product-moment correlation coefficient) were then computed on all anthropometric parameters. Principal Component Analysis (PCA) was employed to evaluate the structural validity of WAMI relative to established anthropometric markers. We suggested diagnostic thresholds aimed at distinguishing between BMI-defined weight categories. These thresholds were determined by analyzing the distribution and midpoints of WAMI scores across four groups: underweight, normal, overweight, and obese. A normal/underweight threshold was set at 70, a value selected to maximize specificity against the normal BMI group and ensure that clinical red flags are reserved for those with the highest adiposity. The intermediate range of 55–70 was defined as overweight. To preliminarily validate these cutoffs, we collapsed the original BMI categories into a three-tier model and calculated classification accuracy using a confusion matrix. The datasets generated and analyzed during the current report, along with the underlying analysis codes used, are available in the Open Science Framework (OSF) repository to support research transparency and reproducibility (URL: https://doi.org/10.17605/OSF.IO/RG59E). Figure 1 illustrates that WAMI consistently demonstrates statistically stronger, or where conceptually relevant, comparable associations with waist-based anthropometric indices compared to BMI, indicating its ability to capture central adiposity. WAMI shows an almost perfect correlation with waist circumference (r = 0.98), surpassing the correlation observed for BMI (r = 0.91), which suggests a closer alignment with abdominal size. WAMI exhibits strong correlations with overall mass (r = 0.95 with weight, r = 0.95 with BMI) and moderate associations with distribution and shape values (r = 0.59 with WHR, r = 0.28 with ABSI). The strong relationship with leading alternatives, such as WHtR (r = 0.92) and BRI (r = 0.92), indicates its applicability for assessing body roundness and central fat distribution with minimal redundancy. The pooled absolute correlation with the group of variables is about 0.73, and its proximity to leading alternatives, such as the WHtR 0.71, indicates its applicability with minimal redundancy Figure 1.Figure 1: Correlations among anthropometric variables. The pooled absolute correlation represents the average strength of the association between a specific variable, such as WAMI, and all other variables in the dataset. High intensity (darker) cells indicate a stronger degree of association. WT = Weight; HT = Height; BMI = Body mass index; WHR = Waist-to-hip ratio; WHtR = Waist-to-height ratio; BRI = Body roundness index; ABSI = A body shape index; WWI = Weight-adjusted waist index; WAMI = Waist-adjusted mass indexThe first principal component (PC1) accounted for the majority of the total variance, representing a latent construct of general adiposity. Analysis of factor loadings revealed that WAIST (0.37) and WAMI (0.36) exhibited the highest contributions to PC1. Notably, WAMI outperformed traditional metrics, including BMI (0.34) and WHR (0.25), and showed comparable strength to leading roundness indices such as WHtR (0.36) and BRI (0.36). Using the proposed thresholds, the model achieved an overall accuracy of 81.1%. The > 70 threshold proved exceptionally effective at identifying obesity, correctly classifying 4,386 out of 4,729 obese individuals (92.7% sensitivity) with zero instances of obese individuals being mislabeled as normal/underweight. It is important to consider the inherent measurement variability associated with waist circumference, which can be influenced by anatomical site selection and technician technique. As this analysis primarily utilizes a specific national dataset, the proposed index and its associated thresholds may require further population-specific calibration to account for known ethnic and demographic variations in adiposity distribution and skeletal morphology. While WAMI demonstrates robust correlation with established anthropometric measures, this out-of-the-box novel analysis is limited by the absence of direct validation against metabolic biomarkers or longitudinal clinical outcomes. Further research utilizing prospective cohorts is necessary to determine the prognostic value of WAMI in predicting cardiometabolic risk compared to traditional metrics like BMI and waist circumference alone. WAMI enhances the waist-to-hip ratio in fat distribution, addressing the variability in hip measurements and demonstrating a larger correlation in measuring central fat and inflammation compared to existing indices. Height scaling will be employed to adjust for height discrepancies that are inadequately addressed by current methodologies. Unlike the ABSI, which is intentionally orthogonal to BMI (with a nearly zero correlation), WAMI retains significant mass context and is simpler to calculate manually compared to the more complex BRI. The near-perfect correlation between WAMI and waist circumference is mathematically anticipated based on the construction of the index. This correlation further validates that WAMI effectively retains the primary indicator of central adiposity while incorporating an allometric adjustment for total body mass. This strong inherent association reflects the formula’s design to prioritize central adiposity while integrating a height-adjusted mass component. While this relationship is partly driven by the mathematical structure of the index, it confirms that WAMI effectively preserves the clinical signal of visceral fat distribution even after accounting for the complexities of overall body size through allometric scaling. Nevertheless, these correlations should be interpreted with the understanding that waist circumference is a direct component of the WAMI calculation. Results from PCA indicated that WAMI captures a greater proportion of the variance related to body composition than standard height-weight or distribution-based ratios. Our preliminary recommendations categorize WAMI scores as follows: below 55 indicates lower concern, 55 to 70 suggests medium concern, and above 70 denotes greater concern, with potential adjustments based on demographic factors as determined by the commission for personalized interpretation. WWI has established the utility of using square-root weight normalization to assess adiposity, WAMI provides a distinct conceptual and mathematical contribution through its integrated allometric scaling. Unlike WWI, which standardizes waist circumference solely by the square root of total weight, WAMI standardizes waist circumference by the square root of the weight-to-height ratio. This distinction is critical: by incorporating height into the denominator’s scaling factor, WAMI attempts to mitigate the potential for stature-related bias that can occur in WWI, where individuals of the same weight but different heights may be incorrectly categorized. WAMI may serve as a possible intermediary measure, bridging the relative accessibility of BMI with the more nuanced, adiposity-focused model of adiposity-based chronic disease currently advocated in obesity and sleep medicine. The clinical relevance and significance of WAMI should be further validated through studies linking it to clinical outcomes such as cardiovascular events, diabetes onset, and the severity of sleep-disordered breathing. Despite these encouraging preliminary findings, some limitations must be acknowledged. The current evaluation relies on cross-sectional NHANES data and simulation-based correlations rather than prospective outcomes, limiting inferences about WAMI’s predictive power for hard clinical endpoints such as cardiovascular events, type 2 diabetes incidence, hypertension progression, or mortality. The proposed thresholds were derived empirically from BMI category distributions rather than outcome-optimized cutpoints, potentially reducing generalizability across diverse populations. Additionally, while WAMI addresses some shortcomings of BMI and alternatives like ABSI or BRI, it has not yet been validated against direct body composition measures, DEXA/magnetic resonance imaging, or adjusted for key confounders such as age, sex, ethnicity, or muscle mass variations in athletic or elderly subgroups. The PCA results highlight that WAMI aligns closely with the primary variance associated with established adiposity metrics, it is important to recognize that PCA serves as a dimensionality reduction technique rather than a formal validation of construct validity. These findings illustrate the covariance structure among the indices but do not, in isolation, establish the superiority of WAMI over BMI. Acknowledging this, we suggest that future research employing confirmatory factor analysis or validation against direct fat-assessment tools, such as DEXA, is necessary to formally confirm WAMI’s diagnostic accuracy and its incremental value in clinical settings. Clinically speaking, WAMI is proposed as a potential “bridging” index to connect initial BMI screenings with more definitive fat assessment tools, its precise operationalization within the clinical workflow remains to be established. It is envisioned to function as a complementary second-tier tool – similar in application to waist circumference or waist-to-height ratio – by offering an alternative method for standardizing central adiposity against height and mass variations. In the context of differentiating pre-clinical from clinical obesity, WAMI may assist in identifying excess visceral fat that BMI alone might overlook. Future research should prioritize prospective cohort studies and longitudinal analyses linking WAMI to incident cardiometabolic diseases, organ dysfunction, and mortality to establish its clinical prognostic value beyond screening. Validation in multiple populations, including diverse ethnic groups and low-resource settings, is essential to refine thresholds and assess demographic-specific adjustments. If substantiated by outcome-based evidence, WAMI could serve as a practical bridge between BMI’s population utility and the more precise, adiposity-focused diagnostics needed for individualized obesity management. Author contributions Haitham Jahrami conceptualized the study, proposed the WAMI, designed the analytical framework, supervised the project, and served as corresponding author. He contributed to data interpretation, manuscript drafting, critical revision of intellectual content, and approved the final version. Waqar Husain contributed to the definition of the intellectual content, theoretical framing of the index, literature review, and manuscript preparation and editing. Omar Alfailakawi contributed to the definition of the intellectual content, assisted with literature search, data handling, preliminary analyses, and manuscript preparation. Seithikurippu R. Pandi‑Perumal contributed to conceptual refinement, critical manuscript review, contextual interpretation within obesity and sleep medicine, and substantive intellectual editing. All authors have given final approval for the current version to be published. Ethical statement Ethical statement is not applicable for this article. Declaration of patient consent Patient consent is not applicable for this article. Data availability statement The datasets generated during and/or analyzed during the current study are available in the National Center for Health Statistics, Centers for Disease Control and Prevention. NHANES August 2021–August 2023 data released with obesity report. 2024. Available from: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Examination&Cycle=2021-2023 (accessed on February 15, 2026). Financial support and sponsorship Nil. Conflicts of interest Dr. Haitham Jahrami is an Editorial Board Member of the Heart and Mind journal. The article was subject to the journal’s standard procedures, with peer review handled independently of Dr. Haitham Jahrami and the research groups. There are no conflicts of interest.