Over the years, I’ve heard complaints from clients that “BMI doesn’t matter” or is “just a number”. While it’s true that BMI doesn’t measure body composition, health professionals still recognize it as an indicator of health and disease risk.
A recent scoring system that could be used alongside BMI was published in Nature Medicine. It may help find those with overweight or obesity who pose the highest risk for having obesity-related conditions and dire health outcomes.
OBSCORE is a new integrated machine-learning model that uses a range of health factors to predict the future risk of obesity-related illnesses. It may offer a way to prioritize those at high risk for disease for earlier interventions.
The study was designed by Kamil Demircan, MD, PhD, a postdoctoral researcher at the Precision Healthcare University Research Institute, Queen Mary University of London, London, England. He noted, “Our study developed and validated a risk prediction model that can stratify individuals into high- and low-risk groups based on their likelihood of developing 18 obesity-related health conditions, including type 2 diabetes, heart disease, or kidney disease”.
BMI Alone Isn’t Enough
Demircan stated that “Overweight and obesity have become a major global health challenge, affecting roughly 60%-70% of adults in many Western countries, with prevalence continuing to rise in most parts of the world”.
But chronic disease development can vary quite a bit among those with similar body weight or BMI.
“Accurately identifying individuals at highest risk of developing obesity-related complications is therefore a challenge, but essential to ensure earlier monitoring, interventions, and improved health outcomes,” he said.
Demircan and his researchers argue that BMI by itself isn’t a complete measure of health risk, and that evaluating weight should include additional measures of health status and disease risk. The measures needed to accurately define “clinical obesity” and predict future consequences, aren’t clear. Currently, there is no data-driven method to assist with intervention based on risk.
Development of OBSCORE
OBSCORE is a solo, integrated model made to predict the future onset of 18 important cardiovascular, metabolic, and physical complications linked with obesity. It was developed to complement BMI as a tool for predicting disease risk.
Data came from 197,264 UK Biobank participants with a BMI ≥ 27 (average age, 58 years). Using machine learning, over 2000 demographic, lifestyle, clinical, biochemical, and health-related factors were utilized.
The latest model included 20 health-related features, which are typically collected in clinical practice. An additional twelve were added in the SURMOUNT-1-trial, which included age, sex, waist-to-height ratio, high blood pressure status, cholesterol levels, hemoglobin A1C, kidney function markers, and smoking status. 2
Other variables such as family history of heart disease, self-reported health status, gamma-glutamyl transferase, chronic illness, pain (abdominal, joint, or chest), and current smoking were included.
Eighteen health-related outcomes were included: type 2 diabetes, hypertension, coronary artery disease, heart failure, atrial fibrillation, stroke, chronic kidney disease, gout, obstructive sleep apnea, fatty liver disease, cirrhosis, gallbladder disease, gastroesophageal reflux disease, osteoarthritis, arthritis, venous thrombosis, obesity-related cancers, chronic obstructive pulmonary disease, and all-cause mortality.
Roughly 48% of subjects were women, nearly 10% had T2D at baseline, and 4.4% had a medical history of a major cardiovascular event (heart attack, etc).
A Complement to BMI
Subjects were divided into five risk quintiles using OBSCORE predictions. This is relevant because all participants had overweight or obesity. Even subjects in the lowest-risk quintile had a high BMI.
OBSCORE showed significant risk stratification in 12 of the 18 outcomes, with rate ratios rose above 10 when compared to the highest- and lowest-risk groups. The biggest differences were seen for CKD, gout, and Type 2 diabetes.
The rate ratio was 47 for cardiovascular mortality. The 10-year event rate was 5.7% in the highest-risk quintile compared with 0.1% for the lowest-risk quintile.
OBSCORE found that weight loss with the GLP-1 medication, Tirzepatide, was similar among baseline risk groups. Predicted risks for obesity-related conditions were lower after treatment. This was seen when OBSCORE was used in participants in the SURMOUNT -1 trial.
Demircan noted, “We found considerable differences in future health risks among individuals with overweight or obesity despite being in similar BMI categories. Our findings suggest that BMI-based approaches to obesity management could be complemented by a more risk-centric assessment of obesity.”
He noted that once OBSCORE is validated in additional studies, the findings could help clinicians target high-risk individuals earlier and support more targeted prevention, monitoring, and treatment interventions.
Participants in the study were primarily middle-aged and older adults. As obesity rates continue to climb in younger adults and teens, future research may be helpful to see if OBSCORE is beneficial in these populations.
Until OBSCORE is available, other measures to identify obesity-related risks include:
1. Waist circumference. A healthy waist circumference is below 35 in women and under 40 in men. 3
2. Waist-to-hip ratio. A healthy waist-to-hip ratio (WHR) is generally below 0.85 for women and below 0.90 for men. 4
3. Elevated blood pressure. A normal blood pressure is 120 (systolic/top number) over 80 (diastolic/bottom number). Clinicians monitor trends in blood pressure.
4. An elevated hemoglobin A1c. This represents a risk for diabetes. A normal hemoglobin A1c is below 5.7%.
5. Increased LFTs (liver function tests). This may be an indicator of metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as NAFLD (non-alcoholic fatty liver disease). 5
6. High uric acid level. Those at risk for gout may have an elevated uric acid level.
7. Hyperlipidemia. Lipids tend to increase with age and menopause. They may be elevated due to genetics, poor diet, or decreased physical activity. 6
8. Poor sleep, snoring, or chronic fatigue. This may trigger a workup for sleep apnea, a condition that may qualify a patient for weight loss medications. 7
Lisa Andrews, MEd, RD, LD
References:
1. Demircan, K., Carrasco-Zanini, J., Williamson, A. et al. Data-driven prioritization of high-risk individuals for weight loss interventions. Nat Med32, 2117–2127 (2026). https://doi.org/10.1038/s41591-026-04353-2
2. Ania M. Jastreboff, M.D., Ph.D., Louis J. Aronne, M.D., Nadia N. Ahmad, M.D., M.P.H., Sean Wharton, M.D.,Pharm.D., Lisa Connery, M.D., Breno Alves, M.D., Arihiro Kiyosue, M.D., Ph.D., Shuyu Zhang, M.S., Bing Liu, Ph.D., Mathijs C. Bunck, M.D., Ph.D., and Adam Stefanski, M.D., Ph.D., for the SURMOUNT-1. Tirzepatide Once Weekly for the Treatment of Obesity. New England Journal of Medicine, Volume 387 • Number 3 • July 21, 2022 Pages: 205-216
3. Waist measurement and heart health | Heart Foundation
4. https://www.bmi-calculator.net/waist-to-hip-ratio-calculator/
5 New MASLD Nomenclature | AASLD
6. Ko SH, Kim HS. Menopause-Associated Lipid Metabolic Disorders and Foods Beneficial for Postmenopausal Women. Nutrients. 2020 Jan 13;12(1):202. doi: 10.3390/nu12010202. PMID: 31941004; PMCID: PMC7019719.
7. Nokes B, Wojeck BS, Schmickl CN. Perspective-weight loss, obstructive sleep apnea therapy, or both for cardiometabolic risk in obesity-related obstructive sleep apnea? Sleep. 2026 Jul 13;49(7):zsag116. doi: 10.1093/sleep/zsag116. PMID: 42033315; PMCID: PMC13357492