Split-image illustration depicting BMI vs. DXA scan misclassification in an Italian study, with adults and researchers in a clinic.
Split-image illustration depicting BMI vs. DXA scan misclassification in an Italian study, with adults and researchers in a clinic.
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Italian study finds BMI misclassifies more than one-third of adults when compared with DXA body-fat scans

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An Italian research team comparing standard BMI categories with dual-energy X-ray absorptiometry (DXA) body-fat measurements found that more than one-third of adults were placed in the wrong weight category, and that BMI slightly overestimated the combined prevalence of overweight and obesity in the sample.

A study led by Professor Marwan El Ghoch of the University of Modena and Reggio Emilia found that body mass index (BMI) frequently misclassified adults’ weight status when compared with body fat percentage measured by dual-energy X-ray absorptiometry (DXA), a technique widely regarded as a gold-standard tool for assessing body composition.

Researchers examined 1,351 White Caucasian adults aged 18 to 98 years, about 60% of whom were women. All participants had been referred to the Department of Neurosciences, Biomedicine and Movement Sciences at the University of Verona in Italy.

Using World Health Organization (WHO) BMI cutoffs, 1.4% of participants were categorized as underweight (BMI 30). That equated to a combined overweight-and-obesity prevalence of about 41%, which the researchers said was consistent with available data from Italy’s Veneto region.

When the same participants were reclassified using DXA-derived body fat percentage, the combined prevalence of overweight and obesity was about 37% (23.4% overweight and 13.2% obesity), compared with 41% under BMI.

The study reported substantial category-level disagreement between the two methods. Among people labeled obese by BMI, 34% were categorized as overweight based on DXA. Among those labeled overweight by BMI, 53% were placed in a different category using DXA; of those misclassified, about three-quarters fell into the normal-weight range and the remaining quarter met criteria for obesity.

Agreement was stronger in the normal-weight BMI range, with BMI and DXA aligning in 78% of cases, according to the researchers. The underweight BMI group showed the largest mismatch: 13 of 19 people (68.4%) categorized as underweight by BMI were reassigned to normal weight based on DXA.

“Our main finding highlights the fact that a large proportion of individuals, exceeding one-third of adults among the Italian general population, is misclassified and placed in an incorrect weight status category, when relying on the traditional WHO BMI classification,” El Ghoch said in a statement distributed by the European Association for the Study of Obesity.

Co-author Professor Chiara Milanese of the University of Verona said that even when BMI and DXA produce a similar overall prevalence of overweight and obesity, they do not necessarily identify the same individuals. “Even though both systems identify a similar overall prevalence of overweight and obesity, we are talking in some cases about different people,” she said.

The findings are due to be presented at the European Congress on Obesity (ECO 2026), scheduled for May 12–15 in Istanbul, Turkey, and are published in the journal Nutrients. The researchers argued that BMI-based public health assessments could be improved by incorporating additional measures of body composition or simpler proxies such as skinfold measurements or waist-to-height ratio, and they suggested similar misclassification patterns may occur in other White Caucasian populations beyond Italy.

사람들이 말하는 것

Initial reactions on X from medical news outlets and obesity organizations highlight the Italian study's findings that BMI misclassifies more than one-third of adults compared to DXA body-fat measurements and slightly overestimates overweight and obesity prevalence. Discussions emphasize BMI's limitations as a metric while some physicians defend it as an effective, accessible screening tool.

관련 기사

Illustration depicting waist measurements in a clinic with a graph showing proposed obesity criteria raising U.S. adult obesity rate from 43% to 69%.
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Study finds proposed obesity criteria based on waist measures could classify nearly 70% of U.S. adults as having obesity

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A proposed update to how obesity is defined—combining body mass index with measures of abdominal fat—would raise the share of U.S. adults classified as having obesity from about 43% to roughly 69%, according to a Mass General Brigham analysis of more than 300,000 participants in the National Institutes of Health’s All of Us Research Program.

Body mass index (BMI) is widely used to assess health, but it has significant flaws for evaluating individuals. Originally developed for population studies, BMI fails to differentiate between muscle, bone, and fat, potentially misclassifying fit people as overweight. Experts recommend alternative metrics that better account for fat distribution and overall health risks.

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Experts in India are urging the recognition of abdominal obesity as a new vital sign in Asian Indians to better assess metabolic health risks. An editorial by Amerta Ghosh and Anoop Misra emphasizes the need to measure waist circumference in all patients. This shift addresses the limitations of BMI as a measure of obesity.

New research finds that blood biomarkers associated with Alzheimer’s disease increase significantly faster in people with obesity than in those without. Drawing on five years of data from 407 volunteers, the study suggests that blood tests can detect obesity‑related changes earlier than brain scans, underscoring obesity as a major modifiable risk factor for Alzheimer’s.

AI에 의해 보고됨

Katie Wells, founder of Wellness Mama, shares insights from her personalized health risk assessment using AI-driven tools, highlighting how lifestyle factors can significantly influence chronic disease risks. The assessment, powered by data from over 10,000 studies, showed her cancer risk below the population average despite family history. It underscores a shift toward proactive prevention over reactive medicine.

고위 마비를 겪은 두 명의 중국 환자가 뇌-기계 인터페이스(BMI) 기술을 사용해 생각만으로 전동 휠체어 조종, 로봇 개에게 배달물 가져오게 하고, 로봇 팔로 컵을 집어 물 마시는 데 성공했다. 이 성과는 수요일 중국과학원 상하이 뇌과학 및 지능기술 우수성 센터의 미디어 브리핑에서 발표됐다. 이는 BMI의 실용적 임상 적용을 향한 중대한 진보를 나타낸다.

AI에 의해 보고됨

Silicon Valley startup Twin Health uses AI and wearable sensors as an alternative to expensive GLP-1 drugs for weight management. Retired firefighter Rodney Buckley lost 100 pounds in under a year through the program. His experience highlights a shift toward personalized health tech for chronic conditions.

 

 

 

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