Alternative Metrics for Evaluating Obesity

We Already Know BMI is Flawed—But What Should We Be Measuring Instead? For years, we’ve known that BMI alone is an oversimplified metric for assessing obesity-related health risks. The Lancet Commission and multiple studies have already called for incorporating waist circumference and metabolic parameters into clinical practice to better capture central adiposity and metabolic risk. But a recent study using machine learning and large datasets (UK Biobank, NHANES) suggests we may need to go even further. The study identified six distinct obesity subtypes, highlighting that muscle function, inflammation, and insulin resistance—beyond just body size—play a critical role in health outcomes. 🔍 Key Findings: ➡️ Some individuals with higher BMI but strong muscle function had no increased mortality risk. ➡️ Muscle weakness, inflammation, and insulin resistance were stronger predictors of poor outcomes than BMI alone. ➡️ A “low strength” phenotype (even with a mildly elevated BMI) was associated with increased mortality risk. 🔬 How Did They Measure Insulin Resistance? Rather than fasting insulin or HOMA-IR, the study used the triglyceride-to-HDL ratio (TG/HDL-C) as a proxy for insulin resistance—a marker already available in standard lipid panels. Given its strong association with metabolic dysfunction, should we be paying more attention to it in clinical practice? ⚠️ Limitations to Consider: 🔹 Many patients may fall into multiple clusters, making real-world classification challenging. 🔹 The study primarily used large epidemiologic datasets, which may lack detailed clinical nuance or unmeasured confounders. 🔹 It’s unclear how well these clusters generalize to younger populations or low-income settings. 🔹 NHANES had a smaller sample size, potentially affecting some subgroup analyses. 🤔 So, what does this mean for clinical practice? We already measure waist circumference and HDL—but should we start incorporating grip strength or the neutrophil-to-lymphocyte ratio (a marker of inflammation)? 📢 What do you think? Will this article change your clinical practice? I would love to hear from people who use  grip strength consistently in their practice. Here is a link to the article: https://lnkd.in/gxmcusVW #ObesityMedicine #HealthResearch #PersonalizedMedicine #PublicHealth #MetabolicHealth

📉 Is BMI outdated? Body Roundness Index (BRI) might offer a better way forward. BMI has long been used to assess health risk—but it doesn’t account for fat distribution or visceral adiposity, two key drivers of metabolic and cardiovascular disease. That’s where BRI comes in. BRI uses only waist circumference and height to estimate body shape, visceral fat, and health risk. It’s gaining attention as a potentially more accurate marker than BMI. 🔍 What the research shows so far: ✅ A large U.S. study (NHANES, ~33K adults) found a U-shaped link between BRI and all-cause mortality ✅ Higher BRI is linked to increased cardiovascular risk across age groups ✅ Higher BRI is associated with earlier onset of hypertension ✅ One study suggests BRI may outperform BMI in predicting hypertension and could be relevant for metabolic syndrome and fatty liver (though more research is needed) 🧠 Why it matters: Visceral fat is inflammatory and metabolically active—while BMI can’t distinguish between fat and muscle, or fat location. ⚠️ But a word of caution: BRI isn’t ready to replace BMI in clinical practice just yet. It shows promise—but still needs more validation across diverse populations. Still, I find this area of research fascinating and worth watching—especially as we move toward precision-based, individualized health metrics. Curious to check your BRI? Try this calculator 👉 https://lnkd.in/g-m3-bbd #MetabolicHealth #VisceralFat #BodyRoundnessIndex #BRI #ObesityMedicine #HealthInnovation #PreventiveHealth #FunctionalMedicine #LongevityScience #PrecisionMedicine #ChronicDisease #LifestyleMedicine #HealthTech #BodyComposition #AbdominalObesity #PublicHealth #InsulinResistance #WearableHealth #CardiovascularRisk #SmartMetrics #PhysicianWellness #FutureOfHealth #DataDrivenMedicine

There’s a new international effort afoot to revise the way physicians diagnose obesity. A clinical study published in The Lancet Diabetes & Endocrinology Journal, “Definition and Diagnostic Criteria of Clinical Obesity,” posits that obesity should be more than just high BMI, and recommends other measures including waist circumference, direct fat measurement, and signs of symptoms of ill health at the individual level. And yes, I am aware that changes in diagnostic criteria take a LONG time to permeate clinical practice. This is just a general heads up—though I am interested to hear your takes on the change, and your current thinking of BMI as a marker of health. The study delineates obesity into “clinical obesity” and “preclinical obesity,” defined as follows: ·     Clinical obesity: A chronic, systemic illness characterised by alterations in the function of tissues, organs, the entire individual, or a combination thereof, due to excess adiposity. Clinical obesity can lead to severe end-organ damage, causing life-altering and potentially life-threatening complications (eg, heart attack, stroke, and renal failure). ·     Preclinical obesity: A state of excess adiposity with preserved function of other tissues and organs and a varying, but generally increased, risk of developing clinical obesity and several other non-communicable diseases (eg, type 2 diabetes, cardiovascular disease, certain types of cancer, and mental disorders). Finally, the study recommends that “BMI should be used only as a surrogate measure of health risk at a population level, for epidemiological studies, or for screening purposes, rather than as an individual measure of health.” From my non-clinical perspective, this makes sense. We all know people who are classified as obese due to BMI, but are otherwise quite healthy (raises hand). And we also know people who have lived with obesity for many years and suffering from its effects. I see this as refinement, along the lines of what happened with malnutrition. Which used to be measured with markers like serum albumin but later evolved to other factors including grip strength and Insufficient energy intake. Given clinical acceptance, I could see a case where clinical obesity is classified as a CC or an MCC, but preclinical obesity is neither and has no additional payment ramification (but might influence risk scoring). Note: This is not so far afield from what we do today. The ACDIS Pocket Guide reminds providers that morbid obesity can be considered in patients with a BMI greater than 35 AND with one or more related comorbid conditions (e.g., DM, hypertension, GERD, cardiovascular disease), and reminds providers that documentation should link the condition(s) to the patient’s BMI. Today, coders require both a morbid/severe obesity diagnosis and a sufficiently high BMI score to code morbid obesity, a CC. That remains true, but based on the work of the international commission may change. Links to the articles below.

Why Our Medical Metrics Are Failing Us Did you know BMI was developed in the 19th century, not to assess health, but as a statistical tool? Adolphe Quetelet, a Belgian mathematician (not a physician), created the Quetelet Index in the 1830s to describe the average body proportions of a population—not to diagnose individuals. Yet today, we still use this outdated metric to define “healthy” weight, despite its failure to account for muscle mass, bone density, or fat distribution. And BMI isn’t the only metric that has been manipulated over time. Consider: • Cholesterol “normal” ranges were lowered in 2004, instantly categorizing millions more people as needing statins—coincidentally, just as new statin drugs hit the market. • Blood pressure thresholds changed in 2017, pushing more people into the hypertensive category, driving the use of antihypertensive meds. But these numbers don’t tell the whole story. Insulin resistance—the root cause of metabolic disease—is rarely measured in routine screenings. • A person can have a “normal” BMI yet be metabolically unhealthy due to high visceral fat. • A person can have “normal” glucose yet be insulin resistant with sky-high fasting insulin—a major predictor of future diabetes and heart disease. If we want to truly reverse chronic disease, we must move beyond outdated and manipulated metrics and start looking at real indicators of metabolic health: ✔ Fasting insulin ✔ Triglyceride-to-HDL ratio ✔ A1c ✔ C-reactive protein (CRP) As a dietitian specializing in metabolic health, I focus on the root causes of disease, not just making numbers “look pretty” for the sake of medication sales. It’s time we challenge the system and prioritize real health over artificial thresholds. Do you track fasting insulin or other metabolic markers? Let’s discuss. #MetabolicHealth #InsulinResistance #BMI #Cholesterol #HealthMetrics

🚨 SO excited to share this weeks NEW PODCAST 🎧   📣 When big things happen in #ObesityCare, I know I need a #TEDtalk! Ted Kyle and I talk about the new framework proposed by the The Lancet Group Diabetes & Endocrinology Commission which redefines the #diagnosis and #understanding of #clinical #obesity. This report represents a significant step forward for healthcare providers, policymakers, and patients, emphasizing a more #nuanced and #evidencebased approach to #obesitycare.   Key Highlights: 1️⃣ Obesity as a Disease ⚕️ Obesity is now clearly defined as a chronic systemic illness caused by excess adiposity, shifting the focus from size or BMI to the health impacts of body fat 2️⃣ Distinguishing Clinical and Preclinical Obesity 🩺 The framework introduces ✔️ clinical obesity (excess adiposity with organ dysfunction or functional impairment) and ✔️ preclinical obesity (excess adiposity without current dysfunction but with elevated risk) 3️⃣ The Limitations of BMI 🤦♀️ While BMI remains a useful screening tool, it is no longer sufficient for diagnosis. Anthropometric measures, such as waist circumference, are now recommended for more precise and individualized assessments. Direct measures of body composition, such as #bioimpedance analysis #BIA are recommended to #confirm excess #bodyfat 4️⃣ Patient-Centered Perspectives 🙋♂️ The report emphasizes the importance of including patient voices in care decisions and addressing stigma and bias in obesity treatment, promoting a more equitable and compassionate approach. 5️⃣ Global Reach and Applicability 🌍 The framework is designed to be adaptable, offering practical tools for both resource-limited areas and advanced clinical settings.   👉 Advancing the Future of Obesity Care Ted emphasizes the transformative potential of this report, which provides clarity and consensus around obesity diagnosis. 📰 Read the Lancet Article: https://lnkd.in/ek-ZTiQp   📸 Check out the Diagnosing Clinical Obesity Infographic: https://lnkd.in/eRmbjhAU 🎧 Listen now: https://lnkd.in/eTk3hqUd Obesity Action Coalition, Obesity Care Advocacy Network (OCAN), Obesity Medicine Association, The Obesity Society, American Society For Metabolic And Bariatric Surgery (ASMBS), Stop Weight Bias, seca – precision for health, seca mBCA North America

Weight is a measurement defined by the gravitational pull of an object. When applied to the human body, it does not discriminate between the types of mass being measured; bones, muscles, clothing, fat mass, etc. Body mass index (BMI) is the ratio of that number to a person's height. Adiposity is a measurement of the amount of fat mass (adipose tissue) present. Decreasing weight/BMI and decreasing adiposity are related, but not interchangeable.  I can decrease my weight by changing my clothes, sitting in a sauna, or emptying my bladder.  None of those things change my adiposity. Not all weight is bad. The amount of muscle mass we have is highly correlated with our longevity - our lifespan. Muscle weight helps prevent frailty, osteoporosis, and cognitive decline.  If, during the process of losing weight, we reduce our muscle weight more than we reduce our adiposity, the outcome could be a body less healthy than it was before. A body with a higher risk of developing frailty, osteoporosis, and/or cognitive decline. A body with a shorter lifespan.  Total weight reduction isn’t synonymous with actual improvement in obesity or quality of weight loss. This is only one of the differences between someone selling “weight loss” and someone practicing Obesity Medicine.  How can we expect people to understand the difference, let alone determine which type of care they are getting, if we aren’t measuring adiposity?  How can we demand clinicians measure adiposity when the studies we use to practice evidence-based medicine only report weight/BMI?  These are some of the questions Rita Glaze-Rowe, the expert team at Real Chemistry, and I posted in the latest #IRIS market-view report. As long as total body weight and BMI continue to serve as primary endpoints used to measure efficacy in clinical trials (instead of adiposity), we will continue to lump weight loss and obesity treatment in the same bucket, potentially doing more harm than good for some patients — a critical consideration as we work to further understand, refine, and optimize #obesity treatment.

3D Imaging and AI Revolutionize Body Fat and Muscle Distribution Analysis Key Takeaways • New AI-Powered Approach: A recent study published in npj Digital Medicine presents a 3D convolutional deep learning method for more accurate body composition analysis. • Enhanced Accuracy: Unlike traditional linear models, this deep-learning-based approach better estimates fat and muscle distribution, offering improved health risk assessments. • Leading Research Institutions: The study was conducted by researchers from Pennington Biomedical Research Center, University of Washington, University of Hawaii, and UCSF. • Clinical & Research Potential: • Could improve early detection of obesity-related health risks. • Enables personalized health interventions based on precise body composition data. • Has applications in medical research, sports science, and metabolic studies. Why This Matters • Beyond BMI: Traditional Body Mass Index (BMI) is an outdated measure that does not account for fat vs. muscle distribution. 3D imaging with AI provides a far more detailed and individualized health assessment. • Obesity & Metabolic Health: Better body composition analysis could lead to improved obesity management, personalized treatment plans, and metabolic disease prevention. • Future Implications: As AI and 3D imaging become more accessible, these methods may soon be widely adopted in clinics, fitness centers, and home health monitoring tools. Final Thoughts By leveraging AI and 3D imaging, researchers are bringing a new level of precision to understanding body composition and metabolic health—a potential game-changer for obesity research and treatment.

This weekend's article in The New York Times addresses the limitations of the traditional Body Mass Index (BMI) and highlights a potential new metric called the Body Roundness Index (BRI) as a possible alternative. Unlike BMI, which relies solely on height and weight (thus unable to accurately differentiate between muscle and fat, especially visceral fat), the BRI considers body shape and fat distribution, offering a more holistic view of an individual's health risks. Clinically, this is long overdue, and it makes sense. Incorporating waist and hip measurements along with height offers a better understanding of body fat distribution, paving the way for more accurate and meaningful health interventions while acknowledging the diverse nature of human bodies. My initial reaction to the word "roundness" was concern for the stigma associated with the term, but I hope this can be addressed effectively with education. By focusing on the health implications of body fat distribution rather than superficial characteristics, we can foster a more inclusive and informed approach to health that respects the diversity of human bodies. #healthcare #healthcareonlinkedin #obesity Full article here --> https://lnkd.in/eU-sxq4b

John Hopkins’ AI tool could transform how over 1 billion people manage obesity risks. We all know that obesity increases the risk of conditions like diabetes, heart disease, and stroke. For years, measuring obesity has relied on Body Mass Index (BMI), a tool that often misses the bigger picture. Waist circumference (cross referenced with age and weight) is a far better predictor of obesity-related risks but it’s hard to measure in digital and health-tech settings. That’s where this AI tool steps in, offering a smarter, simpler solution. Here’s how it works: 1. Data input The person provides basic details such as age, height, weight, ethnicity, and education level. 2. Waist prediction The tool calculates waist circumference based on the simple data provided. 3. Health risk analysis The tool compares the data to known health risks linked to obesity, such as heart disease and diabetes. 4. Suggestions provided Based on its analysis, the AI offers guidance like identifying risks and recommending lifestyle changes or a doctor’s visit. The predictions made by this tool are highly accurate, correctly estimating waist circumference about 95% of the time. This unique metric then helps the tool provide a snapshot of a person’s health. It’s not a replacement for medical advice, but will help patients and doctors make more informed decisions. The researchers from John Hopkins aim to refine it further by including factors like diet and physical activity for even more personalised insights. Could this AI innovation redefine how we tackle obesity? #healthtech #johnhopkinsuniversity #innovation

Is BMI Dead? - This is 'Big' (pardon the pun) "[A] group of 58 researchers is challenging the way obesity is defined and diagnosed, arguing that current methods fail to capture the complexity of the condition. They offer a more nuanced approach." 👉They argue against using BMI as an individual health measure. 👉Direct measures of adiposity should be used or, where not available, a proxy like waist circumference 👉They define "clinical obesity" as excess adiposity to the point where it causes organ dysfunction and/or hinders organismal physical function (e.g. decreased mobility), whereas "preclinical obesity" is a state of excess adiposity but with normal organ function https://lnkd.in/eGeKdTx4 #obesity #BMI #metabolichealth