Cardiovascular And Metabolic Health

➡️ 93% of adults are no longer considered metabolically healthy. If you say “yes” to any of these, your risk of cardiovascular disease doubles (at least). ➡️ Bookmark these 7 markers to check at your next doctor appointment: 1) Waist measurement (not pants size) > 40 inches Visceral fat around your organs is associated with increased risk of nearly every disease. Track waist measurements as part of your journey as this is even more important than just losing weight. 2) Fasting glucose >100mg/dL Fasting glucose above 100 is classified as prediabetic and is a sign your metabolic health is diminishing. Stable blood sugars mean less insulin is required throughout the day. This helps to keep energy levels high and hunger in check. 3) Hemoglobin A1c >5.6% A1c is a snapshot of your blood sugar over the last 3 months. It’s more reliable than a single fasting glucose test. Higher A1c is correlated with stiffer arteries which means a higher risk for heart disease and strokes. 4) Blood pressure >120/80 High blood pressure is the “silent killer” and indicates poor metabolic health. It's a leading cause of heart disease and kidney disease and is often asymptomatic until it's not. Everyone has high blood pressure, but that doesn’t make it less lethal. 5) Triglycerides >150mg/dL Your body stores excess sugar and fat as triglycerides. But often we never use that stored energy because we continue to eat more. High triglycerides indicate poor metabolic health and are associated with increased risk of cancers and heart disease. 6) HDL levels <40mg/dL (<50 for women) HDL carries cholesterol from the rest of the body to the liver where it can be disposed of. Low HDL especially in combination with high triglycerides is correlated with increased risk of heart attacks. 7) Taking medication for any of these While medications can be beneficial to prevent the negative effects in the short term, it’s important to address the root cause. Taking meds for blood pressure, glucose or lipids is still associated with higher disease risk. Heart disease continues to be the leading cause of death by a large margin. If any of these 7 apply to you, your risk for heart disease doubles. This isn't to scare you, these can all be improved with changes to your lifestyle. But only if you take action. ---- Did you find this post helpful? Like and comment below to see more content from me on losing weight, getting off medications, and building strength.

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

The USPSTF could force private insurers (including MAPDs) to cover GLP-1s for prevention of disease. --- CMS decided not to move forward with a Biden administration proposed rule to cover #obesity drugs like #GLP1s. However, the United States Preventive Services Task Force (USPSTF) is developing a draft recommendation statement (story in the comments) on whether #WeightLoss interventions like Wegovy (semaglutide) and Zepbound (tirzepatide) affect health outcomes such as cardiovascular disease. --- The USPSTF uses a systematic and evidence-based process to assign ratings to preventive healthcare services like screenings and chronic disease management medications. These ratings have significant clinical and policy implications. The Affordable Care Act requires most health plans to cover preventive services that receive an “A” or “B” grade from USPSTF without cost-sharing or deductibles. If the task force gives the GLP-1s a grade “A” or “B”, it could mean the drugs must be covered for the approved indication by private insurers, including #MedicareAdvantage plans (but not standalone #PartD plans). Yes--they must cover them for $0 to the member. And with limited ability for utilization management with prior authorizations or step therapy. --- The results of this kind of coverage are pretty easy for most of us to imagine. -Dramatic increase in utilization/demand -Payers redesign formularies and budgets -Major shifting from PDPs to MAPDs for members that qualify -Lobbying from payers -More GLP-1s pursuing similar preventive outcomes This would be a major shift in access compared to current coverage. Are you in favor of a change like this?

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.

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

📊 Thrilled and excited to share our latest publication in the Annals of Internal Medicine from Dr. Iftikhar Kullo’s Laboratory at Mayo Clinic, which is a step toward a more personalized risk prediction for coronary heart disease (CHD)🫀 https://lnkd.in/g7UANvGt 💠 Using machine-learning analysis on 500K UKB, a polysocial risk score (PSS) from >💯 socio-environmental-lifestyle factors 🏃🏻➡️ 🎓💰👩🏻❤️👨🏻🏑 and a polygenic risk score (PRS) 🧬 from >1.7 million single-nucleotide variants. 💠 Key findings: 🏃🏻➡️💰 Among all included factors, physical activity 🏃🏻➡️, sleep 😴 , education 🎓, and income 💰 factors had the strongest association with CHD🫀 👨🏾👩🏾 Non-Whites had higher PSS risk for CHD 🫀compared to Whites 👨🏼👩🏼 🧬🏃🏻➡️ PSS and PRS were not correlated, their joint effect on CHD 🫀 risk were independent and additive 🧬🏃🏻➡️ PSS and PRS had equal 🟰 predictive performance for CHD🫀 🩺🌡 Incorporating PRS and PSS into the PCE, PREVENT, or QRISK3 risk calculators improved CHD 🫀 risk prediction across all actionable thresholds 💠 Special thanks to Kristján Norland and Dr. Dan Schaid for their meaningful contributions.

Rethinking Heart Health: Why Triglycerides and HDL Matter More Than LDL When assessing your risk for a heart attack or stroke, conventional advice often emphasizes lowering LDL cholesterol. However, mounting evidence reveals that triglycerides (TG) and HDL cholesterol are far better indicators of cardiovascular health and risk. The science is clear: Studies consistently demonstrate that a high triglyceride-to-HDL ratio is one of the strongest predictors of cardiovascular disease (CVD) risk. Research published in the American Journal of Cardiology found that this ratio provides critical insight into the likelihood of myocardial infarction (heart attack) (1). Additionally, HDL isn’t cholesterol at all—it’s a high-density lipoprotein that helps transport LDL back to the liver for recycling, reducing arterial plaque formation. The higher your HDL, the more cardio-protective benefit you receive. Optimal markers for heart health: • Triglycerides: Ideally <150 mg/dL, but optimally <100 mg/dL. Elevated triglycerides are often caused by excessive carbohydrate intake, which the liver converts into fat. • HDL cholesterol: Aim for >60 mg/dL to provide significant protection against cardiovascular events. • Triglyceride-to-HDL ratio: Keep this ratio at 1.5 or less, as it is one of the most reliable indicators of insulin sensitivity and metabolic health. Interestingly, metabolic syndrome, a condition that drastically increases the risk of heart attacks and strokes, does not include LDL cholesterol as one of its five diagnostic markers. Instead, it focuses on: 1. High triglycerides 2. Low HDL cholesterol 3. Elevated fasting blood glucose 4. Increased waist circumference 5. High blood pressure What drives these markers? Diabetes, fueled by excessive carbohydrate intake and resulting hyperinsulinemia, is the leading cause of heart disease. Addressing insulin resistance at its root can improve these critical metrics: • Reduce carbohydrates to stabilize blood sugar and insulin. • Incorporate intermittent fasting to boost insulin sensitivity. • Prioritize movement after meals to minimize glucose spikes. • Focus on whole foods, healthy fats, high-quality protein, and fiber. It’s time to reframe the conversation. LDL cholesterol is only part of the story—and often, not the most important part. By optimizing triglycerides, HDL, and their ratio, we can meaningfully lower cardiovascular risk and improve overall metabolic health. What’s your triglyceride-to-HDL ratio? Let’s talk about why this simple calculation could be a lifesaver. References: 1. Gaziano, J. M., et al. (1997). “Fasting triglycerides, high-density lipoprotein, and risk of myocardial infarction.” Circulation. 2. Grundy, S. M. (2016). “Metabolic Syndrome: A Multiplex Cardiovascular Risk Factor.” The Journal of Clinical Endocrinology & Metabolism.

Meta-prediction of coronary artery disease (CAD) risk with genetics at its core. Traditional clinical risk scores like PCE and QRISK3 often miss at-risk individuals, especially younger patients and women. They rely heavily on static clinical factors without fully leveraging the power of genetics and machine learning. 𝗧𝗵𝗲 𝗺𝗲𝘁𝗮-𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗶𝘀 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱 𝗥𝗜𝗦𝗞 𝗠𝗢𝗗𝗘𝗟 𝘁𝗵𝗮𝘁 𝗰𝗼𝗺𝗯𝗶𝗻𝗲𝘀 𝗴𝗲𝗻𝗲𝘁𝗶𝗰 𝗽𝗼𝗹𝘆𝗴𝗲𝗻𝗶𝗰 𝗿𝗶𝘀𝗸 𝘀𝗰𝗼𝗿𝗲𝘀 (𝗣𝗥𝗦) 𝘄𝗶𝘁𝗵 𝗰𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝗮𝗻𝗱 𝗹𝗶𝗳𝗲𝘀𝘁𝘆𝗹𝗲 𝗳𝗮𝗰𝘁𝗼𝗿𝘀 𝘁𝗼 𝗽𝗿𝗼𝗱𝘂𝗰𝗲 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱, 𝗮𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲 𝗖𝗔𝗗 𝗿𝗶𝘀𝗸 𝗲𝘀𝘁𝗶𝗺𝗮𝘁𝗲𝘀.  1. Integrated 50 predictive features, including 22 polygenic risk scores, 13 clinical measurements, and 15 meta-features predicting future diagnoses.  2. Achieved an AUROC of 0.84 and AUPRC of 0.38 for 10-year CAD risk prediction in the UK Biobank cohort (n=32,032), outperforming all standard clinical scores (PCE AUROC 0.73, QRISK3 0.75).  3. Demonstrated that individuals with high genetic risk achieved up to 6.7% greater absolute risk reduction from LDL lowering (to 35 mg/dL), compared to <1% in low-genetic-risk individuals.  4. Validated model generalizability across diverse populations, maintaining AUROC of 0.81 in the All of Us cohort, including European, African, and Hispanic ancestry groups. Couple thoughts:  • I liked how the authors made sure to test generalizability across different demographics and populations. Important for implementation as always. with near-equivalent performance across European, African and Hispanic ancestry groups --> shows strong transportability  • Augmenting static meta-features with sequence models (e.g., transformers) on raw EHR time-series data to detect evolving risk patterns and enhance early warning capabilities could be a cool project Here's the awesome work: https://lnkd.in/gBPV7H38 Congrats to Shang-Fu Chen, Sang Eun Lee, Hossein J., Jun-Bean Park, @Evan Muse, Ali Torkamani, and co! I post my takes on the latest developments in health AI – 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝘄𝗶𝘁𝗵 𝗺𝗲 𝘁𝗼 𝘀𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱! Also, check out my health AI blog here: https://lnkd.in/g3nrQFxW

As we begin the transition between administrations, we find ourselves in a sort of limbo, not unlike the shoulder season in a mountain town.  It’s time to brace ourselves for the uncertainty ahead. A prime example of this uncertainty emerged last week when the Biden administration proposed extending Medicare and Medicaid coverage to weight loss medications like Wegovy and Zepbound. If finalized, this decision would significantly expand access to GLP-1s for millions of Americans and clearly, effectively recognize obesity as a condition requiring medical treatment.  This move could have far-reaching implications for commercial health insurance plans. The proposal’s impact Under the new policy, an estimated 3.4 million Medicare beneficiaries and 4 million Medicaid enrollees would become eligible for obesity medications, at an estimated federal cost of $36 billion over 10 years. With bipartisan support and public interest growing—61% of Americans support Medicare coverage for obesity drugs—this policy could mark a turning point in how obesity is treated in the U.S. The evidence is clear that GLP-1s improve overall health, not just by aiding weight loss but by reducing the severity of obesity-related conditions, but critics are right to be concerned about high costs. A new reality for commercial plans If Medicare and Medicaid expand their coverage, commercial health plans will face heightened pressure to follow suit. It’s soon becoming table stakes to stay competitive in attracting and retaining members. Commercial plans still on the fence about covering obesity care may want to consider the following: Comprehensive Care Models: Just as CMS highlights the importance of using GLP-1s alongside broader obesity management strategies, commercial plans can partner with a solution like Vida that integrates nutrition counseling, mental health, and preventive care to maximize outcomes and mitigate costs. Workforce Implications: Employers are increasingly focused on workforce safety, retention, and productivity. Providing coverage for obesity care could be a way to meet those goals. Expanding Indications: These medications aren’t going away. In fact, the FDA may soon approve them for sleep apnea, kidney disease, fatty liver, and more. It’s better to get a strategy in place now for effective wraparound care and cost management. A catalyst for change For commercial health plans, the expansion of government coverage could be a catalyst for change. As one CMS leader noted, “The more friction there is in the system…the harder it is for people to get the care they need.” Removing those barriers may be the key to ensuring equitable care across all populations. I’m curious: how is your organization responding to the rising demand for GLP-1s?

New 🫀 Heart Health Biomarker! Average daily heart rate 💓 ÷ total steps 👟 is a better predictor of cardiovascular problems than heart rate or steps alone. 💡👇 The research team from Northwestern University - The Feinberg School of Medicine set out to identify a new biomarker that combines daily heart rate and step count. The researchers call this Daily Heart Rate Per Step (DHRPS). Data was analyzed from ~7,000 Fitbit users in the All of Us Research Program, using medical records to determine health conditions. Participants contributed more than 5.8 million person-days of wearable data, including 51 billion total steps. DHRPS is calculated by taking the daily average heart rate (the average of all minute-by-minute heart rate readings each day) and dividing it by the total steps taken that day. This calculation reflects how much a person’s heart rate rises relative to their daily physical activity and potentially captures the body’s physical response (heart rate) to the activity level (steps). The researchers divided people into three DHRPS categories based on a bell curve: - Low DHRPS (lowest 25%) - Medium DHRPS (middle 50%) - High DHRPS (highest 25%) They discovered that people with high DHRPS had a much higher chance of having cardiovascular disease (CVD) compared to those with medium or low DHRPS. In particular, the researchers looked at these cardiovascular conditions: hypertension, type 2 diabetes, stroke, heart failure, coronary atherosclerosis (hardening of the arteries), and heart attack. DHRPS outperformed both heart rate and step count alone in identifying cardiovascular disease risk, showing stronger correlations than either individual metric. This research also found that higher DHRPS was strongly linked to known risk factors for obesity, type 2 diabetes, sleep apnea, and hypertension, among other cardiac conditions. This shows the growing potential for wearable biomarkers in the early detection of adverse health events. DHRPS may also help healthcare providers better understand which patients are at a higher risk for heart disease. A recent Rock Health report showed that 60 million Americans aged 45-79 already use a wearable or in-home health device—the data is out there! Here's the paper in the JAHA — Journal of the American Heart Association: https://lnkd.in/eT38gWyu I'm excited to dig into this and look at the millions of wearable users we support daily at Validic—it may be something we make available for clients! What do you think? Is this a biomarker that health systems and payers should consider tracking?