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Engrams Embedding Entendre: Codesign for Efficient DRAM/SSD Offloading
Engram extends standard token embeddings with learned multi-token lookups. Recurring local patterns retrieve vectors directly, reducing the need to reconstruct them through attention and feed-forward layers. With Engram model architecture optimization, it allows for lower HBM capacity to be needed for models at the same quality. This does not mean there won’t be an insane demand for HBM but it just means that model architecture will continue to innovate around constraints. This model architecture design is naturally codesigned for parameter offloading: each token accesses a few embedding rows whose addresses depend on token IDs, not hidden states. The runtime can prefetch those rows from host DRAM while earlier layers compute, keeping the table outside HBM without transferring entire weight matrices. Our Memory model contains our latest estimates of quarter by quarter HBM, DRAM, & NAND supply and demand. Offloading frees HBM for model weights and KV cache, potentially supporting larger batches or more concurrent sessions. When DRAM becomes the next constraint, NVMe offers another tier. Recommendation systems already cache frequently or recently accessed embedding rows in faster memory while backing colder rows with SSDs. After NVIDIA roadmap had to change due to massively despec’ing Rubin Ultra from 1024GB to now ~200GB of HBM per chip, model architecture optimizations like emgram maybe helpful. Our DeepSeek-V4.1-Flash configuration uses roughly 189 GiB of memory for Engram. We replace it with a memory-mapped (mmap) file and measure serving performance with offload to SSD. Later on in our report, we will show our Engram offloading experiments along with the official InferenceX agentic inference serving results on Engram models like DeepSeekv4.1 Flash across all 6 NVIDIA GPU SKUs along with MI355X. Unsurprisingly, the CUDA Moat is still mogging MI355X on the ultra popular DeepSeekV4.1 Flash model. We also show how even on high capacity HBM SKUs, offloading emgrams to DRAM could result in even better performance for most of the pareto than keeping the emgram in HBM. Our benchmark has been widely reproduced, validated and/or supported by almost every major buyer of compute from Google Cloud to Microsoft Azure to Oracle, to Meta and many more. Furthermore, it has the support of the ML community including from vLLM, LMCache, SGLang, PyTorch, Huggingface and the support of major labs like OpenAI, MiniMax, ZAI, Qwen, Moonshot Kimi, etc. Star the InferenceX GitHub repository if you find the open-source benchmark and data useful!. InferenceX is the only inference benchmark in the world to have TPUv7, Jalapeño, Nvidia Rubin NVL72, AMD, and soon, SambaNova and Trainium. Due to how realistic AgentX scenario is to real world agentic inference workloads, AMD has committed to collaborating on MI455X UALoE72 too. DeepSeek did not release the original paper’s two trained Engram models. We replicated its setup on fineweb-edu using the released code and training hyperparameters, at an estimated 6E18 FLOPs per run. We observed the same U-shape scaling: Engram improved performance over pure MoE baselines. We also reproduced DeepSeek’s results, where earlier-layer representations with Engram resembled those of later layers. Like the original Engram paper, it is possible to probe Engram’s gate scores to see what n-grams DeepSeek-V4.1-Flash makes the most use of. Our gate scan finds names, code fragments, relational phrasing, and boilerplate. These examples prioritize interesting-ness over gate strength. One unexpected result was Wright : Ace Attorney. These examples suggest learned memory optimizes the training objective, not a judgment of which facts deserve storage. Licenses, bibliography fragments, API scaffolding, and website furniture can provide prediction shortcuts, so the value of additional Engram capacity may depend on what survives data preparation. This does not show that table capacity is “wasted”: the evaluation-corpus scan establishes neither training exposure nor the capacity occupied by each category. For offloading, strong gates do not identify cache-hot rows. Low gates do not automatically save reads either: computing the gate requires the retrieved key and undoes the performance gain of a fused kernel. Skipping reads would require a separate usefulness predictor before retrieval. In the original paper’s inference-time ablation, factual-knowledge benchmarks retained just 29–44% of their original performance, while reading comprehension retained 81–93%. This is due to the training–inference mismatch. The resulting degradation therefore measures this trained model’s dependence on Engram, not the performance difference between models trained with and without it. In our ablations, suppressing Engram worsens token likelihood across all evaluated domains, especially encyclopedia text and several code corpora. Surprisingly, GSM8K accuracy stays within measured run-to-run variation and removing Engram has no effect. Engram is not a detachable dictionary beside an unchanged MoE. Removing it changes downstream features and expert selection. We tested whether rerouting hurts or compensates by holding tokens fixed in a teacher-forced experiment on CRUXEval, a code-reasoning benchmark of small Python functions where the model predicts a function’s output from its code and an input, and scoring the reference answer. Removing Engram raised answer loss from 0.2848 to 0.3093 bits/token. Forcing the ablated model to use the original Engram-on expert choices made it worse still, at 0.3375 bits/token. Rerouting partially compensates for the missing memory. Memory features and expert selection work together, rather than following a clean “memory stores facts; experts reason” division. On the same CRUXeval, removing Engram during either phase reduced accuracy and increased generated tokens; removing it throughout produced the largest changes. Keeping Engram for prefill leads to more correct answers than keeping for decode likely due to semantically richer KV cache transferred to decode workers, allowing it to mitigate some of the performance loss. Engram’s table is large, but each lookup is small. DeepSeek-V4.1-Flash requests 24 rows at each of two Engram layers, about 12.4 KiB per processed token position across the model, or 3.1 KiB per GPU when split across four GPUs. Currently as of Day 7 since Model Release, MI355X is still 2-4x worse performance per dollar compared to B200 even when normalized by Mi355X’s lower TCO. Our full total cost of ownership breakdown comes from our AI Cloud TCO Model along with monthly market surveys of over 100+ gpu clouds & gpu cloud customers. On the Day 0 release of DeepSeekv4.1 Flash, NVIDIA vLLM works out of the box with zero issues across all 6 SKUs: H100, H200, B200, B300, GB200, GB300! This was thanks to the amazing work by the NVIDIA & Interact teams! In comparison, AMD vLLM did not work on day 0 for DeepSeekv4.1 Flash.Source: SemiAnalysis InferenceX AMD’s vLLM documentation points to using vllm/vllm-openai-rocm:deepseekv41-flash-0909, but from hour 0 of the model release to hour 23, AMD has not publicly released the image. AMD claims, “SPEED IS THE MOAT,” yet it has still not released it by the 23rd hour. We wish that, going forward, the AMD team has a better process for hour 0 model releases. Eventually when they did publicly release for “day 0” image support, performance-wise, it is currently up to 14.8x worse perf per dollar than H200 and up to 42x worse perf per dollar than B200/B300. The power of the CUDA MOAT is NVIDIA’s collaboration with its massive 6 million-developer community ecosystem including most of the vLLM & SGLang & Tokenspeed maintainers which means that CUDA is optimized on day 0. Overall, AMD did make significant improvements but the performance per dollar is still currently 2-4x worse than B200. AgentX Engram DRAM Offloading Improving Performance The HBM and DRAM offload use the same GPU kernel to select and dequantize rows. With HBM, it reads GPU memory; with Unified Virtual Addressing (UVA), it reads pinned host memory directly. Both support full decode graphs. Moving the table into HBM accelerates only the sparse lookup, leaving decoder computation and communication unchanged, resulting in little overall benefit while consuming memory otherwise available to KV cache. Another benefit of Engram offloaded to DRAM which means you can reduce the communication overhead by using less HBM GPUs per replica. For example, when enabling Engram offloading on B300, we are able to switch from TP4 to now TP2 which improves the pareto curve by up to 1.6x. When iso-model quality, less HBM is required as the engrams could be offloaded to host DRAM. Thus HBM bandwidth matters way more than HBM capacity. For inference workloads where memory bandwidth matters the most, 4-hi HBM provides the best $/bandwidth and therefore lowest cost per token. If China continues to make more and more revolutionary model architecture innovations, soon it could potentially 0Hi HBM stacks. [ ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) [ Long Live the Short King: Why 4-hi HBM Wins ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) Myron Xie, Bryan Shan, and 3 others · Sep 13 [ Read full story ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) Moreover, on B300 and week-0 stack, moving the Engram table back to HBM did not improve results and stay within run-to-run variance. This is a result of the work optimizing DRAM offload, such as async, and overlap. SSD Offloading On B200, we created a unoptimized vLLM fork and stored the Engram tables in memory-mapped files on local SSDs. File backing lets the OS reclaim table pages when other applications need RAM. Pages already cached in memory can be served without reading the SSD again. Furthermore, note that we were unable to turn on GDS The unoptimized SSD implementation changes how rows reach the GPU. It copies row IDs to the CPU, deduplicates them, gathers the requested rows into pinned buffers, copies those rows back to the GPU and dequantizes them. This work runs between segments of the GPU execution graph. Native UVA performs row selection and dequantization directly on the GPU, avoiding the CPU round trip. A warm filesystem cache removes physical SSD reads, but leaves the coordination, row gathering and transfers. This is why a file already cached in RAM can still perform worse than a pinned DRAM table. The comparison measures the whole serving path; it does not separate the time spent on each of these operations. B200 DRAM dominates both measured SSD serving curves in total tokens per dollar and P90 interactivity. Near 125 tokens/s/user, DRAM delivers 121 million total tokens per dollar versus 52 million for SSD. For production serving, SSD offloading is likely not worth the tradeoff. On the B200 configurations we measured, SSD offloading loses on both measures: every observed SSD point has a DRAM alternative that delivers higher P90 interactivity and more total tokens per dollar. The only points where Cheaper storage does not automatically produce a cheaper inference service. Moving Engram to SSD leaves the same four expensive GPUs and the rest of the server in place. Reclaiming RAM only creates an economic benefit if it enables a cheaper server configuration or additional useful capacity. The current unoptimized path provides neither benefit, and the filesystem cache still consumes RAM when table pages are resident. Next we will look at the mechanisms and specific implementation of ngrams from DeepSeekv4.1 Flash, LongCat, Qwen3.8 Flash Next.
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Can researchers trust that AI models won’t leak their ideas?
Research funders are asking how they can stop confidential scientific ideas in grant proposals from leaking out through large language models (LLMs), for example if reviewers use AI to help them judge applications. Last week, a row erupted in mathematics after OpenAI announced it had solved a notoriously difficult problem, only for another mathematician to demand answers over whether the company had used ideas he had fed into its models. The spat, and the broader issue of idea leakage through AI models, was discussed by funders at an R&D policy conference held in Paris on September 14. At worst, it threatens to undermine the correct attribution of breakthroughs, one of the foundations of modern science. The mathematics dispute around OpenAI is “certainly something we're looking at” Jason Gallo, head of the newly established Office of Metascience at the US National Science Foundation (NSF), told the Blue Sky Forum, hosted by the Organisation for Economic Co-operation and Development. The controversy started on September 8, when OpenAI announced it had cracked a mathematical problem that had gone unsolved for 90 years. Using a swarm of AI agents, millions of dollars’ worth of computing power and a new internal model, the company said it had finally proved that a long-used formula for modelling fluid dynamics breaks down under certain conditions. However, Tristan Buckmaster, a New York University mathematician who had been independently working with collaborators on the same problem using OpenAI’s Codex tool, hit back with a statement questioning whether the company had snooped on his Codex sessions to gain an advantage. OpenAI has always denied it looked up the “specific user data” of Buckmaster and colleagues. However, the company initially admitted that “while unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models,” although its proofs “differ significantly” from Buckmaster’s. The firm later said that, following an investigation, it concluded that Buckmaster’s Codex prompts “could not have influenced the [OpenAI] system in any way, including through training.”Funder concernYet OpenAI’s initial admission that its model might have ingested Buckmaster’s ideas, then spat them out to its own mathematicians, has scientistsworriedthat their work could leak out if shared with AI. Some AI tools give users an opt-out option to stop their sessions from training the model. If such guarantees can be trusted, this gives scientists some confidence they can use an LLM to develop an idea without the risk of it leaking out.But there’s a bigger potential vulnerability. To win money to investigate ideas, scientists have to share them with funding bodies. These grant proposals are normally kept strictly confidential, to prevent other scientists from scooping them, or to protect commercially valuable intellectual property. To preserve this confidentiality, when the NSF uses AI models, “we have to be sure that it's not making server calls out to an external party, leaking that data,” said Gallo to delegates in Paris. Leaks through reviewersHowever, funders normally use external academic reviewers to assess proposals. And there are growing worries that these reviewers are feeding grant ideas into AI models to help them make a judgement. Research funders have noticed that peer review reports and funding recommendations have “suddenly become much longer, more detailed and complex,” said Diogo Machado, head of the data science unit at Technopolis, a science and technology-focused consultancy. “The sense is that reviewers are increasingly using generative AI in their review work, even where guidelines sometimes forbid it,” he told Science|Business. “This is very difficult for funders to control or prove.” Happening at scaleThis sharing of confidential proposals with AI models is “happening at scale already,” said James Wilson, executive director of the Research on Research Institute, based at University College London, during the debate in Paris. In response, Gallo said the NSF had “strict guidance” around the uploading of proposals to AI models. But he added the NSF was mulling new types of assessment to make sure applicants were truly the originators of their ideas, such as following a short-form proposal with an oral presentation, which would allow the foundation to “really interrogate the investigator on their intellectual contribution.” Related articlesHow can Europe cope with a flood of AI-assisted research grant proposals?The risks and benefits of AI in screening research grant proposals Viewpoint: AI could make scientific overload worse“For us as funders, a lot of the ways to ensure that the application remains the applicant's own intellectual contribution, those ways are quite regressive,” Peter Clifford, head of strategy at Research Ireland, told delegates. “They're maybe ten or 15 or 20 years old, and they don't scale particularly well.” Disputes over the origin of ideas are as old as modern science. Isaac Newton and Gottfried Wilhelm Leibniz spent more than a decade arguing over who invented calculus, for example. And long before LLMs emerged, academics would agonise over how much of their early work to reveal at conferences, in case their colleagues scooped them, Jack Leahy, a senior metascience advisor to the UK government, told delegates. But AI was “accelerating” the “problems of the system,” he said.
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Bone Health Without the Hype
I am at the Australasian Menopause Society meeting and we had a fantastic lecture on Bone Health on Day 2, called, “Everything you need to know about bones - except the boring bits!” Spoiler alert, there were no boring bits. The lecture was given by Dr. Amanda Beech, who is a senior staff specialist in Obstetric Medicine and Endocrinology at the Royal Hospital for Women, where she is director of the Bone Density Unit. I want to summarize my top 10 take-aways for you. Share Osteoporosis is a disease of low bone mass, disruption of bone microarchitecture, and fragility of the skeleton. It is a silent disease…until someone develops a fracture. The diagnosis of osteoporosis is based on either fragility/minimal trauma fractures or a T-score of less than -2.5 on a DXA (Dual-Energy X-ray Absorptiometry) or bone scan. It is important to recognize that about 50% of minimal trauma fractures occur when women have low bone mass (often referred to as osteopenia), but not osteoporosis. This tells us that bone strength is more complicated than a number on a DXA report. I am going to add a little here that wasn’t in the talk (one can only include so much in 25 minutes), as many women might read the above paragraph and think, “Shouldn’t we be treating everyone with low bone mass if this is when 50% of the fractures occur?” Many fractures happen for women with low bone mass because there are so many women with low bone mass –it is a statement about the population on a whole, not about the fracture risk of low bone mass. For you as an individual, bone density is only one component of fracture risk and many women with lower bone mass will not have a significantly elevated fracture risk. This is why individualized risk assessment matters. Subscribe We build bone in our teens and twenties, and by the time we are about thirty we have reached peak bone mass. This is why, when we discuss risk factors for osteoporosis, it is important to consider factors that might have affected us in our teens and twenties when we were actively building bone, such as nutritional restriction/energy imbalance. It is important to ask women about a history of eating disorders, and for those with such a history, getting a bone scan in perimenopause or at age 50 is likely warranted. (This image below isn’t from the talk, but is similar to one that was). Bone mass is relatively stable until about two years before the final menstrual period, and perimenopause marks a time of rapid bone loss for women. Using urinary markers of bone loss we know that the time of significantly accelerated bone loss starts about 1 year before the final period and ends about 2 years after the final period. The Study of Women’s Health Across the Nation (SWAN) shows that over the menopause transition women lose an average of 10.6% of the bone at their lumbar spine and 9.1% at the femoral neck While this wasn’t covered in the lecture, I want to add that the rapid bone loss during perimenopause is clearly related to more than estradiol, because during this time estradiol levels can be erratic and not uniformly low. Bone loss during perimenopause is likely a multi-hormone event. For example, rising FSH (follicle stimulating hormone) tracks with bone loss better than estradiol, and there is evidence that FSH may act directly on bone. While decreased estrogen matters, there is clearly more going on. FSH and bone is an active area of research. Subscribe Screening for osteoporosis was recommended fairly broadly before age 65, and was taken from the “Practitioner’s Toolkit for Managing Menopause,” from Monash University. Here is a screenshot from the toolkit that was used as a slide: I am going to add a little bit more about body weight here for clarification. Low body mass (typically 152.1 lb): 0 points (risk neutral) 60–69 kg (132.3–152.1 lb): 3 points (adds some risk)
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Viewpoint: Unlocking Europe’s wildfire data
Europe is already investing in the science, technology and infrastructure needed to understand, prevent and fight wildfires. Copernicus provides satellite-based Earth observation and environmental information, while the European Forest Fire Information System provides information on fire danger and forest fires across Europe. Weather and climate systems add forecasts of heat, drought, wind and humidity; historical fire databases show where fires have occurred; and researchers generate increasingly detailed knowledge about fuels, fire behaviour, forecasting, modelling and risk management. Meanwhile, new European research projects are adding digital twins and other tools to this growing ecosystem.However, these data do not necessarily sit together, nor are they necessarily described, structured or governed in ways that allow them to be used together. Much of the information is public, but some is commercially held. Some is sensitive. Some is subject to legal, security or operational restrictions. Even when data can be legally accessed, different systems may use different formats, terminology and rules.Europe therefore faces an unusual paradox: we have vast amounts of relevant information to respond to the wildfire challenge, but not the means to bring it together. For example, a wildfire researcher or emergency service might need to ask whether a particular combination of vegetation, drought, wind and topography has historically been associated with rapid fire spread. The relevant information may exist in several institutions, using different systems and subject to different conditions of access.The challenge is to conduct an authorised analysis that interacts with these sources, combines the results and respects the rules imposed by each data custodian.An important part of the answer can be found in the Fair principles, the idea that data should be findable, accessible, interoperable and reusable. Developed more than a decade ago, these principles have become a central reference point for modern data stewardship, making it easier for people and machines to find data, understand what it means and determine whether it can be reused.Readability by machines is essential, since AI systems will be key to meeting the data challenge. Bring analysis to the dataThe proposition is not to give an AI system unrestricted access to sensitive databases. Quite the opposite. The goal is to make it possible for approved algorithms to go to the data, rather than requiring sensitive data or large datasets to be copied to wherever the analysis happens.Imagine an analysis asking a series of questions of different data stations. At one station it might examine weather and climate data; at another, historical fire records; at another, experimental measurements of fire behaviour. The algorithm does not need to download everything. It can operate within the environment permitted by each data custodian and return only the results that the rules allow to leave.This is sometimes described as data visiting, and related approaches already exist in areas such as federated analysis and federated learning.Sensitive data should therefore not be treated as a free-for-all for machines. This requires appropriate governance at multiple levels. Access needs to be authenticated and authorised. The purpose of an analysis should be defined. The code or model being run should be controlled. Data custodians should be able to specify what can be queried and what results can leave their environment. Activities should be auditable and human oversight should remain where the consequences require it.In other words, the objective is not to trust AI with the data. It is to design systems in which AI never has more access than it needs.As open as possible, as closed as necessaryFair data should also not be confused with open data. Some wildfire data should be openly available. Other information may legitimately need to remain restricted because it concerns critical infrastructure, security, commercial interests, privacy or operational capabilities. The principle should be: as open as possible, as closed as necessary.The important point is that restricted data should not automatically become unusable data. If machines can discover what information exists, understand its meaning, establish where it came from and determine the conditions under which it can be used, authorised analyses can potentially be carried out without the underlying data ever leaving the environment in which it is protected.Restricted data can still be Fair and machine-actionable.This is also an important question of data sovereignty. European institutions should not have to surrender control of their data to a handful of technology platforms in order to benefit from AI. Nor does Europe need to start again by building one giant repository for every piece of wildfire information. The better approach is to connect what already exists.This is the principle behind emerging approaches such as Tryangle, developed through the Leiden Initiative for Fair and Equitable Science. Rather than creating another centralised data warehouse, the approach seeks to provide an interoperable layer through which approved algorithms can visit distributed data sources while respecting the rules of their custodians.In this way, a data station can remain where it is. Its custodians can retain control. Existing databases and applications do not have to be abandoned. What changes is their ability to communicate with other systems through common, machine-readable descriptions, access rules and interoperability mechanisms.But approaches like this need data that meets the Fair principles. Only then can an interoperable ecosystem safely begin to turn fragmented information into collective intelligence.Data infrastructure is wildfire infrastructureThe implications of this approach are entirely practical. Firefighters could gain better intelligence about local and evolving risks. Land managers could test prevention strategies using more comprehensive information about fuels, weather and past fires. Researchers could compare evidence across ecosystems without repeatedly moving huge datasets between institutions. Smaller regions and resource-constrained teams could access analytical capabilities without having to build large data infrastructures themselves.Europe is already investing heavily in satellites, databases, research programmes, forecasting systems and scientific expertise. The question is whether we will connect those investments well enough to turn them into actionable intelligence.That requires policymakers and funders to treat data infrastructure as seriously as physical infrastructure. Data generated or supported through European public funding should be Fair, verifiable and machine actionable by design, wherever legally and ethically possible.Related articlesData Corner: Europe’s burning need for wildfire mitigation researchClimate monitoring must remain a global enterprise, experts sayIn practice, that means funding not only the production of data, but also the infrastructure that makes data usable across systems: metadata, provenance, interoperability, access mechanisms and governance. Where data must remain restricted, we should ensure that it can still be securely discovered, verified and used for authorised analysis.Initiatives such as the European Open Science Cloud offer an existing framework through which these principles can be put into practice. What is needed now is to accelerate implementation and make them a standard part of how Europe funds and builds research and innovation systems.We do not need another generation of disconnected databases. The data is already there. The opportunity is to make it work together, securely, responsibly and quickly enough to help prevent the next fire, rather than simply document the last one.Jean-Claude Burgelman is professor emeritus at the Free University of Brussels (VUB) and editor-in-chief at Frontiers Policy Lab. For more than 20 years he held leading roles in research and innovation policy at the European Commission.
Anthropic posted, then deleted, a $450,000 sales job aimed at Meta
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8 Daily Habits to Support Your Heart, Kidney and Metabolic Health
(Feature Impact) When it comes to protecting your heart, supporting your kidney function and improving your metabolic health, you’ll find the advice for each one looks similar. That’s because your body’s systems are deeply interconnected: what affects one will have ripple effects across the others.Cardiovascular-kidney-metabolic health, or CKM health, is a perfect example. When any one of these systems is compromised, it can lead to problems with the others. Therefore, it’s not uncommon for health problems like heart disease, stroke, diabetes and kidney disease to develop or worsen simultaneously – a condition collectively known as CKM syndrome.The reverse is also true, so habits that are healthy for your heart will have a positive effect on your kidneys and metabolic system and vice versa. Consider these simple strategies that align with the American Heart Association’s Life’s Essential 8, a list of healthy steps that can improve overall health. Learn more about how these habits and health measures affect your CKM health at Heart.org.Choose Healthy Foods – Food is your body’s fuel, so fill your tank wisely. Certain types of foods can help your blood pressure, cholesterol, blood glucose and triglycerides stay within healthy ranges, while poor nutrition can push those numbers into more dangerous territory. Focus on vegetables, fruits, whole grains, plant-based proteins (e.g. beans, legumes and nuts) and lean animal proteins (e.g. seafood, skinless poultry and low-fat dairy). Drink plenty of water throughout the day and limit added sugar and salty or fatty foods.Stay Physically Active – Exercise doesn’t have to feel like a chore – in fact, some of your favorite hobbies may already count. The American Heart Association recommends aiming for 150 minutes per week of moderate physical activity like dancing, gardening or walking, moving quickly enough to elevate your heart rate and break a light sweat. Alternatively, choose 75 weekly minutes of vigorous exercise that gets you breathing hard and fast; think running, swimming, playing tennis or riding your bike on hilly terrain.Avoid Nicotine – Tobacco and other nicotine products can raise your blood pressure, damage blood vessels and increase your risk of heart attack and stroke, which are all bad news for your CKM health. If you’re currently trying to quit, you aren’t alone, so don’t be afraid to seek out support groups and resources to help.#placement_976606_0_i{width:100%;max-width:550px;margin:0 auto;}var rnd = window.rnd || Math.floor(Math.random()*10e6);var pid976606 = window.pid976606 || rnd;var plc976606 = window.plc976606 || 0;var abkw = window.abkw || '';var absrc = 'https://ads.empowerlocal.co/adserve/;ID=181918;size=0x0;setID=976606;type=js;sw='+screen.width+';sh='+screen.height+';spr='+window.devicePixelRatio+';kw='+abkw+';pid='+pid976606+';place='+(plc976606++)+';rnd='+rnd+';click=CLICK_MACRO_PLACEHOLDER';var _absrc = absrc.split("type=js"); absrc = _absrc[0] + 'type=js;referrer=' + encodeURIComponent(document.location.href) + _absrc[1];document.write('');Prioritize Healthy Sleep – Sleep gives your body a chance to rest and recover from the day, but it’s often one of the first things people sacrifice when life gets busy. Poor sleep can increase your risk for heart disease, weight gain and other issues. Aim for a solid 7-9 hours a night. Keep your bedroom cool, dark and comfortable, and do your best to limit distractions like late-night smartphone scrolling.Watch Your Blood Pressure – When your blood pressure is high, it forces your heart to work harder to pump blood through the body, which can weaken it over time. It can also damage the delicate arteries in and around the kidneys, putting CKM health further at risk. Information is power, so check your blood pressure frequently to know whether further action is needed to keep it under control.Manage Your Blood Sugar – High blood sugar can damage the kidneys, eyes, blood vessels and more, thereby increasing your chance of developing harmful CKM conditions. Controlling blood sugar is particularly important for people with diabetes. If you don’t have diabetes and still experience frequent blood sugar spikes and crashes, talk to your doctor.Control Your Cholesterol – Cholesterol comes in both “bad” (LDL) and “good” (HDL) forms, requiring a balancing act to keep your levels within a healthy range. If LDL is high and HDL is low, your risk of clogged blood vessels, heart attack and stroke are higher. Healthy levels keep blood flowing to your heart and kidneys, supporting your metabolic health in the process. See your doctor for regular bloodwork so you can monitor cholesterol changes.Maintain a Healthy Weight – Since excess body weight puts pressure on the heart, kidneys and other organs, a healthy body weight helps lower your risk of CKM syndrome. Whether you’re hoping to maintain or reduce your weight, regular exercise, healthy eating and quality sleep are all great ways to achieve your goals.
8 Daily Habits to Support Your Heart, Kidney and Metabolic Health
(Feature Impact) When it comes to protecting your heart, supporting your kidney function and improving your metabolic health, you’ll find the advice for each one looks similar. That’s because your body’s systems are deeply interconnected: what affects one will have ripple effects across the others.#placement_1126610_0_i{width:100%;max-width:550px;margin:0 auto;}var rnd = window.rnd || Math.floor(Math.random()*10e6);var pid1126610 = window.pid1126610 || rnd;var plc1126610 = window.plc1126610 || 0;var abkw = window.abkw || '';var absrc = 'https://ads.empowerlocal.co/adserve/;ID=181918;size=0x0;setID=1126610;type=js;sw='+screen.width+';sh='+screen.height+';spr='+window.devicePixelRatio+';kw='+abkw+';pid='+pid1126610+';place='+(plc1126610++)+';rnd='+rnd+';click=CLICK_MACRO_PLACEHOLDER';var _absrc = absrc.split("type=js"); absrc = _absrc[0] + 'type=js;referrer=' + encodeURIComponent(document.location.href) + _absrc[1];document.write('');Cardiovascular-kidney-metabolic health, or CKM health, is a perfect example. When any one of these systems is compromised, it can lead to problems with the others. Therefore, it’s not uncommon for health problems like heart disease, stroke, diabetes and kidney disease to develop or worsen simultaneously – a condition collectively known as CKM syndrome.The reverse is also true, so habits that are healthy for your heart will have a positive effect on your kidneys and metabolic system and vice versa. Consider these simple strategies that align with the American Heart Association’s Life’s Essential 8, a list of healthy steps that can improve overall health. Learn more about how these habits and health measures affect your CKM health at Heart.org.Choose Healthy Foods – Food is your body’s fuel, so fill your tank wisely. Certain types of foods can help your blood pressure, cholesterol, blood glucose and triglycerides stay within healthy ranges, while poor nutrition can push those numbers into more dangerous territory. Focus on vegetables, fruits, whole grains, plant-based proteins (e.g. beans, legumes and nuts) and lean animal proteins (e.g. seafood, skinless poultry and low-fat dairy). Drink plenty of water throughout the day and limit added sugar and salty or fatty foods.Stay Physically Active – Exercise doesn’t have to feel like a chore – in fact, some of your favorite hobbies may already count. The American Heart Association recommends aiming for 150 minutes per week of moderate physical activity like dancing, gardening or walking, moving quickly enough to elevate your heart rate and break a light sweat. Alternatively, choose 75 weekly minutes of vigorous exercise that gets you breathing hard and fast; think running, swimming, playing tennis or riding your bike on hilly terrain.Avoid Nicotine – Tobacco and other nicotine products can raise your blood pressure, damage blood vessels and increase your risk of heart attack and stroke, which are all bad news for your CKM health. If you’re currently trying to quit, you aren’t alone, so don’t be afraid to seek out support groups and resources to help.Prioritize Healthy Sleep – Sleep gives your body a chance to rest and recover from the day, but it’s often one of the first things people sacrifice when life gets busy. Poor sleep can increase your risk for heart disease, weight gain and other issues. Aim for a solid 7-9 hours a night. Keep your bedroom cool, dark and comfortable, and do your best to limit distractions like late-night smartphone scrolling.Watch Your Blood Pressure – When your blood pressure is high, it forces your heart to work harder to pump blood through the body, which can weaken it over time. It can also damage the delicate arteries in and around the kidneys, putting CKM health further at risk. Information is power, so check your blood pressure frequently to know whether further action is needed to keep it under control.Manage Your Blood Sugar – High blood sugar can damage the kidneys, eyes, blood vessels and more, thereby increasing your chance of developing harmful CKM conditions. Controlling blood sugar is particularly important for people with diabetes. If you don’t have diabetes and still experience frequent blood sugar spikes and crashes, talk to your doctor.Control Your Cholesterol – Cholesterol comes in both “bad” (LDL) and “good” (HDL) forms, requiring a balancing act to keep your levels within a healthy range. If LDL is high and HDL is low, your risk of clogged blood vessels, heart attack and stroke are higher. Healthy levels keep blood flowing to your heart and kidneys, supporting your metabolic health in the process. See your doctor for regular bloodwork so you can monitor cholesterol changes.Maintain a Healthy Weight – Since excess body weight puts pressure on the heart, kidneys and other organs, a healthy body weight helps lower your risk of CKM syndrome. Whether you’re hoping to maintain or reduce your weight, regular exercise, healthy eating and quality sleep are all great ways to achieve your goals.
8 Daily Habits to Support Your Heart, Kidney and Metabolic Health
(Feature Impact) When it comes to protecting your heart, supporting your kidney function and improving your metabolic health, you’ll find the advice for each one looks similar. That’s because your body’s systems are deeply interconnected: what affects one will have ripple effects across the others.#placement_1126603_0_i{width:100%;max-width:550px;margin:0 auto;}var rnd = window.rnd || Math.floor(Math.random()*10e6);var pid1126603 = window.pid1126603 || rnd;var plc1126603 = window.plc1126603 || 0;var abkw = window.abkw || '';var absrc = 'https://ads.empowerlocal.co/adserve/;ID=181918;size=0x0;setID=1126603;type=js;sw='+screen.width+';sh='+screen.height+';spr='+window.devicePixelRatio+';kw='+abkw+';pid='+pid1126603+';place='+(plc1126603++)+';rnd='+rnd+';click=CLICK_MACRO_PLACEHOLDER';var _absrc = absrc.split("type=js"); absrc = _absrc[0] + 'type=js;referrer=' + encodeURIComponent(document.location.href) + _absrc[1];document.write('');Cardiovascular-kidney-metabolic health, or CKM health, is a perfect example. When any one of these systems is compromised, it can lead to problems with the others. Therefore, it’s not uncommon for health problems like heart disease, stroke, diabetes and kidney disease to develop or worsen simultaneously – a condition collectively known as CKM syndrome.The reverse is also true, so habits that are healthy for your heart will have a positive effect on your kidneys and metabolic system and vice versa. Consider these simple strategies that align with the American Heart Association’s Life’s Essential 8, a list of healthy steps that can improve overall health. Learn more about how these habits and health measures affect your CKM health at Heart.org.Choose Healthy Foods – Food is your body’s fuel, so fill your tank wisely. Certain types of foods can help your blood pressure, cholesterol, blood glucose and triglycerides stay within healthy ranges, while poor nutrition can push those numbers into more dangerous territory. Focus on vegetables, fruits, whole grains, plant-based proteins (e.g. beans, legumes and nuts) and lean animal proteins (e.g. seafood, skinless poultry and low-fat dairy). Drink plenty of water throughout the day and limit added sugar and salty or fatty foods.Stay Physically Active – Exercise doesn’t have to feel like a chore – in fact, some of your favorite hobbies may already count. The American Heart Association recommends aiming for 150 minutes per week of moderate physical activity like dancing, gardening or walking, moving quickly enough to elevate your heart rate and break a light sweat. Alternatively, choose 75 weekly minutes of vigorous exercise that gets you breathing hard and fast; think running, swimming, playing tennis or riding your bike on hilly terrain.Avoid Nicotine – Tobacco and other nicotine products can raise your blood pressure, damage blood vessels and increase your risk of heart attack and stroke, which are all bad news for your CKM health. If you’re currently trying to quit, you aren’t alone, so don’t be afraid to seek out support groups and resources to help. Prioritize Healthy Sleep – Sleep gives your body a chance to rest and recover from the day, but it’s often one of the first things people sacrifice when life gets busy. Poor sleep can increase your risk for heart disease, weight gain and other issues. Aim for a solid 7-9 hours a night. Keep your bedroom cool, dark and comfortable, and do your best to limit distractions like late-night smartphone scrolling.Watch Your Blood Pressure – When your blood pressure is high, it forces your heart to work harder to pump blood through the body, which can weaken it over time. It can also damage the delicate arteries in and around the kidneys, putting CKM health further at risk. Information is power, so check your blood pressure frequently to know whether further action is needed to keep it under control.Manage Your Blood Sugar – High blood sugar can damage the kidneys, eyes, blood vessels and more, thereby increasing your chance of developing harmful CKM conditions. Controlling blood sugar is particularly important for people with diabetes. If you don’t have diabetes and still experience frequent blood sugar spikes and crashes, talk to your doctor.Control Your Cholesterol – Cholesterol comes in both “bad” (LDL) and “good” (HDL) forms, requiring a balancing act to keep your levels within a healthy range. If LDL is high and HDL is low, your risk of clogged blood vessels, heart attack and stroke are higher. Healthy levels keep blood flowing to your heart and kidneys, supporting your metabolic health in the process. See your doctor for regular bloodwork so you can monitor cholesterol changes.Maintain a Healthy Weight – Since excess body weight puts pressure on the heart, kidneys and other organs, a healthy body weight helps lower your risk of CKM syndrome. Whether you’re hoping to maintain or reduce your weight, regular exercise, healthy eating and quality sleep are all great ways to achieve your goals.
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