Derbyshire Healthcare NHS Foundation Trust will roll out Psyomics’digital platform to improve access to community mental health care.
Sleep-related problems among patients with rheumatoid arthritis in the World Health Organization Eastern Mediterranean region: a systematic review and meta-analysis
BackgroundSleep-related problems are common among patients with rheumatoid arthritis (RA) and contribute substantially to disease burden and reduced quality of life. Evidence from the World Health Organization Eastern Mediterranean Region (WHO EMRO) remains fragmented, with variability in reported prevalence, sleep constructs assessed, and associated risk factors. This study aimed to systematically review and meta-analyze the prevalence and correlates of sleep-related problems among adults with RA in WHO EMRO countries.MethodsA systematic search of PubMed, Scopus, Web of Science, Embase, CINAHL, and Google Scholar was conducted to identify observational studies reporting sleep-related outcomes among adults (≥18 years) with RA in WHO EMRO countries from inception to 27 July 2025. Study screening, data extraction, and quality appraisal using Joanna Briggs Institute (JBI) checklists were conducted primarily by a single author, with methodological oversight and consultation from senior collaborators. Eligible studies reported prevalence or sufficient data to calculate prevalence of specific sleep-related constructs, including subjective sleep quality, insomnia symptoms, daytime sleepiness, or obstructive sleep apnea. Random-effects meta-analysis (DerSimonian-Laird) was used to pool prevalence estimates and odds ratios (ORs) for associated risk factors. Heterogeneity was assessed using the I² statistic and Cochran’s Q test. Subgroup analyses were conducted by sleep construct, assessment method, and country. Publication bias was evaluated using funnel plots, Egger’s test, and Begg’s test, with cautious interpretation due to the small number of studies.ResultsTen studies met inclusion criteria for qualitative synthesis, and six studies (n = 2,315 participants) were included in the quantitative meta-analysis. The pooled prevalence of sleep-related problems was 60.9% (95% CI: 56.5%–65.2%), with substantial heterogeneity (I² = 88%, p < 0.001), reflecting differences in sleep constructs and assessment methods. Subgroup analyses yielded pooled prevalence estimates of 58% for insomnia symptoms, 65% for obstructive sleep apnea-related measures, and 61% for poor subjective sleep quality (PSQI above cut-off), with no statistically significant differences between subgroups. Among associated factors, depression showed the strongest association with sleep-related problems (OR = 2.65; 95% CI: 1.87–3.75), followed by pain (OR = 2.18; 95% CI: 1.68–2.84), fatigue (OR = 1.91; 95% CI: 1.45–2.52), female gender (OR = 1.67; 95% CI: 1.25–2.23), and older age (OR = 1.42; 95% CI: 1.12–1.80). Heterogeneity for risk factor analyses ranged from low to moderate. Publication bias assessments did not indicate statistically significant small-study effects but were underpowered.ConclusionSleep-related problems—assessed using heterogeneous subjective and objective measures—are highly prevalent among patients with RA in WHO EMRO countries with available data. Given substantial clinical and methodological heterogeneity and the limited number of contributing countries, findings should be interpreted as reflecting overall sleep-related burden rather than the prevalence of a single clinical disorder. Routine screening, multidisciplinary management, and culturally adapted interventions are recommended. Future large-scale, longitudinal studies using standardized diagnostic criteria across a broader range of WHO EMRO countries are needed to strengthen the evidence base.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD420251109906, identifier CRD420251109906.
NHS SBS and Salesforce launch AI finance support platform
NHS Shared Business Services (NHS SBS) has partnered with Salesforce to launch an AI platform to support NHS finance and procurement.
Antibody Drug Slows Disability Progression in Advanced Multiple Sclerosis
Results from the Phase III ORATORIO-HAND study show that ocrelizumab, an antibody drug already prescribed to some patients with multiple sclerosis (MS), could benefit a much larger patient population, including those who are older or at more advanced stages of the disease. Published today in The Lancet, these findings could change the way clinicians understand and manage MS.
“Our study suggests we should think differently about what successful treatment looks like in advanced MS and that we shouldn’t assume a lack of benefit in certain groups,” said Gavin Giovannoni, MD, PhD, professor of neurology at Queen Mary University of London. “These findings show that treatment can make a meaningful difference to people with more advanced forms of MS and can help preserve hand and arm function, which is important for maintaining independence, daily activities and quality of life.”
The clinical trial recruited over 1,000 patients with primary progressive multiple sclerosis (PPMS), a form of MS where disability worsens over time that affects about 10–15% of people diagnosed with the autoimmune condition. Ocrelizumab is currently available to patients early on in their PPMS with lower levels of disability, delivered as an infusion every six months to reduce disability progression. However, those with advanced disease, including patients who need a wheelchair to move around, are not eligible for this treatment and currently have limited options available to them.
Compared to a placebo, ocrelizumab significantly reduced the risk of disability progression by 30%, with stronger effects in patients showing signs of inflammatory activity on MRI scans, for whom the drug reduced this risk by 55%. The treatment also reduced the worsening of hand and arm function by 41% after 12 weeks and lowered the need for a wheelchair by 52% among patients who were already unable to walk freely at the start.
These results settle a longstanding debate over whether more advanced forms of multiple sclerosis can still benefit from treatment. In contrast with previous studies, which often excluded patients over 55 and those with advanced disability, this clinical trial recruited participants up to 65 years old and those with substantial mobility impairment.
In addition to assessing walking ability, the study focused on assessing hand and arm function. This decision was based on a separate survey conducted by Giovannoni’s team in which patients with worsening multiple sclerosis reported prioritizing upper-limb function due to its importance for their independence, including their ability for cleaning and grooming themselves.
“MS can be debilitating, exhausting and unpredictable, and hand and arm function is essential for helping people to remain independent,” said Catherine Godbold, PhD, senior research communications manager at the MS Society. “Many previous trials have focused solely on walking ability as a measure of whether a drug is effective. But trials like this are vital in helping us find treatments for everyone.”
“Ocrelizumab is already used as a treatment for active relapsing MS and early primary progressive MS,” she added. “These results could mean it is made available for more people with primary progressive MS, who don’t currently have access to any treatment options. The key now is how we work together to see these findings translated into clinical practice.”
The post Antibody Drug Slows Disability Progression in Advanced Multiple Sclerosis appeared first on Inside Precision Medicine.
Hormone Cell Atlas Maps Body’s Chemical Messengers
An initiative to map hormones in the human body has revealed the breadth and complexity of the endocrine system and could deliver transformative insights into disease for the creation of new therapeutics.
The Hormone Cell Atlas provides a comprehensive framework to explore the impact of these chemical messengers in health and disease, combining a system-level overview with detail at a cellular level.
Its creation incorporates transcriptome-level data from dozens of tissue types and involves the expression of hundreds of hormone and receptor genes in millions of cells and nuclei.
The research, in Science, reveal the distributed and interconnected nature of hormonal regulation, involving a wide repertoire of cell types derived from multiple cell lineages.
These cells show the potential to both synthesize or modulate hormones, with most expressing receptor genes that may integrate signals from multiple hormones.
“These findings have implications not only for understanding physiology and its perturbation in disease, but also for understanding the pleiotropic effects of hormone-based therapies,” reported Lijiang Fei, PhD, from the University of Cambridge, and co-workers.
“For example, expression of GLP1R and GIPR in cardiomyocytes and cardiac pacemaker cells raises the possibility that any cardiovascular benefits or risks of these drugs may arise from direct receptor engagement, not solely from weight loss or improvements in glycemic control.”
Hormones are secreted by endocrine glands and act across tissues and organs to coordinate physiological functions, orchestrating metabolism, growth, reproduction, and other essential physiological processes using tightly regulated synthesis and release.
Inspired by the creation of the Human Cell Atlas, which aims to map all the cells in the human body, Fei and co-workers set out to improve understanding of the synthesis and action of hormones.
Using information from that initiative, the team analyzed the expression of 379 hormone and receptor genes in a transcriptomic dataset comprising 14 million single cells and nuclei across 47 human tissues.
The team mapped the cellular architecture of hormone production and action across diverse tissues and lineages, charted long-range endocrine feedback loops, and localized the expression of genes associated with monogenic endocrine and metabolic disease at cell type resolution across a variety of tissues.
Through this, they created a comprehensive, high-resolution, browsable map that predicted hormone production at cellular resolution.
“The atlas not only recovers classical endocrine axes but also illuminates underappreciated sites of hormone production and action,” the authors pointed out.
“We highlight important principles in endocrine biology, including local modulation of steroidogenic signals in peripheral tissues, specialized vascular-endocrine niches, and the role of adipocytes as dynamic hormone-producing and -sensing cell types.”
They conclude: “This accessible, adaptable resource establishes a dynamic framework for dissecting endocrine physiology and paves the way for mechanistic and physiological studies, which together may deliver transformative insights into human endocrine disease and inform rational drug discovery.”
The post Hormone Cell Atlas Maps Body’s Chemical Messengers appeared first on Inside Precision Medicine.
How a new extraction process could unlock the world’s lithium
Researchers say they’ve found a new way to extract lithium, a crucial metal used in the lithium-ion batteries that power electric vehicles and energy storage arrays. This new technique could be more environmentally friendly and cheaper than existing ones.
The research was published today in Science, and a startup called Rock Zero is working to commercialize the process.
“At scale, we believe this will be the lowest-cost way of sourcing lithium in the world,” says Yet-Ming Chiang, one of the study authors, who is an MIT professor and a serial entrepreneur behind climate tech companies including Form Energy and Addis Energy.
The most economical way to get lithium currently is to extract it from brine, salty water that’s pulled the metal out of rock over the course of millennia. But this technique is geographically limited and currently requires vast tracts of land for massive evaporation pools. The more common tactic is hard-rock mining, where large bodies of ore are blasted apart, cooked at high temperatures, and processed using dangerous chemicals.
The researchers’ new method uses a weak acid to dissolve typically nonreactive silicate minerals. That frees not only the lithium but also other useful materials, including alumina and silica.
The origin story for this research, and the resulting company, came from another startup founded by Chiang, Sublime Systems, which makes cement using electrochemistry.
The team was trying to find a source of highly reactive silica in order to form stronger cement. One way to make reactive materials, which can bond easily with other materials, is to take a nonreactive material, dissolve it, and then allow it to become solid in a more reactive form. It’s not impossible to dissolve silicates, but the best-known way is to use hydrofluoric acid, an extremely dangerous chemical. Other fluorine-containing chemicals are candidates too, but some will produce hydrofluoric acid as a side product during reactions.
Chiang drew inspiration from a previous home renovation project involving glass, which is made of silica. “I was remodeling a shower in Framingham, Massachusetts, about 25 years ago,” he says. “So when we started this project, I remembered that glass etching cream and thought, ‘What’s in that?’”
The glass etching cream he remembered, which can be found on shelves at any craft or home improvement store, uses ammonium fluoride, a weak acid. And the MIT researchers discovered that in the right conditions, it can effectively dissolve silicate minerals without producing hydrofluoric acid in the process.
This chemistry could be useful for any silicate minerals—and there are a lot of them. But spodumene, the mineral that’s often mined for lithium, became a prime first target. (Chiang says a suggestion from Doug Wicks, one of the company’s advisors and a former ARPA-E official, pointed the team in spodumene’s direction.)
ROCK ZERO
Today, a key step in processing spodumene ore is to roast it in a kiln at super-high temperatures. This causes a phase transformation, essentially puffing up the material and making the lithium more accessible.
By avoiding the need to reach these temperatures, you could save on energy costs and potentially reduce carbon emissions as well, says Camden Hunt, one of the authors of the study and the CEO and cofounder of Rock Zero.
Avoiding the kiln could also unlock the ability to use some ores that can’t be roasted properly, Hunt adds. Ore that contains too much iron won’t go through the phase change correctly, instead melting and turning into a glassy material.
The new process relies on simple stirred plastic tanks and takes place at temperatures up to about 95 °C (200 °F). The ammonium fluoride dissolves the silicates, which in earlier experiments allowed nearly all of the lithium inside the spodumene ore to be extracted within a couple of days. The researchers have since cut this time to under 12 hours, says Benjamin Mowbray, first author of the study and the CTO and cofounder of Rock Zero.
The products (after some additional steps to clean them up) are lithium carbonate, which can be used to make batteries; alumina, which can go into a smelter to make aluminum; and cementitious silica, which can be added into concrete. And the acid can be reused in the same loop.
Chiang calls this “nose-to-tail” mining—using every part of the ore provided, like eating every part of a butchered animal.
The researchers are currently working to scale and optimize the process. The tanks in the lab in Cambridge, Massachusetts can handle three kilograms of spodumene concentrate in each batch.
They have also estimated the cost of this process once fully scaled up. Assuming that the ammonium fluoride can be recycled at a high level, they should be able to extract lithium for less than $6,000 per metric ton. (They’ve identified a potential cheap industrial source of the acid as well, as an alternative to recycling it.)
The total cost is projected to be lower than that of other processes used to extract lithium from hard-rock ore today, and it could be competitive with brine.
The team has designed a pilot plant and is looking for space to build it. The plan is to have construction done by the end of 2026 and start operating the facility in 2027. Talks are underway with potential partners in the mining industry.
One difficulty for new players in lithium extraction is the volatility of the market: Prices have seen huge swings in recent years, from a peak in 2022 to lows in late 2024 and a slow climb starting in early 2026.
Rising prices might benefit new players like Rock Zero, but there are many projects that could come online if prices continue to rise, and that could bring the market right back down, says Simon Jowitt, chair of exploration geology at the University of Nevada, Reno. “People are waiting to see what happens with the lithium price,” he says. “It’s a crowded market, and there’s some big players out there.”
And even though batteries are driving up demand for lithium, the market is still relatively small, Jowitt adds: “That means it’s going to be volatile.” New lithium extraction technologies like Rock Zero’s will have to compete with methods used by existing giants, and there’s also the potential that technological alternatives, like sodium-ion batteries that don’t need lithium, could make the market more difficult to navigate, Jowitt says. He also thinks some of the company’s economic estimates could be optimistic.
For its part, Rock Zero’s team hopes not only to scale this technology for lithium, but to use it for other minerals in the future. As Mowbray says, “The Earth’s crust is made of silicates.”
SHIMMER: Routine Clinical Data Processed Into Scalable Disease State Markers
Yes, you have a disease. No, you don’t have a disease.
There is too much complexity to human health for this simplistic, binary method of diagnosis. The phenotypic state of a disease is an ongoing process, while the genotype remains static. Even when it comes to infection, the presence or absence of a pathogen captures little of the entire dynamic biological world inside each of us.
A person’s future development can be predicted with near-certainty by an inherited mutation or chromosomal arrangement, such as cystic fibrosis, Huntington’s disease, and trisomy 21 (Down’s syndrome). Disease development is not an on/off switch. Rather, diseases progress along quantifiable biological spectrums.
The question is, how can that be done in an everyday clinical setting with routine data?
Icahn School of Medicine at Mount Sinai researchers developed a machine learning (ML)-based system that uses routine clinical data to estimate a person’s risk for multiple diseases, potentially revealing hidden illness years before diagnosis.
Published in the Cell Press journal Med, the study demonstrates that SHIMMER stands out due to its use of standard clinical measurements instead of highly specialized data. The practical use of many AI systems in medicine is limited because they require extensive imaging, genomic sequencing, or disease-specific testing. The fact that SHIMMER uses data that is already collected in regular healthcare settings instead could make it easier to implement on a larger scale.
Decoding disease states
Led by Iain S. Forrest, MD, PhD, the research team trained ML models on seven diseases—atrial fibrillation, breast cancer, coronary artery disease (CAD), migraine, rheumatoid arthritis (RA), schizophrenia, and type 2 diabetes (T2D)—using records from the BioMe Biobank in New York and the UK Biobank.
The researchers found that the disease-spectrum scores aligned closely with known risk factors and biological markers. For atrial fibrillation, rising SHIMMER scores tracked with increasing age, obesity, hypertension, and stroke risk. In T2D, the scores rose alongside glucose levels, hemoglobin A1c, triglycerides, and inflammatory markers. CAD scores correlated with smoking, high cholesterol, chronic kidney disease, and established cardiovascular risk calculations.
Importantly, these relationships were not abrupt. Instead, biological changes increased gradually across the spectrum, supporting the idea that disease develops gradually rather than suddenly at diagnosis.
Forrest and colleagues also showed that SHIMMER is capable of identifying disease severity and predicting outcomes. Higher SHIMMER scores were associated with earlier disease onset, more complications, and reduced survival. In CAD, increasing scores tracked with worsening artery blockage, heart failure, arrhythmias, and heart attacks. For RA, higher scores aligned with greater inflammation, worsening anemia, and increased use of immunosuppressive medications.
Some of the strongest findings involved diseases that traditionally lack reliable biomarkers. Schizophrenia, for example, is diagnosed largely through behavioral assessment rather than laboratory testing. However, SHIMMER found obesity, smoking, cannabis use, cardiovascular complications, and poorer survival linked to schizophrenia risk and severity. Spectrum associations were also found in migraine, another condition without biomarkers, including symptom severity and medication use.
Perhaps the most clinically intriguing result was SHIMMER’s ability to flag people who appeared biologically ill despite lacking a formal diagnosis. The researchers identified several people whose routine clinical data strongly resembled known disease profiles despite no diagnosis. One patient with a high T2D SHIMMER score had obesity, hypertension, high glucose, and classic diabetic symptoms but was never diagnosed. Similar patterns appeared for coronary artery disease and rheumatoid arthritis.
In larger undiagnosed populations, elevated SHIMMER scores still predicted abnormal biomarkers and mortality risk. That suggests the system may detect diseases that traditional healthcare pathways miss.
Not a shiny object
The findings challenge the long-standing medical convention of classifying disease in binary terms: either a patient has a condition or does not. The implications could be significant for preventive medicine and population health management. Continuous disease-spectrum scores may help healthcare systems identify high-risk patients earlier and prevent complications.
The researchers envision SHIMMER as an EHR background decision-support tool. Even if they do not meet diagnostic criteria, patients with high scores for diabetes or coronary artery disease may be prioritized for testing, monitoring, or preventive treatment. In that sense, it is complementary to genetic screening.
Still, the work remains a proof of concept rather than a ready-to-deploy clinical tool. The study was retrospective, meaning it analyzed existing records rather than prospectively following patients in real time. The researchers also note concerns about healthcare bias, uneven data quality, and the ethical implications of artificial intelligence in medicine.
Another important limitation is that SHIMMER does not provide definitive diagnoses. Its scores reflect disease burden and biological resemblance rather than calibrated predictions of future illness. Determining how clinicians should act on specific score thresholds will require further study.
The work represents a broader shift in how medicine may conceptualize disease in the AI era. Instead of fixed categories, conditions may increasingly be viewed as dynamic biological trajectories measurable through continuously updated clinical data. If confirmed in future studies, SHIMMER could turn routine medical records into early-warning systems that detect disease before symptoms appear.
The post SHIMMER: Routine Clinical Data Processed Into Scalable Disease State Markers appeared first on Inside Precision Medicine.
Non-invasive electrical stimulation for sleep disturbances in adults: protocol for an evidence−mapping umbrella review of systematic reviews and meta−analyses with subgroup analysis by intervention type and population
BackgroundSleep disturbances affect 10%–30% of adults worldwide. Non−invasive electrical stimulation (e.g., transcranial electrical stimulation) has emerged as a promising non−pharmacological intervention. Although numerous systematic reviews and meta−analyses have been published, they vary considerably in methodological quality, populations, intervention types, and conclusions. No umbrella review has yet synthesised the evidence across different modalities and populations. This evidence mapping umbrella review aims to systematically chart the existing systematic reviews, assess methodological quality, quantify overlap, and describe evidence patterns across diverse modalities and populations.MethodsFollowing JBI guidelines, we will search PubMed, Embase, Cochrane Database of Systematic Reviews, Web of Science, PsycINFO, and Scopus (inception to April 2026), restricted to English. Grey literature will be searched via PROSPERO, ClinicalTrials.gov, Google Scholar (first 200 records), and reference list screening (snowballing). We will include systematic reviews and meta−analyses of randomised controlled trials evaluating any non−invasive electrical stimulation for sleep outcomes. Two reviewers will independently screen, extract data, and assess methodological quality using AMSTAR 2. Primary study overlap will be quantified by the Corrected Covered Area. Where feasible, we will calculate 95% prediction intervals, perform Egger’s regression tests, and conduct excess significance tests using review-level summary estimates. Subgroup analyses will be stratified by intervention and population type. Sensitivity analyses will exclude: (1) reviews with critically low AMSTAR 2 ratings, (2) preprints, (3) reviews at high risk of reporting bias, and (4) studies where sleep is not the primary outcome. The primary outcome is subjective sleep quality; total sleep time is a key secondary outcome. Evidence will be graded using the Fusar−Poli classification, with GRADE for key outcomes.DiscussionThis umbrella review will provide the highest level of evidence synthesis, identifying modalities with more consistent or higher certainty evidence and highlighting areas where evidence remains uncertain. Limitations include restriction to English (which may disproportionately impact modalities such as TEAS), expected heterogeneity, and possible insufficient data for some subgroup analyses. All amendments have been documented in PROSPERO (CRD420261357590).Clinical Trial Registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD420261357590.
System C completes Badgernet maternity rollout in Wales
System C has completed the rollout of its BadgerNet maternity platform across all seven NHS health boards in Wales.
Largest Rare Variant–Lipid Association Study Finds Heart Disease Targets
A landmark week for cardiovascular genetics opened with huge news from the gene-editing field: Eli Lilly’s experimental therapy VERVE-102, a single-dose PCSK9 base editor designed for patients with heterozygous familial hypercholesterolemia (FH) and premature coronary artery disease (CAD), demonstrated durable cholesterol-lowering effects in early studies. While the result marks one of the strongest signs yet that in vivo gene editing could become a practical treatment for inherited cardiovascular disease, it’s just scratching the surface of the enormous number of uncharacterized genetic variants that influence cholesterol levels and heart disease risk across global populations.
The largest rare variant association with blood lipids study ever reported may provide the roadmap to heart disease mechanistic and clinical insights. In a Nature Genetics study analyzing data from more than one million individuals, researchers identified thousands of rare coding variants linked to cholesterol and triglyceride (TG) levels, including several genes strongly associated with coronary artery disease. The findings could accelerate the development of precision medicines for dyslipidemia and improve the diagnosis of inherited lipid disorders such as familial hypercholesterolemia.
Diversity and scale
FH is caused by rare mutations that raise LDL-C, increasing the risk of early-onset CAD, a leading cause of premature death worldwide. Although statins and other lipid-lowering therapies can significantly lower cardiovascular risk, FH is underdiagnosed and undertreated. Many FH-associated variants have variable penetrance, so some carriers have severe disease and others have milder symptoms, adding to the complexity.
Accurate variant-specific risk assessment is becoming increasingly important as genetic screening expands. Most genetic databases are biased toward Europeans, making it difficult to classify disease-causing variants in non-Europeans.
To fill this gap, Satoshi Koyama, MD, PhD, led a research team across academic and medical institutions in the Boston area that analyzed exome sequencing and blood lipid data (total cholesterol, LDL-C, HDL-C, and TG) from Million Veteran Program, UK Biobank, and All of Us participants. More than 230,000 participants came from historically underrepresented populations, making this one of the most diverse large-scale lipid genetics studies conducted to date.
Mechanistic and clinical implications
Their analysis uncovered nearly three million rare coding variants, including more than 214,000 predicted loss-of-function mutations, 2.7 million missense variants, and over 23,000 cryptic splice variants that may disrupt gene processing. In total, the team evaluated over 10 million variant-phenotype associations. The results revealed 800 exome-wide significant additive associations across 184 genetic loci, along with 109 recessive associations involving 53 genes. Many of the strongest signals appeared in genes already known to regulate lipid metabolism and cardiovascular disease, including PCSK9, LDLR, APOB, NPC1L1, and APOC3.
The study also identified five lipid-associated genes significantly linked to CAD risk, highlighting potential therapeutic targets. One particularly intriguing gene was RORC, which encodes the transcription factor RORγ. Previous laboratory and animal studies suggested that suppressing RORγ improves metabolic health and reduces atherosclerosis. Consistent with those findings, the study showed that loss-of-function variants in RORC appeared protective against CAD in humans.
Another key finding involved cryptic splice variants, a class of mutations often overlooked in clinical genetics. The researchers used machine-learning-based splice prediction tools to show that these variants had biological effects similar to canonical loss-of-function mutations, suggesting that many clinically important variants may be underestimated.
The study also found that 13% of missense mutations produced hypermorphic alleles that increased gene activity, unlike most loss-of-function variants. Existing computational prediction tools frequently fail to identify these variants, potentially limiting the sensitivity of current genetic testing approaches.
Koyama and colleagues also detected strong recessive genetic effects that standard additive models may miss entirely. Because homozygous rare variants are uncommon, their contribution to disease has historically been difficult to measure. The findings suggest that recessive inheritance may explain part of the “missing heritability” in complex cardiovascular disease.
The researchers found that most rare variants exerted similar effects across populations, even when variant frequencies differed substantially between ancestries. The study identified 130 alleles observed primarily or exclusively in non-European populations, emphasizing the importance of expanding genetic research beyond European cohorts to improve equitable diagnosis and drug discovery.
From screening to saving hearts
Beyond biological discovery, the study carries important clinical implications. By comparing their findings with curated pathogenic variant databases, the investigators confirmed many established classifications while identifying variants that may warrant reclassification. Two newly highlighted variants enriched in non-European populations may represent previously underrecognized causes of familial hypercholesterolemia.
Although the research focused mainly on rare coding variants rather than noncoding DNA, the authors argue that population-scale sequencing studies can now provide clinically actionable insights into disease mechanisms, pathogenicity, penetrance, and prognosis. Together, the findings offer a powerful new resource for cardiovascular genetics at a time when therapies such as PCSK9 gene editing are beginning to move from concept to clinic.
The post Largest Rare Variant–Lipid Association Study Finds Heart Disease Targets appeared first on Inside Precision Medicine.