Category: 8. Health

Prevention and preparedness play central roles in global health security, with the EU’s Health Emergency Preparedness and Response Authority (DG HERA) now working with third-country governments to strengthen cooperation on medical countermeasures for preparedness and response to serious cross-border public health threats. The recently published WHO Pandemic Agreement also represents a significant step forward in strengthening the global health architecture to better address future pandemics.

While timely access to critical medical resources, such as vaccines, therapeutics, and diagnostics often take centre stage in preparedness discussions, a crucial dimension remains underappreciated: animal health. Yet, history tells us that this is precisely where the next global health emergency may begin.

The term “Disease X” a kind of placeholder name adopted by the WHO in 2018 refers to an unknown pathogen with the potential to cause a serious international epidemic or pandemic. Although Disease X is hypothetical, the concept is very real, and one fact is consistently reaffirmed by scientific evidence: pandemics predominantly originate in animals. Zoonotic pathogens (those that can jump from animals to humans) are the likeliest culprits for future pandemics, as SARS, MERS, Ebola, avian influenza, and mostly recently, COVID-19, have all been linked to animal origins. This reality places animal health systems on the front line of prevention, long before the first human case emerges.

The WHO Pandemic Agreement reflects a notable shift toward integrated approaches that span across sectors. A few articles within the text touch on the need to reduce risks of interspecies transmission, strengthen surveillance, and promote the One Health approach, a framework that recognises the interconnectedness of human, animal, and environmental health. The animal health sector is uniquely positioned to play a central role in helping to turn these ambitions into concrete outcomes.

Tackling disease outbreaks in livestock and wildlife at their source prevents them from spreading to other animals, and more importantly to people.

Animal health professionals, including veterinarians, epidemiologists, researchers, and medicines manufacturers, are already deeply engaged in surveillance, prevention, and management of animal disease outbreaks. But continued threats from infectious diseases and evolving pathogens influencing disease distribution and severity have reinforced the need for robust surveillance, early warning systems, and preparedness planning. A recent report from the World Organisation for Animal Health (WOAH) shares some key facts on how animal health impacts human health:

• Animal diseases are migrating into previously unaffected areas and half (47%) of these diseases have zoonotic potential.
• Between 2005 and 2023 68% of the notifications to WOAH for emerging diseases were considered to have zoonotic potential.
• Outbreaks of bird flu in mammals more than doubled in 2024 compared to 2023, increasing the risk of further spread and transmission to people.

Tackling disease outbreaks in livestock and wildlife at their source prevents them from spreading to other animals, and more importantly to people. Moreover, taking bird flu as an example, aside from the devastating loss of poultry, HPAI (highly pathogenic avian influenza) is causing unprecedented mass die-offs in wild-bird populations. This can seriously disrupt ecosystems and threaten biodiversity. And, although in this case the risk of human infection remains low, the more animals are affected, the greater the possibility for the virus to jump from mammal to mammal, and potentially also to people.

It’s clear that decreasing the burden of animal diseases will mitigate the risk of zoonotic disease transmission. Preparedness actions must begin before a pathogen reaches human populations, so investing in disease surveillance, vaccine development, and healthcare infrastructure for animals is not a luxury but a necessity.

Despite their importance, animal health systems often face chronic underfunding. This leaves significant gaps in pandemic preparedness planning, particularly in developing countries where disease emergence risks are high and surveillance capacity is limited. For example, a key vulnerability globally is the inadequate number of trained veterinarians, and Europe is not a stranger to this phenomenon either. An insufficient vet-to-livestock ratio not only means less prevention of zoonotic diseases, but it also means less effective surveillance and a higher likelihood of diseases crossing borders.

The path to pandemic prevention runs not only through our hospitals and laboratories, but also through the world’s ecosystems, our farms, food markets, and veterinary clinics.

By directing greater resources and political attention toward animal health, promoting the development of joint training programmes for the workforce at the human-animal-environment interface, and developing integrated disease surveillance systems the global community can close these gaps and better protect itself from future disease emergencies, while also creating more resilient health systems overall.

The WHO Pandemic Agreement offers a framework to facilitate this shift, as its emphasis on international cooperation, technology transfer, and capacity-building opens the door to greater collaboration between human and animal health sectors. One of the key challenges ahead lies in making sure these ideas are not only endorsed on paper but implemented in practice, which means ensuring that veterinary services are embedded within European and national pandemic preparedness plans and that animal vaccines producers are consulted before a disease outbreak reaches crisis scenario. DG HERA and the EU Preparedness Union Strategy published earlier this year set a good basis for addressing emerging health threats, but the role for animal health is not clearly defined, nor mentioned in the latter.   

It is important that decision-makers understand the value of One Health action, i.e. involving all the health sectors. preventive action over reactive measures, while also fostering a regular dialogue between the public and private sectors, including Chief Veterinary Officers, to ensure strategies are informed by real-world experience and scientific expertise.

The path to pandemic prevention runs not only through our hospitals and laboratories, but also through the world’s ecosystems, our farms, food markets, and veterinary clinics. Ultimately, the global health community must recognise that animal health is public health and that by enhancing animal health systems today, we can reduce the risks and impacts of tomorrow’s pandemics.

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A new AI model is much better than doctors at identifying patients likely to experience cardiac arrest.

The linchpin is the system’s ability to analyze long-underused heart imaging, alongside a full spectrum of medical records, to reveal previously hidden information about a patient’s heart health.

The federally funded work, led by Johns Hopkins University researchers, could save many lives and also spare many people unnecessary medical interventions, including the implantation of unneeded defibrillators.

“Currently we have patients dying in the prime of their life because they aren’t protected and others who are putting up with defibrillators for the rest of their lives with no benefit,” said senior author Natalia Trayanova, a researcher focused on using artificial intelligence in cardiology. “We have the ability to predict with very high accuracy whether a patient is at very high risk for sudden cardiac death or not.”

The findings are published today in Nature Cardiovascular Research.

Hypertrophic cardiomyopathy is one of the most common inherited heart diseases, affecting one in every 200 to 500 individuals worldwide, and is a leading cause of sudden cardiac death in young people and athletes.

Many patients with hypertrophic cardiomyopathy will live normal lives, but a percentage are at significant increased risk for sudden cardiac death. It’s been nearly impossible for doctors to determine who those patients are.

Current clinical guidelines used by doctors across the United States and Europe to identify the patients most at risk for fatal heart attacks have about a 50% chance of identifying the right patients, “not much better than throwing dice,” Trayanova says.

The team’s model significantly outperformed clinical guidelines across all demographics.

Multimodal AI for ventricular Arrhythmia Risk Stratification (MAARS), predicts individual patients’ risk for sudden cardiac death by analyzing a variety of medical data and records, and, for the first time, exploring all the information contained in the contrast-enhanced MRI images of the patient’s heart.

People with hypertrophic cardiomyopathy develop fibrosis, or scarring, across their heart and it’s the scarring that elevates their risk of sudden cardiac death. While doctors haven’t been able to make sense of the raw MRI images, the AI model zeroed right in on the critical scarring patterns.

“People have not used deep learning on those images,” Trayanova said. “We are able to extract this hidden information in the images that is not usually accounted for.”

The team tested the model against real patients treated with the traditional clinical guidelines at Johns Hopkins Hospital and Sanger Heart & Vascular Institute in North Carolina.

Compared to the clinical guidelines that were accurate about half the time, the AI model was 89% accurate across all patients and, critically, 93% accurate for people 40 to 60 years old, the population among hypertrophic cardiomyopathy patients most at-risk for sudden cardiac death.

The AI model also can describe why patients are high risk so that doctors can tailor a medical plan to fit their specific needs.

“Our study demonstrates that the AI model significantly enhances our ability to predict those at highest risk compared to our current algorithms and thus has the power to transform clinical care,” says co-author Jonathan Chrispin, a Johns Hopkins cardiologist.

In 2022, Trayanova’s team created a different multi-modal AI model that offered personalized survival assessment for patients with infarcts, predicting if and when someone would die of cardiac arrest.

The team plans to further test the new model on more patients and expand the new algorithm to use with other types of heart diseases, including cardiac sarcoidosis and arrhythmogenic right ventricular cardiomyopathy.

Authors include Changxin Lai, Minglang Yin, Eugene G. Kholmovski, Dan M. Popescu, Edem Binka, Stefan L. Zimmerman, Allison G. Hays, all of Johns Hopkins; Dai-Yin Lu and M. Roselle Abraham of the Hypertrophic Cardiomyopathy Center of Excellence at University of California San Francisco; and Erica Scherer and Dermot M. Phelan of Atrium Health.

The work was supported by National Institutes of Health grants R01HL166759, R01HL174440, R35HL1431598, and a Leducq Foundation grant.

Dry eye is top of mind for optometrists year-round, but Prevent Blindness has declared July as Dry Eye Month in hopes to raise awareness among the public and the eye care industry. To support this, Prevent Blindness has created a variety of free dry eye resources: a dedicated webpage about the etiology and treatment of dry eye, fact sheets and social media graphics in both English and Spanish for distribution, and how-to videos about how to apply eye drops and other tips and tricks for dry eye relief. For the fifth year in a row, OCuSOFT is partnering with Prevent Blindness in support of Dry Eye Month.¹

“A number of treatment options are available for dry eye that can help address symptoms and save sight,” Jeff Todd, president and CEO of Prevent Blindness, said in a press release. “We invite everyone to check out our free dry eye resources and make an appointment with an eye doctor to find out what type of treatment is best for them.”

The National Eye Institute reports nearly 16.4 million Americans live with dry eye.² Here are some risk factors for dry eye that eye care providers see regularly in their chairs:

• Being over 50 years old
• Hormonal changes or medications that impact hormone levels
• History of refractive eye surgery (such as LASIK)
• Eyelid inflammation (blepharitis)
• Environmental factors, including allergies, smoke exposure, or dry climates
• Wearing contact lenses
• Poor makeup hygiene practices
• Medical conditions like rheumatoid arthritis, rosacea, Sjögren’s syndrome, and other autoimmune diseases
• Reduced blink rate, often due to prolonged screen use or certain neurological conditions like Parkinson’s disease
• Eyelid disorders that prevent complete eyelid closure
• Excessive use of digital devices, including computers, tablets, and smartphones
• Certain medicines that may decrease tear production, including antihistamines, decongestants, hormone replacement therapy, antidepressants, high blood pressure medications, birth control, acne prescriptions, and Parkinson disease therapies

To learn more about Prevent Blindness’s dry eye resources, visit their website. You can download printouts in both English and Spanish, and view patient educational videos. Additionally, there are interviews about dry eye with April Jasper, OD, FAAO, and Stephanie Jones Marioneaux, MD.

References

1. Prevent Blindness Provides a Variety of Free Dry Eye Educational Resources including a dedicated Webpage, Fact Sheets and Graphics in English and Spanish, Expert Interviews and Informative Videos. Prevent Blindness. Press release. Published June 25, 2025. Accessed June 30, 2025. https://preventblindness.org/dry-eye-month-2025/

2. Dry eye. National Eye Institute. Last updated February 18, 2025. Accessed June 30, 2025. https://www.nei.nih.gov/learn-about-eye-health/eye-conditions-and-diseases/dry-eye

For the first time, scientists have systematically studied the genetic effects of chemotherapy on healthy tissues.

Researchers from the Wellcome Sanger Institute, the University of Cambridge, Cambridge University Hospitals NHS Foundation Trust (CUH) and their collaborators analysed blood cell genomes from 23 patients of all ages who had been treated with a range of chemotherapies.

Published today (1 July 2025) in Nature Genetics, the researchers show that many but not all chemotherapy agents cause mutations and premature aging in healthy blood.

As part of Cancer Grand Challenges team Mutographs, the researchers uncovered new patterns of DNA damage, or mutational signatures, associated with specific chemotherapy drugs.

The researchers suggest that the damaging genetic effects of chemotherapy identified by whole genome sequencing could guide the future treatment of patients with effective chemotherapies that have less harmful effects on healthy tissues.

Chemotherapy is a type of anti-cancer treatment that works by killing cancer cells. It is a systemic treatment, meaning it works throughout the body, and can be administered as a single chemotherapy drug or a combination of drugs. In developed countries, it is estimated that around 10 per cent of the population has received chemotherapy treatments for cancer and other diseases at some point in their lifetime.

Chemotherapy can have long-term side effects on healthy, non-cancerous tissues, and is associated with an increased risk of secondary cancers. However, there is limited understanding of the biological mechanisms underlying these side effects.

With new genomic technologies, researchers can explore mutations in normal cells and begin to investigate the extent and long-term consequences of DNA damage from chemotherapy on healthy tissues.

In a new study, scientists set out to research the effects of chemotherapy on healthy blood. The Mutographs team at the Sanger Institute, University of Cambridge, CUH and their collaborators chose to study blood due to its ease in sampling and ability to culture blood in the laboratory. Plus, the numbers of mutations in normal blood are very consistent between people, giving a good baseline to see whether they are higher in individuals who have received chemotherapy.

The researchers sequenced blood cell genomes from 23 individuals aged three to 80 years, who had been treated with a range of chemotherapies for various blood and solid cancers. Most of the patients were treated at Addenbrooke’s Hospital in Cambridge and had received a combination of chemotherapy drugs. Collectively, they had been exposed to 21 drugs from all of the main chemotherapy classes, including alkylating agents, platinum agents and anti-metabolites. The results were compared with genomic data from nine healthy people who had never received chemotherapy.

From analyzing the whole genome sequence data, the team found that many classes of chemotherapeutics, but not all, do produce higher numbers of mutations in normal blood cells. For example, a three-year-old patient who was treated for neuroblastoma, a cancer of nerve tissue, had more than the number of mutations found in 80-year-olds who had never received chemotherapy.

By looking at patterns of damage in the DNA, known as mutational signatures, the researchers showed that different chemotherapeutics have different mutational signatures, and identified four new signatures found in chemotherapy-treated patients.

For instance, the researchers found that some platinum agents, such as carboplatin and cisplatin, caused very high numbers of mutations. Whereas other drugs in the same class, such as oxaliplatin, did not.

The researchers suggest that if these drugs are used interchangeably in cancer treatment, and assuming they have the same effectiveness, then this sort of genetic information could be incorporated in order to administer chemotherapies with fewer harmful effects.

The team also made discoveries around the effects of chemotherapy on the population of cells that generate blood, known as hematopoietic stem cells.

In normal aging, the hematopoietic stem cells producing blood decrease in diversity, due to the expansion of clones of cells that have “driver” mutations in cancer genes. Chemotherapy caused a similar pattern of change, but prematurely in some middle-aged adults. Particularly in children who have had chemotherapy, their blood appeared to prematurely age, which may increase the risk of secondary cancer later in life.

Scientists suggest that genomic data could help in choosing the chemotherapies for children that minimise this premature aging, and genomic technologies could monitor for further changes later in life.

“For the first time, we have taken a systematic view of the genetic effects of chemotherapy on healthy tissues – in this case, blood. We find that some, but not all chemotherapies cause genetic mutations and premature ageing in normal blood. This study lays the groundwork for future research into the effects of chemotherapy on many other normal tissues, including multiple tissue sampling pre and post treatment, across a range of chemotherapies in a larger group of patients. This comprehensive view would reveal the full range of effects of different chemotherapies, and help us to optimise patient health in the long term.”

Dr Emily Mitchell, first author at the Wellcome Sanger Institute and clinician at CUH

“The effects of chemotherapy we see here – increasing numbers of mutations and premature ageing of healthy blood – reasonably contribute to the heightened risk of additional cancers and the patient’s ability to tolerate further treatments in the future. Given that for many cancers, chemotherapy drugs can be switched with other agents to achieve similar results, we hope such genomic data will guide the optimisation of future treatment plans to deliver effective chemotherapies with much fewer damaging side effects for patients.”

Dr Jyoti Nangalia, co-lead author at the Wellcome Sanger Institute and Consultant Haematologist at CUH

“This important research helps us better understand how some chemotherapy drugs can affect healthy cells as well as cancer cells. While many cancers can now be targeted using precision therapies, chemotherapy remains a key way to treat some cancers and saves many lives every year, so it’s vital that patients continue with the treatment recommended by their doctor. At the same time, studies like this are crucial for helping scientists improve cancer treatments in the future – making them not only more effective but also safer for people living with cancer.”

David Scott, Director of Cancer Grand Challenges

“I believe that the results of this study hold implications for the way that chemotherapies are used to treat cancer patients. We are constantly on the lookout for better ways of giving therapy and minimising the side effects of toxic, systemic treatments. I’m hopeful that the genomic information from this and future studies will guide choices of chemotherapies, and their adoption in clinical practice.”

Professor Sir Mike Stratton, Mutographs team lead and co-lead author at the Wellcome Sanger Institute

Reference: Mitchell E, Pham MH, Clay A, et al. The long-term effects of chemotherapy on normal blood cells. Nat Genet. 2025:1-11. doi: 10.1038/s41588-025-02234-x

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source. Our press release publishing policy can be accessed here.

In this population-based study with more than 26 million live births the findings indicated that women belonging to the most vulnerable social group were exposed to a greater burden of factors that increased the likelihood of having a live birth with CA. However, the patterns of risk factors varied according to the group of anomalies. Maternal education was a risk factor only for neural tube defects, while lack of prenatal care and multifetal gestation were associated with greater odds of having a live birth with CA in all groups, except for those with Down syndrome. Advanced maternal age and previous fetal loss were the factors that increased the odds of CA in all groups.

Distal risk factors, known as socioeconomic factors, have been shown to increase the odds of children born with CA. Black maternal race/skin color and low education (0 to 3 years), increased the odds of CA by 16% and 8%, respectively. Similarly, Anele et al. (2022) reported that a low education level was associated with a 2.08 times higher risk of births affected by CA, mainly in mothers with higher incomes, indicating the impact of low education on the outcome [27]. Regarding black maternal race/skin color, studies carried out in the United States showed that the risk of birth with CA among African Americans varied according to the CA group, with a greater risk for musculoskeletal malformations and a lower risk for cardiac anomalies [28, 29]. Furthermore, in a study carried out in the southern region of Brazil, Trevilato et al. (2022) reported that black women had 20% higher odds of having children with CA than white women [6]. Both factors are related to greater social vulnerability and are consequently associated with low income [30, 31].

The contribution of socioeconomic vulnerability to CA has different origins, acts indirectly, and encompasses environmental conditions, such as poor nutrition, as well as social and structural conditions, e.g., lack of access to prenatal care [10, 11, 20, 32]. Thus, we observed that not having had any prenatal consultations during pregnancy or having started late has been shown to increase the odds of birth with CA. Trevilato et al. (2022) reported that women with no prenatal visits had 97% greater odds than women with seven or more prenatal visits of having children with congenital anomalies [6]. Prenatal care assistance allows important guidance on modifiable risks in the mother’s lifestyle, such as smoking, alcohol consumption, diabetes control, and exposure to certain teratogens, to be provided at an early stage of pregnancy, reducing the risk of births with CA [11].

Women in more vulnerable socioeconomic groups can find difficulties accessing prenatal care since women with lower incomes face barriers such as difficulty covering the cost of services, long waiting times, and difficulties obtaining transportation to reach appointment locations, which can lead to negative attitudes toward health care [33, 34]. Furthermore, Dingemann et al. (2019) reported that women with low education attend fewer prenatal consultations, in addition to having a greater chance of future complications in their children with CA [35]. The absence of prenatal consultations may be related to extreme socioeconomic vulnerability [36, 37]. Furthermore, it was observed that in Brazil, among women who underwent prenatal care, the largest proportion underwent (at least once) an ultrasound (99.7%). However, many congenital anomalies require other complementary exams for accurate diagnosis, which are often not available free of charge for the poorest population [38,39,40].

Neural tube defects were strongly associated with not having any prenatal consultations during pregnancy and low maternal education. Mothers exposed to these factors may not correctly supplement folic acid in the diet during the critical period of pregnancy in which neural tube development occurs (up to the fourth week of gestation) [27, 41]. It is recommended that supplementation begin as early as possible; ideally, supplementation should be started before pregnancy during conception planning, to reduce the likelihood of birth with neural tube defects [42]. Cui et al. (2021) reported that women with less education and who had unplanned pregnancies had less knowledge about folic acid and had higher odds of not starting to use it before becoming pregnant [43].

Additionally, there were significant variations in the odds of children being born with CA between regions of the country and CA groups. The leading cause of this variation is underreporting, and the Southeast is the region that best reports births with CA compared to others [7]. The greater chance of mothers living in the Northeast Region having children with neural tube defects has not yet been fully explained. According to a previous study, the Northeast and Southeast regions had the highest prevalence of neural tube defects [44]. The Northeast region of Brazil concentrates almost half of the Brazilian population living in poverty [45], which may help explain the greater odds of residence mothers of having births with neural tube defects, since this condition is highly associated with low income, low education attainment and poor diet (insufficient supplementation) [46, 47]. In addition, the Zika virus epidemic in Brazil in 2014 resulted in an increase in the reporting of live births with microcephaly and other congenital anomalies of the nervous system, especially in the Northeast region [5, 7], which may have contributed to the observed results.

The odds of having children with cardiac CA also varied widely between regions. Women who lived in the North and Northeast regions were less odds to have children affected by cardiac CA. This result reflects considerable underreporting of this group of CA across regions, which is more pronounced in the country’s poorest regions [7]. A similar result was observed by Salim et al. (2020), who reported fewer cardiac CA notifications in these regions [48]. Early diagnosis of cardiac CA may require a more complex structure than some centers can offer, in addition to trained professionals [49], which leads to underreporting of this group, which is more accentuated in the population and economically vulnerable regions.

Multifetal pregnancy and fetal loss were also associated with birth with CA. Previous fetal loss can be an indication of previous gestational problems, such as a fetus with severe anomalies. A history of prior anomalies has been shown in other studies to be a risk factor for birth with CA [6, 50], which, therefore, may be related to fetal loss in previous pregnancies. Furthermore, as noted by Al-Dewik et al. (2023), multifetal gestation increased the chances of birth with different types of CA, including cardiac CA and nervous system CA [51], as seen in the present work.

Maternal biological factors also demonstrated an association with the outcome. Thus, consistent with the literature, advanced maternal age was found to be the factor most strongly associated with the occurrence of births with Down syndrome, as already well known [52]. Additionally, advanced maternal age also elevated the odds of having children born with other CA, such as central nervous system defects and heart defects [52, 53]. The association between advanced maternal age and the risk of chromosomal defects and other CA has been widely recognized, it is seen that the CA risk varies by anomaly type and maternal age. It is worth noting that pregnancies in women under the age of 20 years have also been shown to increase the odds of births with CA, which is primarily attributed to social factors, as early pregnancy may be linked to low income and other lifestyle-related risk factors, such as the use of drugs and alcohol, as previously discussed [54,55,56].

A relevant aspect of this study was the extensive sample size, as it included all births evaluated nationwide over a long period. Additionally, through the linkage process, it was possible to include live births that were not reported in the SINASC database but were registered in the SIM database. Correcting an information error and substantially enhancing the case group’s size. However, it is important to emphasize that the CA recorded in the SIM were those that were severe enough to result in the individual’s death, which may introduce a bias in this regard. In addition, CA that were not recorded in the SINASC at birth and were not registered in the SIM, were not captured in the notifications and consequently were not included in the analyses. Several factors contribute to this underreporting, including the fact that some CA are not detected at birth because they are not noticeable. In addition, the health team is often not trained to recognize certain more important CA, a capability that varies among Brazilian regions, reinforcing the need for active surveillance of the most important defects [17, 57]. Furthermore, there was no information available on the use of folic acid during pregnancy, which made a more detailed analysis in this regard impossible.

In summary, this study showed that socioeconomically vulnerable women have an increased odds of having a pregnancy affected by CA, mainly for neural tube defects, due to the sum of the risk factors to which they are exposed. Maternal characteristics such as low education, region of residence, race/black skin color, and late start of prenatal care were associated with the outcome. Biological characteristics, such as advanced maternal age and multifetal gestation, were also shown to be strongly associated with birth with CA. Advanced maternal age had a strong association with birth with Down syndrome, whereas multifetal gestation was mainly associated with neural tube defects. Thus, although many CA are not preventable, primary care measures to reduce associated factors greatly impact preventing births with CA [58, 59]. As noted in this study, there is a great need to identify the factors associated with CA and outcomes at the population level, thereby supporting the establishment of effective public policies that can effectively reduce the incidence of preventable CA, as a broad-coverage support for families wishing to become pregnant, including genetic counseling for families with a history of congenital anomalies in the family, control of maternal infections before conception, nutritional support and folic acid supplementation before conception also, among others, in addition to health actions to monitor and care of those born and living with CA.

A potential treatment for glioblastoma crafted by scientists at The Wertheim UF Scripps Institute renders the deadly brain cancer newly sensitive to both radiation and chemotherapy drugs, and blocks the cancer’s ability to invade other tissue, a new study shows.

The experimental medication, called MT-125, has received approval from the FDA to move to clinical trials as a possible first-line treatment for the most aggressive form of the brain cancer.

Each year, 14,000 people in the United States receive the devastating news that they have glioblastoma. It is a cancer with an average survival of just 14 to 16 months. Standard treatments include surgery, radiation and chemotherapy. But half of glioblastoma patients have a subtype that doesn’t respond to any approved cancer drugs, said Courtney Miller, PhD, a professor and academic affairs director at The Herbert Wertheim UF Scripps Institute for Biomedical Innovation & Technology.

New options are urgently needed for those patients, said Miller, a member of the University of Florida Health Cancer Center.

“We know glioblastoma patients are awaiting a breakthrough, and we are moving as fast as humanly possible,” she said.

Miller and her colleagues have long focused on molecular “motors” in the cell, nanoscale proteins called myosin. They look and act like machines, converting the cell’s energy into activity. Myosin motors enable cells to move, connect to other cells or contract and expand, Miller said. They are found throughout the body, including in heart, muscle and brain tissue.

As a result, they have potential as therapeutic targets for a wide range of conditions, from cancer to substance use disorders, she said. However, there are no current medications that target them, or even selective drug-like tools that scientists can use to study them.

Miller teamed up with her Wertheim UF Scripps colleagues to design a spectrum of potential drug candidates to block myosin motors in different contexts. Their work was published Tuesday, July 1, in the scientific journal Cell.

Medicinal chemist Theodore Kamenecka, PhD, engineered the array of compounds, in consultation with structural biologist Patrick Griffin, PhD, The Wertheim UF Scripps Institute’s scientific director.

To test the oncology potential of the myosin motor drugs, the team joined forces with Steven Rosenfeld, MD, PhD, a scientist and neuro-oncologist at the Mayo Clinic in Jacksonville.

Their out-of-the-box strategy appears to have opened a new route to attacking the hardest-to-treat glioblastoma. It works in four ways, the scientists reported in a companion paper published in Cell on June 10.

“In animal studies, MT-125 makes malignant cells that were previously resistant to radiation responsive to it,” Miller said. “You also end up with multinucleated cells that cannot separate, and so they get marked for cell death.”

MT-125 also blocks the cells’ ability to squeeze and change shape, which means they cannot proliferate and invade other parts of the brain, she said. And if MT-125 is combined with existing chemotherapy drugs, including sunitinib, the drug appears to deliver a very powerful response, Rosenfeld said. Sunitinib belongs to a class of chemotherapy drugs called kinase inhibitors.

“We found in mice that combining MT-125 with a number of kinase inhibitors created long periods of a disease-free state that we haven’t seen in these mouse models before,” Rosenfeld said. 

The scientists cautioned that many potential drugs that perform well in mice fail in human studies, due to differences in biology, so it will take time and study to learn if MT-125 is the hoped-for breakthrough, Rosenfeld said.

Toxicity is another worry. But because the cancer cells are much more sensitive to MT-125 than healthy cells, and because the drug doesn’t stay in the body long, pulsed administration of the medication over a brief period seems to address the issue, Rosenfeld said.

“I have been in the field for 35 years, and I always thought the solution to this problem would have to come from out-of-the-box thinking,” Rosenfeld said. “The tried-and-true methods don’t seem to work for this disease.”

The compound, MT-125, has been licensed to a Jupiter, Florida-based biotechnology company started by the scientists, Myosin Therapeutics. They are working hard to begin first-in-human clinical trials within the year in glioblastoma patients, Miller said. The US Food and Drug Administration has given them the green light to proceed. They are awaiting release of a federal grant that has internal approval, she said. The National Institutes of Health has provided study funding, as well as the William Potter Glioblastoma Research Fund at The Wertheim UF Scripps Institute, which was established by William Potter’s wife, Ronnie Potter, in his memory.

Looking ahead, Miller says there is evidence that MT-125 could prove beneficial not only against the aggressive variant of glioblastoma, but for malignant gliomas and other cancers.

In parallel, Miller and her collaborators are working to prepare a clinical trial for a related compound, MT-110, which appears to block drug cravings for people with methamphetamine use disorder. This compound is described in more detail in the July 1 Cell study.

Reference: Radnai L, Young EJ, Kikuti C, et al. Development of clinically viable non-muscle myosin II small molecule inhibitors. Cell. 2025. doi: 10.1016/j.cell.2025.06.006

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Alzheimer’s disease (AD) is a debilitating neurodegenerative condition that affects a significant proportion of older people worldwide. Synapses are points of communication between neural cells that are malleable to change based on our experiences. By adding, removing, strengthening, or weakening synaptic contacts, our brain encodes new events or forgets previous ones. In AD, synaptic plasticity, the brain’s ability to regulate the strength of synaptic connections between neurons, is significantly disrupted. This worsens over time, reducing cognitive and memory functions leading to reduced quality of life. To date, there is no effective cure for AD, and only limited treatments for managing the symptoms.

Studies have shown that repetitive transcranial magnetic stimulation (rTMS), a noninvasive brain stimulation technique that uses electromagnetic pulses to target specific brain regions, has therapeutic potential to manage dementia and related diseases. From previous studies, we know that rTMS can promote synaptic plasticity in healthy nervous systems. Moreover, it is already used to treat certain neurodegenerative and neuropsychiatric conditions. However, individual responses to rTMS for AD management are variable, and the underlying mechanisms are not clearly understood.

Recently, researchers from the University of Queensland (Australia) and the Wicking Dementia Research and Education Centre at the University of Tasmania investigated the effects of rTMS on synapses in the brain cortex of mice with Alzheimer’s type dementia. Their report is published in Neurophotonics.

“Since synaptic dysfunction is a key mechanism in AD, in this study, we quantified the changes in synaptic axonal boutons in AD mouse model in response to rTMS, comparing them to those in healthy mice,” explains corresponding author Dr. Barbora Fulopova, a professor at University of Queensland.

Axonal boutons are specialized endings of an axon, which is the long slender part of a neuron that connects neurons by transmitting neural signals. These are sites where synapses form, allowing neurons to communicate. Therefore, any change in the number or function of these boutons can have profound effects on brain connectivity. In this study, the researchers observed structural changes of two types of excitatory boutons, namely “terminaux boutons” (TBs) (short protrusions from the axon shaft typically connecting neurons in a local area) and “en passant boutons” (EPBs) (small bead-like structures along axons typically connecting distal regions). They used two-photon imaging to visualize individual axons and synapses in the brain of a live animal.

The study was conducted on the APP/PS1 xThy-1GFP-M strain of mice, which is a cross between the APP/PS1 strain (genetically modified to show AD-like symptoms seen in humans) and the Thy1-GFP-M strain, which expresses a fluorescent protein in certain neurons. This combination causes axons to glow during imaging, enabling precise tracking of synaptic bouton changes over time. The team monitored the dynamics of the axonal boutons in these mice at 48-hour intervals for eight days, both before and after a single rTMS session. They then compared these findings to healthy wild-type (WT) mice.

They found that both TBs and EPBs in the AD mouse model had comparable density to those in healthy WT mice. However, the turnover of both bouton types was significantly lower in the AD mouse model before rTMS, likely due to the amyloid plaque buildup, a key marker of dementia, and potentially causing diseases like AD. After a single session of low-intensity rTMS, the turnover of TBs in both strains increased significantly, while there was no change in the EPB turnover. Notably, the largest changes were observed two days after stimulation with an 88 percent increase in TB turnover for the WT strain and a 213 percent increase in the APP-GFP strain. However, this increase returned to pre-stimulation levels by the eighth day.

Furthermore, in the AD mouse model, this increased turnover was comparable to the turnover levels in the WT mice seen before stimulation. This indicates that low-intensity rTMS can potentially restore the synaptic plasticity of TBs to those seen in healthy mice. Moreover, the fact that only TBs, and not EPBs, responded to rTMS points to the possibility that the mechanisms of rTMS might be cell-type specific.

“This is the first study to provide evidence of pre-synaptic boutons responding to rTMS in a healthy nervous system as well as a nervous system marked by the presence of dementia,” remarks Fulopova. “Given the established link between synaptic dysfunction and cognitive decline in dementia and the use of rTMS for the treatment of other neurodegenerative conditions, our findings highlight its potential as a powerful addition to currently used AD management strategies.”

This study marks a significant step forward in understanding AD. While further research is required, the findings of this study pave the way for targeted rTMS treatments that could improve the quality of life of patients with Alzheimer’s disease.

Reference: Fulopova B, Bennett W, Canty AJ. Repetitive transcranial magnetic stimulation increases synaptic plasticity of cortical axons in the APP/PS1 amyloidosis mouse model. Neurophoton. 2025;12(S1). doi: 10.1117/1.NPh.12.S1.S14613

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source. Our press release publishing policy can be accessed here.

Introduction

Diabetic Cardiomyopathy (DCM) is a prevalent cardiovascular complication observed in individuals with diabetes, encompassing conditions such as myocardial infarction, heart failure, and various other cardiovascular disorders. The pathogenesis of DCM is intricate and primarily associated with prolonged hyperglycemia, which triggers metabolic disturbances, chronic inflammation, and endothelial dysfunction.¹ Research indicates that the mortality rate from coronary heart disease in diabetic patients is two to four times higher than that of non-diabetic individuals; approximately two-thirds of deaths among diabetic patients are attributable to cardiovascular diseases, with around 40% due to ischemic heart disease, 15% due to other cardiac conditions, primarily congestive heart failure, and about 10% from stroke.² Given that the early identification and intervention for diabetic patients can significantly reduce the risk of severe health complications, enhance patient prognosis, and lower the incidence of cardiovascular events,^3,4 early warning systems become particularly crucial in this context.

Growing evidence has established a significant association between oral health status and diabetic cardiomyopathy. A study focusing on Chinese patients with type 2 diabetes identified a correlation between inflammatory markers, periodontal indices, and increased risks of coronary heart disease, suggesting that periodontitis may influence cardiac health via inflammatory pathways.⁵ In addition, red complex bacteria, including Porphyromonas gingivalis and Fusobacterium nucleatum, may also play an important role in the pathogenesis of cardiovascular diseases in patients with periodontal disease and diabetes. For example, various oral bacteria (such as Streptococcus mutans, Streptococcus sanguinis, Actinomyces, and Aggregatibacter actinomycetemcomitans) have been directly detected in aortic valve specimens.⁶ Research has shown that two or more periodontal bacteria are frequently detected in heart valve samples from patients with high rates of dental caries and periodontal disease.⁷ This evidence directly indicates a causal relationship between oral bacteria and cardiovascular diseases. Periodontal bacteria may contribute to cardiovascular diseases through mechanisms such as direct arterial infection, platelet aggregation, systemic inflammation, and cross-reactivity. Additionally, a prospective cohort study revealed that oral diseases, indicated by tooth loss, were associated with elevated all-cause mortality as well as heightened risks of both cardiovascular and non-cardiovascular deaths in type 2 diabetic patients.⁸ Early interventions targeting oral health in diabetic individuals were shown to mitigate cardiovascular complications. A retrospective cohort study demonstrated that advanced periodontal therapy in patients with type 2 diabetes significantly reduced the incidence of myocardial infarction and heart failure, highlighting the dual benefits of periodontal treatment for both oral and cardiovascular health.⁹ Research also emphasized the impact of lifestyle factors on oral health as mediators of systemic health in diabetic patients, reducing risks of chronic conditions such as cardiovascular complications. Implementing systemic disease risk assessments in dental settings provides a framework for dental professionals to enhance patient outcomes.¹⁰ A Swedish study found that general dental practitioners could effectively identify patients at risk of fatal cardiovascular events within a set timeframe, enabling timely intervention for those unaware of their CVD-related risks.¹¹ Collectively, these findings underscore the importance of oral health management for early detection and intervention of cardiovascular complications in diabetic patients, ultimately improving prognosis.

Current research on the oral-systemic axis in diabetic cardiomyopathy (DCM) reveals several critical knowledge gaps requiring urgent investigation. While observational studies have identified preliminary associations between periodontal indices, salivary biomarkers, oral microbiome profiles and DCM, their small sample sizes and cross-sectional designs preclude definitive conclusions about temporal relationships or causal mechanisms—necessitating large-scale multicenter prospective cohorts. The precise quantification of how socioeconomic and lifestyle confounders modulate DCM progression through oral-inflammatory pathways remains undefined. A standardized analytical framework integrating multimodal omics data (clinical parameters, imaging features, genomics, transcriptomics, proteomics, metabolomics) with optimized AI model weighting schemes is conspicuously absent. Salivary diagnostics face translational challenges including lack of standardized collection protocols, unvalidated longitudinal monitoring utility, and underdeveloped cost-effective detection technologies. Crucially, evidence regarding the long-term cardioprotective effects and cost-effectiveness of oral health interventions (eg, periodontal therapy) on DCM outcomes remains insufficient. Addressing these gaps will require interdisciplinary efforts combining advanced bioinformatics, precision medicine approaches, and health economics analyses.

To enable clinical doctor to identify patients with diabetic cardiomyopathy at an early stage and intervene promptly, we reviewed the mechanisms by which oral health influences the development of diabetic heart lesions. We also summarized potential oral biomarkers for early screening of diabetic cardiomyopathy and outlined future research directions in this field. Ultimately, our goals are to promote the conduct of large-scale multicenter cohort studies and intervention trials in the future to validate the biological performance of multiple oral indicators, facilitate the development of unified multi-omics and multimodal artificial intelligence models to improve diagnostic and predictive efficiency, advance the development of saliva-based noninvasive early diagnostic technologies and standardized sample collection methods to reduce biases from different detection approaches, and extend the application of oral multi-omics indicators in diagnostic and predictive models to other systemic diseases.

Potential Mechanisms of Oral Diseases in Diabetic Heart Disease

In a high blood sugar environment, the oral cavity primarily triggers cardiovascular events through abnormally activated inflammatory responses, dysregulation of the oral microbiota, and immune modulation disorders (Figure 1). The specific mechanisms are as follows:

Figure 1 Potential Mechanisms of Oral Diseases in Diabetic Heart Disease. Abbreviations: DCM, Diabetic Cardiomyopathy; AGEs, Advanced Glycation End-products; HSPs, Heatshock Proteins; CRP, C-reactive Protein; EndMT, Endothelial-mesenchymal transition; IFN, Interferon; ROls, Reactive Oxygen Intermediates; IL-6, Interleukin-6; TNF-α, Tumor Necrosis Factor-alpha; IFN-γ, Interferon-γ.

Abnormal Activation of the Inflammatory Response

Recent studies have established a close relationship between diabetes and localized oral inflammation. Research indicates that the prevalence of periodontitis in patients with type 1 diabetes mellitus (T1DM) is 18.5%, with an odds ratio (OR) of 2.51 compared to the general population.¹² The oxidative stress and reactive oxygen species generated by hyperglycemia in diabetic patients may exacerbate localized damage to periodontal tissues.¹³ This condition leads to elevated levels of inflammatory markers such as IL-1β, TNF-, IL-6, C-reactive protein (CRP), RANKL/OPG, and oxygen metabolites in periodontal tissues.¹⁴ Additionally, there is a significant increase in advanced glycation end products (AGEs) within the periodontal tissues.¹⁵ Increased vascular permeability and microvascular lesions damage vascular walls, facilitating the invasion of pathogenic microorganisms and their toxins, which in turn triggers inflammation.^16,17 These factors contribute to the onset and progression of periodontitis.

Consequently, localized periodontitis can further induce systemic inflammation, influencing the development of cardiovascular complications. Experimental results show that levels of pro-inflammatory mediators, such as IL-1, IL-6, CRP, amyloid A, and MMP-9, are elevated in patients with severe periodontitis compared to healthy controls, along with an increase in neutrophil counts in the bloodstream.^18–20 Studies using mouse models have found a significant correlation between systemic inflammation induced by periodontitis and the formation of arterial plaques,²¹ indicating that severe periodontitis can lead to systemic diseases through the initiation of widespread inflammation.

Systemic inflammation may further promote atherosclerosis through endothelial-mesenchymal transition (EndMT), enhancing platelet aggregation and thrombosis. Research has demonstrated that pro-inflammatory cytokines released by macrophages, such as TNF-α, IL-1β, and IL-6, can induce EndMT in human umbilical vein endothelial cells (HUVECs) in a periodontitis mouse model. During this process, endothelial cells lose their barrier integrity, allowing monocytes and macrophages to infiltrate the vascular intima. Furthermore, mesenchymal-like cells derived from EndMT destabilize atherosclerotic plaques by altering the balance of collagen and matrix metalloproteinases, thereby promoting the progression of atherosclerosis through these endothelial dysfunctions.²¹ Studies have shown that thrombotic and hemostatic markers, such as fibrinogen, plasminogen activator inhibitor-1, von Willebrand factor, and selectins, are significantly elevated in patients with periodontitis.²⁰ These factors play a crucial role in atherosclerosis and thrombosis by facilitating platelet aggregation and clot formation.²²

A series of studies have demonstrated that successful localized periodontal treatment reduces systemic inflammatory markers, thereby attenuating systemic inflammatory responses. This highlights the potential for oral disease interventions to improve overall systemic inflammation,^14,18,23 providing new ideas and insights for future treatment plans to diabetic cardiomyopathy.

Oral Microbiome and Metabolic Disorders

Diabetes exhibits a profound correlation with oral dysbiosis, significantly influencing bacterial and fungal infection dynamics. The oral cavity, being highly vascularized and innervated, is particularly susceptible to diabetic perturbations.²⁴ Diabetes modulates oral microbiome composition through multifaceted mechanisms, including xerostomia, reduced salivary flow, elevated glucose levels, and microvascular degeneration, ultimately disrupting microbial homeostasis and predisposing to oral infections.²⁵ In diabetic conditions, predominant microbial shifts involve increased Gram-positive bacterial species like hemolytic Streptococcus, Staphylococcus, Propionibacterium, Lactobacillus, and Veillonella, alongside elevated oral Candida species. Conversely, Proteus and Bifidobacterium genera experience significant negative metabolic impacts.²⁶

Building upon microbiome metabolic dysregulation, diabetes exacerbates pathogenic oral microbiota’s virulence through enhanced inflammatory processes, osteoclastogenesis, and accelerated periodontal bone loss.²⁷ These mechanisms intensify local inflammatory responses in periodontitis patients, facilitating bacterial dissemination.²⁸ The gingiva’s highly vascularized characteristics enable periodic bacterial and endotoxin entry into systemic circulation, directly triggering and amplifying inflammatory responses while accelerating atherosclerotic progression. This phenomenon manifests through direct endotoxemia and indirect cellular modulation via inflammatory cytokines, including C-reactive protein (CRP), interleukin-6, tumor necrosis factor-α, and interferon-γ.²⁹ Bacterial endotoxin release ultimately induces endothelial cell damage through direct mechanisms and cellular activation of superoxide radical generation, thereby precipitating further endothelial injury and potentially triggering cardiovascular events.³⁰

Immune Regulation Disorder

The intricate pathological association between diabetes, oral immunological dysregulation, and cardiovascular damage represents a complex immunometabolic interaction. Research substantiates significantly elevated immunoglobulin IgG and IgA levels in diabetic patients’ saliva,³¹ with these aberrant immune responses not only exacerbating periodontal disease progression but potentially triggering broader systemic diseases.³²

Diabetic patients exhibit profound immunological microenvironment dysregulation in oral tissues. Hyperglycemia, as the core pathological hallmark, profoundly modulates oral immune equilibrium through multifaceted mechanisms. Primarily, elevated glucose levels suppress salivary gland function, dramatically reducing salivary secretion and decreasing pH, consequently creating an optimal environment for pathogenic bacterial proliferation.³³ Concurrently, immunoglobulin secretion and functionality become severely compromised, substantially weakening local immune defensive capacities.³⁴ Moreover, advanced glycation end-products (AGEs) generated through hyperglycemic pathways directly interfere with immune cell normal functioning. These end-products disrupt phagocytic capabilities, activate pro-inflammatory cytokines, and significantly alter immunological response homeostasis.³⁵ Critically, diabetic patients experience substantial oral microbiome compositional shifts, particularly increased proportions of Gram-negative bacteria, intimately associated with immunological microenvironment perturbations, establishing a vicious pathogenic cycle.³⁶

Oral bacterial translocation through bloodstream exposure can induce chronic systemic immune responses. While potentially protective, this process simultaneously poses cardiovascular risks, potentially precipitating coronary atherosclerosis and myocardial infarction.^30,37 The local oral immunological microenvironment recruits polymorphonuclear and monocytic cells, releasing various immune factors that generate reactive oxygen intermediates (ROIs) through arachidonic acid cascade reactions, ultimately causing endothelial damage and cardiovascular complications.³⁸ Oral pathogens like Porphyromonas gingivalis stimulate heat shock protein (HSP) expression in oral lymphocytes, inducing both specific and non-specific immune responses.^39,40 Elevated HSP levels may trigger detrimental autoimmune reactions, potentially leading to myocardial injury and arrhythmias.⁴¹ Moreover, neutrophil and macrophage functional abnormalities in diabetic patients can exacerbate periodontal disease progression. Macrophage dysfunction impairs pathogen clearance, perpetuating oral health complications, while gingival fibroblasts demonstrate immunoregulatory imbalances further exacerbating oral health deterioration.^35,42 These immunological alterations not only unveil potential interconnections between oral microbiota and cardiovascular diseases but also provide novel theoretical perspectives on diabetic cardiovascular complication mechanisms.

Based on the evidence presented, we propose a model in which hyperglycemia, decreased salivary flow, and dysregulated microbiome metabolism induce caries, subsequently leading to the development of pulpitis, apical periodontitis, and osteomyelitis of the jaw. Apical periodontitis and osteomyelitis can further progress to periodontal disease. All these conditions, including hyperglycemia, reduced salivary flow, microbiome metabolism disturbances, oxidative stress, and microvascular complications, contribute to the development of periodontal disease and oral mucosal lesions. Furthermore, dysregulation of oral immune responses can lead to the onset of immune-related oral conditions such as oral lichen planus, oral fungal infections, and oral leukoplakia. Persistent and recurrent manifestations of these diseases may promote the risk of oral cancer. This cascade of conditions exacerbates the deterioration of overall oral health. As oral health declines, it primarily impacts endothelial function and accelerates atherosclerosis through three major pathways: hyperglycemia, decreased salivary flow, and dysregulated microbiome metabolism, as well as disturbances in the oral microbiome and immune regulation. These processes ultimately contribute to the development of cardiovascular diseases such as coronary heart disease, atrial fibrillation, and myocardial infarction.

Oral Indicators for Early Screening of Diabetic Heart Disease

Recent studies have identified the crucial role of oral health indices, salivary biomarkers, and oral microbiomes in the screening of diabetic heart disease. For example:

Oral Health Status

The assessment of oral health status has shown significant clinical value in the early diagnosis of diabetic cardiomyopathy. Several studies have demonstrated that specific oral diseases, such as recurrent aphthous ulcers, xerostomia, and oral leukoplakia, are significantly associated with the occurrence of systemic diseases. For instance, recurrent aphthous ulcers are closely related to the systemic inflammatory state in diabetic patients,⁴³ which can promote the incidence of systemic cardiovascular events; the symptoms of xerostomia and decreased salivary flow indicate that type 1 diabetic patients may face a higher risk of neurogenic complications,⁴⁴ which could further increase the likelihood of cardiovascular events. Furthermore, oral leukoplakia, recognized as a precancerous lesion, has a higher incidence among diabetic patients, indicating an elevated risk of cardiovascular disease for these individuals.⁴⁵

Additionally, the periodontal index has been utilized in the development and application of diagnostic models for diabetes and cardiovascular complications. For example, Amr Sayed Ghanem et al⁴⁶ examined the impact of the periodontal index—which includes gingival bleeding, active caries, tooth mobility, and tooth loss—on the prevalence of diabetes and constructed a logistic regression model for comprehensive risk assessment and prediction of diabetes. In research conducted by Huiyuan Zhang et al, the periodontal indices, including the plaque index by Silness and Löe and the bleeding points index by Ainamo and Bay, were employed to evaluate periodontal health in diabetic patients and served as crucial indicators for the early diagnosis of diabetic cardiomyopathy.⁴⁷ Moving forward, it may be feasible to predict the risk of diabetic cardiomyopathy based on specific diseases and oral health conditions, although further experimental work and data validation are required to confirm the predictive role of oral health in diabetes and its complications.

Salivary Biomarkers

Recent studies have indicated that biomarkers in saliva and gingival crevicular fluid are considered effective tools for early diagnosis. Various salivary biomarkers, such as glucose, glycosylated hemoglobin (HbA1c), cytokines (eg, TNF-α), C-reactive protein (CRP), IL-6, advanced glycation end-products (AGEs), α-defensins, and insulin-like growth factor (IGF), not only reflect inflammatory states and insulin resistance,⁴⁸ but also provide crucial information for the early diagnosis of diseases such as diabetic cardiomyopathy.⁴⁹ Furthermore, the mRNA transcriptome in saliva possesses potential diagnostic value for diabetic cardiomyopathy; studies have identified elevated expression levels of KRAS, SAT1, SLC13A2, and TMEM72, as well as decreased expression of EGFR and PSMB2, all associated with type 2 diabetes mellitus (T2DM).⁵⁰ These molecules may serve as significant targets for future research and clinical applications.

Salivary biomarkers are widely utilized in the diagnosis and monitoring of diabetes, demonstrating disruptive potential due to their non-invasive nature. However, their role in the diagnosis and prognostic models of diabetes-related cardiovascular events remains in early developmental stages. Raphael-Enrique Tiongco et al applied Pearson correlation and linear regression models, finding that salivary glucose exhibits comparability to blood glucose in diagnosing and monitoring T2DM, and due to its non-invasiveness, it is considered more advantageous than blood sampling.⁵¹ Priya Desai et al noted that salivary biomarkers (such as 1,5-AG, CRP, IL-6) show promise for early diagnosis and risk prediction of diabetes; these markers have demonstrated consistent positive results in diabetic patients, though further research is needed to standardize analytical processes.⁵² Ekhosuehi Theophilus Agho et al discovered that inflammatory biomarkers in saliva (eg, TNF-α, IL-6) correlate with glycemic control, inflammatory status, and cardiovascular disease risk in diabetic patients, serving as important indicators for assessing their health status.⁵³ A review of published data concerning salivary molecular diagnostics for cardiovascular events revealed significant associations between certain salivary biomarkers and cardiovascular disease (CVD), although some existing study details are conflicting.⁵⁴ While the clinical application of salivary biomarkers for diagnosing and predicting heart disease related to diabetes is promising, it remains in the early stages of development. Further research is essential to validate these findings, establish diagnostic thresholds, and compare them with other established biomarkers currently in clinical use.⁵⁵

Oral Microbiome

Changes in the oral microbiome are closely associated with the development of diabetic cardiomyopathy. Research indicates that the oral microbiota in diabetic patients differs significantly from that in healthy individuals. For instance, children and adolescents with type 1 diabetes exhibit a characteristic composition of oral microbiota, showing a high proportion of cariogenic and periodontal pathogens from an early age.⁵⁶ In patients with poor glycemic control, the oral presence of characteristic microbial communities associated with diabetic cardiomyopathy (DCHD), such as Fusobacterium nucleatum, Streptococcus australis, and Lachnospiraceae bacterium oral taxon 096, is more pronounced.⁵⁶ In a study involving adults with type 1 diabetes receiving continuous insulin pump therapy, significant differences in the oral microbiota were observed, with a higher relative abundance of Streptococcus, S. oralis, and Actinomyces in the mouths of diabetic patients.³⁶

Currently, the oral microbiome has been utilized for the diagnosis of diabetes and its cardiovascular complications. Selvasankar Murugesan et al⁵⁷ employed machine learning models to analyze oral microbiome data, categorizing diabetic patients into “low-risk CVD” and “high-risk CVD” groups. This indicates that integrating oral microbiome data with machine learning techniques can enhance the accuracy of cardiovascular disease risk predictions in diabetic patients. Furthermore, Gregorczyk-Maga et al³⁶ identified three optimal sampling sites for oral microbiota in a large cohort of individuals with type 1 diabetes: buccal and palatal mucosa, tooth surfaces, and gingival pockets, providing a standardized approach for the future large-scale application of oral microbiome diagnostics in diabetes. Lastly, Shaalan A et al⁵⁸ proposed that when abnormal microbial communities such as Fusobacterium nucleatum and Streptococcus are detected, patients should undergo further testing for diabetes indicators and cardiovascular diseases to facilitate early diagnosis. In summary, monitoring changes in the oral microbiome in the future may offer new insights for the early diagnosis of diabetic cardiomyopathy.

Future Research Directions

In future research, applying machine learning and artificial intelligence technologies, developing non-invasive early screening methods, and promoting interdisciplinary collaboration will provide new perspectives and solutions for the early diagnosis and management of diabetic heart disease (Figure 2).

Figure 2 Future Research Directions on the Role of Oral Health in Diabetes Cardiomyopathy.

Applications of Machine Learning Models and Artificial Intelligence Technologies

In future research directions, the application of machine learning and artificial intelligence technologies is expected to facilitate early precise diagnosis of diabetic cardiomyopathy through the assessment of oral health status, predict disease prognosis, and develop personalized intervention strategies. Although there are currently machine learning models based on other systemic indicators used in the early diagnosis and prevention of diabetic cardiomyopathy, such as blood biomarker analysis⁵⁹ and standard retinal imaging in patients with type 2 diabetes,⁶⁰ the “oral indicators for early screening of diabetic cardiomyopathy” discussed in this paper also demonstrate significant potential for utilizing oral metrics in constructing profiles for diabetes and cardiovascular complications. However, there is still a lack of standardized machine learning models specifically designed to evaluate oral health in relation to diabetes and cardiovascular complications.

Future studies should focus on designing comprehensive risk assessment models that integrate oral health-related indicators, including oral health indices, salivary biomarkers, and oral microbiomes, as this approach can significantly enhance the convenience and accuracy of risk assessment for diabetic cardiomyopathy. Leveraging machine learning techniques, these models can analyze large amounts of clinical data to identify potential risk factors and provide personalized early warning systems.⁴⁶ Additionally, the deep learning capabilities of artificial intelligence can process extensive patient data rapidly, optimizing model performance and making early diagnosis and interventions more precise and efficient.⁶¹ Dentists can utilize these developed risk assessment tools to promptly identify high-risk patients and make appropriate referrals, thereby improving clinical outcomes.⁶² Therefore, ongoing research and development in this area will pave the way for new insights into the early diagnosis and management of diabetic cardiomyopathy, showcasing significant application potential and clinical value.

Develop Non-Invasive Early Screening Technologies

In recent years, saliva and oral tissues have emerged as non-invasive biological samples with significant potential for early diagnosis and monitoring of diabetes and its cardiovascular complications. Compared to traditional venipuncture, saliva collection has proven to be a non-invasive, convenient, and cost-effective method. Saliva is rich in proteins, DNA, RNA, and microbial communities, which can serve as biomarkers for cardiovascular diseases and diabetes.^52,63 For instance, 1,5-anhydroglucitol (1,5-AG) and C-reactive protein (CRP) in saliva have been identified as clinical biomarkers for diabetes, demonstrating good consistency and predictive value.⁵² Additionally, biomarkers associated with cardiovascular diseases, such as Irisin and ischemia-modified albumin, have also been detected in saliva.⁶³

Future research should focus on utilizing metabolites, biomarkers, and microbiome data from saliva to develop non-invasive early screening technologies through high-throughput sequencing and machine learning modeling.⁵⁷ This includes standardizing the collection, processing, and analysis of saliva samples to minimize inconsistencies and errors across different studies. Although the significance of metabolites, biomarkers, and microbiome data in relation to diabetic cardiomyopathy has been established, diagnostic and prognostic models for cardiovascular events in diabetes are still in the early stages of development, and their efficacy and outcomes require further investigation and validation. By integrating biomarkers and microbiome data from saliva with advanced machine learning techniques, it is possible to develop more precise and efficient non-invasive early screening technologies. This not only aids in increasing the early diagnosis rates of diabetes and its complications but also provides personalized prevention and treatment strategies for patients, ultimately improving overall health outcomes.⁶⁴

Interdisciplinary Diagnosis and Treatment Collaboration

Research indicates a bidirectional relationship between oral health and diabetes;⁶⁵ oral health not only has the potential to exacerbate the condition of diabetes but may also serve as an early warning signal for the disease.⁶⁶ Therefore, interdisciplinary collaboration is particularly important in the prevention and management of diabetic cardiomyopathy.⁶⁷ Specifically, it is essential to establish a referral mechanism among departments such as dentistry, endocrinology, and cardiology, as well as standardized information sharing and multidisciplinary collaboration platforms to ensure that patients’ oral and systemic health information is shared in a timely manner.⁶⁸ Dentists hold a unique advantage in identifying high-risk patients; through regular oral examinations, they can promptly detect potential periodontal issues in diabetic patients and refer them to relevant healthcare professionals for further diagnosis and treatment.⁶⁹ This bidirectional referral mechanism not only enhances the oral health of diabetic patients but also effectively reduces their risk of cardiovascular diseases, facilitating the implementation of comprehensive prevention and treatment strategies. Moreover, interdisciplinary collaboration can promote a comprehensive evaluation of patients’ health status, integrating expertise from various disciplines to formulate personalized treatment plans. Finally, ongoing education and training, along with relevant institutional policies in hospitals, are critical factors in advancing interdisciplinary collaboration, helping healthcare teams better understand the connection between oral health and systemic health, and take appropriate preventive measures for early intervention and comprehensive treatment.

Discussion

Emerging evidence underscores oral health as both a critical modulator in diabetes progression and a potential early warning biomarker for diabetic cardiomyopathy (DCM). The pathophysiological triad of abnormal activation of the inflammatory response, oral microbiome and metabolic disorders constitutes the principal mechanistic framework linking oral pathologies to DCM development. These insights advocate for systematic oral health surveillance in diabetic populations to mitigate cardiovascular complications.

The landmark identification of Porphyromonas gingivalis within coronary thrombi of acute myocardial infarction (AMI) patients⁷⁰ provides definitive histopathological evidence establishing the causal link between periodontal pathogens and cardiovascular events. Expanding beyond previously characterized mechanisms—including virulence factor-induced endothelial inflammation with subsequent plaque destabilization and pro-inflammatory cytokine-mediated fibrous cap degradation (IL-1β/TNF-α upregulation)—contemporary research delineates three novel pathogenic pathways: First, gingipain-mediated proteolytic cleavage of autophagy regulators VAMP8/STX17 disrupts autophagosome-lysosome fusion, precipitating cardiomyocyte proteotoxic stress and programmed cell death that heightens post-infarction cardiac rupture risk.⁷¹ Second, bacterial outer membrane vesicles (OMVs) orchestrate NF-κB-driven transcriptional activation, exacerbating ischemic myocardial injury through TNF-α/IL-6 hyperexpression and neutrophil extracellular trap-mediated endothelial barrier disruption.⁷¹ Third, a dual thrombogenic mechanism emerges via TLR2/4-dependent platelet hyperreactivity coupled with complement system evasion through C3/C5 convertase degradation, establishing a self-perpetuating cycle of immunothrombosis.⁷² These multilayered pathomechanisms—spanning subcellular autophagy dysregulation, paracrine inflammatory amplification, and thromboinflammatory crosstalk—collectively redefine the oral microbiome’s role in cardiovascular pathogenesis, providing a molecular rationale for targeted periodontal interventions in secondary cardiovascular prevention strategies.

It is additionally worth noting that in patients with diabetes mellitus complicated by periodontal disease, the synergistic effect of persistent hyperglycemia and periodontal inflammation can significantly exacerbate glucolipotoxic damage to the myocardium. In a hyperglycemic environment, myocardial cells increase their uptake of free fatty acids (FFAs), leading to the accumulation of lipid intermediates such as diacylglycerol and sphingosine, which in turn trigger oxidative stress, mitochondrial dysfunction, and endoplasmic reticulum stress—thereby impairing myocardial cell function.⁷² The presence of periodontal disease amplifies this process through multiple mechanisms: firstly, lipopolysaccharide (LPS) from Porphyromonas gingivalis induces upregulation of macrophage ACAT1 expression, promoting cholesterol esterification and foam cell formation while inhibiting ABCG1-mediated cholesterol efflux, thus increasing the instability of atherosclerotic plaques;⁷³ secondly, periodontal inflammation-induced systemic low-grade inflammation exacerbates insulin resistance, prompting a shift in FFA metabolism from β-oxidation to non-oxidative pathways and accelerating lipotoxic damage to myocardial cells;⁷⁴ additionally, clinical studies show that periodontitis patients exhibit significantly elevated serum triglyceride (TG) and low-density lipoprotein cholesterol (LDL-C) levels, coupled with reduced high-density lipoprotein cholesterol (HDL-C)—this dyslipidemic profile is closely associated with diastolic dysfunction in diabetic cardiomyopathy.⁷² Notably, P. gingivalis infection can also exacerbate myocardial fibrosis and coronary microvascular endothelial dysfunction by inducing excessive angiotensin II (Ang II) secretion, forming a vicious cycle of glucolipotoxicity-inflammation-fibrosis.⁷⁵ Therefore, alleviating glucolipotoxicity may confer therapeutic benefits in diabetic heart disease patients with periodontitis and related oral local inflammation. In early screening, oral health indices, salivary biomarkers, and oral microbiome analysis have shown significant clinical value. While mounting evidence demonstrates significant associations between oral health parameters and diabetic cardiomyopathy (DCM), several methodological challenges persist in establishing definitive links. First, the predominance of observational studies limits causal inference, as these designs cannot establish temporal relationships or direct causation.⁷⁶ Second, multiple confounding variables – including socioeconomic status, education level, lifestyle factors, smoking habits, alcohol consumption, and dietary patterns may simultaneously influence both oral and cardiovascular health,^77,78 necessitating rigorous statistical control in analytical approaches. Furthermore, current research suffers from sampling limitations, with many studies employing small cohorts restricted to specific diabetic populations,⁶⁶ thereby compromising generalizability. In addition, from a clinical examination perspective, while existing dental protocols adequately assess localized oral pathologies, they prove insufficient for evaluating systemic conditions like DCM. Current oral health assessment methods lack standardized, multidimensional parameters with sufficient diagnostic precision for comprehensive systemic disease evaluation.

To address the current research limitations, future investigations should prioritize several key methodological advancements: First, large-scale prospective cohort studies should be conducted to establish temporal relationships and causal pathways between oral health parameters and diabetic cardiomyopathy (DCM), employing multicenter collaborations to enhance sample diversity across geographic, ethnic, and socioeconomic strata, thereby improving the validity and generalizability of findings. Second, standardized protocols must be implemented for covariate collection and analytical procedures, utilizing stratified analyses or multivariate regression models to adjust for confounding variables including socioeconomic status and lifestyle factors. In addition, a unified sampling framework should be established for oral health assessment in DCM and systemic complications, specifying operational standards for core indicators such as periodontal indices, salivary biomarkers, oral microbiome profiling, and radiographic evaluations. Methodologically, integrating multi-omics approaches (metagenomics, proteomics, metabolomics) with artificial intelligence algorithms will enable construction of robust predictive models, while leveraging electronic health records for longitudinal validation of these predictive tools.

Future research should focus on investigating the relationship between various oral health indicators and diabetic cardiomyopathy, utilizing multi-omics and multimodal data to train machine learning and artificial intelligence models, thereby enhancing the accuracy of early diagnosis (Figure 3). Specifically, current studies are limited to assessing the effects of a single oral health indicator on diabetic heart disease. Our previous research has demonstrated that a comprehensive evaluation of overall oral health status, a variety of biomarker profiles in saliva, and information from omics and microbiomics can effectively diagnose and assess the prognosis of diabetic heart disease. Therefore, future methodologies should integrate multimodal and multi-omics technologies for training artificial intelligence models.⁷⁹ In particular, multimodal data can encompass clinical data, imaging data, and genomic information, while multi-omics data should include genomics, transcriptomics, proteomics, metabolomics, and emerging omics fields. Given the increasing data dimensions and predictive indicators, larger-scale and multi-center clinical data will be necessary for model training. Moreover, before utilizing machine learning or oral biomarkers as standard diagnostic tools, extensive multi-center data must validate the trained artificial intelligence models to enhance the reliability, applicability, and robustness of the research outcomes.⁸⁰ Ultimately, by integrating these vast multimodal and multi-omics data, more precise diagnostic models and risk prediction models can be constructed, and corresponding intervention strategies can be proposed, utilizing electronic health records for long-term follow-up to verify predictive effectiveness.

Figure 3 Machine Learning Models Integrating Multi-omics and Multimodal Data via Comprehensive Oral Health Indicators for Early Diagnosis and Prediction of Diabetic Cardiomyopathy.

A noteworthy challenge in the field is the issue of cost-effectiveness, as both the acquisition of multi-omics data and the training of machine learning models entail significant expenses. The costs associated with multi-omics data collection include genomic sequencing with Illumina NovaSeq, transcriptome sequencing with 10x Genomics, epigenomic sequencing with ATAC-seq, proteomics using LC-MS/MS, metabolomics via GC-MS, and microbiome sequencing through 16S rRNA sequencing. Additionally, expenses related to the machine learning model training process encompass investments in computational hardware, data acquisition and processing, human resources, experimental and clinical validation, as well as ongoing maintenance and updates. These high costs represent potential barriers to the development of effective models in the future. However, from a long-term perspective, advanced systems may significantly reduce healthcare costs by enhancing diagnostic efficiency, decreasing misdiagnosis rates, streamlining detection methods, and optimizing treatment plans. Moreover, advancements in sequencing technologies consistently yield lower costs and higher throughput solutions, enabling more rapid data generation while reducing expenses.^80,81 Furthermore, governments are increasingly providing funding and incentives to support multi-omics and artificial intelligence research, thereby laying a solid foundation for large-scale clinical trials. To effectively minimize costs, future efforts could establish collaborative networks across multiple centers, combining data resources and technologies to distribute expenses, enabling research teams to engage in comprehensive studies. Actively participating in and developing public databases for multi-omics can also decrease data acquisition costs while enhancing the reproducibility and reliability of research. During the model training phase, implementing dimensionality reduction techniques can help streamline the data by extracting the most relevant biomarkers from complex multi-omics datasets, thereby reducing the complexity of data collection and analysis while constructing more efficient and effective models.⁸²

Although this study does not involve human participants, we still believe that integrating machine learning into healthcare raises critical ethical issues, primarily concerning the ethics of future human trials and the protection of patient data. First, the lack of a comprehensive informed consent process may lead to insufficient transparency, causing patients to feel uneasy about how their data is being used. Therefore, obtaining informed consent from patients regarding data usage is essential for ensuring transparency and maintaining ethical standards. To address this issue, it is crucial to implement a clear and flexible consent process that informs patients of their rights and the specific applications of their data. At the same time, as machine learning technology evolves, interdisciplinary collaboration among healthcare professionals, ethicists, and data scientists should be encouraged to develop guidelines that inform responsible research practices and ethical frameworks. Secondly, protecting patient privacy is vital, as unauthorized access to or misuse of sensitive health information can lead to significant harm. To tackle these issues, robust data protection measures must be implemented, including data anonymization, secure storage, and strict access controls. By prioritizing ethical considerations, we can harness the potential of machine learning to improve healthcare outcomes while minimizing risks to patient privacy and autonomy.

Our previous research indicates that developing a non-invasive early screening method using saliva to detect diabetic cardiomyopathy is crucial for reducing associated diagnostic costs and may represent a significant direction for future advancements. However, there are several challenges in standardizing saliva diagnostics. First, the lack of uniform protocols for the collection and processing of saliva samples can lead to reduced comparability of results across different studies. Evidence suggests that the composition of saliva may vary depending on the collection method and timing,⁸³ thus affecting the concentration of salivary biomarkers. Establishing standardized operating procedures encompassing sample collection, storage, and analysis is essential for ensuring the reliability of these biomarkers. Furthermore, continuous monitoring may offer greater insights than single-point measurements, thereby enhancing the analysis of salivary biomarkers.⁸⁴

Currently, the clinical application of salivary biomarkers is still in its nascent stages. Existing studies primarily focus on the associations between individual biomarkers and diabetic heart disease; however, a single biomarker is insufficient to comprehensively explain disease mechanisms. The optimal diagnostic model for saliva may emerge from a combination of biomarkers tailored to each pathological condition. Constructing diagnostic models utilizing multiple biomarkers appears to be the most promising approach, likely enhancing clinical efficacy alongside accuracy and specificity. To identify specific salivary biomarkers for distinct diseases, extensive cohort studies and long-term follow-up investigations are necessary to verify their accuracy. Future research should emphasize multicenter collaborative studies and large-scale clinical trials to validate the effectiveness and feasibility of salivary biomarkers, thereby facilitating their application in clinical practice.

In addition, it is important to note that while considerable research has been conducted on saliva diagnostics, the development of relevant assay kits is still in its exploratory phase. On one hand, further investigation is required to identify salivary biomarkers associated with specific diseases; on the other hand, accurate detection of salivary biomarkers necessitates advanced nanoscale devices that possess high sensitivity and specificity. Additionally, current gold standard detection methods, such as PCR, gel electrophoresis, chromatography, and microarrays, are time-consuming and require skilled personnel for analysis.⁸⁵ These factors significantly hinder the commercialization of saliva-based diagnostic kits for disease detection. Therefore, developing rapid, convenient, and cost-effective testing methodologies should be a central focus of future research in salivary diagnostics, advancing the field further.

This study is a perspective article focusing on an emerging field where substantial literature support is lacking, conducted through a systematic literature search via PubMed and Web of Science. The selection process prioritizes studies published within the past five years to capture the latest advancements, with the timeframe extended to the past decade and further as needed when recent evidence is insufficient, ensuring a comprehensive foundation for the discussion. Adopting a narrative review framework, the analysis synthesizes and interprets core thematic areas within the field, though it does not adhere to the standardized methodological protocols of systematic reviews. This approach inherently involves potential limitations, including risks of selection bias and possible omissions of relevant literature due to the reliance on subjective inclusion criteria and non-transparent search strategies. To address the identified knowledge gaps—particularly in contentious topics or under-investigated domains—future research could benefit from conducting systematic reviews integrated with meta-analyses to quantitatively assess the strength of available evidence, thereby enhancing the robustness of conclusions and providing a more definitive basis for guiding both research and clinical applications in this evolving area.

Ultimately, based on the oral multimodal machine learning model framework proposed in this paper, future research can expand its diagnostic scope from diabetic cardiomyopathy to various systemic diseases, such as breast cancer, gastric cancer, and Alzheimer’s disease. For instance, Assad et al conducted untargeted metabolomic analyses of saliva samples from breast cancer patients and healthy controls, identifying 31 compounds that were upregulated in the breast cancer group.⁸⁶ Similarly, Huang et al utilized 16S rRNA sequencing to compare the salivary microbiome profiles of patients with gastric cancer, superficial gastritis, and atrophic gastritis. By constructing a random forest model, they achieved an impressive AUC of 0.91 in accurately distinguishing gastric cancer patients from non-gastric cancer patients.⁸⁷ Additionally, salivary lactoferrin levels are significantly reduced in Alzheimer’s disease (AD) patients, which can differentiate between preclinical AD/AD and healthy controls, indicating promising detection performance for AD.⁸⁸ By employing machine learning algorithms for feature selection and weight optimization of oral characteristics and indicators, the model can establish a diagnostic prediction system applicable across multiple diseases while retaining its core advantages—non-invasive sampling, multimodal data integration, and real-time monitoring capabilities. Notably, achieving cross-disease applications necessitates the development of a large-scale disease-specific oral biomarker database and addressing the challenges of heterogeneity among different disease datasets, ultimately leading to the formation of adaptable risk assessment, intelligent diagnostic platforms, prognostic analysis, and treatment plan design systems suitable for various clinical scenarios.

Conclusion

Oral health is closely intertwined with diabetic cardiomyopathy, primarily inducing the latter through three mechanisms: Abnormal Activation of the Inflammatory Response, Oral Microbiome and Metabolic Disorders, and Immune Regulation Disorder. Early oral health interventions exert favorable effects on diabetic cardiomyopathy. In the future, oral health is poised to play a pivotal role in the early screening and prevention of diabetic cardiomyopathy. Currently, several categories of oral biomarkers, including Oral Health Status, Salivary Biomarkers, and Oral Microbiome, have been validated as relevant to diabetic cardiomyopathy, demonstrating utility in its diagnosis and prognostic prediction. However, the existing research is predominantly observational and conducted on small scales. To enhance the validity and generalizability of findings, we advocate for future large-scale multicenter cohort studies and interventional trials. Future directions include integrating multi-omics and multimodal data to develop unified artificial intelligence models for improved diagnostic and predictive efficiency; advancing saliva-based noninvasive early diagnostic technologies alongside standardized sample collection methodologies; and promoting interdisciplinary diagnosis and treatment collaboration while extending the application of oral multi-omics indicators in diagnostic and predictive models to other systemic diseases.

Funding

The design of the study and the collection, analysis, and interpretation of data were supported by Young backbone personnel project by Beijing University of Chinese Medicine Dongzhimen Hospital (DZMG-QNGG003); Supported by the National Natural Science Foundation of China (No.82305056); Baoan District Clinical Special Fund of Traditional Chinese Medicine (NO.2023ZYYLCZX-6); “Future Talent” Training Program in the Medical Engineering Field jointly funded by Beijing Chronic Disease Prevention and Control and Health Education Research Association and Zhongguancun Talent Association (MBRCO012025020), and the Youth Talent Support Project of the China Association of Chinese Medicine (2024-2026 Cycle, CACM-2024-0NRC2-A02).

Disclosure

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

1. Serés-Noriega T, Giménez M, Perea V, et al. Use of the steno T1 risk engine identifies preclinical atherosclerosis better than use of ESC/EASD-2019 in adult subjects with type 1 diabetes at high risk. Diabetes Care. 2022;45(10):2412–2421. doi:10.2337/dc22-0118

2. Zhang Y, Niu J, Choi HK. Excess mortality among persons with type 2 diabetes. New Engl J Med. 2016;374(8):788.

3. Manuela R. Positive Effekte einer frühen Diabetestherapie greifen lebenslang. Diabetes-Komplikationen Verhindern, Lebenserwartung Steigern. 2024;22(06):234.

4. Luo S, Zheng X, Bao W, et al. Real-world effectiveness of early insulin therapy on the incidence of cardiovascular events in newly diagnosed type 2 diabetes. Signal Transduct Target Ther. 2024;9(1):154. doi:10.1038/s41392-024-01854-9

5. Chen L, Wei B, Xu L, Wu Y. The association of inflammatory markers and periodontal indexes with the risk of coronary heart disease in Chinese patients with type 2 diabetes mellitus. Diabetes Res Clin Pract. 2018;135:37–44. doi:10.1016/j.diabres.2017.10.008

6. Pardo A, Signoriello A, Signoretto C, et al. Detection of periodontal pathogens in oral samples and cardiac specimens in patients undergoing aortic valve replacement: a pilot study. J Clin Med. 2021;10(17):3874. doi:10.3390/jcm10173874

7. Oliveira FAF, Forte CPF, Silva PGB, et al. Molecular analysis of oral bacteria in heart valve of patients with cardiovascular disease by real-time polymerase chain reaction. Medicine. 2015;94(47):e2067. doi:10.1097/MD.0000000000002067

8. Li Q, Chalmers J, Czernichow S, et al. Oral disease and subsequent cardiovascular disease in people with type 2 diabetes: a prospective cohort study based on the Action in Diabetes and Vascular Disease: preterax and Diamicron Modified-Release Controlled Evaluation (ADVANCE) trial. Diabetologia. 2010;53(11):2320–2327. doi:10.1007/s00125-010-1862-1

9. Peng CH, Yang YS, Chan KC, Kornelius E, Chiou JY, Huang CN. Periodontal treatment and the risks of cardiovascular disease in patients with type 2 diabetes: a retrospective cohort study. Int Med. 2017;56(9):1015–1021. doi:10.2169/internalmedicine.56.7322

10. Cullinan M. The role of the dentist in the management of systemic conditions. Ann R Australas Coll Dent Surg. 2012;21:85–87.

11. Jontell M, Glick M. Oral health care professionals’ identification of cardiovascular disease risk among patients in private dental offices in Sweden. J Am Dental Assoc. 2009;140(11):1385–1391. doi:10.14219/jada.archive.2009.0075

12. Dicembrini I, Serni L, Monami M, et al. Type 1 diabetes and periodontitis: prevalence and periodontal destruction-a systematic review. Acta Diabetol. 2020;57(12):1405–1412. doi:10.1007/s00592-020-01531-7

13. Zhao M, Xie Y, Gao W, Li C, Ye Q, Li Y. Diabetes mellitus promotes susceptibility to periodontitis-novel insight into the molecular mechanisms. Front Endocrinol. 2023;14:1192625. doi:10.3389/fendo.2023.1192625

14. Polak D, Shapira L. An update on the evidence for pathogenic mechanisms that may link periodontitis and diabetes. J Clin Periodontol. 2018;45(2):150–166. doi:10.1111/jcpe.12803

15. Iacopino AM. Diabetic periodontitis: possible lipid-induced defect in tissue repair through alteration of macrophage phenotype and function. Oral Dis. 1995;1(4):214–229. doi:10.1111/j.1601-0825.1995.tb00187.x

16. Malicka B, Kaczmarek U. Periodontal condition in adult patients with diabetes mellitus type 1 and 2. Dent Med Probl. 2011;48:198–207.

17. Bender IB, Bender AB. Diabetes mellitus and the dental pulp. J Endodontics. 2003;29(6):383–389. doi:10.1097/00004770-200306000-00001

18. Schenkein HA, Papapanou PN, Genco R, Sanz M. Mechanisms underlying the association between periodontitis and atherosclerotic disease. Periodontology. 2020;83(1):90–106. doi:10.1111/prd.12304

19. Rodríguez-Lozano B, González-Febles J, Garnier-Rodríguez JL, et al. Association between severity of periodontitis and clinical activity in rheumatoid arthritis patients: a case-control study. Arthritis Res Therapy. 2019;21(1):27. doi:10.1186/s13075-019-1808-z

20. Herrera D, Molina A, Buhlin K, Klinge B. Periodontal diseases and association with atherosclerotic disease. Periodontology. 2020;83(1):66–89. doi:10.1111/prd.12302

21. Suh JS, Kim S, Boström KI, Wang CY, Kim RH, Park NH. Periodontitis-induced systemic inflammation exacerbates atherosclerosis partly via endothelial-mesenchymal transition in mice. Int J Oral Sci. 2019;11(3):21. doi:10.1038/s41368-019-0054-1

22. Davalos D, Akassoglou K. Fibrinogen as a key regulator of inflammation in disease. Semin Immunopathol. 2012;34(1):43–62. doi:10.1007/s00281-011-0290-8

23. D’Aiuto F, Gkranias N, Bhowruth D, et al. Systemic effects of periodontitis treatment in patients with type 2 diabetes: a 12 month, single-centre, investigator-masked, randomised trial. Lancet Diabetes Endocrinol. 2018;6(12):954–965. doi:10.1016/S2213-8587(18)30038-X

24. Verhulst MJL, Loos BG, Gerdes VEA, Teeuw WJ. Evaluating all potential oral complications of diabetes mellitus. Front Endocrinol. 2019;10:56. doi:10.3389/fendo.2019.00056

25. Rodrigues CF, Rodrigues ME, Henriques M. Candida sp. infections in patients with diabetes mellitus. J Clin Med. 2019;8(1):76.

26. Al-Janabi A. A positive or negative connection of diabetes mellitus to the oral microbiota. Eurasian J Med. 2023;55(1):83–89. doi:10.5152/eurasianjmed.2023.21164

27. Graves DT, Corrêa JD, Silva TA. The oral microbiota is modified by systemic diseases. J Dent Res. 2019;98(2):148–156. doi:10.1177/0022034518805739

28. Kudiyirickal MG, Pappachan JM. Periodontitis: an often-neglected complication of diabetes. World J Diabetes. 2024;15(3):318–325. doi:10.4239/wjd.v15.i3.318

29. Priyamvara A, Dey AK, Bandyopadhyay D, et al. Periodontal inflammation and the risk of cardiovascular disease. Curr Atherosclerosis Rep. 2020;22(7):28. doi:10.1007/s11883-020-00848-6

30. Stypułkowska J, Lyszczarz R, Błazowska K. The role of chronic dental bacterial infections in the aetiopathogenesis of ischaemic heart disease. Wiadomosci lekarskie. 2002;55 Suppl 1(Pt 2):922–926.

31. Anil S. Immunoglobulin concentration in gingival tissue of type 2 diabetic patients with periodontitis. Indian J Dent Res. 2006;17(4):151–154. doi:10.4103/0970-9290.29872

32. Nishimura F, Iwamoto Y, Soga Y. The periodontal host response with diabetes. Periodontology. 2007;43:245–253. doi:10.1111/j.1600-0757.2006.00171.x

33. Inchingolo AD, Malcangi G, Semjonova A, et al. Oralbiotica/oralbiotics: the impact of oral microbiota on dental health and demineralization: a systematic review of the literature. Children. 2022;9(7):1014. doi:10.3390/children9071014

34. Negrini TC, Carlos IZ, Duque C, Caiaffa KS, Arthur RA. Interplay among the oral microbiome, oral cavity conditions, the host immune response, diabetes mellitus, and its associated-risk factors-an overview. Front Oral Health. 2021;2:697428. doi:10.3389/froh.2021.697428

35. Portes J, Bullón B, Quiles JL, Battino M, Bullón P. Diabetes mellitus and periodontitis share intracellular disorders as the main meeting point. Cells. 2021;10(9):2411. doi:10.3390/cells10092411

36. Gregorczyk-Maga I, Fiema M, Kania M, et al. Oral microbiota-one habitat or diverse niches? A pilot study of sampling and identification of oral bacterial and fungal biota in patients with type i diabetes mellitus treated with insulin pump. Int J Environ Res Public Health. 2023;20(3):2252. doi:10.3390/ijerph20032252

37. Hu C. Mechanism of cardiovascular disease caused by oral infection. 2010.

38. Hajishengallis G, Chavakis T. Local and systemic mechanisms linking periodontal disease and inflammatory comorbidities. Nat Rev Immunol. 2021;21(7):426–440. doi:10.1038/s41577-020-00488-6

39. Siqueira JF, Rôças IN. Bacterial pathogenesis and mediators in apical periodontitis. Brazilian Dental J. 2007;18(4):267–280. doi:10.1590/S0103-64402007000400001

40. Goulhen F, Grenier D, Mayrand D. Oral microbial heat-shock proteins and their potential contributions to infections. Crit Rev Oral Biol Med. 2003;14(6):399–412. doi:10.1177/154411130301400603

41. Xu Q, Schett G, Seitz CS, Hu Y, Gupta RS, Wick G. Surface staining and cytotoxic activity of heat-shock protein 60 antibody in stressed aortic endothelial cells. Circ Res. 1994;75(6):1078–1085. doi:10.1161/01.RES.75.6.1078

42. Eldzharov A, Kabaloeva D, Nemeryuk D, et al. Evaluation of microcirculation, cytokine profile, and local antioxidant protection indices in periodontal health, and stage II, stage III periodontitis. J Clin Med. 2021;10(6):1262. doi:10.3390/jcm10061262

43. Mauri-Obradors E, Estrugo-Devesa A, Jané-Salas E, Viñas M, López-López J. Oral manifestations of diabetes mellitus. A systematic review. Med Oral Patologia Oral y Cirugia Bucal. 2017;22(5):e586–e594. doi:10.4317/medoral.21655

44. Moore PA, Guggenheimer J, Etzel KR, Weyant RJ, Orchard T. Type 1 diabetes mellitus, xerostomia, and salivary flow rates. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2001;92(3):281–291. doi:10.1067/moe.2001.117815

45. González-Serrano J, Serrano J, López-Pintor RM, Paredes VM, Casañas E, Hernández G. Prevalence of oral mucosal disorders in diabetes mellitus patients compared with a control group. J Diabetes Res. 2016;2016:5048967. doi:10.1155/2016/5048967

46. Ghanem AS, Nagy AC. Oral health’s role in diabetes risk: a cross-sectional study with sociodemographic and lifestyle insights. Front Endocrinol. 2024;15:1342783. doi:10.3389/fendo.2024.1342783

47. Zhang H, Zhang Y, Chen X, Li J, Zhang Z, Yu H. Effects of statins on cytokines levels in gingival crevicular fluid and saliva and on clinical periodontal parameters of middle-aged and elderly patients with type 2 diabetes mellitus. PLoS One. 2021;16(1):e0244806. doi:10.1371/journal.pone.0244806

48. Alam BF, Nayab T, Ali S, et al. Current scientific research trends on salivary biomarkers: a bibliometric analysis. Diagnostics. 2022;12(5):1171. doi:10.3390/diagnostics12051171

49. Marques RCR, da Silva JR, Vieira Lima CP, Stefani CM, Damé-Teixeira N. Salivary parameters of adults with diabetes mellitus: a systematic review and meta-analysis. Oral Surg Oral Med Oral Pathol Oral Radiol. 2022;134(2):176–189. doi:10.1016/j.oooo.2022.03.001

50. Gradisteanu Pircalabioru G, Musat M, Elian V, Iliescu C. Liquid biopsy: a game changer for type 2 diabetes. Int J Mol Sci. 2024;25(5):2661. doi:10.3390/ijms25052661

51. Tiongco RE, Bituin A, Arceo E, Rivera N, Singian E. Salivary glucose as a non-invasive biomarker of type 2 diabetes mellitus. J Clin Exp Dent. 2018;10(9):e902–e907. doi:10.4317/jced.55009

52. Desai P, Donovan L, Janowitz E, Kim JY. The clinical utility of salivary biomarkers in the identification of type 2 diabetes risk and metabolic syndrome. Diabetes Metab Syndr Obes. 2020;13:3587–3599. doi:10.2147/DMSO.S265879

53. Agho ET, Owotade FJ, Kolawole BA, Oyetola EO, Adedeji TA. Salivary inflammatory biomarkers and glycated haemoglobin among patients with type 2 diabetic mellitus. BMC Oral Health. 2021;21(1):101. doi:10.1186/s12903-021-01453-y

54. Gohel V, Jones JA, Wehler CJ. Salivary biomarkers and cardiovascular disease: a systematic review. Clin Chem Lab Med. 2018;56(9):1432–1442. doi:10.1515/cclm-2017-1018

55. Bahbah EI, Noehammer C, Pulverer W, Jung M, Weinhaeusel A. Salivary biomarkers in cardiovascular disease: an insight into the current evidence. FEBS J. 2021;288(22):6392–6405. doi:10.1111/febs.15689

56. Carelli M, Maguolo A, Zusi C, et al. Oral microbiota in children and adolescents with type 1 diabetes mellitus: novel insights into the pathogenesis of dental and periodontal disease. Microorganisms. 2023;11(3):668. doi:10.3390/microorganisms11030668

57. Murugesan S, Elanbari M, Bangarusamy DK, Terranegra A, Al Khodor S. Can the salivary microbiome predict cardiovascular diseases? Lessons learned from the Qatari population. Front Microbiol. 2021;12:772736. doi:10.3389/fmicb.2021.772736

58. Shaalan A, Lee S, Feart C, et al. Alterations in the oral microbiome associated with diabetes, overweight, and dietary components. Front Nutr. 2022;9:914715. doi:10.3389/fnut.2022.914715

59. Yamada T, Iwasaki K, Maedera S, et al. Myocardial infarction in type 2 diabetes using sodium-glucose co-transporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors or glucagon-like peptide-1 receptor agonists: proportional hazards analysis by deep neural network based machine learning. Curr Med Res Opin. 2020;36(3):403–409. doi:10.1080/03007995.2019.1706043

60. Syed MG, Trucco E, Mookiah MRK, et al. Deep-learning prediction of cardiovascular outcomes from routine retinal images in individuals with type 2 diabetes. Cardiovasc Diabetol. 2025;24(1):3. doi:10.1186/s12933-024-02564-w

61. Ji C, Jiang T, Liu L, Zhang J and You L. Continuous glucose monitoring combined with artificial intelligence: redefining the pathway for prediabetes management. Front. Endocrinol., 2025;16. doi:10.3389/fendo.2025.1571362

62. Chapple IL, Genco R. Diabetes and periodontal diseases: consensus report of the Joint EFP/AAP workshop on periodontitis and systemic diseases. J Periodontol. 2013;84(4 Suppl):S106–112. doi:10.1902/jop.2013.1340011

63. Rahim MA, Rahim ZH, Ahmad WA, Hashim OH. Can saliva proteins be used to predict the onset of acute myocardial infarction among high-risk patients? Int J Med Sci. 2015;12(4):329–335. doi:10.7150/ijms.11280

64. Zhang J, Zhang Z, Zhang K, Ge X, Sun R and Zhai X. Early detection of type 2 diabetes risk: limitations of current diagnostic criteriaDataSheet_1.docx. Front. Endocrinol., 2023;14. doi:10.3389/fendo.2023.126062310.3389/fendo.2023.1260623.s001

65. Wang YB, Yan SY, Li XH, et al. Causal association between periodontitis and type 2 diabetes: a bidirectional two-sample Mendelian randomization analysis. Front Genetics. 2021;12:792396. doi:10.3389/fgene.2021.792396

66. Graziani F, Gennai S, Solini A, Petrini M. A systematic review and meta-analysis of epidemiologic observational evidence on the effect of periodontitis on diabetes An update of the EFP-AAP review. J Clin Periodontol. 2018;45(2):167–187. doi:10.1111/jcpe.12837

67. Borgnakke WS, Poudel P. Diabetes and oral health: summary of current scientific evidence for why transdisciplinary collaboration is needed. Front Dent Med. 2021;2:709831. doi:10.3389/fdmed.2021.709831

68. Glurich I, Schwei KM, Lindberg S, Shimpi N, Acharya A. Integrating medical-dental care for diabetic patients: qualitative assessment of provider perspectives. Health Promot Pract. 2018;19(4):531–541. doi:10.1177/1524839917737752

69. Chatzaki N, Zekeridou A, Paroz E, Gastaldi G, Giannopoulou C. Knowledge and practice attitudes regarding the relationship between diabetes and periodontitis: a survey among Swiss endocrinologists and general physicians. BMC Prim Care. 2023;24(1):238. doi:10.1186/s12875-023-02184-5

70. Piñon-Esteban P, Núñez L, Moure R, et al. Presence of bacterial DNA in thrombotic material of patients with myocardial infarction. Sci Rep. 2020;10(1):16299. doi:10.1038/s41598-020-73011-5

71. Shiheido-Watanabe Y, Maejima Y, Nakagama S, Fan Q, Tamura N, Sasano T. Porphyromonas gingivalis, a periodontal pathogen, impairs post-infarcted myocardium by inhibiting autophagosome-lysosome fusion. Int J Oral Sci. 2023;15(1):42. doi:10.1038/s41368-023-00251-2

72. Bregaint S, Boyer E, Fong SB, Meuric V, Bonnaure-Mallet M, Jolivet-Gougeon A. Porphyromonas gingivalis outside the oral cavity. Odontology. 2022;110(1):1–19. doi:10.1007/s10266-021-00647-8

73. Nicolosi G, Donzella M, Polizzi A, et al. Early detection of cardiovascular risk markers through non-invasive ultrasound methodologies in periodontitis patients. Open Med. 2024;19(1):20241003. doi:10.1515/med-2024-1003

74. Amato M, Lupi SM, Polizzi A, et al. New trends in the impact of periodontal treatment on early cardiovascular diseases outcomes: insights and future perspectives. Rev cardiovasc med. 2023;24(10):287. doi:10.31083/j.rcm2410287

75. Guo J, Lin K, Wang S, He X, Huang Z, Zheng M. Effects and mechanisms of Porphyromonas gingivalis outer membrane vesicles induced cardiovascular injury. BMC Oral Health. 2024;24(1):112. doi:10.1186/s12903-024-03886-7

76. Hardan L, Matta A, Bourgi R, et al. Association between dental and cardiovascular diseases: a systematic review. Rev cardiovasc med. 2023;24(6):159. doi:10.31083/j.rcm2406159

77. Fiorillo L. Oral health: the first step to well-being. Medicina. 2019;55(10):676. doi:10.3390/medicina55100676

78. Gomaa N, Glogauer M, Tenenbaum H, Siddiqi A, Quiñonez C. Social-biological interactions in oral disease: a ‘Cells to Society’ view. PLoS One. 2016;11(1):e0146218. doi:10.1371/journal.pone.0146218

79. He X, Liu X, Zuo F, Shi H, Jing J. Artificial intelligence-based multi-omics analysis fuels cancer precision medicine. Semi Cancer Biol. 2023;88:187–200. doi:10.1016/j.semcancer.2022.12.009

80. Acosta JN, Falcone GJ, Rajpurkar P, Topol EJ. Multimodal biomedical AI. Nature Med. 2022;28(9):1773–1784. doi:10.1038/s41591-022-01981-2

81. Jensen TBN, Dall SM, Knutsson S, Karst SM, Albertsen M. High-throughput DNA extraction and cost-effective miniaturized metagenome and amplicon library preparation of soil samples for DNA sequencing. PLoS One. 2024;19(4):e0301446. doi:10.1371/journal.pone.0301446

82. Jeong H, Jeong YW, Park Y, Kim K, Park J, Kang DR. Applications of deep learning methods in digital biomarker research using noninvasive sensing data. Digit Health. 2022;8:20552076221136642. doi:10.1177/20552076221136642

83. Tzimas K, Pappa E. Saliva metabolomic profile in dental medicine research: a narrative review. Metabolites. 2023;13(3):379. doi:10.3390/metabo13030379

84. Liu X and Zhang J. Continuous Glucose Monitoring: A Transformative Approach to the Detection of Prediabetes. JMDH, 2024;17:5513–5519. doi:10.2147/JMDH.S493128

85. Huang Z, Yang X, Huang Y, et al. Saliva – a new opportunity for fluid biopsy. Clin Chem Lab Med. 2023;61(1):4–32. doi:10.1515/cclm-2022-0793

86. Xavier Assad D, Acevedo AC, Cançado Porto Mascarenhas E, et al. Using an untargeted metabolomics approach to identify salivary metabolites in women with breast cancer. Metabolites. 2020;10(12):506. doi:10.3390/metabo10120506

87. Huang K, Gao X, Wu L, et al. Salivary microbiota for gastric cancer prediction: an exploratory study. Front Cell Infect Microbiol. 2021;11:640309. doi:10.3389/fcimb.2021.640309

88. González-Sánchez M, Bartolome F, Antequera D, et al. Decreased salivary lactoferrin levels are specific to Alzheimer’s disease. EBioMedicine. 2020;57:102834. doi:10.1016/j.ebiom.2020.102834