Research Tip Sheet: Diabetic Eye Disease, AI in Heart Care, Prostate Cancer Decisions

Preclinical Study: Protecting Against Diabetic Corneal Disease

The medical journal Diabetologia recently published a Cedars-Sinai study that helps explain why half of diabetes patients experience deterioration of the cornea, the transparent dome-shaped outer layer of the eye that provides protection and focuses incoming light. The findings point to a potential target for therapies to protect vision in these patients.

Investigators performed experiments comparing diabetic versus nondiabetic human corneal cells to explain the molecular changes that diabetes patients experience. Diabetic corneal disease can lead to delayed wound healing and loss of vision.

“Our findings highlight the major role of a molecule called microRNA-10b in the oxidative stress characteristic of diabetic corneal disease and the damage it causes to cells,” said Mehrnoosh Ghiam, PhD, associate professor of Biomedical Sciences, research scientist in the Board of Governors Regenerative Medicine Institute and senior author of the study. “Inhibiting this molecule restored the cornea’s defenses against oxidative stress and cell damage and allowed the outermost layer of cells to be maintained and renewed.”

Additional Cedars-Sinai authors include Daxian Zha, Joshua Gamez, Shaghaiegh M. Ebrahimi, Yizhou Wang, Nagendra Verma, Adam J Poe, Seok White, Ruchi Shah, Andrei A. Kramerov, Chintda Santiskulvong, Aleksandr B. Stotland, Zhiping P. Wang, Jennifer E. Van Eyk, and Alexander V. Ljubimov.

Other authors include Onkar B. Sawant.

Funding: This work was supported by grants from the National Institutes of Health R01 EY025377 (MS); R01EY029829 (MS); R01EY031377 (AVL), R01EY013431 (AVL); the California Institute for Regenerative Medicine (CIRM EDUC4-12751); and funding from the Board of Governors Regenerative Medicine Institute (MS).

Cedars-Sinai Study Highlights AI Limits in Heart Care

There are limits in applying AI to images of the heart, a new study from the Smidt Heart Institute at Cedars-Sinai reveals. The findings were published in the Journal of the American Society of Echocardiography.

Investigators trained multiple artificial intelligence models to read images from echocardiograms, a type of ultrasound test that evaluates the structure and function of the heart. Their goal was to determine whether AI could use these images to calculate measurements like inflammation and scarring that are normally obtained through another, more costly test called cardiac magnetic resonance imaging (CMRI). By examining findings from1,453 patients who had undergone both tests, they found the AI models could not accomplish this task.

“As compared to echocardiograms, cardiac MRI machines are expensive and not available for many patients, especially those in rural areas, so we had hoped that AI could reduce the need for it,” said Alan Kwan, MD, assistant professor in the Department of Cardiology in the Smidt Heart Institute at Cedars-Sinai and co-senior author of the study. “Our results showed the limited powers of AI in this area.”

Other Cedars-Sinai investigators include: Yuki Sahashi, MD, MSc; Milos Vukadinovic, BS; Grant Duffy, BS; Debiao Li, PhD; Susan Cheng, MD, MMSc, MPH; Daniel S. Berman, MD; and David Ouyang, MD (co-senior author).

Acknowledgments: This work was supported in part by grants from the American Heart Association (23CDA1053659), the NIH (KL2TR001882), (75N92020D00021), the NHLBI (R00HL157421), (75N92020D00021, the NBIB through the Medical Imaging and Data Resource Center (MIDRC), Japanese Society of Echocardiography (Foreign research grant), and Japanese Society for the Promotion of Science, Grants-in-Aid for Scientific Research (JSPS-KAKENHI). No funders had a role in the design/conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Competing interests: ACK reports support from the American Heart Association (AHA; 23CDA1053659) and National Institutes of Health (NIH; KL2TR001882), and consulting fees from InVision Medical Technology. DO reports support from the National Institute of Health (NIH; NHLBI R00HL157421) and Alexion, and consulting or honoraria for lectures from EchoIQ, Ultromics, Pfizer, InVision, the Korean Society of Echo, and the Japanese Society of Echo. YS reports honoraria for consulting from m3.com inc.

Racial Differences and Prostate Cancer Treatment Decisions

Black men choose aggressive prostate cancer treatment—regardless of anticipated life expectancy—more often than Hispanic or Caucasian men, according to new research by Cedars-Sinai investigators published in the journal Prostate Cancer and Prostatic Diseases.

In the study, more than 2,000 men selected to mimic the sociodemographics of the U.S. prostate cancer population were asked to choose between conservative management and aggressive treatment in various prostate cancer diagnosis scenarios. These scenarios required participants to make treatment choices in the setting of different risks of cancer death and side effects, all while considering their anticipated life expectancy. Life expectancy is important because prostate cancer is typically slow-growing, and treatment only benefits men with relatively long life expectancies. Among Hispanic and Caucasian men, lower life expectancy was associated with lower likelihood of choosing aggressive treatment. Black men consistently elected to pursue aggressive treatment regardless of life expectancy. This pattern may put Black men at higher risk for overtreatment.

Timothy J. Daskivich, MD, director of Academic Urologic Oncology for the Department of Urology at Cedars-Sinai and corresponding author of the study, said more research is needed to determine why Black men routinely chose aggressive treatment.

“We have to be aware of cultural differences that may make different groups of patients pursue treatment for different reasons,” Daskivich said. “It’s important to have a holistic view and to understand patients’ motivations for making the decisions they do so that we can provide the best possible care.”

Additional Cedars-Sinai authors include John M. Masterson, Rebecca Gale, Brennan Spiegel and Stephen J. Freedland.

Other authors include Renning Zheng, Michael Luu, Adam Murphy, Yaw A. Nyame and Chad Ritch.

Funding: NIH/NCI K08 CA230155; Open access funding provided by SCELC, Statewide California Electronic Library Consortium.

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