Not just diabetes: How slightly high blood sugar wrecks men’s sexual health

Metabolic health factors, including small increases in blood sugar, are the main drivers of change in the reproductive systems and sexual functioning of aging men, according to a study presented at ENDO 2025, the Endocrine Society's annual meeting in San Francisco, Calif.

"Although age and testosterone levels have long been considered an impetus for men's declining sexual health, our research indicates that these changes more closely correlate with modest increases in blood sugar and other metabolic changes," said Michael Zitzmann, M.D., Ph.D., professor and doctor of medicine at University Hospital in Muenster, Germany. "This means that men can take steps to preserve or revive their reproductive health with lifestyle choices and appropriate medical interventions."

These conclusions follow a long-term study of healthy men (without diabetes mellitus, heart disease and/or cancer) aged 18-85 that began in 2014 with 200 participants and concluded in 2020 with 117 participants. Researchers studied progressive changes in participants' semen and hormonal profiles, erectile functioning and metabolic health (BMI and blood sugar levels marked by the HbA1c test).

Findings indicated that over time hormone levels and semen parameters stayed largely within normal ranges. However, sperm movement and erectile function declined in men with minimally elevated blood sugar levels that were below the 6.5% HbA1c diabetes threshold. The study also found that while testosterone levels did not have a direct impact on erectile function, they did correlate with participants' libido assessment.

"We're hopeful that the information gleaned from this study will help doctors and their patients formulate effective male sexual health maintenance plans," Zitzmann added. "We now know that it's in our power to retain sexual and reproductive well-being in men, even as they age."

This research was conducted as part of the FAME 2.0 study.

Citation:

The Endocrine Society. "Not just diabetes: How slightly high blood sugar wrecks men’s sexual health." ScienceDaily. ScienceDaily, 13 July 2025. <www.sciencedaily.com/releases/2025/07/250713031439.htm>. 

 

AI sees what doctors miss: Fatty liver disease hidden in chest x-rays

 

Fatty liver disease, caused by the accumulation of fat in the liver, is estimated to affect one in four people worldwide. If left untreated, it can lead to serious complications, such as cirrhosis and liver cancer, making it crucial to detect early and initiate treatment.

Currently, standard tests for diagnosing fatty liver disease include ultrasounds, CTs, and MRIs, which require costly specialized equipment and facilities. In contrast, chest X-rays are performed more frequently, are relatively inexpensive, and involve low radiation exposure. Although this test is primarily used to examine the condition of the lungs and heart, it also captures part of the liver, making it possible to detect signs of fatty liver disease. However, the relationship between chest X-rays and fatty liver disease has rarely been a subject of in-depth study.

Therefore, a research group led by Associate Professor Sawako Uchida-Kobayashi and Associate Professor Daiju Ueda at Osaka Metropolitan University's Graduate School of Medicine developed an AI model that can detect the presence of fatty liver disease from chest X-ray images.

In this retrospective study, a total of 6,599 chest X-ray images containing data from 4,414 patients were used to develop an AI model utilizing controlled attenuation parameter (CAP) scores. The AI model was verified to be highly accurate, with the area under the receiver operating characteristic curve (AUC) ranging from 0.82 to 0.83.

"The development of diagnostic methods using easily obtainable and inexpensive chest X-rays has the potential to improve fatty liver detection. We hope it can be put into practical use in the future," stated Professor Uchida-Kobayashi.

 

Journal Reference:

1. Daiju Ueda, Sawako Uchida-Kobayashi, Akira Yamamoto, Shannon L. Walston, Hiroyuki Motoyama, Hideki Fujii, Toshio Watanabe, Yukio Miki, Norifumi Kawada. Performance of a Chest Radiograph&amp;#8211;based Deep Learning Model for Detecting Hepatic Steatosis. Radiology: Cardiothoracic Imaging, 2025; 7 (3) DOI: 10.1148/ryct.240402 

Courtesy:

Osaka Metropolitan University. "AI sees what doctors miss: Fatty liver disease hidden in chest x-rays." ScienceDaily. ScienceDaily, 27 June 2025. <www.sciencedaily.com/releases/2025/06/250627021845.htm>.