Category: Research

Helicobacter pylori Infection Is Associated with Long-Term Cognitive Decline in Older Adults: A Two-Year Follow-Up Study

Helicobacter pylori infection is usually known for causing stomach problems, but it may also affect brain health. This research study published in 2023 followed 268 older adults with memory complaints for two years to see whether H. pylori infection was linked to cognitive decline.

While at the beginning of the study, people with and without H. pylori performed similarly on memory tests, over the two-year follow-up, those with a history of infection showed greater declines in their Mini-Mental State Examination (MMSE) scores. After taking into account age, sex, education, genetic risk factors and common medical conditions, H. pylori infection was still associated with a significantly higher risk of cognitive decline, with infected participants more likely to lose three or more MMSE points and showing a faster rate of decline over time.

These findings suggest that H. pylori infection may contribute to progressive cognitive deterioration in older adults with memory complaints and may be relevant in understanding pathways linking infection and dementia.

Learn more about this study here: https://doi.org/10.3233/JAD-221112

Reference

Wang J, Yu N-W, Wang D-Z, et al. Helicobacter pylori Infection Is Associated with Long-Term Cognitive Decline in Older Adults: A Two-Year Follow-Up Study. Journal of Alzheimer’s Disease. 2023;91(4):1351-1358.
The association between psychological status and the development of early gastric cancer from atrophic gastritis

A recent hospital-based, cross-sectional observational study, was conducted in the Chinese population to explore the potential relationship between psychological state and the progression of atrophic gastritis (AG; caused by H. pylori or not) to early stage gastric cancer (EGC).

The study included 258 individuals receiving care in the Department of Gastroenterology at The Affiliated Hospital of Xuzhou Medical University between March 2020 and November 2024, and included 173 patients diagnosed with AG and 85 with EGC. Clinical profiles, demographic characteristics, and laboratory indices were initially recorded, and then comparative analyses were conducted between groups.

Results showed that, compared to the AG group, patients with EGC exhibited significantly higher psychological distress and depressive tendencies. These findings, while not conclusive, imply a possible association between psychological state and the presence of early gastric cancer (EGC) in patients with atrophic gastritis, thus suggesting that they could function as independent indicators and contribute to the malignant progression of EGC in patients.

Incorporating mental health screening tools could, therefore, offer supplementary value in the broader context of evaluating patients who might carry an increased likelihood of disease progression.

Learn more about this study here: http://dx.doi.org/10.1097/MD.0000000000045653

Reference

Liang, Mengmeng BD; Wang, Juan MD; Li, Rui MD; Yang, Jun MD; Liu, Yuping BD; Zhao, Lian BD. The association between psychological status and the development of early gastric cancer from atrophic gastritis. Medicine 104(45):p e45653, November 07, 2025
The association between H. pylori infection and cognitive deterioration: a systematic review and meta-analysis

The association between cognitive decline and H. pylori infection remains controversial, with some evidence suggesting that H. pylori eradication may slow the progression of the disease.

A new meta-analysis reviewed 16 studies to explore whether H. pylori affects cognitive function and whether cognitive decline is linked to higher rates of infection.

The analysis found that people with H. pylori infection had a higher risk of cognitive decline, especially when cognitive dysfunction and dementia were combined. However, the infection was not clearly linked to Alzheimer’s disease. Conversely, people with Alzheimer’s disease were more likely to have H. pylori infection than those without, though the association was weaker for other forms of dementia.

These findings suggest a bidirectional relationship in which H. pylori may contribute to cognitive decline, and certain cognitive conditions may increase susceptibility to infection. The study also highlights the need for more well-designed research to fully understand this complex interaction.

Learn more about this study here: https://doi.org/10.1186/s40001-025-03160-8

Reference

Elhady, M.M., Zidan, A., Rabea, E.M. et al. The association between H. pylori infection and cognitive deterioration: a systematic review and meta-analysis. Eur J Med Res 30, 846 (2025)
Assessing Algorithmic Bias in Language-Based Depression Detection: A Comparison of DNN and LLM Approaches

A study found that large language models (LLMs) outperform traditional deep neural network (DNN) embeddings in automated depression detection and show reduced gender bias, through racial disparities remain. Among DNN fairness-mitigation techniques, the worst-group loss provided the best balance between overall accuracy and demographic fairness, while fairness-regularized loss underperformed.

The identified biases affect the fairness and diagnostic reliability of AI systems for mental health assessment, particularly by disadvantaging underrepresented racial and gender groups, mainly Hispanic participants in the case of this research. Such disparities risk perpetuating inequities in automated mental health screening and could undermine trust and validity in clinical or public health applications.

Learn more about the study here: https://doi.org/10.48550/arXiv.2509.25795

Reference

Junias, O., Kini, P., & Chaspari, T. (2025). Assessing Algorithmic Bias in Language-Based Depression Detection: A Comparison of DNN and LLM Approaches. 2025 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), 1-7.
Developing personalized algorithms for sensing mental health symptoms in daily life

This study investigates algorithmic bias in AI tools that predict depression risk using smartphone-sensed behavioral data.

It finds that these tools underperform in larger, more diverse populations because the behavioral patterns used to predict depression are inconsistent across demographic and socioeconomic subgroups.

Specifically, the AI models often misclassify individuals from certain groups—such as older adults or those from different racial or gender backgrounds—as being at lower risk than they actually are. The authors emphasize the need for tailored, subgroup-aware approaches to improve reliability and fairness in mental health prediction tools. This work highlights the importance of addressing demographic bias to ensure equitable AI deployment in mental healthcare.

Learn more about this study here: https://doi.org/10.1038/s44184-025-00147-5

Reference

Timmons, A.C., Tutul, A.A., Avramidis, K. et al. Developing personalized algorithms for sensing mental health symptoms in daily life. npj Mental Health Res 4, 34 (2025).
Regulatory Effects of Probiotics on Anxiety and Depression‑Like Behaviors in H. pylori‑Infected Rats

In a recent experimental study, researchers used rats to explore whether the use of probiotics such as Lactobacillus can mitigate anxiety- and depression-like behaviors, counteracting the psychological and biological effects of H. pylori infection. Infected rats were treated with each probiotic alone or with the combination, and were then evaluated using standard behavioral tests for anxiety and depression.

Both probiotics, especially when co-administered, reversed the depressive and anxiogenic effects induced by H. pylori. Probiotic supplementation also corrected several brain changes linked to H. pylori, including oxidative stress, inflammation, reduced BDNF/serotonin, and elevated corticosterone.

The findings suggest that multi-strain probiotics may help manage psychiatric symptoms associated with H. pylori infection, and that they merit further clinical evaluation in patients with psychiatric comorbidities.

Learn more about this study here: https://doi.org/10.1007/s12602-025-10674-4

Reference

Ahmadi-Soleimani, S.M., Masoudi, M., Tabrizi, A.M.A. et al. Regulatory Effects of Probiotics on Anxiety and Depression-Like Behaviors in H. pylori-Infected Rats. Probiotics & Antimicro. Prot. (2025)
Racial bias in AI-mediated psychiatric diagnosis and treatment: a qualitative comparison of four large language models

The article investigates racial bias in psychiatric diagnosis and treatment recommendations across four large language models (LLMs): Claude, ChatGPT, Gemini, and NewMes-15. The study evaluates the models’ responses to ten psychiatric cases representing five diagnoses (depression, anxiety, schizophrenia, eating disorders, and ADHD) under three conditions: race-neutral, race-implied, and race-explicitly stated (African American).

Key findings include:

1) Bias in Treatment Recommendations: LLMs often proposed inferior or divergent treatments when racial characteristics were explicitly or implicitly indicated, particularly for schizophrenia and anxiety cases. Diagnostic decisions showed minimal bias overall.

2) Model Performance: NewMes-15 exhibited the highest degree of racial bias, while Gemini demonstrated the least bias across conditions.

3) Statistical Analysis: A Kruskal–Wallis H-test revealed significant differences in bias among the LLMs, with Gemini being significantly less biased than ChatGPT and NewMes-15.

4) Challenges in AI Development: The study highlights that LLMs trained on biased datasets may perpetuate racial disparities in psychiatric care, even when specialized medical training data is used. Local LLMs, despite their cost and privacy advantages, showed higher susceptibility to bias compared to larger, online models.

Learn more about this study here: https://doi.org/10.1038/s41746-025-01746-4

Reference

Bouguettaya, A., Stuart, E.M. & Aboujaoude, E. Racial bias in AI-mediated psychiatric diagnosis and treatment: a qualitative comparison of four large language models. npj Digit. Med. 8, 332 (2025).
Domain Adversarial Training for Mitigating Gender Bias in Speech-based Mental Health Detection

A domain adversarial training (DAT) was developed in a study as a method to reduce gender bias in AI models for depression and PTSD detection using speech data (E-DAIC dataset).

DAT improved F1-scores up to +13% and reduced gender gaps in detection accuracy, improving generalization across male and female participants, specially addressing the effects of the latter’s underrepresentation.

Learn more about this study here: https://doi.org/10.48550/arXiv.2505.03359

Reference

Kim, J., Yoon, H., Oh, W., Jung, D., Yoon, S., Kim, D., Lee, D., Lee, S., & Yang, C. (2025). Domain Adversarial Training for Mitigating Gender Bias in Speech-based Mental Health Detection. 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 1-7.
Genetic correlation, pleiotropic loci and shared risk genes between major depressive disorder and gastrointestinal tract disorders

Although depression is often linked to digestive disorders, the biological connection behind this phenomenon has remained unclear.

A recent genome-wide association study analyzed genetic data from hundreds of thousands of people to explore potential links between major depressive disorder (MDD) and gastrointestinal conditions such as peptic ulcers (mainly caused by H. pylori infection), acid reflux, irritable bowel disease, and inflammatory bowel disease.

The researchers found that depression shares genetic risk factors with most digestive disorders, meaning that some of the same genes and genetic regions influence both mental and gut health. The study also suggested that genetic susceptibility to certain gut conditions may increase the risk of depression, once again highlighting the strong gut-brain connection.

These findings could help scientists better understand the gut-brain connection and may point to new ways to treat gastrointestinal symptoms in patients with depression.

Learn more about this study here: https://doi.org/10.1016/j.jad.2025.01.048

Reference

Zhou, S., Zi, J., Hu, Y., Wang, X., Cheng, G., & Xiong, J. (2025). Genetic correlation, pleiotropic loci and shared risk genes between major depressive disorder and gastrointestinal tract disorders. Journal of affective disorders, 374, 84–90.
Minding the Gaps: Neuroethics, AI, and Depression

In this article, the author highlights the benefits and potential issues regarding the use of AI in depression diagnosis/treatment, focusing on the prevalent gender, racial and ethnicity biases.

It is mentioned that, given the historical, inherent biases in society generally and healthcare specifically, AI-driven advancements are not going to serve minority groups as a matter of course. Unless they are tailored to represent and serve all communities equally, they will exacerbate existing biases and disparities.

Learn more about this article here: https://nonprofitquarterly.org/minding-the-gaps-neuroethics-ai-and-depression/

Reference

Boothroyd, Gemma (2024), “Minding the Gaps: Neuroethics, AI, and Depression”, in Nonprofit Quarterly Magazine, winter 2024, “Health Justice in the Digital Age: Can We Harness AI for Good?”