Objectives: Recent advances in brain age prediction models reveal accelerated brain aging in major depressive disorder (MDD) patients. This review investigates the complex relationship between brain aging and biological age gap (BAG) in MDD, emphasizing the influences of clinical characteristics, treatment responses, and various neuroimaging techniques on this dynamic interplay. Methods: A systematic review of the existing literature was conducted, focusing on 18 studies that analyze brain aging patterns in MDD patients. Key factors such as age, clinical features, and lifestyle choices were examined to assess their impact on BAG and the overall neurobiological health of individuals with MDD. Results: The findings indicate that MDD patients frequently experience accelerated brain aging, particularly in elderly populations, with BAG serving as a valuable biomarker for assessing biological aging rates. The review highlights the urgent need for more granular approaches, considering variables such as age, gender, and socioeconomic status. Specific local brain aging patterns were observed in regions related to emotional regulation, suggesting that localized BAG changes may provide critical insights into the pathophysiology of MDD and its neurobiological underpinnings. Conclusions BAG is a significant biomarker for evaluating accelerated brain aging in MDD, informing personalized treatment strategies. Future research should incorporate diverse clinical characteristics and advanced neuroimaging techniques in representative samples to enhance the clinical applicability of BAG and deepen the understanding of its role in depression and biological aging.

Objectives: Recent advances in brain age prediction models reveal accelerated brain aging in major depressive disorder (MDD) patients. This review investigates the complex relationship between brain aging and biological age gap (BAG) in MDD, emphasizing the influences of clinical characteristics, treatment responses, and various neuroimaging techniques on this dynamic interplay. Methods: A systematic review of the existing literature was conducted, focusing on 18 studies that analyze brain aging patterns in MDD patients. Key factors such as age, clinical features, and lifestyle choices were examined to assess their impact on BAG and the overall neurobiological health of individuals with MDD. Results: The findings indicate that MDD patients frequently experience accelerated brain aging, particularly in elderly populations, with BAG serving as a valuable biomarker for assessing biological aging rates. The review highlights the urgent need for more granular approaches, considering variables such as age, gender, and socioeconomic status. Specific local brain aging patterns were observed in regions related to emotional regulation, suggesting that localized BAG changes may provide critical insights into the pathophysiology of MDD and its neurobiological underpinnings. Conclusions BAG is a significant biomarker for evaluating accelerated brain aging in MDD, informing personalized treatment strategies. Future research should incorporate diverse clinical characteristics and advanced neuroimaging techniques in representative samples to enhance the clinical applicability of BAG and deepen the understanding of its role in depression and biological aging.

Objectives: Recent advances in brain age prediction models reveal accelerated brain aging in major depressive disorder (MDD) patients. This review investigates the complex relationship between brain aging and biological age gap (BAG) in MDD, emphasizing the influences of clinical characteristics, treatment responses, and various neuroimaging techniques on this dynamic interplay. Methods: A systematic review of the existing literature was conducted, focusing on 18 studies that analyze brain aging patterns in MDD patients. Key factors such as age, clinical features, and lifestyle choices were examined to assess their impact on BAG and the overall neurobiological health of individuals with MDD. Results: The findings indicate that MDD patients frequently experience accelerated brain aging, particularly in elderly populations, with BAG serving as a valuable biomarker for assessing biological aging rates. The review highlights the urgent need for more granular approaches, considering variables such as age, gender, and socioeconomic status. Specific local brain aging patterns were observed in regions related to emotional regulation, suggesting that localized BAG changes may provide critical insights into the pathophysiology of MDD and its neurobiological underpinnings. Conclusions BAG is a significant biomarker for evaluating accelerated brain aging in MDD, informing personalized treatment strategies. Future research should incorporate diverse clinical characteristics and advanced neuroimaging techniques in representative samples to enhance the clinical applicability of BAG and deepen the understanding of its role in depression and biological aging.

Objectives: Recent advances in brain age prediction models reveal accelerated brain aging in major depressive disorder (MDD) patients. This review investigates the complex relationship between brain aging and biological age gap (BAG) in MDD, emphasizing the influences of clinical characteristics, treatment responses, and various neuroimaging techniques on this dynamic interplay. Methods: A systematic review of the existing literature was conducted, focusing on 18 studies that analyze brain aging patterns in MDD patients. Key factors such as age, clinical features, and lifestyle choices were examined to assess their impact on BAG and the overall neurobiological health of individuals with MDD. Results: The findings indicate that MDD patients frequently experience accelerated brain aging, particularly in elderly populations, with BAG serving as a valuable biomarker for assessing biological aging rates. The review highlights the urgent need for more granular approaches, considering variables such as age, gender, and socioeconomic status. Specific local brain aging patterns were observed in regions related to emotional regulation, suggesting that localized BAG changes may provide critical insights into the pathophysiology of MDD and its neurobiological underpinnings. Conclusions BAG is a significant biomarker for evaluating accelerated brain aging in MDD, informing personalized treatment strategies. Future research should incorporate diverse clinical characteristics and advanced neuroimaging techniques in representative samples to enhance the clinical applicability of BAG and deepen the understanding of its role in depression and biological aging.

Methods: The study involved 226 people who participated in the Korean Early Psychosis Cohort Study, and we divided the participants into two groups according to the degree of trauma.Positive and Negative Syndrome Scale (PANSS) and Social and Occupational Functioning Assessment Scale (SOFAS) were compared at the start of the study and at 12 months after the treatment using paired t-test and repeated measures analysis of variance. Results: At the beginning of the study, there was no significant difference between the two groups. But after 12 months of treatment, the high trauma group showed less improvement in PANSS negative score, general psychopathological score, total score, and SOFAS than the low trauma group. Conclusion In patients with early psychosis and at least moderate severity of premorbid trauma, negative symptoms, general psychopathological, and social and occupational functional improvements after treatment are less.

Background: The preventable trauma death rate survey is a basic tool for the quality management of trauma treatment because it is a method that can intuitively evaluate the level of national trauma treatment. We conducted this study as a national biennial follow-up survey project and report the results of the review of the 2019 trauma death data in Korea. Methods: From January 1, 2019 to December 31, 2019, of a total of 8,482 trauma deaths throughout the country, 1,692 were sampled from 279 emergency medical institutions in Korea. All cases were evaluated for preventability of death and opportunities for improvement using a multidisciplinary panel review approach. Results: The preventable trauma death rate was estimated to be 15.7%. Of these, 3.1% were judged definitive preventable deaths, and 12.7% were potentially preventable deaths. The odds ratio for preventable traumatic death was 2.56 times higher in transferred patients compared to that of patients who visited the final hospital directly. The group that died 1 hour after the accident had a statistically significantly higher probability of preventable death than that of the group that died within 1 hour after the accident. Conclusion The preventable trauma death rate for trauma deaths in 2019 was 15.7%, which was 4.2%p lower than that in 2017. To improve the quality of trauma treatment, the transfer of severe trauma patients to trauma centers should be more focused.