Xingyu Wan,1,2,* Tinglu Tao,1,2,* Jing Zhang,1,2,* Nan Li,1,2 Yang Gou,1,2 Wuchen Yang,1,2 Xiao Han,1,2 Shengwang Wu,1,2 Cheng Zhang,1,2 Xiangui Peng,1,2 Shuiqing Liu,1,2 Xi Zhang1– 4 

1Medical Center of Hematology, Xinqiao Hospital of Army Medical University, Chongqing, 400037, People’s Republic of China; 2Chongqing Key Laboratory of Hematology and Microenvironment, Chongqing, 400037, People’s Republic of China; 3State Key Laboratory of Trauma and Chemical Poisoning, Chongqing, 400037, People’s Republic of China; 4Jinfeng Laboratory, Chongqing, 401329, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Xiangui Peng, Email pxgpxg1964@tmmu.edu.cn; Shuiqing Liu, Email liushuiqing062@tmmu.edu.cn

Background: Acute myeloid leukemia (AML) is a lethal malignancy of the bone marrow, characterized by rapid proliferation of immature myeloid cells, leading to insufficient hematopoiesis and immune activities. It is well known that AML is closely associated with various molecular and cytogenetic abnormalities. In addition, the long-standing view that non-genetic factors, including age, sex and season, are also associated with the occurrence and development of AML. However, effects of these factors on AML clinical characteristics remain incompletely understood. During clinical practice, we perceived an imbalance distribution of clinical characteristics (including FAB classification, gene mutations, lymphocyte-associated cytokine levels and lymphocyte-subset proportions) in different age, sex and season groups. In order to elucidate the correlations between these factors, we performed a comprehensive data collection and analysis of AML patients in our hospital from 2013 to 2023.
Methods: Totally, 2798 newly diagnosed AML patients and 220 relapsed AML patients who were admitted to our hospital from January 1, 2013 to December 31, 2023 were included for analysis. Chi-square test was conducted to analyze the correlation between categorical variables. T-tests and one-way ANOVA were employed to compare mean values across two and multiple groups respectively. Mann–Whitney U-tests and Kruskal–Wallis H-tests were employed to compare mean values across two and multiple groups respectively, when data did not show normal distribution. Logistic regression was used to analyze the correlation between dependent and independent variables. Log rank test was applied for survival analysis. Waterfall diagram and chord diagram of mutated genes were created using R4.3.3 and RStudio tools.
Results: Overall, the distribution of age, sex and season in AML patients were unbalanced. The relationships among various mutated genes had two sides, co-existence or mutual exclusivity. Additionally, the FAB classification and gene mutation status varied significantly across the subgroups. The levels of cytokines and lymphocyte subsets altered significantly in AML patients, and were associated with prognosis and gene mutations.
Conclusion: Age, sex and season have shown partial correlations with AML clinical characteristics, including FAB classification, gene mutations status, lymphocyte-associated cytokine levels and lymphocyte subset proportions. We hope these findings can contribute to a deeper understanding of AML.

Keywords: acute myeloid leukemia, correlation analysis, mutated genes, FAB classification, cytokine, lymphocyte subset, age, sex, season