Distribution patterns and prediction of lymph, hematopoietic and related tissue cancer incidence in Chinese cancer registration areas from 2005 to 2050.

Introduction

Cancer, as a major chronic disease, poses a substantial threat to human health and survival worldwide. In recent years, its incidence and mortality have continued to increase, not only creating serious public health challenges but also imposing a considerable disease burden and economic pressure on societies and families. According to GLOBOCAN 2022 estimates, there were 19.97 million new cancer cases and 9.74 million deaths worldwide. China accounted for 4.82 million new cancer cases, accounting for 24.2% of the global total and 26.4% of global cancer deaths. Both the incidence and mortality of cancer in China surpassed the global average ( 1 ).

Lymphoma ranks among the relatively common cancer. It is essentially a cancer of the immune system originating in lymph nodes and lymphoid tissue, encompassing Hodgkin lymphoma, non-Hodgkin lymphoma, and their subtypes ( 2 ). According to the latest estimates ( 1 ), there were 635,000 new lymphoma cases worldwide in 2022, with 273,000 deaths due to the disease. In China, there were 85,200 new cases, ranking 15th among all types of cancer, and 41,600 deaths, ranking 13th on the cancer mortality list ( 3 ). With the burden of lymphoma increasing annually ( 4 ), the prevention and control of lymphoma has become a pressing public health challenge for China.

By analyzing the epidemiological characteristics and temporal trends of the disease, important evidence can be provided for prevention and control efforts. However, previous studies on lymphoma have predominantly focused on the global level, developed nations, or localized regions within China ( 5 – 7 ). Studies offering a multidimensional “person-time-space” description from a national perspective in China remain insufficient. This study comprehensively integrated data released by the China Cancer Registry Annual Report from 2008 to 2022, covering nationwide incidence information on lymph, hematopoietic and related tissue cancer stratified by age, sex, and region. To accurately describe incidence trends, the Joinpoint regression model was applied to quantify temporal variations in lymph, hematopoietic and related tissue cancer incidence. Subsequently, the age-period-cohort model was used to decompose the independent contributions of age, period, and cohort effects to incidence risk. To predict future incidence trends, the Bayesian age-period-cohort (BAPC) model was applied. Spatial autocorrelation analysis was also applied to explore geographical distribution patterns of cases. This study provided empirical evidence for formulating lymph, hematopoietic and related tissue cancer prevention and control policies in China, laying a robust data foundation for precision prevention and treatment of this disease.

Methods

Data sources

Data on the incidence and age-standardized incidence rate (ASIR) of lymph, hematopoietic and related tissue cancer in China from 2005 to 2019 were derived from the China Cancer Registry Annual Report, 2008-2022. Lymph, hematopoietic and related tissue cancer cases in this study were selected based on the International Classification of Diseases, Tenth Revision (ICD-10) ( 8 ), with inclusion codes of C81–C85, C88, C90, and C96; specifically, C81 represents Hodgkin lymphoma, C82–C85 and C96 represent Non-Hodgkin lymphoma, C88 represents Immunoproliferative diseases, and C90 represents multiple myeloma. According to the China Cancer Registry Annual Report, prefecture-level and higher administrative divisions were classified as urban areas, while counties and county-level cities were classified as rural areas.

Joinpoint regression model

The Joinpoint regression model is a statistical tool used to describe the continuous trend of disease incidence over time. It is capable of evaluating trend changes across different periods throughout the entire time span. This model creates segmented regressions based on the time characteristics of disease distribution. It divides the study period into distinct intervals using multiple joinpoints, then fits and optimizes the trend within each interval to assess the characteristics of specific disease changes across different periods. The model determines the number of breakpoints, their positions, and corresponding P -values (two-tailed test, significance level α=0.05) using Monte Carlo permutation testing. When analyzing the continuous trend of cancer incidence in a population, the log-linear model is typically employed, expressed as ( 9 ):

In the formula, e is the base of the natural logarithm, k is the number of inflection points, τ k is the number of unknown inflection points, β 0 is the constant parameter, β 1 is the regression coefficient, δ k is the slope of the kth segmental function.

Further, the annual percentage change (APC) was used to assess the trends within each subinterval, the average annual percentage change (AAPC) was used to evaluate the overall trend, and the statistical significance was determined based on the 95% confidence interval (95% CI). When the number of turning points is zero, APC=AAPC. When APC>0 or AAPC>0 and its 95% CI>0, it indicates a significant upward trend during that period; conversely, a significant downward trend is indicated.

Age-period-cohort model

This study applied the age-period-cohort model to quantify the independent effects of age, period, and cohort on incidence, revealing the role of demographic shifts in driving disease trends and estimating the magnitude of each factor’s effect, as expressed by the formula ( 10 , 11 ):

In the formula, α, β and γ are the effects of age, period, and cohort, respectively, while ϵ is the residual. The relative risk (RR) of incidence was used to describe trend changes, where RR<0 indicates a reduced risk of incidence, and RR>0 indicates an increased risk.

Bayesian age-period-cohort model

Using the BAPC model to predict the incidence of lymph, hematopoietic and related tissue cancer in China over the next 30 years. In this model, a second-order stochastic walk model is applied to smooth prior age, period, and cohort effects for predicting posterior incidence. Our method combines ensemble nested Laplace approximation to prevent mixing and convergence issues caused by Markov chain Monte Carlo sampling, thereby yielding more stable and reliable data. The formula is ( 12 ):

Here, k is the precision parameter, Q is the rank, and α i ( i = 1, …, I ) is the age effect.

Spatial autocorrelation analysis

This study employed ArcGIS software to analyze lymph, hematopoietic and related tissue cancer case data at the provincial level using spatial autocorrelation analysis. The analysis encompasses two methodologies: global spatial autocorrelation and local spatial autocorrelation ( 13 ).

Global spatial autocorrelation analysis was adopted to assess the spatial clustering characteristics of lymph, hematopoietic and related tissue cancer incidence overall. Moran’s I index (range: -1 to 1) served as the primary metric to reflect the spatial association degree of these cancer cases.

Local spatial autocorrelation analysis focused on finer spatial scales to precisely identify hotspots of lymph, hematopoietic and related tissue cancer incidence. Similarly conducted at the provincial level, it calculates the Local Indicator of Spatial Association to classify spatial clustering patterns into four types: high-high, low-low, high-low, and low-high. Among these, high-high and low-low clustering represent homogeneous clustering, indicating that the observed values in a province are similar to those in surrounding provinces—i.e., the region and its neighbors share either high or low incidence levels. High-low and low-high clustering represent heterogeneous clustering, indicating that the observed values in a province are higher or lower than those in surrounding provinces.

Statistical methods

Joinpoint regression analysis was performed using the Joinpoint Regression Program 4.9.1.0 software developed by the National Cancer Institute. The age-period-cohort model was constructed using the online web-based analysis tool developed by Rosenberg and Check et al ( 14 ). The BAPC model was created using the BAPC and INLA packages in R 4.3.1 software to predict the incidence trends of lymph, hematopoietic and related tissue cancer in China from 2020 to 2050 based on relevant data. ArcGIS 10.8 software was used to analyze the spatial distribution characteristics of lymph, hematopoietic and related tissue cancer incidence in China from 2010 to 2016. The significance level was set at α = 0.05.

Results

Incidence and its trends of lymph, hematopoietic and related tissue cancer in Chinese cancer registry regions, 2005-2019

From 2005 to 2019, a total of 230,114 new lymph, hematopoietic and related tissue cancer cases were registered in Chinese cancer registry regions. Among these, 132,990 cases (57.8%) occurred in males and 97,124 cases (42.2%) in females. Urban areas accounted for 138,581 cases (60.2%), while rural areas had 91,533 cases (39.8%). The overall incidence was 6.39/100,000 (7.29/100,000 for males, 5.47/100,000 for females; 7.57/100,000 in urban areas, 5.17/100,000 in rural areas), with an ASIR of 4.14/100,000 (4.88/100,000 for males, 3.44/100,000 for females; 4.73/100,000 in urban areas; 3.25/100,000 in rural areas). Results revealed that male’s incidence and ASIR were higher than females; urban areas incidence and ASIR were higher than rural areas ( Table 1 ).

From 2005 to 2019, the ASIR increased from 3.71/100,000 to 4.14/100,000, with an AAPC of 1.2% (95% CI: 0.6%, 1.8%). For males, the ASIR increased from 4.47/100,000 to 4.88/100,000, with an AAPC of 1.0% (95% CI: 0.3%, 1.6%). For females, it increased from 2.98/100,000 to 3.44/100,000, with an AAPC of 1.4% (95% CI: 0.9%, 2.0%). By region, the ASIR in urban areas rose from 4.16/100,000 to 4.73/100,000, with an AAPC of 1.1% (95% CI: -0.4%, 2.6%), but this increase was not statistically significant. This growth included three time periods: from 2005 to 2007, there was a downward trend but it was not statistically significant, with an APC of -2.6% (95% CI: -9.1%, 4.4%); from 2007 to 2010, there was an upward trend, with an APC of 7.3% (95% CI: 0.3%, 14.8%); and from 2010 to 2019, there was a downward trend but it was not statistically significant, with an APC of -0.1% (95% CI: -0.6%, 0.5%). In rural areas, it rose from 2.39/100,000 in 2005 to 3.25/100,000 in 2019, with an AAPC of 4.3% (95% CI: 2.8%, 5.9%) ( Figure 1 ).

Age-period-cohort analysis

Nationwide analyses across genders and urban-rural settings revealed a highly consistent trend in the age-related incidence of lymph, hematopoietic and related tissue cancer. To be specific, among individuals aged 0-45, the incidence risk increased gradually with age. Between the ages of 45-75, the risk grew rapidly with age. After age 75, the risk began to decline with age.

Period effect analysis revealed stable trends in lymph, hematopoietic and related tissue cancer incidence nationwide, among males, and in urban areas. Conversely, incidence among females and rural areas showed an upward trend after 2010, particularly in rural areas.

From the cohort perspective, birth cohorts prior to 1970–1974 exhibited higher nationwide incidence risk than subsequent cohorts. Further analysis revealed that the cohort effect trends for lymph, hematopoietic and related tissue cancer incidence risk among males, females, and urban areas were consistent with the national patterns. However, rural areas exhibited a distinct pattern: cohort groups born after 1970–1974 not only showed a higher cohort effect for incidence risk compared to earlier cohorts but also demonstrated a sustained upward progression overall ( Figure 2 ).

Prediction of lymph, hematopoietic and related tissue cancer incidence in Chinese cancer registry regions

According to the BAPC model predictions, the lymph, hematopoietic and related tissue cancer ASIR will show a sustained upward trend between 2020 and 2050. Predictions indicated that male incidence will remain higher than female incidence, rising from 5.13/100,000 and 3.74/100,000 in 2020 to 6.10/100,000 and 4.21/100,000, respectively. Furthermore, predictions indicated that urban incidence would remain higher than rural incidence in the future, rising from 5.31/100,000 and 3.85/100,000 in 2020 to 14.33/100,000 and 5.73/100,000 in 2050, respectively ( Figure 3 ).

Spatial autocorrelation analysis of lymph, hematopoietic and related tissue cancer incidence in Chinese cancer registry regions, 2010-2016

During the period from 2010 to 2016, lymph, hematopoietic and related tissue cancer incidence increased across all provinces in China, with the highest concentrations observed in the eastern coastal areas, including Zhejiang, Fujian, Jiangsu, and Shanghai ( Figure 4 ).

The global spatial autocorrelation analysis of lymph, hematopoietic and related tissue cancer incidence nationwide from 2010 to 2016 revealed that during 2010-2013, Moran’s I index fluctuated between -0.115 and 0.088, with corresponding P -values consistently exceeding 0.05. This indicated that spatial autocorrelation in lymph, hematopoietic and related tissue cancer incidence was insignificant during this phase, with overall distribution appearing random. From 2014 to 2015, Moran’s I rose to 0.117 and 0.130, with Z-values of 2.032 and 2.192 ( P < 0.05), respectively. This indicated a positive spatial correlation in lymph, hematopoietic and related tissue cancer incidence during these two years, characterized by distinct clustering of high-incidence provinces with other high-incidence provinces and low-incidence provinces with other low-incidence provinces. In 2016, Moran’s I index declined to 0.090, with the P -value rising to 0.097. The spatial positive correlation lost statistical significance, indicating weakened clustering. The 2015 Moran’s coefficient reached its maximum value, reflecting the highest spatial positive correlation and clustering intensity in lymph, hematopoietic and related tissue cancer incidence nationwide ( Table 2 ).

Using ArcGIS software, the local spatial autocorrelation analysis was conducted on the incidence of lymph, hematopoietic and related tissue cancer nationwide from 2010 to 2016. From 2010 to 2016, partial areas exhibited three spatial clustering patterns, including high-high clusters, low-low clusters, and low-high clusters. In 2010, high-high clusters were distributed in the eastern and southern regions. These high-high clusters shifted eastward from 2011 to 2016 and were solely distributed in the eastern regions in 2016. Low-low clusters were located in the northern regions in 2010 and gradually transferred from the northern regions to the western regions during 2010–2016, with low-low clusters concentrated in the western regions in 2016 ( Figure 5 ). Overall, the high-high clusters were mainly distributed in the eastern regions. The low-low clusters showed a shifting trend from the northern regions toward the western regions. The low-high cluster was less abnormal.

Discussion

From 2005 to 2009, the ASIR of lymph, hematopoietic and related tissue cancer in Chinese cancer registry regions showed an upward trend, with noticeable variations across gender, age, and region. This study further revealed that while lymph, hematopoietic and related tissue cancer incidence was higher among males in China, the rate of increase was faster among females. GLOBOCAN data also indicated a global upward trend in lymphoma incidence, particularly among women, younger individuals, and populations from Asian countries ( 1 ). Additional studies have similarly demonstrated that the burden of lymphoma was typically higher in males ( 5 , 15 ). One possible explanation for this gender difference is that overweight and obesity are more prevalent among males ( 16 ). Apart from genetic factors and viral infections, the development of lymphoma is closely associated with metabolic risks such as high body mass index (BMI) ( 17 ). BMI is also associated with clinical efficacy and survival outcomes in patients with hematologic cancer ( 18 , 19 ). A cohort study of 5.8 million individuals found a 10% increased risk of Hodgkin lymphoma for every 5kg/m² increase in BMI ( 20 ). Obesity represents a state of chronic inflammation, leading to alterations in plasma adipokines including growth factors and pro-inflammatory cytokines, all of which participate in biological processes such as immune responses. Furthermore, elevated BMI correlates with reduced secretion of leptin and adiponectin, potentially contributing to insulin resistance and downstream pathway activation, thereby promoting cancer development. Further studies have indicated that obesity can alter the cancer microenvironment to promote cancer progression, affecting immune cell composition and PD-1 expression ( 21 , 22 ).

This association may also relate to physiological and hormonal differences between males and females ( 23 ). Higher androgen levels in males may exert an immunosuppressive effect, reducing surveillance of abnormal cells and increasing the risk of cancer transformation. Furthermore, androgens may promote abnormal proliferation of certain lymphocyte subsets, creating conditions conducive to cancer development. In contrast, estrogen in females exhibits immune-enhancing effects, potentially improving the body’s ability to clear viral infections and cancer cells. Moreover, numerous other risk factors for lymphoma, such as genetic predisposition ( 24 ), physical and chemical agents ( 25 ) (e.g., radiation, air pollution), and unhealthy lifestyles ( 26 – 28 ) (e.g., smoking, alcohol consumption, sedentary habits), may be more prevalent among men. For instance, as China’s economy continues to develop and industrialization advances, residents face increasing exposure to lymphoma risk factors. Men, due to occupational factors, may be more likely to be exposed to these hazards ( 29 ). Therefore, lymphoma prevention and control strategies should persistently incorporate gender-specific approaches.

The age effect observed in this study indicated that the risk of developing lymph, hematopoietic and related tissue cancer increased rapidly with age among individuals aged 45-75. Another study has shown that lymphoma incidence was significantly higher in those over 65, peaking in the 65–84 age group ( 15 ). It is well known that the elderly represent one of the most vulnerable groups for most cancers, facing heightened risks across multiple cancers due to compromised immune function, poor health status, and the cumulative effects of long-term risk factors. This significantly amplifies the challenges posed by the global demographic shift toward aging populations. China has already entered an aging society, with individuals aged 65 and older constituting 13.53% of the population in 2020, and this proportion is projected to exceed 28% by 2040 ( 30 ). Therefore, given the trend of population aging in China and globally, it is crucial to incorporate healthy aging strategies into long-term planning. This includes continuously strengthening health promotion programs and community care services to enhance the quality of life for elderly patients.

Numerous studies have indicated that cancer incidence was more severe in economically developed regions ( 9 , 31 ). Our study found that lymph, hematopoietic and related tissue cancer incidence in China’s urban areas exceeded that in rural areas, although the upward trend was more pronounced in rural areas. A study categorizing global regions based on social development index (SDI, a composite indicator measuring a country’s development level, encompassing per capita income, education levels, and fertility rates to effectively capture socioeconomic factors influencing health outcomes) revealed that lymphoma incidence continued to rise in high-SDI countries and was demonstrably higher than in low-SDI regions. Analysis attributed this to high-SDI countries’ significant advantages in cancer screening, treatment technologies, and drug accessibility, while low-SDI countries face multiple challenges including delayed diagnosis, inadequate treatment, and low survival rates ( 32 ). Concurrently, lifestyle changes in developed regions, such as chronic stress and irregular schedules, may compromise immune function and elevate disease risk ( 33 ). An additional study has indicated that the 5-year survival rate for Hodgkin lymphoma exceeded 90% in regions with high SDI, whereas it fell below 50% in areas with low SDI ( 34 ). Studies have also suggested that urban populations face greater exposure to multiple lymphoma risk factors compared to rural residents. These included pollution from home renovations, psychological stress stemming from fast-paced lifestyles, prolonged exposure to ionizing radiation environments, and air quality issues ( 35 , 36 ). These factors may collectively contribute to the urban–rural disparities in lymphoma incidence. Spatial autocorrelation analysis in this study also identified Zhejiang, Jiangsu, and Shanghai as high-incidence areas for lymph, hematopoietic and related tissue cancer in China, and these areas are also regions with developed economies in China. The rapid rise in lymphoma incidence in China’s rural areas was consistent with previous findings, such as the lower but gradually increasing disease burden in low- and middle- SDI regions. This trend may be linked to economic development, lifestyle changes, and improvements in healthcare standards ( 37 ). In essence, health inequalities observed from this perspective manifest not only in survival rates but also in the geographic distribution and long-term trends of disease burden.

This study has limitations. The data used in this study were obtained from cancer registration programs across various regions of China. Due to their limited coverage, the representativeness of the study population and the generalizability of the findings may be insufficient. The latest cancer statistics reported in the China Cancer Registry Annual Report published by the National Cancer Center have a three-year delay, and the current report is updated to 2019. The study scope encompassed 31 provinces (autonomous regions and municipalities) and the Xinjiang Production and Construction Corps, excluding Hong Kong, Macau, and Taiwan. Future study should integrate data from additional regions to better reflect the national picture. Due to data limitations, this study did not investigate additional risk factors, which limited the comprehensiveness of the findings.

Conclusion

In summary, based on China Cancer Registry Annual Reports, the ASIR of lymph, hematopoietic and related tissue cancer in Chinese cancer registry regions showed an upward trend from 2005 to 2019, and is predicted to continue rising through 2050. Moreover, differences existed in incidence and their trends between males and females, as well as between urban and rural areas. The eastern region may be a hotspot for lymph, hematopoietic and related tissue cancer incidence. These findings suggested that we must continue to focus on and implement differentiated prevention and treatment strategies for lymph, hematopoietic and related tissue cancer, with particular emphasis on high-incidence areas and high-risk populations.

Acknowledgments

We would like to express our gratitude to the National Cancer Center and their dedicated staff for compiling and making these valuable data publicly available.

Edited by: Jianhua Yin , Second Military Medical University, China

Reviewed by: Yuan Xia , University of Ulm, Germany

Goljamal Jorjani , Shahid Beheshti University of Medical Sciences, Iran

Abbreviations: ASIR, age-standardized incidence rate; BAPC, Bayesian age-period-cohort; INLA, Integrated Nested Laplace Approximation; AAPC, Average annual percentage change.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Author contributions

LL: Conceptualization, Formal Analysis, Methodology, Software, Writing – original draft, Writing – review & editing. XP: Formal Analysis, Methodology, Software, Writing – original draft. XC: Formal Analysis, Methodology, Software, Writing – original draft. WN: Conceptualization, Formal Analysis, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing.

Conflict of interest

WN was employed by company National Hematology Clinical Research Center Xinjiang Production and Construction Corps Branch Center.

The remaining author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

1 Bray F Laversanne M Sung H Ferlay J Siegel RL Soerjomataram I . Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries . CA Cancer J Clin . ( 2024 ) 74 : 229–63 . doi: 10.3322/caac.21834 38572751

2 Ansell SM . Hodgkin lymphoma: 2023 update on diagnosis, risk-stratification, and management . Am J Hematol . ( 2022 ) 97 : 1478–88 . doi: 10.1002/ajh.26717 36215668

3 Zheng R Chen R Han B Wang S Li L Sun K . Analysis of the prevalence of Malignant tumors in China in 2022 . J Cancer . ( 2024 ) 46 : 221–31 . doi: 10.3760/cma.J.c.n112152-20240119-00035 30704229

4 Huang J Pang WS Lok V Zhang L Lucero-Prisno DE Xu W . Incidence, mortality, risk factors, and trends for Hodgkin lymphoma: a global data analysis . J Hematol Oncol . ( 2022 ) 15 : 57 . doi: 10.1186/s13045-022-01281-9 35546241 PMC9097358

5 Shi Y Cao T Xu Y Liu L Wen H Zhao Y . Global, regional, and national burden of lymphoma from 1990 to 2021: a comprehensive analysis based on the global burden of disease study 1990-2021 . Eur J Med Res . ( 2026 ) 31 ( 1 ): 338 . doi: 10.1186/s40001-025-03832-5 41508042 PMC12924239

6 GBD 2023 Italian Non-Hodgkin Lymphoma Collaborators . The national and subnational burden of Non-Hodgkin Lymphomas (NHL) in Italy from 1990 to 2023: results from the Global Burden of Disease Study 2023 . EClinicalMedicine . ( 2025 ) 88 : 103475 . doi: 10.1016/j.eclinm.2025.103475 41181827 PMC12572794

7 Luo W Xu W Zhou M Zhou J Miao W Tao R . Analysis of the incidence trend and age changes of lymphoma in cancer registration areas of Jiangsu Province from 2009 to 2019 . Chin J Oncol . ( 2024 ) 33 : 999 – 1005 .

8 Outland B Newman MM William MJ . Health policy basics: Implementation of the International Classification of Disease, 10th Revision . Ann Intern Med . ( 2015 ) 163 : 554–6 . doi: 10.7326/M15-1933 26390305

9 Lin K Jia H Cao M Xu T Chen Z Song X . Epidemiological characteristics of leukemia in China, 2005-2017: a log-linear regression and age-period-cohort analysis . BMC Public Health . ( 2023 ) 23 : 1647 . doi: 10.1186/s12889-023-16226-1 37641011 PMC10464264

10 Zhou K Yuan Q Shi Z Zhang Y Niu Q Li X . Study on the age at diagnosis and incidence trends of female reproductive system Malignancies in China from 2005 to 2018 and their prediction . Chin J Cancer Prev Treat . ( 2025 ) 32 : 1215–26 . doi: 10.16073/j.cnki.cjcpt.2025.20.01

11 Liu X Yu C Bi Y Zhang ZJ . Trends and age-period-cohort effect on incidence and mortality of prostate cancer from 1990 to 2017 in China . Public Health . ( 2019 ) 172 : 70 – 80 . doi: 10.1016/j.puhe.2019.04.016 31220754

12 Xu T Dong C Shao J Huo C Chen Z Shi Z . Global burden of maternal disorders attributable to malnutrition from 1990 to 2019 and predictions to 2035: worsening or improving ? Front Nutr . ( 2024 ) 11 : 1343772 . doi: 10.3389/fnut.2024.1343772 38425484 PMC10902107

13 Shao J Li W Zhang Y Niu Q Song G Jia H . Colorectal cancer registration area China time and space distribution of the disease pattern analysis . Modern Prev Med . ( 2025 ) 52 : 3886–92 . doi: 10.20043/j.carolcarrollnkiMPM.202506401

14 Shi Z Shao J Dong C Song G Hu Y Niu Q . Burden of chronic obstructive pulmonary disease attributable to non-optimal temperature, 1990-2044: six countries on the same isotherm . BMC Public Health . ( 2024 ) 24 : 3407 . doi: 10.1186/s12889-024-20622-6 39695480 PMC11653645

15 Zhou L Shan D Zhang Y Li J Deng R . Global, regional, and national disease burden of lymphoma and leukemia attributable to high body mass index: from 1990 to 2021 . Front Nutr . ( 2025 ) 12 : 1592443 . doi: 10.3389/fnut.2025.1592443 40777176 PMC12328193

16 Chen K Shen Z Gu W Lyu Z Qi X Mu Y . Prevalence of obesity and associated complications in China: A cross-sectional, real-world study in 15.8 million adults . Diabetes Obes Metab . ( 2023 ) 25 : 3390–9 . doi: 10.1111/dom.15238 37589256

17 Dugué PA Yu C Hodge AM Wong EM Joo JE Jung CH . Methylation scores for smoking, alcohol consumption and body mass index and risk of seven types of cancer . Int J Cancer . ( 2023 ) 153 : 489–98 . doi: 10.1002/ijc.34513 36919377

18 Li YJ Yi PY Li JW Liu X Liu X Zhou F . Increased body mass index is associated with improved overall survival in extranodal natural killer/T-cell lymphoma, nasal type . Oncotarget . ( 2017 ) 8 : 4245–56 . doi: 10.18632/oncotarget.13988 28002803 PMC5354828

19 Wang Z Aguilar EG Luna JI Dunai C Khuat LT Le CT . Paradoxical effects of obesity on T cell function during tumor progression and PD-1 checkpoint blockade . Nat Med . ( 2019 ) 25 : 141–51 . doi: 10.1038/s41591-018-0221-5 30420753 PMC6324991

20 Recalde M Davila-Batista V Díaz Y Leitzmann M Romieu I Freisling H . Body mass index and waist circumference in relation to the risk of 26 types of cancer: a prospective cohort study of 3.5 million adults in Spain . BMC Med . ( 2021 ) 19 : 10 . doi: 10.1186/s12916-020-01877-3 33441148 PMC7807518

21 Taranto D Kloosterman DJ Akkari L . Macrophages and T cells in metabolic disorder-associated cancers . Nat Rev Cancer . ( 2024 ) 24 : 744–67 . doi: 10.1038/s41568-024-00743-1 39354070

22 Bader JE Wolf MM Lupica-Tondo GL Madden MZ Reinfeld BI Arner EN . Obesity induces PD-1 on macrophages to suppress anti-tumour immunity . Nature . ( 2024 ) 630 : 968–75 . doi: 10.1038/s41586-024-07529-3 38867043 PMC11456854

23 Radkiewicz C Bruchfeld JB Weibull CE Jeppesen ML Frederiksen H Lambe M . Sex differences in lymphoma incidence and mortality by subtype: A population-based study . Am J Hematol . ( 2023 ) 98 : 23 – 30 . doi: 10.1002/ajh.26744 36178436 PMC10092431

24 Al-Amer OM Mir R Hamadi A Alasseiri MI Altayar MA AlZamzami W . Antiapoptotic gene genotype and allele variations and the risk of lymphoma . Cancers . ( 2023 ) 15 : 1012 . doi: 10.3390/cancers15041012 36831357 PMC9954290

25 Taj T Poulsen AH Ketzel M Geels C Brandt J Christensen JH . Long-term residential exposure to air pollution and Hodgkin lymphoma risk among adults in Denmark: a population-based case-control study . Cancer Causes Control CCC . ( 2021 ) 32 : 935–42 . doi: 10.1007/s10552-021-01446-w 34050843

26 Zhou M Xia J Chen X Wu T Xu K Zou Y . Assessing the causal association between dietary vitamin intake and lymphoma risk: a Mendelian randomisation study . Int J Food Sci Nutr . ( 2024 ) 75 : 92 – 101 . doi: 10.1080/09637486.2023.2278420 37933598

27 Odutola MK van Leeuwen MT Turner J Bruinsma F Seymour JF Prince HM . Associations between smoking and alcohol and follicular lymphoma incidence and survival: a family-based case-control study in Australia . Cancers . ( 2022 ) 14 : 2710 . doi: 10.3390/cancers14112710 35681690 PMC9179256

28 Sun R Lei C Chen L He L Guo H Zhang X . Alcohol-driven metabolic reprogramming promotes development of RORγt-deficient thymic lymphoma . Oncogene . ( 2022 ) 41 : 2287–302 . doi: 10.1038/s41388-022-02257-2 35246617 PMC9018612

29 Naudin S Solans Margalef M Saberi Hosnijeh F Nieters A Kyrø C Tjønneland A . Healthy lifestyle and the risk of lymphoma in the European Prospective Investigation into Cancer and Nutrition study . Int J Cancer . ( 2020 ) 147 : 1649–56 . doi: 10.1002/ijc.32977 32176325

30 Pang G Hu C Yang Z . The trend and countermeasures of population aging in China . Chin Geriatrics Health Med . ( 2021 ) 19 : 3 – 5 . doi: 10.3969/j.issn.1672-2671.2021.01.001

31 Xie L Shi Z Zhang Y Hu Y Yan Y . Study on the characteristics and prediction of non-melanoma skin cancer mortality in China from 2005 to 2018 . Modern Prev Med . ( 2025 ) 52 : 2336–42 . doi: 10.20043/j.cnki.MPM.202503164

32 Allemani C Matsuda T Di Carlo V Harewood R Matz M Nikšić M . Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries . Lancet . ( 2018 ) 391 : 1023–75 . doi: 10.1016/S0140-6736(17)33326-3 29395269 PMC5879496

33 Zhang N Wu J Wang Q Liang Y Li X Chen G . Global burden of hematologic Malignancies and evolution patterns over the past 30 years . Blood Cancer J . ( 2023 ) 13 : 82 . doi: 10.1038/s41408-023-00853-3 37193689 PMC10188596

34 Baissa OT Ben-Shushan T Paltiel O . Lymphoma in Sub-Saharan Africa: a scoping review of the epidemiology, treatment challenges, and patient pathways . Cancer Causes Control CCC . ( 2025 ) 36 : 199 – 230 . doi: 10.1007/s10552-024-01922-z 39417984 PMC11928370

35 Øvlisen AK Jakobsen LH Kragholm KH Nielsen RE de Nully Brown P Dahl-Sørensen RB . Mental health among patients with non-Hodgkin lymphoma: A Danish nationwide study of psychotropic drug use in 8750 patients and 43 750 matched comparators . Am J Hematol . ( 2022 ) 97 : 749–61 . doi: 10.1002/ajh.26538 35298039 PMC9310853

36 Harbron RW Pasqual E . Ionising radiation as a risk factor for lymphoma: a review . J Radiol Prot Off J Soc Radiol Prot . ( 2020 ) 40 . doi: 10.1088/1361-6498/abbe37 33017815

37 Daltveit DS Morgan E Colombet M Steliarova-Foucher E Bendahhou K Marcos-Gragera R . Global patterns of leukemia by subtype, age, and sex in 185 countries in 2022 . Leukemia . ( 2025 ) 39 : 412–9 . doi: 10.1038/s41375-024-02452-y 39567675