Abstract
Background:
Anemia is linked to PM2.5 (particulate matter with aerodynamic diameters of ⩽2.5 μm) exposure, which can increase the risk of various negative health outcomes. It remains unclear which PM2.5 components are associated with anemia and the respective contribution of each component to this association.
Objective:
This study aimed at investigating the association between PM2.5 and anemia in the general population and to identify the most critical PM2.5 toxic components in this association.
Design:
Cross-sectional study.
Methods:
Our study involved a large cohort of 73,511 individuals aged 30–79 from China’s multi-ethnic population. We employed satellite observations and the chemical transport model (GEOS-Chem)to estimate the long-term exposure to PM2.5 and its components. Anemia was defined, according to WHO guidelines, as Hb levels below 130 g/L for men and below 120 g/L for women. Through logistic regression, we investigated the association between PM2.5 components and anemia. By utilizing weighted quantile sum (WQS) analysis, we identified key components and gained insights into their combined impact on anemia. Overall, our study sheds light on the relationship between PM2.5 exposure, its constituents, and the risk of anemia in a large cohort.
Results:
PM2.5 and three components, nitrate (NIT), organic matter (OM), and soil particles (SOIL), were associated with anemia. Per-standard deviation increase in the 3-year average concentrations of PM2.5 [odds ratio (OR): 1.14, 95% confidence interval (CI): 1.01, 1.28], NIT (1.20, 1.06, 1.35), OM (1.17, 1.04, 1.32), and SOIL (1.22, 1.11, 1.33) were associated with higher odds of anemia. In WQS regression analysis, the WQS index was associated with anemia (OR: 1.29, 95% CI: 1.13, 1.47). SOIL has the highest weight among all PM2.5 components.
Conclusions:
Long-term exposure to PM2.5 and its constituents is associated with anemia. Moreover, SOIL might be the most critical component of the relationship between PM2.5 and anemia. Our research increases the evidence of the association between PM2.5 and anemia in the general population, and targeted emission control measures should be taken into consideration to mitigate the adverse effects of PM2.5-related anemia.
Introduction
Anemia is a widely recognized precursor to a host of adverse health outcomes, including cancer, 1 heart failure,2,3 cognitive impairment, 4 and insomnia. 5 Anemia affects approximately one-third of the global population, leading to increased morbidity and mortality, decreased productivity at work, and impaired neurological development. 6 Furthermore, mild anemia or ‘low normal’ hemoglobin (Hb) levels have been found to be associated with all-cause mortality. 7 Common etiology of anemia includes nutritional causes (iron deficiency), 8 non-nutritional causes (infection), and non-modifiable causes (inherited Hb disorders). 6 Nevertheless, a considerable number of cases of anemia remain with unclear etiology, indicating a significant gap in our understanding of its underlying causes. 9
Recent studies indicated that air pollutants may be a potential risk factor for anemia. A systematic review study revealed that there was a link between PM2.5 and hematological abnormalities. 10 Honda et al. 11 found that air pollution exposure was associated with an increased prevalence of anemia in elderly Americans. Similarly, studies conducted in China have shown that air pollution exposure is significantly associated with a decrease in Hb levels among the elderly 12 and an increased incidence of moderate/severe anemia among children under five. 13 Although mounting evidence support the association between PM2.5 exposure and anemia, research examining this relationship in entire populations remains limited.
Importantly, individuals are typically exposed to a blend of pollutants rather than just one. Ambient PM2.5 components are typically the following: black carbon (BC), organic matter (OM), ammonium (NH4), nitrate (NIT), sulfate (SO4), soil particles (SOIL), sea salt (SS), etc. It is noteworthy that different particulate matter constituents can elicit varied toxic effects. 14 In a panel study comprising 135 elderly individuals, it was observed that there was a significant correlation between black/elemental carbon (EC) and Hb, as well as other blood cell parameters related to anemia. 15 However, there has yet to be a comprehensive study exploring the link between PM2.5 components and anemia, especially in the general population. Delving into the precise role of the various constituents of PM2.5 mixtures in developing anemia could offer valuable insights into this association.
Our study aims to investigate the association between anemia prevalence and long-term exposure to ambient particulate matter, as well as different PM2.5 components, using baseline data from the China Multi-Ethnic Cohort (CMEC) study. 16 The results of our study may provide valuable insights into the etiological mechanism of anemia and support the government’s efforts to reduce emissions of key components of PM2.5, thereby effectively reducing the burden of anemia-related diseases.
Materials and methods
Participants
Cross-sectional data from the CMEC study, sponsored by the Chinese National Key Research and Development Program, were used in this study. The CMEC is intended to be representative of the general population of Southwest China, and a multistage stratified cluster sampling strategy has been adopted. The CMEC study included 99,556 individuals between the ages of 30 and 79 from five provinces in Southwest China who belonged to seven ethnic groups (Han, Tibetan, Yi, Miao, Bai, Bouyei, and Dong). All data collectors completed consistent, systematic training prior to the investigation. Throughout the investigation, quality control was implemented. Once the survey was completed, the survey data were systematically cleaned up.
Information on sociodemographic and behavioral patterns was collected through electronic questionnaires. A full physical examination, as well as routine blood biochemistry and electrocardiography, was also performed on all participants. The Sichuan University Medical Ethics Review Committee granted ethical approval. Ethical rules were respected and study participants signed a consent form (K2016038, K2020022). Detailed procedures and methodology information of the CMEC have been reported previously. 17 This study is reported in accordance with the STROBE statement and the checklist is available in the Supplemental Material.
This study included 73,511 participants in total. Specific exclusion criteria were as follows: (1) The Tibetans of Lhasa and Aba, the former due to their incomplete or mobile residential addresses as pastoralists and the latter due to the sparse monitoring sites obtain little variability of exposure; (2) residence period less than 3 years. (3) self-reported anemia or plateau erythropoietic disease, and (4) individuals with missing information for the exposure and covariates and abnormal data (Supplemental Figure S1).
Outcome assessment
As part of the baseline assessment, all participants provided a fasting blood sample. The collected blood samples are tested by a third-party company that meets the corresponding qualifications. The whole process meets the standard process, and the authenticity and reliability of the test results are guaranteed. Anemia was determined by Hb levels <120 g/L in females and <130 g/L in males, respectively, based on WHO guidance. 17
Assessment of PM2.5 and its components
The Global Burden of Disease study provided data about the concentrations of PM2.5 and its components from 2001 to 2017.18–20 In short, primary estimates of the composition of PM2.5 are based on satellite aerosol optical depth observations (on 10 × 10 km spatial resolution) and a chemical transport model (GEOS-Chem). Then, a spatially complete representation of BC, OM, NH4, NIT, SO4, SOIL, and SS can be created by statistically combining these estimates with ground-based observations. 18 Based on the geocoded residential address obtained at the baseline survey, annual exposure levels of PM2.5 and its components were matching to each participant. In the whole analysis, 3-year average levels of PM2.5 and its components were the exposure variable.
Covariates
All information about covariates was obtained from electronic questionnaires with face-to-face interviews. Covariates used in the analysis were age (continuous), body mass index (BMI), gender, ethnicity, residency, occupation, education, household income, season, smoking, alcohol drinking, tea, history of hypertension, stroke, and diabetes. We calculated the dietary approaches to stop hypertension (DASH) diet score to evaluate individual dietary intake. The DASH diet is rich in vegetables, fruits, and low-fat dairy products. 21 The components of individuals’ diets were recorded for fruits, vegetables, nuts, sodium, red meat, and cereals. Overall, a higher score indicates more fruit and vegetable consumption than a lower score indicates more red meat consumption. The non-sedentary metabolic equivalent task is used to measure participants’ physical activity. Indoor air pollution is a summary of cooking behavior, fuel types, and ventilation equipment. Further information on the study variables of this study is shown in the Supplemental Material.
Statistical analysis
Continuous variables were described with means and standard deviations (SDs); categorical variables were described with counts and percentages. We estimated the relationship of individuals’ 3 years of exposure to PM2.5 and its components with anemia through logistic regression. The odds ratio (OR) and 95% confidence interval (CI) corresponding to the increase per SD of PM2.5 and its components are presented as the results. In model 0, the crude model, only exposure was an independent variable. In model 1, age, gender, BMI, ethnicity, residency, occupation, education, household income, season, and indoor air pollution were included as covariates. In model 2, additionally adjusted for tea consumption, smoking, alcohol intake, DASHscore, physical activity, history of hypertension, stroke, and diabetes. Multiple linear regressions were performed for the associations between PM2.5 pollutants and each component’s exposure and anemia-related blood cell parameter levels, including red blood cell (RBC), mean corpuscular hemoglobin (MCH), and mean corpuscular hemoglobin concentration (MCHC). To estimate the effect of PM2.5 and its constituents, the weighted quantile sum (WQS) 22 is adopted. We adjusted for the same covariates as model 2. The WQS generates a score of the WQS and incorporates this score into a regression model to assess the mixture’s overall impact. Each PM2.5 component was assigned a weight estimation to assess its contribution to the mixing effect. The potential modification factor, which included age and gender, was analyzed by adding the interaction terms of stratified variable and exposure components.
To ensure the robustness of our findings, we conducted several sensitivity analyses. (1) We adjusted for the history of coronary heart disease and cancer as additional covariates in our analysis, considering that individuals with a history of cancer or coronary cardiovascular disease often experience comorbidities such as anemia. (2) We tested the relationship between PM2.5 components and anemia using different exposure windows (1, 2, 5, and 7 years), due to the inconsistent definition of the time window for long-term exposure. (3) The ridge regression, the least absolute shrinkage and selection operator (LASSO), and Quantile G-computation (QGC) 23 method were used to estimate the component’s effect. These analytical methods for multiple exposures can be used to corroborate the results obtained from the WQS analysis, ensuring the robustness of our findings.
Furthermore, restricted cubic spline (RCS) analysis was carried out for detecting the linear relationship between exposure and anemia. The relationship between levels of anemia-related blood cell parameters and PM2.5 and its components was also explored as an additional supplement. A significance level of p < 0.05 was utilized to determine statistical significance. All statistical analyses were conducted using R software (version 4.1.0).
Results
Descriptive analysis
The final analysis included 73,511 participants aged from 30 to 79 (Supplemental Figure S1). The prevalence of anemia is 6.07%, meeting the WHO definition. The mean age of the participants is 52.3 years (SD = 11.4), and 60.27% are female (Table 1). Besides, Supplemental Table S2 presents a comparison of PM2.5 concentrations and its composition between the anemic and non-anemic groups, as well as the specific characteristics of these individuals. The standard mean (SD) concentrations of Hb, RBC, MCH, and MCHC in the participants were 145.0 ± 16.4 g/L, 4.8 ± 0.6 × 1012/L, 30.3 ± 2.7 pg, and 328.1 ± 10.6 g/L (Supplemental Table S3), respectively. The 3-year average concentrations of PM2.5, BC, NH4, NIT, OM, SO4, SOIL, and SS are 38.03 ± 21.61, 1.96 ± 1.15, 6.04 ± 3.47, 7.71 ± 5.57, 8.59 ± 5.00, 10.17 ± 5.04, 3.00 ± 1.83, and 0.03 ± 0.03 μg/m3, respectively. More specific information about pollution is shown in Supplemental Table S4.
Baseline characteristics of the participants.
Data are presented as means (standard deviation) for continuous variables and numbers (percentages) for categorical variables. Examining differences between anemia and non-anemia t test (for continuous variables) and χ2 test (for categorical variables).
Indoor air pollution is a summary of cooking behavior, fuel types, and ventilation equipment. Level I is defined as cooking at home often or occasionally, using traditional fuels, and lacking of ventilation devices. Level II is defined as cooking at home often or occasionally, using clean energy, and lacking of ventilation devices. Level III is defined as cooking at home often or occasionally, using traditional fuels, and installing ventilation devices. Level IV is defined as cooking at home often or occasionally, using clean energy, and installing ventilation devices. Level V is defined as little cooking or no kitchen.
Physical activity is measured by metabolic equivalent tasks per day (METs) in non-sedentary and classified based on quartiles (Q1–Q4).
DASHscore is classified based on quartiles (Q1–Q4).
DASHscore, dietary approaches to stop hypertension (DASH) score for fruit, red meat, and sodium salt intake.
Associations of PM2.5 and its components with anemia
The associations of per-SD increase (μg/m3) of PM2.5 and its components with anemia and Hb are shown in Table 2 and Supplemental Table S5. In the fully adjusted model, only PM2.5 and its components NIT, OM, and SOIL were found to be positively and significantly associated with anemia. Specifically, per-SD increase in the 3-year average concentrations of PM2.5 (OR: 1.14, 95% CI: 1.01, 1.28), NIT (1.20, 1.06, 1.35), OM (1.17, 1.04, 1.32), and SOIL (1.22, 1.11, 1.33) was associated with higher odds of anemia. Furthermore, it is worth noting that all PM2.5 components, with the exception of SS, exhibit a significant negative association with Hb concentrations.
Associations between 3-year average PM2.5 and its component’s exposures and prevalence ratio of anemia.
Model 0: unadjusted model; Model 1: adjusted for age, gender, BMI, ethnicity, occupation, education, residency, household income, season, and indoor air pollution; Model 2: additionally adjusted for tea consumption, smoking, alcohol intake, DASHscore, physical activity, hypertension, stroke, and diabetes.
BC, black carbon; BMI, body mass index; CI, confidence interval; DASH, dietary approaches to stop hypertension; NH4, ammonium; NIT, nitrate; OM, organic matter; OR, odds ratio; PM2.5, particulate matter with aerodynamic diameters of ⩽2.5 μm; SO4, sulfate; SOIL, soil particles; SS, sea salt.
For anemia-related blood cell parameters such as Hb, per-SD increase in PM2.5 exposure is associated with a 1.90 g/L decrease in Hb. Similar significant inverse associations are observed for PM2.5 components except for SS (Supplemental Table S5). The results of other anemia-related blood cell parameters such as RBC, MCH, and MCHC are summarized in Supplemental Tables S7–S9.
In WQS regression analysis, the WQS index is significantly associated with anemia (OR: 1.29, 95% CI: 1.13, 1.47). SOIL is the top heavily weighing component. The weights of the remaining several exposed components are small or close to zero (Figure 1).
WQS regression: Weight assigned to each component in the association of anemia with PM2.5 and its components.
Subgroup analyses
Subgroup analyses suggest that the associations between PM2.5 and anemia are modified by age and gender. ORs were higher in subgroups with <45 years or >65 years. For example, the OR (95% CI) of anemia for per-SD increase in SOIL was 1.25 (1.06, 1.47) for the <45 age group, 1.18 (1.03, 1.36) for the 45–65 age group, and 1.34 (1.09, 1.66) for the >65 age group. The results of other exposure components are presented in Table 3.
ORs (95% CIs) of anemia associated with per standard deviation increase in 3-year average concentrations of constituents.
The effects were estimated by logistic regression models with adjustment for age, gender, BMI, ethnicity, occupation, education, residency, household income, season, indoor air pollution, tea consumption, smoking, alcohol intake, DASHscore, physical activity, hypertension, stroke, and diabetes. All stratified estimates were adjusted for the remaining covariates.
p values represent the interaction effects between constituents and possible modifiers.
BC, black carbon; BMI, body mass index; DASH, dietary approaches to stop hypertension; NH4, ammonium; NIT, nitrate; OM, organic matter; PM2.5, particulate matter with aerodynamic diameters of ⩽2.5 μm; SO4, sulfate; SOIL, soil particles; SS, sea salt.
Sensitivity analyses
Sensitivity analyses showed that the estimated ORs of anemia for components are similar when additionally adjusted for a history of cancer and coronary artery heart disease (Supplemental Table S6). And the exposure windows of pollutants at different time periods are also adjusted to verify the robustness of the model. Adjusting for exposure windows of 2, 5, and 7 years yielded results that were generally consistent with the main results (Supplemental Table S7).
For mixture exposure analysis, three other statistical methods for environmental mixtures are used, including ridge regression, LASSO, and QGC method. In the penalized regression approaches (ridge regression, lasso), the λ were 0.04 and 0.002, respectively (Supplemental Figure S6). The ORs of PM2.5 and its components estimated by the ridge regression were smaller than the ORs of model 2, except for SO4 and SS. In the LASSO results, only two covariates SOIL (1.110) and SS (1.060) are selected, while all other ORs drop to 1 (Supplemental Table S11). Weight estimates of different components, by the QGC method, are presented in Supplemental Figure S2. When the WQS and QGC methods are used to evaluate the association between anemia and Hb, it is also determined that SOIL was the most important component in this association (Supplemental Figures S3 and S4). The sensitivity tests yielded similar results to the main analyses, SOIL could be the major contributor to the relationship between PM2.5 and anemia. Furthermore, the result of RCS analysis presented the approximately linear relationship between all components and anemia, except for SO4 and SS (Supplemental Figure S5).
Discussion
Based on the cross-sectional data of the CMEC, this study comprehensively explores the associations of single and joint exposure to PM2.5 and its components with anemia. Notably, our study stands out as the first of its kind to establish a significant association between anemia and prolonged exposure to PM2.5 and its components within the general population. Moreover, our findings indicate that women and individuals aged 65 and above are more susceptible to these effects. Furthermore, the primary contributor to the link between PM2.5 and anemia appears to be SOIL.
Comparison with other studies
The impact of ambient PM2.5 on public health has become the focus of worldwide concern. Numerous studies have demonstrated that even at extremely low levels PM2.5 still poses a nontrivial risk to public health.24 –26 However, studies on the health effects of PM2.5 exposure on the blood system are still lacking and limited to specific populations, such as children, and the elderly. For instance, Elbarbary et al. 12 found that air pollution exposure was significantly associated with an increased prevalence of anemia and reduced Hb levels in the elderly population. However, their study did not specifically investigate the effects of individual PM2.5 components. Similarly, a study of 139,368 Peru’s children showed that children under the age of 5 were more likely to have moderate/severe anemia and lower Hb levels when exposed to outdoor PM2.5 levels. 13 Both of these studies were limited to specific populations. Our study extends these findings by demonstrating a positive association between long-term PM2.5 exposure and anemia in the general population, which aligns with the aforementioned studies.
PM2.5 is a complex mixture with varying components derived from different sources, including industrial activity, coal combustion, and traffic emissions. 27 Identifying and quantifying the effects of specific components is one of the most challenging aspects of environmental health research. 28 Although there is growing evidence that long-term exposure to PM2.5 increases the risk of anemia,29,30 the question of the differential effects of different PM2.5 components in this relationship has not been fully studied. In our study, the analysis of single exposures revealed that PM2.5 and its components, including NIT, OM, and SOIL, were risk factors for anemia. When the response variable was replaced by the continuous Hb concentration, both PM2.5 and its components (except SS) showed a significant association. Some components (e.g. BC, NH4, SO4) have a small effect on Hb, so they do not show a significant association with anemia. In a retrospective birth cohort study involving 7932 pregnant women, Xie et al. investigated the effects of PM2.5 and its components on Hb and anemia during pregnancy. They observed a decrease in Hb levels during the third trimester in multiparous pregnant women associated with increases in PM2.5, BC, NO3, and OM. 31 A study reported in 2021 in Beijing found that in 135 elderly participants, PM2.5 and BC/EC (particle size range: 0–2.5 μm) were significantly associated with levels of RBC count, Hb, and other blood parameters. 15
Overall, the studies examining the relationship between PM2.5 components and anemia demonstrate some variations in their findings. These discrepancies can be attributed to differences in population characteristics, geographic regions, exposure windows, and the composition of PM2.5 across various studies. Consequently, the presence and magnitude of the effects of specific components may vary.
Subgroup and sensitivity analyses
The subgroup analysis showed that the association between PM2.5 and anemia was more significant in women and older people over 65 years of age. This could be attributed to their relatively weaker health status, rendering them more susceptible to the effects of PM2.5 and its components.32,33
Anemia is usually asymptomatic and is often accompanied by other diseases such as cancer, diseases of the cardiovascular system, and chronic inflammatory conditions. 34 Therefore, the disease history is adjusted in the sensitivity analysis. Similarly, we explored the association of PM2.5 and its components with the prevalence of anemia under different exposure windows such as 1, 2, 5, and 7 years. The results were in general agreement with the previous main results, for example, PM2.5 and SOIL were all associated with anemia. To identify the key component in the PM2.5–anemia association, we employed the WQS regression method. This approach assigned weights to different components, generating a summary score for the mixture and evaluating their importance in the PM2.5–anemia relationship.22,35 The WQS index demonstrated a significant association with anemia, with SOIL exhibiting a considerably higher weight compared to other components. This highlights the crucial role of SOIL in the association between PM2.5 and anemia.
Consistent results were obtained through various sensitivity analyses, including ridge regression, lasso, and QGC. All of these analyses reaffirmed the importance of SOIL in the relationship between PM2.5 and anemia. SOIL contains metallic elements and silica suspended in the air due to transportation and human activity. 36 Wang et al. 29 found that there was a significant correlation between the concentration of heavy metal elements in PM2.5 and blood routine indicators. In addition, animal study has shown that the mineral composition of the soil has a positive effect on the inflammatory activity of PM2.5. 37
Potential mechanism
The potential underlying mechanisms of PM2.5-induced anemia have been explored by numerous studies. Alveolar macrophages and lung epithelial cells jointly produce proinflammatory mediators when exposed to particulate matter in the air. 38 An integrated local and systemic inflammatory response is triggered by these mediators. Feng et al. 39 thought that the damaging effects of PM2.5 on health could be caused by immune disorders and the induction of systemic inflammatory responses. PM2.5 can also cause an inflammatory response in a variety of human and animal cells.40,41 With increased inflammatory cytokines, the proliferation and differentiation of red lineage precursor cells are inhibited and the erythropoietin-resistant state is enhanced.42 –44 Chronic hepcidin upregulation by chronic inflammation, (particularly the production of interleukin-6) which results in impaired iron absorption in the gastrointestinal tract and iron sequestration by macrophages. The corresponding iron homeostasis is affected, which in turn leads to anemia.45,46
Our study highlights the critical role of the SOIL component (comprised of metal elements and silica) in the association between PM2.5 exposure and anemia, as indicated by its significantly higher weight compared to other components. It is reasonable to speculate that SOIL is crucial in the potential mechanisms described above. The mechanism of the inflammatory response to heavy metal exposure has also been demonstrated. 47 Air pollution has a wide range of effects across the exposed population. 48 Although the increased risk of anemia from combined exposure to PM2.5 and its constituents was small in this study (OR: 1.14, 95% CI: 1.01, 1.28), it can still cause a very significant disease burden across the population. The exposure–response curves suggested even lower concentrations of PM2.5 and its components are associated with an increased risk of anemia. Children, pregnant women, and the elderly are more vulnerable to air pollution exposure,16,17,22 so effective personal protective measures are necessary for these sensitive populations. Exploring the broader associations between PM2.5 components and multiple diseases and identifying key components contributing to adverse health effects can facilitate the development of region-specific, targeted strategies for population protection.
Limitations and strengths
Our study has several limitations. First, the exposure concentrations of PM2.5 and its components were estimated based on individual residential addresses, which may not fully capture variations in exposure due to individual activity patterns such as outdoor exercise habits and commuting distances. Second, our study design is cross-sectional, and although we used average pollutant concentrations over a 3-year period as exposure metrics, caution is needed when interpreting the causal relationship between PM2.5 and its components and anemia. Third, we did not distinguish clinical types of anemia, which will be studied in the future with the support of richer data.
Despite these limitations, our study has several important advantages. First, this is the first study to examine the association between PM2.5 components and anemia in a general population in Southwest China. The data from the CMEC cohort are of high quality based on standardized survey methods and multiple stringent quality control measures. In addition, our study benefits from a large sample size, enabling robust results regarding the association between anemia and PM2.5 and its components. Second, the CMEC cohort provides a wealth of individual-level information on other risk factors such as indoor air pollution and dietary patterns, allowing for better control of individual-level confounders. Finally, we used different analysis methods for mixed exposures, adjusted for different exposure windows, and the associations were robust to various sensitivity analyses. We also validated the association between PM2.5 components and anemia and identified soil particles as the most important component, which will help in the development of targeted prevention strategies.
Conclusion
In conclusion, our study shows a positive association between long-term PM2.5 exposure and anemia, particularly due to SOIL. Personal protective measures, such as masks and air purifiers, should be adopted during severe air pollution episodes, and targeted interventions to reduce PM2.5 and its components, especially soil particles, are crucial in alleviating the burden of PM2.5-related anemia and protecting vulnerable populations.
Supplemental Material
sj-doc-1-tah-10.1177_20406207231189922 – Supplemental material for Anemia is associated with long-term exposure to PM2.5 and its components: a large population-based study in Southwest China
Supplemental material, sj-doc-1-tah-10.1177_20406207231189922 for Anemia is associated with long-term exposure to PM2.5 and its components: a large population-based study in Southwest China by Congyuan He, Linshen Xie, Lingxi Gu, Hongyu Yan, Shiyu Feng, Chunmei Zeng, Wangjiu Danzhen, Xuehui Zhang, Mingming Han, Zhifeng Li, Zhuoma Duoji, Bing Guo, Juying Zhang, Feng Hong and Xing Zhao in Therapeutic Advances in Hematology
Footnotes
Acknowledgements
We thank all the team members and participants involved in the CMEC study (China Multi-Ethnic Cohort). We appreciate Professor Xiaosong Li of Sichuan University for his leadership and fundamental contribution to the establishment of CMEC. Li passed away in 2019 and his contribution is worth bearing in our hearts forever.
Declarations
Supplemental material
Supplemental material for this article is available online.
References
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