Introduction
Multiple myeloma (MM) is a complex hematological malignancy characterized by the proliferation of malignant plasma cells in the bone marrow [ 1 – 3 ]. Despite significant advancements in treatment strategies, including novel therapies and targeted agents, MM remains a significant public health challenge worldwide [ 4 , 5 ]. The global burden of this disease has been rising, reflecting demographic shifts, improved diagnostic capabilities, and increased life expectancy in various populations [ 6 ].
From 1990 to 2021, the epidemiology of MM has undergone notable changes. Various factors, including age, ethnicity, geographical location, and socioeconomic status, influence the incidence and survival rates of this malignancy [ 7 , 8 ]. Data indicate a rising incidence rate, particularly in developed countries, where enhanced screening and diagnostic techniques have led to earlier detection [ 9 ]. Conversely, the burden in developing regions often remains underreported, compounded by limited access to healthcare and treatment options [ 10 ].
This article aims to provide a comprehensive overview of the global epidemiology of MM over the past three decades. It will assess trends in incidence and mortality rates, analyze risk factors contributing to the disease’s burden, and discuss the implications for healthcare systems. Furthermore, projections to 2045 will be explored, highlighting anticipated changes in disease prevalence due to demographic transitions and advancements in medical interventions. Despite the growing global burden of MM, comprehensive and up-to-date analyses of worldwide epidemiological trends remain limited. Most existing studies focus on specific regions or countries, leaving gaps in our understanding of global patterns and disparities. Furthermore, there is a critical need for reliable projections to guide healthcare resource allocation, workforce planning, and policy development, particularly in low- and middle-income countries where the MM burden is expected to rise substantially due to population aging. This study addresses these unmet needs by providing a systematic assessment of global MM epidemiology spanning over three decades. Understanding the evolving landscape of MM is crucial for developing effective prevention strategies and improving patient outcomes [ 10 – 12 ]. This article seeks to contribute to the existing body of knowledge and encourage ongoing research and policy efforts to address the challenges posed by this malignancy on a global scale.
Methods
Study design
This study employs a descriptive epidemiological analysis design based on the Global Burden of Disease (GBD) 2021 study to assess the global epidemiology and burden of MM from 1990 to 2021, along with projections to 2045. The analysis focuses on extracting and synthesizing MM-related metrics from the GBD database, which integrates data from various epidemiological studies, health surveys, and cancer registries worldwide. By employing this comprehensive database approach, this study aims to provide a thorough overview of the disease’s impact across different populations and regions.
Data sources
The GBD 2021, led by the Institute for Health Metrics and Evaluation (IHME), offers a comprehensive and current assessment of the epidemiology of diseases across 204 countries and territories from 1990 to 2021. Data for this study were obtained from the Global Health Data Exchange GBD Results Tool ( https://vizhub.healthdata.org/gbd-results/ ), which is publicly accessible. We extracted metrics including incidence, prevalence, and death cases, years lived with disability (YLDs), years of life lost (YLLs), disability-adjusted life years (DALYs), age-standardized incidence rates (ASIR), age-standardized prevalence rates (ASPR), age-standardized death rates (ASDR), age-standardized YLDs rates (ASYLD), age-standardized YLLs rates (ASYLL), and age-standardized DALY rates (ASDALY) for MM at global, socio-demographic index (SDI) quintile, regional, and national levels. Regarding age selection, we included both ‘all ages’ and ‘age-standardized’ options for analysis. Additionally, we specified 16 age groups in 5-year intervals from 20 to 24 years to 95 + years. Additionally, burden data associated with high body mass index (BMI) were incorporated. Countries were categorized into five SDI groups—low, low-middle, middle, high-middle, and high—based on total fertility rate, per capita income, and average years of education. This classification allows for an analysis of the relationship between MM and social development status.
Statistical analysis
We used the Joinpoint Regression Program version 4.9.1.0 (Statistical Research and Applications Branch, National Cancer Institute; freely available at https://surveillance.cancer.gov/joinpoint/ ) to calculate average annual percent change (AAPC). Segmented regression (lny = β*x) was performed using the grid search method to identify joinpoints by minimizing mean squared errors. The optimal number of joinpoints was determined via Monte Carlo permutation tests, testing up to five joinpoints. The final model calculated annual percent change (APC) as APC=(e^β^−1)×100% and AAPC by weighting APCs across segments to summarize trends from 1990 to 2021.
The purpose of decomposition analysis is to quantitatively assess the driving factors behind changes in a given indicator, allowing us to determine the specific effects of each factor. In the context of disease burden, we utilized the R package “easyGBDR” (available at https://github.com/Wangxiaoyue-lab/easyGBDR ) to perform decomposition analysis based on data extracted from the GBD 2021 database. This analysis decomposed the changes in the total number of deaths, YLDs, YLLs, and DALYs into contributions from three key components: population growth, population aging, and epidemiological changes (i.e., changes in age-specific rates). The population data used for this analysis, including age- and sex-specific population counts for 1990 and 2021, were obtained from the GBD 2021 population estimates, which are also available through the Global Health Data Exchange GBD Results Tool. The decomposition method follows the approach described by Cheng et al. [ 13 ].
To estimate the future global burden of MM, the Nordpred package in R (available at https://github.com/harveema/nordpred ) was utilized to analyze the number of incident cases, prevalent cases, deaths, and DALYs across different age groups from 2019 to 2045. To ensure the reliability of the projections, additional analyses were conducted using the BAPC package (available at https://CRAN.R-project.org/package=BAPC ) and INLA package (available at https://www.r-inla.org/ ) in R. All statistical analyses in this study were performed using R software (version 4.2.1), with p < 0.05 considered statistically significant.
Results
Global disease burden
The global burden of MM increased substantially from 1990 to 2021 (Fig. 1 ). Incident cases, prevalent cases, and deaths increased approximately 2–3 fold over this period. While the ASIR and ASPR showed notable increases, the ASDR remained relatively stable, suggesting that improvements in survival may have partially offset the rising incidence.
Fig. 1 The global incident cases, ASIR, prevalent cases, ASPR, death cases, ASDR with MM in 2021. (A) Incident cases. (B) Prevalent cases. (C) Deaths. (D) ASIR per 100,000 population. (E) ASPR per 100,000 population. (F) ASDR per 100,000 population. Left panel (A-C) displays absolute numbers with darker shades indicating higher counts. Right panel (D-F) displays age-standardized rates, with blue indicating lower rates and red indicating higher rates. Grey areas indicate missing data
Geographically, the highest absolute case numbers were observed in populous countries such as the United States and China, reflecting their large population bases. Age-standardized rates were highest in high-income countries with well-established cancer registries (e.g., Monaco, New Zealand, Australia), likely reflecting more comprehensive diagnostic capabilities and older population structures rather than inherently higher disease risk. Conversely, the substantial temporal increases observed in some countries (e.g., Mauritius) may reflect improvements in cancer registration and diagnostic infrastructure over the study period rather than true increases in disease occurrence. Detailed country-level data on incidence, prevalence, and deaths are provided in Table S1.
The disability burden of MM also increased substantially from 1990 to 2021 (Fig. 2 ). YLDs showed the largest relative increase, likely reflecting improved survival leading to more patients living with disease-related disability. In contrast, ASYLL and ASDALY rates remained relatively stable, indicating that while more people are being diagnosed and living with MM, the age-adjusted mortality burden has not increased proportionally. Regional and country-level variations in disability burden largely paralleled incidence patterns, with high-income regions showing higher age-standardized rates. Detailed country-level data on YLDs, YLLs, and DALYs are provided in Table S2.
Fig. 2 The global YLDs, ASYLD, YLLs, ASYLL, DALYs, ASDALY with MM in 2021. (A) YLDs. (B) YLLs. (C) DALYs. (D) ASYLD per 100,000 population. (E) ASYLL per 100,000 population. (F) ASDALY per 100,000 population. Left panel (A-C) displays absolute numbers with darker shades indicating higher values. Right panel (D-F) displays age-standardized rates, with blue indicating lower rates and red indicating higher rates. Grey areas indicate missing data
From a regional perspective, in 2021, the all-age disease burden is highest in Western Europe and lowest in Oceania (Table S3 ). In terms of age-standardized disease burden, Australasia has the highest burden, while Oceania has the lowest (Table S4 ).
Sex and age findings
In terms of gender ratio, in 1990, men had higher incident cases, prevalent cases, death cases, and DALYs than women before the age of 75, with this trend reversing after age 75 (Fig. 3 ). By 2021, this reversal in trend shifted to age 90. Analyzing by age group, in 1990, the peak incident cases, prevalent cases, and DALYs occurred in the 65–69 age group, while the peak in death cases occurred in the 75–79 age group. By 2021, DALYs peaked in the 65–69 age group, whereas the highest incidence, prevalence, and death cases were observed in the 70–74 age group.
Fig. 3 The incident cases, prevalent cases, death cases, and DALYs across different age groups and sexes with MM in 1990 and 2021. (A, B) Incident cases. (C, D) Prevalent cases. (E, F) Deaths. (G, H) DALYs. Left column (A, C, E, G): 1990; Right column (B, D, F, H): 2021. Bars represent the number of cases (pink for females, blue for males) with error bars indicating 95% UI. Lines represent age-specific rates with shaded areas indicating 95% UI
Changes in ASR trends
The Joinpoint regression analysis revealed that from 1990 to 2021, the global AAPCs for ASIR, ASPR, ASDR, and ASDALYs were 0.54%, 1.22%, 0.20%, and 0.18%, respectively, all showing statistically significant trends ( p < 0.05) (Fig. 4 , Table S5 ). Among males, the AAPCs for ASIR, ASPR, ASDR, and ASDALYs were 0.73%, 1.52%, 0.35%, and 0.31%, respectively, all of which were statistically significant ( p < 0.001). For females, the AAPCs for ASIR and ASPR were 0.29% and 0.85%, respectively, both showing statistically significant trends ( p < 0.001). However, the AAPCs for ASDR and ASDALYs were both 0.01%, with no statistically significant trends observed ( p > 0.05), suggesting that these APCs have remained largely unchanged over the past three decades.
Fig. 4 The global AAPCs for ASIR, ASPR, ASDR, and ASDALYs from 1990 to 2021. (A) ASIR. (B) ASPR. (C) ASDR. (D) ASDALY. Black dots represent observed data points. Colored lines represent fitted segments between joinpoints, with the APC indicated for each segment. Asterisks (*) indicate statistically significant APC ( P < 0.05). AAPC for the entire study period is shown in the legend
The figure illustrates the variation trends of ASR with SDI across different regions(Fig. 5 ). It shows that in regions like Central Latin America and South Asia, Andean Latin America and Eastern Europe, the ASR trends align closely with expectations. However, some anomalies are also visible in the figure. In high-income regions such as high-income North America are significantly higher than expected, with increased fluctuations. At the national level, we found a positive correlation between ASR and SDI among countries in 2021(Fig. 6 ).
Fig. 5 The variation trends of ASR with SDI across different regions. (A) ASIR. (B) ASPR. (C) ASDR. (D) ASDALYs. Each point represents a GBD region in a specific year, with different colors and shapes indicating different regions. Black solid line represents the expected trend based on SDI. Spearman correlation coefficients (ρ) and P-values are shown in the upper left corner (all P < 0.001)
Fig. 6 The correlation between ASR and SDI among countries in 2021
Burden of MM attributable to high BMI
High BMI is the only GBD risk factor for MM. In 1990, high BMI caused 3,023.5 deaths, 1,802.4 YLDs, 70,207.5 YLLs, and 72,009.9 DALYs, with corresponding age-standardized population attributable fractions (ASPAF) of 6.30%, 6.52%, 6.40%, and 6.40%, respectively. By 2021, high BMI led to 9,165.5 deaths, 6,788.5 YLDs, 200,845.5 YLLs, and 207,633.9 DALYs, with ASPAFs of 7.84%, 8.09%, 7.96%, and 7.96%, respectively (Table S6 ). Over time, high BMI has contributed to an increased global burden of MM. Regionally, in 2021, North Africa and Middle East had the highest population attributable fraction (PAF), while South Asia had the lowest (Fig. 7 , Table S7 ). At the national level, Kuwait had the highest PAF, while Timor-Leste had the lowest (Table S8 - S11 ).
Fig. 7 Different regions burden of MM attributable to high BMI
Decomposition analysis
We conducted a decomposition analysis and illustrated the impacts of population aging, population growth, and epidemiological changes on disease burden over the past 30 years (Fig. 8 ). At the global level, aging, population growth, and epidemiological changes for both sexes contribute to an increased burden of MM (Table S12 ). Specifically, for females, epidemiological changes have led to a reduction in YLLs. In terms of SDI stratification, in low SDI countries, population aging alleviates the MM burden, while in high SDI countries, epidemiological changes result in a reduction in MM-related deaths, DALYs, and YLLs. These findings suggest that high SDI countries have achieved better outcomes in controlling MM, whereas greater attention should be paid to life expectancy issues in low SDI countries.
Fig. 8 The impacts of population aging, population growth, and epidemiological changes on disease burden over the past 30 years. (A) Incidence. (B) Prevalence. (C) Deaths. (D) DALYs. Stacked bar charts showing the contributions of population aging (purple), population growth (teal), and epidemiological change (yellow) to the overall change in MM burden. Results are stratified by sex (both sexes, female, male) and SDI quintiles (global, low SDI, low-middle SDI, middle SDI, high-middle SDI, high SDI). Black dots indicate the net change. Positive values indicate increases in burden; negative values indicate decreases
Projection burden from 2021 to 2045
To provide a comprehensive view of the epidemiological trends of MM over the next three decades, we employed BAPC models to project the global burden for total MM and across various age groups. We anticipate that the incidence, prevalence, and death cases will increase from 148757.88, 394467.77, and 116401.92 in 2021 to 261894.72, 636255.33, and 217206.11 in 2045, respectively (Table S13 - S15 ).
However, our findings indicate that the ASRs for MM will go down annually for both genders, as well as for males and females individually (Table S16 -S18). Specifically, we anticipate that the ASIR, ASPR, and ASDR will decrease from 1.74, 4.55, and 1.38 per 100,000 population in 2021 to 1.71, 4.41, and 1.36 per 100,000 population in 2045, respectively (Fig. 9 ). Over this period, the projected ASRs for MM are expected to remain higher for males compared to females.
Fig. 9 Projection burden from 2021 to 2045. (A) Incidence. (B) Prevalence. (C) Deaths. (D) DALYs. Purple bars represent the number of cases (left y-axis). Solid lines represent observed ASRs (1990–2021) and dashed lines represent projected ASRs (2022–2045) per 100,000 population (right y-axis). Vertical dashed line indicates the transition from observed to projected data at year 2021
Discussion
The global burden of MM has significantly increased over the past three decades, driven by demographic shifts, advancements in diagnostics, and changes in population health dynamics [ 2 ]. This study highlights the evolving epidemiology of MM and provides critical insights into its distribution, risk factors, and future projections.
Our analysis reveals a marked increase in the incidence, prevalence, and DALYs associated with MM between 1990 and 2021. Developed regions such as Western Europe and North America, show higher ASIRs, which may be attributed to better healthcare access, earlier diagnosis, and aging populations, although these interpretations should be made cautiously. Conversely, the apparently lower burden in developing countries may reflect underreporting and limited healthcare infrastructure rather than genuinely lower disease occurrence. It should be noted that GBD estimates for low-SDI regions carry substantial uncertainty due to sparse cancer registry coverage and reliance on statistical modeling to fill data gaps. The true burden of MM in these regions remains unclear, and the observed disparities may represent a combination of genuine epidemiological differences and ascertainment bias. These considerations underscore the importance of strengthening healthcare systems and cancer surveillance globally to ensure equitable access to diagnostic and therapeutic resources [ 14 , 15 ].
To address the underreporting of MM in developing countries, several strategies warrant consideration. First, strengthening cancer registry systems through capacity building and international technical assistance is essential; the Global Initiative for Cancer Registry Development (GICR), coordinated by the International Agency for Research on Cancer (IARC), has established regional hubs to support low- and middle-income countries in improving cancer surveillance [ 16 ]. Second, establishing regional diagnostic centers equipped with essential tools such as serum protein electrophoresis and bone marrow aspiration capabilities could improve MM diagnosis in areas lacking specialized hematology services [ 17 ]. Third, task-shifting approaches that train primary care providers to recognize early MM symptoms (such as unexplained bone pain, anemia, and recurrent infections) could facilitate earlier referral, as delayed diagnosis due to low clinical suspicion has been identified as a major barrier in sub-Saharan Africa [ 18 ]. Fourth, leveraging telemedicine and digital pathology platforms may enable remote consultation with specialists, as telepathology programs have demonstrated feasibility in resource-limited settings [ 19 ]. Finally, international collaborations and the adoption of resource-stratified treatment guidelines can facilitate knowledge transfer and promote evidence-based care adapted to local resource availability [ 20 ].
Age and sex-specific trends further emphasize the need for targeted interventions [ 21 , 22 ]. The shifting peak incidence and prevalence to older age groups in 2021 align with global population aging trends. Moreover, while men exhibited higher rates of MM burden before the age of 75, the reversal of this trend post-90 years highlights potential gender-specific differences in disease biology, healthcare access, and comorbidities that merit further investigation [ 23 – 25 ].
High BMI emerged as the only GBD-identified risk factor for MM, with its attributable burden increasing substantially over the study period. This finding aligns with evidence linking obesity-related metabolic changes to MM pathogenesis [ 24 , 26 ]. Regional variations in PAFs of high BMI indicate the necessity for tailored public health strategies [ 14 , 27 ]. For instance, regions such as North and Central Africa exhibit the highest PAFs, suggesting a pressing need for interventions targeting obesity prevention and management in these areas. It is important to note that the GBD risk attribution framework has inherent limitations in capturing the full etiologic landscape of MM. Other known or suspected risk factors, including occupational exposures (such as pesticides, solvents, and heavy metals), genetic susceptibility, family history, race/ethnicity (with individuals of African descent showing approximately two-fold higher incidence), and chronic antigenic stimulation, are not currently incorporated into the GBD risk factor analysis and thus could not be evaluated in this study. The PAF estimates presented here therefore represent only a partial picture of the preventable MM burden.
Decomposition analysis highlights the distinct contributions of population growth, aging, and epidemiological changes to the MM burden. Notably, epidemiological improvements in high SDI countries have mitigated mortality and disability, reflecting advancements in MM management. However, low SDI regions remain disproportionately affected by the compounded challenges of aging populations and inadequate healthcare systems [ 28 ]. This underscores the urgency of addressing global health inequities [ 29 , 30 ]. Projections to 2045 indicate a continued rise in absolute MM cases, driven primarily by population growth and aging. While ASRs are anticipated to decline slightly, this reduction may not translate into a reduced overall disease burden. These projections rely on historical trends and cannot account for future therapeutic breakthroughs that may substantially alter MM outcomes. These findings highlight the importance of integrating MM prevention and management strategies into broader public health initiatives [ 31 – 33 ]. It should be noted that the decomposition analysis framework used in this study separates changes in disease burden into population growth, population aging, and epidemiological changes. The “epidemiological changes” component captures the net effect of all factors influencing age-specific rates, including but not limited to advances in treatment options. Over the past three decades, the treatment landscape for MM has undergone remarkable transformation, evolving from conventional chemotherapy to the introduction of immunomodulatory drugs (thalidomide, lenalidomide), proteasome inhibitors (bortezomib, carfilzomib), monoclonal antibodies (daratumumab, elotuzumab), and most recently, CAR-T cell therapies and bispecific antibodies [ 34 ]. These therapeutic advances have substantially improved survival outcomes, particularly in high SDI regions where access to novel therapies is more widespread. However, the current decomposition methodology does not allow for the separate quantification of treatment-related contributions, which are inherently embedded within the epidemiological changes component.
This study underscores several challenges in understanding and addressing the global burden of MM. Disparities in data availability, particularly in low- and middle-income countries, hinder accurate burden estimation and policy formulation. Moreover, the biological underpinnings of MM remain incompletely understood, necessitating ongoing research into genetic, environmental, and lifestyle-related risk factors [ 35 – 38 ]. Future efforts should prioritize expanding access to care by ensuring equitable availability of diagnostics, treatments, and supportive care across all regions; implementing policies to address modifiable risk factors such as obesity and physical inactivity while considering regional sociocultural contexts; and strengthening surveillance systems to enhance data collection and monitoring capabilities for more granular insights into MM trends.
This study has several limitations that should be acknowledged. First, the analysis relies entirely on data from the GBD 2021 study, which uses statistical modeling to generate estimates for regions with limited primary data. The accuracy of these estimates varies substantially across countries, with low- and middle-income regions likely having greater uncertainty due to sparse cancer registry coverage and underdiagnosis. Second, the GBD database identifies high BMI as the only attributable risk factor for MM; however, other potentially important factors such as occupational exposures, genetic susceptibility, and environmental factors could not be assessed due to data limitations. Third, no external validation study was conducted to verify our projections to 2045, and the BAPC and Nordpred models assume that historical trends will continue, which may not hold if major changes occur in diagnostic practices, treatment availability, or risk factor prevalence. Specifically, the rapid advancement of immunotherapies such as CAR-T cells and bispecific antibodies has the potential to fundamentally alter MM survival trajectories in ways that cannot be predicted by extrapolating historical trends. Fourth, the ecological nature of this analysis precludes individual-level inference, and associations observed at the country or regional level may not apply to individuals. Fifth, while the decomposition analysis effectively separates the contributions of population growth, aging, and epidemiological changes, it does not allow for the isolated quantification of specific factors such as advances in treatment options. The remarkable improvements in MM therapy over the study period, including the introduction of novel agents and treatment regimens, are captured within the “epidemiological changes” component but cannot be independently assessed using this methodology. Finally, variations in disease classification and diagnostic criteria across countries and time periods may introduce heterogeneity in the reported burden estimates.
Conclusion
The rising global burden of MM poses significant challenges for healthcare systems worldwide. Addressing these challenges requires coordinated efforts across public health, clinical research, and policy domains. By prioritizing equity, prevention, and innovation, we can work toward mitigating the impact of MM and improving outcomes for affected populations.
Supplementary Information
Below is the link to the electronic supplementary material.
Supplementary Material 1.
Supplementary Material 2.
Supplementary Material 3.
Supplementary Material 4.
Supplementary Material 5.
Supplementary Material 6.
Supplementary Material 7.
Supplementary Material 8.
Supplementary Material 9.
Supplementary Material 10.
Supplementary Material 11.
Supplementary Material 12.
Supplementary Material 13.
Supplementary Material 14.
Supplementary Material 15.
Supplementary Material 16.
Supplementary Material 17.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Author contributions
Haiyong Xu and Xiang Chen contributed equally to this work. Haiyong Xu contributed to conceptualization, methodology, investigation, data curation, writing the original draft, and funding acquisition. Xiang Chen participated in methodology, investigation, data curation, formal analysis, and writing review and editing. Wenqiang Wang, Shunan Dong, Hongjie Su, and Puxiang Zhen were involved in investigation, data collection, formal analysis, and validation. Xinyu Nie provided resources, contributed to methodology and validation, and participated in writing review and editing. Qikai Hua contributed to conceptualization, methodology, supervision, project administration and writing review and editing. All authors have read and agreed to the published version of the manuscript.
Funding
None.
Data availability
All data and tools used in this study are publicly accessible. The GBD 2021 data were obtained from the Global Health Data Exchange GBD Results Tool (https://vizhub.healthdata.org/gbd-results/). The Joinpoint Regression Program (version 4.9.1.0) is available at https://surveillance.cancer.gov/joinpoint/. The R packages used include: easyGBDR (https://github.com/Wangxiaoyue-lab/easyGBDR), Nordpred (https://github.com/harveema/nordpred), BAPC (https://CRAN.R-project.org/package=BAPC), and INLA (https://www.r-inla.org/). All extracted datasets and analysis codes are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
This study relied on publicly accessible summary statistics from previously published research and consortia. Each original study involved in this research had received ethical clearance from their respective review boards, and all participants had given informed consent. Notably, this research did not involve the use of individual-level data, thereby eliminating the need for new ethical review board approval. Not applicable. This study utilized publicly available, anonymized data from the GBD 2021 database and did not involve direct participation of human subjects.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
References
1. Palumbo A Anderson K Medical progress multiple myeloma N Engl J Med 2011 364 11 1046 60 10.1056/NEJMra1011442 21410373 Palumbo A, Anderson K. MEDICAL PROGRESS multiple myeloma. N Engl J Med. 2011;364(11):1046–60. 21410373 10.1056/NEJMra1011442
2. Cowan AJ Diagnosis and management of multiple myeloma A review Jama-Journal Am Med Association 2022 327 5 464 77 10.1001/jama.2022.0003 35103762 Cowan AJ, et al. Diagnosis and management of multiple myeloma A review. Jama-Journal Am Med Association. 2022;327(5):464–77. 10.1001/jama.2022.0003 35103762
3. Bladé J Extramedullary disease in multiple myeloma: a systematic literature review Blood Cancer J 2022 12 3 10 10.1038/s41408-022-00643-3 35314675 PMC8938478 Bladé J, et al. Extramedullary disease in multiple myeloma: a systematic literature review. Blood Cancer J. 2022;12(3):10. 35314675 10.1038/s41408-022-00643-3 PMC8938478
4. Rajkumar SV International myeloma working group updated criteria for the diagnosis of multiple myeloma Lancet Oncol 2014 15 12 E538 48 10.1016/S1470-2045(14)70442-5 25439696 Rajkumar SV, et al. International myeloma working group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol. 2014;15(12):E538–48. 25439696 10.1016/S1470-2045(14)70442-5
5. Minnie SA Hill GR Immunotherapy of multiple myeloma J Clin Invest 2020 130 4 1565 75 10.1172/JCI129205 32149732 PMC7108923 Minnie SA, Hill GR. Immunotherapy of multiple myeloma. J Clin Invest. 2020;130(4):1565–75. 32149732 10.1172/JCI129205 PMC7108923
6. Kyle RA Monoclonal gammopathy of undetermined significance (MGUS) and smoldering (asymptomatic) multiple myeloma: IMWG consensus perspectives risk factors for progression and guidelines for monitoring and management Leukemia 2010 24 6 1121 7 10.1038/leu.2010.60 20410922 PMC7020664 Kyle RA, et al. Monoclonal gammopathy of undetermined significance (MGUS) and smoldering (asymptomatic) multiple myeloma: IMWG consensus perspectives risk factors for progression and guidelines for monitoring and management. Leukemia. 2010;24(6):1121–7. 20410922 10.1038/leu.2010.60 PMC7020664
7. Kumar S International myeloma working group consensus criteria for response and minimal residual disease assessment in multiple myeloma Lancet Oncol 2016 17 8 E328 46 10.1016/S1470-2045(16)30206-6 27511158 Kumar S, et al. International myeloma working group consensus criteria for response and minimal residual disease assessment in multiple myeloma. Lancet Oncol. 2016;17(8):E328–46. 27511158 10.1016/S1470-2045(16)30206-6
8. Morgan GJ Walker BA Davies FE The genetic architecture of multiple myeloma Nat Rev Cancer 2012 12 5 335 48 10.1038/nrc3257 22495321 Morgan GJ, Walker BA, Davies FE. The genetic architecture of multiple myeloma. Nat Rev Cancer. 2012;12(5):335–48. 22495321 10.1038/nrc3257
9. Kumar SK Multiple myeloma Nat Reviews Disease Primers 2017 3 20 10.1038/nrdp.2017.46 28726797 Kumar SK, et al. Multiple myeloma. Nat Reviews Disease Primers. 2017;3:20. 10.1038/nrdp.2017.46 28726797
10. Rosiñol L Expert review on soft-tissue Plasmacytomas in multiple myeloma: definition, disease assessment and treatment considerations Br J Haematol 2021 194 3 496 507 10.1111/bjh.17338 33724461 Rosiñol L, et al. Expert review on soft-tissue Plasmacytomas in multiple myeloma: definition, disease assessment and treatment considerations. Br J Haematol. 2021;194(3):496–507. 33724461 10.1111/bjh.17338
11. Ludwig H Recommendations for vaccination in multiple myeloma: a consensus of the European myeloma network Leukemia 2021 35 1 31 44 10.1038/s41375-020-01016-0 32814840 PMC7787974 Ludwig H, et al. Recommendations for vaccination in multiple myeloma: a consensus of the European myeloma network. Leukemia. 2021;35(1):31–44. 32814840 10.1038/s41375-020-01016-0 PMC7787974
12. Raje NS Consensus guidelines and recommendations for infection prevention in multiple myeloma: a report from the international myeloma working group Lancet Haematol 2022 9 2 E143 61 10.1016/S2352-3026(21)00283-0 35114152 Raje NS, et al. Consensus guidelines and recommendations for infection prevention in multiple myeloma: a report from the international myeloma working group. Lancet Haematol. 2022;9(2):E143–61. 35114152 10.1016/S2352-3026(21)00283-0
13. Cheng X Population ageing and mortality during 1990–2017: A global decomposition analysis PLoS Med 2020 17 6 e1003138 10.1371/journal.pmed.1003138 32511229 PMC7279585 Cheng X, et al. Population ageing and mortality during 1990–2017: A global decomposition analysis. PLoS Med. 2020;17(6):e1003138. 32511229 10.1371/journal.pmed.1003138 PMC7279585
14. Moreau P Isatuximab, carfilzomib, and dexamethasone in relapsed multiple myeloma (IKEMA): a multicentre, open-label, randomised phase 3 trial Lancet 2021 397 10292 2361 71 10.1016/S0140-6736(21)00592-4 34097854 Moreau P, et al. Isatuximab, carfilzomib, and dexamethasone in relapsed multiple myeloma (IKEMA): a multicentre, open-label, randomised phase 3 trial. Lancet. 2021;397(10292):2361–71. 34097854 10.1016/S0140-6736(21)00592-4
15. Khorana AA Prediction and prevention of Cancer-Associated thromboembolism Oncologist 2021 26 1 E2 7 10.1002/onco.13569 33274815 PMC7794203 Khorana AA, et al. Prediction and prevention of Cancer-Associated thromboembolism. Oncologist. 2021;26(1):E2–7. 33274815 10.1002/onco.13569 PMC7794203
16. BrayF, et al. Technical publications. Planning and developing population-based cancer registration in low- or middle-income settings. International Agency for Research on Cancer; 2014. (IARC Technical Report, No. 43). https://www.ncbi.nlm.nih.gov/books/NBK566957/ 33502836
17. Mateos MV Global disparities in patients with multiple myeloma: a rapid evidence assessment Blood Cancer J 2023 13 1 109 10.1038/s41408-023-00877-9 37460466 PMC10352266 Mateos MV, et al. Global disparities in patients with multiple myeloma: a rapid evidence assessment. Blood Cancer J. 2023;13(1):109. 37460466 10.1038/s41408-023-00877-9 PMC10352266
18. Razzano D The role of telepathology in improving cancer diagnostic and research capacity in sub-Saharan Africa Front Med (Lausanne) 2022 9 978245 10.3389/fmed.2022.978245 36325383 PMC9618672 Razzano D, et al. The role of telepathology in improving cancer diagnostic and research capacity in sub-Saharan Africa. Front Med (Lausanne). 2022;9:978245. 36325383 10.3389/fmed.2022.978245 PMC9618672
19. Yadav K Telemedicine and cancer care in Low- and Middle-Income countries during the SARS-CoV-2 pandemic JCO Glob Oncol 2021 7 1633 8 10.1200/GO.21.00249 34860567 PMC8654432 Yadav K, et al. Telemedicine and cancer care in Low- and Middle-Income countries during the SARS-CoV-2 pandemic. JCO Glob Oncol. 2021;7:1633–8. 34860567 10.1200/GO.21.00249 PMC8654432
20. Mattar M Epidemiology, treatment Trends, and outcomes of multiple myeloma in the middle East and africa: A systematic review Clin Hematol Int 2024 6 1 67 83 10.46989/001c.92555 38817690 PMC11086989 Mattar M, et al. Epidemiology, treatment Trends, and outcomes of multiple myeloma in the middle East and africa: A systematic review. Clin Hematol Int. 2024;6(1):67–83. 38817690 10.46989/001c.92555 PMC11086989
21. El-Cheikh J Cardiac toxicities in multiple myeloma: an updated and a deeper look into the effect of different medications and novel therapies Blood Cancer J 2023 13 1 10 10.1038/s41408-023-00849-z 37208317 PMC10199017 El-Cheikh J, et al. Cardiac toxicities in multiple myeloma: an updated and a deeper look into the effect of different medications and novel therapies. Blood Cancer J. 2023;13(1):10. 37208317 10.1038/s41408-023-00849-z PMC10199017
22. Urban VS Cegledi A Mikala G Multiple myeloma, a quintessential malignant disease of aging: a geroscience perspective on pathogenesis and treatment Geroscience 2023 45 2 727 46 10.1007/s11357-022-00698-x 36508077 PMC9742673 Urban VS, Cegledi A, Mikala G. Multiple myeloma, a quintessential malignant disease of aging: a geroscience perspective on pathogenesis and treatment. Geroscience. 2023;45(2):727–46. 36508077 10.1007/s11357-022-00698-x PMC9742673
23. Caro J Inflammation and infection in plasma cell disorders: how pathogens shape the fate of patients Leukemia 2022 36 3 613 24 10.1038/s41375-021-01506-9 35110727 PMC8809233 Caro J, et al. Inflammation and infection in plasma cell disorders: how pathogens shape the fate of patients. Leukemia. 2022;36(3):613–24. 35110727 10.1038/s41375-021-01506-9 PMC8809233
24. Larson EA Dalamaga M Magkos F The role of exercise in obesity-related cancers: current evidence and biological mechanisms Sem Cancer Biol 2023 91 16 26 10.1016/j.semcancer.2023.02.008 36871634 Larson EA, Dalamaga M, Magkos F. The role of exercise in obesity-related cancers: current evidence and biological mechanisms. Sem Cancer Biol. 2023;91:16–26. 10.1016/j.semcancer.2023.02.008 36871634
25. Goel U Usmani S Kumar S Current approaches to management of newly diagnosed multiple myeloma Am J Hematol 2022 97 S3 25 10.1002/ajh.26512 35234302 Goel U, Usmani S, Kumar S. Current approaches to management of newly diagnosed multiple myeloma. Am J Hematol. 2022;97:S3–25. 35234302 10.1002/ajh.26512
26. Friedenreich CM Ryder-Burbidge C McNeil J Physical activity, obesity and sedentary behavior in cancer etiology: epidemiologic evidence and biologic mechanisms Mol Oncol 2021 15 3 790 800 10.1002/1878-0261.12772 32741068 PMC7931121 Friedenreich CM, Ryder-Burbidge C, McNeil J. Physical activity, obesity and sedentary behavior in cancer etiology: epidemiologic evidence and biologic mechanisms. Mol Oncol. 2021;15(3):790–800. 32741068 10.1002/1878-0261.12772 PMC7931121
27. Zhou LH Measuring the global, regional, and National burden of multiple myeloma from 1990 to 2019 BMC Cancer 2021 21 1 13 10.1186/s12885-021-08280-y 34034700 PMC8152089 Zhou LH, et al. Measuring the global, regional, and National burden of multiple myeloma from 1990 to 2019. BMC Cancer. 2021;21(1):13. 34034700 10.1186/s12885-021-08280-y PMC8152089
28. White B Does changing healthcare use signal opportunities for earlier detection of cancer? A review of studies using information from electronic patient records Cancer Epidemiol 2022 76 8 10.1016/j.canep.2021.102072 PMC8785122 34876377 White B, et al. Does changing healthcare use signal opportunities for earlier detection of cancer? A review of studies using information from electronic patient records. Cancer Epidemiol. 2022;76:8. 10.1016/j.canep.2021.102072 PMC8785122 34876377
29. Madduri D Real-world treatment patterns, healthcare use and costs in triple-class exposed relapsed and refractory multiple myeloma patients in the united States Future Oncol 2021 17 5 503 15 10.2217/fon-2020-1003 33522834 Madduri D, et al. Real-world treatment patterns, healthcare use and costs in triple-class exposed relapsed and refractory multiple myeloma patients in the united States. Future Oncol. 2021;17(5):503–15. 33522834 10.2217/fon-2020-1003
30. DuMontier C Contemporary analysis of electronic frailty measurement in older adults with multiple myeloma treated in the National US veterans affairs healthcare system Cancers 2021 13 12 18 10.3390/cancers13123053 PMC8233717 34207459 DuMontier C, et al. Contemporary analysis of electronic frailty measurement in older adults with multiple myeloma treated in the National US veterans affairs healthcare system. Cancers. 2021;13(12):18. 10.3390/cancers13123053 PMC8233717 34207459
31. Callander NS Multiple Myeloma, version 3.2022 < i>Featured updates to the NCCN guidelines J Natl Compr Canc Netw 2022 20 1 9 19 10.6004/jnccn.2022.0002 Callander NS, et al. Multiple Myeloma, version 3.2022 < i>Featured updates to the NCCN guidelines. J Natl Compr Canc Netw. 2022;20(1):9–19.
32. Rustad EH Timing the initiation of multiple myeloma Nat Commun 2020 11 1 14 10.1038/s41467-020-15740-9 32317634 PMC7174344 Rustad EH, et al. Timing the initiation of multiple myeloma. Nat Commun. 2020;11(1):14. 32317634 10.1038/s41467-020-15740-9 PMC7174344
33. Gau YC Pathogenesis and treatment of Myeloma-Related bone disease Int J Mol Sci 2022 23 6 18 10.3390/ijms23063112 PMC8951013 35328533 Gau YC, et al. Pathogenesis and treatment of Myeloma-Related bone disease. Int J Mol Sci. 2022;23(6):18. 10.3390/ijms23063112 PMC8951013 35328533
34. Rajkumar SV Multiple myeloma: 2022 update on diagnosis, risk stratification, and management Am J Hematol 2022 97 8 1086 107 10.1002/ajh.26590 35560063 PMC9387011 Rajkumar SV. Multiple myeloma: 2022 update on diagnosis, risk stratification, and management. Am J Hematol. 2022;97(8):1086–107. 35560063 10.1002/ajh.26590 PMC9387011
35. Huang JJ The epidemiological landscape of multiple myeloma: a global cancer registry estimate of disease burden, risk factors, and Temporal trends Lancet Haematol 2022 9 9 E670 7 10.1016/S2352-3026(22)00165-X 35843248 Huang JJ, et al. The epidemiological landscape of multiple myeloma: a global cancer registry estimate of disease burden, risk factors, and Temporal trends. Lancet Haematol. 2022;9(9):E670–7. 35843248 10.1016/S2352-3026(22)00165-X
36. Marinac CR Dissecting Racial disparities in multiple myeloma Blood Cancer J 2020 10 2 8 10.1038/s41408-020-0284-7 32066732 PMC7026439 Marinac CR, et al. Dissecting Racial disparities in multiple myeloma. Blood Cancer J. 2020;10(2):8. 32066732 10.1038/s41408-020-0284-7 PMC7026439
37. Nicol JL Physical activity in people with multiple myeloma: associated factors and exercise program preferences J Clin Med 2020 9 10 11 10.3390/jcm9103277 PMC7601964 33066153 Nicol JL, et al. Physical activity in people with multiple myeloma: associated factors and exercise program preferences. J Clin Med. 2020;9(10):11. 10.3390/jcm9103277 PMC7601964 33066153
38. Kumar V Timeliness of initial therapy in multiple myeloma: trends and factors affecting patient care Jco Oncol Pract 2020 16 4 168 10.1200/JOP.19.00309 32048939 Kumar V, et al. Timeliness of initial therapy in multiple myeloma: trends and factors affecting patient care. Jco Oncol Pract. 2020;16(4):168–. 10.1200/JOP.19.00309 32048939