Session: 907. Outcomes Research: Plasma Cell Disorders: Poster II
Hematology Disease Topics & Pathways:
Adult, Research, Health outcomes research, Clinical Research, Health disparities research, Education, Human, Study Population
Multiple Myeloma (MM) disproportionately affects Black populations. Modifiable risk factors, such as elevated body mass index (BMI) and diabetes mellitus (DM) are linked to increased MM risk. Furthermore, these risk factors also disproportionately affect Black compared to White populations. Therefore, we aim to calculate the MM incidence attributable to elevated BMI and DM among adults ≥18 years and calculate the excess MM risk due to these factors in non-Hispanic Black (NHB) vs non-Hispanic White (NHW) populations.
Methods
This study was conducted using de-identified, publicly available data. The number of MM cancer cases from 2016 to 2021 in the United States (US), including stratification by age, race, and ethnicities, was obtained from the US Cancer Statistics (USCS) Public Use database as reported by National Cancer Institute’s (NCI) Surveillance, Epidemiology, and End Results (SEER) Program.
The incidence of overweight (BMI: 25-30) and obese (BMI: ≥30) BMI categories per race for the US was obtained from the Behavioral Risk Factor Surveillance System (BRFSS), a nationwide telephone survey of adults 18 years or older designed to provide reliable estimates of health-related behavioral risk factors. Lastly, National level DM incidence data per race for age groups 18-44, 45-64, 65-74, and 75+ was obtained from the Centers for Disease Control and Prevention through their US Diabetes Surveillance System from the Division of Diabetes Translation for 2000-2022.
In this study, we estimate the population attributable fraction (PAF) of incident MM cases attributable to BMI and DM. We obtained the hazard ratios (HR) estimates from two large epidemiologic population-based studies evaluating MM risk with elevated BMI (Hofmann et al AJE 2013) and DM (Gong et al. Diabetologia 2021). Based on these papers, the upper and lower range estimates of MM risk for overweight category (HR 1.09 to 1.25), obese category (HR 1.26 to 1.55); and DM (HR 1.15) was utilized.
Results
For NHW populations, the incidence of obesity was 31.3%, and overweight was 36.8%. For NHB populations the incidence of obesity was 43.2%, and overweight was 33.6%. The PAF due to elevated BMI ranged between 11.2-19.3% for NHW and 13.7-22.5% for NHB. Therefore, compared to NHW, in NHB an additional 2.4-3.2% of MM incidence is due to elevated BMI.
For ages 18-44, incidence of DM was 2.2% in NHW and 3.7% in NHB. For ages 45-64, incidence was 9.9% for NHW and 17% for NHB. For ages 65-74 incidence was 17.7% for NHW and 31.2% for NHB. Lastly, for ages 75+, incidence was 16.5% for NHW and 29.1% for NHB populations. The PAF due to DM for the different age groups ranges between 2.5-4.4% in NHB and 1.5-2.6% in NHW. Therefore, compared to NHW, in NHB an additional 1-1.9% of MM incidence is due to DM.
As an example, in 2021, there were 34,920 new MM cases diagnosed (20,192 cases in NHW and 7,068 cases in NHB). Based on our calculated PAF, of these total cases, in NHW between 2261 to 3897 new cases and in NHB between 968 to 1590 new cases can be attributed to an elevated BMI.
Conclusions
The proportion of MM attributable to modifiable risk factors such as elevated BMI and DM (both of which affect black populations disproportionately compared to white populations) is significant.
Based on our analysis, up to approximately 1 in 5 MM cases in NHW and 1 in 4 MM cases in NHB maybe attributable to elevated BMI as a risk factor. Up to approximately 1 in 23 MM cases in NHB and 1 in 38 MM cases in NHW maybe attributable to DM as a risk factor.
Thus, this suggests a need for comprehensive intervention strategies to lower BMI and reduce DM incidence among communities at risk of developing MM, as well as implementing preventive measures to reduce the long-term impact of these chronic conditions.
Disclosures: Usmani: Bristol-Myers Squibb: Consultancy, Research Funding; EdoPharma: Consultancy; GSK: Consultancy, Research Funding; Merck: Research Funding; Pharmacyclics: Research Funding; Oncopeptides: Consultancy; Gilead: Research Funding; Genentech: Consultancy; Pfizer: Consultancy; Sanofi: Consultancy, Research Funding; Gracell: Consultancy; Bristol-Myers Squibb - Celgene:: Consultancy, Research Funding; Abbvie: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding; SkylineDX: Consultancy, Research Funding; TeneoBio: Consultancy; Takeda: Consultancy, Research Funding; SecuraBio: Consultancy; SeaGen: Consultancy, Research Funding; Array Biopharma: Research Funding; Amgen: Consultancy, Research Funding; Bristol-Myers Squibb - Celgene: Consultancy, Research Funding; Johnson & Johnson - Janssen: Consultancy, Research Funding. Shah: Sanofi: Honoraria; Bristol Myers Squibb: Research Funding; Janssen: Honoraria, Research Funding.
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