Quantify the Contribution of Modifiable Risk Factors for Progression of MGUS to Multiple Myeloma

Quantify the Contribution of Modifiable Risk Factors for Progression of MGUS to Multiple Myeloma | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Quantify the Contribution of Modifiable Risk Factors for Progression of MGUS to Multiple Myeloma View ORCID Profile Mei Wang , Byron Sigel , Lawrence Liu , John H. Huber , View ORCID Profile Mengmeng Ji , Martin W. Schoen , Kristen M. Sanfilippo , Theodore S. Thomas , View ORCID Profile Graham A. Colditz , Shi-Yi Wang , Su-Hsin Chang doi: https://doi.org/10.1101/2025.04.21.25326164 Mei Wang 1 Research Service, St. Louis Veterans Affairs Medical Center ; St. Louis, MO 2 Division of Biology and Biomedical Sciences, Washington University School of Medicine ; St. Louis, MO 3 Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine ; St. Louis, MO MS Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mei Wang Byron Sigel 4 Division of Hematology and Medical Oncology, Department of Internal Medicine, Saint Louis University School of Medicine , St. Louis, MO MsC Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lawrence Liu 5 City of Hope Comprehensive Cancer Center ; Duarte, CA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site John H. Huber 3 Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine ; St. Louis, MO PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mengmeng Ji 1 Research Service, St. Louis Veterans Affairs Medical Center ; St. Louis, MO 3 Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine ; St. Louis, MO PhD, MBBS Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mengmeng Ji Martin W. Schoen 1 Research Service, St. Louis Veterans Affairs Medical Center ; St. Louis, MO 6 Department of Medicine, Washington University School of Medicine ; St. Louis, MO MD, MPH Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kristen M. Sanfilippo 1 Research Service, St. Louis Veterans Affairs Medical Center ; St. Louis, MO 4 Division of Hematology and Medical Oncology, Department of Internal Medicine, Saint Louis University School of Medicine , St. Louis, MO MD, MPHS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Theodore S. Thomas 1 Research Service, St. Louis Veterans Affairs Medical Center ; St. Louis, MO 4 Division of Hematology and Medical Oncology, Department of Internal Medicine, Saint Louis University School of Medicine , St. Louis, MO MD, MPHS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Graham A. Colditz 3 Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine ; St. Louis, MO MD, DrPH Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Graham A. Colditz Shi-Yi Wang 7 Yale School of Public Health, Yale University ; New Haven, CT MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Su-Hsin Chang 1 Research Service, St. Louis Veterans Affairs Medical Center ; St. Louis, MO 3 Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine ; St. Louis, MO PhD, SM Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: chang.su-hsin{at}wustl.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF ABSTRACT Background Multiple myeloma (MM), the most common plasma cell dyscrasia in the U.S., is preceded by an asymptomatic precursor monoclonal gammopathy of undetermined significance (MGUS). Although several risk factors for MGUS progression are known, their relative contributions remain unclear. Unlike other malignancies, such evidence is lacking for MM despite its high burden. Objective To quantify contributions of modifiable risk factors to MGUS progression to MM to inform prevention. Design Retrospective cohort study. Setting Nationwide U.S. Veterans Health Administration (VHA). Participants Patients with MGUS (IgG, IgA, or light chain) diagnosed from 10/1/1999-12/31/2023. Interventions Modifiable risk factors including excess body mass index (BMI), chemical exposure, and comorbidities. Measurements Excess body mass index was defined as BMI ≥25 kg/m², chemical exposure was measured by prior exposure to Agent Orange, comorbidities were summarized using Charlson Comorbidity Index. Multivariable-adjusted population attributable fractions (aPAF) was calculated for each modifiable risk factor. The aPAF estimates the proportion of progression in patients diagnosed with MGUS that could have been prevented, if a given risk factor were absent. Results The cohort included 35,073 MGUS patients (33,670 [96.0%] male and 23,218 [66.2%] White), of whom 2,895 (8.3%) progressed to MM. Median age at MGUS diagnosis was 71.8 (IQR: 64.4-78.6) years. Among all evaluated risk factors, excess BMI was the leading factor (Black: aPAF=27.1%, 95% CI 19.5-34.0%; White: 27.2%, 95% CI 20.3-33.4%; All: aPAF=27.1%, 95% CI: 22.1-31.9%). Limitations Potential residual confounding, limited generalizability beyond the VHA population. Conclusion Our study highlights the potential for weight management as a key strategy in reducing the risk of progression to MM in Black and White patients diagnosed with MGUS. Primary Funding Source National Institutes of Health. INTRODUCTION Multiple myeloma (MM) is the most prevalent plasma cell malignancy, comprising ∼10% of hematologic cancers. 1 , 2 In 2024, there were 35,780 new diagnoses and 12,540 deaths in the U.S. 3 MM develops from monoclonal gammopathy of undetermined significance (MGUS), a pre-malignant condition present in 3-10% of individuals ≥50 years and twice as common in those of African descent. 4 – 7 MGUS carries a 1% annual progression risk to MM or other lymphoproliferative disorders. 5 , 8 MM remains incurable, with a 5-year survival rate of 61.1% (2014-2020) 3 and imposes a significant financial burden. 9 , 10 Identifying and quantifying modifiable risk factors for MGUS progression is crucial for risk stratification and targeted interventions. 11 , 12 Prior studies have identified potential modifiable risk factors for MM, including chemical exposure, 13 – 17 cumulative exposure to overweight/obesity, 18 and comorbidities. 19 – 26 However, their contribution to MM burden remains poorly understood. Addressing this gap is essential for efficient healthcare resource allocation and improved MGUS follow-up. MM prevention is further complicated by established MM health disparities, where Black individuals bear a higher burden of MM, compared to their White counterparts. 3 , 26 – 31 Understanding the contribution of modifiable risk factors to MM disparities could inform targeted interventions to reduce racial disparities. We conducted a population-based study to estimate multivariable-adjusted population attributable fractions (aPAFs) overall and by race in the Veteran population. Evidence on risk factors with the highest population attributable risks were identified in other malignancies; 32 – 38 however, as one of the highest burdensome cancers, MM is lacking such evidence. By quantifying the contribution of each modifiable risk factor, we identify key intervention targets to reduce MM burden. METHODS Data and Study Population Data from the nationwide U.S. Veterans Health Administration (VHA) were used. We selected the Veteran population due to their distinct demographic characteristics, including older age, predominantly male composition, and higher prevalence of Agent Orange (AO) exposure. These factors are associated with an increased risk of MM and progression of MGUS to MM, placing this group at a heightened risk compared to the general population. Chemical exposure, a crucial modifiable risk factor typically assessed through occupational history, is often poorly documented in most databases, including VHA. However, the VHA database provides comprehensive records of AO exposure. AO, an herbicide used during the Vietnam War Era, is contaminated with 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), a highly toxic compound. TCDD remains a prevalent occupational hazard due to its prolonged half-life of ∼3.2 years. 39 – 43 Given the well-documented AO exposure in the VHA database, it serves as a reliable proxy for chemical exposure. The study was approved by Institutional Review Boards at both Veterans Affairs Saint Louis Healthcare System and Washington University School of Medicine. Analytic Cohort We confirmed 50,096 Black/White patients diagnosed with MGUS from 10/1/1999-12/31/2023 with immunoglobulin (Ig) subtype data (e Figure 1 ). MGUS and MM diagnosis and the corresponding diagnosis date were determined by validated natural language processing (NLP) models. 44 These models utilized clinical, laboratory, medication, and pathologic data to extract evidence for MGUS and MM achieving 93% accuracy for MGUS and 99% for MM, 44 , 45 as compared to only 20% and 58% using billing codes alone. 44 , 46 Download figure Open in new tab Figure 1. Cumulative incidence of progression to MM among 35,073 MGUS patients for each modifiable risk factor Self-reported race was ascertained and recoded as Black/White following published guidelines. 47 Patients with non-IgG/IgA/light chain subtypes were excluded due to their infrequency and minimal progression risk (n=9,084). Patients progressing <6 months were excluded because they may have had MM at MGUS diagnosis (n=5,898). Finally, patients were excluded if documented date of death was on or before their MGUS diagnosis (n=41). Final analytic cohort consisted of 35,073 patients, among whom 33.8% were Black. Outcome Measure The outcome variable was the progression of MGUS to MM, if any, and the time to progression. MM diagnosis and date were confirmed by the aforementioned NLP model. 44 Risk Factors and Select Modifiable Risk Factors to highlight Multiple data domains were linked to obtain patient-level data on risk factors for progression of MGUS to MM selected based on published work, 6 , 11 , 26 , 48 clinical judgement, and data availability. These risk factors included older age, male sex, Black race, elevated body mass index (BMI), monoclonal (M-)spike level >1.5 g/dL, IgA subtype, comorbidities (summarized by Charlson Comorbidity Index, CCI 49 – 51 ), and chemical exposure (measured by AO exposure) 42 . Among these risk factors, BMI, chemical exposure, and CCI are potentially modifiable. BMI was calculated based on the most frequently reported height and weight ≤1 year prior and closest to MGUS diagnosis. Excess BMI was defined as BMI ≥25 kg/m 2 . 48 Chemical exposure was determined by whether the patient met all of the following criteria: documented AO exposure, AO location designated as Vietnam, and ever served in the Army, Air Force, Navy, or Marine Corps during the Vietnam War Era. 42 CCI was calculated based on comorbidities present ≤1 year before MGUS diagnosis. 50 , 51 Statistical Analysis Patient characteristics Patient characteristics, stratified by progression status were summarized as proportions for categorical variables and medians (interquartile ranges, IQRs) for continuous variables. Statistical significance for differences across groups was assessed using chi-square tests for categorical variables and Kruskal-Wallis tests for continuous variables. Competing risk analyses Death without progression to MM was considered as a competing event. For each of the studied modifiable risk factors, we plotted the cumulative incidence function (CIF) curves, stratified by individual categories of that risk factor. Gray’s test was performed to compare the cumulative incidence of progression between categories. Multivariable-adjusted hazard ratios (aHR) and 95% confidence intervals (CI) for each risk factor were estimated using Fine-Gray distribution hazard models. 52 Follow-up time was defined as time from MGUS to progression, death, or date of censoring (12/31/2023), whichever occurred first. The covariates in the multivariable models included: age, gender (male, female), race (White, Black), Ig subtype (IgG, IgA, light-chain), M-spike level (0, not quantifiable, ≤1.5, >1.5 g/dL, missing), BMI (non-excess 0). Ig subtype was extracted using a published NLP pipeline. 44 aPAF analyses For each target modifiable risk factor significantly associated with MGUS progression in the multivariable analysis, aPAF was calculated. The aPAF quantifies the proportion of progression events from MGUS to MM, that may have been prevented if the specific risk factor were absent in MGUS patients. It incorporates both the prevalence of each risk factor in the cohort and the adjusted relative risk for MM incidence associated with this risk factor during follow-up. It also accounts for competing risk of death to avoid overestimating aPAFs due to ignoring censoring due to death. 53 , 54 We estimated aPAFs for the following risk factor modifications: excess to non-excess BMI, chemical exposure to no chemical exposure, and CCI>0 to CCI=0. Race-specific analyses To assess racial differences in the contribution of modifiable risk factors to the progression of MGUS to MM, the analytic cohort was stratified by race. Differences in aPAFs between the two racial groups were analyzed using two-sample z-tests. Sensitivity analyses We repeated the analyses but replacing excess BMI with BMI classification: underweight <18, normal weight 18-<25, overweight 25-<30, obese ≥30, and missing. We calculated aPAFs for the following modifications: overweight to normal weight, obese to normal weight, chemical exposure to no chemical exposure, and no comorbidities to at least one comorbidity. All tests were two-sided. Statistical significance was assessed at the level of 0.05. Data queries were performed using SQL Server Management Studio 18 (Microsoft, Redmond, WA). Analyses were performed using SAS 9.3 (SAS Institute Inc., Cary, NC). Role of the Funding Source The funders had no role in the design or conduct of the study, collection or analysis of the data, preparation of the manuscript, or the decision to publish the manuscript. RESULTS The analytic cohort included 35,073 MGUS patients, of whom 2,895 (8.3%) progressed to MM, and 15,541 (44.3%) died without progression. Median follow-up was 3.8 (IQR: 1.5-7.3) years. The median age of MGUS diagnosis was 71.8 (IQR: 64.4-78.6) years ( Table 1 ). The cohort was predominantly male (96.0%) and White (66.2%), with 73.1% having excess BMI. Compared to Veterans who died without progression or were censored, Veterans who progressed to MM were younger at MGUS diagnosis (median: 67.6 (progression) versus 75.0 (died) versus 69.9 (censored) years, p<0.0001), had shorter follow-up (median: 2.3 versus 3.7 versus 4.3 years, p<0.0001), were more likely to be Black (39.1% versus 29.3% versus 37.0%, p<0.0001), have abnormal M-spike level at study entry (24.4% versus 8.4% versus 4.2%, p<0.0001), and have IgA subtype (17.7% versus 11.9% versus 11.5%, p<0.0001). View this table: View inline View popup Table 1. Characteristics by progression status in Veterans diagnosed with MGUS in the VHA from 10/1/1999 to 12/31/2023. Patients across racial groups were statistically significantly different in all variables (eTable 1). Competing risk analyses The cumulative incidence of progression varied significantly across categories of each modifiable risk factor ( Figure 1A-C ). Notably, the cumulative incidence of progression was significantly higher for patients who had excess BMI (p<0.0001), chemical exposure (p0 (p=0.0009). Multivariable analyses demonstrated significant associations with the progression of MGUS to MM for all included modifiable factors ( Table 2 ): excess BMI (aHR=1.24, 95% CI=1.13-1.36), chemical exposure (aHR=1.29, 95% CI=1.15-1.45), and comorbidities (aHR=1.34, 95% CI=1.20-1.49). View this table: View inline View popup Download powerpoint Table 2. Overall and race-specific multivariable-adjusted hazard ratios for the progression of MGUS to MM. aPAF analyses Excess BMI was responsible for 27.1% (95% CI=22.1-31.9%) of MM cases in the VHA ( Figure 2A ). Absence of AO exposure may have reduced the MM burden by 3.9% (95% CI=2.5-5.2%). If all patients had no comorbidities, the MM burden may have been 8.0% higher, although not statistically significant (95% CI=-16.7-0.1%). If all patients had ≥1comorbidity (reverse comorbidity from CCI=0 to >0), the MM burden may have been 0.9% lower (95% CI=-0.8-2.6%). See eTable 2 for all risk factors. Age of MGUS diagnosis decreased by one-year, male gender, black race, and IgA subtype accounted for 5.1% (95% CI=4.9-5.4%), 15.1% (95% CI=-2.1-29.4%), 5.2% (95% CI=2.5-7.8%), and 5.2% (95% CI=3.9-6.6%) of MM cases, respectively. Download figure Open in new tab Figure 2. Multivariable-adjusted population attributable fractions for selected modifiable risk factors of the progression of MGUS to MM Race-specific analyses Excess BMI was associated with an elevated progression risk in both White (aHR=1.20, 95% CI=1.07-1.35) and Black patients (aHR=1.30, 95% CI=1.13-1.50) ( Table 2 ). Having comorbidities was associated with a higher risk of MGUS progression in both groups (aHR White =1.37, 95% CI=1.20-1.57; aHR Black =1.28, 95% CI=1.07-1.52). Chemical exposure was significantly associated with a higher progression risk only for White patients (aHR White =1.38, 95% CI=1.20-1.59; aHR Black =1.09, 95% CI=0.89-1.34). Excess BMI emerged as the top modifiable risk factor across both racial groups ( Figure 3A and eTable 2, aPAF White =27.2%, 95% CI=20.3-33.4%; aPAF Black =27.1%, 95% CI=19.5-34.0%; p-difference=0.9806,). The contribution of chemical exposure was greater for White patients (aPAF White =5.6%, 95% CI=3.6-7.6%; aPAF Black =1.7%, 95% CI=0.1-3.3%; p-difference=0.0027). Changing CCI from >0 to 0 increased in MM cases 9.7% among White patients (95% CI=-21.0-0.6%) and 5.8% among Black patients (95% CI=-19.6-6.5%) (p-difference=0.4865). See eTable 2 for race-specific aPAFs for all risk factors. Download figure Open in new tab Figure 3. Race-specific multivariable-adjusted population attributable fractions for selected modifiable risk factors of the progression of MGUS to MM Sensitivity Analyses Obesity and overweight were the top two modifiable risk factors ( Figure 2B , aPAF obesity =13.3%, 95% CI=9.9-16.5%; aPAF overweight =10.1%, 95% CI=7.3-12.7%). The burden of MM cases attributable to obesity was higher for White patients (aPAF White =14.4%, 95% CI=9.9-18.6%), compared to Black patients (aPAF Black =12.0%, 95% CI=6.9-16.8%) ( Figure 3B ). The proportion of MM cases attributable to overweight was lower for White than Black patients (aPAF White =9.7%, 95% CI=6.0-13.2%; aPAF Black =10.7%, 95% CI=6.6-14.7%). DISCUSSION To our knowledge, this is the first study to quantify the burden of MGUS progression attributable to each identified risk factor. In this national cohort of Veterans, excess BMI was the most significant contributor to the progression of MGUS to MM, accounting for 27.1% of the progression cases, consistently observed across both racial groups: White 27.2%; Black 27.1%. Our study highlights the crucial role of obesity in MGUS progression, supported by prior research indicating that obesity promotes the progression through several biological mechanisms. 7 , 19 , 55 – 57 Obesity is linked to elevated levels of inflammatory cytokines, including TNF-α, CRP, and IL-6, which support MM cell survival and proliferation. 55 , 58 , 59 Weight loss has been shown to significantly reduce these cytokines. 60 – 62 Additionally, obesity disrupts adipokine balance, increasing leptin and decreasing adiponectin, further influencing MGUS progression. 63 , 64 Given its strong contribution to risk and modifiability, weight management may mitigate the progression of MGUS to MM. Comorbidities, including type-2 diabetes mellitus and other obesity-related diseases (e.g., hypertension, cardiovascular disease) were linked to increased progression risk based on CIF and multivariable analysis. However, the aPAF for modifying comorbidity was not statistically significant. Both progression and death were considered for estimating the aPAFs for progression. A higher CCI is associated with both outcomes, and reducing CCI from >0 to 0 may reduce more deaths than progression events, potentially increasing observed progression cases. This relationship may be further complicated by shared metabolic and inflammatory mechanisms with obesity. Our study also quantified the contribution of chemical herbicide exposure, which accounted for 3.9% of MM cases. Previous studies have identified chemical exposure, possibly due TCDD contamination, as a risk factor for MGUS and its progression. 39 – 43 TCDD is common in various occupational pesticides. The VHA data provide a unique opportunity to study the impact of chemical exposure via the exposure of AO, offering insights into the long-term effects of environmental and occupational chemical exposures and informing policies for exposure prevention and management. Similar studies have found that occupational pesticide exposure is associated with higher MGUS and MM risk. 13 – 17 Therefore, our findings underscore the importance of considering environmental exposures in MM prevention, particularly for MGUS patients at high risk due to occupational or environmental TCDD exposure. We found that younger age at MGUS diagnosis was associated with a higher risk of progression to MM, aligning with previous studies. 42 , 65 Similar to comorbidities, this negative association may stem from older age being strongly associated with higher all-cause mortality risk. Older individuals with shorter lifespans post-MGUS diagnosis may not live long enough for MGUS to progress to MM, 8 resulting in a negative association with progression risk. Our data also showed that patients diagnosed with MGUS at an older age (≥ median age of 71.8 years) had a shorter average follow-up duration (3.9 years), compared to younger patients (<71.8 years) who had 6.1 years. This difference may be attributed to higher mortality rates in the older population (54.4%) versus the younger cohort (34.2%). Therefore, it is crucial to investigate whether shorter follow-up times or comorbidities in older patients influence the progression of MGUS to MM. While previous research has indicated increased risk of MGUS and MM among Black compared to White populations, 26 , 28 , 31 our race-specific analyses showed larger aPAFs for selected modifiable risk factors in White compared to Black individuals. These disparities may be attributed to socio-contextual factors, potentially linked to underlying health inequities. 66 , 67 Excess BMI emerged as a prominent modifiable risk factor for MGUS progression in both racial groups (aPAF White : 27.2%; aPAF Black : 27.1%). Although this difference was not statistically significant, addressing excess BMI as a modifiable risk factor could substantially reduce MM burden in both racial groups. We found that the contribution of chemical exposure was more pronounced in White compared to Black individuals, and no statistically significant racial difference existed in the contribution of modifying comorbidities from 0 to ≥1. In our cohort, the prevalence of chemical exposure was slightly higher in White than in Black individuals. Multivariable competing-event analyses showed that the impact of chemical exposure was greater in White compared to Black individuals. These findings warrant further studies on racial differences in the impact of chemical exposure on MGUS progression and development of equitable, population-specific intervention strategies. In addition to the analyzed modifiable risk factors, factors like family history/genetics, might contribute to the observed racial differences in MM progression, 67 necessitating further research to elucidate the underlying mechanisms of these findings and to explore potential contributing factors. Ultimately, these results suggest the need to develop tailored interventions for specific subpopulations. 6 , 31 , 67 , 68 Our study has several strengths. First, we used a large national Veterans cohort with NLP-confirmed MGUS/MM diagnoses, improving diagnosis accuracy from 20% to 93% for MGUS and from 58% to 99% for MM compared to administrative codes alone. 44 – 46 Second, we leveraged the well-documented chemical exposure data of the Veterans population to investigate the association of chemical exposure on MGUS progression at the population level. The detailed VHA data allowed a more precise assessment of this usually hard-to-capture risk factor. Third, race-specific aPAFs analysis explored potential racial differences in the contribution of modifiable risk factors to MGUS progression. Identifying higher burden racial group helps in planning targeted prevention strategies to reduce health disparities. As racial disparities in MM are long-established, examining the contribution of modifiable risk factors separately for White and Black populations provides insight into any differential contribution of these factors on MM development, potentially identifying interventions to reduce MM health disparities. Fourth, the application of a comprehensive aPAF method that accounts for the competing risk of death addresses a crucial limitation in studies examining disease progression; specifically, MGUS/MM predominantly affect older adults who face a higher risk of dying from causes unrelated to the disease of interest before progressing to the disease outcome. This enhances the validity of our findings by minimizing the possibility of overestimating aPAF. Our study has limitations. First, risk factors like family history, 11 , 68 , 69 smoking status, 11 , 70 and dietary intake 71 are not recorded as structured data in the VHA. Further data abstraction or development of NLP algorithms is needed to retrieve these data. Second, the male-dominant Veterans population limits the generalizability of our findings to female populations. However, the insights gained from this population can inform future investigation in more diverse cohorts. Third, the retrospective design precludes establishing causality between risk factors and MGUS progression. Fourth, despite using a validated NLP model to confirm MGUS and MM diagnoses, misclassification cannot be completely ruled out. However, we do not anticipate that the misclassification is high as our study shows that the NLP model achieved high accuracy (MGUS: 93%; MM: 99%). Last, the interpretation of aPAFs should focus on the contribution of these risk factors to the progression of MGUS to MM rather than the practical intervention impact, as modifying these risk factors in practice may be complex. Despite these limitations, our study offers insights into the relative contributions of key risk factors in MGUS progression to MM among Veterans. CONCLUSION Our study quantifies the relative contribution of modifiable risk factors to the progression of MGUS to MM among Veterans. Among the variables studied, our results indicate that excess BMI is the top contributor. As one of the few modifiable risk factors identified, these results underscore the potential impact of targeted interventions like lifestyle modifications and weight management on reducing MGUS progression. Further research is needed to validate these findings across different populations and to identify additional risk factors to inform MM prevention. Conflict of Interest All of the authors of this manuscript do not have any conflict of interest to report. Data Sharing Statement The data obtained from the VHA for the analyses in this study contain patient health information, including Social Security numbers and addresses. The institutional review board (IRB)–approved study protocol allows us to share analysis results only in an aggregate format. We also have the following policies in place that do not allow us to share the data: In our original IRB application under “Data Management and Access Plan”: Will final data sets underlying the publications be shared outside the US Department of Veterans Affairs (VA) No. The final data sets contain patient health information and will not be shared outside of the VA. In our data use agreement with VA Information Resource Center for use of VA and Centers for Medicare & Medicaid Services (CMS) data: Data security : VA/CMS data will be protected in accordance with the project’s VA/CMS Data Security Compliance Form and may be stored and used only on VA Office of Information and Technology–managed network servers located at: VA Facility: VA Austin Information Technology Center, Austin, Texas VA Informatics and Computing Infrastructure workspace: ORD_Chang_202011003D In our data access permissions: Access to these data will be restricted to only VA-approved project employees whose roles and responsibilities necessitate access to these data (hereinafter referred to as authorized data users) and who have signed and submitted a VA/CMS Rules of Behavior Agreement. Data Availability The data obtained from the VHA for the analyses in this study contain patient health information, including Social Security numbers and addresses. The institutional review board (IRB) approved study protocol allows us to share analysis results only in an aggregate format. Access to these data will be restricted to only VA-approved project employees whose roles and responsibilities necessitate access to these data (hereinafter referred to as authorized data users) and who have signed and submitted a VA/CMS Rules of Behavior Agreement. Footnotes ↵ † Co-first authors. Funding: This work was supported by the Foundation for Barnes-Jewish Hospital; the Siteman Cancer Center; the National Institutes of Health Grants R01 CA253475 and U01 CA265735. Recategorized covariate M-protein level into more detailed categories: 0, not quantifiable, ≤1.5 g/dL, >1.5 g/dL, and missing. REFERENCES 1. ↵ Rajkumar SV . Multiple myeloma: 2022 update on diagnosis, risk stratification, and management . Am J Hematol . Aug 2022 ; 97 ( 8 ): 1086 – 1107 . doi: 10.1002/ajh.26590 OpenUrl CrossRef PubMed 2. ↵ Rajkumar SV , Dimopoulos MA , Palumbo A , et al. International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma . Lancet Oncol . Nov 2014 ; 15 ( 12 ): e538 – 48 . doi: 10.1016/S1470-2045(14)70442-5 OpenUrl CrossRef PubMed 3. ↵ SEER . National Cancer Institute: Surveillance, Epidemiology, and End Results Program, Cancer Stat Facts: Myeloma . Accessed July 8, 2024. https://seer.cancer.gov/statfacts/html/mulmy.html 4. ↵ El-Khoury H , Lee DJ , Alberge JB , et al. Prevalence of monoclonal gammopathies and clinical outcomes in a high-risk US population screened by mass spectrometry: a multicentre cohort study . Lancet Haematol . May 2022 ; 9 ( 5 ): e340 – e349 . doi: 10.1016/S2352-3026(22)00069-2 OpenUrl CrossRef 5. ↵ Kyle RA , Larson DR , Therneau TM , et al. Long-Term Follow-up of Monoclonal Gammopathy of Undetermined Significance . N Engl J Med. Jan 18 2018 ; 378 ( 3 ): 241 – 249 . doi: 10.1056/NEJMoa1709974 OpenUrl CrossRef PubMed 6. ↵ Landgren O , Gridley G , Turesson I , et al. Risk of monoclonal gammopathy of undetermined significance (MGUS) and subsequent multiple myeloma among African American and white veterans in the United States . Blood . Feb 1 2006 ; 107 ( 3 ): 904 – 6 . doi: 10.1182/blood-2005-08-3449 OpenUrl Abstract / FREE Full Text 7. ↵ Landgren O , Rajkumar SV , Pfeiffer RM , et al. Obesity is associated with an increased risk of monoclonal gammopathy of undetermined significance among black and white women . Blood . Aug 19 2010 ; 116 ( 7 ): 1056 – 9 . doi: 10.1182/blood-2010-01-262394 OpenUrl Abstract / FREE Full Text 8. ↵ Ji M , Huber JH , Schoen MW , et al. Mortality in the US Populations With Monoclonal Gammopathy of Undetermined Significance . JAMA Oncol . Sep 1 2023 ; 9 ( 9 ): 1293 – 1295 . doi: 10.1001/jamaoncol.2023.2278 OpenUrl CrossRef PubMed 9. ↵ Davis J , McGann M , Shockley A , Hashmi H . Idecabtagene vicleucel versus ciltacabtagene autoleucel: a Sophie’s choice for patients with relapsed refractory multiple myeloma . Expert Rev Hematol . Jun 2022 ; 15 ( 6 ): 473 – 475 . doi: 10.1080/17474086.2022.2081147 OpenUrl CrossRef PubMed 10. ↵ de Oliveira C , Pataky R , Bremner KE , et al. Phase-specific and lifetime costs of cancer care in Ontario , Canada. BMC Cancer. Oct 18 2016 ; 16 ( 1 ): 809 . doi: 10.1186/s12885-016-2835-7 OpenUrl CrossRef PubMed 11. ↵ Castaneda-Avila MA , Ulbricht CM , Epstein MM . Risk factors for monoclonal gammopathy of undetermined significance: a systematic review . Ann Hematol . Apr 2021 ; 100 ( 4 ): 855 – 863 . doi: 10.1007/s00277-021-04400-7 OpenUrl CrossRef PubMed 12. ↵ Mouhieddine TH , Weeks LD , Ghobrial IM . Monoclonal gammopathy of undetermined significance . Blood . Jun 6 2019 ; 133 ( 23 ): 2484 – 2494 . doi: 10.1182/blood.2019846782 OpenUrl Abstract / FREE Full Text 13. ↵ Baris D , Silverman DT , Brown LM , et al. Occupation, pesticide exposure and risk of multiple myeloma . Scand J Work Environ Health . Jun 2004 ; 30 ( 3 ): 215 – 22 . doi: 10.5271/sjweh.782 OpenUrl CrossRef PubMed Web of Science 14. Brown LM , Burmeister LF , Everett GD , Blair A . Pesticide exposures and multiple myeloma in Iowa men . Cancer Causes Control . Mar 1993 ; 4 ( 2 ): 153 – 6 . doi: 10.1007/BF00053156 OpenUrl CrossRef PubMed Web of Science 15. Khuder SA , Mutgi AB . Meta-analyses of multiple myeloma and farming . Am J Ind Med . Nov 1997 ; 32 ( 5 ): 510 – 6 . doi: 10.1002/(sici)1097-0274(199711)32:53.0.co;2-5 OpenUrl CrossRef PubMed Web of Science 16. Landgren O , Kyle RA , Hoppin JA , et al. Pesticide exposure and risk of monoclonal gammopathy of undetermined significance in the Agricultural Health Study . Blood. Jun 18 2009 ; 113 ( 25 ): 6386 – 91 . doi: 10.1182/blood-2009-02-203471 OpenUrl Abstract / FREE Full Text 17. ↵ Packard E , Shahid Z , Groff A , Patel R , Jain R . Multiple Myeloma in an Agricultural Worker Exposed to Pesticides . Cureus . May 28 2019 ; 11 ( 5 ): e4762 . doi: 10.7759/cureus.4762 OpenUrl CrossRef 18. ↵ Liu L , Grandhi N , Wang M , et al. Cumulative Excess Body Mass Index and MGUS Progression to Myeloma . JAMA Netw Open . 2024 ;( e2458585 ) doi: 10.1001/jamanetworkopen.2024.58585 (In production) OpenUrl CrossRef 19. ↵ Alexander DD , Mink PJ , Adami HO , et al. Multiple myeloma: a review of the epidemiologic literature . Int J Cancer . 2007 ; 120 Suppl 12 : 40 – 61 . doi: 10.1002/ijc.22718 OpenUrl CrossRef PubMed 20. Birmann BM , Giovannucci EL , Rosner BA , Colditz GA . Regular aspirin use and risk of multiple myeloma: a prospective analysis in the health professionals follow-up study and nurses’ health study . Cancer Prev Res (Phila ) . Jan 2014 ; 7 ( 1 ): 33 – 41 . doi: 10.1158/1940-6207.CAPR-13-0224 OpenUrl Abstract / FREE Full Text 21. Kyle RA , Durie BG , Rajkumar SV , 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 . Jun 2010 ; 24 ( 6 ): 1121 – 7 . doi: 10.1038/leu.2010.60 OpenUrl CrossRef PubMed Web of Science 22. Pasqualetti P , Collacciani A , Casale R . Risk of monoclonal gammopathy of undetermined significance: a case-referent study . Am J Hematol . Jul 1996 ; 52 ( 3 ): 217 – 20 . doi: 10.1002/(SICI)1096-8652(199607)52:33.0.CO;2-C OpenUrl CrossRef PubMed Web of Science 23. Perez-Persona E , Vidriales MB , Mateo G , et al. New criteria to identify risk of progression in monoclonal gammopathy of uncertain significance and smoldering multiple myeloma based on multiparameter flow cytometry analysis of bone marrow plasma cells . Blood . Oct 1 2007 ; 110 ( 7 ): 2586 – 92 . doi: 10.1182/blood-2007-05-088443 OpenUrl Abstract / FREE Full Text 24. Rajkumar SV , Kyle RA , Therneau TM , et al. Serum free light chain ratio is an independent risk factor for progression in monoclonal gammopathy of undetermined significance . Blood . Aug 1 2005 ; 106 ( 3 ): 812 – 7 . doi: 10.1182/blood-2005-03-1038 OpenUrl Abstract / FREE Full Text 25. Wadhera RK , Rajkumar SV . Prevalence of monoclonal gammopathy of undetermined significance: a systematic review . Mayo Clin Proc . Oct 2010 ; 85 ( 10 ): 933 – 42 . doi: 10.4065/mcp.2010.0337 OpenUrl CrossRef PubMed Web of Science 26. ↵ Chang SH , Luo S , Thomas TS , et al. Obesity and the Transformation of Monoclonal Gammopathy of Undetermined Significance to Multiple Myeloma: A Population-Based Cohort Study . J Natl Cancer Inst . May 2017 ; 109 (5) doi: 10.1093/jnci/djw264 OpenUrl CrossRef PubMed 27. Landgren O , Graubard BI , Katzmann JA , et al. Racial disparities in the prevalence of monoclonal gammopathies: a population-based study of 12,482 persons from the National Health and Nutritional Examination Survey . Leukemia . Jul 2014 ; 28 ( 7 ): 1537 – 42 . doi: 10.1038/leu.2014.34 OpenUrl CrossRef PubMed 28. ↵ Huber JH , Ji M , Shih YH , Wang M , Colditz G , Chang SH . Disentangling age, gender, and racial/ethnic disparities in multiple myeloma burden: a modeling study . Nat Commun . Sep 20 2023 ; 14 ( 1 ): 5768 . doi: 10.1038/s41467-023-41223-8 OpenUrl CrossRef PubMed 29. Ji M , Shih YH , Huber JH , et al. Asymptomatic Incidence of Monoclonal Gammopathy of Undetermined Significance and Preclinical Duration to Myeloma Diagnosis: A Modeling Study . Cancer Epidemiol Biomarkers Prev . Aug 6 2024 ; doi: 10.1158/1055-9965.EPI-24-0490 OpenUrl CrossRef 30. Waters EA , Colditz GA , Davis KL . Essentialism and Exclusion: Racism in Cancer Risk Prediction Models . J Natl Cancer Inst . Nov 29 2021 ; 113 ( 12 ): 1620 – 1624 . doi: 10.1093/jnci/djab074 OpenUrl CrossRef PubMed 31. ↵ Waxman AJ , Mink PJ , Devesa SS , et al. Racial disparities in incidence and outcome in multiple myeloma: a population-based study . Blood. Dec 16 2010 ; 116 ( 25 ): 5501 – 6 . doi: 10.1182/blood-2010-07-298760 OpenUrl Abstract / FREE Full Text 32. ↵ Chelmow D , Brooks R , Cavens A , et al. Executive Summary of the Uterine Cancer Evidence Review Conference . Obstet Gynecol . Apr 1 2022 ; 139 ( 4 ): 626 – 643 . doi: 10.1097/AOG.0000000000004711 OpenUrl CrossRef PubMed 33. Cumberbatch MGK , Noon AP . Epidemiology, aetiology and screening of bladder cancer . Transl Androl Urol . Feb 2019 ; 8 ( 1 ): 5 – 11 . doi: 10.21037/tau.2018.09.11 OpenUrl CrossRef PubMed 34. Engmann NJ . Errors in Statistical Programming for Study About Population-Attributable Risk Proportion of Clinical Risk Factors for Breast Cancer . JAMA Oncol . Nov 1 2019 ; 5 ( 11 ): 1637 – 1638 . doi: 10.1001/jamaoncol.2019.4355 OpenUrl CrossRef PubMed 35. Goon S , Kim H , Giovannucci EL . Population attributable risk for colorectal and breast cancer in England, Wales, Scotland, Northern Ireland, and the United Kingdom . AMRC Open Res . 2021 ; 3 : 11 . doi: 10.12688/amrcopenres.12980.2 OpenUrl CrossRef PubMed 36. Leitzmann MF , Rohrmann S . Risk factors for the onset of prostatic cancer: age, location, and behavioral correlates . Clin Epidemiol . 2012 ; 4 : 1 – 11 . doi: 10.2147/CLEP.S16747 OpenUrl CrossRef PubMed 37. Shield KD , Parkin DM , Whiteman DC , et al. Population Attributable and Preventable Fractions: Cancer Risk Factor Surveillance, and Cancer Policy Projection . Curr Epidemiol Rep . Sep 2016 ; 3 ( 3 ): 201 – 211 . doi: 10.1007/s40471-016-0085-5 OpenUrl CrossRef PubMed 38. ↵ Zali H , Rezaei-Tavirani M , Azodi M . Gastric cancer: prevention, risk factors and treatment . Gastroenterol Hepatol Bed Bench . Fall 2011 ; 4 ( 4 ): 175 – 85 . OpenUrl PubMed 39. ↵ Bumma N , Nagasaka M , Hemingway G , et al. Effect of Exposure to Agent Orange on the Risk of Monoclonal Gammopathy and Subsequent Transformation to Multiple Myeloma: A Single-Center Experience From the Veterans Affairs Hospital, Detroit . Clin Lymphoma Myeloma Leuk . May 2020 ; 20 ( 5 ): 305 – 311 . doi: 10.1016/j.clml.2019.11.014 OpenUrl CrossRef PubMed 40. Kort J , O’Brien T . FISH abnormalities in Agent Orange–associated multiple myeloma . Journal of Clinical Oncology . 2023 ; 41 ( 16_suppl ): e20056 - e20056 . doi: 10.1200/JCO.2023.41.16_suppl.e20056 OpenUrl CrossRef 41. Landgren O , Shim YK , Michalek J , et al. Agent Orange Exposure and Monoclonal Gammopathy of Undetermined Significance: An Operation Ranch Hand Veteran Cohort Study . JAMA Oncol . Nov 2015 ; 1 ( 8 ): 1061 – 8 . doi: 10.1001/jamaoncol.2015.2938 OpenUrl CrossRef PubMed 42. ↵ Liu LW , Wang M , Grandhi N , et al. The Association of Agent Orange Exposure with the progression of monoclonal gammopathy of undetermined significance to multiple myeloma: a population-based study of Vietnam War Era Veterans . J Hematol Oncol . Jan 8 2024 ; 17 ( 1 ): 3 . doi: 10.1186/s13045-023-01521-6 OpenUrl CrossRef PubMed 43. ↵ Wang L , Kumar M , Deng Q , et al. 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) induces peripheral blood abnormalities and plasma cell neoplasms resembling multiple myeloma in mice . Cancer Lett . Jan 2019 ; 440-441 : 135 – 144 . doi: 10.1016/j.canlet.2018.10.009 OpenUrl CrossRef 44. ↵ Wang M , Yu YC , Liu L , et al. Natural Language Processing-Assisted Classification Models to Confirm Monoclonal Gammopathy of Undetermined Significance and Progression in Veterans’ Electronic Health Records . JCO Clin Cancer Inform . Sep 2023 ; 7 : e2300081 . doi: 10.1200/cci.23.00081 OpenUrl CrossRef 45. ↵ La J , DuMontier C , Hassan H , et al. Validation of algorithms to select patients with multiple myeloma and patients initiating myeloma treatment in the national Veterans Affairs Healthcare System . Pharmacoepidemiol Drug Saf . May 2023 ; 32 ( 5 ): 558 – 566 . doi: 10.1002/pds.5579 OpenUrl CrossRef PubMed 46. ↵ Wang M , Yu Y-C , Liu L , et al. Natural language processing of Veterans’ electronic health records to confirm diagnoses of monoclonal gammopathy of undetermined significance . Journal of Clinical Oncology . 2022 ; 40 (16_suppl): 1557 – 1557 . doi: 10.1200/JCO.2022.40.16_suppl.1557 OpenUrl CrossRef 47. ↵ Document VOHDQPDQG. Best Practices Guide – Race Data . Accessed July 8, 2024. http://vaww.vhadataquality.va.gov/index.php?option=com_phocadownload&view=category&id=18&Itemid=474&lang=en 48. ↵ Liu L , Grandhi N , Wang M , et al. Cumulative Excess Body Mass Index and MGUS Progression to Myeloma . JAMA Netw Open . Feb 3 2025 ; 8 ( 2 ): e2458585 . doi: 10.1001/jamanetworkopen.2024.58585 OpenUrl CrossRef 49. ↵ Charlson ME , Pompei P , Ales KL , MacKenzie CR . A new method of classifying prognostic comorbidity in longitudinal studies: development and validation . J Chronic Dis . 1987 ; 40 ( 5 ): 373 – 83 . doi: 10.1016/0021-9681(87)90171-8 OpenUrl CrossRef PubMed Web of Science 50. ↵ Deyo RA , Cherkin DC , Ciol MA . Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases . J Clin Epidemiol . Jun 1992 ; 45 ( 6 ): 613 – 9 . doi: 10.1016/0895-4356(92)90133-8 OpenUrl CrossRef PubMed Web of Science 51. ↵ Romano PS , Roos LL , Jollis JG . Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives. Research Support, Non-U.S. Gov’t Research Support, U.S. Gov’t, P.H.S . Journal of clinical epidemiology . Oct 1993 ; 46 ( 10 ): 1075 – 9 ; discussion 1081-90. OpenUrl CrossRef PubMed Web of Science 52. ↵ Fine JP , Gray RJ . A proportional hazards model for the subdistribution of a competing risk . J Am Stat Assoc . Jun 1999 ; 94 ( 446 ): 496 – 509 . doi : Doi 10.2307/2670170 OpenUrl CrossRef Web of Science 53. ↵ Laaksonen MA , Harkanen T , Knekt P , Virtala E , Oja H . Estimation of population attributable fraction (PAF) for disease occurrence in a cohort study design . Stat Med . Mar 30 2010 ; 29 ( 7-8 ): 860 – 74 . doi: 10.1002/sim.3792 OpenUrl CrossRef PubMed 54. ↵ Laaksonen MA , Virtala E , Knekt P , Oja H , Härkänen T . SAS Macros for Calculation of Population Attributable Fraction in a Cohort Study Design . J Stat Softw . Jul 2011 ; 43 ( 7 ): 1 – 25 . OpenUrl CrossRef PubMed 55. ↵ Argiles JM , Busquets S , Toledo M , Lopez-Soriano FJ . The role of cytokines in cancer cachexia . Curr Opin Support Palliat Care . Dec 2009 ; 3 ( 4 ): 263 – 8 . doi: 10.1097/SPC.0b013e3283311d09 OpenUrl CrossRef PubMed 56. Cammisotto PG , Bendayan M . Adiponectin stimulates phosphorylation of AMP-activated protein kinase alpha in renal glomeruli . J Mol Histol . Dec 2008 ; 39 ( 6 ): 579 – 84 . doi: 10.1007/s10735-008-9198-6 OpenUrl CrossRef PubMed Web of Science 57. ↵ Thordardottir M , Lindqvist EK , Lund SH , et al. Obesity and risk of monoclonal gammopathy of undetermined significance and progression to multiple myeloma: a population-based study . Blood Adv . Nov 14 2017 ; 1 ( 24 ): 2186 – 2192 . doi: 10.1182/bloodadvances.2017007609 OpenUrl Abstract / FREE Full Text 58. ↵ Hideshima T , Mitsiades C , Tonon G , Richardson PG , Anderson KC . Understanding multiple myeloma pathogenesis in the bone marrow to identify new therapeutic targets . Nat Rev Cancer . Aug 2007 ; 7 ( 8 ): 585 – 98 . doi: 10.1038/nrc2189 OpenUrl CrossRef PubMed Web of Science 59. ↵ Khanna D , Khanna S , Khanna P , Kahar P , Patel BM . Obesity: A Chronic Low-Grade Inflammation and Its Markers . Cureus . Feb 2022 ; 14 ( 2 ): e22711 . doi: 10.7759/cureus.22711 OpenUrl CrossRef 60. ↵ Bianchi VE . Weight loss is a critical factor to reduce inflammation . Clin Nutr ESPEN . Dec 2018 ; 28 : 21 – 35 . doi: 10.1016/j.clnesp.2018.08.007 OpenUrl CrossRef PubMed 61. Phillips CL , Grayson BE . The immune remodel: Weight loss-mediated inflammatory changes to obesity . Exp Biol Med (Maywood ) . Jan 2020 ; 245 ( 2 ): 109 – 121 . doi: 10.1177/1535370219900185 OpenUrl CrossRef PubMed 62. ↵ Ziccardi P , Nappo F , Giugliano G , et al. Reduction of inflammatory cytokine concentrations and improvement of endothelial functions in obese women after weight loss over one year . Circulation . Feb 19 2002 ; 105 ( 7 ): 804 – 9 . doi: 10.1161/hc0702.104279 OpenUrl Abstract / FREE Full Text 63. ↵ Tie W , Ma T , Yi Z , et al. Obesity as a risk factor for multiple myeloma: insight on the role of adipokines . Pathol Oncol Res . 2023 ; 29 : 1611338 . doi: 10.3389/pore.2023.1611338 OpenUrl CrossRef PubMed 64. ↵ Zorena K , Jachimowicz-Duda O , Slezak D , Robakowska M , Mrugacz M. Adipokines and Obesity. Potential Link to Metabolic Disorders and Chronic Complications . Int J Mol Sci . May 18 2020 ; 21 (10) doi: 10.3390/ijms21103570 OpenUrl CrossRef 65. ↵ Chang SH , Gumbel J , Luo S , et al. Post-MGUS Diagnosis Serum Monoclonal-Protein Velocity and the Progression of Monoclonal Gammopathy of Undetermined Significance to Multiple Myeloma . Cancer Epidemiol Biomarkers Prev . Dec 2019 ; 28 ( 12 ): 2055 – 2061 . doi: 10.1158/1055-9965.EPI-19-0132 OpenUrl Abstract / FREE Full Text 66. ↵ Bunce CM , Drayson MT . Dissecting racial disparities in multiple myeloma-clues from differential immunoglobulin levels . Blood Cancer J . Apr 28 2020 ; 10 ( 4 ): 44 . doi: 10.1038/s41408-020-0314-5 OpenUrl CrossRef PubMed 67. ↵ Marinac CR , Ghobrial IM , Birmann BM , Soiffer J , Rebbeck TR . Dissecting racial disparities in multiple myeloma . Blood Cancer J. Feb 17 2020 ; 10 ( 2 ): 19 . doi: 10.1038/s41408-020-0284-7 OpenUrl CrossRef PubMed 68. ↵ Greenberg AJ , Rajkumar SV , Vachon CM . Familial monoclonal gammopathy of undetermined significance and multiple myeloma: epidemiology, risk factors, and biological characteristics . Blood . Jun 7 2012 ; 119 ( 23 ): 5359 – 66 . doi: 10.1182/blood-2011-11-387324 OpenUrl Abstract / FREE Full Text 69. ↵ Landgren O , Kristinsson SY , Goldin LR , et al. Risk of plasma cell and lymphoproliferative disorders among 14621 first-degree relatives of 4458 patients with monoclonal gammopathy of undetermined significance in Sweden . Blood . Jul 23 2009 ; 114 ( 4 ): 791 – 5 . doi: 10.1182/blood-2008-12-191676 OpenUrl Abstract / FREE Full Text 70. ↵ Boursi B , Weiss BM , Haynes K , Mamtani R , Yang YX . Reappraisal of risk factors for monoclonal gammopathy of undetermined significance . Am J Hematol . Jun 2016 ; 91 ( 6 ): 581 – 4 . doi: 10.1002/ajh.24355 OpenUrl CrossRef PubMed 71. ↵ Joseph JM , Hillengass J , Tang L , et al. Dietary risk factors for monoclonal gammopathy of undetermined significance in a racially diverse population . Blood Adv . Feb 13 2024 ; 8 ( 3 ): 538 – 548 . doi: 10.1182/bloodadvances.2023011608 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted June 28, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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