Dear Editor,
Monoclonal Gammopathy of Undetermined Significance (MGUS) is a clonal plasma-cell disorder characterised by the presence of a monoclonal protein without end-organ damage and is the earliest clinically detectable precursor to multiple myeloma (MM) [ 1 ]. MGUS is common in ageing populations, affecting 5% of adults over 50 years [ 2 , 3 ]. Although all MM is preceded by MGUS, the average annual risk of progression is only ~1% [ 4 , 5 ]. Progression endpoints differ by subtype: non-IgM MGUS typically progresses to MM, IgM MGUS often to lymphoplasmacytic lymphoma/Waldenström macroglobulinaemia, and light-chain MGUS to LC-myeloma or AL amyloidosis [ 6 ]. This long preclinical window makes risk communication and monitoring central to care.
Current International Myeloma Working Group (IMWG) guidance recommends periodic monitoring; however, prospective evidence demonstrating clinical benefit is limited and adherence in routine practice is variable [ 6 , 7 ]. Beyond its role as a precursor, MGUS is associated with a range of comorbidities collectively termed Monoclonal Gammopathy of Clinical Significance (MGCS), including renal impairment and neuropathy, which contribute to morbidity and mortality [ 6 , 8 ]. Importantly, most MGUS is detected incidentally during investigations for unrelated conditions, and up to 90% of patients who develop MM have no prior MGUS diagnosis [ 3 , 9 ]. This highlights missed opportunities for earlier risk stratification and more consistent follow-up, while cautioning against resource-intensive population screening with uncertain clinical yield.
The SECURE study was established to address these information gaps by investigating a UK cohort of incidentally detected MGUS. It aims to generate a real-world reference for MGUS epidemiology, monitoring practices, and progression risk, including MGUS evolution, that can inform risk-adapted monitoring. SECURE integrates routine clinical data with patient-reported outcomes and serial biospecimens (immunology, proteomics, metabolomics, genomics), providing a platform to discover and validate biomarkers linked to progression, lived experience, and service utilisation.
SECURE is a UK, prospective, multicentre, non-interventional observational cohort of incidentally detected MGUS across multiple NHS sites (ClinicalTrials.gov NCT05539079 ; REC 22/WA/0291). Recruitment began September 2022 with planned sample size of 2000 and follow-up of up to 60 months per participant.
Eligibility criteria include adults (≥18) with confirmed MGUS by IMWG criteria (non-IgM, IgM, and light-chain subtypes). Exclusions include inability to consent, a borderline FLC ratio (0.3–3.0) in the absence of a measurable monoclonal protein on serum electrophoresis or immunofixation, and evidence of active/progressive plasma-cell disorder at inclusion. Patients are identified in routine care by haematology teams/specialist nurses/MDT coordinators; written consent follows ICH-GCP/Declaration of Helsinki. Optional consent covers biospecimen storage and linkage to primary/secondary care data for health-economics analyses.
Data collection at baseline and annually includes demographics, comorbidities/medications, laboratory parameters (M-protein, isotype), clinically indicated imaging, and patient-reported outcomes (questionnaires: EORTC QLQ-C30, PHQ-9, GAD-7, HAI, IUS, PCL-5). Data are captured securely in OpenClinica with planned linkage to HES/CPRD. At each visit, 10 mL serum and 10 mL EDTA are banked under SOPs at the Botnar Research Centre (with distribution to OUH Immunology, PC-B, and ICR for immunology/proteomics/metabolomics/genetics).
The primary outcome is progression from MGUS to MM. Secondary outcomes include routes to diagnosis (primary vs secondary care), monitoring patterns, family history, MGCS incidence, quality of life and psychological burden, and healthcare utilisation. Exploratory endpoints focus on biochemical, proteomics, and genomic predictors, such as metabolite signatures (sphingosine-1-phosphate, acetylcarnitine), post-translational modifications of serum free light-chains, and ctDNA dynamics, linked to progression.
To contextualise representativeness, ethnic distribution was benchmarked to ONS 2021 Census (England and Wales), and BMI categories to ONS 2022 survey data. Risk stratification followed Mayo Clinic criteria (non-IgG isotype, M protein ≥ 15 g/L, abnormal FLC ratio), with pathological FLC defined per updated iStopMM recommendations [ 10 ]. For this Correspondence, analyses are descriptive.
As of April 2026, 1244 individuals were recruited from 35 NHS sites. 1133 met inclusion criteria for this interim analysis (Fig. 1A ). Median age was 71 years (range 25–95), and 53.0% were male (Table 1 ). Ethnicity was recorded for 1111 participants and plotted alongside ONS 2021 benchmarks (Fig. 1B ). BMI was available for 970 participants, with category distribution shown alongside ONS 2022 benchmarks (Fig. 1C ). Fig. 1 SECURE cohort recruitment flow and BMI/ethnicity benchmarking. A CONSORT-style recruitment and analysis flow with diagnosis provider breakdown. B Ethnicity distribution in SECURE versus England & Wales 2021 Census. C BMI categories in SECURE versus the ONS Health Survey 2022. Contains public sector information from the Office for National Statistics licensed under the Open Government Licence v3.0. Table 1 Baseline characteristics of SECURE participants. n % Age ( n = 1133) Median (range) 71 (25–95) 20–29 2 0.2 30–39 12 1.1 40–49 41 3.6 50–59 168 14.8 60–69 304 26.8 70–79 447 39.5 80–89 151 13.3 ≥90 8 0.7 Gender ( n = 1133) Male 600 53.0 Female 533 47.0 Ethnicity ( n = 1111) White 989 89.0 Black 47 4.2 Asian 33 3.0 Other 29 2.6 Mixed 13 1.2 BMI ( n = 970) Underweight (<18.5) 16 1.6 Normal (18.5 ≤ x < 25) 312 32.2 Overweight (25 ≤ x < 30) 333 34.3 Obese (30 ≤ x < 40) 270 27.8 Morbidly obese (≥40) 39 4.0 Diagnosis route ( n = 1083) Primary care 241 22.3 Secondary care 834 77.0 Other (e.g. private healthcare) 8 0.7 Risk stratification ( n = 685) Low risk 207 30.2 Low-intermediate risk 307 44.8 High-intermediate risk 159 23.2 High risk 12 1.8 Progression risk factors ( n = 685) Pathological FLC ratio 359 52.4 Non-IgG MGUS 205 29.9 M protein ≥ 15 g/L 97 14.2 Mean number of progression risk factors 0.97 Comorbidities Autoimmune disease 110 16.3 Previous infection (with hospitalisation) 82 16.2 Osteoporosis 120 11.1 Family history ( n = 972) First degree relative with FHx of MGUS/MM 44 4.5 First degree relative with FHx of other cancer 379 39.0 Risk stratification uses Mayo Clinic criteria, with the definition of pathological FLC informed by updated iStopMM recommendations. BMI body-mass index, FLC free light chain, FHx family history, MGUS monoclonal gammopathy of undetermined significance.
Table 1
Baseline characteristics of SECURE participants.
Risk stratification uses Mayo Clinic criteria, with the definition of pathological FLC informed by updated iStopMM recommendations. BMI body-mass index, FLC free light chain, FHx family history, MGUS monoclonal gammopathy of undetermined significance.
| n | % | |
| Age ( n = 1133) | ||
| Median (range) | 71 (25–95) | |
| 20–29 | 2 | 0.2 |
| 30–39 | 12 | 1.1 |
| 40–49 | 41 | 3.6 |
| 50–59 | 168 | 14.8 |
| 60–69 | 304 | 26.8 |
| 70–79 | 447 | 39.5 |
| 80–89 | 151 | 13.3 |
| ≥90 | 8 | 0.7 |
| Gender ( n = 1133) | ||
| Male | 600 | 53.0 |
| Female | 533 | 47.0 |
| Ethnicity ( n = 1111) | ||
| White | 989 | 89.0 |
| Black | 47 | 4.2 |
| Asian | 33 | 3.0 |
| Other | 29 | 2.6 |
| Mixed | 13 | 1.2 |
| BMI ( n = 970) | ||
| Underweight (<18.5) | 16 | 1.6 |
| Normal (18.5 ≤ x < 25) | 312 | 32.2 |
| Overweight (25 ≤ x < 30) | 333 | 34.3 |
| Obese (30 ≤ x < 40) | 270 | 27.8 |
| Morbidly obese (≥40) | 39 | 4.0 |
| Diagnosis route ( n = 1083) | ||
| Primary care | 241 | 22.3 |
| Secondary care | 834 | 77.0 |
| Other (e.g. private healthcare) | 8 | 0.7 |
| Risk stratification ( n = 685) | ||
| Low risk | 207 | 30.2 |
| Low-intermediate risk | 307 | 44.8 |
| High-intermediate risk | 159 | 23.2 |
| High risk | 12 | 1.8 |
| Progression risk factors ( n = 685) | ||
| Pathological FLC ratio | 359 | 52.4 |
| Non-IgG MGUS | 205 | 29.9 |
| M protein ≥ 15 g/L | 97 | 14.2 |
| Mean number of progression risk factors | 0.97 | |
| Comorbidities | ||
| Autoimmune disease | 110 | 16.3 |
| Previous infection (with hospitalisation) | 82 | 16.2 |
| Osteoporosis | 120 | 11.1 |
| Family history ( n = 972) | ||
| First degree relative with FHx of MGUS/MM | 44 | 4.5 |
| First degree relative with FHx of other cancer | 379 | 39.0 |
The route to diagnosis was available for 1083 participants: 22.3% via primary care and 77.0% via secondary care (Fig. 1A ). Among those risk-stratified, 30.2% were low, 44.8% low-intermediate, 23.2% high-intermediate and 1.8% high risk for progression. We had 448 patients with missing risk factor data (majority paraprotein concentration) and therefore could not be risk stratified. Baseline progression factors among those with measurements: abnormal FLC ratio 52.4%, non-IgG MGUS 29.9%, M-protein ≥ 15 g/L 14.2% (Table 1 ). LC-MGUS prevalence was 7.1% (Supplementary 1 ).
Common comorbidities included autoimmune disease (16.3%, Supplementary 1 ), prior infection requiring hospitalisation (16.2%), and osteoporosis (11.1%). Relevant family history included 3.9% with first-degree MGUS/MM and 33.5% with first-degree cancer. To date, ten participants have progressed to MM. Time-to-event analyses are planned with mature follow-up.
Screening cohorts (e.g. iStopMM) answer questions about prevalence and screening logistics [ 11 ]. SECURE instead reflects how MGUS diagnoses are made and followed in clinics. In an iStopMM sub-study comparing screened to clinical (incidental) cohorts, the clinically detected group carried substantially more comorbidity than the screened group, despite broadly similar distributions of progression risk factors, and a slightly higher mean M-protein at baseline [ 12 ]. Complementing this, the Mayo Clinic comparison reported no meaningful difference in long-term progression between screened and clinically detected MGUS once death was treated as a competing risk. ‘Mode of detection’ did not add prognostic value beyond M-protein ≥ 15 g/L, non-IgG isotype, abnormal FLC ratio [ 13 ]. SECURE’s incidentally detected, clinic-based cohort in which MGCS and comorbidity are expected to be more frequent than in screened cohorts, with additional variables, can refine Mayo MGUS risk model. We intend to test age, sex, ethnicity and BMI, and develop risk-adapted follow-up strategy for incidental MGUS patients.
The median diagnosis age in SECURE is 71 years, similar to the clinical (non-screened) MGUS cohort from Mayo Clinic (72 years) and the iStopMM clinical cohort (73.5 years) [ 12 , 13 ]. Although older age is often associated with higher biological risk of progression to MM [ 5 ], effect sizes vary across cohorts [ 14 ]. Given the slow natural history and non-myeloma mortality in older adults, the payoff from frequent surveillance is often small, arguing for risk-based follow-up. In SECURE, many patients carry at least one risk feature but relatively few formally classify as high progression risk (Table 1 ).
SECURE shows a slight male predominance (53.0%), like the clinical cohorts from Mayo (54%) and iStopMM (50.8%) [ 12 , 13 ]. Prior literature on sex differences is varied, with some analyses suggesting lower progression risk in men, and others finding minimal effect size [ 5 ]. As such, sex should be considered as a contextual, not deterministic factor in risk-adapted pathways.
SECURE’s ethnicity distribution follows England & Wales 2021 Census (Fig. 1B ), showing this incidental MGUS cohort is representative. Given reports of higher MGUS prevalence in people of African ancestry, a representative cohort helps plan monitoring practices.
SECURE’s BMI profile is similar to the ONS Health Survey distribution (Fig. 1C ) and is consistent with reports linking adiposity to MGUS and, in some analyses, to progression [ 14 ]. While causality cannot be inferred from these interim data, the prevalence of overweight/obese individuals supports integrating weight management and bone-health optimisation into routine advice. With longitudinal follow-up, SECURE can explore whether changes in weight track with progression signals and prognosis.
Our observed comorbidity pattern (osteoporosis, autoimmune disease, serious infection) is consistent with prior links between MGUS and autoimmune/inflammatory conditions and infection. Some associations will reflect ascertainment (e.g. fracture or renal evaluations prompting protein studies), these patterns may nonetheless serve as cues for targeted case-finding. Data collection is ongoing regarding MGCS prevalence and form, with the aim to show how often MGCS emerges and who needs targeted workup.
Beyond the clinical dataset, SECURE’s key feature is serial biosampling at routine visits, letting us follow trajectories and capture pre-progression material (rare in most cohorts). This supports biomarker discovery for risk-adapted follow-up. Furthermore, early SECURE patient-reported outcomes suggest minimal psychological burden related to follow-up, supporting the feasibility of risk-based pathways [ 15 ].
As an interim descriptive analysis, some missing data is expected under standard-of-care practice. For 203 (17.9%) the paraprotein is confirmed on immunofixation but falls below the limit of quantification or is obscured by β-region overlap, reflecting quantification limitations rather than diagnostic uncertainty. Case capture and testing differ between sites; standardisation is ongoing, and sensitivity analyses will assess bias. This is a clinically detected cohort (mostly secondary care), so is not directly comparable with screened populations. Ethnicity and other subgroup analyses are currently underpowered. With short follow-up, progression events are few; time-to-event and multivariable analyses will follow as the dataset matures. Because MGUS-negative individuals were not captured as a comparator, observed associations may reflect indications for testing rather than MGUS-specific associations.
SECURE is the first UK prospective cohort of incidentally detected MGUS seen in routine care, generating new data for risk-adapted follow-up. With serial samples, patient-reported outcomes, and linked service data, SECURE is positioned to characterise how incidentally detected MGUS differs from screened cohorts, quantify MGCS burden and monitoring patterns in routine UK practice, and generate biomarker data to inform risk-adapted follow-up.
Supplementary information
Supplementary Table S1
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Supplementary information
The online version contains supplementary material available at 10.1038/s41408-026-01576-x.
Acknowledgements
We would like to acknowledge the contributions of Earnest Heartin, Victoria Garvey, Charlotte Kallmeyer, Rachel Newton, Jacqueline Stones, Tiina Elonen, Magda Al-Obaidi, Freter Cubos, Rachel Ryan, Sarah Essex, Saranne Hufton, Richard Kaczmarski, Aoife O’Grady, Kavina Suthaharan, Parizade Raymode, Arun Alfred, Dawn Collier, Dinusha Chathurani, Rowena Thomas-Dewing, Charlotte Bomken, Jessica Reynolds, Simon Hallam, Kashia Dobson, Alice Thomas, Lally De Soysa, Rachel Bradley, Sim-Ning Yee, Shiva Natarajan, Roberto Garcia Consuegra, Wendy Harrison, Tracey Chan, Kamaraj Karunanithi, Amie Martin, Suzanne Hirst, Louise Murray, Chris Millar, Louise Jenkins, Ceri Bygrave, Kyriakos Kaminaris, Jackie Evans, Asha Matthew, Deyna Cardosa, Mary Tamplin, Adam Forbes, Denise Storey, Thomas Erblich, Lauren Ellis, Ibiyemi Sadare, Shaki Balogun, Kassim Javaid, Constantinos Koshiaris, Christopher Bunce, Warwick Dunn, Martin Kaiser, Jenny Bird, Grant Vallance, Gaurav Agarwal, Ashutosh Wechalekar, and Anjan Thakurta.
Author contributions
EK, AK, BB, and KR conceived and designed the study. AK, SBy, RB, GP, RS, SBa, SS, SBo, FW, VG, AM, RO, GK, and PN contributed to the acquisition of data. EK analysed and interpreted the data. EK, AK, and BB drafted the manuscript. All authors critically revised the manuscript for important intellectual content, approved the final version to be published, and agree to be accountable for all aspects of the work.
Funding
KR discloses support for the research of this work from the Medical Research Council [grant number MR/V037439/1].
Competing interests
The authors declare no competing interests.
Ethics approval and consent to participate
This study was performed in accordance with the Declaration of Helsinki and the principles of Good Clinical Practice, and in accordance with all relevant UK guidelines and regulations. Ethical approval was granted by Wales REC 3 (reference 22/WA/0291). Written informed consent was obtained from all participants prior to any study procedures.
References
1. Kyle RA Rajkumar SV Monoclonal gammopathy of undetermined significance Br J Haematol 2006 134 573 89 10.1111/j.1365-2141.2006.06235.x 16938117 Kyle RA, Rajkumar SV. Monoclonal gammopathy of undetermined significance. Br J Haematol. 2006;134:573–89. 16938117 10.1111/j.1365-2141.2006.06235.x
2. Thorsteinsdóttir S Gíslason GK Aspelund T Rögnvaldsson S Óskarsson JÞ Sigurðardóttir GÁ Prevalence of smoldering multiple myeloma based on nationwide screening Nat Med 2023 29 467 72 10.1038/s41591-022-02183-6 36747117 PMC11099842 Thorsteinsdóttir S, Gíslason GK, Aspelund T, Rögnvaldsson S, Óskarsson JÞ, Sigurðardóttir GÁ, et al. Prevalence of smoldering multiple myeloma based on nationwide screening. Nat Med. 2023;29:467–72. 36747117 10.1038/s41591-022-02183-6 PMC11099842
3. Go RS Gundrum JD Neuner JM Determining the clinical significance of monoclonal gammopathy of undetermined significance: a SEER-Medicare population analysis Clin Lymphoma Myeloma Leuk 2015 15 177 86.e4 10.1016/j.clml.2014.09.004 25445471 PMC4344843 Go RS, Gundrum JD, Neuner JM. Determining the clinical significance of monoclonal gammopathy of undetermined significance: a SEER-Medicare population analysis. Clin Lymphoma Myeloma Leuk. 2015;15:177–86.e4. 25445471 10.1016/j.clml.2014.09.004 PMC4344843
4. Landgren O Kyle RA Pfeiffer RM Katzmann JA Caporaso NE Hayes RB Monoclonal gammopathy of undetermined significance (MGUS) consistently precedes multiple myeloma: a prospective study Blood 2009 113 5412 7 10.1182/blood-2008-12-194241 19179464 PMC2689042 Landgren O, Kyle RA, Pfeiffer RM, Katzmann JA, Caporaso NE, Hayes RB, et al. Monoclonal gammopathy of undetermined significance (MGUS) consistently precedes multiple myeloma: a prospective study. Blood. 2009;113:5412–7. 19179464 10.1182/blood-2008-12-194241 PMC2689042
5. Kyle RA Larson DR Therneau TM Dispenzieri A Kumar S Cerhan JR Long-term follow-up of monoclonal gammopathy of undetermined significance N Engl J Med 2018 378 241 9 10.1056/NEJMoa1709974 29342381 PMC5852672 Kyle RA, Larson DR, Therneau TM, Dispenzieri A, Kumar S, Cerhan JR, et al. Long-term follow-up of monoclonal gammopathy of undetermined significance. N Engl J Med. 2018;378:241–9. 29342381 10.1056/NEJMoa1709974 PMC5852672
6. Go RS Rajkumar SV How I manage monoclonal gammopathy of undetermined significance Blood 2018 131 163 73 10.1182/blood-2017-09-807560 29183887 PMC5757684 Go RS, Rajkumar SV. How I manage monoclonal gammopathy of undetermined significance. Blood. 2018;131:163–73. 29183887 10.1182/blood-2017-09-807560 PMC5757684
7. Kyle RA Durie BGM Rajkumar SV Landgren O Blade J Merlini G 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 1121 7 10.1038/leu.2010.60 20410922 PMC7020664 Kyle RA, Durie BGM, Rajkumar SV, Landgren O, Blade J, Merlini G, 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;24:1121–7. 20410922 10.1038/leu.2010.60 PMC7020664
8. Gregersen H Ibsen J Mellemkjoer L Dahlerup J Olsen J Sørensen HT Mortality and causes of death in patients with monoclonal gammopathy of undetermined significance Br J Haematol 2001 112 353 7 10.1046/j.1365-2141.2001.02533.x 11167828 Gregersen H, Ibsen J, Mellemkjoer L, Dahlerup J, Olsen J, Sørensen HT. Mortality and causes of death in patients with monoclonal gammopathy of undetermined significance. Br J Haematol. 2001;112:353–7. 11167828 10.1046/j.1365-2141.2001.02533.x
9. Sigurdardottir EE Turesson I Lund SH Lindqvist EK Mailankody S Korde N The role of diagnosis and clinical follow-up of monoclonal gammopathy of undetermined significance on survival in multiple myeloma JAMA Oncol 2015 1 168 74 10.1001/jamaoncol.2015.23 26181017 Sigurdardottir EE, Turesson I, Lund SH, Lindqvist EK, Mailankody S, Korde N, et al. The role of diagnosis and clinical follow-up of monoclonal gammopathy of undetermined significance on survival in multiple myeloma. JAMA Oncol. 2015;1:168–74. 26181017 10.1001/jamaoncol.2015.23
10. Einarsson Long T Rögnvaldsson S Thorsteinsdottir S Sverrisdottir I Eythorsson E Indridason O Revised definition of free light chains in serum and light chain monoclonal gammopathy of undetermined significance: results of the IstopMM study Blood 2023 142 535 10.1182/blood-2023-188547 Einarsson Long T, Rögnvaldsson S, Thorsteinsdottir S, Sverrisdottir I, Eythorsson E, Indridason O, et al. Revised definition of free light chains in serum and light chain monoclonal gammopathy of undetermined significance: results of the IstopMM study. Blood. 2023;142:535.
11. Rögnvaldsson S Love TJ Thorsteinsdottir S Reed ER Óskarsson J Pétursdóttir Í Iceland screens, treats, or prevents multiple myeloma (iStopMM): a population-based screening study for monoclonal gammopathy of undetermined significance and randomized controlled trial of follow-up strategies Blood Cancer J 2021 11 94 10.1038/s41408-021-00480-w 34001889 PMC8128921 Rögnvaldsson S, Love TJ, Thorsteinsdottir S, Reed ER, Óskarsson J, Pétursdóttir Í, et al. Iceland screens, treats, or prevents multiple myeloma (iStopMM): a population-based screening study for monoclonal gammopathy of undetermined significance and randomized controlled trial of follow-up strategies. Blood Cancer J. 2021;11:94. 34001889 10.1038/s41408-021-00480-w PMC8128921
12. Sigurbergsdóttir A Rögnvaldsson S Thorsteinsdóttir S Sverrisdóttir I Sigurðardóttir G Viðarsson B Disease associations with monoclonal gammopathy of undetermined significance can only be evaluated using screened cohorts: results from the population-based iStopMM study Haematologica 2023 108 3392 8 10.3324/haematol.2023.283191 37439374 PMC10690899 Sigurbergsdóttir A, Rögnvaldsson S, Thorsteinsdóttir S, Sverrisdóttir I, Sigurðardóttir G, Viðarsson B, et al. Disease associations with monoclonal gammopathy of undetermined significance can only be evaluated using screened cohorts: results from the population-based iStopMM study. Haematologica. 2023;108:3392–8. 37439374 10.3324/haematol.2023.283191 PMC10690899
13. Visram A Larson D Norman A Dispenzieri A Murray D Kyle R Comparison of progression risk of monoclonal gammopathy of undetermined significance by method of detection Blood 2025 145 325 33 10.1182/blood.2024025415 39437710 PMC12060158 Visram A, Larson D, Norman A, Dispenzieri A, Murray D, Kyle R, et al. Comparison of progression risk of monoclonal gammopathy of undetermined significance by method of detection. Blood. 2025;145:325–33. 39437710 10.1182/blood.2024025415 PMC12060158
14. Li Y Hsu SH Wang R Theprungsirikul P Neparidze N Chang S-H Associations between patient characteristics and progression to multiple myeloma among patients with monoclonal gammopathy of undetermined significance: a systematic review Clin Lymphoma Myeloma Leukemia 2025 25 e222 e31 10.1016/j.clml.2024.12.006 PMC11911095 39730300 Li Y, Hsu SH, Wang R, Theprungsirikul P, Neparidze N, Chang S-H, et al. Associations between patient characteristics and progression to multiple myeloma among patients with monoclonal gammopathy of undetermined significance: a systematic review. Clin Lymphoma Myeloma Leukemia. 2025;25:e222–e31. 10.1016/j.clml.2024.12.006 PMC11911095 39730300
15. Knight ES, Varghese S, Koshiaris C, Byun S, Brouwer R, Rögnvaldsson S, et al. SECURE study: Incidentally detected MGUS patients have comparable depression and anxiety rates to UK general population. Br J Haematol. 208(1):325–328 10.1111/bjh.70245 PMC12819083 41225752