Learn how you can stall the development of full-blown Multiple Myeloma with evidence-based nutritional and supplementation therapies.
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You decided to enter a high-risk smoldering myeloma clinical trial. You were told that you were high-risk SMM (HR-SMM) and that chemotherapy may slow or even prevent your progress to full multiple myeloma.
Two key questions-
Reducing the risk of a MM diagnosis-
MGUS and SMM are forms of pre-cancer, not cancer. Therefore, there are no FDA approved therapies for either MGUS or SMM. Most oncologists don’t actively treat MGUS or SMM.
However, there are a number of evidence-based, non-conventional therapies that research has shown can reduce the risk of both MGUS and SMM processing to full MM. Further, undergoing these preventative therapies (exercise, diet, supplementation, etc.) “prehabilitates” the SMM patient.
According to research, prehabilitation enables the patient to respond better to therapy, experience fewer side effects and heal faster.
When I was originally diagnosed, my oncologist told me that I had a single plasmacytoma of bone (SPB). My pathologist told me that I had multiple myeloma. Pre-myeloma diagnoses are tricky. As far as I know, both my oncologist as well as my pathologist were well-educated, experienced medical professionals.
But one of them was wrong.
So what is the patient considering a high-risk smoldering myeloma clinical trial to do? If there is one thing I’ve learned since my diagnosis of MM, undergoing evidence-based therapies non-conventional therapies shown to fight MM is a good step when a person is diagnosed with pre-Myeloma.
According to the study linked and excerpted below, diagnosing HR-SMM is difficult. The risk models used in the study below disagreed with each other.
Have you been diagnosed with high risk smoldering multiple myeloma? If you would like to learn more about evidence-based non-conventional therapies shown to reduce the risk of a MM diagnosis email me at David.PeopleBeatingCancer@gmail.com
Good luck,
“Smoldering multiple myeloma (SMM) is a plasma cell disorder with the potential to evolve to multiple myeloma.1 Several risk stratification models—commonly,
have been developed to prognosticate the risk of progression.2,3These models use clinical variables of disease burden rather than biological tumor characteristics to predict
Multiple clinical trials are investigating the role of treatment in HR SMM. Interpreting the results of clinical trials investigating the role of treatment in HR SMM is problematic owing to variation in the risk stratification criteria used.4 Our study attempts to assess the concordance of these 3 models in a cohort of patients with SMM from 2 clinical trials…
The medical records of 145 patients were reviewed, and patients’ risk was stratified according to the 3 models. Baseline patient characteristics included mean (range) age of 66 (40-91) years, and 84 (57.9%) were male; 109 (75.2%) were White, 32 (22.1%) were Black, and 3 (2.1%) were Asian.
The isotype was IgG, 103 (71.0%); IgA, 34 (23.4%); and light chain, 8 (5.5%); the median M protein, percent plasmacytosis, serum free light chain ratio, and percent aberrant plasma cells (flow cytometry) was 1.3 g/dL, 18%, 9.1, and 97%, respectively.
A total of 101 patients (69.7%) had immunoparesis; 38 patients (26.2%) had 1 and 60 patients (41.4%) had 2 normal immunoglobulin isotypes depressed.
Overall, 42 (29.0%) and 72 (49.7%) patients had agreement in risk stratification across LR, IR, and HR categories among the 2008 and 2018 Mayo Clinic models and the PETHEMA model, respectively (Table).
The overall global rate of agreement across all 3 models for all 3 categories was 16.6%.
Of the 145 patients evaluated, 24 patients were categorized in the same risk group using all 3 models. There was a significant difference in the rates of LR, IR, and HR among the 3 models (Figure, A).
Stratifying HR patients is most important in terms of patient monitoring and enrollment into clinical trials. Therefore, discordance across models in detecting HR patients compared with all others (non-HR) was specifically examined. The ability of models to classify patients as HR vs non-HR was significantly different (Figure, B)…”