Medication use and multiple myeloma risk in Los Angeles County

Nuyujukian, Daniel S.; Voutsinas, Jenna; Bernstein, Leslie; Wang, Sophia

doi:10.1007/s10552-014-0424-0

Cited by 6 publications

(9 citation statements)

References 19 publications

(22 reference statements)

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“…With time, the myeloma cell accumulates in the bone marrow, where they crowd out healthy blood cells and may cause damage to the solid part of the bone. Multiple myeloma is therefore caused by the accumulation of the myeloma cells in the bones [2][3][4][5][6][7][8]. Figure 1 below shows the development of the myeloma cells [4,[5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Statistical Modeling and Analysis of the Survival Times of Multiple Myeloma Cancer

Mamudu¹,

Cp²

2020

IPHSJ

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Multiple Myeloma (MM) has been and continues to be the subject of many research studies. The main goal is to improve the therapeutic/treatment process of survival of MM patients. Based on the 2012-2016 MM cases and deaths, the number of new cases was 6.9 per 100,000 men and women per year, and the number of deaths was 3.3 per 100,000 men and women per year. It is therefore imperative to research into MM. In the present study, we proposed a data-driven statistical model for the survival time of 48 patients diagnosed with multiple myeloma as a function of 16 attributable risk factors. We identified 9 attributable risk factors out of 16 and one interaction term to be significantly contributing to the survival time. They are Bence Jone protein in urine, blood urea nitrogen (BUN)/serum creatinine, infections, % myeloid cells in peripheral blood, fractures, serum calcium, gender, platelets and age, and white blood cells & total serum protein an interaction term. The proposed model satisfied all the model assumptions, passes the residual analysis test and has very high prediction accuracy. Thus, it passes the goodnessof-fit test and the qualities of a good model. The identified significant attributable risk factors and the interaction has been ranked based on the percent contribution to the survival time. The proposed model was evaluated and compared with other existing models of survival of multiple myeloma. Our model is very accurate and also identifies some new significant risk factors. The study offers an improved strategy for the therapeutic/treatment process of multiple Myeloma Cancer.

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Section: Introductionmentioning

confidence: 99%

“…Those figures are staggering and overwhelming and cannot be overlooked. This is a rise compared with the estimated number of new MM cases of 24,050 reported in 2014 [3,6]. The established risk factors of MM are common among the age, black race, families with MM history, and being a male [2,7].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Statistical Modeling and Analysis of the Survival Times of Multiple Myeloma Cancer

Mamudu¹,

Cp²

2020

IPHSJ

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“…Details of the studies are given in Table . The studies were conducted in the United States (US) [Connecticut Study (Purdue et al , ), Molecular and Genetic Epidemiology (iMAGE) Study of Myeloma (VanValkenburg et al , ), Los Angeles County Case Control Multiple Myeloma Study (LACCMM; Nuyujukian et al , ), Nebraska Study (Zahm et al , ), Population Health Study (PopHealth; Baris et al , ), University of Southern California (USC; Cozen et al , ), and Utah Study (Camp et al , )] (,); Canada (Canadian Study; Ghosh et al , ; McDuffie et al , ) and Europe [L'Etude des Facteurs environnemen‐taux et genétique des Lymphomes de l'Adulte (ENGELA; Orsi et al , ), Epilymph (Casey et al , ; Perrotta et al , ), Italian Multicentre (Miligi et al , )]. Nearly all of the studies included both men and women, with the exception of the Canadian (McDuffie et al , ; Ghosh et al , ) and Connecticut (Purdue et al , ) studies, which included only men and only women, respectively.…”

Section: Methodsmentioning

confidence: 99%

Multiple myeloma and family history of lymphohaematopoietic cancers: Results from the International Multiple Myeloma Consortium

et al. 2016

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Summary Family clusters of multiple myeloma (MM) suggest disease heritability. Nevertheless, patterns of inheritance and the importance of genetic versus environmental risk factors in MM aetiology remain unclear. We pooled data from eleven case-control studies from the International Multiple Myeloma Consortium to characterize the association of MM risk with having a first-degree relative with a history of a lympho-haematapoietic cancer. Unconditional logistic regression models, adjusted for study, sex, age and education level, were used to estimate associations between MM risk and having a first-degree relative with a history of non-Hodgkin lymphoma, Hodgkin lymphoma, leukaemia or MM. Sex, African American race/ethnicity and age were explored as effect modifiers. A total of 2,843 cases and 11,470 controls were included. MM risk was elevated in association with having a first-degree relative with any lympho-haematapoietic cancer (Odds Ratio (OR)= 1.29, 95% Confidence Interval (CI): 1.08-1.55). The association was particularly strong for having a first-degree relative with MM (OR= 1.90, 95% CI: 1.26-2.87), especially among men (OR= 4.13, 95% CI: 2.17-7.85) and African Americans (OR= 5.52, 95% CI: 1.87-16.27). These results support the hypothesis that genetic inheritance plays a role in MM aetiology. Future studies are warranted to characterize interactions of genetic markers with environmental exposures.

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“…A summary of the nine participating case-control studies is provided in Table 1. [13][14][15][16][17][18][19][20][21][22] The individual studies included persons with histologically confirmed incident primary diagnosis of NHL that occurred during the respective enrollment periods, spanning from 1980 to 2013. Controls were identified in the general population or from participating hospitals/clinics and were frequency-or pair-matched to the cases by variables including age and sex, and in some studies, region or ethnicity.…”

Section: Study Populationmentioning

confidence: 99%

Occupational insecticide exposure and risk of non‐Hodgkin lymphoma: A pooled case‐control study from the InterLymph Consortium

Roos

Schinasi

Miligi³

et al. 2021

Intl Journal of Cancer

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Evidence for the human health effects of pesticides is needed to inform risk assessment. We studied the relationship between occupational insecticide use and risk of non-Hodgkin lymphoma (NHL) by pooling data from nine case-control studies participating in the InterLymph Consortium, including 7909 cases and 8644 controls from North America, the European Union and Australia. Insecticide use was coded using self-report or expert assessment, for insecticide groups (eg, organophosphates, pyrethroids) and active ingredients (eg, malathion, permethrin). Associations with insecticides were estimated using logistic regression to produce odds ratios (ORs) and 95% confidence intervals (CI) for all NHL and NHL subtypes, with adjustment for study site, demographic factors and use of other pesticides. Occupational insecticide use, overall, was not associated with risk of NHL. Use of organophosphate insecticides was associated with increased risk of all NHL and the subtype follicular lymphoma, and an association was found with diazinon, in particular (ever use: OR = 2.05, 95%CI: 1.24-3.37). The carbamate insecticide, carbaryl, was associated with risk of all NHL, and the strongest associations were found with T-cell NHL for ever-use (OR = 2.44, 95%CI: 1.13-5.28) and longer duration (>8 years vs never: OR = 2.90, 95%CI: 1.02-8.25). There was no association of NHL with other broad groups of insecticides, including organochlorines and pyrethroids, and some inverse associations were estimated in relation to historical DDT use. Our findings contribute to the totality of evidence available to help inform risk decisions by public health and regulatory agencies of importance given continued, widespread use of organophosphate and carbamate insecticides.

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Medication use and multiple myeloma risk in Los Angeles County

Cited by 6 publications

References 19 publications

Data-Driven Statistical Modeling and Analysis of the Survival Times of Multiple Myeloma Cancer

Data-Driven Statistical Modeling and Analysis of the Survival Times of Multiple Myeloma Cancer

Multiple myeloma and family history of lymphohaematopoietic cancers: Results from the International Multiple Myeloma Consortium

Occupational insecticide exposure and risk of non‐Hodgkin lymphoma: A pooled case‐control study from the InterLymph Consortium

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