Daniel O. Stram

Monoclonal gammopathy of undetermined significance (MGUS) is a necessary precursor of multiple myeloma (MM). Compared with non-Hispanic White (White) patients, African American individuals experience approximately twofold greater, and Asian American individuals 50% lower, occurrence of MGUS and MM. We examined determinants of MGUS among Japanese American, African American, Latino, Native Hawaiian, and White participants in a case-control study nested in the Multiethnic Cohort (MEC) study. Presumptive cases included MEC participants aged ≥65 years initially screened using the Medicare billing code for paraproteinemia along with those diagnosed with MM or another plasma cell neoplasm at least 1 year after blood draw. Presumptive controls had no Medicare paraproteinemia billing code or MM. Laboratory confirmation resulted in 426 confirmed MGUS cases (370 non-immunoglobulin M [non-IgM] and 56 IgM) and 863 MGUS-free controls. We used multivariable logistic regression to estimate associations with MGUS. The distribution of immunoglobulin isotypes differed by race/ethnicity (P = .005): Japanese American individuals had the lowest (11.0%) and Native Hawaiian individuals the highest (23.5%) proportion of IgA. Each unit increase in body mass index (BMI; kilogram per square meter) was associated with a 6% increase in non-IgM MGUS (95% confidence interval, 3-9) among all racial/ethnic groups combined. BMI was also significantly associated within most racial/ethnic groups, especially among Native Hawaiian individuals (13% increase per BMI unit), with IgA and IgG MGUS, and with high and low risk MGUS. BMI was the main determinant associated with MGUS. Isotype distribution differed between racial/ethnic groups.

Background: The Multiethnic Cohort Study (MEC) is a US prospective cohort of more than 215,000 participants, designed to investigate variation in risk factors and disease across diverse racial and ethnic groups. More than 74,000 participants contributed biospecimens for genetic studies. We describe this subcohort and demonstrate the types of analyses it enables. Methods: The MEC recruited adults aged 45 to 75 in California and Hawaii between 1993 and 1996. Cancer diagnoses were identified via state tumor registries. The MEC Genetics Database includes 73,139 participants with germline genotype data. We evaluated genetic similarity, its relationship with self-reported race/ethnicity, and baseline characteristics, including neighborhood socioeconomic status (nSES). Using breast, colorectal, and prostate cancer as examples, we conducted genome-wide association studies (GWAS), assessed nongenetic risk factors, and performed time-to-event analyses. Results: Participants included 10,962 African Americans, 24,234 Japanese Americans, 17,242 Latinos, 5,488 Native Hawaiians, 14,649 Whites, and 564 others. Principal component analysis showed substantial diversity. Multiethnic GWAS replicated known variants with effective control of population stratification. Polygenic risk score (PRS) effects varied across groups. Time-to-event models revealed associations between cancer incidence and nSES, population descriptors, and genetic similarity. Conclusions: The MEC Genetics Database enables multiancestry analyses of genetic and nongenetic cancer risk, supporting research on disparities, polygenic traits, and integrated risk prediction. Impact: Example analyses using these resources show the relationship between population descriptors, PRSs, and common cancer risk factors that require special consideration in genetic analyses.

Background: Integrating genetic and lifestyle information has the potential to greatly improve the prediction of colorectal cancer (CRC) risk. However, racial and ethnic minorities are generally underrepresented in gene-environment studies of CRC risk. Methods: We investigated the interplay of genetics and lifestyle on CRC risk in a prospective analysis of 68 397 African American, Japanese American, Latino, Native Hawaiian, and White individuals from the Multiethnic Cohort Study. Genetic predisposition was assessed using a 205-variant polygenic risk score. Lifestyle was assessed using a lifestyle risk score based on smoking, alcohol consumption, body mass index, physical activity, and diet. The independent and joint associations of the polygenic risk score and lifestyle risk score on CRC risk were evaluated using Cox regression. Results: We identified 1303 incident CRC cases (median 15.1-year follow-up). The highest quintile of the polygenic risk score was associated with a 2.4-fold increase in CRC risk compared with the lowest quintile (Q5 vs Q1: hazard ratio [HR] = 2.40, 95% confidence interval [CI] = 1.99 to 2.89). The highest quintile of the lifestyle risk score was associated with a 54% increased risk (Q5 vs Q1: HR = 1.54, 95% CI = 1.26 to 1.88). This association was stronger among those with high genetic risk (polygenic risk score ≥ 50%; Q5 vs Q1: HR = 1.82, 95% CI = 1.41 to 2.35) and statistically nonsignificant among those with low genetic risk (polygenic risk score < 50%; Q5 vs Q1: HR = 1.20, 95% CI = 0.88 to 1.64; P for interaction = 0.01). Results were similar across race and ethnicity. Conclusions: Our study suggests that lifestyle modification may offer greater risk reduction among those at higher genetic risk. Future research is warranted to enhance the integration of genetics and lifestyle in CRC risk stratification and screening approaches across populations.

Supplementary Table S5 shows Dietary Approaches to Stop Hypertension (DASH) score and pancreatic cancer risk by individual dietary component

Geometric mean and 95% confidence intervals for urinary biomarkers of smoking-related toxicants among the Multiethnic Cohort (MEC) subcohort of current smokers and incident lung cancer cases

Supplementary Table S3 shows DQIs and pancreatic cancer risk by smoking status

Supplementary Table S2 shows DQIs and pancreatic cancer risk among racial and ethnic groups studied in the Multiethnic Cohort Study (MEC)

Supplementary Table S1 shows the components of diet quality indices (DQIs)

Background:Data on diet quality and pancreatic cancer are limited. We examined the relationship between diet quality, assessed by the Healthy Eating Index-2015 (HEI-2015), the Alternative Healthy Eating Index-2010 (AHEI-2010), the alternate Mediterranean Diet (aMED) score, the Dietary Approaches to Stop Hypertension (DASH) score and the energy-adjusted Dietary Inflammatory Index (E-DII), and pancreatic cancer incidence in the Multiethnic Cohort Study.Methods:Diet quality scores were calculated from a validated food frequency questionnaire administered at baseline. Cox models were used to calculate HR and 95% confidence intervals (CI) adjusted for age, sex, race/ethnicity, education, diabetes, family history of pancreatic cancer, physical activity, smoking variables, total energy intake, body mass index (BMI), and alcohol consumption. Stratified analyses by sex, race/ethnicity, smoking status, and BMI were conducted.Results:Over an average follow-up of 19.3 years, 1,779 incident pancreatic cancer cases were identified among 177,313 participants (average age of 60.2 years at baseline, 1993–1996). Overall, we did not observe associations between the dietary pattern scores and pancreatic cancer (aMED: 0.98; 95% CI, 0.83–1.16; HEI-2015: 1.03; 95% CI, 0.88–1.21; AHEI-2010: 1.03; 95% CI, 0.88–1.20; DASH: 0.92; 95% CI, 0.79–1.08; E-DII: 1.05; 95% CI, 0.89–1.23). An inverse association was observed with DASH for ever smokers (HR, 0.75; 0.61–0.93), but not for nonsmokers (HR, 1.05; 0.83–1.32).Conclusions:The DASH diet showed an inverse association with pancreatic cancer among ever smokers, but does not show a protective association overall.Impact:Modifiable measures are needed to reduce pancreatic cancer burden in these high-risk populations; our study adds to the discussion of the benefit of dietary changes.