Aaron D. Norman

Validity and reproducibility of self-reported total physical activity--differences by relative weight.

OBJECTIVE: Physical activity is hypothesized to reduce the risk of obesity and several other chronic diseases and enhance longevity. However, most of the questionnaires used measure only part of total physical activity, occupational and/or leisure-time activity, which might lead to misclassification of total physical activity level and to dilution of risk estimates. We evaluated the validity and reproducibility of a short self-administered physical activity questionnaire, intended to measure long-term total daily 24 h physical activity.METHOD: The questionnaire included questions on level of physical activity at work, hours per day of walking/bicycling, home/household work, leisure-time activity/inactivity and sleeping and was sent twice during one year (winter/spring and late summer). Two 7-day activity records, performed 6 months apart, were used as the reference method. One-hundred and eleven men, aged 44–78, completed the questionnaire and one or two activity records. The physical activity levels were measured as metabolic equivalents (MET)×h/day.RESULTS: Spearman correlation coefficient between total daily activity score estimated from the first questionnaire and the records (validity) was 0.56 (deattenuated) and between the first and the second questionnaire (reproducibility) 0.65. Significantly higher validity correlations were observed in men with self-reported body mass index below 26 kg/m2 than in heavier men (r=0.73 vs r=0.39).CONCLUSIONS: This study indicates that the average total daily physical activity scores can be estimated satisfactorily in men using this simple self-administered questionnaire.

Total physical activity in relation to age, body mass, health and other factors in a cohort of Swedish men

Background: Despite a large public health interest in physical activity and its role in obesity and other chronic diseases, only a few reports to date have addressed total levels of physical activity in relation to age, body mass, health and other lifestyle factors.Objective: To investigate whether levels of total physical activity among men are associated with age, body mass, self-rated health and other lifestyle factors in a cross-sectional setting.Methods: In a population-based cohort of 33 466 men aged 45–79 y in central Sweden, we collected information about physical activity through a self-administered questionnaire. Level of total physical activity was assessed quantitatively based on six questions on different activities: work/occupation, housework, walking/bicycling, exercise, inactive leisure time and sleeping. The physical activity levels were measured as metabolic equivalents, MET-h/day. The relation between age, body mass index, smoking, education, marital status and self-rated health, and total physical activity was studied in a cross-sectional analysis, using multivariate regression.Results: Total daily physical activity was decreasing systematically between age 45 and 79 (−4.1%, 95% CI −4.6, −3.6). Obese men reported −2.6% (95% CI −3.0, −2.1) lower physical activity than normal weight men. Those with high education had −7.0% (95%CI −7.3, −6.7) lower total physical activity than those with elementary school. Men with self-rated poor health had −11.3% (95%CI −12.1, −10.6) lower physical activity than those reporting very good health. The cross-sectionally observed decrease with age was greatest among obese men (−8.7%), current smokers (−7.9%), low-educated men (−5.6%) and those with poor health (−9.8%); the subgroups with very good health reported almost the same level of total physical activity (−0.6%) for age 74–79 as for age 45–49.Conclusions: The observed decreasing levels of total physical activity with age to large degree depend on health status and other factors. The characterization of subjects with low total physical activity levels is of importance for understanding observed worldwide trends in increasing prevalence of obesity. The better understanding of these phenomena might also facilitate a better planning of public health interventions with messages specifically adjusted for subgroups of population with lower physical activity.

The Contributions of Breast Density and Common Genetic Variation to Breast Cancer Risk

We evaluated whether a 76-locus polygenic risk score (PRS) and Breast Imaging Reporting and Data System (BI-RADS) breast density were independent risk factors within three studies (1643 case patients, 2397 control patients) using logistic regression models. We incorporated the PRS odds ratio (OR) into the Breast Cancer Surveillance Consortium (BCSC) risk-prediction model while accounting for its attributable risk and compared five-year absolute risk predictions between models using area under the curve (AUC) statistics. All statistical tests were two-sided. BI-RADS density and PRS were independent risk factors across all three studies (P interaction = .23). Relative to those with scattered fibroglandular densities and average PRS (2(nd) quartile), women with extreme density and highest quartile PRS had 2.7-fold (95% confidence interval [CI] = 1.74 to 4.12) increased risk, while those with low density and PRS had reduced risk (OR = 0.30, 95% CI = 0.18 to 0.51). PRS added independent information (P < .001) to the BCSC model and improved discriminatory accuracy from AUC = 0.66 to AUC = 0.69. Although the BCSC-PRS model was well calibrated in case-control data, independent cohort data are needed to test calibration in the general population.

Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk

Mammographic density reflects the amount of stromal and epithelial tissues in relation to adipose tissue in the breast and is a strong risk factor for breast cancer. Here we report the results from meta-analysis of genome-wide association studies (GWAS) of three mammographic density phenotypes: dense area, non-dense area and percent density in up to 7,916 women in stage 1 and an additional 10,379 women in stage 2. We identify genome-wide significant (P<5×10−8) loci for dense area (AREG, ESR1, ZNF365, LSP1/TNNT3, IGF1, TMEM184B, SGSM3/MKL1), non-dense area (8p11.23) and percent density (PRDM6, 8p11.23, TMEM184B). Four of these regions are known breast cancer susceptibility loci, and four additional regions were found to be associated with breast cancer (P<0.05) in a large meta-analysis. These results provide further evidence of a shared genetic basis between mammographic density and breast cancer and illustrate the power of studying intermediate quantitative phenotypes to identify putative disease susceptibility loci.

Comparison of Clinical and Automated Breast Density Measurements: Implications for Risk Prediction and Supplemental Screening

Purpose To compare the classification of breast density with two automated methods, Volpara (version 1.5.0; Matakina Technology, Wellington, New Zealand) and Quantra (version 2.0; Hologic, Bedford, Mass), with clinical Breast Imaging Reporting and Data System (BI-RADS) density classifications and to examine associations of these measures with breast cancer risk. Materials and Methods In this study, 1911 patients with breast cancer and 4170 control subjects matched for age, race, examination date, and mammography machine were evaluated. Participants underwent mammography at Mayo Clinic or one of four sites within the San Francisco Mammography Registry between 2006 and 2012 and provided informed consent or a waiver for research, in compliance with HIPAA regulations and institutional review board approval. Digital mammograms were retrieved a mean of 2.1 years (range, 6 months to 6 years) before cancer diagnosis, with the corresponding clinical BI-RADS density classifications, and Volpara and Quantra density estimates were generated. Agreement was assessed with weighted κ statistics among control subjects. Breast cancer associations were evaluated with conditional logistic regression, adjusted for age and body mass index. Odds ratios, C statistics, and 95% confidence intervals (CIs) were estimated. Results Agreement between clinical BI-RADS density classifications and Volpara and Quantra BI-RADS estimates was moderate, with κ values of 0.57 (95% CI: 0.55, 0.59) and 0.46 (95% CI: 0.44, 0.47), respectively. Differences of up to 14% in dense tissue classification were found, with Volpara classifying 51% of women as having dense breasts, Quantra classifying 37%, and clinical BI-RADS assessment used to classify 43%. Clinical and automated measures showed similar breast cancer associations; odds ratios for extremely dense breasts versus scattered fibroglandular densities were 1.8 (95% CI: 1.5, 2.2), 1.9 (95% CI: 1.5, 2.5), and 2.3 (95% CI: 1.9, 2.8) for Volpara, Quantra, and BI-RADS classifications, respectively. Clinical BI-RADS assessment showed better discrimination of case status (C = 0.60; 95% CI: 0.58, 0.61) than did Volpara (C = 0.58; 95% CI: 0.56, 0.59) and Quantra (C = 0.56; 95% CI: 0.54, 0.58) BI-RADS classifications. Conclusion Automated and clinical assessments of breast density are similarly associated with breast cancer risk but differ up to 14% in the classification of women with dense breasts. This could have substantial effects on clinical practice patterns. (©) RSNA, 2015 Online supplemental material is available for this article.

Medical History, Lifestyle, Family History, and Occupational Risk Factors for Chronic Lymphocytic Leukemia/Small Lymphocytic Lymphoma: The InterLymph Non-Hodgkin Lymphoma Subtypes Project

BACKGROUND Chronic lymphocytic leukemia (CLL) and small lymphocytic lymphoma (SLL) are two subtypes of non-Hodgkin lymphoma. A number of studies have evaluated associations between risk factors and CLL/SLL risk. However, these associations remain inconsistent or lacked confirmation. This may be due, in part, to the inadequate sample size of CLL/SLL cases. METHODS We performed a pooled analysis of 2440 CLL/SLL cases and 15186 controls from 13 case-control studies from Europe, North America, and Australia. We evaluated associations of medical history, family history, lifestyle, and occupational risk factors with CLL/SLL risk. Multivariate logistic regression analyses were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS We confirmed prior inverse associations with any atopic condition and recreational sun exposure. We also confirmed prior elevated associations with usual adult height, hepatitis C virus seropositivity, living or working on a farm, and family history of any hematological malignancy. Novel associations were identified with hairdresser occupation (OR = 1.77, 95% CI = 1.05 to 2.98) and blood transfusion history (OR = 0.79, 95% CI = 0.66 to 0.94). We also found smoking to have modest protective effect (OR = 0.9, 95% CI = 0.81 to 0.99). All exposures showed evidence of independent effects. CONCLUSIONS We have identified or confirmed several independent risk factors for CLL/SLL supporting a role for genetics (through family history), immune function (through allergy and sun), infection (through hepatitis C virus), and height, and other pathways of immune response. Given that CLL/SLL has more than 30 susceptibility loci identified to date, studies evaluating the interaction among genetic and nongenetic factors are warranted.

Dense and Nondense Mammographic Area and Risk of Breast Cancer by Age and Tumor Characteristics

Background: Mammographic density (MD) is a strong breast cancer risk factor. We previously reported associations of percent mammographic density (PMD) with larger and node-positive tumors across all ages, and estrogen receptor (ER)–negative status among women ages <55 years. To provide insight into these associations, we examined the components of PMD [dense area (DA) and nondense area (NDA)] with breast cancer subtypes. Methods: Data were pooled from six studies including 4,095 breast cancers and 8,558 controls. DA and NDA were assessed from digitized film-screen mammograms and standardized across studies. Breast cancer odds by density phenotypes and age according to histopathologic characteristics and receptor status were calculated using polytomous logistic regression. Results: DA was associated with increased breast cancer risk [OR for quartiles: 0.65, 1.00 (Ref), 1.22, 1.55; Ptrend <0.001] and NDA was associated with decreased risk [ORs for quartiles: 1.39, 1.00 (Ref), 0.88, 0.72; Ptrend <0.001] across all ages and invasive tumor characteristics. There were significant trends in the magnitude of associations of both DA and NDA with breast cancer by increasing tumor size (Ptrend < 0.001) but no differences by nodal status. Among women <55 years, DA was more strongly associated with increased risk of ER+ versus ER− tumors (Phet = 0.02), while NDA was more strongly associated with decreased risk of ER− versus ER+ tumors (Phet = 0.03). Conclusions: DA and NDA have differential associations with ER+ versus ER− tumors that vary by age. Impact: DA and NDA are important to consider when developing age- and subtype-specific risk models. Cancer Epidemiol Biomarkers Prev; 24(5); 798–809. ©2015 AACR.

Rationale and Design of the International Lymphoma Epidemiology Consortium (InterLymph) Non-Hodgkin Lymphoma Subtypes Project

BACKGROUND Non-Hodgkin lymphoma (NHL), the most common hematologic malignancy, consists of numerous subtypes. The etiology of NHL is incompletely understood, and increasing evidence suggests that risk factors may vary by NHL subtype. However, small numbers of cases have made investigation of subtype-specific risks challenging. The International Lymphoma Epidemiology Consortium therefore undertook the NHL Subtypes Project, an international collaborative effort to investigate the etiologies of NHL subtypes. This article describes in detail the project rationale and design. METHODS We pooled individual-level data from 20 case-control studies (17471 NHL cases, 23096 controls) from North America, Europe, and Australia. Centralized data harmonization and analysis ensured standardized definitions and approaches, with rigorous quality control. RESULTS The pooled study population included 11 specified NHL subtypes with more than 100 cases: diffuse large B-cell lymphoma (N = 4667), follicular lymphoma (N = 3530), chronic lymphocytic leukemia/small lymphocytic lymphoma (N = 2440), marginal zone lymphoma (N = 1052), peripheral T-cell lymphoma (N = 584), mantle cell lymphoma (N = 557), lymphoplasmacytic lymphoma/Waldenström macroglobulinemia (N = 374), mycosis fungoides/Sézary syndrome (N = 324), Burkitt/Burkitt-like lymphoma/leukemia (N = 295), hairy cell leukemia (N = 154), and acute lymphoblastic leukemia/lymphoma (N = 152). Associations with medical history, family history, lifestyle factors, and occupation for each of these 11 subtypes are presented in separate articles in this issue, with a final article quantitatively comparing risk factor patterns among subtypes. CONCLUSIONS The International Lymphoma Epidemiology Consortium NHL Subtypes Project provides the largest and most comprehensive investigation of potential risk factors for a broad range of common and rare NHL subtypes to date. The analyses contribute to our understanding of the multifactorial nature of NHL subtype etiologies, motivate hypothesis-driven prospective investigations, provide clues for prevention, and exemplify the benefits of international consortial collaboration in cancer epidemiology.

Incidence of chronic lymphocytic leukemia and high-count monoclonal B-cell lymphocytosis using the 2008 guidelines.

The 1996 National Cancer Institute Working Group (NCI‐WG 96) guidelines classified disease in individuals who had a B‐cell clone with chronic lymphocytic leukemia (CLL) immunophenotype as CLL if their absolute lymphocyte count was ≥5 × 109/L. The 2008 International Workshop on CLL guidelines (IWCLL 2008) classified disease as CLL if the absolute B‐cell count was ≥5 × 109/L or as monoclonal B‐cell lymphocytosis (MBL) if the absolute B‐cell count was <5 × 109/L. The objective of the current study of Olmsted County, Minnesota, was to assess the effects of these changes on incidence rates and presentation from 2000 to 2010.

Single-nucleotide polymorphism rs1052501 associated with monoclonal gammopathy of undetermined significance and multiple myeloma

Monoclonal gammopathy of undetermined significance (MGUS) is the most prevalent clonal plasma cell proliferative disorder, present in over 3% of the population aged 50 years and older, with a rate of progression to malignancy of 1% per year. (Kyle, et al 2006, Kyle, et al 2002) MGUS has been shown to precede MM in almost all cases. (Landgren, et al 2009b) We, and others, have shown evidence for a familial component to MGUS, with first-degree relatives of MM or MGUS probands having a 2.5 fold increased risk of MGUS, indicative of an underlying genetic predisposition. (Landgren, et al 2009a, Vachon, et al 2009) While several genetic variants have been identified for MM, none have been identified that are associated with MGUS. (Greenberg, et al 2012) Broderick et al. (Broderick, et al 2011) recently conducted the first genome-wide association study (GWAS) of MM using case-control studies from the United Kingdom and Germany and identified three novel loci at 3p22.1 (rs1052501 in ULK4), 7p15.3 (rs4487645) and 2p23.3 (rs6746082) associated with risk of MM, although the latter did not reach genome wide significance (p<5×10−8). Here, we investigate whether these three loci for MM risk are also associated with risk of MGUS in order to provide evidence that genetic variation influences MM through MGUS. We also attempt to replicate the association of these loci with MM within a case-control study of MM.