Low-Fat Dietary Pattern Intervention Trials for the Prevention of Breast and Other Cancers

Transcription

1 Low-Fat Dietary Pattern Intervention Trials for the Prevention of Breast and Other Cancers Ross Prentice Fred Hutchinson Cancer Research Center and University of Washington AICR, November 5, 2009

2 Outline WHI Dietary Modification Trial, design and findings WINS and WHEL intervention trials, design and findings WHI nutrition and physical activity biomarker studies Calibrated energy and protein in relation to cancer risk Aspects of the future research agenda for chronic disease prevention via diet and/or physical activity change

3 Design of WHI DM 48,835 CaD HRT 36,282 27,347 CT=68,132 WHI =161,808 os 93,676

4 Low-Fat Dietary Pattern Trial: Findings and Methodology Intervention Group Goals: 20% energy from fat 5 or more fruit and vegetable servings daily 6 or more grain servings daily Photos courtesy of USDA Agricultural Research Service

5 Mean (SD) of Nutrient Consumption by Randomization Group Year 1 Year 1 Year 3 Year 6 Intervention Control Difference Difference Difference Fat (% of calories) 24.3 (7.5) 35.1 (6.9) -10.7* (7.0) -9.5* (7.4) -8.1* (7.8) Total Fat (g) Energy (kcal) 40.8 (21.4) 63.0 (31.0) -22.4* (31.1) -20.1* (32.0) -18.4* (33.5) (544.2) (644.0) -95.8* (616.2) -92.5* (632.1) * (662.9) *Difference significant at p<0.001 from a two sample t-test

6 WHI DM: Change in Body Weight by Randomization Group 4 Mean Difference (kg) Intervention Control Years of Intervention Howard BV et al. JAMA Jan 2006

7 WHI DM: Breast Cancer Incidence by Treatment Group Cumulative Hazard HR, 0.91 (95% CI, ) Comparison Intervention 1,727 total diagnoses 3.5% of all DM participants Time (y ears) Events Intervention H a z a r d R a t io * Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year Overall Prentice RL, Caan B, Chlebowski RT et al. JAMA 2006

8 WHI DM: Invasive Breast Cancer No. of Cases (Annualized %) Intervention Comparison HR (95% CI) BrCa Incidence 655 (0.42) 1072 (0.45) 0.91 ( )* Mortality 27 (0.02) 53 (0.02) 0.77 ( ) * p=0.07; p=0.09 (adjusted) JAMA 2006;295:

9 Comparison of Cancer Incidence Rates between Intervention and Comparison Groups in the Women s Health Initiative (WHI) Dietary Modification Trial* Prentice et al (JAMA, 2006; JNCI, 2007); Beresford et al (JAMA, 2006) Incidence per 1000 person-years ( Number of cases) Cancer Site Intervention Comparison p HR(95% CI) Breast 4.15 (655) 4.52 (1072) (0.83 to 1.01) Colorectal 1.27 (201) 1.18 (279) (0.90 to 1.29) Ovary 0.36 (57) 0.43 (103) (0.60 to 1.14) Endometrium 0.79 (125) 0.71 (170) (0.88 to 1.40 All other sites 4.56 (720) 4.81 (1140) (0.86 to 1.04) Total cancer (1687) (2661) (0.89 to 1.01)

10 WHI DM: Risk of Breast Cancer by Tumor Characteristics No. of Cases (Annualized %) Tumor characteristics Intervention Comparison Estrogen+ /progesterone+ Estrogen+ /progesterone - Estrogen- /progesterone+ Estrogen- /progesterone- Prentice et al JAMA 295;629, 2006 HR (95% CI) 399 (0.25) 616 (0.26) 0.97 ( ) 77 (0.05) 179 (0.08) 0.64 ( ) 8 (0.01) 18 (0.01) 0.67 ( ) 82 (0.05) 138 (0.06) 0.89 ( ) P- Value

11 Low-Fat Dietary Pattern Intervention Effects on Breast and Ovarian Cancer, in Relation to Baseline 4-Day Food Record % of Energy from Fat % of Energy from Fat Mean (SD) Difference Hazard Ratio Interaction (4DFR) Between Groups (95% CI) P-Value Breast Cancer (1727 cases) < (6.2) 0.97 (0.79, 1.20) (6.5) 1.08 (0.89, 1.30) (6.6) 0.85 (0.70, 1.03) (7.0) 0.78 (0.64, 0.95) Ovary Cancer (160 cases) < (0.76, 2.33) (0.32, 1.12) (0.31, 1.08)

12 Women s s Intervention Nutrition Study (WINS) Evaluating Dietary Fat Reduction in Early Stage Breast Cancer Eligibility Criteria: Women years Early breast cancer Primary surgery ± RTx Systemic therapy (ER+: tamoxifen/chemothera py; ER : chemotherapy) Dietary fat intake > 20% of calories (n = 2437) Randomization 40:60 within a year from primary surgery R A N D O M I Z E Dietary intervention: reduced fat intake (n = 975) Control (n = 1462) Primary Endpoint: Relapse-free survival Chlebowski RT, et al. J Natl Cancer Inst 2006;98:1767.

13 WINS: PERCENT CALORIES FROM FAT Baseline Control Baseline Diet Baseline Density of %Fat 12 mo. Diet Month p< mo Control Percent CAL from Fat Percent CAL from Fat Solid---IIG, Dashed---NIG

14 WINS: Fat Gram Intake By Treatment Group Fat Grams / Day * * * * * Control Diet 0 BL 1 YR 2 YR 3 YR 4 YR 5 YR * Significantly different by T test from control and baseline, p<0.0001

15 Change in BMI and Weight by Group Diet Minus Control Group Variable Year 1 Year 3 Year 5 BMI (kg/m 2 ) (-1.3 to -0.3) Weight (lbs) -5.0 (-8.0 to -2.1) (-1.3 to -0.2) -3.9 (-6.9 to -0.5) -1.1 (-1.9 to -0.4) -6.0 (-9.9 to -1.9) All values, P <.005 versus control BMI = Body Mass Index All values for weight, P =.005, intervention versus control Information on weight and BMI was available for all 975 and 1462 women in the dietary intervention group and the control group, respectively, at baseline; for 854 and 1310 at year 1; 698 and 1044 at year 3; and 386 and 998 at year 5. Chlebowski RT, et al. J Natl Cancer Inst 2006;98:1767.

16 WINS: Relapse Events PATIENTS (%) Diet Control HR, 95% CI p-value* 96/ / , Control Diet Absolute difference:1% 3% 3% 3% 4% 7% Follow-up YEARS time (Years) Diet Control * From adjusted Cox proportional hazards model including: stratification factors, ER status, tumor size, and surgery (mastectomy/lumpectomy), p value = by unadjusted log rank test Chlebowski RT, et al. J Natl Cancer Inst 2006;98:1767.

17 WINS: Relapse-Free Survival by Receptor Status and Intervention Group Number per Group Diet Control HR (95% CI) ER+, PR ( ) ER+, PR ( ) ER-, PR ( ) ER-. PR ( )

18 WINS: Overall Mortality by Receptor Status and Intervention Group Number Per Group Diet Control HR (95% CI) P-value* All ( ) ER+, PR ( ) ER+,PR ( ) ER-, PR ( ) ER-, PR ( ) * For adjusted Cox model analyses, adjusted for strata, tumor size, and ER Of 362 women in ER-, PgR- subgroup: 7.5% mortality in diet group; 18.1% mortality in control group Chlebowski RT, Blackburn GL, Hoy MK, et al Proc AMer Soc Clin Oncol 26: Abstract 522, 2008

19 WINS: Conclusions Dietary fat intake can be reduced in breast cancer patients participating in a multi-center clinical trial. A life-style intervention resulting in dietary fat reduction may increase relapse-free survival in a population of mostly postmenopausal breast cancer patients but overall survival was not significantly increased. Exploratory analyses suggest a greater dietary effect in patients with receptor negative disease Further study of lifestyle interventions designed to improve breast cancer outcome are warranted

20 WHEL Study (Women s s Healthy Eating and Living) RCT: early stage breast cancer survivors ( ) - age (two-thirds < 55 yo) R diet intervention control - 5 vegetable servings - 16 oz vegetable juice -3 fruit servings - 30 gm fiber % calories fat Primary Outcome: - breast cancer events, death - disease-free survival Pierce J et al JAMA 2007;278:289.

21 Pierce et al JAMA 2007;398 WHEL: Disease Free Survival

22 Dietary Intake and Body Weight Change During WINS and WHEL Intervention % Energy from fat WHEL WINS Baseline Yr Yrs Yrs Body Weight (kg) Baseline Yr Yrs Yrs WINS values, all mean + SD, WHEL values, all mean + SE Significantly difference from baseline, p<0.005 Chlebowski RT, et al. Breast Cancer Res Treat 2006; 100(suppl 1):S16 (abstract 32).

23 A Low-Fat, High Carbohydrate Dietary Intervention in Women with > 50% With Mammographic Density of Breast Area Eligibility Criteria: Women years Mammogram > 50% breast density BMI kg/m2 No prior breast cancer (n = 4690) R A N D O M I Z E Dietary intervention: reduce fat intake (n = 2341) Control (n = 2349) Primary Endpoint: Breast cancer incidence Goal: reduce fat to 15%, increase carbohydrate to 65% of energy, without change in total energy intake Boyd et al J Natl Cancer Inst 89;488:1997; Martin et al Breast Cancer Res Treat 113;163:2009

24 Are Biases in Dietary Assessment Dominating Nutritional Epidemiology? Bingham et al (2003, Lancet) report a positive association between breast cancer and total and fat when consumption was assessed using a 7-day food diary, but the association was modest and non-significant when consumption was assessed with a FFQ. Very similar results from 4-day food record and FFQ analyses among DM comparison group women (Freedman et al 2006, IJE). Objective measures (biomarkers) are needed to make progress in this important research area. Biomarker assessments in substudies (such as DLW measures of total energy expenditure) can be used to calibrate self-report assessments.

25 Underreporting of Energy and Protein (Heitmann and Lissner, 1995, BMJ)

26 Nutrient and Physical Activity Biomarkers in the WHI 544 women completed two-week week DLW protocol with urine and blood collection and with FFQ and other questionnaire data collection (50% intervention, 50% control). A 20% reliability subsample repeated protocol separated, by about 6 months from original data collection. (NBS) Biomarker study among 450 women in the WHI Observational Study for calibrating baseline FFQ, 4DFR, and PA questions, and for evaluating measurement properties of prominent dietary and physical activity assessment approaches (frequencies, records, and recalls) and their combination. (NPAAS)

27 Measurement Models for Nutritional Epidemiology (Carroll, Freedman, Kaaks, Kipnis, Spiegelman, Rosner, Prentice ) Recovery Biomarkers: W biomarker = Z + e Q self-report = a 0 + a 1 Z + a 2 V + r + ε

28 Odds Ratio and Hazard Ratio Estimation Under a joint normality assumption for (Z, r + e) given V E (Z Q,V) = b 0 + b 1 Q + b 2 V = E (W Q,V) Calibrated estimates of Z from linear regression of W on (Q,V) in the biomarker subsample Estimation of hazard ratios by inserting calibrated consumption estimates in Cox regression, and using a bootstrap procedure for standard error estimation

29 Regression Calibration Coefficients for Log- Transformed Total Energy, Total Protein and Percent Energy from Protein (Neuhouser et al, AJE, 2008) Characteristic Coefficient (SD) Log Total Energy Coefficient (SD) Log Protein Coefficient (SD) Log % Energy from Protein Intercept 7.61 (0.13) 4.28 (0.024) 2.66 (0.01) Log FFQ (0.018) (0.032) (0.058) BMI (0.001) (0.002) (0.002) Age (0.001) (0.002) (0.002) Black (0.017) (0.047) Hispanic (00.30) (0.056) Other race (0.027) (0.058)

30 Geometric means and 95% confidence intervals for uncalibrated dietary intakes as estimated by the Women s Health Initiative (WHI) Food Frequency Questionnaire, and for calibrated intakes using nutritional biomarker data in the WHI Dietary Modification trial comparison group (DM) and Observational Study (OS) (Prentice et al, AJE, 2009) Geometric Mean (95 % Confidence Interval) Energy (kcal/day) Protein (g/day) % of Energy from Protein Uncalibrated Calibrated* Uncalibrated Calibrated Uncalibrated Calibrated DM (675.9, ) (1796.5, ) 60.9 (26.2, 141.6) 78.3 (58.3, 105.0) 16.6 (11.4, 24.1) 14.4 (12.1, 17.2) OS (642.6, ) (1726.1, ) 58.6 (247.0, 138.7) 74.4 (55.5, 99.9) 16.9 (11.5, 25.1) 14.4 (11.9, 17.6) *Calibrated using measurement model A for women in the Nutrition Biomarker Study, and model B otherwise.

31 Estimated Hazard Ratios and 95% Confidence Intervals for a 20% Increase in Energy Consumption from Combined Analysis of Data from the Women s Health Initiative Dietary Modification Trial Comparison Group and Observational Study, Without and With Biomarker Calibration of Consumption: (Open box uncalibrated, Black circle calibrated)

32 Estimated Hazard Ratios and 95% Confidence Intervals for a 20% Increase in % of energy from protein, from Combined Analysis of Data from the Women s Health Initiative Dietary Modification Trial Comparison Group and Observational Study, Without and With Biomarker Calibration of Consumption: (Open box uncalibrated, Black circle calibrated)

33 NPAAS Regression Calibration Coefficients for Log-transformed Total Energy, Protein and % of Energy from Protein, using FFQ, 4DFR or 24HR Dietary Amount (NPAAS) FFQ 4DFR 24HR Characteristic Coefficient (Std. Error) Coefficient (Std. Error) Coefficient (Std. Error) Log - Total Energy (kcal) Intercept (0.012) 7.61 (0.011) 7.62 (0.012) Log Energy (0.022) 0.24 (0.031) 0.15 (0.032) Body Mass Index (0.002) (0.002) (0.002) Age (0.002) (0.001) (0.002) Log - Protein (gm) Intercept 4.32 (0.016) 4.29 (0.014) 4.32 (0.014) Log Protein 0.14 (0.028) 0.48 (0.047) 0.42 (0.050) Body Mass Index (0.002) (0.002) (0.002) Age (0.003) (0.002) (0.002) Log - % of Energy from Protein Intercept 2.67 (0.021) 2.68 (0.020) 2.71 (0.021) Log (% Energy from 0.44 (0.081) 0.51 (0.090) 0.49 (0.089) Protein) Body Mass Index (0.003) (0.003) (0.003) Age (0.003) (0.003) (0.003)

34 Lessons from Studies of Dietary Consumption Effects on Chronic Disease Adequate control of dietary assessment measurement errors may be key to reliable nutritional (and physical activity) effects on chronic disease Development of biomarkers for other nutrients/aspects of physical activity, or methodologic research as to how to use existing or new (e.g., ASA24 and ACT24) dietary and physical activity assessment strategies should have a high priority in the nutrition, physical activity, and chronic disease research agenda

35 Development and Testing of New Preventive Interventions Newer forms of high-dimensional biologic data may have potential to add to formulation and initial testing of preventive interventions

36 Hormone Therapy Proteomics Project 50 E-alone E women; 50 E+P women Compare baseline to 1-year 1 serum proteome in pools of size 10

37 Protein Identification and Quantification Following the application of strict criteria for protein identification, 378 proteins were evaluated for change with E+P or E-alone Of these, a remarkable 44.7% (169/378) had evidence of change (p<0.05) with E+P and/or E-alone. Altered proteins were in multiple biological pathways relevant to observed clinical effects: coagulation/inflammation, immune function, cell adhesion, osteogenesis, growth factors,

38 Population Science Research Needs An enhanced preventive intervention development enterprise Observational studies of maximal reliability for promising intervention concepts Full-scale intervention trials when rationale strong enough, and public health potential sufficiently great Vigorous methodology development (e.g., to incorporate exposure and intermediate outcome biomarkers into research agenda) Infrastructure to facilitate?

39 Population Science Cooperative Group Identify preventive interventions that merit initial testing or full-scale evaluation Receive and evaluate preventive trial proposals and cohort study development/maintenance proposals Identify and facilitate needed methodologic research Group Composition Population, basic and clinical scientists Leaders in key areas for intervention development Leaders in major chronic disease research areas Representatives from within and outside of NIH

40 Summary A low-fat dietary pattern intervention may reduce the risk of breast and other cancers, and reduce breast cancer recurrence risk, though results are not conclusive. Advancing nutrition and physical activity disease prevention research is a most important public health need in the midst of our national epidemic of obesity and obesity-related diseases. Biomarkers/ objective measures of diet and physical activity provide a practical and logical component to the related research agenda. High-dimensional biologic (e.g. proteomic, metabolomic) ) data may enhance preventive intervention development.