Consideration of Anthropometric Measures in Cancer. S. Lani Park April 24, 2009

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1 Consideration of Anthropometric Measures in Cancer S. Lani Park April 24, 2009

2 Presentation outline Background in anthropometric measures in cancer Examples of anthropometric measures and investigating its associations in cancer Height BMI Weight and BMI Change

3 Why study Anthropometric factors in cancers? They are standardize measures which may serve as markers for exposures and/or indexes for adiposity

4 Types of anthropometric measures Height Sitting height Leg Length Weight BMI (kg/m 2 ) Waist-Hip-Ratios

5 Observational study designs used to investigate associations between anthropometric measures and cancer Ecological Cross-Sectional Case-Control Cohort

6 Example of Ecological study

7 Example of cross-sectional study

8 Markers for childhood exposures Leg length: marker for growth before puberty Sitting height: because growth of trunk can continue, sitting height may be an indicator for energy availability before age of 20. Height: potential marker for childhood exposures

9 Models for height and weight Hu FB, Obesity Epidemiology

10 Explanation to observed associations Chance, bias, confounding Height as a marker for other exposures Reduced dietary intake (short stature) Protects against cancer (Frankel, BMJ, 1998) Diets of affluence (tallness) increase risk of cancer Height as a biomarker for biologic mediators of risk

11 Example: Height and endometrial cancer, cohort study Table 2. Relative Risk (RRs) For Endometrial Cancer in Relation to Height in the Multiethnic Cohort No. Cases RR 1 (95%CI) RR 2 (95%CI) RR 3 (95% CI) Height at baseline (m) < to < (1.08, 1.90) 1.42 (1.07, 1.88) 1.21 (0.91, 1.61) 1.60 to < (1.16, 1.90) 1.44 (1.11, 1.88) 1.09 (0.84, 1.43) (1.22, 1.98) 1.50 (1.13, 1.99) 0.88 (0.65, 1.18) 1 Age-adjusted RR 2 RRs were adjusted for age, ethnicity, education, age at menarche, menopausal status, age at menopause, duration and type of hormone therapy, oral contraceptive use, parity, smoking history, diabetes, hypertension, and baseline BMI. 3 RRs were adjusted for age, ethnicity, education, age at menarche, menopausal status, age at menopause, duration and type of hormone therapy, oral contraceptive use, parity, smoking history, diabetes, and hypertension, and baseline weight.

12 Example: UADT Cancers and height, multi-center case-control study Association between UADT cancer and height Height (cm) Overall Ca Co OR* 95% CI Quintile ( ) Quintile ( ) Quintile Quintile ( ) Quintile ( ) Missing Ptrend *Adjusted for center, education, sex, age, fruit and vegetable intake, tobacco status/frequency, and alcohol frequency.

13 Example 2: UADT Cancers and height, multi-center case-control study Association between UADT cancer and height, stratified by site Height (cm) Oral-oropharyngeal Hypopharynx Esophageal Ca Co OR 95% CI Ca Co OR 95% CI Ca Co OR 95% CI Quintile ( ) ( ) ( ) Quintile ( ) ( ) ( ) Quintile Quintile ( ) ( ) ( ) Quintile ( ) ( ) ( ) Ptrend *Adjusted for center, education, sex, age, fruit and vegetable intake, tobacco status/frequency, and alcohol frequency.

14 Limitations and strengths in using height Limitations Can represent a broad range of childhood exposures In our study examples, ideally it would be nice to have multiple measures of height particularly among >50 years of age Strength- Stable measure: Less likely to be affected by disease or its treatment. Within person variance of adult height is likely smaller than within person variance of adult weight

15 Adiposity and cancer

16 Markers for adiposity Weight: adulthood weight gain (age 18 to 55) usually reflects an increase in body fat. BMI: highly correlated with both absolute body fat and percent body fat. Waist-Hip-Ratio: measure for central adiposity

17 Why study BMI and cancer? (Renehan AG, Lancet, 2008)

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22 Obesity and Cancer biomechanism Calle EE, Kaaks R. Nature, 2004

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26 Hyperglycemia Results in increase of IGF1 levels Brownlee M, diabetes 2005

27 Statistical Models Hu FB, Obesity Epidemiology

28 Considerations for BMI in cancer Reverse causality Confounding variables Ethnic differences Self report validity, information bias

29 Example: Association between Leanness and UADT cancers BMI at study entry (kg/m 2 )** Overall Ca Co OR* 95% CI ( ) ( ) ( ) Missing 12 9 Ptrend <0.001 *Adjusted for center, education, sex, age, fruit and vegetable intake, tobacco status/frequency, and alcohol frequency.

30 Example: Association between Leanness and UADT cancers, earlier measure BMI at 2 years prior to interview (kg/m2) Overall Ca Co OR* 95% CI ( ) ( ) ( ) Missing Ptrend <0.001 *Adjusted for center, education, sex, age, fruit and vegetable intake, tobacco status/frequency, and alcohol frequency.

31 BMI at age 30 (kg/m 2 ) Example: Association between Leanness and UADT cancers, earlier measure Overall Ca Co OR* 95% CI ( ) ( ) ( ) Missing Ptrend *Adjusted for center, education, sex, age, fruit and vegetable intake, tobacco status/frequency, and alcohol frequency.

32 BMI at age 21 (kg/m 2 ) Example: Obesity and endometrial cancer risk No. Cases RR 1 (95%CI) RR 2 (95%CI) Quartile 1: < Quartile 2: to < (1.00, 1.73) 1.28 (0.97, 1.69) Quartile 3: to < (1.01, 1.76) 1.27 (0.96, 1.68) Quartile 4: (1.53, 2.56) 1.78 (1.36, 2.31) Ptrend <0.001 <0.001 BMI at baseline (kg/m 2 ) 3 < to < (1.02, 1.60) 1.29 (1.02, 1.65) (2.62, 3.95) 3.22 (2.48, 4.18) Ptrend <0.001 < Age-adjusted RR. 2 RRs were adjusted for age, ethnicity, education, age at menarche, menopausal status, age at menopause, duration and type of hormone therapy, oral contraceptive use, parity, smoking history, diabetes, and hypertension. 3 Additionally adjusted for BMI at age 21 (quartiles).

33 Example: Obesity and endometrial cancer risk Race/Ethnicity African American Japanese Americans Latina White BMI at age 21 (kg/m 2 ) No. Ca RR 2 (95% CI) No. Ca RR 2 (95% CI) No. Ca RR 2 (95% CI) No. Ca RR 2 (95% CI) < to < (0.85, 2.71) (0.86, 2.01) (0.44, 1.74) (0.61, 1.41) > (0.94, 2.84) (1.20, 2.81) (1.02, 3.22) (0.83, 1.89) P Trend BMI at baseline (kg/m 2 ) < to < (0.60, 2.34) (0.82, 1.93) (0.76, 2.62) (0.80, 1.83) (1.58, 5.54) (2.58, 6.97) (1.91, 6.28) (1.61, 3.78) P trend <0.001 <0.001 <0.001 <0.001

34 Ethnic differences between BMI and body fat Hu FB, Obesity Epidemiology

35 Validity of self-reported height and weight Hu FB, Obesity Epidemiology

36 Factors affecting body weight Genetic Endocrine/regulatory Behavioral Psychosocial Environmental

37 Factors to consider when investigating body weight change Reverse causality Confounding variables Ethnic differences Self report validity, information bias Energy Balance Food consumption Physical Activity Collinearity

38 Energy Balance The balance between energy taken in, generally by food and drink, and energy expended. Energy expenditure is influenced by genetics, body size and amount of muscle, and by physical activity. While calories are probably the most critical element in maintaining your energy balance, other factors in your diet such as how much fiber or calcium you eat may influence your energy expenditure and how much muscle and fat you have. Vaughn P,

39 Measuring Food Consumption

40 Example of Semi-quantitative FFQ DIETARY HABITS [Example, will need to adapt to each country] How often did you consume the following foods and beverages one year ago? Unit Food item How many times per day, week, month, year? (mark one column only) day week month year Never D1 1 portion Beef D2 1 portion Pork D3 1 portion Poultry D4 1 portion Other meat (lamb, etc.) D5 1 portion Fish D6 1 portion Ham, salami, sausages D7 1 portion Raw green vegetables and salads D8 1 portion Cooked green vegetables D9 1 portion Carrots D10 1 portion Fresh tomatoes D11 1 portion Pulses (peas, beans, etc.)

41 Example of a quantitative FFQ

42 Physical Activity

43 metabolic equivalents of energy expenditure (METs) METs= (sleeping [h/d]* sitting [h/d]*1.0+ light activity [h/d]*2.4 + moderate activity [h/d]*4.0 + vigorous activity [h/d]*7.2)/24)

44 Example: Physical activity and endometrial cancer No. Cases RR 1 (95% CI) RR 2 (95% CI) RR 3 (95% CI) Physical activity (METs) 3 Quartile 1: < Quartile 2: to < (0.67,1.10) (0.67, 1.10) (0.70, 1.14) Quartile 3: to < (0.61, 1.01) 0.81 (0.63, 1.05) 0.86 (0.67, 1.11) Quartile 4: P trend (0.63, 1.04) (0.67, 1.10) (0.72, 1.19) Age-adjusted RR. 2 RRs were adjusted for age, ethnicity, education, age at menarche, menopausal status, age at menopause, duration and type of hormone therapy, oral contraceptive use, parity, smoking history, diabetes, and hypertension. 3 Additionally adjusted for BMI at baseline (quartiles).

45 Different measures of body weight change and cancer Weight difference BMI difference % change (weight or BMI) [(measure at baseline minus measure at age 21) / measure at age 21] 100 % Average annual BMI change % BMI change/(time between BMI measures)

46 Example of BMI change in UADT cancers, multicenter case-control study Percent BMI change from age 30 to study entry Overall Ca Co OR* 95% CI <-15% ( ) -15% BMI change <-5% ( ) -5% BMI change <5% % BMI change <15% ( ) 15% BMI change <25% ( ) BMI change 25% ( ) Missing Ptrend <0.001 *Adjusted for center, education, sex, age, fruit and vegetable intake, tobacco status/frequency, and alcohol frequency.

47 Ptrend <0.001 *Adjusted for center, education, sex, age, fruit and vegetable intake, tobacco status/frequency, and alcohol frequency. Example of BMI change in UADT cancers, multicenter case-control study, cont. Percent BMI change from age 30 to 2 years prior study entry Overall Ca Co OR* 95% CI <-15% ( ) -15% BMI change <-5% ( ) -5% BMI change <5% % BMI change <15% ( ) 15% BMI change <25% ( ) BMI change 25% ( ) Missing

48 Possible explanations: Reverse causality Residual confounding Alterations in smoking or drinking behaviors Smoking cessation results in weight gain Biological mechanism

49 Association may be due to smoking cessation Associations between BMI change and UADT SCCs, stratified by smoking Variable BMI change from age 30 to 2 years prior to study entry Loss (<-5%) Stable Gain ( +5%) Ptrend Smoking Status Never smoking case/controls 22/71 36/ /430 OR (95% CI)* 1.19 ( ) ( ) Former smoking case/controls 51/67 94/ /431 OR (95% CI)* 1.17 ( ) ( ) Current smoking case/controls 205/64 440/ /293 OR (95% CI)* 1.06 ( ) ( ) <0.001 *Adjusted for center, education, sex, age, fruit and vegetable intake, tobacco status/frequency, and alcohol frequency.

50 Drinking Status Never drinking Associations between weight change and UADT cancers, stratified by UADT risk factors, cont Loss Stable Gain ptrend case/controls 14/40 23/54 53/138 OR (95% CI)* 0.62 ( ) ( ) Former drinking case/controls 53/32 86/39 112/106 OR (95% CI)* 0.76 ( ) ( ) Current drinking case/controls 210/ / /917 OR (95% CI)* 1.32 ( ) ( ) <0.001 *Adjusted for center, education, sex, age, fruit and vegetable intake, tobacco status/frequency, and alcohol frequency.

51 Loss Stable Gain Smoking and Drinking status Never smokes and drinks case/controls 6/22 9/36 22/93 never smoke and drink 0.97 ( ) ( ) No drinking but smokes case/controls 8/18 14/18 31/45 OR (95% CI)* 0.38 ( ) ( ) No smoking but drinks case/controls 16/47 28/134 80/340 OR (95% CI)* 1.20 ( ) ( ) Smokes and drinks Associations between weight change and UADT cancers, stratified by UADT risk factors, cont. case/controls 247/ / /683 OR (95% CI)* 1.23 ( ) ( ) <0.001

52 Rationale for leanness, UADT cancer association leanness UADT cancer Biological mechanism DNA damage Difference in mean BMI change between former and current smokers, where former smokers showed a greater BMI increase (p<0.001). Sensitivity test showed mean BMI change greater in those who claimed to stop smoking 3 years prior to BMI measure. (n= 51, 12.0% vs. 11.2%; ttest pvalue=0.1952).

53 Confounding, by age Time to Disease Loss Stable Gain Ptrend Quartile 1 case/controls 50/41 166/ /210 OR (95% CI)* 0.79 ( ) ( ) Quartile 2 case/controls 88/42 176/ /327 OR (95% CI)* 1.65 ( ) ( ) Quartile 3 case/controls 68/41 128/ /313 OR (95% CI)* 1.25 ( ) ( ) Quartile 4 case/controls 72/78 100/ /304 OR (95% CI)* 1.00 ( ) ( ) ** Quartile 1: <22 years, quartile 2: 22<time to disease <30, quartile 3: 30< time to disease<38, and quartile 4: >38 years.

54 Example of another smoking related cancer Lung cancer, population based case-control study Association between BMI change and Lung cancer BMI change Case/ Control Crude OR (95% CI) Adjust OR 1 (95% CI) <5%Weight loss 64/ (1.11, 2.72) 1.26 (0.73, 2.17) -5% loss to < 5% gain 113/ % to <15% 132/ (0.47, 0.91) 0.80 (0.54, 1.18) 15% to <25% 113/ (0.48, 0.94) 0.78 (0.52, 1.17) 25% to <35% 67/ (0.38, 0.82) 0.58 (0.37, 0.91) >35% weight gain 122/ (0.50, 0.97) 0.58 (0.39, 0.87) P trend

55 Causal diagram BMI change and endometrial cancer Confounding variables BMI change from age 21 to baseline r= 0.74?? Endometrial Cancer BMI at Age 21 BMI at baseline

56 Body weight change (%) Example of Collinearity using BMI change in Endometrial cancer No. Cases <-5 (weight loss) (0.82, 2.50) - 5 to < to < (1.09, 2.51) 15 to < (1.10, 2.54) 25 to < (1.17, 2.77) (2.08, 4.62) P trend <0.001 RR 1 (95% CI) RR 2 (95% CI) RR 3 (95% CI) 0.98 (0.54, 1.76) 1.46 (0.83, 2.55) (1.12, 2.58) 1.44 (0.83, 1.92) 1.76 (1.16, 2.67) 1.26 (0.75, 1.82) 1.94 (1.26, 2.98) 1.17 (0.75, 1.82) 3.55 (2.38, 5.29) 1.38 (0.89, 2.14) < RRs were adjusted for age, ethnicity, education, age at menarche, age at menopause, duration and type of hormone therapy, oral contraceptive use, parity, smoking history, diabetes, and hypertension. 2 Model 1 and adjusting for BMI at age 21 3 Model 1 and adjusting for BMI at baseline

57 Stratify by BMI measures BMI at 21 (kg/m 2 ) BMI Change Category 1 Category 2 (5% to <35%) Category 3 ( 35%) (<5%) 1 No. cases RR 2 No. cases RR 2 No. cases RR 2 Ptrend Tertile 1: < (0.75, 2.70) (0.98, 3.93) Tertile 2: to < (0.75, 2.16) (1.77, 5.61) <0.001 Tertile 3: (1.20, 3.04) (2.34, 6.28) <0.001 P interaction= BMI at baseline (kg/m 2 ) < (1.03, 2.13) (0.95, 4.61) (0.62, 3.27) (1.23, 6.47) <0.001 P interaction=

58 BMI change, stratified by ethnicity Race/Ethnicity African American Japanese Americans Latina White No. Ca RR 2 (95% CI) No. Ca RR 2 (95% CI) No. Ca RR 2 (95% CI) No. Ca RR 2 (95% CI) BMI change (%) < +5% % to <20% (0.35, 4.63) (1.24, 3.37) (0.48, 4.59) (0.77, 2.27) 20% to <35% (0.43, 5.23) (1.16, 3.51) (0.91, 7.61) (0.93, 2.84) 35% (0.95, 10.2) (1.28, 4.73) (1.41, 11.39) (1.45, 4.34) P trend <0.001 < Multivariate RRs were adjusted for age, education, age at menarche, menopausal status, age at menopause, duration and type of hormone therapy, duration and oral contraceptive use, parity, smoking history, diabetes, and hypertension and BMI at age 21.

59 Race/Ethnicity African American Japanese Americans Latina White Body weight change (%), ethnic specific tertiles 3 No. Ca RR 2 (95% CI) No. Ca RR 2 (95% CI) No. Ca RR 2 (95% CI) No. Ca RR 2 (95% CI) Tertile Tertile (0.70, 2.54) (1.17, 2.88) (1.04, 3.72) (0.84, 2.06) Tertile (1.53, 4.94) (1.17, 2.96) (1.72, 5.86) (1.13, 2.70) P trend < < Multivariate RRs were adjusted for age, education, age at menarche, menopausal status, age at menopause, duration and type of hormone therapy, duration and oral contraceptive use, parity, smoking history, diabetes, and hypertension and BMI at age Tertile distribution for African Americans: <23.59%, 23.59% to <42.80%, and 42.80%; Japanese Americans: <8.18%, 8.18% to <20.10%, > 20.10%; Latinas: <18.46%, 18.46% to <35.45%, > 35.45%; Whites: <10.00%, 10.00% to 26.19%, > 26.19%

60 Average annual weight change, example using endometrial cancer data Average annual weight change (%/year) No. Cases RR (95% CI) < 0.25 (weight loss) (0.49, 1.95) 0.25 to < to < (0.82, 1.52) 0.50 to < (1.00, 1.87) 0.75 to < (1.02, 2.04) 1.0% (1.90, 3.38) Ptrend <0.001 Assumptions: Weight cycling is not a factor in endometrial cancer risk

61 Molecular approach Measure inflammatory markers Measure IGF1 and IGFBP3 levels Genotype sometimes correlates to phenotype (Cheng I, 2007; Al-Zahrani A, 2006) Calle EE, Kaaks R. Nature, 2004

62 Relationship between IGF1 and BMI Native Hawaiian African American White Latino Japanese American Henderson KD, et al., CEBP, 2006

63 Conclusions Limitations and Strengths Summary Future Directions

64 Limitations and Strengths, case control design Limitations Recall bias Reverse causation Strengths Ability to study associations with anthropometric measures in rare cancers More likely to have in-person questionnaire interviews to obtain additional anthropometric measures ie WHR Potential for specific questionnaire design Potentially providing ability to control residual confounding

65 Limitations and strengths of cohort design Limitations Potential for nonspecific questionnaires Strengths Reduction of temporal ambiguity. Potential for multiple weight measures

66 In Summary The study of anthropometric measures is necessary in cancer. Anthropometric measures are fairly valid, cost-effective means to measure adiposity. As of now weight maintenance is an effective means of prevention against obesity related cancer

67 Future Directions Improving epidemiological measures to reducing residual confounding and information bias of our anthropometric measures. Measures of WHR would assist further understanding the observed associations between BMI change and cancer. Further investigating the genetic and biological relationships between obesity and cancer would be beneficial.

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