Dietary patterns derived by reduced rank regression, macronutrients as response variables, and variation by economic status: NHANES 1999-2018.

PURPOSE: Macronutrient intakes vary across people and economic status, leading to a disparity in diet-related metabolic diseases. This study aimed to provide insight into this by: (1) identifying dietary patterns in adults using reduced rank regression (RRR), with macronutrients as response variables, and (2) investigating the associations between economic status and macronutrient based dietary patterns, and between dietary patterns with central obesity (waist circumference) and systemic inflammation (C-reactive protein [CRP]). METHODS: 41,849 US participants from the National Health and Nutrition Examination Survey (NHANES), 1999-2018 were included. The percentages of energy from protein, carbohydrates, saturated fats, and unsaturated fats were used as response variables in RRR. Multivariable generalized linear models with Gaussian distribution were employed to investigate the associations. RESULTS: Four dietary patterns were identified. Economic status was positively associated with both the high fat, low carbohydrate [ßHighVsLow = 0.22; 95% CI: 0.16, 0.28] and high protein patterns [ßHighVsLow = 0.07; 95% CI: 0.03, 0.11], and negatively associated with both the high saturated fat [ßHighVsLow = -0.06; 95% CI: -0.08, -0.03] and the low alcohol patterns [ßHighVsLow = -0.08; 95% CI; -0.10, -0.06]. The high saturated fat pattern was positively associated with waist circumference [ßQ5VsQ1 = 1.71; 95% CI: 0.97, 2.44] and CRP [ßQ5VsQ1 = 0.37; 95% CI: 0.26, 0.47]. CONCLUSION: Macronutrient dietary patterns, which varied by economic status and were associated with metabolic health markers, may explain associations between economic status and health.

Biased effects of pre-diagnostic physical activity on breast cancer survival: Systematic review and meta-analysis.

BACKGROUND: Pre-diagnostic physical activity is reported to improve survival for women with breast cancer. However, studies of pre-diagnostic exposures and cancer survival are susceptible to bias, made clear when applying a target trial framework. We investigated the impact of selection bias, immortal time bias, confounding and bias due to inappropriate adjustment for post-exposure variables in a systematic review and meta-analysis of pre-diagnostic physical activity and survival after breast cancer. METHODS: Medline, Embase and Emcare were searched from inception to November 2021 for studies examining pre-diagnostic physical activity and overall or breast cancer-specific survival for women with breast cancer. Random-effects meta-analysis was used to estimate pooled hazard ratios (HRs) and 95% confidence intervals (CIs) comparing highest versus lowest pre-diagnostic physical activity. Subgroup meta-analyses were used to compare HRs of studies with and without different biases. ROBINS-E was used to assess risk of bias. RESULTS: We included 22 studies. Women with highest versus lowest pre-diagnostic physical activity had higher overall and breast cancer-specific survival across most analyses. The overall risk of bias was high. We observed marked differences in estimated HRs between studies that did and did not adjust for post-exposure variables or have immortal time bias. All studies were at risk of selection bias due to participants becoming eligible for study when they have survived to post-exposure events (e.g., breast cancer diagnosis). Insufficient studies were available to investigate confounding. CONCLUSION: Biases can substantially change effect estimates. Due to misalignment of treatment assignment (before diagnosis), eligibility (survival to post-exposure events) and start of follow-up, bias is difficult to avoid. It is difficult to lend a causal interpretation to effect estimates from studies of pre-diagnostic physical activity and survival after cancer. Biased effect estimates that are difficult to interpret may be less useful for clinical or public health policy applications.

Sex-steroid hormones and risk of postmenopausal estrogen receptor-positive breast cancer: a case-cohort analysis.

Purpose: Sex-steroid hormones are associated with postmenopausal breast cancer but potential confounding from other biological pathways is rarely considered. We estimated risk ratios for sex-steroid hormone biomarkers in relation to postmenopausal estrogen receptor (ER)-positive breast cancer, while accounting for biomarkers from insulin/insulin-like growth factor-signaling and inflammatory pathways. Methods: This analysis included 1,208 women from a case-cohort study of postmenopausal breast cancer within the Melbourne Collaborative Cohort Study. Weighted Poisson regression with a robust variance estimator was used to estimate risk ratios (RRs) and 95% confidence intervals (CIs) of postmenopausal ER-positive breast cancer, per doubling plasma concentration of progesterone, estrogens, androgens, and sex hormone binding globulin (SHBG). Analyses included sociodemographic and lifestyle confounders, and other biomarkers identified as potential confounders. Results: Increased risks of postmenopausal ER-positive breast cancer were observed per doubling plasma concentration of progesterone (RR: 1.22, 95% CI: 1.03 to 1.44), androstenedione (RR: 1.20, 95% CI: 0.99 to 1.45), dehydroepiandrosterone (RR: 1.15, 95% CI: 1.00 to 1.34), total testosterone (RR: 1.11, 95% CI: 0.96 to 1.29), free testosterone (RR: 1.12, 95% CI: 0.98 to 1.28), estrone (RR: 1.21, 95% CI: 0.99 to 1.48), total estradiol (RR: 1.19, 95% CI: 1.02 to 1.39) and free estradiol (RR: 1.22, 95% CI: 1.05 to 1.41). A possible decreased risk was observed for SHBG (RR: 0.83, 95% CI: 0.66 to 1.05). Conclusion: Progesterone, estrogens and androgens likely increase postmenopausal ER-positive breast cancer risk, whereas SHBG may decrease risk. These findings strengthen the causal evidence surrounding the sex hormone-driven nature of postmenopausal breast cancer.