BACKGROUND:Associations of the macronutrient composition of the diet with total energy intake (EI) are uncertain, as are associations of macronutrient composition with self-reported energy underreporting. OBJECTIVES:We aimed to estimate the associations of biomarker-assessed EI with both biomarker-assessed and self-reported macronutrient component densities in a Women's Health Initiative (WHI) subcohort of postmenopausal females in the United States. Secondarily, we examined energy underreporting using food records, recalls, and frequencies, for association with macronutrient densities. METHODS:We used a previously proposed EI biomarker equation based on doubly labeled water (DLW) and updated biomarker equations for several macronutrient component densities, to estimate EI and macronutrient component densities in a WHI nutritional biomarkers subcohort (n = 436; 2007-2009). We used linear regression of EI biomarker values on biomarker and self-reported macronutrient component densities, and of EI underreporting values on biomarker densities, to examine targeted associations. RESULTS:Using biomarker assessments, the geometric mean (95% CI) for EI corresponding to a 20% increment in carbohydrate density was 2.0% (0.1%, 3.9%) higher, and for a 20% protein density increment was 2.1% (0.5%, 3.7%) lower. The former was attributable to added sugars. Similarly, EI values for 20% increments in polyunsaturated (PUFA), and monounsaturated (MUFA) fatty acid densities were 1.4% (0.3%, 2.6%) higher and 1.5% (0.1%, 2.9%) lower, respectively. Pertinent associations were either not detected or were substantially attenuated if instead self-reported macronutrient densities were used. Also, EI underreporting was strongly related to self-reported macronutrient densities using food records, recalls, or frequencies. CONCLUSIONS:Among postmenopausal females in the United States lower EI was associated with diets relatively high in protein or MUFA, and higher EI was associated with diets relatively high in PUFA or added sugars. These associations are of public health importance but are mostly missed using self-reported dietary density assessments. Self-reported energy underestimation is substantially associated with self-reported macronutrient densities. CLINICAL TRIAL REGISTRY:This study is registered with clinicaltrials.gov identifier: NCT00000611.

Global economic development has been associated with an increased prevalence of obesity and related health problems. Increased caloric intake and reduced energy expenditure are both cited as development-related contributors to the obesity crisis, but their relative importance remains unresolved. Here, we examine energy expenditure and two measures of obesity (body fat percentage and body mass index, BMI) for 4,213 adults from 34 populations across six continents and a wide range of lifestyles and economies, including hunter-gatherer, pastoralist, farming, and industrialized populations. Economic development was positively associated with greater body mass, BMI, and body fat, but also with greater total, basal, and activity energy expenditure. Body size-adjusted total and basal energy expenditures both decreased approximately 6 to 11% with increasing economic development, but were highly variable among populations and did not correspond closely with lifestyle. Body size-adjusted total energy expenditure was negatively, but weakly, associated with measures of obesity, accounting for roughly one-tenth of the elevated body fat percentage and BMI associated with economic development. In contrast, estimated energy intake was greater in economically developed populations, and in populations with available data (n = 25), the percentage of ultraprocessed food in the diet was associated with body fat percentage, suggesting that dietary intake plays a far greater role than reduced energy expenditure in obesity related to economic development.

The double-blinded randomized trial is considered the gold standard to estimate the average causal effect (ACE). The naive estimator without adjusting any covariate is consistent. However, incorporating the covariates that are strong predictors of the outcome could reduce the issue of unbalanced covariate distribution between the treated and controlled groups and can improve efficiency. Recent work has shown that thanks to randomization, for linear regression, an estimator under risk consistency (e.g. Random Forest) for the regression coefficients could maintain the convergence rate even when a nonparametric model is assumed for the effect of covariates. Also, such an adjusted estimator will always lead to efficiency gain compared to the naive unadjusted estimator. In this paper, we extend this result to the competing risk data setting and show that under similar assumptions, the augmented inverse probability censoring weighting (AIPCW) based adjusted estimator has the same convergence rate and efficiency gain. Extensive simulations were performed to show the efficiency gain in the finite sample setting. To illustrate our proposed method, we apply it to the Women's Health Initiative (WHI) dietary modification trial studying the effect of a low-fat diet on cardiovascular disease (CVD) related mortality among those who have prior CVD.

Nutritional epidemiology aims to link dietary exposures to chronic disease, but the instruments for evaluating dietary intake are inaccurate. One way to identify unreliable data and the sources of errors is to compare estimated intakes with the total energy expenditure (TEE). In this study, we used the International Atomic Energy Agency Doubly Labeled Water Database to derive a predictive equation for TEE using 6,497 measures of TEE in individuals aged 4 to 96years. The resultant regression equation predicts expected TEE from easily acquired variables, such as body weight, age and sex, with 95% predictive limits that can be used to screen for misreporting by participants in dietary studies. We applied the equation to two large datasets (National Diet and Nutrition Survey and National Health and Nutrition Examination Survey) and found that the level of misreporting was >50%. The macronutrient composition from dietary reports in these studies was systematically biased as the level of misreporting increased, leading to potentially spurious associations between diet components and body mass index.

BACKGROUND:The alanine carbon isotope ratio (CIR) biomarker was positively associated with added sugar (AS) and sugar-sweetened beverage (SSB) intakes in multiple studies from the United States. Association strengths varied, and Ala CIR also correlated with protein source in certain studies. OBJECTIVES:This study examined Ala CIR associations with AS and SSB intakes and animal protein ratio (APR; animal protein/total protein), and adjustment for APR, by pooling data from 4 previous studies. METHODS:We pooled diet and biomarker data from 4 studies (n = 346). These included a cross-sectional study of Yup'ik Alaska Native adults (n = 62), a 12-wk randomized controlled feeding study in males (n = 32), a 2-wk habitual intake controlled feeding study in postmenopausal females (n = 153), and a 15-d habitual intake controlled feeding study of adults (n = 99). We estimated correlations between amino acid CIRs and diet and performed multivariable regression of Ala CIR on standardized intake variables to determine simultaneous associations with AS (g/d) or SSBs (servings/d) and APR. We included study by intake interactions to allow for heterogeneity among studies. We then performed models where leucine (Leu) CIR was included to adjust for APR. RESULTS:There were positive correlations between Ala CIR and AS intake (r = 0.54; 95% CI: 0.46, 0.61), log-SSB intake (r = 0.63; 95% CI: 0.56, 0.69), and APR (r = 0.32; 95% CI: 0.22, 0.41). Study-specific slopes for the relationship between Ala CIR and AS or SSB intake were similar in models with and without adjustment for APR. Across studies, slopes ranged from 0.34 (95% CI: 0.08, 0.61) to 1.75 (95% CI: 1.29, 2.20) for AS intake in models with APR and from 0.35 (95% CI: 0.01, 0.68) to 1.11 (95% CI: 0.81, 1.40) for SSB intake in models with APR. Replacing APR with Leu CIR resulted in similar slopes between Ala CIR and AS/SSB intake. CONCLUSIONS:The Ala CIR is a robust biomarker of AS/SSB intake. Potential associations with APR can be adjusted for using a simultaneously measured biomarker. The DBD study was registered at clinicaltrials.gov as NCT01237093 and the NPAAS, an ancillary study of the Women's Health Initiative, at clinicaltrials.gov as NCT00000611.

BACKGROUND:Due to changes in body composition during aging, the inclusion of body composition measures as a variable within equations to predict resting metabolic rate (RMR) may improve their predictive accuracy. OBJECTIVES:This analysis of cross-sectional data aimed to develop and validate new RMR equations for older adults (≥65 y) incorporating variables for body composition, to predict performance and accuracy, and to explore the relative contribution of body composition variables acting directly or potentially via fat-free mass (FFM) to RMR. METHODS:Analyses were conducted utilizing a unique international dataset of gold standard measures developed for this purpose. RMR was predicted from potential predictive variables using stepwise multiple regression. Predictive performance of the final model was assessed using double cross-validation. The new prediction equation was compared with published prediction equations for similar populations and with previously published RMR prediction equations that did not include FFM. Direct associations between the determined predictor variables and RMR with indirect effects mediated via FFM were examined using mediation final (or pathway) analysis. RESULTS:The dataset contained 1238 participants. The predictive equations {utilizing either FFM (Equation 1) or lean body weight [LBW](Equation 2)} follow. Equation 1: RMR = 8.645 × height + 23.684 × weight - 29.717 × age + 38.213 × FFM + 209.637 × sex + 2693.223; Equation 2: RMR = -30.570 × age + 80.736 × LBW - 186.825 × sex + 3956.822 where RMR (kJ/d); height (cm); weight (kg); age (y); FFM (kg); LBW (kg); sex (M = 1, F = 0). The equation performed similarly to some anthropometric-based prediction equations. Predictors using FFM performed marginally better than those using LBW. All variables had significant (P < 0.001) direct effects upon RMR and significant (P < 0.001) indirect effects for sex, weight, and height. CONCLUSIONS:New prediction equations predict RMR at the population level with minimal bias; however, the difference in performance with anthropometry-based equations is minimal. This may be explained by the contribution of FFM to weight, whereby equations that include weight are already accounting for FFM.

In nutritional epidemiology, self-reported dietary data are commonly used to investigate diet-disease relationships. However, the resulting association estimates are often subject to biases due to random and systematic measurement errors. Regression calibration has emerged as a crucial method for addressing these biases by refining self-reported nutrient intake with objective biomarkers, which differ from the true values only by a random "noise" component. This paper presents methodological tools for analyzing nutritional epidemiology cohort studies involving time-to-event data when a biomarker subsample is available alongside dietary assessments. We introduce novel regression calibration methods to tackle two common challenges in this field. First, a widely used approach assumes that the log hazard ratio (HR) follows a linear function of dietary exposure. However, assessing whether this assumption holds-or if a more flexible model is needed to capture potential deviations from linearity-is often necessary. Second, another prevalent analytical strategy involves estimating HRs based on categorized dietary exposure variables. New methods are critically needed to minimize bias in defining category boundaries and estimating hazard ratios within exposure categories, both of which can be distorted by measurement error. We apply these methods to reassess the relationship between sodium and potassium intake and cardiovascular disease risk using data from the Women's Health Initiative.

Metabolomics has been used extensively to capture the exposome. We investigated whether prospectively measured metabolites provided predictive power beyond well-established risk factors among 758 women with adjudicated cancers [n = 577 breast (BC) and n = 181 colorectal (CRC)] and n = 758 controls with available specimens (collected mean 7.2 years prior to diagnosis) in the Women’s Health Initiative Bone Mineral Density subcohort. Fasting samples were analyzed by LC-MS/MS and lipidomics in serum, plus GC-MS and NMR in 24 h urine. For feature selection, we applied LASSO regression and Super Learner algorithms. Prediction models were subsequently derived using logistic regression and Super Learner procedures, with performance assessed using cross-validation (CV). For BC, metabolites did not increase predictive performance over established risk factors (CV-AUCs~0.57). For CRC, prediction increased with the addition of metabolites (median CV-AUC across platforms increased from ~0.54 to ~0.60). Metabolites related to energy metabolism: adenosine, 2-hydroxyglutarate, N-acetyl-glycine, taurine, threonine, LPC (FA20:3), acetate, and glycerate; protein metabolism: histidine, leucic acid, isoleucine, N-acetyl-glutamate, allantoin, N-acetyl-neuraminate, hydroxyproline, and uracil; and dietary/microbial metabolites: myo-inositol, trimethylamine-N-oxide, and 7-methylguanine, consistently contributed to CRC prediction. Energy metabolism may play a key role in the development of CRC and may be evident prior to disease development.

Background: Although calcium and vitamin D (CaD) supplementation may affect chronic disease in older women, evidence of long-term effects on health outcomes is limited. Objective: To evaluate long-term health outcomes among postmenopausal women in the Women's Health Initiative CaD trial. Design: Post hoc analysis of long-term postintervention follow-up of the 7-year randomized intervention trial of CaD. (ClinicalTrials.gov: NCT00000611) Setting: A multicenter (n = 40) trial across the United States. Participants: 36 282 postmenopausal women with no history of breast or colorectal cancer. Intervention: Random 1:1 assignment to 1000 mg of calcium carbonate (400 mg of elemental calcium) with 400 IU of vitamin D3 daily or placebo. Measurements: Incidence of colorectal, invasive breast, and total cancer; disease-specific and all-cause mortality; total cardiovascular disease (CVD); and hip fracture by randomization assignment (through December 2020). Analyses were stratified on personal supplement use. Results: For women randomly assigned to CaD versus placebo, a 7% reduction in cancer mortality was observed after a median cumulative follow-up of 22.3 years (1817 vs. 1943 deaths; hazard ratio [HR], 0.93 [95% CI, 0.87 to 0.99]), along with a 6% increase in CVD mortality (2621 vs. 2420 deaths; HR, 1.06 [CI, 1.01 to 1.12]). There was no overall effect on other measures, including all-cause mortality (7834 vs. 7748 deaths; HR, 1.00 [CI, 0.97 to 1.03]). Estimates for cancer incidence varied widely when stratified by whether participants reported supplement use before randomization, whereas estimates on mortality did not vary, except for CVD mortality. Limitation: Hip fracture and CVD outcomes were available on only a subset of participants, and effects of calcium versus vitamin D versus joint supplementation could not be disentangled. Conclusion: Calcium and vitamin D supplements seemed to reduce cancer mortality and increase CVD mortality after more than 20 years of follow-up among postmenopausal women, with no effect on all-cause mortality. Primary Funding Source: National Heart, Lung, and Blood Institute of the National Institutes of Health.

Data analysis methods for the study of treatments or exposures in relation to a clinical outcome in the presence of competing risks have a long history, often with inference targets that are hypothetical, thereby requiring strong assumptions for identifiability with available data. Here data analysis methods are considered that are based on single and higher dimensional marginal hazard rates, quantities that are identifiable under standard independent censoring assumptions. These lead naturally to joint survival function estimators for outcomes of interest, including competing risk outcomes, and provide the basis for addressing a variety of data analysis questions. These methods will be illustrated using simulations and Women’s Health Initiative cohort and clinical trial data sets, and additional research needs will be described.

Background Doubly labeled water is gold standard for measuring total energy expenditure (TEE). Measurements using the method are sensitive to the isotope dilution space ratio (DSR). Accuracy and precision of the method might be improved if we could identify factors influencing DSR. Objectives We evaluated the potential associations of age, sex, ethnicity, anthropometry, body composition, turnover rates of the isotopes, and geographical elevation with DSR. Methods We used univariate regression analysis to explore the relationships between the continuous variables and analysis of variance to test the relationships between the categorical variables with DSR. Subsequently, we used general linear model (GLM) and 1-way analysis of variance to evaluate the simultaneous associations of age, sex, ethnicity, fat-free mass (FFM) and fat mass (FM) on DSR. Results From 5678 measurements complied from studies around the world with diverse ethnicity and living at various elevations, the mean DSR was 1.0364 ± 0.0141. No meaningful physiologic effect of any of the continuous and categorical variable on DSR was detected. General linear model analysis revealed no effect of FFM and FM (P > 0.33) on DSR, but DSR decreased with age (P < 0.001) among those aged 60 y and older regardless of sex. Among the Whites who were younger than 60 y, DSR was not related to FFM and FM (P = 0.73) but was affected by both age and sex (P < 0.001). Conclusions Previous estimates of age-related decline in TEE may have overestimated TEE at age 90 y. Validation studies on older participants are required to confirm this finding.

Background: Metabolomics has the potential to enhance dietary assessment by revealing objective measures of many aspects of human food intake. Although metabolomics studies indicate that hundreds of metabolites are associated with dietary intake, correlations have been modest (e.g., r < 0.50), and few have been evaluated in controlled feeding studies. Objectives: The aim of this study was to evaluate associations between metabolites and weighed food and beverage intake in a controlled feeding study of habitual diet. Methods: Healthy postmenopausal females from the Women's Health Initiative (N = 153) were provided with a customized 2-wk controlled diet designed to emulate their usual diet. Metabolites were measured by liquid chromatography tandem mass spectrometry in end-of-study 24-h urine and fasting serum samples (1293 urine metabolites; 1113 serum metabolites). We calculated partial Pearson correlations between these metabolites and intake of 65 food groups, beverages, and supplements during the feeding study. The threshold for significance was Bonferroni-adjusted to account for multiple testing (5.94 x 10(-07) for urine metabolites; 6.91 x 10(-07) for serum metabolites). Results: Significant diet-metabolite correlations were identified for 23 distinct foods, beverages, and supplements (171 distinct metabolites). Among foods, strong metabolite correlations (r >= 0.60) were evident for citrus (highest r = 0.80), dairy (r = 0.65), and broccoli (r = 0.63). Among beverages and supplements, strong correlations were evident for coffee (r = 0.86), alcohol (r = 0.69), multivitamins (r = 0.69), and vitamin E supplements (r = 0.65). Moderate correlations (r = 0.50-0.60) were also observed for avocado, fish, garlic, grains, onion, poultry, and black tea. Correlations were specific; each metabolite correlated with one food, beverage, or supplement, except for metabolites correlated with juice or multivitamins. Conclusions: Metabolite levels had moderate to strong correlations with weighed intake of habitually consumed foods, beverages, and supplements. These findings exceed in magnitude those previously observed in population studies and exemplify the strong potential of metabolomics to contribute to nutrition research.

Regular, long-term aspirin use may act synergistically with genetic variants, particularly those in mechanistically relevant pathways, to confer a protective effect on colorectal cancer (CRC) risk. We leveraged pooled data from 52 clinical trial, cohort, and case-control studies that included 30,806 CRC cases and 41,861 controls of European ancestry to conduct a genome-wide interaction scan between regular aspirin/nonsteroidal anti-inflammatory drug (NSAID) use and imputed genetic variants. After adjusting for multiple comparisons, we identified statistically significant interactions between regular aspirin/NSAID use and variants in 6q24.1 (top hit rs72833769), which has evidence of influencing expression of TBC1D7 (a subunit of the TSC1-TSC2 complex, a key regulator of MTOR activity), and variants in 5p13.1 (top hit rs350047), which is associated with expression of PTGER4 (codes a cell surface receptor directly involved in the mode of action of aspirin). Genetic variants with functional impact may modulate the chemopreventive effect of regular aspirin use, and our study identifies putative previously unidentified targets for additional mechanistic interrogation.

BACKGROUND:Menopausal hormone therapy (MHT), a common treatment to relieve symptoms of menopause, is associated with a lower risk of colorectal cancer (CRC). To inform CRC risk prediction and MHT risk-benefit assessment, we aimed to evaluate the joint association of a polygenic risk score (PRS) for CRC and MHT on CRC risk. METHODS:We used data from 28,486 postmenopausal women (11,519 cases and 16,967 controls) of European descent. A PRS based on 141 CRC-associated genetic variants was modeled as a categorical variable in quartiles. Multiplicative interaction between PRS and MHT use was evaluated using logistic regression. Additive interaction was measured using the relative excess risk due to interaction (RERI). 30-year cumulative risks of CRC for 50-year-old women according to MHT use and PRS were calculated. RESULTS:The reduction in odds ratios by MHT use was larger in women within the highest quartile of PRS compared to that in women within the lowest quartile of PRS (p-value = 2.7 × 10-8). At the highest quartile of PRS, the 30-year CRC risk was statistically significantly lower for women taking any MHT than for women not taking any MHT, 3.7% (3.3%-4.0%) vs 6.1% (5.7%-6.5%) (difference 2.4%, P-value = 1.83 × 10-14); these differences were also statistically significant but smaller in magnitude in the lowest PRS quartile, 1.6% (1.4%-1.8%) vs 2.2% (1.9%-2.4%) (difference 0.6%, P-value = 1.01 × 10-3), indicating 4 times greater reduction in absolute risk associated with any MHT use in the highest compared to the lowest quartile of genetic CRC risk. CONCLUSIONS:MHT use has a greater impact on the reduction of CRC risk for women at higher genetic risk. These findings have implications for the development of risk prediction models for CRC and potentially for the consideration of genetic information in the risk-benefit assessment of MHT use.

Metabolomics profiles from blood, urine, or other body fluids have the potential to assess intakes of foods and nutrients objectively, thereby strengthening nutritional epidemiology research. Metabolomics platforms may include targeted components that estimate the relative concentrations for individual metabolites in a predetermined set, or global components, typically involving mass spectrometry, that estimate relative concentrations more broadly. While a specific metabolite concentration usually correlates with the intake of a single food or food group, multiple metabolites may be correlated with the intake of certain foods or with specific nutrient intakes, each of which may be expressed in absolute terms or relative to total energy intake. Here, I briefly review the progress over the past 20 years on the development and application intake biomarkers for foods/food groups, nutrients, and dietary patterns, primarily by drawing from several recent reviews. In doing so, I emphasize the criteria and study designs for candidate biomarker identification, biomarker validation, and intake biomarker application. The use of intake biomarkers for diet and chronic disease association studies is still infrequent in nutritional epidemiology research. My comments here will derive primarily from our research group’s recent contributions to the Women’s Health Initiative cohorts. I will complete the contribution by describing some opportunities to build on the collective 20 years of effort, including opportunities related to the metabolomics profiling of blood and urine specimens from human feeding studies that approximate habitual diets.

Systematic measurement error in self-reported data creates important challenges in association studies between dietary intakes and chronic disease risks, especially when multiple dietary components are studied jointly. The joint regression calibration method has been developed for measurement error correction when objectively measured biomarkers are available for all dietary components of interest. Unfortunately, objectively measured biomarkers are only available for very few dietary components, which limits the application of the joint regression calibration method. Recently, for single dietary components, controlled feeding studies have been performed to develop new biomarkers for many more dietary components. However, it is unclear whether the biomarkers separately developed for single dietary components are valid for joint calibration. In this paper we show that biomarkers developed for single dietary components cannot be used for joint regression calibration. We propose new methods to utilize controlled feeding studies to develop valid biomarkers for joint regression calibration to estimate the association between multiple dietary components simultaneously with the disease of interest. Asymptotic distribution theory for the proposed estimators is derived. Extensive simulations are performed to study the finite sample performance of the proposed estimators. We apply our methods to examine the joint effects of sodium and potassium intakes on cardiovascular disease incidence using the Women's Health Initiative cohort data. We identify positive associations between sodium intake and cardiovascular diseases as well as negative associations between potassium intake and cardiovascular disease.

Importance Approximately 55 million people in the US and approximately 1.1 billion people worldwide are postmenopausal women. To inform clinical practice about the health effects of menopausal hormone therapy, calcium plus vitamin D supplementation, and a low-fat dietary pattern, the Women's Health Initiative (WHI) enrolled 161 808 postmenopausal US women (N = 68 132 in the clinical trials) aged 50 to 79 years at baseline from 1993 to 1998, and followed them up for up to 20 years. Observations The WHI clinical trial results do not support hormone therapy with oral conjugated equine estrogens plus medroxyprogesterone acetate for postmenopausal women or conjugated equine estrogens alone for those with prior hysterectomy to prevent cardiovascular disease, dementia, or other chronic diseases. However, hormone therapy is effective for treating moderate to severe vasomotor and other menopausal symptoms. These benefits of hormone therapy in early menopause, combined with lower rates of adverse effects of hormone therapy in early compared with later menopause, support initiation of hormone therapy before age 60 years for women without contraindications to hormone therapy who have bothersome menopausal symptoms. The WHI results do not support routinely recommending calcium plus vitamin D supplementation for fracture prevention in all postmenopausal women. However, calcium and vitamin D are appropriate for women who do not meet national guidelines for recommended intakes of these nutrients through diet. A low-fat dietary pattern with increased fruit, vegetable, and grain consumption did not prevent the primary outcomes of breast or colorectal cancer but was associated with lower rates of the secondary outcome of breast cancer mortality during long-term follow-up. Conclusions and Relevance For postmenopausal women, the WHI randomized clinical trials do not support menopausal hormone therapy to prevent cardiovascular disease or other chronic diseases. Menopausal hormone therapy is appropriate to treat bothersome vasomotor symptoms among women in early menopause, without contraindications, who are interested in taking hormone therapy. The WHI evidence does not support routine supplementation with calcium plus vitamin D for menopausal women to prevent fractures or a low-fat diet with increased fruits, vegetables, and grains to prevent breast or colorectal cancer. A potential role of a low-fat dietary pattern in reducing breast cancer mortality, a secondary outcome, warrants further study.

BACKGROUND:Understanding the impact of clonal hematopoiesis of indeterminate potential (CHIP) and mosaic chromosomal alterations (mCAs) on solid tumor risk and mortality can shed light on novel cancer pathways. METHODS:The authors analyzed whole genome sequencing data from the Trans-Omics for Precision Medicine Women's Health Initiative study (n = 10,866). They investigated the presence of CHIP and mCA and their association with the development and mortality of breast, lung, and colorectal cancers. RESULTS:CHIP was associated with higher risk of breast (hazard ratio [HR], 1.30; 95% confidence interval [CI], 1.03-1.64; p = .02) but not colorectal (p = .77) or lung cancer (p = .32). CHIP carriers who developed colorectal cancer also had a greater risk for advanced-stage (p = .01), but this was not seen in breast or lung cancer. CHIP was associated with increased colorectal cancer mortality both with (HR, 3.99; 95% CI, 2.41-6.62; p < .001) and without adjustment (HR, 2.50; 95% CI, 1.32-4.72; p = .004) for advanced-stage and a borderline higher breast cancer mortality (HR, 1.53; 95% CI, 0.98-2.41; p = .06). Conversely, mCA (cell fraction [CF] >3%) did not correlate with cancer risk. With higher CFs (mCA >5%), autosomal mCA was associated with increased breast cancer risk (HR, 1.39; 95% CI, 1.06-1.83; p = .01). There was no association of mCA (>3%) with breast, colorectal, or lung mortality except higher colon cancer mortality (HR, 2.19; 95% CI, 1.11-4.3; p = .02) with mCA >5%. CONCLUSIONS:CHIP and mCA (CF >5%) were associated with higher breast cancer risk and colorectal cancer mortality individually. These data could inform on novel pathways that impact cancer risk and lead to better risk stratification.