Search

BackgroundThere is emerging evidence that cancer and its treatments may accelerate the normal aging process, increasing the magnitude and rate of decline in functional capacity. This accelerated aging process is hypothesized to hasten the occurrence of common adverse age-related outcomes in cancer survivors, including loss of muscle mass and decrease in physical function. However, there is no data describing age-related loss of muscle mass and its relation to physical function in the long-term in cancer survivors.MethodsThis study protocol describes the use of a novel method of muscle mass measurement, D3-creatine dilution method (D3Cr), in a large sample (n~6000) of community dwelling postmenopausal women from the Women's Health Initiative (WHI). D3Cr will be used to obtain a direct measure of muscle mass remotely. Participants will be drawn from two sub-cohorts embedded within the WHI that have recently completed an in-home visit. Cancer survivors will be drawn from the Life and Longevity After Cancer (LILAC) cohort, and cancer-free controls will be drawn from the WHI Long Life Study 2. The overall objective of this study is to examine the antecedents and consequences of low muscle mass in cancer survivors. The study aims are to: 1) create age-standardized muscle mass percentile curves and z-scores to characterize the distribution of D3- muscle mass in cancer survivors and non-cancer controls, 2) compare muscle mass, physical function, and functional decline in cancer survivors and non- cancer controls, and 3) use machine learning approaches to generate multivariate risk-prediction algorithms to detect low muscle mass.DiscussionThe D3Cr method will transform our ability to measure muscle mass in large-scale epidemiologic research. This study is an opportunity to advance our understanding of a key source of morbidity among older and long-term female cancer survivors. This project will fill knowledge gaps, including the antecedents and consequences of low muscle mass, and use innovative methods to overcome common sources of bias in cancer research. The results of this study will be used to develop interventions to mitigate the harmful effects of low muscle mass in older adults and promote healthy survivorship in cancer survivors in the old (>65) and oldest-old (>85) age groups.

A microbial community is a dynamic system undergoing constant change in response to internal and external stimuli. These changes can have significant implications for human health. However, due to the difficulty in obtaining longitudinal samples, the study of the dynamic relationship between the microbiome and human health remains a challenge. Here, we introduce a novel computational strategy that uses massive cross-sectional sample data to model microbiome landscapes associated with chronic disease development. The strategy is based on the rationale that each static sample provides a snapshot of the disease process, and if the number of samples is sufficiently large, the footprints of individual samples populate progression trajectories, which enables us to recover disease progression paths along a microbiome landscape by using computational approaches. To demonstrate the validity of the proposed strategy, we developed a bioinformatics pipeline and applied it to a gut microbiome dataset available from a Crohn's disease study. Our analysis resulted in one of the first working models of microbial progression for Crohn's disease. We performed a series of interrogations to validate the constructed model. Our analysis suggested that the model recapitulated the longitudinal progression of microbial dysbiosis during the known clinical trajectory of Crohn's disease. By overcoming restrictions associated with complex longitudinal sampling, the proposed strategy can provide valuable insights into the role of the microbiome in the pathogenesis of chronic disease and facilitate the shift of the field from descriptive research to mechanistic studies.

The D3-Creatine (D3Cr) dilution method is a direct and accurate measure of skeletal muscle mass. In this study, we examined the association of D3Cr muscle mass with measures of insulin-glucose homeostasis in community dwelling postmenopausal women. Additionally, we examined association of sarcopenic obesity, defined as low D3Cr muscle mass and high percent body fat, with fasting plasma glucose, insulin, hemoglobin A1c and insulin resistance. Insulin resistance was measured by the homeostatic measure of insulin resistance (HOMA-IR). This pilot study included 74 participants (mean age = 82.3 years) from the Women's Health Initiative-Buffalo site. The D3Cr method was initiated at a clinic visit and used to measure muscle mass via remote urine sample collection. Descriptive and graphical approaches and age-adjusted linear regression models were used to analyze study data. We examined muscle mass as an absolute value (kg) and scaled to body weight (D3Cr muscle mass/kg). There was an inverse relationship between skeletal muscle mass, and impaired insulin-glucose homeostasis. Women with low muscle mass had higher levels of insulin (uIU/mL; β = -0.40; 95% CI: -0.79, -0.01), fasting plasma glucose (mg/dL; β = -0.1; 95% CI: -0.2, 0.03), HbA1c (%; β = -2.30; 95% CI: -5.7, 1.1), and calculated homeostatic model of insulin resistance, HOMA-IR, (β = -1.49; 95% CI: -2.9, -0.1). Sarcopenic obesity was common in this population of women; 41% of participants were categorized as having low muscle mass and high percent body fat. Results demonstrate that D3Cr muscle mass is independently associated with measures of insulin-glucose homeostasis, but obesity is a stronger predictor of insulin resistance than muscle mass.

The activity of paraoxonase 1 (PON1), an antioxidant enzyme whose polymorphisms have been associated with cancer risk, may be associated with metals exposure.To evaluate PON1 activity in relation to cadmium, lead, and mercury levels in healthy, premenopausal women.Women from upstate New York were followed for ≥ two menstrual cycles. Repeated measures linear mixed models estimated the association between cadmium, lead, and mercury levels (by tertile: T1, T2, T3) and PON1 arylesterase (PON1A) and PON1 paraoxonase (PON1P) activity, separately. Analyses were stratified by PON1 Q192R phenotype and un-stratified.Median blood cadmium, lead, and mercury concentrations were 0.30 µg/L, 0.87 µg/dL, and 1.15 µg/L. In un-stratified analyses cadmium and mercury were associated with decreased PON1A activity (T2 vs. T1; not T3 vs. T1) but metals were not associated with PON1P. Phenotypes were distributed between QQ (n = 99), QR (n = 117), and RR (n = 34). Cadmium was associated with decreased PON1A activity for QR and RR phenotypes comparing T2 vs. T1 (-14.4% 95% confidence interval [CI] [-20.1, -8.4] and -27.9% [-39.5, -14.0],). Lead was associated with decreased PON1A (RR phenotype, T3 vs. T1 -18.9% [-32.5, -2.5]; T2 vs. T1 -19.6% [-32.4, -4.4]). Cadmium was associated with lower PON1P comparing T2 vs. T1 for the RR (-34.9% [-51.5, -12.5]) and QR phenotypes (-9.5% [-18.1, 0.0]) but not comparing T3 vs. T1. Cadmium was associated with increases in PON1P levels (QQ phenotype, T3 vs. T1 24.5% [7.0, 44.9]) and mercury was associated with increased PON1A levels (QQ phenotype, T3 vs. T1 6.2% [0.2, 12.6]). Mercury was associated with decreased PON1P (RR phenotype, T2 vs. T1 -22.8 [-37.8, -4.1]).Blood metals were associated with PON1 activity and these effects varied by phenotype. However, there was not a linear dose-response and these findings await replication.

We developed an absolute risk model to identify individuals in the general population at elevated risk of pancreatic cancer.Using data on 3,349 cases and 3,654 controls from the PanScan Consortium, we developed a relative risk model for men and women of European ancestry based on non-genetic and genetic risk factors for pancreatic cancer. We estimated absolute risks based on these relative risks and population incidence rates.Our risk model included current smoking (multivariable adjusted odds ratio (OR) and 95% confidence interval: 2.20 [1.84-2.62]), heavy alcohol use (>3 drinks/day) (OR: 1.45 [1.19-1.76]), obesity (body mass index >30 kg/m(2)) (OR: 1.26 [1.09-1.45]), diabetes >3 years (nested case-control OR: 1.57 [1.13-2.18], case-control OR: 1.80 [1.40-2.32]), family history of pancreatic cancer (OR: 1.60 [1.20-2.12]), non-O ABO genotype (AO vs. OO genotype) (OR: 1.23 [1.10-1.37]) to (BB vs. OO genotype) (OR 1.58 [0.97-2.59]), rs3790844(chr1q32.1) (OR: 1.29 [1.19-1.40]), rs401681(5p15.33) (OR: 1.18 [1.10-1.26]) and rs9543325(13q22.1) (OR: 1.27 [1.18-1.36]). The areas under the ROC curve for risk models including only non-genetic factors, only genetic factors, and both non-genetic and genetic factors were 58%, 57% and 61%, respectively. We estimate that fewer than 3/1,000 U.S. non-Hispanic whites have more than a 5% predicted lifetime absolute risk.Although absolute risk modeling using established risk factors may help to identify a group of individuals at higher than average risk of pancreatic cancer, the immediate clinical utility of our model is limited. However, a risk model can increase awareness of the various risk factors for pancreatic cancer, including modifiable behaviors.

Multiplexing arrays increase the throughput and decrease sample requirements for studies employing multiple biomarkers. The goal of this project was to examine the performance of Multiplex arrays for measuring multiple protein biomarkers in saliva and serum. Specimens from the OsteoPerio ancillary study of the Women's Health Initiative Observational Study were used. Participants required the presence of at least 6 teeth and were excluded based on active cancer and certain bone issues but were not selected on any specific condition. Quality control (QC) samples were created from pooled serum and saliva. Twenty protein markers were measured on five multiplexing array panels. Sample pretreatment conditions were optimized for each panel. Recovery, lower limit of quantification (LLOQ) and imprecision were determined for each analyte. Statistical adjustment at the plate level was used to reduce imprecision estimates and increase the number of usable observations. Sample pre-treatment improved recovery estimates for many analytes. The LLOQ for each analyte agreed with manufacturer specifications except for MMP-1 and MMP-2 which were significantly higher than reported. Following batch adjustment, 17 of 20 biomarkers in serum and 9 of 20 biomarkers in saliva demonstrated acceptable precision, defined as

Folic acid is recommended to reproductive-aged women to prevent birth defects, though little is known about the effects of dietary intake on other reproductive outcomes. Improved pregnancy rates have been documented after folic acid supplement use, suggesting a possible link with ovulation, however research is limited. Our objective was to evaluate the association between dietary folate intake, hormone levels, and sporadic anovulation in healthy, regularly menstruating women.The BioCycle study (2005-2007) prospectively followed 259 healthy women aged 18-44 years from the western New York region for up to 2 menstrual cycles. Total folate and specific sources of folate were assessed up to 4 times per cycle by 24-hour recall. Estradiol, progesterone, luteinizing hormone, and follicle-stimulating hormone were measured in serum up to 8 times per cycle, timed using fertility monitors. Anovulation was defined as a cycle with peak progesterone concentration ≤ 5 ng/mL and no LH peak in the mid/late luteal phase. Higher intake of dietary folate (in dietary equivalents) across tertiles had a marginally significant association with greater luteal progesterone levels (P trend 0.08). Higher intake of synthetic folate was significantly associated with higher luteal progesterone levels (P trend 0.05). Specifically, women in the 3(rd) tertile of synthetic folate intake had, on average, 16.0% (95% CI, 0.5-33.8%) higher luteal progesterone levels compared to women in the 1(st) tertile. Moreover, consumption of synthetic folate was significantly and inversely associated with anovulation such that women in the 3(rd) tertile had a 64% (95% CI, 8-86%) decreased odds of anovulation compared to the women in the 1(st) tertile (P trend 0.03).These findings suggest that a diet high in synthetic folate may be associated with increased progesterone levels and lower risk of sporadic anovulation. Further study of the effect of dietary folate and folic acid supplement use on reproductive health is warranted.