Chongzhi Di

Simultaneous Association of Total Energy Consumption and Activity-Related Energy Expenditure With Risks of Cardiovascular Disease, Cancer, and Diabetes Among Postmenopausal Women

Total energy consumption and activity-related energy expenditure (AREE) estimates that have been calibrated using biomarkers to correct for measurement error were simultaneously associated with the risks of cardiovascular disease, cancer, and diabetes among postmenopausal women who were enrolled in the Womens Health Initiative at 40 US clinical centers and followed from 1994 to the present. Calibrated energy consumption was found to be positively related, and AREE inversely related, to the risks of various cardiovascular diseases, cancers, and diabetes. These associations were not evident in most corresponding analyses that did not correct for measurement error. However, an important analytical caveat relates to the role of body mass index (BMI) (weight (kg)/height (m)(2)). In the calibrated variable analyses, BMI was regarded, along with self-reported data, as a source of information on energy consumption and physical activity, and BMI was otherwise excluded from the disease risk models. This approach cannot be fully justified with available data, and the analyses herein imply a need for improved dietary and physical activity assessment methods and for longitudinal self-reported and biomarker data to test and relax modeling assumptions. Estimated hazard ratios for 20% increases in total energy consumption and AREE, respectively, were as follows: 1.49 (95% confidence interval: 1.18, 1.88) and 0.80 (95% confidence interval: 0.69, 0.92) for total cardiovascular disease; 1.43 (95% confidence interval: 1.17, 1.73) and 0.84 (95% confidence interval: 0.73, 0.96) for total invasive cancer; and 4.17 (95% confidence interval: 2.68, 6.49) and 0.60 (95% confidence interval: 0.44, 0.83) for diabetes.

Calibrating physical activity intensity for hip-worn accelerometry in women age 60 to 91 years: The Women's Health Initiative OPACH Calibration Study

Objective We conducted a laboratory-based calibration study to determine relevant cutpoints for a hip-worn accelerometer among women ≥ 60 years, considering both type and filtering of counts. Methods Two hundred women wore an ActiGraph GT3X + accelerometer on their hip while performing eight laboratory-based activities. Oxygen uptake was measured using an Oxycon portable calorimeter. Accelerometer data were analyzed in 15-second epochs for both normal and low frequency extension (LFE) filters. Receiver operating characteristic (ROC) curve analyses were used to calculate cutpoints for sedentary, light (low and high), and moderate to vigorous physical activity (MVPA) using the vertical axis and vector magnitude (VM) counts. Results Mean age was 75.5 years (standard deviation 7.7). The Spearman correlation between oxygen uptake and accelerometry ranged from 0.77 to 0.85 for the normal and LFE filters and for both the vertical axis and VM. The area under the ROC curve was generally higher for VM compared to the vertical axis, and higher for cutpoints distinguishing MVPA compared to sedentary and light low activities. The VM better discriminated sedentary from light low activities compared to the vertical axis. The area under the ROC curves were better for the LFE filter compared to the normal filter for the vertical axis counts, but no meaningful differences were found by filter type for VM counts. Conclusion The cutpoints derived for this study among women ≥ 60 years can be applied to ongoing epidemiologic studies to define a range of physical activity intensities.

Dietary biomarker evaluation in a controlled feeding study in women from the Women’s Health Initiative cohort

BACKGROUND Controlled human feeding studies are necessary for robust nutritional biomarker development and validation. Previous feeding studies have typically evaluated single nutrients and tested relatively few diets. OBJECTIVES The objectives were 1) to simultaneously associate dietary intake with a range of potential nutritional biomarkers in postmenopausal women by using a controlled feeding study whereby each participant was provided a diet similar to her usual diet and 2) to evaluate serum concentrations of select nutrients as potential biomarkers with the use of established urinary recovery biomarkers of energy and protein as benchmarks for evaluation. DESIGN Postmenopausal women from the Womens Health Initiative (n = 153) were provided with a 2-wk controlled diet in which each individuals menu approximated her habitual food intake as estimated from her 4-d food record and adjusted for estimated energy requirements. Serum biomarkers, including carotenoids, tocopherols, folate, vitamin B-12, and phospholipid fatty acids, were collected at the beginning and end of the feeding period. Doubly labeled water and urinary nitrogen biomarkers were used to derive estimates of energy and protein consumption, respectively. RESULTS Linear regression of (ln-transformed) consumed nutrients on (ln-transformed) potential biomarkers and participant characteristics led to the following regression (R2) values for serum concentration biomarkers: folate, 0.49; vitamin B-12, 0.51; α-carotene, 0.53; β-carotene, 0.39; lutein + zeaxanthin, 0.46; lycopene, 0.32; and α-tocopherol, 0.47. R2 values for percentage of energy from polyunsaturated fatty acids and urinary recovery biomarkers of energy and protein intakes were 0.27, 0.53, and 0.43, respectively. Phospholipid saturated fatty acids and monounsaturated fatty acids and serum γ-tocopherol were weakly associated with intake (R2 < 0.25). CONCLUSIONS Serum concentration biomarkers of several vitamins and carotenoids performed similarly to established energy and protein urinary recovery biomarkers in representing nutrient intake variation in a feeding study, and thus are likely suitable for application in this population of postmenopausal women. Further work is needed to identify objective measures of categories of fatty acid intake. This trial was registered at clinicaltrials.gov as NCT00000611.

Genetic Variation Predicts Serum Lycopene Concentrations in a Multiethnic Population of Postmenopausal Women

BACKGROUND The consumption and blood concentrations of lycopene are both positively and inversely associated with the risk of several chronic diseases. The inconsistences in lycopene disease association studies may stem from a lack of knowledge about the genetic variation in the synthesis, metabolism, and deposition of transport and binding proteins, which potentially influence serum lycopene concentrations. OBJECTIVE We examined the association between variation across the genome and serum concentrations of lycopene in a multiethnic population. METHODS Participants included African (n = 914), Hispanic (n = 464), and European (n = 1203) American postmenopausal women from the Womens Health Initiative. We analyzed ∼7 million single nucleotide polymorphisms (SNPs). Linear regression models were used to assess associations between each SNP and serum concentrations (log transformed, continuous) of lycopene; we adjusted for age, body mass index, and population substructure. Models were run separately by ethnicity, and results were combined in a transethnic fixed-effects meta-analysis. RESULTS In the meta-analysis, the scavenger receptor class B, member 1 (SCARB1) gene, which encodes for a cholesterol membrane transporter, was significantly associated with lycopene concentrations (rs1672879; P < 2.68 × 10(-9)). Each additional G allele resulted in a 12% decrease in lycopene concentrations for African Americans, 20% decrease for Hispanic Americans, and 9% decrease for European Americans. In addition, 2 regions were significantly associated with serum lycopene concentrations in African Americans: the slit homolog 3 gene (SLIT3), which serves as a molecular guidance cue in cellular migration, and the dehydrogenase/reductase (SDR family) member 2 (DHRS2) gene, which codes for an oxidoreductase that mitigates the breakdown of steroids. CONCLUSIONS We found 3 novel loci associated with serum lycopene concentrations, 2 of which were specific to African Americans. Future functional studies looking at these specific genes may provide insight into the metabolism and underlying function of lycopene in humans, which may further elucidate lycopenes influence on disease risk and health. This trial was registered at clinicaltrials.gov as NCT00000611.

Associations of the MCM6-rs3754686 proxy for milk intake in Mediterranean and American populations with cardiovascular biomarkers, disease and mortality: Mendelian randomization.

Controversy persists on the association between dairy products, especially milk, and cardiovascular diseases (CVD). Genetic proxies may improve dairy intake estimations, and clarify diet-disease relationships through Mendelian randomization. We meta-analytically (n ≤ 20,089) evaluated associations between a lactase persistence (LP) SNP, the minichromosome maintenance complex component 6 (MCM6)-rs3754686C>T (nonpersistence>persistence), dairy intake, and CVD biomarkers in American (Hispanics, African-American and Whites) and Mediterranean populations. Moreover, we analyzed longitudinal associations with milk, CVD and mortality in PREDIMED), a randomized Mediterranean diet (MedDiet) intervention trial (n = 7185). The MCM6-rs3754686/MCM6-rs309180 (as proxy), LP-allele (T) was strongly associated with higher milk intake, but inconsistently associated with glucose and lipids, and not associated with CVD or total mortality in the whole population. Heterogeneity analyses suggested some sex-specific associations. The T-allele was associated with higher CVD and mortality risk in women but not in men (P-sex interaction:0.005 and 0.032, respectively), mainly in the MedDiet group. However, milk intake was not associated with CVD biomarkers, CVD or mortality either generally or in sub-groups. Although MCM6-rs3754686 is a good milk intake proxy in these populations, attributing its associations with CVD and mortality in Mediterranean women to milk is unwarranted, as other factors limiting the assumption of causality in Mendelian randomization may exist.

An Activity Index for Raw Accelerometry Data and Its Comparison with Other Activity Metrics

Accelerometers have been widely deployed in public health studies in recent years. While they collect high-resolution acceleration signals (e.g., 10–100 Hz), research has mainly focused on summarized metrics provided by accelerometers manufactures, such as the activity count (AC) by ActiGraph or Actical. Such measures do not have a publicly available formula, lack a straightforward interpretation, and can vary by software implementation or hardware type. To address these problems, we propose the physical activity index (AI), a new metric for summarizing raw tri-axial accelerometry data. We compared this metric with the AC and another recently proposed metric for raw data, Euclidean Norm Minus One (ENMO), against energy expenditure. The comparison was conducted using data from the Objective Physical Activity and Cardiovascular Health Study, in which 194 women 60–91 years performed 9 lifestyle activities in the laboratory, wearing a tri-axial accelerometer (ActiGraph GT3X+) on the hip set to 30 Hz and an Oxycon portable calorimeter, to record both tri-axial acceleration time series (converted into AI, AC, and ENMO) and oxygen uptake during each activity (converted into metabolic equivalents (METs)) at the same time. Receiver operating characteristic analyses indicated that both AI and ENMO were more sensitive to moderate and vigorous physical activities than AC, while AI was more sensitive to sedentary and light activities than ENMO. AI had the highest coefficients of determination for METs (0.72) and was a better classifier of physical activity intensity than both AC (for all intensity levels) and ENMO (for sedentary and light intensity). The proposed AI provides a novel and transparent way to summarize densely sampled raw accelerometry data, and may serve as an alternative to AC. The AI’s largely improved sensitivity on sedentary and light activities over AC and ENMO further demonstrate its advantage in studies with older adults.

Accelerometer-Measured Moderate to Vigorous Physical Activity and Incidence Rates of Falls in Older Women

To examine whether moderate to vigorous physical activity (MVPA) measured using accelerometry is associated with incident falls and whether associations differ according to physical function or history of falls.

Accelerometer-Measured Physical Activity and Mortality in Women Aged 63 to 99

To prospectively examine associations between accelerometer‐measured physical activity (PA) and mortality in older women, with an emphasis on light‐intensity PA.

Classifiers for Accelerometer-measured Behaviors in Older Women

Purpose Machine learning methods could better improve the detection of specific types of physical activities and sedentary behaviors from accelerometer data. No studies in older populations have developed and tested algorithms for walking and sedentary time in free-living daily life. Our goal was to rectify this gap by leveraging access to data from two studies in older women. Methods In study 1, algorithms were developed and tested in a sample of older women (N = 39, age range = 55–96 yr) in the field. Women wore accelerometers and SenseCam (ground truth annotation) devices for 7 d, yielding 3191 h and 320 d of data. Images were annotated and time matched to accelerometer data, and random forest classifiers labeled behaviors (sitting, riding in a vehicle, standing still, standing moving, and walking/running). In study 2, we examined the concurrent validity of the algorithms using accelerometer data from an observed 400-m walk test (2983 min of data available) and 6 d of wearing both accelerometers and global positioning systems devices in a sample of 222 women (age range = 67–100; 313,290 min of data available). Analyses included sensitivity, specificity balanced accuracy, and precision, as appropriate, averaged over each test participant at the minute level for each behavior. Results In study 1, the algorithms had 82.2% balanced accuracy. In study 2, the classifier had 87.9% accuracy for predicting walking. Overall machine learning classifiers and global positioning systems had 88.6% agreement. Conclusions Free-living algorithms for walking and sedentary time yielded high levels of accuracy and concurrent validity and can be applied to existing accelerometer data from older women.

Associations of Accelerometer-Measured and Self-Reported Sedentary Time with Leukocyte Telomere Length in Older Women

Few studies have assessed the association of sedentary time with leukocyte telomere length (LTL). In a cross-sectional study conducted in 2012-2013, we examined associations of accelerometer-measured and self-reported sedentary time with LTL in a sample of 1,481 older white and African-American women from the Womens Health Initiative and determined whether associations varied by level of moderate- to vigorous-intensity physical activity (MVPA). The association between sedentary time and LTL was evaluated using multiple linear regression models. Women were aged 79.2 (standard deviation, 6.7) years, on average. Self-reported sedentary time was not associated with LTL. In a model adjusting for demographic characteristics, lifestyle behaviors, and health-related factors, among women at or below the median level of accelerometer-measured MVPA, those in the highest quartile of accelerometer-measured sedentary time had significantly shorter LTL than those in the lowest quartile, with an average difference of 170 base pairs (95% confidence interval: 4, 340). Accelerometer-measured sedentary time was not associated with LTL in women above the median level of MVPA. Findings suggest that, on the basis of accelerometer measurements, higher sedentary time may be associated with shorter LTL among less physically active women.