Supervised Learning of Physical Activity Features From Functional Accelerometer Data.

OBJECTIVE: We propose a new health informatics framework to analyze physical activity (PA) from accelerometer devices. Accelerometry data enables scientists to extract personal digital features useful for precision health decision making. Existing methods in accelerometry data analysis typically begin with discretizing summary counts by certain fixed cutoffs into activity categories. One well-known limitation is that the chosen cutoffs are often validated under restricted settings, and cannot be generalizable across populations, devices, or studies. METHODS: We develop a data-driven approach to overcome this bottleneck in PA data analysis, in which we holistically summarize a subject's activity profile using Occupation-Time curves (OTCs), which describe the percentage of time spent at or above a continuum of activity count levels. We develop multi-step adaptive learning algorithms to perform supervised learning via a scalar-on-function model that involves OTC as the functional predictor of interest as well as other scalar covariates. Our learning analytic first incorporates a hybrid approach of fused lasso for clustering and Hidden Markov Model for changepoint detection, then executes refinement procedures to determine activity windows of interest. RESULTS: We evaluate and illustrate the performance of the proposed learning analytic through simulation experiments and real-world data analyses to assess the influence of PA on biological aging. Our findings indicate a different directional relationship between biological age and PA depending on the specific outcome of interest. SIGNIFICANCE: Our bioinformatics methodology involves the biomedical outcome of interest to detect different critical points, and is thus adaptive to the specific data, study population, and health outcome under investigation.

Longitudinal self-learning of individualized treatment rules in a nutrient supplementation trial with missing data.

Longitudinal outcomes are prevalent in clinical studies, where the presence of missing data may make the statistical learning of individualized treatment rules (ITRs) a much more challenging task. We analyzed a longitudinal calcium supplementation trial in the ELEMENT Project and established a novel ITR to reduce the risk of adverse outcomes of lead exposure on child growth and development. Lead exposure, particularly in the form of in utero exposure, can seriously impair children's health, especially their cognitive and neurobehavioral development, which necessitates clinical interventions such as calcium supplementation intake during pregnancy. Using the longitudinal outcomes from a randomized clinical trial of calcium supplementation, we developed a new ITR for daily calcium intake during pregnancy to mitigate persistent lead exposure in children at age 3 years. To overcome the technical challenges posed by missing data, we illustrate a new learning approach, termed longitudinal self-learning (LS-learning), that utilizes longitudinal measurements of child's blood lead concentration in the derivation of ITR. Our LS-learning method relies on a temporally weighted self-learning paradigm to synergize serially correlated training data sources. The resulting ITR is the first of this kind in precision nutrition that will contribute to the reduction of expected blood lead concentration in children aged 0-3 years should this ITR be implemented to the entire study population of pregnant women.

Early Life Exposure in Mexico to ENvironmental Toxicants (ELEMENT) Project.

PURPOSE: The Early Life Exposure in Mexico to ENvironmental Toxicants (ELEMENT) Project is a mother-child pregnancy and birth cohort originally initiated in the mid-1990s to explore: (1) whether enhanced mobilisation of lead from maternal bone stores during pregnancy poses a risk to fetal and subsequent offspring neurodevelopment; and (2) whether maternal calcium supplementation during pregnancy and lactation can suppress bone lead mobilisation and mitigate the adverse effects of lead exposure on offspring health and development. Through utilisation of carefully archived biospecimens to measure other prenatal exposures, banking of DNA and rigorous measurement of a diverse array of outcomes, ELEMENT has since evolved into a major resource for research on early life exposures and developmental outcomes. PARTICIPANTS: n=1643 mother-child pairs sequentially recruited (between 1994 and 2003) during pregnancy or at delivery from maternity hospitals in Mexico City, Mexico. FINDINGS TO DATE: Maternal bone (eg, patella, tibia) is an endogenous source for fetal lead exposure due to mobilisation of stored lead into circulation during pregnancy and lactation, leading to increased risk of miscarriage, low birth weight and smaller head circumference, and transfer of lead into breastmilk. Daily supplementation with 1200 mg of elemental calcium during pregnancy and lactation reduces lead resorption from maternal bone and thereby, levels of circulating lead. Beyond perinatal outcomes, early life exposure to lead is associated with neurocognitive deficits, behavioural disorders, higher blood pressure and lower weight in offspring during childhood. Some of these relationships were modified by dietary factors; genetic polymorphisms specific for iron, folate and lipid metabolism; and timing of exposure. Research has also expanded to include findings published on other toxicants such as those associated with personal care products and plastics (eg, phthalates, bisphenol A), other metals (eg, mercury, manganese, cadmium), pesticides (organophosphates) and fluoride; other biomarkers (eg, toxicant levels in plasma, hair and teeth); other outcomes (eg, sexual maturation, metabolic syndrome, dental caries); and identification of novel mechanisms via epigenetic and metabolomics profiling. FUTURE PLANS: As the ELEMENT mothers and children age, we plan to (1) continue studying the long-term consequences of toxicant exposure during the perinatal period on adolescent and young adult outcomes as well as outcomes related to the original ELEMENT mothers, such as their metabolic and bone health during perimenopause; and (2) follow the third generation of participants (children of the children) to study intergenerational effects of in utero exposures. TRIAL REGISTRATION NUMBER: NCT00558623.

Associations of the infancy body mass index peak with anthropometry and cardiometabolic risk in Mexican adolescents.

BACKGROUND: Early-life growth dynamics are associated with future health. Little is known regarding timing and magnitude of the infancy body mass index (BMI) peak with adiposity and metabolic biomarkers during adolescence. AIM: To examine associations of the infancy BMI peak with anthropometry and cardiometabolic risk during peripuberty. METHODS: Among 163 ELEMENT participants, this study estimated age and magnitude of the infancy BMI peak from eight anthropometric measurements from birth-36 months using Newton's Growth Models, an acceleration-based process model. Associations were examined of the infancy milestones with anthropometry and cardiometabolic risk at 8-14 years using linear regression models that accounted for maternal calcium supplementation and age; child's birthweight, sex, and age; and the other infancy milestone. RESULTS: Median age at the infancy BMI peak was 9.6 months, and median peak BMI was 16.5 kg/m2. Later age and larger magnitude of the peak predicted higher BMI z-score, waist circumference, and skinfold thicknesses; i.e. each 1 month of age at peak and each 1 kg/m2 of peak BMI corresponded with 0.04 (0.01-0.07) and 0.33 (0.17-0.48) units of higher BMI z-score, respectively. Later age at peak was also a determinant of worse glycaemia and higher blood pressure. CONCLUSION: Later age and larger magnitude of the infancy BMI peak are associated with higher adiposity at 8-14 years of age. Later age but not magnitude of the BMI peak are related to a worse cardiometabolic profile during peripuberty.