Dataset Information

Objective Measures of Physical Activity and Cardiometabolic and Endocrine Biomarkers.

ABSTRACT: PURPOSE:Although physical activity is an established risk factor for chronic disease prevention, the specific mechanisms underlying these relationships are poorly understood. We examined the associations between total activity counts and moderate-vigorous physical activity (MVPA) measured by accelerometer, and physical activity energy expenditure measured by doubly labeled water, with plasma levels of proinsulin, insulin, c-peptide, insulin growth factor binding protein-3, insulin growth factor-1, adiponectin, leptin, and leptin-sR. METHODS:We conducted a cross-sectional analysis of 526 healthy US women in the Women's Lifestyle Validation Study, 2010 to 2012. We performed multiple linear regression models adjusting for potential lifestyle and health-related confounders to assess the associations between physical activity, measured in quartiles (Q) and biomarkers. RESULTS:Participants in Q4 versus Q1 of total activity counts had lower proinsulin (-20%), c-peptide (-7%), insulin (-31%), and leptin (-46%) levels, and higher adiponectin (55%), leptin-sR (25%), and insulin growth factor-1 (9.6%) levels (all P trend ? 0.05). Participants in Q4 versus Q1 of MVPA had lower proinsulin (-26%), c-peptide (-7%), insulin (-32%), and leptin (-40%) levels, and higher adiponectin (31%) and leptin-sR (22%) levels (all P trend ? 0.05). Further adjustment for body mass index (BMI) attenuated these associations, but the associations with adipokines remained significant. Those in Q4 versus Q1 of physical activity energy expenditure had lower leptin (-21%) and higher leptin-sR (10%) levels (all P trend ? 0.05), after additional adjustment for BMI. In the sensitivity analysis, the associations were similar but attenuated when physical activity was measured using the subjective physical activity questionnaire. CONCLUSIONS:Our data suggest that greater physical activity is modestly associated with favorable levels of cardiometabolic and endocrine biomarkers, where the strongest associations were found with accelerometer-measured physical activity. These associations may be only partially mediated through BMI, further supporting the role of physical activity in the reduction of cardiometabolic and endocrine disease risk, independent of adiposity.

SUBMITTER: Alessa HB

PROVIDER: S-EPMC5561485 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Objective Measures of Physical Activity and Cardiometabolic and Endocrine Biomarkers.

Alessa Hala B HB Chomistek Andrea K AK Hankinson Susan E SE Barnett Junaidah B JB Rood Jennifer J Matthews Charles E CE Rimm Eric B EB Willett Walter C WC Hu Frank B FB Tobias Deirdre K DK

Medicine and science in sports and exercise 20170901 9

<h4>Purpose</h4>Although physical activity is an established risk factor for chronic disease prevention, the specific mechanisms underlying these relationships are poorly understood. We examined the associations between total activity counts and moderate-vigorous physical activity (MVPA) measured by accelerometer, and physical activity energy expenditure measured by doubly labeled water, with plasma levels of proinsulin, insulin, c-peptide, insulin growth factor binding protein-3, insulin growth ...[more]

PMID: 28398945

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Project description:BackgroundThe minimal physical activity intensity that would confer health benefits among adolescents is unknown. The purpose of this study was to examine the associations of accelerometer-derived light-intensity (split into low and high) physical activity, and moderate- to vigorous-intensity physical activity with cardiometabolic biomarkers in a large population-based sample.MethodsThe study is based on 1,731 adolescents, aged 12-19 years from the 2003/04 and 2005/06 National Health and Nutrition Examination Survey. Low light-intensity activity (100-799 counts/min), high light-intensity activity (800 counts/min to <4 METs) and moderate- to vigorous-intensity activity (? 4 METs, Freedson age-specific equation) were accelerometer-derived. Cardiometabolic biomarkers, including waist circumference, systolic blood pressure, diastolic blood pressure, HDL-cholesterol, and C-reactive protein were measured. Triglycerides, LDL- cholesterol, insulin, glucose, and homeostatic model assessments of ?-cell function (HOMA-%B) and insulin sensitivity (HOMA-%S) were also measured in a fasting sub-sample (n=807).ResultsAdjusted for confounders, each additional hour/day of low light-intensity activity was associated with 0.59 (95% CI: 1.18-0.01) mmHG lower diastolic blood pressure. Each additional hour/day of high light-intensity activity was associated with 1.67 (2.94-0.39) mmHG lower diastolic blood pressure and 0.04 (0.001-0.07) mmol/L higher HDL-cholesterol. Each additional hour/day of moderate- to vigorous-intensity activity was associated with 3.54 (5.73-1.35) mmHG lower systolic blood pressure, 5.49 (1.11-9.77)% lower waist circumference, 25.87 (6.08-49.34)% lower insulin, and 16.18 (4.92-28.53)% higher HOMA-%S.ConclusionsTime spent in low light-intensity physical activity and high light-intensity physical activity had some favorable associations with biomarkers. Consistent with current physical activity recommendations for adolescents, moderate- to vigorous-intensity activity had favorable associations with many cardiometabolic biomarkers. While increasing MVPA should still be a public health priority, further studies are needed to identify dose-response relationships for light-intensity activity thresholds to inform future recommendations and interventions for adolescents.

Project description:BackgroundCortisol is often used as a biological marker for stress. When measured in urine or serum, representing a short-term measurement of the hormone, it has been associated with unfavorable sleep characteristics and both low and high physical activity levels. However, cortisol in hair represents a long-term stress measure and has been suggested as a promising new marker for chronic stress. Therefore, we aimed to examine the association between objectively measured sleep, physical activity and hair cortisol levels in preschool children.MethodsIn order to obtain objective measures of physical activity and sleep habits, 54 children aged 2-6 years wore an ActiGraph for 5 consecutive days and nights. For chronic stress measurements of each child, hair was cut from the back of the head close to the scalp for analysis of cortisol levels. Associations between measured sleep quality and quantity and level of physical activity and hair cortisol levels were estimated using linear regression analysis, presented as β. Results were adjusted for sex, age and BMI z-score.ResultsWe found no significant association between log-transformed cortisol (pg/mg) and sleep duration (hours) (β = - 0.0016, p = 0.99), sleep efficiency (β = - 3.1, p = 0.18), sleep latency (β = 0.015, p = 0.16) or physical activity level (100 counts per min) (β = 0.014, p = 0.22). However, sleep latency (min) was directly associated with physical activity (counts per min) levels (β = 35.2, p = 0.02), while sleep duration (hours) (β = - 142.1, p = 0.55) and sleep efficiency (%) (β = - 4087, p = 0.26) showed no significant associations.ConclusionsIn our study, a high physical activity level was associated with poorer sleep habits. Neither sleep quality nor physical activity were related to long term cortisol exposure. These results are among the first to study associations between objectively measured sleep, physical activity and chronic cortisol levels among preschool children. More and larger studies are therefore needed.

Project description:The purpose of this study was to examine associations between the Walk Score and physical activity in young, overweight/obese urban women. Project Health included 45 White or African American women (BMI 31.5±3.9 kg/m2; age 26.5±4.6 years; 62% African American) living in the Boston area. An accelerometer estimated steps/day and mins/day in light physical activity (100-2019 counts-per-minute) and moderate-to-vigorous-physical activity (≥2020 cpm). Walk Score was used to estimate the walkability of home address by analyzing proximity to nearby amenities. General linear regression models estimated associations between total Walk Score and physical activity (light physical activity, moderate-to-vigorous-physical activity, steps, total activity counts, METs), adjusting for body mass index, age, race/ethnicity, seasonality, wear time, employment and student status. For physical activity variables that had significant associations with Walk Score (steps/day and steps/min), regression models were estimated for Walk Score sub-scores (parks, grocery, errands, shopping, dining/drinking, culture/entertainment and schools). Logistic regression models estimated the odds of meeting the guidelines for steps (≥10,000/day) and moderate-to-vigorous-physical activity (≥150mins MVPA/week) based on Walk Score. Participants had a Walk Score of 63.9±26.4, took 14,143±3,934 steps/day, and spent 206.2±66.0 mins/day in light physical activity and 46.7±17.5 mins/day in moderate-to-vigorous- physical activity. Walk Score was significantly and positively associated with steps/day (β = 51.4, p = 0.01) and steps/min (β = 0.06, p = 0.009) but was not associated with mins/day of light physical activity, moderate-to-vigorous-physical activity, total activity counts or METs. Parks, grocery, errands, shopping, dining/drinking, and culture/entertainment Walk Score sub-scores were significantly associated with steps and steps/min (all p<0.05), but not significantly associated for schools. Participants who lived in higher Walk Score neighborhoods were more likely to meet the step guidelines (OR, 95% CI: 1.59, 1.04-2.99) and moderate-to-vigorous-physical activity guidelines (1.63, 1.06-3.02), respectively, per 10-unit increase in Walk Score. These results indicate that living in a more walkable neighborhood may support walking behavior in young, urban-dwelling overweight/obese women and provide further evidence for the expanded use of urban planning and transportation policies to improve the walkability of urban neighborhoods.

Project description:Maintaining high levels of daily activity and physical capability have been proposed as important constituents to promote healthy brain and cognitive aging. Studies investigating the associations between brain health and physical activity in late life have, however, mainly been based on self-reported data or measures designed for clinical populations. In the current study, we examined cross-sectional associations between physical activity, recorded by an ankle-positioned accelerometer for seven days, physical capability (grip strength, postural control, and walking speed), and neuroimaging based surrogate markers of brain health in 122 healthy older adults aged 65-88 years. We used a multimodal brain imaging approach offering complementary structural MRI based indicators of brain health: global white matter fractional anisotropy (FA) and mean diffusivity (MD) based on diffusion tensor imaging, and subcortical and global brain age based on brain morphology inferred from T1-weighted MRI data. In addition, based on the results from the main analysis, follow-up regression analysis was performed to test for association between the volume of key subcortical regions of interest (hippocampus, caudate, thalamus and cerebellum) and daily steps, and a follow-up voxelwise analysis to test for associations between walking speed and FA across the white matter Tract-Based Spatial Statistics (TBSS) skeleton. The analyses revealed a significant association between global FA and walking speed, indicating higher white matter integrity in people with higher pace. Voxelwise analysis supported widespread significant associations. We also found a significant interaction between sex and subcortical brain age on number of daily steps, indicating younger-appearing brains in more physically active women, with no significant associations among men. These results provide insight into the intricate associations between different measures of brain and physical health in old age, and corroborate established public health advice promoting physical activity.

Dataset Information

Objective Measures of Physical Activity and Cardiometabolic and Endocrine Biomarkers.

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Objective Measures of Physical Activity and Cardiometabolic and Endocrine Biomarkers.

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