Dataset Information

Combined Inflammation and Metabolism Biomarker Indices of Robust and Impaired Physical Function in Older Adults.

ABSTRACT: OBJECTIVES:To determine whether combinations of inflammatory markers are related to physical function. DESIGN AND SUBJECTS:secondary analysis of baseline of three observational studies of community-dwelling older adults MEASUREMENTS: The baseline data from 3 cohorts of older adults with different health and disease status were employed. Twenty markers of inflammation and metabolism were individually assessed for correlation with usual gait speed and were separated into robust and impairment quartiles. For the robustness and impairment indices, individual markers were selected using step-wise regression over bootstrapping iterations, and regression coefficients were estimated for the markers individually and collectively as an additive score. RESULTS:We developed a robustness index involving 6 markers and an impairment index involving 8 markers corresponding positively and negatively with gait speed. Two markers, glycine and tumor necrosis factor receptor 1 (TNFR1), appeared only in the robustness index, and TNFR2; regulated on activation, normal T-cell expressed and secreted; the amino acid factor; and matrix metallopeptidase 3; appeared only in the impairment index. CONCLUSION:Indices of biomarkers were associated with robust and impaired physical performance but differ, in composition suggesting potential biological differences that may contribute to robustness and impairment.

SUBMITTER: Zuo X

PROVIDER: S-EPMC7326532 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Combined Inflammation and Metabolism Biomarker Indices of Robust and Impaired Physical Function in Older Adults.

Zuo Xintong X Luciano Alison A Pieper Carl F CF Bain James R JR Kraus Virginia B VB Kraus William E WE Morey Miriam C MC Cohen Harvey J HJ

Journal of the American Geriatrics Society 20180508 7

<h4>Objectives</h4>To determine whether combinations of inflammatory markers are related to physical function.<h4>Design and subjects</h4>secondary analysis of baseline of three observational studies of community-dwelling older adults MEASUREMENTS: The baseline data from 3 cohorts of older adults with different health and disease status were employed. Twenty markers of inflammation and metabolism were individually assessed for correlation with usual gait speed and were separated into robust and ...[more]

PMID: 29738072

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Project description:Background Selenium (Se) is a trace element with a narrow safety margin. Objectives To evaluate the cross-sectional and longitudinal dose-response association between Se exposure and measures of impaired physical function and disability in older adults. Design NHANES 2011–2014 cross-sectional (US, n = 1733, age ?60 years) and Seniors-ENRICA-2 2017–2019 cross-sectional and longitudinal (Spain, n = 2548 and 1741, respectively, age ?65 years) data were analyzed. Whole blood and serum Se levels were measured using inductively coupled plasma-mass spectrometry. Lower-extremity performance was assessed with the Short Physical Performance Battery, and muscle weakness with a dynamometer. Incident mobility and agility limitations, and disability in instrumental activities of daily living (IADL) were ascertained with standardized questionnaires. Analyses were adjusted for relevant confounders, including physical activity. Results across studies were pooled using random-effects meta-analysis. Results Meta-analyzed odds ratios (95% confidence interval) per log2 increase in whole blood Se were 0.54 (0.32; 0.76) for weakness, 0.59 (0.34; 0.83) for impaired lower-extremity performance, 0.48 (0.31; 0.68) for mobility limitations, 0.71 (0.45; 0.97) for agility limitations, and 0.34 (0.12; 0.56) for disability in at least one IADL. Analyses for serum Se in NHANES showed similar results. Findings suggest the inverse association with grip strength is progressive below 140 ?g/L (p-value for non-linear trend in the Seniors-ENRICA-2 study = 0.13), and above 140 ?g/L (p-value for non-linear trend in NHANES = 0.11). In the Seniors-ENRICA-2 cohort, with a 2.2 year follow-up period, a doubling in baseline Se levels were associated with a lower incidence of weakness [odds ratio (95% confidence interval): 0.45 (0.22; 0.91)], impaired lower-extremity performance [0.63 (0.32; 1.23)], mobility [0.43 (0.21; 0.91)] and agility [0.38 (0.18; 0.78)] limitations. Discussion In US and Spanish older adults, Se concentrations were inversely associated with physical function limitations. Further studies are needed to elucidate underlying mechanisms. Graphical abstract Image 1

Project description:Sufficient physical activity (PA) reduces the risk of a myriad of diseases and preserves physical capabilities in later life. While there have been significant achievements in mapping accelerations to real-life movements using machine learning (ML), errors continue to be common, particularly for wrist-worn devices. It remains unknown whether ML models are robust for estimating age-related loss of physical function. In this study, we evaluated the performance of ML models (XGBoost and LASSO) to estimate the hallmark measures of PA in low physical performance (LPP) and high physical performance (HPP) groups. Our models were built to recognize PA types and intensities, identify each individual activity, and estimate energy expenditure (EE) using wrist-worn accelerometer data (33 activities per participant) from a large sample of participants (n = 247, 57% females, aged 60+ years). Results indicated that the ML models were accurate in recognizing PA by type and intensity while also estimating EE accurately. However, the models built to recognize individual activities were less robust. Across all tasks, XGBoost outperformed LASSO. XGBoost obtained F1-Scores for sedentary (0.932 ± 0.005), locomotion (0.946 ± 0.003), lifestyle (0.927 ± 0.006), and strength flexibility exercise (0.915 ± 0.017) activity type recognition tasks. The F1-Scores for recognizing low, light, and moderate activity intensity were (0.932&nbsp;±&nbsp;0.005), (0.840 ± 0.004), and (0.869 ± 0.005), respectively. The root mean square error for EE estimation was 0.836 ± 0.059 METs. There was no evidence showing that splitting the participants into the LPP and HPP groups improved the models' performance on estimating the hallmark measures of physical activities. In conclusion, using features derived from wrist-worn accelerometer data, machine learning models can accurately recognize PA types and intensities and estimate EE for older adults with high and low physical function.

Dataset Information

Combined Inflammation and Metabolism Biomarker Indices of Robust and Impaired Physical Function in Older Adults.

Publications

Combined Inflammation and Metabolism Biomarker Indices of Robust and Impaired Physical Function in Older Adults.

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