scholarly journals Independent Association of Plasma Hydroxysphingomyelins With Physical Function in the Atherosclerosis Risk in Communities (ARIC) Study

2017 ◽  
Vol 73 (8) ◽  
pp. 1103-1110 ◽  
Author(s):  
Danni Li ◽  
Jeffrey R Misialek ◽  
Fangying Huang ◽  
Gwen B Windham ◽  
Fang Yu ◽  
...  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Danni Li ◽  
Aniqa B. Alam ◽  
Fang Yu ◽  
Anna Kucharska-Newton ◽  
B. Gwen Windham ◽  
...  

AbstractLong-chain sphingomyelins (SMs) may play an important role in the stability of myelin sheath underlying physical function. The objective of this study was to examine the cross-sectional and longitudinal associations of long-chain SMs [SM (41:1), SM (41:2), SM (43:1)] and ceramides [Cer (41:1) and Cer (43:1)] with physical function in the Atherosclerosis Risk in Communities (ARIC) study. Plasma concentrations of SM (41:1), SM (41:2), SM (43:1), Cer (41:1) and Cer (43:1) were measured in 389 ARIC participants in 2011–13. Physical function was assessed by grip strength, Short Physical Performance Battery (SPPB), 4-m walking speed at both 2011–13 and 2016–17, and the modified Rosow-Breslau questionnaire in 2016–2017. Multivariable linear and logistic regression analyses were performed, controlling for demographic and clinical confounders. In cross-sectional analyses, plasma concentrations of SM 41:1 were positively associated with SPPB score (β-coefficients [95% confidence internal]: 0.33 [0.02, 0.63] per 1 standard deviation [SD] increase in log-transformed concentration, p value 0.04), 4-m walking speed (0.042 m/s [0.01, 0.07], p value 0.003), and negatively with self-reported disability (odds ratio = 0.73 [0.65, 0.82], p value < 0.0001). Plasma concentrations of the five metabolites examined were not significantly associated with longitudinal changes in physical function or incidence of poor mobility. In older adults, plasma concentrations of long-chain SM 41:1 were cross-sectionally positively associated with physical function.


Circulation ◽  
2019 ◽  
Vol 139 (Suppl_1) ◽  
Author(s):  
Yejin Mok ◽  
Junichi Ishigami ◽  
Anna Kucharska-Newton ◽  
Maya Salameh ◽  
Jennifer Schrack ◽  
...  

2017 ◽  
Vol 257 ◽  
pp. 208-215 ◽  
Author(s):  
Kunihiro Matsushita ◽  
Shoshana H. Ballew ◽  
Yingying Sang ◽  
Corey Kalbaugh ◽  
Laura R. Loehr ◽  
...  

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1398-P
Author(s):  
MARY R. ROONEY ◽  
OLIVE TANG ◽  
B. GWEN WINDHAM ◽  
JUSTIN B. ECHOUFFO TCHEUGUI ◽  
PAMELA LUTSEY ◽  
...  

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e047356
Author(s):  
Carlton R Moore ◽  
Saumya Jain ◽  
Stephanie Haas ◽  
Harish Yadav ◽  
Eric Whitsel ◽  
...  

ObjectivesUsing free-text clinical notes and reports from hospitalised patients, determine the performance of natural language processing (NLP) ascertainment of Framingham heart failure (HF) criteria and phenotype.Study designA retrospective observational study design of patients hospitalised in 2015 from four hospitals participating in the Atherosclerosis Risk in Communities (ARIC) study was used to determine NLP performance in the ascertainment of Framingham HF criteria and phenotype.SettingFour ARIC study hospitals, each representing an ARIC study region in the USA.ParticipantsA stratified random sample of hospitalisations identified using a broad range of International Classification of Disease, ninth revision, diagnostic codes indicative of an HF event and occurring during 2015 was drawn for this study. A randomly selected set of 394 hospitalisations was used as the derivation dataset and 406 hospitalisations was used as the validation dataset.InterventionUse of NLP on free-text clinical notes and reports to ascertain Framingham HF criteria and phenotype.Primary and secondary outcome measuresNLP performance as measured by sensitivity, specificity, positive-predictive value (PPV) and agreement in ascertainment of Framingham HF criteria and phenotype. Manual medical record review by trained ARIC abstractors was used as the reference standard.ResultsOverall, performance of NLP ascertainment of Framingham HF phenotype in the validation dataset was good, with 78.8%, 81.7%, 84.4% and 80.0% for sensitivity, specificity, PPV and agreement, respectively.ConclusionsBy decreasing the need for manual chart review, our results on the use of NLP to ascertain Framingham HF phenotype from free-text electronic health record data suggest that validated NLP technology holds the potential for significantly improving the feasibility and efficiency of conducting large-scale epidemiologic surveillance of HF prevalence and incidence.


Sign in / Sign up

Export Citation Format

Share Document