scholarly journals Disparities in HbA1c Levels Between African-American and Non-Hispanic White Adults With Diabetes: A meta-analysis

Diabetes Care ◽  
2006 ◽  
Vol 29 (9) ◽  
pp. 2130-2136 ◽  
Author(s):  
J. K. Kirk ◽  
R. B. D'Agostino ◽  
R. A. Bell ◽  
L. V. Passmore ◽  
D. E. Bonds ◽  
...  
Keyword(s):  
African American ◽  
Meta Analysis ◽  
White Adults ◽  
Hispanic White ◽  
Hba1c Levels

Genetic Markers Explain Differences in HbA1c Levels between African-American (AA) and Non-Hispanic White (NHW) Participants of the Diabetes Prevention Program (DPP)

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 1704-P
Author(s):  
MARIE-FRANCE HIVERT ◽  
COSTAS A. CHRISTOPHI ◽  
KATHLEEN A. JABLONSKI ◽  
SHARON EDELSTEIN ◽  
STEVEN E. KAHN ◽  
...  
Keyword(s):  
African American ◽  
Genetic Markers ◽  
Prevention Program ◽  
Diabetes Prevention ◽  
Diabetes Prevention Program ◽  
Hispanic White ◽  
Hba1c Levels

Why Inclusion Matters for Alzheimer’s Disease Biomarker Discovery in Plasma

2021 ◽  
pp. 1-18
Author(s):  
Mostafa J. Khan ◽  
Heather Desaire ◽  
Oscar L. Lopez ◽  
M. Ilyas Kamboh ◽  
Renã A.S. Robinson
Keyword(s):  
Alzheimer’S Disease ◽  
African American ◽  
Biomarker Discovery ◽  
Ethnic Background ◽  
Differentially Expressed ◽  
Differentially Expressed Proteins ◽  
Black Adults ◽  
American Black ◽  
White Adults ◽  
Hispanic White

Background: African American/Black adults have a disproportionate incidence of Alzheimer’s disease (AD) and are underrepresented in biomarker discovery efforts. Objective: This study aimed to identify potential diagnostic biomarkers for AD using a combination of proteomics and machine learning approaches in a cohort that included African American/Black adults. Methods: We conducted a discovery-based plasma proteomics study on plasma samples (N = 113) obtained from clinically diagnosed AD and cognitively normal adults that were self-reported African American/Black or non-Hispanic White. Sets of differentially-expressed proteins were then classified using a support vector machine (SVM) to identify biomarker candidates. Results: In total, 740 proteins were identified of which, 25 differentially-expressed proteins in AD came from comparisons within a single racial and ethnic background group. Six proteins were differentially-expressed in AD regardless of racial and ethnic background. Supervised classification by SVM yielded an area under the curve (AUC) of 0.91 and accuracy of 86%for differentiating AD in samples from non-Hispanic White adults when trained with differentially-expressed proteins unique to that group. However, the same model yielded an AUC of 0.49 and accuracy of 47%for differentiating AD in samples from African American/Black adults. Other covariates such as age, APOE4 status, sex, and years of education were found to improve the model mostly in the samples from non-Hispanic White adults for classifying AD. Conclusion: These results demonstrate the importance of study designs in AD biomarker discovery, which must include diverse racial and ethnic groups such as African American/Black adults to develop effective biomarkers.

scholarly journals Disparities in A1C Levels Between Hispanic and Non-Hispanic White Adults With Diabetes: A meta-analysis

Diabetes Care ◽  
2007 ◽  
Vol 31 (2) ◽  
pp. 240-246 ◽  
Author(s):  
J. K. Kirk ◽  
L. V. Passmore ◽  
R. A. Bell ◽  
K.M. V. Narayan ◽  
R. B. D'Agostino ◽  
...  
Keyword(s):  
Meta Analysis ◽  
White Adults ◽  
Hispanic White

scholarly journals Short Sleep Duration Is Associated with Higher Energy Intake and Expenditure among African-American and Non-Hispanic White Adults

2014 ◽  
Vol 144 (4) ◽  
pp. 461-466 ◽  
Author(s):  
Ruth E. Patterson ◽  
Jennifer A. Emond ◽  
Loki Natarajan ◽  
Katherine Wesseling-Perry ◽  
Laurence N. Kolonel ◽  
...  
Keyword(s):  
African American ◽  
Energy Intake ◽  
Sleep Duration ◽  
Short Sleep Duration ◽  
Short Sleep ◽  
White Adults ◽  
Hispanic White

Effects of wearable technology on patients with diabetes: A systematic review and meta-analysis (Preprint)

2020 ◽  
Author(s):  
Dongjun Wu ◽  
Nicholas Buys ◽  
Guandong Xu ◽  
Jing Sun
Keyword(s):  
Systematic Review ◽  
Blood Pressure ◽  
Relapse Prevention ◽  
Diabetes Management ◽  
Meta Analysis ◽  
Intervention Group ◽  
Wearable Technologies ◽  
Patients With Diabetes ◽  
Hba1c Levels

UNSTRUCTURED Aims: This systematic review and meta-analysis aimed to evaluate the effects of wearable technologies on HbA1c, blood pressure, body mass index (BMI), and fastening blood glucose (FBG) in patients with diabetes. Methods: We searched PubMed, Scopus, Embase, the Cochrane database, and the Chinese CNKI database from last 15 years until August 2021. The quality of the 16 included studies was assessed using the PEDro scale, and random effect models were used to estimate outcomes, with I2 used for heterogeneity testing. Results: A significant reduction in HbA1c (-0.475% [95% CI -0.692 to -0.257, P<0.001]) was found following telemonitoring. However, the results of the meta-analysis did not show significant changes in blood pressure, BMI, and glucose, in the intervention group (P>0.05), although the effect size for systolic blood pressure (0.389) and diastolic blood pressure may indicate a significant effect. Subgroup analysis revealed statistically significant effects of wearable technologies on HbA1c when supported by dietetic interventions (P<0.001), medication monitoring (P<0.001), and relapse prevention (P<0.001). Online messages and telephone interventions significantly affected HbA1c levels (P<0.001). Trials with additional online face-to-face interventions showed greater reductions in HbA1c levels. Remote interventions including dietetic advice (P<0.001), medication (P<0.001), and relapse prevention (P<0.001) during telemonitoring showed a significant effect on HbA1c, particularly in patients attending ten or more intervention sessions (P<0.001). Conclusion: Wearable technologies can improve diabetes management by simplifying self-monitoring, allowing patients to upload their live measurement results frequently and thereby improving the quality of telemedicine. Wearable technologies also facilitate remote medication management, dietetic interventions, and relapse prevention.

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