Literature DB >> 35535216

Association of Nondiabetic Glucometabolic Status and Aortic Stiffness in Community Hypertension Patients [Letter].

Maryam Sayed Jaffar AlSharaf1, Amna Abdalla Harib AlDhmanie1, Aji Gopakumar1.   

Abstract

Entities:  

Year:  2022        PMID: 35535216      PMCID: PMC9078338          DOI: 10.2147/DMSO.S365973

Source DB:  PubMed          Journal:  Diabetes Metab Syndr Obes        ISSN: 1178-7007            Impact factor:   3.168


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The study by Dan et al1 highlights the importance of nondiabetic glucometabolic status and its association with aortic stiffness. The research revealed the impact of SBP, and elevated FBG on aortic stiffness in hypertensive patients. There are few apprehensions observed in the chosen statistical methods which are discussed here. Analytical results were generated based on a cohort of 1065 hypertensive patients who were recruited continuously from May 2015 to Dec 2016. Although it is an acceptable methodology, an inadequate sample size (large/small) may lead to incorrect inferential results and misleading conclusions. Here the study includes comparatively a larger sample and therefore weak correlation coefficients were identified as statistically significant (table-2). The factors “AP, SBP, eGFR and Age” were found to be significantly correlated with cfPWV, but correlation coefficients were close to zero (weak, 0.07 Another concern was about the choice of statistical testing procedures in the study. Properties of normality distribution needs to be verified prior to the choice of descriptive statistics and statistical tests. Mean & SD was calculated to compare the average of the parameters among 5 MS risk factor groups, and then ANOVA was performed to identify the significance of the difference (Table-1). But there is no indication in the article regarding normality distribution of the quantitative variables. Variable distribution is a fundamental criterion for the choice of testing procedures. There are some parameters for which continuity assumption is violated, but researchers have applied parametric tests instead of non-parametric methods. Relationship between cfPWV and other parameters were identified (table-2), but not stated the status of normality to verify the appropriateness of Pearson’s correlation. Findings in Table-1 shows the use of Pearson’s chi-square test, but there are variable categories with small samples with expected frequency <5. It is an indication of a violation of test assumption and therefore the conclusion based on the association test may not be reliable. Overall, this letter intended to emphasize the results with statistical significance in larger samples does not imply the real significance, and the models developed give a poor prediction.
  3 in total

1.  Sample size estimation in diagnostic test studies of biomedical informatics.

Authors:  Karimollah Hajian-Tilaki
Journal:  J Biomed Inform       Date:  2014-02-26       Impact factor: 6.317

Review 2.  How to calculate sample size for different study designs in medical research?

Authors:  Jaykaran Charan; Tamoghna Biswas
Journal:  Indian J Psychol Med       Date:  2013-04

3.  Association of Nondiabetic Glucometabolic Status and Aortic Stiffness in Community Hypertension Patients.

Authors:  Dan Zhou; Mengqi Yan; Songtao Tang; Yingqing Feng
Journal:  Diabetes Metab Syndr Obes       Date:  2022-02-25       Impact factor: 3.168

  3 in total

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