Effect of mean HbA1c on the association of HbA1c variability and all‐cause mortality in patients with type 2 diabetes

Tseng, Juei-Yu; Chen, Hsin‐Hung; Huang, Kuo‐Tung; Hsu, Sandy Huey-Jen; Chen, Ching‐Chu

doi:10.1111/dom.13951

Cited by 12 publications

(13 citation statements)

References 39 publications

(72 reference statements)

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“…In patients with type 1 diabetes, HbA1c variability was found as an important risk factor for vascular complications such as cardiovascular diseases (CVDs) [8], microvascular diseases (MVDs) [8][9][10][11], and hospitalized hypoglycemia [12]. In patients with type 2 diabetes, although cumulative evidence shows the predictive role of HbA1c variability in the risks of hypoglycemia [12], CVDs [13][14][15][16][17][18][19][20] (such as subclinical left ventricular remodeling and dysfunction [21], reduced baroreflex sensitivity [22], and high thrombotic risk [23]), and allcause mortality [15][16][17][18][19][20][24][25][26][27][28][29][30], studies on MVDs [15,19,22,[31][32][33][34][35][36][37] are relatively limited and yield inconsistent results [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

“…However, there is lack of evidence for examining the comparative predictive abilities of various HbA1c variability measures for diabetic outcomes; most studies considered only one [13-15, 17, 24-28, 31-36] or two [16,29] variability measures. Although Tseng et al used three HbA1c variability measures (i.e., HbA1c-SD, HbA1c-CV, and HVS) to assess the role of mean HbA1c in the association between HbA1c variability and all-cause mortality, they did not further explore the comparative predictive abilities among these HbA1c measures [27].…”

Section: Introductionmentioning

confidence: 99%

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Comparative predictive ability of visit-to-visit HbA1c variability measures for microvascular disease risk in type 2 diabetes

Chen

Hung

et al. 2020

Cardiovasc Diabetol

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Background: To assess the associations of various HbA1c measures, including a single baseline HbA1c value, overall mean, yearly updated means, standard deviation (HbA1c-SD), coefficient of variation (HbA1c-CV), and HbA1c variability score (HVS), with microvascular disease (MVD) risk in patients with type 2 diabetes. Methods: Linked data between National Cheng Kung University Hospital and Taiwan's National Health Insurance Research Database were utilized to identify the study cohort. The primary outcome was the composite MVD events (retinopathy, nephropathy, or neuropathy) occurring during the study follow-up. Cox model analyses were performed to assess the associations between HbA1c measures and MVD risk, with adjustment for patients' baseline HbA1c, demographics, comorbidities/complications, and treatments. Results: In the models without adjustment for baseline HbA1c, all HbA1c variability and mean measures were significantly associated with MVD risk, except HVS. With adjustment for baseline HbA1c, HbA1c-CV had the strongest association with MVD risk. For every unit of increase in HbA1c-CV, the MVD risk significantly increased by 3.42-and 2.81-fold based on the models without and with adjustment for baseline HbA1c, respectively. The associations of HbA1c variability and mean measures with MVD risk in patients with baseline HbA1c < 7.5% (58 mmol/mol) were stronger compared with those in patients with baseline HbA1c ≥ 7.5% (58 mmol/mol). Conclusions: HbA1c variability, especially HbA1c-CV, can supplement conventional baseline HbA1c measure for explaining MVD risk. HbA1c variability may play a greater role in MVD outcomes among patients with relatively optimal baseline glycemic control compared to those with relatively poor baseline glycemic control.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative predictive ability of visit-to-visit HbA1c variability measures for microvascular disease risk in type 2 diabetes

Chen

Hung

et al. 2020

Cardiovasc Diabetol

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“…18 They reported that high variability score led to higher risk of all-cause mortality and the score was less influenced by mean HbAc1 level. 18 These facts were corroborated in 2 subsequent large-scale cohort studies, 19,20 suggesting that the variability score can reflect IPV better and may be used as an alternative indicator. Learning from Forbes, we developed the Tac variability score (TVS) to define IPV and explore its impact on transplant outcomes.…”

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confidence: 81%

Tacrolimus variability score outperforms coefficient of variation in predicting clinical outcomes of living kidney transplantation

Yin

Wang

Huang

et al. 2021

Brit J Clinical Pharma

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Aims: Intrapatient variability (IPV) was previously defined as coefficient of variation (CV) or standard deviation of tacrolimus (Tac) exposure while none of them was easily being interpreted and translated into clinical practice after kidney transplantation. Methods:We developed a novel Tac variability score (TVS) to evaluate IPV by calculating the frequency of clinically significant changes of Tac trough levels after kidney transplantation. Multivariate Cox proportional analyses were conducted to compare the impact of TVS and CV on transplant outcomes.Results: A total of 1343 patients were divided into high TVS (>0.30) and low TVS (<0.30) groups, and low CV (<0.30) and high CV (>0.30) groups. Univariate analyses showed that high TVS (hazard ratio [HR]: 2.323, 95% confidence interval [CI]:1.455-3.709) and high CV (HR: 1.606, 95%CI: 1.044-2.471) were associated with inferior graft survival. However, only TVS was an independent predictor for graft failure in multivariate analyses (HR: 1.972, 95%CI: 1.2-3.24), and the correlation maintained in high CV (P = .020) and low CV (P = .037) subgroups, while CV failed to predict graft loss in neither low (P = .387) nor high TVS (P = .600) subgroups. In addition, TVS had a higher correlation with graft survival in patients with Tac exposure within the therapeutic range and the correlation was less influenced by mean Tac trough levels. Conclusion:TVS is a novel measure of Tac IPV with higher correlation with graft survival and more convenience in clinical use than CV after kidney transplantation.

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“…However, in patients with T2D, the cells in the liver become insulin-resistant, resulting in decreased glucose metabolism in the circulation and the outcome is hyperglycemia [10]. T2D is diagnosed using a variety of serological markers, the most prevalent of which is glycated hemoglobin (HbA1c), which is an estimated average of three months' blood glucose concentrations [11]. Dyslipidemia occurs due to abnormalities of lipid profile such as increased serum triglyceride, low-density lipoprotein-cholesterol, and decreased highdensity lipoprotein cholesterol.…”

Section: Introductionmentioning

confidence: 99%

Serological and Oncoinformatic Analysis of HbA1c as a Prognostic Biomarker in Screening the Risks of Different Cancers among the Male T2D Patients of Bangladesh

Hasibuzzaman¹,

Azad²,

Mia³

et al. 2022

Preprint

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This research aimed to figure out the applications of HbA1c protein and HbA1 gene as the prognostic biomarkers for assessing the risks of different cancers among male T2D patients in Bangladesh considering their serological and oncoinformatic parameters. Depending on the concentrations of HbA1c (%) of the T2D patients (n=300), their individual FBS (mmol/L); THABF (mmol/L); creatinine (mg/dl); SC (mg/dl); STGs (mg/dl); HDLC (mg/dl); and LDLC (mg/dl) were estimated. The values of the patients were compared with the control (n=60) group as the serological analysis. Besides, HbA1 gene (encoding hBA1c protein) overexpression and promotor methylation responsible for BLCA, BRCA, CHOL, COAD, LUAD, LUSC, PAAD, and PRAD cancers in the male T2D patients were profiled as the oncoinformatic parameters based on the sample types; caner stages; racial footprints; gender; age; nodal metastasis; p53 methylations; pancreatitis; diabetes status; smoking behaviors; and overall/disease-free survivability. Finally, the ‘HbA1 gene strings’ responsible for genetic coexpression; endophytic vesicle regulation; antioxidant regulation; oxygen species metabolic regulation; and gene-mediated response to the reactive oxygen molecules were studied comprehensively. A strong correlation between BMI and FBS was observed in both the patients and the control (P<0.0001). Similarly, the values of FBS, THABF, and creatinine resulted in equal significance (P<0.0001) as compared to the HbA1c concentrations of all the T2D and control individuals. The SC, STGs, HDLC, and LDLC concentrations regulated ardently in both the control (P<0.0001), and patients group (P<0.0001), while HbA1c ranged from 3.8-5.8%, and 5.11-15.8% respectively. HbA1 gene is found downregulating with cancer progressed in most of the oncoinformatic parameters. According to the DA, CS, EI, CE, PC, NC, GF, H, and AT profiles; the HbA1 gene interacts with 8 other genes responsible for creating a protein cluster comprising- AHSP, HBA1, HBA2, HBB, HBD, HBE1, HBG2, RPS12, and RPS19 proteins for cancer formation. To recapitulate, HbA1c protein and HbA1 gene can be used as the prognostic serological and molecular biomarkers respectively for determining the risks of cancers among male chronic T2D patients.

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Effect of mean HbA1c on the association of HbA1c variability and all‐cause mortality in patients with type 2 diabetes

Cited by 12 publications

References 39 publications

Comparative predictive ability of visit-to-visit HbA1c variability measures for microvascular disease risk in type 2 diabetes

Comparative predictive ability of visit-to-visit HbA1c variability measures for microvascular disease risk in type 2 diabetes

Tacrolimus variability score outperforms coefficient of variation in predicting clinical outcomes of living kidney transplantation

Serological and Oncoinformatic Analysis of HbA1c as a Prognostic Biomarker in Screening the Risks of Different Cancers among the Male T2D Patients of Bangladesh

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