Next Article in Journal
Retinal OCT Findings in Patients after COVID Infection
Next Article in Special Issue
Acute Kidney Injury Following Admission with Acute Coronary Syndrome: The Role of Diabetes Mellitus
Previous Article in Journal
Echocardiography in Pulmonary Arterial Hypertension: Comprehensive Evaluation and Technical Considerations
Previous Article in Special Issue
Hemoglobin A1c in Patients with Diabetes Predict Long-Term Mortality Following Coronary Artery Surgery
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JCM
Volume 10
Issue 15
10.3390/jcm10153232
jcm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Changes over Time in Hemoglobin A1C (HbA1C) Levels Predict Long-Term Survival Following Acute Myocardial Infarction among Patients with Diabetes Mellitus

by
Ygal Plakht
1,2,*,
Harel Gilutz
3 and
Arthur Shiyovich
4
1
Department of Nursing, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel
2
Department of Emergency Medicine, Soroka University Medical Center, Beer Sheva 84101, Israel
3
Goldman Medical School, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel
4
Department of Cardiology, Rabin Medical Center, Petah Tikva, Israel and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2021, 10(15), 3232; https://doi.org/10.3390/jcm10153232
Submission received: 17 June 2021 / Revised: 15 July 2021 / Accepted: 19 July 2021 / Published: 22 July 2021
(This article belongs to the Collection Current Clinical Treatments and Challenges for Coronary Artery Disease (CAD))
Browse Figures
Review Reports Versions Notes

Abstract

:
Frequent fluctuations of hemoglobin A1c (HbA1C) values predict patient outcomes. However, data regarding prognoses depending on the long-term changes in HbA1C among patients after acute myocardial infarction (AMI) are scarce. We evaluated the prognostic significance of HbA1C levels and changes among diabetic patients (n = 4066) after non-fatal AMI. All the results of HbA1C tests up to the 10-year follow-up were obtained. The changes (∆) of HbA1C were calculated in each patient. The time intervals of ∆HbA1C values were classified as rapid (<one year) and slow (≥one year) changes. The outcome was all-cause mortality. The highest mortality rates of 53.8% and 35.5% were found in the HbA1C < 5.5–7% and ∆HbA1C = −2.5–(−2%) categories. A U-shaped association was observed between HbA1C and mortality: adjOR = 1.887 and adjOR = 1.302 for HbA1C < 5.5% and ≥8.0%, respectively, as compared with 5.5–6.5% (p < 0.001). Additionally, ∆HbA1C was associated with the outcome (U-shaped): adjOR = 2.376 and adjOR = 1.340 for the groups of <−2.5% and ≥2.5% ∆HbA1C, respectively, as compared to minimal ∆HbA1C (±0.5%) (p < 0.001). A rapid increase in HbA1C (but not decrease) was associated with a greater risk of mortality. HbA1C values and their changes are significant prognostic markers for long-term mortality among AMI-DM patients. ∆HbA1C and its timing, in addition to absolute HbA1C values, should be monitored.

1. Introduction

The global burden of diabetes mellitus (DM), a major cardiovascular risk factor, has increased dramatically throughout the last two decades [1,2]. Acute myocardial infarction (AMI) is a leading cause of increased mortality among patients with DM [3,4,5,6,7]. Furthermore, DM is a significant risk factor for short- and long-term morbidity and mortality among patients presenting with AMI [8,9]. Glucose and glycated hemoglobin (HbA1C) levels upon admission with AMI were associated with long-term mortality among patients with and without DM [10]. Thus, one of the cornerstones of treatment is glycemic monitoring and control, usually monitored by serial HbA1C levels [11], based on evidence from several trials reporting improved outcomes with lowering of HbA1C levels [4,12,13].
Recently, glycemic variability emerged as an additional and possibly even superior predictor of diabetic complications than mean HbA1C, with frequent fluctuations independently associated with poor prognosis. In contrast, more stable HbA1C control may benefit patients [14,15,16,17,18,19,20,21]. In addition, Lee et al. recently reported that long-term mean HbA1C was significantly associated with long-term survival following AMI [22]. However, data regarding long-term HbA1C changes following AMI and the association with the survival of these patients are scarce. Thus, this study aimed to evaluate the prognostic significance of HbA1C and its changes among patients with DM with non-fatal AMI.

2. Materials and Methods

2.1. Study Population

This observational study retrospectively evaluated consecutive patients with DM admitted with a non-fatal AMI at Soroka University Medical Center (SUMC), a tertiary medical center in Southern Israel, from 1 January 2002 through 31 December 2017, who survived at least one year post-discharge. Patients were excluded if they had the following criteria: under age 18 years or no HbA1C measurements throughout the follow-up period. The SUMC ethics committee approved the study (SOR-0319-16).

2.2. Data Sources and Classifications

The baseline data were obtained from the electronic medical records of SUMC and included: patient demographics, comorbidities, and results of laboratory tests and echocardiographic, angiographic and revascularization procedures, as previously reported for the Soroka Acute Myocardial Infarction (SAMI) project [23]. The diagnosis of AMI was based on the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes: ST-segment elevation AMI (STEMI) 410.0 *–410.6 * and non-ST-segment elevation AMI (NSTEMI) 410.7 *–410.9 *. The DM definitions (and characteristics) were as follows: ICD-9-CM 250 * and/or HbA1C ≥ 6.5% throughout six months before or after hospitalization. The diagnosis of anemia was based on low hemoglobin blood levels (for males < 13 g/dL; for females < 12 g/dL) during the index hospitalization. Patients were defined as having significant renal failure if they were either on hemodialysis or had an estimated glomerular filtration rate (eGFR) < 30 mL/min/1.73 m2. Either the ICD-9-CM code defined dyslipidemia, or this was given if the low-density lipoproteins (LDLs) were ≥100 mg/dL. Severe left ventricular (LV) dysfunction was defined as an ejection fraction < 30%.

2.3. Follow-Up and the Study Endpoint

Follow-up started after discharge and continued up to 10 years after that, or until 1 July 2020. The outcome was all-cause mortality. The survival status or death date for each patient was obtained from the Ministry of the Interior’s population registry.

2.4. HbA1C Values and Changes during the Follow-Up

All the HbA1C values during the follow-up were obtained and classified as follows: <5.5%, 5.5–6.5%, 6.5–7.0%, 7.0–8.0% and ≥8.0%. In addition, the changes (differences between every pair [later result–earlier result] of HbA1C values; ∆HbA1C) throughout the follow-up of each patient were calculated and classified as follows: <−2.5%, −2.5–(−2.0)%, −2.0–(−1.5)%, −1.5–(−1.0)%, −1–(−0.5)%, −0.5–0.5%, 0.5–1%, 1.0–1.5%, 1.5–2.0%, 2.0–2.5% and ≥2.5%. The time interval of ∆HbA1C change values was classified as rapid (<one year) or slow (≥one year).

2.5. Statistical Analysis

Statistical analysis was performed using IBM SPSS 26 (SPSS Inc., Chicago, IL, USA) software. Patient characteristics were presented as mean and standard deviation (SD) for continuous variables and numbers (n) and as percentages (%) for the categorical data. A visual inspection of the continuous variables showed that their distribution was “normal”. Comparison of the baseline characteristics and the outcomes was performed using the chi-square test, the chi-square test for linear trend of categorical variables and Student’s t-test for continuous variables. A parametric test was used based on the central limit theorem and large sample size. The measure of association between HbA1C levels during the follow-up and the risk of mortality was assessed using generalized estimating equation (GEE), binary logistic type, which considered the repeated measures of HbA1C for each patient. Three different models were built: (1) HbA1C levels alone, (2) ∆HbA1C levels alone and (3) HbA1C and ∆HbA1C levels, with the time from hospital discharge and the baseline characteristics as the potential confounders, in order to assess the disparity of the associations between ∆HbA1C values and outcomes by the groups of time intervals of ∆HbA1C (rapid vs. slow change). This analysis included a multivariate model (GEE), which included the interactive variable of ∆HbA1C by time interval. The reference categories for these models were: HbA1C 5.5–6.5% and the minimal category of HbA1C change (−0.5–0.5%). The results of the models are presented as adjusted odds ratios (adjORs) and 95% confidence intervals (CIs) for ORs. For each test, p < 0.05 was considered statistically significant.

3. Results

A total of 4066 eligible DM patients were analyzed in the current study, with a mean age of 66.4 ± 11.9 years; 36% were females. The median follow-up (after the first year) was 6.1 years, with 1802 (44%) patients dying throughout the follow-up period. The baseline characteristics of the study cohort and comparisons (at the index date) between patients who survived and those that died are presented in Table 1. Patients who died were older and more likely to be women, with higher rates of prior AMI, prior coronary artery bypass graft (CABG), congestive heart failure, renal failure and peripheral arterial disease than the survivors. Furthermore, they had an increased rate of DM-associated target-organ complications compared with the survivors. However, these patients had a lower rate of dyslipidemia, obesity, smoking and family history of early coronary artery disease (CAD) than the survivors. Besides, patients who died had a higher prevalence of non-cardiovascular comorbidity and were more likely to present with NSTEMI (rather than STEMI). Those who died had severe left ventricular dysfunction and were less likely to undergo revascularization due to the index AMI compared to the survivors.
A total of 35,609 HbA1C test results were documented among the study cohort during the follow-up. HbA1C values ranged between 3.7% and 18.6%, with a mean of 7.81 ± 1.74%. The mean HbA1C was lower among patients who died vs. survivors (7.75 ± 1.82% vs. 7.84 ± 1.69%, respectively, p < 0.001). The mean values of HbA1C throughout the follow-up (by follow-up year) among patients who died and among survivors are presented in Figure A1 (Appendix A). A trend of decrease in HbA1C was observed among patients who died, while values remained similar or even increased over time among survivors; changes were most evident after five years of follow-up.
∆HbA1C was calculated between 229,645 pairs of HbA1C values. The time between HbA1C tests ranged between 90 and 3645 days (median of 863 days [interquartile range 420–1518]). HbA1C values decreased among patients who died and increased among survivors (−0.09 ± 1.65% vs. 0.10 ± 1.53%, respectively, p < 0.001).
The mortality rates and the relative risks according to the different HbA1C and ∆HbA1C categories are presented in Figure 1. Significant differences in mortality rates between HbA1C and ∆HbA1C levels were demonstrated (p < 0.001 for each). The highest death rate was observed in the HbA1C category of <5.5% (53.8%) with an adjOR of 1.852 (95% CI: 1.530–2.242, p < 0.001), as compared to the reference group (5.5–6.5%; Figure 1a). However, higher HbA1C values were associated with lower mortality risk: adjORs of 0.798 (95% CI: 0.716–0.890, p < 0.001) and 0.825 (95% CI: 0.721–0.943, p = 0.005) for the HbA1C categories of 6.5–7.0 and 7.0–8.0 were similar to the category of ≥8.0% (adjOR = 0.891; 95%CI: 0.765–1.037, p = 0.136), as compared with the reference category.
Regarding ∆HbA1C (Figure 1b), the risk of mortality increased with negative ∆HbA1C and remained similar with positive ∆HbA1C. A decrease of >2.5% in HbA1C was associated with a higher risk of mortality, as compared with the group with the minimal change (±0.5%): 35.3% vs. 26.7%, adjOR 1.495 (95%CI: 1.355–1.650, p < 0.001).
Following multivariable adjustment for potential confounders, the association of HbA1C and mortality presented a U-shape-like association, with increasing risk with both higher and lower HbA1C (more prominent; Figure 2a). A similar trend was found between ∆HbA1C and mortality (Figure 2b).
The mortality rates according to the HbA1C changes in the rapid- and slow-changing groups are presented in Figure A2 (Appendix A). Figure 3 displays the adjORs for mortality according to ∆HbA1C in the rapid- and slow-changing groups (adjusted to baseline characteristics, HbA1C levels and time lag from hospital discharge). It is evident that when HbA1C increases rapidly, it carries a greater risk of mortality than a slow increase. However, the rate of decreasing HbA1C has the same risk of mortality irrespective of the time of change.

4. Discussion

The current study evaluated the prognostic significance of the levels of and changes in HbA1C in patients with DM following non-fatal AMI. The main findings include: (1) the adjusted risk of mortality increases at HbA1C values ≤ 5.5% and ≥7%; (2) ∆HbA1C is a significant independent prognostic marker for the risk of mortality following AMI with a U-shaped association (stronger association with mortality for a decrease in HbA1C); (3) rapid versus slower increase in HbA1C following AMI is associated with a greater risk of mortality. However, the time interval of decrease in HbA1C does not seem to be a significant determinant of mortality risk.
The finding that HbA1C levels are associated with long-term mortality of AMI patients is overall consistent with previous studies of AMI patients [19,24,25]. Other studies often used admission HbA1C values while we evaluated all values (not necessarily upon admission). The association between low and high HbA1C and ∆HbA1C with long-term mortality following AMI is consistent with a recent study by Lee et al. [22] that evaluated the association of long-term mean HbA1C with long-term survival following AMI. They reported that patients with a mean HbA1C value ≥ 6.0% to <7.5% had a better prognosis than patients with HbA1C values of <6.0% or >7.5%. However, in our study, we did not use mean values and quantified the change and the trajectory of the change more elaborately, which is more informative.
Our findings are also consistent, in some parts, with those of Olsson et al. [5] who evaluated HbA1C as an AMI predictor in a large population of patients with type-2 DM and compared three distinct methods using HbA1C measurements taken from diabetes diagnosis and beyond (baseline-, latest- and mean-HbA1C). The authors found that the baseline HbA1C variable was a much weaker predictor of AMI than the updated latest and updated mean HbA1C variables, which in our study, are equivalent to the ∆HbA1C. In addition, they reported a J-shaped association between the updated latest HbA1C variable and AMI with increased risk associated with HbA1C < 6.0% and >7.0% (reference 6.0–7.0%). Furthermore, a recent secondary analysis from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial [26] showed that substantial changes in HbA1C were significantly associated with a higher risk of heart failure: hazard ratio (HR) 1.32 (95% CI: 1.08–1.75) for ≥10% decrease in HbA1C and 1.55 (95% CI: 1.19–2.04) for ≥10% increase in HbA1C (reference < 10% change in HbA1C). Among ambulatory DM patients, high HbA1C variability was associated with increased risk of all-cause and cardiovascular mortality and DM complications [21].
Interestingly, Carson et al. [27] reported a U-shaped association with HbA1C levels, more robust in the lower categories (like our study), associated with increased all-cause mortality among US adults without diabetes. The reasons for the increased risk of mortality associated with low levels of HbA1C are unknown. However, it has been suggested that patients in the lowest category (HbA1C < 6.0%) may represent a frail and vulnerable group. Lower glycemic levels can be attributed not only to intensive glycemic control. Other factors such as old age and comorbidities may cause low HbA1C [22,27,28,29,30]. In addition, increased oxidative stress and endothelial dysfunction, which are markers of worse outcomes, were reported to be associated with significant glycemic changes [31,32]. Furthermore, a confounding—rather than causal—relationship may explain the observed association between HbA1C changes and reduced long-term survival. These include poor compliance with medications, lifestyle recommendations, comorbidity, lack of support and infections [15,33,34,35,36].
Nevertheless, Lee et al. [21] recently found an association between hypoglycemic frequency, HbA1C variability and mortality. They concluded that these associations suggest that intermittent hypoglycemia may result in poorer outcomes in diabetic patients. Interestingly, we found that rapid versus slow increase in HbA1C following AMI is associated with greater risk of mortality. Nevertheless, when a decrease in HbA1C occurred, its rate had a minimal impact. This could be explained by an association between the rate of changes in HbA1C and the magnitude of the deleterious consequences of DM (and its various complications) on patients following AMI. Furthermore, it is possible that a rapid increase in HbA1C results from a systemic illness (e.g., infection or inflammation) or a stressor that activates counterregulatory hormones and additional mechanisms that support insulin intolerance and hyperglycemia, which are significant and long enough to cause a rapid increase in HbA1C [37]. Additionally, various medications may signal more complicated cardiac disease (e.g., hydrochlorothiazides, beta-blockers) or systemic illness (e.g., corticosteroids) and may cause an increase in glucose levels, explaining this association [38]. Finally, a decrease of HbA1C may occur due to other causes such as hypoglycemia (often associated with older age and other comorbidities), frailty and comorbidities (e.g., chronic inflammation, liver function derangement), rather than related to DM itself, thus potentially explaining the relatively weak association between the rate of decrease in HbA1C and mortality [21,22,28]. Moreover, changes in HbA1C levels could result from false decreases/increases, occurring due to various clinical situations and diseases such as iron deficiency anemia, deficiency of vitamin B12 or folic acid, severe hypertriglyceridemia, severe hyperbilirubinemia, chronic salicylate ingestion, chronic opioid ingestion, lead poisoning, acute or chronic blood loss, splenomegaly, pregnancy, vitamin E ingestion, red blood cell transfusion, ribavirin and interferon-alpha use, hemoglobin variants, vitamin C ingestion, uremia, hemodialysis and erythropoietin injections [39]. Although such clinical situations and diseases could, on the one hand, bias our findings, they could on the other hand (mostly the diseases) explain the observed increased mortality associated with such fluctuations.

Limitations

First, this is a non-randomized retrospective observational study; hence, it has the limitations of such a design and is subject to bias; mainly, it cannot show causality. Second, data regarding therapeutic interventions in general, and particularly some medications for DM treatment, were not available and could be a source of bias, particularly since it is currently known that the way in which glucose levels are reduced has a significant effect on the ultimate outcome of diabetic patients [40]. Third, the HbA1C tests were taken at the discretion of the treating physicians rather than at prespecified intervals. Fourth, we used all-cause mortality rather than cause-specific mortality (e.g., cardiovascular). Fifth, data regarding multiple factors that could bias HbA1C levels were not included.

5. Conclusions

The study shows for the first time that among DM patients with non-fatal AMI, long-term changes in HbA1C values and the time interval for these changes (when increasing) are significant independent prognostic markers for long-term all-cause mortality with a U-shaped association. Thus, ∆HbA1C, in addition to absolute HbA1C values, should be monitored in post-AMI patients. Furthermore, additional studies to further evaluate the mechanisms for these changes and potential targets for interventions are warranted.

Author Contributions

Y.P., H.G. and A.S. contributed to the conception and design of the work. Y.P. contributed to the acquisition and analysis of data for the work. A.S. and H.G. contributed to data interpretation. A.S. drafted the manuscript. Y.P. and H.G. critically revised the manuscript. All gave final approval and agreed to be accountable for all aspects of the work, ensuring its integrity and accuracy. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Ethics Committee of Soroka University Medical Center (protocol code SOR-0319-16, extended 3 December 2020).

Informed Consent Statement

Patient consent was waived due to anonymous retrospective data collection.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Jcm 10 03232 g0a1 550
Figure A1. The mean values of HbA1C throughout the follow-up (by follow-up year) among patients who died and among survivors.
Figure A1. The mean values of HbA1C throughout the follow-up (by follow-up year) among patients who died and among survivors.
Jcm 10 03232 g0a1
Jcm 10 03232 g0a2 550
Figure A2. Mortality rates (%) according to the ∆HbA1C category in the rapid- and slow-changing groups. Rapid change—time interval of ∆HbA1C values < one year; slow change—time interval of ∆HbA1C values ≥ one year.
Figure A2. Mortality rates (%) according to the ∆HbA1C category in the rapid- and slow-changing groups. Rapid change—time interval of ∆HbA1C values < one year; slow change—time interval of ∆HbA1C values ≥ one year.
Jcm 10 03232 g0a2

References

  1. Ezzati, M.; Obermeyer, Z.; Tzoulaki, I.; Mayosi, B.M.; Elliott, P.; Leon, D. Contributions of risk factors and medical care to cardiovascular mortality trends. Nat. Rev. Cardiol. 2015, 12, 508–530. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Stevens, A.G.; The Global Burden of Metabolic Risk Factors of Chronic Diseases Collaborating Group (Body Mass Index); Singh, G.M.; Lu, Y.; Danaei, G.; Lin, J.K.; Finucane, M.M.; Bahalim, A.N.; McIntire, R.K.; Gutierrez, H.R.; et al. National, regional, and global trends in adult overweight and obesity prevalences. Popul. Health Metr. 2012, 10, 22. [Google Scholar] [CrossRef] [Green Version]
  3. Booth, G.L.; Kapral, M.K.; Fung, K.; Tu, J.V. Relation between age and cardiovascular disease in men and women with diabetes compared with non-diabetic people: A population-based retrospective cohort study. Lancet 2006, 368, 29–36. [Google Scholar] [CrossRef]
  4. Holman, R.R.; Paul, S.; Bethel, M.A.; Matthews, D.R.; Neil, H.A.W. 10-Year Follow-up of Intensive Glucose Control in Type 2 Diabetes. N. Engl. J. Med. 2008, 359, 1577–1589. [Google Scholar] [CrossRef] [Green Version]
  5. Olsson, M.; Schnecke, V.; Cabrera, C.; Skrtic, S.; Lind, M. Contemporary Risk Estimates of Three HbA1c Variables for Myocardial Infarction in 101,799 Patients Following Diagnosis of Type 2 Diabetes. Diabetes Care 2015, 38, 1481–1486. [Google Scholar] [CrossRef] [Green Version]
  6. Shiyovich, A.; Gilutz, H.; Plakht, Y. Serum electrolyte/metabolite abnormalities among patients with acute myocardial infarction: Comparison between patients with and without diabetes mellitus. Postgrad. Med. 2020, 133, 395–403. [Google Scholar] [CrossRef] [PubMed]
  7. Hammoud, T.; Tanguay, J.-F.; Bourassa, M.G. Management of coronary artery disease: Therapeutic options in patients with diabetes. J. Am. Coll. Cardiol. 2000, 36, 355–365. [Google Scholar] [CrossRef] [Green Version]
  8. Franklin, K.; Goldberg, R.J.; Spencer, F.; Klein, W.; Budaj, A.; Brieger, D.; Marre, M.; Steg, P.G.; Gowda, N.; Gore, J.M. Implications of Diabetes in Patients with Acute Coronary Syndromes. The Global Registry of Acute Coronary Events. Arch. Intern. Med. 2004, 164, 1457–1463. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Plakht, Y.; Gilutz, H.; Shiyovich, A. Excess long-term mortality among hospital survivors of acute myocardial infarction. Soroka Acute Myocardial Infarction (SAMI) project. Public Health 2017, 143, 25–36. [Google Scholar] [CrossRef]
  10. Su, G.; Zhang, T.; Yang, H.; Dai, W.; Tian, L.; Tao, H.; Mi, S.; Wang, T. Admission glycemic variability correlates with in-hospital outcomes in diabetic patients with non-ST segment elevation acute coronary syndrome undergoing percutaneous coronary intervention. Anatol. J. Cardiol. 2018, 19, 368–373. [Google Scholar] [CrossRef] [PubMed]
  11. Grant, P.J.; Cosentino, F. The 2019 ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD: New features and the ‘Ten Commandments’ of the 2019 Guidelines are discussed by Professor Peter J. Grant and Professor Francesco Cosentino, the Task Force chairmen. Eur. Heart J. 2019, 40, 3215–3217. [Google Scholar] [CrossRef]
  12. Hayward, R.; Reaven, P.D.; Emanuele, N.V.; VADT Investigators; Pothineni, N.V.; Wiitala, W.L.; Bahn, G.D.; Reda, D.J.; Ge, L.; McCarren, M.; et al. Follow-up of Glycemic Control and Cardiovascular Outcomes in Type 2 Diabetes. N. Engl. J. Med. 2015, 373, 977–978. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Zhang, Y.; Hu, G.; Yuan, Z.; Chen, L. Glycosylated Hemoglobin in Relationship to Cardiovascular Outcomes and Death in Patients with Type 2 Diabetes: A Systematic Review and Meta-Analysis. PLoS ONE 2012, 7, e42551. [Google Scholar] [CrossRef] [PubMed]
  14. Li, S.; Nemeth, I.; Donnelly, L.; Hapca, S.; Zhou, K.; Pearson, E.R. Visit-to-Visit HbA1c Variability Is Associated with Cardiovascular Disease and Microvascular Complications in Patients With Newly Diagnosed Type 2 Diabetes. Diabetes Care 2019, 43, 426–432. [Google Scholar] [CrossRef] [Green Version]
  15. Gorst, C.; Kwok, C.S.; Aslam, S.; Buchan, I.; Kontopantelis, E.; Myint, P.K.; Heatlie, G.; Loke, Y.; Rutter, M.K.; Mamas, M.A. Long-term Glycemic Variability and Risk of Adverse Outcomes: A Systematic Review and Meta-analysis. Diabetes Care 2015, 38, 2354–2369. [Google Scholar] [CrossRef] [Green Version]
  16. Penno, G.; Solini, A.; Zoppini, G.; Orsi, E.; Fondelli, C.; Zerbini, G.; Morano, S.; Cavalot, F.; Lamacchia, O.; Trevisan, R.; et al. Hemoglobin A1c variability as an independent correlate of cardiovascular disease in patients with type 2 diabetes: A cross-sectional analysis of the Renal Insufficiency and Cardiovascular Events (RIACE) Italian Multicenter Study. Cardiovasc. Diabetol. 2013, 12, 1–98. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Foo, V.; Quah, J.; Cheung, C.M.G.; Tan, N.-C.; Zar, K.L.M.; Chan, C.M.; Lamoureux, E.; Yin, W.T.; Tan, G.; Sabanayagam, C. HbA1c, systolic blood pressure variability and diabetic retinopathy in Asian type 2 diabetics. J. Diabetes 2016, 9, 200–207. [Google Scholar] [CrossRef]
  18. Takao, T.; Suka, M.; Yanagisawa, H.; Matsuyama, Y.; Iwamoto, Y. Predictive ability of visit-to-visit variability in HbA1c and systolic blood pressure for the development of microalbuminuria and retinopathy in people with type 2 diabetes. Diabetes Res. Clin. Pr. 2017, 128, 15–23. [Google Scholar] [CrossRef]
  19. Sheng, C.-S.; Tian, J.; Miao, Y.; Cheng, Y.; Yang, Y.; Reaven, P.D.; Bloomgarden, Z.T.; Ning, G. Prognostic Significance of Long-term HbA1c Variability for All-Cause Mortality in the ACCORD Trial. Diabetes Care 2020, 43, 1185–1190. [Google Scholar] [CrossRef]
  20. Lee, S.; Zhou, J.; Wong, W.T.; Liu, T.; Wu, W.K.K.; Wong, I.C.K.; Zhang, Q.; Tse, G. Glycemic and lipid variability for predicting complications and mortality in diabetes mellitus using machine learning. BMC Endocr. Disord. 2021, 21, 1–15. [Google Scholar] [CrossRef]
  21. Lee, S.; Liu, T.; Zhou, J.; Zhang, Q.; Wong, W.T.; Tse, G. Predictions of diabetes complications and mortality using hba1c variability: A 10-year observational cohort study. Acta Diabetol. 2020, 58, 171–180. [Google Scholar] [CrossRef] [PubMed]
  22. Lee, W.; Kim, S.-H.; Yoon, C.-H.; Suh, J.-W.; Cho, Y.-S.; Youn, T.-J.; Chae, I.-H. Impact of Long-term Glycosylated Hemoglobin in Patients with Acute Myocardial Infarction: A retrospective cohort study. Sci. Rep. 2020, 10, 6726. [Google Scholar] [CrossRef] [Green Version]
  23. Plakht, Y.; Shiyovich, A.; Weitzman, S.; Fraser, D.; Zahger, D.; Gilutz, H. A new risk score predicting 1- and 5-year mortality following acute myocardial infarction Soroka Acute Myocardial Infarction (SAMI) Project. Int. J. Cardiol. 2012, 154, 173–179. [Google Scholar] [CrossRef]
  24. Timmer, J.R.; Hoekstra, M.; Nijsten, M.W.; van der Horst, I.C.; Ottervanger, J.P.; Slingerland, R.J.; Dambrink, J.-H.E.; Bilo, H.J.G.; Zijlstra, F.; van’t Hof, A.W.J. Prognostic Value of Admission Glycosylated Hemoglobin and Glucose in Nondiabetic Patients With ST-Segment–Elevation Myocardial Infarction Treated with Percutaneous Coronary Intervention. Circulation 2011, 124, 704–711. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Lazzeri, C.; Valente, S.; Chiostri, M.; Attanà, P.; Mattesini, A.; Nesti, M.; Gensini, G.F. Glycated haemoglobin and long-term mortality in patients with ST Elevation Myocardial Infarction. J. Cardiovasc. Med. 2015, 16, 404–408. [Google Scholar] [CrossRef]
  26. Segar, M.W.; Patel, K.V.; Vaduganathan, M.; Caughey, M.C.; Butler, J.; Fonarow, G.C.; Grodin, J.L.; McGuire, D.K.; Pandey, A. Association of Long-term Change and Variability in Glycemia with Risk of Incident Heart Failure Among Patients with Type 2 Diabetes: A Secondary Analysis of the ACCORD Trial. Diabetes Care 2020, 43, 1920–1928. [Google Scholar] [CrossRef]
  27. Carson, A.P.; Fox, C.S.; McGuire, D.K.; Levitan, E.B.; Laclaustra, M.; Mann, D.M.; Muntner, P. Low Hemoglobin A1c and Risk of All-Cause Mortality Among US Adults Without Diabetes. Circ. Cardiovasc. Qual. Outcomes 2010, 3, 661–667. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Kosiborod, M.; Inzucchi, S.E.; Goyal, A.; Krumholz, H.M.; Masoudi, F.A.; Xiao, L.; Spertus, J.A. Relationship Between Spontaneous and Iatrogenic Hypoglycemia and Mortality in Patients Hospitalized with Acute Myocardial Infarction. JAMA 2009, 301, 1556–1564. [Google Scholar] [CrossRef] [PubMed]
  29. Perlstein, T.S.; Weuve, J.; Pfeffer, M.A.; Beckman, J. Red Blood Cell Distribution Width and Mortality Risk in a Community-Based Prospective Cohort. Arch. Intern. Med. 2009, 169, 588–594. [Google Scholar] [CrossRef] [Green Version]
  30. Cohen, R.M.; Franco, R.S.; Khera, P.K.; Smith, E.P.; Lindsell, C.J.; Ciraolo, P.J.; Palascak, M.B.; Joiner, C.H. Red cell life span heterogeneity in hematologically normal people is sufficient to alter HbA1c. Blood 2008, 112, 4284–4291. [Google Scholar] [CrossRef] [Green Version]
  31. Monnier, L.; Mas, E.; Ginet, C.; Michel, F.; Villon, L.; Cristol, J.-P.; Colette, C. Activation of Oxidative Stress by Acute Glucose Fluctuations Compared With Sustained Chronic Hyperglycemia in Patients With Type 2 Diabetes. JAMA 2006, 295, 1681–1687. [Google Scholar] [CrossRef] [Green Version]
  32. Ceriello, A.; Esposito, K.; Piconi, L.; Ihnat, M.A.; Thorpe, J.E.; Testa, R.; Boemi, M.; Giugliano, D. Oscillating Glucose Is More Deleterious to Endothelial Function and Oxidative Stress Than Mean Glucose in Normal and Type 2 Diabetic Patients. Diabetes 2008, 57, 1349–1354. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Plakht, Y.; Greenberg, D.; Gilutz, H.; Arbelle, J.E.; Shiyovich, A. Mortality and healthcare resource utilization following acute myocardial infarction according to adherence to recommended medical therapy guidelines. Health Policy 2020, 124, 1200–1208. [Google Scholar] [CrossRef]
  34. Wadén, J.; Forsblom, C.; Thorn, L.; Gordin, D.; Saraheimo, M.; Groop, P.-H.; The Finnish Diabetic Nephropathy Study Group. A1C Variability Predicts Incident Cardiovascular Events, Microalbuminuria, and Overt Diabetic Nephropathy in Patients with Type 1 Diabetes. Diabetes 2009, 58, 2649–2655. [Google Scholar] [CrossRef] [Green Version]
  35. Luk, A.O.Y.; Ma, R.C.V.; Lau, E.S.H.; Yang, X.; Lau, W.W.Y.; Yu, L.W.L.; Chow, F.C.C.; Chan, J.C.N.; So, W.-Y. Risk association of HbA1cvariability with chronic kidney disease and cardiovascular disease in type 2 diabetes: Prospective analysis of the Hong Kong Diabetes Registry. Diabetes Metab. Res. Rev. 2013, 29, 384–390. [Google Scholar] [CrossRef] [PubMed]
  36. Hamer, M.; Stamatakis, E.; Kivimaki, M.; Kengne, A.P.; Batty, G. Psychological Distress, Glycated Hemoglobin, and Mortality in Adults with and Without Diabetes. Psychosom. Med. 2010, 72, 882–886. [Google Scholar] [CrossRef] [PubMed]
  37. Van Cromphaut, S. Hyperglycaemia as part of the stress response: The underlying mechanisms. Best Pr. Res. Clin. Anaesthesiol. 2009, 23, 375–386. [Google Scholar] [CrossRef] [PubMed]
  38. Fathallah, N.; Slim, R.; Larif, S.; Hmouda, H.; Ben Salem, C. Drug-Induced Hyperglycaemia and Diabetes. Drug Saf. 2015, 38, 1153–1168. [Google Scholar] [CrossRef]
  39. Kaiafa, G.; Veneti, S.; Polychronopoulos, G.; Pilalas, D.; Daios, S.; Kanellos, I.; Didangelos, T.; Pagoni, S.; Savopoulos, C. Is HbA1c an ideal biomarker of well-controlled diabetes? Postgrad. Med. J. 2020, 97, 380–383. [Google Scholar] [CrossRef]
  40. Lipska, K.J.; Krumholz, H.M. Is Hemoglobin A1c the Right Outcome for Studies of Diabetes? JAMA 2017, 317, 1017–1018. [Google Scholar] [CrossRef] [Green Version]
Jcm 10 03232 g001a 550Jcm 10 03232 g001b 550
Figure 1. (a) The rates and the relative risks of mortality by HbA1C (adjusted odds ratios and 95% CI) categories; (b) The rates and the relative risks of mortality (adjusted odds ratios and 95% confidence intervals) by HbA1C categories. (a,b) are the results of separate models. Abbreviations: adjOR—adjusted odds ratio (adjusted for repeated measures), CI—confidence interval, HbA1C—hemoglobin A1C, ∆HbA1C—the difference between two HbA1C tests (later–earlier).
Figure 1. (a) The rates and the relative risks of mortality by HbA1C (adjusted odds ratios and 95% CI) categories; (b) The rates and the relative risks of mortality (adjusted odds ratios and 95% confidence intervals) by HbA1C categories. (a,b) are the results of separate models. Abbreviations: adjOR—adjusted odds ratio (adjusted for repeated measures), CI—confidence interval, HbA1C—hemoglobin A1C, ∆HbA1C—the difference between two HbA1C tests (later–earlier).
Jcm 10 03232 g001aJcm 10 03232 g001b
Jcm 10 03232 g002a 550Jcm 10 03232 g002b 550
Figure 2. The associations (adjusted odds ratios and 95% confidence intervals): (a) Between HbA1C and mortality; (b) Between HbA1C change (∆HbA1C) and mortality, adjusted for the potential confounders. The results of the combined model included HbA1C levels and ∆HbA1C, adjusted for: repeated measures, time from hospital discharge, age, sex, supraventricular arrhythmias, congestive heart failure, chronic ischemic heart disease, history of myocardial infarction, history of percutaneous coronary intervention, history of a coronary artery bypass graft, atrioventricular block, renal failure, diabetes mellitus, hypertension, peripheral vascular disease, chronic obstructive pulmonary disease, neurological disorders, malignancy, anemia, gastrointestinal bleeding, alcohol/drug addiction, type of acute myocardial infarction, severe left ventricular dysfunction, left ventricular hypertrophy and three-vessel/left-main coronary arteries disease. Abbreviations: AdjOR—adjusted odds ratio, HbA1C—hemoglobin A1C, HbA1C change (∆HbA1C)—the difference between two HbA1C tests (later–earlier).
Figure 2. The associations (adjusted odds ratios and 95% confidence intervals): (a) Between HbA1C and mortality; (b) Between HbA1C change (∆HbA1C) and mortality, adjusted for the potential confounders. The results of the combined model included HbA1C levels and ∆HbA1C, adjusted for: repeated measures, time from hospital discharge, age, sex, supraventricular arrhythmias, congestive heart failure, chronic ischemic heart disease, history of myocardial infarction, history of percutaneous coronary intervention, history of a coronary artery bypass graft, atrioventricular block, renal failure, diabetes mellitus, hypertension, peripheral vascular disease, chronic obstructive pulmonary disease, neurological disorders, malignancy, anemia, gastrointestinal bleeding, alcohol/drug addiction, type of acute myocardial infarction, severe left ventricular dysfunction, left ventricular hypertrophy and three-vessel/left-main coronary arteries disease. Abbreviations: AdjOR—adjusted odds ratio, HbA1C—hemoglobin A1C, HbA1C change (∆HbA1C)—the difference between two HbA1C tests (later–earlier).
Jcm 10 03232 g002aJcm 10 03232 g002b
Jcm 10 03232 g003 550
Figure 3. The relative risk (adjusted odds ratios) for mortality according to HbA1C changes in the rapid- and slow-changing groups. Rapid change—time interval of ∆HbA1C values < one year; slow change—time interval of ∆HbA1C values ≥ one year. The numbers represent the p-values for disparity (p for interaction) between the rapid- and slow-changing HbA1C (separately for each ∆HbA1C category). Adjusted for: repeated measures, HbA1C levels, time from hospital discharge, age, sex, supraventricular arrhythmias, congestive heart failure, chronic ischemic heart disease, history of myocardial infarction, history of percutaneous coronary intervention, history of a coronary artery bypass graft, atrioventricular block, renal failure, diabetes mellitus, hypertension, peripheral vascular disease, chronic obstructive pulmonary disease, neurological disorders, malignancy, anemia, gastrointestinal bleeding, alcohol/drug addiction, type of acute myocardial infarction, severe left ventricular dysfunction, left ventricular hypertrophy and three-vessel/left-main coronary arteries disease. Abbreviations: AdjOR—adjusted odds ratio, HbA1C—hemoglobin A1C, HbA1C change (∆HbA1C)—the difference between two HbA1C tests (later–earlier).
Figure 3. The relative risk (adjusted odds ratios) for mortality according to HbA1C changes in the rapid- and slow-changing groups. Rapid change—time interval of ∆HbA1C values < one year; slow change—time interval of ∆HbA1C values ≥ one year. The numbers represent the p-values for disparity (p for interaction) between the rapid- and slow-changing HbA1C (separately for each ∆HbA1C category). Adjusted for: repeated measures, HbA1C levels, time from hospital discharge, age, sex, supraventricular arrhythmias, congestive heart failure, chronic ischemic heart disease, history of myocardial infarction, history of percutaneous coronary intervention, history of a coronary artery bypass graft, atrioventricular block, renal failure, diabetes mellitus, hypertension, peripheral vascular disease, chronic obstructive pulmonary disease, neurological disorders, malignancy, anemia, gastrointestinal bleeding, alcohol/drug addiction, type of acute myocardial infarction, severe left ventricular dysfunction, left ventricular hypertrophy and three-vessel/left-main coronary arteries disease. Abbreviations: AdjOR—adjusted odds ratio, HbA1C—hemoglobin A1C, HbA1C change (∆HbA1C)—the difference between two HbA1C tests (later–earlier).
Jcm 10 03232 g003
Table
Table 1. Baseline characteristics and comparisons between patients who died and those who survived.
Table 1. Baseline characteristics and comparisons between patients who died and those who survived.
GroupSurvivedDiedTotalp
n226418024066
Demographics
Age, years:
Mean (SD)62.18 (11.13)71.78 (10.61)66.44 (11.90)<0.001
<651401 (61.9)457 (25.4)1858 (45.7)<0.001
65–75568 (25.1)638 (35.4)1206 (29.7)
≥75295 (13.0)707 (39.2)1002 (24.6)
Sex: Male1613 (71.2)978 (54.3)2591 (63.7)<0.001
Ethnicity: Arab/other579 (25.6)265 (14.7)844 (20.8)<0.001
Cardiac diseases
Cardiomegaly164 (7.2)206 (11.4)370 (9.1)<0.001
Supraventricular arrhythmias230 (10.2)398 (22.1)628 (15.4)<0.001
CHF280 (12.4)542 (30.1)822 (20.2)<0.001
Pulmonary heart disease125 (5.5)261 (14.5)386 (9.5)<0.001
CIHD2008 (88.7)1336 (74.1)3344 (82.2)<0.001
s/p MI201 (8.9)290 (16.1)491 (12.1)<0.001
s/p PCI325 (14.4)276 (15.3)601 (14.8)0.391
s/p CABG195 (8.6)276 (15.3)471 (11.6)<0.001
AV block81 (3.6)99 (5.5)180 (4.4)0.003
Cardiovascular risk factors
Renal diseases96 (4.2)350 (19.4)446 (11.0)<0.001
Dyslipidemia2021 (89.3)1516 (84.1)3537 (87.0)<0.001
Hypertension1468 (64.8)1122 (62.3)2590 (63.7)0.09
Obesity726 (32.1)418 (23.2)1144 (28.1)<0.001
Smoking1020 (45.1)429 (23.8)1449 (35.6)<0.001
PVD219 (9.7)385 (21.4)604 (14.9)<0.001
Family history of IHD213 (9.4)33 (1.8)246 (6.1)<0.001
Other disorders
COPD125 (5.5)202 (11.2)327 (8.0)<0.001
Neurological disorders310 (13.7)490 (27.2)800 (19.7)<0.001
Malignancy36 (1.6)97 (5.4)133 (3.3)<0.001
Anemia892 (39.4)1148 (63.7)2040 (50.2)<0.001
GI bleeding26 (1.1)30 (1.7)56 (1.4)0.16
Schizophrenia/Psychosis16 (0.7)40 (2.2)56 (1.4)<0.001
Alcohol/drug addiction32 (1.4)23 (1.3)55 (1.4)0.707
History of malignancy91 (4.0)122 (6.8)213 (5.2)<0.001
Characteristics of diabetes mellitus
Type I18 (0.8)21 (1.2)39 (1.0)0.229
Insulin-treated259 (11.4)185 (10.3)444 (10.9)0.233
Complications:
Non-complicated1923 (84.9)1338 (74.3)3261 (80.2)<0.001
Renal121 (5.3)171 (9.5)292 (7.2)<0.001
Peripheral circulation137 (6.1)271 (15.0)408 (10.0)<0.001
Ophthalmic128 (5.7)153 (8.5)281 (6.9)<0.001
Neurological91 (4.0)78 (4.3)169 (4.2)0.624
Other11 (0.5)13 (0.7)24 (0.6)0.330
Results of HbA1C tests (at baseline)
HbA1C tests performance2035 (89.9)1557 (86.4)3592 (88.3)0.001
HbA1C, %: Mean (SD)7.63 (1.69)7.68 (1.78)7.65 (1.73)0.456
Clinical characteristics of the hospitalization
Type of AMI: STEMI940 (41.5)564 (31.3)1504 (37.0)<0.001
Results of echocardiography
Echocardiography performance1948 (86.0)1313 (72.9)3261 (80.2)<0.001
Severe LV dysfunction166 (8.5)221 (16.8)387 (11.9)<0.001
LV hypertrophy113 (5.8)116 (8.8)229 (7.0)0.001
Mitral regurgitation64 (3.3)135 (10.3)199 (6.1)<0.001
Tricuspid regurgitation24 (1.2)82 (6.2)106 (3.3)<0.001
Pulmonary hypertension89 (4.6)192 (14.6)281 (8.6)<0.001
Results of angiography
Angiography performance1897 (83.8)1043 (57.9)2940 (72.3)<0.001
Measure of CAD:
None/non-significant72 (3.8)39 (3.7)111 (3.8)<0.001
One vessel456 (24.0)182 (17.4)638 (21.7)
Two vessels543 (28.6)237 (22.7)780 (26.5)
Three vessels/LM826 (43.5)585 (56.1)1411 (48.0)
Type of treatment:
Noninvasive245 (10.8)725 (40.2)970 (23.9)<0.001
PCI1581 (69.8)882 (48.9)2463 (60.6)
CABG438 (19.3)195 (10.8)633 (15.6)
The data are presented as n (%) unless otherwise stated. Abbreviations: AMI—acute myocardial infarction, AV—Atrioventricular, CABG—coronary artery bypass graft, CAD—coronary arteries disease, CHF—congestive heart failure, CIHD—chronic ischemic heart disease, COPD—chronic obstructive pulmonary disease, HbA1C—hemoglobin A1C, IHD—ischemic heart disease, GI—Gastrointestinal, LM—left main (coronary artery), LV—left ventricular, MI—myocardial infarction, PCI—percutaneous coronary intervention, PVD—peripheral vascular (arterial) disease, SD—standard deviation, s/p—status post (history of), STEMI—ST-segment elevation myocardial infarction.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Plakht, Y.; Gilutz, H.; Shiyovich, A. Changes over Time in Hemoglobin A1C (HbA1C) Levels Predict Long-Term Survival Following Acute Myocardial Infarction among Patients with Diabetes Mellitus. J. Clin. Med. 2021, 10, 3232. https://doi.org/10.3390/jcm10153232

AMA Style

Plakht Y, Gilutz H, Shiyovich A. Changes over Time in Hemoglobin A1C (HbA1C) Levels Predict Long-Term Survival Following Acute Myocardial Infarction among Patients with Diabetes Mellitus. Journal of Clinical Medicine. 2021; 10(15):3232. https://doi.org/10.3390/jcm10153232

Chicago/Turabian Style

Plakht, Ygal, Harel Gilutz, and Arthur Shiyovich. 2021. "Changes over Time in Hemoglobin A1C (HbA1C) Levels Predict Long-Term Survival Following Acute Myocardial Infarction among Patients with Diabetes Mellitus" Journal of Clinical Medicine 10, no. 15: 3232. https://doi.org/10.3390/jcm10153232

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop