Hemoglobin Glycation Index Research Articles

The hemoglobin glycation index predicts the risk of adverse cardiovascular events in coronary heart disease patients with type 2 diabetes mellitus.

Previous studies have shown that the hemoglobin glycation index (HGI) can be used as a predictor of diabetic complications. However, limited information is currently available to indicate the correlation between HGI and comorbidity of coronary heart disease (CHD) and diabetes. This study aimed to evaluate the potential of HGI to predict major adverse cardiovascular events (MACEs) in CHD patients with type 2 diabetes mellitus (T2DM) undergoing percutaneous coronary intervention (PCI). A total of 918 CHD patients with T2DM were enrolled in a 3-year retrospective cohort study, from December 2017 to December 2020 at the First Affiliated Hospital of Zhengzhou University. Data including fasting blood glucose (FPG/FBG) and glycated hemoglobin A1c (HbA1c) were collected. HGI was calculated as actual measured HbA1c minus predicted HbA1c. Three groups were further divided based on the levels of HGI, including low, medium, and high levels. Kaplan Meier analysis indicated that elevated HGI was strongly associated with the occurence of MACE (log-rank P < 0.001). Multivariate Cox regression analysis revealed that elevated HGI was an independent risk factor for incident MACE in CHD patients with T2DM [adjusted hazard ratio (HR): 1.473; 95% confidence interval (CI): 1.365-1.589, P < 0.001]. Hemoglobin glycation index is an independent predictor of MACE events in CHD patients with T2DM. High HGI indicates a higher risk of MACE occurrence.

Association of haemoglobin glycation index with outcomes in patients with acute coronary syndrome: results from an observational cohort study in China

BackgroundThe hemoglobin glycation index (HGI) is the difference between measured and estimated glycation of hemoglobin. However, there is limited evidence to investigate the HGI and the clinical outcomes of acute coronary syndrome patients. This study aimed to evaluate the association between HGI and the clinical outcomes of acute coronary syndrome (ACS) in a China cohort.MethodThis single-center retrospective study was carried out in the Cardiovascular Center of Beijing Friendship Hospital, a total of 11004 consecutive patients with ACS from Dec 2012–Dec 2020 were enrolled in this study. Patients were divided into quintiles according to their HGI levels. The incidence of major adverse cardiac and cerebrovascular events (MACCEs) was recorded.ResultHGI were divided into five quintiles quintiles: −0.906 (−7.188, −0.663), −0.491 (−0.663, −0.343), −0.196 (−0.342, −0.039), 0.170 (−0.039, 0.485), and 1.156 (0.485, 7.875), respectively.Competing risk regression revealed that HGI was positively related to all-cause death, CV death, and composite MACCEs. Multivariate Cox proportional hazards regression analysis indicated that hypertension (HR:1.109, P = 0.013), previous stroke (HR:1.208, P < 0.001), past PCI (HR: 1.268, P < 0.001), age (HR: 1.011, P < 0.001), BMI (HR: 0.987, P = 0.012), heart rate (HR: 1.004, P = 0.001), NSTEMI (HR: 1.205, P < 0.001), WBC (HR: 1.020, P = 0.008), eGFR (HR: 0.993, P < 0.001), HDL-C (HR: 0.809, P = 0.002), LVEF (HR:0.240, P < 0.001), LM/three-vessel or proximal LAD involved (HR: 1.208 P < 0.001; HR:0.914, P = 0.019, respectively), and antiplatelet agents during hospitalization (HR:0.806, P = 0.020) independently predicted the incidence of MACCEs in ACS patients. Restricted cubic spline indicated a U-shaped association between the HGI and risk of MACCEs.ConclusionBoth low HGI and high HGI was associated with an increased risk of adverse outcomes in patients with acute coronary syndrome, compared with moderate HGI.

Association of long-term air pollution exposure with the risk of prediabetes and diabetes: Systematic perspective from inflammatory mechanisms, glucose homeostasis pathway to preventive strategies

BackgroundLimited evidence suggests the association of air pollutants with a series of diabetic cascades including inflammatory pathways, glucose homeostasis disorder, and prediabetes and diabetes. Subclinical strategies for preventing such pollutants-induced effects remain unknown. MethodsWe conducted a cross-sectional study in two typically air-polluted Chinese cities in 2018–2020. One-year average PM1, PM2.5, PM10, SO2, NO2, and O3 were calculated according to participants' residence. GAM multinomial logistic regression was performed to investigate the association of air pollutants with diabetes status. GAM and quantile g-computation were respectively performed to investigate individual and joint effects of air pollutants on glucose homeostasis markers (glucose, insulin, HbA1c, HOMA-IR, HOMA-B and HOMA-S). Complement C3 and hsCRP were analyzed as potential mediators. The ABCS criteria and hemoglobin glycation index (HGI) were examined for their potential in preventive strategy. ResultsLong-term air pollutants exposure was associated with the risk of prediabetes [Prevalence ratio for O3 (PR_O3) = 1.96 (95% CI: 1.24, 3.03)] and diabetes [PR_PM1 = 1.18 (95% CI: 1.05, 1.32); PR_PM2.5 = 1.08 (95% CI: 1.00, 1.16); PR_O3 = 1.35 (95% CI: 1.03, 1.74)]. PM1, PM10, SO2 or O3 exposure was associated with glucose-homeostasis disorder. For example, O3 exposure was associated with increased levels of glucose [7.67% (95% CI: 1.75, 13.92)], insulin [19.98% (95% CI: 4.53, 37.72)], HOMA-IR [34.88% (95% CI: 13.81, 59.84)], and decreased levels of HOMA-S [-25.88% (95% CI: −37.46, −12.16)]. Complement C3 and hsCRP played mediating roles in these relationships with proportion mediated ranging from 6.95% to 60.64%. Participants with HGI ≤ −0.53 were protected from the adverse effects of air pollutants. ConclusionOur study provides comprehensive insights into air pollutant–associated diabetic cascade and suggests subclinical preventive strategies.

The Value of Hemoglobin Glycation Index-Diabetes Mellitus System in Evaluating and Predicting Incident Stroke in the Chinese Population.

We aimed to clarify the effect of the hemoglobin glycation index (HGI)–diabetes mellitus (DM) system in evaluating the risk of incident stroke. We followed up on 2934 subjects in rural regions of Northeast China, established Cox proportional hazards models to evaluate the effects of the HGI–DM system in describing stroke risk, and further conducted a discrimination analysis to confirm the improvement in HGI based on the traditional stroke risk model. After a median of 4.23 years of follow-up, 79 subjects developed stroke or related death. DM-high HGI condition significantly elevated the risk of incident stroke (hazard ratio (HR): 2.655, 95% confidence interval (CI): 1.251–5.636). In addition, higher HGI levels elevated the risk of stroke, even if the patients did not have DM (HR: 1.701, 95% CI: 1.136–2.792), but DM failed to bring an extra risk of incident stroke to patients with lower HGI levels (HR: 1.138, 95% CI: 0.337–3.847). The discrimination analysis indicated that the integrated discrimination index (IDI) of the HGI model was 0.012 (95% CI: 0.007–0.015) and that the net reclassification index (NRI) was 0.036 (95% CI: 0.0198–0.0522). These results indicated HGI was associated with the onset of stroke, and high HGI indicated an aggravated trend in glycemic status and increased risk of incident stroke. The HGI–DM system enabled us to identify the different glucose statuses of patients, to conduct suitable treatment strategies, as well as to improve the predictability of incident stroke based on the traditional model.

Association of hemoglobin glycation index with cardiovascular risk factors in non-diabetic adults: A cross-sectional study

Background and aimsThe study aimed to explore the association of hemoglobin glycation index (HGI) with cardiovascular risk factors in non-diabetic adults. MethodsThis cross-sectional study included 200 adults of 20–60 years of age. Predicted glycated hemoglobin (HbA1c) was calculated from linear regression equation. HGI was calculated using the formula HGI = measured HbA1c– predicted HbA1c. The study subjects were classified into three groups based on their HGI tertiles. Cardiovascular risk factors were compared between the groups and Pearson correlation test was done to correlate HGI with cardiovascular risk factors. ResultsSerum total cholesterol, triglyceride, low density lipoprotein cholesterol (LDL-C) and very low density lipoprotein cholesterol (VLDL-C) showed significant increase with increase in HGI in non diabetic individuals. High HGI group had significantly high serum total cholesterol, triglyceride, LDL-C and VLDL-C compared to low HGI group. Serum total cholesterol, triglyceride, LDL-C and VLDL-C showed a statistically significant positive correlation with HGI. ConclusionWe have found a statistically significant correlation of HGI with serum lipid profile, a significant cardiovascular risk factor in non-diabetic individuals. HGI, a simple derivative of HbA1c and fasting plasma glucose may be used to identify cardiovascular risk in non-diabetic individuals. Further prospective studies are required in larger sample size to confirm the clinical implications of HGI.

Association of hemoglobin glycation index and glycation gap with cardiovascular disease among US adults

AimsTo investigate the association of hemoglobin glycation index (HGI) and glycation gap (GGap), reflecting mismatches between HbA1c and other measures of glycemia, with cardiovascular disease (CVD) in the general population. Methods5966 US adult (age ≥ 20 years) participants were included from the National Health and Nutrition Examination Survey (NHANES) (1999–2004). In this cross-sectional study, predicted HbA1c was calculated based on fasting plasma glucose (FPG) and glycated albumin (GA), respectively. Multivariable binary logistic regression analysis was performed to explore the association of HGI and GGap with CVD prevalence. ResultsCompared to the lowest tertile, the ORs with 95% CIs for CVD across the tertiles were 1.41 (1.01, 1.96) and 0.87 (0.58, 1.31) for HGI (P for trend = 0.535) and 1.06 (0.77, 1.47) and 1.60 (1.18, 2.17) for GGap (P for trend = 0.002) in the fully-adjusted model. Besides, the discordantly high GGap/low HbA1c group was associated with higher CVD prevalence compared with the low GGap/high HbA1c group (OR = 1.50, 95% CI, 1.04–2.16, P = 0.030). ConclusionsGGap derived from GA is associated with CVD independent of traditional risk factors, even HbA1c, in US general adults. Considering the potential limitations of HbA1c, the introduction of GGap is warranted.

143-OR: Mapping Genetic Determinants of Blood Glucose Control in the Action to Control Cardiovascular Risk in Diabetes Study Group (ACCORD)

The hemoglobin glycation index (HGI) is the difference between observed HbA1c and predicted HbA1c from FPG using linear regression. HGI is an important biomarker of clinical management/drug treatment outcomes and can identify individuals at high risk for multiple adverse events and outcomes before the appearance of clinical symptoms. Here, we sought to test if variation in HGI has genetic determinants in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial using a genome-wide association approach (N = 7913) . We subsequently replicated the top hits (P &amp;lt; 1e-5) in the Atherosclerosis Risk in Communities Study (ARIC; N = 3741) . An intergenic SNP rs73407935 (7q11.22) was associated with HGI (P = 5.8e-10) with the locus replicating in ARIC. Further, multiple variants at suggestive genome-wide significance in discovery (P &amp;lt; 5e-6) replicated in ARIC including variants near or in ASAH1 (P = 2.4e-6) , a region previously associated with risk of CAD in T2D, LRRC4C (P = 2.4e-6) , SHLD1 (P = 3.7e-6) and FAM22B (P = 2.5e-6) . The top 2 replicated SNPs in FAM22B are characterized eQTLs for expression of multiple genes in cells from pancreas, brain, and the immune system, including TRAF4, PROCA1, and RPL23A. Many SNPs associated with HGI were distinct from those associated with FPG or HbA1c. In ACCORD, sex-specific HGI associations with SNPs in or near GALNT11 in females (P = 5e-9) and HECW2 (P = 1.5e-8) in males were observed. Further, analysis of 544 Hispanics revealed associations of a strong eQTL variant near USF1 (rs2516837; P = 1.5e-09) and SNPs near NXNL2/SPIN1 (rs141006133; P = 6.9e-9) with HGI. This work represents the first evaluation of the genetic etiology of HGI. We identified and replicated variants that merit further study in the development of precision medicine for treatment of T2D. The results of the stratified analyses highlight the potential importance of heterogeneity in these efforts. Disclosure J.S.House: None. D.M.Rotroff: Research Support; Novo Nordisk, Stock/Shareholder; Clarified Precision Medicine. V.Fonseca: Consultant; Abbott, Asahi Kasei Corporation, Bayer AG, Novo Nordisk, Sanofi, Research Support; Fractyl Health, Inc., Jaguar Gene Therapy, Stock/Shareholder; Abbott, Amgen Inc., BRAVO4Health, Mellitus Health. J.M.Hempe: None. J.Mychaleckyj: None. A.Doria: Research Support; Novo Nordisk Foundation. J.B.Buse: Consultant; Alkahest, Anji, AstraZeneca, Boehringer Ingelheim International GmbH, Cirius Therapeutics, Inc., Eli Lilly and Company, Fortress biotech, GentiBio, Glycadia, Glyscend, Janssen Pharmaceuticals, Inc., Mellitus Health, Moderna, Inc., Pendulum Therapeutics, Praetego, LLC, Stability Health, Valo, Zealand Pharma A/S, Other Relationship; Adocia, AstraZeneca, Eli Lilly and Company, Intarcia Therapeutics, Inc., MannKind Corporation, Novo Nordisk, Sanofi, Senseonics, vTv Therapeutics, Research Support; AstraZeneca, Dexcom, Inc., Eli Lilly and Company, Novo Nordisk, vTv Therapeutics, Stock/Shareholder; Glyscend, Mellitus Health, Pendulum Therapeutics, PhaseBio Pharmaceuticals, Inc., Praetego, LLC, Stability Health. A.Motsinger-reif: None. Funding NHLBI RO1HL110380NIDDK P30DK124723NCATS UL1TR002489

Potential misclassification of diabetes and prediabetes in the U.S.: Mismatched HbA1c and glucose in NHANES 2005–2016

AimsTo assess the prevalence and clinical implications of “mismatches” between HbA1c and glucose levels in the United States across the life course. MethodsParticipants ages 12–79 years from U.S. National Health and Nutrition Examination Survey (NHANES) 2005–2016 without known diagnosis of diabetes and who had a 75 g oral glucose tolerance test were included. Previously undiagnosed diabetes (DM), prediabetes, and normal glucose metabolism (NGM) were defined using American Diabetes Association cut-points. Mismatches were defined by the hemoglobin glycation index (HGI). ResultsIn 10,361 participants, 5% and 41% had diabetes and prediabetes, respectively, by fasting or 2-hour glucose criteria. By HbA1c criteria, the high HGI tertile consisted of mostly abnormal classification (3% DM, 52% prediabetes) and the low HGI tertile contained mostly normal classification (78% NGM). Across all ages, 15% (weighted: 30 million individuals) had clinically significant mismatches of HGI magnitude ≥+0.5% (i.e., high mismatch) or ≤−0.5% (low mismatch). Mismatch was most common in older adults and non-Hispanic Black participants. ConclusionsMismatches of clinically significant magnitude could lead to HbA1c-related misdiagnosis or inappropriate management in up to 30 million Americans. Older adults, non-Hispanic Black individuals, and others with high mismatches may benefit from complementing HbA1c with additional diagnostic and management strategies.

Association of Hemoglobin Glycation Index With Contrast-Induced Acute Kidney Injury in Patients Undergoing Coronary Angiography: A Retrospective Study.

Background: The hemoglobin glycation index (HGI) quantifies interindividual variation in glycation and is positively associated with cardiovascular diseases. However, the association between HGI and contrast-induced acute kidney injury (CI-AKI) remains unclear. Therefore, this study aimed to assess the association of HGI with CI-AKI. Methods: In this observational study, a total of 3,142 patients undergoing coronary angiography (CAG) or percutaneous coronary intervention (PCI) were included. The HGI was calculated as the difference between the measured glycated hemoglobin (HbA1c) and predicted HbA1c. CI-AKI was defined as an increase of either 25% or 0.5 mg/dl (44.2 μmol/L) in the serum creatinine (SCr) level within 72 h following the exposure to contrast medium. Piecewise linear regression analysis was conducted to testify the association of HGI with the proportion of SCr elevation. Modified Poisson’s regression analysis was performed to determine the association between HGI and CI-AKI. Exploratory analysis was also performed according to the stratification of HbA1c levels. Results: Among 3,142 patients, the average age was 66.9 years and 483 of them (15.4%) suffered CI-AKI. Piecewise linear regression analysis demonstrated the linear association of HGI with the proportion of SCr elevation on both positive and negative sides of HGI [HGI <0: β = −9.537, 95% CI (−12.057 to −7.017), p < 0.001; HGI ≥0: β = 1.655, 95% CI (0.125 to 3.186), p = 0.034]. Modified Poisson’s regression analysis showed that the higher absolute value of HGI was strongly associated with higher incidence of CI-AKI [(<−1.0 vs. −0.2 to 0.2): aRR = 1.897, 95% CI [1.467 to 2.452], p < 0.001 (≥1.0 vs. −0.2 to 0.2): aRR = 1.545, 95% CI (1.171 to 2.037), p = 0.002]. Furthermore, the results in exploratory analysis showed that such association still remained irrespective of HbA1c levels. Conclusion: The higher absolute value of HGI was strongly associated with higher incidence of CI-AKI in patients undergoing CAG and PCI.

Hemoglobin A1c, hemoglobin glycation index, and triglyceride and glucose index: Useful tools to predict low feed intake associated with glucose intolerance in lactating sows

The aim of the present study was to evaluated hemoglobin A1c (HbA1c), the hemoglobin glycation index (HGI), and triglyceride and glucose (TG) index as predictive indicators for low feed intake in lactating sows due to glucose intolerance. Cactus (Opuntia ficus-indica) was included in sow diets as a modulating factor of glucose. Thirty-six sows were separated into three groups (Gs). Although the three groups received a conventional diet during gestation and lactation, 2.0 kg per sow per day of steam-cooked cactus (G1) and fresh cactus (G2) were added to the lactation diet as a glycemic modulating factor, with G3 serving as the control group. Glycemia was assessed via glucometer (blood glucose concentrations), HbA1c and HGI. For each indicator of glycemia the triglycerides and glucose (TG) index was evaluated. The highest blood glucose concentration was observed on day 3 of lactation (88.2 mg/dL). The average glycemic concentrations obtained from HbA1c on farrowing day (61.6 mg/dL) and day 21 of lactation (65.6 mg/dL) were lower (p<0.05) than those measured by a glucometer on the same days (71.8 and 77.7 mg/dL for farrowing day and day 21 of lactation, respectively). At farrowing, the TG index obtained from the HGI indicated that 83.0% of sows were glucose intolerant, compared to 100% according to the TG index obtained from a glucometer. At weaning, 50% of G2 did not show glucose intolerance when the TG index was calculated using the HGI, compared to 54% when it was calculated with blood glucose concentrations measured by a glucometer. All G3 sows presented glucose intolerance, regardless of the test used. The HbA1c, HGI, and TG index tests are viable alternatives to predict low feed intake due to glucose intolerance in lactating sows.

Heterogeneous treatment effects of intensive glycemic control on major adverse cardiovascular events in the ACCORD and VADT trials: a machine-learning analysis

BackgroundEvidence to guide type 2 diabetes treatment individualization is limited. We evaluated heterogeneous treatment effects (HTE) of intensive glycemic control in type 2 diabetes patients on major adverse cardiovascular events (MACE) in the Action to Control Cardiovascular Risk in Diabetes Study (ACCORD) and the Veterans Affairs Diabetes Trial (VADT).MethodsCausal forests machine learning analysis was performed using pooled individual data from two randomized trials (n = 12,042) to identify HTE of intensive versus standard glycemic control on MACE in patients with type 2 diabetes. We used variable prioritization from causal forests to build a summary decision tree and examined the risk difference of MACE between treatment arms in the resulting subgroups.ResultsA summary decision tree used five variables (hemoglobin glycation index, estimated glomerular filtration rate, fasting glucose, age, and body mass index) to define eight subgroups in which risk differences of MACE ranged from − 5.1% (95% CI − 8.7, − 1.5) to 3.1% (95% CI 0.2, 6.0) (negative values represent lower MACE associated with intensive glycemic control). Intensive glycemic control was associated with lower MACE in pooled study data in subgroups with low (− 4.2% [95% CI − 8.1, − 1.0]), intermediate (− 5.1% [95% CI − 8.7, − 1.5]), and high (− 4.3% [95% CI − 7.7, − 1.0]) MACE rates with consistent directions of effect in ACCORD and VADT alone.ConclusionsThis data-driven analysis provides evidence supporting the diabetes treatment guideline recommendation of intensive glucose lowering in diabetes patients with low cardiovascular risk and additionally suggests potential benefits of intensive glycemic control in some individuals at higher cardiovascular risk.

IDF21-0415 Hemoglobin glycation index predicts progression of chronic kidney disease in patients with type 2 diabetes

Background: Hemoglobin glycation index (HGI) is used to describe the difference between estimated and measured glycated hemoglobin (HbA1c). Aim: We aimed to study whether HGI can predict the progression of chronic kidney disease (CKD) in patients with type 2 diabetes. Method: This cohort study enrolled 538 patients with type 2 diabetes. Participants were divided into the low and high HGI subgroups according to the baseline HGI calculated by fasting blood glucose and HbA1c. One-to-one propensity score matching (269:269) was performed to match age, sex, hypertension, estimated glomerular filtration rate, urine albumin–creatinine ratio, use of renin-angiotensin-aldosterone system blocker, and follow-up period. Survival analysis with Kaplan–Meier method and Cox proportional hazard models with robust standard error were used to evaluate the risk and hazard ratio of progression of clinical CKD stage according to the criteria of kidney disease: improving global outcomes. Results: The participants were followed for a median of 7.3 years. A high HGI was associated with a higher risk of clinical CKD stage progression. The crude hazard ratio (HR) and 95% confidence interval (95% CI) was 1.30 (1.07–1.57). After adjusting for HbA1c quartiles, the HR (95% CI) was 2.24 (1.84–2.74). Discussion: HGI independently predicts the progression of CKD in patients with type 2 diabetes. Further investigations are warranted to elucidate its potential clinical impact.

Hemoglobin glycation index predicts renal function deterioration in patients with type 2 diabetes and a low risk of chronic kidney disease

AimsHemoglobin glycation index (HGI) is used to describe the difference between estimated and measured glycated hemoglobin (HbA1c). We aimed to study whether HGI can predict renal function deterioration in patients with type 2 diabetes and a low risk of chronic kidney disease (CKD). MethodsThis retrospective cohort study enrolled 780 patients with type 2 diabetes and a low CKD risk according to the criteria of kidney disease: improving global outcomes. Participants were divided into two subgroups according to the baseline HGI calculated by fasting blood glucose and HbA1c. Multivariate Cox proportional hazard models were used to evaluate the hazard ratios of the study endpoints. Longitudinal data was analyzed using generalized estimating equation (GEE). ResultsThe participants were followed for a median of 7.3 years. A high HGI predicted rapid renal function decline without or with a resultant eGFR < 60 ml/min/1.73 m2, but not onset of macroalbuminuria. The longitudinal GEE model demonstrated a negative association between HGI and the predicted eGFR changes in both the 1-year and 3-year intervals. ConclusionsHGI independently predicted renal function deterioration in patients with type 2 diabetes and a low CKD risk. Further investigations are warranted to elucidate its potential clinical impact.

Association between hemoglobin glycation index and subclinical myocardial injury in the general population free from cardiovascular disease

Background and aimsThe relationship between hemoglobin glycation index (HGI) and the diagnosis and prognosis of cardiovascular disease (CVD) has been verified by previous studies. However, it remains unknown whether HGI has a predictive effect on subclinical myocardial injury (SC–MI). The purpose of the present study was to explore the relationship between HGI and SC–MI in the general population free from CVD. Methods and resultsThe present study included 6009 participants free of CVD from the third National Health and Nutrition Examination Survey. Binary Logistic regression analysis was used to tested the association between HGI and SC–MI. As results, the HGI was significantly higher in participants with SC–MI compared with those without, and the HGI was positively correlated with SC–MI and other metabolic disorder parameters. Each 1-unit increase of HGI and glycated hemoglobin A1c (HbA1c) was independently associated with higher risk of SC–MI (P < 0.05), while fasting plasma glucose (FPG) was no longer a predictive indicator of SC–MI with the increase of confounding factors [OR (95% CI): 1.001 (0.999–1.003), P = 0.305]. And in the subgroup analysis, HGI, only in participants without diabetes, was independently associated with higher risk of SC–MI, while HbA1c and FPG had no independent predictive role in both diabetic and non-diabetic participants. ConclusionsHGI was a significant predictor of SC–MI in the general population free from CVD.

Hemoglobin glycation index, calculated from a single fasting glucose value, as a prediction tool for severe hypoglycemia and major adverse cardiovascular events in DEVOTE

IntroductionHemoglobin glycation index (HGI) is the difference between observed and predicted glycated hemoglobin A1c (HbA1c), derived from mean or fasting plasma glucose (FPG). In this secondary, exploratory analysis of data...

High Hemoglobin Glycation Index Is Associated With Telomere Attrition Independent of HbA1c, Mediated by TNFα.

The hemoglobin glycation index (HGI) is correlated with metabolic diseases and inflammation. Whether the HGI is associated with the aging process and how inflammation and oxidative stress affect the relationship remain unclear. We aimed to analyze links between the HGI and aging biomarkers, and to explore a potential role of inflammation and oxidative stress in the correlations. A cross-sectional study of 434 subjects with different glucose intolerances in a rural community was enrolled. The HGI was calculated as the difference between the measured and predicted hemoglobin A1c (HbA1c). The population was categorized into tertiles of the HGI. Telomere length (LTL) and mitochondrial DNA copy number (mtDNAcn) determined by polymerase chain reaction assay. Tumor necrosis factor (TNF) α and interleukin (IL) 6, 8-oxo-2'-deoxyguanosine (8-oxo-dG), superoxide dismutase (SOD) activities, and glutathione reductase (GR) were measured. Participants in the high HGI group were older and reported a shorter LTL, higher levels of TNFα, SOD activities, and HbA1c. Correlation analyses demonstrated that HGI was correlated with LTL (r = -0.25, P < .001) and TNFα (r = 0.19, P < .001) regardless of HbA1c levels. No relationship was found between HGI and mtDNAcn. HGI (β = -0.238, 95% CI -0.430, -0.046, P = .015) and TNFα (β = -0.02, 95% CI -0.030, -0.014, P < .001) were proved to be correlated with LTL independently, using multiple linear regression analysis. Ordinal logistic regression models showed that compared with subjects the high HGI group, the possibilities of a higher-level LTL was 5.29-fold in the low HGI group (OR 5.29, 95% CI (2.45, 11.41), P < .001), 2.41-fold in the moderate HGI group (OR 2.41, 95% CI 1.35, 4.30, P = .003) after controlling for confounding variables. Mediation analyses indicated that TNFα accounted for 30.39% of the effects of the HGI on LTL. HGI was negatively related to telomere attrition, independent of HbA1c. TNFα acted as a mediator of the relationship between HGI and LTL.

Standardizing the haemoglobin glycation index.

AimsA high haemoglobin glycation index (HGI) is associated with greater risk for hypoglycaemia and chronic vascular disease. Standardizing how the HGI is calculated would normalize results between research studies and hospital laboratories and facilitate the clinical use of HGI for assessing risk.MethodsThe HGI is the difference between an observed HbA1c and a predicted HbA1c obtained by inserting fasting plasma glucose (FPG) into a regression equation describing the linear relationship between FPG and HbA1c in a reference population. We used data from the 2005–2016 U.S. National Health and Nutrition Examination Survey (NHANES) to identify a reference population of 18,675 diabetes treatment–naïve adults without self‐reported diabetes. The reference population regression equation (predicted HbA1c = 0.024 FPG + 3.1) was then used to calculate the HGI and divide participants into low (<−0.150), moderate (−0.150 to <0.150) and high (≥0.150) HGI subgroups. Diabetes status was classified by OGTTs.ResultsAs previously reported in multiple studies, a high HGI was associated with black race independent of diabetes status, and with older age, higher BMI and higher CRP in normal and prediabetic but not diabetic participants. The mean HGI was 0.6% higher in self‐reported diabetic adults. The HGI was not associated with plasma insulin, HOMA‐IR or 2 h OGTT in participants classified as normal, prediabetic or diabetic.ConclusionsThe regression equation derived from this demographically diverse diabetes treatment–naïve adult NHANES reference population is suitable for standardizing how the HGI is calculated for both clinical use and in research to mechanistically explain population variation in the HGI and why a high HGI is associated with greater risk for chronic vascular disease.

Effects of the hemoglobin glycation index on hyperglycemia diagnosis: Results from the REACTION study

AimsThis study aimed to assess the effects of the hemoglobin glycation index (HGI) on hyperglycemia diagnosis and summarize the general characteristics of patients with a high-HGI phenotype. MethodsThe fasting plasma glucose and glycated hemoglobin (HbA1c) levels of participants (n = 47,648) were used to estimate a linear regression equation and determine the baseline HGI. Overall, 42,317 participants without a history of diabetes were included in the final analysis. The participants were divided into three groups according to the tertiles (low, moderate, and high) of baseline HGI. Proportions and variables were compared among the three HGI groups. A multivariate ordered logistic regression model was used to explore associations between related variables and the high-HGI phenotype. ResultsRegression analysis indicated that the high-HGI phenotype was positively associated with female sex, advanced age, obesity, increased low-density lipoprotein and triglyceride levels, decreased high-density lipoprotein cholesterol, and postprandial glycemic excursion levels (all P < 0.05). The prevalence of hyperglycemia increased from the low- to the high-HGI groups when using HbA1c for diagnosis. ConclusionsIndividuals with high HGI have similar clinical characteristics. Measuring HbA1c alone for diagnosis could lead to inappropriate diabetes management decisions in people with low or high HGI.

Relationship Between Hemoglobin Glycation and AHI Index in Patients With Non-diabetic OSAS

Relationship Between Hemoglobin Glycation and AHI Index in Patients With Non-diabetic OSAS

Postchallenge glucose increment was associated with hemoglobin glycation index in subjects with no history of diabetes

We investigated the association between postchallenge glucose increment and hemoglobin glycation index (HGI), the difference between observed and predicted glycated hemoglobin (HbA1c), in subjects with no history of diabetes. We...