Alterations in HbA(1c) with advancing age in subjects with normal glucose tolerance: Chandigarh Urban Diabetes Study (CUDS)

Risk factors associated with glycated hemoglobin A1c trajectories progressing to type 2 diabetes

BACKGROUND AND OBJECTIVE

The notion of prediabetes, defined by the ADA as glycated hemoglobin A1c (HbA1c) of 5.7-6.4%, implies increased vascular inflammatory and immunologic processes and higher risk for developing diabetes mellitus and major cardiovascular events. We aimed to determine the risk factors associated with rapid progression of normal and prediabetes patients to type 2 diabetes mellitus (T2DM).

METHODS

Retrospective cohort study in a single 8-hospital health system in southeast Michigan, between 2006 and 2020. All patients with HbA1c <6.5% at baseline and at least 2 other HbA1c measurements were clustered in five trajectories encompassing more than 95% of the study population. Multivariate linear regression analysis was performed to examine the association of demographic and comorbidities with HbA1c trajectories progressing to diabetes.

RESULTS

A total of 5,347 prediabetic patients were clustered based on their HbA1c progression (C1: 4,853, C2: 253, C66: 102, C12: 85, C68: 54). The largest cluster (C1) had a baseline median HbA1c value of 6.0% and exhibited stable HbA1c levels in prediabetic range across all subsequent years. The smallest cluster (C68) had the lowest median baseline HbA1c value and also remained stable across subsequent years. The proportion of normal HbA1c in each of the pre-diabetic trajectories ranged from 0 to 12.7%, whereas 81.5% of the reference cluster (C68) were normal HbA1c at baseline. The C2 (steady rising) trajectory was significantly associated with BMI (adj OR 1.10, 95%CI 1.03-1.17), and family history of DM (adj OR 2.75, 95%CI 1.32-5.74). With respect to the late rising trajectories, baseline BMI was significantly associated with both C66 and C12 trajectory (adj OR 1.10, 95%CI 1.03-1.18) and (adj OR 1.13, 95%CI 1.05-1.23) respectively, whereas, the C12 trajectory was also significantly associated with age (adj OR 1.62, 95%CI 1.04-2.53) and history of MACE (adj OR 3.20, 95%CI 1.14-8.93).

CONCLUSIONS

We suggest that perhaps a more aggressive preventative approach should be considered in patients with a family history of T2DM who have high BMI and year-to-year increase in HbA1c, whether they have normal hemoglobin A1c or they have prediabetes.KEY MESSAGESProgression to diabetes from normal or prediabetic hemoglobin A1c within four years is associated with baseline BMI.A steady rise in HbA1c during a four-year period is associated with age and family history of T2DM, whereas age and personal history of MACE are associated with a rapid rise in HbA1c.A more aggressive preventative approach should be considered in patients with a family history of T2DM who have high BMI and year-to-year increase in HbA1c.

Explainable machine learning framework to predict personalized physiological aging

Attaining personalized healthy aging requires accurate monitoring of physiological changes and identifying subclinical markers that predict accelerated or delayed aging. Classic biostatistical methods most rely on supervised variables to estimate physiological aging and do not capture the full complexity of inter-parameter interactions. Machine learning (ML) is promising, but its black box nature eludes direct understanding, substantially limiting physician confidence and clinical usage. Using a broad population dataset from the National Health and Nutrition Examination Survey (NHANES) study including routine biological variables and after selection of XGBoost as the most appropriate algorithm, we created an innovative explainable ML framework to determine a Personalized physiological age (PPA). PPA predicted both chronic disease and mortality independently of chronological age. Twenty-six variables were sufficient to predict PPA. Using SHapley Additive exPlanations (SHAP), we implemented a precise quantitative associated metric for each variable explaining physiological (i.e., accelerated or delayed) deviations from age-specific normative data. Among the variables, glycated hemoglobin (HbA1c) displays a major relative weight in the estimation of PPA. Finally, clustering profiles of identical contextualized explanations reveal different aging trajectories opening opportunities to specific clinical follow-up. These data show that PPA is a robust, quantitative and explainable ML-based metric that monitors personalized health status. Our approach also provides a complete framework applicable to different datasets or variables, allowing precision physiological age estimation.

Variations in glycated haemoglobin with age among individuals with normal glucose tolerance: Implications for diagnosis and treatment-Results from the ICMR-INDIAB population-based study (INDIAB-12)

AIM

To report on glycated haemoglobin (HbA1c) values among individuals with normal glucose tolerance (NGT) at different age groups, using data acquired from a large national survey in India.

MATERIALS AND METHODS

Data on glycaemic parameters at different age groups were obtained from the Indian Council of Medical Research-INdia DIABetes (ICMR-INDIAB) study, in adults aged ≥ 20 years representing all parts of India. Age-wise distribution of HbA1c was assessed among individuals with NGT (n = 14,222) confirmed by an oral glucose tolerance test using the World Health Organization (WHO) criteria. Results were validated in another large epidemiological study (n = 1077) conducted in Chennai, India.

RESULTS

Among NGT individuals, HbA1c increased gradually with age from 5.16 ± 0.71% (33 mmol/mol) in the age group of 20-29 years to 5.49 ± 0.69% (37 mmol/mol) in those aged 70 + years. In the validation study, conducted in another study population, HbA1c was 5.35 ± 0.43% (35 mmol/mol) in age group of 20-29 years and 5.74 ± 0.50% (39 mmol/mol) in those aged 70 and above. In the INDIAB study, for every decadal increase in age, there is a 0.08% increase in HbA1c and this increase was more significant in females (females: 0.10% vs. males: 0.06%) and in urban (urban: 0.10% vs. rural: 0.08%) population.

CONCLUSIONS

HbA1c levels increase steadily with age. This suggests that age-specific cutoffs be used while utilizing HbA1c to diagnose diabetes and prediabetes, so as to minimize the risk of overdiagnosis and unnecessary initiation of treatment in elderly people who could have physiological increase in HbA1c levels.

Reconsidering the HbA1c Cutoff for Diabetes Diagnosis Based on a Large Chinese Cohort

INTRODUCTION

The HbA1c has been considered as the 'gold standard' in diabetes diagnosis and management, however, age, gender and body mass index (BMI) might have certain effects on HbA1c. We are aiming to further investigate the correlation between age and HbA1c, and whether it was affected by gender and BMI.

METHODS

A cross-sectional survey including 135,893 nondiabetic individuals who took the physical examination between 2013 and 2017 was conducted. The subjects were grouped by gender, age and BMI, and the interactive and independent effects of the 3 factors on the HbA1c were detected. The median and 95% confidence interval (CI) of HbA1c levels were calculated.

RESULTS

The HbA1c levels gradually increased along with age, both in female and male, and there is a positive association between BMI and the HbA1c. The difference on HbA1c in gender was associated with both age and BMI, the age-related increase in HbAlc was accentuated in the subgroup with higher BMI, and there was a marked accentuation of the positive association between BMI and HbA1c as age increased. In almost all the young and middle-aged (aged 20-59) subgroups, the 97.5^th percentiles of HbA1c levels were lower than 6.5%, suggesting that the single HbA1c cutoff value is probably not applicable to the young and middle-aged population.

CONCLUSIONS

We recommend that the effects of age, gender and BMI should be taken into consideration when using HbA1c for the diagnosis and management of diabetes, especially in the young and middle-aged population.

Nonlinear model for the carotid artery disease 10-year risk prediction by fusing conventional cardiovascular factors to carotid ultrasound image phenotypes: A Japanese diabetes cohort study

MOTIVATION

This study presents a novel nonlinear model which can predict 10-year carotid ultrasound image-based phenotypes by fusing nine traditional cardiovascular risk factors (ethnicity, gender, age, artery type, body mass index, hemoglobin A1c, hypertension, low-density lipoprotein, and smoking) with five types of carotid automated image phenotypes (three types of carotid intima-media thickness (IMT), wall variability, and total plaque area).

METHODOLOGY

Two-step process was adapted: First, five baseline carotid image-based phenotypes were automatically measured using AtheroEdge^™ (AtheroPoint^™ , CA, USA) system by two operators (novice and experienced) and an expert. Second, based on the annual progression rates of cIMT due to nine traditional cardiovascular risk factors, a novel nonlinear model was adapted for 10-year predictions of carotid phenotypes.

RESULTS

Institute review board (IRB) approved 204 Japanese patients' left/right common carotid artery (407 ultrasound scans) was collected with a mean age of 69 ± 11 years. Age and hemoglobin were reported to have a high influence on the 10-year carotid phenotypes. Mean correlation coefficient (CC) between 10-year carotid image-based phenotype and age was improved by 39.35% in males and 25.38% in females. The area under the curves for the 10-year measurements of five phenotypes IMT_ave10yr , IMT_max10yr , IMT_min10yr , IMTV_10yr , and TPA_10yr were 0.96, 0.94, 0.90, 1.0, and 1.0. Inter-operator variability between two operators showed significant CC (P < 0.0001).

CONCLUSIONS

A nonlinear model was developed and validated by fusing nine conventional CV risk factors with current carotid image-based phenotypes for predicting the 10-year carotid ultrasound image-based phenotypes which may be used risk assessment.

Influence of iron deficiency on Hb A1c levels in type 2 diabetic patients

AIMS

Hemoglobin A1c (HbA1c) is gold-standard for the assessment of glycemic control in diabetic patients. Previous studies have reported that iron deficiency may elevate A1c concentrations, independent of glycemia. This study aimed to analyze the effect of iron status on HbA1c levels in diabetic patients.

METHODS

661 patients 336 females (228 menopausal and 108 premenopausal) and 325 males (237 age> 50 years and 88 age < 50 years) were recruited. HbA1c, ferritin, fasting plasma glucose, hemogram and medical history were recorded. Analysis of variance ANOVA and Pearson's regression were applied.

RESULTS

patients were divided according gender, age, glycemia and iron status (normal, latent iron deficiency LID, iron deficiency anemia IDA).All groups presented increasing HbA1c values in parallel with iron deficiency, subclinical and anemia, but the level of significance was not homogeneous in the different groups. Controlled premenopausal women HbA1c in normal iron status and IDA groups P = 0.0048, between normal and LID, P = 0.033. Not controlled premenopausal women Normal group and IDAP < 0.001, normal iron status and LID P = 0.019. Controlled menopausal women normal group and IDAP < 0.0001, LID and IDA P = 0.01. Not controlled menopausal women normal group and IDA P = 0.04. Controlled men over 50 years normal and IDA groups P = 0.002, LID and IDA P = 0.02. Controlled young men normal group and LID P = 0.03.

CONCLUSION

This study found a positive correlation between iron deficiency and increased HA1c levels. In diabetic patients with IDA should be interpreted with caution, due to the possibility of spurious increment in HbA1c.

Three-year renal function trajectory and its association with adverse renal event in patients with type 2 diabetes

AIMS

The study evaluated associations between 3-year eGFR trajectory patterns and adverse renal event in diabetic patients.

METHODS

Adverse renal event was defined as sustained eGFR <60 or one ACR >300 mg/g creatinine. Cox proportional hazards models evaluated association between eGFR trajectory patterns and adverse renal event.

RESULTS

We detected six clusters. Cluster 1 had a stable but relatively low baseline eGFR level (n = 823, 20.52%), cluster 2 had a high baseline eGFR level, but slightly decreased afterwards (n = 1708, 42.59%), cluster 3 had an increasing eGFR during the first 15-month follow-up and then a decline rate (n = 505, 12.59%), cluster 4 decreased during the first 9-month follow-up and then remained stable (n = 774, 19.30%), cluster 5 had a sharp decline and then was elevated after 21 months until the end of follow-up (n = 135, 3.37%), and cluster 6 had an extremely fluctuating eGFR and then a sharp increase at the last 12-month period (n = 65, 1.62%). Clusters 1, 3, and 4 show increased adverse renal risks compared with cluster 2 (2.24, 1.69-2.97; 2.70, 2.02-3.61; and 2.15, 1.64-2.83, respectively).

CONCLUSIONS

Patients with sustained low-level renal function, renal decline, or increasing trend in eGFR trajectory encountered an increased CKD risk.

Impact of demographics and disease progression on the relationship between glucose and HbA1c

CONTEXT

Several studies have shown that the relationship between mean plasma glucose (MPG) and glycated haemoglobin (HbA1c) may vary across populations. Especially race has previously been referred to shift the regression line that links MPG to HbA1c at steady-state (Herman & Cohen, 2012).

OBJECTIVE

To assess the influence of demographic and disease progression-related covariates on the intercept of the estimated linear MPG-HbA1c relationship in a longitudinal model.

DATA

Longitudinal patient-level data from 16 late-phase trials in type 2 diabetes with a total of 8927 subjects was used to study covariates for the relationship between MPG and HbA1c. The analysed covariates included age group, BMI, gender, race, diabetes duration, and pre-trial treatment. Differences between trials were taken into account by estimating a trial-to-trial variability component.

PARTICIPANTS

Participants included 47% females and 20% above 65years. 77% were Caucasian, 9% were Asian, 5% were Black and the remaining 9% were analysed together as other races.

ANALYSIS

Estimates of the change in the intercept of the MPG-HbA1c relationship due to the mentioned covariates were determined using a longitudinal model.

RESULTS

The analysis showed that pre-trial treatment with insulin had the most pronounced impact associated with a 0.34% higher HbA1c at a given MPG. However, race, diabetes duration and age group also had an impact on the MPG-HbA1c relationship.

CONCLUSION

Our analysis shows that the relationship between MPG and HbA1c is relatively insensitive to covariates, but shows small variations across populations, which may be relevant to take into account when predicting HbA1c response based on MPG measurements in clinical trials.

Diagnostic values of glycated haemoglobin and diagnosis of diabetes: Results of a cross-sectional survey among general practitioners in the province of Reggio Emilia, Italy

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The multilevel analysis showed a strong GP clustering effect.

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The age was related to the likelihood to be diagnosed as ‘having diabetes’.

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Need to enhance dissemination on the use of HbA1c test as diagnostic tool.

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The exchange between GPs and the register could improve the diagnosis timeliness.

Aims

The aim of this study was to investigate whether subjects included in the diabetes register solely because their HbA1c was over the diagnostic threshold received a diagnosis of diabetes from their general practitioner (GP).

Methods

The study included all registered cases in 2009–2010 aged 18 or over that were identified only by the laboratory database because they had one or more HbA1c over the 6.5% threshold and for whom we did not find any information in the search of full electronic clinical records. Multilevel logistic regression was used to examine the influence of GP and patient characteristics.

Results

There were 228 participating GPs (76.3% of those invited) and 832 assessed subjects (68.8% of study population). There was a strong clustering among the GPs (residual intraclass correlation = 0.52, 95% CI 0.40–0.64). About one in two (55.5%) subjects with two or more HbA1c > =6.5% has been diagnosed as diabetic and the percentage declined – unless zeroing – in case the abnormal value was only one (28.3%). The likelihood of being labelled ‘no diabetes’ was greater in subjects aged less than 65 or over 74 with respect to the reference age group (OR 1.89, 95% CI 1.13–3.15; OR 1.55 95% CI 0.94–2.53). The same likelihood consistently decreased when HbA1c test was accompanied by abnormal fasting plasma glucose (FPG) assay (OR 0.20, 95% CI 0.12–0.32).

Conclusions

A permanent exchange of information between the diabetes register and GPs should be maintained to improve the care of patients and the awareness of criteria for diabetes diagnosis among GPs.

Prevalence and Predictors of Pre-Diabetes and Diabetes among Adults 18 Years or Older in Florida: A Multinomial Logistic Modeling Approach

BACKGROUND

Individuals with pre-diabetes and diabetes have increased risks of developing macro-vascular complications including heart disease and stroke; which are the leading causes of death globally. The objective of this study was to estimate the prevalence of pre-diabetes and diabetes, and to investigate their predictors among adults ≥18 years in Florida.

METHODS

Data covering the time period January-December 2013, were obtained from Florida's Behavioral Risk Factor Surveillance System (BRFSS). Survey design of the study was declared using SVYSET statement of STATA 13.1. Descriptive analyses were performed to estimate the prevalence of pre-diabetes and diabetes. Predictors of pre-diabetes and diabetes were investigated using multinomial logistic regression model. Model goodness-of-fit was evaluated using both the multinomial goodness-of-fit test proposed by Fagerland, Hosmer, and Bofin, as well as, the Hosmer-Lemeshow's goodness of fit test.

RESULTS

There were approximately 2,983 (7.3%) and 5,189 (12.1%) adults in Florida diagnosed with pre-diabetes and diabetes, respectively. Over half of the study respondents were white, married and over the age of 45 years while 36.4% reported being physically inactive, overweight (36.4%) or obese (26.4%), hypertensive (34.6%), hypercholesteremic (40.3%), and 26% were arthritic. Based on the final multivariable multinomial model, only being overweight (Relative Risk Ratio [RRR] = 1.85, 95% Confidence Interval [95% CI] = 1.41, 2.42), obese (RRR = 3.41, 95% CI = 2.61, 4.45), hypertensive (RRR = 1.69, 95% CI = 1.33, 2.15), hypercholesterolemic (RRR = 1.94, 95% CI = 1.55, 2.43), and arthritic (RRR = 1.24, 95% CI = 1.00, 1.55) had significant associations with pre-diabetes. However, more predictors had significant associations with diabetes and the strengths of associations tended to be higher than for the association with pre-diabetes. For instance, the relative risk ratios for the association between diabetes and being overweight (RRR = 2.00, 95% CI = 1.55, 2.57), or obese (RRR = 4.04, 95% CI = 3.22, 5.07), hypertensive (RRR = 2.66, 95% CI = 2.08, 3.41), hypercholesterolemic (RRR = 1.98, 95% CI = 1.61, 2.45) and arthritic (RRR = 1.28, 95% CI = 1.04, 1.58) were all further away from the null than their associations with pre-diabetes. Moreover, a number of variables such as age, income level, sex, and level of physical activity had significant association with diabetes but not pre-diabetes. The risk of diabetes increased with increasing age, lower income, in males, and with physical inactivity. Insufficient physical activity had no significant association with the risk of diabetes or pre-diabetes.

CONCLUSIONS

There is evidence of differences in the strength of association of the predictors across levels of diabetes status (pre-diabetes and diabetes) among adults ≥18 years in Florida. It is important to monitor populations at high risk for pre-diabetes and diabetes, so as to help guide health programming decisions and resource allocations to control the condition.

Glycosylated hemoglobin testing in the National Social Life, Health, and Aging Project

OBJECTIVES

Longitudinal biomeasures of health are still new in nationally representative social science survey research. Data measuring blood sugar control provide opportunities for understanding the development of diabetes and its complications in older adults, but researchers must be aware that some of the differences across time can be due to variations in measurement procedures. This is a well-recognized issue whenever all samples cannot be assayed at the same time and we sought to present the analytic methods to quantify and adjust for the variation.

METHOD

We collected and analyzed HbA1C, glycated hemoglobin, a biomeasure of average blood sugar concentrations within the past few months. Improvements were made in the collection protocol for Wave 2, and assays were performed by a different lab.

RESULTS

The HbA1C data obtained during Wave 1 and Wave 2 are consistent with the expected population distributions for differences by gender, age, race/ethnicity, and diabetes status. Age-adjusted mean HbA1C declined slightly from Wave 1 to Wave 2 by -0.19 (95% confidence interval [CI]: -0.27, -0.10), and the average longitudinal change was -0.12 (95% CI: -0.18, -0.06).

DISCUSSION

Collection of HbA1C in Wave 2 permits researchers to examine the relationship between HbA1C and new health and social measures added in Wave 2, and to identify factors related to the change in HbA1C. Changes in collection protocol and labs between waves may have yielded small systematic differences that require analysts to carefully interpret absolute HbA1C values. We recommend analytic methods for cross wave differences in HbA1C and steps to ensure cross wave comparability in future studies.

Aging is associated with increased HbA1c levels, independently of glucose levels and insulin resistance, and also with decreased HbA1c diagnostic specificity

AIM

To determine whether using HbA1c for screening and management could be confounded by age differences, whether age effects can be explained by unrecognized diabetes and prediabetes, insulin resistance or postprandial hyperglycaemia, and whether the effects of aging have an impact on diagnostic accuracy.

METHODS

We conducted a cross-sectional analysis in adults without known diabetes in the Screening for Impaired Glucose Tolerance (SIGT) study 2005-2008 (n=1573) and the National Health and Nutrition Examination Survey (NHANES) 2005-2006 (n=1184).

RESULTS

Both glucose intolerance and HbA(1c) levels increased with age. In univariate analyses including all subjects, HbA(1c) levels increased by 0.93 mmol/mol (0.085%) per 10 years of age in the SIGT study and by 1.03 mmol/mol (0.094%) per 10 years in the NHANES; in both datasets, the HbA(1c) increase was 0.87 mmol/mol (0.08%) per 10 years in subjects without diabetes, and 0.76 mmol/mol (0.07%) per 10 years in subjects with normal glucose tolerance, all P<0.001. In multivariate analyses of subjects with normal glucose tolerance, the relationship between age and HbA(1c) remained significant (P<0.001) after adjustment for covariates including race, BMI, waist circumference, sagittal abdominal diameter, triglyceride/HDL ratio, and fasting and 2-h plasma glucose and other glucose levels, as assessed by an oral glucose tolerance test. In both datasets, the HbA(1c) of an 80-year-old individual with normal glucose tolerance would be 3.82 mmol/mol (0.35%) greater than that of a 30-year-old with normal glucose tolerance, a difference that is clinically significant. Moreover, the specificity of HbA(1c) -based diagnostic criteria for prediabetes decreased substantially with increasing age (P<0.0001).

CONCLUSIONS

In two large datasets, using different methods to measure HbA(1c), the association of age with higher HbA(1c) levels: was consistent and similar; was both statistically and clinically significant; was unexplained by features of aging; and reduced diagnostic specificity. Age should be taken into consideration when using HbA(1c) for the diagnosis and management of diabetes and prediabetes.

Hemoglobin A1c values are affected by hemoglobin level and gender in non-anemic Koreans

AIMS/INTRODUCTION

To evaluate whether hemoglobin A1c (HbA1c) levels are affected by hemoglobin level and gender.

MATERIALS AND METHODS

A cross-sectional analysis was carried out in a sample of 87,284 non-diabetic Koreans without anemia who participated in comprehensive health check-ups between January and December 2009 at the Kangbuk Samsung Hospital Total Healthcare Center in Seoul, Korea. We categorized men and women separately according to fasting plasma glucose and hemoglobin level to carry out the analysis.

RESULTS

HbA1c increased steadily with increasing fasting plasma glucose level. Both men and women with lower hemoglobin had significantly higher HbA1c at a given fasting glucose level, and this result was consistent across the fasting glucose quintiles within the non-diabetic range. Women had a lower mean hemoglobin value compared with men, and women had higher HbA1c levels at a given fasting glucose level consistently across the fasting glucose deciles. There was also a gender-specific association between age and HbA1c (P < 0.001 for interaction).

CONCLUSIONS

HbA1c values were affected by hemoglobin level and gender in non-anemic Koreans. Thus, hemoglobin level and gender should be considered in the diagnosis of diabetes using HbA1c.

Longitudinal trajectories of HbA1c and fasting plasma glucose levels during the development of type 2 diabetes: the Toranomon Hospital Health Management Center Study 7 (TOPICS 7)

OBJECTIVE

To describe the trajectory of HbA(1c) and glucose concentrations before the diagnosis of diabetes.

RESEARCH DESIGN AND METHODS

The study comprised 1,722 nondiabetic Japanese individuals aged 26-80 years. Fasting plasma glucose (FPG) and HbA(1c) were measured annually for a mean of 9.5 (SD 1.8) years.

RESULTS

Diabetes occurred in 193 individuals (FPG ≥ 7.0 mmol/L, self-reported clinician-diagnosed diabetes, or HbA(1c) ≥ 6.5%). Mean HbA(1c) values were >5.6% each year before diagnosis in diabetes cases. Mean HbA(1c) (5.69% [95% CI 5.50-5.88]) was higher in the 21 individuals who developed diabetes 10 years after the baseline examination than in nondiabetic individuals after 10 years (5.27% [5.25-5.28]). From 3 years to 1 year prediagnosis, HbA(1c) increased 0.09% (SE 0.01)/year, reaching 5.90% (5.84-5.96) 1 year prediagnosis. In the entire group, marked increases in HbA(1c) of 0.3% (SE 0.05%)/year and FPG of 0.63 (0.07) mmol/L/year predicted diabetes.

CONCLUSIONS

HbA(1c) trajectory increased sharply after gradual long-term increases in diabetic individuals.

Ageing and associations of fasting plasma glucose and 2 h plasma glucose with HbA(1C) in apparently healthy population. "FIN-D2D" study

OBJECTIVE

In this FIN-D2D cross-sectional survey the relationship of age with HbA(1c) and fasting and 2h glucose in the oral glucose tolerance test (OGTT) was explored in apparently randomly selected healthy population.

PATIENTS AND METHODS

The glycaemic parameters were measured in 1344 men and 1482 women (aged 45-74 years), and among them we excluded all subjects with known diabetes, hypertension or dyslipidaemia. The final analyses for HbA(1c) and the ratios of fasting glucose/HbA(1c) and 2h glucose/HbA(1c) included 649 men and 804 women.

RESULTS

Mean age was 57 years and BMI 26.1kg/m(2) for both genders. HbA(1c) increased in both genders with age (p<0.001). For a particular fasting glucose level HbA(1c) level was higher in older age groups (p<0.001 for linearity). By contrast, a particular 2h plasma glucose value in OGTT implied significantly lower HbA(1c) in the elderly (p<0.001 for linearity).

CONCLUSION

In apparently healthy population, screened with OGTT, in older individuals compared with younger ones a particular HbA(1c) value implies slightly lower fasting glucose, but relatively higher 2h glucose. These results need to be verified in different populations. The effects of age on relation between HbA(1c) and plasma glucose should be taken into account in classifying people into different dysglycaemia categories.