Diabetes mellitus in HIV-infected patients: Fasting glucose, A1c, or oral glucose tolerance test - which method to choose for the diagnosis?

RESEARCH ARTICLE Open Access Diabetes mellitus in HIV-infected patients: fasting glucose, A1c, or oral glucose tolerance test –which method to choose for the diagnosis? Ana Rita Coelho 1* , Flávia Andreia Moreira 1 , Ana Cristina Santos 2,3 , André Silva-Pinto 4,5 , António Sarmento 4,5 , Davide Carvalho 6 and Paula Freitas 6 Abstract Background: Antiretroviral therapy dramatically reduced HIV-related morbidity and mortality, prolonging the lifespan of HIV-infected patients. Greater duration of infection and exposure to antiretroviral therapy makes these patients susceptible to traditional cardio-metabolic risk factors and pathologies. The optimal diagnostic protocol for Diabetes Mellitus in these patients is still controversial. Haemoglobin A1c (HbA1c) has been shown to underestimate glycaemia levels and the oral glucose tolerance test (OGTT) has been shown to reveal cases of glucose metabolism disturbances in patients with normal fasting glucose. Thus, this study aimed to determine the prevalence of prediabetes and diabetes in a population of HIV-infected patients undergoing combined antiretroviral therapy, using three different diagnostic methods (fasting glucose, OGTT and HbA1c), to determine the agreement between the different methods and the characteristics associated with each one. Methods: This study analyzed 220 HIV-infected patients on antiretroviral therapy. Patient characteristics were collected using a standardized protocol. Disturbances of glucose homeostasis were defined by the ADA 2017 criteria. Patients were characterized according to the presence or absence of clinical lipodystrophy, and distributed into four different categories, according to the presence, or absence of either clinical lipoatrophy, or abdominal prominence. Insulin resistance was assessed by HOMA-IR and QUICKI indexes. Agreement between the diagnostic methods was assessed by Cohen’s kappa coefficient. Results: There were no patients diagnosed with diabetes with HbA1c. 5.9% prevalence was obtained when OGTT was used, and 3.2% prevalence when fasting glucose was used. Prediabetes had a prevalence of 14.1% when using HbA1c, 24.1% when using OGTT, and 20% when using fasting glucose. In all three methods, glucose homeostasis disturbances were associated with older age and higher resistance to insulin. Regarding other characteristics, associations varied between the three methods. The agreement between them was fair, or slight. Conclusions: We observed that HbA1c was the method that diagnosed the least amount of cases and that OGTT was the one that diagnosed the most cases. Accordingly, our results indicate that HbA1c underestimated glycaemia levels in this population and that the use of OGTT might allow an earlier diagnosis of glucose homeostasis disturbances, potentially making it possible to avoid severe complications of DM. Keywords: HIV infection, Diabetes mellitus diagnosis, Fasting glucose, Oral glucose tolerance test, HbA1c * Correspondence: [email protected] 1 Medical Student. Faculty of Medicine, University of Porto. Alameda Prof. Hernâni Monteiro, 4200-319 Porto, Portugal Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Coelho et al. BMC Infectious Diseases (2018) 18:309 https://doi.org/10.1186/s12879-018-3221-7 Background Antiretroviral therapy (ART) dramatically reduces HIV-related morbidity and mortality and prolongs the lifespan of this infected population [1]. The greater survival of patients living with the infection makes them more susceptible to exposure to the same traditional cardio-metabolic risk factors and pathologies as the general population. These complications are likely not only correlated with age, but also with the cumulative exposure to ART [1–5]. Cardio-metabolic pathology is, in fact, becoming an increasing problem associated with HIV infection under ART [2,5,6]. In a large-scale HIV population study, De Wit et al. found that the incidence of new-onset diabetes mellitus (DM) increased with cumulative exposure to ART [5]. In fact, it has been shown that HIV populations can have up to a two-fold higher risk of DM when compared to the general population [4]. Besides ART, traditional risk factors have also been associated with the development of DM in this population, such as family history, obesity, older age, race, abdominal prominence (e.g. waist circumference) and statin use [4,7,8]. In the general population, DM has been identified as a high-risk, and very high-risk factor for the development of cardiovascular disease [9]. This was also demonstrated in the HIV population, where it was revealed that DM is a risk factor that substantially increases the chance of developing coronary heart disease, especially in cases of prolonged infection [6]. Thus, it is essential to regularly screen for this condition in this population, in order to prevent these kinds of complications. The optimal diagnostic methodology for DM in HIV patients is still a controversial topic. The current European AIDS Clinical Society guidelines recommend the evaluation of fasting glucose as an initial assessment of glucose metabolism in patients with new HIV diagnostic and also prior to starting ART. An oral glucose tolerance test (OGTT), or haemoglobin A1c (HbA1c) measurement is only recommended if fasting glucose levels reveal prediabetes [10]. Accordingly, this study aims to determine the prevalence of prediabetes and DM using three differences diagnostic methods (fasting glucose, OGTT and HbA1c) in a population of HIV-infected patients under combined antiretroviral therapy. Additionally, it is our aim to investigate which characteristics differ in each diagnostic method between the different groups, and what is the agreement between the different methods. Methods Participants As part of a cross-sectional study, between 2005 and 2016, 220 non-institutionalized HIV-infected adults, consecutively referred from the Infectious Diseases Department, were evaluated at the Endocrinology Outpatient Clinic of São João Hospital. Patients were included in the study on their first visit, and only patients on combined antiretroviral therapy were included. A history of previously-diagnosed DM and use of anti-diabetic therapy excluded patients from our study. The Ethics Committee for Health of Hospital São João approved this study and each patient provided written informed consent. Clinical assessment For each patient the following information was collected using a standardized protocol: age, known duration of HIV infection and of combined antiretroviral therapy exposure, current type of ART, HIV infection risk factors and characterization of the infection, smoking history (past, current, or never), history of diabetes and hypertension, and use of anti-diabetic, anti-hypertensive, and lipid lowering drugs. We used the Centers for Disease Control and Prevention (CDC) criteria for classifying the degree of infection [11]. Weight, height and waist circumference were measured, and Body Mass Index (BMI) was also calculated. Body weight was measured using TANITA (Tanita®, model TBF 300), and scale and height was measured to the nearest centimeter in the standing position using a wall stadiometer (Holtain Limited Crymych, Dyfed®). BMI was calculated as weight divided by height squared (kg/m2). The waist circumference was measured midway between the lowest rib and iliac crest, at the end of a gentle expiration, with the patient standing upright, face directed forward and shoulders relaxed. Clinical lipodystrophy was defined as peripheral lipoatrophy with or without central fat accumulation assessed by both patient and practitioner [12]. Patients with at least one light, moderate, or severe subjective lipoatrophic feature (identified by lipoatrophy-specific physical examination) were asked to report whether he/she had any change in fat in their cheeks, the side of their face, legs, arms, or buttocks. Patients were classified as being without peripheral lipoatrophy when none of the previously described features were present [13]. Presence of central fat accumulation or abdominal prominence was defined by the measurement of waist circumference using the International Diabetes Federation criteria for metabolic syndrome (waist circumference ≥94 cm for Europid men and ≥80 cm for Europid women). Patients were classified into four different categories, according to the presence or absence of either clinical lipoatrophy or abdominal prominence: 1) no lipodystrophy –patients without lipoatrophy and without abdominal prominence; 2) isolated central fat accumulation –patients without lipoatrophy and with abdominal prominence; 3) lipoatrophy –patients with lipoatrophy and without Coelho et al. BMC Infectious Diseases (2018) 18:309 Page 2 of 13 abdominal prominence; 4) mixed forms of lipodystrophy –patients with lipoatrophy and with abdominal prominence [13]. The clinical assessment was performed by the same practitioner (PF). Laboratory analysis A venous blood sample was taken after a 12-h overnight fast. The 12-h overnight fast was confirmed with the participants prior to the collection of the blood sample. If the 12-h fasting period was not observed the blood sample collection was rescheduled. All the samples were analyzed at the central laboratory of our hospital. The measurements of total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, plasma glucose and HbA1c serum levels were determined using commercial kits. Hepatitis C was diagnosed by serological assays that detect antibody to hepatitis C virus (anti-HCV). All patients without a previous diagnosis of diabetes were submitted to an OGTT. The OGTT was performed as described by the World Health Organization, using a glucose load containing the equivalent of 75 g anhydrous glucose dissolved in water. The CD4 cell count was determined by flow cytometry and plasma HIV-1 RNA loads were measured by a quantitative reverse transcriptase polymerase chain reaction (Roche Diagnostic Systems, Inc., Branchburg, NJ, USA), which has a lower limit of detection of 50 copies/mL. Criteria for the definition of disturbances of glucose homeostasis Disturbances of glucose homeostasis were defined by the American Diabetes Association 2017 criteria [14]. Patients were divided into three groups: no diabetes (No DM), prediabetes and diabetes (DM). No DM was defined as fasting glucose <100 mg/dL, HbA1c <5.7% or 120 min plasma glucose <140 mg/dL during the OGTT. Prediabetes was defined as fasting glucose between 100 and 126 mg/dL, HbA1c between 5.7 and 6.5% or 120 min plasma glucose between 140 and 200 mg/dL during the OGTT. DM was defined as fasting glucose ≥126 mg/dL, HbA1c ≥6.5% or 120 min plasma glucose ≥200 mg/dL during the OGTT. Measurements of insulin resistance Insulin resistance was defined by the homeostasis model assessment of insulin resistance (HOMA), and insulin sensitivity by the quantitative insulin sensitivity check index (QUICKI). These indexes were calculated by the following formulas: HOMA-IR index = (fasting plasma insulin × fasting plasma glucose)/22.5 [15] and QUICKI = 1/[log (fasting insulin in mU/l) + log (fasting plasma glucose in mg/dL)] [16]. Glucose was expressed in mmol/L and insulin in μUI/mL. Insulin resistance was defined when the value of HOMA >4 [8]. Statistical analysis Quantitative variables were described as mean and standard deviation (SD), or median and interquartile range (IQR), and were compared using Student-t and ANOVA or Mann–Whitney and Kruskal-Wallis tests, as appropriate. Categorical variables were described as counts and proportions, and compared using the chi-square or Fisher’s exact test. The kappa coefficient was computed to analyze statistical agreement between the three different diagnostic methods used for defining prediabetes and DM. Statistical analysis was performed using SPSS version 24.0 software (SPSS Inc., Chicago, Illinois, USA). All probabilities were two tailed, and pvalues of <0.05 were regarded as significant. Results Baseline characteristics A total of 220 HIV-infected patients under ART were evaluated. The mean age of patients included was 45.8 ± 11.5 years, and 60.5% of them were males. All the demographic and clinical characteristics accessed in this study are presented in Table 1, according to the presence or absence of clinical lipodystrophy. Patients with clinical lipodystrophy were older [47.5 (11.29) vs 43.82 (11.49) years; P= 0.017], had longer duration of the HIV infection [9.0 (5.0) vs 6.0 (6.0) years; p= 0.001] and of ART use [8.0 (5.0) vs 5.0 (5.5) years; p< 0.001]. Regarding anthropometric measures, patients with clinical lipodystrophy had lower weight (p< 0.001), BMI ((p< 0.001), and waist circumference mean values (p< 0.001). Hypertension was more frequent in patients with clinical lipodystrophy [45 (39.1) vs 23 (21.9) %; p= 0.009], as were current smokers [56 (49.1) vs 36 (34.3) %; p= 0.032], and triglycerides median values were significantly higher than those of patients without clinical lipodystrophy [215.5 (214.5) vs 171 (154) mg/dL; p= 0.007]. With regards to pharmaceutical therapy, the use of statins (p= 0.005) and fibrates (p= 0.054) was more frequent in patients with clinical lipodystrophy. No differences were found between patients with or without clinical lipodystrophy in terms of gender, CD4+ cell count, percentage of viral suppression, prevalence of co-infection with Hepatitis C, type of risk factor for the HIV transmission, CDC clinical categories, type of ART used, and lipid profile. Hemoglobin A1c No patients were diagnosed with DM using this method (HbA1c ≥6.5%). Therefore, in Table 2, the results regarding two groups: no DM (HbA1c ≤5.7%) and prediabetes (5.7 < HbA1c < 6.5) are presented. Coelho et al. BMC Infectious Diseases (2018) 18:309 Page 3 of 13 In our population, 31 patients (14.1%) were diagnosed with prediabetes. These patients were older [50.35 (11.89) vs 44.99 (11.30) years; p=0.016], and had a higher BMI [26.90 (4.46) vs 25.01 (4.49) kg/m2; p= 0.031], compared to the no DM patients, but had lower median level of triglycerides [139 (133.8) vs 203 (198.0) mg/dL, p= 0.010], and less frequently used fibrate [4(12.9) vs 61 (32.3)%; p= 0.048]. The values obtained Table 1 Sample’s baseline characteristics, according to the presence of Clinical Lipodystrophy (CL) With CL Without CL Pvalue n(%) 115 (52.3) 105 (47.7) Sex [n(%)] 0.099 Male 76 (66.1) 57 (54.3) Female 39 (33.9) 48 (45.7) Age [years, mean (SD)] 47.5 (11.3) 43.8 (11.5) 0.017 Duration of HIV infection [years, median (IR)] 9 (5) 6 (6) 0.001 cART [years, median (IR)] 8 (5) 5 (5.5) < 0.001 Weight [Kg, mean (SD)] 64.1 (12.8) 73.9 (12.6) < 0.001 Height [m, mean (SD)] 1.7 (0.1) 1.7 (0.1) 0.765 BMI [(kg/m2), mean (SD)] 23.6 (3.8) 27.2 (4.5) < 0.001 Waist circumference [cm, mean (SD)] 88.4 (10.8) 95.1 (12.1) < 0.001 CD4 cell count [cells/mm3, median (IR)] 554 (385) 479 (300) 0.238 HIV RNA (<50) [n(%)] 100 (100) 92 (100) Hepatitis C co-infection [n(%)] 34 (29.8) 30 (29.4) 0.999 Hypertension [n(%)] 45 (39.1) 23 (21.9) 0.009 HIV risk factor [n(%)] 0.162 Intravenous drug user 1 (25) 3 (15.8) Homosexual contact 0 (0) 2 (10.5) Heterosexual contact 2 (50) 14 (73.7) Others 1 (25) 0 (0) CDC clinical categories [n(%)] 0.389 A 63 (54.8) 56 (53.3) B 1 (0.9) 4 (3.8) C 51 (44.3) 45 (42.9) ART [n(%)] IP 61 (53) 62 (59) 0.447 NNRTI 55 (47.8) 47 (44.8) 0.749 NRTI 113 (98.3) 97 (92.4) 0.051 Smoking history [n(%)] 0.032 Never 38 (33.3) 53 (50.5) Current 56 (49.1) 36 (34.3) Former 20 (17.5) 16 (15.2) Total cholesterol [mg/dL, mean (SD)] 221.9 (53.2) 227.8 (57.8) 0.433 LDLcholesterol [mg/dL, mean (SD)] 129.4 (48.1) 140 (45.4) 0.097 HDLcholesterol [mg/dL, mean (SD)] 46.5 (14.8) 49.5 (13.3) 0.123 Triglycerides [mg/dL, median (IR)] 215.5 (214.5) 171 (154) 0.007 Statin use [n(%)] 33 (28.7) 13 (12.4) 0.005 Fibrate use [n(%)] 41 (35.7) 24 (22.9) 0.054 CL clinical lipodystrophy, cART combination antiretroviral therapy, BMI body mass index, ART antiretroviral therapy, PI protease inhibitor, NNRTI non-nucleoside reverse transcriptase inhibitor, NRTI nucleoside reverse transcriptase inhibitor, HOMA homeostatic model assessment, QUICKI quantitative insulin sensitivity check index, SD standard deviation, IR interquartile range Coelho et al. BMC Infectious Diseases (2018) 18:309 Page 4 of 13 for HOMA-IR were higher among patients with prediabetes, and the difference between the groups was statistically significant [2.51 (5.13) vs 1.62 (1.46); p= 0.023]. There were no differences between the diagnostic groups with regards to sex, duration of HIV infection or ART use, presence or absence of clinical lipodystrophy, body composition types, waist circumference, CD4 cell count, percentage of viral suppression, prevalence of hepatitis C coinfection, CDC clinical category, type of ART used, lipid profile, and frequency of use of statins. Table 2 Sample’s characteristics, according to the presence of no DM or prediabetes, accessed by HbA1c HbA1c Pvalue <5.7% 5.7–6.4% n(%) 189 (85.9) 31 (14.1) Sex [n(%)] 0.623 Male 116 (61.4) 17 (54.8) Female 73 (38.6) 14 (45.2) Age [years, mean (SD)] 45 (11.3) 50.4 (11.9) 0.016 Duration of HIV infection [years, median (IR)] 8 (6) 8 (7.0) 0.698 cART [years, median (IR)] 6 (6.5) 7 (7.0) 0.136 Clinical lipodystrophy [n(%)] 0.909 Without CL 91 (48.1) 14 (45.2) With CL 98 (51.9) 17 (54.8) Body Composition [n(%)] 0.469 No lipodystrophy 28 (15.3) 3 (10) Isolated central fat accumulation 59 (32.2) 10 (33.3) Lipoatrophy 52 (28.4) 6 (20) Mixed form of lipodystrophy 44 (24) 11 (36.7) BMI [(kg/m2), mean (SD)] 25 (4.5) 26.9 (4.5) 0.031 Waist circumference [cm, mean (SD)] 90.9 (11.6) 95.2 (13.2) 0.065 CD4 cell count [cells/mm3, median (IR)] 500 (345) 528 (312) 0.819 HIV RNA (<50) [n(%)] 165 (100) 27 (100) Hepatitis C co-infection [n(%)] 57 (30.8) 7 (22.6) 0.474 CDC clinical categories [n(%)] 0.093 A 107 (56.6) 12 (38.7) B 5 (2.6) 0 (0) C 77 (40.7) 19 (61.3) ART [n(%)] IP 106 (56.1) 17 (54.8) 0.999 NNRTI 88 (46.6) 14 (45.2) 0.999 NRTI 182 (96.3) 28 (90.3) 0.152 HOMA-IR index [median (IR)] 1.6 (1.5) 2.5 (5.1) 0.023 QUICKI index [median (IR)] 0.4 (0.1) 0.3 (0.1) 0.023 Total cholesterol [mg/dL, mean (SD)] 225.6 (55.4) 219 (55.8) 0.544 LDLcholesterol [mg/dL, mean (SD)] 133.5 (47.5) 140.2 (44.6) 0.473 HDLcholesterol [mg/dL, mean (SD)] 48.3 (14.4) 45.8 (11.8) 0.374 Triglycerides [mg/dL, median (IR)] 203 (198) 139 (133.8) 0.010 Statin use [n(%)] 41 (21.7) 5 (16.1) 0.640 Fibrate use [n(%)] 61 (32.3) 4 (12.9) 0.048 DM diabetes mellitus, HbA1c glycated haemoglobin, CL clinical lipodystrophy, cART combination antiretroviral therapy, BMI body mass index, ART antiretroviral therapy, PI protease inhibitor, NNRTI non-nucleoside reverse transcriptase inhibitor, NRTI nucleoside reverse transcriptase inhibitor, HOMA homeostatic model assessment index, QUICKI quantitative insulin sensitivity check index, SD standard deviation, IR interquartile range Coelho et al. BMC Infectious Diseases (2018) 18:309 Page 5 of 13 Oral glucose tolerance test With regards to OGTT, 53 patients (24.1%) were diagnosed with prediabetes (140 mg/dL < glucose at 120 min < 200 mg/dL), and 13 patients (5.9%) with DM (glucose at 120 min ≥200 mg/dL). In Table 3, the results regarding the three categories of glucose homeostasis are presented. Patients diagnosed with DM were older than those with prediabetes, and these, in turn, were older than those without DM [55.00 (13.55) vs 50.36 (12.13) vs 43.38 (10.24) years; p< 0.001]. A progressive stage of glucose metabolism disorder appears related to the use of protease inhibitors [76.9% vs 67.9% vs 50%; p= 0.023], and the opposite is observed with the use of non-nucleoside reverse transcriptase inhibitors [23.1% vs 32.1% vs 53.2%; p= 0.005). The HOMA-IR index was highest among the prediabetes group, and lowest among the no DM group [2.37 (2.49) vs 1.94(3.17) vs 1.41 (1.54); p< 0.001]. There were no differences between the diagnostic groups regarding sex, duration of HIV infection or ART use, presence or absence of clinical lipodystrophy, body composition types, BMI, waist circumference, CD4 cell count, percentage of viral suppression, prevalence of hepatitis C coinfection, CDC clinical category, lipid profile, and frequency of use of statins or fibrates. Fasting glucose Forty four patients (20%) were diagnosed with prediabetes (100 mg/dL < fasting glucose< 126 mg/dL), and seven patients (3.2%) were diagnosed with DM (fasting glucose≥126 mg/dL). In Table 4, we present our results regarding the three categories of glucose homeostasis. We observed that there was a significant difference regarding sex between the diagnostic groups, as all the patients diagnosed with DM were men. In the prediabetes group, 47.7% of the patients were woman. The differences in age were also statistically significant, with the oldest patients being in the prediabetes group, and the youngest patients being in the no DM group [no DM 43.00 (15) vs prediabetes 51.50 (15) vs DM 45.00 (14) years; p= 0.027]. Regarding the different categories of body composition, we observe that patients without DM diagnosis had the highest proportions of patients in the categories “No lipodystrophy”(15.8%) and “Lipoatrophy” (31.5%), the prediabetes group had the highest percentage of patients in the “Isolated central fat accumulation” (53.7%) category, and the DM group had the highest percentage of patients in the category “Mixed form of lipodystrophy”(71.4%). Waist circumference was significantly lower in the no DM group [noDM 88.0 (16.5) vs prediabetes 95.0 (13.5) vs DM 95.0 (15.0); p= 0.005]. Patients with hepatitis C coinfection were more frequently classified as no DM group (33.5%), and less frequently in the prediabetes group (14.3%). The HOMA-IR index was highest among the DM group and lowest among the no DM group [no DM 1.45 (1.31) vs prediabetes 3.18 (2.57) vs DM 9.27 (6.79); p< 0.001]. There were no differences between the diagnostic groups regarding duration of HIV infection or ART use, presence or absence of clinical lipodystrophy, BMI, CD4 cell count, percentage of viral suppression, CDC clinical category, type of ART used, lipid profile, and frequency of use of statins or fibrates. Agreement analyses Kappa coefficients were computed to estimate the agreement between the three diagnostic definitions. In Table 5we present the results from the analyses between OGTT and HbA1c. The kappa coefficient value was 0.141 (p= 0.025), which corresponds to only a slight agreement. In Table 6, results from the agreement between fasting glucose and HbA1c are presented, and the kappa coefficient was 0.013 (p= 0.848). In Table 7, we present the results from the analyses between OGTT and fasting glucose. In this pair the kappa value was 0.206 (p< 0.001), which is considered to be a fair agreement. Discussion To our knowledge, this is the first study that has been carried out with HIV-infected patients, that combines the use of fasting glucose, OGTT and HbA1c, to establish the diagnosis of glucose homeostasis disturbances. Analyzing the prevalence of DM and prediabetes throughout these different methods, we observe that the results varied considerably. Regarding the diagnosis of DM, no patients were identified when HbA1c was used, 13 patients (5.9% prevalence) when OGTT was used, and seven patients (3.2% prevalence) when fasting glucose was used. Prediabetes had a prevalence of 14.1% (31 patients) when using HbA1c, 24.1% (53 patients) when using OGTT, and 20% (44 patients) when using fasting glucose. Thus, we can conclude that HbA1c was the method that least diagnosed cases, and that OGTT was the one that diagnosed the most cases. In accordance with our findings, HbA1c has been found to underestimate glycemic levels in HIV-infected patients when compared with other types of diagnostic methods, in several studies [4,17–22]. Possible explanations for the lower than expected HbA1c values in these patients have been hypothesized. For example, low hemoglobin values [19], and situations that shorten erythrocyte lifespan, such as hemolysis or some hemoglobinopathies, have been associated with lower HbA1c values [18]. Diop et al. have found that the discordance HbA1c-fasting glucose was positively correlated with the mean cell volume, and that hemolysis, diagnosed by a Coelho et al. BMC Infectious Diseases (2018) 18:309 Page 6 of 13 very low haptoglobin level, had a higher prevalence in the HIV-infected patients [18]. In fact, this relationship between mean cell volume and HbA1c-fasting glucose discordance has been observed by several authors [17,18,21–23], and Glesby et al. validated it when they observed that higher mean cell volume values emerged as the single most important factor associated with a lower HbA1c than predicted by fasting glucose. High mean cell volume, as a marker of a greater proportion of younger erythrocytes that had a shorter time to Table 3 Sample’s characteristics according to the presence of no DM, prediabetes and DM, accessed by OGTT Glucose at 120 min Pvalue <140 mg/dL 140–200 mg/dL ≥200 mg/dL n(%) 154 (70) 53 (24.1) 13 (5.9) Sex [n(%)] 0.061 Male 101 (65.6) 26 (49.1) 6 (46.2) Female 53 (34.4) 27 (50.9) 7 (53.8) Age [years, mean (SD)] 43.4 (10.2) 50.4 (12.1) 55 (13.6) < 0.001 Duration of HIV infection [years, median (IR)] 8 (6) 7 (6) 10 (8) 0.980 cART [years, [median (IR)] 6 (5.3) 6 (7) 9 (9) 0.566 Clinical lipodystrophy [n(%)] 0.148 Without CL 78 (50.6) 24 (45.3) 3 (23.1) With CL 76 (49.4) 29 (54.7) 10 (76.9) Body Composition [n(%)] 0.078 No lipodystrophy 23 (15.5) 7 (13.5) 1 (7.7) Isolated central fat accumulation 50 (33.8) 17 (32.7) 2 (15.4) Lipoatrophy 45 (30.4) 11 (21.2) 2 (15.4) Mixed form of lipodystrophy 30 (20.3) 17 (32.7) 8 (61.5) BMI [(kg/m2), [median (IR)] 24.5 (5.8) 25.4 (6.4) 25.5 (3.3) Waist circumference [cm, median (IR)] 90.5 (16) 91 (17.8) 95 (8.5) 0.687 CD4 cell count [cells/mm3, [median (IR)] 512.5 (336) 500 (311) 456 (509) 0.569 HIV RNA (<50) [n(%)] 138 (100) 44 (100) 10 (100) Hepatitis C co-infection [n(%)] 47 (30.9) 15 (28.8) 2 (16.7) 0.655 CDC clinical categories [n(%)] 0.252 A 90 (58.4) 24 (45.3) 5 (38.5) B 4 (2.6) 1 (1.9) 0 (0) C 60 (39) 28 (52.8) 8 (61.5) ART [n(%)] IP 77 (50) 36 (67.9) 10 (76.9) 0.023 NNRTI 82 (53.2) 17 (32.1) 3 (23.1) 0.005 NRTI 148 (96.1) 51 (96.2) 11 (84.6) 0.188 HOMA-IR index [median (IR)] 1.4 (1.5) 2.4 (2.5) 1.9 (3.2) < 0.001 QUICKI index [median (IR)] 0.4 (0.1) 0.3 (0.1) 0.4 (0.1) < 0.001 Total cholesterol [mg/dL, median (IR)] 224 (67) 224 (79) 234 (86) 0.743 LDLcholesterol [mg/dL, median (IR)] 130 (70) 135 (66) 156 (79) 0.843 HDLcholesterol [mg/dL, median (IR)] 49 (19) 44 (20) 52 (15) 0.314 Triglycerides [mg/dL, median (IR)] 186 (176.5) 223 (258.5) 170 (101) 0.131 Statin use [n(%)] 31 (20.1) 9 (17) 6 (46.2) 0.072 Fibrate use [n(%)] 43 (27.9) 21 (39.6) 1 (7.7) 0.061 DM diabetes mellitus, OGTT oral glucose tolerance test, CL clinical lipodystrophy, cART combination antiretroviral therapy, BMI body mass index, ART antiretroviral therapy, PI protease inhibitor, NNRTI non-nucleoside reverse transcriptase inhibitor, NRTI nucleoside reverse transcriptase inhibitor, HOMA homeostatic model assessment index, QUICKI quantitative insulin sensitivity check index, SD standard deviation, IR interquartile range Coelho et al. BMC Infectious Diseases (2018) 18:309 Page 7 of 13 become glycated, suggest a greater red blood cell turnover in the HIV-infected patients [17]. The eventual relationship with drugs used in the treatment of HIV infection with these hematologic findings is difficult to study, as ART is generally used in combination. OGTT was the method that diagnosed the highest percentage of patients with DM and prediabetes. Similar results have been found in the literature [24–26]. Gianotti et al. demonstrated in their study that OGTT revealed that 11% of their cohort with long-standing Table 4 Sample’s characteristics according to the presence of no DM, prediabetes and DM, accessed by fasting glucose Fasting glucose Pvalue <100 mg/dL 100–126 mg/dL ≥126 mg/dL n(%) 169 (76.8) 44 (20) 7 (3.2) Sex [n(%)] 0.049 Male 103 (60.9) 23 (52.3) 7 (100) Female 66 (39.1) 21 (47.7) 0 (0) Age [years, [median (IR)] 43 (15) 51.5 (15) 45 (14) 0.027 Duration of HIV infection [years, [median (IR)] 8 (6) 8 (5.8) 8 (6) 0.782 cART [years, [median (IR)] 6 (7) 6 (5.8) 8 (6) 0.408 Clinical lipodystrophy [n(%)] 0.082 Without CL 76 (45) 27 (61.4) 2 (28.6) With CL 93 (55) 17 (38.6) 5 (71.4) Body Composition [n(%)] 0.004 No lipodystrophy 26 (15.8) 4 (9.8) 1 (14.3) Isolated central fat accumulation 46 (27.9) 22 (53.7) 1 (14.3) Lipoatrophy 52 (31.5) 6 (14.6) 0 (0) Mixed form of lipodystrophy 41 (24.8) 9 (22) 5 (71.4) BMI [(kg/m2), [median (IR)] 24.4 (5.8) 26.1 (5.5) 26 (3.4) 0.052 Waist circumference [cm, [median (IR)] 88 (16.5) 95 (13.5) 95 (15) 0.005 CD4 cell count [cells/mm3, [median (IR)] 486 (344) 525 (298) 605 (382) 0.643 HIV RNA (<50) [n(%)] 151 (100) 35 (100) 6 (100) Hepatitis C co-infection [n(%)] 56 (33.5) 6 (14.3) 2 (28.6) 0.034 CDC clinical categories [n(%)] 0.398 A 89 (52.7) 27 (61.4) 3 (42.9) B 3 (1.8) 2 (4.5) 0 (0) C 77 (45.6) 15 (34.1) 4 (57.1) ART [n(%)] IP 96 (56.8) 23 (52.3) 4 (57.1) 0.885 NNRTI 79 (46.7) 20 (45.5) 3 (42.9) 0.999 NRTI 163 (96.4) 41 (93.2) 6 (85.7) 0.157 HOMA-IR index [median (IR)] 1.5 (1.3) 3.2 (2.6) 9.3 (6,8) < 0.001 QUICKI index [median (IR)] 0.4 (0.1) 0.3 (0.04) 0.3 (0.04) < 0.001 Total cholesterol [mg/dL, median (IR)] 220 (66) 240.5 (86) 234 (79) 0.061 LDLcholesterol [mg/dL, median (IR)] 127 (67) 149 (62) 146 (74) 0.136 HDLcholesterol [mg/dL, median (IR)] 47 (18) 49 (22) 41 (22) 0.651 Triglycerides [mg/dL, median (IR)] 189.5 (197.8) 214.5 (207.8) 184 (88) 0.975 Statin use [n(%)] 35 (20.7) 8 (18.2) 3 (42.9) 0.312 Fibrate use [n(%)] 50 (29.6) 15 (34.1) 0 (0) 0.200 DM diabetes mellitus, CL clinical lipodystrophy, cART combination antiretroviral therapy, BMI body mass index, ART antiretroviral therapy, PI protease inhibitor, NNRTI non-nucleoside reverse transcriptase inhibitor, NRTI nucleoside reverse transcriptase inhibitor, HOMA homeostatic model assessment index, QUICKI quantitative insulin sensitivity check index, SD standard deviation, IR interquartile range Coelho et al. BMC Infectious Diseases (2018) 18:309 Page 8 of 13 HIV infection had prediabetes or DM, undiagnosed on the basis of fasting glucose levels alone [24], while Seang et al. detected a 31% relative increase in the prevalence of DM diagnosis among HIV-infected women [25]. Epidemiological evidence has also supported this observation in the general population, especially among older patients [27]. In light of this knowledge, the 2017 European AIDS Clinical Society Guidelines recommend that HIV-infected patients with a fasting glucose diagnosis of prediabetes should carry out an OGTT in order to identify overt diabetes [10]. With regards to the characteristics that differed significantly between the diagnostic groups, we observed that there were some that were important across all three methods, while others varied specifically, depending on the method used for the assessment. Sex was a factor that showed association with the diagnosis only when fasting glucose was used. With this method, the total number of patients that had the diagnosis of DM were male, and there was also a higher prevalence of male (52.3% versus 47.7% female) in the prediabetes group. Some authors have reported a lack of significant differences between sexes when considering the risk factors for glucose homeostasis disturbances in HIV patients [24], while others, in agreement with our findings, stated that male sex was associated with increased risk of new-onset DM [5,28]. In all three methods, a progressive stage of glucose homeostasis disturbance was associated with older age. This finding is congruent with the great majority studies done on the subject [1–5,7,28,29]. This has been highlighted in recent literature that, after the introduction of ART (which has dramatically reduced HIV-related mortality and morbidity, substantially increasing longevity), HIV-infected individuals have a potential of developing metabolic complications, which is comparable to that of the general population [1]. In these patients, the importance of traditional cardio-metabolic risk factors should be emphasized, as these are likely to exert an equal influence on HIV-infected patients as they do in the general population [1]. On the other hand, there have been studies that suggest that the aging process might be premature or accelerated in these patients, leading to the manifestation of metabolic complications earlier in life [2]. This highlights the importance of closely monitoring for the development of cardio-metabolic abnormalities in these patients. Regarding the duration of HIV infection and ART use, we observed that, regardless of the diagnostic method used, no significant difference between the groups was observed. This contrasts with findings in the literature, that suggest that a higher prevalence of DM is associated with a higher duration of HIV infection and ART use [1,3,30], but, is in agreement with a study done by Araújo et al., in which also, no association between the duration of infection and the development of glucose homeostasis disturbances was found [31]. BMI was considered to be significantly higher in prediabetes patients, compared to the no DM patients, but only when HbA1c was used for the assessment. This finding is corroborated by several authors [3–5,7,28–30] who described a significant association between a higher BMI and the presence of disturbances of glucose homeostasis in HIV-infected patients. Additional to the BMI, abdominal fat accumulation or trunk obesity has been identified as a factor primarily associated with the prevalence of disturbances of glucose homeostasis in HIV-infected patients [8,25,26,30–33], just as described in the general population. In our study, when fasting glucose was used for the assessment of diabetes, there was a significant association between waist circumference and the diagnosis. This parameter was higher in DM and prediabetes patients, when compared with the no DM ones. Furthermore, in this diagnostic method, we obtained significant differences regarding the classes of body composition, with most significant differences observed in the isolated central fat accumulation (highest percentage of patients in the prediabetes group) and mixed forms of lipodystrophy (highest percentage of patients in the DM group), which mirror the effect of these higher waist circumference levels in these diagnostic groups. In fact, it has been reported that abdominal fat accumulation is a major contributor to glucose metabolism disturbances when compared to lipoatrophy associated with the acquired lipodystrophy of HIV infection [8]. Endocrine activity of adipose tissue takes a central place in the pathogenesis of metabolic disorders Table 5 Analysis of the agreement between HbA1c and Glucose at 120 min during an OGTT Glucose at 120 min Total <140 mg/dL 140–200 mg/dL HbA1c <5.7% 140 42 182 5.7–6.4% 14 11 25 Total 154 53 207 Kappa Coefficient = 0.141 (p= 0.025) HbA1c glycated haemoglobin, OGTT oral glucose tolerance test Table 6 Analysis of the agreement between HbA1c and fasting glucose Fasting glucose Total <100 mg/dL 100–126 mg/dL HbA1c <5.7% 148 39 187 5.7–6.4% 21 5 26 Total 169 44 213 Kappa Coefficient = 0.013 (p= 0.848) HbA1c glycated haemoglobin Coelho et al. BMC Infectious Diseases (2018) 18:309 Page 9 of 13