Prognostic value of osteoprotegerin for estimation of cardiovascular risk in patients with long-standing type 1 diabetes mellitus

Б  том ХXXI, 2025, № 3 ДРУЖЕСТВО НА КАРДИОЛОЗИТЕ В БЪЛГАРИЯ АВТОРСКИ СТАТИИ ORIGINAL ARTICLES PROGNOSTIC VALUE OF OSTEOPROTEGERIN FOR ESTIMATION PROGNOSTIC VALUE OF OSTEOPROTEGERIN FOR ESTIMATION OF CARDIOVASCULAR RISK IN PATIENTS WITH LONG-STANDING TYPE 1 OF CARDIOVASCULAR RISK IN PATIENTS WITH LONG-STANDING TYPE 1 DIABETES MELLITUS DIABETES MELLITUS G. Chausheva1, S. Shefket1, K. Tsochev2, T. Chalukova3, Y. Bocheva1, Y. Yotov3, V. Iotova2 1Department of Clinical Laboratory 2Department of Pediatrics 3Ist Department of Internal Diseases, Medical Faculty, Medical University – Varna ПРОГНОСТИЧНА СТОЙНОСТ НА ОСТЕОПРОТЕГЕРИН ЗА ОЦЕНКА ПРОГНОСТИЧНА СТОЙНОСТ НА ОСТЕОПРОТЕГЕРИН ЗА ОЦЕНКА НА СЪРДЕЧНО-СЪДОВ РИСК ПРИ ПАЦИЕНТИ С ДЪЛГОГОДИШЕН НА СЪРДЕЧНО-СЪДОВ РИСК ПРИ ПАЦИЕНТИ С ДЪЛГОГОДИШЕН ЗАХАРЕН ДИАБЕТ ТИП 1 ЗАХАРЕН ДИАБЕТ ТИП 1 Г. Чаушева1, С. Шефкет1, К. Цочев2, Т. Чалъкова3, Я. Бочева1, Й. Йотов3, В. Йотова2 1Катедра по клинична лаборатория 2Катедра по педиатрия 3Първа катедра по вътрешни болести, Медицински факултет, Медицински университет – Варна Abstract. Background: Cardiovascular disease (CVD) is a major complication in patients with long-standing type 1 diabetes (T1D). Osteoprotegerin (OPG) has been proposed as a biomarker for cardiovascular risk (CVR), though its utility in this context requires further evaluation. This study aimed to assess the prognostic signifi cance of OPG in CVR estimation among T1D patients using specifi c CVR assessment tools. Patients and Methods: This prospective case–control study includes 183 participants: 124 with T1D (53.2% men, aged 42.7 ± 10.4 years, diabetes duration 25.3 ± 8.2 years) and 59 healthy controls (54.1% men, aged 45.1 ± 9.1 years). Serum OPG levels were determined via ELISA; CRP – immune-turbidimetric method (Advia chemistry 1800); HbA1C (%) – immuno-inhibition assay (ADVIA chemistry 1800); AlbU (mg/l) – immuno-turbidimetric analysis (Olympus AU600). CVR evaluation tools: STENO Type 1 Risk Engine (ST1RE) and ESC – 2019 guidelines. A RiskFactor3 model combined CRP ≥ 3 mg/l, HbA1C ≥ 7%, and AlbU ≥ 30 mg/l. Results: No signifi cant intergroup differences in OPG levels were observed. However, OPG was observably higher in women across both groups (T1D: 5.34 ± 1.2 pmol/l vs. 5.73 ± 1.83 pmol/l; controls: 5.06 ± 1.65 pmol/l vs. 6.16 ± 2.38 pmol/l, p < 0.05). In T1D patients, age (R² = 3%), disease duration (R² = 4.8%), and AlbU (R² = 3.9%) were signifi cant positive determinants of OPG (p < 0.05). According to ESC-2019, 30.6% of T1D patients had high CVR and 69.4% had very high CVR. ST1RE classifi ed 38.7% as low, 28.2% as moderate, and 33.1% as high CVR. ST1RE’s AUC-ROC for males was 0.716 (p = 0.005) with a cut-off of 5.075 pmol/l, and for females, 0.683 (p = 0.039) with a cut-off of 5.355 pmol/l. For ESC-2019, the AUC-ROC for females was 0.644 (p = 0.102) with a cut-off of 5.025 pmol/l. Conclusion: OPG is a promising biomarker for assessing CVR in patients with long-standing T1D. Higher OPG levels in women and its associations with age, disease duration, and AlbU emphasize its potential prognostic role. Using ST1RE and ESC-2019, OPG effectively stratifi ed patients by CVR. Incorporating OPG into clinical practice could improve early detection and personalized management of CVR in T1D patients. Key words: osteoprotegerin, long-standing type 1 diabetes, cardiovascular disease, cardiovascular risk, STENO Type 1 Risk Engine, ESC – 2019 guideline Address for correspondence: Gergana M. Chausheva, MD, PhD, Chief Assist. Prof., Department of Clinical Laboratory, Medical University “Prof. Dr. Paraskev Stoyanov” – Varna, 55, Marin Drinov Street, 9002 Varna, e-mail: [email protected]; Gergana. [email protected], Tel.: +359 88 353 5722 Резюме.Въведение: Сърдечно-съдовите заболявания (ССЗ) са основно усложнение при пациенти с дългогодишен тип 1 захарен диабет (Т1ЗД), като остеопротегринът (ОПГ) се разглежда като потенциален биомаркер за сърдечно-съдов риск (ССР). Настоящото проучване цели да оцени прогностичната стойност на ОПГ при пациенти с Т1ЗД, This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. doi: 10.3897/bgcardio.31.e169297 69 Prognostic value of osteoprotegerin for estimation of cardiovascular risk... I Diabetes mellitus (DM) is associated with an increased risk of CVD. Most recommendations for preventing CVD in T1D are derived from studies on type 2 diabetes (T2D) [1]. CVR calculation models for the general population and for patients with T2D do not account for risk factors (RFs) like hyperglycaemia, or the presence of microvascular complications, thus underestimating CVR in patients with T1D [1, 2, 3, 4]. In T1D, the reported relative risk of CVD, adjusted for age, is 4 to 10 times higher compared to the general population [5]. Management approaches to minimize the frequency of CVD in T1D are mainly extrapolated from T2D experience, and the pathophysiological mechanisms are not well understood. Increasing scientifi c developments are confi rming the potential of new classes of laboratory biomarkers that characterize cardiovascular changes in patients with T1D. Osteoprotegerin (OPG), also known as TNFRS11B, is a key member of the tumor necrosis factor (TNF) receptor family, initially discovered for its critical role in bone metabolism and later identifi ed in the vascular system [6]. This secretory glycoprotein, comprising 401 amino acids and capable of forming homodimeric structures, functions predominantly to inhibit osteoclastogenesis and has signifi cant implications in cell diff erentiation, survival, and apoptosis, refl ecting its broad biological importance [6]. OPG serves as a soluble decoy receptor for both the receptor activator of nuclear factor kappa-B ligand (RANKL) and the apoptosis-inducing ligands, tumor necrosis factor (TNF) – related apoptosis – inducing ligand (TRAIL) [6, 7]. Regardless of its primary clinical signifi cance in relation to its involvement in bone metabolism, the focus of the current study was on the atherogenic eff ects of the protein. OPG emerges as a reliable laboratory marker with predictive value for CVD. The relationship between circulating OPG and cardiovascular RFs such as age, smoking, hypertension, insulin resistance, obesity, diabetes, renal impairment, and infl ammatory conditions has been extensively described [6]. OPG serves as an indicator of cumulative exposure and the severity of known and emerging vascular RFs, refl ecting the overall activity of the OPG/RANK/RANKL system [6, 7]. It’s hypothesized that prolonged OPG overexpression triggers profi brotic, pro-infl ammatory, pro-apoptotic, and plaque destabilizing properties [7, 8, 9]. In healthy individuals, the pro-atherogenic and anti-atherogenic eff ects of OPG are balanced, however, under constant induction by various RFs like chronic hyperglycaemia in diabetes, the pro-atherogenic pathway may dominate [10]. DM is associated with an increased risk of CVD as well as an increased risk of bone loss and osteoporosis progression [11, 12, 13]. The relationships between bone metabolism and T1D have been extensively studied in recent years. Pathologically elevated plasma OPG levels predict overall and cardiovascular mortality in diabetic complication patients [6, 11]. Moreover, elevated OPG levels have been observed in използвайки STENO Type 1 Risk Engine (ST1RE) и ESC – 2019. Материал и методи: Настоящото проспективно проучване тип случай-контрола включва 183 участници: 124 с Т1ЗД (53.2% мъже на възраст 42.7 ± 10.4 години, продължителност на диабета 25.3 ± 8.2 години) и 59 здрави контроли (54.1% мъже, на възраст 45.1 ± 9.1 години). Серумните нива на ОПГ са определени с ELISA; CRP – имуно-турбидиметричен метод (Advia chemistry 1800); HbA1C (%) – имуно-инхибиционен анализ (ADVIA chemistry 1800); албумин в урина (AlbU) (mg/l) – имуно-турбидиметричен анализ (Olympus AU600). Инструменти за оценка на ССР: STENO Type 1 Risk Engine (ST1RE) и ESC – 2019. Моделът RiskFactor3 е конструиран като комбинация от: CRP ≥ 3 mg/l, HbA1C ≥ 7% и AlbU ≥ 30 mg/l. Резултати: Не открихме значима междугрупова разлика в ОПГ, но и в двете групи OПГ е отчетен по-висок при жените: T1ЗД 5.34 ± 1.2 pmol/l срещу 5.73 ± 1.83 pmol/l и контролна група 5.06 ± 1.65 pmol/l срещу 6.16 ± 2.38 pmol/l, p < 0.05. Възрастта, продължителността на заболяването и AlbU са значими положителни детерминанти за OПГ при пациентите с T1ЗД: R² = 3% – възраст, R² = 4.8% – продължителност на T1ЗД, R² = 3.9% – AlbU, p < 0.05. Според ESC – 2019, 30.6% от пациентите с T1ЗД са с висок ССР, 69.4% – с много висок CСР; според ST1RE 38.7% от пациентите с T1ЗД са с нисък, 28.2% – с умерен, и 33.1% – с висок CСР. Според ST1RE AUC-ROC за мъжете е 0.716 (p = 0.005), cut-off = 5.075 pmol/l (ДЧ 70.8%, ДС 55.9%); за жените – 0.683 (p = 0.039), cut-off = 5.355 pmol/l (ДЧ 66.7%, ДС 60%). Относно ESC-2019, AUC-ROC за жените е 0.644 (p = 0.102), cut-off = 5.025 pmol/l (ДЧ 70%, ДС 60%). Заключение: ОПГ е обещаващ биомаркер за оценка на ССР при Т1ЗД. Връзките на ОПГ с възрастта, продължителността на заболяването и AlbU подчертават неговото прогностично значение. Интегрирането на ОПГ в клиничната практика би могло да подобри ранната диагностика и персонализираното управление на ССР. Ключови думи:остеопротегерин, дългогодишен захарен диабет тип 1, сърдечно-съдово заболяване, сърдечно-съдов риск, STENO Type 1 Risk Engine, ESC–2019 Адрес за кореспонденция: Д-р Гергана М. Чаушева, гл. асистент, Катедра „Клинична лаборатория“, Медицински университет „Проф. д-р Параскев Стоянов“ – Варна, ул. „Марин Дринов“ № 55, 9002 Варна, e-mail: [email protected]; Gergana. [email protected], тел.: +359 88 353 5722 G. Chausheva, S. Shefket, K. Tsochev et al. 70 patients with T1D, with a positive correlation between duration of T1D and biomarker levels, though mechanisms remain unclear [14, 15, 16]. This study aimed to assess the prognostic signifi - cance of OPG in CVR estimation among T1D patients using specifi c CVR assessment tools. M   Study population and design The observation was a prospective case-control comparison type. A total of 183 participants (59 healthy volunteers and 124 patients with T1D) were included in this study, without any targeted selection. The project was carried out at the University Hospital “Saint Marina” – Varna, between 2018 and 2020. The research protocol was approved by the Research Ethics Committee at Medical University – Varna, under Decision No. 72, dated March 1, 2018. Every patient signed informed written consent for the acceptance of participation in the study after explaining the procedures. Inclusion criteria were as follows: individuals with over 15 years of T1D duration and healthy individuals matching in gender, age, and BMI. Exclusion criteria were as follows: involvement in other clinical studies; signifi cant mental or physical disabilities; over 3% body weight change in the last 3 months; recent vascular incidents; acute conditions during the study (except specifi c diabetic emergencies for T1D participants); pregnancy; and, for T1D subjects, recent severe hypoglycaemia or ketoacidosis and severe microvascular complications. Laboratory analyses Blood and urine samples were collected following standardized procedures. Blood for HbA1C analysis was drawn into K2EDTA tubes. For urine, fi rst morning samples of 20 ml were collected, then centrifuged at 2500 G for 15 minutes for AlbU assessment. Serum for CRP and OPG tests was separated using a gel separator vacutainer and similarly centrifuged. OPG levels were determined by an ELISA kit (Human OPG ELISA, BioVendor, Czech Republic), CRP levels – by immuno-turbidimetric analysis on ADVIA Chemistry 1800 system, and HbA1C – by an immuno-inhibition method, standardized to DCCT/NGSP, presenting results as a percentage of total hemoglobin (measured colorimetrically on the same system). AlbU was analyzed by immuno-turbidimetry on Olympus AU600. Detection limits (LOD) and reference ranges (RR) were as follows: LODCRP = 0.04 mg/l (RR: 0-5 mg/l); LODHbA1C = 2% (RR: up to 6%), LODAlbU = 0.25 mg/l (RR: up to 20 mg/24h), and LODOPG = 0.03 pmol/l (RR: 4,1 ± 2,3 pmol/l). The CVR evaluation tools utilized in this study included ST1RE and ESC Guidelines (2019). ST1RE calculates 10-year non-fatal/fatal CVR, considering factors like gender, age, diabetes duration, CVD history, systolic blood pressure, AlbU, HbA1C, eGFR, LDL-cholesterol, smoking, and activity level. Categories: low (< 10%), moderate (10-20%), high (≥ 20%) CVR. Link: ST1RE Calculator. ESC Guidelines (2019) assess CVR based on age, gender, smoking, systolic blood pressure, and total cholesterol levels [17]. Categories: moderate – young T1D patients (< 35 years, < 10 years diabetes duration); high risk – diabetes duration > 10 years or other RFs; very high risk – diabetes with target organ damage, 3 major RFs, or early T1D onset (> 20 years). A RiskFactor3 model was developed for individuals with diabetes, categorizing them based on common CVR factors: CRP levels (below and above 3 mg/l), glycaemic control (good: HbA1C below 7% and poor: HbA1C above 7%), and AlbU levels (normoalbuminuria – AlbU below 30 mg/l, microalbuminuria – AlbU between 30 and 300 mg/l, and macroalbuminuria – AlbU above 300 mg/l). Patients were grouped into: Group 0 (no RFs), Group 1 (1 RF), Group 2 (2 RFs), and Group 3 (all 3 RFs). Statistical analysis Statistical analyses were conducted using SPSS version 19 software package (modifi ed 21 May 2021; IBM Corp., Armonk, New York, United States) with Windows 7.0. software (Microsoft Corporation, Redmond, Washington, United States). Numerical data were presented as mean value ± standard deviation (SD). Descriptive statistics determined data central tendency and dispersion. Pearson’s correlation coeffi cient (r) assessed linear relationships between variables. Single and multiple linear regression examined independent and dependent variables. Factor analysis, independent-samples T-tests, and one-way analysis of variance (ANOVA) identifi ed signifi cant mean diff erences. Nonparametric methods like Chi-square tested nominal data. Receiver – Operating Characteristic (ROC) analysis and the area under the curve (AUC) calculation evaluated laboratory parameter sensitivity (Se) and specifi city (Sp), establishing optimal cut-off values. A signifi cance level (α) of 0.05 was maintained, p-values < 0.05 rejected the null hypothesis. R Clinical characteristics The study comprised 59 healthy subjects and 124 patients with long-standing T1D. Gender distribution was similar between the groups, χ² = 0.118, p = 0.731. Average age was 45.14 ± 9.17 years in the control group and 42.68 ± 10.40 years in the T1D group, with a non-signifi cant diff erence (t = 1.550, p = 0.123). Mean duration of T1D was 25.31 ± 8.22 years, with a median of 24 years (Table 1). 71 Prognostic value of osteoprotegerin for estimation of cardiovascular risk... Diabetic patients were categorized according to ESC-2019 guidelines: 30.6% had high CVR, and 69.4% had very high CVR, with no signifi cant gender diff erence (χ² = 0.480, p = 0.489). ST1RE classifi cation revealed 38.7% with low CVR, 28.2% with moderate, and 33.1% with high CVR. A gender diff erence was signifi cant: 39.6% of men and 60.4% of women had low CVR; 60% of men and 40% of women had high CVR; 63.4% of men and 36.6% of women had very high CVR (χ² = 5.943, p = 0.05). OPG – infl uence of gender, age and duration of diabetes The women in both study groups showed higher mean OPG levels. In patients with T1D, the mean difference (MD) was 0.384 pmol/l with borderline statistical signifi cance (men: 5.342 ± 1.996 pmol/l vs women: 5.726 ± 1.832 pmol/l), p = 0.055. In controls, the MD was signifi cant: MD = 1.108 pmol/l, p = 0.049 (men: 5.056 ± 1.645 pmol/l vs women: 6.163 ± 2.378 pmol/l). When comparing the average levels of OPG against the median duration of T1D, higher values were observed for individuals with a longer duration (5.723 ± 1.506 pmol/l) compared to those with a duration below 24 years (5.317 ± 1.574 pmol/l). The MD was 0.406 pmol/l, t = -1,400, p = 0.164. In the control group, a weak negative correlation was observed between OPG and age (r = -0.284, p = 0.038), notably signifi cant only among men (r = -.565, p = 0.001). Conversely, in the T1D group, a less pronounced positive correlation was found between OPG and age with borderline statistical signifi cance (r = 0.173, p = 0.067). The regression curves illustrating the dependencies between OPG and age, as well as OPG and the duration of diabetes in individuals with T1D, are shown (Fig. 1). The reported coeffi cients of determination are as follows: R² = 3%, p = 0.06 concerning age, and R² = 4.8%, p = 0.019 concerning the duration of diabetes. OPG – associations with AlbU, CRP, HbA1C, and RiskFactor3 In individuals with long-standing T1D, average OPG levels were compared based on HbA1c levels below and above 7% (5.446 ± 1.4691 pmol/l and 5.541 ± 1.563 pmol/l), CRP levels below and above 3 mg/l (5.383 ± 1.557 pmol/l and 5.885 ± 1.499 pmol/l), and AlbU levels categorized as below 30 mg/l, between 30 and 300 mg/l, and above 300 mg/l (5.407 ± 1.423 pmol/l; 5.491 ± 1.848 pmol/l; and 6.937 ± 1.609 pmol/l). A signifi cant diff erence was found in mean OPG levels between the groups matched vs AlbU (F = 3.263, p = 0.042). In the control group, the diff erences observed Table 1. Baseline characteristics Parameter T1D subjects (N = 124) Controls (N = 59) P-value mean SD mean SD Age (years) 42.68 10.4 45.14 9.17 0.123 Male 66 (53.2%) / 33 (55.9%) / / Duration of T1D (years) 25.31 8.22 / / / BMI (kg/m2) 25.71 4,11 24.88 3.81 0.313 CRP (mg/l) 3.56 9.3 2.57 3.76 0.433 HBA1C (%) 8.47 1.62 5.40 0.39 < 0.001 AlbU (mg/l) 63.72 147.38 12.22 19.88 0.009 OPG (pmol/l) 5.528 1.545 5.568 2.073 0.891 N – number; SD – standard deviation Fig. 1. Dependencies between OPG and: age; duration of diabetes in individuals with T1D G. Chausheva, S. Shefket, K. Tsochev et al. 72 in HbA1C, CRP, and AlbU were deemed insignifi cant. However, within the T1D group, a signifi cant, but weak correlation was identifi ed between OPG and AlbU (r = 0.218, p = 0.021). Conversely, in the control subjects, no signifi cant correlations were observed. To establish the linear relationship between AlbU and OPG values in individuals with T1D, a single linear regression analysis was conducted (F = 5.521, p = 0.021) with an adjusted R² = 3.9%, represented by the equation: OPG (pmol/l) = 5.381 + 0.218 × AlbU (mg/l). ANOVA analysis conducted on RiskFactor3 revealed a trend of elevation among males, albeit at a 90% CI. The mean OPG levels for males in each group were as follows: 4.93 ± 0.391 pmol/l for group 0, 5.092 ± 1.05 pmol/l for group 1, 5.565 ± 1.392 pmol/l for group 2, and 6.823 ± 0.567 pmol/l for group 3 (F = 2.550, p = 0.065). In females, the mean OPG levels were 5.004 ± 0.600 pmol/l for group 0, 5.828 ± 1.855 pmol/l for group 1, 5.445 ± 2.024 pmol/l for group 2, and 6.732 ± 1.663 pmol/l for group 3 (F = 1.031, p = 0.387). Additionally, OPG exhibited a positive but weak correlation with RiskFactor3 (r = 0.194, p = 0.039). OPG – associations with ST1RE and ESC – 2019 Guidelines Regarding ST1RE, OPG showed a positive but weak correlation with borderline statistical signifi cance (r = 0.183, p = 0.053), and no signifi cant relationship between OPG and ESC – 2019. Furthermore, there were no signifi cant intergroup diff erences in mean OPG levels relative to both ST1RE (F = 2.322, p = 0.103) and ESC-2019 (t = -1.003, p = 0.318) as shown in Fig. 2. The median and interquartile range of OPG in men with low CVR according to ST1RE were 4.95 (4.31-5.86) pmol/l; in men with moderate CVR were 4.92 (4.04-5.37) pmol/l; in men at high risk were 5.775 (4.855-6.87) pmol/l; in women with low CVR were 5.73 (4.24-5.74) pmol/l; in moderate CVR were 5.60 (4.916.325) pmol/l; in high CVR were 6.25 (5.05-6.78) pmol/l (Fig. 2). The medians and interquartile ranges of OPG in men according to ESC – 2019 were: 5.12 (4.2656.215) pmol/l for high risk and 5.20 (4.56-6.218) pmol/l for very high CVR. In women, they were the following: 4.82 (4.24-5.60) pmol/l for high risk and 5.425 (4.7086.605) pmol/l for a very high CVR as shown in Fig. 3. Fig. 2. Mean OPG levels in males and females according to ST1RE Fig. 3. Mean OPG levels in males and females according to ESC – 2019 guidelines 73 Prognostic value of osteoprotegerin for estimation of cardiovascular risk... ROC analyses for determining cut-off OPG values Regarding ST1RE, the AUC-ROC was 0.687 (p = 0.001), while in the ESC-2019 categories, it was 0.589 (p = 0.138). Notably, OPG did not demonstrate significant prognostic value relative to RiskFactor3, with an AUC of 0.520 (p = 0.734). For the purpose of distinguishing patients with high CVR according to ST1RE, a cut-off value of 5.315 pmol/l was derived for OPG, yielding a Se of 64.1% and Sp of 63.5%. The calculated likelihood ratios were LR+ = 1.75 and LR- = 0.57, with a diagnostic odds ratio (DOR) of 3.07. Additionally, the Youden index was -0.994, and the diagnostic eff ectiveness (DE) was 63.7% (Table 2, Fig. 4). Furthermore, at ST1RE for males, the AUC-ROC was 0.716 (p = 0.005), and for females – 0.683 (p = 0.039). Regarding the ESC – 2019 guidelines, the AUC-ROC for males was 0.537 (p = 0.650), and for females - 0.644 (p = 0.102). For women with a very high CVR according to ESC – 2019, a cut-off value of OPG was derived as 5.025 pmol/l, with a Se of 70% and Sp of 60%, LR+ = 1.75 and LR- = 0.571, and DOR = 3.064. The Youden index was -0.9, and DE was 67.2%. Threshold OPG values for diff erentiation of persons with high CVR according to ST1RE were derived: 5.075 pmol/l with Se 70.8% and Sp 55.9% for men and 5.355 pmol/l for women with Se 66.7% and Sp 60%. The following were determined: LR+ = 1.605, LR - = 0.623, DOR = 2.576, Youden index = -0.851 and DE = 62% for men and LR+ = 1.668, LR - = 0.599, DOR = 2,784, Youden index = -0.933 and DE = 61.8% for women (Table 3). D The study found a notable diff erence in OPG concentration between genders, with women showing higher levels than men. This diff erence is particularly substantial before menopause. Serum OPG levels tend to converge after the age of 50, attributed to estrogen defi ciency in women and testosterone’s inhibitory eff ect in men [6, 18, 19]. In our study, where the Table 2. Diagnostic accuracy for serum OPG levels, relative to ST1RE Parameter Criterion cut-off N TPTNFPFN Se Sp DE OPG (pmol/l) ST1RE – high CVR 5.315 113 25 47 27 14 64.1% 63.5% 63.7% TP – true positive, TN – true negative, FP – false positive, FN – false negative Fig. 4. AUC-ROC to estimate a prognostic value of OPG with T1D relative to ST1RE Table 3. Diagnostic accuracy of OPG relative to ST1RE and ESC – 2019 guidelines for men and women with T1D Parameter Criterion Cut-off value N TP TN FP FN Se Sp DE OPG (pmol/l) Men ST1RE – high CVR 5.075 58 17 19 15 7 70.8% 55.9% 62% OPG (pmol/l) Women ST1RE – high CVR 5.355 55 10 24 16 5 66.7% 60% 61.8% OPG (pmol/l) Women ESC–2019 – very high CVR 5.025 55 28 9 6 12 70% 60% 67.2% TP – true positive, TN – true negative, FP – false positive, FN – false negative G. Chausheva, S. Shefket, K. Tsochev et al. 74 average age was below 50, this gender diff erence was confi rmed. Additionally, our fi ndings support existing literature indicating that age and desease duration are signifi cant positive determinants for serum OPG levels in patients with T1D [12, 16, 20]. Several studies have noted an increase in OPG concentrations with age [19]. The exact mechanisms underlying the relationship between OPG and age remain incompletely understood, although hypotheses include changes in bone metabolism, alterations related to glucose homeostasis, and vascular physiological changes. According to a meta-analysis (2020), the results on the relationship between the duration of the disease and serum OPG levels are contradictory: in some cases, it is positive, while others do not report any association [11]. For instance, Boyadzhieva M et al. (2013) found no analogous relationship between serum OPG levels and the duration of T2D [21]. Given these discrepancies, further research in this direction is warranted to elucidate the complex interplay between OPG levels, age, and disease duration in diff erent types of diabetes. In our study, we discovered a signifi cant positive correlation between serum OPG levels and the proposed RiskFactor3 model. After conducting ANOVA analysis, we observed a noticeable diff erence, although it reached only at 90% CI, indicating a proportional increase in OPG values across diff erent risk groups. Upon further examination by gender, we found a difference with borderline statistical signifi cance (at 90% CI) only between group 1 and group 3 males. During the partial analysis of interactions between serum OPG levels and each variable included in RiskFactor3, a signifi cant association with AlbU was identifi ed. However, contrary to previous studies, we did not observe significant positive correlations between serum OPG levels and HbA1C% or CRP [11, 22, 23]. The current study’s fi ndings validate the well-established link between OPG and AlbU in diabetes, as documented in existing literature. In individuals with long-standing T1D, we observed a signifi cant elevation in serum OPG levels, regarding the AlbU value. Elbana M et al (2022) and Perez de Cirza et al. (2015), have postulated a positive association between OPG and DN in T1D [23, 24]. Our study, refl ecting prevalent poor glycaemic control in the majority of our cohort, underscores DN’s pivotal role as a precursor to CVD development. Longitudinal studies by Gordin et al. (2013) involving nearly 2,000 adults with T1D over a decade identifi ed OPG as an independent predictor of cardiovascular events [25]. Additionally, Jorsal et al. (2008) demonstrated serum OPG levels as an independent predictor of total and cardiovascular mortality in individuals with nephropathy [26]. This supports the hypothesis of OPG accumulation within arterial walls, contributing to generalized vascular changes and calcifi cation in long-standing T1D. Grauslund et al. (2010) reported signifi cant OPG level variations between patients with macro-, microalbuminuria, and normoalbuminuria, particularly in cases with extended T1D duration [14]. Subsequent studies by Elsamahy et al. (2015), Wang et al. (2013), and Fekih et al. (2016) further corroborated these fi ndings, showing signifi cantly elevated OPG levels in patients with AlbU above 30 mg/24h compared to those with AlbU below 30 mg/24h [12, 22, 27]. In summary, our study reinforces the notion that OPG positively correlates with AlbU under conditions of poor glycaemic control, thus contributing to CVD development in individuals with long-standing T1D. The current interdisciplinary study represents the fi rst global assessment of the association between the biomarker OPG and established scales for CVR assessment in individuals with long-standing T1D. While a proportional trend of increasing serum OPG levels relative to the CVR category according to ST1RE and ESC-2019 was observed, it did not reach statistical signifi cance. However, when considering gender diff erences, a signifi cant diff erence was noted in the mean biomarker levels between males with low and high CVR, as well as between males with moderate and those with high CVR. Moreover, a signifi cant positive correlation was identifi ed between serum OPG and ST1RE, demonstrating a direct relationship and no signifi cant association was found between OPG and ESC-2019. Thus, the results of the present study confi rm the association of OPG with CVR in individuals with diabetes, particularly with respect to ST1RE. The reported correlations between the biomarker and several variables from ST1RE (including gender, age, duration of diabetes, AlbU, and glycaemic control) off er an explanation for the observed stronger association of OPG with ST1RE compared to ESC-2019. This fi nding supports the hypothesis proposed by Chalakova et al., suggesting that risk calculators assign diff erent weights to various RFs, indicating the degree of contribution of each factor [28]. In contrast, ESC-2019 does not assign diff erent weights to individual RFs, nor quantifi es them, thus introducing subjectivity into the assessment process. Early studies by Browner et al. and Olesen et al. were among the fi rst to establish the association between serum OPG levels and the progression of diabetic complications [29,30]. Gordin et al. (2013) identifi ed the biomarker as an independent predictor of CVD in diabetes [25]. Subsequent research has consistently described the relationship between the severity of atherosclerotic plaques, CAD and serum OPG levels [10]. Numerous researchers have investigated OPG-associated mechanisms in the development of CVD and its prognostic role in diabetes [7]. High-risk populations such as patients with T1D and T2D have shown elevated OPG values compared to healthy controls with sig- 75 Prognostic value of osteoprotegerin for estimation of cardiovascular risk... nifi cantly higher levels reported in individuals who have experienced cardiovascular events [7,11]. Confl icting data exist regarding the impact of statin treatment on circulating OPG levels in patients with very high CVR. Some studies have reported a decrease, while others have observed an increase, with variations even among diff erent statin drugs [10]. For example, in the study by Boyadzhieva et al. (2013), individuals with newly diagnosed T2D exhibited similar serum OPG results regardless of the presence or absence of known CAD [21]. Those with confi rmed CAD underwent standard statin therapy, which could potentially infl uence OPG levels and explain the lack of diff erences observed with the non-CAD group [21]. In a study by Raaz-Schrauder et al. (2017) involving 414 individuals with moderate CVR, as assessed by the Framingham Risk Score and ESC criteria, a notable correlation was uncovered between OPG levels and various atherogenic cytokines [16]. The precise role of OPG in the development of diabetic macroangiopathy remains incompletely understood. Despite this ambiguity, OPG continues to be linked with the progression of CVD. The origin of OPG expression remains a subject of debate. M. Boyadzhieva et al. (2013) identifi ed a signifi cant association between serum OPG levels and carotid intima-media thickness in men recently diagnosed with T2D [21]. This fi nding led the researchers to speculate that vascular changes might infl uence OPG regulation or that OPG could serve as a crucial regulatory molecule in the early stages of vascular dysfunction during the progression of diabetes [21]. The results of the applied ROC analysis against the established scales for the assessment of CVR in T1D demonstrated that OPG has a good prognostic value (approximately 70%) relative to ST1RE. The derived OPG cut-off values for males and females in diff erentiating high CVR according to ST1RE were 5.075 pmol/l for males and 5.355 pmol/l for females. Furthermore, for women using ESC – 2019, OPG also showed good prognostic value (approximately 65%), with a threshold value of 5.025 pmol/l. These fi ndings suggest that OPG serves as a promising biomarker for assessing CVD risk in individuals with long-standing T1D, particularly in high-risk patients. Study limitations The present study is limited primarily by the relatively small sample size, which may compromise statistical power, particularly in sex-stratifi ed analyses. In addition, several associations achieved only borderline statistical signifi cance and should thus be interpreted as indicative trends rather than conclusive evidence. Furthermore, the absence of long-term follow-up restricts the ability to draw fi rm conclusions, especially regarding OPG levels and their putative contribution to the progressive increase in cardiovascular risk among individuals with long-standing type 1 diabetes. C In conclusion, our study supports the notion that OPG is a promising biomarker for CVD assessment in individuals with long-standing T1D. The study found that OPG levels are higher in women, and factors such as age, desease duration and AlbU are positive determinants of OPG in T1D patients. Using ST1RE and ESC – 2019 guideleins, we demonstrated that OPG could eff ectively stratify patients by their CVR, with speciphic cut-off values providing reasonable Se and Sp. These results underscore that integrating of OPG into routine clinical laboratory practice could enhance CVR stratifi cation and facilitate appropriate treatment strategies for T1D patients. We advocate for further research involving larger patient cohorts to validate these fi ndings and elucidate the underlying pathophysiological mechanisms involved in CVD development in T1D. References 1. Cai X, Li J, Cai W et al. Meta-analysis of type 1 diabetes mellitus and risk of cardiovascular disease. J Diabetes Complications. 2021;35(4):107833. doi: 10.1016/j.jdiacomp.2020.107833. 2. de Ferranti S, de Boer I, Fonseca V. et al. Type 1 diabetes mellitus and cardiovascular disease: a scientifi c statement from the American Heart Association and American Diabetes Association. Diabetes Care. 2014;37(10):2843-2863. doi: 10.2337/dc14-1720. 3. Lee SI, Patel M, Jones CM, Narendran P. Cardiovascular disease and type 1 diabetes: prevalence, prediction and management in an ageing population. Ther Adv Chronic Dis. 2015, 6(6):347-74. doi:10.1177/2040622315598502. 4. Sharma H, Lencioni M, Narendran P. Cardiovascular disease in type 1 diabetes. Cardiovasc Endocrinol Metab. 2019;8(1):28-34. doi: 10.1097/XCE.0000000000000167. 5. International Diabetes Federation. Diabetes atlas 10th edition. 2021. https://diabetesatlas.org/atlas/tenth-edition/. 6. Rochette L, Meloux A, Rigal E et al. The Role of Osteoprotegerin and Its Ligands in Vascular Function. Int J Mol Sci. 2019;20(3):705. doi: 10.3390/ijms20030705. 7. Tschiderer L, Willeit J, Schett G et al. Osteoprotegerin concentration and risk of cardiovascular outcomes in nine general population studies: Literature-based meta-analysis involving 26,442 participants. PLoS One. 2017;12(8):e0183910. doi: 10.1371/journal. pone.0183910. 8. Biscetti F, Giovannini S, Straface G et al. RANK/RANKL/ OPG pathway: genetic association with history of ischemic stroke in Italian population. Eur Rev Med Pharmacol Sci. 2016, 20: 45744580. 9. Song D, Zhou P, Xiu X et al. Relationships of OPG genetic polymorphisms with susceptibility to cardiovascular disease: a Sources of funding: The study is supported by a research grant DN 13/3, 14 Dec 2017, from the Scientifi c Research Fund at the Ministry of Education and Science of Bulgaria and a research grant N 17022, 2017, from the Fund of the Department of Science and Research at Varna Medical University. Author contribution: All authors listed contributed equally to the study. G. Chausheva, S. Shefket, K. Tsochev et al. 76 meta-analysis. Med Sci Monit. 2016;22:1223-1231.doi: 10.12659/ MSM.895434 10. Bjerre M, Hilden J, Winkel P et al. Serum osteoprotegerin as a long-term predictor for patients with stable coronary artery disease and its association with diabetes and statin treatment: A CLARICOR trial 10-year follow-up substudy. Atherosclerosis. 2020;301:8-14. doi: 10.1016/j.atherosclerosis.2020.03.030. 11. Chun-Cui D, Chengcheng M, Hai-Qin T. Increased Circulating Osteoprotegerin Levels in Type 1 Diabetes Mellitus: A Systematic Review and Meta-analysis Based on Observational Studies. Immunol Invest. 2020;50(2-3):101-112. doi: 10.1080/08820139.2019.1710531. 12. Elsamahy M, Elhenawy Y, Nawar M. Plasma osteoprotegerin concentrations in type 1 diabetic patients with albuminuria. J Diabetes Complications. 2015;29(4):563-567. doi: 10.1016/j. jdiacomp.2015.02.008. 13. Chrysis D, Efthymiadou Al, Mermigka Al et al. Osteoprotegerin, RANKL, ADMA, and Fetuin-A serum levels in children with type I diabetes mellitus. Pediatric Diabetes. 2017;18(4):277-282. doi: 10.1111/pedi.12384. 14. Grauslund J, Rasmussen L, Green A, Sjølie A. Does osteoprotegerin relate to microand macrovascular complications in long-term type 1 diabetes? Scand J Clin Lab Invest. 2010;70(3):188193. doi: 10.3109/00365511003653599. 15. Singh D, Winocour P, Summerhayes B et al. Low serum osteoprotegerin levels in normoalbuminuric type 1 diabetes mellitus. Acta Diabetologica. 2010;47(1):105-110. doi: 0.1007/s00592-009-0146-z. 16. Raaz-Schrauder D, Schrauder MG, Stumpf C et al. Plasma levels of sRANKL and OPG are associated with atherogenic cytokines in patients with intermediate cardiovascular risk. Heart Vessels. 2017;32:1304-1313. doi: 10.1007/s00380-017-0998-z. 17. Knuuti J, Wijns W, Saraste A et al. ESC Scientifi c Document Group. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J. 2020 Jan 14;41(3):407477. doi: 10.1093/eurheartj/ehz425. Erratum in: Eur Heart J. 2020 Nov 21;41(44):4242. doi: 10.1093/eurheartj/ehz825. 18. Baud’huin M, Duplomb L, Teletchea S. et al. Osteoprotegerin: multiple partners for multiple functions. Cytokine and Growth Factor Reviews. 2013;24(5):401-409. doi: 10.1016/j.cytogfr.2013.06.001. 19. Khosla S, Arrighi HM, Melton LJ 3rd et al. Correlates of osteoprotegerin levels in women and men. Osteoporos Int. 2002;13(5):394-399. doi: 10.1007/s001980200045. 20. Alkac C, Alkac B, Akbas F et al. Osteoprotegerin as a marker of atherosclerosis in type 1 and type 2 diabetic patients. TJMS, 2015;45(6):19. doi: 10.3906/sag-1408-37. 21. Boyadzhieva M, Hristozov K, Georgiev S et al. High osteoprotegerin serum levels in newly-diagnosed type 2 diabetic males without known coronary artery disease. Scripta Scientifi ca Medica. 2013;45(3):65-68. doi: http://dx.doi.org/10.14748/ssm. v45i3.308. 22. Fekih O, Triki H, Triki S et al. Osteoprotegerin as a marker of cardiovascular risk in children and adolescents with type 1 diabetes. Pediatric Diabetes. 2017;18(3):230-236. doi: 10.1111/pedi.12379. 23. Pérez de Ciriza C, Lawrie A, Varo N. Osteoprotegerin in Cardiometabolic Disorders. Int J Endocrinol. 2015;2015:564934. doi: 10.1155/2015/564934. 24. Elbana M, Said N, Baraka A, Mousa M. Study The Association between Serum Osteoprotegerin and Diabetic Kidney Disease. EJHM. 2022;87(1):1297-1302. doi: 10.21608/EJHM.2022.223598. 25. Gordin D, Soro-Paavonen A, Thomas MC et al. FinnDiane Study Group. Osteoprotegerin is an independent predictor of vascular events in Finnish adults with type 1 diabetes. Diabetes Care. 2013;36(7):1827-33.doi: 10.2337/dc13-er08a. 26. Jorsal A, Tarnow L, Flyvbjerg A, Parving HH, Rossing P, Rasmussen LM. Plasma osteoprotegerin levels predict cardiovascular and all-cause mortality and deterioration of kidney function in type 1 diabetic patients with nephropathy. Diabetologia. 2008;51(11): 21002107. doi: 10.1007/s00125-008-1123-8. 27. Wang S, Xu J, Wang M et al. Increased plasma osteoprotegerin concentrations in Type 1 diabetes with albuminuria. Clin Nephrol. 2013;79(3):192-198. doi: 10.5414/CN107536. 28. Chalakova T, Yotov Y, Tzotchev K et al. Type 1 Diabetes Mellitus – Risk Factor for Cardiovascular Disease Morbidity and Mortality. Curr Diabetes Rev. 2021;17(1):37-54. doi: 10.2174/15733 99816666200511004205. 29. Browner W, Lui L, Cummings S. Associations of serum osteoprotegerin levels with diabetes, stroke, bone density, fractures, and mortality in elderly women. J Clin Endocrinol Metab. 2001;86 (2): 631-637. doi: 10.1210/jcem.86.2.7192 30. Olesen P, Ledet T, Rasmussen M. Arterial osteoprotegerin: increased amounts in diabetes and modifi able synthesis from vascular smooth muscle cells by insulin and TNF-α. Diabetologia. 2005;48:561-568. doi:10.1007/s00125-004-1652-8.