The Role of Mid Regional Pro-adrenomedullin as a Biomarker for Early Cardio-Renal Dysfunction in Prediabetes and Type 2 Diabetes Mellitus _ A Prospective cohort study

The Role of Mid Regional Pro-adrenomedullin as a Biomarker for Early Cardio-Renal Dysfunction in Prediabetes and Type 2 Diabetes Mellitus _ A Prospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Role of Mid Regional Pro-adrenomedullin as a Biomarker for Early Cardio-Renal Dysfunction in Prediabetes and Type 2 Diabetes Mellitus _ A Prospective cohort study Oshmi Rao, J.S. Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5795445/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Cardiovascular and renal complications are leading causes of morbidity and mortality in patients with Prediabetes type 2 diabetes mellitus (T2DM). Identifying reliable biomarkers for early detection of cardio-renal dysfunction is essential to mitigate disease progression and improve clinical outcomes. This prospective cohort study evaluates the association between serum Mid-Regional Pro-adrenomedullin (MR-ProADM) levels and early signs of endothelial dysfunction in patients with prediabetes and T2DM. Method: The study was conducted at SRM Medical College Hospital and Research Centre, involving 90 participants divided equally into prediabetic and T2DM groups. Comprehensive clinical assessments included measurements of MR-ProADM, NT-ProBNP, eGFR, urine albumin-to-creatinine ratio (ACR), lipid profiles, and glycated hemoglobin (HbA1c). All these petameters are measured at baseline as well as after 1 year of follow-up. Finding: The study observed a significant increase in serum MR-ProADM levels in prediabetic and T2DM patients during follow-up compared to baseline. MR-ProADM correlated positively with early markers of endothelial dysfunction and cardio-renal impairment. Linear regression analysis confirmed strong associations with NT-ProBNP, Ejection fraction, eGFR, and ACR, indicating its potential as a predictive biomarker for cardiovascular and renal complications. ROC curve analysis further validated MR-ProADM’s diagnostic utility in identifying patients at risk. This study highlights MR-ProADM’s clinical relevance as a stable, reliable marker for early detection, risk assessment, and timely intervention in prediabetic and T2DM populations. Interpretation: Serum MR-ProADM shows potential as a reliable biomarker for early detection of cardio-renal dysfunction in prediabetes and T2DM. Its significant correlation with NT-ProBNP, Ejection fraction, eGFR, and ACR highlights its utility in predicting cardiovascular and renal complications, enabling timely intervention to improve clinical outcomes. Endocrinology & Metabolism Mid-Regional Pro-adrenomedullin (MR-ProADM) Cardiovascular complications Renal complications Type 2 diabetes mellitus (T2DM) Endothelial dysfunction Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Pre-diabetes and Type 2 diabetes mellitus significantly impacts morbidity, mortality, and overall healthcare expenses. These outcomes are mainly derived due to complications associated with pre-diabetes and T2DM, which are macrovascular complication and microvascular complication. The macrovascular complications are cardiovascular diseases and microvascular complication include neuropathy, retinopathy and nephropathy ( 1 ). The cardiovascular complication and nephropathy are the most significant cause of mortality among Pre-diabetes and T2DM patients. Cardiovascular disease (CVD) remains a leading global health challenge ( 2 ). Evidence from prospective studies shows that individuals with diabetes are two to four times more likely to develop coronary artery disease (CAD) and suffer myocardial infarction (MI), confirming that type 2 diabetes mellitus (DM) is a significant, independent risk factor for both stroke and heart disease ( 3 , 4 ). Remarkably, approximately 70% of individuals with type 2 DM aged 65 and older succumb to CVD. Furthermore, patients with type 2 DM who have no prior history of CAD face a cardiovascular risk equivalent to those with a previous MI ( 5 ). Another leading cause of mortality after CVD among prediabetes and T2DM is diabetic kidney disease (DKD). One major microvascular complication of prediabetes and T2DM is diabetic nephropathy, if left untreated it leads to chronic kidney disease followed by end stage renal dialysis (ESRD) which ultimately leads to renal failure ( 1 ). There is multiple theory explaining the development of macrovascular and microvascular complications of diabetes. However, endothelial dysfunction theory is one of the most accepted hypothesis which clearly explain the development of such complication in diabetes ( 6 ). A key hypothesis for the early development of atherosclerosis and diabetic kidney disease is endothelial dysfunction, which is broadly defined as either an imbalance in chemical messengers released by endothelial cells or a diminished nitric oxide-dependent vasodilation in response to acetylcholine or increased blood flow. This dysfunction has been identified in individuals with diabetes, insulin resistance, or those at high risk of developing type 2 diabetes ( 6 ). Reduced ability of nitric oxide synthase (NOS) to produce nitric oxide (NO) has been experimentally observed in endothelial cells (ECs) exposed to diabetic conditions, both in vitro and in vivo ( 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ). This suggests that ECs are directly impacted by the diabetic environment, and endothelial dysfunction is believed to be a key contributor to the vascular complications associated with diabetes. Extensive evidence in humans shows a strong link between endothelial dysfunction and the development of microangiopathy and atherosclerosis in both type 1 and type 2 diabetes mellitus ( 16 ). Adrenomedullin (ADM) is a 52-amino-acid peptide hormone that plays a role in endothelial dysfunction and the regulation of blood vessels ( 17 , 18 , 19 ). It functions as both a circulating hormone and a local paracrine regulator ( 20 ), with endothelial cells actively producing and secreting ADM ( 21 ). ADM levels are elevated in conditions such as heart failure (HF) and post-acute coronary syndrome (ACS), where it helps dilate blood vessels by increasing cyclic adenosine monophosphate (cAMP) levels ( 22 , 23 , 24 ). It also boosts cardiac output, promotes diuresis, and is associated with negative outcomes in HF and myocardial infarction ( 25 , 26 ). The production of ADM increases specially in glomerulus and renal tubules in response to hypoxia and ischemia, conditions commonly seen in diabetes ( 27 , 28 , 29 , 30 ). This suggests that ADM may help protect against organ damage in ischemic or hypoxic conditions, particularly in the cardiovascular and renal complications of type 2 diabetes mellitus (T2DM) ( 27 , 28 , 29 ). Due to ADM's short half-life and rapid clearance, a more stable mid-regional fragment called mid-regional proadrenomedullin (MR ProADM) is often used as a surrogate marker for measurement ( 31 , 32 ).The exact relationship between serum Mid Regional Pro-adrenomedullin levels and endothelial dysfunction among T2DM and Pre-diabetes patient is unclear. Thus, this study is conducted to find the role of serum Mid Regional Pro-adrenomedullin as marker of endothelial dysfunction in Type 2 diabetes mellitus patients and pre-diabetes patients. MATERIAL AND METHOD The study was carried out at tertiary care center in south India (Chennai) [SRM MEDICAL COLLEGE HOSPITAL AND RESEARCH CENTRE] with ethical approval from institutional ethical committee [ ethical committee's clearance [SRMIEC-ST0723-1771] . Informed consent was obtained from all participants or their legal guardians prior to the study's start. All the methods were performed in accordance with the relevant guidelines and regulations. Throughout the research work, we maintained strict adherence to the ethical principles as per declaration of Helsinki. Study group The total sample size was calculated using prevalence of prediabetes and T2DM, it was 90 (n = 90), which is further subdivided into two equal groups. Group I include 45 prediabetes patients of age above 18 years. Group II includes 45 T2DM patients of age above 18 years. Inclusion criteria Adults aged more than 18 years. Patients with diagnosed Pre-Diabetes including Impaired fasting glucose and impaired glucose tolerance (HbA1c between 5.7–6.4%) or a combination and established Type 2 Diabetes Mellitus (Hba1c =/ > 6.5%) Exclusion criteria Patients with known history of chronic kidney disease / Coronary Artery Disease. Patients with Type 1 Diabetes Mellitus Patients with Heart Failure (EF < 50%) Patients with Uncontrolled Hypertension Patients with active infections/ severe anemia Patients unwilling to give consent Sample collection and analysis technique 5 ml of venous blood was collected in non EDTA vacutainer tubes (red cap vacutainer). The blood samples were allowed to clot than samples were subjected to centrifugation to separate serum. The serum samples were separated into two equal parts. One part was stored at -80°C for estimation of special ELISA parameter (MR ProADM & NT ProBNP) and the second part of serum samples were used to analyzed other biochemical parameters like FBS (Fasting blood sugar), PPBS (Post prandial blood sugar), total cholesterol, triglycerides, LDL-c, VLDL-c, HDL-c, Urea, Creatinine, Albumin, Globulin, A/G ration, ALT, AST, GGT. At the same time 25 ml of urine sample were collected in a clean sterile container for estimation of urine Albumin creatinine ratio. Ejection fraction (EF) of all the patients was measured using 2D Eco. All the parameters were analyzed at baseline and after 1 year of follow-up. Follow-up leads to the loss of 10% of total sample size. Calculated parameters VLDL-c - Calculated using Freidwald’s formula: VLDL-C`=`TGL/5 eGFR – Calculated using CKD EPI Calculator: 2021`CKD-EPI`Creatinine`=`142`x`(Scr/A)B`x`0.9938age`x`(1.012`if`female),`where`A`and`B`are`the`following: Scr = Serum Creatinine STATISTICAL ANALYSIS Normality of data was checked using Shapiro-Wilk test. Mann-Whitney U test was used to compare the levels of serum Mid Regional Pro adrenomedullin and NT Pro BNP among the study participants. The data were expressed in terms of Median [Interquartile range]. Spearman’s Correlation was conducted to correlate MR Pro ADM with Urine ACR, eGFR, NT Pro BNP, Ejection fraction, Lipid Profile and BMI. Linear regression analysis was performed to find the association of MR ProADM with NT-Pro BNP, Ejection Fraction, Urine ACR and eGFR P ≤ 0.05 was taken as the significant value Test of normality The normality of data was analyzed using Shapiro Wilk’s test. The test result conclude that the data is not following Gaussian distribution, as the p value is 0.002 which is less than 0.05 Thus, non-parametric test were used to analyze and interpret the result. RESULT Table 1 Basic anthropometric measurements and laboratory analysis of participants Parameter Participants [(Median) Interquartile range] P value Baseline Follow-up Age Pre-Diabetic 42 (28,58) 42 (28,58) 1.000 T2DM 48 (38,56) 48 (38,56) 1.000 Sex Pre-Diabetic Female ( 9 ), Male ( 36 ) Female ( 8 ), Male ( 33 ) 1.000 T2DM Female ( 10 ), Male ( 35 ) Female ( 7 ), Male ( 34 ) 1.000 BMI (kg/m 2 ) Pre-Diabetic 26.3 (20.7, 30.7) 25 (20.1, 29.4) 0.458 T2DM 27.3 (22.4, 29.6) 27.3 (22.9, 28.5) 0.881 HbA1c (%) Pre-Diabetic 6.1 (5.8, 6.3) 6.1 (5.9, 6.3) 0.597 T2DM 7.7 (7.2, 8.5) 8.1 (7.4, 8.8) 0.131 FBS (mg/dL) Pre-Diabetic 112 (105, 117) 116 (110,121) 0.018* T2DM 156 (145,178.5) 161 (145, 178) 0.878 PPBS (mg/dl) Pre-Diabetic 204 (191,219.5) 206 (193,221) 0.500 T2DM 257 (225.5, 293.5) 262 (230, 296) 0.639 TC (mg/dL) Pre-Diabetic 151 (112,176) 164 (136.5,190) 0.165 T2DM 192 (164, 228) 182 (160, 222.5) 0.337 TGL (mg/dL) Pre-Diabetic 102 (59.5, 149) 102 (63.5, 150) 0.824 T2DM 98 (80, 125) 90 (66, 115.5) 0.240 LDL-c (mg/dL) Pre-Diabetic 108 (89.5, 128) 124 (107, 140) 0.011* T2DM 103 (93, 119) 102 (93, 117.5) 0.796 VLDL-c (mg/dL) Pre-Diabetic 24 (15.5, 34.5) 27 (15.5, 35.0) 0.830 T2DM 30 ( 19 , 37 ) 30 (19, 39) 0.674 HDL-c (mg/dL) Pre-Diabetic 46 (32.5, 66) 47 (34, 66) 0.628 T2DM 32 (26.5, 37.5) 32 (23.5, 37.5) 0.932 Serum Creatinine (mg/dL) Pre-Diabetic 0.8 (0.6, 1.1) 0.8 (0.6, 1.1) 0.718 T2DM 0.9 (0.65, 1.0) 0.8 (0.65, 1.0) 0.729 Serum Urea (mg/dl) Pre-Diabetic 29 (22.5, 36.5) 29 (22.5, 35.0) 0.789 T2DM 33 ( 24 , 38 ) 31 ( 23 , 37 ) 0.662 BUN (mg/dl) Pre-Diabetic 13.55 (10.51, 17.06) 13.55 (10.51, 16.35) 0.921 T2DM 15.42 (11.21, 17.75 14.5 (10.74, 17.3) 0.815 Urine ACR (mg/g) Pre-Diabetic 14 ( 11 , 16 ) 28 (17.4, 43) 0.000* T2DM 17 (14, 19.5) 38 (28, 76) 0.000* eGFR (CKD EPI) Pre-Diabetic 108 (86, 128) 115 (86, 127.5) 0.747 T2DM 104 (92, 119) 108 (94.5, 120.5) 0.698 NT-Pro BNP (pg/ml) Pre-Diabetic 119 (80.5, 192) 232 (145.5, 272) 0.000* T2DM 198 (114.5, 243.0) 224 (182, 313.5) 0.005* BMI: Body Mass Index; TC: Total Cholesterol; TGL: Triglycerides; LDL-c: Low density lipoprotein cholesterol; VLDL: Very low-density lipoprotein cholesterol; HDL-c: High density lipoprotein cholesterol; BUN: Blood Urea Nitrogen; eGFR: Estimated glomerular filtration rate; NT-Pro BNP: : N-terminal pro–B-type natriuretic peptide Table 2 Comparison of serum MR Pro-adrenomedullin between pre-diabetic patients at baseline and follow-up. Parameter Study population [Median (interquartile range)] P value Pre-Diabetics (Baseline) Pre-Diabetic (Follow-up) MR Pro-adrenomedullin (pg/ml) 24.10(4.80, 60.35) 59.9 (13.40, 108.05) P = 0.031 P ≤ 0.05 = Statistically significant P > 0.05 = Statistically non-significant Mann-Whitney U test was performed to compare the serum MR Pro-adrenomedullin between pre-diabetic patients at baseline and follow-up. There was significant difference [ Mann-Whitney U value = 1280.500, P = 0.031 ] in the serum MR Pro-adrenomedullin levels with median levels for pre-diabetic patients at baseline were [24.10(4.80, 60.35)] pg/ml and pre-diabetic patients at follow-up were [59.9 (13.40, 108.05)] pg/ml. Pairwise analysis of Table : Parameter Pairwise analysis Fold change in concentration P Value MR Pro- adrenomedullin (pg/ml) Pre-diabetic (Baseline): Pre-diabetic (Follow-up) 2.4 P = 0.031 P ≤ 0.05 = Statistically significant P > 0.05 = Statistically non-significant The statistically significant fold change in concentration of serum MR Pro- adrenomedullin between pre-diabetic patients at baseline and pre-diabetic patients at follow-up was fond to be [+ 2.4 folds (P = 0.031)] . Box whisker plot representing serum MR Pro-adrenomedullin among the study group participants. Elevated serum MR Pro-adrenomedullin levels among pre-diabetic patients at follow-up is clearly depicted in the figure as compare to pre-diabetics patients at baseline. Table 3 Comparison of serum MR Pro-adrenomedullin between T2DM patients at baseline and follow-up. Parameter Study population [Median (interquartile range)] P value T2DM (Baseline) T2DM (Follow-up) MR Pro-adrenomedullin (pg/ml) 47.00 (16.10, 72.95) 143.10 (75.60, 261.40) P = 0.000 P ≤ 0.05 = Statistically significant P > 0.05 = Statistically non-significant Mann-Whitney test was performed to compare the serum MR Pro-adrenomedullin between T2DM patients at baseline and follow-up. There was significant difference [ Mann-Whitney U value = 1455.000, P = 0.000 ] in the serum MR Pro-adrenomedullin levels with median levels for T2DM patients at baseline were [47.00 (16.10, 72.95)] pg/ml and T2DM patients at follow-up were [143.10 (75.60, 261.40)] pg/ml. Pairwise analysis of Table 3 : Parameter Pairwise analysis Fold change in concentration P Value MR Pro- adrenomedullin (pg/ml) T2DM (Baseline): T2DM (Follow-up) 3.04 P = 0.000 P ≤ 0.05 = Statistically significant P > 0.05 = Statistically non-significant The statistically significant fold change in concentration of serum MR Pro- adrenomedullin between T2DM patients at baseline and T2DM patients at follow-up was fond to be [+ 3.04 folds (P = 0.031)] . Box whisker plot representing serum MR Pro-adrenomedullin among the study group participants. Elevated serum MR Pro-adrenomedullin levels among T2DM patients at follow-up is clearly depicted in the figure as compare to T2DM patients at baseline. Table 4 Represents correlation between MR Pro-ADM and NT Pro BNP, Urine ACR, eGFR, BMI, HbA1c A Spearman’s correlation matrix was calculated to examine the relationship between the variables. The matrix is presented in Table 3 . MR Pro-ADM was found to be positively correlated with NT Pro-BNP, Urine ACR, HbA1c and BMI at the same time MR Pro-ADM has a statistically significant negative correlation with eGFR. Spearman’s correlation Parameter NT Pro-BNP Urine ACR HbA1c BMI eGFR MR Pro-ADM r Value 0.312 0.425 0.236 0.385 -0.179 P Value 0.000** 0.000** 0.001** 0.000** 0.16* **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). NT Pro-BNP: N-terminal pro–B-type natriuretic peptide; Urine ACR: Albumin Creatinine ratio; eGFR: Estimated glomerular filtration rate; BMI: Body Mass Index Table 5 Represents correlation between MR Pro-ADM and Total Cholesterol, Triglyceride, HDL-c, LDL-c, VLDL-c, EF Spearman’s correlation Parameter TC TGL HDL-c LDL-c VLDL-c EF MR Pro-ADM r Value 0.206 -0.111 -0.244 -0.111 0.082 -0.155 P Value 0.006** 0.138 0.001** 0.138 0.271 0.038* **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). TC: Total Cholesterol; TGL: Triglyceride; HDL-c: High Density Lipoprotein cholesterol; LDL-c: Low Density Lipoprotein Cholesterol; VLDL-c : Very Low Density Lipoprotein Cholesterol; EF: Ejection Fraction A Spearman’s correlation matrix was calculated to examine the relationship between the variables. The matrix is presented in Table 4 . MR Pro-ADM was found to be positively correlated with TC, at the same time MR Pro-ADM has a statistically significant negative correlation with HDL-c and EF. Table 6 – Linear logistic regression analysis to find the relationship of MR Pro – ADM and the risk of Cardiac dysfunction. Chi 2 test Chi2 df p 8.07 1 0.005 The table shows the results of a Chi-square (χ²) test associated with a logistic regression model. This test is often used to assess the overall significance of the model Chi2 (χ²) statistic, was found to be 8.07. It measures the difference between the observed and expected frequencies of the outcomes predicted by the model. A higher Chi-square value indicates a greater discrepancy between the expected and observed values, suggesting that the model's predictors have a significant relationship with the outcome. df (Degrees of Freedom) 1, represent the number of predictors in the model for a simple logistic regression. p (p-value) This is the probability of observing the Chi2 test statistic as extreme as the one observed under the null hypothesis. The null hypothesis is that there is no relationship between the observed and expected frequencies of the outcomes predicted by the model. A p-value of 0.005 suggests that there's a 0% probability of observing a Chi-square statistic as extreme as 8.07 if the null hypothesis were true. Parameter Coefficient B Standard error z p Odds Ratio 95% confidence interval MR Pro-ADM 0 0 2.64 0.08 1 1-1.01 A logistic regression analysis was performed to examine the influence of MR Pro-ADM to predict the chances of development of cardiac dysfunction among Pre-diabetic and T2DM patients. Logistic regression analysis showed that the model as a whole was significant (Chi 2 ( 5 ) = 8.07, P = 0.005, n = 180). The coefficient of the variable MR Pro-ADM is b = 0 , which is positive. This means that an increase in MR Pro-ADM is associated with an increase in the probability that the dependent variable has ‘Cardiac dysfunction’’. The p-value of 0.005 indicates that this influence is statistically significant. The odds Ratio of 1 indicates that one unit increase of the variable MR Pro-ADM will increase the odds that the dependent variable has "Cardiac dysfunction" by 1 time. Area under the curve (AUC) for variables of linear logistic regression analysis MR Pro-ADM was found to be 0.594 which is closed 1 (maximum AUC) indicating that these variables are satisfactorily classified diseased (Cardiac dysfunction) and non-diseased (Non-Cardiac dysfunction) patients with high TP vs FP ratio Table 7 – Linear logistic regression analysis to find the relationship of MR Pro – ADM and the risk of Renal dysfunction. Chi 2 test Chi2 df p 7.14 1 0.008 The table shows the results of a Chi-square (χ²) test associated with a logistic regression model. This test is often used to assess the overall significance of the model Chi2 (χ²) statistic, was found to be 7.14. It measures the difference between the observed and expected frequencies of the outcomes predicted by the model. A higher Chi-square value indicates a greater discrepancy between the expected and observed values, suggesting that the model's predictors have a significant relationship with the outcome. df (Degrees of Freedom) 1, represent the number of predictors in the model for a simple logistic regression. p (p-value) This is the probability of observing the Chi2 test statistic as extreme as the one observed under the null hypothesis. The null hypothesis is that there is no relationship between the observed and expected frequencies of the outcomes predicted by the model. A p-value of 0.008 suggests that there's a 0% probability of observing a Chi-square statistic as extreme as 7.14 if the null hypothesis were true. Parameter Coefficient B Standard error z p Odds Ratio 95% confidence interval MR Pro-ADM 0 0 2.59 0.01 1 1-1.01 A logistic regression analysis was performed to examine the influence of MR Pro-ADM to predict the chances of development of cardiac dysfunction among Pre-diabetic and T2DM patients. Logistic regression analysis showed that the model as a whole was significant (Chi 2 ( 5 ) = 7.14, P = 0.008, n = 180). The coefficient of the variable MR Pro-ADM is b = 0 , which is positive. This means that an increase in MR Pro-ADM is associated with an increase in the probability that the dependent variable has ‘Renal dysfunction’’. The p-value of 0.01 indicates that this influence is statistically significant. The odds Ratio of 1 indicates that one unit increase of the variable MR Pro-ADM will increase the odds that the dependent variable has "Renal dysfunction" by 1 time. Area under the curve (AUC) for variables of linear logistic regression analysis MR Pro-ADM was found to be 0.622 which is closed 1 (maximum AUC) indicating that these variables are satisfactorily classified diseased (Renal dysfunction) and non-diseased (Normal renal function) patients with high TP vs FP ratio DISCUSSION The objective of the current study is to find association between MR proADM and early cardio-renal complications in patients with Prediabetes and Type 2 diabetes mellitus. The present study provides evidence that MR-proADM is a significant predictor of Cardiorenal dysfunction among T2DM patients and Pre-diabetic patients. Furthermore, this study confirms that serum MR-proADM was significantly elevated at follow-up (59.9 pg/ml) as compared baseline (24.19 pg/ml) in Pre-Diabetic patients, similarly MR-proADM is found to be significantly elevated at follow-up (143.10 pg/ml) as compared to baseline (47.0 pg/ml) in T2DM patients. The fold change in the concentration of MR-Pro ADM from baseline to follow-up in pre-diabetic patients was 2.4-fold and in case of T2DM patients was 3.04 folds. We also found that this biomarker can be used to predict the Cardiorenal dysfunction among T2DM patients and Pre-diabetic patients. It was found to be more sensitive than NT Pro BNP, Ejection Fraction (Used to detect cardiac dysfunction) and Urine ACR, eGFR (Used to detect renal dysfunction). The current study found elevated MR proADM levels at follow-up as compared to baseline in Pre-diabetic patients. To our knowledge this is the first study which tries to understand the relationship between serum MR Pro ADM levels at baseline and follow-up among Pre-Diabetic patients. Similarly, the study also found elevated levels of MR ProADM at follow-up as compared to baseline among T2DM patient. This finding is in agreement with the finding of Pierre-Jean Saulnier et al. who also found elevated MR ProADM levels at follow-up as compared to baseline among T2DM [33]. This is because diabetes is associated with macrovascular and microvascular complications like cardiovascular disease and retinopathy, nephropathy, neuropathy, the major cause of these complications are endothelial dysfunction and hence vascular damage ( 2 ). Adrenomedullin is mainly released from vascular endothelial cells and smooth muscle thus slow, progressive damage to blood vessel in diabetes patients due to accumulation of AGEs (Advance glycosylated end product), oxidative damage leads to leakage or release of ADM in to blood which leads to elevated levels of MR-ProADM among diabetes patients which can act as highly sensitive biomarker of vascular damage among T2DM patients [34,35]. The study also revealed that MR ProADM has a significant positive correlation with NT-Pro BNP and a significant negative correlation with Ejection Fraction. This clearly suggest that MR ProADM can act as a marker of cardiac dysfunction among Pre-Diabetic and T2DM patients. MR-proADM is a precursor peptide of ADM that is involved in the function of endothelial cells, causes long-lasting vasodilatation, carries natriuretic properties and might have positive inotropic effects on the contractility of the myocardium [36,37,38]. Similarly, the study also found that MR ProADM has significant positive correlation with Urine ACR and a significant negative correlation with eGFR, indicating MR ProADM can also acta sensitive marker of renal dysfunction among T2DM patients. All these finding suggested that MR ProADM can act as a biomarker of cardiorenal dysfunction among Pre-diabetic patients as well as T2DM patients with higher sensitivity than NT-Pro BNP, Ejection Fraction which is used to detect Cardiac dysfunction) and Urine ACR and eGFR which is used to detect renal dysfunction. CONCLUSION The study aimed to investigate the association between Serum Mid Regional Pro adrenomedullin (MR proADM) and early detection of cardio-renal dysfunction in patients with Prediabetes and Type 2 Diabetes Mellitus. The findings indicate MR-proADM as a highly sensitive and reliable biomarker for the prediction of cardiorenal dysfunction in patients with prediabetes and T2DM. The findings demonstrate a significant elevated MR-proADM levels at follow-up as compared to basline among both Pre-Diabetic and T2DM patients as well as the study also demonstrate the positive correlation of MR Pro ADM with NT-Pro BNP and a negative correlation with Ejection Fraction. Similarly, study revealed a positive correlation of MR ProADM with urine ACR and a negative correlation with eGFR. These results suggest the important role of MR-proADM in reflecting endothelial damage and vascular complications, providing a valuable biomarker for the early identification and management of diabetes-associated cardiorenal complications. ADDITIONAL INFORMATION Declarations ADDITIONAL INFORMATION AUTHOR CONTRIBUTION Oshmi Rao Rajesh : Conceptualization (equal); formal analysis (lead); investigation (lead); methodology (lead); validation (equal); visualization (equal); writing – original draft (lead); software (supporting); supervision (lead); writing – review and editing (supporting). J.S. Kumar : Conceptualization (equal); data curation (equal); funding acquisition (supporting); investigation (supporting); methodology (supporting); resources (lead); software (supporting); supervision (lead); writing – review and editing (supporting). ACKNOWLEDGMENTS Both the authors would like to acknowledge the support from the department of General Medicine, SRM Medical College Hospital & Research Centre. FUNDING INFORMATION Nil CONFLICT OF INTEREST STATEMENT The authors declare no conflict of interest. DATA AVAILABILITY STATEMENT The patient details and all the raw data including consent forms will be provided by the corresponding author upon request. ETHICS STATEMENT The above project was reviewed by the institutional ethics committee of SRM Medical College Hospital & Research Centre, and approval was obtained (ETHICS CLEARANCE NUMBER: SRMIEC-ST0723-1771). Informed consent was obtained from all patients whose samples were utilized in the study. References Michael J (April 2008) Fowler; Microvascular and Macrovascular Complications of Diabetes. 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J Cardiovasc Pharmacol 32[Suppl 3]:S54–S61 Ichiki Y Kitamura K Kangawa K, Kawamoto M, Matsuo H, Eto T. Distribution and characterization of immunoreactive adrenomedullin in human tissue and plasma. FEBS Lett 1994 ;338:6–10 Samson WK Adrenomedullin and the control of fluid and electrolyte homeostasis. Annu Rev Physiol 1999 ;61:363–389 Lewis LK Smith MW Yandle TG, Richards AM, Nicholls MG. Adrenomedullin(1–52) measured in human plasma by radioimmunoassay: plasma concentration, adsorption, and storage. Clin Chem 1998 ;44:571–577 Bunton DC Petrie MC Hillier C, Johnston F, McMurray JJ. The clinical relevance of adrenomedullin: a promising profile? Pharmacol Ther 2004;103:179–201 Sugo S Minamino N Shoji H, Production and secretion of adrenomedullin from vascular smooth muscle cells: augmented production by tumor necrosis factor-alpha. Biochem Biophys Res Commun 1994 ;203:719–726 Kato J, Kobayashi K, Etoh T et al (1996) Plasma adrenomedullin concentration in patients with heart failure. J Clin Endocrinol Metab 81:180–183 Miyao Y, Nishikimi T, Goto Y et al (1998) Increased plasma adrenomedullin levels in patients with acute myocardial infarction in proportion to the clinical severity Heart. 79:39–44 Lainchbury JG, Cooper GJ, Coy DH et al (1997) Adrenomedullin: hypotensive hormone man Clin Sci Colch 92:467–472 Vari RC, Adkins SD (1996) Samson Renal effects of adrenomedullin in the rat. Proc Soc Exp Biol Med 211:178–183 Pousset F, Masson F, Chavirovskaia O et al Plasma adrenomedullin, a new independent predictor of prognosis in patients with chronic heart failure Minamino N Shoji H Sugo S, Kangawa K, Matsuo H. Adrenocortical steroids, thyroid hormones and retinoic acid augment the production of adrenomedullin in vascular smooth muscle cells. Biochem Biophys Res Commun 1995 ;211:686–693 Sugo S Minamino N Shoji H, Interleukin-1, tumor necrosis factor and lipopolysaccharide additively stimulate production of adrenomedullin in vascular smooth muscle cells. Biochem Biophys Res Commun 1995;207:25–32 Sugo S Minamino N Shoji H, Kangawa K, Matsuo H. Effects of vasoactive substances and cAMP related compounds on adrenomedullin production in cultured vascular smooth muscle cells. FEBS Lett 1995 ;369:311–314 Hansell P Welch WJ, Blantz RC Palm F Determinants of kidney oxygen consumption and their relationship to tissue oxygen tension in diabetes and hypertension. Clin Exp Pharmacol Physiol 2013 ;40:123–137 Struck J, Tao C, Morgenthaler NG A. Bergmann Identification of an adrenomedullin precursor fragment in plasma of sepsis patients Kitamura K, Sakata J, Kangawa K, Kojima M, Matsuo H (1993) Eto Cloning and characterization of cDNA encoding a precursor for human adrenomedullin. Biochem Biophys Res Commun 194:720–725 Saulnier PJ, Gand E, Velho G, Mohammedi K, Zaoui P, Fraty M, Halimi JM, Roussel R, Ragot S, Hadjadj S, SURDIAGENE Study Group (2017) Association of Circulating Biomarkers (Adrenomedullin, TNFR1, and NT-proBNP) With Renal Function Decline in Patients With Type 2 Diabetes: A French Prospective Cohort. Diabetes Care 40(3):367–374. 10.2337/dc16-1571 Epub 2016 Dec 20. PMID: 27998909 Wong HK, Tang F, Cheung TT, Cheung BM (2014) Adrenomedullin and diabetes. World J Diabetes 5(3):364–371. 10.4239/wjd.v5.i3.364 PMID: 24936257; PMCID: PMC4058740 Kato J, Tsuruda T, Kita T, Kitamura K, Eto T (2005) Adrenomedullin Arterioscler Thromb Vascular Biology 25(12):2480–2487 Jougasaki M, Burnett JC Jr (2000) Adrenomedullin: potential in physiology and pathophysiology. Life Sci 66:855–872 Samson WK (1999) Adrenomedullin and the control of fluid and electrolyte homeostasis. Ann Rev Physiol 61:363–389 Ihara T, Ikeda U, Tate Y, Ishibashi S, Shimada K (2000) Positive inotropic effects of adrenomedullin on rat papillary muscle. Eur J Pharmacol 390:167–172 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5795445","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":399887524,"identity":"ae5be3c9-4e53-4d61-b667-b4cf462d1de0","order_by":0,"name":"Oshmi Rao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIie3PvQrCMBDA8YNAXKJdI0p9hUihKL6MU6YUcZcugi7irG/h1FkI1iU6BxwLDk5CwVFMOjn1YxPMfzgy3A8uAC7XL3YEQADUvJB5Au03IXhqCalLbIQVs1J0blLmczGaeetN/tCLEYGWPB3KSPfKeW+f0PFOXZKJSM1hhHNdRpgiIWonlIGOkkBgQygJq0iQWzLQ4h6Idz3CepYwLVAWrWqQrsK8IEOVhijaUoKr/tJRSJrDYuafl1kuXrHvtWRaSr7DtJh1123o2WTb5XK5/qcPEVZDYyaNEcwAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0004-6793-5035","institution":"SRM MEDICAL COLLEGE HOSPITAL \u0026 RESEARCH CENTRE","correspondingAuthor":true,"prefix":"","firstName":"Oshmi","middleName":"","lastName":"Rao","suffix":""},{"id":399889030,"identity":"d468f817-b2db-45cd-b1f7-6ff2e4e2c3fa","order_by":1,"name":"J.S. Kumar","email":"","orcid":"","institution":"SRM MEDICAL COLLEGE HOSPITAL \u0026 RESEARCH CENTRE","correspondingAuthor":false,"prefix":"","firstName":"J.S.","middleName":"","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2025-01-09 10:29:05","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5795445/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5795445/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73519851,"identity":"e0061dc7-912c-4d26-b21a-6cd179c9454a","added_by":"auto","created_at":"2025-01-10 18:11:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44220,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBox-Whisker plot of serum MR Pro-adrenomedullin levels between pre-diabetic patients at baseline and follow-up\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5795445/v1/75fa9e03509ad8adff8e5707.png"},{"id":73520340,"identity":"a6546ac4-f791-4397-961d-e155838e2f5e","added_by":"auto","created_at":"2025-01-10 18:19:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48882,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBox-Whisker plot of serum MR Pro-adrenomedullin levels between T2DM patients at baseline and follow-up\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5795445/v1/c6571d55fe56f8558718419c.png"},{"id":73519858,"identity":"f5163f4e-1456-4c3a-b7eb-3ba0e137cc1b","added_by":"auto","created_at":"2025-01-10 18:11:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30328,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic representing the area under curve by variable of linear logistic regression analysis\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5795445/v1/f650144435e90f4ac548936a.png"},{"id":73519854,"identity":"197890ce-44ce-453d-a617-0128bbe3d884","added_by":"auto","created_at":"2025-01-10 18:11:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":30062,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure - 3 \u003c/strong\u003eReceiver Operating Characteristic representing the area under curve by variable of linear logistic regression analysis\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5795445/v1/b3c2d8ca6c2ff46f52f49060.png"},{"id":73520861,"identity":"c61e6874-afe6-4ec4-ae9a-f8f4d7b0f49e","added_by":"auto","created_at":"2025-01-10 18:27:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1491877,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5795445/v1/ca1338e4-d48c-4212-8c32-494b13f50e97.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eThe Role of Mid Regional Pro-adrenomedullin as a Biomarker for Early Cardio-Renal Dysfunction in Prediabetes and Type 2 Diabetes Mellitus _ A Prospective cohort study\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003ePre-diabetes and Type 2 diabetes mellitus significantly impacts morbidity, mortality, and overall healthcare expenses. These outcomes are mainly derived due to complications associated with pre-diabetes and T2DM, which are macrovascular complication and microvascular complication. The macrovascular complications are cardiovascular diseases and microvascular complication include neuropathy, retinopathy and nephropathy (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The cardiovascular complication and nephropathy are the most significant cause of mortality among Pre-diabetes and T2DM patients.\u003c/p\u003e \u003cp\u003eCardiovascular disease (CVD) remains a leading global health challenge (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Evidence from prospective studies shows that individuals with diabetes are two to four times more likely to develop coronary artery disease (CAD) and suffer myocardial infarction (MI), confirming that type 2 diabetes mellitus (DM) is a significant, independent risk factor for both stroke and heart disease (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Remarkably, approximately 70% of individuals with type 2 DM aged 65 and older succumb to CVD. Furthermore, patients with type 2 DM who have no prior history of CAD face a cardiovascular risk equivalent to those with a previous MI (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother leading cause of mortality after CVD among prediabetes and T2DM is diabetic kidney disease (DKD). One major microvascular complication of prediabetes and T2DM is diabetic nephropathy, if left untreated it leads to chronic kidney disease followed by end stage renal dialysis (ESRD) which ultimately leads to renal failure (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere is multiple theory explaining the development of macrovascular and microvascular complications of diabetes. However, endothelial dysfunction theory is one of the most accepted hypothesis which clearly explain the development of such complication in diabetes (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). A key hypothesis for the early development of atherosclerosis and diabetic kidney disease is endothelial dysfunction, which is broadly defined as either an imbalance in chemical messengers released by endothelial cells or a diminished nitric oxide-dependent vasodilation in response to acetylcholine or increased blood flow. This dysfunction has been identified in individuals with diabetes, insulin resistance, or those at high risk of developing type 2 diabetes (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eReduced ability of nitric oxide synthase (NOS) to produce nitric oxide (NO) has been experimentally observed in endothelial cells (ECs) exposed to diabetic conditions, both in vitro and in vivo (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This suggests that ECs are directly impacted by the diabetic environment, and endothelial dysfunction is believed to be a key contributor to the vascular complications associated with diabetes. Extensive evidence in humans shows a strong link between endothelial dysfunction and the development of microangiopathy and atherosclerosis in both type 1 and type 2 diabetes mellitus (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdrenomedullin (ADM) is a 52-amino-acid peptide hormone that plays a role in endothelial dysfunction and the regulation of blood vessels (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). It functions as both a circulating hormone and a local paracrine regulator (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), with endothelial cells actively producing and secreting ADM (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). ADM levels are elevated in conditions such as heart failure (HF) and post-acute coronary syndrome (ACS), where it helps dilate blood vessels by increasing cyclic adenosine monophosphate (cAMP) levels (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). It also boosts cardiac output, promotes diuresis, and is associated with negative outcomes in HF and myocardial infarction (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The production of ADM increases specially in glomerulus and renal tubules in response to hypoxia and ischemia, conditions commonly seen in diabetes (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). This suggests that ADM may help protect against organ damage in ischemic or hypoxic conditions, particularly in the cardiovascular and renal complications of type 2 diabetes mellitus (T2DM) (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Due to ADM's short half-life and rapid clearance, a more stable mid-regional fragment called mid-regional proadrenomedullin (MR ProADM) is often used as a surrogate marker for measurement (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).The exact relationship between serum Mid Regional Pro-adrenomedullin levels and endothelial dysfunction among T2DM and Pre-diabetes patient is unclear. Thus, this study is conducted to find the role of serum Mid Regional Pro-adrenomedullin as marker of endothelial dysfunction in Type 2 diabetes mellitus patients and pre-diabetes patients.\u003c/p\u003e"},{"header":"MATERIAL AND METHOD","content":"\u003cp\u003eThe study was carried out at tertiary care center in south India (Chennai) [SRM MEDICAL COLLEGE HOSPITAL AND RESEARCH CENTRE] with ethical approval from institutional ethical committee [\u003cstrong\u003eethical committee\u0026apos;s clearance [SRMIEC-ST0723-1771]\u003c/strong\u003e. Informed consent was obtained from all participants or their legal guardians prior to the study\u0026apos;s start. All the methods were performed in accordance with the relevant guidelines and regulations. Throughout the research work, we maintained strict adherence to the ethical principles as per declaration of \u003cem\u003eHelsinki.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe total sample size was calculated using prevalence of prediabetes and T2DM, it was 90 (n\u0026thinsp;=\u0026thinsp;90), which is further subdivided into two equal groups. Group I include 45 prediabetes patients of age above 18 years. Group II includes 45 T2DM patients of age above 18 years.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eInclusion criteria\u003c/h2\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eAdults aged more than 18 years.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ePatients with diagnosed Pre-Diabetes including Impaired fasting glucose and impaired glucose tolerance (HbA1c between 5.7\u0026ndash;6.4%) or a combination and established Type 2 Diabetes Mellitus (Hba1c =/ \u0026gt; 6.5%)\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n\u003c/div\u003e\n\u003ch3\u003eExclusion criteria\u003c/h3\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003ePatients with known history of chronic kidney disease / Coronary Artery Disease.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ePatients with Type 1 Diabetes Mellitus\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ePatients with Heart Failure (EF\u0026thinsp;\u0026lt;\u0026thinsp;50%)\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ePatients with Uncontrolled Hypertension\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ePatients with active infections/ severe anemia\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ePatients unwilling to give consent\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eSample collection and analysis technique\u003c/strong\u003e 5 ml of venous blood was collected in non EDTA vacutainer tubes (red cap vacutainer). The blood samples were allowed to clot than samples were subjected to centrifugation to separate serum. The serum samples were separated into two equal parts. One part was stored at -80\u0026deg;C for estimation of special ELISA parameter (MR ProADM \u0026amp; NT ProBNP) and the second part of serum samples were used to analyzed other biochemical parameters like FBS (Fasting blood sugar), PPBS (Post prandial blood sugar), total cholesterol, triglycerides, LDL-c, VLDL-c, HDL-c, Urea, Creatinine, Albumin, Globulin, A/G ration, ALT, AST, GGT. At the same time 25 ml of urine sample were collected in a clean sterile container for estimation of urine Albumin creatinine ratio. Ejection fraction (EF) of all the patients was measured using 2D Eco.\u003c/p\u003e\n\u003cp\u003eAll the parameters were analyzed at baseline and after 1 year of follow-up. Follow-up leads to the loss of 10% of total sample size.\u003c/p\u003e\n\u003cp\u003eCalculated parameters\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eVLDL-c - Calculated using Freidwald\u0026rsquo;s formula:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n \u003cli\u003eVLDL-C`=`TGL/5\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003eeGFR \u0026ndash; Calculated using CKD EPI Calculator:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul\u003e\n \u003cli\u003e2021`CKD-EPI`Creatinine`=`142`x`(Scr/A)B`x`0.9938age`x`(1.012`if`female),`where`A`and`B`are`the`following:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" alt=\"image\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Scr = Serum Creatinine\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSTATISTICAL ANALYSIS\u003c/p\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eNormality of data was checked using Shapiro-Wilk test.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eMann-Whitney U test was used to compare the levels of serum Mid Regional Pro adrenomedullin and NT Pro BNP among the study participants. The data were expressed in terms of Median [Interquartile range].\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eSpearman\u0026rsquo;s Correlation was conducted to correlate MR Pro ADM with Urine ACR, eGFR, NT Pro BNP, Ejection fraction, Lipid Profile and BMI.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eLinear regression analysis was performed to find the association of MR ProADM with NT-Pro BNP, Ejection Fraction, Urine ACR and eGFR\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eP\u0026thinsp;\u0026le;\u0026thinsp;0.05 was taken as the significant value\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eTest of normality\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eThe normality of data was analyzed using Shapiro Wilk\u0026rsquo;s test. The test result conclude that the data is not following Gaussian distribution, as the p value is 0.002 which is less than 0.05 Thus, non-parametric test were used to analyze and interpret the result.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n\u003c/div\u003e"},{"header":"RESULT","content":"\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic anthropometric measurements and laboratory analysis of participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eParticipants [(Median) Interquartile range]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFollow-up\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-Diabetic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (28,58)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (28,58)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (38,56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (38,56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-Diabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), Male (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), Male (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), Male (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), Male (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-Diabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.3 (20.7, 30.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (20.1, 29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.3 (22.4, 29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.3 (22.9, 28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHbA1c\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-Diabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.1 (5.8, 6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.1 (5.9, 6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.7 (7.2, 8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.1 (7.4, 8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFBS\u003c/p\u003e \u003cp\u003e(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-Diabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112 (105, 117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116 (110,121)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.018*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156 (145,178.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e161 (145, 178)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePPBS (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-Diabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204 (191,219.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e206 (193,221)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e257 (225.5, 293.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e262 (230, 296)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTC (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-Diabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151 (112,176)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e164 (136.5,190)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192 (164, 228)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e182 (160, 222.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTGL (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-Diabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102 (59.5, 149)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102 (63.5, 150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (80, 125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (66, 115.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLDL-c (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-Diabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108 (89.5, 128)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (107, 140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.011*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103 (93, 119)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102 (93, 117.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVLDL-c\u003c/p\u003e \u003cp\u003e(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-Diabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (15.5, 34.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (15.5, 35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (19, 39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHDL-c\u003c/p\u003e \u003cp\u003e(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-Diabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (32.5, 66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (34, 66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (26.5, 37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (23.5, 37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSerum Creatinine\u003c/p\u003e \u003cp\u003e(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-Diabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8 (0.6, 1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8 (0.6, 1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9 (0.65, 1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8 (0.65, 1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSerum Urea\u003c/p\u003e \u003cp\u003e(mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-Diabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (22.5, 36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (22.5, 35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003cp\u003e(mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-Diabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.55 (10.51, 17.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.55 (10.51, 16.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.42 (11.21, 17.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.5 (10.74, 17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUrine ACR\u003c/p\u003e \u003cp\u003e(mg/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-Diabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (17.4, 43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.000*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (14, 19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (28, 76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.000*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eeGFR (CKD EPI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-Diabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108 (86, 128)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115 (86, 127.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (92, 119)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108 (94.5, 120.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNT-Pro BNP\u003c/p\u003e \u003cp\u003e(pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-Diabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119 (80.5, 192)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e232 (145.5, 272)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.000*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e198 (114.5, 243.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e224 (182, 313.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.005*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eBMI: Body Mass Index; TC: Total Cholesterol; TGL: Triglycerides; LDL-c: Low density lipoprotein cholesterol; VLDL: Very low-density lipoprotein cholesterol; HDL-c: High density lipoprotein cholesterol; BUN: Blood Urea Nitrogen; eGFR: Estimated glomerular filtration rate; NT-Pro BNP: : N-terminal pro\u0026ndash;B-type natriuretic peptide\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of serum MR Pro-adrenomedullin between pre-diabetic patients at baseline and follow-up.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eStudy population [Median (interquartile range)]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-Diabetics (Baseline)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-Diabetic (Follow-up)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMR Pro-adrenomedullin\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(pg/ml)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.10(4.80, 60.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.9 (13.40, 108.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eP\u0026thinsp;\u0026le;\u0026thinsp;0.05\u0026thinsp;=\u0026thinsp;Statistically significant\u003c/p\u003e \u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u0026thinsp;=\u0026thinsp;Statistically non-significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMann-Whitney U test was performed to compare the serum MR Pro-adrenomedullin between pre-diabetic patients at baseline and follow-up. There was significant difference \u003cb\u003e[\u003c/b\u003e\u003cb\u003eMann-Whitney U value\u0026thinsp;=\u0026thinsp;1280.500, P\u0026thinsp;=\u0026thinsp;0.031\u003c/b\u003e\u003cb\u003e]\u003c/b\u003e in the serum MR Pro-adrenomedullin levels with median levels for pre-diabetic patients at baseline were \u003cb\u003e[24.10(4.80, 60.35)] pg/ml\u003c/b\u003e and pre-diabetic patients at follow-up were \u003cb\u003e[59.9 (13.40, 108.05)] pg/ml.\u003c/b\u003e\u003c/p\u003e\n\u003ch3\u003ePairwise analysis of Table :\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePairwise analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFold change in concentration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMR Pro- adrenomedullin\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(pg/ml)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-diabetic (Baseline): Pre-diabetic (Follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eP\u0026thinsp;\u0026le;\u0026thinsp;0.05\u0026thinsp;=\u0026thinsp;Statistically significant\u003c/p\u003e \u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u0026thinsp;=\u0026thinsp;Statistically non-significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe statistically significant fold change in concentration of serum MR Pro- adrenomedullin between pre-diabetic patients at baseline and pre-diabetic patients at follow-up was fond to be \u003cb\u003e[+\u0026thinsp;2.4 folds (P\u0026thinsp;=\u0026thinsp;0.031)]\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBox whisker plot representing serum MR Pro-adrenomedullin among the study group participants. Elevated serum MR Pro-adrenomedullin levels among pre-diabetic patients at follow-up is clearly depicted in the figure as compare to pre-diabetics patients at baseline.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of serum MR Pro-adrenomedullin between T2DM patients at baseline and follow-up.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eStudy population [Median (interquartile range)]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM (Baseline)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT2DM (Follow-up)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMR Pro-adrenomedullin\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(pg/ml)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.00 (16.10, 72.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143.10 (75.60, 261.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eP\u0026thinsp;\u0026le;\u0026thinsp;0.05\u0026thinsp;=\u0026thinsp;Statistically significant\u003c/p\u003e \u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u0026thinsp;=\u0026thinsp;Statistically non-significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMann-Whitney test was performed to compare the serum MR Pro-adrenomedullin between T2DM patients at baseline and follow-up. There was significant difference \u003cb\u003e[\u003c/b\u003e\u003cb\u003eMann-Whitney U value\u0026thinsp;=\u0026thinsp;1455.000, P\u0026thinsp;=\u0026thinsp;0.000\u003c/b\u003e\u003cb\u003e]\u003c/b\u003e in the serum MR Pro-adrenomedullin levels with median levels for T2DM patients at baseline were \u003cb\u003e[47.00 (16.10, 72.95)] pg/ml\u003c/b\u003e and T2DM patients at follow-up were \u003cb\u003e[143.10 (75.60, 261.40)] pg/ml.\u003c/b\u003e\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePairwise analysis of Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e:\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePairwise analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFold change in concentration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMR Pro- adrenomedullin\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(pg/ml)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM (Baseline): T2DM (Follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eP\u0026thinsp;\u0026le;\u0026thinsp;0.05\u0026thinsp;=\u0026thinsp;Statistically significant\u003c/p\u003e \u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u0026thinsp;=\u0026thinsp;Statistically non-significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe statistically significant fold change in concentration of serum MR Pro- adrenomedullin between T2DM patients at baseline and T2DM patients at follow-up was fond to be \u003cb\u003e[+\u0026thinsp;3.04 folds (P\u0026thinsp;=\u0026thinsp;0.031)]\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBox whisker plot representing serum MR Pro-adrenomedullin among the study group participants. Elevated serum MR Pro-adrenomedullin levels among T2DM patients at follow-up is clearly depicted in the figure as compare to T2DM patients at baseline.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eRepresents correlation between MR Pro-ADM and NT Pro BNP, Urine ACR, eGFR, BMI, HbA1c\u003c/b\u003e A Spearman\u0026rsquo;s correlation matrix was calculated to examine the relationship between the variables. The matrix is presented in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. MR Pro-ADM was found to be positively correlated with NT Pro-BNP, Urine ACR, HbA1c and BMI at the same time MR Pro-ADM has a statistically significant negative correlation with eGFR.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eSpearman\u0026rsquo;s correlation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNT Pro-BNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUrine ACR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHbA1c\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eMR Pro-ADM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.000**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.000**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.000**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.16*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e**. Correlation is significant at the 0.01 level (2-tailed).\u003c/p\u003e \u003cp\u003e*. Correlation is significant at the 0.05 level (2-tailed).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eNT Pro-BNP: N-terminal pro\u0026ndash;B-type natriuretic peptide; Urine ACR: Albumin Creatinine ratio; eGFR: Estimated glomerular filtration rate; BMI: Body Mass Index\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRepresents correlation between MR Pro-ADM and Total Cholesterol, Triglyceride, HDL-c, LDL-c, VLDL-c, EF\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eSpearman\u0026rsquo;s correlation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTGL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHDL-c\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLDL-c\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVLDL-c\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eMR Pro-ADM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.006**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.038*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e**. Correlation is significant at the 0.01 level (2-tailed).\u003c/p\u003e \u003cp\u003e*. Correlation is significant at the 0.05 level (2-tailed).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eTC: Total Cholesterol; TGL: Triglyceride; HDL-c: High Density Lipoprotein cholesterol; LDL-c: Low Density Lipoprotein Cholesterol; VLDL-c : Very Low Density Lipoprotein Cholesterol; EF: Ejection Fraction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA Spearman\u0026rsquo;s correlation matrix was calculated to examine the relationship between the variables. The matrix is presented in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. MR Pro-ADM was found to be positively correlated with TC, at the same time MR Pro-ADM has a statistically significant negative correlation with HDL-c and EF.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Linear logistic regression analysis to find the relationship of MR Pro \u0026ndash; ADM and the risk of Cardiac dysfunction. Chi\u003csup\u003e2\u003c/sup\u003e test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChi2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe table shows the results of a Chi-square (χ\u0026sup2;) test associated with a logistic regression model. This test is often used to assess the overall significance of the model\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eChi2 (χ\u0026sup2;) statistic, was found to be 8.07. It measures the difference between the observed and expected frequencies of the outcomes predicted by the model. A higher Chi-square value indicates a greater discrepancy between the expected and observed values, suggesting that the model's predictors have a significant relationship with the outcome.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003edf (Degrees of Freedom) 1, represent the number of predictors in the model for a simple logistic regression.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ep (p-value) This is the probability of observing the Chi2 test statistic as extreme as the one observed under the null hypothesis. The null hypothesis is that there is no relationship between the observed and expected frequencies of the outcomes predicted by the model. A p-value of \u003cb\u003e0.005\u003c/b\u003e suggests that there's a 0% probability of observing a Chi-square statistic as extreme as \u003cb\u003e8.07\u003c/b\u003e if the null hypothesis were true.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% confidence interval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMR Pro-ADM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1-1.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eA logistic regression analysis was performed to examine the influence of \u003cem\u003eMR Pro-ADM to\u003c/em\u003e predict the chances of development of cardiac dysfunction among Pre-diabetic and T2DM patients. Logistic regression analysis showed that the model as a whole was significant (Chi\u003csup\u003e2\u003c/sup\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u0026thinsp;=\u0026thinsp;8.07, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.005, n\u0026thinsp;=\u0026thinsp;180).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe coefficient of the variable \u003cem\u003eMR Pro-ADM\u003c/em\u003e is b\u0026thinsp;=\u0026thinsp;\u003cb\u003e0\u003c/b\u003e, which is positive. This means that an increase in \u003cem\u003eMR Pro-ADM\u003c/em\u003e is associated with an increase in the probability that the dependent variable has \u0026lsquo;Cardiac dysfunction\u0026rsquo;\u0026rsquo;. The p-value of \u003cb\u003e0.005\u003c/b\u003e indicates that this influence is statistically significant. The odds Ratio of 1 indicates that one unit increase of the variable MR Pro-ADM will increase the odds that the dependent variable has \"Cardiac dysfunction\" by 1 time.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eArea under the curve (AUC) for variables of linear logistic regression analysis MR Pro-ADM was found to be 0.594 which is closed 1 (maximum AUC) indicating that these variables are satisfactorily classified diseased (Cardiac dysfunction) and non-diseased (Non-Cardiac dysfunction) patients with high TP vs FP ratio\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003e\u0026ndash;\u003c/b\u003e Linear logistic regression analysis to find the relationship of MR Pro \u0026ndash; ADM and the risk of Renal dysfunction. \u003cb\u003eChi\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e \u003cb\u003etest\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChi2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe table shows the results of a Chi-square (χ\u0026sup2;) test associated with a logistic regression model. This test is often used to assess the overall significance of the model\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eChi2 (χ\u0026sup2;) statistic, was found to be 7.14. It measures the difference between the observed and expected frequencies of the outcomes predicted by the model. A higher Chi-square value indicates a greater discrepancy between the expected and observed values, suggesting that the model's predictors have a significant relationship with the outcome.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003edf (Degrees of Freedom) 1, represent the number of predictors in the model for a simple logistic regression.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ep (p-value) This is the probability of observing the Chi2 test statistic as extreme as the one observed under the null hypothesis. The null hypothesis is that there is no relationship between the observed and expected frequencies of the outcomes predicted by the model. A p-value of \u003cb\u003e0.008\u003c/b\u003e suggests that there's a 0% probability of observing a Chi-square statistic as extreme as \u003cb\u003e7.14\u003c/b\u003e if the null hypothesis were true.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% confidence interval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMR Pro-ADM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1-1.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eA logistic regression analysis was performed to examine the influence of \u003cem\u003eMR Pro-ADM to\u003c/em\u003e predict the chances of development of cardiac dysfunction among Pre-diabetic and T2DM patients. Logistic regression analysis showed that the model as a whole was significant (Chi\u003csup\u003e2\u003c/sup\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u0026thinsp;=\u0026thinsp;7.14, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.008, n\u0026thinsp;=\u0026thinsp;180).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe coefficient of the variable \u003cem\u003eMR Pro-ADM\u003c/em\u003e is b\u0026thinsp;=\u0026thinsp;\u003cb\u003e0\u003c/b\u003e, which is positive. This means that an increase in \u003cem\u003eMR Pro-ADM\u003c/em\u003e is associated with an increase in the probability that the dependent variable has \u0026lsquo;Renal dysfunction\u0026rsquo;\u0026rsquo;. The p-value of \u003cb\u003e0.01\u003c/b\u003e indicates that this influence is statistically significant. The odds Ratio of 1 indicates that one unit increase of the variable MR Pro-ADM will increase the odds that the dependent variable has \"Renal dysfunction\" by 1 time.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\u003cp\u003eArea under the curve (AUC) for variables of linear logistic regression analysis MR Pro-ADM was found to be 0.622 which is closed 1 (maximum AUC) indicating that these variables are satisfactorily classified diseased (Renal dysfunction) and non-diseased (Normal renal function) patients with high TP vs FP ratio\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe objective of the current study is to find association between MR proADM and early cardio-renal complications in patients with Prediabetes and Type 2 diabetes mellitus. The present study provides evidence that MR-proADM is a significant predictor of Cardiorenal dysfunction among T2DM patients and Pre-diabetic patients. Furthermore, this study confirms that serum MR-proADM was significantly elevated at follow-up (59.9 pg/ml) as compared baseline (24.19 pg/ml) in Pre-Diabetic patients, similarly MR-proADM is found to be significantly elevated at follow-up (143.10 pg/ml) as compared to baseline (47.0 pg/ml) in T2DM patients. The fold change in the concentration of MR-Pro ADM from baseline to follow-up in pre-diabetic patients was 2.4-fold and in case of T2DM patients was 3.04 folds. We also found that this biomarker can be used to predict the Cardiorenal dysfunction among T2DM patients and Pre-diabetic patients. It was found to be more sensitive than NT Pro BNP, Ejection Fraction (Used to detect cardiac dysfunction) and Urine ACR, eGFR (Used to detect renal dysfunction).\u003c/p\u003e \u003cp\u003eThe current study found elevated MR proADM levels at follow-up as compared to baseline in Pre-diabetic patients. To our knowledge this is the first study which tries to understand the relationship between serum MR Pro ADM levels at baseline and follow-up among Pre-Diabetic patients. Similarly, the study also found elevated levels of MR ProADM at follow-up as compared to baseline among T2DM patient. This finding is in agreement with the finding of \u003cem\u003ePierre-Jean Saulnier et al.\u003c/em\u003e who also found elevated MR ProADM levels at follow-up as compared to baseline among T2DM [33]. This is because diabetes is associated with macrovascular and microvascular complications like cardiovascular disease and retinopathy, nephropathy, neuropathy, the major cause of these complications are endothelial dysfunction and hence vascular damage (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Adrenomedullin is mainly released from vascular endothelial cells and smooth muscle thus slow, progressive damage to blood vessel in diabetes patients due to accumulation of AGEs (Advance glycosylated end product), oxidative damage leads to leakage or release of ADM in to blood which leads to elevated levels of MR-ProADM among diabetes patients which can act as highly sensitive biomarker of vascular damage among T2DM patients [34,35].\u003c/p\u003e \u003cp\u003eThe study also revealed that MR ProADM has a significant positive correlation with NT-Pro BNP and a significant negative correlation with Ejection Fraction. This clearly suggest that MR ProADM can act as a marker of cardiac dysfunction among Pre-Diabetic and T2DM patients. MR-proADM is a precursor peptide of ADM that is involved in the function of endothelial cells, causes long-lasting vasodilatation, carries natriuretic properties and might have positive inotropic effects on the contractility of the myocardium [36,37,38].\u003c/p\u003e \u003cp\u003eSimilarly, the study also found that MR ProADM has significant positive correlation with Urine ACR and a significant negative correlation with eGFR, indicating MR ProADM can also acta sensitive marker of renal dysfunction among T2DM patients.\u003c/p\u003e \u003cp\u003eAll these finding suggested that MR ProADM can act as a biomarker of cardiorenal dysfunction among Pre-diabetic patients as well as T2DM patients with higher sensitivity than NT-Pro BNP, Ejection Fraction which is used to detect Cardiac dysfunction) and Urine ACR and eGFR which is used to detect renal dysfunction.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe study aimed to investigate the association between Serum Mid Regional Pro adrenomedullin (MR proADM) and early detection of cardio-renal dysfunction in patients with Prediabetes and Type 2 Diabetes Mellitus. The findings indicate MR-proADM as a highly sensitive and reliable biomarker for the prediction of cardiorenal dysfunction in patients with prediabetes and T2DM. The findings demonstrate a significant elevated MR-proADM levels at follow-up as compared to basline among both Pre-Diabetic and T2DM patients as well as the study also demonstrate the positive correlation of MR Pro ADM with NT-Pro BNP and a negative correlation with Ejection Fraction. Similarly, study revealed a positive correlation of MR ProADM with urine ACR and a negative correlation with eGFR. These results suggest the important role of MR-proADM in reflecting endothelial damage and vascular complications, providing a valuable biomarker for the early identification and management of diabetes-associated cardiorenal complications.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eADDITIONAL INFORMATION\u003c/h2\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eADDITIONAL INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTION\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003e Oshmi Rao Rajesh\u003c/strong\u003e: Conceptualization (equal); formal analysis (lead); investigation (lead); methodology (lead); validation (equal); visualization (equal); writing \u0026ndash; original draft (lead); software (supporting); supervision (lead); writing \u0026ndash; review and editing (supporting).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eJ.S. Kumar\u003c/strong\u003e: Conceptualization (equal); data curation (equal); funding acquisition (supporting); investigation (supporting); methodology (supporting); resources (lead); software (supporting); supervision (lead); writing \u0026ndash; review and editing (supporting).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth the authors would like to acknowledge the support from the department of General Medicine, SRM Medical College Hospital \u0026amp; Research Centre.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNil\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe patient details and all the raw data including consent forms will be provided by the\u003c/p\u003e\n\u003cp\u003ecorresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe above project was reviewed by the institutional ethics committee of SRM Medical College Hospital \u0026amp; Research Centre, and approval was obtained (ETHICS CLEARANCE NUMBER: SRMIEC-ST0723-1771). Informed consent was obtained from all patients whose samples were utilized in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMichael J (April 2008) Fowler; Microvascular and Macrovascular Complications of Diabetes. Clin Diabetes 1(2):77\u0026ndash;82\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenjamin EJ, Blaha MJ, Chiuve SE, Cushman M, Das SR, Deo R et al (2017) Heart disease and stroke statistics-2017 update: a report from the American Heart Association. Circulation 135:e146\u0026ndash;603. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/CIR.0000000000000485\u003c/span\u003e\u003cspan address=\"10.1161/CIR.0000000000000485\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKannel WB, McGee DL (1979) Diabetes and cardiovascular disease. 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Adrenocortical steroids, thyroid hormones and retinoic acid augment the production of adrenomedullin in vascular smooth muscle cells. Biochem Biophys Res Commun 1995 ;211:686\u0026ndash;693\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSugo S Minamino N Shoji H, Interleukin-1, tumor necrosis factor and lipopolysaccharide additively stimulate production of adrenomedullin in vascular smooth muscle cells. Biochem Biophys Res Commun 1995;207:25\u0026ndash;32\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSugo S Minamino N Shoji H, Kangawa K, Matsuo H. Effects of vasoactive substances and cAMP related compounds on adrenomedullin production in cultured vascular smooth muscle cells. FEBS Lett 1995 ;369:311\u0026ndash;314\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHansell P Welch WJ, Blantz RC Palm F Determinants of kidney oxygen consumption and their relationship to tissue oxygen tension in diabetes and hypertension. Clin Exp Pharmacol Physiol 2013 ;40:123\u0026ndash;137\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStruck J, Tao C, Morgenthaler NG A. Bergmann Identification of an adrenomedullin precursor fragment in plasma of sepsis patients\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKitamura K, Sakata J, Kangawa K, Kojima M, Matsuo H (1993) Eto Cloning and characterization of cDNA encoding a precursor for human adrenomedullin. Biochem Biophys Res Commun 194:720\u0026ndash;725\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaulnier PJ, Gand E, Velho G, Mohammedi K, Zaoui P, Fraty M, Halimi JM, Roussel R, Ragot S, Hadjadj S, SURDIAGENE Study Group (2017) Association of Circulating Biomarkers (Adrenomedullin, TNFR1, and NT-proBNP) With Renal Function Decline in Patients With Type 2 Diabetes: A French Prospective Cohort. Diabetes Care 40(3):367\u0026ndash;374. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/dc16-1571\u003c/span\u003e\u003cspan address=\"10.2337/dc16-1571\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eEpub 2016 Dec 20. PMID: 27998909\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong HK, Tang F, Cheung TT, Cheung BM (2014) Adrenomedullin and diabetes. World J Diabetes 5(3):364\u0026ndash;371. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4239/wjd.v5.i3.364\u003c/span\u003e\u003cspan address=\"10.4239/wjd.v5.i3.364\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 24936257; PMCID: PMC4058740\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKato J, Tsuruda T, Kita T, Kitamura K, Eto T (2005) Adrenomedullin Arterioscler Thromb Vascular Biology 25(12):2480\u0026ndash;2487\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJougasaki M, Burnett JC Jr (2000) Adrenomedullin: potential in physiology and pathophysiology. Life Sci 66:855\u0026ndash;872\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamson WK (1999) Adrenomedullin and the control of fluid and electrolyte homeostasis. Ann Rev Physiol 61:363\u0026ndash;389\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIhara T, Ikeda U, Tate Y, Ishibashi S, Shimada K (2000) Positive inotropic effects of adrenomedullin on rat papillary muscle. Eur J Pharmacol 390:167\u0026ndash;172\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"SRM Institute of Science and Technology","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mid-Regional Pro-adrenomedullin (MR-ProADM), Cardiovascular complications, Renal complications, Type 2 diabetes mellitus (T2DM), Endothelial dysfunction","lastPublishedDoi":"10.21203/rs.3.rs-5795445/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5795445/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eCardiovascular and renal complications are leading causes of morbidity and mortality in patients with Prediabetes type 2 diabetes mellitus (T2DM). Identifying reliable biomarkers for early detection of cardio-renal dysfunction is essential to mitigate disease progression and improve clinical outcomes. This prospective cohort study evaluates the association between serum Mid-Regional Pro-adrenomedullin (MR-ProADM) levels and early signs of endothelial dysfunction in patients with prediabetes and T2DM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u003c/strong\u003e The study was conducted at SRM Medical College Hospital and Research Centre, involving 90 participants divided equally into prediabetic and T2DM groups. Comprehensive clinical assessments included measurements of MR-ProADM, NT-ProBNP, eGFR, urine albumin-to-creatinine ratio (ACR), lipid profiles, and glycated hemoglobin (HbA1c). All these petameters are measured at baseline as well as after 1 year of follow-up.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinding: \u003c/strong\u003eThe study observed a significant increase in serum MR-ProADM levels in prediabetic and T2DM patients during follow-up compared to baseline. MR-ProADM correlated positively with early markers of endothelial dysfunction and cardio-renal impairment. Linear regression analysis confirmed strong associations with NT-ProBNP, Ejection fraction, eGFR, and ACR, indicating its potential as a predictive biomarker for cardiovascular and renal complications. ROC curve analysis further validated MR-ProADM’s diagnostic utility in identifying patients at risk. This study highlights MR-ProADM’s clinical relevance as a stable, reliable marker for early detection, risk assessment, and timely intervention in prediabetic and T2DM populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation: \u003c/strong\u003eSerum MR-ProADM shows potential as a reliable biomarker for early detection of cardio-renal dysfunction in prediabetes and T2DM. Its significant correlation with NT-ProBNP, Ejection fraction, eGFR, and ACR highlights its utility in predicting cardiovascular and renal complications, enabling timely intervention to improve clinical outcomes.\u003c/p\u003e","manuscriptTitle":"The Role of Mid Regional Pro-adrenomedullin as a Biomarker for Early Cardio-Renal Dysfunction in Prediabetes and Type 2 Diabetes Mellitus _ A Prospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-10 18:11:07","doi":"10.21203/rs.3.rs-5795445/v1","editorialEvents":[{"type":"communityComments","content":1}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eeb65915-63fc-426a-ba6a-5f96cff1f661","owner":[],"postedDate":"January 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42613940,"name":"Endocrinology \u0026 Metabolism"}],"tags":[],"updatedAt":"2025-01-10T18:11:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-10 18:11:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5795445","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5795445","identity":"rs-5795445","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}