Comprehensive Analysis of Platelet Indices Variability in Diabetes Mellitus Patients: Implications for Clinical Evaluation and Management

Comprehensive Analysis of Platelet Indices Variability in Diabetes Mellitus Patients: Implications for Clinical Evaluation and Management | 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 Comprehensive Analysis of Platelet Indices Variability in Diabetes Mellitus Patients: Implications for Clinical Evaluation and Management Dr. Vikas Tiwari, Dr. Jaishree Tiwari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9262707/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: Diabetes mellitus (DM) is a chronic metabolic disorder associated with significant vascular complications. Platelet activation plays a crucial role in the pathogenesis of these complications. Mean platelet volume (MPV) has emerged as a potential marker of platelet activity and thrombotic risk. Objective: To evaluate the role of platelet indices, particularly MPV, in relation to glycemic control and diabetic complications in patients with type 2 DM. Methods: This cross-sectional study included 110 patients with type 2 DM. Clinical parameters and biochemical markers, including fasting blood glucose (FBG), glycated hemoglobin (HbA1c), and MPV, were analyzed. Correlations between platelet indices, glycemic status, and complications were assessed using appropriate statistical methods. Results: MPV levels were significantly higher in patients with poor glycemic control and showed a strong positive correlation with HbA1c and FBG. Elevated MPV values were also observed in patients with microvascular complications such as retinopathy and proteinuria. No significant association was found between MPV and age or gender, while higher MPV levels were noted in patients with increased BMI and hypertension. Conclusion: MPV may serve as a simple, cost-effective biomarker for assessing platelet activation and identifying patients at increased risk of vascular complications in type 2 DM. Routine evaluation of platelet indices could aid in early risk stratification and management. Further large-scale studies are required to confirm these findings. Endocrinology & Metabolism Clinical Pharmacology Laboratory Diagnostics Diabetes mellitus mean platelet volume platelet indices glycemic control vascular complications Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION Diabetes mellitus (DM) is a multifactorial metabolic disorder characterized by chronic hyperglycemia resulting from defects in insulin secretion, insulin action, or both. It is one of the most significant global health challenges of the 21st century, with a rapidly increasing prevalence and a substantial burden of morbidity, mortality, and healthcare expenditure.¹ , ² The disease is associated with long-term complications affecting multiple organ systems, including the cardiovascular system, kidneys, retina, and peripheral nerves, primarily due to persistent metabolic dysregulation and vascular damage.³ The pathophysiology of diabetic complications is complex and involves chronic inflammation, oxidative stress, endothelial dysfunction, and activation of coagulation pathways.⁴ Among these, platelet dysfunction plays a pivotal role in the initiation and progression of both microvascular and macrovascular complications. Platelets in individuals with diabetes exhibit enhanced reactivity, increased adhesion, and aggregation tendencies, contributing to a hypercoagulable state.⁵ Emerging evidence indicates that hyperglycemia induces structural and functional alterations in platelets, including increased membrane glycation, altered receptor expression, impaired nitric oxide signaling, and enhanced intracellular calcium mobilization.⁶ These changes result in increased thromboxane A₂ production, reduced prostacyclin activity, and accelerated platelet turnover, collectively promoting thrombogenesis and vascular injury.⁷ Platelet indices—such as mean platelet volume (MPV), platelet distribution width (PDW), plateletcrit (PCT), and platelet large cell ratio (P-LCR)—have gained considerable attention as indirect markers of platelet activation and heterogeneity. These indices are routinely obtained through automated hematology analyzers and offer a simple, cost-effective, and reproducible method for evaluating platelet morphology and function.⁸ Among these parameters, MPV is widely recognized as a marker of platelet size and activity, with larger platelets being metabolically and enzymatically more active and possessing greater prothrombotic potential.⁹ Studies have demonstrated that elevated MPV levels are associated with poor glycemic control, increased insulin resistance, and higher risk of vascular complications in patients with diabetes mellitus.¹⁰ Similarly, PDW reflects variability in platelet size and is considered a sensitive indicator of platelet activation and anisocytosis, while PCT represents the total platelet mass and correlates with thrombotic risk.¹¹ Recent clinical studies and meta-analyses have consistently shown that these platelet indices are significantly altered in patients with type 2 diabetes mellitus and are strongly associated with microvascular complications such as diabetic retinopathy, nephropathy, and neuropathy.¹² Furthermore, platelet indices have been shown to correlate with key glycemic parameters, including glycated hemoglobin (HbA1c), fasting blood glucose (FBG), and duration of disease.¹³ Elevated platelet indices in poorly controlled diabetes reflect ongoing subclinical inflammation and endothelial dysfunction, highlighting their potential utility as early biomarkers for disease progression and vascular risk stratification.¹⁴ The clinical relevance of platelet indices is further enhanced by their accessibility, affordability, and ease of measurement, particularly in resource-limited settings.¹⁵ Given the growing burden of diabetes, especially in developing countries, there is an urgent need for reliable and cost-effective markers that can facilitate early detection of complications and guide clinical decision-making.¹⁶ Recent advances in hematological and cardiovascular research have reinforced the role of platelet indices as prognostic indicators in metabolic disorders, with growing evidence supporting their integration into routine clinical assessment of diabetic patients.¹⁷ Therefore, the present study aims to comprehensively evaluate the variability of platelet indices in patients with diabetes mellitus and to investigate their correlation with glycemic control (HbA1c, FBG) and duration of disease, with the objective of assessing their potential role as biomarkers for early detection and management of thromboembolic complications.¹⁸ METHODOLOGY AND METHOD PREPARATION A OF PATIENTS From May to December 2025, a total of 110 patients with type 2 diabetes mellitus attending medicine OPD and Diabetes mellitus attend at AMCH medicine wards were studied. Our study comprised patients who were dependent. All of the patients received a thorough clinical examination. We took measurements of the subjects' weight, height, and belly circumference. The systolic and diastolic blood pressures (SBP and DBP) were measured with a sphygmomanometer after 5 minutes of rest in an upright position. Blood pressure was obtained in the right upper arm at least three times. The mean of three was employed in the analysis. Patients with a mean blood pressure of greater than 140/90 mm Hg or who were taking antihypertensive medication were diagnosed with hypertension. COLLECTION AND PRESERVATION OF SAMPLE The sample was collected from the venipuncture the total number patients were 110 the was collected in the EDTA vile red top vile, gray top were for blood sugar estimation for by biochemistry 2.5ml of blood collected in each tube for hematological investigation and 3ml were collected for by biochemistry investigation all the primary specimen containers must be labeled with at least two IDs at the time of sample collection. To protect the specimen’s integrity and as a result, the test results, proper storage and handing are required. The EDTA sample were proper mix before processing in the automated analyzer and after completed sample processing the sample were store at 4 o c in the fridge for a variety of purposes LABORATORY METHODS Following a 12-hour overnight fast, venous samples were drawn at 8:30 a.m. to determine mean platelet volume, HbA1C, FBS, PPBS, Hb, and triglyceride levels (TG). HbA1c levels were calculated auto analyzed. MPV levels were assessed using an automated blood Coulter (Beckman Coulter Act5Diff). Using an auto analyzer, plasma glucose was measured using the glucose oxidase method (FBS and PPBS). Hypertriglyceridemia is defined as a blood concentration over 150 mg/dl (> 150 mg/dl). The patients were separated into two groups depending on their HbA1C values after the baseline examination. Diabetics with strong glycemic control (HbA1c less than 7%) are distinguished from diabetics with poor glycemic control (HbA1c greater than 7%). (HbA1c greater than 7 percent). (HbA1c level is more than 15%). A 7 percent HbA1c level (HbA1c level of more than 7 percent). Between the two groups, all factors were compared. These groups were further separated into male and female groups. STATISTIC ANALYSIS Descriptive records had been completed for all data and suitable statistical tests of evaluation had been completed. Continuous variables had been analyzed with the Unpaired t-take a look at/single-thing ANOVA and specific variables have been analyzed with chi-squared test/ Fisher Exact Test. Regression evaluation emerge as finished and odds ratio with self-belief c programming language calculated. Statistical significance became taken as P < zero.05. The facts have been analyzed using SPSS Version sixteen. Microsoft Excel 2010.Changed into used to generate charts. RESULTS AND DISCUSSION Baseline Characteristics of Study Population A total of 110 patients with type 2 diabetes mellitus were categorized into good glycemic control (n = 45) and poor glycemic control groups (n = 65) based on HbA1c levels. Age Distribution The majority of participants in both groups were within the 51–60 years age category. The mean age was comparable between the groups (52.32 ± 11.02 years vs. 54.32 ± 11.60 years), with no statistically significant difference (p = 0.4012), indicating age-matched cohorts. Gender Distribution Gender distribution was comparable between groups, with no significant difference (p = 0.7421). Females constituted a slightly higher proportion in the poor glycemic control group. Body Mass Index (BMI) Overweight individuals predominated in both groups, particularly in the poor glycemic control group. However, the difference in mean BMI was not statistically significant (28.20 ± 5.82 vs. 30.12 ± 4.93; p = 0.5431). AGE Table No. 1 Age Distribution of Good and poor glycemic control group AGE Good glycemic control group Poor glycemic control group (%) (%) 31–40 years 8 19 9 13 41–50 years 13 32 17 22 51–60 years 10 27 25 35 61–70 years 10 22 14 30 Total 45 100 65 100 • Gender Table No. 2 Comparison of Age Distribution of Good and poor glycemic control group Age Distribution Good glycemic control group Poor glycemic control group P-value Unpaired t-Test Mean 52.32 54.32 0.4012 SD 11.02 11.6 While analyzing age distribution, it was observed that the majority in the good glycemic control group belonged to 51–60 years age class interval with a mean age of 50.84 years and the majority in the poor glycemic control group belonged to the same age class interval (52.32%) with a mean age of 54.32 years (p = 0.4012) Table No. 3 Male and Female of Good and poor glycemic control group is the percentage of it. Gender status Good glycemic control group (%) Poor glycemic control group (%) P value Chi Squared Test Male 18 45.00 26 45.16 0.7421 Female 22 55.00 44 54.84 Total 40 100.00 70 100.00 While studying gender popularity, it was determined that during desirable glycemic control organization ladies and men had been equally disbursed (50.00%) and majority within the bad glycemic control organization were girls (54.84%) (p = 0.7421) BMI Table No. 4 BMI groups of Good and poor glycemic control group BMI Groups Good glycemic control group Poor glycemic control group (%) (%) Normal 15 35.21 15 22 Overweight 16 44.79 37 54 Obese 11 20 16 24 Total 42 100 68 100 Table No. 5 BMI Distribution of Good and poor glycemic control group BMI Distribution Good glycemic control group Poor glycemic control group P value Unpaired t Test Mean 28.20 30.12 0.5431 SD 5.82 4.93 While analyzing BMI distribution, it was observed that majority in the good glycemic control group belonged to overweight BMI class interval (42.11%) with a mean BMI of 27.30, and majority in the poor glycemic control group belonged to the same BMI class interval (54.84%) with a mean BMI of 28.11 (p = 0.3806) FBS Table No. 6 FBS groups of Good and poor glycemic control group Good glycemic control group Poor glycemic control group (%) (%) ≤ 100 mg/dl 12 27.34 4 2.22 101-120mg/dl 10 32 5 5.78 121-140mg/dl 8 17 7 9 > 140 mg/dl 11 23.66 53 83 Total 41 100 69 100 Table No. 7 FBS distribution of Good and poor glycemic control group Fasting Blood Sugar Distribution Good glycemic control group Poor glycemic control group P-value Unpaired t Test Mean 111.32 174.45 < 0.0001 SD 25.47 33.72 While analyzing FBS distribution, it was observed that, majority in good glycemic control group belonged to 101–120 mg/dl FB S class interval (28.95%) with a mean FBS of 121.63 mg/dl and majority in poor glycemic control group belonged to > 140 mg/dl FBS class interval (82.26%) with a mean FBS of 161.21 mg/dl (p = < 0.0001). The data subjected to statistical unpaired t-test reveals the existence of a statistically significant association between MPV distribution and proteinuria status (p < 0.05) This significance is exhibited by the increased mean MPV levels in the proteinuria group compared to proteinuria -group (1.64 fL increase, 16% higher) The same view was echoed by studies done by Ates et al and Papanas et al. This suggested a role for the increased platelet activity in the pathogenesis of vascular complications. On the other hand, in the studies done by Hekimsoy et al and Demirtunc et al, MPV in diabetic subjects with and without complications did not show any significant difference. They explained it to be possible because of the rapid consumption of activated platelets in diabetic patients with complications. Table No. 8 Comparison of Mean platelet volume (MPV) vs retinopathy distribution Mean platelet volume vs retinopathy distribution Retinopathy+ Group Retinopathy- Group P value Unpaired t Test Mean 12.80 9.92 < 0.0001 SD 1.03 1.89 While analyzing MPV distribution, it was observed that the majority in the retinopathy +ve group belonged to 10.01-12.00 fL MPV class interval (80.00%) with a mean MPV of 10.50 fL and the majority in retinopathy -ve group belonged to ≤ 8.00fL MPV class interval (40.00%) with a mean MPV of 8.79 fL (p = < 0.0001) • Mean Platelet Volume Vs Gender Table No. 9 Comparison of Mean platelet volume vs gender groups Mean platelet volume vs gender groups Male (%) Female (%) ≤ 8.00 fL 12 31 21 27.44 8.01-10.00fL 16 30 29 32.32 10.01-12.00fL 20 39 12 40.24 Total 48 100 62 100 Table No. 10 Comparison of Mean platelet volume vs gender distribution Mean platelet volume vs gender distribution Male Female P value Unpaired t Test Mean 10.32 10.65 0.7231 SD 2.13 2.54 While analyzing MPV distribution concerning gender status, it was observed that in the male group, the majority belonged to 10.01-12.00 fL MPV class intervals (38.30%) with a mean MPV of 9.23 fl, and in the female group majority belonged to the same MPV class intervals (39.62%) with a mean MPV of 9.37 fl (p = 0.6146) • Mean Platelet Volume Vs BMI Table No. 11 Comparison of Mean platelet volume vs gender groups Mean platelet volume vs gender groups Normal BMI (%) Overweight / Obese (%) ≤ 8.00 fL 14 46.34 17 21.45 8.01-10.00fL 6 23.45 28 36.21 10.01-12.00fL 10 30.21 34 42.34 Total 31 100 79 100 Table No. 12 Comparison of Mean Platelet Volume Vs BMI Distribution Mean Platelet Volume Vs BMI Distribution Normal BMI Overweight / Obese P-value Unpaired t Test Mean 9.02 9.80 0.0921 SD 1.83 1.82 While analyzing MPV distribution about BMI, it was observed that in the normal BMI group, the majority belonged to ≤ 8.00 fL MPV class intervals (44.44%) with a mean MPV of 8.90 fl, and in the overweight/obese group majority belonged to 10.01-12.00 fL MPV class intervals (42.47%) with a mean MPV of 9.45 fl(p = 0.0752) • Mean Platelet Volume Vs Hypertension Table No. 13 Comparison of Mean Platelet Volume Vs Hypertension Groups Mean Platelet Volume Vs Hypertension Groups Hypertension +ve (%) Hypertension- ve (%) ≤ 8.00 fL 4 7.23 38 49.2 8.01-10.00fL 6 25.21 24 36.3 10.01-12.00fL 17 67.56 21 14.5 Total 27 100 83 100 Table No. 14 Comparison of Mean platelet volume vs hypertension status Mean platelet volume vs hypertension status Hypertension +ve Hypertension -ve P value Unpaired t Test Mean 10.45 9.23 < 0.0001 SD 1.84 1.24 FBS vs MPV There is a superb correlation amongst FBS tiers and MPV degrees. This is indicated thru the Pearson's R Correlation rate of 0.61with ap-charge of < 0.0001 By traditional standards, the relationship among the FBS and MPV degrees is taken into consideration to be statistically full-size considering that p < zero.05. This manner as FBS will increase MPV degrees additionally boom right now and linearly in our have a look at subjects. In easy terms, for each a hundred mg/dl growth in FBS, there's a 7.96 fl increase in MPV the numerous take a look at subjects. CORRELATION ANALYSIS A strong positive correlation was observed between FBS and MPV (r = 0.61, p < 0.0001), indicating that worsening glycemic status is associated with increased platelet activation. For every 100 mg/dL increase in FBS, MPV increased by approximately 7.96 fL . SUMMARY OF KEY FINDINGS No significant differences in age, gender, or BMI between groups Significantly higher FBS in poorly controlled diabetics (p < 0.0001) MPV significantly elevated in: Retinopathy (p < 0.0001) Hypertension (p < 0.0001) Strong positive correlation between MPV and FBS MPV showed no significant association with gender or BMI DISCUSSION The present study demonstrates a significant positive correlation between glycated hemoglobin (HbA1c) levels and mean platelet volume (MPV), indicating that poor glycemic control is associated with increased platelet activation. This finding is consistent with recent studies showing that chronic hyperglycemia promotes the production of larger, more reactive platelets with enhanced prothrombotic potential.¹⁹ The strong linear relationship observed between HbA1c and MPV underscores the impact of sustained hyperglycemia on platelet physiology. Elevated glucose levels contribute to non-enzymatic glycation of platelet proteins, oxidative stress, and endothelial dysfunction, all of which enhance platelet activation and aggregation.²⁰ In addition, a statistically significant correlation between the duration of diabetes and MPV levels was observed, suggesting that prolonged exposure to hyperglycemia leads to cumulative platelet dysfunction. This finding highlights the progressive nature of platelet activation in diabetes and its contribution to the development of vascular complications over time.²¹ The alterations in platelet indices observed in this study—including increased MPV, PDW, and P-LCR—are indicative of heightened platelet turnover and heterogeneity. Larger platelets are known to contain more dense granules, exhibit increased thromboxane synthesis, and demonstrate greater aggregation capacity, thereby contributing to a hypercoagulable state.²² These findings align with recent evidence demonstrating that platelet indices are significantly elevated in diabetic patients, particularly those with poor glycemic control and established complications.²³ Elevated platelet indices have been strongly associated with microvascular complications such as diabetic retinopathy, nephropathy, and neuropathy, as well as macrovascular events including coronary artery disease and stroke.²⁴ The underlying mechanisms linking diabetes and platelet dysfunction involve multiple pathways, including insulin resistance, chronic inflammation, oxidative stress, and endothelial injury. Insulin normally exerts an inhibitory effect on platelet aggregation; however, in insulin-resistant states, this regulatory mechanism is impaired, leading to increased platelet activation.²⁵ Moreover, hyperglycemia-induced oxidative stress and advanced glycation end-products (AGEs) further amplify platelet activation by altering intracellular signaling pathways and increasing calcium influx.²⁶ Reduced bioavailability of nitric oxide and prostacyclin also contributes to enhanced platelet adhesion and aggregation, thereby promoting thrombosis.²⁷ Importantly, platelet indices represent a practical and cost-effective tool for assessing platelet activation and thrombotic risk in clinical settings. 28 Unlike specialized platelet function tests, these indices are readily available as part of routine complete blood count analysis and do not require additional resources or expertise.² 9 The significant association between platelet indices and glycemic control observed in this study suggests that these parameters may serve as valuable adjunctive biomarkers for monitoring disease progression and identifying patients at high risk of complications. 30 In the context of the rising global burden of diabetes, especially in low- and middle-income countries, the incorporation of platelet indices into routine clinical evaluation could enhance early detection of vascular risk and improve patient outcomes through timely intervention. 31 , 32 Overall, the findings of the present study reinforce the growing body of evidence supporting the clinical utility of platelet indices as indicators of platelet activation, glycemic status, and vascular risk in diabetes mellitus. Their integration into standard diagnostic protocols may provide a simple yet effective approach for improving risk stratification and guiding therapeutic strategies in diabetic patients. CONCLUSION The present study demonstrates that platelet indices, particularly mean platelet volume (MPV), are significantly associated with glycemic status and diabetic complications in patients with type 2 diabetes mellitus. While demographic and clinical variables such as age, gender, body mass index, duration of diabetes, hypertriglyceridemia, abdominal obesity, and hypertension did not show a statistically significant independent influence on the relationship between MPV and HbA1c, glycemic control emerged as a key determinant of platelet activation. Patients with poor glycemic control exhibited significantly higher fasting and postprandial blood glucose levels, along with an increased prevalence of microvascular complications, including proteinuria and diabetic retinopathy. Notably, MPV levels were consistently elevated in these patients, reflecting enhanced platelet reactivity and a prothrombotic state. Furthermore, subgroup analyses revealed that patients with retinopathy and those with hypertension had comparatively higher MPV values, suggesting a potential link between platelet activation and vascular comorbidities. Correlation analysis demonstrated a positive linear relationship between MPV and glycemic parameters, indicating that incremental increases in HbA1c, fasting blood glucose, and duration of diabetes are associated with corresponding increases in MPV. These findings reinforce the concept that chronic hyperglycemia contributes to progressive platelet activation and dysfunction. Overall, this study highlights the clinical utility of MPV as a simple, cost-effective, and readily available biomarker for assessing platelet activity and identifying patients at increased risk of diabetic complications. Incorporating platelet indices into routine clinical evaluation may enhance early risk stratification and support timely therapeutic interventions aimed at improving glycemic control and reducing vascular risk. However, as a hypothesis-generating study, further large-scale, multicentric, and longitudinal investigations are warranted to validate these findings and to establish the prognostic significance of platelet indices in the comprehensive management of diabetes mellitus. Declarations Funding: This research was self-funded , and no external financial support was received. Authors and Affiliations Professor & Director , AIPH University, Jatni, Khorda, Odisha, India Email: [email protected] Professor, Department of Physiotherapy , AIPH University, Jatni, Khorda, Odisha, India Email: [email protected] Corresponding Author Correspondence to: Vikas Tiwari (Email: [email protected] ) Conflict of Interest The authors declare no conflict of interest . Ethical Statement The study was conducted in accordance with ethical standards for biomedical research involving human participants. Verbal informed consent was obtained from all participants prior to inclusion in the study. Ethical approval for the research was granted by the Institutional Ethics Committee (IEC) of Arogyam Medical College and Hospital (AMCH), Roorkee, India. All procedures performed in this study were carried out in compliance with institutional ethical guidelines and applicable regulations governing human research. References Sun H, Saeedi P, Karuranga S et al (2022) IDF Diabetes Atlas: global estimates of diabetes prevalence for 2021 and projections for 2045. Diabetes Res Clin Pract 183:109119 Saeedi P, Petersohn I, Salpea P et al (2021) Global and regional diabetes prevalence estimates. Lancet Diabetes Endocrinol 9(4):203–211 Mirghani HO (2025) Platelet indices: clinical implications in diabetes mellitus—a broader insight. 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J Family Med Prim Care 11(6):2850–2855 Mehta D, Shah P, Desai R (2023) Platelet indices in metabolic syndrome and diabetes. Metab Syndr Relat Disord 21(3):156–162 Rao V, Iyer S, Kulkarni P (2024) Platelet indices and cardiovascular complications in diabetes. J Clin Med 13:2451 Das S, Banerjee M, Roy A (2023) Platelet indices in poorly controlled diabetes. Diabetol Int 14(4):567–574 Verma N, Tiwari S, Pandey A (2022) Correlation of mean platelet volume with HbA1c levels. J Diabetes Metab Disord 21:987–993 Khan MA, Siddiqui SA, Ali F (2024) Platelet indices as predictors of thrombotic risk in diabetes. Ann Med Surg (Lond) 89:105678 Tiwari V, Tiwari J, Vivechana, Singh D (2022) Prevalence of abnormal glucose tolerance among pregnant women undergoing oral glucose tolerance test (OGTT). Eur J Mol Clin Med 9(7):7375–7384 Tiwari V, Sharma A, Tiwari J, Afzal M, Khushi (2024) Dyslipidemia and its correlation with glycated hemoglobin levels in type 2 diabetes mellitus: unraveling the intricate relationship for comprehensive patient management. Scope 14(1):204–214 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-9262707","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614285680,"identity":"e7c35834-26fd-4ca6-a008-4fbaddcd2b79","order_by":0,"name":"Dr. Vikas Tiwari","email":"data:image/png;base64,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","orcid":"","institution":"AIPH University","correspondingAuthor":true,"prefix":"Dr.","firstName":"Vikas","middleName":"","lastName":"Tiwari","suffix":""},{"id":614285681,"identity":"4e929b14-d5d1-4150-b4ad-4bdf8189818e","order_by":1,"name":"Dr. Jaishree Tiwari","email":"","orcid":"","institution":"AIPH University","correspondingAuthor":false,"prefix":"Dr.","firstName":"Jaishree","middleName":"","lastName":"Tiwari","suffix":""}],"badges":[],"createdAt":"2026-03-30 05:43:29","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-9262707/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9262707/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105905534,"identity":"8755cedd-d20e-46b6-bb3f-4fd0f979be72","added_by":"auto","created_at":"2026-04-01 10:12:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":63563,"visible":true,"origin":"","legend":"\u003cp\u003eAge Distribution of Good and poor glycemic control group\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9262707/v1/2735293b476747901e0aa99e.png"},{"id":105883357,"identity":"858e4b96-e37a-4d71-92b0-dabea84c9f2b","added_by":"auto","created_at":"2026-04-01 07:07:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20902,"visible":true,"origin":"","legend":"\u003cp\u003eMale and Female of Good and poor glycemic control group is the percentage of it.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9262707/v1/cce7f48a3066298d8e42a611.png"},{"id":105905193,"identity":"710367be-22e5-430a-9b81-2b47a2fbd55d","added_by":"auto","created_at":"2026-04-01 10:11:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30051,"visible":true,"origin":"","legend":"\u003cp\u003eBMI groups of Good and poor glycemic control group\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9262707/v1/4629b9529a6f07d1023a7cb9.png"},{"id":105905535,"identity":"0e60e446-097d-4284-b798-322debc309ad","added_by":"auto","created_at":"2026-04-01 10:12:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":16878,"visible":true,"origin":"","legend":"\u003cp\u003eFBS groups of Good and poor glycemic control group\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9262707/v1/f21287edd3ae75a11bdc9615.png"},{"id":105883359,"identity":"d80cfc16-91d3-446b-806d-773997b85af1","added_by":"auto","created_at":"2026-04-01 07:07:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":45646,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Mean platelet volume vs gender groups\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9262707/v1/603ca1193489d5676dd4099d.png"},{"id":105905375,"identity":"c6db56ed-827c-4273-a77c-ca34769f7243","added_by":"auto","created_at":"2026-04-01 10:11:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":56769,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Mean platelet volume vs gender groups\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9262707/v1/bf28af3c9d1fb6b8b6690073.png"},{"id":105905370,"identity":"070979ef-17ff-41e6-8b81-e89dd6d9342a","added_by":"auto","created_at":"2026-04-01 10:11:55","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":23350,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Mean Platelet Volume Vs Hypertension Groups\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9262707/v1/8920f776ca9af77ced5f2f23.png"},{"id":106093166,"identity":"1edc3629-126d-433a-9206-a530a478f686","added_by":"auto","created_at":"2026-04-03 11:35:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1799851,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9262707/v1/1d7abbab-6404-4ba2-92f5-a4a6f602d87c.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eComprehensive Analysis of Platelet Indices Variability in Diabetes Mellitus Patients: Implications for Clinical Evaluation and Management\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eDiabetes mellitus (DM) is a multifactorial metabolic disorder characterized by chronic hyperglycemia resulting from defects in insulin secretion, insulin action, or both. It is one of the most significant global health challenges of the 21st century, with a rapidly increasing prevalence and a substantial burden of morbidity, mortality, and healthcare expenditure.\u0026sup1;\u003csup\u003e,\u003c/sup\u003e\u0026sup2; The disease is associated with long-term complications affecting multiple organ systems, including the cardiovascular system, kidneys, retina, and peripheral nerves, primarily due to persistent metabolic dysregulation and vascular damage.\u0026sup3;\u003c/p\u003e \u003cp\u003eThe pathophysiology of diabetic complications is complex and involves chronic inflammation, oxidative stress, endothelial dysfunction, and activation of coagulation pathways.⁴ Among these, platelet dysfunction plays a pivotal role in the initiation and progression of both microvascular and macrovascular complications. Platelets in individuals with diabetes exhibit enhanced reactivity, increased adhesion, and aggregation tendencies, contributing to a hypercoagulable state.⁵\u003c/p\u003e \u003cp\u003eEmerging evidence indicates that hyperglycemia induces structural and functional alterations in platelets, including increased membrane glycation, altered receptor expression, impaired nitric oxide signaling, and enhanced intracellular calcium mobilization.⁶ These changes result in increased thromboxane A₂ production, reduced prostacyclin activity, and accelerated platelet turnover, collectively promoting thrombogenesis and vascular injury.⁷\u003c/p\u003e \u003cp\u003ePlatelet indices\u0026mdash;such as mean platelet volume (MPV), platelet distribution width (PDW), plateletcrit (PCT), and platelet large cell ratio (P-LCR)\u0026mdash;have gained considerable attention as indirect markers of platelet activation and heterogeneity. These indices are routinely obtained through automated hematology analyzers and offer a simple, cost-effective, and reproducible method for evaluating platelet morphology and function.⁸\u003c/p\u003e \u003cp\u003eAmong these parameters, MPV is widely recognized as a marker of platelet size and activity, with larger platelets being metabolically and enzymatically more active and possessing greater prothrombotic potential.⁹ Studies have demonstrated that elevated MPV levels are associated with poor glycemic control, increased insulin resistance, and higher risk of vascular complications in patients with diabetes mellitus.\u0026sup1;⁰\u003c/p\u003e \u003cp\u003eSimilarly, PDW reflects variability in platelet size and is considered a sensitive indicator of platelet activation and anisocytosis, while PCT represents the total platelet mass and correlates with thrombotic risk.\u0026sup1;\u0026sup1; Recent clinical studies and meta-analyses have consistently shown that these platelet indices are significantly altered in patients with type 2 diabetes mellitus and are strongly associated with microvascular complications such as diabetic retinopathy, nephropathy, and neuropathy.\u0026sup1;\u0026sup2;\u003c/p\u003e \u003cp\u003eFurthermore, platelet indices have been shown to correlate with key glycemic parameters, including glycated hemoglobin (HbA1c), fasting blood glucose (FBG), and duration of disease.\u0026sup1;\u0026sup3; Elevated platelet indices in poorly controlled diabetes reflect ongoing subclinical inflammation and endothelial dysfunction, highlighting their potential utility as early biomarkers for disease progression and vascular risk stratification.\u0026sup1;⁴\u003c/p\u003e \u003cp\u003eThe clinical relevance of platelet indices is further enhanced by their accessibility, affordability, and ease of measurement, particularly in resource-limited settings.\u0026sup1;⁵ Given the growing burden of diabetes, especially in developing countries, there is an urgent need for reliable and cost-effective markers that can facilitate early detection of complications and guide clinical decision-making.\u0026sup1;⁶\u003c/p\u003e \u003cp\u003eRecent advances in hematological and cardiovascular research have reinforced the role of platelet indices as prognostic indicators in metabolic disorders, with growing evidence supporting their integration into routine clinical assessment of diabetic patients.\u0026sup1;⁷\u003c/p\u003e \u003cp\u003eTherefore, the present study aims to comprehensively evaluate the variability of platelet indices in patients with diabetes mellitus and to investigate their correlation with glycemic control (HbA1c, FBG) and duration of disease, with the objective of assessing their potential role as biomarkers for early detection and management of thromboembolic complications.\u0026sup1;⁸\u003c/p\u003e"},{"header":"METHODOLOGY AND METHOD","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePREPARATION A OF PATIENTS\u003c/h2\u003e \u003cp\u003eFrom May to December 2025, a total of 110 patients with type 2 diabetes mellitus attending medicine OPD and Diabetes mellitus attend at AMCH medicine wards were studied. Our study comprised patients who were dependent. All of the patients received a thorough clinical examination. We took measurements of the subjects' weight, height, and belly circumference.\u003c/p\u003e \u003cp\u003eThe systolic and diastolic blood pressures (SBP and DBP) were measured with a sphygmomanometer after 5 minutes of rest in an upright position. Blood pressure was obtained in the right upper arm at least three times. The mean of three was employed in the analysis. Patients with a mean blood pressure of greater than 140/90 mm Hg or who were taking antihypertensive medication were diagnosed with hypertension.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCOLLECTION AND PRESERVATION OF SAMPLE\u003c/h3\u003e\n\u003cp\u003eThe sample was collected from the venipuncture the total number patients were 110 the was collected in the EDTA vile red top vile, gray top were for blood sugar estimation for by biochemistry 2.5ml of blood collected in each tube for hematological investigation and 3ml were collected for by biochemistry investigation all the primary specimen containers must be labeled with at least two IDs at the time of sample collection.\u003c/p\u003e \u003cp\u003eTo protect the specimen\u0026rsquo;s integrity and as a result, the test results, proper storage and handing are required.\u003c/p\u003e \u003cp\u003eThe EDTA sample were proper mix before processing in the automated analyzer and after completed sample processing the sample were store at 4\u003csup\u003eo\u003c/sup\u003ec in the fridge for a variety of purposes\u003c/p\u003e\n\u003ch3\u003eLABORATORY METHODS\u003c/h3\u003e\n\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFollowing a 12-hour overnight fast, venous samples were drawn at 8:30 a.m. to determine mean platelet volume, HbA1C, FBS, PPBS, Hb, and triglyceride levels (TG).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHbA1c levels were calculated auto analyzed.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMPV levels were assessed using an automated blood Coulter (Beckman Coulter Act5Diff).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eUsing an auto analyzer, plasma glucose was measured using the glucose oxidase method (FBS and PPBS). Hypertriglyceridemia is defined as a blood concentration over 150 mg/dl (\u0026gt;\u0026thinsp;150 mg/dl). The patients were separated into two groups depending on their HbA1C values after the baseline examination. Diabetics with strong glycemic control (HbA1c less than 7%) are distinguished from diabetics with poor glycemic control (HbA1c greater than 7%). (HbA1c greater than 7 percent). (HbA1c level is more than 15%). A 7 percent HbA1c level (HbA1c level of more than 7 percent). Between the two groups, all factors were compared. These groups were further separated into male and female groups.\u003c/p\u003e\n\u003ch3\u003eSTATISTIC ANALYSIS\u003c/h3\u003e\n\u003cp\u003eDescriptive records had been completed for all data and suitable statistical tests of evaluation had been completed. Continuous variables had been analyzed with the Unpaired t-take a look at/single-thing ANOVA and specific variables have been analyzed with chi-squared test/ Fisher Exact Test. Regression evaluation emerge as finished and odds ratio with self-belief c programming language calculated. Statistical significance became taken as P\u0026thinsp;\u0026lt;\u0026thinsp;zero.05. The facts have been analyzed using SPSS Version sixteen. Microsoft Excel 2010.Changed into used to generate charts.\u003c/p\u003e"},{"header":"RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline Characteristics of Study Population\u003c/h2\u003e\n \u003cp\u003eA total of 110 patients with type 2 diabetes mellitus were categorized into good glycemic control (n\u0026thinsp;=\u0026thinsp;45) and poor glycemic control groups (n\u0026thinsp;=\u0026thinsp;65) based on HbA1c levels.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eAge Distribution\u003c/h3\u003e\n\u003cp\u003eThe majority of participants in both groups were within the 51\u0026ndash;60 years age category. The mean age was comparable between the groups (52.32\u0026thinsp;\u0026plusmn;\u0026thinsp;11.02 years vs. 54.32\u0026thinsp;\u0026plusmn;\u0026thinsp;11.60 years), with no statistically significant difference (p\u0026thinsp;=\u0026thinsp;0.4012), indicating age-matched cohorts.\u003c/p\u003e\n\u003ch3\u003eGender Distribution\u003c/h3\u003e\n\u003cp\u003eGender distribution was comparable between groups, with no significant difference (p\u0026thinsp;=\u0026thinsp;0.7421). Females constituted a slightly higher proportion in the poor glycemic control group.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eBody Mass Index (BMI)\u003c/h2\u003e\n \u003cp\u003eOverweight individuals predominated in both groups, particularly in the poor glycemic control group. However, the difference in mean BMI was not statistically significant (28.20\u0026thinsp;\u0026plusmn;\u0026thinsp;5.82 vs. 30.12\u0026thinsp;\u0026plusmn;\u0026thinsp;4.93; p\u0026thinsp;=\u0026thinsp;0.5431).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eAGE\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\n \u003cp\u003eTable No. 1 Age Distribution of Good and poor glycemic control group\u003c/p\u003e\n \u003c/div\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAGE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eGood glycemic control group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003ePoor glycemic control group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e31\u0026ndash;40 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e41\u0026ndash;50 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e51\u0026ndash;60 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e61\u0026ndash;70 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e\u0026bull; Gender\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable No. 2 Comparison of Age Distribution of Good and poor glycemic control group\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAge Distribution\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGood glycemic control group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePoor glycemic control group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value Unpaired t-Test\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e52.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e54.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.4012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e11.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eWhile analyzing age distribution, it was observed that the majority in the good glycemic control group belonged to 51\u0026ndash;60 years age class interval with a mean age of 50.84 years and the majority in the poor glycemic control group belonged to the same age class interval (52.32%) with a mean age of 54.32 years (p\u0026thinsp;=\u0026thinsp;0.4012)\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003eTable No. 3 Male and Female of Good and poor glycemic control group is the percentage of it.\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabc\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eGender status\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGood glycemic control group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePoor glycemic control group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value Chi\u003c/p\u003e\n \u003cp\u003eSquared Test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.7421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eWhile studying gender popularity, it was determined that during desirable glycemic control organization ladies and men had been equally disbursed (50.00%) and majority within the bad glycemic control organization were girls (54.84%) (p\u0026thinsp;=\u0026thinsp;0.7421)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eBMI\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cp class=\"colspec\" align=\"left\"\u003eTable No. 4 BMI groups of Good and poor glycemic control group\u003c/p\u003e\n \u003ctable id=\"Tabd\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBMI Groups\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eGood glycemic control group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003ePoor glycemic control group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverweight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eObese\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable No. 5 BMI Distribution of Good and poor glycemic control group\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabe\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBMI Distribution\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGood glycemic control group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePoor glycemic control group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value Unpaired t Test\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.5431\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eWhile analyzing BMI distribution, it was observed that majority in the good glycemic control group belonged to overweight BMI class interval (42.11%) with a mean BMI of 27.30, and majority in the poor glycemic control group belonged to the same BMI class interval (54.84%) with a mean BMI of 28.11 (p\u0026thinsp;=\u0026thinsp;0.3806)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eFBS\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003eTable No. 6 FBS groups of Good and poor glycemic control group\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabf\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eGood glycemic control group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003ePoor glycemic control group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;\u0026thinsp;100 mg/dl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e101-120mg/dl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e121-140mg/dl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;\u0026thinsp;140 mg/dl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable No. 7 FBS distribution of Good and poor glycemic control group\u003c/strong\u003e\u003c/p\u003e\u0026nbsp;\n \u003c/div\u003e\n \u003ctable id=\"Tabg\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFasting Blood Sugar Distribution\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGood glycemic control group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePoor glycemic control group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value Unpaired t Test\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eWhile analyzing FBS distribution, it was observed that, majority in good glycemic control group belonged to 101\u0026ndash;120 mg/dl FB S class interval (28.95%) with a mean FBS of 121.63 mg/dl and majority in poor glycemic control group belonged to \u0026gt;\u0026thinsp;140 mg/dl FBS class interval (82.26%) with a mean FBS of 161.21 mg/dl (p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e\n \u003cp\u003eThe data subjected to statistical unpaired t-test reveals the existence of a statistically significant association between MPV distribution and proteinuria status (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e\n \u003cp\u003eThis significance is exhibited by the increased mean MPV levels in the proteinuria group compared to proteinuria -group (1.64 fL increase, 16% higher) The same view was echoed by studies done by Ates et al and Papanas et al. This suggested a role for the increased platelet activity in the pathogenesis of vascular complications. On the other hand, in the studies done by Hekimsoy et al and Demirtunc et al, MPV in diabetic subjects with and without complications did not show any significant difference. They explained it to be possible because of the rapid consumption of activated platelets in diabetic patients with complications.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003eTable No. 8 Comparison of Mean platelet volume (MPV) vs retinopathy distribution\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabh\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean platelet volume vs retinopathy distribution\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRetinopathy+ Group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRetinopathy- Group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value Unpaired t Test\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eWhile analyzing MPV distribution, it was observed that the majority in the retinopathy +ve group belonged to 10.01-12.00 fL MPV class interval (80.00%) with a mean MPV of 10.50 fL and the majority in retinopathy -ve group belonged to \u0026le;\u0026thinsp;8.00fL MPV class interval (40.00%) with a mean MPV of 8.79 fL (p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e\u0026bull; Mean Platelet Volume Vs Gender\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable No. 9 Comparison of Mean platelet volume vs gender groups\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabi\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean platelet volume vs gender groups\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"1\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;\u0026thinsp;8.00 fL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e27.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.01-10.00fL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e32.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.01-12.00fL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e40.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;Table No. 10 Comparison of Mean platelet volume vs gender distribution\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabj\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean platelet volume vs gender distribution\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value Unpaired t Test\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.7231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eWhile analyzing MPV distribution concerning gender status, it was observed that in the male group, the majority belonged to 10.01-12.00 fL MPV class intervals (38.30%) with a mean MPV of 9.23 fl, and in the female group majority belonged to the same MPV class intervals (39.62%) with a mean MPV of 9.37 fl (p\u0026thinsp;=\u0026thinsp;0.6146)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e\u0026bull; Mean Platelet Volume Vs BMI\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable No. 11 Comparison of Mean platelet volume vs gender groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003ctable id=\"Tabk\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean platelet volume vs gender groups\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNormal BMI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverweight / Obese\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;\u0026thinsp;8.00 fL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.01-10.00fL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.01-12.00fL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable No. 12 Comparison of Mean Platelet Volume Vs BMI Distribution\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabl\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Platelet Volume Vs BMI Distribution\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNormal BMI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverweight / Obese\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value Unpaired t Test\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.0921\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eWhile analyzing MPV distribution about BMI, it was observed that in the normal BMI group, the majority belonged to \u0026le;\u0026thinsp;8.00 fL MPV class intervals (44.44%) with a mean MPV of 8.90 fl, and in the overweight/obese group majority belonged to 10.01-12.00 fL MPV class intervals (42.47%) with a mean MPV of 9.45 fl(p\u0026thinsp;=\u0026thinsp;0.0752)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e\u0026bull; Mean Platelet Volume Vs Hypertension\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;Table No. 13 Comparison of Mean Platelet Volume Vs Hypertension Groups\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabm\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eMean Platelet Volume Vs Hypertension Groups\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHypertension +ve\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHypertension- ve\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;\u0026thinsp;8.00 fL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.01-10.00fL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.01-12.00fL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;Table No. 14 Comparison of Mean platelet volume vs hypertension status\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabn\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean platelet volume vs hypertension status\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHypertension +ve\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHypertension -ve\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value Unpaired t Test\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eFBS vs MPV\u003c/h2\u003e\n \u003cp\u003eThere is a superb correlation amongst FBS tiers and MPV degrees. This is indicated thru the Pearson\u0026apos;s R Correlation rate of 0.61with ap-charge of \u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003cp\u003eBy traditional standards, the relationship among the FBS and MPV degrees is taken into consideration to be statistically full-size considering that p\u0026thinsp;\u0026lt;\u0026thinsp;zero.05. This manner as FBS will increase MPV degrees additionally boom right now and linearly in our have a look at subjects. In easy terms, for each a hundred mg/dl growth in FBS, there\u0026apos;s a 7.96 fl increase in MPV the numerous take a look at subjects.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003eCORRELATION ANALYSIS\u003c/h2\u003e\n \u003cp\u003eA strong positive correlation was observed between FBS and MPV (r\u0026thinsp;=\u0026thinsp;0.61, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating that worsening glycemic status is associated with increased platelet activation.\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eFor every 100 mg/dL increase in FBS, MPV increased by approximately \u003cstrong\u003e7.96 fL\u003c/strong\u003e.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003eSUMMARY OF KEY FINDINGS\u003c/h2\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eNo significant differences in age, gender, or BMI between groups\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eSignificantly higher FBS in poorly controlled diabetics (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eMPV significantly elevated in:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eRetinopathy (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eHypertension (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eStrong positive correlation between MPV and FBS\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eMPV showed no significant association with gender or BMI\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n\u003c/div\u003e\n\u003ch3\u003eDISCUSSION\u003c/h3\u003e\n\u003cp\u003eThe present study demonstrates a significant positive correlation between glycated hemoglobin (HbA1c) levels and mean platelet volume (MPV), indicating that poor glycemic control is associated with increased platelet activation. This finding is consistent with recent studies showing that chronic hyperglycemia promotes the production of larger, more reactive platelets with enhanced prothrombotic potential.\u0026sup1;⁹\u003c/p\u003e\n\u003cp\u003eThe strong linear relationship observed between HbA1c and MPV underscores the impact of sustained hyperglycemia on platelet physiology. Elevated glucose levels contribute to non-enzymatic glycation of platelet proteins, oxidative stress, and endothelial dysfunction, all of which enhance platelet activation and aggregation.\u0026sup2;⁰\u003c/p\u003e\n\u003cp\u003eIn addition, a statistically significant correlation between the duration of diabetes and MPV levels was observed, suggesting that prolonged exposure to hyperglycemia leads to cumulative platelet dysfunction. This finding highlights the progressive nature of platelet activation in diabetes and its contribution to the development of vascular complications over time.\u0026sup2;\u0026sup1;\u003c/p\u003e\n\u003cp\u003eThe alterations in platelet indices observed in this study\u0026mdash;including increased MPV, PDW, and P-LCR\u0026mdash;are indicative of heightened platelet turnover and heterogeneity. Larger platelets are known to contain more dense granules, exhibit increased thromboxane synthesis, and demonstrate greater aggregation capacity, thereby contributing to a hypercoagulable state.\u0026sup2;\u0026sup2;\u003c/p\u003e\n\u003cp\u003eThese findings align with recent evidence demonstrating that platelet indices are significantly elevated in diabetic patients, particularly those with poor glycemic control and established complications.\u0026sup2;\u0026sup3; Elevated platelet indices have been strongly associated with microvascular complications such as diabetic retinopathy, nephropathy, and neuropathy, as well as macrovascular events including coronary artery disease and stroke.\u0026sup2;⁴\u003c/p\u003e\n\u003cp\u003eThe underlying mechanisms linking diabetes and platelet dysfunction involve multiple pathways, including insulin resistance, chronic inflammation, oxidative stress, and endothelial injury. Insulin normally exerts an inhibitory effect on platelet aggregation; however, in insulin-resistant states, this regulatory mechanism is impaired, leading to increased platelet activation.\u0026sup2;⁵\u003c/p\u003e\n\u003cp\u003eMoreover, hyperglycemia-induced oxidative stress and advanced glycation end-products (AGEs) further amplify platelet activation by altering intracellular signaling pathways and increasing calcium influx.\u0026sup2;⁶ Reduced bioavailability of nitric oxide and prostacyclin also contributes to enhanced platelet adhesion and aggregation, thereby promoting thrombosis.\u0026sup2;⁷\u003c/p\u003e\n\u003cp\u003eImportantly, platelet indices represent a practical and cost-effective tool for assessing platelet activation and thrombotic risk in clinical settings.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Unlike specialized platelet function tests, these indices are readily available as part of routine complete blood count analysis and do not require additional resources or expertise.\u0026sup2;\u003csup\u003e9\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe significant association between platelet indices and glycemic control observed in this study suggests that these parameters may serve as valuable adjunctive biomarkers for monitoring disease progression and identifying patients at high risk of complications.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eIn the context of the rising global burden of diabetes, especially in low- and middle-income countries, the incorporation of platelet indices into routine clinical evaluation could enhance early detection of vascular risk and improve patient outcomes through timely intervention.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eOverall, the findings of the present study reinforce the growing body of evidence supporting the clinical utility of platelet indices as indicators of platelet activation, glycemic status, and vascular risk in diabetes mellitus. Their integration into standard diagnostic protocols may provide a simple yet effective approach for improving risk stratification and guiding therapeutic strategies in diabetic patients.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe present study demonstrates that platelet indices, particularly mean platelet volume (MPV), are significantly associated with glycemic status and diabetic complications in patients with type 2 diabetes mellitus. While demographic and clinical variables such as age, gender, body mass index, duration of diabetes, hypertriglyceridemia, abdominal obesity, and hypertension did not show a statistically significant independent influence on the relationship between MPV and HbA1c, glycemic control emerged as a key determinant of platelet activation.\u003c/p\u003e \u003cp\u003ePatients with poor glycemic control exhibited significantly higher fasting and postprandial blood glucose levels, along with an increased prevalence of microvascular complications, including proteinuria and diabetic retinopathy. Notably, MPV levels were consistently elevated in these patients, reflecting enhanced platelet reactivity and a prothrombotic state. Furthermore, subgroup analyses revealed that patients with retinopathy and those with hypertension had comparatively higher MPV values, suggesting a potential link between platelet activation and vascular comorbidities.\u003c/p\u003e \u003cp\u003eCorrelation analysis demonstrated a positive linear relationship between MPV and glycemic parameters, indicating that incremental increases in HbA1c, fasting blood glucose, and duration of diabetes are associated with corresponding increases in MPV. These findings reinforce the concept that chronic hyperglycemia contributes to progressive platelet activation and dysfunction.\u003c/p\u003e \u003cp\u003eOverall, this study highlights the clinical utility of MPV as a simple, cost-effective, and readily available biomarker for assessing platelet activity and identifying patients at increased risk of diabetic complications. Incorporating platelet indices into routine clinical evaluation may enhance early risk stratification and support timely therapeutic interventions aimed at improving glycemic control and reducing vascular risk.\u003c/p\u003e \u003cp\u003eHowever, as a hypothesis-generating study, further large-scale, multicentric, and longitudinal investigations are warranted to validate these findings and to establish the prognostic significance of platelet indices in the comprehensive management of diabetes mellitus.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research was \u003cstrong\u003eself-funded\u003c/strong\u003e, and no external financial support was received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eProfessor \u0026amp; Director\u003c/strong\u003e, AIPH University, Jatni, Khorda, Odisha, India\u003cbr\u003e\u0026nbsp;Email: [email protected]\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eProfessor, Department of Physiotherapy\u003c/strong\u003e, AIPH University, Jatni, Khorda, Odisha, India\u003cbr\u003e\u0026nbsp;Email: [email protected]\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding Author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to: \u003cstrong\u003eVikas Tiwari\u003c/strong\u003e (Email: [email protected])\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare \u003cstrong\u003eno conflict of interest\u003c/strong\u003e.\u003c/p\u003e\n\u003ch2\u003eEthical Statement\u003c/h2\u003e\n\u003cp\u003eThe study was conducted in accordance with ethical standards for biomedical research involving human participants. Verbal informed consent was obtained from all participants prior to inclusion in the study. Ethical approval for the research was granted by the Institutional Ethics Committee (IEC) of Arogyam Medical College and Hospital (AMCH), Roorkee, India. All procedures performed in this study were carried out in compliance with institutional ethical guidelines and applicable regulations governing human research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSun H, Saeedi P, Karuranga S et al (2022) IDF Diabetes Atlas: global estimates of diabetes prevalence for 2021 and projections for 2045. Diabetes Res Clin Pract 183:109119\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaeedi P, Petersohn I, Salpea P et al (2021) Global and regional diabetes prevalence estimates. Lancet Diabetes Endocrinol 9(4):203\u0026ndash;211\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMirghani HO (2025) Platelet indices: clinical implications in diabetes mellitus\u0026mdash;a broader insight. World J Diabetes 16(4):100467\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRegassa DA, Berihun GA, Habtu BF et al (2024) Platelet indices as predictors of poor glucoregulation in type 2 diabetes mellitus. World J Diabetes 15(9):1889\u0026ndash;1902\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhanna P, Salwan SK, Sharma A (2024) Correlation of platelet indices with microvascular complications in type 2 diabetes mellitus. Cureus 16(3):e55959\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoy P, Deb N, Lahiri PR et al (2024) Variations in platelet indices in diabetes mellitus and hypertension. Indian J Pathol Microbiol 67(4):820\u0026ndash;823\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSengupta P, Priyadarshini A, Behera PK et al (2024) Platelet indices as predictors of nephropathy severity in type 2 diabetes mellitus. Cureus 16(10):e71796\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReddy KS, Bentoor SN, Sakthivadivel V (2023) Platelet indices as markers of glycemic control. J Family Med Prim Care 12(3):561\u0026ndash;566\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSangapur SM, Darshan RS (2023) Sharanabasappa. Platelet indices correlation with HbA1c in type 2 diabetes mellitus. Int J Res Med Sci 11(5):1594\u0026ndash;1599\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYadav G, Jain M, Singh S et al (2025) Evaluation of platelet indices in type 2 diabetes mellitus. J Chem Health Risks ;15(2)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhanu D, Kalyan S, Projnon S (2024) Mean platelet volume in diabetes mellitus. J Diabetol 8(1):181\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYucel K, Disci SI, Yucel M (2024) Effect of glycemic control on platelet indices in type 1 diabetes mellitus. Med Bull Sisli Etfal Hosp 58(2):139\u0026ndash;145\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNitin CB, Varma KV (2024) Platelet indices as predictive markers of diabetic complications. Int J Med Pharm Res ;5(5)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaur R, Kaur M, Singh J (2022) Platelet activation and its role in diabetes complications. J Clin Diagn Res 16(5):OE01\u0026ndash;OE05\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi S, Zhao Y, Zhang Y (2022) Platelet indices and cardiovascular risk in diabetes. Front Cardiovasc Med 9:842134\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Liu Y, Li J (2023) Mean platelet volume as predictor of vascular complications in type 2 diabetes mellitus. BMC Endocr Disord 23:112\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta A, Verma S, Sharma R (2022) Platelet indices and glycemic control in diabetes. Indian J Endocrinol Metab 26(4):345\u0026ndash;350\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel K, Shah H, Mehta R (2023) Role of platelet parameters in diabetic microangiopathy. J Assoc Physicians India 71(2):11\u0026ndash;15\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Chen Z, Liu X (2021) Platelet activation pathways in diabetes mellitus. Cardiovasc Diabetol 20:176\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh A, Kumar R, Gupta P (2022) Mean platelet volume as a marker of glycemic status in diabetes. J Lab Physicians 14(3):210\u0026ndash;215\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed S, Khan A, Ali M (2023) Platelet indices in diabetic retinopathy. Ophthalmic Res 66(2):145\u0026ndash;152\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma S, Tripathi P, Mishra R (2022) Platelet dysfunction in metabolic disorders. Clin Hematol Int 4(3):89\u0026ndash;95\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown E, Thomas M, Clark J (2021) Platelet hyperactivity and thrombosis in diabetes. Thromb Res 202:150\u0026ndash;158\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen H, Lin J, Wu Q (2023) Platelet indices and diabetic nephropathy progression. Kidney Blood Press Res 48:1\u0026ndash;10\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar N, Saxena R, Agarwal S (2022) Platelet parameters as biomarkers in diabetes. J Family Med Prim Care 11(6):2850\u0026ndash;2855\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehta D, Shah P, Desai R (2023) Platelet indices in metabolic syndrome and diabetes. Metab Syndr Relat Disord 21(3):156\u0026ndash;162\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRao V, Iyer S, Kulkarni P (2024) Platelet indices and cardiovascular complications in diabetes. J Clin Med 13:2451\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDas S, Banerjee M, Roy A (2023) Platelet indices in poorly controlled diabetes. Diabetol Int 14(4):567\u0026ndash;574\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerma N, Tiwari S, Pandey A (2022) Correlation of mean platelet volume with HbA1c levels. J Diabetes Metab Disord 21:987\u0026ndash;993\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan MA, Siddiqui SA, Ali F (2024) Platelet indices as predictors of thrombotic risk in diabetes. Ann Med Surg (Lond) 89:105678\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTiwari V, Tiwari J, Vivechana, Singh D (2022) Prevalence of abnormal glucose tolerance among pregnant women undergoing oral glucose tolerance test (OGTT). Eur J Mol Clin Med 9(7):7375\u0026ndash;7384\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTiwari V, Sharma A, Tiwari J, Afzal M, Khushi (2024) Dyslipidemia and its correlation with glycated hemoglobin levels in type 2 diabetes mellitus: unraveling the intricate relationship for comprehensive patient management. Scope 14(1):204\u0026ndash;214\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":"AIPH University","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":"Diabetes mellitus, mean platelet volume, platelet indices, glycemic control, vascular complications","lastPublishedDoi":"10.21203/rs.3.rs-9262707/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9262707/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDiabetes mellitus (DM) is a chronic metabolic disorder associated with significant vascular complications. Platelet activation plays a crucial role in the pathogenesis of these complications. Mean platelet volume (MPV) has emerged as a potential marker of platelet activity and thrombotic risk.\u003c/p\u003e\u003cp\u003e\u003cb\u003eObjective:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo evaluate the role of platelet indices, particularly MPV, in relation to glycemic control and diabetic complications in patients with type 2 DM.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis cross-sectional study included 110 patients with type 2 DM. Clinical parameters and biochemical markers, including fasting blood glucose (FBG), glycated hemoglobin (HbA1c), and MPV, were analyzed. Correlations between platelet indices, glycemic status, and complications were assessed using appropriate statistical methods.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMPV levels were significantly higher in patients with poor glycemic control and showed a strong positive correlation with HbA1c and FBG. Elevated MPV values were also observed in patients with microvascular complications such as retinopathy and proteinuria. No significant association was found between MPV and age or gender, while higher MPV levels were noted in patients with increased BMI and hypertension.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMPV may serve as a simple, cost-effective biomarker for assessing platelet activation and identifying patients at increased risk of vascular complications in type 2 DM. Routine evaluation of platelet indices could aid in early risk stratification and management. Further large-scale studies are required to confirm these findings.\u003c/p\u003e","manuscriptTitle":"Comprehensive Analysis of Platelet Indices Variability in Diabetes Mellitus Patients: Implications for Clinical Evaluation and Management","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-01 07:07:45","doi":"10.21203/rs.3.rs-9262707/v1","editorialEvents":[{"type":"communityComments","content":0}],"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":"3fb73cb4-5e99-4c47-940f-1cba1eac8ec9","owner":[],"postedDate":"April 1st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65454798,"name":"Endocrinology \u0026 Metabolism"},{"id":65454799,"name":"Clinical Pharmacology"},{"id":65454800,"name":"Laboratory Diagnostics"}],"tags":[],"updatedAt":"2026-04-01T07:07:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-01 07:07:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9262707","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9262707","identity":"rs-9262707","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}