Data Management

The Department of Data Management plays a crucial role in overseeing the entire data lifecycle at MDRF. It ensures that all data collected is stored, processed, and utilised in an efficient, secure, and ethical manner, thereby supporting informed decision-making, fostering innovation, and advancing the organisation’s strategic objectives.

As the backbone of MDRF’s data-driven ecosystem, the department ensures the integrity, accuracy, and accessibility of information across clinical, research, and administrative domains. Through robust data governance practices, stringent quality control measures, and adherence to regulatory and ethical standards, it safeguards the reliability of every dataset used in research and operations.

The department also collaborates closely with various scientific and technical teams to streamline data workflows, enable seamless integration of multi-source data, and facilitate advanced analytics. By ensuring that raw information is transformed into meaningful, actionable insights, the Department of Data Management quietly yet powerfully strengthens research excellence and empowers smarter decision-making across the institution.

The Department of Data Management at MDRF plays a vital role in ensuring that all data collected is efficiently stored, processed, and utilized to support informed decision-making and research excellence. Guided by Ms. Jebarani, the department focuses on maintaining data accuracy, security, and accessibility while promoting ethical data practices. By transforming raw information into valuable insights, it empowers innovation and strengthens the organization’s research capabilities. Through its commitment to integrity and efficiency, the department serves as the foundation of MDRF’s data-driven success.

Ms. Jebarani

Software Development

Software development

Data is systematically extracted from multiple sources, including unstructured and structured raw data repositories. Once extracted, the data undergoes pre-processing, during which different datasets are linked by identifying and mapping records related to the same individual or entity. The data is then transformed, cleaned, and subjected to rigorous quality checks to enhance its accuracy, consistency, and reliability.  This refined and structured information forms the metadata necessary for analysis. The metadata is subsequently loaded into the MDRF Safe Haven, also known as the MDRF Data Centre (MDC)—a secure, remotely accessible terminal services environment (via RDP). This setup enables authorized users to remotely access research data and conduct in-depth analyses within a controlled and secure infrastructure.

MDRF Data Centre

The department operates a dedicated data centre facility known as the MDRF Data Centre (MDC), which provides shared access to data through a sophisticated network, computing, and storage infrastructure. The MDC houses a wide range of valuable datasets, including:

  • A comprehensive diabetes data repository containing records from over 665,000 patients, (as on Aug 2025) for more than 30 years of follow up including more than 3 million fasting blood sugar values, 2.4 lakh C-peptide measurements, 25 million drug prescriptions, and 18 million entries related to eye health, etc.
  • Epidemiological research data from over 350,000 participants across major studies such as CURES, CUPS, and INDIAB.
  • Retinal imaging data from over 100,000 patients.

Software development

Our department is responsible for designing, developing, testing, and maintaining software applications and systems to support research activities. We digitize manually collected research questionnaires to enhance accuracy, improve accessibility, and enable effective audit tracking. This process goes beyond programming, it involves understanding user needs, collaborating with stakeholders to define software requirements, creating prototypes, and designing solutions tailored to those needs. We also emphasize quality assurance to ensure reliability. The end product is an application designed to help users efficiently input, manage, and organize data.

The department ensures that stakeholder needs are met precisely, nothing more, nothing less. The software is developed to be free of bugs, with no errors in logic, functionality, or usability. It features a user-friendly, responsive, and accessible interface. Data security is a priority, safeguarded through user ID and password protection. The software is designed to deliver high performance, reliability, compatibility, and efficiency.

Data Sharing process in MDRF Safe Haven

Data is systematically extracted from multiple sources, including unstructured and structured raw data repositories. Once extracted, the data undergoes pre-processing, during which different datasets are linked by identifying and mapping records related to the same individual or entity. The data is then transformed, cleaned, and subjected to rigorous quality checks to enhance its accuracy, consistency, and reliability.  This refined and structured information forms the metadata necessary for analysis. The metadata is subsequently loaded into the MDRF Safe Haven, also known as the MDRF Data Centre (MDC)—a secure, remotely accessible terminal services environment (via RDP). This setup enables authorized users to remotely access research data and conduct in-depth analyses within a controlled and secure infrastructure.

Department Activities

Data Architecture & Data Governance

MDRF have a good built in data architecture so that the organisation data assets are documented and mapped well.  We have good normalised data models; help remove data redundancies and have Read more centralised storage with strong governance to ensure business compliance and data consistency.  The team has well-established processes, policies, rules, and procedures governing the use of organizational data. We maintain robust policies, clear rules, standards, and guidelines for data access. This strong governance framework protects sensitive information and ensures that data is accessed and used appropriately. Additionally, these measures are regularly reviewed and updated to adapt to evolving business needs and regulatory requirements.

Data Linkage & Data Wrangling

Our team specializes in data linkage by integrating information from multiple sources to create the most comprehensive dataset possible.  The team is responsible for data wrangling and Read more preparation, which includes cleaning, organizing, and transforming raw data to ensure it’s ready for analysis. Once processed, this cleaned data is loaded into a data warehouse for long-term storage and business intelligence use. The transformation process includes tasks such as removing duplicates and errors, as well as merging data from diverse sources.

Data Cleansing & Data Quality Control

Our data cleansing team identifies and corrects errors, duplicates, and inconsistencies. Additionally, the data undergoes rigorous quality control to enhance its reliability, accuracy, and credibility.

Data Warehousing

We have a well-structured data warehousing system designed to support business intelligence and analytics. Our data warehouse stores raw data, summarized data, and metadata. Backed by a Read more robust relational database, it efficiently stores and manages data. We also offer comprehensive ETL (Extract, Transform, Load) solutions to facilitate data processing for both internal and external clients. The department follows a defined set of processes to manage metadata, which provides descriptive and structural information that helps users better understand and manage the data effectively.

Database Administration

MDRF has a strong database administration framework that plays a critical role in ensuring data integrity and enhancing operational efficiency. This involves capacity planning, database installation and Read more configuration, performance monitoring, user access management, implementation of security protocols, minimizing downtime, ensuring high availability and disaster recovery (DR), regular backups and recovery procedures, and database performance optimization.

Publications

  1. Baskar V, Vignesh MA, Raman SC, Jijo A, Balaji B, Steckhan N, Roth LMK, Siddiqui MK, Jebarani S, Unnikrishnan R, Mohan V, Anjana RM. Development and Validation of DIANA (Diabetes Novel Subgroup Assessment tool): A web-based precision medicine tool to determine type 2 diabetes endotype membership and predict individuals at risk of microvascular disease. PLOS Digital Health. 2025;4:e0000702
  2. Mohan V, Pramodkumar TA, Deepa M, Pradeepa R, Unnikrishnan R, Routray P, Tharunika S, Lakshmi N, Killivalavan D, Ganesan S, Jebarani S, Venkatesan U, Das S, Anjana RM, Lean diabetes is one end of the spectrum of type 2 diabetes and not a separate entity. Diabetes Research and Clinical Practice, DOI:10.1016/j.diabres.2025.112387
  3. Baskar V, Marlene G, M A Vignesh, Jebarani S, Hubert P, Robert W, Jürgen M, Fritz S, Andreas P, Nikolaos P, Stefan K, Knut M, Stefan K, Michael S, Peter EH Schwarz, Hans H, Jochen S, Ryna Y, Andrea I, Michele S, Julia S, Annette S, Martin H, Matthias B, Michael R, Norbert S, Andreas F, Daniel A, Stefan R. Bornstein, Anjana RM, Andreas L. Birkenfeld, Mohan V+ , Reiner J+ and the PLIS Group. Phenotypic Heterogeneity and Risk for Type 2 Diabetes in Central European and South Asian People with Prediabetes. Diabetology and metabolism. 2025 DOI: 10.2337/db25-1377-P
  4. PramodKumar TA, Pradeepa R, Jebarani S, Ganesan S, Pednekar A, Philips R, Kumar SP, Unnikrishnan R, Anjana RM, Mohan V. Effectiveness of Glipizide and Glipizide Plus Metformin Formulation among Asian Indians with Type 2 Diabetes: a Real‐World, Retrospective Electronic Medical Record Analysis. Drugs – Real World Outcomes. 2025 DOI:10.1007/s40801-025-00502-0.
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Publications

  1. Baskar V, Vignesh MA, Raman SC, Jijo A, Balaji B, Steckhan N, Roth LMK, Siddiqui MK, Jebarani S, Unnikrishnan R, Mohan V, Anjana RM. Development and Validation of DIANA (Diabetes Novel Subgroup Assessment tool): A web-based precision medicine tool to determine type 2 diabetes endotype membership and predict individuals at risk of microvascular disease. PLOS Digital Health. 2025;4:e0000702
  2. Mohan V, Pramodkumar TA, Deepa M, Pradeepa R, Unnikrishnan R, Routray P, Tharunika S, Lakshmi N, Killivalavan D, Ganesan S, Jebarani S, Venkatesan U, Das S, Anjana RM, Lean diabetes is one end of the spectrum of type 2 diabetes and not a separate entity. Diabetes Research and Clinical Practice, DOI:10.1016/j.diabres.2025.112387
  3. Baskar V, Marlene G, M A Vignesh, Jebarani S, Hubert P, Robert W, Jürgen M, Fritz S, Andreas P, Nikolaos P, Stefan K, Knut M, Stefan K, Michael S, Peter EH Schwarz, Hans H, Jochen S, Ryna Y, Andrea I, Michele S, Julia S, Annette S, Martin H, Matthias B, Michael R, Norbert S, Andreas F, Daniel A, Stefan R. Bornstein, Anjana RM, Andreas L. Birkenfeld, Mohan V+ , Reiner J+ and the PLIS Group. Phenotypic Heterogeneity and Risk for Type 2 Diabetes in Central European and South Asian People with Prediabetes. Diabetology and metabolism. 2025 DOI: 10.2337/db25-1377-P
  4. PramodKumar TA, Pradeepa R, Jebarani S, Ganesan S, Pednekar A, Philips R, Kumar SP, Unnikrishnan R, Anjana RM, Mohan V. Effectiveness of Glipizide and Glipizide Plus Metformin Formulation among Asian Indians with Type 2 Diabetes: a Real‐World, Retrospective Electronic Medical Record Analysis. Drugs – Real World Outcomes. 2025 DOI:10.1007/s40801-025-00502-0
  5. Bhuvanesswar KC, Srivastava BK, Amutha A, Damle V, Krishna A, Gupta PK, Routray P, Killivalavan D, Jebarani S, Venkatesan U, Chakkalakal RJ, Shalimar, Kumar JS, Nageshwar Reddy D, Kulkarni AV, Unnikrishnan R, Anjana RM, Seshadri KG, Mohan V. Prevalence of Hepatic Steatosis and Fibrosis in Asian Indian Individuals with Type 2 Diabetes. Diabetes Therapy. 2025 DOI:10.1007/s13300-025-01764-1
  6. Pradeepa R, PramodKumar TA, Anjana RM, Jebarani S, Naziyagulnaaz AS, Ganesan S, Premananth N, Oomman A, Kumar S, Jayagopal PB, Wander GS, Mullasari A, Narula J, Jain S, Swami OC, Mohan V. Association Between Type 2 Diabetes Mellitus and Heart Failure: A Retrospective Study from a Tertiary Care Diabetes Centre in India. Diabetes Therapy. 2025 DOI:10.1007/s13300-025-01746-3
  7. Brian C. Callaghan, Anjana RM, Lavanya M, Evan L. Reynolds, Jebarani S, Arun V, Aswin M, Eva L. Feldman, Mohan V. Severe insulin‐deficient diabetes is at the opposite end of the neuropathy spectrum to mild aged‐related diabetes. Journal of Diabetes Investigation. 2025 DOI: 10.1111/jdi.70033
  8. Rajalakshmi R, PramodKumar TA, Dhara AK, Kumar G, Gulnaaz N, Dey S, Basak S, Shankar U, Goswami R, Mohammed R, Manikandan S, Mitra S, Thethi H, Jebarani S, Mathavan S, Sarveswaran T, Anjana RM, Mohan V, Ghosh S, Bera TK, Raman R.  Creating a retinal image database to develop an automated screening tool for diabetic retinopathy in India. Scientific Reports. 2025;15:7853.
  9. Krishna SA, Venkatesan U, Amutha A, Jebarani S, Sengottuvelu G, Unnikrishnan R, Mohan V, Anjana RM. Cardiovascular disease risk prediction and profiling among Asian Indians with young-onset type 1 and type 2 diabetes. Diabetic Medicine. 2025 DOI: 10.1111/dme.70020
  10. Dupuis T, Anjana RM, Srinivasan S, Dawed AY, Melhem A, Bigossi M, Taylor A, Adedire ET, Jebarani S, Sartori A, Davtian D, Radha V, Hodgson S, McNeilly A, Cantley J, Sattar N, Mathur R, Finer S, Genes & Health Research Team, Pearson ER, Viñuela A, Pradeepa R, Mohan V, Palmer CNA, Brown AA, Siddiqui MK. XBP1 expression in pancreatic islet cells is associated with poor glycaemic control across ancestries especially in young non-obese onset diabetes. Communications Medicine. 2025 doi.org/10.1101/ 2023.05.04. 23289501
  11. Amutha A, Reji S, Hema Aarthi R, Keertan Rao S, Ganesan S, Jebarani S, Praveen G, Unnikrishnan R, Mohan V, Anjana RM. Comparison of the Effect of Teleconsultations, Hybrid Visits, and In-Person Visits on Glycemic and Metabolic Parameters Among Individuals With Type 2 Diabetes in India. Journal of Diabetes Science and Technology. 2025 DOI: 10.1177/19322968251319333
  12. BK Srivastava, RM Anjana, Tukaram N, Jebarani S, Mohan V. Effect of combination therapy with once weekly Dulaglutide and Empagliflozin on noninvasive liver scores: real world experience from India. Endocrine Practice. 2024  DOI: 10.1016/j.eprac. 2024.03. 210
  13. Aarthy R, Aston-Mourney K, Amutha A, Mikocka-Walus A, Anjana RM, Unnikrishnan R, Jebarani S, Venkatesan U, Gopi S, Radha V, Mohan V. Identification of appropriate biochemical parameters and cut points to detect Maturity Onset Diabetes of Young (MODY) in Asian Indians in a clinic setting. Scientific Reports. 2023 DOI:10.1038/s41598-2023-37766-x
  14. Pradeepa R, Shreya L, Anjana RM, Jebarani S, Venkatesan U, Kamal Raj N, Swami OC, Mohan V. Sex-Based Differences in Clinical Profile and Complications among Individuals with Type 2 Diabetes Seen at a Private Tertiary Diabetes Care Centre in India. Healthcare (Basel). 2023;11:1634.
  15. Aarthy R, Aston-Mourney K, Amutha A, Mikocka-Walus A, Anjana RM, Unnikrishnan R, Jebarani S, Venkatesan U, Gopi S, Radha V, Mohan V. Prevalence, clinical features and complications of common forms of Maturity Onset Diabetes of the Young (MODY) seen at a tertiary diabetes centre in south India. Primary Care Diabetes. DOI:10.1016/j.pcd. 2023.04.004
  16. Pradeepa R, Shreya L, Anjana RM, Jebarani S, Raj NK, Madhan SK, Jayaganesh P, Swami OC, Mohan V. Frequency of iron deficiency anemia in type 2 diabetes – Insights from tertiary diabetes care centres across India. Diabetes & Metabolic Syndrome: Clinical Research & Reviews.DOI: 10.1016 /j.dsx.2022. 102632.
  17. Vidyulatha J, Pramodkumar TA, Pradeepa R, Poongothai S, Thenmozhi S, Venkatesan U, Jebarani S, Anjana RM, Mohan V. Association of stress, depression, and anxiety among individuals with microvascular complications in type 2 diabetes. Journal of Diabetology. 2022;13:294-300
  18. Nugawela, MD, Gurudas S, Prevost AT, Mathur R, Robson J, Sathish T, Rafferty JM, Rajalakshmi R, Anjana RM, Jebarani S, Mohan V, Owens DR, Sivaprasad S. Development and validation of predictive risk models for sight threatening diabetic retinopathy in patients with type 2 diabetes to be applied as triage tools in resource limited settings. EClinicalMedicine. 2022; 51, 101578.
  19. R.P. Guha Pradeepa, R.M. Anjana, Amutha Anandakumar, Shanthi Rani, Jebarani S, Venky Venkatesan, R. Unnikrishnan, V. Mohan. Effects of COVID-19 lockdown on lifestyle changes and medical challenges in patients with type 1 diabetes in south India. Diabetes Research and Clinical Practice. 2022 186:109386  
  20. Rajalakshmi R, UmaSankari G, Prathiba V, Anjana RM, Unnikrishnan R, Venkatesan U, JebaRani S, ShanthiRani CS, Sivaprasad S, Mohan V. Tele-ophthalmology versus face-to-face retinal consultation for assessment of diabetic retinopathy in diabetes care centres in India: a multicentre cross-sectional study. Diabetes Technology & Therapeutics. 2022 Mar 16. DOI: 10.1089/dia.2022.0025
  21. M K Siddiqui, Anjana RM, Adem Yesuf Dawed, Cyrielle Martoeau, Sundararajan Srinivasan, Jebarani S, Sathish K. Madanagopal, Ally Taylor, Samira Bell, Abirami Veluchamy, R. Guha Pradeepa, Naveed Sattar, Venkatesan Radha, Colin N A Palmer, Ewan R. Pearson, Viswanathan Mohan. Young-onset diabetes in Asian Indians is associated with lower measured and genetically determined beta cell function. Diabetologia (2022) 65:973–983
  22. Abhinav RP, Williams J, Bennett C, Livingston P, Jebarani S, Pradeepa R, Anjana RM, Mohan V. Frequency and association of self-reported oral cancer among individuals with type 2 diabetes at a tertiary care diabetes centre in South India – A retrospective study. Journal of Diabetes and its Complications. DOI: 10.1016/j.jdiacomp.2022.108129
  23. Mohan V, Shanthi Rani CS, Saboo B, Mukhopadhyay S, Chatterjee S, Panneerselvam D, Gupta SS, Pendsey S, Chandrakanta J, Uma Sankari G, Amutha A, Sheryl S, Datta S, Gupta PK, Routary P, Jebarani S, Sastry NG, Venkatesan U, Anjana RM, Unnikrishnan R. Clinical profile of long-term survivors and non-survivors with type 1 diabetes in India. Diabetes Technology and Therapeutics. doi: 10.1089/dia.2021.0284
  24. Kamalraj N, Sathishkumar M, Arunvignesh M, Baskar V, Jebarani S, Amutha A, Deepa M, Shanthi Rani CS, Chandru S, Unnikrishnan R, Anjana RM, Harish M, Mohan V. Retrospective analysis (2009-2017) of factors associated with progression and regression of non-alcoholic fatty liver disease (Hepatic steatosis) in patients with type 2 diabetes seen at a tertiary diabetes centre in Southern India. Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2021;15:102261.
  25. Sarega Gurudas, Manjula Nugawela, A. Toby Prevost, Thirunavukkarasu Sathish, Rohini Mathur, J. M. Rafferty, Kevin Blighe, Ramachandran Rajalakshmi,  Anjana R. Mohan, Jebarani S, Azeem Majeed, Viswanthan Mohan, David R. Owens, John Robson, Sobha Sivaprasad & the ORNATE India Study Group. Development and validation of resource-driven risk prediction models for incident chronic kidney disease in type 2 diabetes. Scientific Reports (2021) 11:13654
  26. Brijendra Kumar Srivastava, R M Anjana, Amit Singla, Jebarani S, Mohan Viswanathan. Combination Therapy With Premixed & Basal Insulin Analogues in Asian Indians With Type 2 Diabetes. Journal of the Endocrine Society. 2021  DOI:10.1210/jendso/ bvab048. 954
  27. Chandru S, Pramodkumar TA, Pradeepa R, Jebarani S, Prasad YM, Praveen RP, Babu JS, Anjana RM, Mohan V. Impact of bariatric surgery on body composition and metabolism among obese Asian Indians with prediabetes and diabetes. Journal of Diabetology. 2021;12:208 17.
  28. Ahuja V, Aronen P, Pramodkumar TA, Looker H, Chetrit A, Bloigu AH, Juutilainen A, Bianchi C, La Sala L, Anjana RM, Pradeepa R, Venkatesan U, Jebarani S, Baskar V, Fiorentino TV, Timpel P, DeFronzo RA, Ceriello A, Del Prato S, Abdul- Ghani M, Keinänen-Kiukaanniemi S, Dankner R, Bennett PH, Knowler WC, Schwarz P, Sesti G, Oka R, Mohan V, Groop L, Tuomilehto J, Ripatti S, Bergman M, Tuomi T. Accuracy of 1-Hour Plasma Glucose During the Oral Glucose Tolerance Test in Diagnosis of Type 2 Diabetes in Adults: A Meta-analysis. Diabetes Care. 2021;44:1062-1069.
  29. Varghese JS, Ho JC, Anjana RM, Pradeepa R, Patel SA, Jebarani S, Baskar V, Narayan KMV, Mohan V. Profiles of Intraday Glucose in Type 2 Diabetes and Their Association with Complications: An Analysis of Continuous Glucose Monitoring Data. Diabetes Technology and Therapeutics. 2021 DOI:10.1089/dia.2020.0672.
  30. Anjana RM, Siddiqui MK, Jebarani S, Vignesh MA, Kamal Raj N, Unnikrishnan R, Pradeepa R, Panikar VK, Kesavadev J, Saboo B, Gupta S, Sosale AR, Seshadri KG, Deshpande N, Chawla M, Chawla P, Das S, Behera M, Chawla R, Nigam A, Gupta A, Kovil R, Joshi SR, Agarwal S, Bajaj S, Pearson ER, Doney ASF, Palmer CNA, Mohan V. Prescribing Patterns and Response to Antihyperglycemic Agents Among Novel Clusters of Type 2 Diabetes in Asian Indians. Diabetes Technology & Therapeutics. DOI: 10.1089/dia.2021.027.
  31. Ghosh A, Anjana RM, Shanthi Rani CS, Jeba Rani S, Gupta R, Jha A, Gupta V, Kuchay MS, Luthra A, Durrani S, Dutta K, Tyagi K, Unnikrishnan R, Srivastava BK, Ramu M, Sastry NG, Gupta PK, Umasankari G, Jayashri R, Mohan V, Misra A. Glycemic parameters in new-onset diabetes during COVID-19 pandemic are more severe than new-onset diabetes before the pandemic: NOD COVID India study. Diabetes & Metabolic Syndrome: Clinical Research and Reviews. 2021;15:215- 220.
  32. Anjana RM, Baskar V, Nair ATN, Jebarani S, Siddiqui MK, Pradeepa R, Unnikrishnan R, Palmer C, Pearson E, Mohan V. Novel subgroups of type 2 diabetes and their association with microvascular outcomes in an Asian Indian population: a data-driven cluster analysis – The INSPIRED STUDY. BMJ Open Diabetes Research & Care. 2020; 8:e001506.
  33. Anjana RM, Pradeepa R, Deepa M, Jebarani S, Venkatesan U, Parvathi SJ, Balasubramanyam M, Radha V, Poongothai S, Sudha V, Shanthi Rani CS, Ranjani H, Amutha A, Manickam N, Unnikrishnan R, Mohan V. Acceptability and Utilization of Newer Technologies and Effects on Glycemic Control in Type 2 Diabetes: Lessons Learnt from Lockdown. Diabetes Technology & Therapeutics. 2020:22:1-9.
  34. Rajalakshmi R, Shanthi Rani CS, Venkatesan U, Unnikrishnan R, Anjana RM, Jeba Rani S, Umasankari G, Sivaprasad S, Raman R, Mohan V. Correlation between markers of renal function and sight-threatening diabetic retinopathy in type 2 diabetes: a longitudinal study in an Indian clinic population. BMJ Open Diabetes Research and Care. 2020;8:e001325.
  35. Chetan MR, Miksza JK, Lawrence I, Anjana RM, Unnikrishnan R, Amutha A, Shanthi Rani CS, Jebarani S, Mohan V, Khunti K, Narendran P. The increased risk of microvascular complications in South Asians with type 1 diabetes is influenced by migration: results from the UK-India multi-ethnic migration study. Diabetic Medicine. 2019 Nov 13.  doi: 10.1111/DME.14184
  36. Mohan V, Anjana RM, Unnikrishnan R, Gupta PPK, Routray P, Jebarani S, Venkatesan U, Uma Sankari G, Gini Venisha K, Ashok Kumar R, Rahulashankiruthiyayan T, Pradeepa  R, Rani CSS. Clinical profile of elderly patients (over 90 years) with type 2 diabetes seen at a diabetes centre in South India. Diabetes Technology and Therapeutics. DOI: 10.1089/ dia.2019.0219
  37. Anjana RM, Kesavadev J, Neeta D, Tiwaskar M, Pradeepa R, Jebarani S, Thangamani S, Sastry NG, Brijendra Kumar S, Ramu M, Gupta PPK, Vignesh J, Chandru S, Kayalvizhi S, Jagdish PS, Uthra SCB, Lovelena M, Jyoti S, Suguna Priya S, Kannan A, Mohan V, Unnikrishnan R. A Multicenter Real-Life Study on the Effect of Flash Glucose Monitoring on Glycemic Control in Patients with Type 1 and Type 2 Diabetes. Diabetes Technology and Therapeutics. 2017;19:533-540.
  38. Unnikrishnan R, Anjana RM, Amutha A, Ranjani H, Jebarani S, Ali MK, Narayan KMV, Mohan V. Younger-onset versus older-onset type 2 diabetes: Clinical profile and complications. Journal of Diabetes and Its Complications. 2017; 31:971 – 975.
  39. Unnikrishnan R, Shanthi Rani CS, Anjana RM, Uthra SC, Vidya J, Uma Sankari G, Venkatesan U,  Jebarani S, Mohan V. Postpartum development of type 1 diabetes in Asian Indian women with gestational diabetes. Indian Journal of Endocrinology and Metabolism 20(5):p 702-706, Sep–Oct 2016
  40. Pramodkumar TA, Priya M, Jebarani S, Anjana RM, Mohan V, Pradeepa R. Metabolic profile of normal glucose-tolerant subjects with elevated 1-h plasma glucose values. Indian Journal of Endocrinology and Metabolism.  2016;20: 612-618.
  41. Mahalakshmi MM, Bhavadharini B, Maheswari K, Anjana RM, Jebarani S, Ninov L, Kayal A, Malanda B, Belton A, Uma R, Mohan V, Unnikrishnan R. Current practices in the diagnosis and management of gestational diabetes mellitus in India (WINGS – 5). Indian Journal of Endocrinology and Metabolism. 2016; 20: 364-8.
  42. Priya MM, Amutha A, Pramodkumar TA, Ranjani H, Jebarani S, Gokulakrishnan K, Pradeepa R, Unnikrishnan R, Anjana RM, Mohan V. β-Cell Function and Insulin Sensitivity in Normal Glucose-Tolerant Subjects Stratified by 1-Hour Plasma Glucose Values. Diabetes Technology and Therapeutics. 2016;18:29-33.
  43. Billow A, Anjana RM,  Ngai M, Amutha A, Pradeepa R, Jebarani S, Unnikrishnan R, Michael E, Mohan V. Prevalence and clinical profile of metabolic syndrome among type 1 diabetes mellitus patients in southern India. Journal of Diabetes and its Complications. doi: 10.1016/j.jdiacomp.2015.03.014
  44. Eshcol J, Jebarani S, Anjana RM, Mohan V, Pradeepa R.  Prevalence, incidence and progression of peripheral arterial disease in Asian Indian type 2 diabetic patients. Journal of Diabetes and Its Complications. doi: 10.1016/ j.jdiacomp.2014.04.013
  45. Shiny A, Bibin YS, Shanthirani CS, Regin BS, Anjana RM, Balasubramanyam M, Jebarani S, Mohan V. Association of Neutrophil-Lymphocyte Ratio with Glucose Intolerance:  An Indicator of  Systemic  Inflammation in  Patients with  Type 2 Diabetes  Technology and Therapeutics. 2014;16:524-530.  
  46. Pradeepa R, Prabu AV, Jebarani S, Subhasini S, Mohan V.  Use of a Large Diabetes  Electronic Medical Record System in India: Clinical and Research Applications. Journal of Diabetes Science and Technology.  2011;5:543-552.

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