Sabanayagam, Charumathi

Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

Stanaway, J. D., Afshin, A., Gakidou, E., Lim, S. S., Abate, D., Abate, K. H., Abbafati, C., Abbasi, N., Abbastabar, H., Abd-Allah, F., et al. (2018) · The Lancet

Cardiovascular Health and Risk FactorsOccupational Health and Global Justice

Cited by 4,943

Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045

Teo, Z. L., Tham, Y.-C., Yu, M., Chee, M. L., Rim, T. H., Cheung, N., Bikbov, M. M., Wang, Y. X., Tang, Y., Lu, Y., et al. (2021) · Ophthalmology

Retinal Diseases and TreatmentsRetinal Imaging and Analysis

Cited by 2,420

Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes

Ting, D. S. W., Cheung, C. Y.-L., Lim, G., Tan, G. S. W., Quang, N. D., Gan, A., Hamzah, H., Garcia-Franco, R., San Yeo, I. Y., Lee, S. Y., et al. (2017) · JAMA

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 2,402

Worldwide trends in diabetes prevalence and treatment from 1990 to 2022: a pooled analysis of 1108 population-representative studies with 141 million participants

Zhou, B., Rayner, A. W., Gregg, E. W., Sheffer, K. E., Carrillo-Larco, R. M., Bennett, J. E., Shaw, J. E., Paciorek, C. J., Singleton, R. K., Barradas Pires, A., et al. (2024) · The Lancet

Diabetes, Cardiovascular Risks, and LipoproteinsDiabetes Treatment and Management

Cited by 1,013

The power of genetic diversity in genome-wide association studies of lipids

Graham, S. E., Clarke, S. L., Wu, K.-H. H., Kanoni, S., Zajac, G. J. M., Ramdas, S., Surakka, I., Ntalla, I., Vedantam, S., Winkler, T. W., et al. (2021) · Nature

Genetic Associations and EpidemiologyLipoproteins and Cardiovascular Health

Cited by 972

A saturated map of common genetic variants associated with human height

Yengo, L., Vedantam, S., Marouli, E., Sidorenko, J., Bartell, E., Sakaue, S., Graff, M., Eliasen, A. U., Jiang, Y., Raghavan, S., et al. (2022) · Nature

Genetic Associations and EpidemiologyGenetics and Physical Performance

Cited by 825

Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation

Mahajan, A., Spracklen, C. N., Zhang, W., Ng, M. C. Y., Petty, L. E., Kitajima, H., Yu, G. Z., Rüeger, S., Speidel, L., Kim, Y. J., et al. (2022) · Nature Genetics

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

Cited by 704

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Suzuki, K., Hatzikotoulas, K., Southam, L., Taylor, H. J., Yin, X., Lorenz, K. M., Mandla, R., Huerta-Chagoya, A., Melloni, G. E. M., Kanoni, S., et al. (2024) · Nature

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

Cited by 606

Height and body-mass index trajectories of school-aged children and adolescents from 1985 to 2019 in 200 countries and territories: a pooled analysis of 2181 population-based studies with 65 million participants

Rodriguez-Martinez, A., Zhou, B., Sophiea, M. K., Bentham, J., Paciorek, C. J., Iurilli, M. L., Carrillo-Larco, R. M., Bennett, J. E., Di Cesare, M., Taddei, C., et al. (2020) · The Lancet

Obesity, Physical Activity, DietHuman Health and Disease

Cited by 417

Impact of common genetic determinants of hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: A transethnic genome-wide meta-analysis

Wheeler, E., Leong, A., Liu, C.-T., Hivert, M.-F., Strawbridge, R. J., Podmore, C., Li, M., Yao, J., Sim, X., Hong, J., et al. (2017) · PLOS Medicine

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

Cited by 408

A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations

Sabanayagam, C., Xu, D., Ting, D. S. W., Nusinovici, S., Banu, R., Hamzah, H., Lim, C., Tham, Y.-C., Cheung, C. Y., Tai, E. S., et al. (2020) · The Lancet Digital Health

Retinal Imaging and AnalysisChronic Kidney Disease and Diabetes

Cited by 279

Kidney and eye diseases: Common risk factors, etiological mechanisms, and pathways

Wong, C. W., Wong, T. Y., Cheng, C.-Y., & Sabanayagam, C. (2014) · Kidney International

Chronic Kidney Disease and DiabetesRetinal Diseases and Treatments

Cited by 276

Telehealth demand trends during the COVID-19 pandemic in the top 50 most affected countries: infodemiological evaluation

Wong, M. Y. Z., Gunasekeran, D. V., Nusinovici, S., Sabanayagam, C., Yeo, K. K., Cheng, C.-Y., & Tham, Y.-C. (2021) · JMIR Public Health and Surveillance

Telemedicine and Telehealth ImplementationData-Driven Disease Surveillance

Cited by 115

Artificial intelligence for diabetes care: current and future prospects

Sheng, B., Pushpanathan, K., Guan, Z., Lim, Q. H., Lim, Z. W., Yew, S. M. E., Goh, J. H. L., Bee, Y. M., Sabanayagam, C., Sevdalis, N., et al. (2024) · The Lancet Diabetes & Endocrinology

Artificial Intelligence in Healthcare and EducationDiabetes Management and Research

Cited by 106

The War on Diabetic Retinopathy: Where Are We Now?

Wong, T. Y., & Sabanayagam, C. (2019) · Asia-Pacific Journal of Ophthalmology

Retinal Diseases and TreatmentsRetinal Imaging and Analysis

Cited by 84

Do we have enough ophthalmologists to manage vision-threatening diabetic retinopathy? A global perspective

Teo, Z. L., Tham, Y.-C., Yu, M., Cheng, C.-Y., Wong, T. Y., & Sabanayagam, C. (2020) · Eye

Global Health Workforce IssuesRetinal Diseases and Treatments

Cited by 62

Prediction of diabetic kidney disease risk using machine learning models: A population-based cohort study of Asian adults

Sabanayagam, C., He, F., Nusinovici, S., Li, J., Lim, C., Tan, G., & Cheng, C. Y. (2023) · eLife

Chronic Kidney Disease and DiabetesArtificial Intelligence in Healthcare

Cited by 51

A multi-ancestry genome-wide study incorporating gene–smoking interactions identifies multiple new loci for pulse pressure and mean arterial pressure

Sung, Y. J., de las Fuentes, L., Winkler, T. W., Chasman, D. I., Bentley, A. R., Kraja, A. T., Ntalla, I., Warren, H. R., Guo, X., Schwander, K., et al. (2019) · Human Molecular Genetics

Genetic Associations and EpidemiologyBlood Pressure and Hypertension Studies

Cited by 43

Deep learning algorithms to detect diabetic kidney disease from retinal photographs in multiethnic populations with diabetes

Betzler, B. K., Chee, E. Y. L., He, F., Lim, C. C., Ho, J., Hamzah, H., Tan, N. C., Liew, G., McKay, G. J., Hogg, R. E., et al. (2023) · Journal of the American Medical Informatics Association

Retinal Imaging and AnalysisChronic Kidney Disease and Diabetes

Cited by 42

Use of artificial intelligence with retinal imaging in screening for diabetes-associated complications: systematic review

Yang, Q., Bee, Y. M., Lim, C. C., Sabanayagam, C., Yim-Lui Cheung, C., Wong, T. Y., Ting, D. S. W., Lim, L.-L., Li, H., He, M., et al. (2025) · eClinicalMedicine

Retinal Imaging and AnalysisArtificial Intelligence in Healthcare

Cited by 38

Development and External Validation of Machine Learning Models for Diabetic Microvascular Complications: Cross-Sectional Study With Metabolites

He, F., Ng Yin Ling, C., Nusinovici, S., Cheng, C.-Y., Wong, T. Y., Li, J., & Sabanayagam, C. (2024) · Journal of Medical Internet Research

Chronic Kidney Disease and DiabetesArtificial Intelligence in Healthcare

Cited by 29