Jia, Weiping

Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range

Battelino, T., Danne, T., Bergenstal, R. M., Amiel, S. A., Beck, R., Biester, T., Bosi, E., Buckingham, B. A., Cefalu, W. T., Close, K. L., et al. (2019) · Diabetes Care

Diabetes Management and ResearchDiabetes Management and Education

Cited by 4,237

Prevalence of Diabetes among Men and Women in China

Yang, W., Lu, J., Weng, J., Jia, W., Ji, L., Xiao, J., Shan, Z., Liu, J., Tian, H., Ji, Q., et al. (2010) · New England Journal of Medicine

Diabetes, Cardiovascular Risks, and LipoproteinsDiabetes Management and Research

Cited by 3,031

International consensus on use of continuous glucose monitoring

Danne, T., Nimri, R., Battelino, T., Bergenstal, R. M., Close, K. L., DeVries, J. H., Garg, S., Heinemann, L., Hirsch, I., Amiel, S. A., et al. (2017) · Diabetes Care

Diabetes Management and ResearchDiabetes Treatment and Management

Cited by 2,069

Diabetes in Asia: Epidemiology, Risk Factors, and Pathophysiology

Chan, J. C. N., Malik, V., Jia, W., Kadowaki, T., Yajnik, C. S., Yoon, K.-H., & Hu, F. B. (2009) · JAMA

Diabetes, Cardiovascular Risks, and LipoproteinsObesity, Physical Activity, Diet

Cited by 1,997

Bile acid-microbiota crosstalk in gastrointestinal inflammation and carcinogenesis

Jia, W., Xie, G., & Jia, W. (2018) · Nature Reviews Gastroenterology & Hepatology

Gut microbiota and healthDrug Transport and Resistance Mechanisms

Cited by 1,924

The genetic architecture of type 2 diabetes

Fuchsberger, C., Flannick, J., Teslovich, T. M., Mahajan, A., Agarwala, V., Gaulton, K. J., Ma, C., Fontanillas, P., Moutsianas, L., McCarthy, D. J., et al. (2016) · Nature

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

Cited by 1,051

Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility

DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Mahajan, A., Asian Genetic Epidemiology Network Type 2 Diabetes (AGEN-T2D) Consortium, South Asian Type 2 Diabetes (SAT2D) Consortium, Mexican American Type 2 Diabetes (MAT2D) Consortium, Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples (T2D-GENES) Consortium, Go, M. J., Zhang, W., Below, J. E., Gaulton, K. J., et al. (2014) · Nature Genetics

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

Cited by 1,034

Low-dose metformin targets the lysosomal AMPK pathway through PEN2

Ma, T., Tian, X., Zhang, B., Li, M., Wang, Y., Yang, C., Wu, J., Wei, X., Qu, Q., Yu, Y., et al. (2022) · Nature

Metabolism, Diabetes, and CancerAutophagy in Disease and Therapy

Cited by 644

A deep learning system for detecting diabetic retinopathy across the disease spectrum

Dai, L., Wu, L., Li, H., Cai, C., Wu, Q., Kong, H., Liu, R., Wang, X., Hou, X., Liu, Y., et al. (2021) · Nature Communications

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 620

Association of time in range, as assessed by continuous glucose monitoring, with diabetic retinopathy in type 2 diabetes

Lu, J., Ma, X., Zhou, J., Zhang, L., Mo, Y., Ying, L., Lu, W., Zhu, W., Bao, Y., Vigersky, R. A., et al. (2018) · Diabetes Care

Diabetes Management and ResearchRetinal Diseases and Treatments

Cited by 517

A deep learning system for predicting time to progression of diabetic retinopathy

Dai, L., Sheng, B., Chen, T., Wu, Q., Liu, R., Cai, C., Wu, L., Yang, D., Hamzah, H., Liu, Y., et al. (2024) · Nature Medicine

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 339

Incidence of type 1 diabetes in China, 2010-13: population based study

Weng, J., Zhou, Z., Guo, L., Zhu, D., Ji, L., Luo, X., Mu, Y., & Jia, W. (2018) · BMJ

Diabetes and associated disordersDiabetes Management and Research

Cited by 316

Standards of care for type 2 diabetes in China

Weng, J., Ji, L., Jia, W., Lu, J., Zhou, Z., Zou, D., Zhu, D., Chen, L., Chen, L., Guo, L., et al. (2016) · Diabetes/Metabolism Research and Reviews

Diabetes Treatment and ManagementDiabetes, Cardiovascular Risks, and Lipoproteins

Cited by 315

Metabolic profiles and treatment gaps in young-onset type 2 diabetes in Asia (the JADE programme): a cross-sectional study of a prospective cohort

Yeung, R. O., Zhang, Y., Luk, A., Yang, W., Sobrepena, L., Yoon, K.-H., Aravind, S. R., Sheu, W., Nguyen, T. K., Ozaki, R., et al. (2014) · The Lancet Diabetes & Endocrinology

Diabetes Treatment and ManagementDiabetes, Cardiovascular Risks, and Lipoproteins

Cited by 296

Artificial intelligence in diabetes management: Advancements, opportunities, and challenges

Guan, Z., Li, H., Liu, R., Cai, C., Liu, Y., Li, J., Wang, X., Huang, S., Wu, L., Liu, D., et al. (2023) · Cell Reports Medicine

Artificial Intelligence in Healthcare and EducationDiabetes Management and Research

Cited by 287

Frequency, Immunogenetics, and Clinical Characteristics of Latent Autoimmune Diabetes in China (LADA China Study)

Zhou, Z., Xiang, Y., Ji, L., Jia, W., Ning, G., Huang, G., Yang, L., Lin, J., Liu, Z., Hagopian, W. A., et al. (2013) · Diabetes

Diabetes and associated disordersDiabetes Management and Research

Cited by 251

Integrated image-based deep learning and language models for primary diabetes care

Li, J., Guan, Z., Wang, J., Cheung, C. Y., Zheng, Y., Lim, L.-L., Lim, C. C., Ruamviboonsuk, P., Raman, R., Corsino, L., et al. (2024) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 250

Adiponectin Activates AMP-Activated Protein Kinase in Muscle Cells via APPL1/LKB1-Dependent and Phospholipase C/Ca2+/Ca2+/Calmodulin-Dependent Protein Kinase Kinase-Dependent Pathways

Zhou, L., Deepa, S. S., Etzler, J. C., Ryu, J., Mao, X., Fang, Q., Liu, D. D., Torres, J. M., Jia, W., Lechleiter, J. D., et al. (2009) · Journal of Biological Chemistry

Metabolism, Diabetes, and CancerAdipose Tissue and Metabolism

Cited by 212

Toll-like receptor-4 mediates obesity-induced non-alcoholic steatohepatitis through activation of X-box binding protein-1 in mice

Ye, D., Li, F. Y. L., Lam, K. S. L., Li, H., Jia, W., Wang, Y., Man, K., Lo, C. M., Li, X., & Xu, A. (2012) · Gut

Liver Disease Diagnosis and TreatmentLiver physiology and pathology

Cited by 206

High-throughput determination of the antigen specificities of T cell receptors in single cells

Zhang, S.-Q., Ma, K.-Y., Schonnesen, A. A., Zhang, M., He, C., Sun, E., Williams, C. M., Jia, W., & Jiang, N. (2018) · Nature Biotechnology

T-cell and B-cell ImmunologyCancer Immunotherapy and Biomarkers

Cited by 200

High-throughput determination of the antigen specificities of T cell receptors in single cells

Zhang, S.-Q., Ma, K.-Y., Schonnesen, A. A., Zhang, M., He, C., Sun, E., Williams, C. M., Jia, W., & Jiang, N. (2018) · Nature Biotechnology

T-cell and B-cell ImmunologyCancer Immunotherapy and Biomarkers

Cited by 199

Fibroblast growth factor 21 protects against acetaminophen-induced hepatotoxicity by potentiating peroxisome proliferator-activated receptor coactivator protein-1α-mediated antioxidant capacity in mice

Ye, D., Wang, Y., Li, H., Jia, W., Man, K., Lo, C. M., Wang, Y., Lam, K. S. L., & Xu, A. (2014) · Hepatology

Drug-Induced Hepatotoxicity and ProtectionFibroblast Growth Factor Research

Cited by 184

Fibroblast growth factor 21 increases insulin sensitivity through specific expansion of subcutaneous fat

Li, H., Wu, G., Fang, Q., Zhang, M., Hui, X., Sheng, B., Wu, L., Bao, Y., Li, P., Xu, A., et al. (2018) · Nature Communications

Fibroblast Growth Factor ResearchAdipokines, Inflammation, and Metabolic Diseases

Cited by 178

Gut microbiota-bile acid crosstalk contributes to the rebound weight gain after calorie restriction in mice

Li, M., Wang, S., Li, Y., Zhao, M., Kuang, J., Liang, D., Wang, J., Wei, M., Rajani, C., Ma, X., et al. (2022) · Nature Communications

Gut microbiota and healthDietary Effects on Health

Cited by 175