The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
HbA1c Variability as an Independent Correlate of Nephropathy, but Not Retinopathy, in Patients With Type 2 Diabetes
Open accessAbstract
OBJECTIVE: To examine the association of hemoglobin (Hb) A1c variability with microvascular complications in the large cohort of subjects with type 2 diabetes from the Renal Insufficiency And Cardiovascular Events (RIACE) Italian Multicenter Study. RESEARCH DESIGN AND METHODS: Serial (3–5) HbA1c values collected in a 2-year period before enrollment were available from 8,260 subjects from 9 centers (of 15,773 patients from 19 centers). HbA1c variability was measured as the intraindividual SD of 4.52 ± 0.76 values. Diabetic retinopathy (DR) was assessed by dilated funduscopy. Chronic kidney disease (CKD) was defined based on albuminuria, as measured by immunonephelometry or immunoturbidimetry, and estimated glomerular filtration rate (eGFR) was calculated from serum creatinine. RESULTS: Median and interquartile range of average HbA1c (HbA1c-MEAN) and HbA1c-SD were 7.57% (6.86–8.38) and 0.46% (0.29–0.74), respectively. The highest prevalence of microalbuminuria, macroalbuminuria, reduced eGFR, albuminuric CKD phenotypes, and advanced DR was observed when both HbA1c parameters were above the median and the lowest when both were below the median. Logistic regression analyses showed that HbA1c-SD adds to HbA1c-MEAN as an independent correlate of microalbuminuria and stages 1–2 CKD and is an independent predictor of macroalbuminuria, reduced eGFR, and stages 3–5 albuminuric CKD, whereas HbA1c-MEAN is not. The opposite was found for DR, whereas neither HbA1c-MEAN nor HbA1c-SD affected nonalbuminuric CKD. CONCLUSIONS: In patients with type 2 diabetes, HbA1c variability affects (albuminuric) CKD more than average HbA1c, whereas only the latter parameter affects DR, thus suggesting a variable effect of these measures on microvascular complications.
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Self-citationStratton, I. M. (2000) · BMJ · doi:10.1136/bmj.321.7258.405 · Cited by 9,059
Risk Factors for Renal Dysfunction in Type 2 Diabetes
Retnakaran, R., Cull, C. A., Thorne, K. I., Adler, A. I., Holman, R. R., & for the UKPDS Study Group (2006) · Diabetes · doi:10.2337/db05-1620 · Cited by 927
Self-citationLachin, J. M., Genuth, S., Nathan, D. M., Zinman, B., Rutledge, B. N., & for the DCCT/EDIC Research Group (2008) · Diabetes · doi:10.2337/db07-1618 · Cited by 555
Self-citationMetabolic Syndrome and Kidney Disease: A Systematic Review and Meta-analysis
Thomas, G., Sehgal, A. R., Kashyap, S. R., Srinivas, T. R., Kirwan, J. P., & Navaneethan, S. D. (2011) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.02180311 · Cited by 553
Early Worsening of Diabetic Retinopathy in the Diabetes Control and Complications Trial
(1998) · Archives of Ophthalmology · doi:10.1001/archopht.116.7.874 · Cited by 509
Raz, I., Wilson, P. W. F., Strojek, K., Kowalska, I., Bozikov, V., Gitt, A. K., Jermendy, G., Campaigne, B. N., Kerr, L., Milicevic, Z., et al. (2009) · Diabetes Care · doi:10.2337/dc08-1671 · Cited by 351
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Long-term Glycemic Variability and Risk of Adverse Outcomes: A Systematic Review and Meta-analysis
Gorst, C., Kwok, C. S., Aslam, S., Buchan, I., Kontopantelis, E., Myint, P. K., Heatlie, G., Loke, Y., Rutter, M. K., & Mamas, M. A. (2015) · Diabetes Care · doi:10.2337/dc15-1188 · Cited by 530
Machine Learning Methods to Predict Diabetes Complications
Dagliati, A., Marini, S., Sacchi, L., Cogni, G., Teliti, M., Tibollo, V., De Cata, P., Chiovato, L., & Bellazzi, R. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817706375 · Cited by 407
Erectile dysfunction and diabetes: A melting pot of circumstances and treatments
Defeudis, G., Mazzilli, R., Tenuta, M., Rossini, G., Zamponi, V., Olana, S., Faggiano, A., Pozzilli, P., Isidori, A. M., & Gianfrilli, D. (2022) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.3494 · Cited by 285
Cardoso, C. R. L., Leite, N. C., Moram, C. B. M., & Salles, G. F. (2018) · Cardiovascular Diabetology · doi:10.1186/s12933-018-0677-0 · Cited by 222
Pugliese, G., Penno, G., Natali, A., Barutta, F., Di Paolo, S., Reboldi, G., Gesualdo, L., & De Nicola, L. (2019) · Nutrition, Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2019.07.017 · Cited by 156
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Metadata
- Authors
- Penno, G., Solini, A., Bonora, E., Fondelli, C., Orsi, E., Zerbini, G., Morano, S., Cavalot, F., Lamacchia, O., Laviola, L., Nicolucci, A., Pugliese, G., & for the Renal Insufficiency And Cardiovascular Events (RIACE) Study Group
- Venue
- Diabetes Care
- Publisher
- American Diabetes Association
- Published
- 2013-07-11
- Volume
- 36
- Issue
- 8
- Pages
- 2301-2310
- ISSN
- 0149-5992 (ISSN-L)
- 1935-5548
- OpenAlex
- W2088273241
- Cited by
- 168
- References
- 7
- FWCI
- 9.94
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Penno, G., Solini, A., Bonora, E., Fondelli, C., Orsi, E., Zerbini, G., Morano, S., Cavalot, F., Lamacchia, O., Laviola, L., Nicolucci, A., Pugliese, G., & for the Renal Insufficiency And Cardiovascular Events (RIACE) Study Group. (2013). HbA1c Variability as an Independent Correlate of Nephropathy, but Not Retinopathy, in Patients With Type 2 Diabetes. Diabetes Care. https://doi.org/10.2337/dc12-2264
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