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OBSERVATIONS Relationship Between Glycated Hemoglobin and Metabolic Syndrome of Type 1 and Type 2 Diabetes A factor analysis study
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lycated hemoglobin (GHb) is the best-characterized marker of glycemic control in patients with diabetes. Nevertheless, the relationship between GHb itself and aggregated cardiovascular risk factors, collectively denominated metabolic syndrome (MS), requires more studies in both type 1 and type 2 diabetes. Insulin resistance (IR) is notoriously associated with MS, but hyperglycemia, primarily resulting from insulin deficiency, might have as important a role as IR in MS of type 2 diabetes. MS is also observed in a minority of patients with type 1 diabetes, but its pathophysiology in such cases is poorly understood. Factor analyses (FAs) have described from one to five factors underlying MS in type 2 diabetes, using only variables linked to IR (1–3). Factors extraneous to IR (namely insulin deficiency) cannot be detected in this setting because FA doesn’t have a constant analogous to that of regression methods. FA doesn’t use conventional power calculation, but it is recommended that at least 400 individuals be included. Insufficient samples possibly contributed to the heterogeneity of previous findings (4). GHb was not used in a previous FA of MS in type 1 diabetes (5). We have conducted exploratory FAs of GHb and MS components in 520 individuals with type 1 diabetes and 870 with type 2 diabetes. In type 1 diabetes, one factor loaded with triglycerides (0.666 and 0.729) and GHb (0.771 and 0.821), in female and male subjects, respectively. In type 2 diabetes, a common factor loaded with GHb (⫺0.852 and 0.772)
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and HDL (0.455 and ⫺0.860), in female and male subjects, respectively. The factor structure showed results similar to those of existing literature, regarding one factor linked to adiposity (in which BMI and waist loaded), but the lipid factor was weakened by the association of lipids with GHb in the instances described above. Logistic regression models using the same variables above, categorized by the National Cholesterol Education Program criteria (except for GHb) and corrected for age, sex, and time from diagnosis, showed that in type 1 diabetes each 1% GHb increment increased the probability of MS (OR 1.22 [95% CI 1.04 –1.44]) and hypertriglyceridemia (1.24 [1.09 –1.40]). In type 2 diabetes, each 1% GHb increase was associated with MS (1.31 [1.18 – 1.45]) and low HDL (1.63 [1.46 –1.83]), albeit with hypertriglyceridemia also (1.20 [1.09 –1.32]), differently from FA results. Our data demonstrate the influence of chronic hyperglycemia, as marked by GHb, on MS associated with both major types of diabetes. Probability of dyslipidemia also increased alongside GHb levels, albeit differently in each disease. Insulin hypoglycemic effect has a well-known role in some steps of lipid metabolism. These pathways are often overlooked in favor of a more IR-oriented view toward MS. These data reinforce a possible role for insulin deficiency within a multifactorial pathophysiology of MS, therefore contradicting hypotheses of a single etiological factor linked to IR being responsible for MS in diabetes. The role of hyperglycemia on dyslipidemia in diabetes has to be further clarified, and must include a better understanding of IRrelated traits in type 1 diabetes. FERNANDO M.A. GIUFFRIDA, MD, PHD1 CELSO F.C. SALLUM, MD1 MONICA A.L. GABBAY, MD, MSC1 MARILIA B. GOMES, MD, PHD2 ANTONIO C. PIRES, MD, PHD3 SERGIO A. DIB, MD, PHD1 From the 1Diabetes Center, Federal University of Sa˜o Paulo, Sa˜o Paulo, Brazil; the 2Rio de Janeiro State University, Rio de Janeiro, Brazil; and the 3 Sa˜o Jose´ do Rio Preto School of Medicine, Sa˜o Jose´ do Rio Preto, Brazil.
DIABETES CARE, VOLUME 33, NUMBER 6, JUNE 2010
Corresponding author: Fernando M.A. Giuffrida,
[email protected]. DOI: 10.2337/dc09-2280 © 2010 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. See http:// creativecommons.org/licenses/by-nc-nd/3.0/ for details.
Acknowledgments — F.M.A.G. was supported by a PhD grant from Coordenac¸a˜o de Aperfeic¸oamento de Pessoal de Nível Superior (CAPES), Brazil. C.F.C.S. was supported by an undergraduate research fellowship grant from the National Council of Scientific and Technological Development (CNPq), Brazil. No potential conflicts of interest relevant to this article were reported. Parts of this study were presented in poster form at the American Diabetes Association 68th Scientific Sessions, San Francisco, California, 6 –10 June 2008. ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●
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