Soluble CD36 (sCD36) clusters with markers of insulin resistance, and high sCD36 is associated with increased type 2 diabetes risk.
OBJECTIVE
Soluble CD36 (sCD36) may be an early marker of insulin resistance and atherosclerosis. The objective of this prospective study was to evaluate sCD36 as a predictor of type 2 diabetes and to study its relationship with components of the metabolic syndrome (MetSy). DESIGN, SETTING, PARTICIPANTS, AND OUTCOME MEASURES: We conducted a case-referent study nested within a population-based health survey. Baseline variables included sCD36, body mass index, blood pressure, blood lipids, adipokines, inflammatory markers, and beta-cell function. A total of 173 initially nondiabetic cohort members who developed type 2 diabetes during 10 yr of follow-up were matched (1:2) with referents. Exploratory factor analysis was applied to hypothesize affiliation of sCD36 to the MetSy components.


RESULTS
Doubling of baseline sCD36 increases the odds ratio for diabetes development by 1.24 in the general study population and by 1.45 in the female population (P < 0.025). Comparing upper sCD36 quartiles with lower, odds ratio for diabetes was 4.6 in women (P = 0.001), 3.15 in men (P = 0.011), and 2.6 in obese individuals (P < 0.025). Multivariate analysis shows that sCD36 does not predict diabetes independent of fasting plasma glucose and insulin. Factor analysis of 15 variables generates a six-factor model explaining 66-69% of total variance, where sCD36, body mass index, insulin, proinsulin, and leptin were assigned to the obesity/insulin resistance cluster.


CONCLUSIONS
Upper quartile sCD36 is associated with elevated diabetes risk independent of age, gender, and obesity. Baseline sCD36 does not, however, predict diabetes independent of fasting glucose and insulin. sCD36 clusters with important markers of insulin resistance and MetSy that are key predictors of type 2 diabetes.
Handberg
A
A
Department of Clinical Biochemistry, Aarhus University Hospital, Noerrebrogade 44, DK-8000 Aarhus C, Denmark. aah@dadlnet.dk
Norberg
M
M
Stenlund
H
H
Hallmans
G
G
Attermann
J
J
Eriksson
J W
JW
D000328:Adult; D000704:Analysis of Variance; D015415:Biomarkers / Q000097:blood; D015992:Body Mass Index; D016000:Cluster Analysis; D015331:Cohort Studies; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology* / Q000378:metabolism*; D005163:Factor Analysis, Statistical; D005260:Female; D006801:Humans; D007333:Insulin Resistance / Q000502:physiology*; D008297:Male; D024821:Metabolic Syndrome / Q000150:complications / Q000378:metabolism; D008875:Middle Aged; D009765:Obesity / Q000150:complications / Q000503:physiopathology; D011237:Predictive Value of Tests; D017463:Receptors, Complement 3b / Q000378:metabolism*; D012306:Risk; D013548:Sweden / Q000453:epidemiology
D015415:Biomarkers; D017463:Receptors, Complement 3b