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Abstract
Purpose
To investigate the prevalence, clinical and metabolic characteristics of atherosclerosis (AS) in newly diagnosed patients with ketosis-prone type 2 diabetes (KPT2D) or non-ketotic type 2 diabetes (NKPT2D).
Patients and Methods
About 1072 subjects with non-autoimmune new-onset diabetes were included in the cross-sectional study. Patients were classified as non-ketotic type 2 diabetes (NKPT2D, n = 662) or ketosis-prone type 2 diabetes (KPT2D, n = 410). Blood samples were collected to determine the levels of glucose, HbA1c, insulin and C-peptide. Routine liver and kidney function tests were also performed. AS was determined by vascular ultrasonography.
Results
The levels of fasting blood glucose and HbA1c were significant higher in the KPT2D group when compared to the NKPT2D group (P<0.001). The levels of fasting C-peptide, 2 h C-peptide and HOMA-β were lower in the KPT2D group than those in NKPT2D group (P<0.001). However, no significant difference was observed for HOMA-IR between the two groups. The onset age of the patients with KPT2D was significantly lower compared to NKPT2D patients (38±13 vs 49±14, P<0.001). After adjusting age of the two groups, the KPT2D patients had a higher prevalence of AS compared to the NKPT2D patients (31.4% vs 21.1%, P=0.005). In both groups, age and gender were independent risk factors for AS, whereas estimated glomerular filtration rate (eGFR) was an independent risk factor in the NKPT2D patients and 2-h postprandial plasma glucose (2h-PPG) was an independent risk factor in the KPT2D patients.
Conclusion
AS was more prevalent in KPT2D patients compared to the NKPT2D cohort, which was independent of age and gender. These data suggest that KPT2D patients may have a higher risk of macrovascular complications compared to NKPT2D of the same age.
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Acknowledgments
We would like to thank all of the subjects for participating in this study. The investigators are grateful to the dedicated participants and all of the research staff involved in the study particularly Dr. Wenyue Liu for providing guidance on statistical analysis. This study was supported by projects of Wenzhou Science and Technology Bureau (grant No.Y20190129 and Y2020263).
Disclosure
The authors report no conflicts of interest in this work.