Naoto Katakami1, Tomoya Mita2, Naohiro Taya3, Yosuke Okada4, Satomi Wakasugi5, Hidenori Yoshii6, Toshihiko Shiraiwa7, Akihito Otsuka8, Yutaka Umayahara9, Kayoko Ryomoto10, Masahiro Hatazaki11, Tetsuyuki Yasuda12, Tsunehiko Yamamoto13, Masahiko Gosho14, Iichiro Shimomura3, Hirotaka Watada5. 1. Department of Metabolic Medicine, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan. katakami@endmet.med.osaka-u.ac.jp. 2. Department of Metabolism & Endocrinology, Juntendo University Graduate School of Medicine, 2-1-1, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan. tom-m@juntendo.ac.jp. 3. Department of Metabolic Medicine, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan. 4. First Department of Internal Medicine, School of Medicine, University of Occupational and Environmental Health, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka, Japan. 5. Department of Metabolism & Endocrinology, Juntendo University Graduate School of Medicine, 2-1-1, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan. 6. Department of Medicine, Diabetology & Endocrinology, Juntendo Tokyo Koto Geriatric Medical Center, 3-3-20, Shinsuna, Koto-ku, Tokyo, Japan. 7. Shiraiwa Medical Clinic, 4-10-24, Houzenji, Kashiwara, Osaka, Japan. 8. Department of Internal Medicine, Kawasaki Hospital, 3-3-1, Higashiyamacho, Hyogo-ku, Kobe, Hyogo, Japan. 9. Department of Diabetes and Endocrinology, Osaka General Medical Center, 3-1-56, Bandaihigashi, Sumiyoshi-ku, Osaka-shi, Osaka, Japan. 10. Center for Diabetes Mellitus, Osaka Rosai Hospital, 1179-3, Nagasonecho, Kita-ku, Sakai, Osaka, Japan. 11. Department of Internal Medicine, Japan Community Health Care Organization Osaka Hospital, 4-2-78, Fukushima, Fukushima-ku, Osaka-shi, Osaka, Japan. 12. Department of Diabetes and Endocrinology, Osaka Police Hospital, 10-31, Kitayamacho, Tennoji-ku, Osaka-shi, Osaka, Japan. 13. Diabetes and Endocrinology, Kansai Rosai Hospital, 3-1-69, Inabaso, Amagasaki, Hyogo, Japan. 14. Department of Biostatistics, Faculty of Medicine, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, Ibaraki, Japan.
Abstract
BACKGROUND: The association between glucose variability and the progression of atherosclerosis is not completely understood. We aimed to evaluate the associations of glucose variability with the progression of atherosclerosis in the early stages. METHODS: We conducted a cross-sectional analysis to investigate the associations of glucose variability, assessed by continuous glucose monitoring, with intima-media thickness (IMT) and gray-scale median (GSM) of the carotid arteries, which are different indicators for the progression of atherosclerosis. We used baseline data from a hospital-based multicenter prospective observational cohort study among Japanese patients with type 2 diabetes without a history of cardiovascular diseases aged between 30 and 80 years. Continuous glucose monitoring was performed by Freestyle Libre Pro, and glucose levels obtained every 15 min for a maximum of eight days were used to calculate the metrics of glucose variability. IMT and GSM were evaluated by ultrasonography, and the former indicates thickening of intima-media complex in the carotid artery wall, while the latter indicates tissue characteristics. RESULTS: Among 600 study participants (age: 64.9 ± 9.2 (mean ± SD) years; 63.2%: men; HbA1c: 7.0 ± 0.8%), participants with a larger intra- and inter-day glucose variability had a lower GSM and most of these associations were statistically significant. No trend based on glucose variability was shown regarding IMT. Standard deviation of glucose (regression coefficient, β = - 5.822; 95% CI - 8.875 to - 2.768, P < 0.001), glucose coefficient of variation (β = - 0.418; - 0.685 to - 0.151, P = 0.002), mean amplitude of glycemic excursion (β = - 1.689; - 2.567 to - 0.811, P < 0.001), mean of daily differences (β = - 6.500; - 9.758 to - 3.241, P < 0.001), and interquartile range (β = - 4.289; - 6.964 to - 1.614, P = 0.002) had a statistically significant association with mean-GSM after adjustment for conventional cardiovascular risk factors, including HbA1c. No metrics of glucose variability had a statistically significant association with IMT. CONCLUSIONS: Continuous glucose monitoring-assessed glucose variability was associated with the tissue characteristics of the carotid artery wall in type 2 diabetes patients without cardiovascular diseases.
BACKGROUND: The association between glucose variability and the progression of atherosclerosis is not completely understood. We aimed to evaluate the associations of glucose variability with the progression of atherosclerosis in the early stages. METHODS: We conducted a cross-sectional analysis to investigate the associations of glucose variability, assessed by continuous glucose monitoring, with intima-media thickness (IMT) and gray-scale median (GSM) of the carotid arteries, which are different indicators for the progression of atherosclerosis. We used baseline data from a hospital-based multicenter prospective observational cohort study among Japanese patients with type 2 diabetes without a history of cardiovascular diseases aged between 30 and 80 years. Continuous glucose monitoring was performed by Freestyle Libre Pro, and glucose levels obtained every 15 min for a maximum of eight days were used to calculate the metrics of glucose variability. IMT and GSM were evaluated by ultrasonography, and the former indicates thickening of intima-media complex in the carotid artery wall, while the latter indicates tissue characteristics. RESULTS: Among 600 study participants (age: 64.9 ± 9.2 (mean ± SD) years; 63.2%: men; HbA1c: 7.0 ± 0.8%), participants with a larger intra- and inter-day glucose variability had a lower GSM and most of these associations were statistically significant. No trend based on glucose variability was shown regarding IMT. Standard deviation of glucose (regression coefficient, β = - 5.822; 95% CI - 8.875 to - 2.768, P < 0.001), glucose coefficient of variation (β = - 0.418; - 0.685 to - 0.151, P = 0.002), mean amplitude of glycemic excursion (β = - 1.689; - 2.567 to - 0.811, P < 0.001), mean of daily differences (β = - 6.500; - 9.758 to - 3.241, P < 0.001), and interquartile range (β = - 4.289; - 6.964 to - 1.614, P = 0.002) had a statistically significant association with mean-GSM after adjustment for conventional cardiovascular risk factors, including HbA1c. No metrics of glucose variability had a statistically significant association with IMT. CONCLUSIONS: Continuous glucose monitoring-assessed glucose variability was associated with the tissue characteristics of the carotid artery wall in type 2 diabetespatients without cardiovascular diseases.
Authors: Vadim V Klimontov; Elena A Koroleva; Rustam S Khapaev; Anton I Korbut; Alexander P Lykov Journal: J Clin Med Date: 2021-12-24 Impact factor: 4.241
Authors: Seong Ho Kim; Ji Young Kim; Eun Song Kim; Il Rae Park; Eun Yeong Ha; Seung Min Chung; Jun Sung Moon; Ji Sung Yoon; Kyu Chang Won; Hyoung Woo Lee Journal: Ann Med Date: 2022-12 Impact factor: 5.348