Global variation in diabetes diagnosis and prevalence based on fasting glucose and hemoglobin A1c


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Zhou B., Sheffer K. E., Bennett J. E., Gregg E. W., Danaei G., Singleton R. K., ...Daha Fazla

Nature Medicine, cilt.29, sa.11, ss.2885-2901, 2023 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 29 Sayı: 11
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1038/s41591-023-02610-2
  • Dergi Adı: Nature Medicine
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Chemical Abstracts Core, MEDLINE, Public Affairs Index, Veterinary Science Database, DIALNET
  • Sayfa Sayıları: ss.2885-2901
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

Fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) are both used to diagnose diabetes, but these measurements can identify different people as having diabetes. We used data from 117 population-based studies and quantified, in different world regions, the prevalence of diagnosed diabetes, and whether those who were previously undiagnosed and detected as having diabetes in survey screening, had elevated FPG, HbA1c or both. We developed prediction equations for estimating the probability that a person without previously diagnosed diabetes, and at a specific level of FPG, had elevated HbA1c, and vice versa. The age-standardized proportion of diabetes that was previously undiagnosed and detected in survey screening ranged from 30% in the high-income western region to 66% in south Asia. Among those with screen-detected diabetes with either test, the age-standardized proportion who had elevated levels of both FPG and HbA1c was 29–39% across regions; the remainder had discordant elevation of FPG or HbA1c. In most low- and middle-income regions, isolated elevated HbA1c was more common than isolated elevated FPG. In these regions, the use of FPG alone may delay diabetes diagnosis and underestimate diabetes prevalence. Our prediction equations help allocate finite resources for measuring HbA1c to reduce the global shortfall in diabetes diagnosis and surveillance.