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A novel predictive model for optimizing diabetes screening in older adults.
Lin, Yushuang; Shen, Ya; He, Rongbo; Wang, Quan; Deng, Hongbin; Cheng, Shujunyan; Liu, Yu; Li, Yimin; Lu, Xiang; Shen, Zhengkai.
Afiliación
  • Lin Y; Department of Geriatrics, Sir Run Run Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China.
  • Shen Y; Department of Integrated Service and Management, Jiangsu Province Center for Disease Control and Prevention, Nanjing, Jiangsu Province, China.
  • He R; Department of Endocrinology, Sir Run Run Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China.
  • Wang Q; Department of Geriatrics, Sir Run Run Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China.
  • Deng H; Medical School of Nanjing University, Nanjing, Jiangsu Province, China.
  • Cheng S; Health Management Center, Sir Run Run Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China.
  • Liu Y; Department of Endocrinology, Sir Run Run Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China.
  • Li Y; Department of Cardiology, Sir Run Run Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China.
  • Lu X; Department of Geriatrics, Sir Run Run Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China.
  • Shen Z; Department of Integrated Service and Management, Jiangsu Province Center for Disease Control and Prevention, Nanjing, Jiangsu Province, China.
J Diabetes Investig ; 15(10): 1403-1409, 2024 Oct.
Article en En | MEDLINE | ID: mdl-38989799
ABSTRACT

INTRODUCTION:

The fasting blood glucose test is widely used for diabetes screening. However, it may fail to detect early-stage diabetes characterized by elevated postprandial glucose levels. Hence, we developed and internally validated a nomogram to predict the diabetes risk in older adults with normal fasting glucose levels. MATERIALS AND

METHODS:

This study enrolled 2,235 older adults, dividing them into a Training Set (n = 1,564) and a Validation Set (n = 671) based on a 73 ratio. We employed the least absolute shrinkage and selection operator regression to identify predictors for constructing the nomogram. Calibration and discrimination were employed to assess the nomogram's performance, while its clinical utility was evaluated through decision curve analysis.

RESULTS:

Nine key variables were identified as significant factors age, gender, body mass index, fasting blood glucose, triglycerides, alanine aminotransferase, the ratio of alanine aminotransferase to aspartate aminotransferase, blood urea nitrogen, and hemoglobin. The nomogram demonstrated good discrimination, with an area under the receiver operating characteristic curve of 0.824 in the Training Set and 0.809 in the Validation Set. Calibration curves for both sets confirmed the model's accuracy in estimating the actual diabetes risk. Decision curve analysis highlighted the model's clinical utility.

CONCLUSIONS:

We provided a dynamic nomogram for identifying older adults at risk of diabetes, potentially enhancing the efficiency of diabetes screening in primary healthcare units.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Glucemia / Tamizaje Masivo / Nomogramas Límite: Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: J Diabetes Investig Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Japón
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Glucemia / Tamizaje Masivo / Nomogramas Límite: Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: J Diabetes Investig Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Japón