TY - JOUR
T1 - Assess prediabetes risk, as a golden period for prevention of diabetes
AU - Liberty, Iche Andriyani
AU - Kodim, Nasrin
N1 - Publisher Copyright:
© 2017 The Authors.
PY - 2017
Y1 - 2017
N2 - Objective: Prediabetes is a high-risk condition for diabetes development and several other health outcomes later in life, but little is known about the factors associated with this condition. On the other hand, by predicting the risk of prediabetes, it is also a golden period for prevent or delay the diabetes conversion. The aim here was to assess the prevalence, risk factor that associated, and build a model to assess prediabetes risk. Methods: A cross-sectional study was conducted in Palembang, Indonesia. Data were collected during January until May 2016. We recruited adult age >15 years from 16 districts in Palembang. Anthropometric, demographic, and clinical history data were measured by standard methods. Capillary blood glucose was measured by finger prick test, followed by confirmatory oral glucose tolerance tests. Results: Of a total of 1241 participants, the prevalence of prediabetes was 27.8% (345 participants) and 72.2% (896 participants) and those were normal blood glucose. Employment, age, exercise, alcohol consumption, body mass index, systolic pressure, diastolic pressure, waist circumference, and hypercholesterol history were screened out as independent factors to build the prediction risk model. Conclusion: The prediabetes prediction model can be used easily and understood by health-related users to assess prediabetes risk. The intervention program, designed based on our prediabetes model to prevent or delay the conversion of prediabetes to diabetes in the population. The discovery of pharmacological therapies to prevent further conversion is needed.
AB - Objective: Prediabetes is a high-risk condition for diabetes development and several other health outcomes later in life, but little is known about the factors associated with this condition. On the other hand, by predicting the risk of prediabetes, it is also a golden period for prevent or delay the diabetes conversion. The aim here was to assess the prevalence, risk factor that associated, and build a model to assess prediabetes risk. Methods: A cross-sectional study was conducted in Palembang, Indonesia. Data were collected during January until May 2016. We recruited adult age >15 years from 16 districts in Palembang. Anthropometric, demographic, and clinical history data were measured by standard methods. Capillary blood glucose was measured by finger prick test, followed by confirmatory oral glucose tolerance tests. Results: Of a total of 1241 participants, the prevalence of prediabetes was 27.8% (345 participants) and 72.2% (896 participants) and those were normal blood glucose. Employment, age, exercise, alcohol consumption, body mass index, systolic pressure, diastolic pressure, waist circumference, and hypercholesterol history were screened out as independent factors to build the prediction risk model. Conclusion: The prediabetes prediction model can be used easily and understood by health-related users to assess prediabetes risk. The intervention program, designed based on our prediabetes model to prevent or delay the conversion of prediabetes to diabetes in the population. The discovery of pharmacological therapies to prevent further conversion is needed.
KW - Assess
KW - Prediabetes
KW - Prediction
KW - Risk
UR - http://www.scopus.com/inward/record.url?scp=85020856105&partnerID=8YFLogxK
U2 - 10.22159/ajpcr.2017.v10i6.18215
DO - 10.22159/ajpcr.2017.v10i6.18215
M3 - Article
AN - SCOPUS:85020856105
SN - 0974-2441
VL - 10
SP - 349
EP - 353
JO - Asian Journal of Pharmaceutical and Clinical Research
JF - Asian Journal of Pharmaceutical and Clinical Research
IS - 6
ER -