Performance of the Finnish Diabetes Risk Score and a Simplified Finnish Diabetes Risk Score in a Community-Based, Cross-Sectional Programme for Screening of Undiagnosed Type 2 Diabetes Mellitus and Dysglycaemia in Madrid, Spain: The SPREDIA-2 Study

Salinero-Fort, Miguel Á.; Burgos‐Lunar, Carmen de; Lahoz, Carlos; Mostaza, José María; Abánades-Herranz, Juan Carlos; Laguna-Cuesta, F.; Cabo, E. Estirado-de; García-Iglesias, Francisca; González-Alegre, Teresa; Fernández-Puntero, Belén; Montesano-Sanchez, Luis; Vicent-López, D.; Rio, V; Fernandez-Garcia, Pedro J.; Sánchez-Arroyo, V.; Sabín-Rodríguez, C.; López-López, Soraya; Patrón-Barandio, P.; Gómez-Campelo, Paloma

doi:10.1371/journal.pone.0158489

Cited by 49 publications

(62 citation statements)

References 37 publications

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“…Similar findings were also put forward by studies from the US and the UK [12,23]. Interestingly, a study from Spain found a high proportion of undiagnosed diabetes in females by HBA1c criteria and/or OGTT criteria [24], which was in agreement with our results. Altogether, these studies suggest that the distribution of diabetes by sex may be undergoing a steady shift.…”

Section: Discussionsupporting

confidence: 93%

Risk Score for Detecting Dysglycemia: A Cross-Sectional Study of a Working-Age Population in an Oil Field in China

Tian

Liu²,

Han

et al. 2017

Med Sci Monit

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BackgroundDysglycemia (pre-diabetes or diabetes) in young adults has increased rapidly. However, the risk scores for detecting dysglycemia in oil field staff and workers in China are limited. This study developed a risk score for the early identification of dysglycemia based on epidemiological and health examination data in an oil field working-age population with increased risk of diabetes.Material/MethodsMultivariable logistic regression was used to develop the risk score model in a population-based, cross-sectional study. All subjects completed the questionnaires and underwent physical examination and oral glucose tolerance tests. The performance of the risk score models was evaluated using the area under the receiver operating characteristic curve (AUC).ResultsThe study population consisted of 1995 participants, 20–64 years old (49.4% males), with undiagnosed diabetes or pre-diabetes who underwent periodic health examinations from March 2014 to June 2015 in Dagang oil field, Tianjin, China. Age, sex, body mass index, history of high blood glucose, smoking, triglyceride, and fasting plasma glucose (FPG) constituted the Dagang dysglycemia risk score (Dagang DRS) model. The performance of Dagang DRS was superior to m-FINDRISC (AUC: 0.791; 95% confidence interval (CI), 0.773–0.809 vs. 0.633; 95% CI, 0.611–0.654). At the cut-off value of 5.6 mmol/L, the Dagang DRS (AUC: 0.616; 95% CI, 0.592–0.641) was better than the FPG value alone (AUC: 0.571; 95% CI, 0.546–0.596) in participants with FPG <6.1 mmol/L (n=1545, P=0.028).ConclusionsDagang DRS is a valuable tool for detecting dysglycemia, especially when FPG <6.1 mmol/L, in oil field workers in China.

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Section: Discussionsupporting

confidence: 93%

Risk Score for Detecting Dysglycemia: A Cross-Sectional Study of a Working-Age Population in an Oil Field in China

Tian

Liu²,

Han

et al. 2017

Med Sci Monit

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“…Because gender is not collected as a separate question, but can be collected from the question on waist circumference, where a separate set of values is available for men and women, FINDRISC questionnaires can be used to collect data on nine non‐invasive variables: age; gender; BMI; waist circumference; physical activity; daily consumption of fruit and vegetables; history of antihypertensive drug treatment; history of high blood glucose; and family history of diabetes. It has been validated in many countries; for example, Sweden , the Netherlands , Greece , Spain , Hungary , Bulgaria and Slovenia . FINDRISC has shown good results, but it has often been modified because of cultural differences; for example, by adjusting the BMI and waist circumference threshold to meet Asia‐Pacific standards or by adjusting the threshold and excluding some variables from the original FINDRISC .…”

Section: Introductionmentioning

confidence: 99%

Development of a screening tool using electronic health records for undiagnosed Type 2 diabetes mellitus and impaired fasting glucose detection in the Slovenian population

et al. 2018

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“…The cumulative diabetes incidence was more than tripled in the NDS high-risk group (NDS ≥28) at each time point (all P < 0.001, log-rank test) compared with the low-risk group (NDS <28). Among the high-risk individuals, those at higher tertiles had a steeper rise in incident diabetes compared with those at the lowest tertile (28)(29)(30). Individuals in the highest tertile of the high-risk group (NDS ≥38) had a more than sixfold (log-rank test P < 0.001) rate of incident diabetes by the 5 th year when compared with those with a baseline NDS <28.…”

Section: Resultsmentioning

confidence: 92%

Three‐component non‐invasive risk score for undiagnosed diabetes in Chinese people: Development, validation and longitudinal evaluation

Woo

Gao

Lee

et al. 2019

J of Diabetes Invest

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Aims/Introduction To develop a new non‐invasive risk score for undiagnosed diabetes in Chinese people, and to evaluate the incident diabetes risk in those with high‐risk scores, but no diabetes on initial testing. Materials and Methods A total of 2,609 participants with no known diabetes (aged 25–74 years) who underwent oral glucose tolerance tests in Hong Kong (HK) were investigated for independent risk factors of diabetes to develop a categorization point scoring system, the Non‐invasive Diabetes Score (NDS). This NDS was validated in a cross‐sectional study of 2,746 participants in Shaanxi, China. HK participants tested to not have diabetes at baseline were assessed for subsequent incident diabetes rates. Results In the HK cohort, hypertension, age and body mass index were the key independent risk factors selected to develop the NDS, with ≥28 out of 50 NDS points considered as high risk. The area under the receiver operating characteristic curve for undiagnosed diabetes was 0.818 and 0.720 for the HK and Shaanxi cohort, respectively. The negative predictive value was 97.4% (HK) and 95.8% (Shaanxi); the number needed to screen to identify one case of diabetes was five (HK) and 11 (Shaanxi), respectively. Among those that tested non‐diabetes at baseline, individuals with NDS ≥28 had a threefold risk of incident diabetes during the subsequent 20.9 years, compared with those with NDS <28 (P < 0.001), with a steeper rise in incident diabetes observed in those with NDS at higher tertiles. Conclusions This new three‐component risk score is a user‐friendly tool for diabetes screening, and might inform the subsequent testing interval for high‐risk non‐diabetes individuals.

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Performance of the Finnish Diabetes Risk Score and a Simplified Finnish Diabetes Risk Score in a Community-Based, Cross-Sectional Programme for Screening of Undiagnosed Type 2 Diabetes Mellitus and Dysglycaemia in Madrid, Spain: The SPREDIA-2 Study

Cited by 49 publications

References 37 publications

Risk Score for Detecting Dysglycemia: A Cross-Sectional Study of a Working-Age Population in an Oil Field in China

Risk Score for Detecting Dysglycemia: A Cross-Sectional Study of a Working-Age Population in an Oil Field in China

Development of a screening tool using electronic health records for undiagnosed Type 2 diabetes mellitus and impaired fasting glucose detection in the Slovenian population

Three‐component non‐invasive risk score for undiagnosed diabetes in Chinese people: Development, validation and longitudinal evaluation

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