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Salinero-Fort M, Mostaza-Prieto JM, Lahoz-Rallo C, Cárdenas-Valladolid J, Iriarte-Campo V, Estirado-Decabo E, Garcia-Iglesias F, Gonzalez-Alegre T, Fernandez-Puntero B, Cornejo-Del Rio VM, Sanchez-Arroyo V, Sabín-Rodríguez C, López-López S, Gómez-Campelo P, Taulero-Escalera B, Rodriguez-Artalejo F, San Andrés-Rebollo FJ, De Burgos-Lunar C. External validation of three diabetes prediction scores in a Spanish cohort: does adding high risk for depression improve the validation of the FINDRISC score (FINDRISC-MOOD)? BMJ Open 2024; 14:e083121. [PMID: 38844393 PMCID: PMC11163630 DOI: 10.1136/bmjopen-2023-083121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 05/17/2024] [Indexed: 06/12/2024] Open
Abstract
OBJECTIVES To evaluate the external validity of the FINDRISC, DESIR and ADA risk scores for the prediction of diabetes in a Spanish population aged >45 years and to test the possible improvement of FINDRISC by adding a new variable of high risk of depression when Patient Health Questionnaire-9 (PHQ-9) questionnaire score ≥10 (FINDRISC-MOOD). DESIGN Prospective population-based cohort study. SETTING 10 primary healthcare centres in the north of the city of Madrid (Spain). PARTICIPANTS A total of 1242 participants without a history of diabetes and with 2-hour oral glucose tolerance test (OGTT) plasma glucose <200 mg/dL (<11.1 mmol/L) were followed up for 7.3 years (median) using their electronic health records (EHRs) and telephone contact. PRIMARY AND SECONDARY OUTCOME MEASURES Diabetes risk scores (FINDRISC, DESIR, ADA), PHQ-9 questionnaire and 2-hour-OGTT were measured at baseline. Incident diabetes was defined as treatment for diabetes, fasting plasma glucose ≥126 mg/dL (≥7.0 mmol/L), new EHR diagnosis or self-reported diagnosis. External validation was performed according to optimal cut-off, sensitivity, specificity and Youden Index. Comparison between diabetes risk scores, including FINDRISC-MOOD (original FINDRISC score plus five points if PHQ-9 ≥10), was measured by area under the receiver operating characteristic curve (AUROC). RESULTS During follow-up, 104 (8.4%; 95% CI, 6.8 to 9.9) participants developed diabetes and 185 had a PHQ-9 score ≥10. The AUROC values were 0.70 (95% CI, 0.67 to 0.72) for FINDRISC-MOOD and 0.68 (95% CI, 0.65 to 0.71) for the original FINDRISC. The AUROCs for DESIR and ADA were 0.66 (95% CI, 0.63 to 0.68) and 0.66 (95% CI, 0.63 to 0.69), respectively. There were no significant differences in AUROC between FINDRISC-MOOD and the other scores. CONCLUSIONS The results of FINDRISC-MOOD were like those of the other risk scores and do not allow it to be recommended for clinical use.
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Affiliation(s)
- Miguel Salinero-Fort
- FIIBAP, Madrid, Spain
- Frailty, patterns of multimorbidity and mortality in the community-dwelling elderly population, IdiPAZ, Madrid, Spain
| | | | | | - Juan Cárdenas-Valladolid
- Gerencia Asistencial de Atención Primaria, Comunidad de Madrid Servicio Madrileno de Salud, Madrid, Spain
- Enfermería, Universidad Alfonso X El Sabio, Villanueva de la Canada, Spain
| | | | | | | | | | | | | | | | | | | | - Paloma Gómez-Campelo
- Fundación de Investigación, La Paz University Hospital Health Research Institute, Madrid, Spain
| | - Belen Taulero-Escalera
- Foundation for Research and Biomedical Innovation of Primary Care of the Community of Madrid (FIIBAP), Madrid, Spain
| | - Fernando Rodriguez-Artalejo
- Department of Preventive Medicine and Public Health, Universidad Autonoma de Madrid, Madrid, Spain
- CIBERESP, Madrid, Spain
- IMDEA-Food, CEI UAM+CSIC, Madrid, Spain
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Boersma HE, van der Klauw MM, Smit AJ, Wolffenbuttel BHR. A non-invasive risk score including skin autofluorescence predicts diabetes risk in the general population. Sci Rep 2022; 12:21794. [PMID: 36526712 PMCID: PMC9758123 DOI: 10.1038/s41598-022-26313-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
Increased skin autofluorescence (SAF) predicts the development of diabetes-related complications and cardiovascular disease. We assessed the performance of a simple model which includes SAF to identify individuals at high risk for undiagnosed and incident type 2 diabetes, in 58,377 participants in the Lifelines Cohort Study without known diabetes. Newly-diagnosed diabetes was defined as fasting blood glucose ≥ 7.0 mmol/l and/or HbA1c ≥ 6.5% (≥ 48 mmol/mol) or self-reported diabetes at follow-up. We constructed predictive models based on age, body mass index (BMI), SAF, and parental history of diabetes, and compared to results with the concise FINDRISC model. At 2nd visit to Lifelines, 1113 (1.9%) participants were identified with undiagnosed diabetes and 1033 (1.8%) participants developed diabetes during follow-up. A model comprising age, BMI and SAF yielded an AUC of 0.783 and was non-inferior to the concise FINDRISC model, which had an AUC of 0.797 to predict new diabetes. At a score of 5.8, sensitivity was 78% and specificity of 66%. Model 2 which also incorporated parental diabetes history, had an AUC of 0.792, and a sensitivity of 74% and specificity of 70% at a score of 6.5. Net reclassification index (NRI) did not improve significantly (NRI 1.43% (- 0.50-3.37 p = 0.15). The combination of an easy to perform SAF measurement with age and BMI is a good alternative screening tool suitable for medical and non-medical settings. Parental history of diabetes did not significantly improve model performance in this homogeneous cohort.
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Affiliation(s)
- Henderikus E. Boersma
- grid.4494.d0000 0000 9558 4598Department of Endocrinology, University of Groningen, University Medical Center Groningen, HPC AA31, P.O. Box 30001, 9700 RB Groningen, The Netherlands ,grid.4494.d0000 0000 9558 4598Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Melanie M. van der Klauw
- grid.4494.d0000 0000 9558 4598Department of Endocrinology, University of Groningen, University Medical Center Groningen, HPC AA31, P.O. Box 30001, 9700 RB Groningen, The Netherlands
| | - Andries J. Smit
- grid.4494.d0000 0000 9558 4598Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Bruce H. R. Wolffenbuttel
- grid.4494.d0000 0000 9558 4598Department of Endocrinology, University of Groningen, University Medical Center Groningen, HPC AA31, P.O. Box 30001, 9700 RB Groningen, The Netherlands
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Olusanya MO, Ogunsakin RE, Ghai M, Adeleke MA. Accuracy of Machine Learning Classification Models for the Prediction of Type 2 Diabetes Mellitus: A Systematic Survey and Meta-Analysis Approach. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph192114280. [PMID: 36361161 PMCID: PMC9655196 DOI: 10.3390/ijerph192114280] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/22/2022] [Accepted: 10/25/2022] [Indexed: 05/13/2023]
Abstract
Soft-computing and statistical learning models have gained substantial momentum in predicting type 2 diabetes mellitus (T2DM) disease. This paper reviews recent soft-computing and statistical learning models in T2DM using a meta-analysis approach. We searched for papers using soft-computing and statistical learning models focused on T2DM published between 2010 and 2021 on three different search engines. Of 1215 studies identified, 34 with 136952 patients met our inclusion criteria. The pooled algorithm's performance was able to predict T2DM with an overall accuracy of 0.86 (95% confidence interval [CI] of [0.82, 0.89]). The classification of diabetes prediction was significantly greater in models with a screening and diagnosis (pooled proportion [95% CI] = 0.91 [0.74, 0.97]) when compared to models with nephropathy (pooled proportion = 0.48 [0.76, 0.89] to 0.88 [0.83, 0.91]). For the prediction of T2DM, the decision trees (DT) models had a pooled accuracy of 0.88 [95% CI: 0.82, 0.92], and the neural network (NN) models had a pooled accuracy of 0.85 [95% CI: 0.79, 0.89]. Meta-regression did not provide any statistically significant findings for the heterogeneous accuracy in studies with different diabetes predictions, sample sizes, and impact factors. Additionally, ML models showed high accuracy for the prediction of T2DM. The predictive accuracy of ML algorithms in T2DM is promising, mainly through DT and NN models. However, there is heterogeneity among ML models. We compared the results and models and concluded that this evidence might help clinicians interpret data and implement optimum models for their dataset for T2DM prediction.
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Affiliation(s)
- Micheal O. Olusanya
- Department of Computer Science and Information Technology, Sol Plaatje University, Kimberley 8300, South Africa
- Correspondence:
| | - Ropo Ebenezer Ogunsakin
- Biostatistics Unit, Discipline of Public Health Medicine, School of Nursing & Public Health, College of Health Sciences, University of KwaZulu-Natal, Durban 4000, South Africa
| | - Meenu Ghai
- Discipline of Genetics, School of Life Sciences, University of KwaZulu-Natal, Durban 4000, South Africa
| | - Matthew Adekunle Adeleke
- Discipline of Genetics, School of Life Sciences, University of KwaZulu-Natal, Durban 4000, South Africa
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Chen Y, Zhong Q, Luo J, Tang Y, Li M, Lin Q, Willey JA, Chen JL, Whittemore R, Guo J. The 6-Month Efficacy of an Intensive Lifestyle Modification Program on Type 2 Diabetes Risk Among Rural Women with Prior Gestational Diabetes Mellitus: a Cluster Randomized Controlled Trial. PREVENTION SCIENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR PREVENTION RESEARCH 2022; 23:1156-1168. [PMID: 35773443 PMCID: PMC9489585 DOI: 10.1007/s11121-022-01392-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/02/2022] [Indexed: 12/08/2022]
Abstract
This study aimed to evaluate the efficacy of an intensive lifestyle modification program tailored to rural Chinese women with prior gestational diabetes mellitus compared with usual care. In a cluster randomized controlled trial, 16 towns (clusters) in two distinct rural areas in China were randomly selected (8 towns per district); and 320 women with prior gestational diabetes mellitus were recruited from these towns. With stratification for the two study districts, eight towns (160 women) were randomly assigned to the intervention group of a tailored intensive lifestyle modification program and 8 towns (160 women) to the control group. Process measures were collected on attendance, engagement, fidelity, and satisfaction. Primary efficacy outcomes included glycemic and weight-related outcomes, while secondary efficacy outcomes were behavioral outcomes and type 2 diabetes risk score, which were collected at baseline, 3-month, and 6-month follow-up. Generalized estimation equations were used to analyze the data. High attendance (72% of sessions), engagement (67% of interactive activities and group discussions), fidelity (98%), and satisfaction (92%) with the tailored intensive lifestyle modification program were achieved. There were significant reductions in fasting blood glucose, oral glucose tolerance test 2 h, waist circumference, and type 2 diabetes risk score of participants in the intervention group compared to the control group (p < .05). There was no significant intervention effect on body mass index or behavioral outcomes (p > .05). In this study, we demonstrate the successful efficacy of an Intensive Lifestyle Modification Program in reducing type 2 diabetes risk among younger women with prior gestational diabetes mellitus. This tailored program delivered by local healthcare providers is a promising approach for diabetes prevention in rural China, reducing health disparities in rural communities about diabetes prevention. Registered in the Chinese Clinical Trial Registry (ChiCTR2000037956) on 3rd Jan 2018.
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Affiliation(s)
- Yao Chen
- Xiangya School of Nursing, Central South University, 172 Tongzipo Road, Changsha, 410013, Hunan, China
| | - Qinyi Zhong
- Xiangya School of Nursing, Central South University, 172 Tongzipo Road, Changsha, 410013, Hunan, China
| | - Jiaxin Luo
- Xiangya School of Nursing, Central South University, 172 Tongzipo Road, Changsha, 410013, Hunan, China
| | - Yujia Tang
- Xiangya School of Nursing, Central South University, 172 Tongzipo Road, Changsha, 410013, Hunan, China
| | - Mingshu Li
- Xiangya School of Public Health, Central South University, Changsha, 410078, Hinan, China
| | - Qian Lin
- Xiangya School of Public Health, Central South University, Changsha, 410078, Hinan, China
| | - James Allen Willey
- Philip R. Lee Institute for Health Policy Research, University of California, San Francisco, San Francisco, CA, 94118, USA
| | - Jyu-Lin Chen
- School of Nursing, University of California, San Francisco, San Francisco, CA, 94118, USA
| | | | - Jia Guo
- Xiangya School of Nursing, Central South University, 172 Tongzipo Road, Changsha, 410013, Hunan, China.
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Shin J, Kim J, Lee C, Yoon JY, Kim S, Song S, Kim HS. Development of Various Diabetes Prediction Models Using Machine Learning Techniques. Diabetes Metab J 2022; 46:650-657. [PMID: 35272434 PMCID: PMC9353566 DOI: 10.4093/dmj.2021.0115] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 11/14/2021] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND There are many models for predicting diabetes mellitus (DM), but their clinical implication remains vague. Therefore, we aimed to create various DM prediction models using easily accessible health screening test parameters. METHODS Two sets of variables were used to develop eight DM prediction models. One set comprised 62 easily accessible examination results of commonly used variables from a tertiary university hospital. The second set comprised 27 of the 62 variables included in the national routine health checkups. Gradient boosting and random forest algorithms were used to develop the models. Internal validation was performed using the stratified 10-fold cross-validation method. RESULTS The area under the receiver operating characteristic curve (ROC-AUC) for the 62-variable DM model making 12-month predictions for subjects without diabetes was the largest (0.928) among those of the eight DM prediction models. The ROC-AUC dropped by more than 0.04 when training with the simplified 27-variable set but still showed fairly good performance with ROC-AUCs between 0.842 and 0.880. The accuracy was up to 11.5% higher (from 0.807 to 0.714) when fasting glucose was included. CONCLUSION We created easily applicable diabetes prediction models that deliver good performance using parameters commonly assessed during tertiary university hospital and national routine health checkups. We plan to perform prospective external validation, hoping that the developed DM prediction models will be widely used in clinical practice.
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Affiliation(s)
- Juyoung Shin
- Health Promotion Center, Seoul St. Mary’s Hospital, Seoul, Korea
- Department of Endocrinology and Metabolism, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | | | | | | | | | | | - Hun-Sung Kim
- Department of Endocrinology and Metabolism, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Corresponding author: Hun-Sung Kim https://orcid.org/0000-0002-7002-7300 Department of Medical Informatics, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Korea E-mail:
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6
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Stiglic G, Wang F, Sheikh A, Cilar L. Development and validation of the type 2 diabetes mellitus 10-year risk score prediction models from survey data. Prim Care Diabetes 2021; 15:699-705. [PMID: 33896755 DOI: 10.1016/j.pcd.2021.04.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 04/13/2021] [Indexed: 12/23/2022]
Abstract
AIMS In this paper, we demonstrate the development and validation of the 10-years type 2 diabetes mellitus (T2DM) risk prediction models based on large survey data. METHODS The Survey of Health, Ageing and Retirement in Europe (SHARE) data collected in 12 European countries using 53 variables representing behavioural as well as physical and mental health characteristics of the participants aged 50 or older was used to build and validate prediction models. To account for strongly unbalanced outcome variables, each instance was assigned a weight according to the inverse proportion of the outcome label when the regularized logistic regression model was built. RESULTS A pooled sample of 16,363 individuals was used to build and validate a global regularized logistic regression model that achieved an area under the receiver operating characteristic curve of 0.702 (95% CI: 0.698-0.706). Additionally, we measured performance of local country-specific models where AUROC ranged from 0.578 (0.565-0.592) to 0.768 (0.749-0.787). CONCLUSIONS We have developed and validated a survey-based 10-year T2DM risk prediction model for use across 12 European countries. Our results demonstrate the importance of re-calibration of the models as well as strengths of pooling the data from multiple countries to reduce the variance and consequently increase the precision of the results.
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Affiliation(s)
- Gregor Stiglic
- University of Maribor, Faculty of Health Sciences, Zitna ulica 15, 2000 Maribor, Slovenia; University of Maribor, Faculty of Electrical Engineering and Computer Science, Koroska cesta 46, 2000 Maribor, Slovenia; Usher Institute, University of Edinburgh, Old Medical School, Teviot Place, Edinburgh EH8 9AG, UK.
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, 425 East 61 Street, New York, NY 10065
| | - Aziz Sheikh
- Usher Institute, University of Edinburgh, Old Medical School, Teviot Place, Edinburgh EH8 9AG, UK
| | - Leona Cilar
- University of Maribor, Faculty of Health Sciences, Zitna ulica 15, 2000 Maribor, Slovenia
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Katoh S, Peltonen M, Zeniya M, Sakamoto Y, Utsunomiya K, Nishimura R, Tuomilehto J. Non-Alcoholic Fatty Liver Disease Markers Associated with Fasting Serum Insulin and Urinary Albumin Excretion Independent of Fasting Plasma Glucose. J Clin Med 2020; 9:jcm9103161. [PMID: 33003574 PMCID: PMC7650561 DOI: 10.3390/jcm9103161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 09/19/2020] [Accepted: 09/22/2020] [Indexed: 11/18/2022] Open
Abstract
Objective: We examined the association between non-alcoholic fatty liver disease (NAFLD) markers and fasting serum immunoreactive insulin (FIRI) and urinary albumin excretion (UAE). Subjects and methods: This study comprised Periods I and II from January 2007 to May 2009, and from June 2009 to December 2011, respectively. After excluding people with ethanol intake ≥210 g/week in men and ≥140 g/week in women, 961 people (613 men, 348 women; mean age: 44 years) were included. We evaluated the fatty liver using ultrasonography score (FLUS) and measured liver enzymes. Results: The mean observation period was 25 ± 9 months. We stratified people into two groups by fasting plasma glucose (FPG) in Period I. The cutoff point between the lower FPG and higher FPG was 100 mg/dL. In regression analysis, serum alanine aminotransferase (ALT) (p < 0.001), FLUS (p < 0.001) and γ-glutamyl transpeptidase (GGTP) (p = 0.022) in Period I were independently associated with FIRI in Period II, whereas in all participants FPG was not. ALT (p < 0.001) and GGTP (p = 0.001) were also independently associated with UAE in people with FPG < 100 mg/dL in Period II. Conclusions: Some NAFLD markers were associated with FIRI and UAE independently of fasting plasma glucose.
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Affiliation(s)
- Shuichi Katoh
- Division of Diabetes, Metabolism & Endocrinology, The Jikei University School of Medicine, 3-25-8 Nishishimbashi, Minato-ku, Tokyo 105-8461, Japan;
- Correspondence: ; Tel.: +81-3-3433-1111; Fax: +81-3-3578-9753
| | - Markku Peltonen
- Public Health Promotion Unit, Finnish Institute for Health and Welfare, Mannerheimintie 166, FI-00271 Helsinki, Finland; (M.P.); (J.T.)
| | - Mikio Zeniya
- Gastroenterology, Akasaka Sanno Medical Center, 4-1-26W Akasaka, Minato-ku, Tokyo 107-8402, Japan;
| | - Yoichi Sakamoto
- The Jikei University School of Medicine, 3-25-8 Nishishimbashi, Minato-ku, Tokyo 105-8461, Japan;
| | - Kazunori Utsunomiya
- Department of Health-Care Center, The Jikei University School of Medicine, 3-25-8 Nishishimbashi, Minato-ku, Tokyo 105-8461, Japan;
| | - Rimei Nishimura
- Division of Diabetes, Metabolism & Endocrinology, The Jikei University School of Medicine, 3-25-8 Nishishimbashi, Minato-ku, Tokyo 105-8461, Japan;
| | - Jaakko Tuomilehto
- Public Health Promotion Unit, Finnish Institute for Health and Welfare, Mannerheimintie 166, FI-00271 Helsinki, Finland; (M.P.); (J.T.)
- Diabetes Research Group, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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8
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Perry BI, Upthegrove R, Crawford O, Jang S, Lau E, McGill I, Carver E, Jones PB, Khandaker GM. Cardiometabolic risk prediction algorithms for young people with psychosis: a systematic review and exploratory analysis. Acta Psychiatr Scand 2020; 142:215-232. [PMID: 32654119 DOI: 10.1111/acps.13212] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 07/06/2020] [Indexed: 12/25/2022]
Abstract
OBJECTIVE Cardiometabolic risk prediction algorithms are common in clinical practice. Young people with psychosis are at high risk for developing cardiometabolic disorders. We aimed to examine whether existing cardiometabolic risk prediction algorithms are suitable for young people with psychosis. METHODS We conducted a systematic review and narrative synthesis of studies reporting the development and validation of cardiometabolic risk prediction algorithms for general or psychiatric populations. Furthermore, we used data from 505 participants with or at risk of psychosis at age 18 years in the ALSPAC birth cohort, to explore the performance of three algorithms (QDiabetes, QRISK3 and PRIMROSE) highlighted as potentially suitable. We repeated analyses after artificially increasing participant age to the mean age of the original algorithm studies to examine the impact of age on predictive performance. RESULTS We screened 7820 results, including 110 studies. All algorithms were developed in relatively older participants, and most were at high risk of bias. Three studies (QDiabetes, QRISK3 and PRIMROSE) featured psychiatric predictors. Age was more strongly weighted than other risk factors in each algorithm. In our exploratory analysis, calibration plots for all three algorithms implied a consistent systematic underprediction of cardiometabolic risk in the younger sample. After increasing participant age, calibration plots were markedly improved. CONCLUSION Existing cardiometabolic risk prediction algorithms cannot be recommended for young people with or at risk of psychosis. Existing algorithms may underpredict risk in young people, even in the face of other high-risk features. Recalibration of existing algorithms or a new tailored algorithm for the population is required.
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Affiliation(s)
- B I Perry
- Department of Psychiatry, University of Cambridge, Cambridge, UK.,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - R Upthegrove
- Institute for Mental Health, University of Birmingham, Birmingham, UK
| | - O Crawford
- University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - S Jang
- University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - E Lau
- University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - I McGill
- University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - E Carver
- University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - P B Jones
- Department of Psychiatry, University of Cambridge, Cambridge, UK.,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - G M Khandaker
- Department of Psychiatry, University of Cambridge, Cambridge, UK.,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
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Agreement between Type 2 Diabetes Risk Scales in a Caucasian Population: A Systematic Review and Report. J Clin Med 2020; 9:jcm9051546. [PMID: 32443837 PMCID: PMC7290893 DOI: 10.3390/jcm9051546] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 05/05/2020] [Accepted: 05/18/2020] [Indexed: 02/07/2023] Open
Abstract
Early detection of people with undiagnosed type 2 diabetes (T2D) is an important public health concern. Several predictive equations for T2D have been proposed but most of them have not been externally validated and their performance could be compromised when clinical data is used. Clinical practice guidelines increasingly incorporate T2D risk prediction models as they support clinical decision making. The aims of this study were to systematically review prediction scores for T2D and to analyze the agreement between these risk scores in a large cross-sectional study of white western European workers. A systematic review of the PubMed, CINAHL, and EMBASE databases and a cross-sectional study in 59,042 Spanish workers was performed. Agreement between scores classifying participants as high risk was evaluated using the kappa statistic. The systematic review of 26 predictive models highlights a great heterogeneity in the risk predictors; there is a poor level of reporting, and most of them have not been externally validated. Regarding the agreement between risk scores, the DETECT-2 risk score scale classified 14.1% of subjects as high-risk, FINDRISC score 20.8%, Cambridge score 19.8%, the AUSDRISK score 26.4%, the EGAD study 30.3%, the Hisayama study 30.9%, the ARIC score 6.3%, and the ITD score 3.1%. The lowest agreement was observed between the ITD and the NUDS study derived score (κ = 0.067). Differences in diabetes incidence, prevalence, and weight of risk factors seem to account for the agreement differences between scores. A better agreement between the multi-ethnic derivate score (DETECT-2) and European derivate scores was observed. Risk models should be designed using more easily identifiable and reproducible health data in clinical practice.
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Aris A, Khalid MZM, Yahaya H, Yoong LO, Ying NQ. Type 2 Diabetes Risk Among University Students in Malaysia. Curr Diabetes Rev 2020; 16:387-394. [PMID: 31433762 DOI: 10.2174/1573399815666190712192527] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Revised: 04/28/2019] [Accepted: 06/26/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND Type 2 diabetes (T2D) is a preventable condition. Targeting those who are at risk of getting this disease is essential. OBJECTIVE To examine T2D risk among university students in Malaysia and determine its relationship with socio-demographic characteristics and physical activity. METHODS The study was conducted cross-sectionally on 390 students selected using quota sampling method from 13 faculties in Universiti Kebangsaan Malaysia. A short form of the International Physical Activity Questionnaire and Finnish Diabetes Risk Score were used to measure the physical activity and T2D risk. RESULTS The T2D risk was found to be low (M = 5.23, SD = 3.32) with more than two-third of the student population at the low risk level while a significant proportion of 23.8%, 5.6% and 0.3% having slightly elevated, moderate and high risk respectively. The T2D risk was significantly related to their age (rho = 0.197, p < 0.000), gender (U = 12641, p = 0.011), ethnic group (Χ2 = 18.86, p < 0.000), marital status (Χ2 = 6.597, p = 0.037), residence (U = 10345, p = 0.008), academic year (Χ2 = 14.24, p = 0.007) and physical activity (rho = -0.205, p < 0.000 and Χ2 = 13.515, p = 0.001). Of these, only age (β=0.130) and physical activity (β=-0.159) remained significant in the regression analysis. CONCLUSION The findings call for a radical change in the nursing practice to target the amendable factors that are significant in order to prevent the progression of the risk towards type 2 diabetes.
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Affiliation(s)
- Aishairma Aris
- Department of Nursing, Faculty of Medicine, Universiti Kebangsaan Malaysia (UKM), Kuala Lumpur, Malaysia
| | - Mohd Zulhilmy Md Khalid
- Department of Nursing, Faculty of Medicine, Universiti Kebangsaan Malaysia (UKM), Kuala Lumpur, Malaysia
| | - Hasnah Yahaya
- Department of Nursing, Faculty of Medicine, Universiti Kebangsaan Malaysia (UKM), Kuala Lumpur, Malaysia
| | - Lee Onn Yoong
- Department of Nursing, Faculty of Medicine, Universiti Kebangsaan Malaysia (UKM), Kuala Lumpur, Malaysia
| | - Ng Qiu Ying
- Department of Nursing, Faculty of Medicine, Universiti Kebangsaan Malaysia (UKM), Kuala Lumpur, Malaysia
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11
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Driving Type 2 Diabetes Risk Scores into Clinical Practice: Performance Analysis in Hospital Settings. J Clin Med 2019; 8:jcm8010107. [PMID: 30658456 PMCID: PMC6352264 DOI: 10.3390/jcm8010107] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2018] [Revised: 01/09/2019] [Accepted: 01/15/2019] [Indexed: 12/12/2022] Open
Abstract
Electronic health records and computational modelling have paved the way for the development of Type 2 Diabetes risk scores to identify subjects at high risk. Unfortunately, few risk scores have been externally validated, and their performance can be compromised when routine clinical data is used. The aim of this study was to assess the performance of well-established risk scores for Type 2 Diabetes using routinely collected clinical data and to quantify their impact on the decision making process of endocrinologists. We tested six risk models that have been validated in external cohorts, as opposed to model development, on electronic health records collected from 2008-2015 from a population of 10,730 subjects. Unavailable or missing data in electronic health records was imputed using an existing validated Bayesian Network. Risk scores were assessed on the basis of statistical performance to differentiate between subjects who developed diabetes and those who did not. Eight endocrinologists provided clinical recommendations based on the risk score output. Due to inaccuracies and discrepancies regarding the exact date of Type 2 Diabetes onset, 76 subjects from the initial population were eligible for the study. Risk scores were useful for identifying subjects who developed diabetes (Framingham risk score yielded a c-statistic of 85%), however, our findings suggest that electronic health records are not prepared to massively use this type of risk scores. Use of a Bayesian Network was key for completion of the risk estimation and did not affect the risk score calculation (p > 0.05). Risk score estimation did not have a significant effect on the clinical recommendation except for starting pharmacological treatment (p = 0.004) and dietary counselling (p = 0.039). Despite their potential use, electronic health records should be carefully analyzed before the massive use of Type 2 Diabetes risk scores for the identification of high-risk subjects, and subsequent targeting of preventive actions.
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Farran B, AlWotayan R, Alkandari H, Al-Abdulrazzaq D, Channanath A, Thanaraj TA. Use of Non-invasive Parameters and Machine-Learning Algorithms for Predicting Future Risk of Type 2 Diabetes: A Retrospective Cohort Study of Health Data From Kuwait. Front Endocrinol (Lausanne) 2019; 10:624. [PMID: 31572303 PMCID: PMC6749017 DOI: 10.3389/fendo.2019.00624] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 08/28/2019] [Indexed: 12/12/2022] Open
Abstract
Objective: In recent decades, the Arab population has experienced an increase in the prevalence of type 2 diabetes (T2DM), particularly within the Gulf Cooperation Council. In this context, early intervention programmes rely on an ability to identify individuals at risk of T2DM. We aimed to build prognostic models for the risk of T2DM in the Arab population using machine-learning algorithms vs. conventional logistic regression (LR) and simple non-invasive clinical markers over three different time scales (3, 5, and 7 years from the baseline). Design: This retrospective cohort study used three models based on LR, k-nearest neighbours (k-NN), and support vector machines (SVM) with five-fold cross-validation. The models included the following baseline non-invasive parameters: age, sex, body mass index (BMI), pre-existing hypertension, family history of hypertension, and T2DM. Setting: This study was based on data from the Kuwait Health Network (KHN), which integrated primary health and hospital laboratory data into a single system. Participants: The study included 1,837 native Kuwaiti Arab individuals (equal proportion of men and women) with mean age as 59.5 ± 11.4 years. Among them, 647 developed T2DM within 7 years of the baseline non-invasive measurements. Analytical methods: The discriminatory power of each model for classifying people at risk of T2DM within 3, 5, or 7 years and the area under the receiver operating characteristic curve (AUC) were determined. Outcome measures: Onset of T2DM at 3, 5, and 7 years. Results: The k-NN machine-learning technique, which yielded AUC values of 0.83, 0.82, and 0.79 for 3-, 5-, and 7-year prediction horizons, respectively, outperformed the most commonly used LR method and other previously reported methods. Comparable results were achieved using the SVM and LR models with corresponding AUC values of (SVM: 0.73, LR: 0.74), (SVM: 0.68, LR: 0.72), and (SVM: 0.71, LR: 0.70) for 3-, 5-, and 7-year prediction horizons, respectively. For all models, the discriminatory power decreased as the prediction horizon increased from 3 to 7 years. Conclusions: Machine-learning techniques represent a useful addition to the commonly reported LR technique. Our prognostic models for the future risk of T2DM could be used to plan and implement early prevention programmes for at risk groups in the Arab population.
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Affiliation(s)
- Bassam Farran
- Research Division, Dasman Diabetes Institute, Kuwait City, Kuwait
| | - Rihab AlWotayan
- Research Division, Dasman Diabetes Institute, Kuwait City, Kuwait
- Department of Primary Health Care, Ministry of Health, Kuwait City, Kuwait
| | - Hessa Alkandari
- Research Division, Dasman Diabetes Institute, Kuwait City, Kuwait
- Department of Pediatrics, Farwaniya Hospital, Al Farwaniyah, Kuwait
| | - Dalia Al-Abdulrazzaq
- Research Division, Dasman Diabetes Institute, Kuwait City, Kuwait
- Department of Pediatrics, Faculty of Medicine, Kuwait University, Kuwait City, Kuwait
| | | | - Thangavel Alphonse Thanaraj
- Research Division, Dasman Diabetes Institute, Kuwait City, Kuwait
- *Correspondence: Thangavel Alphonse Thanaraj
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Jølle A, Midthjell K, Holmen J, Carlsen SM, Tuomilehto J, Bjørngaard JH, Åsvold BO. Validity of the FINDRISC as a prediction tool for diabetes in a contemporary Norwegian population: a 10-year follow-up of the HUNT study. BMJ Open Diabetes Res Care 2019; 7:e000769. [PMID: 31803483 PMCID: PMC6887494 DOI: 10.1136/bmjdrc-2019-000769] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2019] [Revised: 09/15/2019] [Accepted: 10/20/2019] [Indexed: 12/17/2022] Open
Abstract
OBJECTIVE The Finnish Diabetes Risk Score (FINDRISC) is a recommended tool for type 2 diabetes prediction. There is a lack of studies examining the performance of the current 0-26 point FINDRISC scale. We examined the validity of FINDRISC in a contemporary Norwegian risk environment. RESEARCH DESIGN AND METHODS We followed 47 804 participants without known diabetes and aged ≥20 years in the HUNT3 survey (2006-2008) by linkage to information on glucose-lowering drug dispensing in the Norwegian Prescription Database (2004-2016). We estimated the C-statistic, sensitivity and specificity of FINDRISC as predictor of incident diabetes, as indicated by incident use of glucose-lowering drugs. We estimated the 10-year cumulative diabetes incidence by categories of FINDRISC. RESULTS The C-statistic (95% CI) of FINDRISC in predicting future diabetes was 0.77 (0.76 to 0.78). FINDRISC ≥15 (the conventional cut-off value) had a sensitivity of 38% and a specificity of 90%. The 10-year cumulative diabetes incidence (95% CI) was 4.0% (3.8% to 4.2%) in the entire study population, 13.5% (12.5% to 14.5%) for people with FINDRISC ≥15 and 2.8% (2.6% to 3.0%) for people with FINDRISC <15. Thus, FINDRISC ≥15 had a positive predictive value of 13.5% and a negative predictive value of 97.2% for diabetes within the next 10 years. To approach a similar sensitivity as in the study in which FINDRISC was developed, we would have to lower the cut-off value for elevated FINDRISC to ≥11. This would yield a sensitivity of 73%, specificity of 67%, positive predictive value of 7.7% and negative predictive value of 98.5%. CONCLUSIONS The validity of FINDRISC and the risk of diabetes among people with FINDRISC ≥15 is substantially lower in the contemporary Norwegian population than assumed in official guidelines. To identify ~3/4 of those developing diabetes within the next 10 years, we would have to lower the threshold for elevated FINDRISC to ≥11, which would label ~1/3 of the entire adult population as having an elevated FINDRISC necessitating a glycemia assessment.
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Affiliation(s)
- Anne Jølle
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Levanger, Norway
| | - Kristian Midthjell
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Levanger, Norway
| | - Jostein Holmen
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Levanger, Norway
| | - Sven Magnus Carlsen
- Department of Endocrinology, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- Department of Clinical and Molecular Medicine, NTNU, Trondheim, Norway
| | - Jaakko Tuomilehto
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | | | - Bjørn Olav Åsvold
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Levanger, Norway
- Department of Endocrinology, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Trondheim, Norway
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Han X, Wang J, Li Y, Hu H, Li X, Yuan J, Yao P, Miao X, Wei S, Wang Y, Liang Y, Zhang X, Guo H, Pan A, Yang H, Wu T, He M. Development of a new scoring system to predict 5-year incident diabetes risk in middle-aged and older Chinese. Acta Diabetol 2018; 55:13-19. [PMID: 28918462 DOI: 10.1007/s00592-017-1047-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Accepted: 09/02/2017] [Indexed: 01/19/2023]
Abstract
AIMS The aim of this study was to develop a new risk score system to predict 5-year incident diabetes risk among middle-aged and older Chinese population. METHODS This prospective study included 17,690 individuals derived from the Dongfeng-Tongji cohort. Participants were recruited in 2008 and were followed until October 2013. Incident diabetes was defined as self-reported clinician diagnosed diabetes, fasting glucose ≥7.0 mmol/l, or the use of insulin or oral hypoglycemic agent. A total of 1390 incident diabetic cases were diagnosed during the follow-up period. β-Coefficients were derived from Cox proportional hazard regression model and were used to calculate the risk score. RESULTS The diabetes risk score includes BMI, fasting glucose, hypertension, hyperlipidemia, current smoking status, and family history of diabetes. The β-coefficients of these variables ranged from 0.139 to 1.914, and the optimal cutoff value was 1.5. The diabetes risk score was calculated by multiplying the β-coefficients of the significant variables by 10 and rounding to the nearest integer. The score ranges from 0 to 36. The area under the receiver operating curve of the score was 0.751. At the optimal cutoff value of 15, the sensitivity and specificity were 65.6 and 72.9%, respectively. Based upon these risk factors, this model had the highest discrimination compared with several commonly used diabetes prediction models. CONCLUSIONS The newly established diabetes risk score with six parameters appears to be a reliable screening tool to predict 5-year risk of incident diabetes in a middle-aged and older Chinese population.
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Affiliation(s)
- Xu Han
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Jing Wang
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Yaru Li
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Hua Hu
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Xiulou Li
- Dongfeng Central Hospital, Dongfeng Motor Corporation and Hubei University of Medicine, Shiyan, Hubei, China
| | - Jing Yuan
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Ping Yao
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Xiaoping Miao
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Sheng Wei
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Youjie Wang
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Yuan Liang
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Xiaomin Zhang
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Huan Guo
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - An Pan
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Handong Yang
- Dongfeng Central Hospital, Dongfeng Motor Corporation and Hubei University of Medicine, Shiyan, Hubei, China
| | - Tangchun Wu
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China
| | - Meian He
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd., Wuhan, 430030, Hubei, China.
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15
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Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes. SENSORS 2017; 18:s18010079. [PMID: 29286314 PMCID: PMC5795558 DOI: 10.3390/s18010079] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Revised: 12/18/2017] [Accepted: 12/28/2017] [Indexed: 12/23/2022]
Abstract
Life expectancy is increasing and, so, the years that patients have to live with chronic diseases and co-morbidities. Type 2 diabetes is one of the most prevalent chronic diseases, specifically linked to being overweight and ages over sixty. Recent studies have demonstrated the effectiveness of new strategies to delay and even prevent the onset of type 2 diabetes by a combination of active and healthy lifestyle on cohorts of mid to high risk subjects. Prospective research has been driven on large groups of the population to build risk scores that aim to obtain a rule for the classification of patients according to the odds for developing the disease. Currently, there are more than two hundred models and risk scores for doing this, but a few have been properly evaluated in external groups and integrated into a clinical application for decision support. In this paper, we present a novel system architecture based on service choreography and hybrid modeling, which enables a distributed integration of clinical databases, statistical and mathematical engines and web interfaces to be deployed in a clinical setting. The system was assessed during an eight-week continuous period with eight endocrinologists of a hospital who evaluated up to 8080 patients with seven different type 2 diabetes risk models implemented in two mathematical engines. Throughput was assessed as a matter of technical key performance indicators, confirming the reliability and efficiency of the proposed architecture to integrate hybrid artificial intelligence tools into daily clinical routine to identify high risk subjects.
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16
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Krabbe CEM, Schipf S, Ittermann T, Dörr M, Nauck M, Chenot JF, Markus MRP, Völzke H. Comparison of traditional diabetes risk scores and HbA1c to predict type 2 diabetes mellitus in a population based cohort study. J Diabetes Complications 2017; 31:1602-1607. [PMID: 28886990 DOI: 10.1016/j.jdiacomp.2017.07.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Revised: 07/24/2017] [Accepted: 07/27/2017] [Indexed: 12/18/2022]
Abstract
AIMS Compare performances of diabetes risk scores and glycated hemoglobin (HbA1c) to estimate the risk of incident type 2 diabetes mellitus (T2DM) in Northeast Germany. METHODS We studied 2916 subjects (20 to 81years) from the Study of Health in Pomerania (SHIP) in a 5-year follow-up period. Diabetes risk scores included the Cooperative Health Research in the Region of Augsburg (KORA) base model, the Danish diabetes risk score and the Data from the Epidemiological Study on the Insulin Resistance syndrome (D.E.S.I.R) clinical risk score. We assessed the performance of each of the diabetes risk scores and the HbA1c for 5-year risk of T2DM by the area under the receiver-operating characteristic curve (AUC) and calibration plots. RESULTS In SHIP, the incidence of T2DM was 5.4% (n=157) in the 5-year follow-up period. Diabetes risk scores and HbA1c achieved AUCs ranging from 0.76 for the D.E.S.I.R. clinical risk score to 0.82 for the KORA base model. For diabetes risk scores, the discriminative ability was lower for the age group 55 to 74years. For HbA1c, the discriminative ability also decreased for the group 55 to 74years while it was stable in the age group 30 to 64years old. CONCLUSIONS All diabetes risk scores and the HbA1c showed a good prediction for the risk of T2DM in SHIP. Which model or biomarker should be used is driven by its context of use, e.g. the practicability, implementation of interventions and availability of measurement.
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Affiliation(s)
- Christine Emma Maria Krabbe
- Department of Study of Health in Pomerania/Clinical-Epidemiological Research, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Sabine Schipf
- Department of Study of Health in Pomerania/Clinical-Epidemiological Research, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany; German Center for Diabetes Research (DZD), partner site Greifswald, Greifswald, Germany
| | - Till Ittermann
- Department of Study of Health in Pomerania/Clinical-Epidemiological Research, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Marcus Dörr
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany; German Centre for Cardiovascular Research (DZHK), partner site Greifswald, Greifswald, Germany
| | - Matthias Nauck
- German Centre for Cardiovascular Research (DZHK), partner site Greifswald, Greifswald, Germany; Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Jean-François Chenot
- Department of General Practice, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Marcello Ricardo Paulista Markus
- Department of Study of Health in Pomerania/Clinical-Epidemiological Research, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany; German Center for Diabetes Research (DZD), partner site Greifswald, Greifswald, Germany; Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany; German Centre for Cardiovascular Research (DZHK), partner site Greifswald, Greifswald, Germany.
| | - Henry Völzke
- Department of Study of Health in Pomerania/Clinical-Epidemiological Research, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany; German Centre for Cardiovascular Research (DZHK), partner site Greifswald, Greifswald, Germany
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17
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Fokkens BT, van Waateringe RP, Mulder DJ, Wolffenbuttel BHR, Smit AJ. Skin autofluorescence improves the Finnish Diabetes Risk Score in the detection of diabetes in a large population-based cohort: The LifeLines Cohort Study. DIABETES & METABOLISM 2017; 44:424-430. [PMID: 29097003 DOI: 10.1016/j.diabet.2017.09.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Revised: 09/17/2017] [Accepted: 09/18/2017] [Indexed: 12/15/2022]
Abstract
AIM The aim of the present study was to investigate whether skin autofluorescence would improve the Finnish Diabetes Risk Score (FINDRISC) in detecting undiagnosed diabetes in a large population-based cohort. METHODS Included were participants from the Dutch LifeLines Cohort Study. Skin autofluorescence was assessed in an unselected subset of participants using the AGE Reader. After the exclusion of participants with previously diagnosed diabetes (n=1635), pregnant women (n=58) and those using corticosteroids (n=345), 79,248 subjects were eligible for analysis. Diabetes was defined as fasting plasma glucose ≥7.0mmol/L, non-fasting plasma glucose ≥11.1mmol/L or HbA1c ≥6.5% (48mmol/mol). RESULTS Diabetes was detected in 1042 participants (aged 55±12 years; 54% male). Skin autofluorescence improved the area under the receiver operating characteristic (AUROC) curve of the FINDRISC model from 0.802 to 0.811 (P<0.001). Furthermore, the addition of skin autofluorescence to FINDRISC reclassified 8-15% of all participants into more accurate risk categories (NRI: 0.080, 95% CI: 0.052-0.110). The proportion of reclassified participants was especially high (>30%) in the intermediate (1% to <5% and 5% to<10%) risk categories. When skin autofluorescence was added to a simplified model (age+body mass index), its discriminatory performance was similar to the full model+skin autofluorescence (AUROC: 0.806, P=0.062). CONCLUSION Skin autofluorescence is a non-invasive tool that can be used to further improve the FINDRISC for diabetes detection. The new resultant model is especially useful for reclassifying people in the intermediate-risk categories, where additional blood glucose testing is needed to confirm the presence of diabetes.
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Affiliation(s)
- B T Fokkens
- Division of Vascular Medicine, Department of Internal Medicine, University of Groningen, University Medical Center Groningen (UMCG), Hanzeplein 1, 9713 GZ Groningen, Netherlands.
| | - R P van Waateringe
- Department of Endocrinology, University of Groningen, University Medical Center Groningen (UMCG), Hanzeplein 1, 9713 GZ Groningen, Netherlands
| | - D J Mulder
- Division of Vascular Medicine, Department of Internal Medicine, University of Groningen, University Medical Center Groningen (UMCG), Hanzeplein 1, 9713 GZ Groningen, Netherlands
| | - B H R Wolffenbuttel
- Department of Endocrinology, University of Groningen, University Medical Center Groningen (UMCG), Hanzeplein 1, 9713 GZ Groningen, Netherlands
| | - A J Smit
- Division of Vascular Medicine, Department of Internal Medicine, University of Groningen, University Medical Center Groningen (UMCG), Hanzeplein 1, 9713 GZ Groningen, Netherlands
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18
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Bethel MA, Hyland KA, Chacra AR, Deedwania P, Fulcher GR, Holman RR, Jenssen T, Levitt NS, McMurray JJV, Boutati E, Thomas L, Sun JL, Haffner SM. Updated risk factors should be used to predict development of diabetes. J Diabetes Complications 2017; 31:859-863. [PMID: 28319004 DOI: 10.1016/j.jdiacomp.2017.02.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Revised: 02/23/2017] [Accepted: 02/25/2017] [Indexed: 11/25/2022]
Abstract
AIMS Predicting incident diabetes could inform treatment strategies for diabetes prevention, but the incremental benefit of recalculating risk using updated risk factors is unknown. We used baseline and 1-year data from the Nateglinide and Valsartan in Impaired Glucose Tolerance Outcomes Research (NAVIGATOR) Trial to compare diabetes risk prediction using historical or updated clinical information. METHODS Among non-diabetic participants reaching 1year of follow-up in NAVIGATOR, we compared the performance of the published baseline diabetes risk model with a "landmark" model incorporating risk factors updated at the 1-year time point. The C-statistic was used to compare model discrimination and reclassification analyses to demonstrate the relative accuracy of diabetes prediction. RESULTS A total of 7527 participants remained non-diabetic at 1year, and 2375 developed diabetes during a median of 4years of follow-up. The C-statistic for the landmark model was higher (0.73 [95% CI 0.72-0.74]) than for the baseline model (0.67 [95% CI 0.66-0.68]). The landmark model improved classification to modest (<20%), moderate (20%-40%), and high (>40%) 4-year risk, with a net reclassification index of 0.14 (95% CI 0.10-0.16) and an integrated discrimination index of 0.01 (95% CI 0.003-0.013). CONCLUSIONS Using historical clinical values to calculate diabetes risk reduces the accuracy of prediction. Diabetes risk calculations should be routinely updated to inform discussions about diabetes prevention at both the patient and population health levels.
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Affiliation(s)
- Mary Angelyn Bethel
- Diabetes Trials Unit, Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford, UK.
| | | | | | - Prakash Deedwania
- University of California-San Francisco Program at Fresno and the Veterans Affairs Central California Health Care System, Fresno, CA, USA.
| | - Gregory R Fulcher
- Royal North Shore Hospital, University of Sydney, Sydney, New South Wales, Australia.
| | - Rury R Holman
- Diabetes Trials Unit, Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford, UK.
| | - Trond Jenssen
- Oslo University Hospital Rikshospitalet, Oslo Institute of Clinical Medicine, and the University of Tromsø, Tromsø, Norway.
| | - Naomi S Levitt
- Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa.
| | | | - Eleni Boutati
- National and Kapodistrian University of Athens, Athens, Greece.
| | - Laine Thomas
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA.
| | - Jie-Lena Sun
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA.
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Rauh SP, Heymans MW, Koopman ADM, Nijpels G, Stehouwer CD, Thorand B, Rathmann W, Meisinger C, Peters A, de Las Heras Gala T, Glümer C, Pedersen O, Cederberg H, Kuusisto J, Laakso M, Pearson ER, Franks PW, Rutters F, Dekker JM. Predicting glycated hemoglobin levels in the non-diabetic general population: Development and validation of the DIRECT-DETECT prediction model - a DIRECT study. PLoS One 2017; 12:e0171816. [PMID: 28187151 PMCID: PMC5302787 DOI: 10.1371/journal.pone.0171816] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2016] [Accepted: 01/26/2017] [Indexed: 11/27/2022] Open
Abstract
Aims/hypothesis To develop a prediction model that can predict HbA1c levels after six years in the non-diabetic general population, including previously used readily available predictors. Methods Data from 5,762 initially non-diabetic subjects from three population-based cohorts (Hoorn Study, Inter99, KORA S4/F4) were combined to predict HbA1c levels at six year follow-up. Using backward selection, age, BMI, waist circumference, use of anti-hypertensive medication, current smoking and parental history of diabetes remained in sex-specific linear regression models. To minimize overfitting of coefficients, we performed internal validation using bootstrapping techniques. Explained variance, discrimination and calibration were assessed using R2, classification tables (comparing highest/lowest 50% HbA1c levels) and calibration graphs. The model was externally validated in 2,765 non-diabetic subjects of the population-based cohort METSIM. Results At baseline, mean HbA1c level was 5.6% (38 mmol/mol). After a mean follow-up of six years, mean HbA1c level was 5.7% (39 mmol/mol). Calibration graphs showed that predicted HbA1c levels were somewhat underestimated in the Inter99 cohort and overestimated in the Hoorn and KORA cohorts, indicating that the model’s intercept should be adjusted for each cohort to improve predictions. Sensitivity and specificity (95% CI) were 55.7% (53.9, 57.5) and 56.9% (55.1, 58.7) respectively, for women, and 54.6% (52.7, 56.5) and 54.3% (52.4, 56.2) for men. External validation showed similar performance in the METSIM cohort. Conclusions/interpretation In the non-diabetic population, our DIRECT-DETECT prediction model, including readily available predictors, has a relatively low explained variance and moderate discriminative performance, but can help to distinguish between future highest and lowest HbA1c levels. Absolute HbA1c values are cohort-dependent.
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Affiliation(s)
- Simone P Rauh
- Department of Epidemiology and Biostatistics and the EMGO Institute for Health and Care Research, VU University Medical Centre, Amsterdam, The Netherlands
| | - Martijn W Heymans
- Department of Epidemiology and Biostatistics and the EMGO Institute for Health and Care Research, VU University Medical Centre, Amsterdam, The Netherlands
| | - Anitra D M Koopman
- Department of Epidemiology and Biostatistics and the EMGO Institute for Health and Care Research, VU University Medical Centre, Amsterdam, The Netherlands
| | - Giel Nijpels
- Department of General Practice and the EMGO Institute for Health and Care Research, VU University Medical Centre, Amsterdam, The Netherlands
| | - Coen D Stehouwer
- Department of Internal Medicine and Cardiovascular Research Institute Maastricht, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Barbara Thorand
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Wolfgang Rathmann
- Institute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christa Meisinger
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Annette Peters
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Tonia de Las Heras Gala
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Charlotte Glümer
- Research Centre for Prevention and Health, Glostrup Hospital, Capital Region of Denmark, Denmark
| | - Oluf Pedersen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Henna Cederberg
- Department of Medicine, University of Kuopio and Kuopio University Hospital, Kuopio, Finland
| | - Johanna Kuusisto
- Department of Medicine, University of Kuopio and Kuopio University Hospital, Kuopio, Finland
| | - Markku Laakso
- Department of Medicine, University of Kuopio and Kuopio University Hospital, Kuopio, Finland
| | - Ewan R Pearson
- Medical Research Institute, Ninewells Hospital and Medical School, University of Dundee, Dundee, Scotland, United Kingdom
| | - Paul W Franks
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Skane University Hospital Malmö, Malmö, Sweden.,Department of Public Health and Clinical Medicine, Umea University, Umea, Sweden.,Harvard Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Femke Rutters
- Department of Epidemiology and Biostatistics and the EMGO Institute for Health and Care Research, VU University Medical Centre, Amsterdam, The Netherlands
| | - Jacqueline M Dekker
- Department of Epidemiology and Biostatistics and the EMGO Institute for Health and Care Research, VU University Medical Centre, Amsterdam, The Netherlands
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Diabetes Mellitus Affected Patients Classification and Diagnosis through Machine Learning Techniques. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.procs.2017.08.193] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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21
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Cefalu WT, Buse JB, Tuomilehto J, Fleming GA, Ferrannini E, Gerstein HC, Bennett PH, Ramachandran A, Raz I, Rosenstock J, Kahn SE. Update and Next Steps for Real-World Translation of Interventions for Type 2 Diabetes Prevention: Reflections From a Diabetes Care Editors' Expert Forum. Diabetes Care 2016; 39:1186-201. [PMID: 27631469 PMCID: PMC4915559 DOI: 10.2337/dc16-0873] [Citation(s) in RCA: 96] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
The International Diabetes Federation estimates that 415 million adults worldwide now have diabetes and 318 million have impaired glucose tolerance. These numbers are expected to increase to 642 million and 482 million, respectively, by 2040. This burgeoning pandemic places an enormous burden on countries worldwide, particularly resource-poor regions. Numerous landmark trials evaluating both intensive lifestyle modification and pharmacological interventions have persuasively demonstrated that type 2 diabetes can be prevented or its onset can be delayed in high-risk individuals with impaired glucose tolerance. However, key challenges remain, including how to scale up such approaches for widespread translation and implementation, how to select appropriately from various interventions and tailor them for different populations and settings, and how to ensure that preventive interventions yield clinically meaningful, cost-effective outcomes. In June 2015, a Diabetes Care Editors' Expert Forum convened to discuss these issues. This article, an outgrowth of the forum, begins with a summary of seminal prevention trials, followed by a discussion of considerations for selecting appropriate populations for intervention and the clinical implications of the various diagnostic criteria for prediabetes. The authors outline knowledge gaps in need of elucidation and explore a possible new avenue for securing regulatory approval of a prevention-related indication for metformin, as well as specific considerations for future pharmacological interventions to delay the onset of type 2 diabetes. They conclude with descriptions of some innovative, pragmatic translational initiatives already under way around the world.
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Affiliation(s)
- William T. Cefalu
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA
| | - John B. Buse
- University of North Carolina School of Medicine, Chapel Hill, NC
| | - Jaakko Tuomilehto
- Chronic Disease Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland; Dasman Diabetes Institute, Dasman, Kuwait; Saudi Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia; and Center for Vascular Prevention, Danube University Krems, Krems, Austria
| | | | | | | | | | - Ambady Ramachandran
- India Diabetes Research Foundation and Dr. A. Ramachandran’s Diabetes Hospitals, Chennai, India
| | - Itamar Raz
- Diabetes Unit, Department of Internal Medicine, Hadassah Hebrew University Hospital, Jerusalem, Israel
| | - Julio Rosenstock
- Dallas Diabetes and Endocrine Center at Medical City and The University of Texas Southwestern Medical Center, Dallas, TX
| | - Steven E. Kahn
- VA Puget Sound Health Care System and University of Washington, Seattle, WA
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22
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Ahn CH, Yoon JW, Hahn S, Moon MK, Park KS, Cho YM. Evaluation of Non-Laboratory and Laboratory Prediction Models for Current and Future Diabetes Mellitus: A Cross-Sectional and Retrospective Cohort Study. PLoS One 2016; 11:e0156155. [PMID: 27214034 PMCID: PMC4877115 DOI: 10.1371/journal.pone.0156155] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Accepted: 05/10/2016] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Various diabetes risk scores composed of non-laboratory parameters have been developed, but only a few studies performed cross-validation of these scores and a comparison with laboratory parameters. We evaluated the performance of diabetes risk scores composed of non-laboratory parameters, including a recently published Korean risk score (KRS), and compared them with laboratory parameters. METHODS The data of 26,675 individuals who visited the Seoul National University Hospital Healthcare System Gangnam Center for a health screening program were reviewed for cross-sectional validation. The data of 3,029 individuals with a mean of 6.2 years of follow-up were reviewed for longitudinal validation. The KRS and 16 other risk scores were evaluated and compared with a laboratory prediction model developed by logistic regression analysis. RESULTS For the screening of undiagnosed diabetes, the KRS exhibited a sensitivity of 81%, a specificity of 58%, and an area under the receiver operating characteristic curve (AROC) of 0.754. Other scores showed AROCs that ranged from 0.697 to 0.782. For the prediction of future diabetes, the KRS exhibited a sensitivity of 74%, a specificity of 54%, and an AROC of 0.696. Other scores had AROCs ranging from 0.630 to 0.721. The laboratory prediction model composed of fasting plasma glucose and hemoglobin A1c levels showed a significantly higher AROC (0.838, P < 0.001) than the KRS. The addition of the KRS to the laboratory prediction model increased the AROC (0.849, P = 0.016) without a significant improvement in the risk classification (net reclassification index: 4.6%, P = 0.264). CONCLUSIONS The non-laboratory risk scores, including KRS, are useful to estimate the risk of undiagnosed diabetes but are inferior to the laboratory parameters for predicting future diabetes.
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Affiliation(s)
- Chang Ho Ahn
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Ji Won Yoon
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Department of Internal Medicine, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
| | - Seokyung Hahn
- Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Korea
| | - Min Kyong Moon
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Department of Internal Medicine, Boramae Medical Center, Seoul National University College of Medicine, Seoul, Korea
| | - Kyong Soo Park
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Young Min Cho
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- * E-mail:
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Hjellestad ID, Søfteland E, Nilsen RM, Husebye ES, Jonung T. Abdominal aortic aneurysms--glycaemic status and mortality. J Diabetes Complications 2016; 30:438-43. [PMID: 26794646 DOI: 10.1016/j.jdiacomp.2015.12.015] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2015] [Revised: 11/22/2015] [Accepted: 12/14/2015] [Indexed: 12/17/2022]
Abstract
AIMS The prevalence of diabetes mellitus (DM) and mortality with respect to glycaemic status in patients with abdominal aortic aneurysms (AAA) was evaluated. Glycaemic status was assessed by an oral glucose tolerance test (OGTT) and by HbA1c. METHODS Sixty-six patients with AAA admitted to the vascular surgery unit for elective surgery between October 2006 and September 2007 were included. Seven patients had previously known DM. OGTT and HbA1c results were available from 58 patients. The patients were categorized as having DM, prediabetes and normoglycaemia according to the WHO's and American Diabetes Association's criteria. RESULTS The prevalence of newly diagnosed DM according to the OGTT and HbA1c results were 12% and 14% respectively. Mean follow-up time was 68 months and all-cause mortality 43%. HbA1c was an independent predictor for mortality in the DM category. Hazard ratio of all-cause mortality in the DM category defined by the HbA1c values was 6.35, 95% [CI 1.49-27.1]; p = 0.01. CONCLUSIONS DM defined by HbA1c ≥ 6.5% is an important determinant of mortality following surgical treatment for AAA. Half the patients with AAA and DM were unaware of their DM diagnosis. All patients with AAA should be tested for DM using HbA1c. The results should be confirmed in a larger prospective study.
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Affiliation(s)
- Iren Drange Hjellestad
- Department of Medicine, Haukeland University Hospital, Jonas Lies vei 65, 5021, Bergen, Norway.
| | - Eirik Søfteland
- Department of Medicine, Haukeland University Hospital, Jonas Lies vei 65, 5021, Bergen, Norway
| | - Roy Miodini Nilsen
- Centre for Clinical Research, Haukeland University Hospital, Bergen, Norway
| | - Eystein Sverre Husebye
- Department of Medicine, Haukeland University Hospital, Jonas Lies vei 65, 5021, Bergen, Norway; Department of Clinical Sciences, University of Bergen, Bergen, Norway
| | - Torbjørn Jonung
- Department of Clinical Sciences, University of Bergen, Bergen, Norway; Department of Vascular Surgery, Haukeland University Hospital, Bergen, Norway
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Guclu M, Ali A, Eroglu DU, Büyükuysal SO, Cander S, Ocak N. Serum Levels of sRAGE Are Associated with Body Measurements, but Not Glycemic Parameters in Patients with Prediabetes. Metab Syndr Relat Disord 2016; 14:33-9. [DOI: 10.1089/met.2015.0078] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Affiliation(s)
- Metin Guclu
- Department of Endocrinology and Metabolism, Sevket Yilmaz Research and Training Hospital-Bursa, Bursa, Turkey
| | - Asuman Ali
- Department of Neurology, Sevket Yilmaz Research and Training Hospital-Bursa, Bursa, Turkey
| | - Derya Ustun Eroglu
- Department of Internal Medicine, Sevket Yilmaz Research and Training Hospital-Bursa, Bursa, Turkey
| | - Sema Oral Büyükuysal
- Department of Biochemistry, Sevket Yilmaz Research and Training Hospital-Bursa, Bursa, Turkey
| | - Soner Cander
- Department of Endocrinology and Metabolism, Sevket Yilmaz Research and Training Hospital-Bursa, Bursa, Turkey
| | - Nihal Ocak
- Department of Biochemistry, Sevket Yilmaz Research and Training Hospital-Bursa, Bursa, Turkey
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Katoh S, Peltonen M, Zeniya M, Kaji M, Sakamoto Y, Utsunomiya K, Tuomilehto J. Analysis of the Japanese Diabetes Risk Score and fatty liver markers for incident diabetes in a Japanese cohort. Prim Care Diabetes 2016; 10:19-26. [PMID: 26303223 DOI: 10.1016/j.pcd.2015.07.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2015] [Revised: 07/22/2015] [Accepted: 07/30/2015] [Indexed: 01/07/2023]
Abstract
AIMS We examined the effectiveness of the Japanese Diabetes Risk Score (JPDRISC) and fatty liver markers for predicting incident diabetes. METHODS We created the JPDRISC. The study periods I and II were January 2007 to May 2009 and June 2009 to December 2011, respectively. A total of 2084 people (1389 men, 695 women; mean age: 46 years) were included. People with diabetes in the Period I and those with ethanol intake >140 g/week were excluded. A total of 1515 people were included. Fatty liver using ultrasonography scores (FLUS) were assigned. RESULTS The mean observation period was 26.3 months, and 24 people had developed diabetes between the Periods I and II. In logistic regression analysis, the JPDRISC (OR=1.197, 95% C.I.: 1.062-1.350, p=0.003) and FLUS (OR=2.591, 95% C.I.: 1.411-4.758, p=0.002) in the Period I were independent determinants of incident diabetes. In receiver operating characteristic analysis, sensitivity and specificity for incident diabetes were 0.885 and 0.536, respectively, in people with both FLUS≥1 and the total JPDRISC≥6 in the Period I. The sensitivity was better than the JPDRISC alone (sensitivity 0.696) and FLUS alone (sensitivity 0.750). CONCLUSIONS JPDRISC and FLUS were independently associated with incident diabetes and their combination is useful.
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Affiliation(s)
- Shuichi Katoh
- Division of Diabetes, Metabolism and Endocrinology, Department of Internal Medicine, Jikei University School of Medicine, 3-25-8 Nishishimbashi, Minato-ku, Tokyo, 105-8461, Japan; Jikei University Harumi Triton Clinic, Jikei University School of Medicine, 1-8-8 W3 Harumi, Chuo-ku, Tokyo, 104-0053, Japan.
| | - Markku Peltonen
- Department of Chronic Disease Prevention, National Institute for Health and Welfare, Mannerheimintie 164A, FI-00271, Helsinki, Finland.
| | - Mikio Zeniya
- Jikei University Harumi Triton Clinic, Jikei University School of Medicine, 1-8-8 W3 Harumi, Chuo-ku, Tokyo, 104-0053, Japan; Health-Care Center, Gastroenterology & Division of Oncology, Institute of DNA Medicine, Jikei University School of Medicine, 3-25-8 Nishishimbashi, Minato-ku, Tokyo, 105-8461, Japan.
| | - Masanobu Kaji
- Jikei University Harumi Triton Clinic, Jikei University School of Medicine, 1-8-8 W3 Harumi, Chuo-ku, Tokyo, 104-0053, Japan.
| | - Yoichi Sakamoto
- Division of Diabetes, Metabolism and Endocrinology, Department of Internal Medicine, Jikei University School of Medicine, 3-25-8 Nishishimbashi, Minato-ku, Tokyo, 105-8461, Japan; Jikei University Harumi Triton Clinic, Jikei University School of Medicine, 1-8-8 W3 Harumi, Chuo-ku, Tokyo, 104-0053, Japan.
| | - Kazunori Utsunomiya
- Division of Diabetes, Metabolism and Endocrinology, Department of Internal Medicine, Jikei University School of Medicine, 3-25-8 Nishishimbashi, Minato-ku, Tokyo, 105-8461, Japan.
| | - Jaakko Tuomilehto
- Department of Chronic Disease Prevention, National Institute for Health and Welfare, Mannerheimintie 164A, FI-00271, Helsinki, Finland; Instituto de Investigacion Sanitaria del Hospital Universario LaPaz (IdiPAZ), Hospital Universitario La Paz, Paseo de la Castellana, 261, 28048, Madrid, Spain; Centre for Vascular Prevention, Danube-University Krems, Doktor-Karl-Dorrek-Straße 30, A-3500, Krems, Austria; King Abdulaziz University, Jeddah, Saudi Arabia.
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Salinero-Fort MÁ, de Burgos-Lunar C, Mostaza Prieto J, Lahoz Rallo C, Abánades-Herranz JC, Gómez-Campelo P, Laguna Cuesta F, Estirado De Cabo E, García Iglesias F, González Alegre T, Fernández Puntero B, Montesano Sánchez L, Vicent López D, Cornejo Del Río V, Fernández García PJ, Sabín Rodríguez C, López López S, Patrón Barandío P. Validating prediction scales of type 2 diabetes mellitus in Spain: the SPREDIA-2 population-based prospective cohort study protocol. BMJ Open 2015; 5:e007195. [PMID: 26220868 PMCID: PMC4521512 DOI: 10.1136/bmjopen-2014-007195] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
INTRODUCTION The incidence of type 2 diabetes mellitus (T2DM) is increasing worldwide. When diagnosed, many patients already have organ damage or advance subclinical atherosclerosis. An early diagnosis could allow the implementation of lifestyle changes and treatment options aimed at delaying the progression of the disease and to avoid cardiovascular complications. Different scores for identifying undiagnosed diabetes have been reported, however, their performance in populations of southern Europe has not been sufficiently evaluated. The main objectives of our study are: to evaluate the screening performance and cut-off points of the main scores that identify the risk of undiagnosed T2DM and prediabetes in a Spanish population, and to develop and validate our own predictive models of undiagnosed T2DM (screening model), and future T2DM (prediction risk model) after 5-year follow-up. As a secondary objective, we will evaluate the atherosclerotic burden of the population with undiagnosed T2DM. METHODS AND ANALYSIS Population-based prospective cohort study with baseline screening, to evaluate the performance of the FINDRISC, DANISH, DESIR, ARIC and QDScore, against the gold standard tests: Fasting plasma glucose, oral glucose tolerance and/or HbA1c. The sample size will include 1352 participants between the ages of 45 and 74 years. ANALYSIS sensitivity, specificity, positive predictive value, negative predictive value, likelihood ratio positive, likelihood ratio negative and receiver operating characteristic curves and area under curve. Binary logistic regression for the first 700 individuals (derivation) and last 652 (validation) will be performed. All analyses will be calculated with their 95% CI; statistical significance will be p<0.05. ETHICS AND DISSEMINATION The study protocol has been approved by the Research Ethics Committee of the Carlos III Hospital (Madrid). The score performance and predictive model will be presented in medical conferences, workshops, seminars and round table discussions. Furthermore, the predictive model will be published in a peer-reviewed medical journal to further increase the exposure of the scores.
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Affiliation(s)
- Miguel Ángel Salinero-Fort
- Gerencia Adjunta de Planificación y Calidad, Atención Primaria. Servicio Madrileño de Salud, Instituto de Investigación Sanitaria del Hospital Universitario La Paz-IdiPAZ. Red de Investigación en servicios de salud en enfermedades crónicas (REDISSEC), Madrid, Spain
| | - Carmen de Burgos-Lunar
- Servicio de Medicina Preventiva, Hospital Universitario La Paz, Instituto de Investigación Sanitaria del Hospital Universitario La Paz-IdiPAZ. Red de Investigación en servicios de salud en enfermedades crónicas (REDISSEC), Madrid, Spain
| | | | | | - Juan Carlos Abánades-Herranz
- Dirección Técnica de Docencia e Investigación. Gerencia Adjunta de Planificación y Calidad. Atención Primaria, Servicio Madrileño de Salud. Instituto de Investigación Sanitaria del Hospital Universitario La Paz-IdiPaz, Madrid, Spain
| | - Paloma Gómez-Campelo
- Plataforma de apoyo al Investigador Novel. Instituto de Investigación Sanitaria del Hospital Universitario La Paz-IdiPAZ, Madrid, Spain
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Masconi KL, Echouffo-Tcheugui JB, Matsha TE, Erasmus RT, Kengne AP. Predictive modeling for incident and prevalent diabetes risk evaluation. Expert Rev Endocrinol Metab 2015; 10:277-284. [PMID: 30298773 DOI: 10.1586/17446651.2015.1015989] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
With half of individuals with diabetes undiagnosed worldwide and a projected 55% increase of the population with diabetes by 2035, the identification of undiagnosed and high-risk individuals is imperative. Multivariable diabetes risk prediction models have gained popularity during the past two decades. These have been shown to predict incident or prevalent diabetes through a simple and affordable risk scoring system accurately. Their development requires cohort or cross-sectional type studies with a variable combination, number and definition of included risk factors, with their performance chiefly measured by discrimination and calibration. Models can be used in clinical and public health settings. However, the impact of their use on outcomes in real-world settings needs to be evaluated before widespread implementation.
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Affiliation(s)
- Katya L Masconi
- a 1 Division of Chemical Pathology, Faculty of Health Sciences, National Health Laboratory Service (NHLS) and University of Stellenbosch, Cape Town, South Africa
- b 2 Non-Communicable Diseases Research Unit, South African Medical Research Council, Cape Town, South Africa
| | - Justin Basile Echouffo-Tcheugui
- c 3 Hubert Department of Public Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
- d 4 Department of Medicine, MedStar Health System, Baltimore, MD, USA
| | - Tandi E Matsha
- e 5 Department of Biomedical Technology, Faculty of Health and Wellness Sciences, Cape Peninsula University of Technology, Cape Town, South Africa
| | - Rajiv T Erasmus
- a 1 Division of Chemical Pathology, Faculty of Health Sciences, National Health Laboratory Service (NHLS) and University of Stellenbosch, Cape Town, South Africa
| | - Andre Pascal Kengne
- b 2 Non-Communicable Diseases Research Unit, South African Medical Research Council, Cape Town, South Africa
- f 6 Department of Medicine, University of Cape Town, Cape Town, South Africa
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Hamman RF, Horton E, Barrett-Connor E, Bray GA, Christophi CA, Crandall J, Florez JC, Fowler S, Goldberg R, Kahn SE, Knowler WC, Lachin JM, Murphy MB, Venditti E. Factors affecting the decline in incidence of diabetes in the Diabetes Prevention Program Outcomes Study (DPPOS). Diabetes 2015; 64:989-98. [PMID: 25277389 PMCID: PMC4338587 DOI: 10.2337/db14-0333] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2014] [Accepted: 09/21/2014] [Indexed: 12/21/2022]
Abstract
During the first 7 years of the Diabetes Prevention Program Outcomes Study (DPPOS), diabetes incidence rates, when compared with the Diabetes Prevention Program (DPP), decreased in the placebo (-42%) and metformin (-25%), groups compared with the rates in the intensive lifestyle intervention (+31%) group. Participants in the placebo and metformin groups were offered group intensive lifestyle intervention prior to entering the DPPOS. The following two hypotheses were explored to explain the rate differences: "effective intervention" (changes in weight and other factors due to intensive lifestyle intervention) and "exhaustion of susceptible" (changes in mean genetic and diabetes risk scores). No combination of behavioral risk factors (weight, physical activity, diet, smoking, and antidepressant or statin use) explained the lower DPPOS rates of diabetes progression in the placebo and metformin groups, whereas weight gain was the factor associated with higher rates of progression in the intensive lifestyle intervention group. Different patterns in the average genetic risk score over time were consistent with exhaustion of susceptibles. Results were consistent with exhaustion of susceptibles for the change in incidence rates, but not the availability of intensive lifestyle intervention to all persons before the beginning of the DPPOS. Thus, effective intervention did not explain the lower diabetes rates in the DPPOS among subjects in the placebo and metformin groups compared with those in the DPP.
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Affiliation(s)
- Richard F Hamman
- Department of Epidemiology, Colorado School of Public Health, University of Colorado at Denver, Aurora, CO
| | - Edward Horton
- Section on Clinical Research, Joslin Diabetes Center, Boston, MA
| | | | - George A Bray
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA
| | | | | | | | - Sarah Fowler
- Biostatistics Center, George Washington University, Rockville, MD
| | - Ronald Goldberg
- Diabetes Research Institute, University of Miami School of Medicine, Miami, FL
| | - Steven E Kahn
- Division of Metabolism, Endocrinology and Nutrition, Department of Medicine, VA Puget Sound Health Care System and University of Washington, Seattle, WA
| | - William C Knowler
- National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ
| | - John M Lachin
- Biostatistics Center, George Washington University, Rockville, MD
| | - Mary Beth Murphy
- Division of Endocrinology, University of Tennessee Health Science Center, Memphis, TN
| | - Elizabeth Venditti
- Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center Health Systems, Pittsburgh, PA
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Dunbar JA, Hernan AL, Janus ED, Vartiainen E, Laatikainen T, Versace VL, Reynolds J, Best JD, Skinner TC, O'Reilly SL, Mc Namara KP, Stewart E, Coates M, Bennett CM, Carter R. Challenges of diabetes prevention in the real world: results and lessons from the Melbourne Diabetes Prevention Study. BMJ Open Diabetes Res Care 2015; 3:e000131. [PMID: 26464804 PMCID: PMC4597415 DOI: 10.1136/bmjdrc-2015-000131] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2015] [Revised: 09/01/2015] [Accepted: 09/03/2015] [Indexed: 01/08/2023] Open
Abstract
OBJECTIVE To assess effectiveness and implementability of the public health programme Life! Taking action on diabetes in Australian people at risk of developing type 2 diabetes. RESEARCH DESIGN AND METHODS Melbourne Diabetes Prevention Study (MDPS) was a unique study assessing effectiveness of Life! that used a randomized controlled trial design. Intervention participants with AUSDRISK score ≥15 received 1 individual and 5 structured 90 min group sessions. Controls received usual care. Outcome measures were obtained for all participants at baseline and 12 months and, additionally, for intervention participants at 3 months. Per protocol set (PPS) and intention to treat (ITT) analyses were performed. RESULTS PPS analyses were considered more informative from our study. In PPS analyses, intervention participants significantly improved in weight (-1.13 kg, p=0.016), waist circumference (-1.35 cm, p=0.044), systolic (-5.2 mm Hg, p=0.028) and diastolic blood pressure (-3.2 mm Hg, p=0.030) compared with controls. Based on observed weight change, estimated risk of developing diabetes reduced by 9.6% in the intervention and increased by 3.3% in control participants. Absolute 5-year cardiovascular disease (CVD) risk reduced significantly for intervention participants by 0.97 percentage points from 9.35% (10.4% relative risk reduction). In control participants, the risk increased by 0.11 percentage points (1.3% relative risk increase). The net effect for the change in CVD risk was -1.08 percentage points of absolute risk (p=0.013). CONCLUSIONS MDPS effectively reduced the risk of diabetes and CVD, but the intervention effect on weight and waist reduction was modest due to the challenges in recruiting high-risk individuals and the abbreviated intervention.
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Affiliation(s)
- James A Dunbar
- Faculty of Health, Deakin Population Health Strategic Research Centre, Deakin University, Melbourne Burwood Campus, Burwood, Victoria, Australia
| | - Andrea L Hernan
- Greater Green Triangle University Department of Rural Health, Flinders and Deakin Universities, Warrnambool, Victoria, Australia
| | - Edward D Janus
- Greater Green Triangle University Department of Rural Health, Flinders and Deakin Universities, Warrnambool, Victoria, Australia
- Western Academic Centre, Western Centre for Health Research and Education, Western Health, University of Melbourne, St. Albans, Victoria, Australia
| | - Erkki Vartiainen
- Greater Green Triangle University Department of Rural Health, Flinders and Deakin Universities, Warrnambool, Victoria, Australia
- National Institute for Health and Welfare, Helsinki, Finland
| | - Tiina Laatikainen
- Greater Green Triangle University Department of Rural Health, Flinders and Deakin Universities, Warrnambool, Victoria, Australia
- National Institute for Health and Welfare, Helsinki, Finland
- Faculty of Health Sciences, Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Vincent L Versace
- Greater Green Triangle University Department of Rural Health, Flinders and Deakin Universities, Warrnambool, Victoria, Australia
| | - John Reynolds
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia
| | - James D Best
- Lee Kong Chian School of Medicine, Nanyang Technological University and Imperial College London, Singapore, Singapore
| | - Timothy C Skinner
- School of Psychological and Clinical Sciences, Charles Darwin University, Darwin, Northern Territory, Australia
| | - Sharleen L O'Reilly
- Centre for Physical Activity and Nutrition Research, Deakin University, Melbourne Burwood Campus, Burwood, Victoria, Australia
| | - Kevin P Mc Namara
- Greater Green Triangle University Department of Rural Health, Flinders and Deakin Universities, Warrnambool, Victoria, Australia
- Faculty of Pharmacy and Pharmaceutical Sciences, Centre for Medicine Use and Safety, Monash University, Parkville, Victoria, Australia
| | - Elizabeth Stewart
- Faculty of Health, Deakin Population Health Strategic Research Centre, Deakin University, Melbourne Burwood Campus, Burwood, Victoria, Australia
| | - Michael Coates
- Greater Green Triangle University Department of Rural Health, Flinders and Deakin Universities, Warrnambool, Victoria, Australia
| | - Catherine M Bennett
- Faculty of Health, Deakin Population Health Strategic Research Centre, Deakin University, Melbourne Burwood Campus, Burwood, Victoria, Australia
| | - Rob Carter
- Faculty of Health, Deakin Health Economics, Deakin University, Melbourne Burwood Campus, Burwood, Victoria, Australia
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Abstract
A clustering of metabolic abnormalities such as dyslipidemia, hypertension, and diabetes mellitus, all of which are major risk factors for cardiovascular disease (CVD), occurs more often than by chance. Numerous epidemiological studies, as well as basic researches, have revealed that visceral fat accumulation is closely involved in this risk clustering. This morbid condition is now well recognized as the metabolic syndrome. The concept of the metabolic syndrome, i.e., the involvement of visceral adiposity in the clustering of CVD risk factors, implies that an effective CVD risk reduction will be accomplished by an intervention to reduce visceral fat deposits. The primary strategy of the intervention is lifestyle modification, which can be put into practice in healthcare fields, without necessity of medical treatment. Now that CVD is a leading global health burden, the metabolic syndrome attracts increasing attention in the world. To take global action against the syndrome, several working groups developed its internationally unified diagnostic criteria. Most recently, the International Diabetes Federation (IDF) and the American Heart Association/National Heart, Lung, and Blood Institute (AHA/NHLBI) jointly proposed the criteria, although some cautions will be needed in their practical use. In this review, we mainly focus on the findings observed in clinical and epidemiological studies, to discuss a practical strategy of the management of the metabolic syndrome in healthcare fields.
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Affiliation(s)
- Mitsuyoshi Takahara
- Department of Metabolic Medicine, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
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Mühlenbruch K, Ludwig T, Jeppesen C, Joost HG, Rathmann W, Meisinger C, Peters A, Boeing H, Thorand B, Schulze MB. Update of the German Diabetes Risk Score and external validation in the German MONICA/KORA study. Diabetes Res Clin Pract 2014; 104:459-66. [PMID: 24742930 DOI: 10.1016/j.diabres.2014.03.013] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2013] [Revised: 01/31/2014] [Accepted: 03/21/2014] [Indexed: 01/16/2023]
Abstract
AIMS Several published diabetes prediction models include information about family history of diabetes. The aim of this study was to extend the previously developed German Diabetes Risk Score (GDRS) with family history of diabetes and to validate the updated GDRS in the Multinational MONItoring of trends and determinants in CArdiovascular Diseases (MONICA)/German Cooperative Health Research in the Region of Augsburg (KORA) study. METHODS We used data from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study for extending the GDRS, including 21,846 participants. Within 5 years of follow-up 492 participants developed diabetes. The definition of family history included information about the father, the mother and/or sibling/s. Model extension was evaluated by discrimination and reclassification. We updated the calculation of the score and absolute risks. External validation was performed in the MONICA/KORA study comprising 11,940 participants with 315 incident cases after 5 years of follow-up. RESULTS The basic ROC-AUC of 0.856 (95%-CI: 0.842-0.870) was improved by 0.007 (0.003-0.011) when parent and sibling history was included in the GDRS. The net reclassification improvement was 0.110 (0.072-0.149), respectively. For the updated score we demonstrated good calibration across all tenths of risk. In MONICA/KORA, the ROC-AUC was 0.837 (0.819-0.855); regarding calibration we saw slight overestimation of absolute risks. CONCLUSIONS Inclusion of the number of diabetes-affected parents and sibling history improved the prediction of type 2 diabetes. Therefore, we updated the GDRS algorithm accordingly. Validation in another German cohort study showed good discrimination and acceptable calibration for the vast majority of individuals.
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Affiliation(s)
- Kristin Mühlenbruch
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; German Center for Diabetes Research, Germany
| | - Tonia Ludwig
- Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany; German Center for Diabetes Research, Germany
| | - Charlotte Jeppesen
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; German Center for Diabetes Research, Germany
| | - Hans-Georg Joost
- Department of Pharmacology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; German Center for Diabetes Research, Germany
| | - Wolfgang Rathmann
- Institute of Biometry and Epidemiology, German Diabetes Center, Düsseldorf, Germany; German Center for Diabetes Research, Germany
| | - Christine Meisinger
- Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany; German Center for Diabetes Research, Germany
| | - Annette Peters
- Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany; German Center for Diabetes Research, Germany
| | - Heiner Boeing
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - Barbara Thorand
- Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany; German Center for Diabetes Research, Germany
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; German Center for Diabetes Research, Germany.
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Koivula RW, Heggie A, Barnett A, Cederberg H, Hansen TH, Koopman AD, Ridderstråle M, Rutters F, Vestergaard H, Gupta R, Herrgård S, Heymans MW, Perry MH, Rauh S, Siloaho M, Teare HJA, Thorand B, Bell J, Brunak S, Frost G, Jablonka B, Mari A, McDonald TJ, Dekker JM, Hansen T, Hattersley A, Laakso M, Pedersen O, Koivisto V, Ruetten H, Walker M, Pearson E, Franks PW. Discovery of biomarkers for glycaemic deterioration before and after the onset of type 2 diabetes: rationale and design of the epidemiological studies within the IMI DIRECT Consortium. Diabetologia 2014; 57:1132-42. [PMID: 24695864 PMCID: PMC4018481 DOI: 10.1007/s00125-014-3216-x] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2013] [Accepted: 02/24/2014] [Indexed: 01/23/2023]
Abstract
AIMS/HYPOTHESIS The DIRECT (Diabetes Research on Patient Stratification) Study is part of a European Union Framework 7 Innovative Medicines Initiative project, a joint undertaking between four industry and 21 academic partners throughout Europe. The Consortium aims to discover and validate biomarkers that: (1) predict the rate of glycaemic deterioration before and after type 2 diabetes onset; (2) predict the response to diabetes therapies; and (3) help stratify type 2 diabetes into clearly definable disease subclasses that can be treated more effectively than without stratification. This paper describes two new prospective cohort studies conducted as part of DIRECT. METHODS Prediabetic participants (target sample size 2,200-2,700) and patients with newly diagnosed type 2 diabetes (target sample size ~1,000) are undergoing detailed metabolic phenotyping at baseline and 18 months and 36 months later. Abdominal, pancreatic and liver fat is assessed using MRI. Insulin secretion and action are assessed using frequently sampled OGTTs in non-diabetic participants, and frequently sampled mixed-meal tolerance tests in patients with type 2 diabetes. Biosamples include venous blood, faeces, urine and nail clippings, which, among other biochemical analyses, will be characterised at genetic, transcriptomic, metabolomic, proteomic and metagenomic levels. Lifestyle is assessed using high-resolution triaxial accelerometry, 24 h diet record, and food habit questionnaires. CONCLUSIONS/INTERPRETATION DIRECT will yield an unprecedented array of biomaterials and data. This resource, available through managed access to scientists within and outside the Consortium, will facilitate the development of new treatments and therapeutic strategies for the prevention and management of type 2 diabetes.
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Affiliation(s)
- Robert W. Koivula
- Department of Clinical Sciences, Lund University, Genetic and Molecular Epidemiology, CRC, Skåne University Hospital Malmö, Building 91, Level 10, Jan Waldenströms gata 35, SE-205 02 Malmö, Sweden
| | - Alison Heggie
- Institute of Cellular Medicine (Diabetes), The Medical School, Newcastle University, Framlington Place, Newcastle upon Tyne, NE2 4HH UK
| | - Anna Barnett
- Division of Cardiovascular & Diabetes Medicine, Medical Research Institute, University of Dundee, Dundee, DD1 9SY UK
| | - Henna Cederberg
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Tue H. Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Anitra D. Koopman
- Department of Epidemiology and Biostatistics, VUmc, Amsterdam, the Netherlands
- EMGO+ Institute for Health and Care Research, VUmc, Amsterdam, the Netherlands
| | - Martin Ridderstråle
- Department of Clinical Sciences, Clinical Obesity, Skåne University Hospital Malmö, Malmö, Sweden
- Steno Diabetes Center, Gentofte, Denmark
| | - Femke Rutters
- Department of Epidemiology and Biostatistics, VUmc, Amsterdam, the Netherlands
- EMGO+ Institute for Health and Care Research, VUmc, Amsterdam, the Netherlands
| | - Henrik Vestergaard
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ramneek Gupta
- Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Sanna Herrgård
- Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Martijn W. Heymans
- Department of Epidemiology and Biostatistics, VUmc, Amsterdam, the Netherlands
- EMGO+ Institute for Health and Care Research, VUmc, Amsterdam, the Netherlands
| | - Mandy H. Perry
- NIHR Exeter Clinical Research Facility, University of Exeter, Exeter, UK
- Blood Sciences, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Simone Rauh
- EMGO+ Institute for Health and Care Research, VUmc, Amsterdam, the Netherlands
| | - Maritta Siloaho
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Harriet J. A. Teare
- HeLEX, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Barbara Thorand
- Institute of Epidemiology II, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Jimmy Bell
- Metabolic and Molecular Imaging Group, MRC Clinical Science Centre, Imperial College Hammersmith Campus, London, UK
| | - Søren Brunak
- Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Gary Frost
- Division of Endocrinology and Metabolism, Nutrition and Dietetic Research Group, Imperial College London, London, UK
| | - Bernd Jablonka
- Sanofi-Aventis Deutschland GmbH, R&D, Frankfurt am Main, Germany
| | - Andrea Mari
- Institute of Biomedical Engineering, National Research Council, Padova, Italy
| | - Tim J. McDonald
- NIHR Exeter Clinical Research Facility, University of Exeter, Exeter, UK
- Blood Sciences, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Jacqueline M. Dekker
- Department of Epidemiology and Biostatistics, VUmc, Amsterdam, the Netherlands
- EMGO+ Institute for Health and Care Research, VUmc, Amsterdam, the Netherlands
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Andrew Hattersley
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, UK
- Genetics of Diabetes, University of Exeter Medical School, Exeter, UK
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Oluf Pedersen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
- Hagedorn Research Institute, Gentofte, Denmark
- Institute of Biomedical Science, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Hartmut Ruetten
- Sanofi-Aventis Deutschland GmbH, R&D, Frankfurt am Main, Germany
| | - Mark Walker
- Institute of Cellular Medicine (Diabetes), The Medical School, Newcastle University, Framlington Place, Newcastle upon Tyne, NE2 4HH UK
| | - Ewan Pearson
- Division of Cardiovascular & Diabetes Medicine, Medical Research Institute, University of Dundee, Dundee, DD1 9SY UK
| | - Paul W. Franks
- Department of Clinical Sciences, Lund University, Genetic and Molecular Epidemiology, CRC, Skåne University Hospital Malmö, Building 91, Level 10, Jan Waldenströms gata 35, SE-205 02 Malmö, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, MA USA
- Department of Public Health & Clinical Medicine, Section for Medicine, Umeå University Hospital, Umeå, Sweden
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Zhang L, Zhang Z, Zhang Y, Hu G, Chen L. Evaluation of Finnish Diabetes Risk Score in screening undiagnosed diabetes and prediabetes among U.S. adults by gender and race: NHANES 1999-2010. PLoS One 2014; 9:e97865. [PMID: 24852786 PMCID: PMC4031122 DOI: 10.1371/journal.pone.0097865] [Citation(s) in RCA: 83] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2014] [Accepted: 04/25/2014] [Indexed: 12/25/2022] Open
Abstract
Objective To evaluate the performance of Finnish Diabetes Risk Score (FINDRISC) in detecting undiagnosed diabetes and prediabetes among U.S. adults by gender and race. Methods This cross-sectional analysis included participants (aged ≥20 years) from the National Health and Nutrition Examination Survey (NHANES) 1999–2010. Sensitivity, specificity, area under the receiver operating characteristic (ROC) curve and the optimal cutoff points for identifying undiagnosed diabetes and prediabetes were calculated for FINDRISC by gender and race/ethnicity. Results Among the 20,633 adults (≥20 years), 49.8% were women and 53.0% were non-Hispanic White. The prevalence of undiagnosed diabetes and prediabetes was 4.1% and 35.6%, respectively. FINDRISC was positively associated with the prevalence of diabetes (OR = 1.48 for 1 unit increase, p<0.001) and prediabetes (OR = 1.15 for 1 unit increase, p<0.001). The area under ROC for detecting undiagnosed diabetes was 0.75 for total population, 0.74 for men and 0.78 for women (p = 0.04); 0.76 for White, 0.76 for Black and 0.72 for Hispanics (p = 0.03 for White vs. Hispanics). The area under ROC for detecting prediabetes was 0.67 for total population, 0.66 for men and 0.70 for women (p<0.001); 0.68 for White, 0.67 for Black and 0.65 for Hispanics (p<0.001 for White vs. Hispanics). The optimal cutoff point was 10 (sensitivity = 0.75) for men and 12 (sensitivity = 0.72) for women for detecting undiagnosed diabetes; 9 (sensitivity = 0.61) for men and 10 (sensitivity = 0.69) for women for detecting prediabetes. Conclusions FINDRISC is a simple and non-invasive screening tool to identify individuals at high risk for diabetes in the U.S. adults.
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Affiliation(s)
- Lu Zhang
- Epidemiology Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, Louisiana, United States of America
| | - Zhenzhen Zhang
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan, United States of America
| | - Yurong Zhang
- The First Affiliated Hospital of Medical School, Xi'an Jiaotong University, Xi'an, Shanxi, China
| | - Gang Hu
- Pennington Biomedical Research Center, Baton Rouge, Louisiana, United States of America
| | - Liwei Chen
- Department of Public Health Sciences, Clemson University, Clemson, South Carolina, United States of America
- * E-mail:
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Janghorbani M, Adineh H, Amini M. Evaluation of the Finnish Diabetes Risk Score (FINDRISC) as a screening tool for the metabolic syndrome. Rev Diabet Stud 2014; 10:283-92. [PMID: 24841881 PMCID: PMC4160014 DOI: 10.1900/rds.2013.10.283] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2014] [Revised: 01/20/2014] [Accepted: 02/02/2014] [Indexed: 12/25/2022] Open
Abstract
OBJECTIVES Traditionally, the Finnish Diabetes Risk Score (FINDRISC) questionnaire is a screening tool to estimate risk of type 2 diabetes. In this study, we evaluated the ability of FINDRISC to predict the development of the metabolic syndrome (MetS) in an Iranian population without diabetes and MetS. METHODS A total of 1,010 first-degree relatives of consecutive patients with type 2 diabetes, 30-70 years old (274 men and 736 women), without diabetes and MetS, were examined and followed up over 8.0 ± 1.6 years (mean ± SD) for MetS incidence. The incidence of MetS was examined across quartiles of FINDRISC, and a receiver operating characteristic (ROC) curve was plotted to assess the discrimination. At baseline and through follow-ups, participants underwent a standard 75 g 2-hour oral glucose tolerance test (OGTT). Data for determining FINDRISC were available from each participant. RESULTS During 8,089 person-years of follow-up, 69 men and 209 women without MetS and diabetes at baseline subsequently developed MetS. The incidence of MetS was 31.4 per 1000 person-years in men and 35.5 in women. The FINDRSC at baseline was significantly associated with MetS evolution. Participants in the top quartile of FINDRISC were 4.4 times more likely to develop MetS than those in the bottom quartile (rate ratio 4.4; 95% CI 2.7-7.0). The area under the ROC curve was 65.0% (95% CI 61.3-68.7). CONCLUSION The results of this study suggest that FINDRISC can be applied to detect MetS in a high-risk population.
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Affiliation(s)
- Mohsen Janghorbani
- Isfahan Endocrine and Metabolism Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
- Department of Epidemiology and Biostatistics, School of Public Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hoseinali Adineh
- Department of Epidemiology and Biostatistics, School of Public Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Masoud Amini
- Isfahan Endocrine and Metabolism Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
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Metcalf PA, Scragg RKR, Jackson R. Light to moderate alcohol consumption is protective for type 2 diabetes mellitus in normal weight and overweight individuals but not the obese. J Obes 2014; 2014:634587. [PMID: 25140249 PMCID: PMC4130120 DOI: 10.1155/2014/634587] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2014] [Accepted: 07/10/2014] [Indexed: 11/17/2022] Open
Abstract
OBJECTIVE To examine the association between alcohol consumption and risk of type 2 diabetes mellitus (T2DM) overall and by body mass index. METHODS Cross-sectional study of employed individuals. Daily alcohol intakes were calculated from a self-administered food frequency questionnaire by 5,512 Maori, Pacific Island, and European workers (3,992 men, 1520 women) aged 40 years and above. RESULTS There were 170 new cases of T2DM. Compared to the group with no alcohol consumption and adjusting for age, sex, and ethnicity, the group consuming alcohol had relative risks of T2DM of 0.23 (95% CI: 0.08, 0.65) in normal weight individuals, 0.38 (0.18, 0.81) in overweight individuals, and 0.99 (0.59, 1.67) in obese individuals. After further adjusting for total cholesterol, HDL-cholesterol, triglycerides, smoking habit, physical activity, socioeconomic status, body mass index, and hypertension, the relative risks of T2DM were 0.16 (0.05, 0.50) in normal weight individuals, 0.43 (0.19, 0.97) in overweight individuals, and 0.92 (0.52, 1.60) in overweight individuals. Across the categories of alcohol consumption, there was an approximate U-shaped relationship for new cases of T2DM. There was no significant association between alcohol consumption and IGT. CONCLUSIONS Alcohol consumption was protective against diagnosis of T2DM in normal and overweight individuals but not in the obese.
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Affiliation(s)
- Patricia A. Metcalf
- Department of Statistics, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand
- Division of Epidemiology and Biostatistics, School of Population Health, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand
- *Patricia A. Metcalf:
| | - Robert K. R. Scragg
- Division of Epidemiology and Biostatistics, School of Population Health, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand
| | - Rod Jackson
- Division of Epidemiology and Biostatistics, School of Population Health, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand
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Herder C, Kowall B, Tabak AG, Rathmann W. The potential of novel biomarkers to improve risk prediction of type 2 diabetes. Diabetologia 2014; 57:16-29. [PMID: 24078135 DOI: 10.1007/s00125-013-3061-3] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2013] [Accepted: 08/24/2013] [Indexed: 01/05/2023]
Abstract
The incidence of type 2 diabetes can be reduced substantially by implementing preventive measures in high-risk individuals, but this requires prior knowledge of disease risk in the individual. Various diabetes risk models have been designed, and these have all included a similar combination of factors, such as age, sex, obesity, hypertension, lifestyle factors, family history of diabetes and metabolic traits. The accuracy of prediction models is often assessed by the area under the receiver operating characteristic curve (AROC) as a measure of discrimination, but AROCs should be complemented by measures of calibration and reclassification to estimate the incremental value of novel biomarkers. This review discusses the potential of novel biomarkers to improve model accuracy. The range of molecules that serve as potential predictors of type 2 diabetes includes genetic variants, RNA transcripts, peptides and proteins, lipids and small metabolites. Some of these biomarkers lead to a statistically significant increase of model accuracy, but their incremental value currently seems too small for routine clinical use. However, only a fraction of potentially relevant biomarkers have been assessed with regard to their predictive value. Moreover, serial measurements of biomarkers may help determine individual risk. In conclusion, current risk models provide valuable tools of risk estimation, but perform suboptimally in the prediction of individual diabetes risk. Novel biomarkers still fail to have a clinically applicable impact. However, more efficient use of biomarker data and technological advances in their measurement in clinical settings may allow the development of more accurate predictive models in the future.
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Kengne AP, Beulens JWJ, Peelen LM, Moons KGM, van der Schouw YT, Schulze MB, Spijkerman AMW, Griffin SJ, Grobbee DE, Palla L, Tormo MJ, Arriola L, Barengo NC, Barricarte A, Boeing H, Bonet C, Clavel-Chapelon F, Dartois L, Fagherazzi G, Franks PW, Huerta JM, Kaaks R, Key TJ, Khaw KT, Li K, Mühlenbruch K, Nilsson PM, Overvad K, Overvad TF, Palli D, Panico S, Quirós JR, Rolandsson O, Roswall N, Sacerdote C, Sánchez MJ, Slimani N, Tagliabue G, Tjønneland A, Tumino R, van der A DL, Forouhi NG, Sharp SJ, Langenberg C, Riboli E, Wareham NJ. Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models. Lancet Diabetes Endocrinol 2014; 2:19-29. [PMID: 24622666 DOI: 10.1016/s2213-8587(13)70103-7] [Citation(s) in RCA: 105] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
BACKGROUND The comparative performance of existing models for prediction of type 2 diabetes across populations has not been investigated. We validated existing non-laboratory-based models and assessed variability in predictive performance in European populations. METHODS We selected non-invasive prediction models for incident diabetes developed in populations of European ancestry and validated them using data from the EPIC-InterAct case-cohort sample (27,779 individuals from eight European countries, of whom 12,403 had incident diabetes). We assessed model discrimination and calibration for the first 10 years of follow-up. The models were first adjusted to the country-specific diabetes incidence. We did the main analyses for each country and for subgroups defined by sex, age (<60 years vs ≥60 years), BMI (<25 kg/m(2)vs ≥25 kg/m(2)), and waist circumference (men <102 cm vs ≥102 cm; women <88 cm vs ≥88 cm). FINDINGS We validated 12 prediction models. Discrimination was acceptable to good: C statistics ranged from 0·76 (95% CI 0·72-0·80) to 0·81 (0·77-0·84) overall, from 0·73 (0·70-0·76) to 0·79 (0·74-0·83) in men, and from 0·78 (0·74-0·82) to 0·81 (0·80-0·82) in women. We noted significant heterogeneity in discrimination (pheterogeneity<0·0001) in all but one model. Calibration was good for most models, and consistent across countries (pheterogeneity>0·05) except for three models. However, two models overestimated risk, DPoRT by 34% (95% CI 29-39%) and Cambridge by 40% (28-52%). Discrimination was always better in individuals younger than 60 years or with a low waist circumference than in those aged at least 60 years or with a large waist circumference. Patterns were inconsistent for BMI. All models overestimated risks for individuals with a BMI of <25 kg/m(2). Calibration patterns were inconsistent for age and waist-circumference subgroups. INTERPRETATION Existing diabetes prediction models can be used to identify individuals at high risk of type 2 diabetes in the general population. However, the performance of each model varies with country, age, sex, and adiposity. FUNDING The European Union.
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Affiliation(s)
- Andre Pascal Kengne
- University Medical Center Utrecht, Utrecht, Netherlands; University of Cape Town and South African Medical Research Council, Cape Town, South Africa; The George Institute for Global Health, Sydney, NSW, Australia
| | | | | | | | | | | | | | | | | | - Luigi Palla
- Medical Research Council Epidemiology Unit, Cambridge, UK
| | | | | | - Noël C Barengo
- Hjelt Institute, University of Helsinki, Helsinki, Finland
| | | | - Heiner Boeing
- German Institute of Nutrition, Potsdam-Rehbruecke, Germany
| | | | | | - Laureen Dartois
- Inserm, Centre for Research in Epidemiology and Population Health, U1018, Villejuif, France
| | - Guy Fagherazzi
- Inserm, Centre for Research in Epidemiology and Population Health, U1018, Villejuif, France
| | | | | | - Rudolf Kaaks
- German Cancer Research Centre, Heidelberg, Germany
| | | | | | - Kuanrong Li
- German Cancer Research Centre, Heidelberg, Germany
| | | | | | - Kim Overvad
- Department of Public Health, Aarhus University, Aarhus, Denmark
| | | | - Domenico Palli
- Cancer Research and Prevention Institute, Florence, Italy
| | | | | | | | - Nina Roswall
- Danish Cancer Society Research Center, Danish Cancer Society, Copenhagen, Denmark
| | | | | | - Nadia Slimani
- International Agency for Research on Cancer, Lyon, France
| | | | - Anne Tjønneland
- Danish Cancer Society Research Center, Danish Cancer Society, Copenhagen, Denmark
| | - Rosario Tumino
- Cancer Registry and Histopathology Unit, Azienda Sanitaria Provinciale 7, Ragusa, Italy
| | - Daphne L van der A
- National Institute for Public Health and the Environment, Bilthoven, Netherlands
| | - Nita G Forouhi
- Medical Research Council Epidemiology Unit, Cambridge, UK
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Janghorbani M, Adineh H, Amini M. Finnish Diabetes Risk Score to predict type 2 diabetes in the Isfahan diabetes prevention study. Diabetes Res Clin Pract 2013; 102:202-9. [PMID: 24262944 DOI: 10.1016/j.diabres.2013.10.018] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2013] [Revised: 09/21/2013] [Accepted: 10/28/2013] [Indexed: 11/18/2022]
Abstract
AIM The strong association between the Finnish Diabetes Risk Score (FINDRISC) and risk of diabetes reported in European populations cannot necessarily be generalized to other populations. The aim of this study was to evaluate the ability of FINDRISC to predict progression to diabetes in an Iranian population without diabetes. METHODS A total of 1537 first-degree relatives (FDR) without diabetes of consecutive people with type 2 diabetes 30-70 years old (376 men and 1161 women) were examined and followed for a mean (SD) of 7.8 (1.7) years for diabetes incidence. We examined the incidence of diabetes across quartiles of FINDRISC and plotted a receiver operating characteristic (ROC) curve to assess discrimination. At baseline and through follow-up, participants underwent a standard 75-g 2-h oral glucose tolerance test. Data for the FINDRISC were available from each participant. RESULTS During 12,046 person-years of follow-up, 41 men and 154 women developed diabetes. The incidence of type 2 diabetes was 14.0 per 1000 person-years in men and 16.9 in women. Those in the top quartile of FINDRISC were 21.7 times more likely to develop diabetes than those in the bottom quartile (relative risk 21.7; 95% CI 9.90, 47.39). The area under the ROC was 75.1% (95% CI 71.3, 78.8). CONCLUSIONS The results of this study show that FINDRISC is a robust predictor of type 2 diabetes in high-risk individuals in Iran.
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Affiliation(s)
- Mohsen Janghorbani
- Isfahan Endocrine and Metabolism Research Center, Isfahan University of Medical Sciences, Isfahan, Iran; Department of Epidemiology and Biostatistics, School of Public Health, Isfahan University of Medical Sciences, Isfahan, Iran.
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Wagner R, Thorand B, Osterhoff MA, Müller G, Böhm A, Meisinger C, Kowall B, Rathmann W, Kronenberg F, Staiger H, Stefan N, Roden M, Schwarz PE, Pfeiffer AF, Häring HU, Fritsche A. Family history of diabetes is associated with higher risk for prediabetes: a multicentre analysis from the German Center for Diabetes Research. Diabetologia 2013; 56:2176-80. [PMID: 23979484 DOI: 10.1007/s00125-013-3002-1] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2013] [Accepted: 07/05/2013] [Indexed: 01/15/2023]
Abstract
AIMS/HYPOTHESIS Prediabetes is a collective term for different subphenotypes (impaired glucose tolerance [IGT] and/or impaired fasting glucose [IFG]) with different pathophysiologies. A positive family history for type 2 diabetes (FHD) is associated with increased risk for type 2 diabetes. We assumed that it would also associate with prediabetes, but wondered whether all subphenotypes are related to a positive family history. METHODS In a study population of 8,106 non-diabetic individuals of European origin collected from four study centres (normal glucose tolerance, NGT n = 5,482, IFG and/or IGT n = 2,624), we analysed whether having at least one first degree relative with diabetes is associated with prediabetes. The analyses were performed using the same models in each population separately. Afterwards, a meta-analysis was performed. RESULTS FHD was significantly associated with the risk for prediabetes (IFG and/or IGT, OR 1.40; 95% CI 1.27, 1.54). This association remained significant in multivariable logistic regression models including sex, age and BMI (OR 1.26; 95% CI 1.14, 1.40). When different prediabetic outcomes were considered separately, the association was found for isolated IFG (OR 1.37; 95% CI 1.20, 1.57), isolated IGT (OR 1.25; 95% CI 1.07, 1.46) as well as for the combination IFG+IGT (OR 1.64; 95% CI 1.40, 1.93). After stratification on BMI, association between FHD and prediabetes was seen only in non-obese individuals (BMI < 30 kg/m(2)). CONCLUSIONS/INTERPRETATION We found that FHD is an important risk factor for prediabetes, especially for combined IGT and IFG. Its relevance seems to be more evident in the non-obese.
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Affiliation(s)
- Robert Wagner
- Department of Internal Medicine, Division of Endocrinology, Eberhard Karls University, Otfried-Müller-Str 10, 72076 Tübingen, Germany
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Type 2 Diabetes Risk among Asian Indians in the US: A Pilot Study. Nurs Res Pract 2013; 2013:492893. [PMID: 23970965 PMCID: PMC3736461 DOI: 10.1155/2013/492893] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2013] [Accepted: 06/30/2013] [Indexed: 12/31/2022] Open
Abstract
The purpose of this pilot study was to investigate type 2 diabetes risk among Asian Indians of Kerala ethnicity living in a West Texas County of the USA. The study used a descriptive correlational design with thirty-seven adult nondiabetic Asian Indian subjects between 20 and 70 years of age. The measurement included nonbiochemical indices of obesity, family history of type 2 diabetes, length of immigration in the US, history of hypertension, physical activity pattern, and fruit and vegetable intake. The majority of the subjects showed an increased nonbiochemical indices corresponding with overweight and obesity, placing them at risk for type 2 diabetes and associated cardiovascular complications. The physical activity pattern indicated a sedentary lifestyle. The decreased physical activity was associated with a higher Body Mass Index (BMI) and body fat percentage; length of residence in the US greater than 10 years was associated with increased body fat percentage and BMI; family history of type 2 diabetes was associated with an increase in body fat percentage. Fruit and vegetable intake pattern was not associated with a risk for type 2 diabetes. Further studies are recommended for risk surveillance among Asian Indian population living in the US.
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Handlos LN, Witte DR, Almdal TP, Nielsen LB, Badawi SE, Sheikh ARA, Belhadj M, Nadir D, Zinai S, Vistisen D. Risk scores for diabetes and impaired glycaemia in the Middle East and North Africa. Diabet Med 2013; 30:443-51. [PMID: 23331167 DOI: 10.1111/dme.12118] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/10/2013] [Indexed: 11/28/2022]
Abstract
AIMS To develop risk scores for diabetes and diabetes or impaired glycaemia for individuals living in the Middle East and North Africa region. In addition, to derive national risk scores for Algeria, Saudi Arabia and the United Arab Emirates and to compare the performance of the regional risk scores with the national risk scores. METHODS An opportunistic sample of 6588 individuals aged 30-75 years was screened. Screening consisted of a questionnaire and a clinical examination including measurement of HbA(1c). Two regional risk scores and national risk scores for each of the three countries were derived separately by stepwise backwards multiple logistic regression with diabetes [HbA(1c) ≥ 48 mmol/mol (≥ 6.5%)] and diabetes or impaired glycaemia [HbA(1c) ≥ 42 mmol/mol (≥ 6.0%)] as outcome. The performance of the regional and national risk scores was compared in data from each country by receiver operating characteristic analysis. RESULTS The eight risk scores all included age and BMI, while additional variables differed between the scores. The areas under the receiver operating characteristic curves were between 0.67 and 0.70, and for sensitivities approximately 75%; specificities varied between 50% and 57%. The regional and the national risk scores performed equally well in the three national samples. CONCLUSIONS Two regional risk scores for diabetes and diabetes or impaired glycaemia applicable to the Middle East and North Africa region were identified. The regional risk scores performed as well as the national risk scores derived in the same manner.
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Affiliation(s)
- L N Handlos
- Steno Diabetes Center A/S, Gentofte, Denmark.
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Winkler G, Hidvégi T, Vándorfi G, Balogh S, Jermendy G. Prevalence of undiagnosed abnormal glucose tolerance in adult patients cared for by general practitioners in Hungary. Results of a risk-stratified screening based on FINDRISC questionnaire. Med Sci Monit 2013; 19:67-72. [PMID: 23344680 PMCID: PMC3629009 DOI: 10.12659/msm.883747] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2012] [Accepted: 08/02/2012] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND The prevalence of type 2 diabetes mellitus is rapidly increasing, worldwide and also in Hungary. Timely diagnosis and early treatment could be aided by targeted screening. Recognizing this, the Hungarian Diabetes Association initiated a risk-stratified screening with the involvement of primary care physicians. MATERIAL/METHODS In the first phase of screening, the FINDRISC questionnaire was completed, followed by an oral glucose tolerance test (OGTT) for those with a score of ≥12. Between September 1, 2010 and March 31, 2011, 70,432 non-diabetic adults, who visited their general practitioners for any reason, were involved in the screening. Of these, 68,476 questionnaires proved to be suitable for processing. RESULTS From the questionnaires, 28,077 (41.0%) had a score of ≥12. A valid OGTT was performed in 22,846 cases; of this group 3,217 subjects (14.1%) had elevated fasting glucose levels, 5,663 (24.8%) had impaired glucose tolerance, and 1,750 (7.6%) had manifest, previously undiagnosed, diabetes mellitus. Overall, from the valid OGTT group, 46.5% subjects had some degree of glucose intolerance. CONCLUSIONS Based on the FINDRISC questionnaire, the risk-stratified screening for diabetes mellitus proved to be simple and cost-effective method for the early detection of carbohydrate metabolism disorders. Using this method, the prevalence rate of previously undiagnosed abnormal glucose tolerance was high in adult patients cared for by general practitioners in Hungary.
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Affiliation(s)
- Gábor Winkler
- Department of Medicine and Diabetology, Saint John’s Hospital, Budapest, Hungary
- Department of Health Sciences, Faculty of Health Care, University of Miskolc, Miskolc, Hungary
| | - Tibor Hidvégi
- Department of Medicine, Petz County Teaching Hospital, Győr, Hungary
| | - Győző Vándorfi
- Centre for Medicine, Diabetology and Metabolism, Veszprém County Csolnoky Hospital, Veszprém, Hungary
| | - Sándor Balogh
- National Institute for Primary Health Care, Budapest, Hungary
| | - György Jermendy
- Department of Medicine, Bajcsy-Zsilinszky Hospital, Budapest, Hungary
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Heianza Y, Arase Y, Hsieh SD, Saito K, Tsuji H, Kodama S, Tanaka S, Ohashi Y, Shimano H, Yamada N, Hara S, Sone H. Development of a new scoring system for predicting the 5 year incidence of type 2 diabetes in Japan: the Toranomon Hospital Health Management Center Study 6 (TOPICS 6). Diabetologia 2012; 55:3213-23. [PMID: 22955996 DOI: 10.1007/s00125-012-2712-0] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2012] [Accepted: 08/09/2012] [Indexed: 01/11/2023]
Abstract
AIMS/HYPOTHESIS The aims of this study were to assess the clinical significance of introducing HbA(1c) into a risk score for diabetes and to develop a scoring system to predict the 5 year incidence of diabetes in Japanese individuals. METHODS The study included 7,654 non-diabetic individuals aged 40-75 years. Incident diabetes was defined as fasting plasma glucose (FPG) ≥7.0 mmol/l, HbA(1c) ≥6.5% (48 mmol/mol) or self-reported clinician-diagnosed diabetes. We constructed a risk score using non-laboratory assessments (NLA) and evaluated improvements in risk prediction by adding elevated FPG, elevated HbA(1c) or both to NLA. RESULTS The discriminative ability of the NLA score (age, sex, family history of diabetes, current smoking and BMI) was 0.708. The difference in discrimination between the NLA + FPG and NLA + HbA(1c) scores was non-significant (0.836 vs 0.837; p = 0.898). A risk score including family history of diabetes, smoking, obesity and both FPG and HbA(1c) had the highest discrimination (0.887, 95% CI 0.871, 0.903). At an optimal cut-off point, sensitivity and specificity were high at 83.7% and 79.0%, respectively. After initial screening using NLA scores, subsequent information on either FPG or HbA(1c) resulted in a net reclassification improvement of 42.7% or 52.3%, respectively (p < 0.0001). When both were available, net reclassification improvement and integrated discrimination improvement were further improved at 56.7% (95% CI 47.3%, 66.1%) and 10.9% (9.7%, 12.1%), respectively. CONCLUSIONS/INTERPRETATION Information on HbA(1c) or FPG levels after initial screening by NLA can precisely refine diabetes risk reclassification.
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Affiliation(s)
- Y Heianza
- Department of Internal Medicine, Niigata University Faculty of Medicine, Niigata, Japan
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Abbasi A, Peelen LM, Corpeleijn E, van der Schouw YT, Stolk RP, Spijkerman AMW, van der A DL, Moons KGM, Navis G, Bakker SJL, Beulens JWJ. Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study. BMJ 2012; 345:e5900. [PMID: 22990994 PMCID: PMC3445426 DOI: 10.1136/bmj.e5900] [Citation(s) in RCA: 208] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
OBJECTIVE To identify existing prediction models for the risk of development of type 2 diabetes and to externally validate them in a large independent cohort. DATA SOURCES Systematic search of English, German, and Dutch literature in PubMed until February 2011 to identify prediction models for diabetes. DESIGN Performance of the models was assessed in terms of discrimination (C statistic) and calibration (calibration plots and Hosmer-Lemeshow test).The validation study was a prospective cohort study, with a case cohort study in a random subcohort. SETTING Models were applied to the Dutch cohort of the European Prospective Investigation into Cancer and Nutrition cohort study (EPIC-NL). PARTICIPANTS 38,379 people aged 20-70 with no diabetes at baseline, 2506 of whom made up the random subcohort. OUTCOME MEASURE Incident type 2 diabetes. RESULTS The review identified 16 studies containing 25 prediction models. We considered 12 models as basic because they were based on variables that can be assessed non-invasively and 13 models as extended because they additionally included conventional biomarkers such as glucose concentration. During a median follow-up of 10.2 years there were 924 cases in the full EPIC-NL cohort and 79 in the random subcohort. The C statistic for the basic models ranged from 0.74 (95% confidence interval 0.73 to 0.75) to 0.84 (0.82 to 0.85) for risk at 7.5 years. For prediction models including biomarkers the C statistic ranged from 0.81 (0.80 to 0.83) to 0.93 (0.92 to 0.94). Most prediction models overestimated the observed risk of diabetes, particularly at higher observed risks. After adjustment for differences in incidence of diabetes, calibration improved considerably. CONCLUSIONS Most basic prediction models can identify people at high risk of developing diabetes in a time frame of five to 10 years. Models including biomarkers classified cases slightly better than basic ones. Most models overestimated the actual risk of diabetes. Existing prediction models therefore perform well to identify those at high risk, but cannot sufficiently quantify actual risk of future diabetes.
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Affiliation(s)
- Ali Abbasi
- Department of Epidemiology, University of Groningen, University Medical Centre Groningen, Groningen, Netherlands.
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Alssema M, Newson RS, Bakker SJL, Stehouwer CDA, Heymans MW, Nijpels G, Hillege HL, Hofman A, Witteman JCM, Gansevoort RT, Dekker JM. One risk assessment tool for cardiovascular disease, type 2 diabetes, and chronic kidney disease. Diabetes Care 2012; 35:741-8. [PMID: 22338109 PMCID: PMC3308277 DOI: 10.2337/dc11-1417] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2011] [Accepted: 12/17/2011] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Individuals at high risk for chronic cardiometabolic disease (cardiovascular disease [CVD], type 2 diabetes, and chronic kidney disease [CKD]) share many risk factors and would benefit from early intervention. We developed a nonlaboratory-based risk-assessment tool for identification of people at high cardiometabolic disease risk. RESEARCH DESIGN AND METHODS Data of three population-based cohorts from different regions of the Netherlands were merged. Participants were 2,840 men and 3,940 women, white, aged 28-85 years, free from CVD, type 2 diabetes, and CKD diagnosis at baseline. The outcome was developing cardiometabolic disease during 7 years follow-up. RESULTS Age, BMI, waist circumference, antihypertensive treatment, smoking, family history of myocardial infarction or stroke, and family history of diabetes were significant predictors, whereas former smoking, history of gestational diabetes, and use of lipid-lowering medication were not. The models showed acceptable calibration (Hosmer and Lemeshow statistics, P > 0.05) and discrimination (area under the receiver operating characteristic [ROC] curve 0.82 [95% CI 0.81-0.83] for women and 0.80 [0.78-0.82] for men). Discrimination of individual outcomes was lowest for diabetes (area under the ROC curve 0.70 for men and 0.73 for women) and highest for CVD mortality (0.83 for men and 0.85 for women). CONCLUSIONS We demonstrate that a single risk stratification tool can identify people at high risk for future CVD, type 2 diabetes, and/or CKD. The present risk-assessment tool can be used for referring the highest risk individuals to health care for further (multivariable) risk assessment and may as such serve as an important part of prevention programs targeting chronic cardiometabolic disease.
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Affiliation(s)
- Marjan Alssema
- Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands.
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Abstract
OBJECTIVE To evaluate current risk models and scores for type 2 diabetes and inform selection and implementation of these in practice. DESIGN Systematic review using standard (quantitative) and realist (mainly qualitative) methodology. Inclusion criteria Papers in any language describing the development or external validation, or both, of models and scores to predict the risk of an adult developing type 2 diabetes. DATA SOURCES Medline, PreMedline, Embase, and Cochrane databases were searched. Included studies were citation tracked in Google Scholar to identify follow-on studies of usability or impact. DATA EXTRACTION Data were extracted on statistical properties of models, details of internal or external validation, and use of risk scores beyond the studies that developed them. Quantitative data were tabulated to compare model components and statistical properties. Qualitative data were analysed thematically to identify mechanisms by which use of the risk model or score might improve patient outcomes. RESULTS 8864 titles were scanned, 115 full text papers considered, and 43 papers included in the final sample. These described the prospective development or validation, or both, of 145 risk prediction models and scores, 94 of which were studied in detail here. They had been tested on 6.88 million participants followed for up to 28 years. Heterogeneity of primary studies precluded meta-analysis. Some but not all risk models or scores had robust statistical properties (for example, good discrimination and calibration) and had been externally validated on a different population. Genetic markers added nothing to models over clinical and sociodemographic factors. Most authors described their score as "simple" or "easily implemented," although few were specific about the intended users and under what circumstances. Ten mechanisms were identified by which measuring diabetes risk might improve outcomes. Follow-on studies that applied a risk score as part of an intervention aimed at reducing actual risk in people were sparse. CONCLUSION Much work has been done to develop diabetes risk models and scores, but most are rarely used because they require tests not routinely available or they were developed without a specific user or clear use in mind. Encouragingly, recent research has begun to tackle usability and the impact of diabetes risk scores. Two promising areas for further research are interventions that prompt lay people to check their own diabetes risk and use of risk scores on population datasets to identify high risk "hotspots" for targeted public health interventions.
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Affiliation(s)
- Douglas Noble
- Centre for Primary Care and Public Health, Barts and the London School of Medicine and Dentistry, London E1 2AT, UK.
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Alssema M, Vistisen D, Heymans MW, Nijpels G, Glümer C, Zimmet PZ, Shaw JE, Eliasson M, Stehouwer CDA, Tabák AG, Colagiuri S, Borch-Johnsen K, Dekker JM. Risk scores for predicting type 2 diabetes: using the optimal tool. Diabetologia 2011; 54:2468-70. [PMID: 21660635 DOI: 10.1007/s00125-011-2214-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2011] [Accepted: 05/12/2011] [Indexed: 10/18/2022]
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Wareham NJ, Griffin SJ. Risk scores for predicting type 2 diabetes: comparing axes and spades. Diabetologia 2011; 54:994-5. [PMID: 21380593 DOI: 10.1007/s00125-011-2101-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2011] [Accepted: 01/19/2011] [Indexed: 10/18/2022]
Affiliation(s)
- N J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, CB1 0QQ, UK.
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Li HY, Lin MS, Shih SR, Hua CH, Liu YL, Chuang LM, Sung FC, Chen MF, Lee JC, Chiao CH, Wei JN. The performance of risk scores and hemoglobin A1c to find undiagnosed diabetes with isolated postload hyperglycemia. Endocr J 2011; 58:441-8. [PMID: 21490405 DOI: 10.1507/endocrj.k11e-005] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
The aim of this study is to develop strategies to screen diabetic subjects with isolated postload hyperglycemia (IPH) in Chinese population. We included 1175 adult subjects who did not report diabetes were included. Diabetes was diagnosed by oral glucose tolerance tests. IPH was defined as fasting plasma glucose (FPG)<7 mmol/l and 2-hour post-load plasma glucose (2hPG) greater than 11.1 mmol/l. Using FPG criteria, only 59.8% of diabetic subjects were not identified, showing a poor agreement between FPG and 2hPG criteria (kappa 0.294). Age, FPG, total cholesterol, triglycerides, blood pressure, body mass index, HbA1c and medication for hypertension were associated factors for IPH. Four scores were constructed using all these factors, age and blood test results, age and HbA1c, and data from non-invasive examinations, respectively. The area under the ROC curve were 0.9296(95%CI 0.8948-0.9643), 0.9111(95%CI 0.8713-0.9508), 0.8902(95%CI 0.8341-0.9646), 0.8924(95%CI 0.7835-0.8753), and 0.8654(95%CI 0.7963-0.9345) for score 1, 2, 3, 4, and HbA1c, respectively. The sensitivity of all four risk scores to detect IPH was better than that of impaired fasting glucose (IFG). The sensitivity and specificity of HbA1c at cutoff 6.2% for detecting IPH was also better than that of IFG. In conclusion, the risk scores and HbA1c are useful to identify subjects with undiagnosed IPH, with better performance than IFG.
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Affiliation(s)
- Hung-Yuan Li
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
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