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Tong C, Han Y, Zhang S, Li Q, Zhang J, Guo X, Tao L, Zheng D, Yang X. Establishment of dynamic nomogram and risk score models for T2DM: a retrospective cohort study in Beijing. BMC Public Health 2022; 22:2306. [PMID: 36494707 PMCID: PMC9733342 DOI: 10.1186/s12889-022-14782-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 11/30/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Health interventions can delay or prevent the occurrence and development of diabetes. Dynamic nomogram and risk score (RS) models were developed to predict the probability of developing type 2 diabetes mellitus (T2DM) and identify high-risk groups. METHODS Participants (n = 44,852) from the Beijing Physical Examination Center were followed up for 11 years (2006-2017); the mean follow-up time was 4.06 ± 2.09 years. Multivariable Cox regression was conducted in the training cohort to identify risk factors associated with T2DM and develop dynamic nomogram and RS models using weighted estimators corresponding to each covariate derived from the fitted Cox regression coefficients and variance estimates, and then undergone internal validation and sensitivity analysis. The concordance index (C-index) was used to assess the accuracy and reliability of the model. RESULTS Of the 44,852 individuals at baseline, 2,912 were diagnosed with T2DM during the follow-up period, and the incidence density rate per 1,000 person-years was 16.00. Multivariate analysis indicated that male sex (P < 0.001), older age (P < 0.001), high body mass index (BMI, P < 0.05), high fasting plasma glucose (FPG, P < 0.001), hypertension (P = 0.015), dyslipidaemia (P < 0.001), and low serum creatinine (sCr, P < 0.05) at presentation were risk factors for T2DM. The dynamic nomogram achieved a high C-index of 0.909 in the training set and 0.905 in the validation set. A tenfold cross-validation estimated the area under the curve of the nomogram at 0.909 (95% confidence interval 0.897-0.920). Moreover, the dynamic nomogram and RS model exhibited acceptable discrimination and clinical usefulness in subgroup and sensitivity analyses. CONCLUSIONS The T2DM dynamic nomogram and RS models offer clinicians and others who conduct physical examinations, respectively, simple-to-use tools to assess the risk of developing T2DM in the urban Chinese current or retired employees.
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Affiliation(s)
- Chao Tong
- grid.24696.3f0000 0004 0369 153XSchool of Public Health, Capital Medical University, NO.10 Xitoutiao, Youanmen, Beijing, 100069 China
| | - Yumei Han
- Beijing Physical Examination Center, No. 59, Beiwei Road, Xicheng District, Beijing, China
| | - Shan Zhang
- grid.24696.3f0000 0004 0369 153XSchool of Public Health, Capital Medical University, NO.10 Xitoutiao, Youanmen, Beijing, 100069 China
| | - Qiang Li
- Beijing Physical Examination Center, No. 59, Beiwei Road, Xicheng District, Beijing, China
| | - Jingbo Zhang
- Beijing Physical Examination Center, No. 59, Beiwei Road, Xicheng District, Beijing, China
| | - Xiuhua Guo
- grid.24696.3f0000 0004 0369 153XSchool of Public Health, Capital Medical University, NO.10 Xitoutiao, Youanmen, Beijing, 100069 China
| | - Lixin Tao
- grid.24696.3f0000 0004 0369 153XSchool of Public Health, Capital Medical University, NO.10 Xitoutiao, Youanmen, Beijing, 100069 China
| | - Deqiang Zheng
- grid.24696.3f0000 0004 0369 153XSchool of Public Health, Capital Medical University, NO.10 Xitoutiao, Youanmen, Beijing, 100069 China
| | - Xinghua Yang
- grid.24696.3f0000 0004 0369 153XSchool of Public Health, Capital Medical University, NO.10 Xitoutiao, Youanmen, Beijing, 100069 China
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A systematic review of diabetes risk assessment tools in sub-Saharan Africa. Int J Diabetes Dev Ctries 2022. [DOI: 10.1007/s13410-022-01045-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Abstract
Abstract
Objectives
To systematically review all current studies on diabetes risk assessment tools used in SSA to diagnose diabetes in symptomatic and asymptomatic patients.
Methods
Tools were identified through a systematic search of PubMed, Ovid, Google Scholar, and the Cochrane Library for articles published from January 2010 to January 2020. The search included articles reporting the use of diabetes risk assessment tool to detect individuals with type 2 diabetes in SSA. A standardized protocol was used for data extraction (registry #177726).
Results
Of the 825 articles identified, 39 articles met the inclusion criteria, and three articles reported tools used in SSA population but developed for the Western population. None was validated in SSA population. All but three articles were observational studies (136 and 58,657 study participants aged between the ages of 15 and 85 years). The Finnish Medical Association risk tool, World Health Organization (WHO) STEPS instrument, General Practice Physical Activity Questionnaire (GPPAQ), Rapid Eating and Activity Assessment for Patients (REAP), and an anthropometric tool were the most frequently used non-invasive tools in SSA. The accuracy of the tools was measured using sensitivity, specificity, or area under the receiver operating curve. The anthropometric predictor variables identified included age, body mass index, waist circumference, positive family of diabetes, and activity levels.
Conclusions
This systematic review demonstrated a paucity of validated diabetes risk assessment tools for SSA. There remains a need for the development and validation of a tool for the rapid identification of diabetes for targeted interventions.
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Quang Binh T, Tran Phuong P, Thanh Chung N, Thi Nhung B, Dinh Tung D, Tuan Linh D, Ngoc Luong T, Danh Tuyen L. A simple nomogram for identifying individuals at high risk of undiagnosed diabetes in rural population. Diabetes Res Clin Pract 2021; 180:109061. [PMID: 34597731 DOI: 10.1016/j.diabres.2021.109061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 08/21/2021] [Accepted: 09/14/2021] [Indexed: 11/19/2022]
Abstract
AIMS To sought for an easily applicable nomogram for detecting individuals at high risk of undiagnosed type 2 diabetes. METHODS The development cohort included 2542 participants recruited randomly from a rural population in 2011.The glycemic status of subjects was determined using the fasting plasma glucose test and the oral glucose tolerance test. The Bayesian Model Average approach was used to search for a parsimonious model with minimum number of predictor and maximum discriminatory power. The corresponding prediction nomograms were constructed and checked for discrimination, calibration, clinical usefulness, and generalizability in nationwide population in 2012. RESULTS The non-lab nomogram including waist circumference and systolic blood pressure was the most parsimonious with the area under receiver operating characteristic curve (AUC) of 0.71 (95 %CI = 0.64-0.76). Adding low-density lipoprotein cholesterol in the non-lab nomogram generated the lab-based nomogram with significantly improved AUC of 0.83 (0.78-0.87, P < 0.001). The nomograms had a positive net benefit at threshold probability between 0.01 and 0.15. Applying the non-lab nomogram to the national population yielded the AUC of 0.66 (0.63-0.70) and 0.68 (0.65-0.71) in the cohorts aged 40-64 and 30-69 years, respectively. CONCLUSIONS The novel nomograms could help promote the early detection of undiagnosed diabetes in rural Vietnamese population.
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Affiliation(s)
- Tran Quang Binh
- National Institute of Nutrition, 48B Tang Bat Ho Street, Hanoi, Vietnam; Dinh Tien Hoang Institute of Medicine, 20 Cat Linh, Dong Da, Hanoi, Vietnam.
| | - Pham Tran Phuong
- National Institute of Nutrition, 48B Tang Bat Ho Street, Hanoi, Vietnam
| | - Nguyen Thanh Chung
- National Institute of Hygiene and Epidemiology, 1 Yersin, Hanoi, Vietnam
| | - Bui Thi Nhung
- National Institute of Nutrition, 48B Tang Bat Ho Street, Hanoi, Vietnam
| | - Do Dinh Tung
- National Institute of Diabetes and Metabolic Disorders, 1 Ton That Tung, Hanoi, Vietnam
| | - Duong Tuan Linh
- National Institute of Nutrition, 48B Tang Bat Ho Street, Hanoi, Vietnam
| | - Tran Ngoc Luong
- National Hospital of Endocrinology, 80, Alley 82, Yen Lang Street, Dong Da District, Hanoi, Vietnam
| | - Le Danh Tuyen
- National Institute of Nutrition, 48B Tang Bat Ho Street, Hanoi, Vietnam
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Flynn S, Millar S, Buckley C, Junker K, Phillips C, Harrington J. Comparing non-invasive diabetes risk scores for detecting patients in clinical practice: a cross-sectional validation study. HRB Open Res 2021. [DOI: 10.12688/hrbopenres.13254.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background: Type 2 diabetes (T2DM) is a significant cause of morbidity and mortality, thus early identification is of paramount importance. A high proportion of T2DM cases are undiagnosed highlighting the importance of effective detection methods such as non-invasive diabetes risk scores (DRSs). Thus far, no DRS has been validated in an Irish population. Therefore, the aim of this study was to compare the ability of nine DRSs to detect T2DM cases in an Irish population. Methods: This was a cross-sectional study of 1,990 men and women aged 46–73 years. Data on DRS components were collected from questionnaires and clinical examinations. T2DM was determined according to a fasting plasma glucose level ≥7.0 mmol/l or a glycated haemoglobin A1c level ≥6.5% (≥48 mmol/mol). Receiver operating characteristic curve analysis assessed the ability of DRSs and their components to discriminate T2DM cases. Results: Among the examined scores, area under the curve (AUC) values ranged from 0.71–0.78, with the Cambridge Diabetes Risk Score (AUC=0.78, 95% CI: 0.75–0.82), Leicester Diabetes Risk Score (AUC=0.78, 95% CI: 0.75–0.82), Rotterdam Predictive Model 2 (AUC=0.78, 95% CI: 0.74–0.82) and the U.S. Diabetes Risk Score (AUC=0.78, 95% CI: 0.74–0.81) demonstrating the largest AUC values as continuous variables and at optimal cut-offs. Regarding individual DRS components, anthropometric measures displayed the largest AUC values. Conclusions: The best performing DRSs were broadly similar in terms of their components; all incorporated variables for age, sex, BMI, hypertension and family diabetes history. The Cambridge Diabetes Risk Score, had the largest AUC value at an optimal cut-off, can be easily accessed online for use in a clinical setting and may be the most appropriate and cost-effective method for case-finding in an Irish population.
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Performance of Risk Assessment Models for Prevalent or Undiagnosed Type 2 Diabetes Mellitus in a Multi-Ethnic Population-The Helius Study. Glob Heart 2021; 16:13. [PMID: 33598393 PMCID: PMC7880001 DOI: 10.5334/gh.846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background: Most risk assessment models for type 2 diabetes (T2DM) have been developed in Caucasians and Asians; little is known about their performance in other ethnic groups. Objective(s): We aimed to identify existing models for the risk of prevalent or undiagnosed T2DM and externally validate them in a multi-ethnic population currently living in the Netherlands. Methods: A literature search to identify risk assessment models for prevalent or undiagnosed T2DM was performed in PubMed until December 2017. We validated these models in 4,547 Dutch, 3,035 South Asian Surinamese, 4,119 African Surinamese, 2,326 Ghanaian, 3,598 Turkish, and 3,894 Moroccan origin participants from the HELIUS (Healthy LIfe in an Urban Setting) cohort study performed in Amsterdam. Model performance was assessed in terms of discrimination (C-statistic) and calibration (Hosmer-Lemeshow test). We identified 25 studies containing 29 models for prevalent or undiagnosed T2DM. C-statistics varied between 0.77–0.92 in Dutch, 0.66–0.83 in South Asian Surinamese, 0.70–0.82 in African Surinamese, 0.61–0.81 in Ghanaian, 0.69–0.86 in Turkish, and 0.69–0.87 in the Moroccan populations. The C-statistics were generally lower among the South Asian Surinamese, African Surinamese, and Ghanaian populations and highest among the Dutch. Calibration was poor (Hosmer-Lemeshow p < 0.05) for all models except one. Conclusions: Generally, risk models for prevalent or undiagnosed T2DM show moderate to good discriminatory ability in different ethnic populations living in the Netherlands, but poor calibration. Therefore, these models should be recalibrated before use in clinical practice and should be adapted to the situation of the population they are intended to be used in.
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Félix-Martínez GJ, Godínez-Fernández JR. Comparative analysis of screening models for undiagnosed diabetes in Mexico. ENDOCRINOL DIAB NUTR 2020; 67:333-341. [PMID: 31796340 DOI: 10.1016/j.endinu.2019.08.006] [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: 04/18/2019] [Revised: 08/29/2019] [Accepted: 08/30/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND It is estimated that 37% of Mexican adults have undiagnosed diabetes, and are therefore at high risk of developing the severe and devastating complications associated to it. In recent years, a variety of screening tools based on the characteristics of the adult Mexican population have been proposed in order to reduce the negative effects of the disease. OBJECTIVES To assess the performance of screening models to diagnose diabetes in the Mexican adult population and to propose a screening model based on HbA1c measurements. MATERIALS AND METHODS Data from the 2016 Halfway National Health and Nutrition Survey (NHNS) were used to assess the screening models and to develop and validate the proposed 2016 NHNS model, built using a multivariate logistic regression model. Explanatory variables included in the 2016 NHNS 2016 model were selected through a stepwise backward procedure, using sensitivity and specificity as performance indicators. RESULTS Of the screening models assessed, only the model based on the 2006 NHNS survey showed a performance consistent with previous reports. The proposed 2016 NHNS model included age, waist circumference, and systolic blood pressure as explanatory variables and showed a sensitivity of 0.72 and a specificity of 0.80 in the validation data set. CONCLUSIONS Age, waist circumference, and systolic blood pressure are variables of special importance for early detection of undiagnosed diabetes in Mexican adults. Based on the consistent performance of the 2006 NHNS model in different data sets, its use as a screening tool for adults with undiagnosed diabetes in Mexico is recommended.
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Affiliation(s)
- Gerardo Jorge Félix-Martínez
- Cátedras CONACYT (Consejo Nacional de Ciencia y Tecnología, México), Mexico; Departamento de Ingeniería Eléctrica, Universidad Autónoma Metropolitana, Unidad Iztapalapa, Mexico.
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Carrillo‐Larco RM, Aparcana‐Granda DJ, Mejia JR, Barengo NC, Bernabe‐Ortiz A. Risk scores for type 2 diabetes mellitus in Latin America: a systematic review of population-based studies. Diabet Med 2019; 36:1573-1584. [PMID: 31441090 PMCID: PMC6900051 DOI: 10.1111/dme.14114] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/20/2019] [Indexed: 12/18/2022]
Abstract
AIM To summarize the evidence on diabetes risk scores for Latin American populations. METHODS A systematic review was conducted (CRD42019122306) looking for diagnostic and prognostic models for type 2 diabetes mellitus among randomly selected adults in Latin America. Five databases (LILACS, Scopus, MEDLINE, Embase and Global Health) were searched. type 2 diabetes mellitus was defined using at least one blood biomarker and the reports needed to include information on the development and/or validation of a multivariable regression model. Risk of bias was assessed using the PROBAST guidelines. RESULTS Of the 1500 reports identified, 11 were studied in detail and five were included in the qualitative analysis. Two reports were from Mexico, two from Peru and one from Brazil. The number of diabetes cases varied from 48 to 207 in the derivations models, and between 29 and 582 in the validation models. The most common predictors were age, waist circumference and family history of diabetes, and only one study used oral glucose tolerance test as the outcome. The discrimination performance across studies was ~ 70% (range: 66-72%) as per the area under the receiving-operator curve, the highest metric was always the negative predictive value. Sensitivity was always higher than specificity. CONCLUSION There is no evidence to support the use of one risk score throughout Latin America. The development, validation and implementation of risk scores should be a research and public health priority in Latin America to improve type 2 diabetes mellitus screening and prevention.
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Affiliation(s)
- R. M. Carrillo‐Larco
- Department of Epidemiology and BiostatisticsSchool of Public HealthImperial College LondonLondonUK
- CRONICAS Centre of Excellence in Chronic DiseasesUniversidad Peruana Cayetano HerediaLimaPerú
- Centro de Estudios de PoblacionUniversidad Catolica los Ángeles de Chimbote (ULADECHCatolica)ChimbotePerú
| | - D. J. Aparcana‐Granda
- CRONICAS Centre of Excellence in Chronic DiseasesUniversidad Peruana Cayetano HerediaLimaPerú
| | - J. R. Mejia
- Facultad de Medicina HumanaUniversidad Nacional del Centro del PerúHuancayoPerú
| | - N. C. Barengo
- Department of Medical and Population Health Sciences ResearchHerbert Wertheim College of MedicineFlorida International UniversityMiamiFLUSA
- Department of Public HealthFaculty of MedicineUniversity of HelsinkiHelsinkiFinland
- Faculty of MedicineRiga Stradins UniversityRigaLatvia
| | - A. Bernabe‐Ortiz
- CRONICAS Centre of Excellence in Chronic DiseasesUniversidad Peruana Cayetano HerediaLimaPerú
- Universidad Científica del SurLimaPerú
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Mitchell AJ, Vancampfort D, Manu P, Correll CU, Wampers M, van Winkel R, Yu W, De Hert M. Which clinical and biochemical predictors should be used to screen for diabetes in patients with serious mental illness receiving antipsychotic medication? A large observational study. PLoS One 2019; 14:e0210674. [PMID: 31513598 PMCID: PMC6742458 DOI: 10.1371/journal.pone.0210674] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 12/28/2018] [Indexed: 01/01/2023] Open
Abstract
OBJECTIVE We aimed to investigate which clinical and metabolic tests offer optimal accuracy and acceptability to help diagnose diabetes among a large sample of people with serious mental illness in receipt of antipsychotic medication. METHODS A prospective observational study design of biochemical and clinical factors was used. Biochemical measures were fasting glucose, insulin and lipids, oral glucose tolerance testing (OGTT), hemoglobin A1c, and insulin resistance assessed with the homeostatic model (HOMA-IR) were determined in a consecutive cohort of 798 adult psychiatric inpatients receiving antipsychotics. Clinical variables were gender, age, global assessment of functioning (GAF), mental health clinicians' global impression (CGI), duration of severe mental illness, height, weight, BMI and waist/hip ratio. In addition, we calculated the risk using combined clinical predictors using the Leicester Practice Risk Score (LPRS) and the Topics Diabetes Risk Score (TDRS). Diabetes was defined by older criteria (impaired fasting glucose (IFG) or OGTT) as well as2010 criteria (IFG or OGTT or Glycated haemoglobin (HBA1c)) at conventional cut-offs. RESULTS Using the older criteria, 7.8% had diabetes (men: 6.3%; women: 10.3%). Using the new criteria, 10.2% had diabetes (men: 8.2%, women: 13.2%), representing a 30.7% increase (p = 0.02) in the prevalence of diabetes. Regarding biochemical predictors, conventional OGTT, IFG, and HbA1c thresholds used to identify newly defined diabetes missed 25%, 50% and 75% of people with diabetes, respectively. The conventional HBA1c cut-point of ≥6.5% (48 mmol/mol) missed 7 of 10 newly defined cases of diabetes while a cut-point of ≥5.7% improved sensitivity from 44.4% to up to 85%. Specific algorithm approaches offered reasonable accuracy. Unfortunately no single clinical factor was able to accurately rule-in a diagnosis of diabetes. Three clinical factors were able to rule-out diabetes with good accuracy namely: BMI, waist/hip ratio and height. A BMI < 30 had a 92% negative predictive value in ruling-out diabetes. Of those not diabetic, 20% had a BMI ≥ 30. However, for complete diagnosis a specific biochemical protocol is still necessary. CONCLUSIONS Patients with SMI maintained on antipsychotic medication cannot be reliably screened for diabetes using clinical variables alone. Accurate assessment requires a two-step algorithm consisting of HBA1c ≥5.7% followed by both FG and OGTT which does not require all patients to have OGTT and FG.
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Affiliation(s)
| | - Davy Vancampfort
- University Psychiatric Center, Catholic University Leuven, Kortenberg, Belgium
| | - Peter Manu
- University Psychiatric Center, Kortenberg, Belgium
- School of Mental Health and Neuroscience (EURON), University Medical Center, Maastricht, The Netherlands
| | - Christoph U. Correll
- Zucker Hillside Hospital, Glen Oaks, New York, United States
- Hofstra North Shore–LIJ School of Medicine, Hempstead, New York, United States
| | - Martien Wampers
- University Psychiatric Center, Catholic University Leuven, Kortenberg, Belgium
| | - Ruud van Winkel
- University Psychiatric Center, Catholic University Leuven, Kortenberg, Belgium
| | - Weiping Yu
- University Psychiatric Center, Catholic University Leuven, Kortenberg, Belgium
| | - Marc De Hert
- University Psychiatric Center, Catholic University Leuven, Kortenberg, Belgium
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Bernabe-Ortiz A, Perel P, Miranda JJ, Smeeth L. Diagnostic accuracy of the Finnish Diabetes Risk Score (FINDRISC) for undiagnosed T2DM in Peruvian population. Prim Care Diabetes 2018; 12:517-525. [PMID: 30131300 PMCID: PMC6249987 DOI: 10.1016/j.pcd.2018.07.015] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Revised: 07/23/2018] [Accepted: 07/29/2018] [Indexed: 12/18/2022]
Abstract
AIMS To assess the diagnostic accuracy of the Finnish Diabetes Risk Score (FINDRISC) for undiagnosed T2DM and to compare its performance with the Latin-American FINDRISC (LA-FINDRISC) and the Peruvian Risk Score. MATERIALS AND METHODS A population-based study was conducted. T2DM and undiagnosed T2DM were defined using oral glucose tolerance test (OGTT). Risk scores assessed were FINDRISC, LA-FINDRISC and Peruvian Risk Score. Diagnostic accuracy of risk scores was estimated using the c-statistic and the area under the ROC curve (aROC). A simplified version of FINDRISC was also derived. RESULTS Data from 1609 individuals, mean age 48.2 (SD: 10.6), 810 (50.3%) women, were collected. A total of 176 (11.0%; 95%CI: 9.4%-12.5%) were classified as having T2DM, and 71 (4.7%; 95%CI: 3.7%-5.8%) were classified as having undiagnosed T2DM. Diagnostic accuracy of the FINDRISC (aROC=0.69), LA-FINDRISC (aROC=0.68), and Peruvian Risk Score (aROC=0.64) was similar (p=0.15). The simplified FINDRISC, with 4 variables, had a slightly better performance (aROC=0.71) than the other scores. CONCLUSION The performance of FINDRISC, LA-FINDRISC and Peruvian Risk Score for undiagnosed T2DM was similar. A simplified FINDRISC can perform as well or better for undiagnosed T2DM. The FINDRISC may be useful to detect cases of undiagnosed T2DM in resource-constrained settings.
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Affiliation(s)
- Antonio Bernabe-Ortiz
- CRONICAS Center of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima 18, Peru; Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK.
| | - Pablo Perel
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK.
| | - Juan Jaime Miranda
- CRONICAS Center of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima 18, Peru; Department of Medicine, School of Medicine, Universidad Peruana Cayetano Heredia, Lima 31, Peru.
| | - Liam Smeeth
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK.
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Nombo AP, Mwanri AW, Brouwer-Brolsma EM, Ramaiya KL, Feskens EJM. Gestational diabetes mellitus risk score: A practical tool to predict gestational diabetes mellitus risk in Tanzania. Diabetes Res Clin Pract 2018; 145:130-137. [PMID: 29852237 DOI: 10.1016/j.diabres.2018.05.001] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Accepted: 05/04/2018] [Indexed: 12/16/2022]
Abstract
BACKGROUND Universal screening for hyperglycemia during pregnancy may be in-practical in resource constrained countries. Therefore, the aim of this study was to develop a simple, non-invasive practical tool to predict undiagnosed Gestational diabetes mellitus (GDM) in Tanzania. METHODS We used cross-sectional data of 609 pregnant women, without known diabetes, collected in six health facilities from Dar es Salaam city (urban). Women underwent screening for GDM during ante-natal clinics visit. Smoking habit, alcohol consumption, pre-existing hypertension, birth weight of the previous child, high parity, gravida, previous caesarean section, age, MUAC ≥ 28 cm, previous stillbirth, haemoglobin level, gestational age (weeks), family history of type 2 diabetes, intake of sweetened drinks (soda), physical activity, vegetables and fruits consumption were considered as important predictors for GDM. Multivariate logistic regression modelling was used to create the prediction model, using a cut-off value of 2.5 to minimise the number of undiagnosed GDM (false negatives). RESULTS Mid-upper arm circumference (MUAC) ≥ 28 cm, previous stillbirth, and family history of type 2 diabetes were identified as significant risk factors of GDM with a sensitivity, specificity, positive predictive value, and negative predictive value of 69%, 53%, 12% and 95%, respectively. Moreover, the inclusion of these three predictors resulted in an area under the curve (AUC) of 0.64 (0.56-0.72), indicating that the current tool correctly classifies 64% of high risk individuals. CONCLUSION The findings of this study indicate that MUAC, previous stillbirth, and family history of type 2 diabetes significantly predict GDM development in this Tanzanian population. However, the developed non-invasive practical tool to predict undiagnosed GDM only identified 6 out of 10 individuals at risk of developing GDM. Thus, further development of the tool is warranted, for instance by testing the impact of other known risk factors such as maternal age, pre-pregnancy BMI, hypertension during or before pregnancy and pregnancy weight gain.
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Affiliation(s)
- Anna Patrick Nombo
- Sokoine University of Agriculture, Department of Food Technology, Nutrition and Consumer Sciences, P.O. Box 3006, Morogoro, Tanzania
| | - Akwilina Wendelin Mwanri
- Sokoine University of Agriculture, Department of Food Technology, Nutrition and Consumer Sciences, P.O. Box 3006, Morogoro, Tanzania.
| | - Elske M Brouwer-Brolsma
- Wageningen University and Research Centre, Division of Human Nutrition, Wageningen, The Netherlands
| | | | - Edith J M Feskens
- Wageningen University and Research Centre, Division of Human Nutrition, P.O. Box 17, 6700AA Wageningen, The Netherlands
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Juarez LD, Gonzalez JS, Agne AA, Kulczycki A, Pavela G, Carson AP, Shelley JP, Cherrington AL. Diabetes risk scores for Hispanics living in the United States: A systematic review. Diabetes Res Clin Pract 2018; 142:120-129. [PMID: 29852236 PMCID: PMC6557572 DOI: 10.1016/j.diabres.2018.05.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 04/17/2018] [Accepted: 05/08/2018] [Indexed: 12/21/2022]
Abstract
AIM Undiagnosed diabetes is more prevalent among racial/ethnic minorities in the United States (U.S.). Despite the proliferation of risk scores, few have been validated in Hispanics populations. The aim of this study is to systematically review published studies that developed risk scores to identify undiagnosed Type 2 Diabetes Mellitus based on self-reported information that were validated for Hispanics in the U.S. METHODS The search included PubMed, EMBASE, Cochrane and CINAHL from inception to 2016 without language restrictions. Risk scores whose main outcome was undiagnosed Type 2 diabetes reporting performance measures for Hispanics were included. RESULTS We identified three studies that developed and validated risk scores for undiagnosed diabetes based on questionnaire data. Two studies were conducted in Latin America and one in the U.S. All three studies reported adequate performance (area under the receiving curve (AUC) range between0.68and 0.78). The study conducted in the U.S. reported a higher sensitivity of their risk score for Hispanics than whites. The limited number of studies, small size and heterogeneity of the combined cohorts provide limited evidence of the validity of risk scores for Hispanics. CONCLUSIONS Efforts to develop and validate risk prediction models in Hispanic populations in the U.S are needed, particularly given the diversity of thisfast growing population. Healthcare professionals should be aware of the limitations of applying risk scores developed for the general population on Hispanics.
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Affiliation(s)
- Lucia D Juarez
- Division of Preventive Medicine, Department of Medicine, University of Alabama at Birmingham, USA.
| | - Jeffrey S Gonzalez
- Graduate School of Psychology, Yeshiva University, USA; Medicine (Endocrinology) and Epidemiology & Population Health, Albert Einstein College of Medicine, USA
| | - April A Agne
- Division of Preventive Medicine, Department of Medicine, University of Alabama at Birmingham, USA
| | - Andrzej Kulczycki
- Department of Health Care Organization and Policy, School of Public Health, University of Alabama at Birmingham, USA
| | - Gregory Pavela
- Department of Health Behavior, School of Public Health, University of Alabama at Birmingham, USA
| | - April P Carson
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, USA
| | - John P Shelley
- Division of Preventive Medicine, Department of Medicine, University of Alabama at Birmingham, USA
| | - Andrea L Cherrington
- Division of Preventive Medicine, Department of Medicine, University of Alabama at Birmingham, USA
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Félix-Martínez GJ, Godínez-Fernández JR. Screening models for undiagnosed diabetes in Mexican adults using clinical and self-reported information. ACTA ACUST UNITED AC 2018; 65:603-610. [PMID: 29945768 DOI: 10.1016/j.endinu.2018.04.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Revised: 04/09/2018] [Accepted: 04/13/2018] [Indexed: 01/17/2023]
Abstract
BACKGROUND Prevalence of diabetes in Mexico has constantly increased since 1993. Since type 2 diabetes may remain undiagnosed for many years, identification of subjects at high risk of diabetes is very important to reduce its impact and to prevent its associated complications. OBJECTIVE To develop easily implementable screening models to identify subjects with undiagnosed diabetes based on the characteristics of Mexican adults. SUBJECTS AND METHODS Screening models were developed using datasets from the 2006 and 2012 National Health and Nutrition Surveys (NHNS). Variables used to develop the multivariate logistic regression models were selected using a backward stepwise procedure. Final models were validated using data from the 2000 National Health Survey (NHS). RESULTS The model based on the 2006 NHNS included age, waist circumference, and systolic blood pressure as explanatory variables, while the model based on the 2012 NHNS included age, waist circumference, height, and family history of diabetes. The sensitivity and specificity values obtained from the external validation procedure were 0.74 and 0.62 (2006 NHNS model) and 0.76 and 0.55 (2012 NHNS model) respectively. CONCLUSIONS Both models were equally capable of identifying subjects with undiagnosed diabetes (∼75%), and performed satisfactorily when compared to other models developed for other regions or countries.
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Affiliation(s)
- Gerardo J Félix-Martínez
- Department of Electrical Engineering, Universidad Autónoma Metropolitana, Iztapalapa, Ciudad de México, Mexico; Department of Applied Mathematics and Computer Sciences, Universidad de Cantabria, Santander, Cantabria, Spain.
| | - J Rafael Godínez-Fernández
- Department of Electrical Engineering, Universidad Autónoma Metropolitana, Iztapalapa, Ciudad de México, Mexico
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Liu X, Chen Z, Fine JP, Liu L, Wang A, Guo J, Tao L, Mahara G, Yang K, Zhang J, Tian S, Li H, Liu K, Luo Y, Zhang F, Tang Z, Guo X. A competing-risk-based score for predicting twenty-year risk of incident diabetes: the Beijing Longitudinal Study of Ageing study. Sci Rep 2016; 6:37248. [PMID: 27849048 PMCID: PMC5110955 DOI: 10.1038/srep37248] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Accepted: 10/26/2016] [Indexed: 11/09/2022] Open
Abstract
Few risk tools have been proposed to quantify the long-term risk of diabetes among middle-aged and elderly individuals in China. The present study aimed to develop a risk tool to estimate the 20-year risk of developing diabetes while incorporating competing risks. A three-stage stratification random-clustering sampling procedure was conducted to ensure the representativeness of the Beijing elderly. We prospectively followed 1857 community residents aged 55 years and above who were free of diabetes at baseline examination. Sub-distribution hazards models were used to adjust for the competing risks of non-diabetes death. The cumulative incidence function of twenty-year diabetes event rates was 11.60% after adjusting for the competing risks of non-diabetes death. Age, body mass index, fasting plasma glucose, health status, and physical activity were selected to form the score. The area under the ROC curve (AUC) was 0.76 (95% Confidence Interval: 0.72-0.80), and the optimism-corrected AUC was 0.78 (95% Confidence Interval: 0.69-0.87) after internal validation by bootstrapping. The calibration plot showed that the actual diabetes risk was similar to the predicted risk. The cut-off value of the risk score was 19 points, marking mark the difference between low-risk and high-risk patients, which exhibited a sensitivity of 0.74 and specificity of 0.65.
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Affiliation(s)
- Xiangtong Liu
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Zhenghong Chen
- Beijing Neurosurgical Institute, Capital Medical University, 6, Tiantanxili, Beijing, 100050, China
| | - Jason Peter Fine
- Department of Biostatistics, University of North Carolina, Chapel Hill, 46200, NC, U.S.A.,Department of Statistics &Operations Research, University of North Carolina, Chapel Hill, 319200, NC, U.S.A
| | - Long Liu
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Anxin Wang
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Jin Guo
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Lixin Tao
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Gehendra Mahara
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Kun Yang
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Jie Zhang
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Sijia Tian
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Haibin Li
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Kuo Liu
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Yanxia Luo
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Feng Zhang
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Zhe Tang
- Beijing Geriatric Clinical and Research Center, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Xiuhua Guo
- School of Public Health, Capital Medical University, Beijing 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
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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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Bernabe-Ortiz A, Smeeth L, Gilman RH, Sanchez-Abanto JR, Checkley W, Miranda JJ, Study Group CRONICASC. Development and Validation of a Simple Risk Score for Undiagnosed Type 2 Diabetes in a Resource-Constrained Setting. J Diabetes Res 2016; 2016:8790235. [PMID: 27689096 PMCID: PMC5027039 DOI: 10.1155/2016/8790235] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Accepted: 07/27/2016] [Indexed: 01/14/2023] Open
Abstract
Objective. To develop and validate a risk score for detecting cases of undiagnosed diabetes in a resource-constrained country. Methods. Two population-based studies in Peruvian population aged ≥35 years were used in the analysis: the ENINBSC survey (n = 2,472) and the CRONICAS Cohort Study (n = 2,945). Fasting plasma glucose ≥7.0 mmol/L was used to diagnose diabetes in both studies. Coefficients for risk score were derived from the ENINBSC data and then the performance was validated using both baseline and follow-up data of the CRONICAS Cohort Study. Results. The prevalence of undiagnosed diabetes was 2.0% in the ENINBSC survey and 2.9% in the CRONICAS Cohort Study. Predictors of undiagnosed diabetes were age, diabetes in first-degree relatives, and waist circumference. Score values ranged from 0 to 4, with an optimal cutoff ≥2 and had a moderate performance when applied in the CRONICAS baseline data (AUC = 0.68; 95% CI: 0.62-0.73; sensitivity 70%; specificity 59%). When predicting incident cases, the AUC was 0.66 (95% CI: 0.61-0.71), with a sensitivity of 69% and specificity of 59%. Conclusions. A simple nonblood based risk score based on age, diabetes in first-degree relatives, and waist circumference can be used as a simple screening tool for undiagnosed and incident cases of diabetes in Peru.
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Affiliation(s)
- Antonio Bernabe-Ortiz
- CRONICAS Center of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
- *Antonio Bernabe-Ortiz:
| | - Liam Smeeth
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Robert H. Gilman
- CRONICAS Center of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru
- Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Área de Investigación y Desarrollo, Asociación Benéfica PRISMA, Lima, Peru
| | | | - William Checkley
- CRONICAS Center of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru
- Division of Pulmonary and Critical Care, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - J. Jaime Miranda
- CRONICAS Center of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru
- Department of Medicine, School of Medicine, Universidad Peruana Cayetano Heredia, Lima, Peru
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Mill JG, Rodrigues SL, Baldo MP, Malta DC, Szwarcwald CL. Estudo de validação das equações de Tanaka e de Kawasaki para estimar a excreção diária de sódio através da coleta da urina casual. REVISTA BRASILEIRA DE EPIDEMIOLOGIA 2015; 18 Suppl 2:224-37. [DOI: 10.1590/1980-5497201500060020] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2015] [Accepted: 10/06/2015] [Indexed: 11/22/2022] Open
Abstract
RESUMO: Objetivo: Validar as fórmulas de Tanaka e Kawasaki para cálculo do consumo de sal pela relação sódio/creatinina na urina casual. Métodos: Foram estudados 272 adultos (20 - 69 anos, 52,6% de mulheres) com coleta urinária de 24 h e duas coletas casuais no mesmo dia (em jejum - casual 1 - e fora do jejum - casual 2). Antropometria, pressão arterial e coleta de sangue foram obtidos no mesmo dia. A concordância entre o consumo de sal estimado pela urina de 24 h e pela urina casual foi feita por Pearson (r) e Bland & Altman. Resultados: O consumo médio de sal medido pela urina de 24 h foi de 10,4 ± 5,3 g/dia. A correlação entre a excreção de sódio na urina de 24 h e a estimada pela urina casual 1 ou 2, respectivamente, foi apenas moderada, tanto por Tanaka (r = 0,51 e r = 0,55; p < 0,001) como por Kawasaki (r = 0,52 e r = 0,54; p < 0,001). Observa-se subestimação crescente dos valores estimados em relação ao medido com o aumento do consumo de sal pela fórmula de Tanaka e, ao contrário, superestimação ao usar a fórmula de Kawasaki. As fórmulas estimam adequadamente o consumo diário de sal (diferença entre sal medido e estimado de, no máximo, 1 g/dia) somente com consumo entre 9 - 12 g/dia (Tanaka) e 12 - 18 g/dia (Kawasaki). Conclusão: A coleta de urina casual estima adequadamente o consumo de sal apenas nos indivíduos próximos à média populacional.
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Mbanya V, Hussain A, Kengne AP. Application and applicability of non-invasive risk models for predicting undiagnosed prevalent diabetes in Africa: A systematic literature search. Prim Care Diabetes 2015; 9:317-329. [PMID: 25975760 DOI: 10.1016/j.pcd.2015.04.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2014] [Revised: 04/01/2015] [Accepted: 04/02/2015] [Indexed: 11/28/2022]
Abstract
BACKGROUND AND PURPOSE Prediction algorithms are increasingly advocated in diabetes screening strategies, particularly in developing countries. We conducted a systematic review to assess the application and applicability of existing non-invasive prevalent diabetes risk models to populations within Africa. DESIGN systematic review data sources A systematic search of English literatures in Medline via PubMed from 1999 until June, 2014. Study selection Included studies had to report on the development, validation or implementation of a model that was primarily constructed to predict prevalent undiagnosed diabetes using non-laboratory based predictors. DATA EXTRACTION Data were extracted on the type of statistical model, type and range of predictors in the model, performance measures in both internal and external validation, and whether the model was developed from, validated or implemented in an African population. RESULTS Twenty-three studies reporting on non-invasive prevalent diabetes models were identified. Ten from Europe (some with multiethnic populations), nine models were developed among Asian population, two from the USA and two from the Middle-East. The c-statistics for these models ranged from 0.65 to 0.88 in the development studies, and from 0.63 to 0.80 in the validation studies. Twenty models were validated, and none in Africa. Among predictors commonly included in models, parental/family history of diabetes and personal history of hypertension appear to be more prone to measurement errors in the African context. CONCLUSION Existing prevalent diabetes prediction models have not been applied to African populations, and issues with the measurement of key predictors make their applicability likely inaccurate.
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Affiliation(s)
- Vivian Mbanya
- Department of Community Medicine, University of Oslo, Oslo, Norway; Health of Populations in Transition (HoPiT) Research Group, Faculty of Medicine and Biomedical Sciences, The University of Yaoundé 1, Yaoundé, Cameroon.
| | - Akhtar Hussain
- Department of Community Medicine, University of Oslo, Oslo, Norway
| | - Andre Pascal Kengne
- Non-Communicable Diseases Research Unit, South African Medical Research Council & Department of Medicine, University of Cape Town, Cape Town, South Africa
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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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Masconi KL, Matsha TE, Echouffo-Tcheugui JB, Erasmus RT, Kengne AP. Reporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: a systematic review. EPMA J 2015; 6:7. [PMID: 25829972 PMCID: PMC4380106 DOI: 10.1186/s13167-015-0028-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2014] [Accepted: 02/07/2015] [Indexed: 01/10/2023]
Abstract
Missing values are common in health research and omitting participants with missing data often leads to loss of statistical power, biased estimates and, consequently, inaccurate inferences. We critically reviewed the challenges posed by missing data in medical research and approaches to address them. To achieve this more efficiently, these issues were analyzed and illustrated through a systematic review on the reporting of missing data and imputation methods (prediction of missing values through relationships within and between variables) undertaken in risk prediction studies of undiagnosed diabetes. Prevalent diabetes risk models were selected based on a recent comprehensive systematic review, supplemented by an updated search of English-language studies published between 1997 and 2014. Reporting of missing data has been limited in studies of prevalent diabetes prediction. Of the 48 articles identified, 62.5% (n = 30) did not report any information on missing data or handling techniques. In 21 (43.8%) studies, researchers opted out of imputation, completing case-wise deletion of participants missing any predictor values. Although imputation methods are encouraged to handle missing data and ensure the accuracy of inferences, this has seldom been the case in studies of diabetes risk prediction. Hence, we elaborated on the various types and patterns of missing data, the limitations of case-wise deletion and state-of the-art methods of imputations and their challenges. This review highlights the inexperience or disregard of investigators of the effect of missing data in risk prediction research. Formal guidelines may enhance the reporting and appropriate handling of missing data in scientific journals.
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Affiliation(s)
- Katya L Masconi
- Division of Chemical Pathology, Faculty of Health Sciences, National Health Laboratory Service (NHLS) and University of Stellenbosch, Cape Town, South Africa ; Non-Communicable Diseases Research Unit, South African Medical Research Council, PO Box 19070, , Tygerberg, 7505 Cape Town, South Africa
| | - Tandi E Matsha
- Department of Biomedical Technology, Faculty of Health and Wellness Sciences, Cape Peninsula University of Technology, Cape Town, South Africa
| | - Justin B Echouffo-Tcheugui
- Hubert Department of Public Health, Rollins School of Public Health, Emory University, Atlanta, GA USA ; Department of Medicine, MedStar Health System, Baltimore, MD USA
| | - Rajiv T Erasmus
- Division of Chemical Pathology, Faculty of Health Sciences, National Health Laboratory Service (NHLS) and University of Stellenbosch, Cape Town, South Africa
| | - Andre P Kengne
- Non-Communicable Diseases Research Unit, South African Medical Research Council, PO Box 19070, , Tygerberg, 7505 Cape Town, South Africa ; Department of Medicine, University of Cape Town, Cape Town, South Africa
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Dhippayom T, Chaiyakunapruk N, Krass I. How diabetes risk assessment tools are implemented in practice: a systematic review. Diabetes Res Clin Pract 2014; 104:329-42. [PMID: 24485859 DOI: 10.1016/j.diabres.2014.01.008] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2013] [Revised: 10/08/2013] [Accepted: 01/02/2014] [Indexed: 02/02/2023]
Abstract
This review aimed to explore the extent of the use of diabetes risk assessment tools and to determine influential variables associated with the implementation of these tools. CINAHL, Google Scholar, ISI Citation Indexes, PubMed, and Scopus were searched from inception to January 2013. Studies that reported the use of diabetes risk assessment tools to identify individuals at risk of diabetes were included. Of the 1719 articles identified, 24 were included. Follow-up of high risk individuals for diagnosis of diabetes was conducted in 5 studies. Barriers to the uptake of diabetes risk assessment tools by healthcare practitioners included (1) attitudes toward the tools; (2) impracticality of using the tools and (3) lack of reimbursement and regulatory support. Individuals were reluctant to undertake self-assessment of diabetes risk due to (1) lack of perceived severity of type 2 diabetes; (2) impracticality of the tools; and (3) concerns related to finding out the results. The current use of non-invasive diabetes risk assessment scores as screening tools appears to be limited. Practical follow up systems as well as strategies to address other barriers to the implementation of diabetes risk assessment tools are essential and need to be developed.
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Affiliation(s)
- Teerapon Dhippayom
- Pharmaceutical Care Research Unit, Department of Pharmacy Practice, Faculty of Pharmaceutical Sciences, Naresuan University, Phitsanulok 65000, Thailand; Faculty of Pharmacy, The University of Sydney, Sydney, NSW, Australia.
| | - Nathorn Chaiyakunapruk
- Discipline of Pharmacy, Monash University Malaysia, Bandar Sunway, Selangor, Malaysia; Center of Pharmaceutical Outcomes Research, Department of Pharmacy Practice, Faculty of Pharmaceutical Sciences, Naresuan University, Phitsanulok, Thailand; School of Population Health, University of Queensland, Brisbane, Australia; School of Pharmacy, University of Wisconsin-Madison, Madison, WI, USA
| | - Ines Krass
- Faculty of Pharmacy, The University of Sydney, Sydney, NSW, Australia
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Rodrigues SL, Baldo MP, Machado RC, Forechi L, Molina MDCB, Mill JG. High potassium intake blunts the effect of elevated sodium intake on blood pressure levels. ACTA ACUST UNITED AC 2014; 8:232-8. [DOI: 10.1016/j.jash.2014.01.001] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2013] [Revised: 12/31/2013] [Accepted: 01/02/2014] [Indexed: 12/21/2022]
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Rodrigues SL, Ângelo LCS, Baldo MP, Dantas EM, Barcelos AM, Pereira AC, Krieger JE, Mill JG. Detection of left ventricular hypertrophy by the R-wave voltage in lead aVL: population-based study. Clin Res Cardiol 2013; 102:653-9. [DOI: 10.1007/s00392-013-0578-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2012] [Accepted: 04/30/2013] [Indexed: 10/26/2022]
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Brown N, Critchley J, Bogowicz P, Mayige M, Unwin N. Risk scores based on self-reported or available clinical data to detect undiagnosed type 2 diabetes: a systematic review. Diabetes Res Clin Pract 2012; 98:369-85. [PMID: 23010559 DOI: 10.1016/j.diabres.2012.09.005] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2012] [Revised: 06/19/2012] [Accepted: 09/04/2012] [Indexed: 01/28/2023]
Abstract
OBJECTIVE To systematically review published primary research on the development or validation of risk scores that require only self-reported or available clinical data to identify undiagnosed Type 2 Diabetes Mellitus (T2DM). METHODS A systematic literature search of Medline and EMBASE was conducted until January 2011. Studies focusing on the development or validation of risk scores to identify undiagnosed T2DM were included. Risk scores to predict future risk of T2DM were excluded. RESULTS Thirty-one studies were included; 17 developed a new risk score, 14 validated existing scores. Twenty-six studies were conducted in high-income countries. Age and measures of body mass/fat distribution were the most commonly used predictor variables. Studies developing new scores performed better than validation studies, with 11 reporting an AUC of >0.80 compared to one validation study. Fourteen validation studies reported sensitivities of <80%. The performance of scores did not differ by the number of variables included or the country setting. CONCLUSIONS There is a proliferation of newly developed risk scores using similar variables, which sometimes perform poorly upon external validation. Future research should explore the recalibration, validation and applicability of existing scores to other settings, particularly in low/middle income countries, and on the utility of scores to improve diabetes-related outcomes.
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Body mass index is not independently associated with increased aortic stiffness in a Brazilian population. Am J Hypertens 2012; 25:1064-9. [PMID: 22785410 DOI: 10.1038/ajh.2012.91] [Citation(s) in RCA: 51] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Obesity has been described as a predictor of cardiovascular mortality, and some studies have reported an association with obesity and increased aortic stiffness. Other studies have not identified obesity to be an independent risk factor. Therefore, the purpose of our study was to determine the association between aortic stiffness and obesity in the Brazilian population. METHODS A cross-sectional study recruited 1,662 individuals aged 25-64 years from the population of Vitória, Brazil following the guidelines of the MONICA-WHO Project. Anthropometric, clinical, and hemodynamic measurements and analyses of aortic stiffness (using carotid-femoral pulse wave velocity <PWV) were obtained in 1,608 subjects. RESULTS PWV correlated positively with age, systolic blood pressure (SBP), diastolic blood pressure (DBP), mean blood pressure, heart rate (HR), body mass index (BMI), waist circumference (WC), cholesterol levels, triglyceride levels, and blood glucose levels. A multivariate regression analysis demonstrated that the mean BP (β = 0.405, P < 0.01), age (β = 0.314, P < 0.01), HR (β = 0.107, P < 0.01), BMI (β = -103, P < 0.01), and blood glucose levels (β = 0.093, P < 0.01) explained nearly 37% of the PWV variability. A multivariate regression analysis using the WC instead of the BMI failed to reveal any significant effect of this parameter on the PWV. CONCLUSIONS In conclusion, our study failed to provide evidence of a positive, blood pressure (BP)-independent association between obesity on aortic stiffness. Our data suggests that the previously reported finding of an association between obesity and aortic stiffness was probably confounded by the progressive increase in BP observed in obesity.
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Thoopputra T, Newby D, Schneider J, Li SC. Survey of diabetes risk assessment tools: concepts, structure and performance. Diabetes Metab Res Rev 2012; 28:485-98. [PMID: 22407958 DOI: 10.1002/dmrr.2296] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The objective of this study is to review the effectiveness and limitations of existing diabetes risk screening tools to assess the need for further developing of such tools. An electronic search of the EMBASE, MEDLINE, and Cochrane library supplemented by a manual search was performed from 1995-2010. The search retrieved a total of 2168 articles reporting diabetes risk assessment tools which, after culling, produced 41 tools developed in 22 countries, with the majority (n = 26) developed in North America and Europe. All are short questionnaires of 2-16 questions incorporating common variables including age, gender, waist circumference, BMI, family history of diabetes, history of hypertension or antihypertensive medications. While scoring format and cut-offs point are diverse between questionnaires, overall accuracy value range of 40-97%, 24-86% and 62-87% were reported for sensitivity, specificity and receiver operating characteristic curve respectively. In summary, there is a trend of increasing availability of diabetes prediction tools with the existing risk assessment tools being generally a short questionnaire aiming for ease of use in clinical practice. The overall performance of existing tools showed moderate to high accuracy in their predictive performance. However, further detailed comparison of existing questionnaires is needed to evaluate whether they can serve adequately as diabetes risk assessment tool in clinical practice.
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Affiliation(s)
- Thitaporn Thoopputra
- Discipline of Pharmacy and Experimental Pharmacology, School of Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, Australia
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Sathish T, Kannan S, Sarma PS, Thankappan KR. Achutha Menon Centre Diabetes Risk Score: a type 2 diabetes screening tool for primary health care providers in rural India. Asia Pac J Public Health 2012; 27:147-54. [PMID: 22865719 DOI: 10.1177/1010539512454162] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The authors aimed to develop a diabetes risk score for primary care providers in rural India. They used the baseline data of 451 participants (15-64 years) of a cohort study in a rural area of Kerala, India. The new risk score with age, family history of diabetes, and waist circumference identified 40.8% for confirmatory testing, had a sensitivity of 81.0%, specificity of 68.4%, positive predictive value of 37.0%, and negative predictive value of 94.0% for an optimal cutoff ≥4 with an area under the receiver operating characteristic curve of 0.812 (95% confidence interval = 0.765-0.860). The new risk score with 3 simple, easy-to-measure, less time-consuming, and less expensive variables could be suitable for use in primary care settings of rural India.
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Affiliation(s)
- Thirunavukkarasu Sathish
- Achutha Menon Centre for Health Science Studies, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India Faculty of Medicine, Nursing and Health Sciences, Monash University, VIC, Australia
| | - Srinivasan Kannan
- Achutha Menon Centre for Health Science Studies, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India
| | - P Sankara Sarma
- Achutha Menon Centre for Health Science Studies, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India
| | - Kavumpurathu Raman Thankappan
- Achutha Menon Centre for Health Science Studies, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India
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N-domain isoform of Angiotensin I converting enzyme as a marker of hypertension: populational study. Int J Hypertens 2012; 2012:581780. [PMID: 22666552 PMCID: PMC3362081 DOI: 10.1155/2012/581780] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2012] [Accepted: 03/05/2012] [Indexed: 01/28/2023] Open
Abstract
The aim of this paper was to investigate the presence of the urinary 90 kDa N-domain ACE in a cohort of the population from Vitoria, Brazil, to verify its association with essential hypertension since this isoform could be a possible genetic marker of hypertension. Anthropometric, clinical, and laboratory parameters of the individuals were evaluated (n = 1150) and the blood pressure (BP) was measured. The study population was divided according to ACE isoforms in urine as follows: ACE 65/90/190, presence of three ACE isoforms (n = 795), ACE 90+ (65/90) (n = 186), and ACE 90− (65/190) (n = 169) based on the presence (+) or absence (−) of the 90 kDa ACE isoform. The anthropometric parameters, lipid profile, serum levels of uric acid, glucose, and the systolic and diastolic BP were significantly greater in the ACE 90+ compared with the ACE 90− and ACE 65/90/190 individuals. We found that 98% of individuals from the ACE 90+ group and 38% from the ACE 65/90/190 group had hypertension, compared to only 1% hypertensive individuals in the ACE 90− group. There is a high presence of the 90 kDa N-domain ACE isoform (85%) in the studied population. The percentile of normotensive subjects with three isoforms was 62%. Our findings could contribute to the development of new efficient strategy to prevent and treat hypertension to avoid the development of cardiovascular disease.
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Abstract
Many epidemiological studies showed associations of low birth weight with cardiovascular disease, type 2 diabetes and obesity. The associations seem to be consistent and stronger among subjects with a postnatal catch up growth. It has been suggested that developmental changes in response to adverse fetal exposures might lead to changes in the fetal anatomy and physiology. These adaptations may be beneficial for short term, but may lead to common diseases in adulthood. Maternal smoking during pregnancy is one of the most important adverse fetal exposures in Western countries, and is known to be associated with a 150-200 g lower birth weight. An accumulating body of evidence suggests that maternal smoking during pregnancy might be involved in pathways leading to both low birth weight and common diseases, including cardiovascular disease, type 2 diabetes and obesity, in adulthood. In this review, we discuss epidemiological studies focused on the associations of maternal smoking with fetal growth and development and cardiovascular and metabolic disease in later life. We also discuss potential biological mechanisms, and challenges for future epidemiological studies.
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Bakker H, Jaddoe VWV. Cardiovascular and metabolic influences of fetal smoke exposure. Eur J Epidemiol 2011; 26:763-70. [PMID: 21994150 PMCID: PMC3218270 DOI: 10.1007/s10654-011-9621-2] [Citation(s) in RCA: 55] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2011] [Accepted: 09/30/2011] [Indexed: 01/09/2023]
Abstract
Many epidemiological studies showed associations of low birth weight with cardiovascular disease, type 2 diabetes and obesity. The associations seem to be consistent and stronger among subjects with a postnatal catch up growth. It has been suggested that developmental changes in response to adverse fetal exposures might lead to changes in the fetal anatomy and physiology. These adaptations may be beneficial for short term, but may lead to common diseases in adulthood. Maternal smoking during pregnancy is one of the most important adverse fetal exposures in Western countries, and is known to be associated with a 150–200 g lower birth weight. An accumulating body of evidence suggests that maternal smoking during pregnancy might be involved in pathways leading to both low birth weight and common diseases, including cardiovascular disease, type 2 diabetes and obesity, in adulthood. In this review, we discuss epidemiological studies focused on the associations of maternal smoking with fetal growth and development and cardiovascular and metabolic disease in later life. We also discuss potential biological mechanisms, and challenges for future epidemiological studies.
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Affiliation(s)
- Hanneke Bakker
- The Generation R Study Group (Room Ae-012), Erasmus Medical Centre, PO Box 2040, 3000 CA Rotterdam, The Netherlands
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Collins GS, Mallett S, Omar O, Yu LM. Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Med 2011; 9:103. [PMID: 21902820 PMCID: PMC3180398 DOI: 10.1186/1741-7015-9-103] [Citation(s) in RCA: 328] [Impact Index Per Article: 25.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2011] [Accepted: 09/08/2011] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND The World Health Organisation estimates that by 2030 there will be approximately 350 million people with type 2 diabetes. Associated with renal complications, heart disease, stroke and peripheral vascular disease, early identification of patients with undiagnosed type 2 diabetes or those at an increased risk of developing type 2 diabetes is an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) type 2 diabetes in adults. METHODS We conducted a systematic search of PubMed and EMBASE databases to identify studies published before May 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident type 2 diabetes. We extracted key information that describes aspects of developing a prediction model including study design, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies and aspects of performance. RESULTS Thirty-nine studies comprising 43 risk prediction models were included. Seventeen studies (44%) reported the development of models to predict incident type 2 diabetes, whilst 15 studies (38%) described the derivation of models to predict prevalent type 2 diabetes. In nine studies (23%), the number of events per variable was less than ten, whilst in fourteen studies there was insufficient information reported for this measure to be calculated. The number of candidate risk predictors ranged from four to sixty-four, and in seven studies it was unclear how many risk predictors were considered. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in eight studies (21%), whilst the selection procedure was unclear in ten studies (26%). Twenty-one risk prediction models (49%) were developed by categorising all continuous risk predictors. The treatment and handling of missing data were not reported in 16 studies (41%). CONCLUSIONS We found widespread use of poor methods that could jeopardise model development, including univariate pre-screening of variables, categorisation of continuous risk predictors and poor handling of missing data. The use of poor methods affects the reliability of the prediction model and ultimately compromises the accuracy of the probability estimates of having undiagnosed type 2 diabetes or the predicted risk of developing type 2 diabetes. In addition, many studies were characterised by a generally poor level of reporting, with many key details to objectively judge the usefulness of the models often omitted.
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Affiliation(s)
- Gary S Collins
- Centre for Statistics in Medicine, University of Oxford, Wolfson College Annexe, Oxford, OX2 6UD, UK.
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Hofman A, van Duijn CM, Franco OH, Ikram MA, Janssen HLA, Klaver CCW, Kuipers EJ, Nijsten TEC, Stricker BHC, Tiemeier H, Uitterlinden AG, Vernooij MW, Witteman JCM. The Rotterdam Study: 2012 objectives and design update. Eur J Epidemiol 2011; 26:657-86. [PMID: 21877163 PMCID: PMC3168750 DOI: 10.1007/s10654-011-9610-5] [Citation(s) in RCA: 263] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2011] [Accepted: 08/08/2011] [Indexed: 01/09/2023]
Abstract
The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in The Netherlands. The study targets cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, oncological, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. The findings of the Rotterdam Study have been presented in over a 1,000 research articles and reports (see www.erasmus-epidemiology.nl/rotterdamstudy ). This article gives the rationale of the study and its design. It also presents a summary of the major findings and an update of the objectives and methods.
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Affiliation(s)
- Albert Hofman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands.
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Echouffo-Tcheugui JB, Ali MK, Griffin SJ, Narayan KMV. Screening for type 2 diabetes and dysglycemia. Epidemiol Rev 2011; 33:63-87. [PMID: 21624961 DOI: 10.1093/epirev/mxq020] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Type 2 diabetes mellitus (T2DM) and dysglycemia (impaired glucose tolerance and/or impaired fasting glucose) are increasingly contributing to the global burden of diseases. The authors reviewed the published literature to critically evaluate the evidence on screening for both conditions and to identify the gaps in current understanding. Acceptable, relatively simple, and accurate tools can be used to screen for both T2DM and dysglycemia. Lifestyle modification and/or medication (e.g., metformin) are cost-effective in reducing the incidence of T2DM. However, their application is not yet routine practice. It is unclear whether diabetes-prevention strategies, which influence cardiovascular risk favorably, will also prevent diabetic vascular complications. Cardioprotective therapies, which are cost-effective in preventing complications in conventionally diagnosed T2DM, can be used in screen-detected diabetes, but the magnitude of their effects is unknown. Economic modeling suggests that screening for both T2DM and dysglycemia may be cost-effective, although empirical data on tangible benefits in preventing complications or death are lacking. Screening for T2DM is psychologically unharmful, but the specific impact of attributing the label of dysglycemia remains uncertain. Addressing these gaps will inform the development of a screening policy for T2DM and dysglycemia within a holistic diabetes prevention and control framework combining secondary and high-risk primary prevention strategies.
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Affiliation(s)
- Justin B Echouffo-Tcheugui
- Department of Global Health, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, USA.
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Durmuş B, Ay L, Hokken-Koelega ACS, Raat H, Hofman A, Steegers EAP, Jaddoe VWV. Maternal smoking during pregnancy and subcutaneous fat mass in early childhood. The Generation R Study. Eur J Epidemiol 2011; 26:295-304. [PMID: 21229294 PMCID: PMC3088815 DOI: 10.1007/s10654-010-9544-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2010] [Accepted: 12/27/2010] [Indexed: 01/09/2023]
Abstract
Maternal smoking during pregnancy increases the risk of obesity in the offspring. Not much is known about the associations with other measures of body composition. We assessed the associations of maternal smoking during pregnancy with the development of subcutaneous fat mass measured as peripheral and central skinfold thickness measurements in early childhood, in a population-based prospective cohort study from early fetal life onward in the city of Rotterdam, The Netherlands. The study was performed in 907 mothers and their children at the ages of 1.5, 6 and 24 months. As compared to non-smoking mothers, mothers who continued smoking during pregnancy were more likely to have a younger age and a lower educational level. Their children had a lower birth weight, higher risk of small size for gestational age and were breastfed for a shorter duration (P-values <0.01). We did not observe differences in peripheral, central and total subcutaneous fat mass between the offspring of non-smoking mothers, mothers who smoked in first trimester only and mothers who continued smoking during pregnancy (P > 0.05). Also, the reported number of cigarettes smoked by mothers in both first and third trimester of pregnancy were not associated with peripheral, central and total subcutaneous fat mass in the offspring at the ages of 1.5, 6 and 24 months. Our findings suggest that fetal exposure to cigarette smoke during pregnancy does not influence subcutaneous fat mass in early childhood. Follow-up studies are needed in children at older ages and to identify associations of maternal smoking during pregnancy with other measures of body composition.
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Affiliation(s)
- Büşra Durmuş
- The Generation R Study Group (AE-006), Erasmus Medical Center, PO Box 2040, 3000 CA Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Lamise Ay
- The Generation R Study Group (AE-006), Erasmus Medical Center, PO Box 2040, 3000 CA Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Anita C. S. Hokken-Koelega
- Department of Pediatrics, Subdivision of Endocrinology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Hein Raat
- Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Albert Hofman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Eric A. P. Steegers
- Department of Obstetrics and Gynecology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Vincent W. V. Jaddoe
- The Generation R Study Group (AE-006), Erasmus Medical Center, PO Box 2040, 3000 CA Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
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The Shanghai Changfeng Study: a community-based prospective cohort study of chronic diseases among middle-aged and elderly: objectives and design. Eur J Epidemiol 2010; 25:885-93. [PMID: 21120588 DOI: 10.1007/s10654-010-9525-6] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2010] [Accepted: 11/18/2010] [Indexed: 12/14/2022]
Abstract
The Shanghai Changfeng Study is a community-based prospective cohort study of chronic diseases ongoing since February 2009 in Shanghai, China. The study focuses on multiple chronic diseases, including obesity and metabolic syndrome, diabetes, osteoporosis, liver diseases, cardiovascular diseases and neurologic diseases. 15,000 subjects of 40 years or over are planned to be recruited. The rationale, objectives and design of this study are described in this paper.
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Jaddoe VWV, van Duijn CM, van der Heijden AJ, Mackenbach JP, Moll HA, Steegers EAP, Tiemeier H, Uitterlinden AG, Verhulst FC, Hofman A. The Generation R Study: design and cohort update 2010. Eur J Epidemiol 2010; 25:823-41. [PMID: 20967563 PMCID: PMC2991548 DOI: 10.1007/s10654-010-9516-7] [Citation(s) in RCA: 196] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2010] [Accepted: 09/27/2010] [Indexed: 01/09/2023]
Abstract
The Generation R Study is a population-based prospective cohort study from fetal life until young adulthood. The study is designed to identify early environmental and genetic causes of normal and abnormal growth, development and health during fetal life, childhood and adulthood. The study focuses on four primary areas of research: (1) growth and physical development; (2) behavioural and cognitive development; (3) diseases in childhood; and (4) health and healthcare for pregnant women and children. In total, 9,778 mothers with a delivery date from April 2002 until January 2006 were enrolled in the study. General follow-up rates until the age of 4 years exceed 75%. Data collection in mothers, fathers and preschool children included questionnaires, detailed physical and ultrasound examinations, behavioural observations, and biological samples. A genome wide association screen is available in the participating children. Regular detailed hands on assessment are performed from the age of 5 years onwards. Eventually, results forthcoming from the Generation R Study have to contribute to the development of strategies for optimizing health and healthcare for pregnant women and children.
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Affiliation(s)
- Vincent W V Jaddoe
- The Generation R Study Group (AE006), Erasmus Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands.
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Rodrigues SL, Baldo MP, Sá Cunha R, Angelo LCS, Pereira AC, Krieger JE, Mill JG. Anthropometric measures of increased central and overall adiposity in association with echocardiographic left ventricular hypertrophy. Hypertens Res 2009; 33:83-7. [DOI: 10.1038/hr.2009.188] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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