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Liu Y, Yu S, Feng W, Mo H, Hua Y, Zhang M, Zhu Z, Zhang X, Wu Z, Zheng L, Wu X, Shen J, Qiu W, Lou J. A meta-analysis of diabetes risk prediction models applied to prediabetes screening. Diabetes Obes Metab 2024; 26:1593-1604. [PMID: 38302734 DOI: 10.1111/dom.15457] [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: 09/18/2023] [Revised: 12/26/2023] [Accepted: 12/27/2023] [Indexed: 02/03/2024]
Abstract
AIM To provide a systematic overview of diabetes risk prediction models used for prediabetes screening to promote primary prevention of diabetes. METHODS The Cochrane, PubMed, Embase, Web of Science and China National Knowledge Infrastructure (CNKI) databases were searched for a comprehensive search period of 30 August 30, 2023, and studies involving diabetes prediction models for screening prediabetes risk were included in the search. The Quality Assessment Checklist for Diagnostic Studies (QUADAS-2) tool was used for risk of bias assessment and Stata and R software were used to pool model effect sizes. RESULTS A total of 29 375 articles were screened, and finally 20 models from 24 studies were included in the systematic review. The most common predictors were age, body mass index, family history of diabetes, history of hypertension, and physical activity. Regarding the indicators of model prediction performance, discrimination and calibration were only reported in 79.2% and 4.2% of studies, respectively, resulting in significant heterogeneity in model prediction results, which may be related to differences between model predictor combinations and lack of important methodological information. CONCLUSIONS Numerous models are used to predict diabetes, and as there is an association between prediabetes and diabetes, researchers have also used such models for screening the prediabetic population. Although it is a new clinical practice to explore, differences in glycaemic metabolic profiles, potential complications, and methods of intervention between the two populations cannot be ignored, and such differences have led to poor validity and accuracy of the models. Therefore, there is no recommended optimal model, and it is not recommended to use existing models for risk identification in alternative populations; future studies should focus on improving the clinical relevance and predictive performance of existing models.
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Affiliation(s)
- Yujin Liu
- Nursing Department, The second Hosiptal of Jinhua, Jinhua, China
- School of Medicine, Huzhou University, Huzhou, China
| | - Sunrui Yu
- Department of Anesthesiology, Jinhua Municipal Central Hospital, Jinhua, China
| | | | - Hangfeng Mo
- School of Medicine, Huzhou University, Huzhou, China
| | - Yuting Hua
- School of Medicine, Huzhou University, Huzhou, China
| | - Mei Zhang
- School of Medicine, Huzhou University, Huzhou, China
| | - Zhichao Zhu
- School of Medicine, Huzhou University, Huzhou, China
- Emergency Department, Jinhua Municipal Central Hospital Medical Group, Jinhua, China
| | - Xiaoping Zhang
- Nursing Department, The second Hosiptal of Jinhua, Jinhua, China
| | - Zhen Wu
- Nursing Department, The second Hosiptal of Jinhua, Jinhua, China
| | - Lanzhen Zheng
- Nursing Department, The second Hosiptal of Jinhua, Jinhua, China
| | - Xiaoqiu Wu
- Nursing Department, The second Hosiptal of Jinhua, Jinhua, China
| | - Jiantong Shen
- School of Medicine, Huzhou University, Huzhou, China
| | - Wei Qiu
- Department of Endocrinology, Huzhou Central Hospital, Huzhou, China
| | - Jianlin Lou
- Huzhou Key Laboratory of Precise Prevention and Control of Major Chronic Diseases, Huzhou University, Huzhou, China
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Peng Y, Wang P, Gong J, Liu F, Qiao Y, Si C, Wang X, Zhou H, Song F. Association between the Finnish Diabetes Risk Score and cancer in middle-aged and older adults: Involvement of inflammation. Metabolism 2023; 144:155586. [PMID: 37164309 DOI: 10.1016/j.metabol.2023.155586] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 04/22/2023] [Accepted: 05/02/2023] [Indexed: 05/12/2023]
Abstract
BACKGROUND Diabetes is associated with increased risk of common cancers. However, evidence of cancer risk in individuals with different diabetes risk is still scarce, and the underlying mechanism remains unknown. Therefore, we aimed to evaluate the relationship between the Finnish Diabetes Risk Score (FINDRISC) and risks of cancer incidence and mortality in a prospective study, and to explore whether low-grade inflammation partially mediated the association. METHODS A total of 330,384 participants aged 37 to 73 at baseline from the UK Biobank database was included in this study. The Cox proportional hazards model was used to examine the relationship of the FINDRISC and low-grade inflammation with risks of cancer incidence and mortality. Then, we estimated the contribution of higher FINDRISC to risks of overall and site-specific cancers. In addition, the role of low-grade inflammation in the association between FINDRISC and cancer risks was investigated through mediation analysis. RESULTS The increased FINDRISC was dose-dependently associated with higher incidence and mortality risks of overall cancer and an overwhelming majority of site-specific cancers. The higher FINDRISC was a strong contributor to incidence of eighteen site-specific cancers and mortality of fourteen site-specific cancers, with a population-attributable risk of 8.1 %-39.1 %, 14.2 %-39.7 %, respectively. Additionally, low-grade inflammation mainly mediated the association between the FINDRISC and risks of incidence and mortality of overall cancer, colorectal cancer, etc. CONCLUSIONS: Our findings highlighted the higher FINDRISC as critical risk factors of cancer incidence and mortality, partially mediated by low-grade inflammation. Individuals with increased risk of diabetes are also needed to be concerned about cancer prevention.
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Affiliation(s)
- Yu Peng
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Tianjin, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Peng Wang
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Tianjin, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Jianxiao Gong
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Tianjin, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Fubin Liu
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Tianjin, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Yating Qiao
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Tianjin, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Changyu Si
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Tianjin, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Xixuan Wang
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Tianjin, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Huijun Zhou
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Tianjin, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Fangfang Song
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Tianjin, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China.
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Cheng WHG, Mi Y, Dong W, Tse ETY, Wong CKH, Bedford LE, Lam CLK. Non-Laboratory-Based Risk Prediction Tools for Undiagnosed Pre-Diabetes: A Systematic Review. Diagnostics (Basel) 2023; 13:diagnostics13071294. [PMID: 37046512 PMCID: PMC10093270 DOI: 10.3390/diagnostics13071294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 03/28/2023] [Indexed: 04/03/2023] Open
Abstract
Early detection of pre-diabetes (pre-DM) can prevent DM and related complications. This review examined studies on non-laboratory-based pre-DM risk prediction tools to identify important predictors and evaluate their performance. PubMed, Embase, MEDLINE, CINAHL were searched in February 2023. Studies that developed tools with: (1) pre-DM as a prediction outcome, (2) fasting/post-prandial blood glucose/HbA1c as outcome measures, and (3) non-laboratory predictors only were included. The studies’ quality was assessed using the CASP Clinical Prediction Rule Checklist. Data on pre-DM definitions, predictors, validation methods, performances of the tools were extracted for narrative synthesis. A total of 6398 titles were identified and screened. Twenty-four studies were included with satisfactory quality. Eight studies (33.3%) developed pre-DM risk tools and sixteen studies (66.7%) focused on pre-DM and DM risks. Age, family history of DM, diagnosed hypertension and obesity measured by BMI and/or WC were the most common non-laboratory predictors. Existing tools showed satisfactory internal discrimination (AUROC: 0.68–0.82), sensitivity (0.60–0.89), and specificity (0.50–0.74). Only twelve studies (50.0%) had validated their tools externally, with a variance in the external discrimination (AUROC: 0.31–0.79) and sensitivity (0.31–0.92). Most non-laboratory-based risk tools for pre-DM detection showed satisfactory performance in their study populations. The generalisability of these tools was unclear since most lacked external validation.
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Affiliation(s)
- Will Ho-Gi Cheng
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Yuqi Mi
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Weinan Dong
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Emily Tsui-Yee Tse
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Department of Family Medicine, The University of Hong Kong Shenzhen Hospital, Shenzhen 518009, China
| | - Carlos King-Ho Wong
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Laura Elizabeth Bedford
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Cindy Lo-Kuen Lam
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Department of Family Medicine, The University of Hong Kong Shenzhen Hospital, Shenzhen 518009, China
- Correspondence: ; Tel.: +852-2518-5657
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Identifying Glucose Metabolism Status in Nondiabetic Japanese Adults Using Machine Learning Model with Simple Questionnaire. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:1026121. [PMID: 36118835 PMCID: PMC9481387 DOI: 10.1155/2022/1026121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 08/01/2022] [Accepted: 08/29/2022] [Indexed: 11/17/2022]
Abstract
We aimed to identify the glucose metabolism statuses of nondiabetic Japanese adults using a machine learning model with a questionnaire. In this cross-sectional study, Japanese adults (aged 20–64 years) from Tokyo and surrounding areas were recruited. Participants underwent an oral glucose tolerance test (OGTT) and completed a questionnaire regarding lifestyle and physical characteristics. They were classified into four glycometabolic categories based on the OGTT results: category 1: best glucose metabolism, category 2: low insulin sensitivity, category 3: low insulin secretion, and category 4: combined characteristics of categories 2 and 3. A total of 977 individuals were included; the ratios of participants in categories 1, 2, 3, and 4 were 46%, 21%, 14%, and 19%, respectively. Machine learning models (decision tree, support vector machine, random forest, and XGBoost) were developed for identifying the glycometabolic category using questionnaire responses. Then, the top 10 most important variables in the random forest model were selected, and another random forest model was developed using these variables. Its areas under the receiver operating characteristic curve (AUCs) to classify category 1 and the others, category 2 and the others, category 3 and the others, and category 4 and the others were 0.68 (95% confidence intervals: 0.62–0.75), 0.66 (0.58–0.73), 0.61 (0.51–0.70), and 0.70 (0.62–0.77). For external validation of the model, the same dataset of 452 Japanese adults in Hokkaido was obtained. The AUCs to classify categories 1, 2, 3, and 4 and the others were 0.66 (0.61–0.71), 0.57 (0.51–0.62), 0.60 (0.50–0.69), and 0.64 (0.57–0.71). In conclusion, our model could identify the glucose metabolism status using only 10 factors of lifestyle and physical characteristics. This model may help the larger general population without diabetes to understand their glucose metabolism status and encourage lifestyle improvement to prevent diabetes.
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Ibrahim MS, Pang D, Randhawa G, Pappas Y. Development and Validation of a Simple Risk Model for Predicting Metabolic Syndrome (MetS) in Midlife: A Cohort Study. Diabetes Metab Syndr Obes 2022; 15:1051-1075. [PMID: 35418767 PMCID: PMC8995775 DOI: 10.2147/dmso.s336384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Accepted: 01/15/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose To develop and validate a simple risk model for predicting metabolic syndrome in midlife using a prospective cohort data. Design Prospective cohort study. Participants A total of 7626 members of the 1958 British birth cohort (individuals born in the first week of March 1958) participated in the biomedical survey at age 45 and have completed information on metabolic syndrome. Methods Variables utilised were obtained prospectively at birth, 7, 16, 23 and 45 years. Multivariable logistic regression was used to develop a total of ten (10) MetS risk prediction models taking the life course approach. Measures of discrimination and calibration were used to evaluate the performance of the models. A pragmatic criteria developed was used to select one model with the most potential to be useful. The internal validity (overfitting) of the selected model was assessed using bootstrap technique of Stata. Main Outcome Measure Metabolic syndrome was defined based on the NCEP-ATP III clinical criteria. Results There is high prevalence of MetS among the cohort members (19.6%), with males having higher risk as compared to females (22.8% vs 16.4%, P < 0.001). Individuals with MetS are more likely to have higher levels of HbA1c and low HDL-cholesterol. Similarly, regarding the individual components of MetS, male cohort members are more likely to have higher levels of glycaemia (HbA1c), BP and serum triglycerides. In contrast, female cohort members have lower levels of HDL-cholesterol and higher levels of waist circumference. Furthermore, a total of ten (10) MetS risk prediction models were developed taking the life course approach. Of these, one model with the most potential to be applied in practical setting was selected. The model has good accuracy (AUROC 0.91 (0.90, 0.92)), is well calibrated (Hosmer-Lemeshow 6.47 (0.595)) and has good internal validity. Conclusion Early life factors could be included in a risk model to predict MetS in midlife. The developed model has been shown to be accurate and has good internal validity. Therefore, interventions targeting socioeconomic inequality could help in the wider prevention of MetS. However, the validity of the developed model needs to be further established in an external population.
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Affiliation(s)
- Musa S Ibrahim
- Institute for Health Research, University of Bedfordshire, Putteridge Bury Luton, Bedfordshire, LU2 8LE, England
| | - Dong Pang
- Institute for Health Research, University of Bedfordshire, Putteridge Bury Luton, Bedfordshire, LU2 8LE, England
| | - Gurch Randhawa
- Institute for Health Research, University of Bedfordshire, Putteridge Bury Luton, Bedfordshire, LU2 8LE, England
| | - Yannis Pappas
- Institute for Health Research, University of Bedfordshire, Putteridge Bury Luton, Bedfordshire, LU2 8LE, England
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Zatońska K, Basiak-Rasała A, Połtyn-Zaradna K, Różańska D, Karczewski M, Wołyniec M, Szuba A. Characteristic of FINDRISC Score and Association with Diabetes Development in 6-Year Follow-Up in PURE Poland Cohort Study. Vasc Health Risk Manag 2021; 17:631-639. [PMID: 34611406 PMCID: PMC8486267 DOI: 10.2147/vhrm.s321700] [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] [Received: 05/25/2021] [Accepted: 08/24/2021] [Indexed: 11/29/2022] Open
Abstract
Purpose The aim of the study was to assess if FINDRISC score was associated with diabetes development after 6 years of observation. Methods Polish cohort is a part of global PURE study. Hereby analysis presents data from baseline (2007–2010) and 6-year follow-up (2013–2016) and was conducted on 1090 participants (702 women) from urban and rural areas in Lower Silesia region (Poland) without diabetes at the baseline and with complete data throughout course of the study. Results At the baseline, women had significantly higher FINDRISC score than men (10.43 vs 8.91; p=0.000) and participants from rural areas had higher score than from urban areas (10.97 vs 9.33; p=0.000). At the baseline, 25.87% of the participants had low risk of diabetes according to FINDRISC score, 38.90% had slightly elevated risk, 16.79% moderate risk, 16.42% high risk and 2.02% very high risk. Participants, who were healthy at baseline, but developed diabetes after 6 years of observation had significantly higher FINDRISC, than those who did not (13.39 vs 9.36; p=0.000). In 6-year follow-up, diabetes was diagnosed in 2.8% of participants, who were ascertained to “low risk” according to FINDRISC score in baseline; in 9.9% of participants of “slightly elevated risk”, 17.5% of participants of “moderate risk”, 26.8% in participants of “high risk” and 50.0% of participants of “very high risk”. Conclusions Results of PURE Poland cohort study indicates that higher FINDRISC score at the baseline was associated with higher risk of diabetes development during 6 years of observation.
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Affiliation(s)
- Katarzyna Zatońska
- Department of Social Medicine, Wroclaw Medical University, Wrocław, Poland
| | | | | | - Dorota Różańska
- Department of Dietetics, Wroclaw Medical University, Wrocław, Poland
| | - Maciej Karczewski
- The Faculty of Environmental Engineering and Geodesy, Department of Mathematics, Wroclaw University of Environmental and Life Sciences, Wrocław, Poland
| | - Maria Wołyniec
- Department of Social Medicine, Wroclaw Medical University, Wrocław, Poland
| | - Andrzej Szuba
- Department of Angiology, Hypertension and Diabetology, Wroclaw Medical University, Wroclaw, Poland
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Vásquez-Alvarez S, Bustamante-Villagomez SK, Vazquez-Marroquin G, Porchia LM, Pérez-Fuentes R, Torres-Rasgado E, Herrera-Fomperosa O, Montes-Arana I, Gonzalez-Mejia ME. Metabolic Age, an Index Based on Basal Metabolic Rate, Can Predict Individuals That are High Risk of Developing Metabolic Syndrome. High Blood Press Cardiovasc Prev 2021; 28:263-270. [PMID: 33666897 DOI: 10.1007/s40292-021-00441-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 02/21/2021] [Indexed: 12/12/2022] Open
Abstract
INTRODUCTION Every 10 years, an adult's basal metabolic rate (BMR), independent of their BMI, decreases 1-2% due to skeletal muscle loss, thus decreasing an adult's energy requirement and promoting obesity. Increased obesity augments the risk of developing Metabolic Syndrome (MetS); however, an adult's healthy lifestyle, which increases BMR, can mitigate MetS development. To compare different BMRs for certain ages, Metabolic age (Met-age) was developed. AIM To assess the association between Met-age and MetS and to determine if Met-age is an indicator of high-risk individuals for MetS. METHODS Four hundred thirty-five attendees at 2 clinics agreed to participate and gave signed informed consent. MetS risk was assessed by the ESF-I questionnaire. Met-age was determined using a TANITA bio-analyzer. Strengthen of association was determined by calculating Spearman's rho and predictability was evaluated by the area-under-a-receiver-operating characteristic curve (AUC). Difference-in-age (DIA) = [chronological age - Met-age]. RESULTS There was a difference between the low-risk (n = 155) and the high-risk (n = 280) groups' Met-age (37.8±16.7 v. 62.9±17.3) and DIA (1.3±17.4 v. - 10.5±20.8, p < 0.001). There was a positive correlation between the ESF-I questionnaire and Met-age (rho = - 0.624, p < 0.001) and a negative correlation for DIA (rho = - 0.358, p < 0.001). Met-age was strongly predictive (AUC = 0.84, 95% CI 0.80-0.88), suggesting a 45.5 years cutoff (sensitivity = 83.2%, specificity = 72.3%). DIA was a good predictor (AUC = 0.68, 95% CI 0.63-0.74) with a - 11.5 years cutoff (sensitivity = 52.5%, specificity = 82.8%). CONCLUSION Met-age highly associated with and is an indicator of high-risk individuals for MetS. This would suggest that increases in Met-age are associated with augmented MetS severity, independent of the individual's chronological age.
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Affiliation(s)
- Sarahi Vásquez-Alvarez
- Facultad de Medicina, Benemérita Universidad Autónoma de Puebla, Calle 13 Sur 2901 Colonia Volcanes, C.P. 72420, Puebla, Puebla, Mexico
| | - Sergio K Bustamante-Villagomez
- Facultad de Medicina, Benemérita Universidad Autónoma de Puebla, Calle 13 Sur 2901 Colonia Volcanes, C.P. 72420, Puebla, Puebla, Mexico
| | - Gabriela Vazquez-Marroquin
- Facultad de nutrición, Benemérita Universidad Autónoma de Puebla, Calle 13 Sur 2901 Colonia Volcanes, C.P. 72420, Puebla, Puebla, Mexico
| | - Leonardo M Porchia
- Laboratorio de Investigación en Fisiopatología de Enfermedades Crónicas, Centro de Investigación Biomédica de Oriente, Instituto Mexicano del Seguro Social, Delegación Puebla, Carretera Federal Atlixco-Metepec Km 4.5, C.P. 42730, Atlixco, Puebla, Mexico
| | - Ricardo Pérez-Fuentes
- Facultad de Medicina, Benemérita Universidad Autónoma de Puebla, Calle 13 Sur 2901 Colonia Volcanes, C.P. 72420, Puebla, Puebla, Mexico.,Laboratorio de Investigación en Fisiopatología de Enfermedades Crónicas, Centro de Investigación Biomédica de Oriente, Instituto Mexicano del Seguro Social, Delegación Puebla, Carretera Federal Atlixco-Metepec Km 4.5, C.P. 42730, Atlixco, Puebla, Mexico
| | - Enrique Torres-Rasgado
- Facultad de Medicina, Benemérita Universidad Autónoma de Puebla, Calle 13 Sur 2901 Colonia Volcanes, C.P. 72420, Puebla, Puebla, Mexico
| | - Oscar Herrera-Fomperosa
- Facultad de Medicina, Benemérita Universidad Autónoma de Puebla, Calle 13 Sur 2901 Colonia Volcanes, C.P. 72420, Puebla, Puebla, Mexico
| | - Ivette Montes-Arana
- Unidad de Medicina Familiar 2 (UMF-2) del IMSS, Delegación Puebla, Calle 9 Oriente 404, Colonia Centro, C.P. 72000, Puebla, Puebla, Mexico
| | - M Elba Gonzalez-Mejia
- Facultad de Medicina, Benemérita Universidad Autónoma de Puebla, Calle 13 Sur 2901 Colonia Volcanes, C.P. 72420, Puebla, Puebla, Mexico.
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Porchia LM, Lara-Solis B, Torres-Rasgado E, Gonzalez-Mejia ME, Ruiz-Vivanco G, Pérez-Fuentes R. Validation of a non-laboratorial questionnaire to identify Metabolic Syndrome among a population in central Mexico. Rev Panam Salud Publica 2019; 43:e9. [PMID: 31093233 PMCID: PMC6393732 DOI: 10.26633/rpsp.2019.9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2018] [Accepted: 09/17/2018] [Indexed: 02/06/2023] Open
Abstract
OBJECTIVE To determine the reliability of a non-laboratorial questionnaire, the Encuesta de Identificación de Sujetos Metabólicamente Comprometidos en Fase-I (ESF-I) for identifying Metabolic Syndrome among a population in central Mexico. METHODS Clinical and biochemical parameters were collected for 232 participants from 1 June 2012 - 31 August 2013. Three definitions of Metabolic Syndrome (Harmonizing, National Cholesterol Education Program Expert Panel and Adult Treatment Panel III [ATPIII], and International Diabetes Federation [IDF]) were used to allocate subjects to either the normal or Metabolic Syndrome positive (MetS+) group. The predictability of the questionnaire was determined by the Area-Under-the-Receiver-Operating Characteristic curve (AUC). Youden's index was calculated and the highest score was considered the optimal cutoff value. Cohen´s kappa (κ) was calculated to determine the level of agreement between the ESF-I questionnaire (max score: 15 based on 15 items) and Metabolic Syndrome. RESULTS From 53.8% - 60.7% of the participants were determined to be MetS+. The average questionnaire score was significantly higher in the MetS+ group for each definition (4.0 vs. 8.0, P < 0.05). The ESF-I questionnaire was predictive for the Harmonizing definition (AUC = 0.841, 95%CI: 0.790 - 0.892), the ATPIII definition (AUC = 0.827, 95%CI: 0.774 - 0.880), and the IDF definition (AUC = 0.836, 95%CI: 0.785 - 0.887). A cutoff value of 7 was determined for each definition; therefore, the cohort was re-categorized based on questionnaire results. There was a strong agreement between the ESF-I questionnaire and MetS (Harmonizing: accuracy = 77.6%, κ = 0.554; ATPIII: accuracy = 74.1%, κ = 0.489; IDF: accuracy = 74.6%, κ = 0.495, P < 0.001). CONCLUSION The ESF-I questionnaire can identify MetS+ patients, and therefore, lead to earlier diagnoses, reduced number of consultations, and lower costs with easier application.
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Affiliation(s)
- Leonardo M. Porchia
- Independent researcher and consultant. Laboratorio de Investigación en Fisiopatología de Enfermedades Crónicas, Centro de Investigación Biomédica de Oriente, Instituto Mexicano del Seguro Social, Puebla, Mexico.
| | - Beatriz Lara-Solis
- Independent researcher and consultant. Laboratorio de Investigación en Fisiopatología de Enfermedades Crónicas, Centro de Investigación Biomédica de Oriente, Instituto Mexicano del Seguro Social, Puebla, Mexico.
| | | | | | - Guadalupe Ruiz-Vivanco
- Programa de Ciencias Biológicas, Centro Tlaxcala de Biología de la Conducta, Universidad Autónoma de Tlaxcala, Tlaxcala, Mexico.
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