Noninvasive screening tool to detect undiagnosed diabetes among young and middle-aged people in Chinese community

Zhang, Min; Li, Gang; Xu, Xiaoyue; Wu, Xuesen; Jin, Qili; Liu, Huaqing

doi:10.1007/s13410-018-0698-y

Cited by 4 publications

(10 citation statements)

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“…10,24 The performance of each tool evaluated by ROC curve and the value of the AUC ranges from 0.65-0.93 by. FBS, 3,7,16,17,19 two studies used FBS and HbA1c (9%), 9,24 three studies (13.6%) used only HbA1C, 8,10,12 one study (4.5%) used FBS, 1h PG and HbA1C, 21 one study (4.5%) used FBS, 2h PG and HbA1c, 13 one study (4.5%) used RBS and HbA1C, 14 and one study (4.5%) used 2h PG to define pre-DM and DM. 4 to calibrate the developed Tools.…”

Section: Tesfaye Hailementioning

confidence: 99%

“…Fourteen studies (63.6%) 6,7,8,10,11,14,15,16,17,19,21,22,23,24 reported the performance of their prediction power using sensitivity, specificity, PPV, and NPV. However, in five studies (22.7%), 3,5,9,12,20 PPV and NPV were not reported, and the remaining three studies (13.6%) did not report all. 4,13,18 To evaluate the predictive power of the developed noninvasive screening tool, the biochemical parameters utilized as the 'Gold standard' varied a lot among the studies.…”

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confidence: 97%

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Literature review of Diabetes noninvasive screening tools for previously undiagnosed. Scoping review

Haile

2023

Preprint

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BackgroundDiabetes is a severe chronic disease that needs due attention. Worldwide distribution of the disease is increasing in alarming trend marked by its pandemic nature. Numerous people are disabled and dead due to the complexity of the disease and its management. A recent CDC report revealed that in the USA, 37.3 million people have Diabetes (11.3% of the US population). Ninety-six million people aged 18 years and older have prediabetes (38.0% of the adult US population). Worldwide, the number of people with Diabetes rose from 108 million in 1980 to 422 million in 2014. WHO on its sustainable development goal (SDG) report made clear the existing diabetic patient evaluation, intervention, and monitoring method does not adequately address the problem. Paradoxically, the ambitious goal of the American Diabetes Association is: “A Future Without Diabetes.”The current Diabetes screening method is invasive, provided that scarcity of resources and high cost of test kit makes it nonpractical to frequently checkup. We must proactively engage in its prevention by making screening cheap and easily accessible. So, this review is designed to answer the question: What is an available Diabetes noninvasive screening tool for undiagnosed?MethodThe relevant Article was searched from PubMed, MEDLINE, Embase, Web of Science, SCOPUS, and the CINHAL using a combination of the following key constructs: Diabetes screening, Diabetes noninvasive screening, Diabetes AND screening, Diabetes and (“noninvasive” or “non-invasive” or “non invasive”) and “screening tool,” Diabetes AND noninvasive AND screening AND Tool, “Diabetes noninvasive Screening Tool.” The selection process was conducted based on the PRISMA framework statement. Exclusion and inclusion criteria were applied to select the final relevant literature, after which 22 relevant studies were selected.ResultThe review included studies conducted from 2013 to 2023, 36% in 2019 and 2022. The included articles are from 13 countries. The data sources for the articles are surveys, databanks, random sampling, and opportunity sampling. A total of 24 key predictors were used to develop 22 types of tools. The researchers used the logistic regression (LR) method while developing most studies, and ML in a couple of them. The Authors described the Tool’s performance using the ROC curve, and the AUC is 0.65-0.93. The developed tool predictive power of the screening Prediabetes used Sensitivity, Specificity, PPV, NPV metrics and the result is encouraging.ConclusionDiabetes noninvasive Screening tests have the potential to identify prediabetes at an early stage and, thus, a more treatable disease that, as a result, saves lives. The developed tools had promising designate ability in pre-DM case findings, as shown by their ROC curve, AUC, sensitivity/specificity, and PPV/NPV. The Tool developed by these researchers looks promising for our goal of screening Diabetes by noninvasive method to answer this review research question using non-laboratory methods, which can be applied by common people regardless of the involvement of health professional skill. This review’s limitations are that the included article’s bias assessment was not performed, and non-English language articles were not included, so this could miss some pertinent tools. Finally, a systematic review, meta-analysis, and RCT on the Tool are recommended to identify any bias during the development of the Tool and possible generalizability of the best Tool for worldwide applicability.

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Section: Tesfaye Hailementioning

confidence: 99%

mentioning

confidence: 97%

Literature review of Diabetes noninvasive screening tools for previously undiagnosed. Scoping review

Haile

2023

Preprint

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“…Since 2009, several DM risk prediction models and scoring algorithms have been developed specifically for Chinese populations 3 17–22. The majority of these models and algorithms were developed and validated in Mainland China,17–21 with only three models developed for the Hong Kong Chinese population 3 17 22. The first Hong Kong model used self-reported factors and laboratory measurements to create a scoring algorithm 3.…”

Section: Introductionmentioning

confidence: 99%

“…There is a need for research to incorporate more lifestyle factors to improve risk prediction models’ predictive validity and impact 13. Lifestyle factors that may be associated with DM and pre-DM include PA level,17 dietary factors (eg, fibre, sugar or fat intake), alcohol consumption, smoking and sleep 18 19…”

Section: Introductionmentioning

confidence: 99%

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Development and validation of a diabetes mellitus and prediabetes risk prediction function for case finding in primary care in Hong Kong: a cross-sectional study and a prospective study protocol paper

et al. 2022

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IntroductionDiabetes mellitus (DM) is a major non-communicable disease with an increasing prevalence. Undiagnosed DM is not uncommon and can lead to severe complications and mortality. Identifying high-risk individuals at an earlier disease stage, that is, pre-diabetes (pre-DM), is crucial in delaying progression. Existing risk models mainly rely on non-modifiable factors to predict only the DM risk, and few apply to Chinese people. This study aims to develop and validate a risk prediction function that incorporates modifiable lifestyle factors to detect DM and pre-DM in Chinese adults in primary care.Methods and analysisA cross-sectional study to develop DM/Pre-DM risk prediction functions using data from the Hong Kong’s Population Health Survey (PHS) 2014/2015 and a 12-month prospective study to validate the functions in case finding of individuals with DM/pre-DM. Data of 1857 Chinese adults without self-reported DM/Pre-DM will be extracted from the PHS 2014/2015 to develop DM/Pre-DM risk models using logistic regression and machine learning methods. 1014 Chinese adults without a known history of DM/Pre-DM will be recruited from public and private primary care clinics in Hong Kong. They will complete a questionnaire on relevant risk factors and blood tests on Oral Glucose Tolerance Test (OGTT) and haemoglobin A1C (HbA1c) on recruitment and, if the first blood test is negative, at 12 months. A positive case is DM/pre-DM defined by OGTT or HbA1c in any blood test. Area under receiver operating characteristic curve, sensitivity, specificity, positive predictive value and negative predictive value of the models in detecting DM/pre-DM will be calculated.Ethics and disseminationEthics approval has been received from The University of Hong Kong/Hong Kong Hospital Authority Hong Kong West Cluster (UW19-831) and Hong Kong Hospital Authority Kowloon Central/Kowloon East Cluster (REC(KC/KE)-21-0042/ER-3). The study results will be submitted for publication in a peer-reviewed journal.Trial registration numberUS ClinicalTrial.gov: NCT04881383; HKU clinical trials registry: HKUCTR-2808; Pre-results.

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A computer aided diagnostic method for the evaluation of type II diabetes mellitus in facial thermograms

et al. 2020

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Noninvasive screening tool to detect undiagnosed diabetes among young and middle-aged people in Chinese community

Cited by 4 publications

References 20 publications

Literature review of Diabetes noninvasive screening tools for previously undiagnosed. Scoping review

Literature review of Diabetes noninvasive screening tools for previously undiagnosed. Scoping review

Development and validation of a diabetes mellitus and prediabetes risk prediction function for case finding in primary care in Hong Kong: a cross-sectional study and a prospective study protocol paper

A computer aided diagnostic method for the evaluation of type II diabetes mellitus in facial thermograms

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