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Rehani MM, Xu XG. Dose, dose, dose, but where is the patient dose? RADIATION PROTECTION DOSIMETRY 2024; 200:945-955. [PMID: 38847407 DOI: 10.1093/rpd/ncae137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/14/2024] [Accepted: 05/17/2024] [Indexed: 06/25/2024]
Abstract
The article reviews the historical developments in radiation dose metrices in medical imaging. It identifies the good, the bad, and the ugly aspects of current-day metrices. The actions on shifting focus from International Commission on Radiological Protection (ICRP) Reference-Man-based population-average phantoms to patient-specific computational phantoms have been proposed and discussed. Technological developments in recent years involving AI-based automatic organ segmentation and 'near real-time' Monte Carlo dose calculations suggest the feasibility and advantage of obtaining patient-specific organ doses. It appears that the time for ICRP and other international organizations to embrace 'patient-specific' dose quantity representing risk may have finally come. While the existing dose metrices meet specific demands, emphasis needs to be also placed on making radiation units understandable to the medical community.
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Affiliation(s)
- Madan M Rehani
- Massachusetts General Hospital, Radiology Department, Boston, MA, 02114, United States
| | - Xie George Xu
- University of Science and Technology of China (USTC), College of Nuclear Science & Technology, Hefei, Anhui Province, 230026, China
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Basu S, Maheshwari V, Roy D, Saiyed M, Gokalani R. Risk assessment of diabetes using the Indian Diabetes Risk Score among older adults: Secondary analysis from the Longitudinal Ageing Study in India. Diabetes Metab Syndr 2024; 18:103040. [PMID: 38761608 DOI: 10.1016/j.dsx.2024.103040] [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: 11/10/2023] [Revised: 05/10/2024] [Accepted: 05/15/2024] [Indexed: 05/20/2024]
Abstract
BACKGROUND The Indian Diabetes Risk Score (IDRS) is a simple tool to assess the probability of an individual having type 2 diabetes (T2DM) but its applicability in community-dwelling older adults is lacking. This study aimed to estimate the risk of T2DM and its determinants among older adults without prior diabetes (DM) using the IDRS, while also assessing its sensitivity and specificity in individuals with a history of diabetes. METHODS We analyzed cross-sectional data from the Longitudinal Ageing Study in India (LASI) wave-1 (2017-18). IDRS was calculated amongst individuals aged ≥45 years considering waist circumference, physical activity, age and family history of DM. Risk was categorized as high (≥60), moderate (30-50), and low (<30). RESULTS Among 64541 individuals, 7.27 % (95 % CI: 6.78, 7.80) were at low risk, 61.80 % (95 % CI: 60.99, 62.61) at moderate risk, and 30.93 % (95 % CI: 30.19, 31.67) at high risk for T2DM. Adjusted analysis showed higher risk of T2DM among men, widowed/divorced, urban residents, minority religions, overweight, obese, and individuals with hypertension. ROC curve yielded an AUC of 0.67 (95 % CI: 0.66, 0.67, P < 0.001). The IDRS cutoff ≥50 had 73.69 % sensitivity and 51.40 % specificity for T2DM detection. CONCLUSION More than 9 in 10 older adults in India without history of DM have high-moderate risk of T2DM when assessed with the IDRS risk-prediction tool. However, the low specificity and moderate sensitivity of IDRS in existing DM cases constraints its practical utility as a decision tool for screening.
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Affiliation(s)
- Saurav Basu
- Indian Institute of Public Health - Delhi, Public Health Foundation of India, India.
| | - Vansh Maheshwari
- Indian Institute of Public Health - Delhi, Public Health Foundation of India, India
| | - Debolina Roy
- Indian Institute of Public Health - Delhi, Public Health Foundation of India, India
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Liu X, Littlejohns TJ, Bešević J, Bragg F, Clifton L, Collister JA, Trichia E, Gray LJ, Khunti K, Hunter DJ. Incorporating polygenic risk into the Leicester Risk Assessment score for 10-year risk prediction of type 2 diabetes. Diabetes Metab Syndr 2024; 18:102996. [PMID: 38608567 DOI: 10.1016/j.dsx.2024.102996] [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: 09/05/2023] [Revised: 02/22/2024] [Accepted: 03/25/2024] [Indexed: 04/14/2024]
Abstract
AIMS We evaluated whether incorporating information on ethnic background and polygenic risk enhanced the Leicester Risk Assessment (LRA) score for predicting 10-year risk of type 2 diabetes. METHODS The sample included 202,529 UK Biobank participants aged 40-69 years. We computed the LRA score, and developed two new risk scores using training data (80% sample): LRArev, which incorporated additional information on ethnic background, and LRAprs, which incorporated polygenic risk for type 2 diabetes. We assessed discriminative and reclassification performance in a test set (20% sample). Type 2 diabetes was ascertained using primary care, hospital inpatient and death registry records. RESULTS Over 10 years, 7,476 participants developed type 2 diabetes. The Harrell's C indexes were 0.796 (95% Confidence Interval [CI] 0.785, 0.806), 0.802 (95% CI 0.792, 0.813), and 0.829 (95% CI 0.820, 0.839) for the LRA, LRArev and LRAprs scores, respectively. The LRAprs score significantly improved the overall reclassification compared to the LRA (net reclassification index [NRI] = 0.033, 95% CI 0.015, 0.049) and LRArev (NRI = 0.040, 95% CI 0.024, 0.055) scores. CONCLUSIONS Polygenic risk moderately improved the performance of the existing LRA score for 10-year risk prediction of type 2 diabetes.
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Affiliation(s)
- Xiaonan Liu
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Jelena Bešević
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Fiona Bragg
- Nuffield Department of Population Health, University of Oxford, Oxford, UK; MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Lei Clifton
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Eirini Trichia
- Nuffield Department of Population Health, University of Oxford, Oxford, UK; MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Laura J Gray
- Department of Population Health Sciences, University of Leicester, UK
| | - Kamlesh Khunti
- Diabetes Research Centre, University of Leicester, Leicester, UK
| | - David J Hunter
- Nuffield Department of Population Health, University of Oxford, Oxford, UK; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA
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Khalil MAM, Sadagah NM, Tan J, Syed FO, Chong VH, Al-Qurashi SH. Pros and cons of live kidney donation in prediabetics: A critical review and way forward. World J Transplant 2024; 14:89822. [PMID: 38576756 PMCID: PMC10989475 DOI: 10.5500/wjt.v14.i1.89822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/11/2023] [Accepted: 01/16/2024] [Indexed: 03/15/2024] Open
Abstract
There is shortage of organs, including kidneys, worldwide. Along with deceased kidney transplantation, there is a significant rise in live kidney donation. The prevalence of prediabetes (PD), including impaired fasting glucose and impaired glucose tolerance, is on the rise across the globe. Transplant teams frequently come across prediabetic kidney donors for evaluation. Prediabetics are at risk of diabetes, chronic kidney disease, cardiovascular events, stroke, neuropathy, retinopathy, dementia, depression and nonalcoholic liver disease along with increased risk of all-cause mortality. Unfortunately, most of the studies done in prediabetic kidney donors are retrospective in nature and have a short follow up period. There is lack of prospective long-term studies to know about the real risk of complications after donation. Furthermore, there are variations in recommendations from various guidelines across the globe for donations in prediabetics, leading to more confusion among clinicians. This increases the responsibility of transplant teams to take appropriate decisions in the best interest of both donors and recipients. This review focuses on pathophysiological changes of PD in kidneys, potential complications of PD, other risk factors for development of type 2 diabetes, a review of guidelines for kidney donation, the potential role of diabetes risk score and calculator in kidney donors and the way forward for the evaluation and selection of prediabetic kidney donors.
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Affiliation(s)
- Muhammad Abdul Mabood Khalil
- Center of Renal Diseases and Transplantation, King Fahad Armed Forces Hospital Jeddah, Jeddah 23311, Saudi Arabia
| | - Nihal Mohammed Sadagah
- Center of Renal Diseases and Transplantation, King Fahad Armed Forces Hospital Jeddah, Jeddah 23311, Saudi Arabia
| | - Jackson Tan
- Department of Nephrology, RIPAS Hospital Brunei Darussalam, Brunei Muara BA1710, Brunei Darussalam
| | - Furrukh Omair Syed
- Center of Renal Diseases and Transplantation, King Fahad Armed Forces Hospital Jeddah, Jeddah 23311, Saudi Arabia
| | - Vui Heng Chong
- Division of Gastroenterology and Hepatology, Department of Medicine, Raja Isteri Pengiran Anak Saleha Hospital, Bandar Seri Begawan BA1710, Brunei Darussalam
| | - Salem H Al-Qurashi
- Center of Renal Diseases and Transplantation, King Fahad Armed Forces Hospital Jeddah, Jeddah 23311, Saudi Arabia
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Tang Y, Dong W, Shen J, Jiang G, Wang Q, Hao J, Hu Z. Life's Essential 8 and osteoporosis in adults aged 50 years or older: data from the National Health and Nutrition Examination Survey. Arch Osteoporos 2024; 19:13. [PMID: 38363413 DOI: 10.1007/s11657-024-01368-5] [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: 10/24/2023] [Accepted: 01/05/2024] [Indexed: 02/17/2024]
Abstract
In this cross-sectional study, we examined the association between Life's Essential 8 (LE8) and bone mineral density (BMD) as well as osteoporosis risk among adults aged 50 and over. The findings of this study revealed that higher LE8 scores were associated with higher BMD and reduced osteoporosis risk. PURPOSE The objective of the present study was to evaluate the association between Life's Essential 8 (LE8) and bone mineral density (BMD), as well as osteoporosis risk, in adults aged 50 years or over. METHODS This cross-sectional study recruited individuals who were 50 years old or older from the National Health and Nutrition Examination Survey. LE8 scores were evaluated and calculated according to the scoring algorithm based on the American Heart Association recommendations, which were further categorized into health behaviors (LE8-HB) and health factors (LE8-HF) scores. Furthermore, the present study utilized multivariate linear regression models to examine the correlations between BMD and LE8 scores. In addition, ordinal logistic regression models were employed to determine the associations between the risk of osteoporosis (normal BMD, osteopenia, and osteoporosis) and LE8 scores. RESULTS The final analysis included a total of 2910 participants, whose mean age was 64.49 ± 9.28 years. LE8 and LE8-HF scores exhibited a negative association with BMD and a positive association with osteoporosis risk in unadjusted models. Nevertheless, after adjustment for covariates, LE8 and LE8-HB scores exhibited a positive association with BMD and a negative association with osteoporosis risk, regardless of age, sex, or menopausal status. CONCLUSIONS Scoring systems based on multiple lifestyle and behavior factors, similar to LE8, have the potential to become a novel option and be used for osteoporosis risk assessment.
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Affiliation(s)
- Yuchen Tang
- Department of Orthopedics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Department of Orthopedics, University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Wei Dong
- Department of Orthopedics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Department of Spinal Surgery, Chongqing Orthopedic Hospital of Traditional Chinese Medicine, Chongqing, China
| | - Jieliang Shen
- Department of Rehabilitation Medicine, Bishan Hospital of Chongqing Medical University, Bishan Hospital of Chongqing, Chongqing, China
| | - Guanyin Jiang
- Department of Orthopedics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Department of Orthopedics, University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Qiufu Wang
- Department of Orthopedics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Department of Orthopedics, University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Jie Hao
- Department of Orthopedics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Zhenming Hu
- Department of Orthopedics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
- Department of Orthopedics, University-Town Hospital of Chongqing Medical University, Chongqing, China.
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Osei-Yeboah J, Kengne AP, Owusu-Dabo E, Schulze MB, Meeks KA, Klipstein-Grobusch K, Smeeth L, Bahendeka S, Beune E, Moll van Charante EP, Agyemang C. Validation of prevalent diabetes risk scores based on non-invasively measured predictors in Ghanaian migrant and non-migrant populations - The RODAM study. PUBLIC HEALTH IN PRACTICE 2023; 6:100453. [PMID: 38034345 PMCID: PMC10687695 DOI: 10.1016/j.puhip.2023.100453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/06/2023] [Accepted: 11/15/2023] [Indexed: 12/02/2023] Open
Abstract
Background Non-invasive diabetes risk models are a cost-effective tool in large-scale population screening to identify those who need confirmation tests, especially in resource-limited settings. Aims This study aimed to evaluate the ability of six non-invasive risk models (Cambridge, FINDRISC, Kuwaiti, Omani, Rotterdam, and SUNSET model) to identify screen-detected diabetes (defined by HbA1c) among Ghanaian migrants and non-migrants. Study design A multicentered cross-sectional study. Methods This analysis included 4843 Ghanaian migrants and non-migrants from the Research on Obesity and Diabetes among African Migrants (RODAM) Study. Model performance was assessed using the area under the receiver operating characteristic curves (AUC), Hosmer-Lemeshow statistics, and calibration plots. Results All six models had acceptable discrimination (0.70 ≤ AUC <0.80) for screen-detected diabetes in the overall/combined population. Model performance did not significantly differ except for the Cambridge model, which outperformed Rotterdam and Omani models. Calibration was poor, with a consistent trend toward risk overestimation for screen-detected diabetes, but this was substantially attenuated by recalibration through adjustment of the original model intercept. Conclusion Though acceptable discrimination was observed, the original models were poorly calibrated among populations of African ancestry. Recalibration of these models among populations of African ancestry is needed before use.
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Affiliation(s)
- James Osei-Yeboah
- Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Meibergdreef 9, Amsterdam, the Netherlands
- Department of Global and International Health, School of Public Health, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Andre-Pascal Kengne
- Non-communicable Disease Research Unit, South African Medical Research Council, Cape Town, South Africa
| | - Ellis Owusu-Dabo
- Department of Global and International Health, School of Public Health, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Matthias B. Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam‐Rehbruecke, Nuthetal, Germany
- German Center for Diabetes Research (DZD), Germany
- Institute of Nutritional Science, University of Potsdam, Germany
| | - Karlijn A.C. Meeks
- Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Meibergdreef 9, Amsterdam, the Netherlands
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Kerstin Klipstein-Grobusch
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Liam Smeeth
- Department of Non‐Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Erik Beune
- Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Meibergdreef 9, Amsterdam, the Netherlands
| | - Eric P. Moll van Charante
- Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Meibergdreef 9, Amsterdam, the Netherlands
- Department of General Practice, Amsterdam UMC, University of Amsterdam, Amsterdam Public health Research Institute, Meibergdreef 9, Amsterdam, the Netherlands
| | - Charles Agyemang
- Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Meibergdreef 9, Amsterdam, the Netherlands
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Prakoso DA, Mahendradhata Y, Istiono W. Family Involvement to Stop the Conversion of Prediabetes to Diabetes. Korean J Fam Med 2023; 44:303-310. [PMID: 37582666 PMCID: PMC10667073 DOI: 10.4082/kjfm.23.0019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 05/01/2023] [Indexed: 08/17/2023] Open
Abstract
Prediabetes is a condition associated with an increased risk of developing diabetes, in which blood glucose levels are high but not high enough to be diagnosed as diabetes. The rapid increase in the prevalence of prediabetes is a major global health challenge. The incidence of prediabetes has increased to pandemic levels and can lead to serious consequences. Unfortunately, nearly 90% of prediabetic individuals are unaware of their ailment. A quarter of prediabetic individuals develop type 2 diabetes mellitus (T2DM) within 3-5 years. Although prediabetes is a reversible condition, the prevention of diabetes has received little attention. It is essential for prediabetic individuals to implement new health-improvement techniques. Focusing on family systems is one strategy to promote health, which is determined by health patterns that are often taught, established, and adjusted within family contexts. For disease prevention, a family-based approach may be beneficial. Family support is essential for the metabolic control of the disease. This study aimed to show several strategies for involving the patient's family members in preventing the conversion of prediabetes to T2DM and to emphasize that the patient's family members are a valuable resource to reduce the incidence of diabetes.
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Affiliation(s)
- Denny Anggoro Prakoso
- Postgraduate Programme in Public Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Yodi Mahendradhata
- Center for Health Policy and Management, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Wahyudi Istiono
- Department of Family and Community Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
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Mehta N, Dangas K, Ditmarsch M, Rensen PCN, Dicklin MR, Kastelein JJP. The evolving role of cholesteryl ester transfer protein inhibition beyond cardiovascular disease. Pharmacol Res 2023; 197:106972. [PMID: 37898443 DOI: 10.1016/j.phrs.2023.106972] [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: 02/28/2023] [Revised: 09/21/2023] [Accepted: 10/25/2023] [Indexed: 10/30/2023]
Abstract
The main role of cholesteryl ester transfer protein (CETP) is the transfer of cholesteryl esters and triglycerides between high-density lipoprotein (HDL) particles and triglyceride-rich lipoprotein and low-density lipoprotein (LDL) particles. There is a long history of investigations regarding the inhibition of CETP as a target for reducing major adverse cardiovascular events. Initially, the potential effect on cardiovascular events of CETP inhibitors was hypothesized to be mediated by their ability to increase HDL cholesterol, but, based on evidence from anacetrapib and the newest CETP inhibitor, obicetrapib, it is now understood to be primarily due to reducing LDL cholesterol and apolipoprotein B. Nevertheless, evidence is also mounting that other roles of HDL, including its promotion of cholesterol efflux, as well as its apolipoprotein composition and anti-inflammatory, anti-oxidative, and anti-diabetic properties, may play important roles in several diseases beyond cardiovascular disease, including, but not limited to, Alzheimer's disease, diabetes, and sepsis. Furthermore, although Mendelian randomization analyses suggested that higher HDL cholesterol is associated with increased risk of age-related macular degeneration (AMD), excess risk of AMD was absent in all CETP inhibitor randomized controlled trial data comprising over 70,000 patients. In fact, certain HDL subclasses may, in contrast, be beneficial for treating the retinal cholesterol accumulation that occurs with AMD. This review describes the latest biological evidence regarding the relationship between HDL and CETP inhibition for Alzheimer's disease, type 2 diabetes mellitus, sepsis, and AMD.
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Affiliation(s)
- Nehal Mehta
- Mobius Scientific, Inc., JLABS @ Washington, DC, Washington, DC, USA
| | | | | | - Patrick C N Rensen
- Department of Medicine, Division of Endocrinology, and Einthoven Laboratory of Experimental Vascular Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | | | - John J P Kastelein
- Department of Vascular Medicine, Academic Medical Center, University of Amsterdam, the Netherlands.
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Atehortúa A, Gkontra P, Camacho M, Diaz O, Bulgheroni M, Simonetti V, Chadeau-Hyam M, Felix JF, Sebert S, Lekadir K. Cardiometabolic risk estimation using exposome data and machine learning. Int J Med Inform 2023; 179:105209. [PMID: 37729839 DOI: 10.1016/j.ijmedinf.2023.105209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 08/11/2023] [Accepted: 08/30/2023] [Indexed: 09/22/2023]
Abstract
BACKGROUND The human exposome encompasses all exposures that individuals encounter throughout their lifetime. It is now widely acknowledged that health outcomes are influenced not only by genetic factors but also by the interactions between these factors and various exposures. Consequently, the exposome has emerged as a significant contributor to the overall risk of developing major diseases, such as cardiovascular disease (CVD) and diabetes. Therefore, personalized early risk assessment based on exposome attributes might be a promising tool for identifying high-risk individuals and improving disease prevention. OBJECTIVE Develop and evaluate a novel and fair machine learning (ML) model for CVD and type 2 diabetes (T2D) risk prediction based on a set of readily available exposome factors. We evaluated our model using internal and external validation groups from a multi-center cohort. To be considered fair, the model was required to demonstrate consistent performance across different sub-groups of the cohort. METHODS From the UK Biobank, we identified 5,348 and 1,534 participants who within 13 years from the baseline visit were diagnosed with CVD and T2D, respectively. An equal number of participants who did not develop these pathologies were randomly selected as the control group. 109 readily available exposure variables from six different categories (physical measures, environmental, lifestyle, mental health events, sociodemographics, and early-life factors) from the participant's baseline visit were considered. We adopted the XGBoost ensemble model to predict individuals at risk of developing the diseases. The model's performance was compared to that of an integrative ML model which is based on a set of biological, clinical, physical, and sociodemographic variables, and, additionally for CVD, to the Framingham risk score. Moreover, we assessed the proposed model for potential bias related to sex, ethnicity, and age. Lastly, we interpreted the model's results using SHAP, a state-of-the-art explainability method. RESULTS The proposed ML model presents a comparable performance to the integrative ML model despite using solely exposome information, achieving a ROC-AUC of 0.78±0.01 and 0.77±0.01 for CVD and T2D, respectively. Additionally, for CVD risk prediction, the exposome-based model presents an improved performance over the traditional Framingham risk score. No bias in terms of key sensitive variables was identified. CONCLUSIONS We identified exposome factors that play an important role in identifying patients at risk of CVD and T2D, such as naps during the day, age completed full-time education, past tobacco smoking, frequency of tiredness/unenthusiasm, and current work status. Overall, this work demonstrates the potential of exposome-based machine learning as a fair CVD and T2D risk assessment tool.
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Affiliation(s)
- Angélica Atehortúa
- BCN-AIM laboratory, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain.
| | - Polyxeni Gkontra
- BCN-AIM laboratory, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Marina Camacho
- BCN-AIM laboratory, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Oliver Diaz
- BCN-AIM laboratory, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | | | | | - Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, MRC-HPA Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Janine F Felix
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Sylvain Sebert
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Karim Lekadir
- BCN-AIM laboratory, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
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Bahrami HSZ, Jørgensen PG, Hove JD, Dixen U, Biering-Sørensen T, Rossing P, Jensen MT. Prognostic value of myocardial performance index in individuals with type 1 and type 2 diabetes: Thousand&1 and Thousand&2 studies. Eur Heart J Cardiovasc Imaging 2023; 24:1555-1562. [PMID: 37638773 DOI: 10.1093/ehjci/jead178] [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: 04/20/2023] [Accepted: 07/15/2023] [Indexed: 08/29/2023] Open
Abstract
AIMS Cardiovascular disease (CVD) is the leading cause of mortality and morbidity in type 1 (T1D) and type 2 diabetes (T2D). Despite diabetes affects the myocardium, risk prediction models do not include myocardial function parameters. Myocardial performance index (MPI) reflects left ventricular function. The prognostic value of MPI has not been evaluated in large-scale diabetes populations. METHODS AND RESULTS We evaluated two prospective cohort studies: Thousand&1 (1093 individuals with T1D) and Thousand&2 (1030 individuals with T2D). Clinical data, including echocardiography, were collected at baseline. We collected follow-up data from national registries. We defined major adverse cardiovascular events (MACE) as incident events of hospital admission for acute coronary syndrome, heart failure, stroke, or all-cause mortality. For included individuals (56% male, 54 ± 15 years, MPI 0.51 ± 0.1, 63% T1D), follow-up was 100% after median of 5.3 years (range: 4.8-6.3). MPI was associated with MACE (HR 1.2, 95%CI 1.0-1.3, P = 0.012, per 0.10-unit increase) and heart failure (HR 1.3, 95%CI 1.1-1.6, P = 0.005, per 0.10-unit increase) after adjusting for clinical and echocardiographic variables. MPI predicted MACE and heart failure better in T1D than T2D (P = 0.031 for interaction). MPI added discriminatory power to the Steno T1 Risk Engine, based on clinical characteristics, in predicting MACE [area under the curve (AUC) from 0.77 to 0.79, P = 0.030] and heart failure (AUC from 0.77 to 0.83, P = 0.009) in T1D. CONCLUSION MPI is independently associated with MACE and heart failure in T1D but not T2D and improves prediction in T1D. Echocardiographic assessment in T1D may enhance risk prediction.
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Affiliation(s)
- Hashmat Sayed Zohori Bahrami
- Department of Cardiology, Copenhagen University Hospital, Amager & Hvidovre, Kettegård Alle 30, 2650 Hvidovre, Denmark
- Department of Clinical Research, Steno Diabetes Center Copenhagen, Borgmester Ib Juuls Vej 83, 2730 Herlev, Denmark
| | - Peter Godsk Jørgensen
- Department of Clinical Research, Steno Diabetes Center Copenhagen, Borgmester Ib Juuls Vej 83, 2730 Herlev, Denmark
- Department of Cardiology, Copenhagen University Hospital, Herlev & Gentofte, Borgmester Ib Juuls Vej 1, 2730 Herlev, Denmark
| | - Jens Dahlgaard Hove
- Department of Cardiology, Copenhagen University Hospital, Amager & Hvidovre, Kettegård Alle 30, 2650 Hvidovre, Denmark
- Department of Clinical Medicine, Faculty of Health Sciences, University of Copenhagen, Blegdamsvej 3b, 2200 Copenhagen, Denmark
| | - Ulrik Dixen
- Department of Cardiology, Copenhagen University Hospital, Amager & Hvidovre, Kettegård Alle 30, 2650 Hvidovre, Denmark
- Department of Clinical Medicine, Faculty of Health Sciences, University of Copenhagen, Blegdamsvej 3b, 2200 Copenhagen, Denmark
| | - Tor Biering-Sørensen
- Department of Clinical Medicine, Faculty of Health Sciences, University of Copenhagen, Blegdamsvej 3b, 2200 Copenhagen, Denmark
- Center for Translational Cardiology and Pragmatic Randomized Trials, Gentofte Hospitalsvej 1, 2900 Hellerup, Denmark
| | - Peter Rossing
- Department of Clinical Research, Steno Diabetes Center Copenhagen, Borgmester Ib Juuls Vej 83, 2730 Herlev, Denmark
- Department of Clinical Medicine, Faculty of Health Sciences, University of Copenhagen, Blegdamsvej 3b, 2200 Copenhagen, Denmark
| | - Magnus T Jensen
- Department of Cardiology, Copenhagen University Hospital, Amager & Hvidovre, Kettegård Alle 30, 2650 Hvidovre, Denmark
- Department of Clinical Research, Steno Diabetes Center Copenhagen, Borgmester Ib Juuls Vej 83, 2730 Herlev, Denmark
- William Harvey Research Institute, NIHR Barts Biomedical Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
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11
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Seah JYH, Yao J, Hong Y, Lim CGY, Sabanayagam C, Nusinovici S, Gardner DSL, Loh M, Müller-Riemenschneider F, Tan CS, Yeo KK, Wong TY, Cheng CY, Ma S, Tai ES, Chambers JC, van Dam RM, Sim X. Risk prediction models for type 2 diabetes using either fasting plasma glucose or HbA1c in Chinese, Malay, and Indians: Results from three multi-ethnic Singapore cohorts. Diabetes Res Clin Pract 2023; 203:110878. [PMID: 37591346 DOI: 10.1016/j.diabres.2023.110878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 08/07/2023] [Accepted: 08/14/2023] [Indexed: 08/19/2023]
Abstract
AIMS To assess three well-established type 2 diabetes (T2D) risk prediction models based on fasting plasma glucose (FPG) in Chinese, Malays, and Indians, and to develop simplified risk models based on either FPG or HbA1c. METHODS We used a prospective multiethnic Singapore cohort to evaluate the established models and develop simplified models. 6,217 participants without T2D at baseline were included, with an average follow-up duration of 8.3 years. The simplified risk models were validated in two independent multiethnic Singapore cohorts (N = 12,720). RESULTS The established risk models had moderate-to-good discrimination (area under the receiver operating characteristic curves, AUCs 0.762 - 0.828) but a lack of fit (P-values < 0.05). Simplified risk models that included fewer predictors (age, BMI, systolic blood pressure, triglycerides, and HbA1c or FPG) showed good discrimination in all cohorts (AUCs ≥ 0.810), and sufficiently captured differences between the ethnic groups. While recalibration improved fit the simplified models in validation cohorts, there remained evidence of miscalibration in Chinese (p ≤ 0.012). CONCLUSIONS Simplified risk models including HbA1c or FPG had good discrimination in predicting incidence of T2D in three major Asian ethnic groups. Risk functions with HbA1c performed as well as those with FPG.
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Affiliation(s)
- Jowy Yi Hong Seah
- Centre for Population Health Research and Implementation, SingHealth, Singapore 150167, Singapore; Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
| | - Jiali Yao
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
| | - Yueheng Hong
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
| | - Charlie Guan Yi Lim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore; Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Simon Nusinovici
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore
| | - Daphne Su-Lyn Gardner
- Department of Endocrinology, Singapore General Hospital, Singapore 169608, Singapore
| | - Marie Loh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore; Research Division, National Skin Centre, Singapore 308205, Singapore
| | - Falk Müller-Riemenschneider
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
| | - Chuen Seng Tan
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
| | - Khung Keong Yeo
- Department of Cardiology, National Heart Centre, Singapore 169609, Singapore; Duke-NUS Medical School, Singapore
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore; Tsinghua Medicine, Tsinghua University, Beijing, China; Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore 117597, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore; Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore 169857, Singapore; Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore 117597, Singapore; Center for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore 117597, Singapore
| | - Stefan Ma
- Epidemiology & Disease Control Division, Ministry of Health, Singapore 169854, Singapore
| | - E Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore; Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore 117597, Singapore
| | - John C Chambers
- Department of Endocrinology, Singapore General Hospital, Singapore 169608, Singapore; Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London W2 1PG, United Kingdom
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore; Departments of Exercise and Nutrition Sciences and Epidemiology, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, United States.
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore.
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12
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Stefan N, Schulze MB. Metabolic health and cardiometabolic risk clusters: implications for prediction, prevention, and treatment. Lancet Diabetes Endocrinol 2023; 11:426-440. [PMID: 37156256 DOI: 10.1016/s2213-8587(23)00086-4] [Citation(s) in RCA: 45] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 03/13/2023] [Accepted: 03/13/2023] [Indexed: 05/10/2023]
Abstract
Among 20 leading global risk factors for years of life lost in 2040, reference forecasts point to three metabolic risks-high blood pressure, high BMI, and high fasting plasma glucose-as being the top risk variables. Building upon these and other risk factors, the concept of metabolic health is attracting much attention in the scientific community. It focuses on the aggregation of important risk factors, which allows the identification of subphenotypes, such as people with metabolically unhealthy normal weight or metabolically healthy obesity, who strongly differ in their risk of cardiometabolic diseases. Since 2018, studies that used anthropometrics, metabolic characteristics, and genetics in the setting of cluster analyses proposed novel metabolic subphenotypes among patients at high risk (eg, those with diabetes). The crucial point now is whether these subphenotyping strategies are superior to established cardiometabolic risk stratification methods regarding the prediction, prevention, and treatment of cardiometabolic diseases. In this Review, we carefully address this point and conclude, firstly, regarding cardiometabolic risk stratification, in the general population both the concept of metabolic health and the cluster approaches are not superior to established risk prediction models. However, both subphenotyping approaches might be informative to improve the prediction of cardiometabolic risk in subgroups of individuals, such as those in different BMI categories or people with diabetes. Secondly, the applicability of the concepts by treating physicians and communication of the cardiometabolic risk with patients is easiest using the concept of metabolic health. Finally, the approaches to identify cardiometabolic risk clusters in particular have provided some evidence that they could be used to allocate individuals to specific pathophysiological risk groups, but whether this allocation is helpful for prevention and treatment still needs to be determined.
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Affiliation(s)
- Norbert Stefan
- Department of Internal Medicine IV, University Hospital Tübingen, Tübingen, Germany; Institute of Diabetes Research and Metabolic Diseases of the Helmholtz Centre Munich, Tübingen, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany.
| | - Matthias B Schulze
- German Center for Diabetes Research (DZD), Neuherberg, Germany; Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany
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13
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Tsao CW, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Beaton AZ, Boehme AK, Buxton AE, Commodore-Mensah Y, Elkind MSV, Evenson KR, Eze-Nliam C, Fugar S, Generoso G, Heard DG, Hiremath S, Ho JE, Kalani R, Kazi DS, Ko D, Levine DA, Liu J, Ma J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge MP, Thacker EL, Virani SS, Voeks JH, Wang NY, Wong ND, Wong SS, Yaffe K, Martin SS. Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association. Circulation 2023; 147:e93-e621. [PMID: 36695182 DOI: 10.1161/cir.0000000000001123] [Citation(s) in RCA: 1288] [Impact Index Per Article: 1288.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
BACKGROUND The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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14
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Fletcher E, Burns A, Wiering B, Lavu D, Shephard E, Hamilton W, Campbell JL, Abel G. Workload and workflow implications associated with the use of electronic clinical decision support tools used by health professionals in general practice: a scoping review. BMC PRIMARY CARE 2023; 24:23. [PMID: 36670354 PMCID: PMC9857918 DOI: 10.1186/s12875-023-01973-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 01/05/2023] [Indexed: 01/21/2023]
Abstract
BACKGROUND Electronic clinical decision support tools (eCDS) are increasingly available to assist General Practitioners (GP) with the diagnosis and management of a range of health conditions. It is unclear whether the use of eCDS tools has an impact on GP workload. This scoping review aimed to identify the available evidence on the use of eCDS tools by health professionals in general practice in relation to their impact on workload and workflow. METHODS A scoping review was carried out using the Arksey and O'Malley methodological framework. The search strategy was developed iteratively, with three main aspects: general practice/primary care contexts, risk assessment/decision support tools, and workload-related factors. Three databases were searched in 2019, and updated in 2021, covering articles published since 2009: Medline (Ovid), HMIC (Ovid) and Web of Science (TR). Double screening was completed by two reviewers, and data extracted from included articles were analysed. RESULTS The search resulted in 5,594 references, leading to 95 full articles, referring to 87 studies, after screening. Of these, 36 studies were based in the USA, 21 in the UK and 11 in Australia. A further 18 originated from Canada or Europe, with the remaining studies conducted in New Zealand, South Africa and Malaysia. Studies examined the use of eCDS tools and reported some findings related to their impact on workload, including on consultation duration. Most studies were qualitative and exploratory in nature, reporting health professionals' subjective perceptions of consultation duration as opposed to objectively-measured time spent using tools or consultation durations. Other workload-related findings included impacts on cognitive workload, "workflow" and dialogue with patients, and clinicians' experience of "alert fatigue". CONCLUSIONS The published literature on the impact of eCDS tools in general practice showed that limited efforts have focused on investigating the impact of such tools on workload and workflow. To gain an understanding of this area, further research, including quantitative measurement of consultation durations, would be useful to inform the future design and implementation of eCDS tools.
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Affiliation(s)
- Emily Fletcher
- grid.8391.30000 0004 1936 8024College of Medicine and Health, University of Exeter Medical School, St Luke’s Campus, Heavitree Road, Exeter, Devon EX1 2LU England
| | - Alex Burns
- grid.8391.30000 0004 1936 8024College of Medicine and Health, University of Exeter Medical School, St Luke’s Campus, Heavitree Road, Exeter, Devon EX1 2LU England
| | - Bianca Wiering
- grid.8391.30000 0004 1936 8024College of Medicine and Health, University of Exeter Medical School, St Luke’s Campus, Heavitree Road, Exeter, Devon EX1 2LU England
| | - Deepthi Lavu
- grid.8391.30000 0004 1936 8024College of Medicine and Health, University of Exeter Medical School, St Luke’s Campus, Heavitree Road, Exeter, Devon EX1 2LU England
| | - Elizabeth Shephard
- grid.8391.30000 0004 1936 8024College of Medicine and Health, University of Exeter Medical School, St Luke’s Campus, Heavitree Road, Exeter, Devon EX1 2LU England
| | - Willie Hamilton
- grid.8391.30000 0004 1936 8024College of Medicine and Health, University of Exeter Medical School, St Luke’s Campus, Heavitree Road, Exeter, Devon EX1 2LU England
| | - John L. Campbell
- grid.8391.30000 0004 1936 8024College of Medicine and Health, University of Exeter Medical School, St Luke’s Campus, Heavitree Road, Exeter, Devon EX1 2LU England
| | - Gary Abel
- grid.8391.30000 0004 1936 8024College of Medicine and Health, University of Exeter Medical School, St Luke’s Campus, Heavitree Road, Exeter, Devon EX1 2LU England
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15
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Seto H, Oyama A, Kitora S, Toki H, Yamamoto R, Kotoku J, Haga A, Shinzawa M, Yamakawa M, Fukui S, Moriyama T. Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data. Sci Rep 2022; 12:15889. [PMID: 36220875 PMCID: PMC9553945 DOI: 10.1038/s41598-022-20149-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 09/09/2022] [Indexed: 01/04/2023] Open
Abstract
We sought to verify the reliability of machine learning (ML) in developing diabetes prediction models by utilizing big data. To this end, we compared the reliability of gradient boosting decision tree (GBDT) and logistic regression (LR) models using data obtained from the Kokuho-database of the Osaka prefecture, Japan. To develop the models, we focused on 16 predictors from health checkup data from April 2013 to December 2014. A total of 277,651 eligible participants were studied. The prediction models were developed using a light gradient boosting machine (LightGBM), which is an effective GBDT implementation algorithm, and LR. Their reliabilities were measured based on expected calibration error (ECE), negative log-likelihood (Logloss), and reliability diagrams. Similarly, their classification accuracies were measured in the area under the curve (AUC). We further analyzed their reliabilities while changing the sample size for training. Among the 277,651 participants, 15,900 (7978 males and 7922 females) were newly diagnosed with diabetes within 3 years. LightGBM (LR) achieved an ECE of 0.0018 ± 0.00033 (0.0048 ± 0.00058), a Logloss of 0.167 ± 0.00062 (0.172 ± 0.00090), and an AUC of 0.844 ± 0.0025 (0.826 ± 0.0035). From sample size analysis, the reliability of LightGBM became higher than LR when the sample size increased more than [Formula: see text]. Thus, we confirmed that GBDT provides a more reliable model than that of LR in the development of diabetes prediction models using big data. ML could potentially produce a highly reliable diabetes prediction model, a helpful tool for improving lifestyle and preventing diabetes.
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Affiliation(s)
- Hiroe Seto
- grid.136593.b0000 0004 0373 3971Health Care Division, Health and Counseling Center, Osaka University, Osaka, 560-0043 Japan ,grid.136593.b0000 0004 0373 3971Graduate School of Human Sciences, Osaka University, Osaka, 565-0871 Japan
| | - Asuka Oyama
- grid.136593.b0000 0004 0373 3971Health Care Division, Health and Counseling Center, Osaka University, Osaka, 560-0043 Japan
| | - Shuji Kitora
- grid.136593.b0000 0004 0373 3971Health Care Division, Health and Counseling Center, Osaka University, Osaka, 560-0043 Japan
| | - Hiroshi Toki
- grid.136593.b0000 0004 0373 3971Health Care Division, Health and Counseling Center, Osaka University, Osaka, 560-0043 Japan ,grid.136593.b0000 0004 0373 3971Research Center for Nuclear Physics, Osaka University, Osaka, 567-0047 Japan
| | - Ryohei Yamamoto
- grid.136593.b0000 0004 0373 3971Health Care Division, Health and Counseling Center, Osaka University, Osaka, 560-0043 Japan ,grid.136593.b0000 0004 0373 3971Department of Nephrology, Graduate School of Medicine, Osaka University, Osaka, 565-0871 Japan ,grid.136593.b0000 0004 0373 3971Health Promotion and Regulation, Department of Health Promotion Medicine, Osaka University Graduate School of Medicine, Osaka, 565-0871 Japan
| | - Jun’ichi Kotoku
- grid.136593.b0000 0004 0373 3971Health Care Division, Health and Counseling Center, Osaka University, Osaka, 560-0043 Japan ,grid.264706.10000 0000 9239 9995Graduate School of Medical Care and Technology, Teikyo University, Tokyo, 173-8605 Japan
| | - Akihiro Haga
- grid.136593.b0000 0004 0373 3971Health Care Division, Health and Counseling Center, Osaka University, Osaka, 560-0043 Japan ,grid.267335.60000 0001 1092 3579Graduate School of Biomedical Sciences, Tokushima University, Tokushima, 770-8503 Japan
| | - Maki Shinzawa
- grid.136593.b0000 0004 0373 3971Department of Nephrology, Graduate School of Medicine, Osaka University, Osaka, 565-0871 Japan
| | - Miyae Yamakawa
- grid.136593.b0000 0004 0373 3971Division of Health Sciences, Graduate School of Medicine, Osaka University, Osaka, 565-0871 Japan
| | - Sakiko Fukui
- grid.136593.b0000 0004 0373 3971Division of Health Sciences, Graduate School of Medicine, Osaka University, Osaka, 565-0871 Japan ,grid.265073.50000 0001 1014 9130Department of Home and Palliative Care Nursing, Graduate School of Health Care Sciences, Tokyo Medical and Dental University, Tokyo, 113-8519 Japan
| | - Toshiki Moriyama
- grid.136593.b0000 0004 0373 3971Health Care Division, Health and Counseling Center, Osaka University, Osaka, 560-0043 Japan ,grid.136593.b0000 0004 0373 3971Department of Nephrology, Graduate School of Medicine, Osaka University, Osaka, 565-0871 Japan ,grid.136593.b0000 0004 0373 3971Health Promotion and Regulation, Department of Health Promotion Medicine, Osaka University Graduate School of Medicine, Osaka, 565-0871 Japan
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16
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Martínez-Hervás S, Morales-Suarez-Varela MM, Andrés-Blasco I, Lara-Hernández F, Peraita-Costa I, Real JT, García-García AB, Chaves FJ. Developing a simple and practical decision model to predict the risk of incident type 2 diabetes among the general population: The Di@bet.es Study. Eur J Intern Med 2022; 102:80-87. [PMID: 35570127 DOI: 10.1016/j.ejim.2022.05.005] [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: 01/26/2022] [Revised: 04/08/2022] [Accepted: 05/03/2022] [Indexed: 11/28/2022]
Abstract
AIMS To develop a simple multivariate predictor model of incident type 2 diabetes in general population. METHODS Participants were recruited from the Spanish Di@bet.es cohort study with 2570 subjects meeting all criteria to be included in the at-risk sample studied here. Information was collected using an interviewer-administered structured questionnaire, followed by physical and clinical examination. CHAID algorithm, which collects the information of individuals with and without type 2 diabetes, was used to develop a decision tree based type 2 diabetes prediction model. RESULTS 156 individuals were identified as having developed type 2 diabetes (6.5% incidence). Fasting plasma glucose (FPG) at the beginning of the study was the main predictive variable for incident type 2 diabetes: FPG ≤ 92 mg/dL (ref.), 92-106 mg/dL (OR = 3.76, 95%CI = 2.36-6.00), > 106 mg/dL (OR = 13.21; 8.26-21.12). More than 25% of subjects starting follow-up with FPG levels > 106 mg/dL developed type 2 diabetes. When FPG <106 mg/dL, other variables (fasting triglycerides (FTGs), BMI or age) were needed. For levels ≤ 92 mg/dL, higher FTGs levels increased risk of incident type 2 diabetes (FTGs > 180 mg/dL, OR = 14.57; 4.89-43.40) compared with the group of FTGs ≤ 97 mg/dL (FTGs = 97-180 mg/dL, OR = 3.12; 1.05-9.24). This model correctly classified 93.5% of individuals. CONCLUSIONS The type 2 diabetes prediction model is based on FTGs, FPG, age, gender, and BMI values. Utilizing commonly available clinical data and a simple blood test, a simple tree diagram helps identify subjects at risk of developing type 2 diabetes, even in apparently low risk subjects with normal FPG.
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Affiliation(s)
- Sergio Martínez-Hervás
- Department of Medicine, University of Valencia, Avenida Blasco Ibañez 15, Valencia 46010, Spain; Service of Endocrinology and Nutrition, Valencia University Clinical Hospital, Avenida Blasco Ibañez 17, Valencia 46010, Spain; INCLIVA Biomedical Research Institute, Menendez Pelayo 4acc, Valencia 46010, Spain; CIBER of Diabetes and Associated Metabolic Diseases CIBERDEM, Monforte de Lemos 3-5, Madrid 28029, Spain
| | - María M Morales-Suarez-Varela
- Department of Preventive Medicine, Unit of Public Health and Environmental Care, University of Valencia, Vicente Andres Estelles Avenue, Burjassot, Valencia 46100, Spain; CIBER of Epidemiology and Public Health (CIBERESP), Monforte de Lemos 3-5, Madrid 28029, Spain
| | - Irene Andrés-Blasco
- Genomic and Diabetes Unit, INCLIVA Biomedical Research Institute, Menendez Pelayo 4acc, Valencia 46010, Spain
| | - Francisco Lara-Hernández
- Genomic and Diabetes Unit, INCLIVA Biomedical Research Institute, Menendez Pelayo 4acc, Valencia 46010, Spain
| | - Isabel Peraita-Costa
- Department of Preventive Medicine, Unit of Public Health and Environmental Care, University of Valencia, Vicente Andres Estelles Avenue, Burjassot, Valencia 46100, Spain; CIBER of Epidemiology and Public Health (CIBERESP), Monforte de Lemos 3-5, Madrid 28029, Spain
| | - José T Real
- Department of Medicine, University of Valencia, Avenida Blasco Ibañez 15, Valencia 46010, Spain; Service of Endocrinology and Nutrition, Valencia University Clinical Hospital, Avenida Blasco Ibañez 17, Valencia 46010, Spain; INCLIVA Biomedical Research Institute, Menendez Pelayo 4acc, Valencia 46010, Spain; CIBER of Diabetes and Associated Metabolic Diseases CIBERDEM, Monforte de Lemos 3-5, Madrid 28029, Spain.
| | - Ana-Bárbara García-García
- CIBER of Diabetes and Associated Metabolic Diseases CIBERDEM, Monforte de Lemos 3-5, Madrid 28029, Spain; Genomic and Diabetes Unit, INCLIVA Biomedical Research Institute, Menendez Pelayo 4acc, Valencia 46010, Spain.
| | - F Javier Chaves
- CIBER of Diabetes and Associated Metabolic Diseases CIBERDEM, Monforte de Lemos 3-5, Madrid 28029, Spain; Genomic and Diabetes Unit, INCLIVA Biomedical Research Institute, Menendez Pelayo 4acc, Valencia 46010, Spain
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17
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Heider AK, Mang H. Integration of Risk Scores and Integration Capability in Electronic Patient Records. Appl Clin Inform 2022; 13:828-835. [PMID: 36070800 PMCID: PMC9451952 DOI: 10.1055/s-0042-1756367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 07/13/2022] [Indexed: 11/02/2022] Open
Abstract
BACKGROUND Digital availability of patient data is continuously improving with the increasing implementation of electronic patient records in physician practices. The emergence of digital health data defines new fields of application for data analytics applications, which in turn offer extensive options of using data. Common areas of data analytics applications include decision support, administration, and fraud detection. Risk scores play an important role in compiling algorithms that underlay tools for decision support. OBJECTIVES This study aims to identify the current state of risk score integration and integration capability in electronic patient records for cardiovascular disease and diabetes in German primary care practices. METHODS We developed an evaluation framework to determine the current state of risk score integration and future integration options for four cardiovascular disease risk scores (arriba, Pooled Cohort Equations, QRISK3, and Systematic Coronary Risk Evaluation) and two diabetes risk scores (Finnish Diabetes Risk Score and German Diabetes Risk Score). We then used this framework to evaluate the integration of risk scores in common practice software solutions by examining the software and inquiring the respective software contact person. RESULTS Our evaluation showed that the most widely integrated risk score is arriba, as recommended by German medical guidelines. Every software version in our sample provided either an interface to arriba or the option to implement one. Our assessment of integration capability revealed a more nuanced picture. Results on data availability were mixed. Each score contains at least one variable, which requires laboratory diagnostics. Our analysis of data standardization showed that only one score documented all variables in a standardized way. CONCLUSION Our assessment revealed that the current state of risk score integration in physician practice software is rather low. Integration capability currently faces some obstacles. Future research should develop a comprehensive framework that considers the reasonable integration of risk scores into practice workflows, disease prevention programs, and the awareness of physicians and patients.
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Affiliation(s)
- Ann-Kathrin Heider
- Faculty of Medicine, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Harald Mang
- Universitätsklinikum Erlangen, Erlangen, Germany
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18
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Anyasodor AE, Nwose EU, Bwititi PT, Richards RS. Cost-effectiveness of community diabetes screening: Application of Akaike information criterion in rural communities of Nigeria. Front Public Health 2022; 10:932631. [PMID: 35958851 PMCID: PMC9357922 DOI: 10.3389/fpubh.2022.932631] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 06/28/2022] [Indexed: 11/13/2022] Open
Abstract
Background The prevalence of diabetes mellitus (DM) is increasing globally, and this requires several approaches to screening. There are reports of alternative indices for prediction of DM, besides fasting blood glucose (FBG) level. This study, investigated the ability of combination of biochemical and anthropometric parameters and orodental disease indicators (ODIs) to generate models for DM prediction, using Akaike information criterion (AIC) to substantiate health economics of diabetes screening. Methods Four hundred and thirty-three subjects were enrolled in the study in Ndokwa communities, Delta State, Nigeria, and their glycaemic status was determined, using the CardioChek analyser® and previous data from the Prediabetes and Cardiovascular Complications Study were also used. The cost of screening for diabetes (NGN 300 = $0.72) in a not-for-profit organization/hospital was used as basis to calculate the health economics of number of individuals with DM in 1,000 participants. Data on the subjects' anthropometric, biochemical and ODI parameters were used to generate different models, using R statistical software (version 4.0.0). The different models were evaluated for their AIC values. Lowest AIC was considered as best model. Microsoft Excel software (version 2020) was used in preliminary analysis. Result The cost of identifying <2 new subjects with hyperglycemia, in 1,000 people was ≥NGN 300,000 ($ 716). A total of 4,125 models were generated. AIC modeling indicates FBG test as the best model (AIC = 4), and the least being combination of random blood sugar + waist circumference + hip circumference (AIC ≈ 34). Models containing ODI parameters had AIC values >34, hence considered as not recommendable. Conclusion The cost of general screening for diabetes in rural communities may appear high and burdensome in terms of health economics. However, the use of prediction models involving AIC is of value in terms of cost-benefit and cost-effectiveness to the healthcare consumers, which favors health economics.
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Affiliation(s)
- Anayochukwu Edward Anyasodor
- School of Dentistry and Medical Sciences, Charles Sturt University, Orange, NSW, Australia
- *Correspondence: Anayochukwu Edward Anyasodor
| | - Ezekiel Uba Nwose
- School of Dentistry and Medical Sciences, Charles Sturt University, Orange, NSW, Australia
- Department of Public and Community Health, Novena University, Kwale, Nigeria
| | | | - Ross Stuart Richards
- School of Dentistry and Medical Sciences, Charles Sturt University, Orange, NSW, Australia
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19
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Chekin N, Ayatollahi H, Karimi Zarchi M. A Clinical Decision Support System for Assessing the Risk of Cervical Cancer: Development and Evaluation Study. JMIR Med Inform 2022; 10:e34753. [PMID: 35731549 PMCID: PMC9260527 DOI: 10.2196/34753] [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: 11/06/2021] [Revised: 02/05/2022] [Accepted: 02/25/2022] [Indexed: 11/21/2022] Open
Abstract
Background Cervical cancer has been recognized as a preventable type of cancer. As the assessment of all the risk factors of a disease is challenging for physicians, information technology and risk assessment models have been used to estimate the degree of risk. Objective The aim of this study was to develop a clinical decision support system to assess the risk of cervical cancer. Methods This study was conducted in 2 phases in 2021. In the first phase of the study, 20 gynecologists completed a questionnaire to determine the essential parameters for assessing the risk of cervical cancer, and the data were analyzed using descriptive statistics. In the second phase of the study, the prototype of the clinical decision support system was developed and evaluated. Results The findings revealed that the most important parameters for assessing the risk of cervical cancer consisted of general and specific parameters. In total, the 8 parameters that had the greatest impact on the risk of cervical cancer were selected. After developing the clinical decision support system, it was evaluated and the mean values of sensitivity, specificity, and accuracy were 85.81%, 93.82%, and 91.39%, respectively. Conclusions The clinical decision support system developed in this study can facilitate the process of identifying people who are at risk of developing cervical cancer. In addition, it can help to increase the quality of health care and reduce the costs associated with the treatment of cervical cancer.
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Affiliation(s)
- Nasrin Chekin
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Haleh Ayatollahi
- Health Management and Economics Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran
| | - Mojgan Karimi Zarchi
- Department of Obstetrics and Gynecology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.,Endometriosis Research Center, Iran University of Medical Sciences, Tehran, Iran
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20
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Dong W, Cheng WHG, Tse ETY, Mi Y, Wong CKH, Tang EHM, Yu EYT, Chin WY, Bedford LE, Ko WWK, Chao DVK, Tan KCB, Lam CLK. 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. BMJ Open 2022; 12:e059430. [PMID: 35613775 PMCID: PMC9131118 DOI: 10.1136/bmjopen-2021-059430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 04/28/2022] [Indexed: 12/01/2022] Open
Abstract
INTRODUCTION Diabetes 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 ANALYSIS A 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 DISSEMINATION Ethics 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 NUMBER US ClinicalTrial.gov: NCT04881383; HKU clinical trials registry: HKUCTR-2808; Pre-results.
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Affiliation(s)
- Weinan Dong
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Will Ho Gi Cheng
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Emily Tsui Yee Tse
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
- Department of Family Medicine, The University of Hong Kong Shenzhen Hospital, Shenzhen, People's Republic of China
| | - Yuqi Mi
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Carlos King Ho Wong
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Eric Ho Man Tang
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Esther Yee Tak Yu
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Weng Yee Chin
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Laura Elizabeth Bedford
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Welchie Wai Kit Ko
- Family Medicine and Primary Healthcare Department, Queen Mary Hospital, Hong Kong West Cluster, Hospital Authority, Hong Kong, People's Republic of China
| | - David Vai Kiong Chao
- Department of Family Medicine & Primary Health Care, United Christian Hospital, Kowloon East Cluster, Hospital Authority, Hong Kong, People's Republic of China
- Department of Family Medicine & Primary Health Care, Tseung Kwan O Hospital, Kowloon East Cluster, Hospital Authority, Hong Kong, People's Republic of China
| | - Kathryn Choon Beng Tan
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Cindy Lo Kuen Lam
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
- Department of Family Medicine, The University of Hong Kong Shenzhen Hospital, Shenzhen, People's Republic of China
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21
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Allaoui G, Rylander C, Averina M, Wilsgaard T, Fuskevåg O, Berg V. Longitudinal changes in blood biomarkers and their ability to predict type 2 diabetes mellitus—The Tromsø study. Endocrinol Diabetes Metab 2022; 5:e00325. [PMID: 35147293 PMCID: PMC8917864 DOI: 10.1002/edm2.325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 01/31/2022] [Accepted: 02/02/2022] [Indexed: 11/07/2022] Open
Abstract
Introduction Methods Results Conclusion
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Affiliation(s)
- Giovanni Allaoui
- Division of Diagnostic Services Department of Laboratory Medicine University Hospital of North Norway Tromsø Norway
- Department of Medical Biology Faculty of Health Sciences UiT‐The Arctic University of Norway Tromsø Norway
| | - Charlotta Rylander
- Department of Community Medicine Faculty of Health Sciences UIT‐The Arctic University of Norway Tromsø Norway
| | - Maria Averina
- Division of Diagnostic Services Department of Laboratory Medicine University Hospital of North Norway Tromsø Norway
- Department of Community Medicine Faculty of Health Sciences UIT‐The Arctic University of Norway Tromsø Norway
| | - Tom Wilsgaard
- Department of Community Medicine Faculty of Health Sciences UIT‐The Arctic University of Norway Tromsø Norway
| | - Ole‐Martin Fuskevåg
- Division of Diagnostic Services Department of Laboratory Medicine University Hospital of North Norway Tromsø Norway
| | - Vivian Berg
- Division of Diagnostic Services Department of Laboratory Medicine University Hospital of North Norway Tromsø Norway
- Department of Medical Biology Faculty of Health Sciences UiT‐The Arctic University of Norway Tromsø Norway
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Ortiz-Martínez M, González-González M, Martagón AJ, Hlavinka V, Willson RC, Rito-Palomares M. Recent Developments in Biomarkers for Diagnosis and Screening of Type 2 Diabetes Mellitus. Curr Diab Rep 2022; 22:95-115. [PMID: 35267140 PMCID: PMC8907395 DOI: 10.1007/s11892-022-01453-4] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/27/2022] [Indexed: 11/03/2022]
Abstract
PURPOSE OF REVIEW Diabetes mellitus is a complex, chronic illness characterized by elevated blood glucose levels that occurs when there is cellular resistance to insulin action, pancreatic β-cells do not produce sufficient insulin, or both. Diabetes prevalence has greatly increased in recent decades; consequently, it is considered one of the fastest-growing public health emergencies globally. Poor blood glucose control can result in long-term micro- and macrovascular complications such as nephropathy, retinopathy, neuropathy, and cardiovascular disease. Individuals with diabetes require continuous medical care, including pharmacological intervention as well as lifestyle and dietary changes. RECENT FINDINGS The most common form of diabetes mellitus, type 2 diabetes (T2DM), represents approximately 90% of all cases worldwide. T2DM occurs more often in middle-aged and elderly adults, and its cause is multifactorial. However, its incidence has increased in children and young adults due to obesity, sedentary lifestyle, and inadequate nutrition. This high incidence is also accompanied by an estimated underdiagnosis prevalence of more than 50% worldwide. Implementing successful and cost-effective strategies for systematic screening of diabetes mellitus is imperative to ensure early detection, lowering patients' risk of developing life-threatening disease complications. Therefore, identifying new biomarkers and assay methods for diabetes mellitus to develop robust, non-invasive, painless, highly-sensitive, and precise screening techniques is essential. This review focuses on the recent development of new clinically validated and novel biomarkers as well as the methods for their determination that represent cost-effective alternatives for screening and early diagnosis of T2DM.
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Affiliation(s)
- Margarita Ortiz-Martínez
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Nuevo León, México
| | - Mirna González-González
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Nuevo León, México.
- Tecnologico de Monterrey, The Institute for Obesity Research, Monterrey, Nuevo León, México.
| | - Alexandro J Martagón
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Nuevo León, México
- Tecnologico de Monterrey, The Institute for Obesity Research, Monterrey, Nuevo León, México
- Unidad de Investigación de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, México City, México
| | - Victoria Hlavinka
- Department of Chemical and Biomolecular Engineering, University of Houston, Houston, TX, USA
| | - Richard C Willson
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Nuevo León, México
- Department of Chemical and Biomolecular Engineering, University of Houston, Houston, TX, USA
| | - Marco Rito-Palomares
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Nuevo León, México
- Tecnologico de Monterrey, The Institute for Obesity Research, Monterrey, Nuevo León, México
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Tsao CW, Aday AW, Almarzooq ZI, Alonso A, Beaton AZ, Bittencourt MS, Boehme AK, Buxton AE, Carson AP, Commodore-Mensah Y, Elkind MSV, Evenson KR, Eze-Nliam C, Ferguson JF, Generoso G, Ho JE, Kalani R, Khan SS, Kissela BM, Knutson KL, Levine DA, Lewis TT, Liu J, Loop MS, Ma J, Mussolino ME, Navaneethan SD, Perak AM, Poudel R, Rezk-Hanna M, Roth GA, Schroeder EB, Shah SH, Thacker EL, VanWagner LB, Virani SS, Voecks JH, Wang NY, Yaffe K, Martin SS. Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association. Circulation 2022; 145:e153-e639. [PMID: 35078371 DOI: 10.1161/cir.0000000000001052] [Citation(s) in RCA: 2489] [Impact Index Per Article: 1244.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2022 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population and an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, and the global burden of cardiovascular disease and healthy life expectancy. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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Jin S, Chen Q, Han X, Liu Y, Cai M, Yao Z, Lu H. Comparison of the Finnish Diabetes Risk Score Model With the Metabolic Syndrome in a Shanghai Population. Front Endocrinol (Lausanne) 2022; 13:725314. [PMID: 35273562 PMCID: PMC8902815 DOI: 10.3389/fendo.2022.725314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 01/27/2022] [Indexed: 11/13/2022] Open
Abstract
AIMS This study aimed to compare the diagnostic accuracy of the metabolic syndrome with the Finnish Diabetes Risk Score (FINDRISC) to screen for type 2 diabetes mellitus (T2DM) in a Shanghai population. METHODS Participants aged 25-64 years were recruited from a Shanghai population from July 2019 to March 2020. Each participant underwent a standard metabolic work-up, including clinical examination with anthropometry. Glucose status was tested using hemoglobin A1c (HbAlc), 2h-post-load glucose (2hPG), and fasting blood glucose (FBG). The FINDRISC questionnaire and the metabolic syndrome were examined. The performance of the FINDRISC was assessed using the area under the receiver operating characteristic curve (AUC-ROC). RESULTS Of the 713 subjects, 9.1% were diagnosed with prediabetes, whereas 5.2% were diagnosed with T2DM. A total of 172 subjects had the metabolic syndrome. A higher FINDRISC score was positively associated with the prevalence of T2DM and the metabolic syndrome. Multivariable linear regression analysis demonstrated that the FINDRISC had a linear regression relationship with 2hPG levels (b'= 036, p < 0.0001). The AUC-ROC of the FINDRISC to identify subjects with T2DM among the total population was 0.708 (95% CI 0.639-0.776), the sensitivity was 44.6%, and the specificity was 90.1%, with 11 as the cut-off point. After adding FBG or 2hPG to the FINDRISC, the AUC-ROC among the total population significantly increased to 0.785 (95% CI 0.671-0.899) and 0.731 (95% CI 0.619-0.843), respectively, while the AUC-ROC among the female group increased to 0.858 (95% CI 0.753-0.964) and 0.823 (95% CI 0.730-0.916), respectively (p < 0.001). The AUC-ROC of the metabolic syndrome to identify subjects with T2DM among the total and female population was 0.805 (95% CI 0.767-0.844) and 0.830 (95% CI 0.788-0.872), respectively, with seven as the cut-off point. CONCLUSIONS The metabolic syndrome performed better than the FINDRISC model. The metabolic syndrome and the FINDRISC with FBG or 2hPG in a two-step screening model are both efficacious clinical practices for predicting T2DM in a Shanghai population.
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Affiliation(s)
| | | | | | | | | | - Zheng Yao
- *Correspondence: Zheng Yao, ; Hao Lu,
| | - Hao Lu
- *Correspondence: Zheng Yao, ; Hao Lu,
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25
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De Pergola G, Castellana F, Zupo R, De Nucci S, Panza F, Castellana M, Lampignano L, Di Chito M, Triggiani V, Sardone R, Giannelli G. A family history of type 2 diabetes as a predictor of fatty liver disease in diabetes-free individuals with excessive body weight. Sci Rep 2021; 11:24084. [PMID: 34916558 PMCID: PMC8677812 DOI: 10.1038/s41598-021-03583-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 12/06/2021] [Indexed: 12/12/2022] Open
Abstract
Comprehensive screening for non-alcoholic fatty liver disease (NAFLD) may help prompt clinical management of fatty liver disease. A family history, especially of diabetes, has been little studied as a predictor for NAFLD. We characterized the cross-sectional relationship between a family history of type 2 diabetes (FHT2D) and NAFLD probability in 1185 diabetes-free Apulian (Southern-Italy) subjects aged > 20 years with overweight or obesity not receiving any drug or supplementation. Clinical data and routine biochemistry were analysed. NAFLD probability was defined using the fatty liver index (FLI). A first-degree FHT2D was assessed by interviewing subjects and assigning a score of 0, 1, or 2 if none, only one, or both parents were affected by type 2 diabetes mellitus (T2DM). Our study population featured most females (70.9%, N = 840), and 48.4% (N = 574) of the sample had first-degree FHT2D. After dividing the sample by a FHT2D, we found a higher BMI, Waist Circumference (WC), and diastolic blood pressure shared by FHT2D subjects; they also showed altered key markers of glucose homeostasis, higher triglyceride levels, and worse liver function. FLI scores were significantly lower in subjects without a first-degree FHT2D. After running logistic regression models, a FHT2D was significantly associated with the NAFLD probability, even adjusting for major confounders and stratifying by age (under and over 40 years of age). A FHT2D led to an almost twofold higher probability of NAFLD, regardless of confounding factors (OR 2.17, 95% CI 1.63 to 2.89). A first-degree FHT2D acts as an independent determinant of NAFLD in excess weight phenotypes, regardless of the age group (younger or older than 40 years). A NAFLD risk assessment within multidimensional screening might be useful in excess weight subjects reporting FHT2D even in the absence of diabetes.
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Affiliation(s)
- Giovanni De Pergola
- Unit of Geriatrics and Internal Medicine, National Institute of Gastroenterology "Saverio de Bellis", Research Hospital, 70013, Castellana Grotte, BA, Italy.
| | - Fabio Castellana
- Unit of Data Sciences and Technology Innovation for Population Health, National Institute of Gastroenterology "Saverio de Bellis", Research Hospital, 70013, Castellana Grotte, BA, Italy
| | - Roberta Zupo
- Unit of Data Sciences and Technology Innovation for Population Health, National Institute of Gastroenterology "Saverio de Bellis", Research Hospital, 70013, Castellana Grotte, BA, Italy
| | - Sara De Nucci
- Unit of Data Sciences and Technology Innovation for Population Health, National Institute of Gastroenterology "Saverio de Bellis", Research Hospital, 70013, Castellana Grotte, BA, Italy
| | - Francesco Panza
- Unit of Data Sciences and Technology Innovation for Population Health, National Institute of Gastroenterology "Saverio de Bellis", Research Hospital, 70013, Castellana Grotte, BA, Italy
| | - Marco Castellana
- Unit of Data Sciences and Technology Innovation for Population Health, National Institute of Gastroenterology "Saverio de Bellis", Research Hospital, 70013, Castellana Grotte, BA, Italy
| | - Luisa Lampignano
- Unit of Data Sciences and Technology Innovation for Population Health, National Institute of Gastroenterology "Saverio de Bellis", Research Hospital, 70013, Castellana Grotte, BA, Italy
| | - Martina Di Chito
- Unit of Geriatrics and Internal Medicine, National Institute of Gastroenterology "Saverio de Bellis", Research Hospital, 70013, Castellana Grotte, BA, Italy
| | - Vincenzo Triggiani
- Section of Internal Medicine, Geriatrics, Endocrinology, and Rare Disease, Interdisciplinary Department of Medicine, School of Medicine, University of Bari, 70124, Bari, Italy
| | - Rodolfo Sardone
- Unit of Data Sciences and Technology Innovation for Population Health, National Institute of Gastroenterology "Saverio de Bellis", Research Hospital, 70013, Castellana Grotte, BA, Italy
| | - Gianluigi Giannelli
- Scientific Direction, National Institute of Gastroenterology "Saverio de Bellis", Research Hospital, 70013, Castellana Grotte, BA, Italy
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Kavian F, Benton F, Mcgill J, Luscombe-Marsh N. Characterizing screening strategies for type 2 diabetes in high-risk ethnic communities: a scoping review protocol. JBI Evid Synth 2021; 19:3402-3411. [PMID: 34545015 DOI: 10.11124/jbies-20-00492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
OBJECTIVE This review aims to identify the characteristics of screening strategies for type 2 diabetes to determine the most pragmatic approach to improve relevance to high-risk community groups from ethnically diverse backgrounds. INTRODUCTION Type 2 diabetes is increasingly contributing to the global burden of disease and is more common in some community groups. Although screening underpins the success of primary prevention programs for type 2 diabetes, screening of high-risk community groups from ethnically diverse backgrounds require different screening protocols and can be challenging. These strategies have never been systematically scoped. INCLUSION CRITERIA This scoping review will consider screening strategies for type 2 diabetes that target high-risk ethnic community groups. Studies with adults older than 18 years will be considered for inclusion. Screening strategies may include, but are not limited to, risk-assessment questionnaires, blood tests, or both, using an opportunistic approach involving general practices or a targeted approach toward high-risk community groups from ethnically diverse backgrounds. Experimental and observational quantitative studies and mixed methods studies will be included. METHODS MEDLINE, CINAHL, PsycINFO, Informit, ProQuest, Web of Science, and Scopus will be searched. Studies will be screened for inclusion by two independent reviewers, and data will be extracted using the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework. Results will be summarized in tables accompanied by narrative text.
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Affiliation(s)
- Foorough Kavian
- Research and Program Development, Diabetes SA, Adelaide, SA, Australia
| | - Fiona Benton
- Research and Program Development, Diabetes SA, Adelaide, SA, Australia
| | - Josephine Mcgill
- Corporate Services, Library, Flinders University, Adelaide, SA, Australia
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Thamakaison S, Anothaisintawee T, Sukhato K, Unwanatham N, Rattanasiri S, Reutrakul S, Thakkinstian A. Hemoglobin A1c in combination with fasting plasma glucose trumps fasting plasma glucose alone as predictive indicators for diabetes mellitus: an ambidirectional cohort study of Thai people with impaired fasting glucose. BMJ Open Diabetes Res Care 2021; 9:9/2/e002427. [PMID: 34845059 PMCID: PMC8634022 DOI: 10.1136/bmjdrc-2021-002427] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 11/05/2021] [Indexed: 01/16/2023] Open
Abstract
INTRODUCTION This ambidirectional cohort study aimed to assess the performance of combining hemoglobin A1c (HbA1c) to fasting plasma glucose (FPG) for estimation of progression rate to diabetes mellitus (DM) and to explore the risk factors of DM in patients with impaired fasting glucose (IFG). RESEARCH DESIGN AND METHODS Patients with IFG were eligible for this study. IFG was defined as FPG of 100-125 mg/dL. Progression rates to DM were estimated using Kaplan-Meier analysis. Risk factors of DM were explored by Cox regression analysis. RESULTS 3011 patients were enrolled with median follow-up time of 8 years (range: 6 months-29 years). Progression rates to DM in patients with FPG 100-109 mg/dL and 110-125 mg/dL were 2.64 and 4.79 per 100 person-years. After adjusting covariables, compared with patients with FPG 100-109 mg/dL plus normal HbA1c (<5.7%), hazard ratios (95% CI) of patients with FPG 110-125 plus normal HbA1c, FBG 100-109 plus abnormal HbA1c (5.7%-6.49%), and FPG 110-125 plus abnormal HbA1c were 5.89 (2.37 to 14.63), 16.30 (8.59 to 30.92), and 33.84 (16.41 to 69.78), respectively. Body mass index ≥27.5 kg/m2, serum triglyceride level ≥150 mg/dL, family history of DM, and low level of high-density lipoprotein-cholesterol were independently associated with risk of DM in patients with IFG. CONCLUSIONS Patients with both IFG and abnormal HbA1c had higher risk of DM than patients with IFG alone. Therefore, performing HbA1c in combination with FPG helps to identify subgroups of people with IFG at highest risk of DM. These patients should have the highest priority in diabetes prevention programs, especially in countries with low and limited resources.
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Affiliation(s)
- Sangsulee Thamakaison
- Department of Family Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Thunyarat Anothaisintawee
- Department of Family Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Kanokporn Sukhato
- Department of Family Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Nattawut Unwanatham
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Sasivimol Rattanasiri
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Sirimon Reutrakul
- Department of Endocrinology, Diabetes, and Metabolism, Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Ammarin Thakkinstian
- Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
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28
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Screening for identifying individuals at risk of developing type 2 diabetes using the Canadian diabetes risk (CANRISK) questionnaire. J Public Health (Oxf) 2021. [DOI: 10.1007/s10389-021-01606-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
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Buccheri E, Dell'Aquila D, Russo M. Artificial intelligence in health data analysis: The Darwinian evolution theory suggests an extremely simple and zero-cost large-scale screening tool for prediabetes and type 2 diabetes. Diabetes Res Clin Pract 2021; 174:108722. [PMID: 33647331 DOI: 10.1016/j.diabres.2021.108722] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 02/02/2021] [Accepted: 02/09/2021] [Indexed: 12/11/2022]
Abstract
AIMS The effective identification of individuals with early dysglycemia status is key to reduce the incidence of type 2 diabetes. We develop and validate a novel zero-cost tool that significantly simplifies the screening of undiagnosed dysglycemia. METHODS We use NHANES cross-sectional data over 10 years (2007-2016) to derive an equation that links non-laboratory exposure variables to the possible presence of undetected dysglycemia. For the first time, we adopt a novel artificial intelligence approach based on the Darwinian evolutionary theory to analyze health data. We collected data for 47 variables. RESULTS Age and waist circumference are the only variables required to use the model. To identify undetected dysglycemia, we obtain an area under the curve (AUC) of 75.3%. Sensitivity and specificity are 0.65 and 0.73 by using the optimal threshold value determined from external validation data. CONCLUSIONS The use of uniquely two variables allows to obtain a zero-cost screening tool of analogous precision than that of more complex tools widely adopted in the literature. The newly developed tool has clinical use as it significantly simplifies the screening of dysglycemia. Furthermore, we suggest that the definition of an age-related waist circumference cut-off might help to improve existing diabetes risk factors.
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Affiliation(s)
| | - Daniele Dell'Aquila
- Department of Chemistry and Pharmacy, University of Sassari, Sassari, Italy; INFN - Laboratori Nazionali del Sud, Catania, Italy
| | - Marco Russo
- Department of Physics and Astronomy, University of Catania, Catania, Italy; INFN - Sezione di Catania, Catania, Italy
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30
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Thorand B, Zierer A, Büyüközkan M, Krumsiek J, Bauer A, Schederecker F, Sudduth-Klinger J, Meisinger C, Grallert H, Rathmann W, Roden M, Peters A, Koenig W, Herder C, Huth C. A Panel of 6 Biomarkers Significantly Improves the Prediction of Type 2 Diabetes in the MONICA/KORA Study Population. J Clin Endocrinol Metab 2021; 106:e1647-e1659. [PMID: 33382400 PMCID: PMC7993565 DOI: 10.1210/clinem/dgaa953] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Indexed: 12/29/2022]
Abstract
CONTEXT Improved strategies to identify persons at high risk of type 2 diabetes are important to target costly preventive efforts to those who will benefit most. OBJECTIVE This work aimed to assess whether novel biomarkers improve the prediction of type 2 diabetes beyond noninvasive standard clinical risk factors alone or in combination with glycated hemoglobin A1c (HbA1c). METHODS We used a population-based case-cohort study for discovery (689 incident cases and 1850 noncases) and an independent cohort study (262 incident cases, 2549 noncases) for validation. An L1-penalized (lasso) Cox model was used to select the most predictive set among 47 serum biomarkers from multiple etiological pathways. All variables available from the noninvasive German Diabetes Risk Score (GDRSadapted) were forced into the models. The C index and the category-free net reclassification index (cfNRI) were used to evaluate the predictive performance of the selected biomarkers beyond the GDRSadapted model (plus HbA1c). RESULTS Interleukin-1 receptor antagonist, insulin-like growth factor binding protein 2, soluble E-selectin, decorin, adiponectin, and high-density lipoprotein cholesterol were selected as the most relevant biomarkers. The simultaneous addition of these 6 biomarkers significantly improved the predictive performance both in the discovery (C index [95% CI], 0.053 [0.039-0.066]; cfNRI [95% CI], 67.4% [57.3%-79.5%]) and the validation study (0.034 [0.019-0.053]; 48.4% [35.6%-60.8%]). Significant improvements by these biomarkers were also seen on top of the GDRSadapted model plus HbA1c in both studies. CONCLUSION The addition of 6 biomarkers significantly improved the prediction of type 2 diabetes when added to a noninvasive clinical model or to a clinical model plus HbA1c.
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Affiliation(s)
- Barbara Thorand
- Institute of Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Correspondence: Barbara Thorand, PhD, MPH, Helmholtz Zentrum München GmbH, Institute of Epidemiology, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany.
| | - Astrid Zierer
- Institute of Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
| | - Mustafa Büyüközkan
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Jan Krumsiek
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Alina Bauer
- Institute of Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
| | - Florian Schederecker
- Institute of Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
| | | | - Christa Meisinger
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Chair of Epidemiology, Ludwig-Maximilians-Universität München, UNIKA-T Augsburg, Augsburg, Germany
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
| | - Harald Grallert
- Institute of Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Wolfgang Rathmann
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Michael Roden
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- German Centre for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany
| | - Wolfgang Koenig
- German Centre for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
- Institute of Epidemiology and Medical Biometry, University of Ulm, Ulm, Germany
| | - Christian Herder
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Cornelia Huth
- Institute of Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
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31
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Virani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Cheng S, Delling FN, Elkind MSV, Evenson KR, Ferguson JF, Gupta DK, Khan SS, Kissela BM, Knutson KL, Lee CD, Lewis TT, Liu J, Loop MS, Lutsey PL, Ma J, Mackey J, Martin SS, Matchar DB, Mussolino ME, Navaneethan SD, Perak AM, Roth GA, Samad Z, Satou GM, Schroeder EB, Shah SH, Shay CM, Stokes A, VanWagner LB, Wang NY, Tsao CW. Heart Disease and Stroke Statistics-2021 Update: A Report From the American Heart Association. Circulation 2021; 143:e254-e743. [PMID: 33501848 DOI: 10.1161/cir.0000000000000950] [Citation(s) in RCA: 3101] [Impact Index Per Article: 1033.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2021 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors related to cardiovascular disease. RESULTS Each of the 27 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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Schwatka NV, Smith DE, Golden A, Tran M, Newman LS, Cragle D. Development and validation of a diabetes risk score among two populations. PLoS One 2021; 16:e0245716. [PMID: 33493190 PMCID: PMC7833146 DOI: 10.1371/journal.pone.0245716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 01/06/2021] [Indexed: 11/18/2022] Open
Abstract
The purpose of this study was to assess the validity of a practical diabetes risk score amongst two heterogenous populations, a working population and a non-working population. Study population 1 (n = 2,089) participated in a large-scale screening program offered to retired workers to discover previously undetected/incipient chronic illness. Study population 2 (n = 3,293) was part of a Colorado worksite wellness program health risk assessment. We assessed the relationship between a continuous diabetes risk score at baseline and development of diabetes in the future using logistic regression. Receiver operating curves and sensitivity/specificity of the models were calculated. Across both study populations, we observed that participants with diabetes at follow-up had higher diabetes risk scores at baseline than participants who did not have diabetes at follow-up. On average, the odds ratio of developing diabetes in the future was 1.38 (95% CI: 1.26-1.50, p < 0.0001) for study population 1 and 1.68 (95% CI: 1.45-1.95, p-value < 0.0001) for study population 2. These findings indicate that the diabetes risk score may be generalizable to diverse individuals, and thus potentially a population level diabetes screening tool. Minimally-invasive diabetes risk scores can aid in the identification of sub-populations of individuals at risk for diabetes.
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Affiliation(s)
- Natalie V. Schwatka
- Center for Health, Work & Environment, Colorado School of Public Health, University of Colorado, Anschutz Medical Campus, Aurora, Colorado, United States of America
- Department of Environmental and Occupational Health, Colorado School of Public Health, University of Colorado, Anschutz Medical Campus, Aurora, Colorado, United States of America
- * E-mail:
| | - Derek E. Smith
- Department of Pediatrics, Cancer Center Biostatistics Core, University of Colorado and Children’s Hospital Colorado, Aurora, Colorado, United States of America
| | - Ashley Golden
- Oak Ridge Associated Universities, Oak Ridge, Tennessee, United States of America
| | - Molly Tran
- Center for Health, Work & Environment, Colorado School of Public Health, University of Colorado, Anschutz Medical Campus, Aurora, Colorado, United States of America
- OpenPlans, New York, New York, United States of America
| | - Lee S. Newman
- Center for Health, Work & Environment, Colorado School of Public Health, University of Colorado, Anschutz Medical Campus, Aurora, Colorado, United States of America
- Department of Environmental and Occupational Health, Colorado School of Public Health, University of Colorado, Anschutz Medical Campus, Aurora, Colorado, United States of America
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
- Division of Pulmonary Sciences and Critical Care Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Donna Cragle
- Oak Ridge Associated Universities, Oak Ridge, Tennessee, United States of America
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Awad SF, Dargham SR, Toumi AA, Dumit EM, El-Nahas KG, Al-Hamaq AO, Critchley JA, Tuomilehto J, Al-Thani MHJ, Abu-Raddad LJ. A diabetes risk score for Qatar utilizing a novel mathematical modeling approach to identify individuals at high risk for diabetes. Sci Rep 2021; 11:1811. [PMID: 33469048 PMCID: PMC7815783 DOI: 10.1038/s41598-021-81385-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 01/06/2021] [Indexed: 12/23/2022] Open
Abstract
We developed a diabetes risk score using a novel analytical approach and tested its diagnostic performance to detect individuals at high risk of diabetes, by applying it to the Qatari population. A representative random sample of 5,000 Qataris selected at different time points was simulated using a diabetes mathematical model. Logistic regression was used to derive the score using age, sex, obesity, smoking, and physical inactivity as predictive variables. Performance diagnostics, validity, and potential yields of a diabetes testing program were evaluated. In 2020, the area under the curve (AUC) was 0.79 and sensitivity and specificity were 79.0% and 66.8%, respectively. Positive and negative predictive values (PPV and NPV) were 36.1% and 93.0%, with 42.0% of Qataris being at high diabetes risk. In 2030, projected AUC was 0.78 and sensitivity and specificity were 77.5% and 65.8%. PPV and NPV were 36.8% and 92.0%, with 43.0% of Qataris being at high diabetes risk. In 2050, AUC was 0.76 and sensitivity and specificity were 74.4% and 64.5%. PPV and NPV were 40.4% and 88.7%, with 45.0% of Qataris being at high diabetes risk. This model-based score demonstrated comparable performance to a data-derived score. The derived self-complete risk score provides an effective tool for initial diabetes screening, and for targeted lifestyle counselling and prevention programs.
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Affiliation(s)
- Susanne F Awad
- Infectious Disease Epidemiology Group, Weill Cornell Medicine - Qatar, Cornell University, Qatar Foundation - Education City, Doha, Qatar.,World Health Organization Collaborating Centre for Disease Epidemiology Analytics On HIV/AIDS, Sexually Transmitted Infections, and Viral Hepatitis, Weill Cornell Medicine - Qatar, Cornell University, Qatar Foundation - Education City, P.O. Box 24144, Doha, Qatar.,Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, USA
| | - Soha R Dargham
- Infectious Disease Epidemiology Group, Weill Cornell Medicine - Qatar, Cornell University, Qatar Foundation - Education City, Doha, Qatar.,World Health Organization Collaborating Centre for Disease Epidemiology Analytics On HIV/AIDS, Sexually Transmitted Infections, and Viral Hepatitis, Weill Cornell Medicine - Qatar, Cornell University, Qatar Foundation - Education City, P.O. Box 24144, Doha, Qatar
| | - Amine A Toumi
- Public Health Department, Ministry of Public Health, Doha, Qatar
| | | | | | | | - Julia A Critchley
- Population Health Research Institute, St George's, University of London, London, UK
| | - Jaakko Tuomilehto
- Public Health Promotion Unit, Finnish Institute for Health and Welfare, Helsinki, Finland.,Department of Public Health, University of Helsinki, Helsinki, Finland.,Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | | | - Laith J Abu-Raddad
- Infectious Disease Epidemiology Group, Weill Cornell Medicine - Qatar, Cornell University, Qatar Foundation - Education City, Doha, Qatar. .,World Health Organization Collaborating Centre for Disease Epidemiology Analytics On HIV/AIDS, Sexually Transmitted Infections, and Viral Hepatitis, Weill Cornell Medicine - Qatar, Cornell University, Qatar Foundation - Education City, P.O. Box 24144, Doha, Qatar. .,Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, USA.
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Evans M, Morgan AR, Patel D, Dhatariya K, Greenwood S, Newland-Jones P, Hicks D, Yousef Z, Moore J, Kelly B, Davies S, Dashora U. Risk Prediction of the Diabetes Missing Million: Identifying Individuals at High Risk of Diabetes and Related Complications. Diabetes Ther 2021; 12:87-105. [PMID: 33190216 PMCID: PMC7843706 DOI: 10.1007/s13300-020-00963-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 10/28/2020] [Indexed: 01/08/2023] Open
Abstract
Early diagnosis and effective management of type 2 diabetes (T2D) are crucial in reducing the risk of developing life-changing complications such as heart failure, stroke, kidney disease, blindness and amputation, which are also associated with significant costs for healthcare providers. However, as T2D symptoms often develop slowly it is not uncommon for people to live with T2D for years without being aware of their condition-commonly known as the undiagnosed missing million. By the time a diagnosis is received, many individuals will have already developed serious complications. While the existence of undiagnosed diabetes has long been recognised, wide-reaching awareness among the general public, clinicians and policymakers is lacking, and there is uncertainty in how best to identify high-risk individuals. In this article we have used consensus expert opinion alongside the available evidence, to provide support for the diabetes healthcare community regarding risk prediction of the missing million. Its purpose is to provide awareness of the risk factors for identifying individuals at high, moderate and low risk of T2D and T2D-related complications. The awareness of risk predictors, particularly in primary care, is important, so that appropriate steps can be taken to reduce the clinical and economic burden of T2D and its complications.
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Affiliation(s)
- Marc Evans
- Diabetes Resource Centre, University Hospital Llandough, Cardiff, UK.
| | | | - Dipesh Patel
- Department of Diabetes, Division of Medicine, University College London, Royal Free NHS Trust, London, UK
| | - Ketan Dhatariya
- Elsie Bertram Diabetes Centre, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, UK
| | - Sharlene Greenwood
- Renal Medicine, King's College Hospital, London, UK
- Renal Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | | | | | - Zaheer Yousef
- Wales Heart Research Institute, Cardiff University, Cardiff, UK
| | - Jim Moore
- Stoke Road Surgery, Bishop's Cleeve, Cheltenham, UK
| | | | | | - Umesh Dashora
- East Sussex Healthcare NHS Trust, St Leonards-on-Sea, UK
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Montero E, Matesanz P, Nobili A, Luis Herrera-Pombo J, Sanz M, Guerrero A, Bujaldón A, Herrera D. Screening of undiagnosed hyperglycaemia in the dental setting: The DiabetRisk study. A field trial. J Clin Periodontol 2020; 48:378-388. [PMID: 33263197 DOI: 10.1111/jcpe.13408] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Revised: 10/05/2020] [Accepted: 11/22/2020] [Indexed: 11/26/2022]
Abstract
AIM To evaluate the efficacy of different screening protocols for undiagnosed hyperglycaemia in a Research Network of Dental Clinics coordinated by the Spanish Society of Periodontology (SEPA). MATERIAL AND METHODS A total of 1143 patients were included in the study. Participants filled a questionnaire considering diabetes risk factors (FINDRISC) and received a periodontal screening examination. Patients with a slightly elevated score according to the Findrisc (≥7), received a point-of-care HbA1c and were eventually referred to their physician for confirmatory diagnosis. Receiver Operating Characteristic (ROC) curves were used to assess the performance of various predictive models with confirmed hyperglycaemia as outcome. RESULTS From this population, 97 (8.5%) were finally diagnosed of diabetes (n = 28; 2.5%) or prediabetes (n = 69; 6.0%). When only including the results from the FINDRISC questionnaire, the model reported an area under the curve (AUC) of 0.866 (95% confidence interval - CI [0.833; 0.900]). This model significantly improved when a basic periodontal examination (EPB Code; AUC = 0.876; 95% CI [0.845: 0.906]; p = .042) or a point-of-care HbA1c were added (AUC = 0.961; 95% CI [0.941; 0.980]; p < .001). CONCLUSIONS The tested protocol, combining the FINDRISC questionnaire and a point-of-care HbA1c, showed to be feasible when carried out in a dental clinic setting and was efficient to identify subjects with undiagnosed diabetes or prediabetes.
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Affiliation(s)
- Eduardo Montero
- ETEP (Etiology and Therapy of Periodontal and Peri-implant Diseases) Research Group, University Complutense of Madrid, Madrid, Spain.,Working Group "Diabetes and Periodontal Diseases" of the Spanish Society of Diabetes (SED) and the Spanish Society of Periodontology (SEPA), Madrid, Spain
| | - Paula Matesanz
- ETEP (Etiology and Therapy of Periodontal and Peri-implant Diseases) Research Group, University Complutense of Madrid, Madrid, Spain.,Fundación SEPA de Periodoncia e Implantes Dentales and Spanish Society of Periodontology (SEPA), Madrid, Spain
| | - Antonio Nobili
- ETEP (Etiology and Therapy of Periodontal and Peri-implant Diseases) Research Group, University Complutense of Madrid, Madrid, Spain
| | - José Luis Herrera-Pombo
- Working Group "Diabetes and Periodontal Diseases" of the Spanish Society of Diabetes (SED) and the Spanish Society of Periodontology (SEPA), Madrid, Spain.,Endocrinology and Nutrition Department, University Hospital Fundación Jiménez Díaz, Madrid, Spain
| | - Mariano Sanz
- ETEP (Etiology and Therapy of Periodontal and Peri-implant Diseases) Research Group, University Complutense of Madrid, Madrid, Spain.,Fundación SEPA de Periodoncia e Implantes Dentales and Spanish Society of Periodontology (SEPA), Madrid, Spain
| | - Adrián Guerrero
- Fundación SEPA de Periodoncia e Implantes Dentales and Spanish Society of Periodontology (SEPA), Madrid, Spain
| | - Antonio Bujaldón
- Fundación SEPA de Periodoncia e Implantes Dentales and Spanish Society of Periodontology (SEPA), Madrid, Spain
| | - David Herrera
- ETEP (Etiology and Therapy of Periodontal and Peri-implant Diseases) Research Group, University Complutense of Madrid, Madrid, Spain.,Working Group "Diabetes and Periodontal Diseases" of the Spanish Society of Diabetes (SED) and the Spanish Society of Periodontology (SEPA), Madrid, Spain.,Fundación SEPA de Periodoncia e Implantes Dentales and Spanish Society of Periodontology (SEPA), Madrid, Spain
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Atayoglu AT, Inanc N, Başmisirli E, Çapar AG. Evaluation of the Finnish Diabetes Risk Score (FINDRISC) for diabetes screening in Kayseri, Turkey. Prim Care Diabetes 2020; 14:488-493. [PMID: 32029385 DOI: 10.1016/j.pcd.2020.01.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 01/08/2020] [Accepted: 01/14/2020] [Indexed: 12/19/2022]
Abstract
AIM Diabetes is a major health problem worldwide, as its prevalence is increasing dramatically. Lifestyle modification can delay or prevent the onset of type 2 diabetes. Therefore, screening for prediabetes and type 2 diabetes risk through an assessment of risk factors is important. The objective of the study was to investigate the risk for type 2 diabetes using the Finnish Diabetes Risk Score (FINDRISC) in Kayseri. METHODS In total, 1500 adults aged 18 years or older were interviewed using the FINDRISC as Diabetes Risk Questionnaire and fasting serum glucose levels. The findings of FINDRISC were grouped according to gender and a score of 15 and above was accepted as a high risk in terms of Type 2 diabetes. RESULTS 13.5% of the participants were in the high- risk group. There was a statistically significant relationship between total FINDRISC score and gender (p < 0.001). While 15.2% of the women were in the high -risk group, 12.4% of the men were in the high- risk group. The percentage of women in the low-risk group (35.9%) is less than the men with low- risk of diabetes (38.5%). As the BMI increased, the individuals were found to have a high risk evaluated with the use of FINDRISC. 14.3% of women with waist circumference >88 and 6.7% of men with waist circumference >102 were in the high- risk group. (p < 0.001) CONCLUSIONS: Risk of diabetes was found to be higher with the FINDRISC score as the BMI and waist circumference increased. FINDRISC can be used in the primary care for this purpose; fast and easy to be applied.
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Affiliation(s)
- A Timucin Atayoglu
- Department of Family Medicine, Medical Faculty, International Medipol University, Istanbul, Turkey.
| | - Neriman Inanc
- Department of Nutrition and Dietetics, Faculty of Health Sciences, Naci Yazgan University, Kayseri, Turkey.
| | - Eda Başmisirli
- Department of Nutrition and Dietetics, Faculty of Health Sciences, Naci Yazgan University, Kayseri, Turkey.
| | - Aslı Gizem Çapar
- Department of Nutrition and Dietetics, Faculty of Health Sciences, Naci Yazgan University, Kayseri, Turkey.
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Mühlenbruch K, Menzel J, Dörr M, Ittermann T, Meisinger C, Peters A, Kluttig A, Medenwald D, Bergmann M, Boeing H, Schulze MB, Weikert C. Association of familial history of diabetes or myocardial infarction and stroke with risk of cardiovascular diseases in four German cohorts. Sci Rep 2020; 10:15373. [PMID: 32958955 PMCID: PMC7505832 DOI: 10.1038/s41598-020-72361-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 08/25/2020] [Indexed: 12/12/2022] Open
Abstract
Since family history of diabetes is a very strong risk factor for type 2 diabetes, which is one of the most important risk factors for cardiovascular disease (CVD), it might be also useful to assess the risk for CVD. Therefore, we aimed to investigate the relationship between a familial (parents and siblings) history of diabetes and the risk of incident CVD. Data from four prospective German cohort studies were used: EPIC-Potsdam study (n = 26,054), CARLA study (n = 1,079), SHIP study (n = 3,974), and KORA study (n = 15,777). A multivariable-adjusted Cox regression was performed to estimate associations between familial histories of diabetes, myocardial infarction or stroke and the risk of CVD in each cohort; combined hazard ratios (HRMeta) were derived by conducting a meta-analysis. The history of diabetes in first-degree relatives was not related to the development of CVD (HRMeta 0.99; 95% CI 0.88–1.10). Results were similar for the single outcomes myocardial infarction (MI) (HRMeta 1.07; 95% CI 0.92–1.23) and stroke (HRMeta 1.00; 95% CI 0.86–1.16). In contrast, parental history of MI and stroke were associated with an increased CVD risk. Our study indicates that diabetes in the family might not be a relevant risk factor for the incidence of CVD. However, the study confirmed the relationship between a parental history of MI or stroke and the onset of CVD.
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Affiliation(s)
- Kristin Mühlenbruch
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Juliane Menzel
- Department of Food Safety, German Federal Institute for Risk Assessment, Max-Dohrn-Str. 8-10, Berlin, Germany
| | - Marcus Dörr
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany.,German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
| | - Till Ittermann
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany.,German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
| | - Christa Meisinger
- Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.,Chair of Epidemiology, Ludwig-Maximilians-Universität München, UNIKA-T Augsburg, Augsburg, Germany
| | - Annette Peters
- German Center for Diabetes Research (DZD), Neuherberg, Germany.,Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.,Chair of Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany.,German Centre for Cardiovascular Research (DZHK), Partner Site Munich, Neuherberg, Germany
| | - Alexander Kluttig
- Institute of Medical Epidemiology, Biostatistics and Informatics, Interdisciplinary Center for Health Sciences, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Daniel Medenwald
- Institute of Medical Epidemiology, Biostatistics and Informatics, Interdisciplinary Center for Health Sciences, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany.,Department of Radiation Oncology, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Manuela Bergmann
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - Heiner Boeing
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany.,Institute of Nutritional Sciences, University of Potsdam, Nuthetal, Germany
| | - Cornelia Weikert
- Department of Food Safety, German Federal Institute for Risk Assessment, Max-Dohrn-Str. 8-10, Berlin, Germany.
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Alazzam MF, Darwazeh AMG, Hassona YM, Khader YS. Diabetes mellitus risk among Jordanians in a dental setting: a cross-sectional study. Int Dent J 2020; 70:482-488. [PMID: 32705689 DOI: 10.1111/idj.12591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Dental offices can be useful to screen and identify patients at risk of developing diabetes mellitus (DM) using risk prediction tools. The Finnish Diabetes Risk Score (FINDRISC) is a validated, questionnaire-based tool used to predict the 10-year risk of developing type II DM. OBJECTIVES To determine the 10-year DM risk among Jordanians using the FINDRISC questionnaire in a dental setting. MATERIALS AND METHODS Participants attending two university dental teaching centres between March 2017 and February 2018 were interviewed using an Arabic translated version of the FINDRISC questionnaire. Anthropometrics including weight, height, waist circumference (WC) and body mass index (BMI) were recorded. Random capillary blood glucose level was measured for each participant. Statistical analysis was done using Chi-square and independent t-tests. RESULTS A total of 1,247 (436 males and 811 females) participants were included. As defined by BMI, 1,012 (81.2%) participants were either overweight or obese. Abdominal adiposity as determined by WC was seen in 738 (59.2%) participants. The mean (± SD) FINDRISC score for females (11.3 ± 4.3) was significantly higher (P = 0.001) than males (10.4 ± 4.9). After age adjustment, more females were in the high-risk categories (FINDRISC ≥ 15) compared with males. This trend was seen among all age groups, but was statistically significant in the older age groups; 55-64 years (P = 0.037) and ≥ 65 years (P = 0.004). CONCLUSION In a developing Middle Eastern country such as Jordan, almost half of Jordanians attending university dental clinics are at a moderate to high risk of developing type II DM in 10 years. The risk of DM should be considered in dental patients, particularly older females.
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Affiliation(s)
- Melanie Fawaz Alazzam
- Department of Oral Medicine and Oral Surgery, School of Dentistry, Jordan University of Science and Technology, Irbid, Jordan
| | - Azmi Mohammad-Ghaleb Darwazeh
- Department of Oral Medicine and Oral Surgery, School of Dentistry, Jordan University of Science and Technology, Irbid, Jordan
| | - Yazan Mansour Hassona
- Department of Oral and Maxillofacial Surgery, Oral Medicine and Periodontology, School of Dentistry, University of Jordan, Amman, Jordan
| | - Yousef Saleh Khader
- Department of Public Health, School of Medicine, Jordan University of Science and Technology, Irbid, Jordan
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Chamroonkiadtikun P, Ananchaisarp T, Wanichanon W. The triglyceride-glucose index, a predictor of type 2 diabetes development: A retrospective cohort study. Prim Care Diabetes 2020; 14:161-167. [PMID: 31466834 DOI: 10.1016/j.pcd.2019.08.004] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 08/08/2019] [Indexed: 01/28/2023]
Abstract
AIMS The triglycerides-glucose (TyG) index, the product of fasting plasma glucose (FPG) and triglycerides (TG) is a novel index. Many previous studies have reported that the TyG index might be a strong predictor of incident type 2 diabetes. We determined whether the TyG index could be a useful predictor for diabetes diagnosis and compared it to the FPG and TG as predictors of type 2 diabetes. METHODS A total of 617 subjects without baseline diabetes were examined and followed up for a median period of 9.2 years. We performed a mixed effect cox regression analysis to evaluate the risk of developing diabetes across the quartiles of the TyG index, calculated as ln[triglyceride (mg/dl)×FPG (mg/dl)/2], and plotted a receiver operating characteristic (ROC) curve to assess discrimination among TyG, FPG and TG. RESULTS During 4,871.56 person-years of follow-up, there were 163 incident cases of diabetes. The risk of diabetes increased across the quartiles of the TyG index. Those in the highest quartile of TyG had a higher risk of developing diabetes (adjusted HR 3.38 95% CI 2.38-4.8, ptrend<0.001) than those in the lowest quartile. The area under the curve (AUC) of the ROC plots were 0.79 (95% CI 0.74-0.83) for FPG, 0.64 (95% CI 0.60-0.69) for TyG and 0.59 (95% CI 0.54-0.64) for TG. CONCLUSION The TyG index was significantly associated with risk of incident diabetes and could be a valuable biomarker of developing diabetes. However, FPG appeared to be a more robust predictor of diabetes.
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Affiliation(s)
- Panya Chamroonkiadtikun
- Department of Family and Preventive Medicine, Prince of Songkla University, Hat Yai, Songkhla, 90110, Thailand.
| | - Thareerat Ananchaisarp
- Department of Family and Preventive Medicine, Prince of Songkla University, Hat Yai, Songkhla, 90110, Thailand.
| | - Worawit Wanichanon
- Department of Family and Preventive Medicine, Prince of Songkla University, Hat Yai, Songkhla, 90110, Thailand.
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Han X, Wei Y, Hu H, Wang J, Li Z, Wang F, Long T, Yuan J, Yao P, Wei S, Wang Y, Zhang X, Guo H, Yang H, Wu T, He M. Genetic Risk, a Healthy Lifestyle, and Type 2 Diabetes: the Dongfeng-Tongji Cohort Study. J Clin Endocrinol Metab 2020; 105:5696594. [PMID: 31900493 DOI: 10.1210/clinem/dgz325] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 12/31/2019] [Indexed: 02/07/2023]
Abstract
OBJECTIVE The objective of this study is to examine whether healthy lifestyle could reduce diabetes risk among individuals with different genetic profiles. DESIGN A prospective cohort study with a median follow-up of 4.6 years from the Dongfeng-Tongji cohort was performed. PARTICIPANTS A total of 19 005 individuals without diabetes at baseline participated in the study. MAIN VARIABLE MEASURE A healthy lifestyle was determined based on 6 factors: nonsmoker, nondrinker, healthy diet, body mass index of 18.5 to 23.9 kg/m2, waist circumference less than 85 cm for men and less than 80 cm for women, and higher level of physical activity. Associations of combined lifestyle factors and incident diabetes were estimated using Cox proportional hazard regression. A polygenic risk score of 88 single-nucleotide polymorphisms previously associated with diabetes was constructed to test for association with diabetes risk among 7344 individuals, using logistic regression. RESULTS A total of 1555 incident diabetes were ascertained. Per SD increment of simple and weighted genetic risk score was associated with a 1.39- and 1.34-fold higher diabetes risk, respectively. Compared with poor lifestyle, intermediate and ideal lifestyle were reduced to a 23% and 46% risk of incident diabetes, respectively. Association of lifestyle with diabetes risk was independent of genetic risk. Even among individuals with high genetic risk, intermediate and ideal lifestyle were separately associated with a 29% and 49% lower risk of diabetes. CONCLUSION Genetic and combined lifestyle factors were independently associated with diabetes risk. A healthy lifestyle could lower diabetes risk across different genetic risk categories, emphasizing the benefit of entire populations adhering to a healthy lifestyle.
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Affiliation(s)
- Xu Han
- Department of Occupational and Environmental Health and Key Laboratory of Environmental and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Yue Wei
- Department of Occupational and Environmental Health and Key Laboratory of Environmental and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Hua Hu
- Department of Occupational and Environmental Health and Key Laboratory of Environmental and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Jing Wang
- Department of Occupational and Environmental Health and Key Laboratory of Environmental and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Zhaoyang Li
- Department of Occupational and Environmental Health and Key Laboratory of Environmental and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Fei Wang
- Department of Occupational and Environmental Health and Key Laboratory of Environmental and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Tengfei Long
- Department of Occupational and Environmental Health and Key Laboratory of Environmental and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Jing Yuan
- Department of Occupational and Environmental Health and Key Laboratory of Environmental and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Ping Yao
- Department of Occupational and Environmental Health and Key Laboratory of Environmental and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Sheng Wei
- Department of Occupational and Environmental Health and Key Laboratory of Environmental and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Youjie Wang
- Department of Occupational and Environmental Health and Key Laboratory of Environmental and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Xiaomin Zhang
- Department of Occupational and Environmental Health and Key Laboratory of Environmental and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Huan Guo
- Department of Occupational and Environmental Health and Key Laboratory of Environmental and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Handong Yang
- Dongfeng Central Hospital, Dongfeng Motor Corporation and Hubei University of Medicine, Shiyan, Hubei, P.R. China
| | - Tangchun Wu
- Department of Occupational and Environmental Health and Key Laboratory of Environmental and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Meian He
- Department of Occupational and Environmental Health and Key Laboratory of Environmental and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
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Mühlenbruch K, Zhuo X, Bardenheier B, Shao H, Laxy M, Icks A, Zhang P, Gregg EW, Schulze MB. Selecting the optimal risk threshold of diabetes risk scores to identify high-risk individuals for diabetes prevention: a cost-effectiveness analysis. Acta Diabetol 2020; 57:447-454. [PMID: 31745647 PMCID: PMC7093341 DOI: 10.1007/s00592-019-01451-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 10/31/2019] [Indexed: 01/21/2023]
Abstract
AIMS Although risk scores to predict type 2 diabetes exist, cost-effectiveness of risk thresholds to target prevention interventions are unknown. We applied cost-effectiveness analysis to identify optimal thresholds of predicted risk to target a low-cost community-based intervention in the USA. METHODS We used a validated Markov-based type 2 diabetes simulation model to evaluate the lifetime cost-effectiveness of alternative thresholds of diabetes risk. Population characteristics for the model were obtained from NHANES 2001-2004 and incidence rates and performance of two noninvasive diabetes risk scores (German diabetes risk score, GDRS, and ARIC 2009 score) were determined in the ARIC and Cardiovascular Health Study (CHS). Incremental cost-effectiveness ratios (ICERs) were calculated for increasing risk score thresholds. Two scenarios were assumed: 1-stage (risk score only) and 2-stage (risk score plus fasting plasma glucose (FPG) test (threshold 100 mg/dl) in the high-risk group). RESULTS In ARIC and CHS combined, the area under the receiver operating characteristic curve for the GDRS and the ARIC 2009 score were 0.691 (0.677-0.704) and 0.720 (0.707-0.732), respectively. The optimal threshold of predicted diabetes risk (ICER < $50,000/QALY gained in case of intervention in those above the threshold) was 7% for the GDRS and 9% for the ARIC 2009 score. In the 2-stage scenario, ICERs for all cutoffs ≥ 5% were below $50,000/QALY gained. CONCLUSIONS Intervening in those with ≥ 7% diabetes risk based on the GDRS or ≥ 9% on the ARIC 2009 score would be cost-effective. A risk score threshold ≥ 5% together with elevated FPG would also allow targeting interventions cost-effectively.
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Affiliation(s)
- Kristin Mühlenbruch
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Xiaohui Zhuo
- Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Barbara Bardenheier
- Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Hui Shao
- Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Michael Laxy
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Institute of Health Economics and Health Care Management, Neuherberg, Germany
| | - Andrea Icks
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Health Services Research and Health Economics, German Diabetes Centre, Leibniz-Centre for Diabetes Research, Düsseldorf, Germany
- Institute of Health Services Research and Health Economics, Medical Faculty, Heinrich-Heine-University, Düsseldorf, Germany
| | - Ping Zhang
- Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Edward W Gregg
- Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.
- German Center for Diabetes Research (DZD), Neuherberg, Germany.
- Institute of Nutritional Sciences, University of Potsdam, Potsdam, Germany.
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Virani SS, Alonso A, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Chang AR, Cheng S, Delling FN, Djousse L, Elkind MSV, Ferguson JF, Fornage M, Khan SS, Kissela BM, Knutson KL, Kwan TW, Lackland DT, Lewis TT, Lichtman JH, Longenecker CT, Loop MS, Lutsey PL, Martin SS, Matsushita K, Moran AE, Mussolino ME, Perak AM, Rosamond WD, Roth GA, Sampson UKA, Satou GM, Schroeder EB, Shah SH, Shay CM, Spartano NL, Stokes A, Tirschwell DL, VanWagner LB, Tsao CW. Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association. Circulation 2020; 141:e139-e596. [PMID: 31992061 DOI: 10.1161/cir.0000000000000757] [Citation(s) in RCA: 4856] [Impact Index Per Article: 1214.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND The American Heart Association, in conjunction with the National Institutes of Health, annually reports on the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2020 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, metrics to assess and monitor healthy diets, an enhanced focus on social determinants of health, a focus on the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors, implementation strategies, and implications of the American Heart Association's 2020 Impact Goals. RESULTS Each of the 26 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, healthcare administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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Perveen S, Shahbaz M, Ansari MS, Keshavjee K, Guergachi A. A Hybrid Approach for Modeling Type 2 Diabetes Mellitus Progression. Front Genet 2020; 10:1076. [PMID: 31969896 PMCID: PMC6958689 DOI: 10.3389/fgene.2019.01076] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 10/09/2019] [Indexed: 12/31/2022] Open
Abstract
Type 2 Diabetes Mellitus (T2DM) is a chronic, progressive metabolic disorder characterized by hyperglycemia resulting from abnormalities in insulin secretion, insulin action, or both. It is associated with an increased risk of developing vascular complication of micro as well as macro nature. Because of its inconspicuous and heterogeneous character, the management of T2DM is very complex. Modeling physiological processes over time demonstrating the patient’s evolving health condition is imperative to comprehending the patient’s current status of health, projecting its likely dynamics and assessing the requisite care and treatment measures in future. Hidden Markov Model (HMM) is an effective approach for such prognostic modeling. However, the nature of the clinical setting, together with the format of the Electronic Medical Records (EMRs) data, in particular the sparse and irregularly sampled clinical data which is well understood to present significant challenges, has confounded standard HMM. In the present study, we proposed an approximation technique based on Newton’s Divided Difference Method (NDDM) as a component with HMM to determine the risk of developing diabetes in an individual over different time horizons using irregular and sparsely sampled EMRs data. The proposed method is capable of exploiting available sequences of clinical measurements obtained from a longitudinal sample of patients for effective imputation and improved prediction performance. Furthermore, results demonstrated that the discrimination capability of our proposed method, in prognosticating diabetes risk, is superior to the standard HMM.
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Affiliation(s)
- Sajida Perveen
- Department of Computer Science & Engineering, University of Engineering & Technology, Lahore, Pakistan
| | - Muhammad Shahbaz
- Department of Computer Science & Engineering, University of Engineering & Technology, Lahore, Pakistan.,Research Lab for Advanced System Modelling, Ryerson University, Toronto, ON, Canada
| | | | - Karim Keshavjee
- Research Lab for Advanced System Modelling, Ryerson University, Toronto, ON, Canada.,Institute for Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Aziz Guergachi
- Research Lab for Advanced System Modelling, Ryerson University, Toronto, ON, Canada.,Ted Rogers School of Information Technology Management, Ryerson University, Toronto, ON, Canada.,Department of Mathematics & Statistics, York University, Toronto, ON, Canada
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Abdallah M, Sharbaji S, Sharbaji M, Daher Z, Faour T, Mansour Z, Hneino M. Diagnostic accuracy of the Finnish Diabetes Risk Score for the prediction of undiagnosed type 2 diabetes, prediabetes, and metabolic syndrome in the Lebanese University. Diabetol Metab Syndr 2020; 12:84. [PMID: 33014142 PMCID: PMC7526372 DOI: 10.1186/s13098-020-00590-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 09/19/2020] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Risk scores were mainly proved to predict undiagnosed type 2 diabetes mellitus (UT2DM) in a non-invasive manner and to guide earlier clinical treatment. The objective of the present study was to assess the performance of the Finnish Diabetes Risk Score (FINDRISC) for detecting three outcomes: UT2DM, prediabetes, and the metabolic syndrome (MS). METHODS This was a prospective, cross-sectional study during which employees aged between 30 and 64, with no known diabetes and working within the faculties of the Lebanese University (LU) were conveniently recruited. Participants completed the FINDRISC questionnaire and their glucose levels were examined using both fasting blood glucose (FBG) and oral glucose tolerance tests (OGTT). Furthermore, they underwent lipid profile tests with anthropometry. RESULTS Of 713 subjects, 397 subjects (55.2% female; 44.8% male) completed the blood tests and thus were considered as the sample population. 7.6% had UT2DM, 22.9% prediabetes and 35.8% had MS, where men had higher prevalence than women for these 3 outcomes (P = 0.001, P = 0.003 and P = 0.001) respectively. The AUROC value with 95% Confidence Interval (CI) for detecting UT2DM was 0.795 (0.822 in men and 0.725 in women), 0.621(0.648 in men and 0.59 in women) for prediabetes and 0.710 (0.734 in men and 0.705 in women) for MS. The correspondent optimal cut-off point for UT2DM was 11.5 (sensitivity = 83.3% and specificity = 61.3%), 9.5 for prediabetes (sensitivity = 73.6% and specificity = 43.1%) and 10.5 (sensitivity = 69.7%; specificity = 56.5%) for MS. CONCLUSION The FINDRISC can be considered a simple, quick, inexpensive, and non-invasive instrument to use in a Lebanese community of working people who are unaware of their health status and who usually report being extremely busy because of their daily hectic work for the screening of UT2DM and MS. However, it poorly screens for prediabetes in this context.
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Affiliation(s)
- Maher Abdallah
- Faculty of Public Health, Lebanese University, Hadat, Beirut, Lebanon
| | - Safa Sharbaji
- Department of Nutrition and Dietetics, Faculty of Public Health, Lebanese University, Hadat, Beirut, Lebanon
| | - Marwa Sharbaji
- Department of Nutrition and Dietetics, Faculty of Public Health, Lebanese University, Hadat, Beirut, Lebanon
| | - Zeina Daher
- Faculty of Public Health, Lebanese University, Hadat, Beirut, Lebanon
| | - Tarek Faour
- Medical Laboratory, Lebanese University Medical Center, Lebanese University, Hadat, Beirut, Lebanon
| | - Zeinab Mansour
- Medical Laboratory, Lebanese University Medical Center, Lebanese University, Hadat, Beirut, Lebanon
| | - Mohammad Hneino
- Sciences Department, Faculty of Public Health, Lebanese University Hadat, Hadat, Beirut, Lebanon
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Nagarathna R, Kaur N, Anand A, Sharma K, Dada R, Sridhar P, Sharma P, Kumar Singh A, Patil S, Nagendra HR. Distribution of glycated haemoglobin and its determinants in Indian young adults. Diabetes Res Clin Pract 2020; 159:107982. [PMID: 31846666 DOI: 10.1016/j.diabres.2019.107982] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 11/23/2019] [Accepted: 12/12/2019] [Indexed: 01/15/2023]
Abstract
AIM The aim of the present study is to understand the distribution of A1c in four different age groups in young adults and its relation to other co-variants. METHODS The countrywide data was collected in 2017 in Individuals with high risk analysed by Indian Diabetes Risk Score (IDRS) and self-declared diabetics were identified after screening a sample of 240,968 individuals from rural (4 villages of about 500 adults each) and urban (4 census enumeration blocks of about 500 adults each) population spanning 65 districts of 29 states/UTs of Indian subcontinent. Blood tests and other detailed assessments were carried out on this selected group. This study presents the analysis of the A1c values of 2862 young adults (<35 years). RESULTS In the age group of 31-34 years, the proportion of Diabetes (22.36%) and Prediabetes (9.86%) was higher in comparison with younger age groups. Also, Diabetes (7.3%) and Prediabetes (22%) were highest among those who had parental history of DM in both parents as compared to those with Diabetes history in one parent [Diabetes (7.1%) or Prediabetes (19.0%)] and no Diabetes Parental History (Diabetes (7.3%) and Prediabetes (18.3%) cases. BMI was found to play a significant positive correlation with Diabetes and Prediabetes (p < 0.001) with range of A1c. CONCLUSION Age, BMI and parental history were found to be correlated with A1c levels in IDRS screened high-risk population. With increasing age, the proportion of Diabetics and Prediabetics also increased with positive correlation of age with A1c levels.
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Affiliation(s)
| | - Navneet Kaur
- Department of Physical Education, Panjab University Chandigarh, India; Neuroscience Research Lab, Department of Neurology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Akshay Anand
- Neuroscience Research Lab, Department of Neurology, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
| | - Kanupriya Sharma
- Neuroscience Research Lab, Department of Neurology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Rima Dada
- Department of Anatomy, Laboratory of Molecular Reproduction & Genetics, All India Institute of Medical Sciences, New Delhi, India
| | | | - Purnendu Sharma
- Swami Vivekananda Yoga Anusandhana Samsthana, Bengaluru, India
| | | | - Suchitra Patil
- Swami Vivekananda Yoga Anusandhana Samsthana, Bengaluru, India
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Mambiya M, Shang M, Wang Y, Li Q, Liu S, Yang L, Zhang Q, Zhang K, Liu M, Nie F, Zeng F, Liu W. The Play of Genes and Non-genetic Factors on Type 2 Diabetes. Front Public Health 2019; 7:349. [PMID: 31803711 PMCID: PMC6877736 DOI: 10.3389/fpubh.2019.00349] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 11/04/2019] [Indexed: 12/12/2022] Open
Abstract
Diabetes has been a disease of public health concern for a number of decades. It was in the 1930s when scientists made an interesting discovery that the disease is actually divided into two types as some patients were insensitive to insulin treatment then. Type 2 Diabetes which happens to be the non-insulin dependent one is the most common form of the disease and is caused by the interaction between genetic and non-genetic factors. Despite conflicting results, numerous studies have identified genetic and non-genetic factors associated with this common type of diabetes. This review has summarized literature on some genes and non-genetic factors which have been identified to be associated with Type 2 diabetes. It has sourced literature from PubMed, Web of Science and Medline without any limitation to regions, publication types, or languages. The paper has started with the introduction, the play of non-genetic factors, the impact of genes in general, and ended with the interaction between some genes and environmental factors.
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Affiliation(s)
- Michael Mambiya
- Department of Nutrition and Food Hygiene, School of Public Health, China Medical University, Shenyang, China
| | - Mengke Shang
- Department of Nutrition and Food Hygiene, School of Public Health, China Medical University, Shenyang, China
| | - Yue Wang
- Department of Nutrition and Food Hygiene, School of Public Health, China Medical University, Shenyang, China
| | - Qian Li
- Department of Nutrition and Food Hygiene, School of Public Health, China Medical University, Shenyang, China
| | - Shan Liu
- Department of Nutrition and Food Hygiene, School of Public Health, China Medical University, Shenyang, China
| | - Luping Yang
- Department of Nutrition and Food Hygiene, School of Public Health, China Medical University, Shenyang, China
| | - Qian Zhang
- Department of Nutrition and Food Hygiene, School of Public Health, China Medical University, Shenyang, China
| | - Kaili Zhang
- Department of Nutrition and Food Hygiene, School of Public Health, China Medical University, Shenyang, China
| | - Mengwei Liu
- Department of Nutrition and Food Hygiene, School of Public Health, China Medical University, Shenyang, China
| | - Fangfang Nie
- Department of Nutrition and Food Hygiene, School of Public Health, China Medical University, Shenyang, China
| | - Fanxin Zeng
- Department of Nutrition and Food Hygiene, School of Public Health, China Medical University, Shenyang, China
| | - Wanyang Liu
- Department of Nutrition and Food Hygiene, School of Public Health, China Medical University, Shenyang, China
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Prognostic Modeling and Prevention of Diabetes Using Machine Learning Technique. Sci Rep 2019; 9:13805. [PMID: 31551457 PMCID: PMC6760163 DOI: 10.1038/s41598-019-49563-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Accepted: 08/20/2019] [Indexed: 02/07/2023] Open
Abstract
Stratifying individuals at risk for developing diabetes could enable targeted delivery of interventional programs to those at highest risk, while avoiding the effort and costs of prevention and treatment in those at low risk. The objective of this study was to explore the potential role of a Hidden Markov Model (HMM), a machine learning technique, in validating the performance of the Framingham Diabetes Risk Scoring Model (FDRSM), a well-respected prognostic model. Can HMM predict 8-year risk of developing diabetes in an individual effectively? To our knowledge, no study has attempted use of HMM to validate the performance of FDRSM. We used Electronic Medical Record (EMR) data, of 172,168 primary care patients to derive the 8-year risk of developing diabetes in an individual using HMM. The Area Under Receiver Operating Characteristic Curve (AROC) in our study sample of 911 individuals for whom all risk factors and follow up data were available is 86.9% compared to AROCs of 78.6% and 85% reported in a previously conducted validation study of FDRSM in the same Canadian population and the Framingham study respectively. These results demonstrate that the discrimination capability of our proposed HMM is superior to the validation study conducted using the FDRSM in a Canadian population and in the Framingham population. We conclude that HMM is capable of identifying patients at increased risk of developing diabetes within the next 8-years.
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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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Gray LJ, Brady EM, Albaina O, Edwardson CL, Harrington D, Khunti K, Miksza J, Raposo JF, Smith E, Vazeou A, Vergara I, Weihrauch-Blüher S, Davies MJ. Evaluation and refinement of the PRESTARt tool for identifying 12-14 year olds at high lifetime risk of developing type 2 diabetes compared to a clinicians assessment of risk: a cross-sectional study. BMC Endocr Disord 2019; 19:79. [PMID: 31345191 PMCID: PMC6659313 DOI: 10.1186/s12902-019-0410-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 07/16/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Traditionally Type 2 Diabetes Mellitus (T2DM) was associated with older age, but is now being increasingly diagnosed in younger populations due to the increasing prevalence of obesity and inactivity. We aimed to evaluate whether a tool developed for community use to identify adolescents at high lifetime risk of developing T2DM agreed with a risk assessment conducted by a clinician using data collected from five European countries. We also assessed whether the tool could be simplified. METHODS To evaluate the tool we collected data from 636 adolescents aged 12-14 years from five European countries. Each participant's data were then assessed by two clinicians independently, who judged each participant to be at either low or high risk of developing T2DM in their lifetime. This was used as the gold standard to which the tool was evaluated and refined. RESULTS The refined tool categorised adolescents at high risk if they were overweight/obese and had at least one other risk factor (High waist circumference, family history of diabetes, parental obesity, not breast fed, high sugar intake, high screen time, low physical activity and low fruit and vegetable intake). Of those found to be at high risk by the clinicians, 93% were also deemed high risk by the tool. The specificity shows that 67% of those deemed at low risk by the clinicians were also found to be a low risk by the tool. CONCLUSIONS We have evaluated a tool for identifying adolescents with risk factors associated with the development of T2DM in the future. Future work to externally validate the tool using prospective data including T2DM incidence is required.
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Affiliation(s)
- Laura J. Gray
- Department of Health Sciences, College of Life Sciences, University of Leicester, George Davies Centre, University Road, Leicester, LE1 7RH UK
| | - Emer M. Brady
- Leicester Diabetes Centre, University Hospitals of Leicester, Leicester, LE5 4PW UK
| | - Olatz Albaina
- Kronikgune, Torre del BEC (Bilbao Exhibition Centre), Ronda de Azkue, 1, 48902 Barakaldo, Bizkaia Spain
| | | | - Deirdre Harrington
- Diabetes Research Centre, University of Leicester, Leicester, LE5 4PW UK
| | - Kamlesh Khunti
- Diabetes Research Centre, University of Leicester, Leicester, LE5 4PW UK
| | - Joanne Miksza
- Diabetes Research Centre, University of Leicester, Leicester, LE5 4PW UK
| | | | - Ellesha Smith
- Department of Health Sciences, College of Life Sciences, University of Leicester, George Davies Centre, University Road, Leicester, LE1 7RH UK
| | - Andriani Vazeou
- Diabetes Center, Department of Pediatrics, P&A Kyriakou Children’s Hospital, Athens, Greece
| | - Itziar Vergara
- Kronikgune, Torre del BEC (Bilbao Exhibition Centre), Ronda de Azkue, 1, 48902 Barakaldo, Bizkaia Spain
- Unidad de Investigación APOSIs Gipuzkoa, Osakidetza, Instituto Biodonostia, San Sebastián, Spain
- Red de Investigación en Servicios de Salud y Cronicidad REDISSEC, San Sebastián, Spain
| | - Susann Weihrauch-Blüher
- Integrated Research and Treatment Center (IFB) Adiposity Diseases, University of Leipzig, Leipzig, Germany
- Department of Pediatrics/ Pediatric Endorinology I, University Hospital of Halle/S, Halle, Germany
| | - Melanie J. Davies
- Leicester Diabetes Centre, University Hospitals of Leicester, Leicester, LE5 4PW UK
- Diabetes Research Centre, University of Leicester, Leicester, LE5 4PW UK
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Xiong XL, Zhang RX, Bi Y, Zhou WH, Yu Y, Zhu DL. Machine Learning Models in Type 2 Diabetes Risk Prediction: Results from a Cross-sectional Retrospective Study in Chinese Adults. Curr Med Sci 2019; 39:582-588. [PMID: 31346994 DOI: 10.1007/s11596-019-2077-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 06/10/2019] [Indexed: 02/08/2023]
Abstract
Type 2 diabetes mellitus (T2DM) has become a prevalent health problem in China, especially in urban areas. Early prevention strategies are needed to reduce the associated mortality and morbidity. We applied the combination of rules and different machine learning techniques to assess the risk of development of T2DM in an urban Chinese adult population. A retrospective analysis was performed on 8000 people with non-diabetes and 3845 people with T2DM in Nanjing. Multilayer Perceptron (MLP), AdaBoost (AD), Trees Random Forest (TRF), Support Vector Machine (SVM), and Gradient Tree Boosting (GTB) machine learning techniques with 10 cross validation methods were used with the proposed model for the prediction of the risk of development of T2DM. The performance of these models was evaluated with accuracy, precision, sensitivity, specificity, and area under receiver operating characteristic (ROC) curve (AUC). After comparison, the prediction accuracy of the different five machine models was 0.87, 0.86, 0.86, 0.86 and 0.86 respectively. The combination model using the same voting weight of each component was built on T2DM, which was performed better than individual models. The findings indicate that, combining machine learning models could provide an accurate assessment model for T2DM risk prediction.
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Affiliation(s)
- Xiao-Lu Xiong
- Department of Endocrinology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210008, China
| | - Rong-Xin Zhang
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166, China
| | - Yan Bi
- Department of Endocrinology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210008, China
| | - Wei-Hong Zhou
- Department of Endocrinology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210008, China.
| | - Yun Yu
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166, China.
| | - Da-Long Zhu
- Department of Endocrinology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210008, China.
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