Literature DB >> 33520838

Predicting type 2 diabetes mellitus among fishermen in Cape Coast: a comparison between the FINDRISC score and the metabolic syndrome.

Richard K D Ephraim1, Victor Boachie Owusu1, Jephthah Asiamah1, Arnold Mills1, Albert Abaka-Yawson2, Godsway Edem Kpene2, Precious Kwablah Kwadzokpui2, Samuel Adusei3.   

Abstract

BACKGROUND: Studies over the past decades have observed a sharp rise in the prevalence and incidence of type 2 diabetes mellitus (T2DM). A highly sensitive and specific predictive tool for risky populations is essential. This study assessed two significant diabetes mellitus predictive tools for effectiveness and accuracy among people living in fishing communities in Cape Coast, Ghana.
METHOD: In April 2019, we recruited one hundred and thirty-five (135) fishermen from three fishing communities in Cape Coast in the Central Region of Ghana. Each participant underwent a standard metabolic procedure including clinical examination as well as taking of anthropometric variables such as weight, height, waist and hip circumference were also measured. The FINDRISC questionnaire was used to gather data from the respective participants. Serum glucose and lipids were estimated with enzymatic techniques, and metabolic syndrome (MetS) screened with the international diabetes federation (IDF) criteria.
RESULTS: Of the 135 participants, 71 (52.6%) were women. The average age of study participants was 52 ± 16 years with females averagely older (56.6 ± 15.0) than the males (47.3 ± 15.0). This study recorded 31.1% and 8.9% prediabetic and diabetic fishermen respectively. Frequency of both prediabetes and diabetes was significantly predominant among females (71.4% vs 83.3%) than males (26.2% vs 25.0%) (p < 0.001) respectively. Prevalence of MetS according to the IDF criteria was 18.5%, significantly higher among females (92.0%) than recorded among the males (18.5%). The discriminatory accuracy of FINDRISC [aROC = 0.76 (95% CI 0.68 to 0.83); sensitivity = 58.3% and specificity = 86.9%; p = 0.003; optimal cut-off point = 13.50] and the MetS [aROC = 0.74 (95% CI 0.66 to 0.81); sensitivity = 75.0% and specificity = 71.5%; p = 0.002] despite demonstrating a significantly good capacity to detect T2DM were statistically comparable [aROC = 0.018 (95% CI -0.152 to 0.189); p = 0.834] in our study.
CONCLUSION: Our findings indicate that both FINDRISC (with a suitable cut-off value of 13.5) and MetS screening tools possess a good predictive capacity for the detection of T2DM. Additionally, FINDRISC can be employed to detect MetS in a high-risk population. © Springer Nature Switzerland AG 2020.

Entities:  

Keywords:  Diabetes; FINDRISC; Fishing communities; Metabolic syndrome

Year:  2020        PMID: 33520838      PMCID: PMC7843673          DOI: 10.1007/s40200-020-00650-w

Source DB:  PubMed          Journal:  J Diabetes Metab Disord        ISSN: 2251-6581


  16 in total

Review 1.  Type 2 diabetes mellitus and obesity in sub-Saharan Africa.

Authors:  Vivian C Tuei; Geoffrey K Maiyoh; Chung-Eun Ha
Journal:  Diabetes Metab Res Rev       Date:  2010-09       Impact factor: 4.876

Review 2.  Metabolic syndrome--a new world-wide definition. A Consensus Statement from the International Diabetes Federation.

Authors:  K G M M Alberti; P Zimmet; J Shaw
Journal:  Diabet Med       Date:  2006-05       Impact factor: 4.359

Review 3.  The metabolic syndrome.

Authors:  Robert H Eckel; Scott M Grundy; Paul Z Zimmet
Journal:  Lancet       Date:  2005 Apr 16-22       Impact factor: 79.321

Review 4.  Global aetiology and epidemiology of type 2 diabetes mellitus and its complications.

Authors:  Yan Zheng; Sylvia H Ley; Frank B Hu
Journal:  Nat Rev Endocrinol       Date:  2017-12-08       Impact factor: 43.330

Review 5.  Impaired glucose tolerance and impaired fasting glycaemia: the current status on definition and intervention.

Authors:  N Unwin; J Shaw; P Zimmet; K G M M Alberti
Journal:  Diabet Med       Date:  2002-09       Impact factor: 4.359

6.  Smoking and type 2 diabetes mellitus.

Authors:  Sang Ah Chang
Journal:  Diabetes Metab J       Date:  2012-12-12       Impact factor: 5.376

7.  Prevalence of hypertension, obesity, diabetes, and metabolic syndrome in Nepal.

Authors:  Sanjib Kumar Sharma; Anup Ghimire; Jeyasundar Radhakrishnan; Lekhjung Thapa; Nikesh Raj Shrestha; Navaraj Paudel; Keshar Gurung; Maskey R; Anjali Budathoki; Nirmal Baral; David Brodie
Journal:  Int J Hypertens       Date:  2011-04-19       Impact factor: 2.420

8.  Predicting type 2 diabetes mellitus: a comparison between the FINDRISC score and the metabolic syndrome.

Authors:  Abraham S Meijnikman; Christophe E M De Block; An Verrijken; Ilse Mertens; Luc F Van Gaal
Journal:  Diabetol Metab Syndr       Date:  2018-03-01       Impact factor: 3.320

9.  The relationship between alcohol consumption and vascular complications and mortality in individuals with type 2 diabetes.

Authors:  Juuso I Blomster; Sophia Zoungas; John Chalmers; Qiang Li; Clara K Chow; Mark Woodward; Giuseppe Mancia; Neil Poulter; Bryan Williams; Stephen Harrap; Bruce Neal; Anushka Patel; Graham S Hillis
Journal:  Diabetes Care       Date:  2014-02-27       Impact factor: 19.112

Review 10.  Metabolic Syndrome in Apparently "Healthy" Ghanaian Adults: A Systematic Review and Meta-Analysis.

Authors:  Richard Ofori-Asenso; Akosua Adom Agyeman; Amos Laar
Journal:  Int J Chronic Dis       Date:  2017-10-09
View more
  2 in total

1.  Diabetes Risk Assessment and Awareness in a University Academics and Employees.

Authors:  Tulin Yildiz; Senay Zuhur; Sayid Shafi Zuhur
Journal:  Sisli Etfal Hastan Tip Bul       Date:  2021-12-29

2.  Non-invasive type 2 diabetes risk scores do not identify diabetes when the cause is β-cell failure: The Africans in America study.

Authors:  Annemarie Wentzel; Arielle C Patterson; M Grace Duhuze Karera; Zoe C Waldman; Blayne R Schenk; Christopher W DuBose; Anne E Sumner; Margrethe F Horlyck-Romanovsky
Journal:  Front Public Health       Date:  2022-09-23
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.