Incidence of diabetes and its predictors in the Greater Beirut Area: a five-year longitudinal study

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Abstract

Background

Type 2 Diabetes (T2D) remains a world epidemic. Obtaining accurate estimates of its incidence and their predictors will aid in targeting preventive measures, allocating resources, and strategizing its management. The Middle East North Africa region has high T2D prevalence and rates of rise. Few incidence studies exist for the region, and none from Lebanon. The current study objective was to determine diabetes incidence and diabetes predictors in a community-based Lebanese sample. A secondary objective was to describe the metabolic control over time in adults with preexisting diabetes.

Methods

This is a five-year (2014–2019) follow-up study on a random sample of 501 residents of the Greater Beirut area. Out of 478 people eligible to participate in the follow-up study, 198 returned (response rate 39.5%). Assessment included medical history, anthropometric measures, food frequency, sleep, and lifestyle questionnaires. Laboratory data included glycemic indices (fasting glucose and HbA1C) and other biological markers. The diagnosis of probable diabetes (PD) was based on one abnormal test for either fasting glucose ≥ 126 mg/dL or HbA1C ≥ 6.5% or having history of diabetes.

Results

The incidence of diabetes was 17.2 (95% CI 9.6–28.7) per 1000 person-years. Cardiometabolic risk factors independently associated with diabetes were: older age, higher BMI, family history of diabetes, metabolic syndrome, higher CRP and triglyceride level; whereas an independent predictor of diabetes was previous BMI.

In addition, the 42 participants with preexisting diabetes had worsening of their metabolic profile over a five-year period.

Conclusions

The incidence of diabetes was high as compared to some reported world rates, and in line with the high prevalence in the MENA region. The risk was highest in those with positive family history and the presence of the metabolic syndrome or its components. Preventive measures should particularly target participants with that specific risk profile. This becomes particularly important when observing that metabolic control gets worse over time in individuals with diabetes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13098-022-00833-w.

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Most cited references 49

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WITHDRAWN: Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9th edition

Pouya Saeedi, Inga Petersohn, Paraskevi Salpea … (2019)

To provide global estimates of diabetes prevalence for 2019 and projections for 2030 and 2045.

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Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity.

K Alberti, Robert H. Eckel, Scott M. Grundy … (2009)

A cluster of risk factors for cardiovascular disease and type 2 diabetes mellitus, which occur together more often than by chance alone, have become known as the metabolic syndrome. The risk factors include raised blood pressure, dyslipidemia (raised triglycerides and lowered high-density lipoprotein cholesterol), raised fasting glucose, and central obesity. Various diagnostic criteria have been proposed by different organizations over the past decade. Most recently, these have come from the International Diabetes Federation and the American Heart Association/National Heart, Lung, and Blood Institute. The main difference concerns the measure for central obesity, with this being an obligatory component in the International Diabetes Federation definition, lower than in the American Heart Association/National Heart, Lung, and Blood Institute criteria, and ethnic specific. The present article represents the outcome of a meeting between several major organizations in an attempt to unify criteria. It was agreed that there should not be an obligatory component, but that waist measurement would continue to be a useful preliminary screening tool. Three abnormal findings out of 5 would qualify a person for the metabolic syndrome. A single set of cut points would be used for all components except waist circumference, for which further work is required. In the interim, national or regional cut points for waist circumference can be used.

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Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man.

D. Matthews, J Hosker, A Rudenski … (1985)

The steady-state basal plasma glucose and insulin concentrations are determined by their interaction in a feedback loop. A computer-solved model has been used to predict the homeostatic concentrations which arise from varying degrees beta-cell deficiency and insulin resistance. Comparison of a patient's fasting values with the model's predictions allows a quantitative assessment of the contributions of insulin resistance and deficient beta-cell function to the fasting hyperglycaemia (homeostasis model assessment, HOMA). The accuracy and precision of the estimate have been determined by comparison with independent measures of insulin resistance and beta-cell function using hyperglycaemic and euglycaemic clamps and an intravenous glucose tolerance test. The estimate of insulin resistance obtained by homeostasis model assessment correlated with estimates obtained by use of the euglycaemic clamp (Rs = 0.88, p less than 0.0001), the fasting insulin concentration (Rs = 0.81, p less than 0.0001), and the hyperglycaemic clamp, (Rs = 0.69, p less than 0.01). There was no correlation with any aspect of insulin-receptor binding. The estimate of deficient beta-cell function obtained by homeostasis model assessment correlated with that derived using the hyperglycaemic clamp (Rs = 0.61, p less than 0.01) and with the estimate from the intravenous glucose tolerance test (Rs = 0.64, p less than 0.05). The low precision of the estimates from the model (coefficients of variation: 31% for insulin resistance and 32% for beta-cell deficit) limits its use, but the correlation of the model's estimates with patient data accords with the hypothesis that basal glucose and insulin interactions are largely determined by a simple feed back loop.

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Author and article information

Contributors

Hani Tamim: htamim@alfaisal.edu , htamim@aub.edu.lb

Journal

Journal ID (nlm-ta): Diabetol Metab Syndr

Journal ID (iso-abbrev): Diabetol Metab Syndr

Title: Diabetology & Metabolic Syndrome

Publisher: BioMed Central (London )

ISSN (Electronic): 1758-5996

Publication date (Electronic): 4 May 2022

Publication date PMC-release: 4 May 2022

Publication date Collection: 2022

Volume: 14

Electronic Location Identifier: 67

Affiliations

[1 ]GRID grid.411654.3, ISNI 0000 0004 0581 3406, Faculty of Medicine, Department of Internal Medicine, Division of Endocrinology, , American University of Beirut Medical Center, ; Beirut, Lebanon

[2 ]GRID grid.22903.3a, ISNI 0000 0004 1936 9801, Vascular Medicine Program, , American University of Beirut, ; Beirut, Lebanon

[3 ]GRID grid.411654.3, ISNI 0000 0004 0581 3406, Faculty of Medicine, Clinical Research Institute, , American University of Beirut Medical Center, ; Beirut, Lebanon

[4 ]GRID grid.411654.3, ISNI 0000 0004 0581 3406, Faculty of Medicine, Department of Internal Medicine, , American University of Beirut Medical Center, ; Beirut, Lebanon

[5 ]GRID grid.22903.3a, ISNI 0000 0004 1936 9801, Faculty of Agricultural and Food Sciences, Department of Nutrition and Food Sciences, , American University of Beirut, ; Beirut, Lebanon

[6 ]GRID grid.411654.3, ISNI 0000 0004 0581 3406, Faculty of Medicine, Department of Internal Medicine, Division of Pulmonary and Critical Care, , American University of Beirut Medical Center, ; Beirut, Lebanon

[7 ]GRID grid.22903.3a, ISNI 0000 0004 1936 9801, Faculty of Medicine, Department of Internal Medicine, Division of Cardiology, , American University of Beirut, ; Beirut, Lebanon

[8 ]GRID grid.411335.1, ISNI 0000 0004 1758 7207, College of Medicine, , Alfaisal University, ; Riyadh, Kingdom of Saudi Arabia

Article

Publisher ID: 833

DOI: 10.1186/s13098-022-00833-w

PMC ID: 9066987

PubMed ID: 35509100

SO-VID: 987d239c-5911-470b-8edc-3a7c110f5420

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 21 May 2021

Date accepted : 11 April 2022

Custom metadata

ScienceOpen disciplines: Nutrition & Dietetics

Keywords: type 2 diabetes,incidence,lebanon,beirut,mena region,predictors,metabolic syndrome

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ScienceOpen disciplines: Nutrition & Dietetics

Keywords: type 2 diabetes, incidence, lebanon, beirut, mena region, predictors, metabolic syndrome

Incidence of diabetes and its predictors in the Greater Beirut Area: a five-year longitudinal study

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Conclusions

Supplementary Information

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Karger: Nutrition and Dietetics

Most cited references 49

WITHDRAWN: Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9th edition

Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man.

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