Association of risk factors with type 2 diabetes: A systematic review

Ismail, Leila; Materwala, Huned; Kaabi, Juma Al

doi:10.1016/j.csbj.2021.03.003

Cited by 156 publications

(126 citation statements)

References 190 publications

(69 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Generally, obesity and uneven diet were considered as part of risk factors for type 2 diabetes mellitus. However, sleep quality, hypertension and smoking were also found to be strongly associated with an elevated risk of type 2 diabetes mellitus (19). However, as our results showed, APMP and HAP ranked third and seventh respectively for type 2 diabetes mellitus deaths among all risk factors, the impact of which was more profound than some behavior risks, such as smoking and dietary risks.…”

Section: Discussionmentioning

confidence: 40%

Estimates of Type 2 Diabetes Mellitus Burden Attributable to Particulate Matter Pollution and Its 30-Year Change Patterns: A Systematic Analysis of Data From the Global Burden of Disease Study 2019

Chen

et al. 2021

Front. Endocrinol.

View full text Add to dashboard Cite

BackgroundEpidemiological trends of type 2 diabetes mellitus attributable to fine particulate matter (PM2.5) pollution remain unclear. Here, we estimated spatiotemporal trends of type 2 diabetes mellitus burden attributable to PM2.5 pollution, including ambient particulate matter pollution (APMP) and household air pollution (HAP), from 1990–2019.MethodsData were obtained from the Global Burden of Disease Study 2019 and were analyzed by age, sex, year, and location. Joinpoint regression analysis was applied in the analysis of temporal trends in type 2 diabetes mellitus burden over the 30 years.ResultsGlobally, PM2.5 pollution contributed to 292.5 thousand deaths and 13 million disability-adjusted life-years (DALYs) in 2019. APMP ranked third among all risk factors, causing an increase in type 2 diabetes mellitus burden from 1990, whereas the impact of HAP significantly fell during the same period. Both APMP and HAP contributed the most to deaths and DALYs of type 2 diabetes mellitus among older people. However, the age-standardized death and DALY rates of type 2 diabetes mellitus attributable to APMP were greater among males and people in the middle socio-demographic index countries, especially in Southern Sub-Saharan Africa. For HAP, type 2 diabetes mellitus burden was modestly higher in females and was highest in Oceania, which was the only region with an increase from 1990.ConclusionsPM2.5 pollution resulted in substantial and increasing type 2 diabetes mellitus burden worldwide. Hence, governments and health systems should take steps to reduce air pollution to mitigate this increasing burden.

show abstract

Section: Discussionmentioning

confidence: 40%

Estimates of Type 2 Diabetes Mellitus Burden Attributable to Particulate Matter Pollution and Its 30-Year Change Patterns: A Systematic Analysis of Data From the Global Burden of Disease Study 2019

Chen

et al. 2021

Front. Endocrinol.

View full text Add to dashboard Cite

show abstract

“…In the selected studies on TL, PA was the most studied lifestyle factor, followed by smoking and sleep. In general, being physical active, not smoking and having good sleep quality are associated with a reduced risk of morbimortality for the main chronic diseases [ 8 , 9 , 10 , 11 , 144 , 145 , 146 , 147 ]. There is a challenge in searching for the best biomarker of aging and despite these limitations and the advantage of measuring a combination of biomarkers instead of only one [ 148 , 149 ], TL is the most extensively studied biomarker of age-related diseases [ 7 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ].…”

Section: Discussionmentioning

confidence: 99%

“…Life expectancy has increased in recent decades, and it is expected to rise in the coming years [ 1 , 2 , 3 , 4 ]. Therefore, there is a higher probability of suffering from diseases related to an aging population, such as cardiovascular diseases [ 1 , 5 , 6 , 7 ], diabetes [ 7 , 8 , 9 ], hypertension [ 10 , 11 , 12 ], cancer [ 7 , 13 ], and neurological diseases [ 7 , 14 ]. Owing to the social, psychological, and economic conditions associated with aging, this has now become one of the major issues facing public health [ 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Effect of Physical Activity, Smoking, and Sleep on Telomere Length: A Systematic Review of Observational and Intervention Studies

Barragán

Ortega

Sorlí

et al. 2021

JCM

View full text Add to dashboard Cite

Aging is a risk factor for several pathologies, restricting one’s health span, and promoting chronic diseases (e.g., cardiovascular and neurodegenerative diseases), as well as cancer. Telomeres are regions of repetitive DNA located at chromosomal ends. Telomere length has been inversely associated with chronological age and has been considered, for a long time, a good biomarker of aging. Several lifestyle factors have been linked with telomere shortening or maintenance. However, the consistency of results is hampered by some methodological issues, including study design, sample size, measurement approaches, and population characteristics, among others. Therefore, we aimed to systematically review the current literature on the effects of three relevant lifestyle factors on telomere length in human adults: physical activity, smoking, and sleep. We conducted a qualitative systematic review of observational and intervention studies using the Preferred Reporting Item for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The systematic literature search covered articles published in MEDLINE and EMBASE databases (from 2010 to 2020). A total of 1400 studies were identified; 83 were included after quality control. Although fewer sedentary activities, optimal sleep habits, and non- or ex-smoker status have been associated with less telomere shortening, several methodological issues were detected, including the need for more targeted interventions and standardized protocols to better understand how physical activity and sleep can impact telomere length and aging. We discuss the main findings and current limitations to gain more insights into the influence of these lifestyle factors on the healthy aging process.

show abstract

“…The prevalence of type 2 diabetes in an individual is found to be associated with the interactions of several risk factors that are related to lifestyle, psychosocial, medical conditions, demographic, and genetic (Hereditary) [3][4][5]. Diagnosis of type 2 diabetes if not performed at an early stage can lead to several serious life-threatening complications [6].…”

Section: Introductionmentioning

confidence: 99%

Type 2 Diabetes with Artificial Intelligence Machine Learning: Methods and Evaluation

Ismail

Materwala

Tayefi³

et al. 2021

Arch Computat Methods Eng

Self Cite

View full text Add to dashboard Cite

Diabetes, one of the top 10 causes of death worldwide, is associated with the interaction between lifestyle, psychosocial, medical conditions, demographic, and genetic risk factors. Predicting type 2 diabetes is important for providing prognosis or diagnosis support to allied health professionals, and aiding in the development of an efficient and effective prevention plan. Several works proposed machine-learning algorithms to predict type 2 diabetes. However, each work uses different datasets and evaluation metrics for algorithms’ evaluation, making it difficult to compare among them. In this paper, we provide a taxonomy of diabetes risk factors and evaluate 35 different machine learning algorithms (with and without features selection) for diabetes type 2 prediction using a unified setup, to achieve an objective comparison. We use 3 real-life diabetes datasets and 9 feature selection algorithms for the evaluation. We compare the accuracy, F-measure, and execution time for model building and validation of the algorithms under study on diabetic and non-diabetic individuals. The performance analysis of the models is elaborated in the article.

show abstract

Association of risk factors with type 2 diabetes: A systematic review

Cited by 156 publications

References 190 publications

Estimates of Type 2 Diabetes Mellitus Burden Attributable to Particulate Matter Pollution and Its 30-Year Change Patterns: A Systematic Analysis of Data From the Global Burden of Disease Study 2019

Estimates of Type 2 Diabetes Mellitus Burden Attributable to Particulate Matter Pollution and Its 30-Year Change Patterns: A Systematic Analysis of Data From the Global Burden of Disease Study 2019

Effect of Physical Activity, Smoking, and Sleep on Telomere Length: A Systematic Review of Observational and Intervention Studies

Type 2 Diabetes with Artificial Intelligence Machine Learning: Methods and Evaluation

Contact Info

Product

Resources

About