Role of Intermittent Fasting in the Management of Prediabetes and Type 2 Diabetes Mellitus

Ojo, Tioluwani K; Joshua, Olajide O; Ogedegbe, Oboseh J; Oluwole, Oluwapelumi; Ademidun, Ayoade; Jesuyajolu, Damilola

doi:10.7759/cureus.28800

Cited by 7 publications

(11 citation statements)

References 28 publications

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“…Numerous studies have demonstrated that high FBS levels are necessary for transition from a healthy state to diabetes mellitus. 67 , 68 Yeboah et al 69 claimed that FBS is a distinct risk factor for the emergence of type 2 diabetes, implying that efforts to lower the FBS incidence will also reduce the incidence of type 2 diabetes mellitus in the long run. Therefore, the incidence of diabetes mellitus can be effectively controlled through early diagnosis and FBS treatment.…”

Section: Discussionmentioning

confidence: 99%

Predictive model for early detection of type 2 diabetes using patients' clinical symptoms, demographic features, and knowledge of diabetes

Ojurongbe,

Afolabi,

Oyekale

et al. 2024

Health Science Reports

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Background and AimsWith the global rise in type 2 diabetes, predictive modeling has become crucial for early detection, particularly in populations with low routine medical checkup profiles. This study aimed to develop a predictive model for type 2 diabetes using health check‐up data focusing on clinical details, demographic features, biochemical markers, and diabetes knowledge.MethodsData from 444 Nigerian patients were collected and analysed. We used 80% of this data set for training, and the remaining 20% for testing. Multivariable penalized logistic regression was employed to predict the disease onset, incorporating waist‐hip ratio (WHR), triglycerides (TG), catalase, and atherogenic indices of plasma (AIP).ResultsThe predictive model demonstrated high accuracy, with an area under the curve of 99% (95% CI = 97%–100%) for the training set and 94% (95% CI = 89%–99%) for the test set. Notably, an increase in WHR (adjusted odds ratio [AOR] = 70.35; 95% CI = 10.04–493.1, p‐value < 0.001) and elevated AIP (AOR = 4.55; 95% CI = 1.48–13.95, p‐value = 0.008) levels were significantly associated with a higher risk of type 2 diabetes, while higher catalase levels (AOR = 0.33; 95% CI = 0.22–0.49, p < 0.001) correlated with a decreased risk. In contrast, TG levels (AOR = 1.04; 95% CI = 0.40–2.71, p‐value = 0.94) were not associated with the disease.ConclusionThis study emphasizes the importance of using distinct clinical and biochemical markers for early type 2 diabetes detection in Nigeria, reflecting global trends in diabetes modeling, and highlighting the need for context‐specific methods. The development of a web application based on these results aims to facilitate the early identification of individuals at risk, potentially reducing health complications, and improving diabetes management strategies in diverse settings.

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Section: Discussionmentioning

confidence: 99%

Predictive model for early detection of type 2 diabetes using patients' clinical symptoms, demographic features, and knowledge of diabetes

Ojurongbe,

Afolabi,

Oyekale

et al. 2024

Health Science Reports

View full text Add to dashboard Cite

show abstract

“…Numerous studies have demonstrated that high FBS levels are necessary for the transition from a healthy state to diabetes mellitus 47,48 . Yeboah et al 49 claimed that FBS is a distinct risk factor for the emergence of type 2 diabetes, implying that efforts to lower FBS incidence will also reduce the incidence of type 2 diabetes mellitus in the long run.…”

Section: Discussionmentioning

confidence: 99%

Predictive Machine Learning Model for Early Detection of Type 2 Diabetes Using Patients’ Clinical Symptoms, Demographic Features, and Knowledge of Diabetes

Ojurongbe,

Afolabi,

Oyekale

et al. 2023

Preprint

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Background: Type 2 diabetes is commonly diagnosed at a later stage due to low attendance to health examinations, especially among certain demographic groups with financial constraints, differences in self-care routines, and medical habits. Given these circumstances, the need to develop reliable predictive tools for type 2 diabetes is becoming increasingly pressing. Methods: To address this, we routinely collected health checkup data from patients with diabetes and used their clinical symptoms, demographics, and diabetes knowledge to predict the disease onset. Data from 444 Nigerian patients were used to develop a predictive model, with 80% of the dataset selected for training and the remaining 20% for testing. Results: Using multivariable penalised logistic regression, we predicted type 2 diabetes with an AUC of 99% (95% confidence interval [CI] = 97% - 100%) for the training set and 94% (95% CI = 89% - 99%) for the test set, incorporating waist-hip ratio (WHR), triglycerides (TG), catalase, and atherogenic indices of plasma (AIP) as informative features. A significant increase in the adjusted odds ratios (AOR) of type 2 diabetes was observed with WHR (AOR = 70.35; 95% CI = 10.04 - 493.1, p-value < 0.0001) and AIP (AOR = 4.55; 95% CI = 1.48 - 13.95, p-value = 0.0081). Meanwhile, TG had an increased adjusted odds ratio of 1.04 for type 2 diabetes (95% CI = 0.4 - 2.71), although this association was not statistically significant (p-value = 0.9377). Conversely, catalase exhibited a decreased adjusted odds ratio for type 2 diabetes (AOR = 0.33; 95% CI = 0.22 - 0.49, p-value < 0.0001). Conclusion: Our study revealed associations between clinical symptoms and type 2 diabetes, emphasising the crucial role of early diagnosis. A web application is provided to aid the early identification of at-risk individuals, reduce health complications, and address inconsistent checkup gaps.

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“…One such intervention is intermittent fasting. 9 , 10 Although there are many types of intermittent fasting, they all share the general principle of alternating periods of fasting (abstinence or very low energy intake) with periods of eating. Time‐restricted eating (TRE) is a common intermittent fasting method that limits the number of daily hours during which a person consumes food and calorie‐containing drinks.…”

Section: Introductionmentioning

confidence: 99%

The effectiveness of mobile app usage in facilitating weight loss: An observational study

Huntriss,

Salimgaraev,

Nikogosov

et al. 2024

Obesity Science & Practice

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AimWith increasing rates of global obesity and associated health issues, there is an ever‐increasing need for weight management solutions to be more accessible. Mobile applications offer accessible support systems and have the potential to offer a viable and effective weight management solution as an alternative to traditional healthcare models.ObjectiveTo evaluate the effectiveness of the SIMPLE mobile application for time‐restricted eating in achieving weight loss (WL).MethodsUser data were analyzed between January 2021 and January 2023. In‐app activity was calculated as the proportion of active days over 12, 26 and 52 weeks. A day is considered active if it contains at least one in‐app action (e.g., logging weight, food, fasting, or physical activity). Users were categorized into four in‐app activity levels: inactive (in‐app activity <33%), medium activity (33%–66%), high activity (66%–99%), and maximal activity (100%). Weight change among in‐app activity groups was assessed at 12, 26, and 52 weeks.ResultsOut of 53,482 users, a positive association was found between the use of the SIMPLE app and WL. Active app users lost more weight than their less active counterparts. Active users had a median WL of 4.20%, 5.04%, and 3.86% at 12, 26, and 52 weeks, respectively. A larger percentage of active users—up to 50.26%—achieved clinically significant WL (≥5%) when compared to inactive users. A dose‐response relationship between WL and app usage was found after adjusting for gender, age, and initial Body Mass Index; a 10% increase in app activity correlated with increased WL by 0.43, 0.66 and 0.69 kg at 12, 26, and 52 weeks, respectively.ConclusionsThe study demonstrates that the SIMPLE app enables effective WL directly associated with the level of app engagement. Mobile health applications offer an accessible and effective weight management solution and should be considered when supporting adults to lose weight.

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Role of Intermittent Fasting in the Management of Prediabetes and Type 2 Diabetes Mellitus

Cited by 7 publications

References 28 publications

Predictive model for early detection of type 2 diabetes using patients' clinical symptoms, demographic features, and knowledge of diabetes

Predictive model for early detection of type 2 diabetes using patients' clinical symptoms, demographic features, and knowledge of diabetes

Predictive Machine Learning Model for Early Detection of Type 2 Diabetes Using Patients’ Clinical Symptoms, Demographic Features, and Knowledge of Diabetes

The effectiveness of mobile app usage in facilitating weight loss: An observational study

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