Review Article
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The author describes one of his hypothetical theories on the relationship between life longevity and overall metabolism, the macrosystem view, specifically the stress and daily life routine regularity, two micro-categories. He has spent ~25,000 h over 7.5 years (2010–2019) to conduct research on metabolism, endocrinology, and chronic diseases, specifically diabetes. These big data analytics is based on ~600,000 data over 2.5 years. His developed metabolism model has shed some light about the impact on his life longevity due to his overall metabolic changes, especially his stress level and life routine regularity. Having a strong lifestyle management leads into a good metabolic state, which then converts into a strong immunity to fight against three major disease categories, chronic diseases and complications (50% of death), cancers (29% of death), and infectious diseases (11% of death), with the remaining 10% of non-diseases related to death cases. This is a logical way to achieve longevity which is the core of geriatrics.
Systematic Review
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The world has been facing a novel coronavirus, COVID-19 pandemic since the beginning of 2020. Until the end of May, 5.9 million confirmed cases and 350.000 deaths have been reported. Diabetes, as a prevalent chronic disease is known to be a risk factor for infection onset and disease severity. In this study, a systematic review has been planned to determine the relation between COVID-19 and diabetes among other comorbidities. For this aim, 564 researches have been determined about the topic and 48 of them have been evaluated in the review. The researches have been done with 91.172 COVID-19 patients, and diabetes ratio among the researches differ from 3.3% to 40%. Besides, age, hypertension, cardiovascular diseases, smoking status, and respiratory diseases have been evaluated in the review as common comorbidities. As a result of the study, diabetes and hypertension have been determined to be important risk factors in COVID-19 onset and severity. However, further detailed multidisciplinary researches about COVID-19, diabetes and comorbidities will be valuable in the COVID-19 pandemic process and future aspects.
Review Article
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Haptoglobin (HAP) is genetically polymorphic with three primary genotypes, HAP 1-1, 2-1 and 2-2. Each genotype differs phenotypically in HAP structure and ability to perform its main function, scavenging free hemoglobin (Hb) released from old red blood cells. Patients with both diabetes and the genotype 2-2 appear to be at an increased cardiovascular risk than those with the other genotypes or patients without diabetes. This risk appears elevated with worse glycemic control. The exact mechanism for this increased risk is unknown but there are several proposed causes. Vitamin E has shown to reduce cardiovascular events in patients with both diabetes and the 2-2 genotype but the safety of implementing such therapy remains unknown. Recent post-hoc evaluation of a landmark study originally designed to assess the benefits and risks of more aggressive glycemic control suggests there may be a cardiovascular benefit in patients with diabetes and the 2-2 genotype that is not seen in those with the other genotypes. This information, if confirmed with post-hoc evaluation of other similar landmark studies as well as evaluation of genotype differences in recent cardiovascular safety studies with glucagon-like peptide agonists or sodium-glucose cotransporter inhibitors, could provide clinicians with an avenue to better identify patients most at risk for cardiovascular events and who may benefit the most from more aggressive glycemic control or use of other antihyperglycemic agents.
Research Article
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Introduction: Approximate Entropy (ApEn) is a widely enforced metric to evaluate the chaotic response and irregularities of RR intervals from an electrocardiogram. We applied the metric to estimate these responses in subjects with type 1 diabetes mellitus (DM1). So far, as a technique it has one key problem - the accurate choices of the tolerance (r) and embedding dimension (M). So, we attempted to overcome this drawback by applying different groupings to detect the optimum.
Methods: We studied 46 subjects split into two equal groups: DM1 and control. To evaluate autonomic modulation the heart rate was measured for 30 min in a supine position without any physical, sensory, or pharmacological stimuli. For the time-series, the ApEn was applied with set values for r (0.1→0.5 in intervals of 0.1) and M (1→5 in intervals of 1) and the differences between the two groups and their effect size by two measures (Cohen’s ds and Hedges’s gs) were computed.
Results: The highest value of statistical significance accomplished for the effect sizes (ES) for any of the combinations performed was -0.7137 for Cohen’s ds and -0.7015 for Hedges’s gs with M = 2 and r = 0.08.
Conclusion: ApEn was able to identify the reduction in chaotic response in DM1 subjects. Still, ApEn is relatively unreliable as a mathematical marker to determine this.
Original Research
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This paper focuses on the author’s invented robotic software technology, the artificial intelligence glucometer (AIG) product, to provide a diagnosis for diabetes disease and glucose control. From 2010–2013, he self-studied internal medicine and food nutrition. In 2014, he further utilized topology concept, partial differential equation, non-linear algebra, and finite element engineering concept to develop a human metabolism’s mathematical model. It consists of 10 categories and ~500 elements with ~1.5 million collected data of his own body health, disease conditions, and lifestyle details. Starting from 2015, he focused on the root cause of diabetes, which is “glucose”. By applying wave theory, signal processing, energy theory, optical physics, structural & fluid dynamics from physics and engineering modeling; pattern and segmentation analysis, time/space/frequency domain analyses, big data analytics, machine learning and self-correction, prediction equations from mathematics and computer science, he decided to utilize his robotic software as the foundation to further build up his needed medical research and clinical tools. By using the artificial intelligence (AI) robotic software, the author’s average glucose decreased from 280 mg/dL to 118 mg/dL and his hemoglobin A1C (HbA1C or A1C) reduced from 10%+ to below 6.5%, without diabetes medications. All his diabetes complications are either under control or have subsided. This innovative technology of his robotic software for glucose prediction and diabetes control has also been proven by many other patients, who have achieved equally remarkable medical results.
Research Article
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The present analysis was to identify the socioeconomic factors responsible for prevalence of obesity and diabetes simultaneously among adults of 18 years and above residing in both urban and rural localities of Bangladesh. Accordingly, information was collected from 960 adults by some doctors and nurses from and nearby their working places. Among the investigated adults, 66.9% were diabetic patients and 20.2% of them were obese. In the sample, total obese adults were 29.3%. Obesity and diabetes were significantly associated. Prevalence of obesity and diabetes were significantly associated with age, marital status and utilization of time. Income was the most responsible factor for this simultaneous health hazard followed by expenditure, physical activity, marital status, religion and occupation. This conclusion was drawn from the results of odds ratio and discriminant analysis.
