Metabolic Control Of Diabetes Research Articles

Machine learning for prediction of diabetes risk in middle-aged Swedish people

AimsTo study if machine learning methodology can be used to detect persons with increased type 2 diabetes or prediabetes risk among people without known abnormal glucose regulation. MethodsMachine learning and interpretable machine learning models were applied on research data from Stockholm Diabetes Preventive Program, including more than 8000 people initially with normal glucose tolerance or prediabetes to determine high and low risk features for further impairment in glucose tolerance at follow-up 10 and 20 years later. ResultsThe features with the highest importance on the outcome were body mass index, waist-hip ratio, age, systolic and diastolic blood pressure, and diabetes heredity. High values of these features as well as diabetes heredity conferred increased risk of type 2 diabetes. . The machine learning model was used to generate individual, comprehensible risk profiles, where the diabetes risk was obtained for each person in the data set. Features with the largest increasing or decreasing effects on the risk were determined. ConclusionsThe primary application of this machine learning model is to predict individual type 2 diabetes risk in people without diagnosed diabetes, and to which features the risk relates. However, since most features affecting diabetes risk also play a role for metabolic control in diabetes, e.g. body mass index, diet composition, tobacco use, and stress, the tool can possibly also be used in diabetes care to develop more individualized, easily accessible health care plans to be utilized when encountering the patients.

Is Vitamin D Deficiency Related to Increased Cancer Risk in Patients with Type 2 Diabetes Mellitus?

There is mounting evidence that type 2 diabetes mellitus (T2DM) is related with increased risk for the development of cancer. Apart from shared common risk factors typical for both diseases, diabetes driven factors including hyperinsulinemia, insulin resistance, hyperglycemia and low grade chronic inflammation are of great importance. Recently, vitamin D deficiency was reported to be associated with the pathogenesis of numerous diseases, including T2DM and cancer. However, little is known whether vitamin D deficiency may be responsible for elevated cancer risk development in T2DM patients. Therefore, the aim of the current review is to identify the molecular mechanisms by which vitamin D deficiency may contribute to cancer development in T2DM patients. Vitamin D via alleviation of insulin resistance, hyperglycemia, oxidative stress and inflammation reduces diabetes driven cancer risk factors. Moreover, vitamin D strengthens the DNA repair process, and regulates apoptosis and autophagy of cancer cells as well as signaling pathways involved in tumorigenesis i.e., tumor growth factor β (TGFβ), insulin-like growth factor (IGF) and Wnt-β-Cathenin. It should also be underlined that many types of cancer cells present alterations in vitamin D metabolism and action as a result of Vitamin D Receptor (VDR) and CYP27B1 expression dysregulation. Although, numerous studies revealed that adequate vitamin D concentration prevents or delays T2DM and cancer development, little is known how the vitamin affects cancer risk among T2DM patients. There is a pressing need for randomized clinical trials to clarify whether vitamin D deficiency may be a factor responsible for increased risk of cancer in T2DM patients, and whether the use of the vitamin by patients with diabetes and cancer may improve cancer prognosis and metabolic control of diabetes.

Stress Induced Diabetes

Many evidences suggested that stressful experience might affect diabetes. The estimate of the actual number of diabetes in India is around 40 million. This means India actually has the highest number of diabetic’s patient of any one country in entire world. No doubt, one of the natural element of life i.e. Stress is a major contributor to the diabetes. Stress can bring many unexpected changes in the blood sugar level; this could trigger symptoms associated with diabetes. Some retrospective human studies have suggested that the stress can participate in Type-1 diabetes, animal studies have shown that stress can cause Type-1 diabetes. impaired glucose tolerance and metabolic disease, however human studies have shown that stress can stimulate hyperglycaemia, insulin resistance. In contrast more consistentevidence support the role of stress in Type-2 diabetes. In recent year the complexities of the relationship between stress and diabetes have become well known but have been less well researched. Some studies have suggested that stressful experience may affect the onset and the metabolic control of diabetes, but finding have often been inconclusive. In this article we reviewed some of this research going on to consider how stress might affect the diabetes and physiological mechanism through which this may occurs and their preventions and management.

Effect of non-surgical periodontal therapy on glycemic control of patients with chronic periodontitis and type-2 diabetes mellitus: A 3 month follow-up intervention study

Periodontal pathogens and their by-products trigger production of cytokines, acute phase proteins and oxidative stress molecules that impair insulin sensitivity or action. The effect of mechanical treatment alone on metabolic control in diabetes is controversial. The present study compared changes in glycaemic control following full-mouth scaling and root planing (SRP) by measuring glycated haemoglobin (HbA1c) level in 15 chronic periodontitis patients with diabetes. At baseline, periodontal examination, HbA1c evaluation and full-mouth SRP was done. Periodontal examination and HbA1c evaluation were repeated after 3 months. Change in HbA1c levels at 3 months was the primary outcome and changes in clinical parameters were secondary outcomes. Statistically significant improvement in periodontal status and HbA1c levels were seen after a follow up of 3 months following SRP. Periodontal treatment improved clinical measures of chronic periodontitis in patients with diabetes, supporting use of nonsurgical periodontal treatment for lowering HbA1c levels.

Gut microbial richness as an earlier biomarker of Mediterranean diet intervention in type 2 diabetes metabolic control

Gut microbial richness as an earlier biomarker of Mediterranean diet intervention in type 2 diabetes metabolic control

PREVALENCE OF ASYMPTOMATIC BACTERIURIA AMONG WOMEN WITH DIABETES MELLITUS - A PROSPECTIVE CROSS-SECTIONAL STUDY

Background: There are many studies demonstrating the risk factors of asymptomatic bacteriuria (ASBU) in diabetic women. ASBU is a well-established risk factor for frequent urinary tract infections (UTIs) in women, and the risk factors that predispose diabetic women to ASBU has to be further evaluated. Aim: To estimate the prevalence of asymptomatic bacteriuria among women with diabetes mellitus .To verify if there is a correlation between bacteriuria and the duration of diabetes and also the correlation between bacteriuria with the metabolic control of diabetes. Materials And Methods: Patients who were admitted in Sree Mookambika Institute of Medical Sciences, Kulasekharam from February 2019 to November, 2020 were included in our study. Two hundred women with diabetes mellitus were enrolled in the study. A control group of 75 healthy women were randomly selected without diabetes was also evaluated in the same period.All relevant clinical information, data was collected. Patients were categorised into variables and mean, SD, Levene’s test of homogeneity of variance, linear regression was calculated. Results: 200 diabetic women were included in our study and 75 healthy women were selected as control. 150 patients were included in the study out of which 95 were males and 55 were females 40% had unstable Angina, 7% had NSTEMI and 53% had STEMI. prevalence of anaemia was 50% of the study population. Anaemia varied according to gender and was more in women 70.9% when compared to men 27.3%. Anaemia was also found to be higher in higher age groups and was more in patients with associated co morbidities such as chronic kidney disease,hypothyroidism and diabetes . Anaemia patients were also found to have more incidence of unstable angina when compared to other forms of ACS. The most common anaemia found was Microcytic hypochromic iron deficiency anaemia 32%. Out of those with iron deficiency, upper GI bleed was the most common cause in our patients. Triple Vessel Disease (TVD) patients were also found to have more incidence of anaemia than those with DVD and SVD. Conclusion: Urinary tract infection is one of the major disease burden for many patients having diabetes mellitus. Asymptomatic bacteriuria (ASB) is several-fold more common among diabetic women than healthy women. The risk of asymptomatic bacteriuria increases with increasing age. The duration of diabetes mellitus has significant correlation with development of asymptomatic bacteriuria among diabetic women. A significant impairment of metabolic control of diabetes shows an overall increase in the risk of developing asymptomatic bacteriuria. Keywords: Asymptomatic Bacteriuria, Diabetic Women, UTI

Using GH-Method: Math-Physical Medicine to Investigate Different Contribution Margins of Fasting Plasma Glucose vs. Postprandial Plasma Glucose on HbA1C

This paper displays results of the contribution margin calculation of fasting plasma glucose (FPG) vs. postprandial plasma glucose (PPG) on HbA1C. The dataset is provided by the author, who uses his own type 2 diabetes metabolic conditions control, as a case study via the “math-physical medicine” approach of a nontraditional methodology in medical research.

Bolus calculator in personal insulin pumps — advantages, differences and practical tips

The widespread use of personal insulin pumps in the treatment of type 1 diabetes has significantly improved the effects of therapy. Patients achieved better metabolic control of diabetes, but also increased comfort of life and professional opportunities. One of the func­tions that increased the effectiveness of insulin therapy using a personal insulin pump is the bolus calculator. The bolus calculator function allows the user to dose their insulin more accurately before meals and to correct hyperglycaemia. The bolus calculator algorithms in insulin pumps of individual manufacturers differ in the way they calculate the correction dose and the amount of so-called active insulin. Consequently, with the same treatment parameters, individual bolus calculators may offer a different dose of insulin. Understanding the principles of the bolus calculator by therapeutic team members and patients is very important for proper diabetes education and diabetes management.

Using GH-Method: Math-Physical Medicine to Investigate the Impact of Different Intensity of Exercise on Postprandial Plasma Glucose (No. 65)

Introduction The data-set is provided by the author, who uses his own type 2 diabetes (T2D) metabolic conditions control, as a case study via the “math-physical medicine” approach of a non-traditional methodology in medical research.

Implications of diabetes self-management education in metabolic control

Diabetes is a chronic, progressive and disabling disease that affects millions of people around the world, with a high mortality rate derived from poor control. The paradigm of the treatment of diabetes has shifted to focus on empowering the person with diabetes to manage the disease successfully and to improve their quality of life. Diabetes self-management education, or DSME, is a process where people with diabetes gain the knowledge and skills needed to make informed decisions, modify their behaviour and to self-manage, in collaboration with health personnel, successfully the disease and its related conditions. To achieve these goals a structured educational program is used, in 4 stages which include assessment, planning, implementation and evaluation; to carry on this program, the model of the seven self-care behaviours known as AADE7®, developed by the American Association of Diabetes Educators, is used as a frame of reference. The objective of this paper is to analyse the components of the education program for diabetes self-care as well as its reference framework, the AADE7® model, and its importance in the metabolic control of diabetes.

Using Candlestick Charting, Segmentation Pattern Analysis, and GHMethod: Math-Physical Medicine to calculate Effective Glucoses based on Finger-Piercing Glucose Measurement

This paper describes glucose measurements and their extensive calculation results over a period of 7.5 years based on Finger-Piercing Data (Finger) using both candlestick charting and glucose segmentation pattern analysis. The dataset is provided by the author, who uses his own type 2 diabetes metabolic conditions control, as a case study via the “math-physical medicine” approach of a non- traditional methodology in medical research. Math-Physical Medicine (MPM) starts with the observation of the human body’s physical phenomena (not biological or chemical characteristics), collecting elements of the disease related data (preferring big data), utilizing applicable engineering modeling techniques, developing appropriate mathematical equations (not just statistical analysis), and finally predicting the direction of the development and control mechanism of the disease.

Using Three Analysis Methods: Time-Series, Spatial, And Frequency-Domain to Analyze and Forecast Key Diabetes Biomedical Variables (GH-Method: MathPhysical Medicine)

This paper describes the author’s application of Time-Series Analysis, Spatial Analysis, and Frequency-Domain Analysis (including wave theory, Candlestick charting, segmentation pattern analysis) to analyze and forecast certain biomedical key variables of type 2 diabetes (T2D) conditions. The dataset is provided by the author, who uses his own type 2 diabetes metabolic conditions control, as a case study via the “math-physical medicine” approach of a nontraditional methodology in medical research.

Using GH-Method: Math-Physical Medicine, Fourier Transform, and Frequency Segmentation Pattern Analysis to Investigate Relative Energy Associated with Glucose

This paper provides research findings on glucose created relative energy by using sensor collected glucose data from a period of 376 days from 5/5/2018 to 5/15/20. The dataset is provided by the author, who uses his own type 2 diabetes metabolic conditions control, as a case study via the “math-physical medicine” approach of a nontraditional methodology in medical research. Math-physical medicine (MPM) starts with the observation of the human body’s physical phenomena (not biological or chemical characteristics), collecting elements of the disease related data (preferring big data), utilizing applicable engineering modeling techniques, developing appropriate mathematical equations (not just statistical analysis), and finally predicting the direction of the development and control mechanism of the disease.

Using Three Analysis Methods: Time-Series, Spatial, And Frequency-Domain to Analyze and Forecast Key Diabetes Biomedical Variables (GH-Method: MathPhysical Medicine)

This paper describes the author’s application of Time-Series Analysis, Spatial Analysis, and Frequency-Domain Analysis (including wave theory, Candlestick charting, segmentation pattern analysis) to analyze and forecast certain biomedical key variables of type 2 diabetes (T2D) conditions. The dataset is provided by the author, who uses his own type 2 diabetes metabolic conditions control, as a case study via the “math-physical medicine” approach of a nontraditional methodology in medical research.

Using GH-Method: Math-Physical Medicine, Fourier Transform, and Frequency Segmentation Pattern Analysis to Investigate Relative Energy Associated With Glucose

This paper provides research findings on glucose created relative energy by using sensor collected glucose data from a period of 376 days from 5/5/2018 to 5/15/20. The dataset is provided by the author, who uses his own type 2 diabetes metabolic conditions control, as a case study via the “math-physical medicine” approach of a nontraditional methodology in medical research. Math-physical medicine (MPM) starts with the observation of the human body’s physical phenomena (not biological or chemical characteristics), collecting elements of the disease related data (preferring big data), utilizing applicable engineering modeling techniques, developing appropriate mathematical equations (not just statistical analysis), and finally predicting the direction of the development and control mechanism of the disease.

Type 2 Diabetes Nursing Guidelines based on Wave and Energy Theories Associated with Three Distinctive Postprandial Plasma Glucose Waveforms (GH-Method: Math-Physical Medicine)

This paper summarizes tips and guidelines of optimized combination of food and exercise for type 2 diabetes (T2D) patients to control their glucoses. The dataset is provided by the author, who uses his own type 2 diabetes metabolic conditions control, as a case study via the “math-physical medicine” approach of a non-traditional methodology in medical research.

Using GH-Method: Math-Physical Medicine to Conduct the Accuracy Comparison of Two different Postprandial Plasma Glucosa Prediction Methods

This paper describes the accuracy of two different methods of postprandial plasma glucose (PPG) prediction in comparison with the actual measured PPG by using the finger-piercing and test-strip (Finger) method. The dataset is provided by the author, who uses his own type 2 diabetes metabolic conditions control, as a case study via the “math-physical medicine” approach of a non-traditional methodology in medical research.

Using GH-Method: Math-Physical Medicine to Conduct Segmentation Analysis to Investigate the Impact of Weather Temperatures on Glucose (Both FPG And PPG)

This paper is based on big data collected from a period of 1,420daysfrom 6/1/2015 to 4/21/2019 with a total of 4,260 data, including highest ambient temperature (weather) of each day in degree Fahrenheit (°F), fasting plasma glucose (FPG) and postprandial plasma glucose (PPG) in mg/dL. The dataset is provided by the author, who uses his own type 2 diabetes metabolic conditions control, as a case study via the “math-physical medicine” approach of a non-traditional methodology in medical research.

Differences in Risk Factors for Diabetic Retinopathy in Type 1 and Type 2 Diabetes Mellitus Patients in North-East Poland.

Background and Objective: Nowadays, diabetes is one of the main causes of blindness in the world. Identification and differentiation of risk factors for diabetic retinopathy depending on the type of diabetes gives us the opportunity to fight and prevent this complication. Aim of the research: To assess differences in the risk factors for diabetic retinopathy in type 1 and type 2 diabetes mellitus patients in Warmia and Mazury Region, Poland. Materials and Methods: Risk factors for diabetic retinopathy (DR) were assessed on the basis of an original questionnaire, which included: personal data, clinical history of diabetes and eye disease. Elements of clinical examination: blood pressure, BMI, waist circumference. Indicators of diabetes metabolic control: mean glycemia, glycated hemoglobin (HbA1c), total cholesterol and triglycerides, creatinine, glomerular filtration rate (GFR), albumin–creatinine ratio in urine. Results: The study group included 315 (26%) patients with DM1 and 894 (74%) patients with DM2. Risk factors were estimated on the basis of logistic regression and verified with Student’s t-test. Statistically significant dependencies were found in both groups between the occurrence of diabetic retinopathy and diabetes duration, HbA1c, triglyceride concentrations, indicators of kidney function and cigarette smoking status. In the DM2 group, the development of DR was significantly influenced by the implemented models of diabetic treatment. Conclusions: In the whole study group, the risk of DR was associated with the duration of diabetes, HbA1c, triglyceride concentrations and smoking. In DM1 patients, the risk of DR was associated with diabetic kidney disease in the G1A1/A2 stage of chronic kidney disease, and in DM2 patients with the G2 stage of chronic kidney disease. An important risk factor for DR in DM2 patients was associated with late introduction of insulin therapy.

Association of Adverse Childhood Experiences with Glycemic Control and Lipids in Children with Type 1 Diabetes.

Adverse childhood experiences (ACE) have been associated with a greater prevalence of risky behaviors and chronic health conditions, such as diabetes in adulthood. While adolescents with risk taking behaviors experience worsening of diabetic metabolic control, it is yet to be determined whether glycemic management in children and adolescents is negatively and independently influenced by ACEs. This study examines the relationship between ACEs in children and adolescents with type 1 diabetes (T1DM) and glycemic control, BMI and lipids. For such children, we hypothesized that hemoglobin A1c (HbA1c) is positively correlated with ACE scores. Parents of children (age 2–18 years) with T1DM completed a validated ACE questionnaire. The associations between parent and child ACE score and HbA1c, lipids and BMI z-scores were assessed using linear regression. The prevalence of any ACE was 27.9% among children and 49.0% among parents. HbA1c was significantly higher in children who had exposure to three or more ACEs (β: 0.63 (4.5 mmol/mol); p = 0.02), in those who had a parent exposed to four or more ACEs (β: 0.87 (7.2 mmol/mol); p = 0.03), in children who had exposure to household incarceration (β: 0.62 (4.4 mmol/mol); p = 0.05) and children who witnessed or had been victim of violence in the neighborhood (β: 0.71 (5.4 mmol/mol); p = 0.02). ACEs were highly prevalent among children with T1DM and had a positive association with glycemic control.