Average Glucose Levels Research Articles

Dexamethasone Is Associated With a Statistically Significant Increase in Postoperative Blood Glucose Levels Following Primary Total Knee Arthroplasty

BackgroundDexamethasone has the potential to cause a transient increase in blood glucose levels. Recent evidence has suggested the potential for a linearly increased risk of periprosthetic joint infection beginning at blood glucose levels of ≥115 mg/dL and an optimal cutoff of 137 mg/dL. We designed the following study to determine (1) what percentage of our patients had postoperative day 1 (POD1) glucose levels above 137 mg/dL and (2) if the administration of dexamethasone further increased this risk. MethodsAll primary total knee arthroplasties performed from 1998 to 2021 at our institution were identified and retrospectively reviewed. Patient demographics, dexamethasone administration, and perioperative glucose levels were recorded. Outcomes included POD1 glucose levels, infection rate, and all-cause reoperations and revisions. ResultsThe average POD1 glucose level for the entire cohort (n = 5353) was 138.7 mg/dL. The percentage of patients with a glucose level of 137 mg/dL or higher was significantly greater in patients that received dexamethasone (55.2% vs 37.7%; P < .0001). Significantly higher glucose levels were seen with dexamethasone administration in both diabetic (187.7 vs 173.4 mg/dL; P < .0001) and nondiabetic patients (137.7 vs 128.0 mg/dL; P < .0001). Dexamethasone use was associated with a nonstatistically significant increase in infection rates (1.7% vs 1.0%; P = .177). ConclusionsAdministration of dexamethasone is associated with a statistically significant increase in POD1 glucose levels, regardless of diabetic status. Dexamethasone use should continue to be closely monitored given the potential risks of elevated postoperative glucose levels and the potential for periprosthetic infection.

Relationships of habitual daily alcohol consumption with all-day and time-specific average glucose levels among non-diabetic population samples.

Alcohol consumption is a prevalent behavior that is bi-directionally related to the risk of type 2 diabetes. However, the effect of daily alcohol consumption on glucose levels in real-world situations in the general population has not been well elucidated. This study aimed to clarify the relationship between alcohol consumption and all-day and time-specific glucose levels among non-diabetic individuals. We investigated 913 non-diabetic males and females, aged 40-69 years, during 2018-2020 from four communities across Japan. The daily alcohol consumption was assessed using a self-report questionnaire. All-day and time-specific average glucose levels were estimated from the interstitial glucose concentrations measured using the Flash glucose monitoring system for a median duration of 13 days. Furthermore, we investigated the association between all-day and time-specific average glucose levels and habitual daily alcohol consumption levels, using never drinkers as the reference, and performed multiple linear regression analyses after adjusting for age, community, and other diabetes risk factors for males and females separately. All-day average glucose levels did not vary according to alcohol consumption categories in both males and females. However, for males, the average glucose levels between 5:00 and 11:00 h and between 11:00 and 17:00 h were higher in moderate and heavy drinkers than in never drinkers, with the difference values of 4.6 and 4.7 mg/dL for moderate drinkers, and 5.7 and 6.8 mg/dL for heavy drinkers. Conversely, the average glucose levels between 17:00 and 24:00 h were lower in male moderate and heavy drinkers and female current drinkers than in never drinkers; the difference values of mean glucose levels were -5.8 for moderate drinkers, and -6.1 mg/dL for heavy drinkers in males and -2.7 mg/dL for female current drinkers. Alcohol consumption was associated with glucose levels in a time-dependent biphasic pattern.

Analysis of Glycaemic Changes and their Outcome in Critically Ill Non-diabetic Patients Admitted to the ICU: A Cohort Study

Introduction: Critical illness results in physiological and metabolic changes that lead to dysglycaemia, which is associated with morbidity and mortality. There exists a J- or U-shaped relationship between average glucose levels and mortality, emphasising the importance of evaluating glycaemic variability in critical illness. Aim: To assess glycaemic changes in critically ill patients and their association with Intensive Care Unit (ICU) outcomes. Materials and Methods: The prospective cohort study was conducted from August 2018 to August 2019. A total of 100 non-diabetic critically ill patients admitted to the ICU were observed for seven days. The severity of illness was evaluated using the Glasgow Coma Score (GCS) and Sequential Organ Failure Assessment (SOFA) scores. Plasma glucose levels were recorded every four hours in the ICU. Patients were followed for a maximum of seven days or until discharge or death. They were categorised into hypoglycaemia, normoglycaemia, or stress hyperglycaemia groups for analysis. Statistical analysis was performed using IBM SPSS Statistics for Windows, version 24.0. Results: The study included 64 male and 36 female patients, with an average mean age of 55.90±16.51 years (range: 18- 86 years). Among the 100 patients, 21 died within the sevenday hospitalisation period. Among these, two were in the hypoglycaemic group, 13 were in the normoglycaemic group, and six were in the stress hyperglycaemic group. The patients had a mean SOFA score of 11.55±2.20, which was significantly higher compared to patients without organ failure (mean score: 2.54±2.55), with a statistically significant association (p&lt;0.01). Similarly, patients who died during hospitalisation had a very high SOFA score (mean score: 9.76±3.36), also statistically significant (p&lt;0.01). Conclusion: Critically ill patients in the stress hyperglycaemia and hypoglycaemia groups during their ICU stay had a worse prognosis compared to patients with normoglycaemia. Hypoglycaemia during the ICU stay was associated with the poorest outcome. Maintaining normoglycaemia can significantly reduce morbidity and mortality in critically ill non-diabetic patients; therefore, considering Continuous Glucose Monitoring Systems (CGMS) for more frequent glycaemic monitoring and reducing glycaemic variability may lead to better outcomes in the ICU.

The impact of lockdown measures during the SARS-CoV-2 pandemic on the management of diabetes in a Northern Gauteng Region of South Africa.

coronavirus disease 2019 (SARS-CoV-2), a global pandemic, popularised the term "lockdown" due to its rapid spread around the world. "Lockdown" was used as an emergency measure to temporarily prevent people from entering or leaving their communities in an effort to reduce the spread of the virus. The effects of the "lockdown" measures on the management of chronic medical conditions in African populations have been inconsistent. This study aimed to assess the effects of the lockdown on glycaemic control in patients with diabetes. retrospective study that examined metadata from 1st January 2019 to 31st December 2021, to assess the impact of the national SARS-CoV-2 response on the quantity and average level of haemoglobin A1c and random glucose in patients with diabetes at Dr. George Mukhari Academic Hospital. The data was retrieved from the National Health Laboratory Services corporate data warehouse. from 2019 to 2021, a total of 9,039 tests were performed, with females accounting for 63.21% (n = 5,714) and males for 36.08% (n = 3,261), while 0.7% (n = 70) did not have an assigned gender. Mean age was 49, with a standard deviation (SD) of 21.71. The testing rate (TR) in 2019 was 10.74 per day, 2020 had a TR of 6.07, and 2021 had a TR of 7.95. During the pandemic phase, all other age groups had TRs below 1.85, except the 50-59, 60-69, and 70+ year-old groups. the study revealed that SARS-CoV-2 lockdown measures were linked to poor diabetes control in patients. As a result, the consequences of managing SARS-CoV-2 had a direct influence on diabetes management.

Ambulatory Glucose Profile of Type 2 Diabetes Patients with End-Stage Renal Disease Undergoing Hemodialysis

Background: Management of glucose levels in patients with diabetes mellitus and end-stage renal disease (ESRD) is challenging. There are various factors contributing to glycemic variability (GV), including altered metabolism of glucose and insulin in the ESRD stage. Continuous glucose monitoring (CGM) system measures glucose levels continuously and helps to study the glucose profile of patients undergoing hemodialysis (HD). This pilot study aimed to analyze the ambulatory glucose profile (AGP) of type 2 diabetes patients with ESRD undergoing HD and study the GV when patients were on HD (referred to as ON days) versus when patients were not on HD (referred as OFF days). Materials and Methods: We enrolled 10 patients with type 2 diabetes mellitus and ESRD undergoing maintenance HD in the study. Data regarding patient characteristics, including age, gender, duration of diabetes mellitus, HbA1c, and serum fructosamine, were collected. The AGP data obtained by the CGM sensor on the HD ON days was compared to AGP data on HD OFF days. Results: There was a significant linear correlation of CGM-derived average blood glucose with HbA1c value; however, the association was not significant with fructosamine level. The CGM-derived average glucose level was significantly lower during HD ON days compared to HD OFF days, difference being 29.9 mg/dl (−33.7, −26.0; P &lt; 0.001), whereas GV was significantly higher during HD OFF days compared to ON days. Conclusion: The variation in the glucose levels was higher during the HD OFF days compared to HD ON days. Future AGP studies recruiting more number of patients on HD will help better understand the clinical impact of glucose profile and GV in ESRD patients.

Viscoelastic or Viscoplastic Glucose Theory (VGT #47): Using Viscoplasticity/Viscoplasticity to Explore the Inter-Relationships on Cancer Risk % Versus Sensor Measured Fasting Plasma Glucose and Body Weight Based on Eleven Collected Annual Data Over A 10+ Year Period From 1/1/2012 to 3/20/2022 using the GHMethod: Math-Physical Medicine (No. 632)

The purpose of this research article is to explore a possible link between cancer risk probability percentage (Cancer %) and body weight (BW) in the early morning. Body weight is very easy to measure by standing on a weight scale. Weight in the early morning is highly correlated with fasting plasma glucose (FPG) measured in the early morning or averaged glucose during sleeping hours. FPG also serves as the baseline of postprandial plasma glucose (PPG) and contributes around 25% to 29% of daily average glucose (eAG). Daily average glucose levels or quarterly HbA1C values (average eAG during the past 90-115 days) determine the severity of diabetes disease. Finally, diabetes is one of the major influential chronic diseases for developing many kinds of diseases and complications, including cancers. From the author’s mathematical view, in this biomedical field, an output biomarker sometimes turns into an input biomarker of a third biomarker. Therefore, many biomedical problems can be described as a scenario of the matrix with “m by n by p” dimensions.

New Insights into the Use of Empagliflozin-A Comprehensive Review.

Empagliflozin is a relatively new drug that, as an inhibitor of the sodium-glucose cotransporter 2 (SGLT2), causes increased urinary glucose excretion and thus contributes to improved glycemic control, better glucose metabolism, reduced glucotoxicity and insulin resistance. Although its original use was to induce a hypoglycemic effect in patients with type 2 diabetes mellitus (T2DM), empagliflozin has also shown a number of other beneficial effects by demonstrating a nephroprotective effect, and it has proven to be a breakthrough in the treatment of heart failure (HF). Empagliflozin has been shown to reduce hospitalizations for HF and the number of deaths from cardiovascular causes. Empagliflozin treatment also reduces the incidence of renal events, including death from renal causes, as well as the risk of end-stage renal failure. Empagliflozin appears to be a fairly well-tolerated and safe drug. In patients with inadequate glycemic control, empagliflozin used in monotherapy or as an adjunct to therapy effectively lowers fasting blood glucose, postprandial blood glucose, average daily glucose levels, glycated hemoglobin A1C (HbA1C) and also leads to significant weight reduction in patients with T2DM. Unfortunately, there are some limitations, e.g., severe hypersensitivity reaction to the drug and a glomerular filtration rate (GFR) &lt; 30 mL/min/1.73 m2. As with any drug, empagliflozin is also characterized by several side effects among which symptomatic hypotension, troublesome genital fungal infections, urinary tract infections and rare ketoacidosis are characteristic.

ISPAD Clinical Practice Consensus Guidelines 2022: Glycemic targets and glucose monitoring for children, adolescents, and young people with diabetes.

ISPAD Clinical Practice Consensus Guidelines 2022: Glycemic targets and glucose monitoring for children, adolescents, and young people with diabetes.

Efficacy of metformin versus sulfonylurea derivative in HNF4A-MODY.

This study compares the effects of metformin, sulfonylurea derivative (SU) and no treatment in HNF4A-MODY on glycemic control. In two patients with HNF4A-MODY, we changed the existing metformin treatment to SU derivative. The effect on the glycemic control was registered with a Freestyle Libre Flash glucose monitoring device. Each treatment period had a duration of 2 consecutive weeks, and in between, an intermediate period without medication. Data from the first 2 days after changing medications were excluded. We calculated time in range (TIR), and differences in the mean glucose level were tested with a one-way ANOVA test. The 24-h average glucose levels were significantly lower with either metformin (7.7 mmol/L; P < 0.001 and 6.3 mmol/L; P < 0.001) or gliclazide (7.6 mmol/L; P < 0.001 and 5.8 mmol/L; P < 0.001) compared to no treatment (9.4 and 8.9 mmol/L). The TIR with metformin or gliclazide was higher than without treatment (patient 1: 87 and 83 vs 61% and patient 2: 83 and 93 vs 67%). Treatment with either metformin or gliclazide effectively decreases blood glucose, rendering both drugs appropriate for treating HNF4A-MODY. HNF4A-MODY has a mild phenotype. Blood glucose was responsive to long-term metformin treatment in HNF4A-MODY. Metformin and gliclazide seem appropriate treatments for HNF4A-MODY.

Risk Factors of Postpartum Cardiovascular Disease in Patients with Gestational Diabetes Mellitus

Objective: To investigate the risk factors of postpartum cardiovascular disease in patients with gestational diabetes mellitus (GDM). Methods: From December 2020 to December 2021, pregnant women who underwent 75-g oral glucose tolerance test (OGTT) between 24 and 28 weeks of gestation and were diagnosed with GDM were selected as the research subjects. These women were followed-up after delivery. Results: The average fasting plasma glucose (FPG) levels of these women were 6.25 ± 1.36 mmol/L before discharge and 5.01 ± 1.45 mmol/L at 42 days after delivery; the average 2-hour plasma glucose (2hPG) levels were 11.23 ± 2.01 mmol/L before discharge and 7.98 ± 1.23 mmol/L at 42 days after delivery; the average insulin levels were 8.36 ± 1.98 mmol/L before discharge and 2.98 ± 1.36 mmol/L at 42 days after delivery. There were 46 patients with postpartum cardiovascular disease, with an incidence rate of 22.89%. Conclusion: By improving the postpartum management of patients, the weight of GDM patients can be better controlled, their insulin sensitivity can be increased, and the occurrence of glucose and lipid metabolism disorders can be reduced.

Heart Stroke Prediction using ML

As we all know, the human body functions through a variety of organs, with the heart being one of the most important. We can see that the number of deaths from heart attacks has increased in recent days. Even at a young age, people are being affected by heart attacks. People are realizing that they have a heart attack at the end stage, when there are fewer chances of curing it. As a result, many people are losing their lives because they are unable to detect it at an early stage. If we can predict whether a person will have a heart attack or not at an early stage, we may be able to cure that person and save their life. This paper focuses on developing a prediction model for heart stroke using age, hypertension, previous heart disease status, average body glucose level, bmi, and smoking status as parameters. A random forest algorithm is used to create the prediction model.

ODP285 A Case of Encephalitis and Adrenal Insufficiency in a Patient With Panhypopituitarism After COVID-19 Vaccination

Abstract Background Since COVID-19 vaccination was introduced, various adverse effects have been linked to the vaccines. In patients with hypopituitarism, adrenal insufficiency due to the side reactions including fever of COVID-19 vaccination is concerned. Clinical Case A 33-year woman was on medical therapy including hydrocortisone (HC) for panhypopituitarism arising from surgical treatment of a pituitary adenoma in 2006. She received a COVID-19 vaccination on day X-3. On day X-2, she developed fever in the morning and became unconscious in the evening. She was brought to our hospital by her family at night on day X. She had fever of 40.5°C, low blood pressure, and Glasgow Coma Scale (GCS) of 11. Her neck was supple and she had no quadriplegia. A COVID-19 PCR test was negative. Blood tests showed elevated white blood cell count (8900/μL; reference range: 3300–8600/µL) and C-reactive protein (138.3 mg/l; reference range: 0-1.44 mg/l). Blood glucose (81 mg/dL), ACTH (&amp;lt;3. 00 pg/mL; reference range: 7.2–63.3 pg/mL), and cortisol (1.9 μg/dL; reference range: 2.9–19.4 µg/dL) were low. Serum electrolytes were normal. A computed tomography scan showed no abnormality. Adrenal insufficiency was suspected, and she received HC intravenously. Her blood glucose and blood pressure increased, but her disorientation persisted. Lumbar puncture with cerebrospinal fluid (CSF) examination revealed slightly elevated cell counts (8 μ/L; reference range ≤4 μ/L) with average protein and glucose levels. Magnetic resonance imaging (MRI) of the brain revealed abnormal hyperintensity in the splenium of the corpus callosum on diffusion-weighted images and decreased apparent diffusion coefficient in the lesion, suggesting clinically mild encephalitis/encephalopathy with a reversible splenial lesion (MERS). During her hospital stay, she received a 7-day course of meropenem and acyclovir for suspected meningoencephalitis. Her consciousness disturbance improved to GCS of 15 on day X+1 and her fever decreased on day X+2. HSV and VZV PCR tests were negative on CSF examination, and antibiotics and antivirals were discontinued on day X+7. On day X+8, brain MRI showed complete resolution of the corpus callosum lesion. She discharged on day X+18 without any neurological sequelae. Conclusions For most vaccines, the incidence rates of encephalitis are low at 0.1–0.2 per 100,000 vaccinated individuals (1). The present patient developed fever and adrenal insufficiency after COVID-19 vaccination, and her prolonged disturbance of consciousness after HC administration led to the diagnosis of MERS. MERS should be considered in patients with adrenocortical insufficiency who show delayed recovery from unconsciousness with HC administration after COVID-19 vaccination. Reference: (1) Huynh W, Cordato DJ, Kehdi E, Masters LT, Dedousis C. Post-vaccination encephalomyelitis: literature review and illustrative case. J Clin Neurosci. 2008 Dec;15(12): 1315-22. Presentation: No date and time listed

PERBANDINGAN KADAR GLUKOSA SERUM DARI DARAH YANG LANGSUNG DISENTRIFUGE DAN DITUNGGU BEKU SEBELUM DISENTRIFUGE METODE GOD-PAP TAHUN 2021

Background : Glucose examination, can use whole blood, serum, and urine samples. Serum was obtained from blood which was awaited to freezing first and then centrifuged at 3000 rpm for 15 minutes. However, the reality in the employment to shorten the time, blood is often centrifuged immediatelly without waiting for the blood to freezing. Purpose : To determine whether there is differences in serum glucose levels from the blood that is immediately centrifuged and waited for freezing before centrifuge. Methods :This research is an analytical research using cross sectional approach. The research sample was taken from 30 students of Medical Laboratory Technology, Poltekkes Palembang. Result :The data obtained were analyzed statistically by using the test t dependet we got the average serum glucose level from blood that is immediately centrifuge was 83,80 mg/dL. The average serum glucose from blood waited for freezing before centrifuge was 76,50 mg/dL. Obtained result P value = 0,000012 means p&lt;0,05. Conclusion : There is statistically meaningful differences beetwen serum glucose levels from the blood that is immediately centrifuge and waited for freezing before centrifuge. Suggestion :it is recommended for laboratory technology should be examined against blood that has waited for freezing before centrifuge, because blood that is immediately centrifuge can increase glucose levels so that the result examination increase falsely.&#x0D;

Correlation between Estimated Average Glucose Levels Calculated from HbA1c Values and Random Blood Glucose Levels in a Cohort of Subjects.

Objective Hemoglobin A1c (HbA1c) level remains the gold standard test for the assessment of glycemic control, and it reflects the mean glucose values in the previous 3-month period. HbA1c is expressed as a percentage, whereas the monitoring and treatment of diabetes are based on blood glucose levels expressed as mg/dL. It is appropriate to make it easy for the patient to understand both random blood sugar (RBS) and estimated average glucose (eAG) expressed with the same units. This will enhance the usefulness of eAG. This article determines the statistical correlation between eAG derived from HBA1C with RBS values both in diabetic and prediabetic subjects. Methods The RBS and HbA1c levels of 178 males and 283 females (12-90 years) were obtained and the eAG levels were calculated using Nathan's regression equation. The samples were divided into four groups based on HbA1c levels-group 1: HbA1c greater than 9%, group 2: HbA1c 6.5 to 9%, group 3: HbA1c 5.7 to 6.4%; and group 4: HbA1c less than 5.7%. Results There was a statistically significant positive correlation between RBS and eAG values for the study group 1 and 2. Also, the median values of RBS and eAG showed a significant difference ( p < 0.001). Conclusion As the association between the RBS and eAG levels is strong in a fairly and poorly controlled diabetic population, reporting the eAG level together with the HbA1c level at no additional cost may assist in effective blood glucose control in clinical care. However, eAG and RBS values cannot be used interchangeably.

Evaluating the role of homoeopathically potentised magnesium sulphate 30C on insulin resistance in type 2 diabetes mellitus patients in surat, India: A randomised controlled crossover clinical trial

The World Health Organization has declared type 2 diabetes mellitus (T2DM) to have reached pandemic proportions. The affected population worldwide has increased from 108 million in 1980 to 537 million in 2021. Recent studies have shown that T2DM is often associated with altered magnesium levels. The study aimed to assess the effects of homoeopathically potentised Magnesium sulphate 30C on insulin resistance in T2DM patients in Surat, India. The study design was a randomised controlled crossover trial conducted at the ZFR Multi-specialty Homoeopathy Clinic, Surat, India. A total of 79 participants were divided randomly into two groups. One group received the placebo and the other received the potentised Magnesium sulphate 30C in the first period (12 weeks). This was followed by a washout period and the groups were crossed over to receive the other intervention, again for 12 weeks. The analysis of variance was used to find the comparisons between both groups for the mean value of glycosylated haemoglobin (HbA1c), fasting blood sugar (FBS), fasting insulin, serum magnesium, homeostatic model assessment for insulin resistance (HOMA-IR), and estimated average glucose levels. HbA1c (%) (p&lt;0.001) showed a significant difference between both groups.

Extending sleep to improve glycemia: The Family Routines Enhancing Adolescent Diabetes by Optimizing Management (FREADOM) randomized clinical trial protocol

Sleep deficiencies amongst individuals with type 1 diabetes mellitus (T1DM) have been linked with dysregulated glycemic control and greater morbidities. Sleep extension (EXT) has been identified as a viable intervention target to improve adolescent outcomes. The intervention aims to emphasize collaborative work with families to engage in behaviors that increase the likelihood of the youth increasing their sleep duration consistently. This study will randomize up to 175 youth with T1DM and at least one caregiver to either an EXT intervention or a family routines support (FRS) consultation. It is hypothesized that the EXT condition will lead to improvements in sleep, which in turn, will contribute to improved glycemic control. The primary endpoint is improved glycemic control assessed via a continuous glucose monitor (CGM) to ascertain average glucose levels across a week, glycemic variability, and percent time in the target range at one month and HbA1c at three months. Analyses will control for co-morbid conditions, including sleep-disordered breathing and obesity. This study will provide the needed data to support addressing sleep as part of the standards of care in youth with T1DM.

The Fast-Glycator Phenotype, Skin Advanced Glycation End Products, and Complication Burden Among People With Type 1 Diabetes.

Existence of a fast-glycator phenotype among people with type 1 diabetes (T1D) is debated. Routine use of glucose sensors allows the comparison of long-term average glucose levels with laboratory HbA1c values. We herein evaluated whether participants with T1D and HbA1c values higher than their glucose management indicator (GMI) had greater accumulation of advanced glycation end products (AGEs) and chronic complications. We included participants with T1D using the intermittently scanned continuous glucose monitoring system consecutively for at least 90 days and having a laboratory-determined HbA1c at the end of observation. Skin AGEs were estimated using the skin autofluorescence (SAF) method. The complication burden was assessed by a standardized screening. The fast-glycator phenotype was defined as having a GMI to HbA1c ratio <0.9. We included 135 individuals with T1D (58% men; mean age, 44.4 years) with a mean diabetes duration of 21 years and a mean HbA1c value of 7.7%. Thirty (22.2%) were defined as having the fast-glycator phenotype. As expected, fast glycators had higher HbA1c (8.6% vs. 7.5%; P < 0.001) with similar 90-day mean glucose level (172 vs. 168 mg/dL; P = 0.52). Fast glycators had higher SAF than did other participants (2.5 vs. 2.1 arbitrary units; P = 0.005) and had a significantly higher prevalence of dyslipidemia (73% vs. 44%; P = 0.005), macroangiopathy (38% vs. 9%; P = 0.001), albuminuria (25% vs. 7%; P = 0.038), and retinopathy (61% vs. 38%; P = 0.022). After adjusting for age and dyslipidemia, the fast-glycator phenotype remained significantly associated with macroangiopathy (odds ratio 3.72; 95% CI 1.22-11.4). In T1D, a fast-glycator phenotype defined by the GMI to HbA1c ratio is characterized by elevated skin AGEs and is associated with the complication burden.

Application of machine learning methods for the prediction of true fasting status in patients performing blood tests

The fasting blood glucose (FBG) values extracted from electronic medical records (EMR) are assumed valid in existing research, which may cause diagnostic bias due to misclassification of fasting status. We proposed a machine learning (ML) algorithm to predict the fasting status of blood samples. This cross-sectional study was conducted using the EMR of a medical center from 2003 to 2018 and a total of 2,196,833 ontological FBGs from the outpatient service were enrolled. The theoretical true fasting status are identified by comparing the values of ontological FBG with average glucose levels derived from concomitant tested HbA1c based on multi-criteria. In addition to multiple logistic regression, we extracted 67 features to predict the fasting status by eXtreme Gradient Boosting (XGBoost). The discrimination and calibration of the prediction models were also assessed. Real-world performance was gauged by the prevalence of ineffective glucose measurement (IGM). Of the 784,340 ontologically labeled fasting samples, 77.1% were considered theoretical FBGs. The median (IQR) glucose and HbA1c level of ontological and theoretical fasting samples in patients without diabetes mellitus (DM) were 94.0 (87.0, 102.0) mg/dL and 5.6 (5.4, 5.9)%, and 92.0 (86.0, 99.0) mg/dL and 5.6 (5.4, 5.9)%, respectively. The XGBoost showed comparable calibration and AUROC of 0.887 than that of 0.868 in multiple logistic regression in the parsimonious approach and identified important predictors of glucose level, home-to-hospital distance, age, and concomitantly serum creatinine and lipid testing. The prevalence of IGM dropped from 27.8% based on ontological FBGs to 0.48% by using algorithm-verified FBGs. The proposed ML algorithm or multiple logistic regression model aids in verification of the fasting status.

Viscoelastic or Viscoplastic Glucose Theory (VGT #46): Using Viscoplasticity/ Viscoplasticity to Explore the Inter-Relationships on CVD Risk % Versus Sensor Measured Fasting Plasma Glucose and Body Weight Based on Eight Collected Semi-Annual Data over an ~4-Year Period from 5/8/2018 to 3/20/2022 using the GH-Method: Math-Physical Medicine (No. 631)

The purpose of this research article is to explore a possible link between CVD risk probability percentage (CVD %) and body weight (BW) in the early morning. Body weight is very easy to measure by standing on a weight scale. Weight in the early morning is highly correlated with fasting plasma glucose (FPG) measured in the early morning or averaged glucose during sleeping hours. FPG also serves as the baseline of postprandial plasma glucose (PPG) and contributes around 25% to 29% of daily average glucose (eAG). Daily average glucose levels or quarterly HbA1C values (averaged eAG during the past 90-115 days) determine the severity of diabetes disease. Finally, diabetes is one of the major influential chronic diseases for developing many kinds of heart diseases, such as cardiovascular diseases (CVD). From the author’s mathematical view, in this biomedical field, an output biomarker sometimes turns into an input biomarker of a third biomarker. Therefore, many biomedical problems can be described as a scenario of the matrix with “m by n by p” dimensions. Since 2012, the author has been collecting his body weight (BW) and finger-piercing glucose values each day. In addition, he accumulates medical conditions data including blood pressure (BP), heart rate (HR), and blood lipids along with lifestyle details (LD). Based on the collected big data, he further organized them into two main groups. The first is the medical conditions group (MC) with 4 categories: weight, glucose, BP, and blood lipids. The second is the LD group with 6 categories: food &amp; diet, exercise, water intake, sleep, stress, and daily routines. At first, he collected his data daily since Y2012 and then calculated a unique combined daily score for each of the 10 categories within the MC and LD groups. The combined scores of the 2 groups, 10 categories, and 500+ detailed elements constitute an overall “metabolism index (MI) model”. This MI model includes the root causes from 6 major lifestyle inputs and symptoms from 4 rudimentary chronic diseases: obesity, diabetes, hypertension, and hyperlipidemia. Therefore, it can serve as the foundation and building block for his additional research work that can expand into various diseases associated with different organs, such as cardiovascular diseases (CVD), and cancers.

Viscoelastic or Viscoplastic Glucose Theory (VGT #47): Using Viscoplasticity/ Viscoplasticity to Explore the Inter-Relationships on Cancer risk % Versus Sensor Measured Fasting Plasma Glucose and Body Weight Based on Eleven Collected Annual Data over a 10+ Year Period From 1/1/2012 to 3/20/2022 using the GH-Method: Math-Physical Medicine (No. 632)

The purpose of this research article is to explore a possible link between cancer risk probability percentage (Cancer %) and body weight (BW) in the early morning. Body weight is very easy to measure by standing on a weight scale. Weight in the early morning is highly correlated with fasting plasma glucose (FPG) measured in the early morning or averaged glucose during sleeping hours. FPG also serves as the baseline of postprandial plasma glucose (PPG) and contributes around 25% to 29% of daily average glucose (eAG). Daily average glucose levels or quarterly HbA1C values (average eAG during the past 90-115 days) determine the severity of diabetes disease. Finally, diabetes is one of the major influential chronic diseases for developing many kinds of diseases and complications, including cancers. From the author’s mathematical view, in this biomedical field, an output biomarker sometimes turns into an input biomarker of a third biomarker. Therefore, many biomedical problems can be described as a scenario of the matrix with “m by n by p” dimensions. Since 2012, the author has been collecting his body weight (BW) and finger-piercing glucose values each day. In addition, he accumulates medical conditions data including blood pressure (BP), heart rate (HR), and blood lipids along with lifestyle details (LD). Based on his collected big data, he further organized them into two main groups. The first is the medical conditions group (MC) with 4 categories: weight, glucose, BP, and blood lipids. The second is the LD group with 6 categories: food &amp; diet, exercise, water intake, sleep, stress, and daily routines. At first, he collected his data on a daily basis since Y2012 and then calculated a unique combined daily score for each of the 10 categories within the MC and LD groups. The combined scores of the 2 groups, 10 categories, and 500+ detailed elements constitute an overall “metabolism index (MI) model”. This MI model includes the root causes from 6 major lifestyle inputs and symptoms from 4 rudimentary chronic diseases: obesity, diabetes, hypertension, and hyperlipidemia. Therefore, it can serve as the foundation and building block for his additional research work that can expand into various diseases associated with different organs, such as cancers. As we know, lifestyle details cause rudimentary chronic diseases which further influence more complicated diseases, such as heart problems (CVD &amp; CHD), chronic kidney disease (CKD), stroke, diabetic retinopathy (DR), neuropathy, hypothyroidism, diabetic constipation, diabetic skin fungal infection, and others including cancers. Some genetic conditions and lifetime unhealthy habits, such as smoking, alcohol consumption, and illicit drug use would account for approximately 15% to 25% of the root cause of chronic diseases &amp; their complications, including cancers and dementia. Some external factors - environmental factors and viral infections, such as radiation, air and water pollution, food poison and pollution, toxic chemicals, and hormonal therapy can also contribute to the causes of a variety of cancers.