Risk Of Poor Glycemic Control Research Articles

Public health insurance: An approach to mitigate the burden of diabetes in low resource settings

Background: Diabetes mellitus is a significant public health concern globally as well as in Owerri, Nigeria. The deleterious effects of diabetes have been linked to poor glycemic control. According to the International Diabetes Federation, poor glycemic control is reflected in glycosylated hemoglobin levels greater than 7.0%, which are associated with substantial morbidity and mortality. Studies have shown a dramatic rise in diabetic complications in Nigeria, particularly in Owerri. However, evidence is lacking on specific risk factors associated with poor glycemic control among diabetes mellitus patients in Owerri. There is a gap in the literature regarding the association between health insurance and glycemic control in diabetic patients in Owerri. With health insurance assuming a significant position in healthcare service delivery in Nigeria, addressing this gap is valuable. Methods: We performed a cross-sectional study of health insurance as a determinant of glycemic control among 160 type 2 diabetic patients attending the family medicine clinic at the Federal University Teaching Hospital, formerly the Federal Medical Center, Owerri. Participants were measured as not insured, insured-private, and insured-public/National Health Insurance Scheme. The dependent variable was glycemic control measured using glycosylated hemoglobin. We used ecosocial theory as the theoretical framework of this research. SPSS was used for data analysis; multiple logistic regression was applied to assess the association between insurance status and glycemic control in the participants. Results:In the patients without health insurance coverage, the prevalence of poor glycemic control was 93.8% whereas in those with health insurance coverage, the prevalence was 60.0%. Logistic regression analysis showed that lack of access to health insurance was a determinant of glycemic control, with uninsured subjects at 28 times and 6 times increased risk of poor glycemic control compared with insured-private and insured-public subjects, respectively. Conclusion: We have shown in our study that insured enrollees have an increased likelihood of good glycemic control relative to uninsured subjects. This finding has the potential to promote positive social change through optimization of the National Health Insurance Scheme. Enabling regulations and designing policies to explicitly cover diabetes preventive and control services in the scheme could lead to improved glycemic control, and, thus, reduce the burden of the disease.

Glycemic control and associated factors among type 2 diabetes mellitus patients: a cross-sectional study of Azar cohort population

BackgroundDespite the growing prevalence of diabetes and its complications, there is a dearth of data regarding factors associated with glycemic control. Therefore, in this cross-sectional study, we aimed to identify factors influencing glycemic control in patients with type 2 diabetes mellitus (T2DM) in the Iranian population.MethodsThis cross-sectional study was conducted among the Azar cohort population and the glycemic control status of patients with T2DM was investigated. Possible risk factors including age, sex, marital status, educational level, smoking status, sleep duration, family history of diabetes and hypertension, socioeconomic status, physical activity level, and co-existence of other chronic diseases and their relationship with glycemic control status were also assessed. Multivariate logistic regression analysis was used to identify determinants of glycemic control.ResultsAmong 1,710 T2DM patients (60.2% female), the overall prevalence of poor glycemic control was 56.8%. In the unadjusted logistic regression analysis model, a low wealth score index significantly increased the risk of poor glycemic control (OR: 1.49;1.10–2.02). Variables significantly associated with poor glycemic control even after adjusting for confounding factors were first-degree family history of diabetes (OR: 1.34; 1.08–1.65), and sleep duration (OR: 1.29 ;1.02–1.62 for 6.6-8 h/d; OR:1.42;1.10–1.88 for > 8 h/d). Interestingly, we found that the co-existence of ≥ 3 chronic diseases with diabetes decreased the risk of poor glycemic control.ConclusionsIn the current study, most of the patients with T2DM had uncontrolled glycemic control. Due to the individual and social costs of diabetes complications, it is necessary to suggest tailored and effective interventions for controlling blood glucose levels in people with diabetes.

A-163 Abnormal HbA1c Results Lead to an Unexpected Chronic Myeloid Leukemia Diagnosis

Abstract Background Hemoglobin A1c (HbA1c) is used to assess and monitor glycemic control, as it reflects average levels of blood glucose over the previous two to three months. HbA1c can be measured by capillary electrophoresis (CE) which calculates percent HbA1c from the HbA1c and HbA peaks in the electropherogram. CE is a prominent method in reference labs as it provides complete automation with fast separation and less interference than what can be observed with other assay formats. The high-resolution capability of CE allows the quantification of HbA1c, even in the presence of labile HbA1c, carbamylated and acetylated hemoglobins, and major hemoglobin variants. These variants are commonly recognized as causes of interference in HbA1c measurements. Case presentation A 70-year-old male with a history of type 2 diabetes mellitus presented to an outpatient laboratory for routine venipuncture to evaluate glycemic management and cardiovascular disease risk. HbA1c analysis by CE resulted with a flag for an atypical profile in the electropherogram and no HbA1c value was reported. HbA1c measurements throughout the past year demonstrated HbA1c values ≥6.5% yet trending downward, consistent with his diagnosis and treatment of diabetes mellitus. HbA1c was repeated with a different instrument and fresh specimen collected at a different phlebotomy location and showed similar inconclusive results. Unusual shifts in hemoglobin peak migration times were observed upon close review of the electropherogram, suggesting a potential analytical interference of the HbA1c measurement. Results Because atypical peak migration times due to elevated leukocyte counts or increased viscosity are documented by the manufacturer, a follow up complete blood count (CBC) was recommended for this patient. CBC results revealed a highly elevated leukocyte count of 175.80 k/μL (reference interval: 4.30–11.30 k/μL), suggesting the abnormal HbA1c migration profile was possibly due to leukocytosis. The elevated leukocyte count led the patient to visit an oncology clinic where leukemia/lymphoma phenotyping, ancillary studies, and cytogenic testing, confirmed a diagnosis of chronic myeloid leukemia. The diagnosis was completely unexpected, as the patient’s only complaint was feeling fatigued over the last few weeks. The patient began chemotherapy treatment immediately. Conclusion The present case is an example of how leukocytosis and hyperviscosity can interfere with HbA1c measurement by CE. Although this specific clinical situation is uncommon, clinicians and laboratorians should be aware of these potential interferences, as they have not been discussed in detail in the literature. Further, these interferences warrant recognition in the cancer patient population, as this population may be directly impacted by elevated leukocyte count and hyperviscosity in their specimens, which may impact glycemic control monitoring during treatment. The incidence of diabetes in cancer patients is six times greater than in the general population, making glycemic control monitoring key for cancer patients. Many diabetic patients undergoing chemotherapy will prioritize their cancer treatment over management of their diabetes, which increases their risk for poor glycemic control. These patients should be carefully monitored to ensure HbA1c results are accurate as poor glycemic control can lead to increased risk for infections and hospitalizations, which may decrease survivorship.

Serious Mental Illness, Glycemic Control, and Neighborhood Factors within an Urban Diabetes Cohort.

Serious mental illness (SMI) may compromise diabetes self-management. This study assessed the association between SMI and glycemic control, and explored sociodemographic predictors and geographic clustering of this outcome among patients with and without SMI. We used electronic health record data for adult primary care patients with diabetes from 2 San Francisco health care delivery systems. The primary outcome was poor glycemic control (hemoglobin A1c >9.0%), which was modeled on SMI diagnosis status and sociodemographics. Geospatial analyses examined hotspots of poor glycemic control and neighborhood characteristics. The study included 11 694 participants with diabetes, 21% with comorbid SMI, of whom 22% had a schizophrenia spectrum or bipolar disorder. Median age was 62 years; 52% were female and 79% were Asian, Black, or Hispanic. In adjusted models, having schizophrenia spectrum disorder or bipolar disorder was associated with greater risk for poor glycemic control (vs participants without SMI, adjusted relative risk [aRR] = 1.24; 95% confidence interval, 1.02, 1.49), but having broadly defined SMI was not. People with and without SMI had similar sociodemographic correlates of poor glycemic control including younger versus older age, Hispanic versus non-Hispanic White race/ethnicity, and English versus Chinese language preference. Hotspots for poor glycemic control were found in neighborhoods with more lower-income, Hispanic, and Black residents. Poor diabetes control was significantly related to having a schizophrenia spectrum or bipolar disorder, and to sociodemographic factors and neighborhood. Community-based mental health clinics in hotspots could be targets for implementation of diabetes management services.

610-P: Comparison of Dietary Habits among Rural Americans With and Without Diabetes

Introduction: Residents of rural communities face unique geographic challenges in accessing food and healthcare, which may contribute to poor diet quality and increase the risk of diabetes. We compared Alternative Healthy Eating Index (aHEI) scores among residents with and without a history of diabetes from a rural county in New York known to have poor health outcomes. Methods: In 2021, we performed a cross-sectional survey and validated food frequency questionnaire (FFQ) among residents in Sullivan County, NY, a rural county that has the second-worst health outcomes among all counties in New York. We compared overall total aHEI scores and the 11 component scores of the aHEI between 155 rural participants with diabetes and 961 without a history of diagnosed diabetes. Results: We found statistically lower healthy eating scores among rural study participants with diabetes compared to those without diabetes (p=0.01). While the overall magnitude of this difference in total aHEI was not large (54.1 versus 56.7), our analysis of aHEI component scores found that study participants with diabetes had statistically worse eating habits for nuts/legumes, red meat, trans-fats, and alcoholic drinks (p-values of 0.036, 0.001, 0.002 and 0.002 respectively). Notably, one category where rural residents with diabetes had better eating habits than those without diabetes was for sugary beverages/juice. Discussion: Our study found worse eating habits among rural residents with diabetes compared to those without diabetes in a rural county with particularly poor health outcomes. Additionally, this study highlights the potential need for accessible nutritional education in high-risk rural communities. While study participants with diabetes on average, consumed fewer sugary beverages and juice, more attention may need to be focused on reducing other food categories that might increase the risk of poor glycemic control. Disclosure M.Rony: None. C.Quintero arias: None. N.N.Wise: None. A.Prabu: None. K.Doran: None. R.Anthopolos: None. B.Elbel: None. L.Thorpe: None. D.C.Lee: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (R01DK124400)

50 Metformin versus Insulin: Glucose Control and Safety in Burn Patients

Abstract Introduction Patients suffering from moderate-to-severe burns are known to experience significant metabolic perturbations, such as hyperglycemia, as the result of a systemic hypermetabolic response. Hyperglycemia in these patients is in part due to the development of peripheral insulin resistance. Glycemic control is important, as hyperglycemia in patients with moderate-to-severe burns is associated with increased morbidity and mortality. Insulin and metformin are the first lines of treatment, with previous studies showing metformin to be superior to insulin in reducing burn-induced hyperglycemia. We queried a large multi-institutional national database to investigate whether the use of metformin in burn patients is associated with improved glycemic control and morbidity/mortality outcomes compared to insulin therapy. Methods We accessed TriNetX, a global health research network, and queried ICD-10 codes for burn injuries (T20-25, T30-32) across 54 participating health care organizations. We then identified cohorts of patients who were administered either metformin or insulin, but not both, within 1 week of burn. These cohorts were balanced using propensity score matching for age, gender, ethnicity, race, and type 2 diabetes mellitus and subsequently compared for the following outcomes: hyperglycemia (R73), hypoglycemia (E16.1, E16.2), lactic acidosis (E87.2), sepsis (A41), and death. Results A total of 16,569 patients were given insulin within 1 week of burn. This patient cohort had a mean age of 54 years, with 60% being male, 67% White, and 19% Black or African American. In contrast, 7,308 patients received metformin within 1 week of burn, with a mean age of 56 years, 55% female, 70% White, and 16% Black or African American. Burn patients who received insulin within 1 week of burn showed a comparative increased risk of 3.009% (p < 0.0001) for the development of hyperglycemia compared to those who were given metformin within 1 week of burn (RR: 3.165, 95% CI: 2.433-4.117). Additionally, burn patients given insulin displayed an increased risk of 2.452% for development of hypoglycemia (p < 0.0001, RR: 4.555, 95% CI: 3.317-6.255), 3.078% for development of lactic acidosis (p < 0.0001, RR: 17.541, 95% CI: 10.003-30.76), 3.67% for development of sepsis (p < 0.0001, RR: 12.106, 95% CI: 7.898-18.557), and 7.422% for death (p < 0.0001, RR: 13.996, 95% CI: 10.212-19.182). Conclusions Patients receiving insulin within 1 week of burn showed increased risk of poor glycemic control than those receiving metformin. Morbidity and mortality are also higher in those receiving insulin than those receiving metformin. Additional research is necessary to further understand these findings. Applicability of Research to Practice These findings should encourage clinicians to appreciate the importance of glycemic control in burn patients, as well as recognize the importance of choosing the most appropriate pharmacologic agent for glycemic control.

Barriers of Good Glycemic Control Of Known Diabetic Patients in a Tertiary Care Hospital

Introduction: uncontrolled Diabetes mellitus is a growing cause of disability and premature death. HbA1C is an indicator of glycemic control status over the previous three months. Therefore, to minimize the morbidity and mortality of diabetic patients, it would be necessary to identify the factors associated with poor glycemic control, which may not be similar to developed countries. Objective: To identify factors acting as a barrier to reasonable glycemic control of known diabetic patients in Bangladesh Materials and methods: This is a hospital-based descriptive cross-sectional study among diabetic patients with poor glycemic control in the indoor and outdoor Medicine and Endocrinology departments of Dhaka medical college hospital, Dhaka, from July 2019 to December 2019 (6 months). For at least three months, patients who had uncontrolled Diabetes were selected by purposive and convenient sampling method. Results: One hundred and six patients with poor glycemic status were included in the analysis. Aged persons were at risk for poor glycemic control, especially age range of 50-69 years (65.0%) P-value was .004. Duration of Diabetes and educational status were significantly associated with poor glycemic control. P values were 0.001 and .003, respectively. Conclusion: Patient education plays a pivotal role in controlling glycemic control, favoring treatment success, reducing adverse drug events, and preventing further complications of Diabetes. J Dhaka Med Coll. 2022; 31(1) : 9-14

Leptin reduces the risk of poor glycemic control in type two diabetes mellitus patients on metformin therapy

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

Impact of Diabetes Distress on Glycemic Control

Introduction: Diabetes related distress is a recent entity that was found to be associated with poor glycemic control but it was never assessed in Tunisia nor in african countries. This study aimed at exploring the relationship between diabetes related distress and glycemic control. Methods: Cross-sectional study assessing diabetes related distress (using Problem Areas in Diabetes Scale PAID-5) and treatment adherence (using Medication Adherence Questionnaire de Morisky-Green) and glycemic control using HbA1c in Tunisian patients with Type 2 diabetes. Results: There was a statistically significant association between high PAID-5 scores and poor glycemic control. A threshold of 8.5 on PAID-5 predicts poor glycemic control and complications. Conclusion: It is recommended to screen for diabetes related distress using PAID-5 in order to detect patients with high risk of poor glycemic control and complications.

Dietary Pattern Associated with the Risk of Poor Glycemic Control in Chinese Diabetic Adults: Results from the China Nutrition and Health Surveillance 2015–2017 Survey

The present study was conducted to investigate the nutrition status of dietary patterns and the association between dietary patterns and the risk of poor glycemic control in Chinese diabetics. The relevant data was collected from the China Nutrition and Health Surveillance 2015-2017 survey, which is a national cross-sectional surveillance program. A total of 2031 participants were included in the present statistical analysis. Food consumption was assessed through a validated and standard food frequency questionnaire. Dietary patterns were derived with reduced rank regression using hemoglobin A1c. Diabetes was diagnosed by medical institutions, glycemic control was defined as hemoglobin A1c less than 7%, poor glycemic control was defined as hemoglobin A1c greater than 7%. A multiple-variable-adjusted logistic regression, including age, living area, income level, educational attainment, body mass index, occupational physical activity, energy intake, current smoking status, current drinking status, diabetic medication use, insulin use, following diabetic diets, increased exercise, and glucose monitoring, was adjusted to explore the association between dietary patterns and the risk of poor glycemic control in diabetes. Two gender-specific dietary patterns have an increased risk of poor glycemic control and are characterized by a low intake of freshwater fish, poultry, and fruits. For male participants, the dietary pattern was characterized by a high intake of wheat and its products, a low intake of vegetables, corn and its products, shrimp and crab, and beans (Q4 vs. Q1, OR = 2.69, 95% CI: 1.76 to 4.10). For female participants, the dietary pattern was characterized by a low intake of snacks and nuts, and algae and mushroom (Q4 vs. Q1, OR = 2.18, 95% CI: 1.48 to 3.20).

Potential Role of Copper in Diabetes and Diabetic Kidney Disease.

Copper is a fundamental element for the homeostasis of the body. It is the third most abundant essential transition metal in humans. Changes in the concentration of copper in the blood are responsible for numerous diseases affecting various organs, including the heart, brain, kidneys, and liver. Even small copper deficiencies can lead to the development and progression of several pathologies. On the other hand, excessive exposure to copper can cause toxicity in many human organs, leading to various systemic alterations. In the kidney, increased copper concentration in the blood can cause deposition of this element in the kidneys, leading to nephrotoxicity. One of the most interesting aspects of copper balance is its influence on diabetes and the progression of its complications, such as Diabetic Kidney Disease (DKD). Several studies have shown a close relationship between copper serum levels and altered glycemic control. An imbalance of copper can lead to the progression of diabetes-related complications and impaired antioxidant homeostasis. A high Zinc/Copper (Zn/Cu) ratio is associated with improved renal function and reduced risk of poor glycemic control in patients with type two diabetes mellitus (T2DM). Furthermore, the progression of DKD appears to be related to the extent of urinary copper excretion, while regulation of adequate serum copper concentration appears to prevent and treat DKD. The aim of this review is to evaluate the possible role of copper in DKD patients.

Association Between Physical Activity, Sedentary Behavior and Physical Fitness and Glycated Hemoglobin in Youth with Type 1 Diabetes: A Systematic Review and Meta-analysis.

Scientific literature suggests poor glycemic control in youth with type 1 diabetes (T1D) and physical inactivity, sedentary behavior and low physical fitness levels, although results are not entirely consistent. To meta-analyze the association between glycated hemoglobin and physical activity, sedentary behavior, and physical fitness in children and adolescents with T1D. Our meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines(PRISMA). Three databases were searched for studies. All studies meeting the following criteria were included: (1) Population: children and adolescents with a mean age between 3 and 18years diagnosed with T1D; (2) Exposition: physical activity and/or sedentary behavior and/or cardiorespiratory fitness and/or muscular fitness; (3) Outcome: glycated hemoglobin; (4) Study design: cross-sectional, longitudinal, and case-control studies. Pooled effects were calculated using a random effects inverse-variance model with the Hartung-Knapp-Sidik-Jonkman adjustment. Thirty-seven studies were included, accounting for a total of 34,863 youths with T1D (51.9% girls). Twenty-nine studies evaluated physical activity, eight sedentary behavior, 14 cardiorespiratory fitness, and two muscular fitness. A negative association between physical activity (r = - 0.09, 95% CI - 0.14 to - 0.04; I2 = 63.5%), cardiorespiratory fitness (r = - 0.31, 95% CI - 0.44 to - 0.19; I2 = 57.0%) and glycated hemoglobin was found. Also, the association with sedentary behavior was positive (r = 0.20, 95% CI 0.04 to 0.35; I2 = 92.6%). All the associations were independent of the glycated hemoglobin levels and diabetes duration. Low levels of physical activity and cardiorespiratory fitness and extensive sedentary behavior may explain part of the variance in glycated hemoglobinand part of the risk for poor glycemic control in youth with T1D. Registration number: CRD42021254362.

Association Between Working Hours and Poor Glycemic Control in Patients With Diabetes

This study aimed to evaluate the relationship between working hours and glycemic control. Study was performed among Korean participants who underwent at least two health screening examinations between 2012 and 2018. The study included 2169 participants who were older than 40 years and undergoing treatment for diabetes at baseline. A hemoglobin A1c level >9% at the follow-up visit was defined as poor glycemic control. The weekly working hours were divided into three groups for analysis. Compared with participants with 45-54 weekly working hours, multivariate-adjusted hazard ratios (95% confidence intervals) for incident poor glycemic control among participants with ≥55 and 35-44 working hours were 1.40 (1.01-1.96) and 1.51 (1.09-2.09), respectively. Standard working hours and long working hours were independent risk factors for poor diabetes control in patients with diabetes.

Race/Ethnicity versus cotinine-based smoking status in relation to glycemic control in US adults with type 2 diabetes mellitus

ObjectivesTo examine the independent and combined relationship of race/ethnicity and cotinine-based smoking status in relation to poor glycemic control in US adults with T2DM in a sex-stratified analysis. MethodsWe abstracted data from the 1999 to 2018 NHANES examination. We defined poor glycemic control as glycated hemoglobin A1C ≥ 8% (if aged ≥ 75 years) or A1C≥ 7% (aged 20–65 years). Participants were nonpregnant individuals aged ≥ 20 years with known diabetes. Race/ethnicity was self-reported and categorized as non-Hispanic white (NHW), non-Hispanic black (NHB) and Hispanic. We used generalized linear models to estimate prevalence ratio of poor glycemic control, after adjusting for confounders. ResultsWe included 7,537 adults with T2DM. The overall prevalence of poor glycemic control was 44.9%. Among nonsmokers, the risk of poor glycemic control increased by 14% (P=.06) in NHB and 30% (P<.0001) in Hispanic, compared with their relatives in NHW group. When compared with NHW not exposed to tobacco smoking, the risk of poor glycemic control was significantly higher in Hispanic men, independent of smoking status. The prevalence ratio was 1.31 (95% CI: 1.10–1.57) in nonsmokers and 1.32 (95% CI: 1.00–1.74) in smokers. In women not exposed to tobacco smoke, the prevalence ratio was significantly higher in both NHB [1.29 (95% CI: 1.04–1.58)] and Hispanic [1.33 (95% CI: 1.08–1.63)]. ConclusionsOverall, tobacco smoking did not appear to confer additional risk for poor glycemic control beyond that of race/ethnicity.

Glycemic Control of Diabetes Mellitus (DM) in Acute Coronary Syndrome (ACS) Patient

Introduction: Diabetes mellitus (DM) is a predictor of recurrent ischaemic events in patients with the acute coronary syndrome (ACS) whilst cardiovascular disease remains the leading cause of mortality among patients with diabetes. This study aims to determine the prevalence of DM, glycemic control and predictors of poor glycemic control patients diagnosed with ACS in our population. Methods: This is a single centre, cross-sectional study of ACS patients admitted to cardiology wards, Hospital Serdang. A chi-square test was used to test the association between the prescribing pattern of antihyperglycemic agents and the glycemic control of DM patients. Logistic regression analysis was performed to determine the predictors of poorly glycemic controlled among DM patients with ACS. Results: A total of 486 patients were included (male 73.4%; mean age 57.3(12.5) years). The prevalence of DM among ACS patients was 207 (42.6%). Of these, 88 (42.1%) had poorly-controlled DM with the HbA1c &gt; 8% and 81 (47.9%) had well control the HbA1c≤8%. Significant association was found between antihyperglycemic agents, i.e. insulin (p&lt;0.034), metformin (p&lt;0.038) and sulphonylurea (p&lt;0.022). In poor control group, insulin is the most prescribed antihyperglycemic agents, 35 (37.2%) compared to well control, metformin is the highest proportion, 25 (34.7%). In multivariate analysis, only age was independently predictive of poorly controlled among DM patients with ACS (adjusted OR 0.94; 95% CI 0.90-0.97; p&lt;0.001). Conclusion: This study found, the prevalence of DM was high among ACS patient, with half of them demonstrate poor glycemic control. This study found that increasing age was associated with a lower risk of poor glycemic control among DM patients with recent ACS.

Protein Intake Among Patients with Insulin-Treated Diabetes is Linked to Poor Glycemic Control: Findings of NHANES Data.

BackgroundNutrition therapy is considered a key component of diabetes management. Recently, high-protein diets have gained in popularity. Understanding the glycemic effect of dietary protein among patients with diabetes has been of particular interest.MethodsThis study is based on NHANES data of 990 participants aged 40 years and older from the NHANES cohorts of 2011–2012 and 2013–2014 surveys. Glycosylated hemoglobin (HbA1c level) was used as indicator of glycemic control. Patients were categorized into quartiles for daily protein intake. Average protein intakes for patients in quartile 1 to quartile 4 were 35.5 g, 58.7 g, 79.9 g, and 131.6 g, respectively. Covariates included in the analysis were gender, age, race, caloric intake, muscle strength, education, income, smoking, sedentary lifestyle, alcohol consumption, and BMI.ResultsAfter controlling for covariates, patients in quartile 4 of protein intake had 261% increased risk of poor glycemic control compared with those in quartile 1 (95% CI: 1.29–5.30). The association was not attenuated after further control for insulin use (OR = 1.94; 95% CI: 0.89–4.24). In conclusion, higher protein intake seems related to a worse glycemic control in diabetic subjects.

Sex Differences in the Effects of CDKAL1 Variants on Glycemic Control in Diabetic Patients: Findings from the Korean Genome and Epidemiology Study.

Using long-term data from the Korean Genome and Epidemiology Study, we defined poor glycemic control and investigated possible risk factors, including variants related to type 2 diabetes mellitus (T2DM). In addition, we evaluated interaction effects among risk factors for poor glycemic control. Among 436 subjects with newly diagnosed diabetes, poor glycemic control was defined based on glycosylated hemoglobin trajectory patterns by group-based trajectory modeling. For the variants related to T2DM, genetic risk scores (GRSs) were calculated and divided into quartiles. Risk factors for poor glycemic control were assessed using a logistic regression model. Of the subjects, 43% were in the poor-glycemic-control group. Body mass index (BMI) and triglyceride (TG) were associated with poor glycemic control. The risk for poor glycemic control increased by 11.0% per 1 kg/m2 increase in BMI and by 3.0% per 10 mg/dL increase in TG. The risk for GRS with poor glycemic control was sex-dependent (Pinteraction=0.07), and a relationship by GRS quartiles was found in females but not in males. Moreover, the interaction effect was found to be significant on both additive and multiplicative scales. The interaction effect was evident in the variants of cyclin-dependent kinase 5 regulatory subunit-associated protein 1-like (CDKAL1). Females with risk alleles of variants in CDKAL1 associated with T2DM had a higher risk for poor glycemic control than males.

Glycemic control and its association with sociodemographics, comorbid conditions, and medication adherence among patients with type 2 diabetes in southwestern Nigeria.

ObjectiveWe determined the prevalence of poor glycemic control and associations with sociodemographics, comorbid conditions, and medication adherence among patients with type 2 diabetes mellitus (T2DM) at a tertiary hospital in southwestern Nigeria.MethodsWe conducted a retrospective observational study among 300 patients with T2DM using systematic random sampling. We used a semi-structured questionnaire to collect information on respondents’ sociodemographic profile, lifestyle, comorbid conditions, and antidiabetic medications. Adherence was determined using the Morisky Medication Adherence Scale. Fasting blood samples were tested using a glycated hemoglobin marker. Multivariate logistic regression was used to identify factors associated with poor glycemic control.ResultsRespondents’ mean age was 61.9 ± 11.8 years. The prevalence of poor glycemic control was 40.0% (95% confidence interval [CI]: 34.4%–45.8%). The adjusted odds ratio (95% CI) for factors associated with poor glycemic control was 2.522 (1.402–4.647) for older age, 1.882 (1.021–3.467) for low income, 1.734 (1.013–3.401) for obesity, 2.014 (1.269–5.336) for non-initiation of insulin therapy, and 1.830 (1.045–3.206) for poor medication adherence.ConclusionOlder age, lower income, obesity, non-initiation of insulin, and poor medication adherence were associated with poor glycemic control. These variables may help clinicians identify patients at high risk of poor glycemic control.

Barriers and facilitators of type 1 diabetes self-care in adolescents and young adults

PurposeThis study explores the disease experience of adolescents and young adults with T1DM focusing on the barriers and facilitators that characterise their disease self-care. Self-care requires complex decision making and cooperation between patients, their families, the healthcare team, and the social support system. Personal and social factors affect self-care, and specific challenges impact adolescents and young adults, putting them at a higher risk of poor glycaemic control and more severe complications. Design and methodsThe study uses a qualitative description approach. Twenty-two people (15 women; 10–30 years old; 2–24 years from diagnosis) were purposefully recruited through snowballing techniques. Data were collected with semi-structured interviews and analysed inductively with semantic thematic analysis. ResultsFour themes and nine subthemes conceptualise the patients' experience as a life-long journey that has its difficult beginning at the time of diagnosis and continues through the resolution of the initial crisis by integrating disease at the identity level and acquiring expertise. Technology and social environment act both as self-care barriers and facilitators. ConclusionsFindings highlight the importance of designing and improving technology related to diabetes accounting for patients' experiences. Second, it is imperative to work towards a de-stigmatisation of diabetes. Finally, health professionals should work with people with T1DM on the psychological aspects of the disease and identity integration. Practice implicationsDiabetes-related technology should promote usability and acceptability while addressing visibility and device burden issues. Clinicians should pay particular attention during the transition from the paediatric to the adult centres and offer global assessments and treatment.

Association between Inflammatory Markers and Glycemic Control in Korean Diabetic Patients

Objectives: This study evaluated the association between inflammatory markers and glycemic control in Korean patients with diabetes mellitus.Methods: Using data from the 2007-2018 Korean National Health and Nutritional Examination, 4,186 diabetic subjects aged between 19 and 80 years were included in this study. Poor glycemic control was defined as level of HbA1c higher than 7%. Inflammation was measured by inflammatory markers including white blood cell (WBC) count and high-sensitivity C-reactive protein (hsCRP), and those inflammatory markers were categorized into 4 groups by quartiles. A multiple logistic regression analysis was performed to evaluate the association between quartile of inflammatory markers and poor glycemic control after adjusting for demographic factors, lifestyle, and comorbidities.Results: Levels of WBC count and hsCRP were positively associated with the risk of poor glycemic control even after fully adjusting for potential confounders. For WBC count, compared with the first quartile, the second quartile was 1.50 (95% confidence interval, CI: 1.26-1.78), 1.71 (95 % CI:1.43-2.05) for the third, 2.00 (95% CI: 1.67-2.40) for the fourth. In case of hsCRP, compared with the first quartile, the second quartile was 1.15 (95% CI: 0.87-1.53), 1.59 (95% CI: 1.20-2.11) for the third, and 1.59 (95% CI: 1.19-2.12) for the fourth. There was significant age difference in the association between hsCRP and glycemic control. The association was more evident in the younger group than in the elderly group. Conclusions: High level of WBC and hsCRP were positively associated with the risk of poor glycemic control. These results suggest that in a Korean diabetic patient, inflammation was significantly associated with poor glycemic control.