Glycemic Control Risk Factors Research Articles

Risk Factors for Glycemic Control in Hospitalized Patients with Type 2 Diabetes Receiving Continuous Subcutaneous Insulin Infusion Therapy.

Patients with diabetes are confronted with numerous obstacles to achieve adequate glycemic control during hospitalization. The aim of this study was to explore the risk factors associated with glycemic control in hospitalized patients with type 2 diabetes mellitus (T2DM) treated with continuous subcutaneous insulin infusion (CSII). This cross-sectional study included 5223 patients hospitalized with T2DM in a tertiary hospital in Xiamen (China) between January 2017 and December 2019. All patients were managed according to established protocols for glycemic monitoring and insulin pump treatment regimens. Demographic information and clinical profiles were collected from electronic health records. Multiple linear regression analysis was used to identify the risk factors associated with glycemic control. Among the 5223 hospitalized patients with T2DM receiving CSII therapy, 55.2% achieved their ideal blood glucose level (3.9-10.0mmol/L), 44.5% experienced hyperglycemia (> 10.0mmol/L), and 0.3% experienced hypoglycemia (< 3.9mmol/L) during their hospitalization. Multivariate analyses showed that among inpatients with T2DM, older age, male gender, higher low-density lipoprotein-cholesterol (LDL-C) level, lower C-peptide (C-P) level, lower body mass index (BMI), longer duration of diabetes, previous insulin prescriptions, nephropathy, and retinopathy were factors more likely to be associated with a blood glucose level in the hyperglycemic range (P < 0.05). We also observed that among hospitalized patients with T2DM, those with lower BMI, lower C-P, lower LDL-C, longer disease duration, and previous insulin prescriptions were more likely to correlate with a higher proportion of hypoglycemia range (all P < 0.05). Older age, male gender, lower BMI, lower C-P, higher LDL-C, previous insulin prescriptions, longer duration of diabetes, nephropathy, and retinopathy may be risk factors for a higher proportion of hyperglycemic events in hospitalized patients with T2DM under CSII therapy. Furthermore, lower BMI, lower C-P, lower LDL-C, longer duration of diabetes, and previous insulin prescriptions were found to be important factors for a higher proportion of hypoglycemic events. Evaluating the clinical features, comorbidities, and complications of hospitalized patients is essential to achieve reasonable glycemic control.

A cross-sectional study clarifying profiles of patients with diabetes who discontinued pharmacotherapy: reasons and consequences

BackgroundAlthough diabetes is one of the fastest increasing diseases in prevalence worldwide and demands significant medical resources, more than half of all patients with diabetes do not achieve the expected target level of blood glucose. As a potential cause of poor glycemic control, insufficient adherence to medication has long been discussed and variably studied. However, dropout from treatment as another plausible cause has not been fully examined. The aim of this study was to clarify profiles of patients with diabetes who discontinued pharmacotherapy (Discont group) by extracting reasons of their decisions and by comparing with those who continued (Cont group) in terms of the related factors to glycemic control.MethodsA cross-sectional, internet-based survey was conducted among Japanese with diabetes registered in a database. A self-administered questionnaire consisting of the 8-item version of the Morisky Medication Adherence Scale (MMAS-8), glycosylated haemoglobin (HbA1c) level, and demographic and disease characteristics was completed by all participants. Reasons for medication discontinuation and resumption were also received retrospectively from participants in the Discont group. To examine the risk of uncontrolled HbA1c, logistic regression analysis was conducted in each group.ResultsIn the Discont group (148 cases), older age at resumption of pharmacotherapy and current smoking habit increased the probability of uncontrolled HbA1c, whereas in the Cont group (146 cases), a familial history of diabetes and better medication adherence in MMAS-8 scores decreased the probability of uncontrolled HbA1c. A relationship between medication adherence and HbA1c level was seen in the Cont but not in the Discont group. About 70 % of those in the Discont group made their decision to terminate diabetes treatment without consulting physicians and half of them perceived their situations inappropriately.ConclusionsThose who discontinued pharmacotherapy were less adherent to medication than those who did not discontinue. Risk factors for glycemic control also differed between those who discontinued and those who did not. More than one-third of participants with diabetes who discontinued pharmacotherapy had inappropriate perceptions of their disease, which medical professionals should be aware of for better interventions.

Factors contributing to glycemic control in diabetes mellitus patients complying with home quarantine during the coronavirus disease 2019 (COVID-19) epidemic

AimsThis study assessed factors contributing to glycemic control among diabetes mellitus patients complying with home quarantine during the epidemic of coronavirus disease 2019 (COVID-19). MethodsWe conducted an analytical cross-sectional study by telephone with 1159 patients with type 2 diabetes mellitus (T2DM) and 96 patients with type 1 diabetes mellitus (T1DM) who were discharged from the endocrinology department of a hospital from January 1, 2019, to January 24, 2020. According to their fasting blood glucose (FBG) and 2-h postprandial BG (2hPBG) values, the patients were divided into the well-controlled BG group and the poorly controlled BG group. The main evaluation indicators included sociodemographic variables, health risk variables and adherence to self-management behaviors. ResultsIn total, 74.46% of the T2DM patients and 64.89% of the T1DM patients had poor glycemic control. T2DM patients with poor glycemic control were more likely to be older (odds ratio (OR): 1.017 [95% confidence interval (CI) 1.003–1.030]; P = 0.013), have fewer than 12 years of education (OR: 1.646 [95% CI 1.202–2.255]; P = 0.002), lack a BG meter at home (OR: 2.728 [95% CI 1.205–6.179]; P = 0.016), have a lower degree of medicationcompliance (OR: 1.627 [95% CI 1.076–2.460]; P = 0.021), and engage in less self-monitoring of BG (SMBG) per week (OR: 10.884 [95% CI 5.883–20.139]; P < 0.001). Fewer than 12 years of education (OR: 3.031 [95% CI 1.112–8.263]; P = 0.030) was a risk factor for glycemic control in T1DM. ConclusionsGlycemic control among patients with T1DM and T2DM during home quarantine amid the COVID-19 pandemic is poor. Our results showed that more eduction, a higher frequency of SMBG, and improved medication compliance may contribute to glycemic control. Therefore, diabetic patients should be advised to increase the frequency of blood glucose measurements during home quarantine and be re-educated regarding the importance of medication compliance.

The risk factors of glycemic control, blood pressure control, lipid control in Chinese patients with newly diagnosed type 2 diabetes _ A nationwide prospective cohort study

Nationwide data on glycemic control, blood pressure (BP) control and lipid control in patients with newly diagnosed type 2 diabetes were vacant in China. The aim of this study was to assess the clinical outcomes for these patients. This is an observational prospective cohort study with 12 months of follow up. Patients with a diagnosis of type 2 diabetes less than 6 months were enrolled. Hemoglobin A1c (HbA1c) levels, BP levels and lipid levels were collected at baseline and the follow-ups. This study was registered at www.clinicaltrials.gov (NCT01525693). A total of 5770 participants from 79 hospitals across six geographic regions of China were recruited. After 12 months of treatment, 68.5% of these patients achieved HbA1c <7.0%; 83.7% reached BP <140/90 mmHg; 48.2% met low density lipoprotein cholesterol (LDL-c) <2.6 mmol/L; and 29.5% of patients reached the combined three therapeutic targets. Compared to those patients with baseline HbA1c <7.0%, patients with baseline HbA1c ≥7.0% had higher failure rate to reach glycemic control (relative risk (RR) = 2.04, p < 0.001), BP control (RR = 1.21, p < 0.001) and LDL-c control (RR = 1.11, p < 0.001). Obese patients had higher possibilities of failure in glucose control (RR = 1.05, p = 0.004), BP control (RR = 1.62, p < 0.001) and lipid control (RR = 1.09, p = 0.001) than patients with normal weight. The active smokers were more likely to fail in glycemic control than non-smokers (RR = 1.06, p = 0.002), and patients with physical activities were less likely to fail in lipid control than patients without exercises (RR = 0.93, p = 0.008). This study outlined the burdens of glycemic control, blood pressure control, lipid control in newly diagnosed type 2 diabetic patients in China, identified gaps in the quality of care and risk-factor control and revealed the factors influencing these gaps.

Association between smoking and glycemic control in diabetic patients: Results from the Risk Evaluation of cAncers in Chinese diabeTic Individuals: A lONgitudinal (REACTION) study.

A number of primary studies suggested that active smoking could be independently associated with incident diabetes. However less is known about the effect of active smoking and smoking cessation on glycemic control in patients with diabetes. The aim of this study was to evaluate the associations of active smoking and smoking cessation with glycemic control in diabetic patients. The present was a cross-sectional study of 10 551 men and 15 297 women with diabetes from the Risk Evaluation of cAncers in Chinese diabeTic Individuals: a lONgitudinal (REACTION) study. Risk factors for glycemic control and the association of active smoking with glycemic control were evaluated using logistic regression models. Poor glycemic control was defined as HbA1c ≥7.0%. Current smokers have an increased risk of poor glycemic control, and the multivariable-adjusted odds ratio (OR) and 95% confidence intervals (CI) of HbA1c ≥7.0% with current smoking were 1.49 (1.35-1.66) in men and 1.56 (1.13-2.15) in women. Further analysis demonstrated a dose-dependent relationship between active smoking and the risk of poor glycemic control in men. Former smokers who quit smoking for <10 years remained at increased risk of poor glycemic control, with the risk leveling off after 10 years of smoking cessation compared with non-smokers, but risk in former smokers was significantly lower than that in current smokers. Active smoking is a modifiable risk factor for poor glycemic control in Chinese diabetic patients.

The prevalence and determinants of poor glycemic control among adults with type 2 diabetes mellitus in Saudi Arabia

BackgroundAlthough the prevalence of type 2 diabetes mellitus (T2DM) is rising sharply in Saudi Arabia, data on glycemic control, crucial to reducing diabetes mellitus complications, remain scarce. This study therefore investigated glycemic control status and the factors influencing poor glycemic control among adult T2DM patients in Saudi Arabia.MethodsThis cross-sectional study examined 423 T2DM patients at a diabetic center in Tabuk, Saudi Arabia between September 2016 and July 2017. Glycemic levels were measured via fasting blood glucose (FBG) levels, and “poor glycemic control” was defined as FBG >130 mg/dL. Poor glycemic control’s risk factors were identified using a logistic regression.ResultsIn the sample, 74.9% of the patients had poor blood glycemic control. Logistic regression revealed that T2DM patients had an increased chance of poorly controlled diabetes if they had family histories of diabetes (adjusted odds ratio [AOR] =7.38, 95% CI 4.09–13.31), longer diabetic durations (AOR =2.33, 95% CI 1.14–4.78 for 5–10 years and AOR =5.19, 95% CI 2.50–10.69 for >10 years), insufficient physical exercise (AOR =19.02, 95% CI 6.23–58.06), or were overweight (AOR =3.79, 95% CI 2.00–7.18), or obese (AOR =5.35, 95% CI 2.72–12.59).ConclusionA high proportion of the sampled patients had poor glycemic control, therefore, health care professionals should manage the associated risk factors to limit disease complications and improve the health of patients with diabetes.

Racial and Economic Disparities in Diabetes in a Large Primary Care Patient Population.

We sought to determine if, after adjusting for economic status, race is an independent risk factor for glycemic control among diabetic patients in a large primary care patient population. We performed a retrospective chart review of 264,000 primary care patients at our large, urban academic medical center to identify patients with a diagnosis of diabetes (n=25,123). Zip code was used to derive median income levels using US Census Bureau demographic information. Self-reported race was extracted from registration data. The prevalence of diabetes, average glycated hemoglobin (A1c), and prevalence of uncontrolled diabetes of White and Black patients at all income levels were determined. White patients had a lower average A1c level and a lower prevalence of diabetes than Black patients in all income quartiles (P<.001). Among White patients, the prevalence of diabetes (P<.001), uncontrolled diabetes (P<.001), and A1c level (P=.014) were inversely proportional to income level. No significant difference in the prevalence of diabetes (P=.214), A1c level (P=.282), or uncontrolled diabetes related to income was seen in Black patients (P=.094). Race had an independent association with diabetes prevalence and glycemic control. Our study does not support two prominent theories that economic and insurance status are the main factors in diabetes disparities, as we attempted to control for economic status and nearly every patient had insurance. It will be important for future analysis to explore how health care system factors affect these observed gaps in quality.

Interaction of sleep quality and sleep duration on glycemic control in patients with type 2 diabetes mellitus

Background Copious evidence from epidemiological and laboratory studies has revealed that sleep status is associated with glucose intolerance, insulin resistance, thus increasing the risk of developing type 2 diabetes. The aim of this study was to reveal the interaction of sleep quality and sleep quantity on glycemic control in patients with type 2 diabetes mellitus. Methods From May 2013 to May 2014, a total of 551 type 2 diabetes patients in Tianjin Metabolic Diseases Hospital were enrolled. Blood samples were taken to measure glycosylated hemoglobin (HbA1c), and all the patients completed the Chinese version of the Pittsburgh Sleep Quality Index (PSQI) questionnaire to evaluate their sleep status. “Good sleep quality” was defined as PQSI &lt;5, “average sleep quality” was defined as PQSI 6–8, and “poor sleep quality” was defined as PQSI &gt;8. Poor glycemic control was defined as HbA1c ≥7%. Sleep quantity was categorized as &lt;6, 6–8, and &gt;8 hours/night. Short sleep time was defined as sleep duration &lt;6 hours/night. Results In the poor glycemic control group, the rate of patients who had insufficient sleep was much higher than that in the other group (χ2=11.16, P=0.037). The rate of poor sleep quality in poor glycemic control group was much greater than that in the average control group (χ2=9.79, P=0.007). After adjusted by gender, age, body mass index, and disease duration, the adjusted PSQI score's OR was 1.048 (95% CI 1.007–1.092, P=0.023) for HbA1c level. The sleep duration's OR was 0.464 (95% CI 0.236–0.912, P=0.026) for HbA1c level. One-way analysis of variance showed that the poor sleep quality group had the highest homeostasis model assessment-insulin resistance (P &lt;0.01). Conclusions Inadequate sleep, in both quality and quantity, should be regarded as a plausible risk factor for glycemic control in type 2 diabetes. Poor sleep might bring much more serious insulin resistance and could be the reason for bad glycemic control. A good night's sleep should be seen as a critical health component tool in the prevention and treatment of type 2 diabetes. It is important for clinicians to target the root causes of short sleep duration and/or poor sleep quality.

Associations Between Media Consumption Habits, Physical Activity, Socioeconomic Status, and Glycemic Control in Children, Adolescents, and Young Adults With Type 1 Diabetes

OBJECTIVETo evaluate the relationship between media consumption habits, physical activity, socioeconomic status, and glycemic control in youths with type 1 diabetes.RESEARCH DESIGN AND METHODSIn the cross-sectional study, self-report questionnaires were used to assess media consumption habits, physical activity, and socioeconomic status in 296 children, adolescents, and young adults with type 1 diabetes. Clinical data and HbA1c levels were collected. Risk factors were analyzed by multiple regression.RESULTSYouths with type 1 diabetes (aged 13.7 ± 4.1 years, HbA1c 8.7 ± 1.6%, diabetes duration 6.1 ± 3.3 years) spent 2.9 ± 1.8 h per day watching television and using computers. Weekly physical activity was 5.1 ± 4.5 h. Multiple regression analysis identified diabetes duration, socioeconomic status, and daily media consumption time as significant risk factors for glycemic control.CONCLUSIONSDiabetes duration, socioeconomic status, and daily media consumption time, but not physical activity, were significant risk factors for glycemic control in youths with type 1 diabetes.

Linking Glycemic Control and Executive Function in Rural Older Adults with Diabetes Mellitus

To examine the association between glycemic control and the executive functioning domain of cognition and to identify risk factors for inadequate glycemic control that may explain this relationship. Cross-sectional study. In-person interviews conducted in participants' homes. Ninety-five rural older African Americans, American Indians, and whites with diabetes mellitus (DM) from three counties in south-central North Carolina. Participants underwent uniform evaluations. Glycemic control was measured using a validated method, and executive function was assessed using a previously established set of measures and scoring procedure. Information pertaining to medication for treatment of DM, knowledge of DM, and DM self-care behaviors were obtained. In linear regression models adjusting for sex, age, education, ethnicity, duration of DM, and depressive symptoms, executive function was significantly associated with glycemic control. A 1-point higher executive function score was associated with a 0.47 lower glycosylated hemoglobin value (P=.01). The association between glycemic control and executive function became nonsignificant (P=.08) when controlling for several glycemic control risk factors, including use of DM medication and DM knowledge. These results suggest that poor glycemic control is associated with impairments in performance on composite measures of executive function and that modifiable risk factors for glycemic control such as use of DM medication and DM knowledge may explain this relationship.