Higher glucose variability in type 1 than in type 2 diabetes patients admitted to the intensive care unit: A retrospective cohort study

Olfactory function in diabetes mellitus

Diabetes mellitus (DM) is an increasingly common disease in both children and adults. In addition to neuronal and/or vascular disorders, it can cause chemosensory abnormalities including olfactory deterioration. The purpose of this article is to summarize current knowledge on olfactory function in DM, highlighting the impact of co-morbidities, especially obesity, thyroid dysfunction, chronic kidney disease and COVID-19 on olfactory outcomes. Research to date mostly shows that olfactory impairment is more common in people with diabetes than in the general population. In addition, the presence of concomitant diseases is a factor increasing olfactory impairment. Such a correlation was shown for type 1 diabetes, type 2 diabetes and gestational diabetes. At the same time, not only chronic diseases, but also DM in acute conditions such as COVID-19 leads to a higher prevalence of olfactory disorders during infection. Analyzing the existing literature, it is important to be aware of the limitations of published studies. These include the small number of patients studied, the lack of uniformity in the methods used to assess the sense of smell, frequently relying on rated olfactory function only, and the simultaneous analysis of patients with different types of diabetes, often without a clear indication of diabetes type. In addition, the number of available publications is small. Certainly, further research in this area is needed. From a practical point of view decreased olfactory performance may be an indicator for central neuropathy and an indication for assessing the patient's nutritional status, examining cognitive function, especially in older patients and performing additional diagnostic tests, such as checking thyroid function, because all those changes were correlated with smell deterioration.

Association between dysglycemia and mortality by diabetes status and risk factors of dysglycemia in critically ill patients: a retrospective study

AIMS

Dysglycemia, including the three domains hyperglycemia, hypoglycemia, and increased glycemic variability (GV), is associated with high mortality among critically ill patients. However, this association differs by diabetes status, and reports in this regard are limited. This study aimed to evaluate the associations between the three dysglycemia domains and mortality in critically ill patients by diabetes status and determined the contributing factors for dysglycemia.

METHODS

This retrospective study included 958 critically ill patients (admitted to the ICU) with or without DM. Dysglycemia was defined as abnormality of any of the three dimensions. We evaluated the effects of the three domains of glucose control on mortality using binary logistic regression and then adjusted for confounders. The associations between dysglycemia and other variables were investigated using cumulative logistic regression analysis.

RESULT

GV independently and similarly affected mortality in both groups after adjustment for confounders (DM: odds ratio [OR], 1.05; 95% confidence interval [CI]: 1.03-1.08; p <0.001; non-DM: OR, 1.07; 95% CI, 1.03-1.11; p = 0.002). Hypoglycemia was strongly associated with ICU mortality among patients without DM (3.12; 1.76-5.53; p <0.001) and less so among those with DM (1.18; 0.49-2.83; p = 0.72). Hyperglycemia was non-significantly associated with mortality in both groups. However, the effects of dysglycemia seemed cumulative. The factors contributing to dysglycemia included disease severity, insulin treatment, glucocorticoid use, serum albumin level, total parenteral nutrition, duration of diabetes, elevated procalcitonin level, and need for mechanical ventilation and renal replacement therapy.

CONCLUSION

The association between the three dimensions of dysglycemia and mortality varied by diabetes status. Dysglycemia in critical patients is associated with excess mortality; however, glucose management in patients should be specific to the patient's need considering the diabetes status and broader dimensions. The identified factors for dysglycemia could be used for risk assessment in glucose management requirement in critically ill patients, which may improve clinical outcomes.

In-Hospital Mortality and Glycemic Control in Patients with Hospital Hyperglycemia

BACKGROUND

Stress-induced hyperglycemia is a phenomenon that occurs typically in patients hospitalized for acute disease and resolves spontaneously after regression of the acute illness. However, it can also occur in diabetes patients, a fact that is sometimes overlooked. It is thus important to make a proper diabetes diagnosis if hospitalized patients with episodes of hyperglycemia with and without diabetes are studied.

AIMS

To estimate the extent of the association between stress-induced hyperglycemia and in-hospital mortality in patients with hospital hyperglycemia (HH), and to explore potential differences between patients diagnosed with diabetes (HH-DBT) and those with stress-induced hyperglycemia (SH), but not diagnosed with diabetes.

METHODS

A cohort of adults with hospital hyperglycemia admitted to a tertiary, university hospital in Buenos Aires, Argentina, was analyzed retrospectively.

RESULTS

In the study, 2,955 patients were included and classified for analysis as 1,579 SH and 1,376 HH-DBT. Significant differences were observed in glycemic goal (35.53% SH versus 25.80% HH-DBT, p < 0.01), insulin use rate (26.66% SH versus 46.58% HH-DBT, p < 0.01), and severe hypoglycemia rate (1.32% SH versus 1.74% HH-DBT, p < 0.01). There were no differences in hypoglycemia rate (8.23% SH versus 10.53% HH-DBT) and hospital mortality. There was no increase in risk of mortality in the SH group adjusted for age, non-scheduled hospitalization, major surgical intervention, critical care, hypoglycemia, oncological disease, cardiovascular comorbidity, and prolonged hospitalization.

CONCLUSIONS

In this study, we observed better glycemic control in patients with SH than in those with HH-DBT, and there was no difference in hospital mortality.

Glucose variability and mood in adults with diabetes: A systematic review

Aims

To systematically review the literature regarding the association between glucose variability (GV) and mood in adults with diabetes, appraise the used methods and make suggestions for future research.

Methods

A systematic review of literature published up to May 2019 was performed. Abstracts and full texts were screened independently in duplicate. Experimental and observational studies reporting the association between GV and mood in adults with type 1 diabetes or type 2 diabetes were evaluated. A descriptive analysis of the extracted data was conducted, along with a quality assessment.

Results

Out of the 2.316 studies screened, eight studies met our criteria. Studies used a variety of measures and metrics to determine GV and mood. Four studies used continuous glucose monitoring (CGM). An association between GV and mood was found in four studies when correlating either postprandial glucose rate of increase with current mood or multiday GV with mood measured retrospectively. The other four studies did not find any association.

Conclusions

There is no clear empirical support for a link between GV and mood in adults with type 1 and type 2 diabetes. More rigorous research is warranted using CGM and ecological momentary assessment of mood to assess if and under what conditions an association between GV and mood exists.

Issues for the management of people with diabetes and COVID-19 in ICU

In the pandemic “Corona Virus Disease 2019” (COVID-19) people with diabetes have a high risk to require ICU admission. The management of diabetes in Intensive Care Unit is always challenging, however, when diabetes is present in COVID-19 the situation seems even more complicated. An optimal glycemic control, avoiding acute hyperglycemia, hypoglycemia and glycemic variability may significantly improve the outcome. In this case, intravenous insulin infusion with continuous glucose monitoring should be the choice. No evidence suggests stopping angiotensin-converting-enzyme inhibitors, angiotensin-renin-blockers or statins, even it has been suggested that they may increase the expression of Angiotensin-Converting-Enzyme-2 (ACE2) receptor, which is used by “Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to penetrate into the cells. A real issue is the usefulness of several biomarkers, which have been suggested to be measured during the COVID-19. N-Terminal-pro-Brain Natriuretic-Peptide, D-dimer and hs-Troponin are often increased in diabetes. Their meaning in the case of diabetes and COVID-19 should be therefore very carefully evaluated. Even though we understand that in such a critical situation some of these requests are not so easy to implement, we believe that the best possible action to prevent a worse outcome is essential in any medical act.

Impact of Carbohydrate on Glucose Variability in Patients with Type 1 Diabetes Assessed Through Professional Continuous Glucose Monitoring: A Retrospective Study

INTRODUCTION

The aim of this study was to objectively analyze the correlation between dietary components and blood glucose variation by means of continuous glucose monitoring (CGM).

METHODS

Patients with type 1 diabetes mellitus (T1DM) who received CGM to manage their blood glucose levels were enrolled into the study, and the components of their total caloric intake were analyzed. Glycemic variation parameters were calculated, and dietary components, including percentages of carbohydrate, protein and fat in the total dietary intake, were analyzed by a dietitian. The interaction between parameters of glycemic variability and dietary components was analyzed.

RESULTS

Sixty-one patients with T1DM (33 females, 28 males) were enrolled. The mean age of the participants was 34.7 years, and the average duration of diabetes was 14 years. Glycated hemoglobin before CGM was 8.54%. Participants with a carbohydrate intake that accounted for < 50% of their total caloric intake had a longer DM duration and a higher protein and fat intake than did those with a carbohydrate intake that accounted for ≥ 50% of total caloric intake, but there was no between-group difference in total caloric intake per day. The group with a carbohydrate intake that accounted for < 50% of their total caloric intake also had lower nocturnal continuous overlapping net glycemic action (CONGA) 1, - 2 and - 4 values. The percentage of protein intake had a slightly negative correlation with mean amplitude of glycemic excursions (MAGE) (r = - 0.286, p < 0.05) and a moderately negative correlation with coefficient of variation (CV) (r = 0.289, p < 0.05). One additional percentage of protein calories of total calories per day decreased the MAGE to 4.25 mg/dL and CV to 0.012 (p < 0.05). The optimal dietary protein percentage for MAGE < 140 mg/dL was 15.13%. The performance of predictive models revealed the beneficial effect of adequate carbohydrate intake on glucose variation when combined with protein consumption.

CONCLUSIONS

Adequate carbohydrate consumption-but not more than half the daily total calories-combined with protein calories that amount to approximately 15% of the daily caloric intake is important for glucose stability and beneficial for patients with T1DM.

Comparison of the HAT study, the largest global hypoglycaemia study to date, with similar large real-world studies

AIMS

Optimal diabetes care requires clear understanding of the incidence of hypoglycaemia in real-world clinical practice. Current data on hypoglycaemia are generally limited to those reported from randomised controlled clinical trials. The Hypoglycaemia Assessment Tool (HAT) study, a non-interventional real-world study of hypoglycaemia, assessed hypoglycaemia in 27 585 individuals across 24 countries. The present study compared the incidence of hypoglycaemia from the HAT study with other similarly designed, large, real-world studies.

MATERIALS AND METHODS

A literature search of PubMed (1995-2017) for population-based studies of insulin-treated patients with type 1 or type 2 diabetes (T1D, T2D), excluding clinical trials and reviews, identified comparable population-based studies reporting the incidence of hypoglycaemia.

RESULTS

The 24 comparative studies, including more than 24 000 participants with T1D and more than 160 000 participants with T2D, varied in design, size, inclusion criteria, definitions of hypoglycaemia and method of recording hypoglycaemia. Reported rates (events per patient-year [PPY]) of hypoglycaemia were higher in patients with T1D than in those with T2D (overall T1D, 21.8-73.3 and T2D, 1.3-37.7; mild/non-severe T1D, 29.0-126.7 and T2D, 1.3-41.5; severe T1D, 0.7-5.8 and T2D, 0.0-2.5; nocturnal T1D, 2.6-11.3 and T2D, 0.38-9.7) and were similar to the ranges found in the HAT study.

CONCLUSIONS

The HAT data on hypoglycaemia incidence were comparable with those from other real-world studies and indicate a high incidence of hypoglycaemia among insulin-treated patients. Differences in rates among studies are mostly explained by differences in patient populations and study methodology. The goal of reducing hypoglycaemia should be a target for continued educational and evidence-based pharmacological interventions.

Comparison of perioperative glucose regulation in patients with type 1 vs type 2 diabetes mellitus: A retrospective cross-sectional study

BACKGROUND

Most perioperative diabetes mellitus (DM) guidelines do not distinguish between patients with type 1 (DM1) and type 2 (DM2). We hypothesised that similar treatment of DM1 and DM2 patients leads to differences in their perioperative glucose control.

METHODS

We performed a retrospective cross-sectional study, of all DM patients undergoing surgery between May 2013 and November 2015 in a Dutch university hospital. We compared DM1 with DM2 patients, treated according to the same perioperative glucose protocol. Our primary outcome was the incidence of hyperglycaemia (glucose ≥10 mmol/L). Secondary outcomes were short-term glycaemic control (glucose before surgery and peak glucose perioperatively), long-term glycaemic control (HbA1c in 90 days before and after surgery) and the incidence of hypoglycaemia (glucose <4 mmol/L).

RESULTS

We included 2259 patients with DM, 216 (10%) of which had DM1. The calculated incidences in our population were 7 out of 1000 patients with DM1 and 69 out of 1000 patients with DM2. Compared to those with DM2, patients with DM1 were younger, had a lower BMI, a higher glucose concentration before surgery, and a higher perioperative peak glucose concentration (11.0 [8.2-14.7] vs 9.4 [7.7-11.7], P < 0.001). The incidence of the primary endpoint, perioperative hyperglycaemia, was significantly higher in DM1 compared to DM2 patients (63% vs 43%, P < 0.001). Hypoglycaemia occurred more often in the DM1 population (7.1% vs 1.3%, P < 0.001).

CONCLUSION

Providing similar perioperative treatment to patients with DM1 and DM2 is associated with poorer short-term and long-term glycaemic control in DM1 throughout the perioperative period as well as an increased risk of hypoglycaemia.

Stress-Induced and Diabetic Hyperglycemia Associated with Higher Mortality among Intensive Care Unit Trauma Patients: Cross-Sectional Analysis of the Propensity Score-Matched Population

Background: This study was designed to measure the effect of stress-induced hyperglycemia (SIH) and diabetic hyperglycemia (DH) versus non-diabetic normoglycemia (NDN) on the outcomes of trauma patients in the intensive care unit (ICU). Methods: Diabetes mellitus (DM) was determined based on patient history and/or a hemoglobin A1c (HbA1c) level of ≥6.5% at admission. The patients who had serum glucose levels of ≥200 mg/dL in the absence or presence of DM were assigned into the groups SIH and DH, respectively. Diabetic normoglycemia (DN) and NDN were determined based on serum glucose levels of <200 mg/dL in patients with and without DM, respectively. Patients with burn injury or incomplete data were excluded. Detailed data of trauma patients in the ICU of a Level-I trauma center from 1 January 2009 to 31 December 2016 were retrieved from the database of the Trauma Registry System. These patients were classified into four exclusive groups, including NDN (n = 1745), DN (n = 306), SIH (n = 225) and DH (n = 206). The Pearson chi-square test was used to compare categorical data between groups. Continuous variables were compared using one-way analysis of variance along with the Games–Howell post hoc test. To decrease the confounding effect of the differences in sex and age, preexisting comorbidities and injury severity score (ISS) among different groups of patients, 1:1 ratio propensity score-matched cohorts were assigned using the NCSS software. The effect of hyperglycemia on the outcomes of patients with and without DM was assessed with a logistic regression analysis. Results: Among those selected propensity score-matched patient cohorts, the patients with SIH and DH had a 3.88-fold (95% CI, 2.13–7.06; p < 0.001) and 1.83-fold (95% CI, 1.00–3.34; p = 0.048) higher mortality, respectively, than those with NDN. Moreover, the patients in the SIH group (10.0 vs. 7.4 days; p = 0.005) and those in the DH group (10.1 vs. 7.4 days; p = 0.006) who were admitted to the ICU had a significantly longer length of stay than those in the NDN group. In addition, the SIH group had a 2.13-fold (95% CI, 1.04–4.36; p = 0.038) higher adjusted odds ratio for mortality than the DH group. Conclusions: This study revealed significantly worse outcomes in terms of mortality among patients with SIH and DH who were admitted to the ICU after controlling for sex and age, preexisting comorbidities and ISS. In addition, patients who had SIH presented significantly higher adjusted odds for mortality than those DH patients. These results suggest that hyperglycemia is detrimental in patients with or without DM who were admitted to the ICU, and there is a different pathophysiological mechanisms behind the SIH and DH.