The role of the immune system in the insulin resistance syndrome

Hyperglycemia Associated With Raynaud Phenomenon and Liver Dysfunction After COVID-19 Vaccination in Type 1 Diabetes Mellitus

Background/Objective

The association of COVID-19 vaccinations and the changes in glycemic control remains debatable. We report a case of a patient with type 1 diabetes mellitus (DM) with previously well-controlled glucose on a hybrid closed-loop insulin pump who developed significant glucose variation, new onset Raynaud phenomenon, and liver dysfunction after the vaccination.

Case Report

A 33-year-old man with type 1 DM since the age of 5 years was on an insulin pump for 17 years. He had a reasonable controlled glucose level with a hemoglobin A1c level of 6.8% (51 mmol/mol). Three days after he received the COVID-19 vaccination, his glucose level started to fluctuate in the range of 46 to 378 mg/dL with 3.5 times higher total daily insulin requirement. The patient developed white-pale cold hands, weight gain, fatigue, and liver dysfunction. Computed tomography of the abdomen revealed mild hepatomegaly, and laboratory workup was negative for hepatitis. One month later, his glucose level became better controlled, and his liver function improved. Continuous glucose monitoring revealed that his glucose profile returned to baseline after 6 weeks.

Discussion

COVID-19 vaccination resulted in significant glucose variation and fluctuations in this patient. It could be explained by the vaccine-induced immune response causing an increase in insulin resistance, such as in adipose tissue and muscle cells. Immune stimulation could have also caused the abnormal liver function and explain his new onset Raynaud phenomenon.

Conclusion

We described, for the first time, the long-term continuous glucose monitoring glucose profile with a hybrid closed-loop system in type 1 DM after COVID-19 vaccination. Clinicians need to keep alert to glycemic excursion and side effects after immunization in type 1 DM.

Investigation of neutrophil lymphocyte ratio and blood glucose regulation in patients with type 2 diabetes mellitus

Objective

Leukocytosis is thought to be directly associated with the pathogenesis of atherosclerosis and metabolic syndrome. Increased white blood cell (WBC) count is related to cardiovascular disease in patients with type 2 diabetes mellitus; raised neutrophil lymphocyte ratio (NLR) is associated with metabolic syndrome. There is little information, however, concerning a correlation between glycosylated haemoglobin (HbA1c) and NLR. The aim of the present study was to investigate the relationship between NLR and blood glucose regulation.

Methods

This retrospective study was conducted in patients with type 2 diabetes mellitus, divided into two groups according to HbA1c levels: group 1, HbA1c levels ≤ 7%; group 2, HbA1c levels > 7%. Venous WBC, neutrophil and lymphocyte counts were determined.

Results

Of 71 patients included, fasting serum glucose, neutrophil and WBC counts were significantly higher in group 2 compared with group 1. NLR had a positive correlation with HbA1c.

Conclusion

There may be a significant relationship between NLR and blood glucose regulation. The authors propose that increased NLR may be associated with elevated HbA1c in patients with type 2 diabetes mellitus.

Deciphering normal blood gene expression variation--The NOWAC postgenome study

There is growing evidence that gene expression profiling of peripheral blood cells is a valuable tool for assessing gene signatures related to exposure, drug-response, or disease. However, the true promise of this approach can not be estimated until the scientific community has robust baseline data describing variation in gene expression patterns in normal individuals. Using a large representative sample set of postmenopausal women (N = 286) in the Norwegian Women and Cancer (NOWAC) postgenome study, we investigated variability of whole blood gene expression in the general population. In particular, we examined changes in blood gene expression caused by technical variability, normal inter-individual differences, and exposure variables at proportions and levels relevant to real-life situations. We observe that the overall changes in gene expression are subtle, implying the need for careful analytic approaches of the data. In particular, technical variability may not be ignored and subsequent adjustments must be considered in any analysis. Many new candidate genes were identified that are differentially expressed according to inter-individual (i.e. fasting, BMI) and exposure (i.e. smoking) factors, thus establishing that these effects are mirrored in blood. By focusing on the biological implications instead of directly comparing gene lists from several related studies in the literature, our analytic approach was able to identify significant similarities and effects consistent across these reports. This establishes the feasibility of blood gene expression profiling, if they are predicated upon careful experimental design and analysis in order to minimize confounding signals, artifacts of sample preparation and processing, and inter-individual differences.

As a major defence and transport system, blood cells are capable of adjusting gene expression in response to various clinical, biochemical, and pathological conditions. Here, we expand our understanding about the nature and extent of variation in gene expression from blood among healthy individuals. Using a large representative sample of postmenopausal women (N = 286) in the Norwegian Women and Cancer (NOWAC) postgenome study, we investigated blood gene expression changes due to normal inter-individuality (age, body mass index, fasting status), and exposure variables (smoking, hormone therapy, and medication use) at proportions and levels found in real life situations. Host genes were found to vary by inter-individual (i.e. fasting, BMI) and exposure (i.e. smoking) factors, and these gene lists may be used as a basis for further hypothesis development. Our study also establishes the feasibility of blood gene expression profiling for disease prediction, diagnosis, or prognosis, but underscores the necessity of care in study design and analysis to account for inter-individual differences and confounding signals.