What Clinical Laboratorians Should Do in Response to Extremely Low Hemoglobin A1c Results

Effects of glycaemic control on memory performance, hippocampal volumes and depressive symptomology

BACKGROUND

Diabetes and poor glycaemic control have been shown to negatively impact cognitive abilities, while also raising risk of both mood disorders and brain structural atrophy. Sites of atrophy include the hippocampus, which has been implicated in both memory performance and depression. The current study set out to better characterise the associations between poor glycaemic control, memory performance, and depression symptoms, and investigate whether loss of hippocampal volume could represent a neuropathological mechanism underlying these.

METHODS

1331 participants (60.9% female, age range 18-88 (Mean = 44.02), 6.5% with likely diabetes) provided HbA1c data (as an index of glycaemic control), completed a word list learning task, and a validated depression scale. A subsample of 392 participants underwent structural MRI; hippocampal volumes were extracted using FreeSurfer.

RESULTS

Partial correlation analyses (controlling for age, gender, and education) showed that, in the full sample, poorer glycaemic control was related to lower word list memory performance. In the MRI sub-sample, poorer glycaemic control was related to higher depressive symptoms, and lower hippocampal volumes. Total hippocampus volume partially mediated the association between HbA1c levels and depressive symptoms.

CONCLUSIONS

Results emphasise the impact of glycaemic control on memory, depression and hippocampal volume and suggest hippocampal volume loss could be a pathophysiological mechanism underlying the link between HbA1c and depression risk; inflammatory and stress-hormone related processes might have a role in this.

Falsely low glycosylated haemoglobin levels probably secondary to hypersplenism in a patient with diabetes mellitus

A man in his 70s presented with a history of low glycated haemoglobin (HbA1c) values despite a diagnosis of type 2 diabetes. His blood glucose readings ranged between 8 and 15 mmol/L, but his HbA1c values were below 27 mmol/mol. Initial investigations demonstrated evidence of reduced red blood cell lifespan as a cause of misleadingly low HbA1c values. Further investigation revealed chronic liver disease and splenomegaly, with hypersplenism being the probable cause of increased red blood cell turnover. HbA1c estimation was no longer reliable, so ongoing diabetic care was guided by home capillary blood glucose monitoring. Healthcare providers and clinical laboratorians need to be aware of the possible clinical implications of very low HbA1c values in patients with type 2 diabetes.

Markovchart: an R package for cost-optimal patient monitoring and treatment using control charts

Control charts originate from industrial statistics, but are constantly seeing new areas of application, for example in health care (Thor et al. in BMJ Qual Saf 16(5):387–399, 2007. https://doi.org/10.1136/qshc.2006.022194; Suman and Prajapati in Int J Metrol Qual Eng, 2018. https://doi.org/10.1051/ijmqe/2018003). This paper is about the package, an implementation of generalised Markov chain-based control charts with health care applications in mind and with a focus on cost-effectiveness. The methods are based on Zempléni et al. (Appl Stoch Model Bus Ind 20(3):185–200, 2004. https://doi.org/10.1002/asmb.521), Dobi and Zempléni (Qual Reliab Eng Int 35(5):1379–1395, 2019a. https://doi.org/10.1002/qre.2518, Ann Univ Sci Budapestinensis Rolando Eötvös Nomin Sect Comput 49:129–146, 2019b). The implemented ideas in the package were motivated by problems encountered by health care professionals and biostatisticians when assessing the effects and costs of different monitoring schemes and therapeutic regimens. However, the implemented generalisations may be useful in other (e.g., engineering) applications too, as they mainly revolve around the loosening of assumptions seen in traditional control chart theory. The package is able to model processes with random shift sizes (i.e., the degradation of the patient’s health), random repair (i.e., treatment) and random time between samplings (i.e., visits) as well. The article highlights the flexibility of the methods through the modelling of different disease progression and treatment scenarios and also through an application on real-world data of diabetic patients.

Metabolic Dysfunction in Spinal Muscular Atrophy

Spinal muscular atrophy (SMA) is an autosomal recessive genetic disorder leading to paralysis, muscle atrophy, and death. Significant advances in antisense oligonucleotide treatment and gene therapy have made it possible for SMA patients to benefit from improvements in many aspects of the once devastating natural history of the disease. How the depletion of survival motor neuron (SMN) protein, the product of the gene implicated in the disease, leads to the consequent pathogenic changes remains unresolved. Over the past few years, evidence toward a potential contribution of gastrointestinal, metabolic, and endocrine defects to disease phenotype has surfaced. These findings ranged from disrupted body composition, gastrointestinal tract, fatty acid, glucose, amino acid, and hormonal regulation. Together, these changes could have a meaningful clinical impact on disease traits. However, it is currently unclear whether these findings are secondary to widespread denervation or unique to the SMA phenotype. This review provides an in-depth account of metabolism-related research available to date, with a discussion of unique features compared to other motor neuron and related disorders.

Relationship Between Glycosylated Hemoglobin and Short-Term Mortality of Spontaneous Intracerebral Hemorrhage

Background: The relationship between glycosylated hemoglobin (HbA1c) and prognosis of spontaneous intracerebral hemorrhage (SICH) patients has not been fully elucidated. This study aimed to reveal the relationship between HbA1c levels and short-term mortality after patient admission with SICH. Methods: It was a large-scale, multicenter, cross-sectional study. From August 1, 2015, to July 31, 2019, a total of 41910 SICH patients were included in the study from the Chinese Stroke Center Alliance (CSCA) program. Finally, we comprehensively analyzed the data from 21,116 patients with SICH. HbA1c was categorized into four groups by quartile. Univariate and multivariate logistic regression analyses were used to assess the association between HbA1c levels and short-term mortality in SICH patients. Results: The average age of the 21,116 patients was 62.8 ± 13.2 years; 13,052 (61.8%) of them were male, and 507 (2.4%) of them died. Compared to the higher three quartiles of HbA1c, the lowest quartile (≤5.10%) had higher short-term mortality. In subgroup analysis with or without diabetes mellitus (DM) patients, the mortality of the Q3 group at 5.60-6.10% was significantly lower than that of the Q1 group at ≤5.10%. After adjustment for potential influencing factors, the ROC curve of HbA1c can better predict the short-term mortality of patients with SICH (AUC = 0.6286 P < 0.001). Conclusions: Therefore, we concluded that low or extremely low HbA1c levels (≤5.10%) after stroke were associated with higher short-term mortality in SICH patients, with or without DM.