Dataset Information

A case of polyneuropathy associated with diabetic ketoacidosis in new-onset type 1 diabetes.

ABSTRACT: Although diabetic peripheral neuropathy is the most common diabetic microangiopathic complication, several other neuropathy syndromes can occur in the context of diabetes. We describe a rare case of polyneuropathy associated with diabetic ketoacidosis in a patient with new-onset type 1 diabetes. A 42-year-old man with diabetic ketoacidosis was admitted to our hospital with complications of respiratory and renal failure requiring mechanical ventilation and hemodialysis, respectively. After diabetic ketoacidosis improved from the critical state, he developed upper- and lower-limb paralysis with sensory disturbances and pain, as well as right facial paralysis, left recurrent nerve paralysis, and left hypoglossal nerve paralysis. Autonomic nerve function was also impaired. As the pathophysiology, prevention, and treatment of polyneuropathy associated with diabetic ketoacidosis are unclear, the neurologic function of patients with diabetic ketoacidosis should be closely monitored.

SUBMITTER: Sada K

PROVIDER: S-EPMC9077735 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A case of polyneuropathy associated with diabetic ketoacidosis in new-onset type 1 diabetes.

Sada Kokoro K Hidaka Shuji S Takemaru Makoto M Ueno Daisuke D Shibata Hirotaka H

Journal of diabetes investigation 20211229 5

Although diabetic peripheral neuropathy is the most common diabetic microangiopathic complication, several other neuropathy syndromes can occur in the context of diabetes. We describe a rare case of polyneuropathy associated with diabetic ketoacidosis in a patient with new-onset type 1 diabetes. A 42-year-old man with diabetic ketoacidosis was admitted to our hospital with complications of respiratory and renal failure requiring mechanical ventilation and hemodialysis, respectively. After diabet ...[more]

PMID: 34845866

Similar Datasets

Project description:Diabetic ketoacidosis (DKA) is a serious complication of complete insulin deficiency and insulin resistance in Type 1 diabetes (T1D). This results in the body producing high levels of serum ketones in an attempt to compensate for the insulin deficiency and decreased glucose utilization. DKA's metabolic and immunologic dysregulation results in gradual increase of systemic and cerebral oxidative stress, along with low grade systemic and cerebral inflammation and the development of pretreatment subclinical BE. During treatment the early progression of oxidative stress and inflammation is hypothesized to advance the possibility of occurrence of crisis of clinical brain edema (BE), which is the most important cause of morbidity and mortality in pediatric DKA. Longitudinal neurocognitive studies after DKA treatment show progressive and latent deficits of cognition and emphasize the need for more effective DKA treatment of this long-standing conundrum of clinical BE, in the presence of systemic osmotic dehydration, metabolic acidosis and immune dysregulation. Candidate biomarkers of several systemic and neuroinflammatory pathways prior to treatment also progress during treatment, such as the neurotoxic and neuroprotective molecules in the well-recognized tryptophan (TRP)/kynurenine pathway (KP) that have not been investigated in DKA. We used LC-MS/MS targeted mass spectrometry analysis to determine the presence and initiation of the TRP/KP at three time points: A) 6-12 hours after initiation of treatment; B) 2 weeks; and C) 3 months following DKA treatment to determine if they might be involved in the pathogenesis of the acute vasogenic complication of DKA/BE. The Trp/KP metabolites TRP, KYN, quinolinic acid (QA), xanthurnenic acid (XA), and picolinic acid (PA) followed a similar pattern of lower levels in early treatment, with subsequent increases. Time point A compared to Time points B and C were similar to the pattern of sRAGE, lactate and pyruvic acid. The serotonin/melatonin metabolites also followed a similar pattern of lower quantities at the early stages of treatment compared to 3 months after treatment. In addition, glutamate, n-acetylglutamate, glutamine, and taurine were all lower at early treatment compared to 3 months, while the ketones 3-hydroxybutaric acid and acetoacetate were significantly higher in the early treatment compared to 3 months. The two major fat metabolites, L-carnitine and acetyl-L-carnitine (ALC) changed inversely, with ALC significantly decreasing at 2 weeks and 3 months compared to the early stages of treatment. Both anthranilic acid (AA) and 3-OH-anthranilic acid (3OH-AA) had overall higher levels in the early stages of treatment (A) compared to Time points (B and C). Interestingly, the levels of AA and 3OH-AA early in treatment were higher in Caucasian females compared to African American females. There were also differences in the metabolite levels of QA and kynurenic acid (KA) between genders and between races that may be important for further development of custom targeted treatments. We hypothesize that the TRP/KP, along with the other inflammatory pathways, is an active participant in the metabolic and immunologic pathogenesis of DKA's acute and chronic insults.

Project description:Background/objectiveThe prevalence of diabetic ketoacidosis (DKA) in gestational diabetes mellitus (GDM) is very low. We describe a patient with GDM in whom severe DKA with intrauterine fetal demise developed in the setting of nonadherence to therapy.Case reportA 33-year-old woman, G2P0010, with no preexisting diabetes mellitus (DM) presented at 30 weeks of gestation with acute-onset altered sensorium, nausea, and emesis. GDM was diagnosed at 15 weeks of gestation with a serum glucose level of 266 mg/dL (70-134 mg/dL) after 1-hour 50-gram glucose challenge test. Glycated hemoglobin (HbA1C) was 5.9% (41 mmol/mol) at the time of GMD diagnosis. Insulin was initiated at week 20 of gestation. On presentation, serum glucose level of 920 mg/dL (70-110 mg/dL), pH of 7.02 (7.32-7.43), anion gap level of 38 mmol (5-17 mmol), bicarbonate level of 5.0 mEq/L (22-29 mEq/L), and large serum ketones were found. Ultrasound showed intrauterine fetal demise. She received intravenous fluids and continuous insulin. Following the spontaneous delivery of a nonviable fetus, DKA was resolved. Negative antiglutamic acid decarboxylase, islet cell, and zinc transporter 8 antibodies, C-peptide level of 2.4 ng/dL (1.1-4.4 ng/dL), and HbA1C level of 9% (75 mmol/mol) were found. Inpatient management included basal-bolus and sliding scale insulin therapies. Metformin was added upon discharge 7 days after admission. The HbA1C levels were 5.3% (34 mmol/mol) and 5% (31 mmol/mol) at the 3- and 6-month follow-ups, respectively. Insulin was discontinued. Currently, the patient is on metformin and glucagon-like peptide 1 receptor agonist.DiscussionThe development of insulin resistance during pregnancy is driven by multiple factors. Approximately 1% to 2% of pregnant women with impaired glucose tolerance develop DKA; most cases occur in women with type 1 DM. The approximate incidence of DKA in GDM is 0.02%.ConclusionDKA complicating GDM is extremely infrequent, but it cannot be dismissed. Early recognition along with prompt and appropriate medical and obstetrical management is critical.

Project description:BackgroundMethods of data mining and analytics can be efficiently applied in medicine to develop models that use patient-specific data to predict the development of diabetic polyneuropathy. However, there is room for improvement in the accuracy of predictive models. Existing studies of diabetes polyneuropathy considered a limited number of predictors in one study to enable a comparison of efficiency of different machine learning methods with different predictors to find the most efficient one. The purpose of this study is the implementation of machine learning methods for identifying the risk of diabetes polyneuropathy based on structured electronic medical records collected in databases of medical information systems.MethodsFor the purposes of our study, we developed a structured procedure for predictive modelling, which includes data extraction and preprocessing, model adjustment and performance assessment, selection of the best models and interpretation of results. The dataset contained a total number of 238,590 laboratory records. Each record 27 laboratory tests, age, gender and presence of retinopathy or nephropathy). The records included information about 5846 patients with diabetes. Diagnosis served as a source of information about the target class values for classification.ResultsIt was discovered that inclusion of two expressions, namely "nephropathy" and "retinopathy" allows to increase the performance, achieving up to 79.82% precision, 81.52% recall, 80.64% F1 score, 82.61% accuracy, and 89.88% AUC using the neural network classifier. Additionally, different models showed different results in terms of interpretation significance: random forest confirmed that the most important risk factor for polyneuropathy is the increased neutrophil level, meaning the presence of inflammation in the body. Linear models showed linear dependencies of the presence of polyneuropathy on blood glucose levels, which is confirmed by the clinical interpretation of the importance of blood glucose control.ConclusionDepending on whether one needs to identify pathophysiological mechanisms for one's prospective study or identify early or late predictors, the choice of model will vary. In comparison with the previous studies, our research makes a comprehensive comparison of different decisions using a large and well-structured dataset applied to different decision support tasks.

Dataset Information

A case of polyneuropathy associated with diabetic ketoacidosis in new-onset type 1 diabetes.

Publications

A case of polyneuropathy associated with diabetic ketoacidosis in new-onset type 1 diabetes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets