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Record W2142087289 · doi:10.1373/clinchem.2013.208777

d-Lactate: A Novel Contributor to Metabolic Acidosis and High Anion Gap in Diabetic Ketoacidosis

2013· letter· en· W2142087289 on OpenAlexaff
Jinshuang Bo, Wei Li, Zengqiang Chen, Daniel G Wadden, Edward Randell, Huaibin Zhou, Jianxin Lü, Qing H. Meng

Bibliographic record

VenueClinical Chemistry · 2013
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiabetes and associated disorders
Canadian institutionsMemorial University of Newfoundland
FundersNational Natural Science Foundation of China
KeywordsAnion gapDiabetic ketoacidosisMetabolic acidosisKetoacidosisInternal medicineAcidosisEndocrinologyKetone bodiesDiabetes mellitusMedicineMetabolismType 1 diabetes

Abstract

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To the Editor: Diabetic ketoacidosis (DKA),the most common and serious acute complication of diabetes, is characterized by hyperglycemia and severe high–anion-gap metabolic acidosis with ketonemia (1). In DKA, the high anion gap is attributed largely to excessive production of blood ketone bodies, and serum β-hydroxybutyrate quantification is recommended for the diagnosis and monitoring of DKA (2). However, even counting of all the ketone bodies, including β-hydroxybutyrate, does not account for the entire anion gap, suggesting that there are additional sources of anion production in DKA. We recently demonstrated that plasma d-lactate concentrations were greatly increased in DKA compared with the concentrations in diabetic patients or a healthy control group (3). Nevertheless, the clinical value of d-lactate measurement in metabolic acidosis, especially the contribution of d-lactate to the metabolic acidosis and high anion gap in DKA, is not well appreciated. We report here that decreasing d-lactate concentrations are associated with improved clinical situations, whereas increased lactate concentrations are associated with the severity of metabolic acidosis and high anion gap in patients with DKA. The study included 38 diabetic patients with DKA, 42 diabetic patients without DKA, and 40 healthy controls. The institutional ethics review board of the First Affiliated Hospital of Wenzhou Medical College approved the study, and written informed consent was obtained from all study participants. For patients with DKA, blood samples were collected at the time of admission to the emergency room and following medical treatment after admission, when the patient's condition became stabilized. Plasma methylglyoxal was assayed by LC-MS (3). Plasma d-lactate concentration was determined by an enzymatic assay kit (BioVision Corporation). Other biochemical analyses were performed on automated chemistry analyzers. Concentrations of plasma glucose [mean (SD) 450.45 (201.80) mg/dL], β-hydroxybutyrate [58.41 (37.38) mg/dL], and methylglyoxal [75.72 (46.25) ng/mL] were greatly increased compared with the concentrations in diabetic patients without DKA and healthy controls (all P < 0.001). Interestingly, plasma d-lactate concentrations were markedly increased in diabetic patients with DKA [3.44 (1.99) mmol/L] compared to diabetic patients without DKA [0.48 (0.56) mmol/L] and healthy controls [0.32 (0.30) mmol/L] (P < 0.001). Increased d-lactate concentrations were greatly reduced following treatment [3.44 (1.99) vs 0.53 (0.35) mmol/L, P < 0.001]. The reduction of d-lactate concentration was consistent with the changes in and improvement of plasma glucose [450.45 (201.80) vs 170.81 (52.43) mg/dL], β-hydroxybutyrate [58.41 (37.38) vs 12.49 (14.89) mg/dL], bicarbonate [13.12 (6.72) vs 21.94 (3.45) mEq/L], and anion gap [20.09 (5.80) vs 8.27 (2.69) mmol/L] following treatment (all P < 0.001). Plasma l-lactate concentrations were also increased in DKA, but to a lesser degree compared to d-lactate concentrations [2.60 (1.55) vs 1.21 (0.69) mmol/L, P = 0.01]. Linear regression analyses identified a significant correlation of plasma d-lactate concentration with acidosis (bicarbonate, r = −0.575, P < 0.001) and high anion gap (r = 0.593, P < 0.001) (Fig. 1). The contribution of d-lactate to acidosis and anion gap was comparable to that of β-hydroxybutyrate. The contribution of d-lactate and β-hydroxybutyrate to the high anion gap found in DKA was statistically significant (r = 0.593, P < 0.001, and r = 0.642, P < 0.001, respectively). Under physiologic conditions, d-lactate is present in the human body at low concentrations (4). Blood concentrations of d-lactate are increased in diabetes, and particularly in DKA in humans (3). d-lactate is generated by degradation of methylglyoxal, an intermediate glucose metabolite, through the glyoxalase system (3, 5). High concentrations of d-lactate can induce severe metabolic acidosis, resulting in neurological symptoms and encephalopathy. In hyperglycemic disorders such as diabetes mellitus and DKA, methylglyoxal production is greatly increased (3, 5). Consistent with our previous finding, the increased d-lactate concentration is inversely associated with bicarbonate concentration and positively correlated with the increasing anion gap. Reduction of plasma d-lactate concentrations correlated well with improvement of bicarbonate concentrations and anion gap following treatment. In conclusion, our findings suggest a large contribution of plasma d-lactate to the metabolic acidosis and high anion gap in DKA. Inclusion of the measurement of plasma d-lactate concentrations helps to account for the anion gap and the severity of metabolic acidosis in patients with DKA. Measurement of plasma d-lactate is important in predicting the severity of DKA as characterized by acidosis and high anion gap and monitoring DKA progression.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0140.014
Insufficient payload (model declined to judge)0.0020.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.278
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations23
Published2013
Admission routes1
Has abstractyes

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