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Enregistrement 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 sur OpenAlexaff
Jinshuang Bo, Wei Li, Zengqiang Chen, Daniel G Wadden, Edward Randell, Huaibin Zhou, Jianxin Lü, Qing H. Meng

Notice bibliographique

RevueClinical Chemistry · 2013
Typeletter
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueDiabetes and associated disorders
Établissements canadiensMemorial University of Newfoundland
Organismes subventionnairesNational Natural Science Foundation of China
Mots-clésAnion gapDiabetic ketoacidosisMetabolic acidosisKetoacidosisInternal medicineAcidosisEndocrinologyKetone bodiesDiabetes mellitusMedicineMetabolismType 1 diabetes

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,008
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Étude de cas · Signal consensuel: Étude de cas
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,014
Score d'incertitude au seuil0,011

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,008
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,002
Communication savante0,0020,002
Science ouverte0,0020,001
Intégrité de la recherche0,0140,014
Charge utile insuffisante (le modèle a refusé de juger)0,0020,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,017
Tête enseignante GPT0,278
Écart entre enseignants0,260 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeÉtude de cas
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations23
Publié2013
Routes d'admission1
Résumé présentoui

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