Effects of obesity, metabolic syndrome, and non-alcoholic or alcoholic elevated liver enzymes on incidence of diabetes following lifestyle intervention: A subanalysis of the J-DOIT1

Sakane, Naoki; Kotani, Kazuhiko; Suganuma, Akiko; Takahashi, Kaoru; Sato, Juichi; Suzuki, Sadao; Izumi, Kazuo; Kato, Masayuki; Noda, Mitsuhiko; Nirengi, Shinsuke; Kuzuya, Hideshi

doi:10.1002/1348-9585.12109

Cited by 7 publications

(7 citation statements)

References 30 publications

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“…In previous diabetes prevention trials, stratification by IFG and IGT status indicated subtype-specific responses to lifestyle treatments, consistently showing the NAFLD-prone IFG patients achieve better improvements in response to low-fat or fiber-rich diets [24][25][26][27][28][29]. In support of these findings, elevated transaminase levels predicted diabetes progression in the control group of the J-DOIT1 trial, but a better response to lifestyle intervention [30]. Even though all of these studies indicated a particularly good response of patients with NAFLD surrogates to lifestyle treatment, none of them actually measured liver fat content [24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 69%

Predicting Factors for Metabolic Non-Response to a Complex Lifestyle Intervention—A Replication Analysis to a Randomized-Controlled Trial

et al. 2022

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Background: T2DM heterogeneity affects responsiveness to lifestyle treatment. Beta-cell failure and nonalcoholic fatty liver disease (NAFLD) independently predict T2DM, but NAFLD inconsistently predicts metabolic response to lifestyle intervention. Aim: We attempt to replicate a prediction model deducted from the Tübinger Lifestyle Intervention Program by assessing similar metabolic factors to predict conversion to normal glucose regulation (NGR) in a comparable lifestyle intervention trial. Methods: In the Optimal Fiber Trial (OptiFiT), 131 Caucasian participants with prediabetes completed a one-year lifestyle intervention program and received a fiber or placebo supplement. We compared baseline parameters for responders and non-responders, assessed correlations of major metabolic changes and conducted a logistic regression analysis for predictors of remission to NGR. Results: NGR was achieved by 33 participants, respectively. At baseline, for the placebo group only, 1 h and 2 h glucose levels, glucose AUC and Cederholm index predicted conversion to NGR. HOMA-beta, HOMA-IR or liver fat indices did not differ between responders and non-responders of the placebo or the fiber group. Changes in waist circumference or fatty liver index correlated with changes in glycemia and insulin resistance, but not with changes in insulin secretion. Insulin-resistant NAFLD did not predict non-response. Differences in compliance did not explain the results. Conclusions: Higher post-challenge glucose levels strongly predicted the metabolic non-response to complex lifestyle intervention in our cohort. Depending on the specific intervention and the investigated cohort, fasting glucose levels and insulin sensitivity might contribute to the risk pattern. Beta-cell function did not improve in accordance with other metabolic improvements, qualifying as a potential risk factor for non-response. We could not replicate previous data suggesting that an insulin-resistant fatty liver is a specific risk factor for treatment failure. Replication studies are required.

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Section: Introductionmentioning

confidence: 69%

Predicting Factors for Metabolic Non-Response to a Complex Lifestyle Intervention—A Replication Analysis to a Randomized-Controlled Trial

et al. 2022

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“…[ 33 ] Cross sectional study 19 MetS + 28.07 ± 4.48 MetS- 26.86 ± 4.49 70/390 Total 460 Buscemi et al (46) MetS + subjects had increased activity of liver enzymes ALT, AST and GGTP. MetS + 30.61 ± 26.97 (ALT) MetS- 18.74 ± 16.01 (ALT) MetS + 31.06 ± 54.85 (AST) MetS- 20.58 ± 9.7 (AST) MetS + 36.27 ± 38.78 (GGTP) MetS- 16.56 ± 9.65 (GGTP) Sakane et al, 2020, Japan [ 34 ]. Cluster randomised controlled trial 20 MetS + 49.4 ± 6.7 MetS- 48.4 ± 7.9 490/844 Total 1334 NCEP ATP III MetS + group has elevated AST and ALT levels compared to the MetS- group.…”

Section: Resultsmentioning

confidence: 99%

Metabolic syndrome and transaminases: systematic review and meta-analysis

Raya-Cano,

Molina-Luque,

Vaquero-Abellán

et al. 2023

Diabetol Metab Syndr

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Background Metabolic syndrome (MetS) is a group of metabolic abnormalities characterised by hypertension, central obesity, dyslipidaemia and dysregulation of blood glucose, associated with the risk of diabetes, cardiovascular disease and overall mortality. The presence of elevated liver enzymes may precede the development of MetS, with alterations of the liver being observed that are directly related to metabolic problems. The study aims to provide the best evidence on the association between liver enzymes (ALT, AST, GGT) and MetS by determining the effect size of these biomarkers. Methods A systematic review and meta-analysis of studies indexed in PubMed and Scopus databases were performed. Study quality was assessed using the STROBE tool. The Grade Pro tool was used to evaluate the evidence, and the quantitative synthesis was performed using RevMan (Cochrane Collaboration). Results Seventeen articles comparing liver enzyme concentrations between 76,686 with MetS (MetS+) and 201,855 without MetS (MetS-) subjects were included. The concentration of ALT, AST and GGT in the MetS + subjects was significantly higher than in the control group 7.13 IU/L (CI95% 5.73–8.54; p < 0.00001; I2 = 96%), 2.68 IU/L (CI95% 1.82–3.54; p < 0.00001; I2 = 96%) and 11.20 IU/L (CI95% 7.11–15.29; p < 0.00001; I2 = 96%), respectively. Conclusions The evaluation of the relationship of liver enzymes in the pathophysiological process of MetS could lead to new insights into early diagnosis.

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“…En estudios clínicos revisados, la hiperglucemia está vinculada con una presentación futura de obesidad, SM, elevación de enzimas hepáticas, DM2 y prediabetes y consecuentemente con EHGNA, esteatosis hepática, insulinorresistencia, obesidad (Sakane, N., et al, 2020;Sampson, M. J., et al, 2021;Belfort-DeAguiar, R., et al, 2018;Yeung, K.-F., et al, 2022) En la revisión de ensayos clínicos se ha obtenido que la hipertrigliceridemia aumenta el riesgo cardiovascular en un 30%, dislipidemia, la probabilidad de presentación en diabéticos aumenta, sobrepeso y obesidad, consecuentemente a largo plazo la hipertrigliceridemia está relacionado con enfermedad cardiovascular ateroesclerótica, infarto agudo de miocardio (IM), SM, resistencia a la insulina, DM2, enfermedad arterial coronaria, eventos cerebrovasculares (ECV), cardiopatía isquémica y pancreatitis (Woo, J. Sato, D., et al, 2019;Kim CH, et al, 2018;Maroofi M, et al,2020).…”

Section: Discussionunclassified

Alteraciones metabólicas y su relación con el índice de masa corporal

Tsenkush-Chamik,

Peña-Cordero,

Mora-Dominguez

2023

MQRInvestigar

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Introducción: La población con sobrepeso y obesos están dentro de los parámetros donde presentan factores que aumentan el riesgo de desarrollar síndrome metabólico como es la dislipidemia, hipertensión arterial, e insulinorresistencia o prediabetes u obesidad central. Objetivo: Analizar las alteraciones metabólicas y su relación con el índice de masa corporal. Métodos: Este estudio consiste en una revisión para la cual se recopiló información científica de artículos publicados en revistas indexadas como PubMed y Scopus. Para realizar la búsqueda de información se utilizarán términos relacionados proporcionados por MeSH, así como los operadores booleanos "AND", "NOT" y "OR", que podrán combinarse para obtener resultados más precisos. Para seleccionar la información relevante, se aplicarán criterios de inclusión y exclusión. Resultados: El IMC es un indicador que, junto con la alteración de uno de los parámetros del síndrome metabólico, sugiere una asociación con enfermedades metabólicas a largo plazo en el paciente. Conclusiones: un IMC elevado se relaciona con disfunciones metabólicas que aumentan el riesgo de enfermedades crónicas como DM2, CVD y enfermedades del hígado. Es importante detectar tempranamente las alteraciones metabólicas y adoptar un estilo de vida saludable para prevenir complicaciones a largo plazo.

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Effects of obesity, metabolic syndrome, and non-alcoholic or alcoholic elevated liver enzymes on incidence of diabetes following lifestyle intervention: A subanalysis of the J-DOIT1

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 7 publications

References 30 publications

Predicting Factors for Metabolic Non-Response to a Complex Lifestyle Intervention—A Replication Analysis to a Randomized-Controlled Trial

Predicting Factors for Metabolic Non-Response to a Complex Lifestyle Intervention—A Replication Analysis to a Randomized-Controlled Trial

Metabolic syndrome and transaminases: systematic review and meta-analysis

Alteraciones metabólicas y su relación con el índice de masa corporal

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