Association between hemoglobin glycation index and non‐alcoholic fatty liver disease in the patients with type 2 diabetes mellitus

Wang, Meng; Li, Shiwei; Zhang, Xinxin; Li, Xin; Cui, Jingqiu

doi:10.1111/jdi.14066

Cited by 4 publications

(2 citation statements)

References 50 publications

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“…Several previous investigations have shown that HGI is strongly associated with adverse cardiovascular events [ 18 ], kidney injury [ 24 , 25 ], and nonalcoholic fatty liver disease [ 26 ]. However, both high HGI and low HGI appear to be associated with poor prognosis to varying degrees.…”

Section: Discussionmentioning

confidence: 99%

Risk analysis of the association between different hemoglobin glycation index and poor prognosis in critical patients with coronary heart disease-A study based on the MIMIC-IV database

Wei,

Chen,

Zhang

et al. 2024

Cardiovasc Diabetol

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Background The hemoglobin glycation index (HGI) is the difference between the observed and predicted values of glycosylated hemoglobin (HbA1c), which is closely associated with a variety of poor prognoses. However, there are still no studies on the correlation between HGI and poor prognosis in patients with critical coronary artery disease. The purpose of this study was to analyze the correlation between HGI and all-cause mortality in patients with critical coronary artery disease using the MIMIC-IV database. Methods The HGI was calculated by constructing a linear regression equation between HbA1c and fasting plasma glucose (FPG). A Kaplan‒Meier survival analysis model was constructed based on the HGI quartiles to clarify the differences in all-cause mortality rates between groups, and the log-rank test was used to assess the differences between groups. The hazard ratio (HR) of HGI as a risk factor for outcome events was assessed using the Cox proportional risk model and restricted cubic spline (RCS), with the Q2 group serving as the reference group. Results A total of 5260 patients were included in this study. The 30-day mortality rate of the patients was 4.94% and the mortality rate within 365 days was 13.12%. A low HGI was significantly associated with 30-day mortality (HR, 1.96; 95% CI, (1.38, 2.78); P < 0.001) and 365-day mortality (HR, 1.48; 95% CI, (1.19, 1.85); P < 0.001) in patients with critical coronary artery disease in the completely adjusted Cox proportional risk model. In addition, high levels of HGI were associated with 365-day mortality (HR, 1.31; 95% CI, (1.02, 1.69); P < 0.05). RCS analysis revealed a U-shaped relationship between HGI and outcome events. According to the stratified analysis, the interaction test revealed that the correlation between HGI and outcome events remained stable. Conclusion There was a significant correlation between HGI and all-cause mortality in patients with critical coronary artery disease, particularly in those with low HGI. HGI can be used as a potential indicator for assessing the short- and long-term risk of mortality in such patients.

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Section: Discussionmentioning

confidence: 99%

Risk analysis of the association between different hemoglobin glycation index and poor prognosis in critical patients with coronary heart disease-A study based on the MIMIC-IV database

Wei,

Chen,

Zhang

et al. 2024

Cardiovasc Diabetol

View full text Add to dashboard Cite

show abstract

“…However, the impact of this variability on cardiovascular disease and mortality remains controversial (20). Several previous studies have shown that HGI is strongly associated with adverse cardiovascular events (21), kidney injury (22) and NAFLD (23). Some studies suggest that both high HGI and low HGI seem to be associated with poor prognosis to varying degrees.…”

Section: Discussionmentioning

confidence: 99%

Association between different hemoglobin glycation index and poor prognosis in patients with a first diagnosis of acute myocardial infarction-a study based on the MIMIC-IV database

Hu,

Hou

2024

Preprint

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Background The relationship between HGI and short-term mortality risk in patients with a first diagnosis of acute myocardial infarction (AMI) remains unclear. This study sought to understand better the relationship between HGI and mortality risk in patients with a first diagnosis of AMI. Methods We conducted a cohort study using data from 1961 patients with a first diagnosis of AMI from the MIMIC-IV (version 2.2) database. Patients were divided into four groups based on HGI quartiles. The Cox proportional hazards model and a two-segmented Cox proportional hazards model were used to elucidate the nonlinear relationship between HGI in patients with a first diagnosis of AMI and mortality were used to elucidate the nonlinear relationship between HGI in patients with a first diagnosis of AMI and mortality. Results Of the surveyed population, 175 patients (8.92%) died within 90 days, and 210 patients (10.71%) died within 180 days. A low HGI was significantly associated with 90-day mortality [HR, 1.96; 95% CI, (1.26, 3.05); P < 0.001] and 180-day mortality [HR, 1.62; 95% CI, (1.10, 2.38); P < 0.001] in patients with a first diagnosis of AMI in the completely adjusted Cox proportional risk model, showing a non-linear correlation with an inflection point at 0.16 and 0.44. In the subgroup analysis, patients with pre-diabetes mellitus (pre-DM) and lower HGI levels had increased 90-day (HR 8.30; 95% CI 2.91, 23.68) and 180-day mortality risks (HR 6.84; 95% CI 2.86, 16.34). Conclusion There is a significant correlation between HGI and all-cause mortality in patients diagnosed with AMI, especially those with lower HGI. HGI can serve as a potential indicator for evaluating the 90 and 180-day death risk of such patients.

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A Systematic Review of Proteomics in Obesity: Unpacking the Molecular Puzzle

Rodriguez-Muñoz,

Motahari-Rad,

Martin-Chaves

et al. 2024

Curr Obes Rep

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Purpose of Review The present study aims to review the existing literature to identify pathophysiological proteins in obesity by conducting a systematic review of proteomics studies. Proteomics may reveal the mechanisms of obesity development and clarify the links between obesity and related diseases, improving our comprehension of obesity and its clinical implications. Recent Findings Most of the molecular events implicated in obesity development remain incomplete. Proteomics stands as a powerful tool for elucidating the intricate interactions among proteins in the context of obesity. This methodology has the potential to identify proteins involved in pathological processes and to evaluate changes in protein abundance during obesity development, contributing to the identification of early disease predisposition, monitoring the effectiveness of interventions and improving disease management overall. Despite many non-targeted proteomic studies exploring obesity, a comprehensive and up-to-date systematic review of the molecular events implicated in obesity development is lacking. The lack of such a review presents a significant challenge for researchers trying to interpret the existing literature. Summary This systematic review was conducted following the PRISMA guidelines and included sixteen human proteomic studies, each of which delineated proteins exhibiting significant alterations in obesity. A total of 41 proteins were reported to be altered in obesity by at least two or more studies. These proteins were involved in metabolic pathways, oxidative stress responses, inflammatory processes, protein folding, coagulation, as well as structure/cytoskeleton. Many of the identified proteomic biomarkers of obesity have also been reported to be dysregulated in obesity-related disease. Among them, seven proteins, which belong to metabolic pathways (aldehyde dehydrogenase and apolipoprotein A1), the chaperone family (albumin, heat shock protein beta 1, protein disulfide-isomerase A3) and oxidative stress and inflammation proteins (catalase and complement C3), could potentially serve as biomarkers for the progression of obesity and the development of comorbidities, contributing to personalized medicine in the field of obesity. Our systematic review in proteomics represents a substantial step forward in unravelling the complexities of protein alterations associated with obesity. It provides valuable insights into the pathophysiological mechanisms underlying obesity, thereby opening avenues for the discovery of potential biomarkers and the development of personalized medicine in obesity

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Association between hemoglobin glycation index and non‐alcoholic fatty liver disease in the patients with type 2 diabetes mellitus

Cited by 4 publications

References 50 publications

Risk analysis of the association between different hemoglobin glycation index and poor prognosis in critical patients with coronary heart disease-A study based on the MIMIC-IV database

Risk analysis of the association between different hemoglobin glycation index and poor prognosis in critical patients with coronary heart disease-A study based on the MIMIC-IV database

Association between different hemoglobin glycation index and poor prognosis in patients with a first diagnosis of acute myocardial infarction-a study based on the MIMIC-IV database

A Systematic Review of Proteomics in Obesity: Unpacking the Molecular Puzzle

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