&lt;p&gt;High level of individual lipid profile and lipid ratio as a predictive marker of poor glycemic control in type-2 diabetes mellitus&lt;/p&gt;

Artha, I Made Junior Rina; Bhargah, Agha; Dharmawan, Nyoman Khrisna; Pande, Utami Wijayaswari; Triyana, Komang Agus; Mahariski, Pande Agung; Yuwono, Jessica; Bhargah, Varennia; Prabawa, I Putu Yuda; Manuaba, Ida Bagus Putra; Rina, I Ketut

doi:10.2147/vhrm.s209830

Cited by 100 publications

(97 citation statements)

References 25 publications

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“…The levels of TC and TC in our study were lower than the study conducted by A Hussain et al [16] in T2DM with HbA1c>7%. As compared to a another study by Artha IMJR et al [17] involving 56 poor glycemic control patients (HbA1c>7%), the level of TG in our study was higher, and the level of TG was similar. Although the level of lipid pro le in these studies was inconsistency, diabetic patients with poor and worse glycemic control had signi cantly higher levels of TC, TG, LDL-C and low level of HDL-C than group with good glycemic control [18,19].…”

Section: Discussioncontrasting

confidence: 60%

Dyslipidemia in Type 2 Diabetes Mellitus With Poor Glycemic Control: Prevalence, Associated Factors, and Relationship Between HbA1c and Lipid Profile.

Li¹,

Xu²,

Qiu³

et al. 2020

Preprint

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BackgroundCcompared with HbA1c<7% ,T2DM patients with HbA1c≥7% had significantly higher levels of lipid profile, however, it is unknown whether there was a linear and significant increase levels of lipid profile associated with HbA1c value of ≥7%. This study identified the lipid profile across a full range of poor glycemic control and the association between lipid profiles with different specific HbA1c cutoffs.MethodsA total of 1183 type 2 diabetes patients with poor glycemic control (HbA1c>7%) selected through convenience sampling in three hospitals of Jiangsu province were surveyed. Dyslipidemia was defined according to criteria of the Third Report of the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III). ResultsThe prevalence of dyslipidemia was 55.2 % overall. Of 1183 subjects, 13.0% had high TC, 33.1% had low HDL-C, 9.9% had high LDL-C, and 28.4% had high TG concentrations. There was an increase in frequency of dyslipidemia in patients with different cutoff values of HbA1c (P<0.05).The prevalence of high TC was closely related with different cutoff values of HbA1c (adjusted OR =1.77, 2.56 3.82, respectively). Patients with HbA1c values 9%≤HbA1c<11% and HbA1c≥13% had significantly higher prevalence of dyslipidemia compared with the patients who had 7≤HbA1c<9%.ConclusionDiabetic patients with 9%≤HbA1c<11% and HbA1c≥13% tend to have moderate and severe dyslipidemia respectively and suggest the importance of glycemic control in normalizing dyslipidemia.

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Section: Discussioncontrasting

confidence: 60%

Dyslipidemia in Type 2 Diabetes Mellitus With Poor Glycemic Control: Prevalence, Associated Factors, and Relationship Between HbA1c and Lipid Profile.

Li¹,

Xu²,

Qiu³

et al. 2020

Preprint

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“…Lipid ratios have been proven to be a useful biomarker for several diseases, including cardiovascular disease, Alzheimer’s disease, and diabetes [ 49 – 51 ]. In addition, using lipid ratios as a disease indicator would carry more information and be more biologically meaningful.…”

Section: Discussionmentioning

confidence: 99%

Identification of lipid biomarker from serum in patients with chronic obstructive pulmonary disease

et al. 2020

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Background Chronic obstructive pulmonary disease (COPD) is the third leading cause of death in the United States with no effective treatment. The current diagnostic method, spirometry, does not accurately reflect the severity of COPD disease status. Therefore, there is a pressing unmet medical need to develop noninvasive methods and reliable biomarkers to detect early stages of COPD. Lipids are the fundamental components of cell membranes, and dysregulation of lipids was proven to be associated with COPD. Lipidomics is a comprehensive approach to all the pathways and networks of cellular lipids in biological systems. It is widely used for disease diagnosis, biomarker identification, and pathology disorders detection relating to lipid metabolism. Methods In the current study, a total of 25 serum samples were collected from 5 normal control subjects and 20 patients with different stages of COPD according to the global initiative for chronic obstructive lung disease (GOLD) (GOLD stages I ~ IV, 5 patients per group). After metabolite extraction, lipidomic analysis was performed using electrospray ionization mass spectrometry (ESI-MS) to detect the serum lipid species. Later, the comparisons of individual lipids were performed between controls and patients with COPD. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) and receiver operating characteristic (ROC) analysis were utilized to test the potential biomarkers. Finally, correlations between the validated lipidomic biomarkers and disease stages, age, FEV1% pack years and BMI were evaluated. Results Our results indicate that a panel of 50 lipid metabolites including phospholipids, sphingolipids, glycerolipids, and cholesterol esters can be used to differentiate the presence of COPD. Among them, 10 individual lipid species showed significance (p < 0.05) with a two-fold change. In addition, lipid ratios between every two lipid species were also evaluated as potential biomarkers. Further multivariate data analysis and receiver operating characteristic (ROC: 0.83 ~ 0.99) analysis suggest that four lipid species (AUC:0.86 ~ 0.95) and ten lipid ratios could be potential biomarkers for COPD (AUC:0.94 ~ 1) with higher sensitivity and specificity. Further correlation analyses indicate these potential biomarkers were not affected age, BMI, stages and FEV1%, but were associated with smoking pack years. Conclusion Using lipidomics and statistical methods, we identified unique lipid signatures as potential biomarkers for diagnosis of COPD. Further validation studies of these potential biomarkers with large population may elucidate their roles in the development of COPD.

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“…1 On the other hand, patients of T2DM in Indonesia have reached 10 million people. 2,65 T2DM treatments to this date, including oral antidiabetics (metformin, sulfonylurea, thiazolidinedione), both monotherapy and combination, and insulin injection, are targeted to establish normal blood glucose level. 3,4 However, conventional medications are limited to curing the symptoms and have not been able to solve the main problem, which is insulin resistance.…”

Section: Introductionmentioning

confidence: 99%

<p>Chaperone-Based Therapeutic Target Innovation: Heat Shock Protein 70 (HSP70) for Type 2 Diabetes Mellitus</p>

Mulyani

Sanjiwani

Sandra

et al. 2020

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Type 2 diabetes mellitus (T2DM) is still a global health problem. Current T2DM treatments are limited to curing the symptoms and have not been able to restore insulin sensitivity in insulin-sensitive tissues that have become resistant. In the past decade, some studies have shown the significant role of a chaperone family, heat shock protein 70 (HSP70), in insulin resistance pathogenesis that leads to T2DM. HSP70 is a cytoprotective molecular chaperone that functions in protein folding and degradation. In general, studies have shown that decreased concentration of HSP70 is able to induce inflammation process through JNK activation, inhibit fatty acid oxidation by mitochondria through mitophagy decrease and mitochondrial biogenesis, as well as activate SREBP-1c, one of the lipogenic gene transcription factors in ER stress. The overall molecular pathways are potentially leading to insulin resistance and T2DM. Increased expression of HSP70 in brain tissues is able to improve insulin sensitivity and glycemic control specifically. HSP70 modulation-targeting strategies (including long-term physical exercise, hot tub therapy (HTT), and administration of alfalfa-derived HSP70 (aHSP70)) in subjects with insulin resistance are proven to have therapeutic and preventive potency that are promising in T2DM management.

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<p>High level of individual lipid profile and lipid ratio as a predictive marker of poor glycemic control in type-2 diabetes mellitus</p>

Cited by 100 publications

References 25 publications

Dyslipidemia in Type 2 Diabetes Mellitus With Poor Glycemic Control: Prevalence, Associated Factors, and Relationship Between HbA1c and Lipid Profile.

Dyslipidemia in Type 2 Diabetes Mellitus With Poor Glycemic Control: Prevalence, Associated Factors, and Relationship Between HbA1c and Lipid Profile.

Identification of lipid biomarker from serum in patients with chronic obstructive pulmonary disease

<p>Chaperone-Based Therapeutic Target Innovation: Heat Shock Protein 70 (HSP70) for Type 2 Diabetes Mellitus</p>

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