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- Wu, Chuanyan, et al.
(författare)
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Elevated circulating follistatin associates with an increased risk of type 2 diabetes
- 2021
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 12, s. 1-10
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Tidskriftsartikel (refereegranskat)abstract
- The hepatokine follistatin is elevated in patients with type 2 diabetes (T2D) and promotes hyperglycemia in mice. Here we explore the relationship of plasma follistatin levels with incident T2D and mechanisms involved. Adjusted hazard ratio (HR) per standard deviation (SD) increase in follistatin levels for T2D is 1.24 (CI: 1.04-1.47, p < 0.05) during 19-year follow-up (n = 4060, Sweden); and 1.31 (CI: 1.09-1.58, p < 0.01) during 4-year follow-up (n = 883, Finland). High circulating follistatin associates with adipose tissue insulin resistance and non-alcoholic fatty liver disease (n = 210, Germany). In human adipocytes, follistatin dose-dependently increases free fatty acid release. In genome-wide association study (GWAS), variation in the glucokinase regulatory protein gene (GCKR) associates with plasma follistatin levels (n = 4239, Sweden; n = 885, UK, Italy and Sweden) and GCKR regulates follistatin secretion in hepatocytes in vitro. Our findings suggest that GCKR regulates follistatin secretion and that elevated circulating follistatin associates with an increased risk of T2D by inducing adipose tissue insulin resistance.
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| 2. |
- Prystupa, Katsiaryna, et al.
(författare)
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Comprehensive validation of fasting-based and oral glucose tolerance test-based indices of insulin secretion against gold standard measures
- 2022
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Ingår i: BMJ Open Diabetes Research & Care. - : BMJ Publishing Group Ltd. - 2052-4897. ; 10:5
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Tidskriftsartikel (refereegranskat)abstract
- INTRODUCTION: With pre-diabetes and diabetes increasingly recognized as heterogeneous conditions, assessment of beta-cell function is gaining clinical importance to identify disease subphenotypes. Our study aims to comprehensively validate all types of surrogate indices based on oral glucose tolerance test (OGTT) and fasting measurements in comparison with gold standard methods.RESEARCH DESIGN AND METHODS: The hyperglycemic clamp extended with glucagon-like peptide 1 (GLP-1) infusion and intravenous glucose tolerance test (IVGTT), as well as OGTT, was performed in two well-phenotyped cohorts. The gold standard-derived indices were compared with surrogate insulin secretion markers, derived from fasting state and OGTT, using both Pearson's and Spearman's correlation coefficients. The insulin-based and C-peptide-based indices were analyzed separately in different groups of glucose tolerance and the entire cohorts.RESULTS: The highest correlation coefficients were found for area under curve (AUC) (I0-30)/AUC (G0-30), I30/G30, first-phase Stumvoll and Kadowaki model. These indices have high correlation coefficients with measures obtained from both insulin and C-peptide levels from IVGTT and hyperglycemic clamp. AUC (I0-120)/AUC (G0-120), BIGTT-AIR0-60-120, I30/G30, first-phase Stumvoll and AUC (I0-30)/AUC (G0-30) demonstrated the strongest association with incretin-stimulated insulin response.CONCLUSIONS: We have identified glucose-stimulated and GLP-1-stimulated insulin secretion indices, derived from OGTT and fasting state, that have the strongest correlation with gold standard measures and could be potentially used in future researches and clinical practice.
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| 3. |
- Dietz, Benedikt, et al.
(författare)
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Detection of diabetes from whole-body MRI using deep learning
- 2021
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Ingår i: JCI Insight. - : American Society for Clinical Investigation. - 2379-3708. ; 6:21
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Tidskriftsartikel (refereegranskat)abstract
- Obesity is one of the main drivers of type 2 diabetes, but it is not uniformly associated with the disease. The location of fat accumulation is critical for metabolic health. Specific patterns of body fat distribution, such as visceral fat, are closely related to insulin resistance. There might be further, hitherto unknown, features of body fat distribution that could additionally contribute to the disease. We used machine learning with dense convolutional neural networks to detect diabetes-related variables from 2371 T1-weighted whole-body MRI data sets. MRI was performed in participants undergoing metabolic screening with oral glucose tolerance tests. Models were trained for sex, age, BMI, insulin sensitivity, HbA1c, and prediabetes or incident diabetes. The results were compared with those of conventional models. The area under the receiver operating characteristic curve was 87% for the type 2 diabetes discrimination and 68% for prediabetes, both superior to conventional models. Mean absolute regression errors were comparable to those of conventional models. Heatmaps showed that lower visceral abdominal regions were critical in diabetes classification. Subphenotyping revealed a group with high future diabetes and microalbuminuria risk. Our results show that diabetes is detectable from whole-body MRI without additional data. Our technique of heatmap visualization identifies plausible anatomical regions and highlights the leading role of fat accumulation in the lower abdomen in diabetes pathogenesis.
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| 4. |
- Rossing, Peter, et al.
(författare)
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Finerenone in Patients With Chronic Kidney Disease and Type 2 Diabetes by Sodium-Glucose Cotransporter 2 Inhibitor Treatment : The FIDELITY Analysis.
- 2022
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Ingår i: Diabetes Care. - : American Diabetes Association. - 0149-5992 .- 1935-5548. ; 45:12, s. 2991-2998
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: Finerenone reduced the risk of kidney and cardiovascular events in people with chronic kidney disease (CKD) and type 2 diabetes in the FIDELIO-DKD and FIGARO-DKD phase 3 studies. Effects of finerenone on outcomes in patients taking sodium-glucose cotransporter 2 inhibitors (SGLT2is) were evaluated in a prespecified pooled analysis of these studies.RESEARCH DESIGN AND METHODS: Patients with type 2 diabetes and urine albumin-to-creatinine ratio (UACR) ≥30 to ≤5,000 mg/g and estimated glomerular filtration rate (eGFR) ≥25 mL/min/1.73 m2 were randomly assigned to finerenone or placebo; SGLT2is were permitted at any time. Outcomes included cardiovascular composite (cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure) and kidney composite (kidney failure, sustained ≥57% eGFR decline, or renal death) end points, changes in UACR and eGFR, and safety outcomes.RESULTS: Among 13,026 patients, 877 (6.7%) received an SGLT2i at baseline and 1,113 (8.5%) initiated one during the trial. For the cardiovascular composite, the hazard ratios (HRs) were 0.87 (95% CI 0.79-0.96) without SGLT2i and 0.67 (95% CI 0.42-1.07) with SGLT2i. For the kidney composite, the HRs were 0.80 (95% CI 0.69-0.92) without SGLT2i and 0.42 (95% CI 0.16-1.08) with SGLT2i. Baseline SGLT2i use did not affect risk reduction for the cardiovascular or kidney composites with finerenone (Pinteraction = 0.46 and 0.29, respectively); neither did SGLT2i use concomitant with study treatment.CONCLUSIONS: Benefits of finerenone compared with placebo on cardiorenal outcomes in patients with CKD and type 2 diabetes were observed irrespective of SGLT2i use.
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