Triglyceride-glucose index predicts future chronic kidney disease development in all populations, including normotensive and isolated diastolic hypertension

Hypertension and insulin resistance are established risk factors for chronic kidney disease. However, the association between chronic kidney disease and insulin resistance in detailed hypertension pattern groups such as isolated diastolic hypertension remains unclear. Triglyceride-glucose index has been noted as an indicator of insulin resistance. This study investigated the association between the triglyceride-glucose index and chronic kidney disease in four blood pressure groups: isolated diastolic hypertension, isolated systolic hypertension, systolic diastolic hypertension, and normotension. Using a database of 41,811 middle-aged men who had two or more annual health checkups from 2007 to 2019, those with chronic kidney disease at the first visit, antihypertensive/diabetes/dyslipidemia medication users, and incomplete data were excluded. Four groups were categorized using the 140/90 mmHg threshold. A COX proportional hazards model was used to assess the triglyceride-glucose index with incident chronic kidney disease. Participants were divided: isolated diastolic hypertension: 2207 (6.72%), isolated systolic hypertension: 2316 (7.06%), systolic-diastolic hypertension: 3299 (10.05%), normal: 24,996 (76.17%). The follow-up period was 6.78 years. Adjusted hazard ratios (HRs) and 95% CIs per unit increase in triglyceride-glucose index: isolated diastolic hypertension (HR = 1.31, 95% CI (1.06-1.62)), isolated systolic hypertension (HR = 1.36, 95% CI (1.12-1.64)), systolic-diastolic hypertension (HR = 1.40, 95% CI (1.19-1.64)), normal (HR = 1.18, 95% CI (1.09-1.28)). Triglyceride-glucose index is relevant for predicting chronic kidney disease development in all subtypes of hypertension. The results may lead to early prediction and prevention of the development of chronic kidney disease.

A machine learning-based classification of adult-onset diabetes identifies patients at risk of liver-related complications

Background & aims

Diabetes mellitus is a major risk factor for fatty liver disease development and progression. A novel machine learning method identified five clusters of patients with diabetes, with different characteristics and risk of diabetic complications using six clinical and biological variables. We evaluated whether this new classification could identify individuals with an increased risk of liver-related complications.

Methods

We used a prospective cohort of patients with a diagnosis of type 1 or type 2 diabetes without evidence of advanced fibrosis at baseline recruited between 2000 and 2020. We assessed the risk of each diabetic cluster of developing liver-related complications (i.e. ascites, encephalopathy, variceal haemorrhage, hepatocellular carcinoma), using competing risk analyses.

Results

We included 1,068 patients, of whom 162 (15.2%) were determined to be in the severe autoimmune diabetes subgroup, 266 (24.9%) had severe insulin-deficient diabetes, 95 (8.9%) had severe insulin-resistant diabetes (SIRD), 359 (33.6%) had mild obesity-related diabetes, and 186 (17.4%) were in the mild age-related diabetes subgroup. In multivariable analysis, patients in the SIRD cluster and those with excessive alcohol consumption at baseline had the highest risk for liver-related events. The SIRD cluster, excessive alcohol consumption, and hypertension were independently associated with clinically significant fibrosis, evaluated by liver biopsy or transient elastography. Using a simplified classification, patients assigned to the severe and mild insulin-resistant groups had a three- and twofold greater risk, respectively, of developing significant fibrosis compared with those in the insulin-deficient group.

Conclusions

A novel clustering classification adequately stratifies the risk of liver-related events in a population with diabetes. Our results also underline the impact of the severity of insulin resistance and alcohol consumption as key prognostic risk factors for liver-related complications.

Impact and implications

Diabetes represents a major risk factor for NAFLD development and progression. This study examined the ability of a novel machine-learning approach to identify at-risk diabetes subtypes for liver-related complications. Our results suggest that patients that had severe insulin resistance had the highest risk of liver-related outcomes and fibrosis progression. Moreover, excessive alcohol consumption at the diagnosis of diabetes was the strongest risk factor for developing liver-related events.

Metformin improves lipid metabolism disorders through reducing the expression of microsomal triglyceride transfer protein in OLETF rats

OBJECTIVE

This study aimed to investigate the role of MTP on lipid metabolism disorders in insulin-resistant rats and the potential mechanism through which metformin can improve lipid metabolism disorders.

METHODS

30 OLETF rats served as research subjects and 18 LETO rats of the same strain served as the control group (LETO group). After the first oral glucose tolerance test (at 8-week-old), 6 rats were randomly killed from each group. The remaining 24 OLETF rats were randomly divided into untreated group (OLETF group) and treated group (OLETF/M group, cured with metformin). By the end of the 10th and 20th week of treatment, MTP in the liver was measured for all rats in the study.

RESULTS

All OLETF rats exhibited diabetic phenotypes at 18-week-old, with their triglyceride level higher than in LETO rats at the same age. In OLETF rats, MTP level in the liver was higher than in LETO rats at 18-week-old, and the difference was significant at 28-week-old [(13.79±1.47) vs. (8.20±1.14), p<0.05]. Treatment with metformin for 20weeks decreased triglyceride [(1.06±0.23) vs. (2.20±0.62) mmol/L, p<0.05] and total cholesterol [(1.90±0.19) vs. (2.36±0.14) mmol/L, p<0.05] in OLETF rats. Metformin also decreased MTP level in the liver [(7.65±1.31) vs. (13.79±1.47), p<0.01].

CONCLUSIONS

MTP may be associated with the lipid metabolism disorder in OLETF rats and metformin could improve lipid metabolism through reducing the expression of MTP.