Huntington disease (HD) is an autosomal dominant progressive neurodegenerative disorder characterized by involuntary movements, especially chorea; frank dementia; behavioral and psychiatric disturbances; and functional disability. It is caused by an unstable expanded CAG trinucleotide repeat in exon 1 on the huntingtin gene (HD [OMIM 143100) on the tip of the short arm of chromosome 4.¹

Type 2 diabetes (T2D), one of the most prevalent noncommunicable diseases, is often preceded by insulin resistance (IR), which underlies the inability of tissues to respond to insulin and leads to disturbed metabolic homeostasis. Mitochondria, as a central player in the cellular energy metabolism, are involved in the mechanisms of IR and T2D. Mitochondrial function is affected by insulin resistance in different tissues, among which skeletal muscle and liver have the highest impact on whole-body glucose homeostasis. This review focuses on human studies that assess mitochondrial function in liver, muscle and blood cells in the context of T2D. Furthermore, different interventions targeting mitochondria in IR and T2D are listed, with a selection of studies using respirometry as a measure of mitochondrial function, for better data comparison. Altogether, mitochondrial respiratory capacity appears to be a metabolic indicator since it decreases as the disease progresses but increases after lifestyle (exercise) and pharmacological interventions, together with the improvement in metabolic health. Finally, novel therapeutics developed to target mitochondria have potential for a more integrative therapeutic approach, treating both causative and secondary defects of diabetes.

Increased body weight as well as type 2 diabetes (T2D) are found to be associated with increased incidence of hypertension, although the mechanisms facilitating hypertension in T2D or nondiabetic individuals are not clear. Therefore, in this study we compared the levels of insulin resistance (IR:OGIS), plasma insulin (PI:RIA) levels, and pro-inflammatory cytokines (IL-6 and TNF-α: ELISA), being risk factors previously found to be associated with hypertension, in T2D patients showing increased body weight (obese and overweight, BMI ≥ 25 kg/m²) with hypertension (group A, N = 30), or without hypertension (group B, N = 30), and in nonobese (BMI < 25 kg/m²), normotensive controls (group C, N = 15). We found that OGIS index was the lowest (A: 267 ± 35.42 vs. B: 342.89 ± 32.0, p < 0.01) and PI levels were the highest (A: 31.05 ± 8.24 vs. B: 17.23 ± 3.23, p < 0.01) in group A. In addition, IL-6 levels were higher in group A (A: 15.46 ± 5.15 vs. B: 11.77 ± 6.09; p < 0.05) while there was no difference in TNF-α levels. Our results have shown that appearance of hypertension in T2D patients with increased body weight was dependent on further increase in IR which was associated with the rise in pro-inflammatory IL-6 cytokine. The results imply that lifestyle intervention aimed to decrease IR might be beneficial in reducing the risk for hypertension in those T2D individuals.

This study aimed to analyse the impact of obesity in type 2 diabetes (T2D) on adipocytokines (adiponectin, leptin and resistin) and inflammatory markers (TNF-α, IL-6 and hsCRP) as cardiovascular risk factors. A cross-sectional study comparing the basal levels of adipocytokines and inflammatory markers was done in 18 obese (BMI ≥ 30 kg/m²) (group A), 21 overweight (25 kg/m² ≤ BMI < 30 kg/m²) (group B), 25 non-obese T2D patients (group C) and 15 non-obese controls (group D). The lowest levels of adiponectin and the highest levels of leptin, resistin, TNF-α, IL-6 and hsCRP were found in group A. Adiponectin levels were significantly lower, and resistin, TNF-α, and hsCRP levels were elevated in group C vs. D. However, leptin and IL-6 levels differed significantly between groups A and B, but not between groups C and D. Moreover, we found a significant negative correlation between adiponectin and TNF-α, but not with other markers, which was independent of the presence of obesity. In contrast, leptin and resistin correlated with the inflammatory markers, and this correlation was obesity-dependent. Our results suggest that obesity influences cardiovascular risk primarily through changes in leptin and resistin and less efficiently at the level of adiponectin.

Left ventricular mass index (LVMI) increase has been described in hypertension (HTN), but less is known about its association with type 2 diabetes (T2DM). As these conditions frequently co-exist, we investigated the association of T2DM, HTN and both with echocardiographic parameters, and hypothesized that patients with both had highest LVMI, followed by patients with only T2DM or HTN. Study population included 101 T2DM patients, 62 patients with HTN and no T2DM, and 76 patients with T2DM and HTN, excluded for ischemic heart disease. Demographic and clinical data, biochemical measurements, stress echocardiography, transthoracic 2D Doppler and tissue Doppler echocardiography were performed. Multivariable logistic regression was used to determine the independent association with T2DM. Linear regression models and Pearson’s correlation were used to assess the correlations between LVMI and other parameters. Patients with only T2DM had significantly greater LVMI (84.9 ± 20.3 g/m²) compared to patients with T2DM and HTN (77.9 ± 16 g/m²) and only HTN (69.8 ± 12.4 g/m²). In multivariate logistic regression analysis, T2DM was associated with LVMI (OR 1.033, 95%CI 1.003–1.065, p = 0.029). A positive correlation of LVMI was found with fasting glucose (p < 0.001) and HbA1c (p = 0.0003). Increased LVMI could be a potential, pre-symptomatic marker of myocardial structural change in T2DM.

Aims: To compare insulin dose adjustments made by physicians to those made by an artificial intelligence-based decision support system, the Advisor Pro, in people with type 1 diabetes (T1D) using an insulin pump and self-monitoring blood glucose (SMBG). Methods: This was a multinational, non-interventional study surveying 17 physicians from 11 countries. Each physician was asked to provide insulin dose adjustments for the settings of the pump including basal rate, carbohydrate-to-insulin ratios (CRs), and correction factors (CFs) for 15 data sets of pumps and SMBG of people with T1D (mean age 18.4 ± 4.8 years; eight females; mean glycated hemoglobin 8.2% ± 1.4% [66 ± 11mmol/mol]). The recommendations were compared among the physicians and between the physicians and the Advisor Pro. The study endpoint was the percentage of comparison points for which there was an agreement on the direction of insulin dose adjustments. Results: The percentage (mean ± SD) of agreement among the physicians on the direction of insulin pump dose adjustments was 51.8% ± 9.2%, 54.2% ± 6.4%, and 49.8% ± 11.6% for the basal, CR, and CF, respectively. The automated recommendations of the Advisor Pro on the direction of insulin dose adjustments were comparable )49.5% ± 6.4%, 55.3% ± 8.7%, and 47.6% ± 14.4% for the basal rate, CR, and CF, respectively( and noninferior to those provided by physicians. The mean absolute difference in magnitude of change between physicians was 17.1% ± 13.1%, 14.6% ± 8.4%, and 23.9% ± 18.6% for the basal, CR, and CF, respectively, and comparable to the Advisor Pro 11.7% ± 9.7%, 10.1% ± 4.5%, and 25.5% ± 19.5%, respectively, significant for basal and CR. Conclusions: Considerable differences in the recommendations for changes in insulin dosing were observed among physicians. Since automated recommendations by the Advisor Pro were similar to those given by physicians, it could be considered a useful tool to manage T1D.