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Platinum and platinum-based alloys are used as the electrode material in cochlear implants because of the biocompatibility and the favorable electrochemical properties. Still, these implants can fail over time. The present study was conducted to shed light on the effects of microstructure on the electrochemical degradation of platinum. After three days of stimulation with a square wave signal, corrosive attack appeared on the platinum surface. The influence of mechanical deformation, in particular rolling, on the corrosion resistance of platinum was also prominent. The cyclic voltammetry showed a clear dependence on the electrolyte used, which was interpreted as an influence of the buffer in the artificial perilymph used. In addition, the polarization curves showed a shift with grain size that was not expected. This could be attributed to the defects present on the surface. These findings are crucial for the manufacture of cochlear implants to ensure their long-term functionality.

Type 2 diabetes (T2D) and cardiovascular disease (CVD) represent significant disease burdens for most societies and susceptibility to these diseases is strongly influenced by diet and lifestyle. Physiological changes associated with T2D or CVD, such has high blood pressure and cholesterol and glucose levels in the blood, are often apparent prior to disease incidence. Here we integrated genetics, lipidomics, and standard clinical diagnostics to assess future T2D and CVD risk for 4,067 participants from a large prospective population-based cohort, the Malmö Diet and Cancer-Cardiovascular Cohort. By training Ridge regression-based machine learning models on the measurements obtained at baseline when the individuals were healthy, we computed several risk scores for T2D and CVD incidence during up to 23 years of follow-up. We used these scores to stratify the participants into risk groups and found that a lipidomics risk score based on the quantification of 184 plasma lipid concentrations resulted in a 168% and 84% increase of the incidence rate in the highest risk group and a 77% and 53% decrease of the incidence rate in lowest risk group for T2D and CVD, respectively, compared to the average case rates of 13.8% and 22.0%. Notably, lipidomic risk correlated only marginally with polygenic risk, indicating that the lipidome and genetic variants may constitute largely independent risk factors for T2D and CVD. Risk stratification was further improved by adding standard clinical variables to the model, resulting in a case rate of 51.0% and 53.3% in the highest risk group for T2D and CVD, respectively. The participants in the highest risk group showed significantly altered lipidome compositions affecting 167 and 157 lipid species for T2D and CVD, respectively. Our results demonstrated that a subset of individuals at high risk for developing T2D or CVD can be identified years before disease incidence. The lipidomic risk, which is derived from only one single mass spectrometric measurement that is cheap and fast, is informative and could extend traditional risk assessment based on clinical assays. Risk score analysis of genetic and lipidomic data from a large population cohort reveals that in a subset of patients large-scale alterations in lipidome composition may be prognostic of future type 2 diabetes and cardiovascular disease.

Type 2 diabetes (T2D) and cardiovascular disease (CVD) represent significant disease burdens for most societies and susceptibility to these diseases is strongly influenced by diet and lifestyle. Physiological changes associated with T2D or CVD, such has high blood pressure and cholesterol and glucose levels in the blood, are often apparent prior to disease incidence. Here we integrated genetics, lipidomics, and standard clinical diagnostics to assess future T2D and CVD risk for 4,067 participants from a large prospective population-based cohort, the Malmö Diet and Cancer-Cardiovascular Cohort. By training Ridge regression-based machine learning models on the measurements obtained at baseline when the individuals were healthy, we computed several risk scores for T2D and CVD incidence during up to 23 years of follow-up. We used these scores to stratify the participants into risk groups and found that a lipidomics risk score based on the quantification of 184 plasma lipid concentrations resulted in a 168% and 84% increase of the incidence rate in the highest risk group and a 77% and 53% decrease of the incidence rate in lowest risk group for T2D and CVD, respectively, compared to the average case rates of 13.8% and 22.0%. Notably, lipidomic risk correlated only marginally with polygenic risk, indicating that the lipidome and genetic variants may constitute largely independent risk factors for T2D and CVD. Risk stratification was further improved by adding standard clinical variables to the model, resulting in a case rate of 51.0% and 53.3% in the highest risk group for T2D and CVD, respectively. The participants in the highest risk group showed significantly altered lipidome compositions affecting 167 and 157 lipid species for T2D and CVD, respectively. Our results demonstrated that a subset of individuals at high risk for developing T2D or CVD can be identified years before disease incidence. The lipidomic risk, which is derived from only one single mass spectrometric measurement that is cheap and fast, is informative and could extend traditional risk assessment based on clinical assays.

Obesity is associated with changes in the plasma lipids. Although simple lipid quantification is routinely used, plasma lipids are rarely investigated at the level of individual molecules. We aimed at predicting different measures of obesity based on the plasma lipidome in a large population cohort using advanced machine learning modeling. A total of 1,061 participants of the FINRISK 2012 population cohort were randomly chosen, and the levels of 183 plasma lipid species were measured in a novel mass spectrometric shotgun approach. Multiple machine intelligence models were trained to predict obesity estimates, i.e., body mass index (BMI), waist circumference (WC), waist-hip ratio (WHR), and body fat percentage (BFP), and validated in 250 randomly chosen participants of the Malmö Diet and Cancer Cardiovascular Cohort (MDC-CC). Comparison of the different models revealed that the lipidome predicted BFP the best (R2 = 0.73), based on a Lasso model. In this model, the strongest positive and the strongest negative predictor were sphingomyelin molecules, which differ by only 1 double bond, implying the involvement of an unknown desaturase in obesity-related aberrations of lipid metabolism. Moreover, we used this regression to probe the clinically relevant information contained in the plasma lipidome and found that the plasma lipidome also contains information about body fat distribution, because WHR (R2 = 0.65) was predicted more accurately than BMI (R2 = 0.47). These modeling results required full resolution of the lipidome to lipid species level, and the predicting set of biomarkers had to be sufficiently large. The power of the lipidomics association was demonstrated by the finding that the addition of routine clinical laboratory variables, e.g., high-density lipoprotein (HDL)- or low-density lipoprotein (LDL)- cholesterol did not improve the model further. Correlation analyses of the individual lipid species, controlled for age and separated by sex, underscores the multiparametric and lipid species-specific nature of the correlation with the BFP. Lipidomic measurements in combination with machine intelligence modeling contain rich information about body fat amount and distribution beyond traditional clinical assays.

Adipose tissue lipolysis occurs during the development of heart failure as a consequence of chronic adrenergic stimulation. However, the impact of enhanced adipose triacylglycerol hydrolysis mediated by adipose triglyceride lipase (ATGL) on cardiac function is unclear. To investigate the role of adipose tissue lipolysis during heart failure, we generated mice with tissue-specific deletion of ATGL (atATGL-KO). atATGL-KO mice were subjected to transverse aortic constriction (TAC) to induce pressure-mediated cardiac failure. The cardiac mouse lipidome and the human plasma lipidome from healthy controls (n = 10) and patients with systolic heart failure (HFrEF, n = 13) were analyzed by MS-based shotgun lipidomics. TAC-induced increases in left ventricular mass (LVM) and diastolic LV inner diameter were significantly attenuated in atATGL-KO mice compared to wild type (wt) -mice. More importantly, atATGL-KO mice were protected against TAC-induced systolic LV failure. Perturbation of lipolysis in the adipose tissue of atATGL-KO mice resulted in the prevention of the major cardiac lipidome changes observed after TAC in wt-mice. Profound changes occurred in the lipid class of phosphatidylethanolamines (PE) in which multiple PE-species were markedly induced in failing wt-hearts, which was attenuated in atATGL-KO hearts. Moreover, selected heart failure-induced PE species in mouse hearts were also induced in plasma samples from patients with chronic heart failure. TAC-induced cardiac PE induction resulted in decreased PC/ PE-species ratios associated with increased apoptotic marker expression in failing wt-hearts, a process absent in atATGL-KO hearts. Perturbation of adipose tissue lipolysis by ATGL-deficiency ameliorated pressure-induced heart failure and the potentially deleterious cardiac lipidome changes that accompany this pathological process, namely the induction of specific PE species. Non-cardiac ATGL-mediated modulation of the cardiac lipidome may play an important role in the pathogenesis of chronic heart failure.

Mass spectrometry-based shotgun lipidomics has enabled the quantitative and comprehensive assessment of cellular lipid compositions. The yeast Saccharomyces cerevisiae has proven to be a particularly valuable experimental system for studying lipid-related cellular processes. Here, by applying our shotgun lipidomics platform, we investigated the influence of a variety of commonly used growth conditions on the yeast lipidome, including glycerophospholipids, triglycerides, ergosterol as well as complex sphingolipids. This extensive dataset allowed for a quantitative description of the intrinsic flexibility of a eukaryotic lipidome, thereby providing new insights into the adjustments of lipid biosynthetic pathways. In addition, we established a baseline for future lipidomic experiments in yeast. Finally, flexibility of lipidomic features is proposed as a new parameter for the description of the physiological state of an organism.