Clusters of longitudinal risk profile trajectories are associated with cardiometabolic diseases: Results from the population-based KORA cohort.

BACKGROUND: Multiple risk factors contribute jointly to the development and progression of cardiometabolic diseases. Therefore, joint longitudinal trajectories of multiple risk factors might represent different degrees of cardiometabolic risk. METHODS: We analyzed population-based data comprising three examinations (Exam 1: 1999-2001, Exam 2: 2006-2008, Exam 3: 2013-2014) of 976 male and 1004 female participants of the KORA cohort (Southern Germany). Participants were followed up for cardiometabolic diseases, including cardiovascular mortality, myocardial infarction and stroke, or a diagnosis of type 2 diabetes, until 2016. Longitudinal multivariate k-means clustering identified sex-specific trajectory clusters based on nine cardiometabolic risk factors (age, systolic and diastolic blood pressure, body-mass-index, waist circumference, Hemoglobin-A1c, total cholesterol, high- and low-density lipoprotein cholesterol). Associations between clusters and cardiometabolic events were assessed by logistic regression models. RESULTS: We identified three trajectory clusters for men and women, respectively. Trajectory clusters reflected a distinct distribution of cardiometabolic risk burden and were associated with prevalent cardiometabolic disease at Exam 3 (men: odds ratio (OR)ClusterII = 2.0, 95% confidence interval: (0.9-4.5); ORClusterIII = 10.5 (4.8-22.9); women: ORClusterII = 1.7 (0.6-4.7); ORClusterIII = 5.8 (2.6-12.9)). Trajectory clusters were furthermore associated with incident cardiometabolic cases after Exam 3 (men: ORClusterII = 3.5 (1.1-15.6); ORClusterIII = 7.5 (2.4-32.7); women: ORClusterII = 5.0 (1.1-34.1); ORClusterIII = 8.0 (2.2-51.7)). Associations remained significant after adjusting for a single time point cardiovascular risk score (Framingham). CONCLUSIONS: On a population-based level, distinct longitudinal risk profiles over a 14-year time period are differentially associated with cardiometabolic events. Our results suggest that longitudinal data may provide additional information beyond single time-point measures. Their inclusion in cardiometabolic risk assessment might improve early identification of individuals at risk.

Omentin associates with serum metabolite profiles indicating lower diabetes risk: KORA F4 Study.

INTRODUCTION: Circulating omentin levels have been positively associated with insulin sensitivity. Although a role for adiponectin in this relationship has been suggested, underlying mechanisms remain elusive. In order to reveal the relationship between omentin and systemic metabolism, this study aimed to investigate associations of serum concentrations of omentin and metabolites. RESEARCH DESIGN AND METHODS: This study is based on 1124 participants aged 61-82 years from the population-based KORA (Cooperative Health Research in the Region of Augsburg) F4 Study, for whom both serum omentin levels and metabolite concentration profiles were available. Associations were assessed with five multivariable regression models, which were stepwise adjusted for multiple potential confounders, including age, sex, body mass index, waist-to-hip ratio, lifestyle markers (physical activity, smoking behavior and alcohol consumption), serum adiponectin levels, high-density lipoprotein cholesterol, use of lipid-lowering or anti-inflammatory medication, history of myocardial infarction and stroke, homeostasis model assessment 2 of insulin resistance, diabetes status, and use of oral glucose-lowering medication and insulin. RESULTS: Omentin levels significantly associated with multiple metabolites including amino acids, acylcarnitines, and lipids (eg, sphingomyelins and phosphatidylcholines (PCs)). Positive associations for several PCs, such as diacyl (PC aa C32:1) and alkyl-alkyl (PC ae C32:2), were significant in models 1-4, whereas those with hydroxytetradecenoylcarnitine (C14:1-OH) were significant in all five models. Omentin concentrations were negatively associated with several metabolite ratios, such as the valine-to-PC ae C32:2 and the serine-to-PC ae C32:2 ratios in most models. CONCLUSIONS: Our results suggest that omentin may influence insulin sensitivity and diabetes risk by changing systemic lipid metabolism, but further mechanistic studies investigating effects of omentin on metabolism of insulin-sensitive tissues are needed.

Association of plasma proteomics with incident coronary heart disease in individuals with and without type 2 diabetes: results from the population-based KORA study.

BACKGROUND: Coronary heart disease (CHD) is a major global health concern, especially among individuals with type 2 diabetes (T2D). Given the crucial role of proteins in various biological processes, this study aimed to elucidate the aetiological role and predictive performance of protein biomarkers on incident CHD in individuals with and without T2D. METHODS: The discovery cohort included 1492 participants from the Cooperative Health Research in the Region of Augsburg (KORA) S4 study with 147 incident CHD cases (45 vs. 102 cases in the group with T2D and without T2D, respectively) during 15.6 years of follow-up. The validation cohort included 888 participants from the KORA-Age1 study with 70 incident CHD cases (19 vs. 51 cases in the group with T2D and without T2D, respectively) during 6.9 years of follow-up. We measured 233 plasma proteins related to cardiovascular disease and inflammation using proximity extension assay technology. Associations of proteins with incident CHD were assessed using Cox regression and Mendelian randomization (MR) analysis. Predictive models were developed using priority-Lasso and were evaluated on top of Framingham risk score variables using the C-index, category-free net reclassification index (cfNRI), and relative integrated discrimination improvement (IDI). RESULTS: We identified two proteins associated with incident CHD in individuals with and 29 in those without baseline T2D, respectively. Six of these proteins are novel candidates for incident CHD. MR suggested a potential causal role for hepatocyte growth factor in CHD development. The developed four-protein-enriched model for individuals with baseline T2D (ΔC-index: 0.017; cfNRI: 0.253; IDI: 0.051) and the 12-protein-enriched model for individuals without baseline T2D (ΔC-index: 0.054; cfNRI: 0.462; IDI: 0.024) consistently improved CHD prediction in the discovery cohort, while in the validation cohort, significant improvements were only observed for selected performance measures (with T2D: cfNRI: 0.633; without T2D: ΔC-index: 0.038; cfNRI: 0.465). CONCLUSIONS: This study identified novel protein biomarkers associated with incident CHD in individuals with and without T2D and reaffirmed previously reported protein candidates. These findings enhance our understanding of CHD pathophysiology and provide potential targets for prevention and treatment.

Deep learning to estimate impaired glucose metabolism from Magnetic Resonance Imaging of the liver: An opportunistic population screening approach.

AIM: Diabetes is a global health challenge, and many individuals are undiagnosed and not aware of their increased risk of morbidity/mortality although dedicated tests are available, which indicates the need for novel population-wide screening approaches. Here, we developed a deep learning pipeline for opportunistic screening of impaired glucose metabolism using routine magnetic resonance imaging (MRI) of the liver and tested its prognostic value in a general population setting. METHODS: In this retrospective study a fully automatic deep learning pipeline was developed to quantify liver shape features on routine MR imaging using data from a prospective population study. Subsequently, the association between liver shape features and impaired glucose metabolism was investigated in individuals with prediabetes, type 2 diabetes and healthy controls without prior cardiovascular diseases. K-medoids clustering (3 clusters) with a dissimilarity matrix based on Euclidean distance and ordinal regression was used to assess the association between liver shape features and glycaemic status. RESULTS: The deep learning pipeline showed a high performance for liver shape analysis with a mean Dice score of 97.0±0.01. Out of 339 included individuals (mean age 56.3±9.1 years; males 58.1%), 79 (23.3%) and 46 (13.6%) were classified as having prediabetes and type 2 diabetes, respectively. Individuals in the high risk cluster using all liver shape features (n = 14) had a 2.4 fold increased risk of impaired glucose metabolism after adjustment for cardiometabolic risk factors (age, sex, BMI, total cholesterol, alcohol consumption, hypertension, smoking and hepatic steatosis; OR 2.44 [95% CI 1.12-5.38]; p = 0.03). Based on individual shape features, the strongest association was found between liver volume and impaired glucose metabolism after adjustment for the same risk factors (OR 1.97 [1.38-2.85]; p<0.001). CONCLUSIONS: Deep learning can estimate impaired glucose metabolism on routine liver MRI independent of cardiometabolic risk factors and hepatic steatosis.

Diabetes incidence before and after COVID-19 vaccination - Results from the German Disease Analyzer database.

Objective: We investigated whether COVID-19 vaccination had an impact on diabetes risk. Methods: We used data of 6,198 patients (mean age 64.3 years) from the nationwide Disease Analyzer database, a representative panel of physicians' practices in Germany. Patients received their first COVID-19 vaccination between 1 April 2021 and 31 March 2022, and all were newly diagnosed with diabetes within 183 days before or after this vaccination. Incident rates of diabetes after vaccination were compared to incident rates before vaccination. Results: The incidence rate of diabetes was lower after vaccination than before vaccination (incidence rate ratio = 0.79, 95% confidence interval: 0.75-0.83). The number of incident cases of diabetes was not greater in 2021 than in 2019. Conclusion: Our study did not confirm an increased risk of diabetes after COVID-19 vaccination. Further studies are needed to show whether the vaccination may be associated with a reduced diabetes risk.

Trajectories of glycaemic traits exhibit sex-specific associations with hepatic iron and fat content: Results from the KORA-MRI study.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) represents a major disease burden in the population. While the bidirectional association between NAFLD and diabetes is established, little is known about the association of hepatic iron content and glycaemia. Moreover, analyses of sex-specific effects and of dynamic changes in glycaemia are scarce. METHODS: We investigated 7-year sex-specific trajectories of glycaemia and related traits (HbA1c, fasting glucose, fasting insulin, HOMA-IR, 2-h glucose and cross-sectional 2-h insulin) in a sample from a population-based cohort (N = 365; 41.1% female). Hepatic iron and fat content were assessed by 3T-Magnetic Resonance Imaging (MRI). Two-step multi-level models adjusted for glucose-lowering medication and confounders were applied. RESULTS: In women and men, markers of glucose metabolism correlated with hepatic iron and fat content. Deterioration of glycaemia was associated with increased hepatic iron content in men (normoglycaemia to prediabetes: beta = 2.21 s-1 , 95% CI [0.47, 3.95]). Additionally, deterioration of glycaemia (e.g. prediabetes to diabetes: 1.27 log(%), [0.84, 1.70]) and trajectories of glucose, insulin and HOMA-IR were significantly associated with hepatic fat content in men. Similarly, deterioration of glycaemia as well as trajectories of glucose, insulin and HOMA-IR was significantly associated with increased hepatic fat content in women (e.g. trajectory of fasting insulin: 0.63 log(%), [0.36, 0.90]). CONCLUSIONS: Unfavourable 7-year trajectories of markers of glucose metabolism are associated with increased hepatic fat content, particularly in women, whereas the association with hepatic iron content was less clear. Monitoring changes of glycaemia in the sub-diabetic range might enable early identification of hepatic iron overload and steatosis.

Associations of plasma proteomics with type 2 diabetes and related traits: results from the longitudinal KORA S4/F4/FF4 Study.

AIMS/HYPOTHESIS: This study aimed to elucidate the aetiological role of plasma proteins in glucose metabolism and type 2 diabetes development. METHODS: We measured 233 proteins at baseline in 1653 participants from the Cooperative Health Research in the Region of Augsburg (KORA) S4 cohort study (median follow-up time: 13.5 years). We used logistic regression in the cross-sectional analysis (n=1300), and Cox regression accounting for interval-censored data in the longitudinal analysis (n=1143). We further applied two-level growth models to investigate associations with repeatedly measured traits (fasting glucose, 2 h glucose, fasting insulin, HOMA-B, HOMA-IR, HbA1c), and two-sample Mendelian randomisation analysis to investigate causal associations. Moreover, we built prediction models using priority-Lasso on top of Framingham-Offspring Risk Score components and evaluated the prediction accuracy through AUC. RESULTS: We identified 14, 24 and four proteins associated with prevalent prediabetes (i.e. impaired glucose tolerance and/or impaired fasting glucose), prevalent newly diagnosed type 2 diabetes and incident type 2 diabetes, respectively (28 overlapping proteins). Of these, IL-17D, IL-18 receptor 1, carbonic anhydrase-5A, IL-1 receptor type 2 (IL-1RT2) and matrix extracellular phosphoglycoprotein were novel candidates. IGF binding protein 2 (IGFBP2), lipoprotein lipase (LPL) and paraoxonase 3 (PON3) were inversely associated while fibroblast growth factor 21 was positively associated with incident type 2 diabetes. LPL was longitudinally linked with change in glucose-related traits, while IGFBP2 and PON3 were linked with changes in both insulin- and glucose-related traits. Mendelian randomisation analysis suggested causal effects of LPL on type 2 diabetes and fasting insulin. The simultaneous addition of 12 priority-Lasso-selected biomarkers (IGFBP2, IL-18, IL-17D, complement component C1q receptor, V-set and immunoglobulin domain-containing protein 2, IL-1RT2, LPL, CUB domain-containing protein 1, vascular endothelial growth factor D, PON3, C-C motif chemokine 4 and tartrate-resistant acid phosphatase type 5) significantly improved the predictive performance (ΔAUC 0.0219; 95% CI 0.0052, 0.0624). CONCLUSIONS/INTERPRETATION: We identified new candidates involved in the development of derangements in glucose metabolism and type 2 diabetes and confirmed previously reported proteins. Our findings underscore the importance of proteins in the pathogenesis of type 2 diabetes and the identified putative proteins can function as potential pharmacological targets for diabetes treatment and prevention.

Identification of candidate metabolite biomarkers for metabolic syndrome and its five components in population-based human cohorts.

BACKGROUND: Metabolic Syndrome (MetS) is characterized by risk factors such as abdominal obesity, hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C), hypertension, and hyperglycemia, which contribute to the development of cardiovascular disease and type 2 diabetes. Here, we aim to identify candidate metabolite biomarkers of MetS and its associated risk factors to better understand the complex interplay of underlying signaling pathways. METHODS: We quantified serum samples of the KORA F4 study participants (N = 2815) and analyzed 121 metabolites. Multiple regression models adjusted for clinical and lifestyle covariates were used to identify metabolites that were Bonferroni significantly associated with MetS. These findings were replicated in the SHIP-TREND-0 study (N = 988) and further analyzed for the association of replicated metabolites with the five components of MetS. Database-driven networks of the identified metabolites and their interacting enzymes were also constructed. RESULTS: We identified and replicated 56 MetS-specific metabolites: 13 were positively associated (e.g., Val, Leu/Ile, Phe, and Tyr), and 43 were negatively associated (e.g., Gly, Ser, and 40 lipids). Moreover, the majority (89%) and minority (23%) of MetS-specific metabolites were associated with low HDL-C and hypertension, respectively. One lipid, lysoPC a C18:2, was negatively associated with MetS and all of its five components, indicating that individuals with MetS and each of the risk factors had lower concentrations of lysoPC a C18:2 compared to corresponding controls. Our metabolic networks elucidated these observations by revealing impaired catabolism of branched-chain and aromatic amino acids, as well as accelerated Gly catabolism. CONCLUSION: Our identified candidate metabolite biomarkers are associated with the pathophysiology of MetS and its risk factors. They could facilitate the development of therapeutic strategies to prevent type 2 diabetes and cardiovascular disease. For instance, elevated levels of lysoPC a C18:2 may protect MetS and its five risk components. More in-depth studies are necessary to determine the mechanism of key metabolites in the MetS pathophysiology.

Routine care for people with newly diagnosed type 2 diabetes before and during the SARS-CoV-2 pandemic - results from the German Disease Analyzer database.

AIM: To investigate whether the SARS-CoV-2 pandemic affected care for people with newly diagnosed type 2 diabetes in Germany. METHODS: The Disease Analyzer database (IQVIA, Germany) contains routine data on diagnoses and treatments (ICD-10 and ATC codes) from patients followed in selected physician practices across Germany. We compared 21,747 individuals with a first diagnosis of type 2 diabetes between January 2018 and September 2019 with 20,513 individuals with a first diabetes diagnosis between March 2020 and November 2021. RESULTS: In March and April 2020, the number of new diagnoses of diabetes decreased by 18.3% and 35.7%, respectively, compared to March and April of the previous two years. The previous diabetes incidence level was reached again in June 2020. Mean pre-treatment glucose levels were higher during the pandemic than before (fasting plasma glucose: +6.3 mg/dl (95% confidence interval: 4.6-8.0)). In the first six months after diabetes diagnosis, the mean number of GP visits, specialist referrals and HbA1c measurements decreased. CONCLUSION: We observed a decrease in diabetes incidence in the early phase of the pandemic and slightly higher pretreatment blood glucose levels during the pandemic than before. Care for newly diagnosed diabetes was slightly worse during the pandemic than before.

Development and validation of DNA methylation scores in two European cohorts augment 10-year risk prediction of type 2 diabetes.

Type 2 diabetes mellitus (T2D) presents a major health and economic burden that could be alleviated with improved early prediction and intervention. While standard risk factors have shown good predictive performance, we show that the use of blood-based DNA methylation information leads to a significant improvement in the prediction of 10-year T2D incidence risk. Previous studies have been largely constrained by linear assumptions, the use of cytosine-guanine pairs one-at-a-time and binary outcomes. We present a flexible approach (via an R package, MethylPipeR) based on a range of linear and tree-ensemble models that incorporate time-to-event data for prediction. Using the Generation Scotland cohort (training set ncases = 374, ncontrols = 9,461; test set ncases = 252, ncontrols = 4,526) our best-performing model (area under the receiver operating characteristic curve (AUC) = 0.872, area under the precision-recall curve (PRAUC) = 0.302) showed notable improvement in 10-year onset prediction beyond standard risk factors (AUC = 0.839, precision-recall AUC = 0.227). Replication was observed in the German-based KORA study (n = 1,451, ncases = 142, P = 1.6 × 10-5).

HbA1c trajectories over 3 years in people with type 2 diabetes starting second-line glucose-lowering therapy: The prospective global DISCOVER study.

AIM: To identify distinct HbA1c trajectories in people with type 2 diabetes (T2D) starting second-line glucose-lowering therapy. MATERIALS AND METHODS: DISCOVER was a 3-year observational study of individuals with T2D beginning second-line glucose-lowering therapy. Data were collected at initiation of second-line treatment (baseline) and at 6, 12, 24 and 36 months. Latent class growth modelling was used to identify groups with distinct HbA1c trajectories. RESULTS: After exclusions, 9295 participants were assessed. Four distinct HbA1c trajectories were identified. Mean HbA1c levels decreased between baseline and 6 months in all groups; 72.4% of participants showed stable good levels of glycaemic control over the remainder of follow-up, 18.0% showed stable moderate levels of glycaemic control and 2.9% showed stable poor levels of glycaemic control. Only 6.7% of participants showed highly improved glycaemic control at month 6 and stable control over the rest of follow-up. For all groups, dual oral therapy use decreased over time, compensated for by the increasing use of other treatment regimens. Use of injectable agents increased over time in groups with moderate and poor glycaemic control. Logistic regression models suggested that participants from high-income countries were more probable to be in the stable good trajectory group. CONCLUSIONS: Most people receiving second-line glucose-lowering treatment in this global cohort achieved stable good or highly improved long-term glycaemic control. One-fifth of participants showed moderate or poor glycaemic control during follow-up. Further large-scale studies are required to characterize possible factors associated with patterns of glycaemic control to inform personalized diabetes treatment.

Associations of myosteatosis with disc degeneration: A 3T magnetic resonance imaging study in individuals with impaired glycaemia.

BACKGROUND: Intervertebral disc degeneration (IVDD) may be linked to dysregulations of skeletal muscle glucose metabolism and fatty alterations of muscle composition (Myosteatosis). Our aim was to evaluate the different associations of magnetic resonance imaging (MRI)-based paravertebral myosteatosis with lumbar disc degeneration in individuals with impaired glucose metabolism and normoglycaemic controls. METHODS: In total, 304 individuals (mean age: 56.3 ± 9.1 years, 53.6% male sex, mean body mass index [BMI]: 27.6 ± 4.7 kg/m2 ) from a population-based cohort study who underwent 3-Tesla whole-body chemical-shift-encoded (six echo times) and T2-weighted single-shot-fast-spin-echo MRI were included. Lumbar disc degeneration was assessed at motion segments L1 to L5, categorized according to the Pfirrmann score and defined as Pfirrmann grade > 2 and/or disc bulging/herniation on at least one segment. Fat content of the autochthonous back muscles and the quadratus lumborum muscle was quantified as proton density fat fraction (PDFFmuscle ). Logistic regression models adjusted for age, sex, BMI and regular physical activity were calculated to evaluate the association between PDFFmuscle and outcome IVDD. RESULTS: The overall prevalence of IVDD was 79.6%. There was no significant difference in the prevalence or severity distribution of IVDD between participants with or without impaired glucose metabolism (77.7% vs. 80.7%, P = 0.63 and P = 0.71, respectively). PDFFmuscle was significantly and positively associated with an increased risk for the presence of IVDD in participants with impaired glycaemia when adjusted for age, sex and BMI (PDFFautochthonous back muscles : odds ratio [OR] 2.16, 95% confidence interval [CI] [1.09, 4.3], P = 0.03; PDFFquadratus lumborum : OR 2.01, 95% CI [1.04, 3.85], P = 0.04). After further adjustment for regular physical activity, the results attenuated, albeit approaching statistical significance (PDFFautochthonous back muscles : OR 1.97, 95% CI [0.97, 3.99], P = 0.06; PDFFquadratus lumborum : OR 1.86, 95% CI [0.92, 3.76], P = 0.09). No significant associations were shown in healthy controls (PDFFautochthonous back muscles : OR 0.62, 95% CI [0.34, 1.14], P = 0.13; PDFFquadratus lumborum : OR 1.06, 95% CI [0.6, 1.89], P = 0.83). CONCLUSIONS: Paravertebral myosteatosis is positively associated with intervertebral disc disease in individuals with impaired glucose metabolism, independent of age, sex and BMI. Regular physical activity may confound these associations. Longitudinal studies will help to better understand the pathophysiological role of skeletal muscle in those with concomitant disturbed glucose haemostasis and intervertebral disc disease, as well as possible underlying causal relationships.

Metabolic Signatures Elucidate the Effect of Body Mass Index on Type 2 Diabetes.

Obesity plays an important role in the development of insulin resistance and diabetes, but the molecular mechanism that links obesity and diabetes is still not completely understood. Here, we used 146 targeted metabolomic profiles from the German KORA FF4 cohort consisting of 1715 participants and associated them with obesity and type 2 diabetes. In the basic model, 83 and 51 metabolites were significantly associated with body mass index (BMI) and T2D, respectively. Those metabolites are branched-chain amino acids, acylcarnitines, lysophospholipids, or phosphatidylcholines. In the full model, 42 and 3 metabolites were significantly associated with BMI and T2D, respectively, and replicate findings in the previous studies. Sobel mediation testing suggests that the effect of BMI on T2D might be mediated via lipids such as sphingomyelin (SM) C16:1, SM C18:1 and diacylphosphatidylcholine (PC aa) C38:3. Moreover, mendelian randomization suggests a causal relationship that BMI causes the change of SM C16:1 and PC aa C38:3, and the change of SM C16:1, SM C18:1, and PC aa C38:3 contribute to T2D incident. Biological pathway analysis in combination with genetics and mice experiments indicate that downregulation of sphingolipid or upregulation of phosphatidylcholine metabolism is a causal factor in early-stage T2D pathophysiology. Our findings indicate that metabolites like SM C16:1, SM C18:1, and PC aa C38:3 mediate the effect of BMI on T2D and elucidate their role in obesity related T2D pathologies.

Association of Aldosterone with Mortality in the General Population.

INTRODUCTION: Aldosterone excess is linked to cardiovascular events and mortality as well as to low-grade inflammation in the context of metabolic diseases. Whether mildly elevated aldosterone levels in the general population promote cardiovascular risk is still under debate. We analyzed the association of plasma aldosterone concentrations with incident cardiovascular events, cardiovascular and all-cause mortality as well as with biomarkers of subclinical inflammation in the population-based KORA F4 study. METHODS: Plasma aldosterone concentrations were measured with an in-house immunoflurometric assay. The analyses included 2935 participants (n=1076 for selected biomarkers of subclinical inflammation) with a median follow-up of 8.7 (8.2; 9.1) years. The associations were estimated using Cox proportional hazard and linear regression models adjusted for renin, sex, age, body mass index, arterial hypertension, diabetes, estimated glomerular filtration rate, low- and high-density lipoprotein cholesterol, physical activity, smoking, use of angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, beta-blockers, diuretics and calcium channel blockers. RESULTS: Aldosterone was significantly associated with all-cause mortality (hazard ratio per standard deviation increase: 1.20; 95% confidence interval 1.04-1.37), but not with cardiovascular mortality, incident cardiovascular events, or with biomarkers of subclinical inflammation. CONCLUSIONS: Aldosterone was associated with all-cause mortality in the population-based KORA F4 study, but the previously described associations of excess aldosterone with cardiovascular complications and biomarkers of subclinical inflammation could not be shown.

Environmental risk factors of incident distal sensorimotor polyneuropathy: Results from the prospective population-based KORA F4/FF4 study.

Distal sensorimotor polyneuropathy (DSPN) is a common condition in older populations with high prevalence of obesity and type 2 diabetes. We hypothesised that the risk of DSPN is increased by multiple ubiquitous environmental risk factors, particularly in people with obesity. This study was based on 423 individuals aged 62-81 years without DSPN who participated in the population-based Cooperative Health Research in the Region of Augsburg (KORA) F4 survey (2006-2008) in Southern Germany. During 6.5 years of follow-up, 188 participants developed clinical DSPN as assessed by the Michigan Neuropathy Screening Instrument. Environmental exposures, including air temperature, surrounding greenness (assessed with the normalized difference vegetation index [NDVI]), long-term road traffic noise and air pollution, were assessed at participants' residences. The cumulative risk index (CRI) evaluated the joint effects of co-occurring exposures on DSPN risk based on effect estimates from multi-exposure Poisson regression models. The models were adjusted for age, sex, height, waist circumference, smoking, alcohol consumption, physical activity, education and neighbourhood socioeconomic status. In the entire cohort, the co-occurrence of an interquartile range (IQR) decrease in temperature of the warm season and NDVI in a 100-m buffer and of an IQR increase in night-time average traffic noise and in annual average particle number concentration (PNC) was positively associated with incident DSPN (CRI [95 % CI] 1.39 [1.02, 1.91]). Effect estimates for exposure combinations were generally higher in individuals with obesity (CRI 1.34-2.01) than in those without obesity (CRI 0.90-1.33). The four-exposure model showed a twofold increased risk of DSPN among obese (CRI [95 % CI] 2.01 [1.10, 3.67]), but not among non-obese individuals (CRI [95 % CI] 1.18 [0.83, 1.67]). Thus, ubiquitous environmental exposures jointly augment the risk of DSPN in the older population. Lower air temperature in the warm season, less greenness, and higher noise levels and ultrafine particle concentrations identified people with obesity as a particularly vulnerable subgroup.

Serum neurofilament light chain: a novel biomarker for early diabetic sensorimotor polyneuropathy.

AIMS/HYPOTHESIS: No established blood-based biomarker exists to monitor diabetic sensorimotor polyneuropathy (DSPN) and evaluate treatment response. The neurofilament light chain (NFL), a blood biomarker of neuroaxonal damage in several neurodegenerative diseases, represents a potential biomarker for DSPN. We hypothesised that higher serum NFL levels are associated with prevalent DSPN and nerve dysfunction in individuals recently diagnosed with diabetes. METHODS: This cross-sectional study included 423 adults with type 1 and type 2 diabetes and known diabetes duration of less than 1 year from the prospective observational German Diabetes Study cohort. NFL was measured in serum samples of fasting participants in a multiplex approach using proximity extension assay technology. DSPN was assessed by neurological examination, nerve conduction studies and quantitative sensory testing. Associations of serum NFL with DSPN (defined according to the Toronto Consensus criteria) were estimated using Poisson regression, while multivariable linear and quantile regression models were used to assess associations with nerve function measures. In exploratory analyses, other biomarkers in the multiplex panel were also analysed similarly to NFL. RESULTS: DSPN was found in 16% of the study sample. Serum NFL levels increased with age. After adjustment for age, sex, waist circumference, height, HbA1c, known diabetes duration, diabetes type, cholesterol, eGFR, hypertension, CVD, use of lipid-lowering drugs and use of non-steroidal anti-inflammatory drugs, higher serum NFL levels were associated with DSPN (RR [95% CI] per 1-normalised protein expression increase, 1.92 [1.50, 2.45], p<0.0001), slower motor (all p<0.0001) and sensory (all p≤0.03) nerve conduction velocities, lower sural sensory nerve action potential (p=0.0004) and higher thermal detection threshold to warm stimuli (p=0.023 and p=0.004 for hand and foot, respectively). There was no evidence for associations between other neurological biomarkers and DSPN or nerve function measures. CONCLUSIONS/INTERPRETATION: Our findings in individuals recently diagnosed with diabetes provide new evidence associating higher serum NFL levels with DSPN and peripheral nerve dysfunction. The present study advocates NFL as a potential biomarker for DSPN.

Combined Effects of Diabetes and Education on Decline of Cognitive Performance in the Older Population: The Survey of Health, Ageing, and Retirement in Europe.

AIMS: Diabetes management requires cognitive abilities. Unfortunately, diabetes is a risk factor for cognitive decline. However, the effects of diabetes in persons with reduced cognitive reserves are unclear. We aimed to examine whether the effects of diabetes and education on cognitive skills simply add up or deviate from simple additivity. METHODS: We used data from waves 1 to 7 (except wave 3) of the Survey of Health, Ageing, and Retirement in Europe (SHARE) collected between 2004 and 2017. SHARE includes 140,000 persons aged ≥50 years from 28 European countries and Israel. Diabetes was assessed by self-report and education was coded according to the International Standard Classification of Education levels. Verbal fluency, immediate, and delayed memory were assessed using standard tests. Interaction between diabetes and education was estimated from interaction contrasts and from interaction terms in adjusted regression models. RESULTS: At baseline, cognitive performance declined in the order "no diabetes/high education" > "diabetes/high education" > "no diabetes/low education" > "diabetes/low education" for all three tests - e.g., the first group named 21.4 ± 7.2, the fourth group 14.6 ± 6.1 animals in a minute in the test of verbal fluency. Interaction contrasts and regression coefficients of interaction terms were close to zero, showing that the effects of diabetes and education on cognitive performance added up without interaction both in cross-sectional and longitudinal analyses. CONCLUSION: We have observed no interaction between education and diabetes on cognitive skills. Yet, people with diabetes and low education showed poor cognitive performance and should receive particular support in managing diabetes.