Differences in Biomarkers of Inflammation Between Novel Subgroups of Recent-Onset Diabetes

Lipid profile, inflammatory biomarkers, endothelial dysfunction, and heart rate variability in adolescents with type 1 diabetes. A case-control study among UAE population

Background

Type 1 diabetes mellitus (T1DM) is an autoimmune disease characterized by the chronic inflammation and cause of endothelial dysfunction (ED). Heart rate variability (HRV) is a marker of sympathetic and parasympathetic autonomic nervous system dysfunction. We investigated the association of lipid profile, inflammatory biomarkers, endothelial dysfunction, and heart rate variability in adolescents with T1DM among UAE population.

Method

In this case-control study we recruited 126 adolescents (13-22 years) from Abu Dhabi, UAE (United Arab Emirates). Demographic, anthropometric, blood and urine samples were collected after an overnight fasting. HRV measurements were determined per Task Force recommendations. Independent t-test or Mann-Whitney U test and Pearson's Chi-squared test were used to compare groups. Adjusted conditional logistic regression model was used to identify the determinants independently associated with T1DM.

Results

The mean ages in control (n = 47) and patient (n = 79) groups were 17.5 ± 4.6 and 18.6 ± 4.8 years, respectively. A family history of diabetes and waist and hip circumferences significantly differed between the groups (p = 0.030 and 0.010). The patients with T1DM exhibited significantly higher levels of atherogenic markers than control. Endothelial dysfunction biomarkers such as levels of sICAM-1 (p < 0.001), adiponectin (p < 0.001) and 25-hydroxyvitamin D (p < 0.001) were significantly different in the control group compared with those in the T1DM group. There was a significant difference in SDNN intervals, NN50, pNN50, and SD1/SD2 among the two groups. In adjusted analysis, total cholesterol (adjusted Odds Ratio (aOR): 2.78, 95 % CI:1.37-5.64; p = 0.005), LDL (2.66, 95%CI:1.19-5.92; p = 0.017), and triglycerides (5.51, 95%CI:1.57-19.41; p = 0.008) were significantly associated with developing T1DM. The HRV indicators were significantly associated with decrease odds of T1DM after controlling for SBP, BMI, and family history of DM.

Conclusion

In this study, adolescents with T1DM showed a significant association with lipid profile, ED, and HRV compared with controls. Thus, an early attention to diabetes control is required to reduce the risk of cardiac autonomic neuropathy leading to various cardiovascular diseases.

Harnessing the power of proteomics in precision diabetes medicine

Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.

Multiplex proteomics identifies inflammation-related plasma biomarkers for aging and cardio-metabolic disorders

BACKGROUND

Cardio-metabolic disorders (CMDs) are common in aging people and are pivotal risk factors for cardiovascular diseases (CVDs). Inflammation is involved in the pathogenesis of CVDs and aging, but the underlying inflammatory molecular phenotypes in CMDs and aging are still unknown.

METHOD

We utilized multiple proteomics to detect 368 inflammatory proteins in the plasma of 30 subjects, including healthy young individuals, healthy elderly individuals, and elderly individuals with CMDs, by Proximity Extension Assay technology (PEA, O-link). Protein-protein interaction (PPI) network and functional modules were constructed to explore hub proteins in differentially expressed proteins (DEPs). The correlation between proteins and clinical traits of CMDs was analyzed and diagnostic value for CMDs of proteins was evaluated by ROC curve analysis.

RESULT

Our results revealed that there were 161 DEPs (adjusted p < 0.05) in normal aging and EGF was the most differentially expressed hub protein in normal aging. Twenty-eight DEPs were found in elderly individuals with CMDs and MMP1 was the most differentially expressed hub protein in CMDs. After the intersection of DEPs in aging and CMDs, there were 10 overlapping proteins: SHMT1, MVK, EGLN1, SLC39A5, NCF2, CXCL6, IRAK4, REG4, PTPN6, and PRDX5. These proteins were significantly correlated with the level of HDL-C, TG, or FPG in plasma. They were verified to have good diagnostic value for CMDs in aging with an AUC > 0.7. Among these, EGLN1, NCF2, REG4, and SLC39A2 were prominently increased both in normal aging and aging with CMDs.

CONCLUSION

Our results could reveal molecular markers for normal aging and CMDs, which need to be further expanded the sample size and to be further investigated to predict their significance for CVDs.

Association of dietary patterns with diabetes-related comorbidities varies among diabetes endotypes

BACKGROUND AND AIMS

Differences of dietary pattern adherence across the novel diabetes endotypes are unknown. This study assessed adherence to pre-specified dietary patterns and their associations with cardiovascular risk factors, kidney function, and neuropathy among diabetes endotypes.

METHODS AND RESULTS

The cross-sectional analysis included 765 individuals with recent-onset (67 %) and prevalent diabetes (33 %) from the German Diabetes Study (GDS) allocated into severe autoimmune diabetes (SAID, 35 %), severe insulin-deficient diabetes (SIDD, 3 %), severe insulin-resistant diabetes (SIRD, 5 %), mild obesity-related diabetes (MOD, 28 %), and mild age-related diabetes (MARD, 29 %). Adherence to a Mediterranean diet score (MDS), Dietary Approaches to Stop Hypertension (DASH) score, overall plant-based diet (PDI), healthful (hPDI) and unhealthful plant-based diet index (uPDI) was derived from a food frequency questionnaire and associated with cardiovascular risk factors, kidney function, and neuropathy using multivariable linear regression analysis. Differences in dietary pattern adherence between endotypes were assessed using generalized mixed models. People with MARD showed the highest, those with SIDD and MOD the lowest adherence to the hPDI. Adherence to the MDS, DASH, overall PDI, and hPDI was inversely associated with high-sensitivity C-reactive protein (hsCRP) among people with MARD (β (95%CI): -9.18 % (-15.61; -2.26); -13.61 % (-24.17; -1.58); -19.15 % (-34.28; -0.53); -16.10 % (-28.81; -1.12), respectively). Adherence to the PDIs was associated with LDL cholesterol among people with SAID, SIRD, and MOD.

CONCLUSIONS

Minor differences in dietary pattern adherence (in particular for hPDI) and associations with markers of diabetes-related complications (e.g. hsCRP) were observed between endotypes. So far, evidence is insufficient to derive endotype-specific dietary recommendations.

TRIAL REGISTRATION

Clinicaltrials.gov: NCT01055093.

Correlates of Skeletal Muscle Mass and Differences Between Novel Subtypes in Recent-Onset Diabetes

CONTEXT

Low skeletal muscle mass (SMM) is associated with long-standing diabetes but little is known about SMM in newly diagnosed diabetes.

OBJECTIVE

We aimed to identify correlates of SMM in recent-onset diabetes and to compare SMM between novel diabetes subtypes.

METHODS

SMM was normalized to body mass index (SMM/BMI) in 842 participants with known diabetes duration of less than 1 year from the German Diabetes Study (GDS). Cross-sectional associations between clinical variables, 79 biomarkers of inflammation, and SMM/BMI were assessed, and differences in SMM/BMI between novel diabetes subtypes were analyzed with different degrees of adjustment for confounders.

RESULTS

Male sex and physical activity were positively associated with SMM/BMI, whereas associations of age, BMI, glycated hemoglobin A1c, homeostatic model assessment for β-cell function, and estimated glomerular filtration rate with SMM/BMI were inverse (all P < .05; model r2 = 0.82). Twenty-three biomarkers of inflammation showed correlations with SMM/BMI after adjustment for sex and multiple testing (all P < .0006), but BMI largely explained these correlations. In a sex-adjusted analysis, individuals with severe autoimmune diabetes had a higher SMM/BMI whereas individuals with severe insulin-resistant diabetes and mild obesity-related diabetes had a lower SMM/BMI than all other subtypes combined. However, differences were attenuated after adjustment for the clustering variables.

CONCLUSION

SMM/BMI differs between diabetes subtypes and may contribute to subtype differences in disease progression. Of note, clinical variables rather than biomarkers of inflammation explain most of the variation in SMM/BMI.

Analysis of type 2 diabetes heterogeneity with a tree-like representation: insights from the prospective German Diabetes Study and the LURIC cohort

BACKGROUND

Heterogeneity in type 2 diabetes can be represented by a tree-like graph structure by use of reversed graph-embedded dimensionality reduction. We aimed to examine whether this approach can be used to stratify key pathophysiological components and diabetes-related complications during longitudinal follow-up of individuals with recent-onset type 2 diabetes.

METHODS

For this cohort analysis, 927 participants aged 18-69 years from the German Diabetes Study (GDS) with recent-onset type 2 diabetes were mapped onto a previously developed two-dimensional tree based on nine simple clinical and laboratory variables, residualised for age and sex. Insulin sensitivity was assessed by a hyperinsulinaemic-euglycaemic clamp, insulin secretion was assessed by intravenous glucose tolerance test, hepatic lipid content was assessed by 1 H magnetic resonance spectroscopy, serum interleukin (IL)-6 and IL-18 were assessed by ELISA, and peripheral and autonomic neuropathy were assessed by functional and clinical measures. Participants were followed up for up to 16 years. We also investigated heart failure and all-cause mortality in 794 individuals with type 2 diabetes undergoing invasive coronary diagnostics from the Ludwigshafen Risk and Cardiovascular Health (LURIC) cohort.

FINDINGS

There were gradients of clamp-measured insulin sensitivity (both dimensions: p<0·0001) and insulin secretion (pdim1<0·0001, pdim2=0·00097) across the tree. Individuals in the region with the lowest insulin sensitivity had the highest hepatic lipid content (n=205, pdim1<0·0001, pdim2=0·037), pro-inflammatory biomarkers (IL-6: n=348, pdim1<0·0001, pdim2=0·013; IL-18: n=350, pdim1<0·0001, pdim2=0·38), and elevated cardiovascular risk (nevents=143, pdim1=0·14, pdim2<0·00081), whereas individuals positioned in the branch with the lowest insulin secretion were more prone to require insulin therapy (nevents=85, pdim1=0·032, pdim2=0·12) and had the highest risk of diabetic sensorimotor polyneuropathy (nevents=184, pdim1=0·012, pdim2=0·044) and cardiac autonomic neuropathy (nevents=118, pdim1=0·0094, pdim2=0·06). In the LURIC cohort, all-cause mortality was highest in the tree branch showing insulin resistance (nevents=488, pdim1=0·12, pdim2=0·0032). Significant gradients differentiated individuals having heart failure with preserved ejection fraction from those who had heart failure with reduced ejection fraction.

INTERPRETATION

These data define the pathophysiological underpinnings of the tree structure, which has the potential to stratify diabetes-related complications on the basis of routinely available variables and thereby expand the toolbox of precision diabetes diagnosis.

FUNDING

German Diabetes Center, German Federal Ministry of Health, Ministry of Culture and Science of the state of North Rhine-Westphalia, German Federal Ministry of Education and Research, German Diabetes Association, German Center for Diabetes Research, European Community, German Research Foundation, and Schmutzler Stiftung.

Inter-organ crosstalk during development and progression of type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is characterized by tissue-specific insulin resistance and pancreatic β-cell dysfunction, which result from the interplay of local abnormalities within different tissues and systemic dysregulation of tissue crosstalk. The main local mechanisms comprise metabolic (lipid) signalling, altered mitochondrial metabolism with oxidative stress, endoplasmic reticulum stress and local inflammation. While the role of endocrine dysregulation in T2DM pathogenesis is well established, other forms of inter-organ crosstalk deserve closer investigation to better understand the multifactorial transition from normoglycaemia to hyperglycaemia. This narrative Review addresses the impact of certain tissue-specific messenger systems, such as metabolites, peptides and proteins and microRNAs, their secretion patterns and possible alternative transport mechanisms, such as extracellular vesicles (exosomes). The focus is on the effects of these messengers on distant organs during the development of T2DM and progression to its complications. Starting from the adipose tissue as a major organ relevant to T2DM pathophysiology, the discussion is expanded to other key tissues, such as skeletal muscle, liver, the endocrine pancreas and the intestine. Subsequently, this Review also sheds light on the potential of multimarker panels derived from these biomarkers and related multi-omics for the prediction of risk and progression of T2DM, novel diabetes mellitus subtypes and/or endotypes and T2DM-related complications.

Identification and multicentric validation of soluble CDCP1 as a robust serological biomarker for risk stratification of NASH in obese Chinese

The definitive diagnosis of non-alcoholic steatohepatitis (NASH) currently relies on invasive and labor-intensive liver biopsy. Here, we identified soluble CUB domain-containing protein 1 (sCDCP1) as a top-ranked non-invasive biomarker for NASH using Olink-based proteomics in 238 obese individuals with liver biopsies. Both the circulating concentration and hepatic mRNA abundance of sCDCP1 were significantly elevated in patients with NASH and correlated closely with each histological feature of NASH. In the pooled multicenter validation cohort, sCDCP1 as a standalone biomarker achieved an area under the receiver operating characteristic (AUROC) of 0.838 (95% confidence interval [CI] 0.789-0.887) for diagnosing NASH, which is better than those achieved with cytokeratin-18 and other non-invasive tests. Furthermore, the C-DAG model established by the combination of sCDCP1 with diabetes, aspartate aminotransferase (AST), and gender accurately rules in and rules out both NASH and fibrotic NASH (gray zones <20%). Thus, sCDCP1-based non-invasive tests can be potentially implemented for screening and early diagnosis of NASH and for ruling out low-risk individuals to avoid unnecessary liver biopsies.

Characteristics of glucolipid metabolism and complications in novel cluster-based diabetes subgroups: a retrospective study

BACKGROUND

Glucolipid metabolism plays an important role in the occurrence and development of diabetes mellitus. However, there is limited research on the characteristics of glucolipid metabolism and complications in different subgroups of newly diagnosed diabetes. This study aimed to investigate the characteristics of glucolipid metabolism and complications in novel cluster-based diabetes subgroups and explore the contributions of different glucolipid metabolism indicators to the occurrence of complications and pancreatic function.

METHODS

This retrospective study included 547 newly diagnosed type 2 diabetes patients. Age, body mass index (BMI), glycated hemoglobin (HbA1C), homeostasis model assessment-2 beta-cell function (HOMA2-β), and homeostasis model assessment-2 insulin resistance (HOMA2-IR) were used as clustering variables. The participants were divided into 4 groups by k-means cluster analysis. The characteristics of glucolipid indicators and complications in each subgroup were analyzed. Regression analyses were used to evaluate the impact of glucolipid metabolism indicators on complications and pancreatic function.

RESULTS

Total cholesterol (TC), triglycerides (TG), triglyceride glucose index (TyG), HbA1C, fasting plasma glucose (FPG), and 2-h postprandial plasma glucose (2hPG) were higher in the severe insulin-resistant diabetes (SIRD) and severe insulin-deficient diabetes (SIDD) groups. Fasting insulin (FINS), fasting C-peptide (FCP), 2-h postprandial insulin (2hINS), 2-h postprandial C-peptide (2hCP), and the monocyte-to-high-density lipoprotein cholesterol ratio (MHR) were higher in mild obesity-related diabetes (MOD) and SIRD. 2hCP, FCP, and FINS were positively correlated with HOMA2-β, while FPG, TyG, HbA1C, and TG were negatively correlated with HOMA2-β. FINS, FPG, FCP, and HbA1C were positively correlated with HOMA2-IR, while high-density lipoprotein (HDL) was negatively correlated with HOMA2-IR. FINS (odds ratio (OR),1.043;95% confidence interval (CI) 1.006 ~ 1.081), FCP (OR,2.881;95%CI 2.041 ~ 4.066), and TyG (OR,1.649;95%CI 1.292 ~ 2.104) contributed to increase the risk of nonalcoholic fatty liver disease (NAFLD); 2hINS (OR,1.015;95%CI 1.008 ~ 1.022) contributed to increase the risk of atherosclerotic cardiovascular disease (ASCVD); FCP (OR,1.297;95%CI 1.027 ~ 1.637) significantly increased the risk of chronic kidney disease (CKD).

CONCLUSIONS

There were differences in the characteristics of glucolipid metabolism as well as complications among different subgroups of newly diagnosed type 2 diabetes. 2hCP, FCP, FINS, FPG, TyG, HbA1C, HDL and TG influenced the function of insulin. FINS, TyG, 2hINS, and FCP were associated with ASCVD, NAFLD, and CKD in newly diagnosed T2DM patients.

Multi-Tiered Assessment of Gene Expression Provides Evidence for Mechanisms That Underlie Risk for Type 2 Diabetes

Introduction

Integrated transcriptome and microRNA differential gene expression (DEG) analyses may help to explain type 2 diabetes (T2D) pathogenesis in at-risk populations. The purpose of this study was to characterize DEG in banked biospecimens from underactive adult participants who responded to a randomized clinical trial measuring the effects of lifestyle interventions on T2D risk factors. DEGs were further examined within the context of annotated biological pathways.

Methods

Participants (n = 52) in a previously completed clinical trial that assessed a 12-week behavioural intervention for T2D risk reduction were included. Participants who showed >6mg/dL decrease in fasting blood glucose were identified as responders. Gene expression was measured by RNASeq, and overrepresentation analysis within KEGG pathways and weighted gene correlation network analysis (WGCNA) were performed.

Results

No genes remained significantly differentially expressed after correction for multiple comparisons. One module derived by WGCNA related to body mass index was identified, which contained genes located in KEGG pathways related to known mechanisms underlying risk for T2D as well as pathways related to neurodegeneration and protein misfolding. A network analysis showed indirect connections between genes in this module and islet amyloid polypeptide (IAPP), which has previously been hypothesized as a mechanism for T2D.

Discussion

We validated prior studies that showed pathways related to metabolism, inflammation/immunity, and endocrine/hormone function are related to risk for T2D. We identified evidence for new potential mechanisms that include protein misfolding. Additional studies are needed to determine whether these are potential therapeutic targets to decrease risk for T2D.

Precision subclassification of type 2 diabetes: a systematic review

BACKGROUND

Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients.

METHODS

We searched PubMed and Embase for publications that used 'simple subclassification' approaches using simple categorisation of clinical characteristics, or 'complex subclassification' approaches which used machine learning or 'omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches.

RESULTS

Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes.

CONCLUSION

Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.

Machine learning based study for the classification of Type 2 diabetes mellitus subtypes

PURPOSE

Data-driven diabetes research has increased its interest in exploring the heterogeneity of the disease, aiming to support in the development of more specific prognoses and treatments within the so-called precision medicine. Recently, one of these studies found five diabetes subgroups with varying risks of complications and treatment responses. Here, we tackle the development and assessment of different models for classifying Type 2 Diabetes (T2DM) subtypes through machine learning approaches, with the aim of providing a performance comparison and new insights on the matter.

METHODS

We developed a three-stage methodology starting with the preprocessing of public databases NHANES (USA) and ENSANUT (Mexico) to construct a dataset with N = 10,077 adult diabetes patient records. We used N = 2,768 records for training/validation of models and left the remaining (N = 7,309) for testing. In the second stage, groups of observations -each one representing a T2DM subtype- were identified. We tested different clustering techniques and strategies and validated them by using internal and external clustering indices; obtaining two annotated datasets Dset A and Dset B. In the third stage, we developed different classification models assaying four algorithms, seven input-data schemes, and two validation settings on each annotated dataset. We also tested the obtained models using a majority-vote approach for classifying unseen patient records in the hold-out dataset.

RESULTS

From the independently obtained bootstrap validation for Dset A and Dset B, mean accuracies across all seven data schemes were [Formula: see text] ([Formula: see text]) and [Formula: see text] ([Formula: see text]), respectively. Best accuracies were [Formula: see text] and [Formula: see text]. Both validation setting results were consistent. For the hold-out dataset, results were consonant with most of those obtained in the literature in terms of class proportions.

CONCLUSION

The development of machine learning systems for the classification of diabetes subtypes constitutes an important task to support physicians for fast and timely decision-making. We expect to deploy this methodology in a data analysis platform to conduct studies for identifying T2DM subtypes in patient records from hospitals.

Advanced Proteomics and Cluster Analysis for Identifying Novel Obstructive Sleep Apnea Subtypes before and after Continuous Positive Airway Pressure Therapy

Rationale: Studies have shown elevated inflammatory biomarkers in obstructive sleep apnea (OSA), but data after continuous positive airway pressure (CPAP) treatment are inconsistent. Objectives: We used the Olink proteomics panel to identify unique OSA clusters on the basis of inflammatory protein expression and assess the impact of CPAP therapy. Methods: Adults with newly diagnosed OSA had blood drawn at baseline and three to four months after CPAP. Samples were analyzed using the Olink proteomics platform, which measures 92 prespecified inflammatory proteins using proximity extension assay. Linear mixed-effects models were used to model changes in protein expression during the period of CPAP use, adjusting for batch, age, and sex. Unsupervised hierarchical clustering was performed to identify unique inflammatory OSA clusters on the basis of inflammatory biomarkers. Within-cluster impact of CPAP on inflammatory protein expression was assessed. Results: Among 46 patients, the mean age was 46 ± 12 years (22% women), mean body mass index was 31 ± 5 kg/m2, and mean respiratory disturbance index was 33 ± 17 events/hour. Unsupervised cluster and heatmap analysis revealed three unique proteomic clusters, with low (n = 21), intermediate (n = 19), and high (n = 6) inflammatory protein expression. After CPAP, there were significant within-cluster differences in protein expression. The low inflammatory cluster had a significant increase in protein expression (16%; P = 0.02), and the high inflammatory cluster had a significant decrease in protein expression (-20%; P = 0.003), more significant among those compliant with CPAP in the low (25%; P = 0.04) and high (-22%; P = 0.01) clusters. Conclusions: We identified three unique inflammatory clusters in patients with OSA using plasma proteomics, with a differential response to CPAP by cluster. Our results are hypothesis generating and require further investigation in larger longitudinal studies for enhanced cardiovascular risk profiling in OSA.

Diabetes and climate change: current evidence and implications for people with diabetes, clinicians and policy stakeholders

Climate change will be a major challenge for the world's health systems in the coming decades. Elevated temperatures and increasing frequencies of heat waves, wildfires, heavy precipitation and other weather extremes can affect health in many ways, especially if chronic diseases are already present. Impaired responses to heat stress, including compromised vasodilation and sweating, diabetes-related comorbidities, insulin resistance and chronic low-grade inflammation make people with diabetes particularly vulnerable to environmental risk factors, such as extreme weather events and air pollution. Additionally, multiple pathogens show an increased rate of transmission under conditions of climate change and people with diabetes have an altered immune system, which increases the risk for a worse course of infectious diseases. In this review, we summarise recent studies on the impact of climate-change-associated risk for people with diabetes and discuss which individuals may be specifically prone to these risk conditions due to their clinical features. Knowledge of such high-risk groups will help to develop and implement tailored prevention and management strategies to mitigate the detrimental effect of climate change on the health of people with diabetes.

SYSTEMI - systemic organ communication in STEMI: design and rationale of a cohort study of patients with ST-segment elevation myocardial infarction

BACKGROUND

ST-segment elevation myocardial infarction (STEMI) still causes significant mortality and morbidity despite best-practice revascularization and adjunct medical strategies. Within the STEMI population, there is a spectrum of higher and lower risk patients with respect to major adverse cardiovascular and cerebral events (MACCE) or re-hospitalization due to heart failure. Myocardial and systemic metabolic disorders modulate patient risk in STEMI. Systematic cardiocirculatory and metabolic phenotyping to assess the bidirectional interaction of cardiac and systemic metabolism in myocardial ischemia is lacking.

METHODS

Systemic organ communication in STEMI (SYSTEMI) is an all-comer open-end prospective study in STEMI patients > 18 years of age to assess the interaction of cardiac and systemic metabolism in STEMI by systematically collecting data on a regional and systemic level. Primary endpoint will be myocardial function, left ventricular remodelling, myocardial texture and coronary patency at 6 month after STEMI. Secondary endpoint will be all-cause death, MACCE, and re-hospitalisation due to heart failure or revascularisation assessed 12 month after STEMI. The objective of SYSTEMI is to identify metabolic systemic and myocardial master switches that determine primary and secondary endpoints. In SYSTEMI 150-200 patients are expected to be recruited per year. Patient data will be collected at the index event, within 24 h, 5 days as well as 6 and 12 months after STEMI. Data acquisition will be performed in multilayer approaches. Myocardial function will be assessed by using serial cardiac imaging with cineventriculography, echocardiography and cardiovascular magnetic resonance. Myocardial metabolism will be analysed by multi-nuclei magnetic resonance spectroscopy. Systemic metabolism will be approached by serial liquid biopsies and analysed with respect to glucose and lipid metabolism as well as oxygen transport. In summary, SYSTEMI enables a comprehensive data analysis on the levels of organ structure and function alongside hemodynamic, genomic and transcriptomic information to assess cardiac and systemic metabolism.

DISCUSSION

SYSTEMI aims to identify novel metabolic patterns and master-switches in the interaction of cardiac and systemic metabolism to improve diagnostic and therapeutic algorithms in myocardial ischemia for patient-risk assessment and tailored therapy.

TRIAL REGISTRATION

Trial Registration Number: NCT03539133.

C-reactive protein partially mediates the inverse association between coffee consumption and risk of type 2 diabetes: The UK Biobank and the Rotterdam study cohorts

BACKGROUND

Coffee is among the most consumed beverages worldwide. Coffee consumption has been associated with lower risk of type 2 diabetes mellitus (T2D), but underlying mechanisms are not well understood. We aimed to study the role of classic and novel-T2D biomarkers with anti- or pro-inflammatory activity in the association between habitual coffee intake and T2D risk. Furthermore, we studied differences by coffee types and smoking status in this association.

METHODS

Using two large population-based cohorts, the UK-Biobank (UKB; n = 145,368) and the Rotterdam Study (RS; n = 7111), we investigated associations of habitual coffee consumption with incident T2D and repeated measures of insulin resistance (HOMA-IR), using Cox proportional hazards and mixed effect models, respectively. Additionally, we studied associations between coffee and subclinical inflammation biomarkers including C-reactive protein (CRP) and IL-13, and adipokines, such as adiponectin and leptin, using linear regression models. Next, we performed formal causal mediation analyses to investigate the role of coffee-associated biomarkers in the association of coffee with T2D. Finally, we evaluated effect modification by coffee type and smoking. All models were adjusted for sociodemographic, lifestyle and health-related factors.

RESULTS

During a median follow-up of 13.9 (RS) and 7.4 (UKB) years, 843 and 2290 incident T2D cases occurred, respectively. A 1 cup/day increase in coffee consumption was associated with 4% lower T2D risk (RS, HR = 0.96 [95%CI 0.92; 0.99], p = 0.045; UKB, HR = 0.96 [0.94; 0.98], p < 0.001), with lower HOMA-IR (RS, log-transformed β = -0.017 [-0.024;-0.010], p < 0.001), and with lower CRP (RS, log-transformed β = -0.014 [-0.022;-0.005], p = 0.002; UKB, β = -0.011 [-0.012;-0.009], p < 0.001). We also observed associations of higher coffee consumption with higher serum adiponectin and IL-13 concentrations, and with lower leptin concentrations. Coffee-related CRP levels partially mediated the inverse association of coffee intake with T2D incidence (average mediation effect RS β = 0.105 (0.014; 0.240), p = 0.016; UKB β = 6.484 (4.265; 9.339), p < 0.001), with a proportion mediated by CRP from 3.7% [-0.012%; 24.4%] (RS) to 9.8% [5,7%; 25.8%] (UKB). No mediation effect was observed for the other biomarkers. Coffee-T2D and coffee-CRP associations were generally stronger among consumers of ground (filtered or espresso) coffee and among never and former smokers.

CONCLUSIONS

Lower subclinical inflammation may partially mediate the beneficial association between coffee consumption and lower T2D risk. Consumers of ground coffee and non-smokers may benefit the most. KEYWORDS (MESH TERMS): coffee consumptions; diabetes mellitus, type 2; inflammation; adipokines; biomarkers; mediation analysis; follow-up studies.

Systematic review of precision subclassification of type 2 diabetes

Heterogeneity in type 2 diabetes presentation, progression and treatment has the potential for precision medicine interventions that can enhance care and outcomes for affected individuals. We undertook a systematic review to ascertain whether strategies to subclassify type 2 diabetes are associated with improved clinical outcomes, show reproducibility and have high quality evidence. We reviewed publications that deployed 'simple subclassification' using clinical features, biomarkers, imaging or other routinely available parameters or 'complex subclassification' approaches that used machine learning and/or genomic data. We found that simple stratification approaches, for example, stratification based on age, body mass index or lipid profiles, had been widely used, but no strategy had been replicated and many lacked association with meaningful outcomes. Complex stratification using clustering of simple clinical data with and without genetic data did show reproducible subtypes of diabetes that had been associated with outcomes such as cardiovascular disease and/or mortality. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into meaningful groups. More studies are needed to test these subclassifications in more diverse ancestries and prove that they are amenable to interventions.

Screening for Diabetes Mellitus in Patients with Hidradenitis Suppurativa—A Monocentric Study in Germany

Hidradenitis suppurativa (HS) is a chronic skin disease that is often associated with metabolic disorders. Diabetes mellitus (DM) is a frequent comorbidity in HS. There is currently no established screening for DM in HS patients. The aim of our study was to identify high-risk groups of HS patients that develop DM and to assess the frequency of different types of DM present in HS patients. To do so, we conducted a monocentric study in 99 patients with HS. All patients underwent detailed clinical and laboratory assessments, including the determination of glycated hemoglobin. Among the 20.2% of patients that presented with DM, type 2 was by far the most prevalent (19 out of 20 patients). Moreover, male gender, age, BMI, Hurley stage, modified Hidradenitis Suppurativa Score (mHSS), DLQI and hypertension all correlated with the glycated hemoglobin levels in the HS patients. In the multivariable analysis, Hurley stage III, older age, and higher BMI were significantly associated with DM. Specifically, patients at Hurley stage III were at a 5.3-fold increased risk of having DM type II compared to patients at earlier Hurley stages. Since many of the HS patients had not been diagnosed, our study reveals shortcomings in the screening for DM and suggest that this should be routinely performed in HS patients at high risk to avoid secondary complications.

Identifying blood biomarkers for type 2 diabetes subtyping: a report from the ORIGIN trial

AIMS/HYPOTHESIS

Individuals with diabetes can be clustered into five subtypes using up to six routinely measured clinical variables. We hypothesised that circulating protein levels might be used to distinguish between these subtypes. We recently used five of these six variables to categorise 7017 participants from the Outcome Reduction with an Initial Glargine Intervention (ORIGIN) trial into these subtypes: severe autoimmune diabetes (SAID, n=241), severe insulin-deficient diabetes (SIDD, n=1594), severe insulin-resistant diabetes (SIRD, n=914), mild obesity-related diabetes (MOD, n=1595) and mild age-related diabetes (MARD, n=2673).

METHODS

Forward-selection logistic regression models were used to identify a subset of 233 cardiometabolic protein biomarkers that were independent determinants of one subtype vs the others. We then assessed the performance of adding identified biomarkers (one after one, from the most discriminant to the least) to predict each subtype vs the others using area under the receiver operating characteristic curve (AUC ROC). Models were adjusted for age, sex, ethnicity, C-peptide level, diabetes duration and glucose-lowering medication usage at blood collection.

RESULTS

A total of 25 biomarkers were independent determinants of subtypes, including 13 for SIDD, 2 for SIRD, 7 for MOD and 11 for MARD (all p<4.3 × 10^-5). The performance of the biomarker sets (comprising 1 to 25 biomarkers), assessed through the AUC ROC, ranged from 0.611 to 0.734, 0.723 to 0.861, 0.672 to 0.742, and 0.651 to 0.751, for SIDD, SIRD, MOD and MARD, respectively. No biomarkers other than GAD antibodies were determinants of SAID.

CONCLUSIONS/INTERPRETATION

We identified 25 serum biomarkers, as independent determinants of type 2 diabetes subtypes, that could be combined into a diagnostic test for subtyping.

TRIAL REGISTRATION

ORIGIN trial, ClinicalTrials.gov NCT00069784.

Inflammatory and immune etiology of type 2 diabetes

Type 2 diabetes (T2D) represents a global threat affecting millions of patients worldwide. However, its causes remain incompletely dissected and we lack the tools to predict which individuals will develop T2D. Although there is a clear proven clinical association of T2D with metabolic disorders such as obesity and nonalcoholic fatty liver disease (NAFLD), the existence of a significant number of nondiabetic obese subjects suggests yet-uncovered features of such relationships. Here, we propose that a significant proportion of individuals may harbor an immune profile that renders them susceptible to developing T2D. We note the heterogeneity of circulating monocytes and tissue macrophages in organs that are key to metabolic disorders such as liver, white adipose tissue (WAT), and endocrine pancreas, as well as their contribution to T2D genesis.

Clinical variable-based cluster analysis identifies novel subgroups with a distinct genetic signature, lipidomic pattern and cardio-renal risks in Asian patients with recent-onset type 2 diabetes

AIMS/HYPOTHESIS

We sought to subtype South East Asian patients with type 2 diabetes by de novo cluster analysis on clinical variables, and to determine whether the novel subgroups carry distinct genetic and lipidomic features as well as differential cardio-renal risks.

METHODS

Analysis by k-means algorithm was performed in 687 participants with recent-onset diabetes in Singapore. Genetic risk for beta cell dysfunction was assessed by polygenic risk score. We used a discovery-validation approach for the lipidomics study. Risks for cardio-renal complications were studied by survival analysis.

RESULTS

Cluster analysis identified three novel diabetic subgroups, i.e. mild obesity-related diabetes (MOD, 45%), mild age-related diabetes with insulin insufficiency (MARD-II, 36%) and severe insulin-resistant diabetes with relative insulin insufficiency (SIRD-RII, 19%). Compared with the MOD subgroup, MARD-II had a higher polygenic risk score for beta cell dysfunction. The SIRD-RII subgroup had higher levels of sphingolipids (ceramides and sphingomyelins) and glycerophospholipids (phosphatidylethanolamine and phosphatidylcholine), whereas the MARD-II subgroup had lower levels of sphingolipids and glycerophospholipids but higher levels of lysophosphatidylcholines. Over a median of 7.3 years follow-up, the SIRD-RII subgroup had the highest risks for incident heart failure and progressive kidney disease, while the MARD-II subgroup had moderately elevated risk for kidney disease progression.

CONCLUSIONS/INTERPRETATION

Cluster analysis on clinical variables identified novel subgroups with distinct genetic, lipidomic signatures and varying cardio-renal risks in South East Asian participants with type 2 diabetes. Our study suggests that this easily actionable approach may be adapted in other ethnic populations to stratify the heterogeneous type 2 diabetes population for precision medicine.

Increased serum S100A12 levels are associated with higher risk of acute heart failure in patients with type 2 diabetes

AIMS

The hyperglycaemic stress induces the release of inflammatory proteins such as S100A12, one of the endogenous ligands of the receptors for advanced glycation end products (RAGE). Chronic activation of RAGE has multiple deleterious effects in target tissues such as the heart and the vessels by promoting oxidative stress, inflammation by the release of cytokines, macrophages infiltration, and vascular cell migration and proliferation, causing ultimately endothelial cell and cardiomyocyte dysfunction. The aim of our study was to investigate the prognostic value of circulating S100A12 beyond established cardiovascular risk factors (CVRF) for heart failure (HF) and major adverse cardiovascular events (MACE) in a cohort of patients with type 2 diabetes.

METHODS AND RESULTS

Serum S100A12 concentrations were measured at baseline in 1345 type 2 diabetes patients (58% men, 64 ± 11 years) recruited in the SURDIAGENE prospective cohort. Endpoints were the occurrence of acute HF requiring hospitalization (HHF) and MACE. We used a proportional hazard model adjusted for established CVRF (age, sex, duration of diabetes, estimated glomerular filtration rate, albumin/creatinine ratio, history of coronary artery disease) and serum S100A12. During the median follow-up of 84 months, 210 (16%) and 505 (38%) patients developed HHF and MACE, respectively. Baseline serum S100A12 concentrations were associated with an increased risk of HHF [hazard ratio (HR) (95% confidence interval) 1.28 (1.01-1.62)], but not MACE [1.04 (0.90-1.20)]. After adjustment for CVRF, S100A12 concentrations remained significantly associated with an increased risk of HHF [1.29 (1.01-1.65)]. In a sub-analysis, patients with high probability of pre-existing HF [N terminal pro brain natriuretic peptide (NT-proBNP) >1000 pg/mL, n = 87] were excluded. In the remaining 1258 patients, the association of serum S100A12 with the risk of HHF tended to be more pronounced [1.39 (1.06-1.83)]. When including the gold standard HF marker NT-proBNP in the model, the prognostic value of S100A12 for HHF did not reach significance. Youden method performed at 7 years for HHF prediction yielded an optimal cut-off for S100A12 concentration of 49 ng/mL (sensitivity 53.3, specificity 52.2). Compared with those with S100A12 ≤ 49 ng/mL, patients with S100A12 > 49 ng/mL had a significantly increased risk of HHF in the univariate model [HR = 1.58 (1.19-2.09), P = 0.0015] but also in the multivariate model [HR = 1.63 (1.23-2.16), P = 0.0008]. After addition of NT-proBNP to the multivariate model, S100A12 > 49 ng/mL remained associated with an increased risk of HHF [HR = 1.42 (1.07-1.90), P = 0.0160]. However, the addition of S100A12 categories on top of multivariate model enriched by NT-pro BNP did not improve the ability of the model to predict HHF (relative integrated discrimination improvement = 1.9%, P = 0.1500).

CONCLUSIONS

In patients with type 2 diabetes, increased serum S100A12 concentration is independently associated with risk of HHF, but not with risk of MACE. Compared with NT-proBNP, the potential clinical interest of S100A12 for the prediction of HF events remains limited. However, S100A12 could be a candidate for a multimarker approach for HF risk assessment in diabetic patients.

A novel diabetes typology: towards precision diabetology from pathogenesis to treatment

The current classification of diabetes, based on hyperglycaemia, islet-directed antibodies and some insufficiently defined clinical features, does not reflect differences in aetiological mechanisms and in the clinical course of people with diabetes. This review discusses evidence from recent studies addressing the complexity of diabetes by proposing novel subgroups (subtypes) of diabetes. The most widely replicated and validated approach identified, in addition to severe autoimmune diabetes, four subgroups designated severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes and mild age-related diabetes subgroups. These subgroups display distinct patterns of clinical features, disease progression and onset of comorbidities and complications, with severe insulin-resistant diabetes showing the highest risk for cardiovascular, kidney and fatty liver diseases. While it has been suggested that people in these subgroups would benefit from stratified treatments, RCTs are required to assess the clinical utility of any reclassification effort. Several methodological and practical issues also need further study: the statistical approach used to define subgroups and derive recommendations for diabetes care; the stability of subgroups over time; the optimal dataset (e.g. phenotypic vs genotypic) for reclassification; the transethnic generalisability of findings; and the applicability in clinical routine care. Despite these open questions, the concept of a new classification of diabetes has already allowed researchers to gain more insight into the colourful picture of diabetes and has stimulated progress in this field so that precision diabetology may become reality in the future.

S100 Proteins in Fatty Liver Disease and Hepatocellular Carcinoma

Non-alcoholic fatty liver disease (NAFLD) is a highly prevalent and slow progressing hepatic pathology characterized by different stages of increasing severity which can ultimately give rise to the development of hepatocellular carcinoma (HCC). Besides drastic lifestyle changes, few drugs are effective to some extent alleviate NAFLD and HCC remains a poorly curable cancer. Among the deregulated molecular mechanisms promoting NAFLD and HCC, several members of the S100 proteins family appear to play an important role in the development of hepatic steatosis, non-alcoholic steatohepatitis (NASH) and HCC. Specific members of this Ca²⁺-binding protein family are indeed significantly overexpressed in either parenchymal or non-parenchymal liver cells, where they exert pleiotropic pathological functions driving NAFLD/NASH to severe stages and/or cancer development. The aberrant activity of S100 specific isoforms has also been reported to drive malignancy in liver cancers. Herein, we discuss the implication of several key members of this family, e.g., S100A4, S100A6, S100A8, S100A9 and S100A11, in NAFLD and HCC, with a particular focus on their intracellular versus extracellular functions in different hepatic cell types. Their clinical relevance as non-invasive diagnostic/prognostic biomarkers for the different stages of NAFLD and HCC, or their pharmacological targeting for therapeutic purpose, is further debated.

Physical Fitness and Cardiovascular Risk Factors in Novel Diabetes Subgroups

CONTEXT

Physical inactivity promotes insulin resistance and increases the risk of diabetes and cardiovascular disease. Recently introduced clustering based on simple clinical measures identified diabetes subgroups (clusters) with different risks of diabetes-related comorbidities and complications.

OBJECTIVE

This study aims to determine differences in physical fitness and cardiovascular risk between diabetes subgroups and a glucose-tolerant control group (CON). We hypothesized that the severe insulin-resistant diabetes (SIRD) subgroup would be associated with lower physical fitness and increased cardiovascular risk.

METHODS

The physical fitness and cardiovascular risk of 746 participants with recent-onset diabetes (diabetes duration of < 12 months, aged 18-69 years) and 74 CONs of the German Diabetes Study (GDS), a prospective longitudinal cohort study, were analyzed. Main outcome measures included physical fitness (VO2max from spiroerogometry), endothelial function (flow- and nitroglycerin-mediated dilation), and cardiovascular risk scores (Framingham Risk Scores for Coronary Heart Disease [FRS-CHD] and Atherosclerotic CardioVascular Disease [ASCVD] risk score).

RESULTS

VO2max was lower in SIRD than in CON, severe autoimmune diabetes (SAID) (both P < .001), and mild age-related diabetes (MARD) (P < .01) subgroups, but not different compared to severe insulin-deficient diabetes (SIDD) (P = .98) and moderate obesity-related diabetes (MOD) subgroups (P = .07) after adjustment for age, sex, and body mass index. Endothelial function was similar among all groups, whereas SAID had lower FRS-CHD and ASCVD than SIRD, MOD, and MARD (all P < .001).

CONCLUSION

Despite comparable endothelial function across all groups, SIRD showed the lowest physical fitness. Of note, SAID had the lowest cardiovascular risk within the first year after diabetes diagnosis compared to the other diabetes subgroups.

The Role of Racial and Ethnic Factors in MicroRNA Expression and Risk for Type 2 Diabetes

Prior studies focused on circulating microRNAs and the risk for complex diseases have shown inconsistent findings. The majority of studies focused on European and East Asian racial or ethnic groups, however, ancestry was not typically reported. We evaluated the risk for type 2 diabetes as an exemplar to show that race and ethnic group may contribute to inconsistent validation of previous findings of associations with microRNAs.

Co‑expressed microRNAs, target genes and pathways related to metabolism, inflammation and endocrine function in individuals at risk for type 2 diabetes

MicroRNAs (miRNAs) may be considered important regulators of risk for type 2 diabetes (T2D). The aim of the present study was to identify novel sets of miRNAs associated with T2D risk, as well as their gene and pathway targets. Circulating miRNAs (n=59) were measured in plasma from participants in a previously completed clinical trial (n=82). An agnostic statistical approach was applied to identify novel sets of miRNAs with optimal co-expression patterns. In silico analyses were used to identify the messenger RNA and biological pathway targets of the miRNAs within each factor. A total of three factors of miRNAs were identified, containing 18, seven and two miRNAs each. Eight biological pathways were revealed to contain genes targeted by the miRNAs in all three factors, 38 pathways contained genes targeted by the miRNAs in two factors, and 55, 18 and two pathways were targeted by the miRNAs in a single factor, respectively (all q<0.05). The pathways containing genes targeted by miRNAs in the largest factor shared a common theme of biological processes related to metabolism and inflammation. By contrast, the pathways containing genes targeted by miRNAs in the second largest factor were related to endocrine function and hormone activity. The present study focused on the pathways uniquely targeted by each factor of miRNAs in order to identify unique mechanisms that may be associated with a subset of individuals. Further exploration of the genes and pathways related to these biological themes may provide insights about the subtypes of T2D and lead to the identification of novel therapeutic targets.

Differences in the prevalence of erectile dysfunction between novel subgroups of recent-onset diabetes

AIMS/HYPOTHESIS

In men with diabetes, the prevalence of erectile dysfunction increases with advanced age and longer diabetes duration and is substantially higher in men with type 2 diabetes than those with type 1 diabetes. This study aimed to evaluate the prevalence of erectile dysfunction among the five novel subgroups of recent-onset diabetes and determine the strength of associations between diabetes subgroups and erectile dysfunction.

METHODS

A total of 351 men with recent-onset diabetes (<1 year) from the German Diabetes Study baseline cohort and 124 men without diabetes were included in this cross-sectional study. Erectile dysfunction was assessed with the International Index of Erectile Function (IIEF) questionnaire. Poisson regression models were used to estimate associations between diabetes subgroups (each subgroup tested against the four other subgroups as reference) and erectile dysfunction (dependent binary variable), adjusting for variables used to define diabetes subgroups, high-sensitivity C-reactive protein and depression.

RESULTS

The prevalence of erectile dysfunction was markedly higher in men with diabetes than in men without diabetes (23% vs 11%, p = 0.004). Among men with diabetes, the prevalence of erectile dysfunction was highest in men with severe insulin-resistant diabetes (SIRD) (52%), lowest in men with severe autoimmune diabetes (SAID) (7%), and intermediate in men with severe insulin-deficient diabetes (SIDD), mild obesity-related diabetes (MOD) and mild age-related diabetes (MARD) (31%, 18% and 29%, respectively). Men with SIRD had an adjusted RR of 1.93 (95% CI 1.04, 3.58) for prevalent erectile dysfunction (p = 0.038). Similarly, men with SIDD had an adjusted RR of 3.27 (95% CI 1.18, 9.10) (p = 0.023). In contrast, men with SAID and those with MARD had unadjusted RRs of 0.26 (95% CI 0.11, 0.58) (p = 0.001) and 1.52 (95% CI 1.04, 2.22) (p = 0.027), respectively. However, these associations did not remain statistically significant after adjustment.

CONCLUSIONS/INTERPRETATION

The high RRs for erectile dysfunction in men with recent-onset SIRD and SIDD point to both insulin resistance and insulin deficiency as major contributing factors to this complication, suggesting different mechanisms underlying erectile dysfunction in these subgroups.

Islet Inflammation and β Cell Dysfunction in Type 2 Diabetes

Pancreatic islets are the body's central rheostat that regulates glucose homeostasis through the production of different hormones, including β cell-derived insulin. During obesity-induced type 2 diabetes (T2D), islet β cells become dysfunctional and inadequate insulin secretion no longer ensures glycemic control. T2D is associated with a chronic low-grade inflammation that manifests in several metabolic organs including the pancreatic islets. Growing evidence suggests that components of the innate immune system, and especially macrophages, play a crucial role in regulating islet homeostasis. Yet, the phenotypes and functions of islet macrophages in physiology and during T2D have only started to attract attention and remain unclear. In this review, the current knowledge about islet inflammation and macrophages will be summarized in humans and rodent models. Recent findings on the cellular and molecular mechanisms involved in islet remodeling and β cell function during obesity and T2D will be discussed.

Leukocyte Counts and T-Cell Frequencies Differ Between Novel Subgroups of Diabetes and Are Associated With Metabolic Parameters and Biomarkers of Inflammation

Frequencies of circulating immune cells are altered in those with type 1 and type 2 diabetes compared with healthy individuals and are associated with insulin sensitivity, glycemic control, and lipid levels. This study aimed to determine whether specific immune cell types are associated with novel diabetes subgroups. We analyzed automated white blood cell counts (n = 669) and flow cytometric data (n = 201) of participants in the German Diabetes Study with recent-onset (<1 year) diabetes, who were allocated to five subgroups based on data-driven analysis of clinical variables. Leukocyte numbers were highest in severe insulin-resistant diabetes (SIRD) and mild obesity-related diabetes (MOD) and lowest in severe autoimmune diabetes (SAID). CD4⁺ T-cell frequencies were higher in SIRD versus SAID, MOD, and mild age-related diabetes (MARD), and frequencies of CCR4⁺ regulatory T cells were higher in SIRD versus SAID and MOD and in MARD versus SAID. Pairwise differences between subgroups were partially explained by differences in clustering variables. Frequencies of CD4⁺ T cells were positively associated with age, BMI, HOMA2 estimate of β-cell function (HOMA2-B), and HOMA2 estimate of insulin resistance (HOMA2-IR), and frequencies of CCR4⁺ regulatory T cells with age, HOMA2-B, and HOMA2-IR. In conclusion, different leukocyte profiles exist between novel diabetes subgroups and suggest distinct inflammatory processes in these diabetes subgroups.

The Interplay between Housing Environmental Attributes and Design Exposures and Psychoneuroimmunology Profile-An Exploratory Review and Analysis Paper in the Cancer Survivors' Mental Health Morbidity Context

Adult cancer survivors have an increased prevalence of mental health comorbidities and other adverse late-effects interdependent with mental illness outcomes compared with the general population. Coronavirus Disease 2019 (COVID-19) heralds an era of renewed call for actions to identify sustainable modalities to facilitate the constructs of cancer survivorship care and health care delivery through physiological supportive domestic spaces. Building on the concept of therapeutic architecture, psychoneuroimmunology (PNI) indicators-with the central role in low-grade systemic inflammation-are associated with major psychiatric disorders and late effects of post-cancer treatment. Immune disturbances might mediate the effects of environmental determinants on behaviour and mental disorders. Whilst attention is paid to the non-objective measurements for examining the home environmental domains and mental health outcomes, little is gathered about the multidimensional effects on physiological responses. This exploratory review presents a first analysis of how addressing the PNI outcomes serves as a catalyst for therapeutic housing research. We argue the crucial component of housing in supporting the sustainable primary care and public health-based cancer survivorship care model, particularly in the psychopathology context. Ultimately, we illustrate a series of interventions aiming at how housing environmental attributes can trigger PNI profile changes and discuss the potential implications in the non-pharmacological treatment of cancer survivors and patients with mental morbidities.

Understanding the heterogeneity and functions of metabolic tissue macrophages

Growing evidence places tissue-resident macrophages as essential gatekeepers of metabolic organ homeostasis, including the adipose tissue and the pancreatic islets. Therein, macrophages may adopt specific phenotypes and ensure local functions. Recent advances in single cell genomic analyses provide a comprehensive map of adipose tissue macrophage subsets and their potential roles are now better apprehended. Whether they are beneficial or detrimental, macrophages overall contribute to the proper adipose tissue expansion under steady state and during obesity. By contrast, macrophages residing inside pancreatic islets, which may exert fundamental functions to fine tune insulin secretion, have only started to attract attention and their cellular heterogeneity remains to be established. The present review will focus on the latest findings exploring the phenotype and the properties of macrophages in adipose tissue and pancreatic islets, questioning early beliefs and future perspectives in the field of immunometabolism.