Lower Hepatic Fat Is Associated With Improved Insulin Secretion in a High-Risk Prediabetes Subphenotype During Lifestyle Intervention

Subphenotype-Dependent Benefits of Bariatric Surgery for Individuals at Risk for Type 2 Diabetes

OBJECTIVE

Bariatric surgery is an effective treatment option for individuals with obesity and type 2 diabetes (T2D). However, whether outcomes in subtypes of individuals at risk for T2D and/or comorbidities (Tübingen Clusters) differ, is unknown. Of these, cluster 5 (C5) and cluster 6 (C6) are high-risk clusters for developing T2D and/or comorbidities, while cluster 4 (C4) is a low-risk cluster. We investigated bariatric surgery outcomes, hypothesizing that high-risk clusters benefit most due to great potential for metabolic improvement.

RESEARCH DESIGN AND METHODS

We allocated participants without T2D but at risk for T2D, defined by elevated BMI, to the Tübingen Clusters. Participants had normal glucose regulation or prediabetes according to American Diabetes Association criteria. Two cohorts underwent bariatric surgery: a discovery (Lille, France) and a replication cohort (Rome, Italy). A control cohort (Tübingen, Germany) received behavioral modification counseling. Main outcomes included alteration of glucose regulation parameters and prediabetes remission.

RESULTS

In the discovery cohort, 15.0% of participants (n = 121) were allocated to C4, 22.3% (n = 180) to C5, and 62.4% (n = 503) to C6. Relative body weight loss was similar among all clusters; however, reduction of insulin resistance and improvement of β-cell function were strongest in C5. Prediabetes remission rate was lowest in low-risk C4 and highest in high-risk C5. Individuals from high-risk clusters changed to low-risk clusters in both bariatric surgery cohorts but not in the control cohort.

CONCLUSIONS

Participants in C5 had the highest benefit from bariatric surgery in terms of improvement in insulin resistance, β-cell function, and prediabetes remission. This novel classification might help identify individuals who will benefit specifically from bariatric surgery.

Differential risk assessment in persons at risk of type 2 diabetes using urinary peptidomics

OBJECTIVE

Individuals at increased risk of type 2 diabetes have recently been classified into six prediabetes clusters, which stratify the risk of progression to diabetes and diabetes complications. Clusters 1, 2 and 4 are low-risk clusters while clusters 3, 5 and 6 are high-risk clusters; individuals in cluster 6 have an elevated risk of nephropathy and all-cause mortality despite delayed onset of diabetes. The urinary peptidome classifiers CKD273 (chronic kidney disease, CKD), HF2 (heart failure, HF) and CAD238 (coronary artery disease, CAD) are based on unique urinary peptide patterns and have shown potential for identifying individuals at risk for CKD and cardiovascular pathologies. This observational study investigates whether peptidome classifiers can differentiate complication risks across the prediabetes clusters and if a novel combination of peptides can distinguish high-risk from low-risk prediabetes clusters.

METHODS

Urine peptidome analysis was performed on spot urine samples from individuals across 6 prediabetes clusters (n = 249) and 19 individuals with screen-detected diabetes (study cohorts at University Hospital Tübingen, Germany from 11/2004 to 11/2012). Predefined urinary classifiers were calculated for each participant. Lasso regression analysis was used to identify an optimal combination of peptides distinguishing low- Schlesinger et al. (2022), Wagner et al. (2021) [1,2,4] and high-risk (Rooney et al., 2021; Wagner, 2023; Latosinska et al., 2021 [3,5,6]) clusters.

RESULTS

The predefined urinary peptidome classifiers CKD273, HF2 and CAD238 differed significantly across prediabetes clusters, particularly with elevated values in cluster 6 compared to the healthiest cluster 2. CKD273, HF2 and CAD238 were inversely associated with insulin sensitivity indexes. Machine Learning identified a combination of 112 urinary peptides that differentiated low-risk from high-risk prediabetes clusters (AUC-ROC 0.868 (95 % CI 0.755-0.981)).

CONCLUSIONS

Urinary peptidome classifiers support the increased risk of CKD and suggest an elevated risk of heart failure and coronary artery disease in the high-risk prediabetes cluster 6. Urine peptidomics show promising potential as a tool for identifying high-risk prediabetes individuals and guiding early preventive interventions.

Prediabetes remission to reduce the global burden of type 2 diabetes

Prediabetes is a highly prevalent and increasingly common condition affecting a significant proportion of the global population. The heterogeneous nature of prediabetes presents a challenge in identifying individuals who particularly benefit from lifestyle or other therapeutic interventions aiming at preventing type 2 diabetes (T2D) and associated comorbidities. The phenotypic characteristics of individuals at risk for diabetes are associated with both specific risk profiles for progression and a differential potential to facilitate prediabetes remission and reduce the risk of future T2D. This review examines the current definition and global prevalence of prediabetes and evaluates the potential of prediabetes remission to reduce the alarming increase in the global burden of T2D.

Health and Liver Diagnostic Markers Influencing Glycemia in Subjects with Prediabetes: Preview Study

INTRODUCTION

Glucose homeostasis may be dependent on liver conditions and influence health-related markers and quality of life (QoL) objective measurements. This study aimed to analyze the interactions of glycemia with liver and health status in a prediabetic population.

SUBJECTS AND METHODS

This study included 2220 overweight/obese prediabetics from the multinational PREVIEW project. Anthropometrics; clinical, metabolic and other health-related markers; and QoL variables were analyzed. Univariate and multilinear-adjusted regression models were run to explain the interrelationships and effect modification between glycemia, health-related QoL (applying SF-12) and metabolic/liver health (using the HSI, a putative marker of fatty liver).

RESULTS

Relevant age/sex interactions were found concerning the levels of insulin, HOMA-IR, C peptide and transaminases in this prediabetic population. Multivariate models identified age, sex, glucose, WC and QoL as important predictors of HSI variability (adj. R value = 0.1393, p < 0.001), whereas the QoL status was statistically related to age, sex, HOMA-IR and HSI (adj. R value = 0.1130, p < 0.001) in this glycemia-impaired group. Furthermore, the QoL values declined with increased HSI scores, where a significant interaction was found (p = 0.011) when the data were analyzed when comparing lower glycemia vs. higher glycemia in prediabetics. Indeed, an effect modification was featured depending on the glycemia levels concerning the QoL and HSI worsening.

CONCLUSION

Glycemia associations with the QoL status and liver metabolism markers were evidenced, with clinical implications for diabetes and liver disease precision management given the modification of the QoL outcomes depending on the liver status and glycemia concentrations. Notably, independent associations of circulating glucose with age, sex, adiposity, inflammation and C peptide levels were found.

The insulin resistant brain: impact on whole-body metabolism and body fat distribution

Insulin exerts its actions not only on peripheral organs but is also transported into the brain where it performs distinct functions in various brain regions. This review highlights recent advancements in our understanding of insulin's actions within the brain, with a specific emphasis on investigations in humans. It summarises current knowledge on the transport of insulin into the brain. Subsequently, it showcases robust evidence demonstrating the existence and physiological consequences of brain insulin action, while also introducing the presence of brain insulin resistance in humans. This pathophysiological condition goes along with an impaired acute modulation of peripheral metabolism in response to brain insulin action, particularly in the postprandial state. Furthermore, brain insulin resistance has been associated with long-term adiposity and an unfavourable adipose tissue distribution, thus implicating it in the pathogenesis of subgroups of obesity and (pre)diabetes that are characterised by distinct patterns of body fat distribution. Encouragingly, emerging evidence suggests that brain insulin resistance could represent a treatable entity, thereby opening up novel therapeutic avenues to improve systemic metabolism and enhance brain functions, including cognition. The review closes with an outlook towards prospective research directions aimed at further elucidating the clinical implications of brain insulin resistance. It emphasises the critical need to establish feasible diagnostic measures and effective therapeutic interventions.

Mechanisms of weight loss-induced remission in people with prediabetes: a post-hoc analysis of the randomised, controlled, multicentre Prediabetes Lifestyle Intervention Study (PLIS)

BACKGROUND

Remission of type 2 diabetes can occur as a result of weight loss and is characterised by liver fat and pancreas fat reduction and recovered insulin secretion. In this analysis, we aimed to investigate the mechanisms of weight loss- induced remission in people with prediabetes.

METHODS

In this prespecified post-hoc analysis, weight loss-induced resolution of prediabetes in the randomised, controlled, multicentre Prediabetes Lifestyle Intervention Study (PLIS) was assessed, and the results were validated against participants from the Diabetes Prevention Program (DPP) study. For PLIS, between March 1, 2012, and Aug 31, 2016, participants were recruited from eight clinical study centres (including seven university hospitals) in Germany and randomly assigned to receive either a control intervention, a standard lifestyle intervention (ie, DPP-based intervention), or an intensified lifestyle intervention for 12 months. For DPP, participants were recruited from 23 clinical study centres in the USA between July 31, 1996, and May 18, 1999, and randomly assigned to receive either a standard lifestyle intervention, metformin, or placebo. In both PLIS and DPP, only participants who were randomly assigned to receive lifestyle intervention or placebo and who lost at least 5% of their bodyweight were included in this analysis. Responders were defined as people who returned to normal fasting plasma glucose (FPG; <5·6 mmol/L), normal glucose tolerance (<7·8 mmol/L), and HbA1c less than 39 mmol/mol after 12 months of lifestyle intervention or placebo or control intervention. Non-responders were defined as people who had FPG, 2 h glucose, or HbA1c more than these thresholds. The main outcomes for this analysis were insulin sensitivity, insulin secretion, visceral adipose tissue (VAT), and intrahepatic lipid content (IHL) and were evaluated via linear mixed models.

FINDINGS

Of 1160 participants recruited to PLIS, 298 (25·7%) had weight loss of 5% or more of their bodyweight at baseline. 128 (43%) of 298 participants were responders and 170 (57%) were non-responders. Responders were younger than non-responders (mean age 55·6 years [SD 9·9] vs 60·4 years [8·6]; p<0·0001). The DPP validation cohort included 683 participants who lost at least 5% of their bodyweight at baseline. Of these, 132 (19%) were responders and 551 (81%) were non-responders. In PLIS, BMI reduction was similar between responders and non-responders (responders mean at baseline 32·4 kg/m2 [SD 5·6] to mean at 12 months 29·0 kg/m2 [4·9] vs non-responders 32·1 kg/m2 [5·9] to 29·2 kg/m2 [5·4]; p=0·86). However, whole-body insulin sensitivity increased more in responders than in non-responders (mean at baseline 291 mL/[min × m2], SD 60 to mean at 12 months 378 mL/[min × m2], 56 vs 278 mL/[min × m2], 62, to 323 mL/[min × m2], 66; p<0·0001), whereas insulin secretion did not differ within groups over time or between groups (responders mean at baseline 175 pmol/mmol [SD 64] to mean at 12 months 163·7 pmol/mmol [60·6] vs non-responders 158·0 pmol/mmol [55·6] to 154·1 pmol/mmol [56·2]; p=0·46). IHL decreased in both groups, without a difference between groups (responders mean at baseline 10·1% [SD 8·7] to mean at 12 months 3·5% [3·9] vs non-responders 10·3% [8·1] to 4·2% [4·2]; p=0·34); however, VAT decreased more in responders than in non-responders (mean at baseline 6·2 L [SD 2·9] to mean at 12 months 4·1 L [2·3] vs 5·7 L [2·3] to 4·5 L [2·2]; p=0·0003). Responders had a 73% lower risk of developing type 2 diabetes than non-responders in the 2 years after the intervention ended.

INTERPRETATION

By contrast to remission of type 2 diabetes, resolution of prediabetes was characterised by an improvement in insulin sensitivity and reduced VAT. Because return to normal glucose regulation (NGR) prevents development of type 2 diabetes, we propose the concept of remission of prediabetes in analogy to type 2 diabetes. We suggest that remission of prediabetes should be the primary therapeutic aim in individuals with prediabetes.

FUNDING

German Federal Ministry for Education and Research via the German Center for Diabetes Research; the Ministry of Science, Research and the Arts Baden-Württemberg; the Helmholtz Association and Helmholtz Munich; the Cluster of Excellence Controlling Microbes to Fight Infections; and the German Research Foundation.

[Prediabetes as a therapeutic challenge in internal medicine]

The term prediabetes describes a fasting blood glucose level that is elevated but not yet in the diabetic range, a blood glucose level that is elevated after 120 min in a standard 75‑g oral glucose tolerance test, or both. The American Diabetes Association definition also includes glycated hemoglobin A (HbA1c). The incidence of prediabetes is rapidly increasing. Progression from normal glucose tolerance to diabetes is a continuous process. Insulin resistance and insulin secretory dysfunction, the simultaneous presence of which characterizes manifest diabetes, are already present in the prediabetic stage. Prediabetes is associated with an increased risk of diabetes; however, by no means all people with prediabetes go on to develop diabetes. Nevertheless, the identification of an increased risk of diabetes is still relevant insofar as it requires the adoption of diabetes prevention measures. Structured lifestyle intervention has been shown to be the most effective strategy for treating prediabetes. To increase its efficiency, it should, as far as possible, be made exclusively available to those people on whom it is most likely to confer a benefit. This would make it necessary to stratify people with prediabetes according to their risk profile. In a population of people at increased risk of diabetes (Tübingen Diabetes Family Study), a cluster analysis was performed, resulting in six clusters/subgroups. Within these, three high-risk subgroups were identified: Two of these risk groups show predominant insulin secretory dysfunction or predominant insulin resistance and high diabetes and cardiovascular risk. The third group shows a high risk of nephropathy and high mortality, but a comparatively lower diabetes risk. In general, prediabetes cannot yet be treated in a targeted pathophysiologically oriented manner. The new classification of prediabetes-based on pathophysiology-is now opening up new avenues for diabetes prevention. Current and future studies should confirm the assumption that the effectiveness of established, or not yet established, preventive measures depends on the respective subgroup.

Deconvoluting prediabetes: A path to understanding the origins of complications

A study of 56,000 people with prediabetes in China stratified the population into 6 clusters, among which were two clusters of impaired insulin secretion and a cluster with hyperinsulinemic insulin resistance.¹ A follow-up showed different risk of complications, demonstrating prediabetes heterogeneity.