Novel strategies for glycaemic control and preventing diabetic complications applying the clustering-based classification of adult-onset diabetes mellitus: A perspective

A collectanea of food insulinaemic index: 2023

BACKGROUND AND AIMS

To systematically update and publish the lnsulinaemic Index (II) value compilation of food/beverages.

METHODS

A literature search identified around 400 scholarly articles published between inception and December 2023. II values were pooled according to the selection criteria of at least 10 healthy, non-diabetic subjects with normal BMI. In addition, the II reported should have been derived from incremental area under the curve (iAUC) calculation of the insulin concentration over time. The reference food used from the pooled articles were either glucose or bread.

RESULTS

The II of 629 food/beverage items were found from 80 distinct articles. This is almost a five-fold increase in the number of entries from a previous compilation in 2011. Furthermore, these articles originated from 32 different countries, and were cleaved into 25 food categories. The II values ranged from 1 to 209. The highest overall recorded II was for a soy milk-based infant formula while the lowest was for both acacia fibre and gin. Upon clustering to single food, the infant formula retained the highest II while both acacia fibre and gin maintained the lowest recording. As for mixed meal, a potato dish served with a beverage recorded the highest II while a type of taco served with a sweetener, vegetable and fruit had the lowest II. Our minimum and maximum II data values replace the entries reported by previous compilations.

CONCLUSION

Acknowledging some limitations, these data would facilitate clinical usage of II for various applications in research, clinical nutrition, clinical medicine, diabetology and precision medicine. Future studies concerning II should investigate standardisation of reference food, including glucose and the test food portion. Although this collectanea adds up new food/beverages II values, priority should be given to populate this database.

Differential manifestation of type 2 diabetes in Black Africans and White Europeans with recently diagnosed type 2 diabetes: A systematic review

AIMS

The clinical manifestation of type 2 diabetes (T2D) varies across populations. We compared the phenotypic characteristics of Black Africans and White Europeans with recently diagnosed T2D to understand the ethnic differences in the manifestation of T2D.

METHODS

We searched Medline, EMBASE, CINAHL, Google Scholar, African Index Medicus, and Global Health for studies reporting information on phenotypic characteristics in Black Africans and White Europeans with recently diagnosed T2D.

RESULTS

A total of 28 studies were included in this systematic review (14 studies conducted on 2586 Black Africans in eight countries and 14 studies conducted on 279,621 White Europeans in nine countries). Compared with White Europeans, Black Africans had a lower pooled mean (95 % confidence interval) age (51.5 [48.5-54.4] years vs. 60.2 [57.9-62.6] years), body mass index (27.0 [24.2-29.8] kg/m2 vs. 31.3 [30.5-32.1] kg/m2), and a higher pooled median glycated haemoglobin (9.0 [8.0-10.3]% vs. 7.1 [6.7-7.7]%). Ugandan and Tanzanian participants had lower markers of beta-cell function and insulin resistance when compared with four White European populations.

CONCLUSION

These findings provide evidence of the ethnic differences in the manifestation of T2D, underscoring the importance of understanding the underlying factors influencing these differences and formulating ethnic-specific approaches for managing and preventing T2D.

Cluster analysis of adult individuals with type 1 diabetes: Treatment pathways and complications over a five-year follow-up period

AIMS

To identify subgroups of adults with type 1 diabetes and analyse their treatment pathways and risk of diabetes-related complications over a 5-year follow-up.

METHODS

We performed a k-means cluster analysis using the T1DExchange Registry (n = 6,302) to identify subgroups based on demographic and clinical characteristics. Annual reassessments linked treatment trajectories with these clusters, considering drug and technology use. Complication risks were analysed using Cox regression.

RESULTS

Five clusters were identified: 1) A favourable combination of all variables (31.67 %); 2) Longer diabetes duration (22.63 %); 3) Higher HbA1c levels (13.28 %); 4) Higher BMI (15.25 %); 5) Older age at diagnosis (17.17 %). Two-thirds of patients remained in their initial cluster annually. Technology adoption showed improved glycaemic control over time. Cox proportional hazards showed different risk patterns: Cluster 1 had low complication risk; Cluster 2 had the highest risk for retinopathy, coronary artery disease and autonomic neuropathy; Cluster 3 had the highest risk for albuminuria, depression and diabetic ketoacidosis; Cluster 4 had increased risk for multiple complications; Cluster 5 had the highest risk for hypertension and severe hypoglycaemia, with elevated coronary artery disease risk.

CONCLUSIONS

Clinical characteristics can identify subgroups of patients with T1DM showing differences in treatment and complications during follow-up.

Identification and validation of gestational diabetes subgroups by data-driven cluster analysis

AIMS/HYPOTHESIS

Gestational diabetes mellitus (GDM) is a heterogeneous condition. Given such variability among patients, the ability to recognise distinct GDM subgroups using routine clinical variables may guide more personalised treatments. Our main aim was to identify distinct GDM subtypes through cluster analysis using routine clinical variables, and analyse treatment needs and pregnancy outcomes across these subgroups.

METHODS

In this cohort study, we analysed datasets from a total of 2682 women with GDM treated at two central European hospitals (1865 participants from Charité University Hospital in Berlin and 817 participants from the Medical University of Vienna), collected between 2015 and 2022. We evaluated various clustering models, including k-means, k-medoids and agglomerative hierarchical clustering. Internal validation techniques were used to guide best model selection, while external validation on independent test sets was used to assess model generalisability. Clinical outcomes such as specific treatment needs and maternal and fetal complications were analysed across the identified clusters.

RESULTS

Our optimal model identified three clusters from routinely available variables, i.e. maternal age, pre-pregnancy BMI (BMIPG) and glucose levels at fasting and 60 and 120 min after the diagnostic OGTT (OGTT0, OGTT60 and OGTT120, respectively). Cluster 1 was characterised by the highest OGTT values and obesity prevalence. Cluster 2 displayed intermediate BMIPG and elevated OGTT0, while cluster 3 consisted mainly of participants with normal BMIPG and high values for OGTT60 and OGTT120. Treatment modalities and clinical outcomes varied among clusters. In particular, cluster 1 participants showed a much higher need for glucose-lowering medications (39.6% of participants, compared with 12.9% and 10.0% in clusters 2 and 3, respectively, p<0.0001). Cluster 1 participants were also at higher risk of delivering large-for-gestational-age infants. Differences in the type of insulin-based treatment between cluster 2 and cluster 3 were observed in the external validation cohort.

CONCLUSIONS/INTERPRETATION

Our findings confirm the heterogeneity of GDM. The identification of subgroups (clusters) has the potential to help clinicians define more tailored treatment approaches for improved maternal and neonatal outcomes.

Diabetic kidney disease: the kidney disease relevant to individuals with diabetes

In individuals with diabetes, chronic kidney disease (CKD) is a major comorbidity. However, it appears that there is worldwide confusion regarding which term should be used to describe CKD complicated with diabetes: diabetic nephropathy, diabetic kidney disease (DKD), CKD with diabetes, diabetes and CKD, etc. Similar confusion has also been reported in Japan. Therefore, to provide clarification, the Japanese Diabetes Society and the Japanese Society of Nephrology collaborated to update the corresponding Japanese term to describe DKD and clearly define the concept of DKD. In this review, we briefly described the history of kidney complications in individuals with diabetes and the Japanese definition of the DKD concept and provided our rationale for these changes.

Twenty-four-hour physical behaviour profiles across type 2 diabetes mellitus subtypes

AIM

To investigate how 24-h physical behaviours differ across type 2 diabetes (T2DM) subtypes.

MATERIALS AND METHODS

We included participants living with T2DM, enrolled as part of an ongoing observational study. Participants wore an accelerometer for 7 days to quantify physical behaviours across 24 h. We used routinely collected clinical data (age at onset of diabetes, glycated haemoglobin level, homeostatic model assessment index of beta-cell function, homeostatic model assessment index of insulin resistance, body mass index) to replicate four previously identified subtypes (insulin-deficient diabetes [INS-D], insulin-resistant diabetes [INS-R], obesity-related diabetes [OB] and age-related diabetes [AGE]), via k-means clustering. Differences in physical behaviours across the diabetes subtypes were assessed using generalized linear models, with the AGE cluster as the reference.

RESULTS

A total of 564 participants were included in this analysis (mean age 63.6 ± 8.4 years, 37.6% female, mean age at diagnosis 53.1 ± 10.0 years). The proportions in each cluster were as follows: INS-D: n = 35, 6.2%; INS-R: n = 88, 15.6%; OB: n = 166, 29.4%; and AGE: n = 275, 48.8%. Compared to the AGE cluster, the OB cluster had a shorter sleep duration (-0.3 h; 95% confidence interval [CI] -0.5, -0.1), lower sleep efficiency (-2%; 95% CI -3, -1), lower total physical activity (-2.9 mg; 95% CI -4.3, -1.6) and less time in moderate-to-vigorous physical activity (-6.6 min; 95% CI -11.4, -1.7), alongside greater sleep variability (17.9 min; 95% CI 8.2, 27.7) and longer sedentary time (31.9 min; 95% CI 10.5, 53.2). Movement intensity during the most active continuous 10 and 30 min of the day was also lower in the OB cluster.

CONCLUSIONS

In individuals living with T2DM, the OB subtype had the lowest levels of physical activity and least favourable sleep profiles. Such behaviours may be suitable targets for personalized therapeutic lifestyle interventions.

Are we ready for an adipocentric approach in people living with type 2 diabetes and chronic kidney disease?

We are entering a new era in the management of adiposity-based chronic disease (ABCD) with type 2 diabetes (T2D) and related chronic kidney disease (CKD). ABCD, T2D and CKD can affect almost every major organ system and have a particularly strong impact on the incidence of cardiovascular disease (CVD) and heart failure. ABCD and the associated insulin resistance are at the root of many cardiovascular, renal and metabolic (CKM) disorders, thus an integrated therapeutic framework using weight loss (WL) as a disease-modifying intervention could simplify the therapeutic approach at different stages across the lifespan. The breakthrough of highly effective WL drugs makes achieving a WL of >10% possible, which is required for a potential T2D disease remission as well as for prevention of microvascular disease, CKD, CVD events and overall mortality. The aim of this review is to discuss the link between adiposity and CKM conditions as well as placing weight management at the centre of the holistic CKM syndrome approach with a focus on CKD. We propose the clinical translation of the available evidence into a transformative Dysfunctional Adipose Tissue Approach (DATA) for people living with ABCD, T2D and CKD. This model is based on the interplay of four essential elements (i.e. adipocentric approach and target organ protection, dysfunctional adiposity, glucose homeostasis, and lifestyle intervention and de-prescription) together with a multidisciplinary person-centred care. DATA could facilitate decision-making for all clinicians involved in the management of these individuals, and if we do this in a multidisciplinary way, we are prepared to meet the adipocentric challenge.

Role of perirenal adiposity in renal dysfunction among CKD individuals with or without diabetes: a Japanese cross-sectional study

INTRODUCTION

It remains unclear whether increased perirenal fat (PRF) accumulation is equally related to renal involvement in patients with and without diabetes mellitus (DM). We evaluated the association between PRF volume (PRFV) and low glomerular filtration rate (GFR) and proteinuria in people with or without type 2 diabetes mellitus (T2DM).

RESEARCH DESIGN AND METHODS

We performed a cross-sectional analysis of 473 individuals without T2DM (non-DM, n=202) and with T2DM (DM, n=271). PRFV (cm3), obtained from non-contrast CT, was indexed as PRF index (PRFV/body surface area, cm3/m2). Multivariate-adjusted models were used to determine the ORs of PRFV and PRFV index for detecting estimated GFR (eGFR) decrease of <60 mL/min/1.73 m2 proteinuria onset, or both.

RESULTS

Although body mass index (BMI), visceral fat area, and waist circumference were comparable between the non-DM and DM groups, kidney volume, PRFV, and PRFV index were higher in individuals with T2DM than in those without T2DM. In the multivariate analysis, after adjusting for age, sex, BMI, hypertension, smoking history, and visceral fat area ≥100 cm2, the cut-off values of PRFV index were associated with an eGFR<60 in individuals with DM (OR 6.01, 95% CI 2.20 to 16.4, p<0.001) but not in those without DM.

CONCLUSIONS

PRFV is associated with low eGFR in patients with T2DM but not in those without T2DM. This suggests that PRF accumulation is more closely related to the onset and progression of diabetic kidney disease (DKD) than non-DKD. Clarifying the mechanisms through which PRF influences DKD development could pave the way for novel prevention and treatment strategies.

NAFLD and type 2 diabetes: A practical guide for the joint management

Non-alcoholic fatty liver disease (NAFLD) is becoming a major cause of liver disease-related morbidity, as well as mortality. Importantly, NAFLD is considered a mediator of systemic diseases including cardiovascular disease. Its prevalence is expected to increase, mainly due to its close association with obesity and type 2 diabetes mellitus (T2D). In addition, T2D and NAFLD share common pathophysiological mechanisms, and one can lead to or worsen the other. Therefore, a close collaboration between primary care physician, endocrinologists and hepatologists is essential to optimize the management of patients with NAFLD and T2D. Here, we summarize relevant aspects about NAFLD and T2D that all clinician managing these patients should know as well as current therapeutic options for the treatment of T2D associated with NAFLD.

Lifestyle behaviour patterns in the prevention of type 2 diabetes mellitus: the Fukushima Health Database 2015-2020

OBJECTIVES

Lifestyle behaviours associated with the incidence of type 2 diabetes mellitus (T2DM) need further clarification using health insurance data.

STUDY DESIGN

This is a cohort study.

METHODS

In 2015, 193,246 participants aged 40-74 years attended the specific health checkups and were observed up to 2020 in Fukushima, Japan. Using the principal component analysis, we identified two patterns from ten lifestyle behaviour questions, namely, the "diet-smoking" pattern (including smoking, alcohol drinking, skipping breakfast, eating fast, late dinner, and snacking) and the "physical activity-sleep" pattern (including physical exercise, walking equivalent activity, walking fast, and sufficient sleep). Then, individual pattern scores were calculated; the higher the scores, the healthier the behaviours.

RESULTS

The accumulative incidence rate of T2DM was 630.5 in men and 391.9 in women per 100,000 person-years in an average of 4 years of follow-up. Adjusted for the demographic and cardiometabolic factors at the baseline, the hazard ratio (95% confidence interval) of the highest versus lowest quartile scores of the "diet-smoking" pattern for T2DM risk was 0.82 (0.72, 0.92; P for trend = 0.002) in men and 0.87 (0.76, 1·00; P for trend = 0.034) in women; that of the "physical activity-sleep" pattern was 0.92 (0.82, 1·04; P for trend = 0.0996) in men and 0.92 (0.80, 1·06; P for trend = 0.372) in women. The "physical activity-sleep" pattern showed a significant inverse association in non-overweight men.

CONCLUSIONS

Lifestyle behaviour associated with a healthy diet and lack of smoking may significantly lower the risk of T2DM in middle-aged Japanese adults.

Clusters of prediabetes and type 2 diabetes stratify all-cause mortality in a cohort of participants undergoing invasive coronary diagnostics

BACKGROUND

Heterogeneous metabolic clusters have been identified in diabetic and prediabetic states. It is not known whether such pathophysiologic clusters impact survival in at-risk persons being evaluated for coronary heart disease.

METHODS

The LURIC Study recruited patients referred for coronary angiography at a median age of 63 (IQR 56-70) with a follow-up of 16.1 (IQR 9.6, 17.7) years. Clustering of 1269 subjects without diabetes was performed with oGTT-derived glucose and insulin; fasting triglyceride, high-density lipoprotein, BMI, waist and hip circumference. Patients with T2D (n = 794) were clustered using age, BMI, glycemia, homeostasis model assessment, and islet autoantibodies. Associations of clusters with mortality were analysed using Cox regression.

RESULTS

Individuals without diabetes were classified into six subphenotypes, with 884 assigned to subjects at low-risk (cluster 1,2,4) and 385 at high-risk (cluster 3,5,6) for diabetes. We found significantly increased mortality in clusters 3 (hazard ratio (HR)1.42), 5 (HR 1.43), and 6 (HR 1.46) after adjusting for age, BMI, HbA1c and sex. In the T2D group, 508 were assigned to mild age-related diabetes (MARD), 183 to severe insulin-resistant diabetes (SIRD), 84 to mild obesity-related diabetes (MOD), 19 to severe insulin-deficient diabetes (SIDD). Compared to the low-risk non-diabetes group, crude mortality was not different in MOD. Increased mortality was found for MARD (HR 2.2), SIRD (HR 2.2), and SIDD (HR 2.5).

CONCLUSIONS

Metabolic clustering successfully stratifies survival even among persons undergoing invasive coronary diagnostics. Novel clustering approaches based on glucose metabolism can identify persons who require special attention as they are at risk of increased mortality.

Subgroups of adult-onset diabetes: a data-driven cluster analysis in a Ghanaian population

Adult-onset diabetes mellitus (here: aDM) is not a uniform disease entity. In European populations, five diabetes subgroups have been identified by cluster analysis using simple clinical variables; these may elucidate diabetes aetiology and disease prognosis. We aimed at reproducing these subgroups among Ghanaians with aDM, and establishing their importance for diabetic complications in different health system contexts. We used data of 541 Ghanaians with aDM (age: 25-70 years; male sex: 44%) from the multi-center, cross-sectional Research on Obesity and Diabetes among African Migrants (RODAM) Study. Adult-onset DM was defined as fasting plasma glucose (FPG) ≥ 7.0 mmol/L, documented use of glucose-lowering medication or self-reported diabetes, and age of onset ≥ 18 years. We derived subgroups by cluster analysis using (i) a previously published set of variables: age at diabetes onset, HbA1c, body mass index, HOMA-beta, HOMA-IR, positivity of glutamic acid decarboxylase autoantibodies (GAD65Ab), and (ii) Ghana-specific variables: age at onset, waist circumference, FPG, and fasting insulin. For each subgroup, we calculated the clinical, treatment-related and morphometric characteristics, and the proportions of objectively measured and self-reported diabetic complications. We reproduced the five subgroups: cluster 1 (obesity-related, 73%) and cluster 5 (insulin-resistant, 5%) with no dominant diabetic complication patterns; cluster 2 (age-related, 10%) characterized by the highest proportions of coronary artery disease (CAD, 18%) and stroke (13%); cluster 3 (autoimmune-related, 5%) showing the highest proportions of kidney dysfunction (40%) and peripheral artery disease (PAD, 14%); and cluster 4 (insulin-deficient, 7%) characterized by the highest proportion of retinopathy (14%). The second approach yielded four subgroups: obesity- and age-related (68%) characterized by the highest proportion of CAD (9%); body fat-related and insulin-resistant (18%) showing the highest proportions of PAD (6%) and stroke (5%); malnutrition-related (8%) exhibiting the lowest mean waist circumference and the highest proportion of retinopathy (20%); and ketosis-prone (6%) with the highest proportion of kidney dysfunction (30%) and urinary ketones (6%). With the same set of clinical variables, the previously published aDM subgroups can largely be reproduced by cluster analysis in this Ghanaian population. This method may generate in-depth understanding of the aetiology and prognosis of aDM, particularly when choosing variables that are clinically relevant for the target population.

Diabetes Self-Care Activities and Their Relationship With Glycemic Control in Patients Visiting Hayatabad Medical Complex, Peshawar

Background A significant portion of the Pakistani population is affected by diabetes, which has emerged as a global healthcare concern. Objective This study aimed to assess the correlation between glycemic control in diabetes patients and their engagement in diabetes self-care activities (DSCA). Methodology Cross-sectional research was conducted at Hayatabad Medical Complex in Peshawar between June 2019 and May 2020. A total of 280 carefully selected patients with type 2 diabetes mellitus (T2DM) were included. Data collection involved an interviewer-administered questionnaire encompassing sociodemographic information, diabetes-related data, and the summary of the Diabetes Self-Care Activities (SDSCA) scale. Descriptive statistics and Pearson's chi-square test were employed for data analysis. Results The study observed that the majority of participants (40.36%) were females, and the age range of the participants was between 42 and 53 years (68.22%). According to the study, 55.00% of participants had a normal body mass index (BMI), and 71.08% of participants had diabetes in their family. Regarding glycemic control, 55.71% of individuals exhibited good control based on fasting blood sugar (FBS) levels while 74.64% showed poor control according to hemoglobin A1C (HbA1c) values. HbA1c was substantially linked with a general diet (healthy eating plan), physical activities, and adherence to medication ((odds ratios (OR): 3.12), (95% confidence interval (CI): 1.02 - 8.78), (P value: 0.031)); ((OR: 2.19, 95%), (CI:1.18 - 3.79), (P value: 0.003)); ((OR: 2.85), (95% CI: 1.22 - 6.59), P value: 0.021)). Conclusion The findings indicated that health professionals need to create health education programs on DSCA in order to increase DSCA adherence in people with T2DM while maintaining glycemic control.

Response to insulin glargine 100 U/mL treatment in newly-defined subgroups of type 2 diabetes: Post hoc pooled analysis of insulin-naïve participants from nine randomised clinical trials

AIMS

To assess insulin glargine 100 U/mL (IGlar-100) treatment outcomes according to newly-defined subgroups of type 2 diabetes mellitus (T2DM).

METHODS

Insulin-naïve T2DM participants (n = 2684) from nine randomised clinical trials initiating IGlar-100 were pooled and assigned to subgroups "Mild Age-Related Diabetes (MARD)", "Mild Obesity Diabetes (MOD)", "Severe Insulin Resistant Diabetes (SIRD)", and "Severe Insulin Deficient Diabetes (SIDD)", according to age at onset of diabetes, baseline HbA1c, BMI, and fasting C-peptide using sex-specific nearest centroid approach. HbA1c, FPG, hypoglycemia, insulin dose, and body weight were analysed at baseline and 24 weeks.

RESULTS

Subgroup distribution was MARD 15.3 % (n = 411), MOD 39.8 % (n = 1067), SIRD 10.5 % (n = 283), SIDD 34.4 % (n = 923). From baseline HbA1c 8.0-9.6% adjusted least square mean reductions after 24 weeks were similar between subgroups (1.4-1.5 %). SIDD was less likely to achieve HbA1c < 7.0 % (OR: 0.40 [0.29, 0.55]) than MARD. While the final IGlar-100 dose (0.36 U/kg) in MARD was lower than in other subgroups (0.46-0.50 U/kg), it had the highest hypoglycemia risk. SIRD had lowest hypoglycemia risk and SIDD exhibited greatest body weight gain.

CONCLUSIONS

IGlar-100 lowered hyperglycemia similarly in all T2DM subgroups, but level of glycemic control, insulin dose, and hypoglycemia risk differed between subgroups.

Effect of dapagliflozin on 24-hour glycemic variables in Japanese patients with type 2 diabetes mellitus receiving basal insulin supported oral therapy (DBOT): a multicenter, randomized, open-label, parallel-group study

INTRODUCTION

This study aimed to evaluate the impacts of dapagliflozin on 24-hour glucose variability and diabetes-related biochemical variables in Japanese patients with type 2 diabetes who had received basal insulin supported oral therapy (BOT).

RESEARCH DESIGN AND METHODS

Changes in mean daily blood glucose level before and after 48-72 hours of add-on or no add-on of dapagliflozin (primary end point) and diabetes-related biochemical variables and major safety variables during the 12 weeks (secondary end point) were evaluated in the multicenter, randomized, two-arm, open-label, parallel-group comparison study.

RESULTS

Among 36 participants, 18 were included in the no add-on group and 18 were included in the dapagliflozin add-on group. Age, gender, and body mass index were comparable between the groups. There were no changes in continuous glucose monitoring metrics in the no add-on group. In the dapagliflozin add-on group, mean glucose (183-156 mg/dL, p=0.001), maximum glucose (300-253, p<0.01), and SD glucose (57-45, p<0.05) decreased. Time in range increased (p<0.05), while time above the range decreased in the dapagliflozin add-on group but not in the no add-on group. After 12-week treatment with dapagliflozin add-on, 8-hydroxy-2'-deoxyguanosine (8OHdG), as well as hemoglobin A1c (HbA1c), decreased.

CONCLUSIONS

This study showed that the mean daily blood glucose and other daily glucose profiles were amended after 48-72 hours of dapagliflozin add-on in Japanese patients with type 2 diabetes who received BOT. The diabetes-related biochemical variables such as HbA1c and urinary 8OHdG were also obtained during the 12 weeks of dapagliflozin add-on without major adverse events. A preferable 24-hour glucose profile in 'time in ranges' and an improvement in reactive oxygen species by dapagliflozin warrant us to evaluate these benefits in larger clinical studies.

TRIAL REGISTRATION NUMBER

UMIN000019457.

Type 2 Diabetes and Parkinson's Disease: A Focused Review of Current Concepts

Highly reproducible epidemiological evidence shows that type 2 diabetes (T2D) increases the risk and rate of progression of Parkinson's disease (PD), and crucially, the repurposing of certain antidiabetic medications for the treatment of PD has shown early promise in clinical trials, suggesting that the effects of T2D on PD pathogenesis may be modifiable. The high prevalence of T2D means that a significant proportion of patients with PD may benefit from personalized antidiabetic treatment approaches that also confer neuroprotective benefits. Therefore, there is an immediate need to better understand the mechanistic relation between these conditions and the specific molecular pathways affected by T2D in the brain. Although there is considerable evidence that processes such as insulin signaling, mitochondrial function, autophagy, and inflammation are involved in the pathogenesis of both PD and T2D, the primary aim of this review is to highlight the evidence showing that T2D-associated dysregulation of these pathways occurs not only in the periphery but also in the brain and how this may facilitate neurodegeneration in PD. We also discuss the challenges involved in disentangling the complex relationship between T2D, insulin resistance, and PD, as well as important questions for further research. © 2022 International Parkinson and Movement Disorder Society.

Review: Machine learning in precision pharmacotherapy of type 2 diabetes-A promising future or a glimpse of hope?

Precision pharmacotherapy of diabetes requires judicious selection of the optimal therapeutic agent for individual patients. Artificial intelligence (AI), a swiftly expanding discipline, holds substantial potential to transform current practices in diabetes diagnosis and management. This manuscript provides a comprehensive review of contemporary research investigating drug responses in patient subgroups, stratified via either supervised or unsupervised machine learning approaches. The prevalent algorithmic workflow for investigating drug responses using machine learning involves cohort selection, data processing, predictor selection, development and validation of machine learning methods, subgroup allocation, and subsequent analysis of drug response. Despite the promising feature, current research does not yet provide sufficient evidence to implement machine learning algorithms into routine clinical practice, due to a lack of simplicity, validation, or demonstrated efficacy. Nevertheless, we anticipate that the evolving evidence base will increasingly substantiate the role of machine learning in molding precision pharmacotherapy for diabetes.

Ethnic differences between Asians and non-Asians in clustering-based phenotype classification of adult-onset diabetes mellitus: A systematic narrative review

Several international studies have stratified people with diabetes into phenotypical clusters. However, there has not been a systematic examination of the variation in these clusters across ethnic groups. For example, some clusters appear more frequent among Asians and may have lower weight, age at diagnosis and poorer beta cell function.

Dietary Patterns and New-Onset Type 2 Diabetes Mellitus in Evacuees after the Great East Japan Earthquake: A 7-Year Longitudinal Analysis in the Fukushima Health Management Survey

Background: Dietary patterns may be linked to the incidence of type 2 diabetes mellitus (T2DM) after disasters. We investigated the association between dietary patterns and new-onset T2DM in evacuees of the Great East Japan Earthquake and the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident. Methods: Among the 22,740 non-diabetic participants aged 20-89 years who completed the dietary assessment in the Fukushima Health Management Survey between July 2011 and November 2012, the incidence of T2DM was evaluated until 2018. Principal component analysis with varimax rotation was applied to derive dietary patterns based on a validated, short-form food frequency questionnaire. The identified dietary patterns were categorized as typical Japanese, juice, and meat. Results: The cumulative incidence of T2DM was 18.0 and 9.8 per 1000 person-years in men and women, respectively, during the follow-up period. The multiple-adjusted hazard ratio (95% confidence interval) of the highest vs. lowest quartile of the typical Japanese pattern scores for T2DM was 0.80 (0.68, 0.94; P for trend = 0.015) in total, 0.85 (0.68, 1.06; P for trend = 0.181) in men, and 0.76 (0.60, 0.95; P for trend = 0.04) in women. Conclusions: A typical Japanese dietary pattern may be associated with a reduced new-onset T2DM risk in evacuees, especially women, after the Great East Japan Earthquake and the FDNPP accident.

Detecting Sarcopenia Risk by Diabetes Clustering: A Japanese Prospective Cohort Study

CONTEXT

Previous studies have assessed the usefulness of data-driven clustering for predicting complications in patients with diabetes mellitus. However, whether the diabetes clustering is useful in predicting sarcopenia remains unclear.

OBJECTIVE

To evaluate the predictive power of diabetes clustering for the incidence of sarcopenia in a prospective Japanese cohort.

DESIGN

Three-year prospective cohort study.

SETTING AND PATIENTS

We recruited Japanese patients with type 1 or type 2 diabetes mellitus (n = 659) between January 2018 and February 2020 from the Fukushima Diabetes, Endocrinology, and Metabolism cohort.

INTERVENTIONS

Kaplan-Meier and Cox proportional hazards models were used to measure the predictive values of the conventional and clustering-based classification of diabetes mellitus for the onset of sarcopenia. Sarcopenia was diagnosed according to the Asian Working Group for Sarcopenia (AWGS) 2019 consensus update.

MAIN OUTCOME MEASURES

Onset of sarcopenia.

RESULTS

Cluster analysis of a Japanese population revealed 5 diabetes clusters: cluster 1 [severe autoimmune diabetes (SAID)], cluster 2 [severe insulin-deficient diabetes (SIDD)], cluster 3 (severe insulin-resistant diabetes, cluster 4 (mild obesity-related diabetes), and cluster 5 (mild age-related diabetes). At baseline, 38 (6.5%) patients met the AWGS sarcopenia criteria, and 55 had newly developed sarcopenia within 3 years. The SAID and SIDD clusters were at high risk of developing sarcopenia after correction for known risk factors.

CONCLUSIONS

This study reveals that among the 5 diabetes clusters, the SAID and SIDD clusters are at a high risk for developing sarcopenia. Clustering-based stratification may be beneficial for predicting and preventing sarcopenia in patients with diabetes.

Psychological burden predicts new-onset diabetes in men: A longitudinal observational study in the Fukushima Health Management Survey after the Great East Japan earthquake

BACKGROUND

The burden of psychological distress and post-traumatic stress disorder (PTSD) has been suggested as a factor in developing type 2 diabetes mellitus. However, longitudinal features in psychological distress- and PTSD-related new-onset diabetes mellitus have not been thoroughly evaluated.

METHODS

The association between probable depression and probable PTSD and the risk of developing new-onset diabetes mellitus was evaluated in a 7-year prospective cohort of evacuees of the Great East Japan Earthquake in 2011. Probable depression was defined as a Kessler 6 scale (K6) ≥ 13 and probable PTSD as a PTSD Checklist-Stressor-Specific Version (PCL-S) ≥ 44.

RESULTS

The log-rank test for the Kaplan-Meier curve for new-onset diabetes mellitus was significant between K6 ≥ 13 vs. < 13 and PCL-S ≥ 44 vs. < 44 in men but not in women. In men, both K6 ≥ 13 and PCL-S ≥ 44 remained significant in the Cox proportional hazards model after multivariate adjustment for established risk factors and disaster-related factors, including evacuation, change in work situation, sleep dissatisfaction, and education.

CONCLUSION

The post-disaster psychological burden of probable depression and probable PTSD was related to new-onset diabetes in men but not in women. In post-disaster circumstances, prevention strategies for new-onset diabetes might consider sex differences in terms of psychological burden.