ADA/EASD Precision Medicine in Diabetes Initiative: An International Perspective and Future Vision for Precision Medicine in Diabetes

Associations of Cognitive Impairment with All-Cause and Cardiovascular Mortality Among Individuals with Diabetes: A Prospective Cohort Study

This study explored the association between diabetes, cognitive imFpairment (CI), and mortality in a cohort of 2931 individuals aged 60 and above from the 2011 to 2014 NHANES. Mortality data was gathered through 2019, and multivariable Cox proportional hazards models were used to determine the association between diabetes, CI, and mortality adjusting for sociodemographic characteristics, lifestyle factors, and comorbidity conditions. The study spanned up to 9.17 years, observing 579 deaths, with individuals having both diabetes and CI showing the highest all-cause mortality (23.6 events per 100 patient-years). Adjusted analysis revealed a 2.34-fold higher risk of all-cause mortality for this group, surpassing those with diabetes or CI alone. These results held after a series of stratified and sensitivity analyses. In conclusion, CI was linked to higher all-cause mortality in individuals with diabetes, emphasizing the need to address cognitive dysfunction in diabetic patients.

Islet autoantibodies as precision diagnostic tools to characterize heterogeneity in type 1 diabetes: a systematic review

BACKGROUND

Islet autoantibodies form the foundation for type 1 diabetes (T1D) diagnosis and staging, but heterogeneity exists in T1D development and presentation. We hypothesized that autoantibodies can identify heterogeneity before, at, and after T1D diagnosis, and in response to disease-modifying therapies.

METHODS

We systematically reviewed PubMed and EMBASE databases (6/14/2022) assessing 10 years of original research examining relationships between autoantibodies and heterogeneity before, at, after diagnosis, and in response to disease-modifying therapies in individuals at-risk or within 1 year of T1D diagnosis. A critical appraisal checklist tool for cohort studies was modified and used for risk of bias assessment.

RESULTS

Here we show that 152 studies that met extraction criteria most commonly characterized heterogeneity before diagnosis (91/152). Autoantibody type/target was most frequently examined, followed by autoantibody number. Recurring themes included correlations of autoantibody number, type, and titers with progression, differing phenotypes based on order of autoantibody seroconversion, and interactions with age and genetics. Only 44% specifically described autoantibody assay standardization program participation.

CONCLUSIONS

Current evidence most strongly supports the application of autoantibody features to more precisely define T1D before diagnosis. Our findings support continued use of pre-clinical staging paradigms based on autoantibody number and suggest that additional autoantibody features, particularly in relation to age and genetic risk, could offer more precise stratification. To improve reproducibility and applicability of autoantibody-based precision medicine in T1D, we propose a methods checklist for islet autoantibody-based manuscripts which includes use of precision medicine MeSH terms and participation in autoantibody standardization workshops.

Glucagon-like peptide-1 and interleukin-6 interaction in response to physical exercise: An in-silico model in the framework of immunometabolism

BACKGROUND AND OBJECTIVE

Glucagon-like peptide 1 (GLP-1) is classically identified as an incretin hormone, secreted in response to nutrient ingestion and able to enhance glucose-stimulated insulin secretion. However, other stimuli, such as physical exercise, may enhance GLP-1 plasma levels, and this exercise-induced GLP-1 secretion is mediated by interleukin-6 (IL-6), a cytokine secreted by contracting skeletal muscle. The aim of the study is to propose a mathematical model of IL-6-induced GLP-1 secretion and kinetics in response to physical exercise of moderate intensity.

METHODS

The model includes the GLP-1 subsystem (with two pools: gut and plasma) and the IL-6 subsystem (again with two pools: skeletal muscle and plasma); it provides a parameter of possible clinical relevance representing the sensitivity of GLP-1 to IL-6 (k0). The model was validated on mean IL-6 and GLP-1 data derived from the scientific literature and on a total of 100 virtual subjects.

RESULTS

Model validation provided mean residuals between 0.0051 and 0.5493 pg⋅mL-1 for IL-6 (in view of concentration values ranging from 0.8405 to 3.9718 pg⋅mL-1) and between 0.0133 and 4.1540 pmol⋅L-1 for GLP-1 (in view of concentration values ranging from 0.9387 to 17.9714 pmol⋅L-1); a positive significant linear correlation (r = 0.85, p<0.001) was found between k0 and the ratio between areas under GLP-1 and IL-6 curve, over the virtual subjects.

CONCLUSIONS

The model accurately captures IL-6-induced GLP-1 kinetics in response to physical exercise.

Whole-exome sequencing in familial type 2 diabetes identifies an atypical missense variant in the RyR2 gene

Genome-wide association studies have identified several hundred loci associated with type 2 diabetes mellitus (T2DM). Additionally, pathogenic variants in several genes are known to cause monogenic diabetes that overlaps clinically with T2DM. Whole-exome sequencing of related individuals with T2DM is a powerful approach to identify novel high-penetrance disease variants in coding regions of the genome. We performed whole-exome sequencing on four related individuals with T2DM - including one individual diagnosed at the age of 33 years. The individuals were negative for mutations in monogenic diabetes genes, had a strong family history of T2DM, and presented with several characteristics of metabolic syndrome. A missense variant (p.N2291D) in the type 2 ryanodine receptor (RyR2) gene was one of eight rare coding variants shared by all individuals. The variant was absent in large population databases and affects a highly conserved amino acid located in a mutational hotspot for pathogenic variants in Catecholaminergic polymorphic ventricular tachycardia (CPVT). Electrocardiogram data did not reveal any cardiac abnormalities except a lower-than-normal resting heart rate (< 60 bpm) in two individuals - a phenotype observed in CPVT individuals with RyR2 mutations. RyR2-mediated Ca2+ release contributes to glucose-mediated insulin secretion and pathogenic RyR2 mutations cause glucose intolerance in humans and mice. Analysis of glucose tolerance testing data revealed that missense mutations in a CPVT mutation hotspot region - overlapping the p.N2291D variant - are associated with complete penetrance for glucose intolerance. In conclusion, we have identified an atypical missense variant in the RyR2 gene that co-segregates with diabetes in the absence of overt CPVT.

Association of multiple blood metals and systemic atherosclerosis: A cross-sectional study in the CAD population

BACKGROUND

Coronary atherosclerotic disease (CAD) is often accompanied by peripheral atherosclerosis, resulting in a higher risk of ischemia and cardiovascular death. Exposure to metals is associated with atherosclerotic plaques at specific sites. However, less is known about the effects of mixed metals on systemic atherosclerotic burden in CAD patients.

OBJECTIVES

To investigate the association of metal mixtures with systemic atherosclerotic burden in a CAD population.

METHODS

A cross-sectional study including 1562 CAD patients from Southwest China was conducted. The levels of 10 blood metals were measured via inductively coupled plasma spectrometry. More than one vessel with a stenosis ≥50% vessel diameter was defined as CAD. Carotid and lower limb atherosclerosis was assessed by using ultrasound, and coronary atherosclerosis was quantified via arterial angiography. Systemic atherosclerosis was scored according to the presence or absence of lesions at the three sites and the total number of lesions. To investigate the combined impacts and interaction effects of metals, Bayesian kernel machine regression was used. Weighted quantile regression was used to identify the contributions of the metals.

RESULTS

Significant overall associations of mixed metals with systemic atherosclerotic burden were found. These positive overall associations were mainly driven by Cd, Cu and Pb in systemic atherosclerosis. The main contributing factors were As and Cu for coronary atherosclerosis as well as Cd, Cu and Pb for carotid and lower limb atherosclerosis. Cd and Pb or Cr can interact, and Pb interacts with age, sex and alcohol.

CONCLUSIONS

In CAD patients, exposure to combinations of metals was highly positively associated with systemic atherosclerotic burden. These significant trends were more pronounced in the peripheral arteries and carotid arteries. Controlling environmental metal exposure can contribute to reducing systemic atherosclerosis in CAD patients.

Addressing uncertainty about the role of structured lifestyle modification for metabolic surgery patients

There is good evidence that structured lifestyle modification programmes improve health in patients with metabolic and cardiovascular disorders, but there is no specific evidence that they improve outcomes in patients undergoing metabolic or obesity surgery. Despite expert consensus guidelines stating this fact, some healthcare systems still compel patients to participate in a structured lifestyle modification programme prior to metabolic or obesity surgery. There is a well-established need for individualised multidisciplinary dietetic and physical activity care for metabolic and obesity surgery patients, and the benefits of intentional weight loss prior to surgery are well proven, but these are distinct from potentially harmful requirements for patients to undertake compulsory structured lifestyle programmes of fixed duration, frequency and intensity, which may delay surgery and reinforce obesity stigma. A critical step in rejuvenating metabolic surgery is to reframe patient participation in structured lifestyle modification programmes as an opportunity for education and empowerment, not as an indicator of motivation or suitability for metabolic surgery. Large, well-designed and adequately powered clinical trials are needed to address uncertainties in the evidence base for these programmes. Given genuine equipoise, they will need to determine whether "surgery plus lifestyle" is superior to "surgery plus placebo". Moreover, they will need to determine the cost-effectiveness of these programmes and identify some of the factors giving rise to the substantial heterogeneity in responses to structured lifestyle modification.

Precision prognostics for cardiovascular disease in Type 2 diabetes: a systematic review and meta-analysis

BACKGROUND

Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with Type 2 diabetes (T2D).

METHODS

We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies.

RESULTS

Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort.

CONCLUSIONS

Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

Precision stratification of prognostic risk factors associated with outcomes in gestational diabetes mellitus: a systematic review

BACKGROUND

The objective of this systematic review is to identify prognostic factors among women and their offspring affected by gestational diabetes mellitus (GDM), focusing on endpoints of cardiovascular disease (CVD) and type 2 diabetes (T2D) for women, and cardiometabolic profile for offspring.

METHODS

This review included studies published in English language from January 1st, 1990, through September 30th, 2021, that focused on the above outcomes of interest with respect to sociodemographic factors, lifestyle and behavioral characteristics, traditional clinical traits, and 'omics biomarkers in the mothers and offspring during the perinatal/postpartum periods and across the lifecourse. Studies that did not report associations of prognostic factors with outcomes of interest among GDM-exposed women or children were excluded.

RESULTS

Here, we identified 109 publications comprising 98 observational studies and 11 randomized-controlled trials. Findings indicate that GDM severity, maternal obesity, race/ethnicity, and unhealthy diet and physical activity levels predict T2D and CVD in women, and greater cardiometabolic risk in offspring. However, using the Diabetes Canada 2018 Clinical Practice Guidelines for studies, the level of evidence was low due to potential for confounding, reverse causation, and selection biases.

CONCLUSIONS

GDM pregnancies with greater severity, as well as those accompanied by maternal obesity, unhealthy diet, and low physical activity, as well as cases that occur among women who identify as racial/ethnic minorities are associated with worse cardiometabolic prognosis in mothers and offspring. However, given the low quality of evidence, prospective studies with detailed covariate data collection and high fidelity of follow-up are warranted.

Refining the diagnosis of gestational diabetes mellitus: a systematic review and meta-analysis

BACKGROUND

Perinatal outcomes vary for women with gestational diabetes mellitus (GDM). The precise factors beyond glycemic status that may refine GDM diagnosis remain unclear. We conducted a systematic review and meta-analysis of potential precision markers for GDM.

METHODS

Systematic literature searches were performed in PubMed and EMBASE from inception to March 2022 for studies comparing perinatal outcomes among women with GDM. We searched for precision markers in the following categories: maternal anthropometrics, clinical/sociocultural factors, non-glycemic biochemical markers, genetics/genomics or other -omics, and fetal biometry. We conducted post-hoc meta-analyses of a subset of studies with data on the association of maternal body mass index (BMI, kg/m2) with offspring macrosomia or large-for-gestational age (LGA).

RESULTS

A total of 5905 titles/abstracts were screened, 775 full-texts reviewed, and 137 studies synthesized. Maternal anthropometrics were the most frequent risk marker. Meta-analysis demonstrated that women with GDM and overweight/obesity vs. GDM with normal range BMI are at higher risk of offspring macrosomia (13 studies [n = 28,763]; odds ratio [OR] 2.65; 95% Confidence Interval [CI] 1.91, 3.68), and LGA (10 studies [n = 20,070]; OR 2.23; 95% CI 2.00, 2.49). Lipids and insulin resistance/secretion indices were the most studied non-glycemic biochemical markers, with increased triglycerides and insulin resistance generally associated with greater risk of offspring macrosomia or LGA. Studies evaluating other markers had inconsistent findings as to whether they could be used as precision markers.

CONCLUSIONS

Maternal overweight/obesity is associated with greater risk of offspring macrosomia or LGA in women with GDM. Pregnancy insulin resistance or hypertriglyceridemia may be useful in GDM risk stratification. Future studies examining non-glycemic biochemical, genetic, other -omic, or sociocultural precision markers among women with GDM are warranted.

Factors associated with weight loss and health gains in a structured lifestyle modification programme for adults with severe obesity: a prospective cohort study

Background

Individual responses to behavioural weight loss interventions can vary significantly, and a better understanding of the factors associated with successful treatment might help to target interventions for those who will benefit the most. We sought to identify demographic and clinical characteristics that predicted intervention "success" (defined as ≥5% weight loss) and other health gains in patients with severe obesity attending a ten-week structured lifestyle modification programme.

Methods

We conducted a prospective cohort study of all 1122 patients (751 (66.9%) female, mean age 47.3 ± 11.9 years, mean body mass index (BMI) 46.7 ± 7.8 kgm-2) referred from our hospital-based obesity clinic, who started the structured lifestyle programme between 2012-2019. We compared routine clinical measures such as weight, fitness, blood pressure, lipids and HbA1c at baseline and follow-up. We also used validated questionnaires to quantify anxiety, depression and health-related quality of life.

Results

Of 1122 patients who started, 877 (78.2%) completed the programme and attended for follow up. Of these, 12.8% lost ≥5% body weight. The amount of weight lost was a strong and consistent predictor of improvements in metabolic, cardiovascular, and mental health, even after adjusting for age, sex, programme attendance and baseline fitness. Older age, male sex, being physically active and having lower anxiety and depression scores at baseline predicted greater weight loss. Younger age, depression and longer wait time to start the intervention were associated with drop-out.

Conclusions

In adults with severe obesity completing a structured lifestyle modification programme, older age and good mental health were associated with programme completion and attaining ≥5% weight loss. The magnitude of weight lost was a strong predictor of improvements in cardiovascular, metabolic and mental health associated with programme completion.

Multi-ancestry Polygenic Mechanisms of Type 2 Diabetes Elucidate Disease Processes and Clinical Heterogeneity

We identified genetic subtypes of type 2 diabetes (T2D) by analyzing genetic data from diverse groups, including non-European populations. We implemented soft clustering with 650 T2D-associated genetic variants, capturing known and novel T2D subtypes with distinct cardiometabolic trait associations. The twelve genetic clusters were distinctively enriched for single-cell regulatory regions. Polygenic scores derived from the clusters differed in distribution between ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a BMI of 30 kg/m2 in the European subpopulation and 24.2 (22.9-25.5) kg/m2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg/m2 in the East Asian group, explaining about 75% of the difference in BMI thresholds. Thus, these multi-ancestry T2D genetic subtypes encompass a broader range of biological mechanisms and help explain ancestry-associated differences in T2D risk profiles.

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.

Genotype-stratified treatment for monogenic insulin resistance: a systematic review

BACKGROUND

Monogenic insulin resistance (IR) includes lipodystrophy and disorders of insulin signalling. We sought to assess the effects of interventions in monogenic IR, stratified by genetic aetiology.

METHODS

Systematic review using PubMed, MEDLINE and Embase (1 January 1987 to 23 June 2021). Studies reporting individual-level effects of pharmacologic and/or surgical interventions in monogenic IR were eligible. Individual data were extracted and duplicates were removed. Outcomes were analysed for each gene and intervention, and in aggregate for partial, generalised and all lipodystrophy.

RESULTS

10 non-randomised experimental studies, 8 case series, and 23 case reports meet inclusion criteria, all rated as having moderate or serious risk of bias. Metreleptin use is associated with the lowering of triglycerides and haemoglobin A1c (HbA1c) in all lipodystrophy (n = 111), partial (n = 71) and generalised lipodystrophy (n = 41), and in LMNA, PPARG, AGPAT2 or BSCL2 subgroups (n = 72,13,21 and 21 respectively). Body Mass Index (BMI) is lowered in partial and generalised lipodystrophy, and in LMNA or BSCL2, but not PPARG or AGPAT2 subgroups. Thiazolidinediones are associated with improved HbA1c and triglycerides in all lipodystrophy (n = 13), improved HbA1c in PPARG (n = 5), and improved triglycerides in LMNA (n = 7). In INSR-related IR, rhIGF-1, alone or with IGFBP3, is associated with improved HbA1c (n = 17). The small size or absence of other genotype-treatment combinations preclude firm conclusions.

CONCLUSIONS

The evidence guiding genotype-specific treatment of monogenic IR is of low to very low quality. Metreleptin and Thiazolidinediones appear to improve metabolic markers in lipodystrophy, and rhIGF-1 appears to lower HbA1c in INSR-related IR. For other interventions, there is insufficient evidence to assess efficacy and risks in aggregated lipodystrophy or genetic subgroups.

The use of precision diagnostics for monogenic diabetes: a systematic review and expert opinion

BACKGROUND

Monogenic diabetes presents opportunities for precision medicine but is underdiagnosed. This review systematically assessed the evidence for (1) clinical criteria and (2) methods for genetic testing for monogenic diabetes, summarized resources for (3) considering a gene or (4) variant as causal for monogenic diabetes, provided expert recommendations for (5) reporting of results; and reviewed (6) next steps after monogenic diabetes diagnosis and (7) challenges in precision medicine field.

METHODS

Pubmed and Embase databases were searched (1990-2022) using inclusion/exclusion criteria for studies that sequenced one or more monogenic diabetes genes in at least 100 probands (Question 1), evaluated a non-obsolete genetic testing method to diagnose monogenic diabetes (Question 2). The risk of bias was assessed using the revised QUADAS-2 tool. Existing guidelines were summarized for questions 3-5, and review of studies for questions 6-7, supplemented by expert recommendations. Results were summarized in tables and informed recommendations for clinical practice.

RESULTS

There are 100, 32, 36, and 14 studies included for questions 1, 2, 6, and 7 respectively. On this basis, four recommendations for who to test and five on how to test for monogenic diabetes are provided. Existing guidelines for variant curation and gene-disease validity curation are summarized. Reporting by gene names is recommended as an alternative to the term MODY. Key steps after making a genetic diagnosis and major gaps in our current knowledge are highlighted.

CONCLUSIONS

We provide a synthesis of current evidence and expert opinion on how to use precision diagnostics to identify individuals with monogenic diabetes.

Treatment effect heterogeneity following type 2 diabetes treatment with GLP1-receptor agonists and SGLT2-inhibitors: a systematic review

BACKGROUND

A precision medicine approach in type 2 diabetes requires the identification of clinical and biological features that are reproducibly associated with differences in clinical outcomes with specific anti-hyperglycaemic therapies. Robust evidence of such treatment effect heterogeneity could support more individualized clinical decisions on optimal type 2 diabetes therapy.

METHODS

We performed a pre-registered systematic review of meta-analysis studies, randomized control trials, and observational studies evaluating clinical and biological features associated with heterogenous treatment effects for SGLT2-inhibitor and GLP1-receptor agonist therapies, considering glycaemic, cardiovascular, and renal outcomes. After screening 5,686 studies, we included 101 studies of SGLT2-inhibitors and 75 studies of GLP1-receptor agonists in the final systematic review.

RESULTS

Here we show that the majority of included papers have methodological limitations precluding robust assessment of treatment effect heterogeneity. For SGLT2-inhibitors, multiple observational studies suggest lower renal function as a predictor of lesser glycaemic response, while markers of reduced insulin secretion predict lesser glycaemic response with GLP1-receptor agonists. For both therapies, multiple post-hoc analyses of randomized control trials (including trial meta-analysis) identify minimal clinically relevant treatment effect heterogeneity for cardiovascular and renal outcomes.

CONCLUSIONS

Current evidence on treatment effect heterogeneity for SGLT2-inhibitor and GLP1-receptor agonist therapies is limited, likely reflecting the methodological limitations of published studies. Robust and appropriately powered studies are required to understand type 2 diabetes treatment effect heterogeneity and evaluate the potential for precision medicine to inform future clinical care.

Participant characteristics in the prevention of gestational diabetes as evidence for precision medicine: a systematic review and meta-analysis

BACKGROUND

Precision prevention involves using the unique characteristics of a particular group to determine their responses to preventive interventions. This study aimed to systematically evaluate the participant characteristics associated with responses to interventions in gestational diabetes mellitus (GDM) prevention.

METHODS

We searched MEDLINE, EMBASE, and Pubmed to identify lifestyle (diet, physical activity, or both), metformin, myoinositol/inositol and probiotics interventions of GDM prevention published up to May 24, 2022.

RESULTS

From 10347 studies, 116 studies (n = 40940 women) are included. Physical activity results in greater GDM reduction in participants with a normal body mass index (BMI) at baseline compared to obese BMI (risk ratio, 95% confidence interval: 0.06 [0.03, 0.14] vs 0.68 [0.26, 1.60]). Combined diet and physical activity interventions result in greater GDM reduction in participants without polycystic ovary syndrome (PCOS) than those with PCOS (0.62 [0.47, 0.82] vs 1.12 [0.78-1.61]) and in those without a history of GDM than those with unspecified GDM history (0.62 [0.47, 0.81] vs 0.85 [0.76, 0.95]). Metformin interventions are more effective in participants with PCOS than those with unspecified status (0.38 [0.19, 0.74] vs 0.59 [0.25, 1.43]), or when commenced preconception than during pregnancy (0.21 [0.11, 0.40] vs 1.15 [0.86-1.55]). Parity, history of having a large-for-gestational-age infant or family history of diabetes have no effect on intervention responses.

CONCLUSIONS

GDM prevention through metformin or lifestyle differs according to some individual characteristics. Future research should include trials commencing preconception and provide results disaggregated by a priori defined participant characteristics including social and environmental factors, clinical traits, and other novel risk factors to predict GDM prevention through interventions.

Impact of individual and environmental factors on dietary or lifestyle interventions to prevent type 2 diabetes development: a systematic review

BACKGROUND

The variability in the effectiveness of type 2 diabetes (T2D) preventive interventions highlights the potential to identify the factors that determine treatment responses and those that would benefit the most from a given intervention. We conducted a systematic review to synthesize the evidence to support whether sociodemographic, clinical, behavioral, and molecular factors modify the efficacy of dietary or lifestyle interventions to prevent T2D.

METHODS

We searched MEDLINE, Embase, and Cochrane databases for studies reporting on the effect of a lifestyle, dietary pattern, or dietary supplement interventions on the incidence of T2D and reporting the results stratified by any effect modifier. We extracted relevant statistical findings and qualitatively synthesized the evidence for each modifier based on the direction of findings reported in available studies. We used the Diabetes Canada Clinical Practice Scale to assess the certainty of the evidence for a given effect modifier.

RESULTS

The 81 publications that met our criteria for inclusion are from 33 unique trials. The evidence is low to very low to attribute variability in intervention effectiveness to individual characteristics such as age, sex, BMI, race/ethnicity, socioeconomic status, baseline behavioral factors, or genetic predisposition.

CONCLUSIONS

We report evidence, albeit low certainty, that those with poorer health status, particularly those with prediabetes at baseline, tend to benefit more from T2D prevention strategies compared to healthier counterparts. Our synthesis highlights the need for purposefully designed clinical trials to inform whether individual factors influence the success of T2D prevention strategies.

Precision gestational diabetes treatment: a systematic review and meta-analyses

BACKGROUND

Gestational Diabetes Mellitus (GDM) affects approximately 1 in 7 pregnancies globally. It is associated with short- and long-term risks for both mother and baby. Therefore, optimizing treatment to effectively treat the condition has wide-ranging beneficial effects. However, despite the known heterogeneity in GDM, treatment guidelines and approaches are generally standardized. We hypothesized that a precision medicine approach could be a tool for risk-stratification of women to streamline successful GDM management. With the relatively short timeframe available to treat GDM, commencing effective therapy earlier, with more rapid normalization of hyperglycaemia, could have benefits for both mother and fetus.

METHODS

We conducted two systematic reviews, to identify precision markers that may predict effective lifestyle and pharmacological interventions.

RESULTS

There was a paucity of studies examining precision lifestyle-based interventions for GDM highlighting the pressing need for further research in this area. We found a number of precision markers identified from routine clinical measures that may enable earlier identification of those requiring escalation of pharmacological therapy (to metformin, sulphonylureas or insulin). This included previous history of GDM, Body Mass Index and blood glucose concentrations at diagnosis.

CONCLUSIONS

Clinical measurements at diagnosis could potentially be used as precision markers in the treatment of GDM. Whether there are other sensitive markers that could be identified using more complex individual-level data, such as omics, and if these can feasibly be implemented in clinical practice remains unknown. These will be important to consider in future studies.

Utility and precision evidence of technology in the treatment of type 1 diabetes: a systematic review

BACKGROUND

The greatest change in the treatment of people living with type 1 diabetes in the last decade has been the explosion of technology assisting in all aspects of diabetes therapy, from glucose monitoring to insulin delivery and decision making. As such, the aim of our systematic review was to assess the utility of these technologies as well as identify any precision medicine-directed findings to personalize care.

METHODS

Screening of 835 peer-reviewed articles was followed by systematic review of 70 of them (focusing on randomized trials and extension studies with ≥50 participants from the past 10 years).

RESULTS

We find that novel technologies, ranging from continuous glucose monitoring systems, insulin pumps and decision support tools to the most advanced hybrid closed loop systems, improve important measures like HbA1c, time in range, and glycemic variability, while reducing hypoglycemia risk. Several studies included person-reported outcomes, allowing assessment of the burden or benefit of the technology in the lives of those with type 1 diabetes, demonstrating positive results or, at a minimum, no increase in self-care burden compared with standard care. Important limitations of the trials to date are their small size, the scarcity of pre-planned or powered analyses in sub-populations such as children, racial/ethnic minorities, people with advanced complications, and variations in baseline glycemic levels. In addition, confounders including education with device initiation, concomitant behavioral modifications, and frequent contact with the healthcare team are rarely described in enough detail to assess their impact.

CONCLUSIONS

Our review highlights the potential of technology in the treatment of people living with type 1 diabetes and provides suggestions for optimization of outcomes and areas of further study for precision medicine-directed technology use in type 1 diabetes.

Precision medicine in complex diseases-Molecular subgrouping for improved prediction and treatment stratification

Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease-both with regards to symptoms and underlying causal mechanisms-and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic-based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases-including psychiatric disorders and allergies-available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high-throughput molecular technologies, together with large collections of health data and novel data-driven approaches, offers promise toward improved individual health through precision medicine.

All-Cause Mortality and Cardiovascular and Microvascular Diseases in Latent Autoimmune Diabetes in Adults

OBJECTIVE

Latent autoimmune diabetes in adults (LADA) is a heterogenous, slowly progressing autoimmune diabetes. We aim to contribute new knowledge on the long-term prognosis of LADA with varying degrees of autoimmunity by comparing it to type 2 diabetes and adult-onset type 1 diabetes.

RESEARCH DESIGN AND METHODS

This Swedish population-based study included newly diagnosed LADA (n = 550, stratified into LADAlow and LADAhigh by median autoimmunity level), type 2 diabetes (n = 2,001), adult-onset type 1 diabetes (n = 1,573), and control subjects without diabetes (n = 2,355) in 2007-2019. Register linkages provided information on all-cause mortality, cardiovascular diseases (CVDs), diabetic retinopathy, nephropathy, and clinical characteristics during follow-up.

RESULTS

Mortality was higher in LADA (hazard ratio [HR] 1.44; 95% CI 1.03, 2.02), type 1 (2.31 [1.75, 3.05]), and type 2 diabetes (1.31 [1.03, 1.67]) than in control subjects. CVD incidence was elevated in LADAhigh (HR 1.67; 95% CI 1.04, 2.69) and type 2 diabetes (1.53 [1.17, 2.00]), but not in LADAlow or type 1 diabetes. Incidence of retinopathy but not nephropathy was higher in LADA (HR 2.25; 95% CI 1.64, 3.09), including LADAhigh and LADAlow than in type 2 diabetes (unavailable in type 1 diabetes). More favorable blood pressure and lipid profiles, but higher HbA1c levels, were seen in LADA than type 2 diabetes at baseline and throughout follow-up, especially in LADAhigh, which resembled type 1 diabetes in this respect.

CONCLUSIONS

Despite having fewer metabolic risk factors than type 2 diabetes, LADA has equal to higher risks of death, CVD, and retinopathy. Poorer glycemic control, particularly in LADAhigh, highlights the need for improved LADA management.

Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine

Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.

Phenotype similarities in automatically grouped T2D patients by variation-based clustering of IL-1β gene expression

Background

Analyzing longitudinal gene expression data is extremely challenging due to limited prior information, high dimensionality, and heterogeneity. Similar difficulties arise in research of multifactorial diseases such as Type 2 Diabetes. Clustering methods can be applied to automatically group similar observations. Common clinical values within the resulting groups suggest potential associations. However, applying traditional clustering methods to gene expression over time fails to capture variations in the response. Therefore, shape-based clustering could be applied to identify patient groups by gene expression variation in a large time metabolic compensatory intervention.

Objectives

To search for clinical grouping patterns between subjects that showed similar structure in the variation of IL-1β gene expression over time.

Methods

A new approach for shape-based clustering by IL-1β expression behavior was applied to a real longitudinal database of Type 2 Diabetes patients. In order to capture correctly variations in the response, we applied traditional clustering methods to slopes between measurements.

Results

In this setting, the application of K-Medoids using the Manhattan distance yielded the best results for the corresponding database. Among the resulting groups, one of the clusters presented significant differences in many key clinical values regarding the metabolic syndrome in comparison to the rest of the data.

Conclusions

The proposed method can be used to group patients according to variation patterns in gene expression (or other applications) and thus, provide clinical insights even when there is no previous knowledge on the subject clinical profile and few timepoints for each individual.

Multi-ancestry Polygenic Mechanisms of Type 2 Diabetes Elucidate Disease Processes and Clinical Heterogeneity

We identified genetic subtypes of type 2 diabetes (T2D) by analyzing genetic data from diverse groups, including non-European populations. We implemented soft clustering with 650 T2D-associated genetic variants, capturing known and novel T2D subtypes with distinct cardiometabolic trait associations. The twelve genetic clusters were distinctively enriched for single-cell regulatory regions. Polygenic scores derived from the clusters differed in distribution between ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a BMI of 30 kg/m2 in the European subpopulation and 24.2 (22.9-25.5) kg/m2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg/m2 in the East Asian group, explaining about 75% of the difference in BMI thresholds. Thus, these multi-ancestry T2D genetic subtypes encompass a broader range of biological mechanisms and help explain ancestry-associated differences in T2D risk profiles.

Systematic Review of Treatment of Beta-Cell Monogenic Diabetes

Background

Beta-cell monogenic forms of diabetes are the area of diabetes care with the strongest support for precision medicine. We reviewed treatment of hyperglycemia in GCK-related hyperglycemia, HNF1A-HNF4A- and HNF1B-diabetes, Mitochondrial diabetes (MD) due to m.3243A>G variant, 6q24-transient neonatal diabetes (TND) and SLC19A2-diabetes.

Methods

Systematic reviews with data from PubMed, MEDLINE and Embase were performed for the different subtypes. Individual and group level data was extracted for glycemic outcomes in individuals with genetically confirmed monogenic diabetes.

Results

147 studies met inclusion criteria with only six experimental studies and the rest being single case reports or cohort studies. Most studies had moderate or serious risk of bias.For GCK-related hyperglycemia, six studies (N=35) showed no deterioration in HbA1c on discontinuing glucose lowering therapy. A randomized trial (n=18 per group) showed that sulfonylureas (SU) were more effective in HNF1A-diabetes than in type 2 diabetes, and cohort and case studies supported SU effectiveness in lowering HbA1c. Two crossover trials (n=15 and n=16) suggested glinides and GLP-1 receptor agonists might be used in place of SU. Evidence for HNF4A-diabetes was limited. While some patients with HNF1B-diabetes (n=301) and MD (n=250) were treated with oral agents, most were on insulin. There was some support for the use of oral agents after relapse in 6q24-TND, and for thiamine improving glycemic control and reducing insulin requirement in SLC19A2-diabetes (less than half achieved insulin-independency).

Conclusion

There is limited evidence to guide the treatment in monogenic diabetes with most studies being non-randomized and small. The data supports: no treatment in GCK-related hyperglycemia; SU for HNF1A-diabetes. Further evidence is needed to examine the optimum treatment in monogenic subtypes.

How amenable is type 2 diabetes treatment for precision diabetology? A meta-regression of glycaemic control data from 174 randomised trials

AIMS/HYPOTHESIS

There are two prerequisites for the precision medicine approach to be beneficial for treated individuals. First, there must be treatment heterogeneity; second, in the case of treatment heterogeneity, we need to detect clinical predictors to identify people who would benefit from one treatment more than from others. There is an established meta-regression approach to assess these two prerequisites that relies on measuring the variability of a clinical outcome after treatment in placebo-controlled randomised trials. Our aim was to apply this approach to the treatment of type 2 diabetes.

METHODS

We performed a meta-regression analysis using information from 174 placebo-controlled randomised trials with 178 placebo and 272 verum (i.e. active treatment) arms including 86,940 participants with respect to the variability of glycaemic control as assessed by HbA1c after treatment and its potential predictors.

RESULTS

The adjusted difference in log(SD) values between the verum and placebo arms was 0.037 (95% CI: 0.004, 0.069). That is, we found a small increase in the variability of HbA1c values after treatment in the verum arms. In addition, one potentially relevant predictor for explaining this increase, drug class, was observed, and GLP-1 receptor agonists yielded the largest differences in log(SD) values.

CONCLUSIONS/INTERPRETATION

The potential of the precision medicine approach in the treatment of type 2 diabetes is modest at best, at least with regard to an improvement in glycaemic control. Our finding of a larger variability after treatment with GLP-1 receptor agonists in individuals with poor glycaemic control should be replicated and/or validated with other clinical outcomes and with different study designs.

FUNDING

The research reported here received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

DATA AVAILABILITY

Two datasets (one for the log[SD] and one for the baseline-corrected log[SD]) to reproduce the analyses from this paper are available on https://zenodo.org/record/7956635 .

Single-tablet-scale direct-compression: An on-demand manufacturing route for personalized tablets

Today's pharmaceutical industry is facing various challenges. Two of them are issues with supply chain security and the increasing demand for personalized medicine. Both can be addressed by increasing flexibility and a more decentralized approach to pharmaceutical manufacturing. In this study, we present a setup that provides flexibility in terms of supplied raw materials and the product, i.e., a direct-compression setup for personalized tablets operating at a single-tablet-scale. The performance of the implemented single-tablet-scale technology for dosing and mixing was investigated. In addition, an analysis of the critical quality attributes (CQAs) of immediate release ibuprofen and loratadine tablets was performed. The developed dosing device achieved acceptance rates of > 90 % for doses ≥ 20 mg for various pharmaceutical powders. Regarding the vibratory mixing process, a dependency of the performance on the applied frequencies and acceleration was observed, with 100 Hz and ∼ 90 G performing best, yet still exhibiting varying mixing efficacies depending on the granular system. The tablets produced met U.S. Pharmacopeia requirements regarding mechanical stability and dissolution characteristics. Given these results, we consider the developed setup a proof of concept of a tool to provide personalized tablets to patients while minimizing the dependency on complex supply chains.

Precision Prognostics for Cardiovascular Disease in Type 2 Diabetes: A Systematic Review and Meta-analysis

Background Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with type 2 diabetes (T2D). Methods We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies. Results Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort. Conclusions Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

GIP receptor agonism improves dyslipidemia and atherosclerosis independently of body weight loss in preclinical mouse model for cardio-metabolic disease

BACKGROUND

Agonism at the receptor for the glucose-dependent insulinotropic polypeptide (GIPR) is a key component of the novel unimolecular GIPR:GLP-1R co-agonists, which are among the most promising drugs in clinical development for the treatment of obesity and type 2 diabetes. The therapeutic effect of chronic GIPR agonism to treat dyslipidemia and thus to reduce the cardiovascular disease risk independently of body weight loss has not been explored yet.

METHODS

After 8 weeks on western diet, LDL receptor knockout (LDLR-/-) male mice were treated with daily subcutaneous injections of long-acting acylated GIP analog (acyl-GIP; 10nmol/kg body weight) for 28 days. Body weight, food intake, whole-body composition were monitored throughout the study. Fasting blood glucose and intraperitoneal glucose tolerance test (ipGTT) were determined on day 21 of the study. Circulating lipid levels, lipoprotein profiles and atherosclerotic lesion size was assessed at the end of the study. Acyl-GIP effects on fat depots were determined by histology and transcriptomics.

RESULTS

Herein we found that treatment with acyl-GIP reduced dyslipidemia and atherogenesis in male LDLR-/- mice. Acyl-GIP administration resulted in smaller adipocytes within the inguinal fat depot and RNAseq analysis of the latter revealed that acyl-GIP may improve dyslipidemia by directly modulating lipid metabolism in this fat depot.

CONCLUSIONS

This study identified an unanticipated efficacy of chronic GIPR agonism to improve dyslipidemia and cardiovascular disease independently of body weight loss, indicating that treatment with acyl-GIP may be a novel approach to alleviate cardiometabolic disease.

Role of sociodemographic, clinical, behavioral, and molecular factors in precision prevention of type 2 diabetes: a systematic review

The variability in the effectiveness of type 2 diabetes (T2D) preventive interventions highlights the potential to identify the factors that determine treatment responses and those that would benefit the most from a given intervention. We conducted a systematic review to synthesize the evidence to support whether sociodemographic, clinical, behavioral, and molecular characteristics modify the efficacy of dietary or lifestyle interventions to prevent T2D. Among the 80 publications that met our criteria for inclusion, the evidence was low to very low to attribute variability in intervention effectiveness to individual characteristics such as age, sex, BMI, race/ethnicity, socioeconomic status, baseline behavioral factors, or genetic predisposition. We found evidence, albeit low certainty, to support conclusions that those with poorer health status, particularly those with prediabetes at baseline, tend to benefit more from T2D prevention strategies compared to healthier counterparts. Our synthesis highlights the need for purposefully designed clinical trials to inform whether individual factors influence the success of T2D prevention strategies.

Precision Medicine and/or Biomarker Based Therapy in T2DM: Ready for Prime Time?

Approximately 30-40% of people with type 2 diabetes mellitus develop chronic kidney disease. This is characterised by elevated blood pressure, declining kidney function and enhanced cardiovascular morbidity and mortality. Increased albuminuria and decreasing estimated glomerular function has to be evaluated regularly to diagsnose kidney disease. New biomarkers may facilitate early diagnosis and provide infomation on undlying pathology thereby supporting early precision intervention for the optimal benefit. A number of biomarkers have been suggested but are not yet implemented in clinical practice. iI the future such bimarkers may pave the way for personalized treatment.

Precision medicine in type 2 diabetes: A systematic review of treatment effect heterogeneity for GLP1-receptor agonists and SGLT2-inhibitors

Background

A precision medicine approach in type 2 diabetes requires identification of clinical and biological features that are reproducibly associated with differences in clinical outcomes with specific anti-hyperglycaemic therapies. Robust evidence of such treatment effect heterogeneity could support more individualized clinical decisions on optimal type 2 diabetes therapy.

Methods

We performed a pre-registered systematic review of meta-analysis studies, randomized control trials, and observational studies evaluating clinical and biological features associated with heterogenous treatment effects for SGLT2-inhibitor and GLP1-receptor agonist therapies, considering glycaemic, cardiovascular, and renal outcomes.

Results

After screening 5,686 studies, we included 101 studies of SGLT2-inhibitors and 75 studies of GLP1-receptor agonists in the final systematic review. The majority of papers had methodological limitations precluding robust assessment of treatment effect heterogeneity. For glycaemic outcomes, most cohorts were observational, with multiple analyses identifying lower renal function as a predictor of lesser glycaemic response with SGLT2-inhibitors and markers of reduced insulin secretion as predictors of lesser response with GLP1-receptor agonists. For cardiovascular and renal outcomes, the majority of included studies were post-hoc analyses of randomized control trials (including meta-analysis studies) which identified limited clinically relevant treatment effect heterogeneity.

Conclusions

Current evidence on treatment effect heterogeneity for SGLT2-inhibitor and GLP1-receptor agonist therapies is limited, likely reflecting the methodological limitations of published studies. Robust and appropriately powered studies are required to understand type 2 diabetes treatment effect heterogeneity and evaluate the potential for precision medicine to inform future clinical care.

Plain language summary

This review identifies research that helps understand which clinical and biological factors that are associated with different outcomes for specific type 2 diabetes treatments. This information could help clinical providers and patients make better informed personalized decisions about type 2 diabetes treatments. We focused on two common type 2 diabetes treatments: SGLT2-inhibitors and GLP1-receptor agonists, and three outcomes: blood glucose control, heart disease, and kidney disease. We identified some potential factors that are likely to lessen blood glucose control including lower kidney function for SGLT2-inhibitors and lower insulin secretion for GLP1-receptor agonists. We did not identify clear factors that alter heart and renal disease outcomes for either treatment. Most of the studies had limitations, meaning more research is needed to fully understand the factors that influence treatment outcomes in type 2 diabetes.

Systematic review of genotype-stratified treatment for monogenic insulin resistance

Objective

To assess the effects of pharmacologic and/or surgical interventions in monogenic insulin resistance (IR), stratified by genetic aetiology.

Design

Systematic review.

Data sources

PubMed, MEDLINE and Embase, from 1 January 1987 to 23 June 2021.

Review methods

Studies reporting individual-level effects of pharmacologic and/or surgical interventions in monogenic IR were eligible. Individual subject data were extracted and duplicate data removed. Outcomes were analyzed for each affected gene and intervention, and in aggregate for partial, generalised and all lipodystrophy.

Results

10 non-randomised experimental studies, 8 case series, and 21 single case reports met inclusion criteria, all rated as having moderate or serious risk of bias. Metreleptin was associated with lower triglycerides and hemoglobin A1c in aggregated lipodystrophy (n=111), in partial lipodystrophy (n=71) and generalised lipodystrophy (n=41)), and in LMNA , PPARG , AGPAT2 or BSCL2 subgroups (n=72,13,21 and 21 respectively). Body Mass Index (BMI) was lower after treatment in partial and generalised lipodystrophy overall, and in LMNA or BSCL2 , but not PPARG or AGPAT2 subgroups. Thiazolidinedione use was associated with improved hemoglobin A1c and triglycerides in aggregated lipodystrophy (n=13), improved hemoglobin A1c only in the PPARG subgroup (n=5), and improved triglycerides only in the LMNA subgroup (n=7). In INSR -related IR, use of rhIGF-1, alone or with IGFBP3, was associated with improved hemoglobin A1c (n=15). The small size or absence of all other genotype-treatment combinations precluded firm conclusions.

Conclusions

The evidence guiding genotype-specific treatment of monogenic IR is of low to very low quality. Metreleptin and Thiazolidinediones appear to have beneficial metabolic effects in lipodystrophy, and rhIGF-1 appears to lower hemoglobin A1c in INSR-related IR. For other interventions there is insufficient evidence to assess efficacy and risks either in aggregated lipodystrophy or in genetic subgroups. There is a pressing need to improve the evidence base for management of monogenic IR.

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.

A Systematic Review of the use of Precision Diagnostics in Monogenic Diabetes

Monogenic forms of diabetes present opportunities for precision medicine as identification of the underlying genetic cause has implications for treatment and prognosis. However, genetic testing remains inconsistent across countries and health providers, often resulting in both missed diagnosis and misclassification of diabetes type. One of the barriers to deploying genetic testing is uncertainty over whom to test as the clinical features for monogenic diabetes overlap with those for both type 1 and type 2 diabetes. In this review, we perform a systematic evaluation of the evidence for the clinical and biochemical criteria used to guide selection of individuals with diabetes for genetic testing and review the evidence for the optimal methods for variant detection in genes involved in monogenic diabetes. In parallel we revisit the current clinical guidelines for genetic testing for monogenic diabetes and provide expert opinion on the interpretation and reporting of genetic tests. We provide a series of recommendations for the field informed by our systematic review, synthesizing evidence, and expert opinion. Finally, we identify major challenges for the field and highlight areas for future research and investment to support wider implementation of precision diagnostics for monogenic diabetes.

Predictors and risk factors of short-term and long-term outcomes among women with gestational diabetes mellitus (GDM) and their offspring: Moving toward precision prognosis?

As part of the American Diabetes Association Precision Medicine in Diabetes Initiative (PMDI) - a partnership with the European Association for the Study of Diabetes (EASD) - this systematic review is part of a comprehensive evidence evaluation in support of the 2 ^nd International Consensus Report on Precision Diabetes Medicine. Here, we sought to synthesize evidence from empirical research papers published through September 1 ^st , 2021 to evaluate and identify prognostic conditions, risk factors, and biomarkers among women and children affected by gestational diabetes mellitus (GDM), focusing on clinical endpoints of cardiovascular disease (CVD) and type 2 diabetes (T2D) among women with a history of GDM; and adiposity and cardiometabolic profile among offspring exposed to GDM in utero. We identified a total of 107 observational studies and 12 randomized controlled trials testing the effect of pharmaceutical and/or lifestyle interventions. Broadly, current literature indicates that greater GDM severity, higher maternal body mass index, belonging to racial/ethnic minority group; and unhealthy lifestyle behaviors would predict a woman's risk of incident T2D and CVD, and an unfavorable cardiometabolic profile among offspring. However, the level of evidence is low (Level 4 according to the Diabetes Canada 2018 Clinical Practice Guidelines for diabetes prognosis) largely because most studies leveraged retrospective data from large registries that are vulnerable to residual confounding and reverse causation bias; and prospective cohort studies that may suffer selection and attrition bias. Moreover, for the offspring outcomes, we identified a relatively small body of literature on prognostic factors indicative of future adiposity and cardiometabolic risk. Future high-quality prospective cohort studies in diverse populations with granular data collection on prognostic factors, clinical and subclinical outcomes, high fidelity of follow-up, and appropriate analytical approaches to deal with structural biases are warranted.

A systematic review and meta-analysis of participant characteristics in the prevention of gestational diabetes: a summary of evidence for precision medicine

Background and aims

Precision prevention involves using the unique characteristics of a particular group to determine their responses to preventive interventions. This study aimed to systematically evaluate the participant characteristics associated with interventions in gestational diabetes mellitus (GDM) prevention.

Methods

We searched MEDLINE, EMBASE, and Pubmed to identify lifestyle (diet, physical activity, or both), metformin, myoinositol/inositol and probiotics interventions of GDM prevention published up to May 24, 2022.

Results

From 10347 studies, 116 studies (n=40940 women) were included. Physical activity resulted in greater GDM reduction in participants with a normal body mass index (BMI) at baseline compared to obese BMI (risk ratio, 95% confidence interval: 0.06 [0.03, 0.14] vs 0.68 [0.26, 1.60]). Diet and physical activity interventions resulted in greater GDM reduction in participants without polycystic ovary syndrome (PCOS) than those with PCOS (0.62 [0.47, 0.82] vs 1.12 [0.78-1.61]) and in those without a history of GDM than those with unspecified history (0.62 [0.47, 0.81] vs 0.85 [0.76, 0.95]). Metformin interventions were more effective in participants with PCOS than those with unspecified status (0.38 [0.19, 0.74] vs 0.59 [0.25, 1.43]), or when commenced preconception than during pregnancy (0.22 [0.11, 0.45] vs 1.15 [0.86-1.55]). Parity, history of having a large-for-gestational-age infant or family history of diabetes had no effect.

Conclusions

GDM prevention through metformin or lifestyle differs according to some individual characteristics. Future research should include trials commencing preconception and provide results stratified by participant characteristics including social and environmental factors, clinical traits, and other novel risk factors to predict GDM prevention through interventions.

Plain language summary

Precision prevention involves using a group’s unique context to determine their responses to preventive interventions. This study aimed to evaluate the participant characteristics associated with interventions in GDM prevention. We searched medical literature databases to identify lifestyle (diet, physical activity), metformin, myoinositol/inositol and probiotics interventions. A total of 116 studies (n=40903 women) were included. Diet and physical activity interventions resulted in greater GDM reduction in participants without polycystic ovary syndrome (PCOS) and those without a history of GDM. Metformin interventions resulted in greater GDM reduction in participants with PCOS or when started during the preconception period. Future research should include trials starting in the preconception period, and provide results stratified by participant characteristics to predict GDM prevention through interventions.

A Strategic Research Framework for Defeating Diabetes in India: A 21st-Century Agenda

Indian people are at high risk for type 2 diabetes (T2DM) even at younger ages and lower body weights. Already 74 million people in India have the disease, and the proportion of those with T2DM is increasing across all strata of society. Unique aspects, related to lower insulin secretion or function, and higher hepatic fat deposition, accompanied by the rise in overweight (related to lifestyle changes) may all be responsible for this unrelenting epidemic of T2DM. Yet, research to understand the causes, pathophysiology, phenotypes, prevention, treatment, and healthcare delivery of T2DM in India seriously lags behind. There are major opportunities for scientific discovery and technological innovation, which if tapped can generate solutions for T2DM relevant to the country's context and make leading contributions to global science. We analyze the situation of T2DM in India, and present a four-pillar (etiology, precision medicine, implementation research, and health policy) strategic research framework to tackle the challenge. We offer key research questions for each pillar, and identify infrastructure needs. India offers a fertile environment for shifting the paradigm from imprecise late-stage diabetes treatment toward early-stage precision prevention and care. Investing in and leveraging academic and technological infrastructures, across the disciplines of science, engineering, and medicine, can accelerate progress toward a diabetes-free nation.

Type 2 Diabetes Prevention Programs-From Proof-of-Concept Trials to National Intervention and Beyond

The prevention of type 2 diabetes (T2D) in high-risk people with lifestyle interventions has been demonstrated by several randomized controlled trials. The intervention effect has sustained up to 20 years in post-trial monitoring of T2D incidence. In 2000, Finland launched the national T2D prevention plan. For screening for high T2D risk, the non-laboratory Finnish Diabetes Risk Score was developed and widely used, also in other countries. The incidence of drug-treated T2D has decreased steadily since 2010. The US congress authorized public funding for a national diabetes prevention program (NDPP) in 2010. It was built around a 16-visit program that relies on referral from primary care and self-referral of persons with either prediabetes or by a diabetes risk test. The program uses a train-the-trainer program. In 2015 the program started the inclusion of online programs. There has been limited implementation of nationwide T2D prevention programs in other countries. Despite the convincing results from RCTs in China and India, no translation to the national level was introduced there. T2D prevention efforts in low-and middle-income countries are still limited, but results have been promising. Barriers to efficient interventions are greater in these countries than in high-income countries, where many barriers also exist. Health disparities by socioeconomic status exist for T2D and its risk factors and form a challenge for preventive interventions. It seems that a stronger commitment to T2D prevention is needed, such as the successful WHO Framework Convention on Tobacco Control, which legally binds the countries to act.

Independent association of blood cadmium with subclinical lower extremity atherosclerosis: An observational study based on dose-response analysis

BACKGROUND

Atherosclerosis is an increasingly public health issue globally. Previous studies have showed a causal link between heavy metal exposure and atherosclerosis. However, the association of cadmium concentration with subclinical lower extremity atherosclerosis (SLEA) remains unclear.

AIMS

To investigate the association of blood cadmium with SLEA and its extent, and further analyze the potential dose-response relationship.

METHODS

Blood cadmium concentration was measured using inductively coupled plasma mass spectrometry. SLEA and its extent were assessed by ultrasound diagnosis system. Multivariate models were applied to evaluate the association of blood cadmium with SLEA and its extent. Restricted cubic splines were performed to explore the potential dose-response relationship.

RESULTS

This observational study consisted of 1664 participants from cardiovascular outpatient, with an average age of 62.4 years and 1218 (73.2%) men. When blood cadmium was included as a categorical variable in multivariate models, logistic regression analysis showed that high quartile in blood cadmium was an independent risk factor of SLEA (OR = 2.704, 95%CI 1.866-3.919). After log-transformed for SLEA extent parameters, linear regression analysis indicated that high quartile in blood cadmium was significantly associated with higher Crouse score (GMR = 1.21, 95%CI 1.15-1.28), plaque maximum thickness (GMR = 1.13, 95%CI 1.09-1.18) and diseased vessel count (GMR = 1.14, 95%CI 1.10-1.19), respectively. When blood cadmium was used as a continuous variable in restricted cubic splines, the dose-response relationship presented a positive progression in SLEA (P = 0.302), plaque maximum thickness (P = 0.145) and diseased vessel count (P = 0.055) apparently that did not deviate from linearity.

CONCLUSIONS

Blood cadmium exhibited an independent association with SLEA, and this dose-response relationship was progressive without significant departure from linearity.

Chemical Compounds and Ambient Factors Affecting Pancreatic Alpha-Cells Mass and Function: What Evidence?

The exposure to different substances present in the environment can affect the ability of the human body to maintain glucose homeostasis. Some review studies summarized the current evidence about the relationships between environment and insulin resistance or beta-cell dysfunction. Instead, no reviews focused on the relationships between the environment and the alpha cell, although in recent years clear indications have emerged for the pivotal role of the alpha cell in glucose regulation. Thus, the aim of this review was to analyze the studies about the effects of chemical, biological, and physical environmental factors on the alpha cell. Notably, we found studies focusing on the effects of different categories of compounds, including air pollutants, compounds of known toxicity present in common objects, pharmacological agents, and compounds possibly present in food, plus studies on the effects of physical factors (mainly heat exposure). However, the overall number of relevant studies was limited, especially when compared to studies related to the environment and insulin sensitivity or beta-cell function. In our opinion, this was likely due to the underestimation of the alpha-cell role in glucose homeostasis, but since such a role has recently emerged with increasing strength, we expect several new studies about the environment and alpha-cell in the near future.

Artificial Intelligence Methodologies Applied to Technologies for Screening, Diagnosis and Care of the Diabetic Foot: A Narrative Review

Diabetic foot syndrome is a multifactorial pathology with at least three main etiological factors, i.e., peripheral neuropathy, peripheral arterial disease, and infection. In addition to complexity, another distinctive trait of diabetic foot syndrome is its insidiousness, due to a frequent lack of early symptoms. In recent years, it has become clear that the prevalence of diabetic foot syndrome is increasing, and it is among the diabetes complications with a stronger impact on patient's quality of life. Considering the complex nature of this syndrome, artificial intelligence (AI) methodologies appear adequate to address aspects such as timely screening for the identification of the risk for foot ulcers (or, even worse, for amputation), based on appropriate sensor technologies. In this review, we summarize the main findings of the pertinent studies in the field, paying attention to both the AI-based methodological aspects and the main physiological/clinical study outcomes. The analyzed studies show that AI application to data derived by different technologies provides promising results, but in our opinion future studies may benefit from inclusion of quantitative measures based on simple sensors, which are still scarcely exploited.

TyGIS: improved triglyceride-glucose index for the assessment of insulin sensitivity during pregnancy

Background

The triglyceride-glucose index (TyG) has been proposed as a surrogate marker of insulin resistance, which is a typical trait of pregnancy. However, very few studies analyzed TyG performance as marker of insulin resistance in pregnancy, and they were limited to insulin resistance assessment at fasting rather than in dynamic conditions, i.e., during an oral glucose tolerance test (OGTT), which allows more reliable assessment of the actual insulin sensitivity impairment. Thus, first aim of the study was exploring in pregnancy the relationships between TyG and OGTT-derived insulin sensitivity. In addition, we developed a new version of TyG, for improved performance as marker of insulin resistance in pregnancy.

Methods

At early pregnancy, a cohort of 109 women underwent assessment of maternal biometry and blood tests at fasting, for measurements of several variables (visit 1). Subsequently (26 weeks of gestation) all visit 1 analyses were repeated (visit 2), and a subgroup of women (84 selected) received a 2 h-75 g OGTT (30, 60, 90, and 120 min sampling) with measurement of blood glucose, insulin and C-peptide for reliable assessment of insulin sensitivity (PREDIM index) and insulin secretion/beta-cell function. The dataset was randomly split into 70% training set and 30% test set, and by machine learning approach we identified the optimal model, with TyG included, showing the best relationship with PREDIM. For inclusion in the model, we considered only fasting variables, in agreement with TyG definition.

Results

The relationship of TyG with PREDIM was weak. Conversely, the improved TyG, called TyGIS, (linear function of TyG, body weight, lean body mass percentage and fasting insulin) resulted much strongly related to PREDIM, in both training and test sets (R² > 0.64, p < 0.0001). Bland–Altman analysis and equivalence test confirmed the good performance of TyGIS in terms of association with PREDIM. Different further analyses confirmed TyGIS superiority over TyG.

Conclusions

We developed an improved version of TyG, as new surrogate marker of insulin sensitivity in pregnancy (TyGIS). Similarly to TyG, TyGIS relies only on fasting variables, but its performances are remarkably improved than those of TyG.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-022-01649-8.

Cardiovascular Disease in Adults with Type 1 Diabetes: Looking Beyond Glycemic Control

PURPOSE OF REVIEW

Despite improvements in treatment, people with type 1 diabetes continue to have increased cardiovascular disease (CVD) risk. Glycemic control does not fully explain this excess CVD risk, so a greater understanding of other risk factors is needed.

RECENT FINDINGS

The authors review the relationship between glycemia and CVD risk in adults with type 1 diabetes and summarize evidence regarding other factors that may explain risk beyond glycemia. Insulin resistance, weight gain, sex differences, genetics, inflammation, emerging markers of risk, including lipid subclasses and epigenetic modifications, and future directions are discussed. As glycemic control improves, an increased focus on other CVD risk factors is warranted in type 1 diabetes. Novel markers and precision medicine approaches may improve CVD prediction, but a lack of type 1 diabetes-specific guidelines for lipids, blood pressure, and physical activity are likely impediments to optimal CVD prevention in this high-risk population.

Precision Diabetes: Lessons Learned from Maturity-Onset Diabetes of the Young (MODY)

Maturity‐onset of diabetes of the young (MODY) are monogenic forms of diabetes characterized by early onset diabetes with autosomal dominant inheritance. Since its first description about six decades ago, there have been significant advancements in our understanding of MODY from clinical presentations to molecular diagnostics and therapeutic responses. The prevalence of MODY is estimated as at least 1.1–6.5% of the pediatric diabetes population with a high degree of geographic variability that might arise from several factors in the criteria used to ascertain cases. GCK‐MODY, HNF1A‐MODY, and HNF4A‐MODY account for >90% of MODY cases. While some MODY forms do not require treatment (i.e., GCK‐MODY), some others are highly responsive to oral agents (i.e., HNF1A‐MODY). The risk of micro‐ and macro‐vascular complications of diabetes also differ significantly between MODY forms. Despite its high clinical impact, 50–90% of MODY cases are estimated to be misdiagnosed as type 1 or type 2 diabetes. Although there are many clinical features suggestive of MODY diagnosis, there is no single clinical criterion. An online MODY Risk Calculator can be a useful tool for clinicians in the decision‐making process for MODY genetic testing in some situations. Molecular genetic tests with a commercial gene panel should be performed in cases with a suspicion of MODY. Unresolved atypical cases can be further studied by exome or genome sequencing in a clinical or research setting, as available.

Assessing the Effect of Incretin Hormones and Other Insulin Secretagogues on Pancreatic Beta-Cell Function: Review on Mathematical Modelling Approaches

Mathematical modelling in glucose metabolism has proven very useful for different reasons. Several models have allowed deeper understanding of the relevant physiological and pathophysiological aspects and promoted new experimental activity to reach increased knowledge of the biological and physiological systems of interest. Glucose metabolism modelling has also proven useful to identify the parameters with specific physiological meaning in single individuals, this being relevant for clinical applications in terms of precision diagnostics or therapy. Among those model-based physiological parameters, an important role resides in those for the assessment of different functional aspects of the pancreatic beta cell. This study focuses on the mathematical models of incretin hormones and other endogenous substances with known effects on insulin secretion and beta-cell function, mainly amino acids, non-esterified fatty acids, and glucagon. We found that there is a relatively large number of mathematical models for the effects on the beta cells of incretin hormones, both at the cellular/organ level or at the higher, whole-body level. In contrast, very few models were identified for the assessment of the effect of other insulin secretagogues. Given the opportunities offered by mathematical modelling, we believe that novel models in the investigated field are certainly advisable.