Obesity, clinical, and genetic predictors for glycemic progression in Chinese patients with type 2 diabetes: A cohort study using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank

Precision Medicine to Redefine Insulin Secretion and Monogenic Diabetes-Randomized Controlled Trial (PRISM-RCT) in Chinese patients with young-onset diabetes: design, methods and baseline characteristics

INTRODUCTION

We designed and implemented a patient-centered, data-driven, holistic care model with evaluation of its impacts on clinical outcomes in patients with young-onset type 2 diabetes (T2D) for which there is a lack of evidence-based practice guidelines.

RESEARCH DESIGN AND METHODS

In this 3-year Precision Medicine to Redefine Insulin Secretion and Monogenic Diabetes-Randomized Controlled Trial, we evaluate the effects of a multicomponent care model integrating use of information and communication technology (Joint Asia Diabetes Evaluation (JADE) platform), biogenetic markers and patient-reported outcome measures in patients with T2D diagnosed at ≤40 years of age and aged ≤50 years. The JADE-PRISM group received 1 year of specialist-led team-based management using treatment algorithms guided by biogenetic markers (genome-wide single-nucleotide polymorphism arrays, exome-sequencing of 34 monogenic diabetes genes, C-peptide, autoantibodies) to achieve multiple treatment goals (glycated hemoglobin (HbA1c) <6.2%, blood pressure <120/75 mm Hg, low-density lipoprotein-cholesterol <1.2 mmol/L, waist circumference <80 cm (women) or <85 cm (men)) in a diabetes center setting versus usual care (JADE-only). The primary outcome is incidence of all diabetes-related complications.

RESULTS

In 2020-2021, 884 patients (56.6% men, median (IQR) diabetes duration: 7 (3-12) years, current/ex-smokers: 32.5%, body mass index: 28.40±5.77 kg/m2, HbA1c: 7.52%±1.66%, insulin-treated: 27.7%) were assigned to JADE-only (n=443) or JADE-PRISM group (n=441). The profiles of the whole group included positive family history (74.7%), general obesity (51.4%), central obesity (79.2%), hypertension (66.7%), dyslipidemia (76.4%), albuminuria (35.4%), estimated glomerular filtration rate <60 mL/min/1.73 m2 (4.0%), retinopathy (13.8%), atherosclerotic cardiovascular disease (5.2%), cancer (3.1%), emotional distress (26%-38%) and suboptimal adherence (54%) with 5-item EuroQol for Quality of Life index of 0.88 (0.87-0.96). Overall, 13.7% attained ≥3 metabolic targets defined in secondary outcomes. In the JADE-PRISM group, 4.5% had pathogenic/likely pathogenic variants of monogenic diabetes genes; 5% had autoantibodies and 8.4% had fasting C-peptide <0.2 nmol/L. Other significant events included low/large birth weight (33.4%), childhood obesity (50.7%), mental illness (10.3%) and previous suicide attempts (3.6%). Among the women, 17.3% had polycystic ovary syndrome, 44.8% required insulin treatment during pregnancy and 17.3% experienced adverse pregnancy outcomes.

CONCLUSIONS

Young-onset diabetes is characterized by complex etiologies with comorbidities including mental illness and lifecourse events.

TRIAL REGISTRATION NUMBER

NCT04049149.

An omics-based machine learning approach to predict diabetes progression: a RHAPSODY study

AIMS/HYPOTHESIS

People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA1c and diabetes duration are associated with glycaemic progression, it is unclear how well such variables predict insulin initiation or requirement and whether newly identified markers have added predictive value.

METHODS

In two prospective cohort studies as part of IMI-RHAPSODY, we investigated whether clinical variables and three types of molecular markers (metabolites, lipids, proteins) can predict time to insulin requirement using different machine learning approaches (lasso, ridge, GRridge, random forest). Clinical variables included age, sex, HbA1c, HDL-cholesterol and C-peptide. Models were run with unpenalised clinical variables (i.e. always included in the model without weights) or penalised clinical variables, or without clinical variables. Model development was performed in one cohort and the model was applied in a second cohort. Model performance was evaluated using Harrel's C statistic.

RESULTS

Of the 585 individuals from the Hoorn Diabetes Care System (DCS) cohort, 69 required insulin during follow-up (1.0-11.4 years); of the 571 individuals in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) cohort, 175 required insulin during follow-up (0.3-11.8 years). Overall, the clinical variables and proteins were selected in the different models most often, followed by the metabolites. The most frequently selected clinical variables were HbA1c (18 of the 36 models, 50%), age (15 models, 41.2%) and C-peptide (15 models, 41.2%). Base models (age, sex, BMI, HbA1c) including only clinical variables performed moderately in both the DCS discovery cohort (C statistic 0.71 [95% CI 0.64, 0.79]) and the GoDARTS replication cohort (C 0.71 [95% CI 0.69, 0.75]). A more extensive model including HDL-cholesterol and C-peptide performed better in both cohorts (DCS, C 0.74 [95% CI 0.67, 0.81]; GoDARTS, C 0.73 [95% CI 0.69, 0.77]). Two proteins, lactadherin and proto-oncogene tyrosine-protein kinase receptor, were most consistently selected and slightly improved model performance.

CONCLUSIONS/INTERPRETATION

Using machine learning approaches, we show that insulin requirement risk can be modestly well predicted by predominantly clinical variables. Inclusion of molecular markers improves the prognostic performance beyond that of clinical variables by up to 5%. Such prognostic models could be useful for identifying people with diabetes at high risk of progressing quickly to treatment intensification.

DATA AVAILABILITY

Summary statistics of lipidomic, proteomic and metabolomic data are available from a Shiny dashboard at https://rhapdata-app.vital-it.ch .

Young-Onset Diabetes in East Asians: From Epidemiology to Precision Medicine

Precision diagnosis is the keystone of clinical medicine. In East Asians, classical type 1 diabetes is uncommon in patients with youngonset diabetes diagnosed before age of 40, in whom a family history, obesity, and beta-cell and kidney dysfunction are key features. Young-onset diabetes affects one in five Asian adults with diabetes in clinic settings; however, it is often misclassified, resulting in delayed or non-targeted treatment. Complex aetiologies, long disease duration, aggressive clinical course, and a lack of evidence-based guidelines have contributed to variable care standards and premature death in these young patients. The high burden of comorbidities, notably mental illness, highlights the numerous knowledge gaps related to this silent killer. The majority of adult patients with youngonset diabetes are managed as part of a heterogeneous population of patients with various ages of diagnosis. A multidisciplinary care team led by physicians with special interest in young-onset diabetes will help improve the precision of diagnosis and address their physical, mental, and behavioral health. To this end, payors, planners, and providers need to align and re-design the practice environment to gather data systematically during routine practice to elucidate the multicausality of young-onset diabetes, treat to multiple targets, and improve outcomes in these vulnerable individuals.

Lessons and Applications of Omics Research in Diabetes Epidemiology

PURPOSE OF REVIEW

Recent advances in genomic technology and molecular techniques have greatly facilitated the identification of disease biomarkers, advanced understanding of pathogenesis of different common diseases, and heralded the dawn of precision medicine. Much of these advances in the area of diabetes have been made possible through deep phenotyping of epidemiological cohorts, and analysis of the different omics data in relation to detailed clinical information. In this review, we aim to provide an overview on how omics research could be incorporated into the design of current and future epidemiological studies.

RECENT FINDINGS

We provide an up-to-date review of the current understanding in the area of genetic, epigenetic, proteomic and metabolomic markers for diabetes and related outcomes, including polygenic risk scores. We have drawn on key examples from the literature, as well as our own experience of conducting omics research using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank, as well as other cohorts, to illustrate the potential of omics research in diabetes. Recent studies highlight the opportunity, as well as potential benefit, to incorporate molecular profiling in the design and set-up of diabetes epidemiology studies, which can also advance understanding on the heterogeneity of diabetes. Learnings from these examples should facilitate other researchers to consider incorporating research on omics technologies into their work to advance the field and our understanding of diabetes and its related co-morbidities. Insights from these studies would be important for future development of precision medicine in diabetes.

Recent advances in polygenic scores: translation, equitability, methods and FAIR tools

Polygenic scores (PGS) can be used for risk stratification by quantifying individuals' genetic predisposition to disease, and many potentially clinically useful applications have been proposed. Here, we review the latest potential benefits of PGS in the clinic and challenges to implementation. PGS could augment risk stratification through combined use with traditional risk factors (demographics, disease-specific risk factors, family history, etc.), to support diagnostic pathways, to predict groups with therapeutic benefits, and to increase the efficiency of clinical trials. However, there exist challenges to maximizing the clinical utility of PGS, including FAIR (Findable, Accessible, Interoperable, and Reusable) use and standardized sharing of the genomic data needed to develop and recalculate PGS, the equitable performance of PGS across populations and ancestries, the generation of robust and reproducible PGS calculations, and the responsible communication and interpretation of results. We outline how these challenges may be overcome analytically and with more diverse data as well as highlight sustained community efforts to achieve equitable, impactful, and responsible use of PGS in healthcare.

Understanding diabetes heterogeneity: key steps towards precision medicine in diabetes

Diabetes is a highly heterogeneous condition; yet, it is diagnosed by measuring a single blood-borne metabolite, glucose, irrespective of aetiology. Although pragmatically helpful, disease classification can become complex and limit advances in research and medical care. Here, we describe diabetes heterogeneity, highlighting recent approaches that could facilitate management by integrating three disease models across all forms of diabetes, namely, the palette model, the threshold model and the gradient model. Once diabetes has developed, further worsening of established diabetes and the subsequent emergence of diabetes complications are kept in check by multiple processes designed to prevent or circumvent metabolic dysfunction. The impact of any given disease risk factor will vary from person-to-person depending on their background, diabetes-related propensity, and environmental exposures. Defining the consequent heterogeneity within diabetes through precision medicine, both in terms of diabetes risk and risk of complications, could improve health outcomes today and shine a light on avenues for novel therapy in the future.

Associations between body mass index and all-cause mortality: A systematic review and meta-analysis

Fasting insulin and c-reactive protein confound the association between mortality and body mass index. An increase in fat mass may mediate the associations between hyperinsulinemia, hyperinflammation, and mortality. The objective of this study was to describe the "average" associations between body mass index and the risk of mortality and to explore how adjusting for fasting insulin and markers of inflammation might modify the association of BMI with mortality. MEDLINE and EMBASE were searched for studies published in 2020. Studies with adult participants where BMI and vital status was assessed were included. BMI was required to be categorized into groups or parametrized as non-first order polynomials or splines. All-cause mortality was regressed against mean BMI squared within seven broad clinical populations. Study was modeled as a random intercept. β coefficients and 95% confidence intervals are reported along with estimates of mortality risk by BMIs of 20, 30, and 40 kg/m2 . Bubble plots with regression lines are drawn, showing the associations between mortality and BMI. Splines results were summarized. There were 154 included studies with 6,685,979 participants. Only five (3.2%) studies adjusted for a marker of inflammation, and no studies adjusted for fasting insulin. There were significant associations between higher BMIs and lower mortality risk in cardiovascular (unadjusted β -0.829 [95% CI -1.313, -0.345] and adjusted β -0.746 [95% CI -1.471, -0.021]), Covid-19 (unadjusted β -0.333 [95% CI -0.650, -0.015]), critically ill (adjusted β -0.550 [95% CI -1.091, -0.010]), and surgical (unadjusted β -0.415 [95% CI -0.824, -0.006]) populations. The associations for general, cancer, and non-communicable disease populations were not significant. Heterogeneity was very large (I2  ≥ 97%). The role of obesity as a driver of excess mortality should be critically re-examined, in parallel with increased efforts to determine the harms of hyperinsulinemia and chronic inflammation.

Identification of biomarkers for glycaemic deterioration in type 2 diabetes

We identify biomarkers for disease progression in three type 2 diabetes cohorts encompassing 2,973 individuals across three molecular classes, metabolites, lipids and proteins. Homocitrulline, isoleucine and 2-aminoadipic acid, eight triacylglycerol species, and lowered sphingomyelin 42:2;2 levels are predictive of faster progression towards insulin requirement. Of ~1,300 proteins examined in two cohorts, levels of GDF15/MIC-1, IL-18Ra, CRELD1, NogoR, FAS, and ENPP7 are associated with faster progression, whilst SMAC/DIABLO, SPOCK1 and HEMK2 predict lower progression rates. In an external replication, proteins and lipids are associated with diabetes incidence and prevalence. NogoR/RTN4R injection improved glucose tolerance in high fat-fed male mice but impaired it in male db/db mice. High NogoR levels led to islet cell apoptosis, and IL-18R antagonised inflammatory IL-18 signalling towards nuclear factor kappa-B in vitro. This comprehensive, multi-disciplinary approach thus identifies biomarkers with potential prognostic utility, provides evidence for possible disease mechanisms, and identifies potential therapeutic avenues to slow diabetes progression.

Genetic predictors of lifelong medication-use patterns in cardiometabolic diseases

Little is known about the genetic determinants of medication use in preventing cardiometabolic diseases. Using the Finnish nationwide drug purchase registry with follow-up since 1995, we performed genome-wide association analyses of longitudinal patterns of medication use in hyperlipidemia, hypertension and type 2 diabetes in up to 193,933 individuals (55% women) in the FinnGen study. In meta-analyses of up to 567,671 individuals combining FinnGen with the Estonian Biobank and the UK Biobank, we discovered 333 independent loci (P < 5 × 10-9) associated with medication use. Fine-mapping revealed 494 95% credible sets associated with the total number of medication purchases, changes in medication combinations or treatment discontinuation, including 46 credible sets in 40 loci not associated with the underlying treatment targets. The polygenic risk scores (PRS) for cardiometabolic risk factors were strongly associated with the medication-use behavior. A medication-use enhanced multitrait PRS for coronary artery disease matched the performance of a risk factor-based multitrait coronary artery disease PRS in an independent sample (UK Biobank, n = 343,676). In summary, we demonstrate medication-based strategies for identifying cardiometabolic risk loci and provide genome-wide tools for preventing cardiovascular diseases.

Associations of the HOMA2-%B and HOMA2-IR with progression to diabetes and glycaemic deterioration in young and middle-aged Chinese

AIMS

Insulin deficiency (ID) and resistance (IR) contribute to progression from normal glucose tolerance to diabetes to insulin requirement although their relative contributions in young-onset diabetes is unknown.

METHODS

We examined the associations of HOMA2 using fasting plasma glucose and C-peptide in Chinese aged 20-50 years with (1) progression to type 2 diabetes (T2D) in participants without diabetes in a community-based cohort (1998-2013) and (2) glycaemic deterioration in patients with T2D in a clinic-based cohort (1995-2014). We defined ID as HOMA2-%B below median and insulin IR as HOMA2-IR above median.

RESULTS

During 10-year follow-up, 62 (17.9%) of 347 community-dwelling participants progressed to T2D. After 8.6 years, 291 (48.1%) of 609 patients with T2D had glycaemic deterioration. At baseline, progressors for T2D had higher HOMA2-IR, while in patients with T2D, progressors for glycaemic deterioration had higher HOMA2-IR and lower HOMA2-%B than non-progressors. The non-ID/IR group and the ID/IR group had an adjusted odds ratios of 2.47 (95% CI: 1.28, 4.94) and 5.36 (2.26, 12.79), respectively, for incident T2D versus the ID/non-IR group. In patients with T2D, 50% of the ID/IR group required insulin at 6.7 years versus around 11 years in the non-ID/IR or ID/non-IR, and more than 15 years in the non-ID/non-IR group. Compared with the latter group, the adjusted hazard ratios were 2.74 (1.80, 4.16) in the ID/non-IR, 2.73 (1.78, 4.19) in the non-ID/IR and 4.46 (2.87, 6.91) in the ID/IR group (p-interaction = 0.049).

CONCLUSIONS

In young Chinese adults, IR and ID contributed to progression to T2D and glycaemic deterioration.

Pathophysiology, phenotypes and management of type 2 diabetes mellitus in Indian and Chinese populations

Nearly half of all adults with type 2 diabetes mellitus (T2DM) live in India and China. These populations have an underlying predisposition to deficient insulin secretion, which has a key role in the pathogenesis of T2DM. Indian and Chinese people might be more susceptible to hepatic or skeletal muscle insulin resistance, respectively, than other populations, resulting in specific forms of insulin deficiency. Cluster-based phenotypic analyses demonstrate a higher frequency of severe insulin-deficient diabetes mellitus and younger ages at diagnosis, lower β-cell function, lower insulin resistance and lower BMI among Indian and Chinese people compared with European people. Individuals diagnosed earliest in life have the most aggressive course of disease and the highest risk of complications. These characteristics might contribute to distinctive responses to glucose-lowering medications. Incretin-based agents are particularly effective for lowering glucose levels in these populations; they enhance incretin-augmented insulin secretion and suppress glucagon secretion. Sodium-glucose cotransporter 2 inhibitors might also lower blood levels of glucose especially effectively among Asian people, while α-glucosidase inhibitors are better tolerated in east Asian populations versus other populations. Further research is needed to better characterize and address the pathophysiology and phenotypes of T2DM in Indian and Chinese populations, and to further develop individualized treatment strategies.

Shortened Leukocyte Telomere Length Is Associated With Glycemic Progression in Type 2 Diabetes: A Prospective and Mendelian Randomization Analysis

OBJECTIVE

Several studies support associations between relative leukocyte telomere length (rLTL), a biomarker of biological aging and type 2 diabetes. This study investigates the relationship between rLTL and the risk of glycemic progression in patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

In this cohort study, consecutive Chinese patients with type 2 diabetes (N = 5,506) from the Hong Kong Diabetes Register with stored baseline DNA and available follow-up data were studied. rLTL was measured using quantitative PCR. Glycemic progression was defined as the new need for exogenous insulin.

RESULTS

The mean (SD) age of the 5,349 subjects was 57.0 (13.3) years, and mean (SD) follow-up was 8.8 (5.4) years. Baseline rLTL was significantly shorter in the 1,803 subjects who progressed to insulin requirement compared with the remaining subjects (4.43 ± 1.16 vs. 4.69 ± 1.20). Shorter rLTL was associated with a higher risk of glycemic progression (hazard ratio [95% CI] for each unit decrease [to ∼0.2 kilobases]: 1.10 [1.06-1.14]), which remained significant after adjusting for confounders. Baseline rLTL was independently associated with glycemic exposure during follow-up (β = -0.05 [-0.06 to -0.04]). Each 1-kilobase decrease in absolute LTL was on average associated with a 1.69-fold higher risk of diabetes progression (95% CI 1.35-2.11). Two-sample Mendelian randomization analysis showed per 1-unit genetically decreased rLTL was associated with a 1.38-fold higher risk of diabetes progression (95% CI 1.12-1.70).

CONCLUSIONS

Shorter rLTL was significantly associated with an increased risk of glycemic progression in individuals with type 2 diabetes, independent of established risk factors. Telomere length may be a useful biomarker for glycemic progression in people with type 2 diabetes.

Clinical Predictors and Long-term Impact of Acute Kidney Injury on Progression of Diabetic Kidney Disease in Chinese Patients With Type 2 Diabetes

We aim to assess the long-term impact of acute kidney injury (AKI) on progression of diabetic kidney disease (DKD) and all-cause mortality and investigate determinants of AKI in Chinese patients with type 2 diabetes (T2D). A consecutive cohort of 9,096 Chinese patients with T2D from the Hong Kong Diabetes Register was followed for 12 years (mean ± SD age 57 ± 13.2 years; 46.9% men; median duration of diabetes 5 years). AKI was defined based on the Kidney Disease: Improving Global Outcomes (KDIGO) criteria using serum creatinine. Estimated glomerular filtration rate measurements were used to identify the first episode with chronic kidney disease (CKD) and end-stage renal disease (ESRD). Polygenic risk score (PRS) composed of 27 single nucleotide polymorphisms (SNPs) known to be associated with serum uric acid (SUA) in European populations was used to examine the role of SUA in pathogenesis of AKI, CKD, and ESRD. Validation was sought in an independent cohort including 6,007 patients (age 61.2 ± 10.9 years; 59.5% men; median duration of diabetes 10 years). Patients with AKI had a higher risk for developing incident CKD (hazard ratio 14.3 [95% CI 12.69-16.11]), for developing ESRD (12.1 [10.74-13.62]), and for all-cause death (7.99 [7.31-8.74]) compared with those without AKI. Incidence rate for ESRD among patients with no episodes of AKI and one, two, and three or more episodes of AKI was 7.1, 24.4, 32.4, and 37.3 per 1,000 person-years, respectively. Baseline SUA was a strong independent predictor for AKI. A PRS composed of 27 SUA-related SNPs was associated with AKI and CKD in both discovery and replication cohorts but not ESRD. Elevated SUA may increase the risk of DKD through increasing AKI. The identification of SUA as a modifiable risk factor and PRS as a nonmodifiable risk factor may facilitate the identification of individuals at high risk to prevent AKI and its long-term impact in T2D.

Skin autofluorescence is associated with progression of kidney disease in type 2 diabetes: A prospective cohort study from the Hong Kong diabetes biobank

BACKGROUND AND AIMS

Skin autofluorescence (SAF) can non-invasively assess the accumulation of tissue AGEs. We investigated the association between SAF and kidney dysfunction in participants with T2D.

METHODS

Of 4030 participants consecutively measured SAF at baseline, 3725 participants free of end-stage kidney disease (ESKD) were included in the analyses. The association of SAF with incident ESKD or ≥30% reduction in estimated glomerular filtration rate (eGFR) was examined with Cox regression, linear mixed-effects model for the association with annual eGFR decline, and mediation analyses for the mediating roles of renal markers.

RESULTS

During a median (IQR) 1.8 (1.1-3.1) years of follow-up, 411 participants developed the outcome. SAF was associated with progression of kidney disease (hazard ratio 1.15 per SD, 95% confidence interval [CI] [1.04, 1.28]) and annual decline in eGFR (β -0.39 per SD, 95% CI [-0.71, -0.07]) after adjustment for risk factors, including baseline eGFR and urinary albumin-creatinine ratio (UACR). Decreased eGFR (12.9%) and increased UACR (25.8%) accounted for 38.7% of the effect of SAF on renal outcome.

CONCLUSIONS

SAF is independently associated with progression of kidney disease. More than half of its effect is independent of renal markers. SAF is of potential to be a prognostic marker for kidney dysfunction.

Mitochondrial haplogroups have a better correlation to insulin requirement than nuclear genetic variants for type 2 diabetes mellitus in Taiwanese individuals

AIMS/INTRODUCTION

Identifying diabetes-susceptible genetic variants will help to provide personalized therapy for the management of type 2 diabetes. Previous studies have reported a genetic risk score (GRS), computed by the sum of nuclear DNA (nDNA) risk alleles, that may predict the future requirement for insulin therapy. Although mitochondrial dysfunction has a close association with insulin resistance (IR), there are few studies investigating whether genetic variants of mitochondrial DNA (mtDNA) will affect the clinical characteristics of type 2 diabetes.

MATERIALS AND METHODS

Mitochondrial haplogroups were determined using mtDNA whole genome next generation sequencing and 13 single nucleotide polymorphisms (SNPs) in nDNA susceptibility loci of 13 genes in 604 Taiwanese subjects with type 2 diabetes. A GRS of nDNA was computed by summation of the number of risk alleles. The correlation between the mtDNA haplogroup and the clinical characteristics of type 2 diabetes was assessed by logistic regression analysis. The results were compared with the GRS subgroups for the risk of insulin requirement.

RESULTS

Mitochondrial haplogroups modulate the clinical characteristics of type 2 diabetes, in which patients harboring haplogroup D4, compared with those harboring non-D4 haplotypes, were less prone to require insulin treatment, after adjusting for age, gender, and diabetes duration. However, there was no association between insulin requirement and GRS calculated from nuclear genetic variants.

CONCLUSIONS

Mitochondrial haplogroups, but not nuclear genetic variants, have a better association with the insulin requirement. The results highlight the role of mitochondria in the management of common metabolic diseases.

Cost-Effectiveness of Flash Glucose Monitoring for the Management of Patients with Type 1 and Patients with Type 2 Diabetes in China

INTRODUCTION

To compare the cost-effectiveness of flash glucose monitoring versus self-monitoring of blood glucose/point of care testing (SMBG/POCT) in both patients with type 1 and patients with type 2 diabetes (T1D/T2D) receiving insulin therapy.

METHODS

The IQVIA CORE Diabetes Model (version 9.5) was used to project the lifetime costs and health outcomes of flash glucose monitoring and SMBG/POCT from a Chinese societal perspective. We considered both hospital and individual version flash glucose monitoring to reflect the clinical practice in China. The clinical inputs leveraged the outcomes from both clinical trials and real-world studies. Cohort characteristics, intervention costs, treatment-related disutility and mortality were extracted from the literature. We also conducted scenario analyses and probabilistic sensitivity analyses to test the robustness of results.

RESULTS

Compared with SMBG/POCT using efficacy results from clinical trial, flash glucose monitoring brought the incremental costs of Chinese yuan (CNY) 58,021 and CNY 90,997 and additional quality-adjusted life years (QALYs) of 1.22 and 0.65 for patients with T1D and patients with T2D, respectively. According to the "WHO-CHOICE threshold" of three times the gross domestic product per capita in China (CNY 217,341 in 2020) as cost-effectiveness threshold, flash glucose monitoring was cost-effective for both patients with T1D and patients with T2D with incremental cost-effectiveness ratios (ICER) of CNY 47,636 and CNY 140,297 per QALY gained, respectively. According to the real-world effectiveness data, flash glucose monitoring was dominant for patients with T1D (lower costs and better effectiveness) and cost-effective for patients with T2D with an ICER of CNY 124,169 per QALY gained compared with SMBG/POCT. Scenario analyses and probabilistic sensitivity analyses confirmed the robustness of the results.

CONCLUSION

Flash glucose monitoring is likely to be considered as a cost-effective strategy compared to SMBG/POCT for Chinese patients with T1D and patients with T2D receiving insulin therapy.

Responsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps

Polygenic risk scores (PRSs) aggregate the many small effects of alleles across the human genome to estimate the risk of a disease or disease-related trait for an individual. The potential benefits of PRSs include cost-effective enhancement of primary disease prevention, more refined diagnoses and improved precision when prescribing medicines. However, these must be weighed against the potential risks, such as uncertainties and biases in PRS performance, as well as potential misunderstanding and misuse of these within medical practice and in wider society. By addressing key issues including gaps in best practices, risk communication and regulatory frameworks, PRSs can be used responsibly to improve human health. Here, the International Common Disease Alliance's PRS Task Force, a multidisciplinary group comprising expertise in genetics, law, ethics, behavioral science and more, highlights recent research to provide a comprehensive summary of the state of polygenic score research, as well as the needs and challenges as PRSs move closer to widespread use in the clinic.

CYP2C19 Loss-of-function Polymorphisms are Associated with Reduced Risk of Sulfonylurea Treatment Failure in Chinese Patients with Type 2 Diabetes

Sulfonylureas (SUs) are predominantly metabolized by cytochrome p450 2C9 (CYP2C9) and cytochrome p450 2C19 (CYP2C19) enzymes. CYP2C9 polymorphisms are associated with greater treatment response and hypoglycemic risk in SU users. However, there are no large scale pharmacogenetic studies investigating the effect of loss‐of‐function alleles CYP2C19*2 and CYP2C19*3, which occur frequently in East Asians. Retrospective pharmacogenetic analysis was performed in 11,495 genotyped patients who were enrolled in the Hong Kong Diabetes Register between 1995 and 2017, with follow‐up to December 31, 2019. The associations of CYP2C19 polymorphisms with SU treatment failure, early HbA1c response, and severe hypoglycemia were analyzed by Cox regression or logistic regression assuming an additive genetic model. There were 2341 incident SU users that were identified (mean age 59 years, median diabetes duration 9 years), of which 324 were CYP2C19 poor metabolizers (CYP2C19 *2/*2 or *2/*3 or *3/*3). CYP2C19 poor metabolizers had lower risk of SU treatment failure (hazard ratio 0.83, 95% confidence interval (CI) 0.72–0.97, P = 0.018) and were more likely to reach the HbA1c treatment target < 7% (odds ratio 1.52, 95% CI 1.02–2.27, P = 0.039) than wild‐type carriers (CYP2C19 *1/*1) following adjustment for multiple covariates. There were no significant differences in severe hypoglycemia rates among different CYP2C19 genotype groups. CYP2C19 polymorphisms should be considered during personalization of SU therapy.

Patterns of drug therapy, glycemic control, and predictors of escalation - non-escalation of treatment among diabetes outpatients at a tertiary care center

OBJECTIVES

The study was conducted to assess patterns of prescribed drug therapy and clinical predictors of need for therapy escalation in outpatients with diabetes mellitus (DM).

METHODS

This was a prospective cohort study, conducted at an apex tertiary care teaching hospital in central India for a period of 18 months. The demographic, clinical, and treatment details on the baseline and follow up visits were collected from the patients' prescription charts. Glycemic control, adherence, pill burdens along with pattern of antidiabetic therapy escalation, and deescalations were analyzed.

RESULTS

A total of 1,711 prescriptions of 925 patients of diabetes with a mean age of 53.81 ± 10.42 years and duration of disease of 9.15 ± 6.3 years were analyzed. Approximately half of the patients (n=450) came for ≥1 follow up visits. Hypertension (59.35%) was the most common comorbidity followed by dyslipidemia and hypothyroidism. The mean total daily drugs and pills per prescription were 4.03 ± 1.71 and 4.17 ± 1.38, respectively. Metformin (30.42%) followed by sulphonylureas (SUs) (21.39%) constituted majority of the AHA's and dual and triple drug therapy regimens were most commonly prescribed. There were improvements in HbA_1c, fasting/postprandial/random blood sugar (FBS/PPBS/RBS) as well as adherence to medication, diet, and exercise in the follow up visits. Among patients with follow ups, therapy escalations were found in 31.11% patients, among whom dose was increased in 12.44% and drug was added in 17.28%. Apart from Hb1Ac, FBS, and PPBS levels (p<0.001), characteristics such as age, BMI, duration of diagnosed diabetes, presence of hypertension and dyslipidemia, and daily pill burdens were found to be significantly higher in the therapy escalation group (p<0.05). Inadequate medication adherence increased the relative risk (RR) of therapy escalation by almost two times.

CONCLUSIONS

Disease and therapy patterns are reflective of diabetes care as expected at a tertiary care center. Higher BMI, age, pill burden, duration of diabetes, presence of comorbidities, and poor medication adherence may be the predictors of therapy escalation independent of glycemic control and such patients should be more closely monitored.

Genetics of Type 2 Diabetes: Opportunities for Precision Medicine: JACC Focus Seminar

Type 2 diabetes (T2D) is highly prevalent and is a strong contributor for cardiovascular disease. However, there is significant heterogeneity in disease pathogenesis and the risk of complications. Enormous progress has been made in our ability to catalog genetic variation associated with T2D risk and variation in disease-relevant quantitative traits. These discoveries hold the potential to shed light on tractable targets and pathways for safe and effective therapeutic development, but the promise of precision medicine has been slow to be realized. Recent studies have identified subgroups of individuals with differential risk for intermediate phenotypes (eg, lipid levels, fasting insulin, body mass index) that contribute to T2D risk, helping to account for the observed clinical heterogeneity. These "partitioned genetic risk scores" not only have the potential to identify patients at greatest risk of cardiovascular disease and rapid disease progression, but also could aid patient stratification bridging the gap toward precision medicine for T2D.

Risk of Typical Diabetes-Associated Complications in Different Clusters of Diabetic Patients: Analysis of Nine Risk Factors

Objectives: Diabetic patients are often diagnosed with several comorbidities. The aim of the present study was to investigate the relationship between different combinations of risk factors and complications in diabetic patients. Research design and methods: We used a longitudinal, population-wide dataset of patients with hospital diagnoses and identified all patients (n = 195,575) receiving a diagnosis of diabetes in the observation period from 2003–2014. We defined nine ICD-10-codes as risk factors and 16 ICD-10 codes as complications. Using a computational algorithm, cohort patients were assigned to clusters based on the risk factors they were diagnosed with. The clusters were defined so that the patients assigned to them developed similar complications. Complication risk was quantified in terms of relative risk (RR) compared with healthy control patients. Results: We identified five clusters associated with an increased risk of complications. A combined diagnosis of arterial hypertension (aHTN) and dyslipidemia was shared by all clusters and expressed a baseline of increased risk. Additional diagnosis of (1) smoking, (2) depression, (3) liver disease, or (4) obesity made up the other four clusters and further increased the risk of complications. Cluster 9 (aHTN, dyslipidemia and depression) represented diabetic patients at high risk of angina pectoris “AP” (RR: 7.35, CI: 6.74–8.01), kidney disease (RR: 3.18, CI: 3.04–3.32), polyneuropathy (RR: 4.80, CI: 4.23–5.45), and stroke (RR: 4.32, CI: 3.95–4.71), whereas cluster 10 (aHTN, dyslipidemia and smoking) identified patients with the highest risk of AP (RR: 10.10, CI: 9.28–10.98), atherosclerosis (RR: 4.07, CI: 3.84–4.31), and loss of extremities (RR: 4.21, CI: 1.5–11.84) compared to the controls. Conclusions: A comorbidity of aHTN and dyslipidemia was shown to be associated with diabetic complications across all risk-clusters. This effect was amplified by a combination with either depression, smoking, obesity, or non-specific liver disease.

Low-Frequency Genetic Variant in the Hepatic Glucokinase Gene Is Associated With Type 2 Diabetes and Insulin Resistance in Chinese Population

Glucokinase (GCK) regulates insulin secretion and hepatic glucose metabolism, and its inactivating variants could cause diabetes. We aimed to evaluate the association of a low-frequency variant of GCK (rs13306393) with type 2 diabetes (T2D), prediabetes, or both (impaired glucose regulation [IGR]) in a Chinese population. An association study was first conducted in a random cluster sampling population (sample 1: 537 T2D, 768 prediabetes, and 1,912 control), and then another independent population (sample 2: 3,896 T2D, 2,301 prediabetes, and 868 control) was used to confirm the findings in sample 1. The A allele of rs13306393 was associated with T2D (odds ratio 3.08 [95% CI 1.77-5.36], P = 0.00007) in sample 1; rs13306393 was also associated with prediabetes (1.67 [1.05-2.65], P = 0.03) in sample 2. In a pooled analysis of the two samples, the A allele increased the risk of T2D (1.57 [1.15-2.15], P = 0.005), prediabetes (1.83 [1.33-2.54], P = 0.0003) or IGR (1.68 [1.26-2.25], P = 0.0004), insulin resistance estimated by HOMA (β = 0.043, P = 0.001), HbA_1c (β = 0.029, P = 0.029), and urinary albumin excretion (β = 0.033, P = 0.025), irrespective of age, sex, and BMI. Thus, the Chinese-specific low-frequency variant increased the risk of T2D through reducing insulin sensitivity rather than islet β-cell function, which should be considered in the clinical use of GCK activators in the future.

Associations of young onset age and genetic risk of beta cell dysfunction with glycaemic progression in individuals with type 2 diabetes

AIM

To study the relationship between genetic risk of beta cell dysfunction, young onset age and glycaemic progression in individuals with type 2 diabetes (T2D).

MATERIALS AND METHODS

1385 T2D outpatients were included in cross-sectional sub-study and 730 insulin-naïve outpatients were followed for 3 years in prospective sub-study. Genetic risk score (GRS) was derived from 24 beta cell dysfunction-related single nucleotide polymorphisms, with lower and upper 25 percentiles defined as low and high genetic risk. Glycaemic progression was defined as requirement for sustained insulin therapy.

RESULTS

388 participants in cross-sectional and 128 in prospective sub-study experienced glycaemic progression. Young onset age (T2D diagnosis below 40 year-old) was associated with high risk of glycaemic progression as compared to usual-onset counterparts (adjusted OR 1.64 [95% CI 1.14-2.36], and 2.92 [95% CI 1.76-4.87] in cross-sectional and prospective sub-study, respectively). As compared to those with intermediate risk, a low GRS was associated with lower risk for glycaemic progression (adjusted OR 0.72 [95% CI 0.49-1.06], and 0.51 [95% CI 0.29-0.90]) whereas a high GRS was not significantly associated with glycaemic progression. Notably, the association of young-onset T2D with high risk of glycaemic progression was independent of known clinical risk factors and beta cell dysfunction GRS (P interaction > 0.10).

CONCLUSION

Young onset age and low genetic risk of beta cell dysfunction are independently associated with risk of glycaemic progression. Our data do not support that genetic risk modulates the risk of glycaemic progression in individuals with young-onset T2D.

Development of genome-wide polygenic risk scores for lipid traits and clinical applications for dyslipidemia, subclinical atherosclerosis, and diabetes cardiovascular complications among East Asians

Background

The clinical utility of personal genomic information in identifying individuals at increased risks for dyslipidemia and cardiovascular diseases remains unclear.

Methods

We used data from Biobank Japan (n = 70,657–128,305) and developed novel East Asian-specific genome-wide polygenic risk scores (PRSs) for four lipid traits. We validated (n = 4271) and subsequently tested associations of these scores with 3-year lipid changes in adolescents (n = 620), carotid intima-media thickness (cIMT) in adult women (n = 781), dyslipidemia (n = 7723), and coronary heart disease (CHD) (n = 2374 cases and 6246 controls) in type 2 diabetes (T2D) patients.

Results

Our PRSs aggregating 84–549 genetic variants (0.251 < correlation coefficients (r) < 0.272) had comparably stronger association with lipid variations than the typical PRSs derived based on the genome-wide significant variants (0.089 < r < 0.240). Our PRSs were robustly associated with their corresponding lipid levels (7.5 × 10^− 103 < P < 1.3 × 10^− 75) and 3-year lipid changes (1.4 × 10^− 6 < P < 0.0130) which started to emerge in childhood and adolescence. With the adjustments for principal components (PCs), sex, age, and body mass index, there was an elevation of 5.3% in TC (β ± SE = 0.052 ± 0.002), 11.7% in TG (β ± SE = 0.111 ± 0.006), 5.8% in HDL-C (β ± SE = 0.057 ± 0.003), and 8.4% in LDL-C (β ± SE = 0.081 ± 0.004) per one standard deviation increase in the corresponding PRS. However, their predictive power was attenuated in T2D patients (0.183 < r < 0.231). When we included each PRS (for TC, TG, and LDL-C) in addition to the clinical factors and PCs, the AUC for dyslipidemia was significantly increased by 0.032–0.057 in the general population (7.5 × 10^− 3 < P < 0.0400) and 0.029–0.069 in T2D patients (2.1 × 10^− 10 < P < 0.0428). Moreover, the quintile of TC-related PRS was moderately associated with cIMT in adult women (β ± SE = 0.011 ± 0.005, P_trend = 0.0182). Independent of conventional risk factors, the quintile of PRSs for TC [OR (95% CI) = 1.07 (1.03–1.11)], TG [OR (95% CI) = 1.05 (1.01–1.09)], and LDL-C [OR (95% CI) = 1.05 (1.01–1.09)] were significantly associated with increased risk of CHD in T2D patients (4.8 × 10^− 4 < P < 0.0197). Further adjustment for baseline lipid drug use notably attenuated the CHD association.

Conclusions

The PRSs derived and validated here highlight the potential for early genomic screening and personalized risk assessment for cardiovascular disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00831-z.

Characterization and Treatment of Inflammation and Insulin Resistance in Obese Adipose Tissue

Adipose tissue is the largest energy storage and protection organ. It is distributed subcutaneously and around the internal organs. It regulates metabolism by storing and releasing fatty acids and secreting adipokines. Excessive nutritional intake results in adipocyte hypertrophy and proliferation, leading to local hypoxia in adipose tissue and changes in the release of adipokines. These lead to recruit of more immune cells into adipose tissue and release of inflammatory signaling factors. Excess free fatty acids and inflammatory factors interfere with intracellular insulin signaling. In this review, we summarize the characteristics of obese adipose tissue and analyze how its inflammation causes insulin resistance. We further discuss the latest clinical research progress on the control of insulin resistance and inflammation resulting from obesity through anti-inflammatory therapy and bariatric surgery. Our review shows that targeted anti-inflammatory therapy is of great significance for obese patients with insulin resistance.