Download PDF
Perspective  |  Open Access  |  21 Jan 2024

Multifaceted nature of young-onset diabetes - can genomic medicine improve the precision of diagnosis and management?

Views: 361 |  Downloads: 107 |  Cited:   1
J Transl Genet Genom 2024;8:13-34.
10.20517/jtgg.2023.36 |  © The Author(s) 2024.
Author Information
Article Notes
Cite This Article


Young-onset type 2 diabetes (YOD), defined as diabetes diagnosis before age 40, has an aggressive clinical course with premature mortality, in part due to long disease duration and lack of evidence to guide diagnosis and management. Autoimmune type 1 diabetes, maturity-onset diabetes of the young (MODY), and latent autoimmune diabetes in adults (LADA) are subtypes of diabetes in young people, which, however, cannot fully explain their complex clinical course. Similarly, family members carrying the same rare genetic variant of monogenic diabetes can have different presentations and outcomes. Ancestral heterogeneity, ecological transition, inter-ethnic differences in genomic architecture, and variations in living environment, lifestyles, access to care, and timeliness of diagnosis and treatment can influence the age of diagnosis and exposure to these cardiometabolic-renal risk factors. Despite the wealth of literature on genetic associations with diabetes, the familial cosegregation of rare variants and their relevance to YOD remains uncertain. This perspective was motivated by decades of clinical observations and learnings from an ongoing randomized controlled trial that uses biogenetic markers to classify patients with YOD for improving outcomes. Apart from highlighting the need to use family-based studies to improve the precision of diagnosis, we discussed atypical causes for diabetic ketoacidosis and the importance of lifecourse and psychosocial-behavioral factors in patients with YOD. Apart from detailed clinical evaluation, we propose using plasma C peptide, homeostasis model of assessment (HOMA) indexes, autoantibodies, and polygenic risk scores to stratify risk, classify diabetes subtypes, and personalize treatment in YOD. To achieve these goals, we advocate changing the practice environment and team structure to enable physicians to use the insights they learn from patients and their family members to implement precision medicine and improve the outlook of these high-risk individuals.


Young-onset type 2 diabetes, YOD, genomic medicine, precision medicine, autoimmunity, type 1 diabetes, monogenic diabetes, MODY, LADA, PRISM, randomized controlled trial


In 2021, diabetes affected 537 million people, with 80% coming from low- and middle-income countries[1]. Age, obesity, and family history of diabetes are major risk factors for diabetes[2]. Thus, people who develop diabetes at a young age, especially if lean, require comprehensive investigations to explain their early metabolic decompensation. In part due to childhood obesity, there is a growing prevalence of young-onset type 2 diabetes (YOD), defined as diabetes diagnosis before the age of 40[3], which affects one in five adults with type 2 diabetes in Asia[4].

Patients with YOD may be exposed to decades of glycemic burden with rapid deterioration of glycemic control[5] and shortened lifespan by 10 years or more[6]. Compared to their peers with late-onset diabetes, patients with YOD had a 2-6 times higher risk of cardiovascular-renal events, recurrent hospitalizations, and premature death, with mental illness and kidney dysfunction being prominent clinical features[7-9].

While there are ongoing large-scale epidemiological, genetic[10], and interventional studies[11] in people with youth-onset diabetes (less than 18 years old), there are research gaps in YOD diagnosed during adulthood to guide diagnosis and management[12]. In high-income jurisdictions with well-developed healthcare systems, such as Hong Kong, despite overall falling trends of diabetes and related death[13,14], the incidence of YOD continued to rise[15]. Individuals with YOD have experienced mortality rates that are 5-8 times higher than those with late-onset diabetes[16].


During the last three decades, the Chinese University of Hong Kong Diabetes Care and Research Team has combined practice and research to gather data and use data to inform practice and policies[3,17]. In the ongoing Precision Medicine to Redefine Insulin Secretion and Monogenic Diabetes Randomized Controlled Trial (PRISM-RCT) in Chinese Patients with Young-Onset Diabetes, we performed comprehensive clinical assessment and used biogenetic information to increase the precision of diagnosis and management of these high-risk patients ([18].

In part motivated by learnings from the PRISM-RCT, in this invited perspective not intended to be a review article, we shared our three decades of insights learned from diagnosing, classifying, and managing patients with YOD while pursuing active research in the field of genetics/genomics of diabetes focusing on translation. We first highlighted the minute amount of islets endowment at birth and the many lifecourse factors that may damage their structure and function, ultimately leading to diabetes. We briefly discussed Latent Autoimmune Diabetes in Adults (LADA) and monogenic diabetes as well-recognized subtypes of YOD. In our discussion on autoimmune type 1 diabetes, we highlighted the caveat that atypical diabetes due to acute destruction of islets related to viral infections and immunomodulating therapies may also present with diabetic ketoacidosis. In our discussion on monogenic diabetes, including Maturity-Onset Diabetes of the Young (MODY), we highlighted the pitfalls of sole reliance on bioinformatics and few reported cases to classify the pathogenic nature of variants, which can lead to missed opportunities for early diagnosis and intervention, especially in populations where common genetic variants may contribute to trajectories of YOD different from that reported in European populations. With the availability of genome sequencing and preconception counseling services, we used clinical examples, albeit rare, to highlight how the detection of mutations with autosomal recessive inheritance in patients with YOD may alert the possibility of syndromic diabetes in homozygous carriers, which calls for genetic and preconception counseling.

We then discussed the clinical application of polygenetic risk scores, now widely accepted by the scientific community to have potential utilities for improving the precision of prediction, prevention, diagnosis, and therapies in complex diseases such as diabetes, and their relevance to YOD. We concluded by summarizing the design of the PRISM-RCT aimed at closing these knowledge gaps but at the same time advocating the need to create an environment conducive to reducing genomic medicine to practice, taking into consideration the unmet psychosocial-behavioral needs in YOD.

Against this background, the motivation underlying this perspective is to encourage more physicians who stand between patients (and their families) and technologies, to gather data systematically to improve our understanding of the nature of this complex syndrome and implement person-centered solutions with ongoing evaluation to inform practice and policies. To non-physicians involved in creating these genomic/genetic data, we emphasize to them the myriad of factors that need to be considered when interpreting these data and translating them into a technology or service aimed at beneficiating the patients and those at risk.


Blood glucose is maintained within a narrow range of 4-8 mmol/L most of the time, irrespective of energy intake or expenditure, due to efficient glucose sensing and insulin release by the pancreatic beta-cells[19-21]. Stress hormones, including catecholamines, glucagon, growth hormone, and cortisol, can increase blood glucose, while insulin is the only hormone that lowers blood glucose[19,20]. Other mechanisms of type 2 diabetes include non-suppression of glucagon and hepatic glucose production, excessive lipolysis, insulin resistance in peripheral tissues, dysregulation of appetite control, and abnormal incretin physiology[22]. Recent meta-analyses of multi-ethnic genome-wide association studies (GWAS) discovered hundreds of loci implicated in pancreas, adipose, and muscle tissue biology in type 2 diabetes[23-25].

In an autopsy series of 100 deceased subjects, the weight of pancreatic islets increased from approximately 0.2 grams at birth to a plateau of 1.0 gram at the age of 21 with marked inter-individual variations [Figure 1][26]. Other autopsy series revealed close correlations between body mass index (BMI) and pancreatic islet mass, with diabetes cases having lower beta-cell mass and larger alpha-cell mass than cases without diabetes[27]. Oxidized proteins, fat infiltration, amyloid deposits, and atherosclerosis were common features in diabetes pancreas[28]. Insufficient islet mass may lead to diabetes and early insulin requirement, so-called type 3c diabetes, due to chronic pancreatitis, pancreatic ductal adenocarcinoma, hemochromatosis, cystic fibrosis, and previous pancreatic surgery[29].

Multifaceted nature of young-onset diabetes - can genomic medicine improve the precision of diagnosis and management?

Figure 1. A quantitative analysis of 100 pancreases obtained from individuals in the United Kingdom in the mid-1930, including newly-born infants and adults up to the age of 64 years, all of whom apparently had normal nutrition and died from different causes including pneumonia, cerebral hemorrhage, perforated gastric ulcer burns, showing the proportional weight of islets and their increase in size over time, reaching a plateau in early adulthood. The small islet mass and its plateau in early adulthood might contribute to the marked increase in diabetes prevalence given the average adult body weight of 60-70 kg today compared to 50 kg nearly 100 years ago (reproduced with permission)[26].

Islet autoimmunity and type 1 diabetes

Autoimmune destruction of islets can lead to progressive and severe insulin deficiency with rising blood glucose. This is accompanied by lipolysis as an alternative fuel with weight loss and ketone formation, culminating in diabetic ketoacidosis and coma[30]. In 2021, 8 million people had type 1 diabetes. Amongst them, 1.5 million were children and adolescents, with the majority diagnosed after the age of 18[31]. There was familial clustering of type 1 diabetes with 50% concordance amongst monozygotic twins[32]. Human leukocyte antigen (HLA) haplotypes were associated with increased (e.g., HLA DQ2/DQ8) and decreased (e.g., HLA DQ6/x) risk of islet autoimmunity[33,34]. Genetic risk score incorporating HLA haplotypes and non-HLA genetic variants predicted the onset of type 1 diabetes in European populations[35-37].

In a proof-of-concept RCT conducted in the 1980s, researchers reported that 1-year treatment with cyclosporine, an immuno-modulating therapy, was more effective than placebo in causing remission in patients with type 1 diabetes (10-35 years old)[38]. In 2022, teplizumab, a modified mono-antibody against C3 component on cytotoxic T cells, with a considerably safer profile, was approved by the regulatory agency for delaying the onset of type 1 diabetes[39,40]. In the latest publication, teplizumab was also found to improve beta-cell function in children newly diagnosed with type 1 diabetes[41]. These therapeutic advancements have opened up avenues for using biogenetic markers, such as polygenetic risk scores, to identify high-risk individuals for early prevention and intervention[33,42]. From a treatment perspective, severe insulin deficiency and dysregulation of glucagon secretion put patients with type 1 diabetes at high risk of hyperglycemia and severe hypoglycemia. Advanced insulin formulation and delivery systems[43], supplemented by continuous glucose monitoring devices[44], had improved the safety and effectiveness of intensive insulin therapy, making early diagnosis of type 1 diabetes imperative to improve their prognosis[45,46].

Atypical forms of type 1 diabetes

There are rare forms of type 1 diabetes due to islet destruction. One example is fulminant type 1 diabetes, which occurs within a few days after a viral illness presenting with diabetic ketoacidosis, often accompanied by increased biomarkers of exocrine dysfunction. These patients usually require life-long insulin treatment[47]. These rare examples inspire new insights into alternative pathogenesis for acute diabetic ketoacidosis other than autoimmunity. Acute infections such as the coronavirus infectious disease of 2019 (COVID-19) have been shown to increase the risk of type 2 diabetes[48]. It remains to be proven whether these viral infections might precipitate acute or subacute forms of type 1 diabetes[49-51].

Our immune system is tightly regulated to control viral infections and suppress cancer growth while avoiding host damage. In developing areas with endemic viral infections, chronic activation of the immune system may lead to immunological tolerance. The latter can contribute to the low prevalence of autoimmune disease but a high prevalence of chronic viral infection and cancer in these areas. Immune checkpoint inhibitor (ICI) increases the activity of cytotoxic T cells of the host to kill cancer cells, but may activate the autoimmune process and cause type 1 diabetes and other endocrine dysfunction[52,53]. Given the increasing trend of young-onset cancer[54] and the use of immune-modulating treatment, physicians should be aware of these atypical forms of ICI-associated type 1 diabetes[55].

Latent autoimmune diabetes in adults

A subacute form of type 1 diabetes, often referred to as LADA, can be missed or undiagnosed, especially in adult patients with obesity or slow disease progression. The availability of many oral glucose-lowering drugs can inadvertently delay insulin treatment. Given the legacy effect of early glycemic control on increasing glycemic durability[56,57] and reducing long-term complications[58-60] in both type 1 and type 2 diabetes, misdiagnosis of LADA should be avoided.

In patients with type 2 diabetes, the presence of one anti-islet autoantibody, for example, glutamic acid decarboxylase antibodies (GADA), and that against IA2 and ZnT8 can be use used to diagnose LADA. There is no agreed cut-off age for diagnosing LADA, although some experts proposed an age below 35. There is a need to standardize the assays of these autoantibodies. The suboptimal quality of these assays hinders the interpretation of low titers with uncertain significance. Additionally, many reports related to the diagnosis and treatment of LADA were of low quality in terms of study design, definitions, and methodologies. That said, the prevalence of LADA appeared to range from 3% to 12% in patients with type 2 diabetes, in whom high titers of antibodies predicted early insulin requirement[61]. Although titers of these autoantibodies tend to decline after diagnosis, GADA could be detected up to 8 years after initial diagnosis. Patients with LADA tended to have an earlier age of diagnosis and lower BMI and were highly responsive to insulin treatment compared to their counterparts without LADA. In patients with LADA and residual beta-cell function, the use of sulphonylureas (SU) might hasten, while that of dipeptidyl peptidase 4 inhibitor (DPP4i) might slow the decline in beta-cell function[62]. These findings underlie the need to assess beta-cell function and autoimmunity, especially in lean patients with type 2 diabetes, to avoid misclassification and inappropriate treatment.


The 90% concordance rates for type 2 diabetes amongst monozygotic twins[63] and the 3- to 9-fold increased lifetime risk of diabetes amongst family members of affected individuals supported its strong genetic component[64-66]. Monogenic diabetes is caused by a single gene mutation and maturity onset diabetes of the young (MODY) is the most common form of monogenic diabetes. Traditionally, MODY is characterized by absence of obesity, presentation before the age of 25 years, strong family history suggestive of autosomal dominant inheritance, absence of β-cell autoimmunity, and sustained pancreatic β-cell function. However, many patients with MODY had older age of diagnosis with considerable phenotypic heterogeneity. This complexity calls for more phenotypic and multiomic analysis in family-based cohorts to test for cosegregation of genetic variants amongst affected family members[67].

In 1975, Tattersall first reported a MODY family affecting three generations. The affected members had a young age of diagnosis with non-ketotic presentation and mild clinical course[68]. The use of family-based linkage analysis and sequencing technology has uncovered the cosegregation of mutations of multiple genes amongst affected family members. The “loss-of-function” and “gain-of-function” of these mutations supported the causal roles of these genes in pancreatic islets and other insulin-sensitive tissues. These genes often encode proteins implicated in neogenesis, differentiation, and maturation of islets, transcription factors, enzymes, ion channels, and mitochondria. Collectively, they coordinate the complex processes of glucose entry and sensing as well as synthesis, processing, and secretion of insulin to maintain glucose metabolism[69-71]. Amongst the 40 subtypes of monogenic diabetes, MODY due to mutations in genes encoding glucokinase (GCK) and transcription factors including hepatocyte nuclear factor (HNF4α), HNF1 homeobox A (HNF1α), and HNF1 homeobox B (HNF1β) were best described and most frequently reported. These mutations may be de-novo or transmitted from one generation to another with varying ages of diagnosis depending on the presence of other risk factors. Other rare mutations with Mendelian recessive mode of inheritance were associated with neonatal diabetes, severe insulin resistance, lipodystrophy, and syndromic features (e.g., deafness, visual impairment, liver/renal cysts, developmental abnormality)[67][Figure 2 and Table 1].

Multifaceted nature of young-onset diabetes - can genomic medicine improve the precision of diagnosis and management?

Figure 2. A schematic diagram showing the different types of monogenic diabetes often due to a mutation in a single gene with different clinical presentation, phenotypes, and mode of inheritance.

Table 1

List of genes implicated in monogenic diabetes. Adapted from[67]

Maturity-onset diabetes of the young
ABCC8ATP binding cassette subfamily C member 8
APPL1Adaptor protein, phosphotyrosine interacting with PH domain and leucine zipper 1
CELCarboxyl ester lipase
HNF1AHNF1 homeobox A
HNF1BHNF1 homeobox B
HNF4AHepatocyte nuclear factor 4 alpha
KCNJ11Potassium inwardly rectifying channel subfamily J member 11
NEUROD1Neuronal differentiation 1
PAX4Paired box 4
PDX1Pancreatic and duodenal homeobox 1
RFX6Regulatory factor X6
ZFP57ZFP57 zinc finger protein
Syndromic diabetes
CELCarboxyl ester lipase
CISD2CDGSH iron sulfur domain 2
DCAF17DDB1 and CUL4 associated factor 17
DNAJC3DnaJ heat shock protein family (Hsp40) member C3
DYRK1BDual-specificity tyrosine-phosphorylation-regulated kinase 1B
GATA4GATA binding protein 4
GATA6GATA binding protein 6
HNF1BHNF1 homeobox B
PAX6Paired box 6
PCBD1Pterin-4 alpha-carbinolamine dehydratase 1
PIK3R1Phosphoinositide-3-kinase regulatory subunit 1
PPP1R15BProtein phosphatase 1 regulatory subunit 15B
SLC29A3Solute carrier family 29 member 3
TRMT10AtRNA methyltransferase 10A
WFS1Wolframin ER transmembrane glycoprotein
ZBTB20Zinc finger and BTB domain containing 20
Neonatal diabetes
ABCC8ATP binding cassette subfamily C member 8
CISD2CDGSH iron sulfur domain 2
GATA4GATA binding protein 4
GATA6GATA binding protein 6
HNF1BHNF1 homeobox B
INSRInsulin receptor
KCNJ11Potassium inwardly rectifying channel subfamily J member 11
NEUROD1Neuronal differentiation 1
PDX1Pancreatic and duodenal homeobox 1
RFX6Regulatory factor X6
SLC29A3Solute carrier family 29 member 3
WFS1Wolframin ER transmembrane glycoprotein
Mitochondrial diabetes
Insulin resistance
AKT2AKT serine/threonine kinase 2
INSRInsulin receptor
PLIN1Perilipin 1
POLD1DNA polymerase delta 1, catalytic subunit
PPARGPeroxisome proliferator-activated receptor gamma
AGPAT21-acylglycerol-3-phosphate O-acyltransferase 2
BSCL2BSCL2 lipid droplet biogenesis associated, seipin
CAV1Caveolin 1
CIDECCell death-inducing DFFA-like effector c
AKT2AKT serine/threonine kinase 2
PLIN1Perilipin 1
PPARGPeroxisome proliferator-activated receptor gamma
CAVIN1 (PTRF)Caveolae-associated protein 1
ZMPSTE24Zinc metallopeptidase STE24

There are considerable overlaps in the phenotypes amongst individuals with MODY, LADA, type 1 diabetes, or type 2 diabetes in whom autoimmunity, common and rare variants may coexist. In a recent review article, the authors summarized the epidemiology, pathophysiology, diagnosis, and management of monogenic diabetes. As many as 90% of patients with monogenic diabetes were not diagnosed or misclassified in part due to the prohibitive cost of incorporating sequencing in routine service. Depending on the selection criteria, amongst young patients with type 2 diabetes without autoantibodies, the frequency of MODY ranged from less than 10% to more than 50%, with the majority having mutations in HNF1α, GCK, and HNF1β[67].

In a multi-ethnic cohort of 3,333 young patients under the age of 20 with a presumptive diagnosis of type 2 diabetes, whole exome sequencing indicated that 93 (2.8%) patients carried a pathogenic/likely pathogenic (P/LP) variant in genes encoding HNF4α (n = 16), GCK (n = 23), HNF1α (n = 44), pancreatic and duodenal homeobox 1 (PDX1) (n = 5), insulin (INS) (n = 4) and carboxyl ester lipase (CEL) (n = 1). Compared to those without P/LP variants, patients with MODY had a younger age of diagnosis and lower fasting plasma C-peptide (CP) levels. They were less likely to have hypertension and had higher HDL-C levels, although there were no distinct features that reliably distinguished MODY from other forms of diabetes. Importantly, the diagnosis of MODY would have changed clinical management in 89% of them[72]. Although MODY risk calculators using age, age of diagnosis, family history of diabetes, HbA1c, fasting plasma glucose and CP, high sensitivity C-reactive protein, autoantibodies, and syndromic features had been developed in Europeans, their sensitivity and specificity remained suboptimal, especially in non-European populations[73].

Clinical implications of identifying families with MODY or monogenic diabetes

There is good evidence that patients with HNF1α and HNF4α-MODY, as well as K-ATP channel mutations, respond particularly well to SU agents, while adding DPP4i may have incremental benefits[74]. The use of these oral glucose-lowering drugs enabled insulin discontinuation in some patients misdiagnosed with type 1 diabetes. However, in many patients with MODY, notably those with transcription factor MODY, different background genetic profiles might contribute to progressive β-cell failure. Within a MODY family, carriers of the same rare variant might have different presentations and outcomes depending on the presence and control of other risk factors[75,76].

Hepatic nuclear factor 1 alpha

In the early 1990s, our group reported the first Chinese MODY family. The index patient was a young woman who presented with polyuria and polydipsia in her early 20s. Clinical examination showed severe retinopathy and proteinuria. An inquiry of family history revealed that her mother died of combined heart and kidney failure in her 50s and her grandmother died in her early 60s. Her elder sister presented with blindness due to severe retinopathy in her late 20s. One younger sister was detected to have diabetes during a minor operation at the age of 12 and has been on insulin ever since. The remaining two siblings underwent oral glucose tolerance tests and the younger brother had a high 2-h plasma glucose level of 17 mmol/L at the age of 17. All siblings were lean. The two younger siblings detected by screening and put on insulin remained complications-free in their late 40s, while the index case died of end-stage kidney disease (ESKD) in her 50s. We later confirmed the cosegregation of a splice site mutation of HNF1α amongst all affected members despite their different outcomes depending on the timing of diagnosis and intervention[77].

Glucokinase MODY

Distinct from transcription factor MODY, GCK-MODY is characterized by isolated fasting hyperglycemia due to suboptimal glucose sensing with normal post-prandial insulin secretion once glucose sensing is triggered[78]. In European patients, carriers of GCK mutations usually have mild disease and do not require treatment[79]. In Asia, patients with GCK-MODY share features similar to those of their European counterparts, with multiple generations being affected, suggestive of autosomal dominant inheritance. However, the high prevalence of common variants for type 2 diabetes genes and beta-cell dysfunction in young Asian patients, along with other lifecourse factors, may lead to considerable variations in terms of age of diagnosis and subphenotypes both within and across GCK-MODY families[4,80,81].

Additionally, maternal-offspring GCK genotype concordance or discordance can affect the pregnancy outcome, which is highly relevant to young women with diabetes. Intensive insulin treatment in a GCK-MODY non-carrier may cause low birth weight in an offspring carrier who requires a high fasting plasma glucose to trigger insulin secretion. On the other hand, high fasting plasma glucose in an affected mother may lead to high birthweight in an offspring non-carrier due to fetal hyperinsulinemia[82,83]. The newly developed GCK activator[84] improved glucose sensing and triggered early insulin secretion. This new class of drug may provide precision therapy in patients with GCK-MODY, although more RCTs are needed to confirm this hypothesis[85].

The discovery of these MODY families due to “gain-of-function” or “loss-of-function” of genetic mutations supports the biological importance of proteins encoded by these genes. Some of their common variants may cause qualitative or quantitative changes in gene expression to increase the risk of type 2 diabetes and its complications. Indeed, some of the loci associated with type 2 diabetes lie within the coding or non-coding regions of MODY genes[24,25,86]. One example is the common variants of GCK and GCK-regulating proteins associated with glucose/lipid traits and ESKD[87]. Mendelian randomization analysis suggested that genetic proxies of GCK were causally linked to cardiovascular disease[88].

Thus, using multiomic analysis including whole exome sequencing focusing on coding regions and whole genome SNP arrays is complementary in identifying high-risk individuals with abnormal biology. Multiomic analysis in prospective cohorts with risk factors, interventions, and outcomes can provide valuable information in our pursuit of genomic medicine. However, the under-presentation of non-European populations, along with differences in ancestry, genomic architecture, and lifecourse factors, means major knowledge gaps in the heritability of YOD remain. Here, supported by other scientists, physicians with knowledge in human biology, pathophysiology, clinical medicine, drug mechanisms, technologies, and care delivery are in a good position to use these technologies and analytical tools to formulate hypotheses for translational purposes.

Wolfram syndrome/DIDMOAD

Currently, there are no guidelines on cascade family screening for heterozygous carriers of MODY genes with recessive mode of inheritance. While the homozygous carriers may suffer from severe disease with multiple organ damage, the heterozygous carriers may only have "mild" diabetes as a feature. One such example is Wolfram Syndrome, also known as DIDMOAD, the acronym for Diabetes Insipidus, Diabetes Mellitus, Optic Atrophy and Deafness[89,90]. The latter is a rare, autosomal recessive neurodegenerative disease due to mutations in the gene encoding Wolframin ER transmembrane glycoprotein (WFS1 and WFS2) located in the Chromosome 4p16.1 region. These genes encode a transmembrane protein causing abnormal calcium metabolism and protein misfolding in the endoplasmic reticulum. These patients might also have mitochondrial dysfunction with marked heterogeneity in phenotypes[89,91].

In the 1990s, we reported a Chinese man diagnosed with diabetes at the age of 12 with progressive loss of vision due to optic atrophy. At the age of 17, he presented with severe hyperglycemia and polyuria which was due to diabetes insipidus. Ultrasound imaging revealed severe hydroureters associated with autonomic dysfunction. With the loss of vision, the patient learned to tune the piano but later developed high-tone deafness. Both his parents had mild diabetes. Although genetic diagnosis was not available at the time, his clinical presentation suggested Wolfram Syndrome/DIDMOAD[91]. Given the severe disabilities and premature mortality in these homozygous carriers, preconceptual counseling would have prevented these tragic stories while there are ongoing efforts to discover druggable pathways or use gene editing to correct these genetic abnormalities and their consequences[89,90].

Clinical diagnosis versus prediction by bioinformatics and algorithms

Most of the recommendations on diagnosis and treatment of MODY were based on experiences in Europeans[79]. The current classification of P/LP variants is largely determined by the rarity of variants, functional annotations, experimental studies, and/or reported cases. Many of these variants were predicted to cause amino acid change, protein truncation, premature initiation, or termination of gene transcription. Due to their high frequency and incomplete penetration, they were often classified as variants of uncertain significance (VUS). Although heterozygous carriers of variants with recessive mode of inheritance or carriers of compound heterozygous mutations are not considered to have monogenic diabetes[92], these carriers may develop diabetes in the presence of additional risk factors[51]. The significance of these VUS requires evaluation through detailed phenotyping, systematic family screening, and testing for cosegregation, especially in populations with a high prevalence of familial YOD[71].

Discounting the significance of these VUS may lead to missed opportunities for early diagnosis and intervention. As an analogy, 7% of Asians have thalassemia minor due to a single copy of mutation affecting one of the oxygen-carrying globin proteins in the erythrocytes[93]. The high prevalence of these mutations may have selection advantages. Heterozygous carriers have mild anemia and an increased risk of diabetes[94] due to the close link between glucose and iron metabolism[95]. Given the recessive mode of inheritance, one in four offsprings of parents who are both carriers may suffer from thalassemia major, requiring lifelong blood transfusion, iron overload, multi-organ failure, and premature death[96]. In some countries, preconception counseling for couples who are both carriers has prevented the birth of these homozygous carriers[97,98].


Since the launching of the Human Genome Project in 1993[99], a wealth of knowledge has been amassed confirming the inter-ethnic differences in genomic architecture with hundreds of loci scattered throughout the genome associated with complex diseases including diabetes, cardiovascular-renal disease, and cancer[24,86]. In keeping with their frequent, albeit not invariable clustering, these diseases share common variants, which are mainly non-coding with small effect size, suggesting perturbation of common pathways in these disease clusters[86,100-102]. While some of these genetic loci are potential drug targets[103,104], other researchers advocate the use of polygenic risk scores to increase the cost-effectiveness of screening for high-risk subjects for intervention and individualized treatment[105-107].

In people with youth-onset diabetes (less than 18 years old), there are ongoing large-scale epidemiological, genetic[10], and interventional studies[11] to discover the causes and evaluate interventions. By contrast, there are major research gaps in adults with YOD often diagnosed by physicians who are less familiar with rare diseases[12]. Amongst patients diagnosed before the age of 40 years, there is heterogeneity in etiologies and phenotypes depending on whether they present during infancy, childhood, adolescence, or early adulthood. Large-scale GWAS have identified different genetic variants associated with the age of diagnosis, with many of them being associated with common forms of type 2 diabetes[108]. These variants can be used to generate polygenic risk scores to stratify risk for prevention and early intervention. Structured lifestyle modification and medications including metformin and alpha-glucosidase inhibitors prevent or delay the onset of type 2 diabetes in people with impaired glucose tolerance[3]. In the United States[109] and China Diabetes Prevention Program[110], metformin was most effective in people under the age of 45. Thus, by combining familial, genetic, and clinical risk scores, it is possible to segment the 20%-30% of individuals at the highest risk of developing YOD with less biological resilience for early intervention[111].

Likewise, while the early use of cheap generic medications such as statin and renin-angiotensin system inhibitors are likely to be cost-effective in preventing cardiovascular-renal events in young patients with type 2 diabetes[3], the cost-effectiveness of new drugs such as glucagon-like peptide 1 receptor agonist (GLP1-RA), sodium-glucose-cotransporter 2 inhibitor (SGLT2i), and non-steroidal mineralocorticoid receptor antagonist[112] in young patients remain uncertain despite their high lifetime risk for complications[113]. In this connection, diagnosis of familial hypercholesterolemia followed by cascade screening has been shown to be cost-effective for preventing premature cardiovascular disease through early detection and intervention[114]. While definitive trials are needed to prove the value of genetic testing, RCTs have demonstrated that the provision of personalized[115,116] and genetic information on the risk of diabetes complications empowered self-management and reduced negative emotions[117-119].


There are considerable overlaps in clinical presentations and phenotypes amongst people with autoimmune type 1 diabetes, LADA, MODY, type 2 diabetes, and other atypical forms of diabetes in young people. Once diabetes develops, glucolipotoxicity, inflammation, and oxidative stress can cause dedifferentiation and apoptosis of beta-cells and perpetuate glycemic deterioration[22,120]. For the same set of risk factors (e.g., smoking, obesity), there are considerable inter-individual variations in rates of decline of beta-cell function[81]. An estimated 50% of beta-cell function might have been lost at the time of diagnosis of diabetes[121]. Early optimization of glycemic control could restore beta-cell function[122] and delay treatment escalation[56,57]. These data support the use of biomarkers to improve the precision of diagnosis and treatment to preserve beta-cell function[123]. In the ongoing PRISM-RCT, we observed the importance of nature and nurture in these young patients in whom varying combinations of genetic predispositions, lifecourse factors, timeliness of diagnosis, lifestyles, and access to care can result in different presentations, trajectories, and outcomes [Figures 3 and 4 and Table 2].

Multifaceted nature of young-onset diabetes - can genomic medicine improve the precision of diagnosis and management?

Figure 3. A conceptual framework indicating the decline in beta-cell function due to natural aging, metabolic stress, and inflammation (e.g., obesity, use of tobacco, psychosocial stress, poor sleep, infection), which can influence the age of onset of diabetes. For a given trajectory of beta-cell function, those with reduced beta-cell capacity are more likely to decompensate early. With the onset of diabetes, ongoing lipoglucotoxicity and inflammation can further accelerate beta-cell loss, resulting in insulin requirement. Reducing metabolic stress, especially in people with vulnerable beta-cell function, may delay the onset of diabetes and insulin requirement[128].

Multifaceted nature of young-onset diabetes - can genomic medicine improve the precision of diagnosis and management?

Figure 4. A conceptual diagram showing the complex interactions between nature and nurture where multiple lifecourse factors can predispose, precipitate, and perpetuate the onset and trajectories of diabetes resulting in markedly different outcomes, strongly influenced by genetic factors, early childhood development, environmental exposures, lifestyles, and access to care and education (reproduced with permission from GemVCare)[151].

Table 2

Lifecourse factors that may influence the onset, trajectory, and consequences of diabetes (also refer to Figure 3)

Infancy and childhoodAdolescenceMiddle ageOld age
• Ethnicity and migration
• Common and rare genetic variants
• Other familial factors
(e.g., hemoglobinopathy, chronic hepatitis B infection)
• Epigenetics and perinatal development
• Low birth weight
• Childhood illness (e.g., malignancy and steroid use)
• Childhood obesity
• Formation of habits and lifestyles
• Education
• Socioeconomic status
• Obesity, metabolic syndrome, and fatty liver
• Lifestyles (e.g., unhealthy diet, physical inactivity, poor sleep)
• Psychosocial stress
•Other risk conditions
(e.g., depression, gestational diabetes, PCOS…)
• Endocrinopathy and drug use
• Micro/macrovascular complications
• Chronic kidney disease
• Heart failure
• Cancer
• Dementia
• Frailty

In Hong Kong Chinese patients with YOD, analysis of stored samples indicated that 8% had LADA, but the majority had not been given early insulin treatment. Compared to their peers with classical type 1 diabetes, patients with LADA had a 2.8 times higher risk of ESKD, likely due to poor glycemic control with delayed insulin treatment. Amongst patients treated with insulin, the mean reduction in HbA1c at 6 months was 2.3% in patients with LADA versus 0.7% in those without autoantibodies, calling for early detection of LADA to avoid delayed insulin treatment[124,125].

Between 1995 and 1998, fasting, stimulated, and random CPs were measured in 280,539 inhabitants in Skaraborg from Sweden, of whom 3.2% had diabetes. Amongst 1,093 patients with well-defined diabetes types, all three CP measurement protocols were robust in discriminating type 1 from type 2 diabetes, based on receiver operator curve (ROC) analysis, with random CP having the best performance. The optimal cut-off value was 0.50 nmol/L for random CP, 0.42 nmol/L for fasting CP, and 0.60 nmol/L for stimulated CP[126]. Other researchers proposed using fasting plasma CP < 250 pmol/L with or without autoantibodies to indicate absolute or severe insulin deficiency[46]. In our prospective analysis, Chinese patients with type 2 diabetes who had GADA positivity and low CP had the fastest progression rate to insulin treatment with a high risk of severe hypoglycemia. These patients should benefit from a basal-bolus insulin regimen to optimize glycemic control. Patients with GADA but residual CP level had a similar risk of insulin requirement as their peers without autoimmune type 2 diabetes. Counter-intuitively, patients with high CP, suggestive of insulin resistance, were more likely to progress to insulin treatment[125]. In a cohort of Chinese of working age, we used fasting plasma CP and glucose to derive Homeostasis model of assessment insulin resistance (HOMA-IR) and HOMA-beta to estimate insulin resistance and deficiency. In keeping with their contributory roles in diabetes[127], both HOMA-IR and HOMA-beta predicted incident diabetes in people with normal glucose tolerance. In patients with YOD, these indexes independently predicted early insulin requirement[128]. Due to these non-linear relationships of CP, the estimation of HOMA-IR and HOMA-beta, which takes prevailing PG into consideration, should be more informative. The use of HOMA indexes and autoantibodies will help physicians make more precise diagnoses and treatments.

The growing number of glucose-lowering drugs with different mechanisms of action calls for better patient segmentation to prioritize treatment selection[22]. Using HOMA indexes, GADA, age, BMI, and age of diagnosis, researchers classified patients with type 2 diabetes into five subtypes. Patients with severe insulin resistance had a high risk of chronic kidney disease, while those with autoimmune or severe insulin deficiency required early insulin treatment[129]. These studies had been replicated in other populations, including Chinese, in whom severe insulin-deficient type was more common and mild age-related diabetes was less common than their European counterparts. In all five subtypes of diabetes, Chinese patients had an earlier age of diagnosis, lower BMI, HOMA-beta, and higher HbA1c[130,131]. In a cross-sectional analysis, Chinese patients with YOD had lower beta-cell function with a steeper negative relationship between beta-cell function and disease duration than that in their late-onset counterparts[81].


Despite the organ-protective effects of SGLT2i and GLP1-RA, such evidence mainly came from high-risk patients with complications. Optimal glycemic control remained the cornerstone in diabetes management, and given the variable phenotypes suggestive of contribution from different etiologies, more studies are needed to guide treatment based on phenotypes or genotypes[132]. For example, in the Trimaster Study, patients with type 2 diabetes and an estimated glomerular filtration rate of 60-90 mL/min/1.73m2 were more responsive to DPP4i than SGLT2i. Patients with low BMI (< 30 kg/m2) were also more responsive to DPP4i than thiazolidinediones[133]. Apart from its proven benefits in preventing diabetes, metformin also reduces the risk of vascular, renal, cancer, and pneumonia events and all-cause death in type 2 diabetes[134]. There is also evidence suggesting that patients with predominant insulin deficiency may benefit from the addition of insulin secretagogues (e.g., SU), prandial insulin regulators (e.g., GLP1-RA, DPP4i, AGI), or insulin treatment, while those with insulin resistance, often due to obesity, might benefit more from weight-neutral or weight-reducing therapies such as GLP1-RA and SGLT2i[135,136]. In a proof-of-concept analysis, we stratified patients with type 2 diabetes by CP and insulin treatment and reported that patients with low CP and treated with insulin had the lowest mortality rate[137].

In the VERIFY Trial, newly diagnosed patients with type 2 diabetes treated with combination therapy of metformin and DPP4i had more durable glycemic control and 30% reduced risk of progression to insulin treatment compared to those treated with metformin monotherapy followed by DPP4i only with rising HbA1c[138]. These effects were particularly evident in patients with YOD[123]. In a real-world database, patients with type 2 diabetes, the majority of whom were on metformin and/or SU, those with additional DPP4i within 2 years of diagnosis had a 30% reduced risk of insulin treatment compared to those with additional DPP4 after 3-5 years of diagnosis[57]. These findings highlight the importance of early diagnosis, early intervention, and early control in preserving beta-cell function, especially in patients with compromised beta-cell function. Against a backdrop of the declining use of SU, increasing popularity of SGLT2i, and advocacy of using GLP1-RA to replace insulin, the importance of better classification cannot be emphasized enough to ensure timely and appropriate treatment for maximizing efficacy (e.g., SU in patients with HNF1α- and HNF4α-MODY) and reducing side-effects (e.g., insulin analogs in patients with LADA or severe insulin insufficiency to avoid diabetic ketoacidosis and severe hypoglycemia). Despite their heterogeneous phenotypes and treatment responses, there is a lack of RCT data to guide diagnosis and treatment in these high-risk patients with YOD[12].


Perinatal development, environmental exposure, socioeconomic status, migration, education, and health behaviors[139] can interact in a complex manner to cause childhood obesity, which can track into adulthood and bring forward the age of onset of diabetes. Other familial factors, such as chronic hepatitis B infection and hemoglobinopathy, have been associated with diabetes in some ethnic groups[94,140]. Medical histories such as pancreatic disease, gestational diabetes, polycystic ovary syndrome, thyroid disease, tuberculosis, and mental illness may provide clues regarding the predisposition, precipitation, and perpetuation of YOD[71,141,142]. A family history of diabetes affecting multiple generations with or without syndromic features may alert the possibility of MODY or monogenic diabetes[67]. Understanding patient-reported outcome measures (PROMs) such as quality of life, competing priorities, psychosocial stress from work or family, interpersonal relationships, and life events[143] may help care providers address negative emotions, maladjustment, and poor adherence frequently encountered in young patients with diabetes[101,144-146][Figure 3 and 4, Table 2].


A correct diagnosis is key to a meaningful dialogue between doctors and patients for informed and shared decision-making[147,148]. This can only be achieved through comprehensive profiling, good doctor-patient relationships, regular reviews, and quality care. However, the busy working environment, short consultation time, and frequent changes of care providers had made this approach challenging. The many technological advances focusing on procedures have further contributed to the increasing organ-based and fragmented healthcare practices[149].

Reducing genomic medicine to practice for improving the precision of diagnosis and therapy[150,151] must be aligned with reform in undergraduate/professional education and practice environments to facilitate implementation[117]. Genomic medicine is only one of the many facets of person-orientated care which begins with good history taking, physical examination, and value-based investigations. This should allow physicians to prioritize a list of differential diagnoses followed by definitive or empirical treatment with anticipated outcomes, and action plans if the outcome is not achieved. Doctors interested in the field of diabetes need to stay abreast of the advances in genomic medicine, data analytics, and drug development and learn how to use lay language to communicate probabilities, uncertainties, and complexities. They are in the best position to assess the utility of using clinical/genetic risk scores or algorithms to segment patients for targeted treatment, exclude hormonal or drug-induced forms of diabetes, and order comprehensive genetic profiling to diagnose rare or syndromic forms of diabetes. For research-orientated physicians, setting up registers[152] will provide a powerful tool to assess the values of using new technologies and approaches aimed at addressing the many needs of a young person with diabetes[17,101,153-155]. The adoption of this person-orientated approach will bring back the science and arts of clinical medicine which is particularly relevant to patients with YOD given the implications of misdiagnosis, misclassification, and mismatched treatment.

PRISM: precision medicine to redefine insulin secretion and monogenic diabetes (PRISM) in Chinese patients with young-onset diabetes

Complexity is a key feature in internal medicine. For the same disease, different people can have different clinical presentations. For the same clinical presentation, different people can have different underlying causes. For the same treatment, different people can have different responses. It is against this background that the authors embarked upon a pragmatic 3-year RCT [Precision medicine to redefine insulin secretion and monogenic diabetes (PRISM)] where 884 patients with type 2 diabetes diagnosed before the age of 40 and aged less than 50 years underwent structured clinical assessment and comprehensive biogenetic profiling including measurement of HOMA-indices, CP, and GADA to diagnose LADA and assess beta-cell function. These patients had genome-wide genotyping for computing polygenic risk scores for beta-cell function and complications. They also had targeted gene-sequencing to detect mutations of genes for MODY and monogenic diabetes. Other PROMs included psychosocial-behavioral factors and quality of life. Half of these patients were randomized to receive 1-year intensive counseling and personalized treatment guided by their biogenetic profiles and psychosocial needs, delivered by a specialist-led multidisciplinary team in a diabetes center away from busy clinics aimed at attaining multiple treatment targets. After this 1-year multi-component management[154,156], these patients will return to their usual clinics for follow-up with yearly review at the diabetes center while the other half receive usual care. All patients will undergo re-evaluation at 3 years. The primary outcome of PRISM is the incidence of all diabetes-related endpoints and the secondary outcome is control of cardiometabolic risk factors The results will be analyzed within the RE-AIM framework (Reach, Effectiveness, Adoption, Implementation and Evaluation)[157] to inform planners, practitioners, and policymakers about the resources, infrastructure, personnel, logistics, and technology needed to reduce precision medicine in YOD to practice and their cost-effectiveness. This project commenced in January 2020 and completed recruitment in September 2021, and the 3-year study period will end in September 2024[18].


Diabetes is a societal, public health and personal challenge. The rapid changes in our ecosystem, physical and food environment, cultures, lifestyles, values, and perspectives have unmasked biological defects in vulnerable individuals at risk of developing diabetes at a young age. These individuals need to be diagnosed, treated, and controlled early to prevent complications, maintain earning power, and preserve quality of life. Decision makers including governments, payors, and healthcare planners are tasked with creating a health-enabling environment, building capacity, and ensuring access to affordable medications, care, and support in collaboration with industry. Likewise, care providers have the responsibility to identify unmet needs and discover new knowledge to improve outcomes. Against this backdrop, physicians standing between patients and technologies, equipped with knowledge in human biology, pathophysiology, clinical medicine, drug mechanisms, and care delivery, should spearhead the use of genomic medicine and holistic care to reclassify diabetes and implement personalized solutions in people with or at risk of developing YOD. By combining research and practice, there is a real possibility that we can use personalized and genomic data to transform care and save patient lives[3].



We thank all staff, patients, and their relatives and the support of the Hong Kong Hospital Authority and the Hong Kong Genome Institute in the implementation of the PRISM Project, supported by the Hong Kong Government Health and Medical Research Fund Commissioned Research (CFS-CUHK2). We also acknowledge the support of the Impact Research Fund supported by the Research Grant Committee.

Authors’ contributions

Conceptualized and wrote the first draft with contribution and finalized the paper for submission: Chan JCN

Provided critical comments and approved the final manuscript: all authors

Availability of data and materials

Not applicable.

Financial support and sponsorship


Conflicts of interest

Chan JCN and Ma R hold patents for using genetic markers to predict diabetes and its complications for personalized care. Chan JCN, Ma R, and Lim C are cofounders of a start-up biotech company partially supported by the Technology Start-up Support Scheme for Universities (TSSSU) of the Hong Kong Government Innovation and Technology Commission. All other coauthors have no conflict of interest to declare.

Ethical approval and consent to participate

Not applicable.

Consent for publication

Not applicable.


© The Author(s) 2024.


1. Sun H, Saeedi P, Karuranga S, et al. IDF diabetes atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract 2022;183:109119.

2. Xu Y, Wang L, He J, et al. 2010 China Noncommunicable Disease Surveillance Group. Prevalence and control of diabetes in Chinese adults. JAMA 2013;310:948-59.

3. Chan JCN, Lim LL, Wareham NJ, et al. The lancet commission on diabetes: using data to transform diabetes care and patient lives. Lancet 2021;396:2019-82.

4. Yeung RO, Zhang Y, Luk A, et al. Metabolic profiles and treatment gaps in young-onset type 2 diabetes in Asia (the JADE programme): a cross-sectional study of a prospective cohort. Lancet Diabetes Endocrinol 2014;2:935-43.

5. Ke C, Stukel TA, Shah BR, et al. Age at diagnosis, glycemic trajectories, and responses to oral glucose-lowering drugs in type 2 diabetes in Hong Kong: a population-based observational study. PLoS Med 2020;17:e1003316.

6. Seshasai SR, Kaptoge S, Thompson A, et al. Emerging Risk Factors Collaboration. Diabetes mellitus, fasting glucose, and risk of cause-specific death. N Engl J Med 2011;364:829-41.

7. Wu H, Lau ESH, Yang A, et al. Young age at diabetes diagnosis amplifies the effect of diabetes duration on risk of chronic kidney disease: a prospective cohort study. Diabetologia 2021;64:1990-2000.

8. Wu H, Yang A, Lau ESH, et al. Age- and sex-specific hospital bed-day rates in people with and without type 2 diabetes: a territory-wide population-based cohort study of 1.5 million people in Hong Kong. PLoS Med 2023;20:e1004261.

9. Ke C, Lau E, Shah BR, et al. Excess burden of mental illness and hospitalization in young-onset type 2 diabetes: a population-based cohort study. Ann Intern Med 2019;170:145-54.

10. Srinivasan S, Chen L, Todd J, et al. ProDiGY Consortium. The first genome-wide association study for type 2 diabetes in youth: the progress in diabetes genetics in youth (ProDiGY) consortium. Diabetes 2021;70:996-1005.

11. Zeitler P, Hirst K, Pyle L, et al. TODAY Study Group. A clinical trial to maintain glycemic control in youth with type 2 diabetes. N Engl J Med 2012;366:2247-56.

12. Ke C, Shah BR, Luk AO, Di Ruggiero E, Chan JCN. Cardiovascular outcomes trials in type 2 diabetes: time to include young adults. Diabetes Obes Metab 2020;22:3-5.

13. Magliano DJ, Chen L, Carstensen B, et al. Trends in all-cause mortality among people with diagnosed diabetes in high-income settings: a multicountry analysis of aggregate data. Lancet Diabetes Endocrinol 2022;10:112-9.

14. Magliano DJ, Islam RM, Barr ELM, et al. Trends in incidence of total or type 2 diabetes: systematic review. BMJ 2019;366:l5003.

15. Luk AOY, Ke C, Lau ESH, et al. Secular trends in incidence of type 1 and type 2 diabetes in Hong Kong: a retrospective cohort study. PLoS Med 2020;17:e1003052.

16. Wu H, Lau ESH, Ma RCW, et al. Secular trends in all-cause and cause-specific mortality rates in people with diabetes in Hong Kong, 2001-2016: a retrospective cohort study. Diabetologia 2020;63:757-66.

17. Chan JCN, Lim LL, Luk AOY, et al. From Hong Kong diabetes register to JADE program to RAMP-DM for data-driven actions. Diabetes Care 2019;42:2022-31.

18. Luk A, Fan Y, Fan B, et al. 1829-PUB: precision medicine to redefine insulin secretion and monogenic diabetes randomised controlled trial (PRISM-RCT) in Chinese patients with young-onset diabetes - study design and baseline profile. Diabetes 2023;72:1829.

19. Polonsky KS, Given BD, Hirsch LJ, et al. Abnormal patterns of insulin secretion in non-insulin-dependent diabetes mellitus. N Engl J Med 1988;318:1231-9.

20. Gerich JE. Control of glycaemia. Baillieres Clin Endocrinol Metab 1993;7:551-86.

21. Polonsky KS. The past 200 years in diabetes. N Engl J Med 2012;367:1332-40.

22. Defronzo RA. Banting Lecture. From the triumvirate to the ominous octet: a new paradigm for the treatment of type 2 diabetes mellitus. Diabetes 2009;58:773-95.

23. McCarthy M, Zeggini E. Genetics of type 2 diabetes. Curr Rep Diabetes 2006;6:147-54.

24. Fuchsberger C, Flannick J, Teslovich TM, et al. The genetic architecture of type 2 diabetes. Nature 2016;536:41-7.

25. Spracklen CN, Horikoshi M, Kim YJ, et al. Identification of type 2 diabetes loci in 433,540 East Asian individuals. Nature 2020;582:240-5.

26. Ogilvie RF. A quantitative estimation of pancreatic islet tissue. QJM 1937;6:287-300.

27. Yoon KH, Ko SH, Cho JH, et al. Selective beta-cell loss and alpha-cell expansion in patients with type 2 diabetes mellitus in Korea. J Clin Endocrinol Metab 2003;88:2300-8.

28. Zhao HL, Lai FM, Tong PC, et al. Prevalence and clinicopathological characteristics of islet amyloid in chinese patients with type 2 diabetes. Diabetes 2003;52:2759-66.

29. Hart PA, Bellin MD, Andersen DK, et al. Consortium for the Study of Chronic Pancreatitis; Diabetes; and Pancreatic Cancer(CPDPC). Type 3c (pancreatogenic) diabetes mellitus secondary to chronic pancreatitis and pancreatic cancer. Lancet Gastroenterol Hepatol 2016;1:226-37.

30. Lernmark A, Freedman ZR, Hofmann C, et al. Islet-cell-surface antibodies in juvenile diabetes mellitus. N Engl J Med 1978;299:375-80.

31. Gregory GA, Robinson TIG, Linklater SE, et al. International Diabetes Federation Diabetes Atlas Type 1 Diabetes in Adults Special Interest Group. Global incidence, prevalence, and mortality of type 1 diabetes in 2021 with projection to 2040: a modelling study. Lancet Diabetes Endocrinol 2022;10:741-60.

32. Metcalfe KA, Hitman GA, Rowe RE, et al. Concordance for type 1 diabetes in identical twins is affected by insulin genotype. Diabetes Care 2001;24:838-42.

33. Sharp SA, Weedon MN, Hagopian WA, Oram RA. Clinical and research uses of genetic risk scores in type 1 diabetes. Curr Opin Genet Dev 2018;50:96-102.

34. Noble JA, Valdes AM. Genetics of the HLA region in the prediction of type 1 diabetes. Curr Diab Rep 2011;11:533-42.

35. Barrett JC, Clayton DG, Concannon P, et al. Type 1 diabetes genetics consortium. genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nat Genet 2009;41:703-7.

36. Redondo MJ, Geyer S, Steck AK, et al. Type 1 Diabetes TrialNet Study Group. A Type 1 diabetes genetic risk score predicts progression of islet autoimmunity and development of type 1 diabetes in individuals at risk. Diabetes Care 2018;41:1887-94.

37. Bonifacio E, Beyerlein A, Hippich M, et al. TEDDY Study Group. Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabetes: a prospective study in children. PLoS Med 2018;15:e1002548.

38. Cyclosporin-induced remission of IDDM after early intervention. Association of 1 yr of cyclosporin treatment with enhanced insulin secretion. The Canadian-European randomized control trial group. Diabetes 1988;37:1574-82.

39. Mullard A. FDA approves anti-CD3 antibody to delay type 1 diabetes onset. Nat Rev Drug Discov 2023;22:6-7.

40. Herold KC, Bundy BN, Long SA, et al. Type 1 Diabetes TrialNet Study Group. An Anti-CD3 antibody, teplizumab, in relatives at risk for type 1 diabetes. N Engl J Med 2019;381:603-13.

41. Ramos EL, Dayan CM, Chatenoud L, et al. PROTECT Study Investigators. Teplizumab and β-Cell function in newly diagnosed type 1 diabetes. N Engl J Med 2023;389:2151-61.

42. Padilla-Martínez F, Collin F, Kwasniewski M, Kretowski A. Systematic review of polygenic risk scores for type 1 and type 2 diabetes. Int J Mol Sci 2020;21:1703.

43. Sims EK, Carr ALJ, Oram RA, DiMeglio LA, Evans-Molina C. 100 years of insulin: celebrating the past, present and future of diabetes therapy. Nat Med 2021;27:1154-64.

44. Rodbard D. Continuous glucose monitoring: a review of recent studies demonstrating improved glycemic outcomes. Diabetes Technol Ther 2017;19:S25-37.

45. Nathan DM, Genuth S, Lachin J, et al. Diabetes Control and Complications Trial Research Group. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med 1993;329:977-86.

46. Buzzetti R, Tuomi T, Mauricio D, et al. Management of latent autoimmune diabetes in adults: a consensus statement from an international expert panel. Diabetes 2020;69:2037-47.

47. Imagawa A. Two types of fulminant type 1 diabetes mellitus: immune checkpoint inhibitor-related and conventional. J Diabetes Investig 2021;12:917-9.

48. Bornstein SR, Rubino F, Ludwig B, et al. Consequences of the COVID-19 pandemic for patients with metabolic diseases. Nat Metab 2021;3:289-92.

49. Ata A, Jalilova A, Kırkgöz T, et al. Does COVID-19 predispose patients to type 1 diabetes mellitus? Clin Pediatr Endocrinol 2022;31:33-7.

50. Lim S, Bae JH, Kwon HS, Nauck MA. COVID-19 and diabetes mellitus: from pathophysiology to clinical management. Nat Rev Endocrinol 2021;17:11-30.

51. Rothman KJ, Greenland S. Causation and causal inference in epidemiology. Am J Public Health 2005;95 Suppl 1:S144-50.

52. Akturk HK, Kahramangil D, Sarwal A, Hoffecker L, Murad MH, Michels AW. Immune checkpoint inhibitor-induced type 1 diabetes: a systematic review and meta-analysis. Diabet Med 2019;36:1075-81.

53. Postow MA, Sidlow R, Hellmann MD. Immune-related adverse events associated with immune checkpoint blockade. N Engl J Med 2018;378:158-68.

54. Ugai T, Sasamoto N, Lee HY, et al. Is early-onset cancer an emerging global epidemic? Nat Rev Clin Oncol 2022;19:656-73.

55. Collier JL, Weiss SA, Pauken KE, Sen DR, Sharpe AH. Not-so-opposite ends of the spectrum: CD8+ T cell dysfunction across chronic infection, cancer and autoimmunity. Nat Immunol 2021;22:809-19.

56. Matthews DR, Paldánius PM, Proot P, Chiang Y, Stumvoll M, Del Prato S. VERIFY study group. Glycaemic durability of an early combination therapy with vildagliptin and metformin versus sequential metformin monotherapy in newly diagnosed type 2 diabetes (VERIFY): a 5-year, multicentre, randomised, double-blind trial. Lancet 2019;394:1519-29.

57. Cheung JTK, Yang A, Wu H, et al. Early treatment with dipeptidyl-peptidase 4 inhibitors reduces glycaemic variability and delays insulin initiation in type 2 diabetes: a propensity score-matched cohort study. Diabetes Metab Res Rev 2023:e3711.

58. Laiteerapong N, Ham SA, Gao Y, et al. The legacy effect in type 2 diabetes: impact of early glycemic control on future complications (the diabetes & aging study). Diabetes Care 2019;42:416-26.

59. Holman RR, Paul SK, Bethel MA, Matthews DR, Neil HA. 10-year follow-up of intensive glucose control in type 2 diabetes. N Engl J Med 2008;359:1577-89.

60. Nathan DM, Cleary PA, Backlund JY, et al. Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. N Engl J Med 2005;353:2643-53.

61. Turner R, Stratton I, Horton V, et al. UKPDS 25: autoantibodies to islet-cell cytoplasm and glutamic acid decarboxylase for prediction of insulin requirement in type 2 diabetes. UK prospective diabetes study group. Lancet 1997;350:1288-93.

62. Buzzetti R, Zampetti S, Maddaloni E. Adult-onset autoimmune diabetes: current knowledge and implications for management. Nat Rev Endocrinol 2017;13:674-86.

63. Medici F, Hawa M, Ianari A, Pyke DA, Leslie RD. Concordance rate for type II diabetes mellitus in monozygotic twins: actuarial analysis. Diabetologia 1999;42:146-50.

64. Zhang J, Yang Z, Xiao J, et al. China National Diabetes and Metabolic Disorders Study Group. Association between family history risk categories and prevalence of diabetes in Chinese population. PLoS One 2015;10:e0117044.

65. Li JK, Ng MC, So WY, et al. Phenotypic and genetic clustering of diabetes and metabolic syndrome in Chinese families with type 2 diabetes mellitus. Diabetes Metab Res Rev 2006;22:46-52.

66. Zhang Y, Luk AOY, Chow E, et al. High risk of conversion to diabetes in first-degree relatives of individuals with young-onset type 2 diabetes: a 12-year follow-up analysis. Diabet Med 2017;34:1701-9.

67. Bonnefond A, Unnikrishnan R, Doria A, et al. Monogenic diabetes. Nat Rev Dis Primers 2023;9:12.

68. Tattersall RB. Mild familial diabetes with dominant inheritance. Q J Med 1974;43:339-57.

69. Fajans SS, Bell GI, Polonsky KS. Molecular mechanisms and clinical pathophysiology of maturity-onset diabetes of the young. N Engl J Med 2001;345:971-80.

70. Feingold K. Atypical forms of diabetes. In: Feingold KR, Anawalt B, Boyce A, et al., editors. Endotext [Internet]. South Dartmouth:, Inc.; 2022. Available from: [Last accessed on 15 Jan 2024].

71. Chan JC, Malik V, Jia W, et al. Diabetes in Asia: epidemiology, risk factors, and pathophysiology. JAMA 2009;301:2129-40.

72. Todd JN, Kleinberger JW, Zhang H, et al. Monogenic diabetes in youth with presumed type 2 diabetes: results from the progress in diabetes genetics in youth (ProDiGY) collaboration. Diabetes Care 2021;44:2312-9.

73. Shields BM, Shepherd M, Hudson M, et al. UNITED study team. Population-based assessment of a biomarker-based screening pathway to aid diagnosis of monogenic diabetes in young-onset patients. Diabetes Care 2017;40:1017-25.

74. Christensen AS, Hædersdal S, Støy J, et al. Efficacy and safety of glimepiride with or without linagliptin treatment in patients with hnf1a diabetes (maturity-onset diabetes of the young type 3): a randomized, double-blinded, placebo-controlled, crossover trial (GLIMLINA). Diabetes Care 2020;43:2025-33.

75. Thewjitcharoen Y, Nakasatien S, Tsoi TF, Lim CKP, Himathongkam T, Chan JCN. Hypertriglyceridemia as a main feature associated with 17q12 deletion syndrome-related hepatocyte nuclear factor 1β-maturity-onset diabetes of the young. Endocrinol Diabetes Metab Case Rep 2022;2022:22-0297.

76. Thewjitcharoen Y, Wanothayaroj E, Krittiyawong S, et al. Phenotypic and genetic heterogeneity in a thai glucokinase MODY family reveals the complexity of young-onset diabetes. Front Endocrinol 2021;12:690343.

77. Ng MC, Li JK, So WY, et al. Nature or nurture: an insightful illustration from a Chinese family with hepatocyte nuclear factor-1 alpha diabetes (MODY3). Diabetologia 2000;43:816-8.

78. Matschinsky FM. Regulation of pancreatic beta-cell glucokinase: from basics to therapeutics. Diabetes 2002;51 Suppl 3:S394-404.

79. Hattersley AT, Patel KA. Precision diabetes: learning from monogenic diabetes. Diabetologia 2017;60:769-77.

80. Yabe D, Seino Y. Type 2 diabetes via β-cell dysfunction in east Asian people. Lancet Diabetes Endocrinol 2016;4:2-3.

81. Fan Y, Fan B, Lau ESH, et al. Comparison of beta-cell function between Hong Kong Chinese with young-onset type 2 diabetes and late-onset type 2 diabetes. Diabetes Res Clin Pract 2023;205:110954.

82. López Tinoco C, Sánchez Lechuga B, Bacon S, et al. Evaluation of pregnancy outcomes in women with GCK-MODY. Diabet Med 2021;38:e14488.

83. Dickens LT, Letourneau LR, Sanyoura M, Greeley SAW, Philipson LH, Naylor RN. Management and pregnancy outcomes of women with GCK-MODY enrolled in the US Monogenic Diabetes Registry. Acta Diabetol 2019;56:405-11.

84. Zhu D, Gan S, Liu Y, et al. Dorzagliatin monotherapy in Chinese patients with type 2 diabetes: a dose-ranging, randomised, double-blind, placebo-controlled, phase 2 study. Lancet Diabetes Endocrinol 2018;6:627-36.

85. Chow E, Wang K, Lim CKP, et al. Dorzagliatin, a dual-acting glucokinase activator, increases insulin secretion and glucose sensitivity in glucokinase maturity-onset diabetes of the young and recent-onset type 2 diabetes. Diabetes 2023;72:299-308.

86. Khera AV, Chaffin M, Aragam KG, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet 2018;50:1219-24.

87. Wang K, Shi M, Yang A, et al. GCKR and GCK polymorphisms are associated with increased risk of end-stage kidney disease in Chinese patients with type 2 diabetes: the Hong Kong Diabetes Register (1995-2019). Diabetes Res Clin Pract 2022;193:110118.

88. Wang K, Shi M, Huang C, et al. Evaluating the impact of glucokinase activation on risk of cardiovascular disease: a Mendelian randomisation analysis. Cardiovasc Diabetol 2022;21:192.

89. Urano F. Wolfram syndrome: diagnosis, management, and treatment. Curr Diab Rep 2016;16:6.

90. Domenech E, Gomez-Zaera M, Nunes V. Wolfram/DIDMOAD syndrome, a heterogenic and molecularly complex neurodegenerative disease. Pediatr Endocrinol Rev 2006;3:249-57.

91. Chan JC, Woo J, Cockram CS, Teoh R. DIDMOAD syndrome in a Chinese male with HLA DR7 DRw12. Postgrad Med J 1987;63:1109.

92. Richards S, Aziz N, Bale S, et al. ACMG Laboratory Quality Assurance Committee. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 2015;17:405-24.

93. Lau YL, Chan LC, Chan YY, et al. Prevalence and genotypes of α- and β-thalassemia carriers in Hong Kong - implications for population screening. N Engl J Med 1997;336:1298-301.

94. Lao TT, Ho LF. α-Thalassaemia trait and gestational diabetes mellitus in Hong Kong. Diabetologia 2001;44:966-71.

95. Fernández-Real JM, McClain D, Manco M. Mechanisms linking glucose homeostasis and iron metabolism toward the onset and progression of type 2 diabetes. Diabetes Care 2015;38:2169-76.

96. Bazi A, Sharifi-Rad J, Rostami D, Sargazi-Aval O, Safa A. Diabetes mellitus in thalassaemia major patients: a report from the southeast of Iran. J Clin Diagn Res 2017;11:BC01-4.

97. Hussein N, Henneman L, Kai J, Qureshi N. Preconception risk assessment for thalassaemia, sickle cell disease, cystic fibrosis and Tay-Sachs disease. Cochrane Database Syst Rev 2021;10:CD010849.

98. Cousens NE, Gaff CL, Metcalfe SA, Delatycki MB. Carrier screening for beta-thalassaemia: a review of international practice. Eur J Hum Genet 2010;18:1077-83.

99. Guyer MS, Collins FS. The human genome project and the future of medicine. Am J Dis Child 1993;147:1145-52.

100. Pociot F. Type 1 diabetes genome-wide association studies: not to be lost in translation. Clin Transl Immunology 2017;6:e162.

101. McCarthy M, Birney E. Personalized profiles for disease risk must capture all facets of health. Nature 2021;597:175-7.

102. Ma RC, Cooper ME. Genetics of diabetic kidney disease-from the worst of nightmares to the light of dawn? J Am Soc Nephrol 2017;28:389-93.

103. Stadler LKJ, Farooqi IS. A new drug target for type 2 diabetes. Cell 2017;170:12-4.

104. Hara K, Fujita H, Johnson TA, et al. DIAGRAM consortium. Genome-wide association study identifies three novel loci for type 2 diabetes. Hum Mol Genet 2014;23:239-46.

105. Udler MS, McCarthy MI, Florez JC, Mahajan A. Genetic risk scores for diabetes diagnosis and precision medicine. Endocr Rev 2019;40:1500-20.

106. Guinan K, Beauchemin C, Tremblay J, et al. Economic evaluation of a new polygenic risk score to predict nephropathy in adult patients with type 2 diabetes. Can J Diabetes 2021;45:129-36.

107. Tremblay J, Haloui M, Attaoua R, et al. Polygenic risk scores predict diabetes complications and their response to intensive blood pressure and glucose control. Diabetologia 2021;64:2012-25.

108. Noordam R, Läll K, Smit RAJ, et al. Stratification of type 2 diabetes by age of diagnosis in the UK biobank reveals subgroup-specific genetic associations and causal risk profiles. Diabetes 2021;70:1816-25.

109. Herman W, Hoerger T, Brandle M, et al. The cost-effectiveness of lifestyle modification or metformin in preventing type 2 diabetes in adults with impaired glucose tolerance. Ann Intern Med 2005;142:323-32.

110. Zhang L, Zhang Y, Shen S, et al. China Diabetes Prevention Program Study Group. Safety and effectiveness of metformin plus lifestyle intervention compared with lifestyle intervention alone in preventing progression to diabetes in a Chinese population with impaired glucose regulation: a multicentre, open-label, randomised controlled trial. Lancet Diabetes Endocrinol 2023;11:567-77.

111. Hahn SJ, Kim S, Choi YS, Lee J, Kang J. Prediction of type 2 diabetes using genome-wide polygenic risk score and metabolic profiles: A machine learning analysis of population-based 10-year prospective cohort study. EBioMedicine 2022;86:104383.

112. Lim LL, Chow E, Chan JCN. Cardiorenal diseases in type 2 diabetes mellitus: clinical trials and real-world practice. Nat Rev Endocrinol 2023;19:151-63.

113. Choi JG, Winn AN, Skandari MR, et al. First-line therapy for type 2 diabetes with sodium-glucose cotransporter-2 inhibitors and glucagon-like peptide-1 receptor agonists : a cost-effectiveness study. Ann Intern Med 2022;175:1392-400.

114. Knowles JW, Rader DJ, Khoury MJ. Cascade screening for familial hypercholesterolemia and the use of genetic testing. JAMA 2017;318:381-2.

115. Chan JC, Sui Y, Oldenburg B, et al. JADE and PEARL Project Team. Effects of telephone-based peer support in patients with type 2 diabetes mellitus receiving integrated care: a randomized clinical trial. JAMA Intern Med 2014;174:972-81.

116. Chan JC, Ozaki R, Luk A, et al. JADE Collaborative Study Group. Delivery of integrated diabetes care using logistics and information technology - the Joint Asia Diabetes Evaluation (JADE) program. Diabetes Res Clin Pract 2014;106 Suppl 2:S295-304.

117. Ma RCW, Chan JCN. Implementation of precision genetic approaches for type 1 and 2 diabetes. In: Basu R, editor. Precision medicine in diabetes. Cham: Springer International Publishing; 2022. pp. 111-29.

118. Ma RCW, Xie F, Lim CKP, et al. A randomized clinical trial of genetic testing and personalized risk counselling in patients with type 2 diabetes receiving integrated care -the genetic testing and patient empowerment (GEM) trial. Diabetes Res Clin Pract 2022;189:109969.

119. Grant RW, Hivert M, Pandiscio JC, Florez JC, Nathan DM, Meigs JB. The clinical application of genetic testing in type 2 diabetes: a patient and physician survey. Diabetologia 2009;52:2299-305.

120. Bensellam M, Jonas JC, Laybutt DR. Mechanisms of β-cell dedifferentiation in diabetes: recent findings and future research directions. J Endocrinol 2018;236:R109-43.

121. Holman RR. Assessing the potential for α-glucosidase inhibitors in prediabetic states. Diabetes Res Clin Pract 1998;40 Suppl:S21-5.

122. Weng J, Li Y, Xu W, et al. Effect of intensive insulin therapy on beta-cell function and glycaemic control in patients with newly diagnosed type 2 diabetes: a multicentre randomised parallel-group trial. Lancet 2008;371:1753-60.

123. Chan JCN, Paldánius PM, Mathieu C, Stumvoll M, Matthews DR, Del Prato S. Early combination therapy delayed treatment escalation in newly diagnosed young-onset type 2 diabetes: a subanalysis of the VERIFY study. Diabetes Obes Metab 2021;23:245-51.

124. Luk AOY, Lau ESH, Lim C, et al. Diabetes-related complications and mortality in patients with young-onset latent autoimmune diabetes: a 14-year analysis of the prospective Hong Kong diabetes register. Diabetes Care 2019;42:1042-50.

125. Fan B, Lim CKP, Poon EWM, et al. Differential associations of GAD antibodies (GADA) and C-peptide with insulin initiation, glycemic responses, and severe hypoglycemia in patients diagnosed with type 2 diabetes. Diabetes Care 2023;46:1282-91.

126. Berger B, Stenström G, Sundkvist G. Random C-peptide in the classification of diabetes. Scand J Clin Lab Invest 2000;60:687-93.

127. Kahn SE. The relative contributions of insulin resistance and beta-cell dysfunction to the pathophysiology of type 2 diabetes. Diabetologia 2004;46:3-19.

128. Fan B, Wu H, Shi M, et al. Associations of the HOMA2-%B and HOMA2-IR with progression to diabetes and glycaemic deterioration in young and middle-aged Chinese. Diabetes Metab Res Rev 2022;38:e3525.

129. Ahlqvist E, Storm P, Käräjämäki A, et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol 2018;6:361-9.

130. Li X, Yang S, Cao C, et al. Validation of the Swedish Diabetes re-grouping scheme in adult-onset diabetes in China. J Clin Endocrinol Metab 2020;105:e3519-28.

131. Ke C, Narayan KMV, Chan JCN, Jha P, Shah BR. Pathophysiology, phenotypes and management of type 2 diabetes mellitus in Indian and Chinese populations. Nat Rev Endocrinol 2022;18:413-32.

132. Chan SP, Lim LL, Chan JCN, Matthews DR. Adjusting the use of glucose-lowering agents in the real-world clinical management of people with type 2 diabetes: a narrative review. Diabetes Ther 2023;14:823-38.

133. Shields BM, Dennis JM, Angwin CD, et al. TriMaster Study group. Patient stratification for determining optimal second-line and third-line therapy for type 2 diabetes: the TriMaster study. Nat Med 2023;29:376-83.

134. Chow E, Yang A, Chung CHL, Chan JCN. A clinical perspective of the multifaceted mechanism of metformin in diabetes, infections, cognitive dysfunction, and cancer. Pharmaceuticals 2022;15:442.

135. Tahrani AA, Bailey CJ, Del Prato S, Barnett AH. Management of type 2 diabetes: new and future developments in treatment. Lancet 2011;378:182-97.

136. Dennis JM. Precision medicine in type 2 diabetes: using individualized prediction models to optimize selection of treatment. Diabetes 2020;69:2075-85.

137. Ko GT, So WY, Tong PC, et al. Effect of interactions between C peptide levels and insulin treatment on clinical outcomes among patients with type 2 diabetes mellitus. CMAJ 2009;180:919-26.

138. Matthews D, Del Prato S, Mohan V, et al. Insights from VERIFY: early combination therapy provides better glycaemic durability than a stepwise approach in newly diagnosed type 2 diabetes. Diabetes Ther 2020;11:2465-76.

139. Diabetes Association Professional Practice Committee. 5. Facilitating behavior change and well-being to improve health outcomes: standards of medical care in diabetes-2022. Diabetes Care 2022;45:S60-82.

140. Lao TT, Chan BC, Leung WC, Ho LF, Tse KY. Maternal hepatitis B infection and gestational diabetes mellitus. J Hepatol 2007;47:46-50.

141. Magee MJ, Khakharia A, Gandhi NR, et al. Increased risk of incident diabetes among individuals with latent tuberculosis infection. Diabetes Care 2022;45:880-7.

142. Golden SH, Lazo M, Carnethon M, et al. Examining a bidirectional association between depressive symptoms and diabetes. JAMA 2008;299:2751-9.

143. Harris ML, Oldmeadow C, Hure A, Luu J, Loxton D, Attia J. Stress increases the risk of type 2 diabetes onset in women: a 12-year longitudinal study using causal modelling. PLoS One 2017;12:e0172126.

144. Fisher EB, Chan JC, Nan H, Sartorius N, Oldenburg B. Co-occurrence of diabetes and depression: conceptual considerations for an emerging global health challenge. J Affect Disord 2012;142 Suppl:S56-66.

145. Peyrot M, Burns KK, Davies M, et al. Diabetes attitudes wishes and needs 2 (DAWN2): a multinational, multi-stakeholder study of psychosocial issues in diabetes and person-centred diabetes care. Diabetes Res Clin Pract 2013;99:174-84.

146. Nicolucci A, Kovacs Burns K, Holt RI, et al. DAWN2 Study Group. Diabetes attitudes, wishes and needs second study (DAWN2™): cross-national benchmarking of diabetes-related psychosocial outcomes for people with diabetes. Diabet Med 2013;30:767-77.

147. Riaz H, Krasuski RA. Best practice advisories should not replace good clinical acumen. Am J Med 2017;130:245-6.

148. TODD JW. The superior clinical acumen of the old physicians, a myth. Lancet 1953;1:482-4.

149. Fuller J. The new medical model: a renewed challenge for biomedicine. CMAJ 2017;189:E640-1.

150. Chung WK, Erion K, Florez JC, et al. Precision Medicine in Diabetes: a consensus report from the American diabetes association (ADA) and the European association for the study of diabetes (EASD). Diabetes Care 2020;43:1617-35.

151. Xie F, Chan JC, Ma RC. Precision medicine in diabetes prevention, classification and management. J Diabetes Investig 2018;9:998-1015.

152. Chan JCN, So WY, Ma RCW, Tong PCY, Wong R, Yang X. The complexity of vascular and non-vascular complications of diabetes: the Hong Kong diabetes registry. Curr Cardiovasc Risk Rep 2011;5:230-9.

153. Hannawa AF, Wu AW, Kolyada A, Potemkina A, Donaldson LJ. The aspects of healthcare quality that are important to health professionals and patients: a qualitative study. Patient Educ Couns 2022;105:1561-70.

154. Lim LL, Lau ESH, Kong APS, et al. Aspects of multicomponent integrated care promote sustained improvement in surrogate clinical outcomes: a systematic review and meta-analysis. Diabetes Care 2018;41:1312-20.

155. Backhouse A, Ogunlayi F. Quality improvement into practice. BMJ 2020;368:m865.

156. Lim LL, Lau ESH, Ozaki R, et al. Association of technologically assisted integrated care with clinical outcomes in type 2 diabetes in Hong Kong using the prospective JADE program: a retrospective cohort analysis. PLoS Med 2020;17:e1003367.

157. Glasgow RE, Harden SM, Gaglio B, et al. RE-AIM planning and evaluation framework: adapting to new science and practice with a 20-year review. Front Public Health 2019;7:64.

Cite This Article

OAE Style

Chan JCN, Chow E, Kong A, Cheung E, O T, Lim C, Fan B, Tsoi S, Fan Y, Shi M, Ozaki R, Ma R, Luk A. Multifaceted nature of young-onset diabetes - can genomic medicine improve the precision of diagnosis and management?. J Transl Genet Genom 2024;8:13-34.

AMA Style

Chan JCN, Chow E, Kong A, Cheung E, O T, Lim C, Fan B, Tsoi S, Fan Y, Shi M, Ozaki R, Ma R, Luk A. Multifaceted nature of young-onset diabetes - can genomic medicine improve the precision of diagnosis and management?. Journal of Translational Genetics and Genomics. 2024; 8(1): 13-34.

Chicago/Turabian Style

Chan, Juliana C. N., Elaine Chow, Alice Kong, Elaine Cheung, Tony O, Cadmon Lim, Baoqi Fan, Sandra Tsoi, Yingnan Fan, Mai Shi, Risa Ozaki, Ronald Ma, Andrea Luk. 2024. "Multifaceted nature of young-onset diabetes - can genomic medicine improve the precision of diagnosis and management?" Journal of Translational Genetics and Genomics. 8, no.1: 13-34.

ACS Style

Chan, JCN.; Chow E.; Kong A.; Cheung E.; O T.; Lim C.; Fan B.; Tsoi S.; Fan Y.; Shi M.; Ozaki R.; Ma R.; Luk A. Multifaceted nature of young-onset diabetes - can genomic medicine improve the precision of diagnosis and management?. J. Transl. Genet. Genom. 2024, 8, 13-34.

About This Article

Special Issue

This article belongs to the Special Issue Genomics & Precision Health
© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License (, which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Data & Comments




Comments must be written in English. Spam, offensive content, impersonation, and private information will not be permitted. If any comment is reported and identified as inappropriate content by OAE staff, the comment will be removed without notice. If you have any queries or need any help, please contact us at

Download PDF
Cite This Article 12 clicks
Like This Article 3 likes
Share This Article
Scan the QR code for reading!
See Updates
Journal of Translational Genetics and Genomics
ISSN 2578-5281 (Online)
Follow Us


All published articles are preserved here permanently:


All published articles are preserved here permanently: