Horizon placement: Top editorial feature

A new review in Diabetologia looks at how genetic risk scores are already being applied, both at the pre-clinical stages of type 1 diabetes and to more accurate classification of type 1, type 2 and other forms of diabetes. This exciting technology could lead to more accurate diagnosis and tailored care. Dr Susan Aldridge reports. 

Type 1 diabetes develops in stages, with genetic risk and environmental triggers contributing to the start of autoimmunity, which leads to the presence of islet-specific autoantibodies. Stage 1 is marked by the presence of these autoantibodies, stage 2 involves progression to dysglycaemia, while stage 3 is clinical diabetes. 

More than 70 genetic regions have now been associated with type 1 diabetes, with particularly strong associations in the human leukocyte antigen (HLA) Class II regions. In the past, an individual’s genetic risk was assessed by family history, HLA typing or genotyping of other diabetes-associated loci. However, the increased ease and lower cost of acquiring genetic data has led to the development of scores, which combine risk variants from associated loci into a single number. 

The polygenic risk score (PRS) includes the risk of many possibly correlated variants from across the genome, even if they do not reach genome-wide significance. The genetic risk score (GRS) estimates the cumulative contribution of a smaller subset of genetic variants that do reach genome-wide significance. Richard Oram of the University of Exeter and colleagues elsewhere describe the genetic architecture of type 1 diabetes, focusing on the development of GRSs. They look at the application of the risk scores in clinical studies and the opportunities and challenges involved in their translation into mainstream diabetes care. 

Heritability of type 1 diabetes

Twin and family studies provide evidence for a substantial heritable component in type 1 diabetes, which declines with age at diagnosis. Concordance rates for monozygotic and dizygotic twins are more than 50% and around 8%, respectively. Sibling concordance rates range from 6-10%, while there is a risk of 6-9% for offspring of an affected father and 1-4% for those of an affected mother. 

Research has attributed a large amount of type 1 diabetes heritability to variation in the class II HLA genes located within the major histocompatibility complex (MHC) region on chromosome 6. Some specific haplotypes, (combinations of alleles at multiple loci on the same chromosome) that confer the highest risk of type 1 diabetes have been identified. 

Meanwhile, other studies have identified more than 70 non-HLA type 1 diabetes risk loci. The greater than 50% heritable risk explained by the variants in the HLA region is similar to that seen in other autoimmune diseases, but differs from that seen in other complex diseases, such as type 2 diabetes, where heritability is more equally distributed across all chromosomes. 

Identifying variants with small effect sizes, which can combine to have an additive effect on disease risk, is necessary to uncover the full range of genetic risk in order to fully understand the pathogenesis of the disease. This is the rationale behind the development of the genetic risk score. 

Genetic risk scores for type 1 diabetes

A number of GRSs have already been developed, making use of both HLA haplotypes and non-HLA variants. For instance, the author and his team have put together a 30 single nucleotide polymorphism (SNP) GRS known as GRS1. Using SNPs as ‘tags’ for key HLA alleles has the advantage of reducing the cost of genetic screening. GRS1 proved highly effective in discriminating type 1 from type 2 diabetes. It was then combined with another GRS that included 12 non-HLA loci and applied in The Environmental Determinants of Diabetes in the Young (TEDDY) study, which is looking at type 1 diabetes progression in individuals with high HLA risk. This combined GRS only needed cheap custom SNP assays, opening the door to population-wide genetic screening. 

Meanwhile, GRS2 is another GRS tool, which includes some extra variants and tags, intended to capture further HLA gene contributions to genetic risk. It has been applied in the Type 1 Diabetes Genetics Consortium where its ability to identify cases was close to 100% accurate. The highest discrimination was found at younger ages, emphasising the importance of capturing HLA-based risk and the stronger contribution of genetics in very young children. 

The GRS models described above were derived from individuals of European ancestry and may not have the same predictive power among those of different ancestries. For example, these early GRSs have worked well in people from Europe, South Asia and Iran and in Hispanic populations, but were less discriminative in African-American populations. Accordingly, an African ancestry type 1 diabetes GRS has been developed and has now been validated in several cohorts. There is also research developing GRSs more specific to East Asian and other populations, opening up the prospect of eventually making screening for type 1 genetic risk available and relevant world wide. 

GRS for classification of diabetes

The increasing prevalence of obesity has led to more people being diagnosed with type 2 diabetes at a younger age, while levels of obesity among those who have type 1 diabetes is also on the increase. Moreover, type 1 diabetes can present at any age and, despite popular belief, onset during adulthood is actually more frequent than during childhood. All of this makes distinguishing between the two types of diabetes challenging, but using a GRS in addition to clinical features and antibody status could make for a more accurate diagnosis. 

GRS1 can distinguish between type 1 diabetes and type 2 diabetes and between type 1 diabetes and monogenic diabetes (MODY). And, when combined with clinical features and biomarkers, it provided the best discrimination of progression to severe insulin deficiency. This approach has been validated among those with adult-onset type 1 diabetes and GADA antibody-positive type 2 diabetes. Genetic risk scores can also distinguish MODY from type 1 diabetes (as mentioned above), as a high GRS score is indicative of type 1 and a lower score suggests MODY. So adding a GRS to antibody and C-peptide testing can help identify those with monogenic diabetes.  

Recent research using GRS tools is also providing new insights into early-onset diabetes, helping distinguish between early-onset type 1 diabetes and monogenic diabetes. It is also being applied in adult-onset type 1 diabetes, particularly in the identification of latent autoimmune diabetes of adults (LADA), which has genetic signals associated with both type 1 and type 2 diabetes. Here, a type 1 diabetes GRS was found to predict LADA more accurately than a type 2 diabetes GRS, showing that LADA is more similar to type 1 than type 2 diabetes.  

Looking to the future

Now that disease-modifying therapy in the form of teplizumab is available for those in the early stages of type 1 diabetes, population screening to identify those at risk has never been more important. Including a type 1 diabetes GRS as part of a newborn screen could help those who would benefit from more intense monitoring going forward. Simulation studies with GRS2 showed that over 77% of future type 1 diabetes cases could be identified in 10% of the general population, showing the value of this approach. Another study has shown that children in the TEDDY study with a specific HLA haplotype and a GRS of greater than 14.4 had an 11% risk of developing multiple autoantibodies by the age of six, while those who had a lower GRS had a corresponding lower risk of 4.1%. 

Accurate prediction models generally include multiple, time-dependent clinical biomarkers, such as autoantibodies in type 1 diabetes. Combining multiple predictive factors, such as family history, autoantibody status and GRSs may improve the prediction of type 1 diabetes through a combined risk score (CRS). A study has shown that using a CRS in nearly 8000 high-risk children aged two years significantly enhanced the prediction of type 1 diabetes, compared with the use of autoantibody status alone. Meanwhile, a 30 SNP GRS combined with age, autoantibody status and Diabetes Prevention Trial-Type 1 (DPT-1) Risk Score predicted progression to type 1 diabetes in at-risk family members, initially without diabetes, in the TrialNet Pathway to Prevention programme.  

It is the pace of research in genetics that has driven the development of GRSs. SNP genotyping is becoming ever cheaper, with GRS development costs being related mainly to blood sampling and DNA extraction. Once performed, SNP genotyping information can be interpreted across an individual’s lifetime, with no need for repeat testing. The discriminative power of most type 1 diabetes GRSs is attributed to just nine SNPs, so determining type 1 diabetes risk will become ever cheaper. And with the increasing availability of direct-to-consumer testing, individuals are starting to gain access to their own genetic information. It may be that type 1 diabetes GRSs will find their way into these tests, although there are obviously risks involved with respect to direct consumer interpretation of the results.

At present, there are limited guidelines and regulations around GRSs and these will need to be put in place to ensure accuracy and appropriate application in clinical practice. There is considerable potential for identification of high-risk individuals who could benefit from follow-up care and treatment. However, there is a need for further research into how GRSs should be implemented into diabetes care. 

In conclusion, advances in genetics have allowed the development of GRS models that can distinguish between type 1 diabetes and other diabetes phenotypes. This technology has significant potential for ‘test and treat’ approaches that could tailor care for individuals with type 1 diabetes.  

To read this paper, go to: Luckett AM, Weedon MN, Hawkes G, Leslie RD, Oram RA, Grant SFA. Utility of genetic risk scores in type 1 diabetes. Diabetologia 13 July 2023. https://doi.org/10.1007/s00125-023-05955-y

To learn more, enrol on the EASD e-Learning course ‘Phenotypic variability’.

Any opinions expressed in this article are the responsibility of the EASD e-Learning Programme Director, Dr Eleanor D Kennedy.

A new study in Diabetologia finds that people with type 2 diabetes who are socioeconomically disadvantaged are less likely to be prescribed incretin-based therapies, including GLP-1 receptor agonists, even though they may have more to gain from such treatments. The authors suggest a number of ways of moving forward so that better value for money for society can be obtained from these new cardioprotective therapies. Dr Susan Aldridge reports. 

Low socioeconomic status has a negative impact on morbidity and mortality and is also a risk factor for type 2 diabetes via mediators including obesity, alcohol, reduced physical activity, stress, low health literacy and limited access to healthy food and exercise facilities. The result is often poor diabetes management and increased risk of cardiovascular complications.

Disparities in care, including the unequal use of incretin-based therapies, are also influenced by socioeconomic status. The incretin-based therapies are glucose-lowering drugs, including glucagon-like peptide-1 receptor agonists (GLP-1 RAs), dipeptidyl peptidase-4 (DPP-4) inhibitors and the recently developed dual glucose-dependent insulinotropic polypeptide (GIP)/GLP-1 RAs, such as tirzepatide. The ADA and EASD recommend use of agents that have demonstrated cardiovascular benefit in those individuals with type 2 diabetes with cardiovascular risk. Certain GLP-1 RAs are included among these agents and emerging data suggests that tirzepatide, too, may have cardioprotective effects.  

In a new review, Apostolos Tsapas and colleagues at Aristotle University of Thessaloniki, Greece, have summarised real-world evidence on the use of incretin-based therapies in clinical practice across the socioeconomic spectrum. They look at socioeconomic disparities in the adoption of these therapies, the possible factors driving these and how these might be addressed in the future. The study consisted of a literature search focusing on incretin-based therapy use with regard to the following aspects of socioeconomic status: area-level indexes, income, education, sociodemographic variables. 

Area-level indexes and income

One study in the US showed that individuals with type 2 diabetes and cardiovascular disease with increased area-level socioeconomic deprivation were less likely to receive GLP-1 RAs compared with those living in more privileged areas. In Australia, the Index of Relative Socioeconomic Disadvantage ranks areas according to information on income, education, employment, occupation, housing and other indicators. A study assessing the relationship between use of incretin-based therapies and the Index found that those in the most disadvantaged areas were consistently less likely to receive GLP-1 RAs, while the opposite was so for DPP-4 inhibitors.

The study also found a connection between low socioeconomic status and a reduced probability of receiving SGLT-2 inhibitors, while no such relationship was observed for metformin, sulphonylureas or insulin. And, according to a UK study, when fully accounting for various confounding factors, those belonging to the most deprived group, as identified by the Index of Multiple Deprivation, had a lower likelihood of being prescribed GLP-1 RAs.  

In another study from the US, the odds of being treated with a GLP-1 RA were greater among individuals with diabetes who had a high household income compared with those whose annual income was less than $50,000. Higher income individuals were also more likely to be on GLP-1 RAs in the All of Us Research Program, a US contemporary cohort study. Finally, in Denmark, metformin-treated patients with a high household income were more likely to initiate second-line treatment with a GLP-1 RA compared with those with a low household income. 

Education and sociodemographic factors

Education is often used as an indicator of socioeconomic status because it captures the knowledge-related assets of a person and determines their future employment, occupation and income. In the US, those with a high-school education had lower odds of receiving a GLP-1 RA prescription in comparison with those who had a postgraduate degree. Similarly, in the All of Us Research Program, a higher percentage of those who went to college were on GLP-1 RAs compared with those with less than a high-school diploma. In Denmark, the probability of initiating a GLP-1 RA was higher in those with a college education compared with lower educational levels. 

In addition, multinational data from the global DISCOVER programme suggested that those who had less than 13 years of education had lower odds of receiving a GLP-1 RA than a sulphonylurea. Similar, although less marked, associations with education level and drug utilisation were also found for DPP-4 inhibitors, but not for insulin.

Meanwhile, sociodemographic factors, such as race/ethnicity and age can also influence the uptake of newer medications. For instance, retrospective cohort data from the US suggest that, compared with White individuals with type 2 diabetes, Asian, Black and Hispanic individuals were less likely to receive GLP-1 RA therapy. In the UK, compared with White individuals, Asian and Black minorities were more likely to be prescribed metformin or sulphonylureas than GLP-1 RAs or SGLT-2 inhibitors. And in Denmark, inequalities in GLP-1 RA therapy between those with a high income and those with a low income were more pronounced in immigrants than the native Danish population. Finally, in the US, older age has been associated with a lower probability of receiving GLP-1 RAs or SGLT-2 inhibitors in those with type 2 diabetes and atherosclerotic cardiovascular disease.  

Drivers of inequalities in incretin-based therapy

The authors have identified some key mechanisms that underlie the inequalities reported above. Firstly, people may simply not be able to afford incretin-based therapies and this will, of course, vary according to the country where they live. In a system that provides universal reimbursement, such as the UK and many other European countries, access to these therapies may be more equitable because the treatments are available to more of the population, regardless of their financial status. However, access to GLP-1 RAs is not consistent across European populations. For example, in the UK, they can only be prescribed to people with type 2 diabetes who also have obesity, while other countries do not impose such restrictions. 

Where insurance coverage is not universal, as in the US, people with lower socioeconomic backgrounds may face barriers in accessing these therapies, due to higher out-of-pocket costs or limited insurance coverage. In the US, for instance, many states lack expanded Medicare coverage and one study has shown that even Medicare beneficiaries are less likely to receive GLP-1 RAs than those with private insurance. A similar trend has been uncovered in Germany, with private health insurance being a strong predictor of GLP-1 RA prescription.

The disparity in use of GLP-1 RAs according to income is currently being exacerbated by the current global shortage of semaglutide and dulaglutide, which is being driven, in part, by increasing off-label use for weight loss. So a considerable proportion of limited supply is being redirected towards well-off individuals seeking weight reduction, regardless of their diabetes status. This situation disproportionately affects those of lower socioeconomic status with type 2 diabetes and cardiovascular disease, who are totally dependent upon affordable reimbursement to access these medications. 

Secondly, there is the issue of actual access to incretin-based therapies. People living in rural or disadvantaged areas may find it hard to get to an appointment with a diabetes specialist who is well-versed in these new drugs. Finding a pharmacy that stocks them may be another challenge. Primary care physicians may not be fully aware of the cardiovascular benefits of incretin-based therapies and may be reluctant to prescribe them, even to their patients with cardiovascular disease. And, of course, most people with type 2 diabetes are treated in primary care. Research has confirmed that diabetologists and endocrinologists are more likely to prescribe GLP-1 RAs, perhaps because they are more familiar with injectables. 

Thirdly, disadvantaged groups have been consistently reported to have lower health literacy than more privileged groups in society and this has been associated with poorer health outcomes and decreased uptake of therapeutic and preventive interventions. Those with low health literacy may find it difficult to understand health information and communicate effectively with healthcare professionals. 

In some countries, they may be confused about insurance coverage options and the availability and out-of-pocket costs of GLP-1 RAs. These concerns may lead them to avoid or postpone treatment. Furthermore, those with low health literacy are less likely to be well-informed about the cardiovascular benefits of GLP-1 RAs and might not feel confident in trying to access, or get a referral, to a diabetes specialist in order to get a prescription. And during a consultation with a healthcare provider, they may find it challenging to raise their concerns about cost or the need for subcutaneous administration. Language and cultural barriers may also arise. 

Finally, on the other side of the consultation, there may be conscious or unconscious bias on the part of the healthcare provider. A recent study has shown that GLP-1 RAs are less likely to be prescribed to patients from lower socioeconomic backgrounds or minority groups, who are perceived to be less compliant to treatment and medical advice, or unable to afford the cost of these drugs. 

Increasing societal benefit from incretin-based therapy

Reflecting on the above, the authors propose some strategies for increasing the uptake of incretin-based therapy. There are consistent data from clinical trials supporting the use of incretin-based therapies, particularly GLP-1 RAs, in socioeconomically disadvantaged people. They have much to gain as they are at increased risk of cardiovascular complications in type 2 diabetes and should therefore be a priority for receiving these treatments. 

Addressing barriers to accessing incretin-based therapies is vital for increasing and widening their uptake. This might mean providing transportation to appointments for those living in rural or low-income regions and increasing the availability and retention of both primary care and specialist staff in underserved areas. Meanwhile, primary care physicians’ familiarity with GLP-1 RAs should be improved through targeted education, training and resources, given that most people with type 2 diabetes are treated in primary care. 

The authors emphasise that the availability of beneficial medications at low cost is the key to increasing their value for money from a broader societal perspective. This is especially pertinent when it comes to considering the newer, high-cost incretin-based therapies such as semaglutide or tirzepatide. 

A recent study from the US concluded that, as a first-line therapy, the cost of GLP-1 RAs needs to fall by at least 70% to be cost-effective in comparison with metformin. Another Australian study says that GLP-1 RAs are not cost-effective at current prices for either primary or secondary cardiovascular prevention – although SGLT-2 inhibitors are. Similar findings emerged from an analysis of data from 67 low- and middle-income countries. Finally, a review from high-income countries has suggested that GLP-1 RAs were not cost-effective compared with DPP-4 inhibitors, sulphonylureas or thiazolidinediones. 

These economic evaluations highlight the need for country-specific strategies to improve the cost-effectiveness of GLP-1 RAs. These would include collaborative efforts between governments and pharmaceutical companies to lower drug prices, establish product listing agreements and promote generics. The authors note that the manufacturer of liraglutide and semaglutide has recently enjoyed a significant increase in market cap, reflecting its robust financial position. Thus, there is scope for negotiating lower prices for these medications without adversely impacting company profitability or scope for research and investment.

Improving health literacy in people with type 2 diabetes can help them appreciate the importance of preventing cardiovascular complications and help them actively participate in discussion with their physicians about potentially cardioprotective drugs. Through shared decision-making, patients may be more able to accept co-payments for these drugs and overcome barriers such as the need for injection with GLP-1 RAs. This collaborative approach can enhance treatment adherence and persistence, ultimately leading to better outcomes. 

Finally, it is necessary to address physician-patient barriers. This can be a complex issue, requiring a multifaceted approach, including implicit bias training and promoting diversity within healthcare professions. The former helps healthcare professionals to become aware of any unconscious prejudice that might be affecting their decision-making and contributing to disparities in prescribing incretin-based therapies. The latter creates a more inclusive healthcare environment, which may help promote better understanding of those from disadvantaged groups.  

In conclusion

The authors have highlighted disparities in the uptake of incretin-based therapies, particularly GLP-1 RAs, in people with type 2 diabetes according to socioeconomic status. An essential first step in addressing this problem is advocating for a reduction in the price of GLP-1 RAs, which would improve their value for money for society at large. This approach would complement other measures, such as increasing accessibility, improving health literacy and overcoming physician-patient barriers. 

However, much of the evidence informing this paper comes from the US and findings may not be generalisable to other settings, so additional research on uptake disparities and context-specific strategies for improvement is needed. Collaborative efforts to implement these strategies will boost the societal outcomes of the incretin-based therapies and improve health outcomes for those with type 2 diabetes.

To read this paper, go to: Karagiannis T, Bekiari E, Tsapas A. Socioeconomic aspects of incretin-based therapy. Diabetologia 12 July 2023. https://doi.org/10.1007/s00125-023-05962-z

To learn more, enrol on the EASD e-Learning course ‘GLP-1 receptor agonists’.

Any opinions expressed in this article are the responsibility of the EASD e-Learning Programme Director, Dr Eleanor D Kennedy.

One in five people with type 2 diabetes have a normal or low body weight, with reduced muscle mass relative to their fat mass. A new study reported in Diabetologia shows that they have more to gain from strength training than weight loss when it comes to glycaemic control. Dr Susan Aldridge reports. 

While the majority of people with type 2 diabetes are overweight or obese, around 20% have a healthy weight with a BMI of 25 or less. This is known as normal-weight type 2 diabetes – it is now recognised as being a particular phenotype and it’s more common among Asians and older people. Normal-weight type 2 diabetes is associated with sarcopenia or loss of muscle mass, and research has suggested that this feature mediates the elevated mortality risk seen in this phenotype compared with diabetes with overweight.

Exercise is always recommended for people with type 2 diabetes. Guidelines are similar to those for the general population: three to five days per week of moderate-to-vigorous aerobic activity to reach a minimum duration of 150 min per week, plus two to three sessions of strength training. Trials comparing the impact of aerobic versus strength exercise on HbA1c have mostly been carried out in people with type 2 diabetes who also have overweight or obesity. For instance, the Diabetes Aerobic and Resistance Exercise (DARE) study and the Health Benefits of Aerobic and Resistance Training in individuals with type 2 diabetes (HART-D) study both found a combination of aerobic and strength training to be superior to either modality on its own in lowering HbA1c.

Individuals with obesity have both increased fat mass and increased lean muscle mass, while those with normal-weight type 2 diabetes are more likely to have a different body composition – namely, sarcopenia, especially related to their fat mass, which is known as relative sarcopenia. This suggests that the most effective exercise training for those with normal-weight type 2 diabetes may not be the same as for those who have overweight and obesity. That is why Yukari Kobayashi at Stanford University and colleagues set up the Strength Training Regimen for Normal Weight Diabetics (STRONG-D) study, which looks at the effects of strength training alone, aerobic training alone and a combination of the two upon glycaemic control in normal-weight people with diabetes. They hypothesised that these individuals might respond better to strength training than aerobic training, given their phenotype. The study looked at changes in body composition and muscle strength from these interventions and how these impacted HbA1c.

Focus on strength training

The 186 participants, of whom 83% were Asian, were assigned to either strength training only (ST), aerobic training (AER) or a combination of the two (COMB), which they did for three days a week for nine months. Strength training consisted of two sets of upper-body exercises (bench press, seated row, shoulder press and pull-down), three sets of leg exercises (leg press, extension and flexion) and abdominal crunches and back extensions. They worked up, increasing weight, to eight to 12 repetitions in a set. 

The aerobic group worked on a treadmill or exercise bike to 50-80% of their metabolic equivalent of task, which is energy expenditure related to their weight. Combination was two strength-training sessions and a slightly reduced amount of aerobic training. The primary outcome was change in HbA1c and secondary outcomes were changes in body composition and muscle strength at nine months. 

Mean HbA1c was 59.6 mmol/mol at the start of the trial and 131 participants actually completed it with data for analysis. At the end, there was a significant mean decrease in HbA1c in the ST group of 0.44%, compared with non-significant decreases of 0.35% in the COMB group and 0.24% in the AER group. Therefore, strength training alone was better than aerobic training or a combination of the two in reducing HbA1c levels in normal-weight individuals and combination training had an intermediate effect. Strength training also increased appendicular (arms and legs) lean mass relative to fat mass and was an independent predictor of a reduction in HbA1c. 

This was the first clinical trial of exercise in normal-weight individuals but there was no significant difference in the AER or COMB group. Of course, the findings need to be confirmed in further, larger studies, but there is no reason not to apply a recommendation of strength training immediately to people with normal-weight diabetes, the authors say.

In the STRONG-D study, only the ST group showed a significant reduction in HbA1c, which suggests a potentially unique benefit of strength training in normal-weight individuals with type 2 diabetes. In comparison with the strength-training groups in the HART-D and DARE studies, the ST group achieved a higher absolute mean reduction in HbA1c. The participants in STRONG-D had a lower fat mass and much lower lean mass than the participants in the other two studies. Given that 80% of the insulin-mediated glucose uptake occurs in skeletal muscle or lean mass, it may be important to look at increasing lean mass for improving glycaemic control in this population. 

Focus on body composition

An important finding of STRONG-D is that body composition – increase in lean mass, loss of fat mass – was independently associated with a reduction in HbA1c, while an increase in lean mass or decrease in fat mass alone was not. This adds to the growing body of evidence that estimates of muscle mass adjusted for fat mass show stronger associations with metabolic abnormalities than lean mass alone. 

Strength training led to increased muscle mass relative to decreased fat mass and it is this that seems to be more beneficial for lowering HbA1c in individuals with normal-weight HbA1c. In contrast, individuals with overweight or obesity can lower HbA1c by lowering fat mass. At present, there isn’t enough data to support body composition as a central target for exercise training in type 2 diabetes. However, these new findings, along with previous studies that have shown a relationship between body composition and cardiovascular mortality, show the benefits of strength training in the normal-weight diabetes population. 

Furthermore, weight loss is well established as being associated with a reduction in HbA1c in people with overweight or obesity and type 2 diabetes. In the STRONG-D study, significant weight loss was found only in the AER group and there was no relationship between weight loss and reduction in HbA1c. Thus, the most effective exercise regimen for overweight and obese individuals with type 2 diabetes may not necessarily be applicable to normal-weight individuals with type 2 diabetes. 

The findings of STRONG-D make an important contribution to exercise recommendations for lean individuals with type 2 diabetes. They could also inform personalised exercise recommendations for different diabetes phenotypes. In the current clinical guidelines for people with type 2 diabetes, there are no recommended strength-training regimens, so the authors used the strength-training programme from the HART-D study. 

In conclusion, the STRONG-D study shows that strength training alone was effective and superior to aerobic training alone for reducing HbA1c levels in individuals with normal-weight type 2 diabetes. Individuals with normal-weight type 2 diabetes present with relative sarcopenia and achieving increased muscle mass relative to decreased fat mass via strength training plays an important role in glycaemic control in this population. The findings of this study could help refine physical activity recommendations in type 2 diabetes by weight status.  

To read this paper, go to: Kobayashi Y, Long J, Dan S, Johannsen NM, Talamoa R, Raghuram A, Chung S, Kent K, Basina M, Lamendola C, Haddad F, Leonard Mb, Church TS, Palaniappan L. Strength training is more effective than aerobic exercise for improving glycaemic control and body composition in people with normal-weight type 2 diabetes: a randomised controlled trial.

Diabetologia 26 July 2023. https://doi.org/10.1007/s00125-023-05958-9

To learn more, enrol on the EASD e-Learning course ‘Management of hyperglycaemia in type 2 diabetes’.

Any opinions expressed in this article are the responsibility of the EASD e-Learning Programme Director, Dr Eleanor D Kennedy.

The presence of severe mental illness can worsen cardiovascular morbidity and mortality suggests a study based on a nationwide type 2 diabetes cohort, reported in Diabetes Care. This high-risk group therefore merits special attention when it comes to prevention and management of heart disease. Dr Susan Aldridge reports.

People who have severe mental illness (SMI), including schizophrenia, bipolar disorder and major depression, have a 10 to 20 years shorter life expectancy than the general population. This premature mortality is largely due to a higher risk of physical disease, particularly cardiovascular disease (CVD), where diabetes is a major risk factor. Having SMI is also associated with a two to three-fold higher risk of type 2 diabetes – a risk that may even be on the increase.  

While we already know that depression in people with diabetes is associated with increased risk of CVD and cardiac death, other complications and all-cause mortality, fewer studies have focused on severe depression, schizophrenia and bipolar disorder. In the few existing studies, SMI is consistently associated with an increased risk of mortality among people with diabetes, but findings on associations between SMI and macrovascular and microvascular complications have been inconsistent. 

In a new study, Caroline Jackson of the University of Edinburgh and on behalf of the Scottish Diabetes Research Network Epidemiology Group, looked at a large, nationally representative diabetes cohort to determine the association between SMI and clinical outcomes. Their aim was to determine the independent effects of schizophrenia, bipolar disorder and major depression on the risk of major CVD events, CVD-specific mortality and all-cause mortality in people with type 2 diabetes. 

The study drew upon data from the Scottish Diabetes Research Network National Diabetes Dataset (SDRN-NDS), which covers 99% of people with diabetes in Scotland. It includes information on type of diabetes, sociodemographics, routine diabetes care, including retinopathy screening, and linked acute and psychiatric hospital records and death records. The study population comprised 259,875 adults diagnosed with type 2 diabetes in Scotland between 2004 and 2018 whose data could be linked with hospital and death records. 

The primary outcomes were major CVD events – myocardial infarction (MI) or stroke – in the whole cohort and also in the subgroup without a history of CVD, CVD-specific mortality and all-cause mortality. Secondary outcomes were related to other diabetes complications, consisting of retinopathy, renal replacement therapy and lower-limb amputation.

Characteristics of people with SMI and type 2 diabetes

The SDRN-NDS hospital record data revealed that 2,621 (1%) of this cohort had a diagnosis of schizophrenia, 1,211 (0.5%) had bipolar disorder and 7,903 (3%) had depression. The cohort was mainly of White ethnicity and there was a higher prevalence of type 2 diabetes among those from more deprived areas. This was even more striking among those with SMI. For instance, more than one-third of those with schizophrenia came from the most deprived fifth of areas in Scotland. 

Diabetes was diagnosed at a younger mean age in those with a history of schizophrenia (52.1 years), bipolar disorder (57.5 years) or depression (58.9 years), compared with those without a history of SMI (60.8 years). 

Furthermore, history of prior CVD, comorbidity and high cholesterol at the time of diabetes diagnosis were also more common among those with depression and bipolar disorder compared with those without SMI. This wasn’t seen for those with schizophrenia, maybe reflecting their younger age at diabetes diagnosis. Smoking, history of alcohol use disorder and overweight or obesity were also more common among those with SMI.

The impact of SMI

The researchers carried out a statistical analysis that adjusted for all the factors that could affect the findings – sociodemographics, HbA1c, hypertension, smoking and so on. This fully adjusted model helped clarify the link between the presence of SMI and health outcomes. 

During a mean of 6.9 years of follow-up, there were nearly 25,000 major CVD events. All three SMIs were associated with an increased risk of having a major CVD event. The hazard ratios were 1.07, 1.37 and 1.22, for schizophrenia, bipolar disorder and depression, respectively, for the fully adjusted model. The association was similar, whether or not the individual had a history of CVD. 

There were also 51,029 deaths occurring during a mean follow-up of 7.1 years. Deaths from CVD were higher among those with SMI than those without, with hazard ratios of 2.38 for schizophrenia, 1.70 for bipolar disorder and 1.84 for depression. This was for a model adjusted for sociodemographic factors – the hazard ratios were attenuated only slightly when the fully adjusted model was applied, so there is a persistent higher risk of CVD mortality in all groups with SMI and type 2 diabetes. Similar increased risk also applied to all-cause mortality. 

When it came to the secondary outcomes, numbers with lower limb amputation and renal replacement therapy were low – at around 0.6% and 0.2%, respectively – in those with and without a history of SMI. Referable retinopathy occurred in around 5% of both groups. Numbers for these three complications were too low to make comparisons between those with and without SMI in this study. 

Previous studies of SMI and macrovascular complications among people with diabetes report conflicting findings. For instance, a study from South Korea found a similar excess risk of heart attack and stroke among those with diabetes and schizophrenia, bipolar disorder or depression. A Taiwanese study found an association between clinical depression with diabetes and increased risk of acute coronary syndrome and stoke. And these new findings also echo those of a Danish study that found that SMI, as a composite exposure, was associated with higher CVD risk – although that was more broadly defined than in the current study. In contrast, however, there have been two studies that actually reported a lower risk of CVD events among people with schizophrenia or major depression. 

Only two previous studies have reported an association between SMI and CV-specific mortality in diabetes, with similar findings. There has also been a meta-analysis of studies examining depression of any severity and the risk and the risk of CV mortality in people with diabetes – again with similar findings. And the observed excess all-cause mortality among people with diabetes and SMI compared with no mental illness is consistent with previous literature. This new study adds to scarce data on bipolar disorder, which has been less studied in this context in comparison with schizophrenia and major depression. 

Underlying reasons

The mechanisms behind the increased CVD morbidity and mortality among those with diabetes and SMI shown by this new study are complicated and poorly understood. We already know that shared risk factors for poor physical and mental health include low socioeconomic status, adverse childhood experiences and lifestyle. In this study, the higher prevalence of smoking, overweight and obesity and comorbidities was made evident through statistical analysis that adjusted for these factors and showed an attenuation of the risk. Meanwhile, a study from Denmark of people with type 2 diabetes showed that excess mortality among those with depression was largely explained through smoking, physical activity and comorbidities. 

There is also emerging evidence that brain insulin resistance might be part of the pathophysiology of schizophrenia and bipolar disorder. This might go some way to explaining poorer diabetes outcomes in those with SMI. 

Inequalities in care for physical disease may also explain the poorer outcomes among those with SMI and diabetes. A recent study from Denmark reports lower rates of diabetes monitoring and achievement of HbA1c and cholesterol targets in those with SMI, compared with those without. However, disparities in care do not explain the findings in this new study – in Scotland, receipt of diabetes care processes is actually similar or better in those with SMI than for those without. 

It may be that this does not translate into optimal treatment of those with CVD risk factors or established CVD. Previous studies of populations with and without diabetes have uncovered suboptimal CVD risk management in those with SMI. The excess risk of CV death in people with diabetes and SMI found in this new study may reflect more severe cardiovascular events and potential differences in cardiac care, both in the acute phase and subsequently. For instance, previous work from these authors has shown that patients with SMI were less likely to receive coronary revascularisation after MI, were less likely to survive for 30 days post-MI and were more likely to have a further vascular event than those without mental illness. 

Cardiac and metabolic adverse effects of some antipsychotic medications may also play a role in poorer outcomes. The impact of antipsychotic and antidepressant drugs upon CV and mortality outcomes in people with diabetes is an underinvestigated topic. 

This new study shines a light on an area where previous studies are scarce or contradictory and addresses a number of limitations and gaps in the literature. It draws on data from a nationally representative cohort of people with diabetes with and without SMI. This large study population and long follow-up allowed the researchers to investigate individual SMIs, analyse specific CVD outcomes and obtain reliable, precise effect estimates for outcomes. The richness of the diabetes register allowed associations to be adjusted for key lifestyle factors, which hasn’t always been done in other studies. Finally, the study also adds to scarce data on associations between SMI and CVD-specific mortality in people with diabetes and on all-cause mortality in those with bipolar disorder specifically. 

In conclusion, among people with new-onset type 2 diabetes, those with a prior history of SMI have a markedly higher risk of major CVD events, CVD-specific mortality and all-cause mortality than people with no mental illness. Some of this excess risk is due to modifiable risk factors, including smoking, alcohol misuse and obesity, highlighting the need for effective lifestyle modification in people with SMI. However, there are other emerging mechanisms, including possible shared pathophysiology between SMI and diabetes, which require further investigation. 

Another future avenue of research should be the role of psychotropic medication use and receipt of optimal cardiac care in primary and secondary care settings. Meanwhile, effective prevention and management of cardiovascular risk factors is needed in this high-risk group to improve clinical outcomes. 

To read this paper, go to: Fleetwood KJ, Wild SH, Licence KAM, Mercer SW, Smith DJ, Jackson CA on behalf of the Scottish Diabetes Research Network Epidemiology Group. Diabetes Care 2023;46:1363–1371. https://doi.org/10.2337/dc23-0177

To learn more, enrol on the EASD e-Learning course ‘Cardiovascular health and diabetes’.

Any opinions expressed in this article are the responsibility of the EASD e-Learning Programme Director, Dr Eleanor D Kennedy.

A new study, based on type 1 diabetes audit data and reported in Diabetes Care, shows that failure to engage with services, hyperglycaemia and diabetic ketoacidosis peak in late adolescence. Therefore, innovative approaches to optimising glycaemia during the transition from paediatric to adult care are needed to avoid later-life complications. Dr Susan Aldridge reports. 

In young people who have type 1 diabetes, hyperglycaemia increases during adolescence and continues to do so as they transition to adult services. Data from the T1D Exchange Clinical Network confirm this and their 2016-2018 report shows that glycaemic control in those aged 15 to 18 years has deteriorated further, despite increased use of diabetes technology.  

It is generally assumed that it is the transition from paediatric to adult services at the age of 16 to 18 years, with changes in continuity and support, which leads to hyperglycaemia at this stage of life. There are also personal psychological issues and the influence of growth-related hormonal change on insulin requirements to consider. Whatever the cause, even these few years of hyperglycaemia can have long-term consequences, as seen from the follow-up of the Diabetes Control and Complications Trial.

In a new study, Naomi Holman at Imperial College London and colleagues looked at data from diabetes audits in England and Wales to investigate the age-related changes in HbA1c measurement, HbA1c levels and hospital admissions for diabetic ketoacidosis (DKA) in children and young people.

Exploring the age-related trajectory of hyperglycaemia

The National Diabetes Audit (NDA) for England and Wales collects data on people of all ages with a diabetes diagnosis from primary care records and adult specialist diabetes services. Each audit includes data for a 15-month period from 1 January to 31 March in the following year. Meanwhile, the National Paediatric Diabetes Audit (NPDA) collects data on children and young people from paediatric diabetes services in England and Wales for a 12-month time period from 1 April to 31 March in the following year. 

The researchers identified sequential cohorts of people with type 1 diabetes aged between five and 30 years from the 2017/2018, 2018/2019 and 2019/2020 data collections of the NDA and NPDA, from which they created a pooled cohort of 93,125 individuals. Then, for each year of age, the latest HbA1c measurement was identified and recorded, along with hospital admissions for DKA. 

The existence of an HbA1c record was used as a measure of clinic attendance and engagement with health services. The proportion of records lacking an HbA1c measurement was low (around 5%) up to age 16 and then it rose rapidly, peaking at 22.3% at age 21 for men and 17.3% at age 19 to 20 years for women. It then fell gradually to 17.9% for men and 13.1% for women at the age of 30. This reflects a peak in lack of engagement with services around the ages of 19 to 21, which never falls back to the level achieved in childhood.  

As has been previously observed, HbA1c rises with age, peaking in young adulthood, then falling again. At the age of nine, median HbA1c was 60 mmol/mol for males and 61 mmol/mol for females, peaking at age 19 at 72 mmol/mol for young men and 74 mmol/mol for young women. By the age of 39, it had fallen to 68 mmol/mol for men and 66 mmol/mol for women. Between the ages of 16 and 22 years, median HbA1c was consistently higher for women than men. 

When it came to glycaemic control, those aged 20 had the lowest proportion reaching a target of 58 mmol/mol or lower. And the greatest proportion of those with a very high HbA1c (more than 86 mmol/mol) was among young men aged 17 (31.4%) and young women aged 18 (35.8%). These age-related patterns persisted after adjustment for ethnicity, social deprivation and duration of diabetes. 

Finally, annual prevalence of one or more hospital admissions for DKA rose from 2% for boys and 1.4% for girls at the age of six, to a peak of 7.9% at age 19 for young men and 12.7% at age 18 for young women. After this, prevalence fell and, at age 30, it was 4.3% for men and 5.4% for women. Each year from the age of nine, the proportion of females with one or more annual hospitalisations for DKA was significantly higher than in males.

A significant drop in attendance

This analysis of over 93,000 individuals in England and Wales highlights the changes that occur when children with type 1 diabetes pass through adolescence to early adulthood. We’ve learned that almost all children have annual records for HbA1c – only 5% do not – reflecting faithful attendance at clinic. When they transition to adult services between the ages of 16 and 20, this proportion drops to around 80%. Median HbA1c, along with hospitalisation for DKA, starts to rise earlier but peaks around the time of transition. It then decreases again, but never goes back to childhood levels. 

So the HbA1c upward trend starts before, and continues after, the transition period. This hyperglycaemia pattern reflects that found in the SWEET project, which compiled data on 66,418 individuals with type 1 diabetes from 22 centres across 19 countries. It found that, from 2016 to 2018, HbA1c rose steadily from the age of six to 18 years. The scale in the rise of HbA1c from age nine to 18 years was 12 mmol/mol for males and 11 mmol/mol for females, and is similar to the 11 mmol/mol increase in HbA1c reported in another study for a combined cohort of individuals living in Germany or Austria and the US. 

The sudden increase at age 17 to 19 years in those lacking annual HbA1c records suggests a dramatic reduction in attendance at clinic at the time of changing provision from paediatric to adult. This age is also a time of increasing psychological and social pressures – leaving home for university, for instance. Adolescence is a time of profound psychological change for all young people and the more so if they have to manage a chronic condition like diabetes. The need for continual self-care is relentless and can create a considerable burden. Both very high HbA1c levels and DKA have been associated with insulin omission and psychological stress. An additional personal and clinical challenge is the effects of the surge in puberty-associated growth hormones. This demands physiologically matched adjustments of insulin, which will only add to the burden. 

While this study was not designed to identify the underlying reasons for lack of engagement, the authors suggest that it is likely due to a combination of personal and health-service factors. Whatever the reasons, this lack of engagement in late adolescence is concerning – research shows that people with type 1 diabetes for whom routine care processes have not been recorded are at increased risk of future morbidity and mortality. This is particularly so for those with the highest HbA1c levels and repeated admissions for DKA. 

This study found greater adverse changes among young women with type 1 diabetes. They were less likely to reach a glycaemic target of 58 mmol/mol and more likely to have an HbA1c of more than 86 mmol/mol. And, between the ages of 15 and 25, they were more likely to have at least one episode of DKA, compared with men of the same age. One reason for this inequality might be the higher prevalence of disordered eating among women. Psychological and societal pressures, including approaches to body image and peer pressure, may play out differently between men and women in this age group. 

The authors point out that this new study may even underestimate problematic hyperglycaemia among children and young people. The data comes from routine health records, which means that it is limited to those individuals who are actually engaging with health services. It therefore misses those who are potentially at greatest risk. The peak age for highest HbA1c coincides with the peak age for lack of records. One-third of the cohort had HbA1c of 86 mmol/mol or more at a time when another fifth had not even had their HbA1c recorded. While we can’t know for sure, it is likely that non-attenders have high HbA1c. So the scale of the increase in HbA1c in the late teens might be even greater than that recorded in this study. 

In conclusion, these findings point to the need for novel approaches to the support of children and young adults with type 1 diabetes. The aim should be to flatten the rise in HbA1c during the teenage years and reduce the peak in DKA admissions that occurs around the same time. It is clear from this study that improved, innovative, well-resourced and age-appropriate service designs oriented to deliver optimal diabetes care over the course of adolescence to young adulthood are warranted.

To read this paper, go to Holman N, Woch E, Dayan C, Warner J, Robinson H, Young B, Elliott J. National trends in hyperglycaemia and diabetic ketoacidosis in children, adolescents and young adults with type 1 diabetes: a challenge due to age of stage of development or is new thinking about service provision needed? Diabetes Care 2023; 46(7): 1404–1408. https://doi.org/10.2337/dc23-0180

To learn more about how to manage patients after the transition into adult care, enrol on the EASD e-Learning course ‘Management of type 1 diabetes in adults’.

Any opinions expressed in this article are the responsibility of the EASD e-Learning Programme Director, Dr Eleanor D Kennedy.