The prevalence and incidence of young-onset type 2 diabetes is increasing globally, especially in low- and middle-income countries, and predominantly affects non-White ethnic and racial populations. Young-onset type 2 is heterogeneous in terms of the genetic and environmental contributions to its underlying pathophysiology, which poses challenges for glycemic management. Young at-risk individuals remain underrepresented in clinical trials, including diabetes prevention studies, and there is still an insufficient evidence base to inform practice for this age group. Improvements in diabetes care delivery have not reached young people who will progress to have disabling complications at an age when they are most productive. This review summarizes recent studies on the epidemiology of young-onset type 2 diabetes and its complications. We discuss the genetic and environmental risk factors that act in concert to promote glycemic dysregulation and early onset of type 2 diabetes. We provide perspectives on diabetes prevention and management, and propose strategies to address the unique medical and psychosocial issues associated with young-onset type 2 diabetes. The Precision Medicine to Redefine Insulin Secretion and Monogenic Diabetes Randomized Controlled Trial (PRISM-RCT) is the first large-scale clinical trial designed to evaluate the effect of a structured care model that integrates biogenetic markers with communication and information technology on attaining strict metabolic targets and improving clinical outcomes in individuals with young-onset type 2 diabetes. The results of this study will inform the scientific community about the impact of multifactorial intervention and precision care in young patients, for whom the legacy effect is particularly significant.

In this article, we discuss Ye et al's recent article on the association between age at diabetes diagnosis and subsequent risk of age-related ocular diseases. The study, which utilized United Kingdom Biobank data, highlighted a strong link between early diabetes onset and major eye conditions, such as cataracts, glaucoma, age-related macular degeneration, and vision loss, independent of glycemic control and disease duration. This finding challenges the previous belief that diabetic eye disease primarily correlates with hyperglycemia. As lifestyles evolve and the age of diabetes diagnosis decreases, understanding this relationship may reveal the complex pathogenesis underlying diabetes-related complications. This editorial summarizes potential mechanisms connecting the age of diabetes onset with four types of ocular diseases, emphasizing the significance of early diagnosis.

Few studies have examined the clinical characteristics associated with changes in weight before and after diagnosis of type 2 diabetes. Using a large real-world cohort, we derived trajectories of BMI before and after diabetes diagnosis, and examined the clinical characteristics associated with these trajectories, including assessing the impact of pre-diagnosis weight change on post-diagnosis weight change.

We performed an observational cohort study using electronic medical records from individuals in the Scottish Care Information Diabetes Collaboration database. Two trajectories were calculated, based on observed BMI measurements between 3 years and 6 months before diagnosis and between 1 and 5 years after diagnosis. In the post-diagnosis trajectory, each BMI measurement was time-dependently adjusted for the effects of diabetes medications and HbA1c change.

A total of 2736 individuals were included in the study. There was a pattern of pre-diagnosis weight gain, with 1944 individuals (71%) gaining weight overall, and 875 (32%) gaining more than 0.5 kg/m2 per year. This was followed by a pattern of weight loss after diagnosis, with 1722 individuals (63%) losing weight. Younger age and greater social deprivation were associated with increased weight gain before diagnosis. Pre-diagnosis weight change was unrelated to post-diagnosis weight change, but post-diagnosis weight loss was associated with older age, female sex, higher BMI, higher HbA1c and weight gain during the peri-diagnosis period. When considering the peri-diagnostic period (defined as from 6 months before to 12 months after diagnosis), we identified 986 (36%) individuals who had a high HbA1c at diagnosis but who lost weight rapidly and were most aggressively treated at 1 year; this subgroup had the best glycaemic control at 5 years.

Average weight increases before diagnosis and decreases after diagnosis; however, there were significant differences across the population in terms of weight changes. Younger individuals gained weight pre-diagnosis, but, in older individuals, type 2 diabetes is less associated with weight gain, consistent with other drivers for diabetes aetiology in older adults. We have identified a substantial group of individuals who have a rapid deterioration in glycaemic control, together with weight loss, around the time of diagnosis, and who subsequently stabilise, suggesting that a high HbA1c at diagnosis is not inevitably associated with a poor outcome and may be driven by reversible glucose toxicity.

Over the past 20 years, the incidence and prevalence of type 2 diabetes mellitus (T2DM) in children and adolescents have increased, particularly in racial and ethnic minorities. Despite the rise in T2DM in children and adolescents, the pathophysiology and progression of disease in this population are not clearly understood. Youth-onset T2DM has a more adverse clinical course than is seen in those who develop T2DM in adulthood or those with T1DM. Furthermore, the available therapeutic options are more limited for children and adolescents with T2DM compared to adult patients, mostly due to the challenges of implementing clinical trials. A better understanding of the mechanisms underlying the de-velopment and aggressive disease phenotype of T2DM in youth is important to finding effective prevention and management strategies. This review highlights the key evidence about T2DM in children and adolescents and its current burden and challenges both in clinical care and research activities.

In high-income countries, rates of atherosclerotic complications in type 2 diabetes have declined markedly over time due to better management of traditional risk factors including lipids, blood pressure, and glycemia levels. Population-wide reductions in smoking have also helped lower atherosclerotic complications and so reduce premature mortality in type 2 diabetes. However, as excess adiposity is a stronger driver for heart failure (HF), and obesity levels have remained largely unchanged, HF risks have not declined as much and may even be rising in the increasing number of people developing type 2 diabetes at younger ages. Excess weight is also an underrecognized risk factor for chronic kidney disease (CKD). Based on evidence from a range of sources, we explain how excess adiposity must be influencing most risks well before diabetes develops, particularly in younger-onset diabetes, which is linked to greater excess adiposity. We also review potential mechanisms linking excess adiposity to HF and CKD and speculate on how some of the responsible pathways-e.g., hemodynamic, cellular overnutrition, and inflammatory-could be favorably influenced by intentional weight loss (via lifestyle or drugs). On the basis of available evidence, we suggest that the cardiorenal outcome benefits seen with sodium-glucose cotransporter 2 inhibitors may partially derive from their interference of some of these same pathways. We also note that many other complications common in diabetes (e.g., hepatic, joint disease, perhaps mental health) are also variably linked to excess adiposity, the aggregated exposure to which has now increased in type 2 diabetes. All such observations suggest a greater need to tackle excess adiposity earlier in type 2 diabetes.

Cardiometabolic disease is a major threat to global health. Precision medicine has great potential to help to reduce the burden of this common and complex disease cluster, and to enhance contemporary evidence-based medicine. Its key pillars are diagnostics; prediction (of the primary disease); prevention (of the primary disease); prognosis (prediction of complications of the primary disease); treatment (of the primary disease or its complications); and monitoring (of risk exposure, treatment response, and disease progression or remission). To contextualise precision medicine in both research and clinical settings, and to encourage the successful translation of discovery science into clinical practice, in this Series paper we outline a model (the EPPOS model) that builds on contemporary evidence-based approaches; includes precision medicine that improves disease-related predictions by stratifying a cohort into subgroups of similar characteristics, or using participants' characteristics to model treatment outcomes directly; includes personalised medicine with the use of a person's data to objectively gauge the efficacy, safety, and tolerability of therapeutics; and subjectively tailors medical decisions to the individual's preferences, circumstances, and capabilities. Precision medicine requires a well functioning system comprised of multiple stakeholders, including health-care recipients, health-care providers, scientists, health economists, funders, innovators of medicines and technologies, regulators, and policy makers. Powerful computing infrastructures supporting appropriate analysis of large-scale, well curated, and accessible health databases that contain high-quality, multidimensional, time-series data will be required; so too will prospective cohort studies in diverse populations designed to generate novel hypotheses, and clinical trials designed to test them. Here, we carefully consider these topics and describe a framework for the integration of precision medicine in cardiometabolic disease.

The rate of progression of type 2 diabetes following diagnosis varies across individuals and populations. Studies investigating the progression of type 2 diabetes in adult African populations with newly diagnosed diabetes are limited. We aimed to investigate the prevalence and predictors of short-term (one year) diabetes progression in an adult Ugandan population with new-onset type 2 diabetes (type 2 diabetes diagnosed in < 3 months) initiated on oral hypoglycaemic agents (OHA).

Two hundred and seven adult participants with type 2 diabetes diagnosed within the previous three months were followed up for 12 months. We investigated the association of specific demographic, clinical, and metabolic characteristics, and short-term diabetes progression (defined as glycated haemoglobin or HbA1c ≥ 8% on ≥ 2 OHA and/or treatment intensification).

One hundred sixteen participants (56%) completed the follow-up period. Sixty-four participants (55.2%, 95% CI 45.7-64.4) showed evidence of diabetes progression during the 12-month period of follow-up. An HbA1c ≥ 8% on ≥ 2 OHA and treatment intensification were noted in 44.8% and 29.3% of the participants, respectively. On multivariate analysis, only the female gender (AOR 3.2, 95% CI 1.1-9.2, p = 0.03) was noted to be independently associated with short-term diabetes progression.

Short-term diabetes progression was relatively common in this study population and was independently associated with the female gender. Early intensified diabetes therapy in adult Ugandan female patients with new-onset type 2 diabetes should be emphasised to avert rapid short-term diabetes progression.

Type 2 diabetes diagnosed in childhood or early adulthood is termed early-onset type 2 diabetes. Cases of early-onset type 2 diabetes are increasing rapidly globally, alongside rising obesity. Compared with a diagnosis later in life, an earlier-onset diagnosis carries an unexplained excess risk of microvascular complications, adverse cardiovascular outcomes, and earlier death. Women with early-onset type 2 diabetes also have a higher risk of adverse pregnancy outcomes. The high burden of complications renders individuals with early-onset type 2 diabetes at future risk of multimorbidity and interventions to reverse these concerning trends should be a priority. Within the early-onset cohort, disease pathophysiology and interventions have been better studied in paediatric-onset (<19 years) type 2 diabetes compared to adults; however, young adults aged 19-39 years (a larger number proportionally) are not well characterised and are also invisible in the current evidence base supporting management, which is derived from trials in later-onset type 2 diabetes. Young adults with type 2 diabetes face challenges in self-management that older individuals are less likely to experience (being in education or of working age, higher diabetes distress, and possible obesity-related stigma and diabetes-related stigma). There is a major research gap as to the optimal strategies to deploy in managing type 2 diabetes in adolescents and young adults, given that current models of care appear to not work as well in this age group. In the face of manifold risk factors (obesity, female sex, social deprivation, non-White European ethnicity, and genetic risk factors) prevention strategies with tailored lifestyle interventions, where needed, are likely to have greater success, but more evidence is needed. In this Review, we draw on evidence from both adolescents and young adults to provide a contemporary update on the current insights and emerging trends in early-onset type 2 diabetes.

Precision medicine aims to treat an individual based on their clinical characteristics. A differential drug response, critical to using these features for therapy selection, has never been examined directly in type 2 diabetes. In this study, we tested two hypotheses: (1) individuals with body mass index (BMI) > 30 kg/m², compared to BMI ≤ 30 kg/m², have greater glucose lowering with thiazolidinediones than with DPP4 inhibitors, and (2) individuals with estimated glomerular filtration rate (eGFR) 60-90 ml/min/1.73 m², compared to eGFR >90 ml/min/1.73 m², have greater glucose lowering with DPP4 inhibitors than with SGLT2 inhibitors. The primary endpoint for both hypotheses was the achieved HbA1c difference between strata for the two drugs. In total, 525 people with type 2 diabetes participated in this UK-based randomized, double-blind, three-way crossover trial of 16 weeks of treatment with each of sitagliptin 100 mg once daily, canagliflozin 100 mg once daily and pioglitazone 30 mg once daily added to metformin alone or metformin plus sulfonylurea. Overall, the achieved HbA1c was similar for the three drugs: pioglitazone 59.6 mmol/mol, sitagliptin 60.0 mmol/mol and canagliflozin 60.6 mmol/mol (P = 0.2). Participants with BMI > 30 kg/m², compared to BMI ≤ 30 kg/m², had a 2.88 mmol/mol (95% confidence interval (CI): 0.98, 4.79) lower HbA1c on pioglitazone than on sitagliptin (n = 356, P = 0.003). Participants with eGFR 60-90 ml/min/1.73 m², compared to eGFR >90 ml/min/1.73 m², had a 2.90 mmol/mol (95% CI: 1.19, 4.61) lower HbA1c on sitagliptin than on canagliflozin (n = 342, P = 0.001). There were 2,201 adverse events reported, and 447/525 (85%) randomized participants experienced an adverse event on at least one of the study drugs. In this precision medicine trial in type 2 diabetes, our findings support the use of simple, routinely available clinical measures to identify the drug class most likely to deliver the greatest glycemic reduction for a given patient. (ClinicalTrials.gov registration: NCT02653209 ; ISRCTN registration: 12039221 .).

In the treatment of type 2 diabetes, GLP-1 receptor agonists lower blood glucose concentrations, body weight, and have cardiovascular benefits. The efficacy and side effects of GLP-1 receptor agonists vary between people. Human pharmacogenomic studies of this inter-individual variation can provide both biological insight into drug action and provide biomarkers to inform clinical decision making. We therefore aimed to identify genetic variants associated with glycaemic response to GLP-1 receptor agonist treatment.

In this genome-wide analysis we included adults (aged ≥18 years) with type 2 diabetes treated with GLP-1 receptor agonists with baseline HbA_1c of 7% or more (53 mmol/mol) from four prospective observational cohorts (DIRECT, PRIBA, PROMASTER, and GoDARTS) and two randomised clinical trials (HARMONY phase 3 and AWARD). The primary endpoint was HbA_1c reduction at 6 months after starting GLP-1 receptor agonists. We evaluated variants in GLP1R, then did a genome-wide association study and gene-based burden tests.

4571 adults were included in our analysis, of these, 3339 (73%) were White European, 449 (10%) Hispanic, 312 (7%) American Indian or Alaskan Native, and 471 (10%) were other, and around 2140 (47%) of the participants were women. Variation in HbA_1c reduction with GLP-1 receptor agonists treatment was associated with rs6923761G→A (Gly168Ser) in the GLP1R (0·08% [95% CI 0·04-0·12] or 0·9 mmol/mol lower reduction in HbA_1c per serine, p=6·0 × 10^-5) and low frequency variants in ARRB1 (optimal sequence kernel association test p=6·7 × 10^-8), largely driven by rs140226575G→A (Thr370Met; 0·25% [SE 0·06] or 2·7 mmol/mol  [SE 0·7] greater HbA_1c reduction per methionine, p=5·2 × 10^-6). A similar effect size for the ARRB1 Thr370Met was seen in Hispanic and American Indian or Alaska Native populations who have a higher frequency of this variant (6-11%) than in White European populations. Combining these two genes identified 4% of the population who had a 30% greater reduction in HbA_1c than the 9% of the population with the worse response.

This genome-wide pharmacogenomic study of GLP-1 receptor agonists provides novel biological and clinical insights. Clinically, when genotype is routinely available at the point of prescribing, individuals with ARRB1 variants might benefit from earlier initiation of GLP-1 receptor agonists.

Innovative Medicines Initiative and the Wellcome Trust.

Tayside is a region in the East of Scotland and forms one of nine local government regions in the country. It is home to approximately 416,000 individuals who fall under the National Health Service (NHS) Tayside health board, which provides health care services to the population. In Tayside, Scotland, a comprehensive informatics network for diabetes care and research has been established for over 25 years. This has expanded more recently to a comprehensive Scotland-wide clinical care system, Scottish Care Information - Diabetes (SCI-Diabetes). This has enabled improved diabetes screening and integrated management of diabetic retinopathy, neuropathy, nephropathy, cardiovascular health, and other comorbidities. The regional health informatics network links all of these specialized services with comprehensive laboratory testing, prescribing records, general practitioner records, and hospitalization records. Not only do patients benefit from the seamless interconnectedness of these data, but also the Tayside bioresource has enabled considerable research opportunities and the creation of biobanks. In this article we describe how health informatics has been used to improve care of people with diabetes in Tayside and Scotland and, through anonymized data linkage, our understanding of the phenotypic and genotypic etiology of diabetes and associated complications and comorbidities.

Type 2 diabetes (T2D) is a progressive disease often associated with comorbidities that complicate the management of T2D and affect the achievement of treatment targets. However, adherence to guidelines and individualized treatments can potentially improve treatment outcomes. This study assessed the association between different glucose lowering and lipid lowering medication lines and the achievement of treatment targets with different comorbidities among a T2D cohort in North Karelia, Finland (2011-12 to 2015-16).

The data on all diagnosed T2D patients (n = 10,190) in North Karelia were collated retrospectively from regional electronic health records (EHRs). Analyses were performed considering the age, sex, and comorbidities such as cardiovascular diseases (CVD) and any mental disorders (AMD). We analyzed the trends in using glucose lowering and lipid lowering medications and the effect of changes in medication on the achievement of treatment targets among different patient groups.

Metformin was the most common treatment in all patient groups. The use of only metformin declined and the use of metformin and/or other non-insulin medications increased during the follow-up. A Combination of insulin and non-insulin medication was mostly used by T2D patients with both cardiovascular diseases and mental disorders (T2D + CVD + AMD), and the use of insulin increased among this group in follow-up. Achievement of the glucose treatment target deteriorated even after the intensification of medication among all patient groups during the follow-up. A considerably higher number of patients with T2D + AMD and T2D + CVD + AMD did not use lipid lowering medication when compared to the T2D + CVD patients both at baseline and follow-up. However, the achievement of the LDL treatment target improved during the follow-up.

Achievement of the glucose target deteriorated even after the intensification of treatment, and especially among patients with multiple diseases. Many T2D patients with AMD and CVD remained without lipid lowering medication, which needs further attention.

Adult-onset autoimmune (AOA) diabetes pathophysiology starts with immune changes, followed by dysglycaemia and overt disease. AOA diabetes can occur as classic type 1 diabetes when associated with severe loss of insulin secretion. More frequently, it is diagnosed as latent autoimmune diabetes in adults, a slowly progressing form with late onset, a long period not requiring insulin, and it is often misdiagnosed as type 2 diabetes. As its clinical presentation varies remarkably and immune markers often lack specificity, it is challenging to classify each case ad hoc, especially when insulin treatment is not required at diagnosis. Proper care of AOA diabetes aims to prevent complications and to improve quality of life and life expectancy. To achieve these goals, attention should be paid to lifestyle factors, with the aid of pharmacological therapies properly tailored to each individual clinical setting. Given the heterogeneity of the disease, choosing the right therapy for AOA diabetes is challenging. Most of the trials testing disease-modifying therapies for autoimmune diabetes are conducted in people with childhood onset, whereas non-insulin diabetes therapies have mostly been studied in the larger population with type 2 diabetes. More randomized controlled trials of therapeutic agents in AOA diabetes are needed.

It is well established that low birthweight is associated with subsequent risk of type 2 diabetes (T2DM). The aim of our study was to use a large birth cohort linked to a national diabetes registry to investigate how birthweight impacts the phenotype at diagnosis of T2DM and the subsequent rate of glycaemic deterioration.

We linked the Walker Birth Cohort (48,000 births, 1952-1966, Tayside, Scotland) to the national diabetes registry in Scotland (SCI-Diabetes). Birthweight was adjusted for gestational age. Simple linear regression was performed to assess the impact of the adjusted birthweight on the diabetes phenotype at diagnosis. This was then built up into a multiple regression model to allow for the adjustment of confounding variables. A cox proportional hazards model was then used to evaluate the impact of birthweight on diabetes progression.

Lower birthweights were associated with a 293 day younger age of diagnosis of T2DM per 1 kg reduction in birthweight, p = 0.005; and a 1.29 kg/m² lower BMI at diagnosis per 1 kg reduction in birthweight, p < 0.001. There was no significant association of birthweight on diabetes progression.

For the first time, we have shown that a lower birthweight is associated with younger onset of T2DM, with those with lower birthweight also being slimmer at diagnosis. These results suggest that lower birthweight impacts on T2DM phenotype via reduced beta-cell function rather than insulin resistance.

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

The UK Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS-OM) developed using 30-year (1977-2007) data from the UKPDS is widely used for health outcomes' projections and economic evaluations of therapies for patients with type 2 diabetes (T2D). Nevertheless, its reliability for contemporary UK T2D populations is unclear. We assessed the performance of version 2 of the model (UKPDS-OM2) using data from A Study of Cardiovascular Events in Diabetes (ASCEND), which followed participants with diabetes in the UK between 2005 and 2017.

The UKPDS-OM2 was used to predict the occurrence of myocardial infarction (MI), other ischemic heart disease, stroke, cardiovascular (CV) death, and other death among the 14 569 participants with T2D in ASCEND, all without previous CV disease at study entry. Calibration (comparison of predicted and observed year-on-year cumulative incidence over 10 years) and discrimination (c-statistics) of the model were assessed for each endpoint. The percentage error in event rates at year 7 (mean duration of follow up) was used to quantify model bias.

The UKPDS-OM2 substantially overpredicted MI, stroke, CV death, and other death over the 10-year follow-up period (by 149%, 42%, 269%, and 52%, respectively, at year 7). Discrimination of the model for MI and other ischemic heart disease (c-statistics 0.58 and 0.60, respectively) was poorer than that for other outcomes (c-statistics ranging from 0.66 to 0.72).

The UKPDS-OM2 substantially overpredicted risks of key CV outcomes and death in people with T2D in ASCEND. Appropriate adjustments or a new model may be required for assessments of long-term effects of treatments in contemporary T2D cohorts.

Knowing the collective clinical factors that determine patient response to glucose-lowering medication would be beneficial in the treatment of type 2 diabetes. We carried out a retrospective cohort study to explore the combination of clinical factors involved in its therapeutic efficacy.

The results of cohort studies retrieved using the CoDiC® database across Japan from January 2005 to July 2018 were analyzed based on criterion that using insulin therapy indicates severe type 2 diabetes.

A logistic regression analysis showed that age at diagnosis, disease duration, hemoglobin A1c (HbA1c) and serum C-peptide reactivity (CPR) at medication commencement were associated with the probability of insulin treatment. Receiver operating characteristic curve showed that these clinical factors predicted insulin treatment positivity with an area under the curve of >0.600. The area under the curve increased to 0.674 and 0.720 for the disease duration-to-age at diagnosis ratio and HbA1c-to-CPR ratio, respectively. Furthermore, area under the curve increased to 0.727 and 0.750 in the indices (duration-to-age ratio at diagnosis × 43 + HbA1c) and (duration-to-age ration at diagnosis × 21 + HbA1c-to-CPR ratio), respectively. After stratification to three groups according to the indices, monthly HbA1c levels during 6 months of treatment were higher in the upper one-third than in the lower one-third of patients, and many patients did not achieve the target HbA1c level (53 mmol/mol) in the upper one-third, although greater than fourfold more patients were administered insulin in the upper one-third.

The combination of disease duration-to-age at diagnosis and HbA1c-to-CPR ratios is a collective risk factor that predicts response to the medications.

Studies investigating neighborhood walkability and physical activity in people with type 2 diabetes (T2D) mainly used self-report measures, and only few studies assessed the association with glycemic control. This study assessed the associations between objectively measured (i.e. GIS based) and subjectively measured (i.e. questionnaire-based) neighborhood walkability and changes in glycemic markers in people with T2D, and whether this association was mediated by device-measured physical activity (PA), in the Diabetes Care System Cohort (n = 1230). Neither objective or subjectively measured walkability was associated with glycemic control. In mediation analyses we observed no overall mediation by PA.

We investigated the processes underlying glycemic deterioration in type 2 diabetes (T2D).

A total of 732 recently diagnosed patients with T2D from the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) study were extensively phenotyped over 3 years, including measures of insulin sensitivity (OGIS), β-cell glucose sensitivity (GS), and insulin clearance (CLIm) from mixed meal tests, liver enzymes, lipid profiles, and baseline regional fat from MRI. The associations between the longitudinal metabolic patterns and HbA_1c deterioration, adjusted for changes in BMI and in diabetes medications, were assessed via stepwise multivariable linear and logistic regression.

Faster HbA_1c progression was independently associated with faster deterioration of OGIS and GS and increasing CLIm; visceral or liver fat, HDL-cholesterol, and triglycerides had further independent, though weaker, roles (R ² = 0.38). A subgroup of patients with a markedly higher progression rate (fast progressors) was clearly distinguishable considering these variables only (discrimination capacity from area under the receiver operating characteristic = 0.94). The proportion of fast progressors was reduced from 56% to 8-10% in subgroups in which only one trait among OGIS, GS, and CLIm was relatively stable (odds ratios 0.07-0.09). T2D polygenic risk score and baseline pancreatic fat, glucagon-like peptide 1, glucagon, diet, and physical activity did not show an independent role.

Deteriorating insulin sensitivity and β-cell function, increasing insulin clearance, high visceral or liver fat, and worsening of the lipid profile are the crucial factors mediating glycemic deterioration of patients with T2D in the initial phase of the disease. Stabilization of a single trait among insulin sensitivity, β-cell function, and insulin clearance may be relevant to prevent progression.

We present the adaptation for Spain of the updated European Cardiovascular Prevention Guidelines. In this update, greater stress is laid on the population approach, and especially on the promotion of physical activity and healthy diet through dietary, leisure and active transport policies in Spain. To estimate vascular risk, note should be made of the importance of recalibrating the tables used, by adapting them to population shifts in the prevalence of risk factors and incidence of vascular diseases, with particular attention to the role of chronic kidney disease. At an individual level, the key element is personalised support for changes in behaviour, adherence to medication in high-risk individuals and patients with vascular disease, the fostering of physical activity, and cessation of smoking habit. Furthermore, recent clinical trials with PCSK9 inhibitors are reviewed, along with the need to simplify pharmacological treatment of arterial hypertension to improve control and adherence to treatment. In the case of patients with type 2 diabetes mellitus and vascular disease or high vascular disease risk, when lifestyle changes and metformin are inadequate, the use of drugs with proven vascular benefit should be prioritised. Lastly, guidelines on peripheral arterial disease and other specific diseases are included, as is a recommendation against prescribing antiaggregants in primary prevention.

To analyse quantitatively the association between the durability of glycaemic control and body weight changes during treatment.

This study adhered to an appropriate methodology according to Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines. Studies with follow-ups >12 months, and final and intermediate assessments of haemoglobin A1c (HbA1c) and body weight were included. Four outcomes assessing therapeutic durability were extracted and synthesized using Stata statistical software, including changes in HbA1c, goal-achievement rate, failure rate and coefficient of failure (CoF).

After 8.9 months of treatment, HbA1c levels declined from 8.03% [95% confidence interval (CI), 7.91-8.15; I² = 99.2%] to 7.15% (95% CI, 7.02-7.27; I² = 99.4%) and then gradually increased up to 7.72% (95% CI, 7.50-7.94; I² = 99.0%) 5 years later. The goal-achievement rate decreased from 54.8% (after 1 year of treatment) to 19.4% 5 years later. The CoF was 0.123 ± 0.022%/year (P < .001). After stratification, the CoFs were 0.224 ± 0.025%/year (P < .001) for weight gain, 0.137 ± 0.034%/year (P < .001) for neutral weight and -0.024 ± 0.032%/year (P = .450) for weight loss. After stratification by treatment approaches, the CoFs were 0.45%/year for insulin, 0.43%/year for sulphonylurea, 0.34%/year for thiazolidinediones, 0.29%/year for metformin, 0.16% for glucagon-like polypeptide-1 receptor agonists, 0.12% for surgery, -0.03% for sodium-glucose cotransporter-2 inhibitors and -0.21% for dipeptidyl peptidase-IV inhibitors.

Modest weight loss with a goal of 2-3% of body weight should be recommended to improve therapeutic durability and prevent beta-cell deterioration.

A new special reimbursement scheme (SRS) for non-insulin medications used for treatment of hyperglycaemia in type 2 diabetes (T2D) was implemented in Finland on January 1, 2017. The new SRS affected all community-dwelling Finnish T2D patients as all community-dwelling residents are eligible for reimbursement for prescription medications. The aim of the study was to evaluate the impact of this co-payment increase on glycaemic control among Finnish T2D patients.

Data on glycaemic control were collected with HbA1c measures from electronic health records from primary health care and specialized care in the North Karelia region, Finland, from patients with a confirmed T2D diagnosis in 2012 who were alive on January 1, 2017 (n = 8436). Average HbA1c levels were measured monthly 36 months before and 33 months after the policy change. Consumption of diabetes medications was measured with defined daily doses (DDDs) based on reimbursed medication purchases. Interrupted time series design analysed with segmented regression model was applied to examine the effect of the policy change on average HbA1c levels.

Eight thousand one hundred forty-three T2D patients had at least one HbA1c measurement within 01/2014-9/2019. Mean age of the patients was 68.1 (SD 11.3) years and 53.0% were women. Average time since T2D diagnosis was 11.5 (SD 6.1) years. An estimated increase of 0.81 (95% confidence interval, CI, 0.04-1.58) mmol/mol in average HbA1c levels was detected at the time of the policy change. In subgroup analyses, strongest effects were detected among patients who used only other diabetes medications than insulin or metformin in 2016 (3.56 mmol/mol, 95% CI 2.50-4.62). Meanwhile, yearly consumption of diabetes medications decreased slightly from 618.9 (SD 487.8) DDDs/patient in 2016 to 602.9 (SD 475.6) DDDs/patient in 2017 (p = 0.048).

Simultaneously with the increase of the co-payment level, the average HbA1c level increased among T2D patients from the North Karelia region, Finland. This may be explained by the decreased consumption of diabetes medications between 2016 and 2017. Special attention should be allocated to glycaemic control of patients utilizing only other antidiabetic medications than metformin or insulin.

We present the adaptation for Spain of the updated European Cardiovascular Prevention Guidelines. In this update, greater stress is laid on the population approach, and especially on the promotion of physical activity and healthy diet through dietary, leisure and active transport policies in Spain. To estimate vascular risk, note should be made of the importance of recalibrating the tables used, by adapting them to population shifts in the prevalence of risk factors and incidence of vascular diseases, with particular attention to the role of chronic kidney disease. At an individual level, the key element is personalised support for changes in behaviour, adherence to medication in high-risk individuals and patients with vascular disease, the fostering of physical activity, and cessation of smoking habit. Furthermore, recent clinical trials with PCSK9 inhibitors are reviewed, along with the need to simplify pharmacological treatment of arterial hypertension to improve control and adherence to treatment. In the case of patients with type 2 diabetes mellitus and vascular disease or high vascular disease risk, when lifestyle changes and metformin are inadequate, the use of drugs with proven vascular benefit should be prioritised. Lastly, guidelines on peripheral arterial disease and other specific diseases are included, as is a recommendation against prescribing antiaggregants in primary prevention.

We present the adaptation for Spain of the updated European Cardiovascular Prevention Guidelines. In this update, greater stress is laid on the population approach, and especially on the promotion of physical activity and healthy diet through dietary, leisure and active transport policies in Spain. To estimate vascular risk, note should be made of the importance of recalibrating the tables used, by adapting them to population shifts in the prevalence of risk factors and incidence of vascular diseases, with particular attention to the role of chronic kidney disease. At an individual level, the key element is personalised support for changes in behaviour, adherence to medication in high-risk individuals and patients with vascular disease, the fostering of physical activity, and cessation of smoking habit. Furthermore, recent clinical trials with PCSK9 inhibitors are reviewed, along with the need to simplify pharmacological treatment of arterial hypertension to improve control and adherence to treatment. In the case of patients with type 2 diabetes mellitus and vascular disease or high vascular disease risk, when lifestyle changes and metformin are inadequate, the use of drugs with proven vascular benefit should be prioritised. Lastly, guidelines on peripheral arterial disease and other specific diseases are included, as is a recommendation against prescribing antiaggregants in primary prevention.

Lifetime glycemic exposure and its relationship with age at diagnosis in type 2 diabetes (T2D) are unknown. Pharmacologic glycemic management strategies for young-onset T2D (age at diagnosis <40 years) are poorly defined. We studied how age at diagnosis affects glycemic exposure, glycemic deterioration, and responses to oral glucose-lowering drugs (OGLDs).

In a population-based cohort (n = 328,199; 47.2% women; mean age 34.6 and 59.3 years, respectively, for young-onset and usual-onset [age at diagnosis ≥40 years] T2D; 2002-2016), we used linear mixed-effects models to estimate the association between age at diagnosis and A1C slope (glycemic deterioration) and tested for an interaction between age at diagnosis and responses to various combinations of OGLDs during the first decade after diagnosis. In a register-based cohort (n = 21,016; 47.1% women; mean age 43.8 and 58.9 years, respectively, for young- and usual-onset T2D; 2000-2015), we estimated the glycemic exposure from diagnosis until age 75 years. People with young-onset T2D had a higher mean A1C (8.0% [standard deviation 0.15%]) versus usual-onset T2D (7.6% [0.03%]) throughout the life span (p < 0.001). The cumulative glycemic exposure was >3 times higher for young-onset versus usual-onset T2D (41.0 [95% confidence interval 39.1-42.8] versus 12.1 [11.8-12.3] A1C-years [1 A1C-year = 1 year with 8% average A1C]). Younger age at diagnosis was associated with faster glycemic deterioration (A1C slope over time +0.08% [0.078-0.084%] per year for age at diagnosis 20 years versus +0.02% [0.016-0.018%] per year for age at diagnosis 50 years; p-value for interaction <0.001). Age at diagnosis ≥60 years was associated with glycemic improvement (-0.004% [-0.005 to -0.004%] and -0.02% [-0.027 to -0.0244%] per year for ages 60 and 70 years at diagnosis, respectively; p-value for interaction <0.001). Responses to OGLDs differed by age at diagnosis (p-value for interaction <0.001). Those with young-onset T2D had smaller A1C decrements for metformin-based combinations versus usual-onset T2D (metformin alone: young-onset -0.15% [-0.105 to -0.080%], usual-onset -0.17% [-0.179 to -0.169%]; metformin, sulfonylurea, and dipeptidyl peptidase-4 inhibitor: young-onset -0.44% [-0.476 to -0.405%], usual-onset -0.48% [-0.498 to -0.459%]; metformin and α-glucosidase inhibitor: young-onset -0.40% [-0.660 to -0.144%], usual-onset -0.25% [-0.420 to -0.077%]) but greater responses to other combinations containing sulfonylureas (sulfonylurea alone: young-onset -0.08% [-0.099 to -0.065%], usual-onset +0.06% [+0.059 to +0.072%]; sulfonylurea and α-glucosidase inhibitor: young-onset -0.10% [-0.266 to 0.064%], usual-onset: 0.25% [+0.196% to +0.312%]). Limitations include possible residual confounding and unknown generalizability outside Hong Kong.

In this study, we observed excess glycemic exposure and rapid glycemic deterioration in young-onset T2D, indicating that improved treatment strategies are needed in this setting. The differential responses to OGLDs between young- and usual-onset T2D suggest that better disease classification could guide personalized therapy.

To estimate time in suboptimal glycemic control among patients with incident type 2 diabetes (T2D) over 10 years.

We calculated percent of time in suboptimal glycemic control using three A1C thresholds (8%, 7.5%, 7%) following T2D diagnosis. Stratified analyses were conducted based on age and A1C levels at T2D diagnosis.

We identified 28,315 patients with incident T2D. Percent of time in suboptimal glycemic control increased with T2D duration. Mean percent time in suboptimal A1C control in the first 2 years following diagnosis was 30%, 34% and 40% for the 8%, 7.5%, and 7% thresholds, respectively. In the 6-10 years following T2D diagnosis, the percent time in suboptimal A1C control increased to 39%, 48% and 61%, for the 8%, 7.5%, and 7% thresholds, respectively. Time in suboptimal glycemic control was longer among younger patients aged 20-44 versus ≥65 years and those with higher A1C (>8%) versus lower A1C (<7%) at diagnosis.

Over 10 years following diagnosis, T2D patients spent one-third to over one-half of their time in suboptimal glycemic control. Reducing time spent above desired A1C targets could lower risk of microvascular and macrovascular complications.

New-onset diabetes is the most common sequela of acute pancreatitis (AP). Yet, prospective changes in glycaemia over time have never been investigated comprehensively in this study population. The primary aim was to determine the cumulative incidence of new-onset prediabetes and new-onset diabetes after AP over 24 months of follow-up in a prospective cohort study. The secondary aim was to identify trajectories of glycaemia during follow-up and their predictors at the time of hospitalisation.

Patients with a prospective diagnosis of AP and no diabetes based on the American Diabetes Association criteria were followed up every 6 months up to 24 months after hospital discharge. Incidence of new-onset prediabetes/diabetes over each follow-up period was calculated. Group-based trajectory modelling was used to identify common changes in glycaemia. Multinomial regression analyses were conducted to investigate the associations between a wide array of routinely available demographic, anthropometric, laboratory, imaging, and clinical factors and membership in the trajectory groups.

A total of 152 patients without diabetes were followed up. The cumulative incidence of new-onset prediabetes and diabetes was 20% at 6 months after hospitalisation and 43% over 24 months of follow-up (p trend < 0.001). Three discrete trajectories of glycaemia were identified: normal-stable glycaemia (32%), moderate-stable glycaemia (60%), and high-increasing glycaemia (8%). Waist circumference was a significant predictor of moderate-stable glycaemia. None of the studied predictors were significantly associated with high-increasing glycaemia.

This first prospective cohort study of changes in glycaemia (determined at structured time points in unselected AP patients) showed that at least one out of five patients develops new-onset prediabetes or diabetes at 6 months of follow-up and more than four out of ten-in the first 2 years. Changes in glycaemia after AP follow three discrete trajectories. This may inform prevention or early detection of critical changes in blood glucose metabolism following an attack of AP and, hence, reduce the burden of new-onset diabetes after acute pancreatitis.

Progression from pre-diabetes to type 2 diabetes (T2D) and from T2D to insulin requirement proceeds at very heterogenous rates among patient populations, and the risk of developing different types of secondary complications is also different between patients. The diagnosis of pre-diabetes and T2D solely based on blood glucose measurements cannot capture this heterogeneity, thereby preventing proposition of therapeutic strategies adapted to individual needs and pathogenetic mechanisms. There is, thus, a need to identify novel means to stratify patient populations based on a molecular knowledge of the diverse underlying causes of the disease. Such knowledge would form the basis for a precision medicine approach to preventing and treating T2D according to the need of identified patient subgroups as well as allowing better follow up of pharmacological treatment.

Here, we review a systems biology approach that aims at identifying novel biomarkers for T2D susceptibility and identifying novel beta-cell and insulin target tissue genes that link the selected plasma biomarkers with insulin secretion and insulin action. This work was performed as part of two Innovative Medicine Initiative projects. The focus of the review will be on the use of preclinical models to find biomarker candidates for T2D prediction and novel regulators of beta-cell function. We will demonstrate that the study of mice with different genetic architecture and widely different adaptation to metabolic stress can be a powerful approach to identify biomarkers of T2D susceptibility in humans or for the identification of so far unrecognized genes controlling beta-cell function.

The examples developed in this review will highlight the power of the systems biology approach, in particular as it allowed the discovery of dihydroceramide as a T2D biomarker candidate in mice and humans and the identification and characterization of novel regulators of beta-cell function.

Type 2 diabetes mellitus (T2DM) is a condition defined by hyperglycaemia, but also often presents with dyslipidaemia and suppressed HDL cholesterol. Mendelian randomization studies have suggested a causal link between low HDL cholesterol and T2DM. However, influences of gender, polymorphisms and lifestyle, all known to influence HDL cholesterol, have not been fully explored in a prospective cohort.

In 2001-2002, a random sample of 1514 males (18-87 years old) and 1528 females (18-89 years old) were recruited in the ATTICA study. The 10-year follow-up (2011-2012) included 1485 participants. Lipids and lipoproteins levels, glucose and insulin levels were measured together with apolipoprotein A1 (apoA1) 75 G/A genotype, which is known to influence HDL-cholesterol. In total, 12.9% of the study sample developed T2DM within the 10-year follow-up period. In multivariable models, for each mg/dL increase in apoA1 levels in males, 10-year T2DM risk decreased 1.02%; while every unit increase in apoB/LDL-cholesterol ratio increased risk 4-fold. Finally, for every unit increase in triglycerides/apoA1 ratio, the risk increased 85%. HOMA-IR independently predicted T2DM 10-year incidence only for carriers of GG polymorphism (all, p < 0.05), but not in carriers of the GA polymorphism (all, p > 0.05).

ApoA1 was associated with decreased T2DM risk and TG/ApoA1 and apoB/LDL were associated with increased risk of T2DM, only in males. ApoA1 polymorphism, which is associated with lower HDL cholesterol, influenced the predictive effects of HOMA-IR on T2DM incidence, which appeared to be moderated by physical activity, suggesting potential scope for more targeted preventative strategies.

Risk of cardiovascular disease (CVD) and mortality for patients with versus without type 2 diabetes mellitus (T2DM) appears to vary by the age at T2DM diagnosis, but few population studies have analyzed mortality and CVD outcomes associations across the full age range.

With use of the Swedish National Diabetes Registry, everyone with T2DM registered in the Registry between 1998 and 2012 was included. Controls were randomly selected from the general population matched for age, sex, and county. The analysis cohort comprised 318 083 patients with T2DM matched with just <1.6 million controls. Participants were followed from 1998 to 2013 for CVD outcomes and to 2014 for mortality. Outcomes of interest were total mortality, cardiovascular mortality, noncardiovascular mortality, coronary heart disease, acute myocardial infarction, stroke, heart failure, and atrial fibrillation. We also examined life expectancy by age at diagnosis. We conducted the primary analyses using Cox proportional hazards models in those with no previous CVD and repeated the work in the entire cohort.

Over a median follow-up period of 5.63 years, patients with T2DM diagnosed at ≤40 years had the highest excess risk for most outcomes relative to controls with adjusted hazard ratio (95% CI) of 2.05 (1.81-2.33) for total mortality, 2.72 (2.13-3.48) for cardiovascular-related mortality, 1.95 (1.68-2.25) for noncardiovascular mortality, 4.77 (3.86-5.89) for heart failure, and 4.33 (3.82-4.91) for coronary heart disease. All risks attenuated progressively with each increasing decade at diagnostic age; by the time T2DM was diagnosed at >80 years, the adjusted hazard ratios for CVD and non-CVD mortality were <1, with excess risks for other CVD outcomes substantially attenuated. Moreover, survival in those diagnosed beyond 80 was the same as controls, whereas it was more than a decade less when T2DM was diagnosed in adolescence. Finally, hazard ratios for most outcomes were numerically greater in younger women with T2DM.

Age at diagnosis of T2DM is prognostically important for survival and cardiovascular risks, with implications for determining the timing and intensity of risk factor interventions for clinical decision making and for guideline-directed care. These observations amplify support for preventing/delaying T2DM onset in younger individuals.

To conduct a comprehensive review of studies of glycaemic deterioration in type 2 diabetes and identify the major factors influencing progression.

We conducted a systematic literature search with terms linked to type 2 diabetes progression. All the included studies were summarized based upon the factors associated with diabetes progression and how the diabetes progression was defined.

Our search yielded 2785 articles; based on title, abstract and full-text review, we included 61 studies in the review. We identified seven criteria for diabetes progression: 'Initiation of insulin', 'Initiation of oral antidiabetic drug', 'treatment intensification', 'antidiabetic therapy failure', 'glycaemic deterioration', 'decline in beta-cell function' and 'change in insulin dose'. The determinants of diabetes progression were grouped into phenotypic, ethnicity and genotypic factors. Younger age, poorer glycaemia and higher body mass index at diabetes diagnosis were the main phenotypic factors associated with rapid progression. The effect of genotypic factors on progression was assessed using polygenic risk scores (PRS); a PRS constructed from the genetic variants linked to insulin resistance was associated with rapid glycaemic deterioration. The evidence of impact of ethnicity on progression was inconclusive due to the small number of multi-ethnic studies.

We have identified the major determinants of diabetes progression-younger age, higher BMI, higher HbA1c and genetic insulin resistance. The impact of ethnicity is uncertain; there is a clear need for more large-scale studies of diabetes progression in different ethnic groups.

Glycated hemoglobin (HbA1c) level reflects chronic glycemic status if reliable tests are used, however, in some regions worldwide high performing assays might not be readily available. This study aimed to asses two HbA1c immunoassays, comparing them with high-performance liquid chromatography (HPLC) assay, three methods available in Ecuador.

HbA1c were measured in 114 fresh whole blood-samples by DCA-Vantage point-of-care analyzer, I-Chroma portable fluorescent scanner immunoassay and BioRad Variant II Turbo HPLC. Normal and pathological HbA1c ranges were included. Blood samples with variants of hemoglobin were excluded. HbA1c values were expressed in National Glycohemoglobin Standardization Program percentages and mmol/mol, as mean±standard deviation.

HbA1c results by HPLC and DCA-Vantage were similar: 6.3±1.7% (45±18.6mmol/mol) vs. 6.3±1.8% (45±19.7mmol/mol), respectively, P=0.057; while HbA1c values by I-Chroma were lower than HPLC, 5.8±1.9% (40±20.8mmol/mol), P<0.001. The coefficient of variation was below 2% for high and low HbA1c levels, in all methods studied. HbA1c values by HPLC and DCA-Vantage were highly correlated (Spearman's Rank Correlation [SRC]: 0.916), while the correlation among HPLC and I-Chroma was weak (SRC: 0.368). The mean bias between DCA-Vantage and HPLC was -0.02±0.29% (-0.2±3.2mmol/mol), while for I-Chroma and HPLC mean bias was -0.50±1.62% (-5.5±17.7mmol/mol).

HbA1c immunoassays DCA-Vantage was comparable to HPLC assay, showing good correlation, appropriate precision and low bias, whereas I-Chroma assay was precise but inaccurate. Therefore, DCA-Vantage has better performance than I-Chroma. These findings suggest that is convenient to assess the HbA1c immunoassays commercially available in our country, Ecuador.

Research using data-driven cluster analysis has proposed five subgroups of diabetes with differences in diabetes progression and risk of complications. We aimed to compare the clinical utility of this subgroup-based approach for predicting patient outcomes with an alternative strategy of developing models for each outcome using simple patient characteristics.

We identified five clusters in the ADOPT trial (n=4351) using the same data-driven cluster analysis as reported by Ahlqvist and colleagues. Differences between clusters in glycaemic and renal progression were investigated and contrasted with stratification using simple continuous clinical features (age at diagnosis for glycaemic progression and baseline renal function for renal progression). We compared the effectiveness of a strategy of selecting glucose-lowering therapy using clusters with one combining simple clinical features (sex, BMI, age at diagnosis, baseline HbA1c) in an independent trial cohort (RECORD [n=4447]).

Clusters identified in trial data were similar to those described in the original study by Ahlqvist and colleagues. Clusters showed differences in glycaemic progression, but a model using age at diagnosis alone explained a similar amount of variation in progression. We found differences in incidence of chronic kidney disease between clusters; however, estimated glomerular filtration rate at baseline was a better predictor of time to chronic kidney disease. Clusters differed in glycaemic response, with a particular benefit for thiazolidinediones in patients in the severe insulin-resistant diabetes cluster and for sulfonylureas in patients in the mild age-related diabetes cluster. However, simple clinical features outperformed clusters to select therapy for individual patients.

The proposed data-driven clusters differ in diabetes progression and treatment response, but models that are based on simple continuous clinical features are more useful to stratify patients. This finding suggests that precision medicine in type 2 diabetes is likely to have most clinical utility if it is based on an approach of using specific phenotypic measures to predict specific outcomes, rather than assigning patients to subgroups.

UK Medical Research Council.

This study assessed type 2 diabetes treatment outcomes and process indicators using a comprehensive type 2 diabetes patient cohort in North Karelia, Finland, from 2011 to 2016.

Data from all diagnosed type 2 diabetes patients (n = 8429) living in North Karelia were collated retrospectively from regional electronic patient records. We assessed whether HbA1c and low-density lipoprotein (LDL) were measured and managed as recommended.

The HbA1c measurement rate improved (78% vs 89%) during 2011-2012 and 2015-2016, but a gradual deterioration in glycaemic control (HbA1c < 7.0% or 53 mmol/mol) was observed among both females (75% vs 67%) and males (72% vs 64%). The LDL measurement rate initially improved from the baseline. LDL control (<2.5 mmol/L) improved among both females (52% vs 59%) and males (58% vs 66%). A gender difference was observed in the achievement of the treatment target for LDL, with females showing worse control.

Low-density lipoprotein (LDL) control in type 2 diabetes patients has improved, but the existence of gender disparities needs further attention. Maintaining appropriate HbA1c control among type 2 diabetes patients over time appears to be difficult. Active follow-up and tailored treatment have the potential to improve the quality of care. Electronic patient records could be more efficiently used to improve the quality of care and to support decision-making.

The aim of this review was to investigate existing guidelines and scientific evidence on determining insulin dosage in people with type 1 and type 2 diabetes, and in particular to check whether the prandial insulin dose should be calculated based on glycemia or the meal composition, including the carbohydrates, protein and fat content in a meal. By exploring the effect of the meal composition on postprandial glycemia we demonstrated that several factors may influence the increase in glycemia after the meal, which creates significant practical difficulties in determining the appropriate prandial insulin dose. Then we reviewed effects of the existing insulin therapy regimens on glycemic control. We demonstrated that in most existing algorithms aimed at calculating prandial insulin doses in type 1 diabetes only carbohydrates are counted, whereas in type 2 diabetes the meal content is often not taken into consideration. We conclude that prandial insulin doses in treatment of people with diabetes should take into account the pre-meal glycemia as well as the size and composition of meals. However, there are still open questions regarding the optimal way to adjust a prandial insulin dose to a meal and the possible benefits for people with type 1 and type 2 diabetes if particular parameters of the meal are taken into account while calculating the prandial insulin dose. The answers to these questions may vary depending on the type of diabetes.

To investigate the metabolic profiles of newly diagnosed type 2 diabetes (NEW2D) in Chinese older adults (≥65 years) and assess the proportion of patients who achieved the targeted goals of blood glucose, blood pressure, and blood lipid.

NEW2D study was an observational, longitudinal, prospective cohort study involving patients who were diagnosed with type 2 diabetes (T2D) within the past 6 months and had a follow-up of 12 months. Participants were divided into younger NEW2D group (aged 20-65 years old) and older NEW2D group (aged ≥65 years old) according to age of diabetes onset. The baseline metabolic profiles were compared and the proportion of patients achieving adequate control of blood glucose, blood pressure, and blood lipids in reference to target goals were assessed during treatment.

The older NEW2D (n = 1362) had a lower BMI, HbA1c%, diastolic blood pressure, triglyceride, low-density lipoprotein cholesterol (LDL-C), and total cholesterol, higher systolic blood pressure, and high-density lipoprotein cholesterol levels at baseline. 47.8%, 66.7%, and 39.4% reached the target of HbA1c < 7.0%, BP < 140/90 mmHg, and LDL - C < 2.6 mmol/L, respectively. After 12 months, the proportion achieving above three targets increased to 70.2%, 76.1%, and 47.5%, respectively. The proportions of patients achieving three combined therapeutic targets doubled from 13.5% to 26.7%.

The older NEW2D patients have special metabolic profiles compared with younger patients. The control of cardiovascular disease risk factors was suboptimal in older adults with type 2 diabetes.