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To compare the validity and robustness of five methods for handling missing characteristics when using cardiovascular disease risk prediction models for individual patients in a real-world clinical setting. The performance of the missing data methods was assessed using data from the Swedish National Diabetes Registry (n = 419,533) with external validation using the Scottish Care Information ˗ diabetes database (n = 226,953). Five methods for handling missing data were compared. Two methods using submodels for each combination of available data, two imputation methods: conditional imputation and median imputation, and one alternative modeling method, called the naïve approach, based on hazard ratios and populations statistics of known risk factors only. The validity was compared using calibration plots and c-statistics. C-statistics were similar across methods in both development and validation data sets, that is, 0.82 (95% CI 0.82–0.83) in the Swedish National Diabetes Registry and 0.74 (95% CI 0.74–0.75) in Scottish Care Information-diabetes database. Differences were only observed after random introduction of missing data in the most important predictor variable (i.e., age). Validity and robustness of median imputation was not dissimilar to more complex methods for handling missing values, provided that the most important predictor variables, such as age, are not missing.

A collection of short stories explore what it is like to be young and black, centering on the experiences of black teenagers and emphasizing that one person's experiences, reality, and personal identity are different than someone else.

Aims/hypothesisThe aim of this study was to investigate the risks of all-cause and cause-specific mortality among participants with neither, one or both of diabetes and depression in a large prospective cohort study in the UK.MethodsOur study population included 499,830 UK Biobank participants without schizophrenia and bipolar disorder at baseline. Type 1 and type 2 diabetes and depression were identified using self-reported diagnoses, prescribed medication and hospital records. Mortality was identified from death records using the primary cause of death to define cause-specific mortality. We performed Cox proportional hazards models to estimate the risk of all-cause mortality and mortality from cancer, circulatory disease and causes of death other than circulatory disease or cancer among participants with either depression (n=41,791) or diabetes (n=22,677) alone and with comorbid diabetes and depression (n=3597) compared with the group with neither condition (n=431,765), adjusting for sociodemographic and lifestyle factors, comorbidities and history of CVD or cancer. We also investigated the interaction between diabetes and depression.ResultsDuring a median of 6.8 (IQR 6.1–7.5) years of follow-up, there were 13,724 deaths (cancer, n=7976; circulatory disease, n=2827; other causes, n=2921). Adjusted HRs of all-cause mortality and mortality from cancer, circulatory disease and other causes were highest among people with comorbid depression and diabetes (HRs 2.16 [95% CI 1.94, 2.42]; 1.62 [95% CI 1.35, 1.93]; 2.22 [95% CI 1.80, 2.73]; and 3.60 [95% CI 2.93, 4.42], respectively). The risks of all-cause, cancer and other mortality among those with comorbid depression and diabetes exceeded the sum of the risks due to diabetes and depression alone.Conclusions/interpretationWe confirmed that depression and diabetes individually are associated with an increased mortality risk and also identified that comorbid depression and diabetes have synergistic effects on the risk of all-cause mortality that are largely driven by deaths from cancer and causes other than circulatory disease and cancer.

Aims/hypothesisData on type 1 diabetes incidence and prevalence are limited, particularly for adults. This study aims to estimate global numbers of incident and prevalent cases of type 1 diabetes in 2017 for all age groups, by country and areas defined by income and region.MethodsIncidence rates of type 1 diabetes in children (available from 94 countries) from the IDF Atlas were used and extrapolated to countries without data. Age-specific incidence rates in adults (only known across full age range for fewer than ten countries) were obtained by applying scaling ratios for each adult age group relative to the incidence rate in children. Age-specific incidence rates were applied to population estimates to obtain incident case numbers. Duration of diabetes was estimated from available data and adjusted using differences in childhood mortality rate between countries from United Nations demographic data. Prevalent case numbers were derived by modelling the relationship between prevalence, incidence and disease duration. Sensitivity analyses were performed to quantify the impact of alternative assumptions and model inputs.ResultsGlobal numbers of incident and prevalent cases of type 1 diabetes were estimated to be 234,710 and 9,004,610, respectively, in 2017. High-income countries, with 17% of the global population, accounted for 49% of global incident cases and 52% of prevalent cases. Asia, which has the largest proportion of the world’s population (60%), had the largest number of incident (32%) and prevalent (31%) cases of type 1 diabetes. Globally, 6%, 35%, 43% and 16% of prevalent cases were in the age groups 0–14, 15–39, 40–64 and 65+ years, respectively. Based on sensitivity analyses, the estimates could deviate by ±15%.Conclusions/interpretationGlobally, type 1 diabetes represents about 2% of the estimated total cases of diabetes, ranging from less than 1% in certain Pacific countries to more than 15% in Northern European populations in 2017. This study provides information for the development of healthcare and policy approaches to manage type 1 diabetes. The estimates need further validation due to limitations and assumptions related to data availability and estimation methods.

The incidence of early-onset type 2 diabetes is increasing, with a growing number of cases now occurring in children, adolescents, and young adults. This transition is primarily driven by the rising prevalence of obesity in younger populations, especially in high-income countries. However, the relationship between obesity and early-onset type 2 diabetes varies across ethnic groups, with some populations exhibiting a higher risk at lower BMI thresholds, possibly due to differences in insulin resistance and β-cell function. Socioeconomic factors further shape disease patterns, with early-onset type 2 diabetes disproportionately affecting lower-income populations in high-income settings, whereas in low-income and middle-income countries, economic development and urbanisation have contributed to increasing incidence among more affluent groups. The consequences of this transition to early-onset type 2 diabetes are severe, with accelerated disease progression, heightened risks of microvascular and macrovascular complications, and considerable societal and health-care burdens compared with later-onset disease. Given the continuing rise in childhood and adolescent obesity, the incidence of early-onset type 2 diabetes is expected to increase further, placing mounting pressure on health-care systems worldwide. In the first of three papers in this Series, we examine global trends in the incidence and prevalence of early-onset type 2 diabetes, identify key drivers of this transition to diagnosis at younger ages, and review the evidence for risk factors both at population and individual level.

Early-onset type 2 diabetes (defined as type 2 diabetes diagnosed in people aged <40 years) is an increasingly prevalent condition with a more aggressive disease trajectory than late-onset type 2 diabetes. It is associated with accelerated microvascular and macrovascular complications, reduced life expectancy, and adverse pregnancy outcomes. Despite its rising incidence, global management strategies have mostly been extrapolated from studies in older adults with limited evidence specific to younger populations. In this Series paper, we aim to highlight the unique challenges in the management of early-onset type 2 diabetes and why current models of care are inadequate. We emphasise that early-onset type 2 diabetes necessitates proactive and combination treatment strategies to address weight, faster β-cell decline, worse insulin resistance, and rapidly progressing hyperglycaemia compared with late-onset type 2 diabetes. However, there is minimal evidence on how best to address these factors and clinical inertia risks contributing to glycaemic burden. Cardiovascular risk assessment tools underestimate long-term risk, contributing to low use of statin and antihypertensive therapy. Reproductive health remains a key concern, yet preconception and pregnancy care are inadequate, with low adherence to recommended interventions. Health-care systems are not optimised to address the distinct needs of young adults, and gaps in transitional care (from paediatric to adult services) contribute to disengagement and adverse outcomes. Addressing these challenges requires tailored management strategies that consider the unique metabolic and psychosocial factors in this population. In this Series paper, we summarise the evidence base for the management of early-onset type 2 diabetes, key evidence gaps, and discuss the multisectoral and transdisciplinary elements needed to achieve population-level prevention to reverse these concerning trends.

Healthy eating and fitness mobile apps are designed to promote healthier living. However, for young people, body dissatisfaction is commonplace, and these types of apps can become a source of maladaptive eating and exercise behaviors. Furthermore, such apps are designed to promote continuous engagement, potentially fostering compulsive behaviors. The aim of this study was to identify potential risks around healthy eating and fitness app use and negative experience and behavior formation among young people and to inform the understanding around how current commercial healthy eating and fitness apps on the market may, or may not, be exasperating such behaviors. Our research was conducted in 2 phases. Through a survey (n=106) and 2 workshops (n=8), we gained an understanding of young people's perceptions of healthy eating and fitness apps and any potential harm that their use might have; we then explored these further through interviews with experts (n=3) in eating disorder and body image. Using insights drawn from this initial phase, we then explored the degree to which leading apps are preventing, or indeed contributing to, the formation of maladaptive eating and exercise behaviors. We conducted a review of the top 100 healthy eating and fitness apps on the Google Play Store to find out whether or not apps on the market have the potential to elicit maladaptive eating and exercise behaviors. Participants were aged between 18 and 25 years and had current or past experience of using healthy eating and fitness apps. Almost half of our survey participants indicated that they had experienced some form of negative experiences and behaviors through their app use. Our findings indicate a wide range of concerns around the wider impact of healthy eating and fitness apps on individuals at risk of maladaptive eating and exercise behavior, including (1) guilt formation because of the nature of persuasive models, (2) social isolation as a result of personal regimens around diet and fitness goals, (3) fear of receiving negative responses when targets are not achieved, and (4) feelings of being controlled by the app. The app review identified logging functionalities available across the apps that are used to promote the sustained use of the app. However, a significant number of these functionalities were seen to have the potential to cause negative experiences and behaviors. In this study, we offer a set of responsibility guidelines for future researchers, designers, and developers of digital technologies aiming to support healthy eating and fitness behaviors. Our study highlights the necessity for careful considerations around the design of apps that promote weight loss or body modification through fitness training, especially when they are used by young people who are vulnerable to the development of poor body image and maladaptive eating and exercise behaviors.

People with comorbidities are underrepresented in clinical trials. Empirical estimates of treatment effect modification by comorbidity are lacking, leading to uncertainty in treatment recommendations. We aimed to produce estimates of treatment effect modification by comorbidity using individual participant data (IPD). We obtained IPD for 120 industry-sponsored phase 3/4 trials across 22 index conditions (n = 128,331). Trials had to be registered between 1990 and 2017 and have recruited ≥300 people. Included trials were multicentre and international. For each index condition, we analysed the outcome most frequently reported in the included trials. We performed a two-stage IPD meta-analysis to estimate modification of treatment effect by comorbidity. First, for each trial, we modelled the interaction between comorbidity and treatment arm adjusted for age and sex. Second, for each treatment within each index condition, we meta-analysed the comorbidity-treatment interaction terms from each trial. We estimated the effect of comorbidity measured in 3 ways: (i) the number of comorbidities (in addition to the index condition); (ii) presence or absence of the 6 commonest comorbid diseases for each index condition; and (iii) using continuous markers of underlying conditions (e.g., estimated glomerular filtration rate (eGFR)). Treatment effects were modelled on the usual scale for the type of outcome (absolute scale for numerical outcomes, relative scale for binary outcomes). Mean age in the trials ranged from 37.1 (allergic rhinitis trials) to 73.0 (dementia trials) and percentage of male participants range from 4.4% (osteoporosis trials) to 100% (benign prostatic hypertrophy trials). The percentage of participants with 3 or more comorbidities ranged from 2.3% (allergic rhinitis trials) to 57% (systemic lupus erythematosus trials). We found no evidence of modification of treatment efficacy by comorbidity, for any of the 3 measures of comorbidity. This was the case for 20 conditions for which the outcome variable was continuous (e.g., change in glycosylated haemoglobin in diabetes) and for 3 conditions in which the outcomes were discrete events (e.g., number of headaches in migraine). Although all were null, estimates of treatment effect modification were more precise in some cases (e.g., sodium-glucose co-transporter-2 (SGLT2) inhibitors for type 2 diabetes-interaction term for comorbidity count 0.004, 95% CI -0.01 to 0.02) while for others credible intervals were wide (e.g., corticosteroids for asthma-interaction term -0.22, 95% CI -1.07 to 0.54). The main limitation is that these trials were not designed or powered to assess variation in treatment effect by comorbidity, and relatively few trial participants had >3 comorbidities. Assessments of treatment effect modification rarely consider comorbidity. Our findings demonstrate that for trials included in this analysis, there was no empirical evidence of treatment effect modification by comorbidity. The standard assumption used in evidence syntheses is that efficacy is constant across subgroups, although this is often criticised. Our findings suggest that for modest levels of comorbidities, this assumption is reasonable. Thus, trial efficacy findings can be combined with data on natural history and competing risks to assess the likely overall benefit of treatments in the context of comorbidity.