Background:Major Depressive Disorder (MDD) is clinically and biologically heterogeneous. Here, we leveraged the genetics of individual depressive symptoms to dissect the disorder's underlying heterogeneity. Methods:We utilized the BIObanks Netherlands Internet Collaboration (BIONIC). A series of genome-wide association studies (effective-N range: 14,407 - 47,110) compared controls (N=48,286) with partially different subsets of lifetime MDD cases (range: 3,892-15,577), each endorsing one of 12 individual DSM-based depressive symptoms. Results were combined in genetic correlations that informed factor analyses with Genomic Structural Equation Modeling, decomposing underlying MDD liability dimensions. The identified factors were assessed and further characterized using multivariate regression of neurodevelopmental/psychiatric and cardiometabolic traits. Results:All symptoms demonstrated substantial SNP-based heritability (h 2 SNP : 0.088 - 0.127). Despite high between-symptom genetic correlations, factor analyses yielded two highly correlated (rg=0.85) but still distinct latent factors: factor 1 (F1), capturing appetite/weight loss, insomnia, guilt/worthlessness, psychomotor slowing and suicidality, and factor 2 (F2), reflecting concentration problems, anhedonia, depressed mood, appetite/weight gain and fatigue. Overall, F1 had a stronger genetic overlap with neurodevelopmental/psychiatric phenotypes (e.g., autism: standardized estimate β=0.45, p=4.49×10-4; schizophrenia: β=0.40, p=1.73×10-4), while F2 significantly overlapped with cardiometabolic traits (e.g., metabolic syndrome: β=0.44, p=8.69×10-4; coronary artery disease: β=0.31, p=0.009). Conclusions:We identified two genetic dimensions of MDD, each linked to partially distinct clinical manifestations and underlying biology, with one reflecting neurodevelopmental/psychiatric liabilities and the other capturing a strong cardiometabolic vulnerability. Disentangling such distinct dimensions may help guide patient stratification and targeted treatment, thereby advancing precision psychiatry.

Background Cardiovascular disease (CVD) is an important complication of type 2 diabetes (T2D). Current CVD-prediction models use single baseline measurements and achieve moderate performance (C-indices ≈0.7). Repeated measurements available in modern healthcare registries may provide incremental predictive value. In this case study, we investigated whether HbA1c, LDL-cholesterol and eGFR trajectory measures might improve incident CVD-risk prediction. Methods We studied 83,326 people with T2D from Danish nationwide registers without CVD-history at baseline (1st January 2015), who had ≥2 HbA1c, LDL-cholesterol and eGFR measurements between 2012–2014; their last measurement defined baseline. For each risk-factor we derived three paired trajectory measures across 2012–2014: mean & SD, median & IQR, and intercept & slope from growth models. A reference Cox-regression model for CVD-events (ICD-10 codes, until 31st December 2020) included baseline age, sex, age-at-T2D-onset, HbA1c, LDL-cholesterol, HDL-cholesterol, eGFR, and medication use. Trajectory measures were added sequentially and jointly using an offset-procedure, computing hazard ratios (HRs), C-indices and continuous net reclassification index (NRI) with 95% CIs. Results Mean age was 65 (SD±12), and 48% were female. Over median 6 years 11,280 (13.5%) people experienced a CVD-event. Accounting for the reference model, trajectory measures of dispersion and change were associated with CVD-events (HRs ≈ 1.1 for HbA1c and eGFR; >1.4 for LDL-cholesterol), centrality measures were not. Adding trajectory measures produced minimal discrimination gains (C-index Δ +0.001-+0.003) but modest NRI improvements (+3-+9%). Conclusions Within-person dispersion or change measures for HbA1c, eGFR, and especially LDL-cholesterol, obtained from routine data, might moderately enhance incident CVD-risk prediction in people with T2D.

Harmonized phenotyping and diverse population-specific studies are crucial for advancing gene discovery in psychiatric genetics. We conducted a genome-wide association (GWAS) mega-analysis of DSM-defined lifetime major depressive disorder (MDD) in 64 941 participants (25.7% cases) from the Dutch BIObanks Netherlands Internet Collaboration (BIONIC) consortium. Liability-scale SNP-based heritability was 12.0% (SE = 1.4%) as estimated by LDSC (assuming a lifetime prevalence of 15%) and 26.6% (SE = 1.1%) when estimated by LDAK-REML on individual-level genotype data, indicating substantial common-variant signal in this clinically harmonized sample. The genetic correlation with the latest major depression GWAS from the Psychiatric Genomics Consortium (PGC-MD) was high (rG = 0.89, SE = 0.048). Polygenic scores (PGSs) based on BIONIC predicted depression in UK Biobank, and PGSs derived from PGC-MD predicted MDD in BIONIC, supporting transferability of depression polygenic signal across cohorts and phenotype definitions. Within-family PGS analyses in twins suggested that the observed prediction was not primarily driven by detectable family-level confounding, and twin concordance for MDD increased with polygenic burden. We identified one genome-wide significant locus, indexed by rs3818852 in PALMD, but this finding currently lacks independent replication and should be interpreted cautiously. Finally, genetic correlation and latent causal variable analyses identified multiple traits showing shared or directionally consistent genetic associations with MDD. Together, these findings underscore the value of clinically harmonized phenotyping in regional biobank collaborations for studying the genetic architecture of MDD.

The OMED2 (Optimization of Medication in Elderly with Diabetes) study addresses the effect and implementation of integrating a deprescribing programme (DPP) in general practice. The aim of the DPP is to reduce glucose-lowering medication (SU/insulin) in overtreated older patients. The protocol for this study has been published previously. This statistical analysis plan (SAP) contains a more elaborate outline of the (statistical) methods we plan to use for data analysis. The OMED2 study is a randomized mixed-methods study with a 2-year follow-up period that compares the effect of the implementation of a DPP in general practice to regular care (control). In this SAP, we report on the (statistical) approaches that we plan to use to address the study objectives. The main objective of the OMED2 study is to examine the effect of the implementation of the DPP on diabetes complications, whereby the total number of diabetes complications related to undertreatment and overtreatment will be summed. Generalized linear mixed models with a Poisson distribution and the DPP as the main determinant will be used to test whether the total number of diabetes complications occurring from the start of the 2-year follow-up until the end of follow-up differs between intervention and control. The incident rate of the number of diabetes complications will be calculated to correct for possible differences in follow-up duration. The model will also include a random effect variable to allow for possible clustering effects by general practice. We will perform intention-to-treat analyses, which include all patients eligible for deprescribing, as well as per protocol analyses, which omit patients who were not deprescribed in the intervention arm. Additionally, approaches to study the implementation of the DPP and the cost-effectiveness of the implementation are outlined in the SAP. ISRCTN Registry ISRCTN50008265. Registered on 1 November 2024.

OBJECTIVE:To examine whether IgG N-glycosylation patterns are prospectively associated with incident diabetic nephropathy and neuropathy. RESEARCH DESIGN AND METHODS:We analyzed IgG N-glycosylation profiles in four cohorts: EPIC-Potsdam, DiaGene, GenoDiabMar, and Hoorn DCS. Among 3,263 individuals with and without type 2 diabetes at profiling, 674 incident neuropathy and 639 incident nephropathy cases occurred after diabetes diagnosis. Associations of IgG N-glycan peaks (IgG-GPs) and traits with incident outcomes were examined using Cox models and meta-analyzed across cohorts. RESULTS:Agalactosylated, asialylated, and bisected IgG-GPs were associated with higher nephropathy risk (IgG-GP3: hazard ratio [HR] 1.13 [95% CI 1.04-1.24]; IgG-GP4: HR 1.15 [95% CI 1.05-1.26]), while galactosylated and sialylated IgG-GPs were associated with lower risk (IgG-GP14: HR 0.86 [95% CI 0.78-0.94]; IgG-GP18: HR 0.85 [95% CI 0.77-0.94]) (all false discovery rate <0.05). Associations of IgG-GPs with incident neuropathy were rendered nonsignificant after multiple testing correction. CONCLUSIONS:IgG N-glycosylation may reflect immune-related mechanisms relevant to diabetic nephropathy; but the evidence between IgG-GPs and diabetic neuropathy remains inconclusive.

Diabetes distress (DD) refers to the emotional and psychological burden experienced by individuals living with diabetes. Randomized controlled trials (RCTs) have investigated the efficacy of interventions on DD among adults with type 1 diabetes or type 2 diabetes. We aimed to systematically identify, summarize and critically appraise all available evidence from RCTs assessing the efficacy of interventions for reducing DD (either as primary or secondary outcome) among adults with type 1 diabetes or type 2 diabetes. Four electronic databases (PubMed, Cochrane Database of Systematic Reviews, PsycINFO and CINAHL) were searched from inception until 23 September 2024. Retrieved papers were screened for eligibility by two independent reviewers, who also screened the reference lists of all included publications. Studies were included if the study was an RCT performed in an adult population (≥18 years), in which the efficacy of an active intervention on DD as primary or secondary outcome was described. Data were extracted using an a priori developed form. Two independent reviewers assessed the risk of bias of included studies using the Cochrane RoB 2 tool for RCTs. For each type of intervention, a narrative data synthesis was conducted, and, if possible, meta-analyses were conducted when two or more studies reported on the same outcome. Heterogeneity between studies was assessed using the I2 statistic and a certainty of evidence assessment (GRADE) was conducted. In case of substantial heterogeneity, subgroup analysis was conducted. Data regarding the efficacy of interventions on DD was summarized and discussed. Future implications and recommendations for clinical care were presented. This systematic review and meta-analysis served as the basis for a European Association for the Study of Diabetes (EASD) guideline on the management of DD. PROSPERO CRD42024598512.

BACKGROUND:Continuity of care (CoC) is linked to better outcomes. Particularly, older adults and those with chronic conditions, like type 2 diabetes (T2D) and dementia, may benefit from CoC. Individuals with a migration background (MB) face challenges in accessing adequate healthcare. Our aim was to study associations between MB and personal continuity of general practitioner (GP) care among older adults, and in subgroups with T2D and dementia. METHODS:Observational cohort study (2013-8) based on electronic records from 48 Dutch general practices linked to data from Statistics Netherlands. We specifically compared adults who migrated to the Netherlands to those without MB. The Herfindahl-Hirschman Index (HHI; low/medium/high) was used to measure CoC. We used multilevel ordinal regression to estimate associations between MB and CoC, adjusted for follow-up time/age/gender/comorbidity/income/practice. RESULTS:46 663 individuals aged ≥50 years were included: 72.9% with no MB, 5.7% with Surinamese, 4.3% Moroccan, 2.7% Turkish, 5.1% European, and 9.3% other MB. Compared with those without MB, persons with a Moroccan MB had lower odds of having moderate or high CoC [odds ratio (OR) 0.81, 95% CI 0.74-0.89], and persons with a European MB had higher odds of having moderate or high CoC (OR 1.16, 95% CI 1.07-1.26). Persons with a Moroccan MB in the T2D subgroup had lower odds of having moderate or high CoC (OR 0.75, 95% CI 0.64-0.89). No differences were found in the dementia subgroup. CONCLUSIONS:This study reveals inequalities in personal continuity of GP care by MB in the Netherlands. Interventions to improve CoC should actively incorporate MB groups to promote equitable CoC.

Background: The effectiveness of COVID-19 vaccines appears to decline rapidly over time due to waning immunity and immune evasion by emerging variants of concern, and may be reduced in high-risk populations. We aimed to evaluate the rates of SARS-CoV-2 breakthrough infection or severe COVID-19, both in individuals who had completed their primary COVID-19 vaccination, and in those who had received their first booster vaccination. Specifically, we aimed to evaluate whether persons with certain risk factors, such as age, gender, socioeconomic status (SES), and specified comorbidities have an increased risk of either breakthrough infection or severe COVID-19, compared to those without the respective risk factors. Methods: Data on COVID-19 vaccinations, infections, hospitalizations, and deaths were collected from the PHARMO Data Network, consisting of health records from Dutch residents. Two cohorts were established: (1) all persons who have completed their primary COVID-19 vaccination regimen, and (2) those who have received their first booster vaccination. The outcomes were SARS-CoV-2 breakthrough infection, and severe COVID-19, defined as either hospitalization or death following SARS-CoV-2 infection. Incidence rates of these outcomes were calculated in both cohorts. The adjusted incidence rate ratios of these outcomes in persons with certain risk factors were calculated, using generalized linear models with a Poisson distribution. Results: In 2021, a total of 1,090,567 individuals received either two doses of BNT162b2, AZD1222, or mRNA-1273, or one dose of Ad26.COV2.S and were included in the primary vaccination cohort, of which 344,153 (31.6%) received a booster vaccination. Overall incidence rates of SARS-CoV-2 breakthrough infection and severe COVID-19 after primary vaccination were 29.9 and 3.1 per 1000 person-years, respectively, and after booster vaccination were 256.4 and 2.3, respectively. Male gender, older age, lower SES, history of COVID-19, and recent hospitalization were factors associated with a lower risk of breakthrough infection after primary vaccination, and a higher risk of severe COVID-19. The risk of severe COVID-19 after primary vaccination was increased in persons with several comorbidities, compared to those without, and remained elevated after booster vaccination in persons with diabetes or lung disease. Conclusions: Our study emphasizes the crucial role of boosters in reducing breakthrough infections, particularly in high-risk populations. The varied impact on severe outcomes in individuals with comorbidities underscores the need for ongoing surveillance and tailored vaccination strategies.

AIMS:Adherence to an ideal cardiovascular health (CVH) might contribute to lower the burden of sudden cardiac death (SCD) in the community. We aimed to examine the association between the number of ideal CVH metrics at baseline and of its change over 10 years with the risk of SCD. METHODS AND RESULTS:The Copenhagen City Heart Study is a community-based prospective cohort study. The number of ideal CVH metrics (range 0-6; non-smoking and ideal level of body mass index, physical activity, untreated glucose, untreated systolic blood pressure, and untreated total cholesterol levels) at baseline in 1991-94 and its 10-year change thereof between 1981-83 and 1991-94 were evaluated. Definite SCD was defined as a death occurring within 1 h (eye-witnessed case) or within 24 h (non-eye-witnessed) of symptoms onset, with the presence of confirmed ventricular tachycardia and the exclusion of non-cardiac cause at autopsy. Fine and Gray sub-distribution HRs (sHRs) were calculated to account for competing risk. The study population includes 8837 participants (57% women; mean age 57 years, ±15 years) in 1991-94. After a median follow-up of 22.6 years from 1 January 1993 up to 31 December 2016, 56 definite SCD occurred. The risk of definite SCD decreased gradually with the number of ideal metrics in 1991-94 [sHR = 0.58; 95% confidence interval (CI): 0.44-0.75 per additional ideal metric] and with the change (i.e. improvement) in the number of ideal metrics between 1981-83 and 1991-94 (sHR = 0.68; 0.50-0.93 per change in the number of ideal metrics). Effect size was lower for coronary death, all-cause mortality, and coronary heart disease events. CONCLUSION:Adherence to a higher number of ideal cardiovascular health was related to a substantial lower risk of definite SCD.

Aims To investigate whether adding electrocardiogram (ECG) abnormalities as a predictor improves the performance of incident cardiovascular disease (CVD) risk prediction models for people with Type 2 diabetes (T2D).Methods and results We evaluated the four major prediction models that are recommended by the guidelines of the American College of Cardiology/American Heart Association and European Society of Cardiology, in 11 224 people with T2D without CVD (coronary heart disease, heart failure, stroke, and thrombosis) from the Hoorn Diabetes Care System cohort (1998-2018). Baseline measurements included CVD risk factors and ECG recordings coded according to the Minnesota Classification as no, minor, or major abnormalities. After confirming good reference model fit, model performance was assessed before and after addition of ECG abnormalities and compared using c-statistics, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). c-statistics [95% confidence interval (CI)] of reference models (ASCVD, AD-ON, ADVANCE, and SCORE2-Diabetes) were 0.67 (0.65-0.70), 0.73 (0.71-0.76), 0.71 (0.68-0.74), and 0.67 (0.65-0.69), respectively. Adding ECG abnormalities as a predictor improved c-statistics with +0.02 (0.01-0.03), +0.01 (0.00-0.01), +0.02 (0.01-0.03), and +0.02 (0.01-0.02), respectively. Reclassification indicators also showed improvement: categorical NRI (+6%, +3%, +8%, and +5%, respectively), continuous NRI (95% CI) [0.25 (0.08-0.37), 0.32 (0.23-0.42), 0.54 (0.34-0.69), and 0.28 (0.09-0.33)], respectively, and IDI (95% CI) [0.005 (0.001-0.010), 0.002 (-0.001-0.007), 0.006 (0.001-0.007), and 0.004 (0.000-0.006), respectively]. Sensitivity analyses yielded similar results.Conclusion The addition of ECG abnormalities to incident CVD risk prediction models moderately but consistently improves the ability of models to correctly classify people with T2D in the appropriate CVD risk category with up to 8%, which is approximately equivalent to many established predictors and (bio)markers. This study in 11 224 people with Type 2 diabetes (T2D) without cardiovascular disease (CVD) at baseline demonstrates the impact of including electrocardiogram (ECG) abnormalities as an additional predictor in the four major cardiovascular risk prediction models recommended by international clinical guidelines for use in primary prevention of CVD in people with T2D. Adding ECG abnormalities to the ASCVD, AD-ON, ADVANCE, and SCORE2-Diabetes CVD risk prediction models, respectively, moderately but consistently improved their ability to correctly classify people with T2D in the appropriate CVD risk category with up to 8%, which is approximately equivalent to many established predictors and (bio)markers.Electrocardiogram abnormalities can have clinical value as a predictor for CVD risk in primary prevention of CVD in people with T2D, because they are common, independently associated with CVD, and low cost and consistently improve risk classification.

OBJECTIVE To assess longitudinal associations with sudden cardiac arrest (SCA) of clinical characteristics recorded in primary care in people with type 2 diabetes (T2D), both with and without cardiovascular disease (CVD). RESEARCH DESIGN AND METHODS We performed a case-control study, with SCA case subjects with T2D from the Amsterdam Resuscitation Studies (ARREST) registry of out-of-hospital resuscitation attempts in the Dutch Noord-Holland region (2010-2020) and up to five matched (age, sex, T2D, general practitioner [GP] practice) non-SCA control subjects. We collected relevant clinical measurements, medication use, and medical history from GPs' electronic health care records. We analyzed the associations of clinical characteristics and medication use with SCA in the total sample and in subgroups with or without CVD using multivariable time-dependent Cox regression (hazard ratios, 95% confidence intervals). RESULTS We included 689 SCA case subjects and 3,230 non-SCA control subjects. In multivariable models, low fasting glucose (<4.5 mmol/mol: 1.91 [1.00-3.64]), antihypertensive (1.80 [1.39-2.33]), glucose lowering (oral only: 1.32 [1.06-1.63]; insulin only: 2.31 [1.71-3.12]; oral and insulin: 1.64 [1.21-2.22]), heart failure (1.91 [1.55-2.35]), and QTc-prolonging prokinetic (1.78 [1.27-2.50]), antibiotic (1.35 [1.05-1.73]), and antipsychotic (2.10 [1.42-3.09]) medication were associated with SCA in the total sample. In subgroup effect modification analyses, QTc-prolonging antibiotic (1.82 [1.26-2.63]) and antipsychotic (3.10 [2.09-4.59]) medication use were associated with SCA only in those without CVD. CONCLUSIONS In people with T2D, low fasting glucose and QTc-prolonging prokinetic, antibiotic, or antipsychotic medication use and a history of heart failure are associated with SCA risk. Subgroup analyses indicate antibiotic and antipsychotic medication use increases SCA risk specifically in those without CVD.

OBJECTIVES:This study aimed to assess construct validity against commonly used patient-reported outcome measures (PROMs), test-retest reliability and responsiveness of seven Dutch-Flemish Patient-Reported Outcomes Measurement Information System (PROMIS) computerised adaptive testing (CATs) in Dutch adults with type 2 diabetes (T2D), and assess their acceptability in healthcare providers and people with T2D. DESIGN:A cross-sectional observational study in people with T2D and qualitative study involving both people with T2D and healthcare professionals. SETTING:Participants with T2D were recruited from the ongoing Hoorn Diabetes Care System cohort in the West-Friesland area of the Netherlands. Additionally, people with T2D and advanced chronic kidney disease were recruited at the outpatient clinics of Amsterdam University Medical Centre and 'Niercentrum aan de Amstel', both in the Amsterdam area of the Netherlands. The healthcare professionals involved in the qualitative part were recruited at the Amsterdam University Medical Centre. PARTICIPANTS:314 people with T2D (age 64.0±10.8 years, 63.7% men). PRIMARY AND SECONDARY OUTCOME MEASURES:Participants completed seven PROMIS CATs (assessing (1) Physical Function, (2) Pain Interference, (3) Fatigue, (4) Sleep Disturbance, (5) Anxiety, (6) Depression and (7) Ability to Participate in Social Roles and Activities), and PROMs measuring similar constructs. After 2 weeks and 6 months, participants completed the CATs measures again, together with seven Global Rating Scales (GRS) on perceived change in each domain. Construct validity was assessed using Pearson's correlations. Test-retest reliability was assessed by the intraclass correlation coefficient (ICC). Measurement error was assessed by the standard error of measurement (SEM) and minimal detectable change (MDC). Responsiveness was assessed by correlations between change scores on the PROMIS CAT and GRS. Acceptability was assessed through focus groups and interviews in healthcare providers and people with T2D. RESULTS:Except for Fatigue, all PROMIS CAT domains demonstrated sufficient construct validity, since ≥75% of the results was in accordance with a priori hypotheses. All seven PROMIS CATs showed sufficient test-retest reliability (ICCs 0.73-0.91). SEM and MDC ranged from 2.1 to 2.7 and from 5.7 to 7.4, respectively. Responsiveness was rated as insufficient in this study design as there was almost no change in participants' own rating of their health compared with 6 months ago according to a global rating of change.During the focus groups and interviews, healthcare providers and people with T2D agreed that CATs could serve as a conversation starter in routine care, but should never replace personal consultations with a doctor. If implemented, participants would be willing to spend 15 min to complete the PROMIS CATs. CONCLUSIONS:The PROMIS CATs showed sufficient construct validity and test-retest reliability in most domains in people with T2D. Responsiveness needs to be evaluated in a population with poorer diabetes control or in a study design with longer follow-up. The CATs are well accepted to be used in care to identify relevant topics, but should not replace personal contact with the doctor.

BACKGROUND:Elderly patients with Type 2 diabetes (T2D) are frequently overtreated with glucose-lowering medication. OBJECTIVE:This feasibility study evaluated the implementation of a deprescribing programme (DPP) for general practices, consisting of education, a patient selection tool, practice visits, and an expert support panel, before scaling it in a randomized controlled trial. METHODS:Quantitative evaluation included the number of patients with T2D eligible for deprescribing using medical records and study progress data. Qualitative evaluation entailed the analysis of minutes made during training, and interviews with health care providers (HCPs). The extended normalization process theory guided analysis. RESULTS:In 10 practices, 55 out of 65 eligible patients were deprescribed glucose-lowering medication, with 22 restarts. Most execution steps were perceived as the practice nurse's responsibility, whereas the general practitioner needed to approve the deprescribing. Practice nurses found the educational training, including peer-to-peer sessions and practice visits, supportive of integrating deprescribing into practice. DPP procedures and tasks not part of the regular care process were not consistently performed. The DPP was adapted to minimize study tasks for HCPs and align study procedures to existing routine procedures. CONCLUSION:Implementation of a DPP in general practice requires education, practice visits, and alignment of DPP components to regular care.

Background During the COVID-19 pandemic, older patients in primary care were triaged based on their frailty or assumed vulnerability for poor outcomes, while evidence on the prognostic value of vulnerability measures in COVID-19 patients in primary care was lacking. Still, knowledge on the role of vulnerability is pivotal in understanding the resilience of older people during acute illness, and hence important for future pandemic preparedness. Therefore, we assessed the predictive value of different routine care-based vulnerability measures in addition to age and sex for 28-day mortality in an older primary care population of patients with COVID-19. Methods From primary care medical records using three routinely collected Dutch primary care databases, we included all patients aged 70 years or older with a COVID-19 diagnosis registration in 2020 and 2021. All-cause mortality was predicted using logistic regression based on age and sex only (basic model), and separately adding six vulnerability measures: renal function, cognitive impairment, number of chronic drugs, Charlson Comorbidity Index, Chronic Comorbidity Score, and a Frailty Index. Predictive performance of the basic model and the six vulnerability models was compared in terms of area under the receiver operator characteristic curve (AUC), index of prediction accuracy and the distribution of predicted risks. Results Of the 4,065 included patients, 9% died within 28 days after COVID-19 diagnosis. Predicted mortality risk ranged between 7–26% for the basic model including age and sex, changing to 4–41% by addition of comorbidity-based vulnerability measures (Charlson Comorbidity Index, Chronic Comorbidity Score), more reflecting impaired organ functioning. Similarly, the AUC of the basic model slightly increased from 0.69 (95%CI 0.66 – 0.72) to 0.74 (95%CI 0.71 – 0.76) by addition of either of these comorbidity scores. Addition of a Frailty Index, renal function, the number of chronic drugs or cognitive impairment yielded no substantial change in predictions. Conclusion In our dataset of older COVID-19 patients in primary care, the 28-day mortality fraction was substantial at 9%. Six different vulnerability measures had little incremental predictive value in addition to age and sex in predicting short-term mortality.

In this cohort profile article we describe the lifetime major depressive disorder (MDD) database that has been established as part of the BIObanks Netherlands Internet Collaboration (BIONIC). Across the Netherlands we collected data on Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) lifetime MDD diagnosis in 132,850 Dutch individuals. Currently, N = 66,684 of these also have genomewide single nucleotide polymorphism (SNP) data. We initiated this project because the complex genetic basis of MDD requires large population-wide studies with uniform in-depth phenotyping. For standardized phenotyping we developed the LIDAS (LIfetime Depression Assessment Survey), which then was used to measure MDD in 11 Dutch cohorts. Data from these cohorts were combined with diagnostic interview depression data from 5 clinical cohorts to create a dataset of N = 29,650 lifetime MDD cases (22%) meeting DSM-5 criteria and 94,300 screened controls. In addition, genomewide genotype data from the cohorts were assembled into a genomewide association study (GWAS) dataset of N = 66,684 Dutch individuals (25.3% cases). Phenotype data include DSM-5-based MDD diagnoses, sociodemographic variables, information on lifestyle and BMI, characteristics of depressive symptoms and episodes, and psychiatric diagnosis and treatment history. We describe the establishment and harmonization of the BIONIC phenotype and GWAS datasets and provide an overview of the available information and sample characteristics. Our next step is the GWAS of lifetime MDD in the Netherlands, with future plans including fine-grained genetic analyses of depression characteristics, international collaborations and multi-omics studies.

Objectives: Type 2 diabetes mellitus (T2DM) is a chronic disease associated with overweight and obesity. Evidence suggests that 24-hour movement behaviors (24 h-MBs) play a crucial role in cardiometabolic health. However, it is not yet known if 24 h-MBs differ between weight status groups among people with T2DM (PwT2DM) and how 24 h-MBs are associated with their cardiometabolic health. Design: Cross-sectional study. Methods: Cardiometabolic variables (i.e. Body Mass Index (BMI), waist circumference (WC), HbA1c, fasting glucose, triglycerides, total -cholesterol, HDL-cholesterol, LDL-cholesterol, blood pressure) and 24 h-MBs (accelerometry and sleep-diary) of 1001 PwT2DM were collected. Regression models using compositional data analysis explored differences in 24 h-MBs between weight status groups and analyzed associations with cardiometabolic variables. Results: The 24 h-MBs of PwT2DM being obese consisted of less sleep, light physical activity (LPA) and moderate to vigorous physical activity (MVPA) and more sedentary time (ST) per day as compared to PwT2DM being overweight or normal weight (p < 0.001). Regardless of weight status, the largest associations were found when reallocating 20 mina day from ST into MVPA for BMI (-0.32 kg/m2; [-0.55; -0.09], -1.09 %), WC (-1.44 cm, [-2.26; -0.62], -1.35 %) and HDL-cholesterol (0.02 mmol/l, [0.01, 0.02], +1.59 %), as well as from ST into LPA for triglycerides (-0.04 mmol/l, [-0.05; -0.03], -2.3 %). Moreover, these associations were different when stratifying people by short -to -average (7.7 h/night) versus long sleep (9.3 h /night) period. Conclusions: This study highlights the importance of 24 h-MBs in the cardiometabolic health of PwT2DM. Shifting time from ST and/or sleep toward LPA or MVPA might theoretically benefit cardiometabolic health among relatively inactive PwT2DM, irrespective of weight status. (c) 2023 The Authors. Published by Elsevier Ltd on behalf of Sports Medicine Australia. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Aims/hypothesis The aim of this study was to describe the metabolome in diabetic kidney disease (DKD) and its association with incident CVD in type 2 diabetes, and identify prognostic biomarkers. Methods From a prospective cohort of individuals with type 2 diabetes, baseline sera ( N =1991) were quantified for 170 metabolites using NMR spectroscopy with median 5.2 years of follow-up. Associations of chronic kidney disease (CKD, eGFR<60 ml/min per 1.73 m 2 ) or severely increased albuminuria with each metabolite were examined using linear regression, adjusted for confounders and multiplicity. Associations between DKD (CKD or severely increased albuminuria)-related metabolites and incident CVD were examined using Cox regressions. Metabolomic biomarkers were identified and assessed for CVD prediction and replicated in two independent cohorts. Results At false discovery rate (FDR)<0.05, 156 metabolites were associated with DKD (151 for CKD and 128 for severely increased albuminuria), including apolipoprotein B-containing lipoproteins, HDL, fatty acids, phenylalanine, tyrosine, albumin and glycoprotein acetyls. Over 5.2 years of follow-up, 75 metabolites were associated with incident CVD at FDR<0.05. A model comprising age, sex and three metabolites (albumin, triglycerides in large HDL and phospholipids in small LDL) performed comparably to conventional risk factors (C statistic 0.765 vs 0.762, p =0.893) and adding the three metabolites further improved CVD prediction (C statistic from 0.762 to 0.797, p =0.014) and improved discrimination and reclassification. The 3-metabolite score was validated in independent Chinese and Dutch cohorts. Conclusions/interpretation Altered metabolomic signatures in DKD are associated with incident CVD and improve CVD risk stratification. Graphical Abstract

Micro- and macrovascular complications are common among persons with type 2 diabetes. Recently there has been growing interest to investigate the potential of circulating small non-coding RNAs (sncRNAs) as contributors to the development of diabetic complications. In this study we investigate to what extent circulating sncRNAs levels associate with prevalent diabetic kidney disease (DKD) in persons with type 2 diabetes. Plasma sncRNAs levels were determined using small RNA-seq, allowing detection of miRNAs, snoRNAs, piRNAs, tRNA fragments, and various other sncRNA classes. We tested for differentially expressed sncRNAs in persons with type 2 diabetes, with DKD (n = 69) or without DKD (n = 405). In secondary analyses, we also tested the association with eGFR, albuminuria (UACR), and the plasma proteome. In total seven sncRNAs were negatively associated with prevalent DKD (all PFDR ≤ 0.05). Including one microRNA (miR-143-5p), five snoRNAs (U8, SNORD118, SNORD24, SNORD107, SNORD87) and a piRNA (piR-019825 | DQ597218). Proteomic analyses showed that the seven sncRNAs, and especially the piRNA piR-019825, were associated with plasma levels of 24 proteins of which several have known associations with kidney function including TNF sR-I (TNFRFS1A), DAN (NBL1) and cystatin C (CST3). We have identified novel small non-coding RNAs, primarily from classes other than microRNAs, that are associated with diabetic kidney disease. Our results show that the involvement of small non-coding RNAs in DKD goes beyond the already known microRNAs and also involves other classes of sncRNA, in particular snoRNAs and the piRNA piR-019825, that have never been studied before in relation to kidney function.