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STUDY OBJECTIVES: We investigated the prevalence of self-reported insomnia symptoms in people with type 2 diabetes and assessed the association with metabolic outcomes and the mediating role of lifestyle factors. METHODS: In a prospective cohort of 1,272 participants with type 2 diabetes (63.4% male, age 68.7 ± 9 years) we measured insomnia symptoms using the Insomnia Severity Index and metabolic outcomes as hemoglobin A1c, glucose, lipids, and body mass index at baseline and at 1 year follow-up. Linear regression analyses assessed the association between insomnia symptoms and metabolic outcomes, corrected for demographic factors, comorbidities, and body mass index. Mediation analyses were conducted for lifestyle factors. RESULTS: The prevalence of mild and severe insomnia symptoms was 23.0% and 10.7%, respectively. When adjusted for demographic factors and comorbidities, cross-sectionally severe insomnia symptoms were associated with higher body mass index (β = 0.97 kg/m2; 95% confidence interval 0.04: 1.89) compared to no insomnia symptoms. Cross-sectionally, no associations were observed for the other metabolic outcomes. Additionally, no prospective associations were observed with any of the outcomes. Finally, physical activity mediated the association between severe insomnia symptoms and body mass index by 29.3%. CONCLUSIONS: About a third of people with type 2 diabetes experience self-reported insomnia symptoms, but insomnia symptoms were not associated with metabolic outcomes in people with type 2 diabetes. CITATION: Groeneveld L, den Braver NR, Beulens JWJ, et al. The prevalence of self-reported insomnia symptoms and association with metabolic outcomes in people with type 2 diabetes: the Hoorn Diabetes Care System cohort. J Clin Sleep Med. 2023;19(3):539-548.

BACKGROUND: It is hypothesized that inflammation leads to heart failure. Results from observational studies thus far have been inconsistent and it is unclear whether inflammation is causally associated with new-onset heart failure. Mendelian randomization analyses are less prone to biases common in observational studies such as reverse causation and unmeasured confounding. The aim of this study was to investigate the causal relation between various inflammatory biomarkers with risk of new-onset heart failure by using a two-sample Mendelian randomization approach. METHODS: Ten inflammatory biomarkers with available genome-wide association studies (GWAS) among individuals of European ancestry were identified and included C-reactive protein (CRP), immunoglobulin E, tumour necrosis factor (TNF), toll-like receptor 4, interleukin 1 receptor antagonist, interleukin 2 receptor subunit α, interleukin 6 receptor subunit α, interleukin 16, 17 and 18. For the associations between the identified SNPs and heart failure, we used the largest GWAS meta-analysis performed by the Heart Failure Molecular Epidemiology for Therapeutic Targets Consortium with 47 309 participants with heart failure and 930 014 controls. For our main analyses, we used the inverse-variance weighted method. RESULTS: We included 63 SNPs. CRP, TNF, interleukin 2, 16 and 18 were not associated with heart failure with odds ratios (ORs) of 1.01 [95% confidence interval (95% CI: 0.94-1.09), 1.11 (95% CI: 0.80-1.48), 0.97 (95% CI: 0.93-1.02), 0.99 (95% CI: 0.96-1.03) and 1.01 (95% CI: 0.97-1.06), respectively. The other biomarkers were also not associated with the risk of heart failure and suffered from weak instrument bias. CONCLUSION: This Mendelian randomization study could not determine a causal relationship between inflammation and risk of heart failure. However, some biomarkers suffered from weak instrument bias. ispartof: JOURNAL OF CARDIOVASCULAR MEDICINE vol:23 issue:11 pages:728-735 ispartof: location:United States status: published

AIMS: The HFA-PEFF score was developed to optimize diagnosis and to aid in early recognition of heart failure (HF) with preserved ejection fraction (HFpEF) in patients who present with HF-like symptoms. Recognizing early-HFpEF phenogroups is essential to better understand progression towards overt HFpEF and pave the way for early intervention and treatment. Whether the HFA-PEFF domain scores can identify 'early-HFpEF' phenogroups remains unknown. The aims of this pilot study are to (i) identify distinct phenogroups by cluster analysis of HFA-PEFF domain scores in subjects that present with HF-like symptoms and (ii) study whether these phenogroups may be associated with distinct blood proteome profiles. METHODS AND RESULTS: Subjects referred to the Cardiology Centers of the Netherlands, location Utrecht, with non-acute possibly cardiac-related symptoms (such as dyspnoea or fatigue) were prospectively enrolled in the HELPFul cohort (N = 507) and were included in the current analysis. Inclusion criteria for this study were (i) age ≥ 45 years and (ii) a left ventricular ejection fraction (LVEF) ≥ 50%, in the absence of a history of HF, coronary artery disease, congenital heart disease, or any previous cardiac interventions. Multinominal-based clustering with latent class model using the HFA-PEFF domain scores (functional, structural, and biomarker scores) as input was used to detect distinct phenotypic clusters. For each bootstrapping run, the 92 Olink proteins were analysed for their association with the identified phenogroups. Four distinct phenogroups were identified in the current analysis (validated by bootstrapping 1000×): (i) no left ventricular diastolic dysfunction (no LVDD, N = 102); (ii) LVDD with functional left ventricular (LV) abnormalities (N = 204); (iii) LVDD with functional and structural LV abnormalities (N = 204); and (iv) LVDD with functional and structural LV abnormalities and elevated BNP (N = 107). The HFA-PEFF total score risk categories significantly differed between the phenogroups (P

Chapter 2 gives an overview of the available evidence on the diagnostic performance of natriuretic peptides (NPs) to detect left ventricular diastolic dysfunction (DD) and heart failure with preserved ejection fraction (HFpEF) based on a systematic review and meta-analysis of 51 studies. The meta-analysis indicated a reasonable diagnostic performance for both NPs for the detection of DD and HFpEF. As the risk of bias of the included studies is high and sensitivity of NPs for detection of DD or HFpEF is low compared to specificity, use of NPs alone to detect DD or HFpEF should be discouraged as recommended by current guidelines. In Chapter 3, we give an overview of the available evidence on the diagnostic performance and risk of bias of novel biomarkers in the detection of HFpEF. We included a total of 28 studies. Use of case-control/two-gated designs, exclusion of difficult-to-diagnose patients, absence of a pre-specified cutoff value for the index test without external validation, the use of inappropriate reference standards and unclear timing of the index test and/or reference standard were the most important forms of bias. In Chapters 4 and 5, we studied the association between inflammation and heart failure (HF), and HFpEF-associated echocardiographic measures. In Chapter 4, we performed two-sample Mendelian Randomization analyses. Nine inflammatory biomarkers with available genome-wide association studies (GWAS) among individuals of European ancestry were identified and included: C-reactive protein, immunoglobulin E, tumor necrosis factor, interleukin 1 receptor antagonist, interleukin 2 receptor subunit α, interleukin 6 receptor subunit α, interleukin 16, interleukin 17 and interleukin 18. For the associations between the identified SNPs and HF, we used the largest GWAS meta-analysis performed by the Heart Failure Molecular Epidemiology for Therapeutic Targets Consortium with 47,309 participants with HF and 930,014 controls. In the analyses, we included 63 SNPs, of which 51 were for C-reactive protein. C-reactive protein, tumor necrosis factor and interleukin 18 were not associated with risk of incident HF: ORs per 1 unit increase in log-transformed biomarker levels of 1.01, 1.11 and 1.01, respectively. Other inflammatory biomarkers were also not associated with risk of incident HF, but may have suffered from weak instrument bias. In Chapter 5, we investigated within-person and betweenpersons associations of biomarkers of inflammation and endothelial dysfunction with left ventricular ejection fraction (LVEF), left ventricular mass index (LVMI) and left atrial volume index (LAVI). We included 383 participants of the Hoorn Study, a Dutch population-based cohort, and 491 participants of FLEMENGHO, a Flemish population-based cohort. Both cohorts had inflammatory biomarker and echocardiographic measurements at baseline and ~8 and ~5 years of follow-up, respectively. Overall Z-scores for low-grade inflammation or endothelial dysfunction were not associated with echocardiographic measures. Effect modification by sex was apparent for TNF-α in the Hoorn Study and E-selectin in FLEMENGHO with LVEF. In the Hoorn Study, women whose TNF-α levels increased with 1-SD over time had a decrease in LVEF. In FLEMENGHO, men whose E-selectin levels increased with 1-SD over time had a decrease in LVEF. In Chapter 6, we determined the sex-specific prospective associations between body composition measures and cardiac structure and function after 8 years of follow-up in 321 participants of the Hoorn Study. Total body fat, trunk fat and leg fat were associated with increased LVMI, while lean mass was associated with lower LVMI. Body composition measures were not associated with LVEF or LAVI. These associations were not modified by sex or mediated by inflammation. In Chapter 7, we studied the associations between serum and dietary advanced glycation endproducts and cardiac structure and function. Increased pentosidine and overall serum AGEs Z-scores over time were associated with decreased LVEF at follow-up.

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Additional file 3: Figure S1. Prisma Flow diagram of the included studies. Figure S2. Risk of Bias score and applicability assessment of the 51 included studies. Figure S3. Funnel plots for summary estimates of NPs for the detection of DD and HFpEF.

Additional file 2: Additional information about the methods. Table S1. Information of the reference diagnoses of the 51 included studies. Table S2. Diagnostic performance measures of the 51 included studies. Table S3. Subgroup analyses stratified by geographic location or assay.