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Left ventricular diastolic dysfunction (LVDD) without symptoms, and heart failure (HF) with preserved ejection fraction (HFpEF) represent the most common phenotypes of HF in individuals with type 2 diabetes mellitus, and are more common than HF with reduced ejection fraction (HFrEF), HF with mildly reduced ejection fraction (HFmrEF) and left ventricular systolic dysfunction (LVSD) in these individuals. However, diagnostic criteria for HF have changed over the years, resulting in heterogeneity in the prevalence/incidence rates reported in different studies. We aimed to give an overview of the diagnosis and epidemiology of HF in type 2 diabetes, using both a narrative and systematic review approach; we focus narratively on diagnosing (using the 2021 European Society of Cardiology [ESC] guidelines) and screening for HF in type 2 diabetes. We performed an updated (2016-October 2022) systematic review and meta-analysis of studies reporting the prevalence and incidence of HF subtypes in adults ≥18 years with type 2 diabetes, using echocardiographic data. Embase and MEDLINE databases were searched and data were assessed using random-effects meta-analyses, with findings presented as forest plots. From the 5015 studies found, 209 were screened using the full-text article. In total, 57 studies were included, together with 29 studies that were identified in a prior meta-analysis; these studies reported on the prevalence of LVSD (n=25 studies, 24,460 individuals), LVDD (n=65 studies, 25,729 individuals), HFrEF (n=4 studies, 4090 individuals), HFmrEF (n=2 studies, 2442 individuals) and/or HFpEF (n=8 studies, 5292 individuals), and on HF incidence (n=7 studies, 17,935 individuals). Using Hoy et al's risk-of-bias tool, we found that the studies included generally had a high risk of bias. They showed a prevalence of 43% (95% CI 37%, 50%) for LVDD, 17% (95% CI 7%, 35%) for HFpEF, 6% (95% CI 3%, 10%) for LVSD, 7% (95% CI 3%, 15%) for HFrEF, and 12% (95% CI 7%, 22%) for HFmrEF. For LVDD, grade I was found to be most prevalent. Additionally, we reported a higher incidence rate of HFpEF (7% [95% CI 4%, 11%]) than HFrEF 4% [95% CI 3%, 7%]). The evidence is limited by the heterogeneity of the diagnostic criteria over the years. The systematic section of this review provides new insights on the prevalence/incidence of HF in type 2 diabetes, unveiling a large pre-clinical target group with LVDD/HFpEF in which disease progression could be halted by early recognition and treatment.Registration PROSPERO ID CRD42022368035.
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Diabetes Mellitus Tipo 2 , Insuficiencia Cardíaca , Humanos , Diabetes Mellitus Tipo 2/epidemiología , Diabetes Mellitus Tipo 2/complicaciones , Insuficiencia Cardíaca/epidemiología , Insuficiencia Cardíaca/fisiopatología , Incidencia , Prevalencia , Volumen Sistólico/fisiología , Disfunción Ventricular Izquierda/epidemiología , Disfunción Ventricular Izquierda/fisiopatología , EcocardiografíaRESUMEN
AIMS/HYPOTHESIS: This study aimed to explore the added value of subgroups that categorise individuals with type 2 diabetes by k-means clustering for two primary care registries (the Netherlands and Scotland), inspired by Ahlqvist's novel diabetes subgroups and previously analysed by Slieker et al. METHODS: We used two Dutch and Scottish diabetes cohorts (N=3054 and 6145; median follow-up=11.2 and 12.3 years, respectively) and defined five subgroups by k-means clustering with age at baseline, BMI, HbA1c, HDL-cholesterol and C-peptide. We investigated differences between subgroups by trajectories of risk factor values (random intercept models), time to diabetes-related complications (logrank tests and Cox models) and medication patterns (multinomial logistic models). We also compared directly using the clustering indicators as predictors of progression vs the k-means discrete subgroups. Cluster consistency over follow-up was assessed. RESULTS: Subgroups' risk factors were significantly different, and these differences remained generally consistent over follow-up. Among all subgroups, individuals with severe insulin resistance faced a significantly higher risk of myocardial infarction both before (HR 1.65; 95% CI 1.40, 1.94) and after adjusting for age effect (HR 1.72; 95% CI 1.46, 2.02) compared with mild diabetes with high HDL-cholesterol. Individuals with severe insulin-deficient diabetes were most intensively treated, with more than 25% prescribed insulin at 10 years of diagnosis. For severe insulin-deficient diabetes relative to mild diabetes, the relative risks for using insulin relative to no common treatment would be expected to increase by a factor of 3.07 (95% CI 2.73, 3.44), holding other factors constant. Clustering indicators were better predictors of progression variation relative to subgroups, but prediction accuracy may improve after combining both. Clusters were consistent over 8 years with an accuracy ranging from 59% to 72%. CONCLUSIONS/INTERPRETATION: Data-driven subgroup allocations were generally consistent over follow-up and captured significant differences in risk factor trajectories, medication patterns and complication risks. Subgroups serve better as a complement rather than as a basis for compressing clustering indicators.
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Diabetes Mellitus Tipo 2 , Humanos , Diabetes Mellitus Tipo 2/epidemiología , Diabetes Mellitus Tipo 2/sangre , Diabetes Mellitus Tipo 2/complicaciones , Masculino , Femenino , Persona de Mediana Edad , Anciano , Factores de Riesgo , Países Bajos/epidemiología , Hemoglobina Glucada/metabolismo , Escocia/epidemiología , HDL-Colesterol/sangre , Sistema de Registros , Péptido C/sangre , Progresión de la Enfermedad , Adulto , Análisis por Conglomerados , Resistencia a la Insulina/fisiología , Índice de Masa CorporalRESUMEN
AIMS/HYPOTHESIS: People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA1c and diabetes duration are associated with glycaemic progression, it is unclear how well such variables predict insulin initiation or requirement and whether newly identified markers have added predictive value. METHODS: In two prospective cohort studies as part of IMI-RHAPSODY, we investigated whether clinical variables and three types of molecular markers (metabolites, lipids, proteins) can predict time to insulin requirement using different machine learning approaches (lasso, ridge, GRridge, random forest). Clinical variables included age, sex, HbA1c, HDL-cholesterol and C-peptide. Models were run with unpenalised clinical variables (i.e. always included in the model without weights) or penalised clinical variables, or without clinical variables. Model development was performed in one cohort and the model was applied in a second cohort. Model performance was evaluated using Harrel's C statistic. RESULTS: Of the 585 individuals from the Hoorn Diabetes Care System (DCS) cohort, 69 required insulin during follow-up (1.0-11.4 years); of the 571 individuals in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) cohort, 175 required insulin during follow-up (0.3-11.8 years). Overall, the clinical variables and proteins were selected in the different models most often, followed by the metabolites. The most frequently selected clinical variables were HbA1c (18 of the 36 models, 50%), age (15 models, 41.2%) and C-peptide (15 models, 41.2%). Base models (age, sex, BMI, HbA1c) including only clinical variables performed moderately in both the DCS discovery cohort (C statistic 0.71 [95% CI 0.64, 0.79]) and the GoDARTS replication cohort (C 0.71 [95% CI 0.69, 0.75]). A more extensive model including HDL-cholesterol and C-peptide performed better in both cohorts (DCS, C 0.74 [95% CI 0.67, 0.81]; GoDARTS, C 0.73 [95% CI 0.69, 0.77]). Two proteins, lactadherin and proto-oncogene tyrosine-protein kinase receptor, were most consistently selected and slightly improved model performance. CONCLUSIONS/INTERPRETATION: Using machine learning approaches, we show that insulin requirement risk can be modestly well predicted by predominantly clinical variables. Inclusion of molecular markers improves the prognostic performance beyond that of clinical variables by up to 5%. Such prognostic models could be useful for identifying people with diabetes at high risk of progressing quickly to treatment intensification. DATA AVAILABILITY: Summary statistics of lipidomic, proteomic and metabolomic data are available from a Shiny dashboard at https://rhapdata-app.vital-it.ch .
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Diabetes Mellitus Tipo 2 , Humanos , Diabetes Mellitus Tipo 2/metabolismo , Estudios Prospectivos , Péptido C , Proteómica , Insulina/uso terapéutico , Biomarcadores , Aprendizaje Automático , ColesterolRESUMEN
BACKGROUND: Supermarket interventions are promising to promote healthier dietary patterns, but not all individuals may be equally susceptible. We explored whether the effectiveness of nudging and pricing strategies on diet quality differs by psychological and grocery shopping characteristics. METHODS: We used data of the 12-month Supreme Nudge parallel cluster-randomised controlled supermarket trial, testing nudging and pricing strategies to promote healthier diets. Participants were Dutch speaking adults aged 30-80 years and regular shoppers of participating supermarkets (n = 12) in socially disadvantaged neighbourhoods. Data on psychological characteristics (food-related behaviours; price sensitivity; food decision styles; social cognitive factors; self-control) and grocery shopping characteristics (time spent in the supermarket; moment of the day; average supermarket visits; shopping at other retailers; supermarket proximity) were self-reported at baseline. These characteristics were tested for their moderating effects of the intervention on diet quality (scored 0-150) in linear mixed models. RESULTS: We included 162 participants from intervention supermarkets and 199 from control supermarkets (73% female, 58 (± 10.8) years old, 42% highly educated). The interventions had no overall effect on diet quality. Only five out of 23 potential moderators were statistically significant. Yet, stratified analyses of these significant moderators showed no significant effects on diet quality for one of the subgroups and statistically non-significant negative effects for the other. Negative effects were suggested for individuals with lower baseline levels of meal planning (ß - 2.6, 95% CI - 5.9; 0.8), healthy shopping convenience (ß - 3.0, 95% CI - 7.2; 1.3), and healthy food attractiveness (ß - 3.5, 95% CI - 8.3; 1.3), and with higher levels of price consciousness (ß - 2.6, 95% CI - 6.2; 1.0) and weekly supermarket visits (ß - 2.4, 95% CI - 6.8; 1.9). CONCLUSIONS: Adults with varying psychological and grocery shopping characteristics largely seem equally (un)susceptible to nudging and pricing strategies. It might be that certain characteristics lead to adverse effects, but this is not plausible, and the observed negative effects were small and statistically non-significant and may be explained by chance findings. Verification of these findings is needed in real-world trials based on larger sample sizes and with the use of more comprehensive interventions. TRIAL REGISTRATION: Dutch Trial Register ID NL7064, 30th of May, 2018, https://onderzoekmetmensen.nl/en/trial/20990.
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Supermercados , Humanos , Femenino , Masculino , Persona de Mediana Edad , Adulto , Anciano , Países Bajos , Anciano de 80 o más Años , Comercio , Promoción de la Salud/métodos , Dieta Saludable/economía , Costos y Análisis de CostoRESUMEN
BACKGROUND: Context-specific interventions may contribute to sustained behaviour change and improved health outcomes. We evaluated the real-world effects of supermarket nudging and pricing strategies and mobile physical activity coaching on diet quality, food-purchasing behaviour, walking behaviour, and cardiometabolic risk markers. METHODS: This parallel cluster-randomised controlled trial included supermarkets in socially disadvantaged neighbourhoods across the Netherlands with regular shoppers aged 30-80 years. Supermarkets were randomised to receive co-created nudging and pricing strategies promoting healthier purchasing (N = 6) or not (N = 6). Nudges targeted 9% of supermarket products and pricing strategies 3%. Subsequently, participants were individually randomised to a control (step counter app) or intervention arm (step counter and mobile coaching app) to promote walking. The primary outcome was the average change in diet quality (low (0) to high (150)) over all follow-up time points measured with a validated 40-item food frequency questionnaire at baseline and 3, 6, and 12 months. Secondary outcomes included healthier food purchasing (loyalty card-derived), daily step count (step counter app), cardiometabolic risk markers (lipid profile and HbA1c via finger prick, and waist circumference via measuring tape), and supermarket customer satisfaction (questionnaire-based: very unsatisfied (1) to very satisfied (7)), evaluated using linear mixed-models. Healthy supermarket sales (an exploratory outcome) were analysed via controlled interrupted time series analyses. RESULTS: Of 361 participants (162 intervention, 199 control), 73% were female, the average age was 58 (SD 11) years, and 42% were highly educated. Compared to the control arm, the intervention arm showed no statistically significant average changes over time in diet quality (ß ï»¿- 1.1 (95% CI - 3.8 to 1.7)), percentage healthy purchasing (ß 0.7 ( - 2.7 to 4.0)), step count (ß ï»¿- 124.0 (- 723.1 to 475.1), or any of the cardiometabolic risk markers. Participants in the intervention arm scored 0.3 points (0.1 to 0.5) higher on customer satisfaction on average over time. Supermarket-level sales were unaffected (ß - 0.0 (- 0.0 to 0.0)). CONCLUSIONS: Co-created nudging and pricing strategies that predominantly targeted healthy products via nudges were unable to increase healthier food purchases and intake nor improve cardiometabolic health. The mobile coaching intervention did not affect step count. Governmental policy measures are needed to ensure more impactful supermarket modifications that promote healthier purchases. TRIAL REGISTRATION: Dutch Trial Register ID NL7064, 30 May 2018, https://www.onderzoekmetmensen.nl/en/trial/20990.
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Enfermedades Cardiovasculares , Tutoría , Humanos , Femenino , Persona de Mediana Edad , Masculino , Supermercados , Estilo de Vida , Ejercicio Físico , Enfermedades Cardiovasculares/epidemiología , Enfermedades Cardiovasculares/prevención & controlRESUMEN
AIM: To uncover differences in small non-coding RNAs (sncRNAs) in individuals with type 2 diabetes (T2D) categorized into five clusters based on individual characteristics, which may aid in the identification of those prone to rapid progression. MATERIALS AND METHODS: In the Hoorn Diabetes Care System (DCS) cohort, participants were clustered by age, body mass index (BMI), and glycated haemoglobin, C-peptide and high-density lipoprotein (HDL) cholesterol levels, yielding severe insulin-deficient diabetes, severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), mild diabetes, and mild diabetes with high HDL cholesterol clusters (n = 412). Utilizing plasma sncRNA-sequencing, we identified distinct cluster-specific sncRNAs. Validation was performed in a smaller DCS Hoorn dataset (n = 138). To elucidate their potential functions, we examined tissue expression, identified potential targets or (co-)regulated proteins, conducted gene set enrichment analyses on the targets through Reactome, and examined tissue expression of the (co-)regulated proteins. RESULTS: The insulin-resistant cluster exhibited aberrant expression of 10 sncRNAs, while the high BMI cluster featured eight differentially expressed sncRNAs. Multiple (co-)regulated proteins were identified for sncRNAs associated with both clusters. Proteins associated with both clusters showed enrichment for metabolism. Proteins that specifically and only associated with the SIRD cluster showed enrichment for immune-related signalling. Furthermore, MOD cluster-specific associated proteins showed enrichment for the complement system. CONCLUSIONS: Our research showed differential sncRNA levels among type 2 diabetes clusters. This may reflect and could deepen our understanding of molecular mechanisms, in development, progression, and risk factors for each cluster.
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AIM: To investigate the association of plasma metabolites with incident and prevalent chronic kidney disease (CKD) in people with type 2 diabetes and establish whether this association is causal. MATERIALS AND METHODS: The Hoorn Diabetes Care System cohort is a large prospective cohort consisting of individuals with type 2 diabetes from the northwest part of the Netherlands. In this cohort we assessed the association of baseline plasma levels of 172 metabolites with incident (Ntotal = 462/Ncase = 81) and prevalent (Ntotal = 1247/Ncase = 120) CKD using logistic regression. Additionally, replication in the UK Biobank, body mass index (BMI) mediation and causality of the association with Mendelian randomization was performed. RESULTS: Elevated levels of total and individual branched-chain amino acids (BCAAs)-valine, leucine and isoleucine-were associated with an increased risk of incident CKD, but with reduced odds of prevalent CKD, where BMI was identified as an effect modifier. The observed inverse effects were replicated in the UK Biobank. Mendelian randomization analysis did not provide evidence for a causal relationship between BCAAs and prevalent CKD. CONCLUSIONS: Our study shows the intricate relationship between plasma BCAA levels and CKD in individuals with type 2 diabetes. While an association exists, its manifestation varies based on disease status and BMI, with no definitive evidence supporting a causal link between BCAAs and prevalent CKD.
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Diabetes Mellitus Tipo 2 , Insuficiencia Renal Crónica , Humanos , Diabetes Mellitus Tipo 2/complicaciones , Diabetes Mellitus Tipo 2/epidemiología , Factores de Riesgo , Estudios Prospectivos , Aminoácidos de Cadena Ramificada/efectos adversos , Aminoácidos de Cadena Ramificada/metabolismo , Insuficiencia Renal Crónica/complicaciones , Insuficiencia Renal Crónica/epidemiología , Insuficiencia Renal Crónica/inducido químicamenteRESUMEN
OBJECTIVE: Social jetlag is a discordance between the social and biological rhythm and is associated with higher HbA1c, higher BMI, and higher odds of obesity. The pathways that could explain these associations are still debated. This study aims to assess the mediating role of several lifestyle factors in the cross-sectional association between social jetlag and BMI. METHODS: We used cross-sectional data from 1784 adults from urban areas in the Netherlands, collected in 2019. Social jetlag (difference in midpoint of sleep between week and weekend nights) was categorized as low(<1 h), moderate(1-2h), and high(>2 h). BMI(kg/m2) was calculated from self-reported height and weight. The association between social jetlag and BMI was assessed using linear regression, adjusted for sex, age, education, and sleep duration and stratified for the effect modifier stress (high vs. low). Mediation analysis was performed for self-reported smoking, physical activity, alcohol consumption, and adherence to a healthy diet. RESULTS: High social jetlag was associated with higher BMI (0.69 kg/m2,95%CI 0.05;1.33). This association was stronger in people with high stress (0.93 kg/m2,95%CI 0.09;1.76). Social jetlag was also associated with higher odds of smoking, lower physical activity, higher alcohol consumption, and lower healthy diet adherence. In people with high stress, these factors mediated 10-15% of the association between social jetlag and BMI. CONCLUSIONS: Social jetlag is associated with higher BMI and this association is stronger in people with high stress. In people with high stress, healthy diet adherence mediated 12% of this association. Other pathways involved in this association should be further investigated.
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OBJECTIVE: This study aimed to investigate cross-sectional associations of total, animal, and plant-based protein intake and depressive symptoms in Dutch adults with type 2 diabetes (T2D). METHODS: We included 1137 individuals with T2D (aged 68.6 ± 9.0) from the Hoorn Diabetes Care System cohort. Energy-adjusted protein intake was assessed using a validated Food Frequency Questionnaire. The nine-item Patient Health Questionnaire (PHQ-9) was used to assess the prevalence of depressive symptoms (PHQ-9 ≥ 10 and/or anti-depressant use) and the severity of depressive symptoms (continuous PHQ-9 score). Associations between total, animal, and plant-based protein (quartiles) with depressive symptoms were assessed using multiple logistic and linear regression. RESULTS: Highest intake of total, animal, and plant-based protein was not associated with the prevalence of depressive symptoms, compared to lowest intake (e.g., total protein, ORQ4vsQ1:0.75, 95%CI 0.42;1.32). For the severity of depressive symptoms, highest total protein intake was significantly associated with lower PHQ-9 scores (ORQ4vsQ1:0.87, 95%CI 0.75;1.00), compared to lowest intake. Animal protein was not associated with the severity of depressive symptoms (ß â¼ 1), while the association for plant-based protein was marginally non-significant (ßQ4vsQ1:0.88, 95%CI 0.76;1.02). CONCLUSION: In individuals with T2D, higher total protein intake was associated with reduced severity of depressive symptoms, but not with the prevalence of depressive symptoms. Further prospective research with a larger sample size is needed to confirm these associations.
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Depresión , Diabetes Mellitus Tipo 2 , Humanos , Diabetes Mellitus Tipo 2/epidemiología , Diabetes Mellitus Tipo 2/psicología , Masculino , Estudios Transversales , Femenino , Países Bajos/epidemiología , Anciano , Prevalencia , Depresión/epidemiología , Persona de Mediana Edad , Proteínas en la Dieta/administración & dosificación , Encuestas y Cuestionarios , AnimalesRESUMEN
BACKGROUND: Obesity is a key risk factor for major chronic diseases such as type 2 diabetes and cardiovascular diseases. To extensively characterise the obesogenic built environment, we recently developed a novel Obesogenic Built environment CharacterisTics (OBCT) index, consisting of 17 components that capture both food and physical activity (PA) environments. OBJECTIVES: We aimed to assess the association between the OBCT index and body mass index (BMI) in a nationwide health monitor. Furthermore, we explored possible ways to improve the index using unsupervised and supervised methods. METHODS: The OBCT index was constructed for 12,821 Dutch administrative neighbourhoods and linked to residential addresses of eligible adult participants in the 2016 Public Health Monitor. We split the data randomly into a training (two-thirds; n = 255,187) and a testing subset (one-third; n = 127,428). In the training set, we used non-parametric restricted cubic regression spline to assess index's association with BMI, adjusted for individual demographic characteristics. Effect modification by age, sex, socioeconomic status (SES) and urbanicity was examined. As improvement, we (1) adjusted the food environment for address density, (2) added housing price to the index and (3) adopted three weighting strategies, two methods were supervised by BMI (variable selection and random forest) in the training set. We compared these methods in the testing set by examining their model fit with BMI as outcome. RESULTS: The OBCT index had a significant non-linear association with BMI in a fully-adjusted model (p<0.05), which was modified by age, sex, SES and urbanicity. However, variance in BMI explained by the index was low (<0.05%). Supervised methods increased this explained variance more than non-supervised methods, though overall improvements were limited as highest explained variance remained <0.5%. DISCUSSION: The index, despite its potential to highlight disparity in obesogenic environments, had limited association with BMI. Complex improvements are not necessarily beneficial, and the components should be re-operationalised.
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Índice de Masa Corporal , Entorno Construido , Obesidad , Características de la Residencia , Humanos , Femenino , Masculino , Obesidad/epidemiología , Persona de Mediana Edad , Adulto , Países Bajos , Ejercicio Físico , Anciano , Adulto Joven , AdolescenteRESUMEN
In this observational cross-sectional study, we investigated the relationship between combined obesogenic neighbourhood characteristics and various cardiovascular disease risk factors in adults, including BMI, systolic blood pressure, and blood lipids, as well as the prevalence of overweight/obesity, hypertension, and dyslipidaemia. We conducted a large-scale pooled analysis, comprising data from five Dutch cohort studies (n = 183,871). Neighbourhood obesogenicity was defined according to the Obesogenic Built-environmental CharacterisTics (OBCT) index. The index was calculated for 1000m circular buffers around participants' home addresses. For each cohort, the association between the OBCT index and prevalence of overweight/obesity, hypertension and dyslipidaemia was analysed using robust Poisson regression models. Associations with continuous measures of BMI, systolic blood pressure, LDL-cholesterol, HDL-cholesterol, and triglycerides were analysed using linear regression. All models were adjusted for age, sex, education level and area-level socio-economic status. Cohort-specific estimates were pooled using random-effects meta-analyses. The pooled results show that a 10 point higher OBCT index score was significantly associated with a 0.17 higher BMI (95%CI: 0.10 to 0.24), a 0.01 higher LDL-cholesterol (95% CI: 0.01 to 0.02), a 0.01 lower HDL cholesterol (95% CI: -0.02 to -0.01), and non-significantly associated with a 0.36 mmHg higher systolic blood pressure (95%CI: -0.14 to 0.65). A 10 point higher OBCT index score was also associated with a higher prevalence of overweight/obesity (PR = 1.03; 95% CI: 1.02 to 1.05), obesity (PR = 1.04; 95% CI: 1.01 to 1.08) and hypertension (PR = 1.02; 95% CI: 1.00 to 1.04), but not with dyslipidaemia. This large-scale pooled analysis of five Dutch cohort studies shows that higher neighbourhood obesogenicity, as measured by the OBCT index, was associated with higher BMI, higher prevalence of overweight/obesity, obesity, and hypertension. These findings highlight the importance of considering the obesogenic environment as a potential determinant of cardiovascular health.
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Presión Sanguínea , Obesidad , Humanos , Estudios Transversales , Masculino , Obesidad/epidemiología , Obesidad/sangre , Femenino , Persona de Mediana Edad , Países Bajos/epidemiología , Adulto , Estudios de Cohortes , Hipertensión/epidemiología , Hipertensión/sangre , Anciano , Lípidos/sangre , Prevalencia , Dislipidemias/epidemiología , Dislipidemias/sangre , Características de la Residencia , Índice de Masa Corporal , Peso CorporalRESUMEN
BACKGROUND: Geographic access to food may affect dietary choices and health outcomes, but the strength and direction of associations may depend on the operationalization of exposure measures. We aimed to systematically review the literature on up-to-date evidence on the association between food environment exposures based on Global Positioning System (GPS) and diet-related and cardiometabolic health outcomes. METHODS: The databases PubMed, Embase.com, APA PsycInfo (via Ebsco), Cinahl (via Ebsco), the Web of Science Core Collection, Scopus, and the International Bibliography of the Social Sciences (via ProQuest) were searched from inception to October 31, 2022. We included studies that measured the activity space through GPS tracking data to identify exposure to food outlets and assessed associations with either diet-related or cardiometabolic health outcomes. Quality assessment was evaluated using the criteria from a modified version of the Newcastle-Ottawa Scale (NOS) for cross-sectional studies. We additionally used four items from a quality assessment tool to specifically assess the quality of GPS measurements. RESULTS: Of 2949 studies retrieved, 14 studies fulfilled our inclusion criteria. They were heterogeneous and represent inconsistent evidence. Yet, three studies found associations between food outlets and food purchases, for example, more exposure to junk food outlets was associated with higher odds of junk food purchases. Two studies found associations between greater exposure to fast food outlets and higher fast food consumption and out of three studies that investigated food environment in relation to metabolic outcomes, two studies found that higher exposure to an unhealthy food environment was associated with higher odds of being overweight. CONCLUSIONS: The current and limited evidence base does not provide strong evidence for consistent associations of GPS-based exposures of the food environment with diet-related and cardiometabolic health outcomes.
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Enfermedades Cardiovasculares , Sistemas de Información Geográfica , Humanos , Estudios Transversales , Ambiente , DietaRESUMEN
Apolipoprotein-CIII (apo-CIII) inhibits the clearance of triglycerides from circulation and is associated with an increased risk of diabetes complications. It exists in four main proteoforms: O-glycosylated variants containing either zero, one, or two sialic acids and a non-glycosylated variant. O-glycosylation may affect the metabolic functions of apo-CIII. We investigated the associations of apo-CIII glycosylation in blood plasma, measured by mass spectrometry of the intact protein, and genetic variants with micro- and macrovascular complications (retinopathy, nephropathy, neuropathy, cardiovascular disease) of type 2 diabetes in a DiaGene study (n = 1571) and the Hoorn DCS cohort (n = 5409). Mono-sialylated apolipoprotein-CIII (apo-CIII1) was associated with a reduced risk of retinopathy (ß = -7.215, 95% CI -11.137 to -3.294) whereas disialylated apolipoprotein-CIII (apo-CIII2) was associated with an increased risk (ß = 5.309, 95% CI 2.279 to 8.339). A variant of the GALNT2-gene (rs4846913), previously linked to lower apo-CIII0a, was associated with a decreased prevalence of retinopathy (OR = 0.739, 95% CI 0.575 to 0.951). Higher apo-CIII1 levels were associated with neuropathy (ß = 7.706, 95% CI 2.317 to 13.095) and lower apo-CIII0a with macrovascular complications (ß = -9.195, 95% CI -15.847 to -2.543). In conclusion, apo-CIII glycosylation was associated with the prevalence of micro- and macrovascular complications of diabetes. Moreover, a variant in the GALNT2-gene was associated with apo-CIII glycosylation and retinopathy, suggesting a causal effect. The findings facilitate a molecular understanding of the pathophysiology of diabetes complications and warrant consideration of apo-CIII glycosylation as a potential target in the prevention of diabetes complications.
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Apolipoproteína C-III , Diabetes Mellitus Tipo 2 , Anciano , Femenino , Humanos , Masculino , Persona de Mediana Edad , Apolipoproteína C-III/genética , Apolipoproteína C-III/metabolismo , Diabetes Mellitus Tipo 2/metabolismo , Diabetes Mellitus Tipo 2/complicaciones , Diabetes Mellitus Tipo 2/genética , Angiopatías Diabéticas/metabolismo , Angiopatías Diabéticas/genética , Angiopatías Diabéticas/etiología , Retinopatía Diabética/metabolismo , Retinopatía Diabética/genética , Retinopatía Diabética/etiología , Glicosilación , Polimorfismo de Nucleótido SimpleRESUMEN
AIMS/HYPOTHESIS: Both manifestations of kidney disease in diabetes, reduced eGFR (ml/min per 1.73 m2) and increased urinary albumin/creatinine ratio (UACR, mg/mmol), may increase the risk of specific CVD subtypes in adults with diabetes. METHODS: We assessed the prospective association between annually recorded measures of eGFR and UACR and the occurrence of myocardial infarction (MI), CHD, stroke, heart failure (HF) and cardiovascular mortality in 13,657 individuals with diabetes (53.6% male, age 62.3±12.1 years) from the Hoorn Diabetes Care System cohort, using data obtained between 1998 and 2018. Multivariate time-dependent Cox regression models adjusted for cardiovascular risk factors were used to estimate HRs and 95% CI. Associations of eGFR were adjusted for UACR values and vice versa. Effect modification by sex was investigated for all associations. RESULTS: After a mean follow-up period of 7 years, event rates per 1000 person-years were 3.08 for MI, 3.72 for CHD, 1.12 for HF, 0.84 for stroke and 6.25 for cardiovascular mortality. Mildly reduced eGFR (60-90 ml/min per 1.73 m2) and moderately to severely reduced eGFR (<59 ml/min per 1.73 m2) were associated with higher risks of MI (HR 1.52; 95% CI 1.10, 2.12 and HR 1.69; 95% CI 1.09, 2.64) and CHD (HR 1.67; 95% CI 1.23, 2.26 and HR 2.01; 95% CI 1.34, 3.02) compared with normal eGFR (>90 ml/min per 1.73 m2). Mildly reduced eGFR was associated with a higher risk of stroke (HR 2.53; 95% CI 1.27, 5.03). Moderately increased UACR (3-30 mg/mmol) and severely increased UACR (>30 mg/mmol) were prospectively associated with a higher cardiovascular mortality risk in men and women (HR 1.87; 95% CI 1.41, 2.47 and HR 2.78; 95% CI 1.78, 4.34) compared with normal UACR (<3 mg/mmol). Significant effect modification by sex was observed for the association between UACR and HF. Because there were a limited number of HF events within the category of UACR >30 mg/mmol, categories were combined into UACR <3.0 and >3.0 mg/mmol in the stratified analysis. Women but not men with UACR >3.0 mg/mmol had a significantly higher risk of HF compared with normal UACR (HR 2.79; 95% CI 1.47, 5.28). CONCLUSIONS/INTERPRETATION: This study showed differential and independent prospective associations between manifestations of early kidney damage in diabetes and several CVD subtypes, suggesting that regular monitoring of both kidney function measures may help to identify individuals at higher risk of specific cardiovascular events.
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Enfermedades Cardiovasculares , Diabetes Mellitus Tipo 2 , Insuficiencia Cardíaca , Insuficiencia Renal Crónica , Accidente Cerebrovascular , Adulto , Humanos , Masculino , Femenino , Persona de Mediana Edad , Anciano , Diabetes Mellitus Tipo 2/epidemiología , Tasa de Filtración Glomerular , Riñón , Accidente Cerebrovascular/epidemiología , AlbuminuriaRESUMEN
AIMS/HYPOTHESIS: The aim of this study was to identify differentially expressed long non-coding RNAs (lncRNAs) and mRNAs in whole blood of people with type 2 diabetes across five different clusters: severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), mild diabetes (MD) and mild diabetes with high HDL-cholesterol (MDH). This was to increase our understanding of different molecular mechanisms underlying the five putative clusters of type 2 diabetes. METHODS: Participants in the Hoorn Diabetes Care System (DCS) cohort were clustered based on age, BMI, HbA1c, C-peptide and HDL-cholesterol. Whole blood RNA-seq was used to identify differentially expressed lncRNAs and mRNAs in a cluster compared with all others. Differentially expressed genes were validated in the Innovative Medicines Initiative DIabetes REsearCh on patient straTification (IMI DIRECT) study. Expression quantitative trait loci (eQTLs) for differentially expressed RNAs were obtained from a publicly available dataset. To estimate the causal effects of RNAs on traits, a two-sample Mendelian randomisation analysis was performed using public genome-wide association study (GWAS) data. RESULTS: Eleven lncRNAs and 175 mRNAs were differentially expressed in the MOD cluster, the lncRNA AL354696.2 was upregulated in the SIDD cluster and GPR15 mRNA was downregulated in the MDH cluster. mRNAs and lncRNAs that were differentially expressed in the MOD cluster were correlated among each other. Six lncRNAs and 120 mRNAs validated in the IMI DIRECT study. Using two-sample Mendelian randomisation, we found 52 mRNAs to have a causal effect on anthropometric traits (n=23) and lipid metabolism traits (n=10). GPR146 showed a causal effect on plasma HDL-cholesterol levels (p = 2×10-15), without evidence for reverse causality. CONCLUSIONS/INTERPRETATION: Multiple lncRNAs and mRNAs were found to be differentially expressed among clusters and particularly in the MOD cluster. mRNAs in the MOD cluster showed a possible causal effect on anthropometric traits, lipid metabolism traits and blood cell fractions. Together, our results show that individuals in the MOD cluster show aberrant RNA expression of genes that have a suggested causal role on multiple diabetes-relevant traits.
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Diabetes Mellitus Tipo 2 , Insulinas , ARN Largo no Codificante , Humanos , Diabetes Mellitus Tipo 2/genética , Metabolismo de los Lípidos/genética , ARN Largo no Codificante/genética , ARN Largo no Codificante/metabolismo , Estudio de Asociación del Genoma Completo , HDL-Colesterol , Expresión Génica , Obesidad/complicaciones , Obesidad/genética , Receptores de Péptidos/genética , Receptores de Péptidos/metabolismo , Receptores Acoplados a Proteínas G/metabolismoRESUMEN
Patient-reported outcomes (PROs) are valuable for shared decision making and research. Patient-reported outcome measures (PROMs) are questionnaires used to measure PROs, such as health-related quality of life (HRQL). Although core outcome sets for trials and clinical practice have been developed separately, they, as well as other initiatives, recommend different PROs and PROMs. In research and clinical practice, different PROMs are used (some generic, some disease-specific), which measure many different things. This is a threat to the validity of research and clinical findings in the field of diabetes. In this narrative review, we aim to provide recommendations for the selection of relevant PROs and psychometrically sound PROMs for people with diabetes for use in clinical practice and research. Based on a general conceptual framework of PROs, we suggest that relevant PROs to measure in people with diabetes are: disease-specific symptoms (e.g. worries about hypoglycaemia and diabetes distress), general symptoms (e.g. fatigue and depression), functional status, general health perceptions and overall quality of life. Generic PROMs such as the 36-Item Short Form Health Survey (SF-36), WHO Disability Assessment Schedule (WHODAS 2.0), or Patient-Reported Outcomes Measurement Information System (PROMIS) measures could be considered to measure commonly relevant PROs, supplemented with disease-specific PROMs where needed. However, none of the existing diabetes-specific PROM scales has been sufficiently validated, although the Diabetes Symptom Self-Care Inventory (DSSCI) for measuring diabetes-specific symptoms and the Diabetes Distress Scale (DDS) and Problem Areas in Diabetes (PAID) for measuring distress showed sufficient content validity. Standardisation and use of relevant PROs and psychometrically sound PROMs can help inform people with diabetes about the expected course of disease and treatment, for shared decision making, to monitor outcomes and to improve healthcare. We recommend further validation studies of diabetes-specific PROMs that have sufficient content validity for measuring disease-specific symptoms and consider generic item banks developed based on item response theory for measuring commonly relevant PROs.
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Diabetes Mellitus , Calidad de Vida , Humanos , Medición de Resultados Informados por el Paciente , Encuestas y Cuestionarios , Encuestas Epidemiológicas , Diabetes Mellitus/terapiaRESUMEN
BACKGROUND: Microvascular dysfunction (MVD) is an important contributor to major clinical disease such as stroke, dementia, depression, retinopathy, and chronic kidney disease. Alcohol consumption may be a determinant of MVD. OBJECTIVE: Main objectives were (1) to study whether alcohol consumption was associated with MVD as assessed in the brain, retina, skin, kidney and in the blood; and (2) to investigate whether associations differed by history of cardiovascular disease or sex. DESIGN: We used cross-sectional data from The Maastricht Study (N = 3,120 participants, 50.9% men, mean age 60 years, and 27.5% with type 2 diabetes [the latter oversampled by design]). We used regression analyses to study the association between total alcohol (per unit and in the categories, i.e. none, light, moderate, high) and MVD, where all measures of MVD were combined into a total MVD composite score (expressed in SD). We adjusted all associations for potential confounders; and tested for interaction by sex, and history of cardiovascular disease. Additionally we tested for interaction with glucose metabolism status. RESULTS: The association between total alcohol consumption and MVD was non-linear, i.e. J-shaped. Moderate versus light total alcohol consumption was significantly associated with less MVD, after full adjustment (beta [95% confidence interval], -0.10 [-0.19; -0.01]). The shape of the curve differed with sex (Pinteraction = 0.03), history of cardiovascular disease (Pinteraction < 0.001), and glucose metabolism status (Pinteraction = 0.02). CONCLUSIONS: The present cross-sectional, population-based study found evidence that alcohol consumption may have an effect on MVD. Hence, although increasing alcohol consumption cannot be recommended as a policy, this study suggests that prevention of MVD may be possible through dietary interventions.
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Enfermedades Cardiovasculares , Diabetes Mellitus Tipo 2 , Masculino , Humanos , Persona de Mediana Edad , Femenino , Enfermedades Cardiovasculares/diagnóstico , Enfermedades Cardiovasculares/epidemiología , Enfermedades Cardiovasculares/complicaciones , Estudios Transversales , Consumo de Bebidas Alcohólicas/efectos adversos , Consumo de Bebidas Alcohólicas/epidemiología , GlucosaRESUMEN
AIMS/HYPOTHESIS: Inflammation is important in the development of type 2 diabetes complications. The N-glycosylation of IgG influences its role in inflammation. To date, the association of plasma IgG N-glycosylation with type 2 diabetes complications has not been extensively investigated. We hypothesised that N-glycosylation of IgG may be related to the development of complications of type 2 diabetes. METHODS: In three independent type 2 diabetes cohorts, plasma IgG N-glycosylation was measured using ultra performance liquid chromatography (DiaGene n = 1815, GenodiabMar n = 640) and mass spectrometry (Hoorn Diabetes Care Study n = 1266). We investigated the associations of IgG N-glycosylation (fucosylation, galactosylation, sialylation and bisection) with incident and prevalent nephropathy, retinopathy and macrovascular disease using Cox- and logistic regression, followed by meta-analyses. The models were adjusted for age and sex and additionally for clinical risk factors. RESULTS: IgG galactosylation was negatively associated with prevalent and incident nephropathy and macrovascular disease after adjustment for clinical risk factors. Sialylation was negatively associated with incident diabetic nephropathy after adjustment for clinical risk factors. For incident retinopathy, similar associations were found for galactosylation, adjusted for age and sex. CONCLUSIONS: We showed that IgG N-glycosylation, particularly galactosylation and to a lesser extent sialylation, is associated with a higher prevalence and future development of macro- and microvascular complications of diabetes. These findings indicate the predictive potential of IgG N-glycosylation in diabetes complications and should be analysed further in additional large cohorts to obtain the power to solidify these conclusions.
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This study aims to determine the association between social jetlag and parameters of metabolic syndrome and type 2 diabetes (T2D) in a systematic review and meta-analysis. A systematic literature search was conducted in PubMed/Embase/Scopus until May 2022. Included studies described an association between social jetlag and parameters of the metabolic syndrome and/or T2D, were available full text and written in English or Dutch. Data extraction and quality assessment were performed on pre-piloted forms independently by two reviewers. Results were meta-analysed using random-effects analysis. A total of 6,290 titles/abstracts were screened, 176 papers were read full-text, 68 studies were included. Three studies were rated as low quality, 27 were moderate, and 38 were high quality. High quality studies showed that having social jetlag compared to no social jetlag was significantly associated with higher body mass index in 20 studies (0.49 kg/m2 , 95% confidence interval [CI] 0.21-0.77; I2 = 100%), higher waist circumference in seven studies (1.11 cm, 95% CI 0.42-1.80; I2 = 25%), higher systolic blood pressure in 10 studies (0.37 mmHg, 95% CI 0.00-0.74; I2 = 94%) and higher glycated haemoglobin in 12 studies (0.42%, 95% CI 0.12- 0.72; I2 = 100%). No statistically significant associations were found for obesity, abdominal obesity, high- and low-density lipoprotein levels, cholesterol, triglycerides, diastolic blood pressure, hypertension, fasting glucose, homeostatic model assessment for insulin resistance, metabolic syndrome or T2D. Sensitivity analyses did not reduce heterogeneity. Despite substantial heterogeneity, social jetlag is associated with certain parameters of the metabolic syndrome and T2D, but not with prevalent metabolic syndrome or T2D. These findings should be interpreted with caution as the level of evidence is low and mostly based on cross-sectional data. Longitudinal studies are needed to further assess the direction of causality.
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Diabetes Mellitus Tipo 2 , Resistencia a la Insulina , Síndrome Metabólico , Humanos , Síndrome Metabólico/complicaciones , Diabetes Mellitus Tipo 2/complicaciones , Estudios Transversales , Obesidad/complicaciones , Síndrome Jet Lag/complicacionesRESUMEN
OBJECTIVE: Low dietary guideline adherence is persistent, but there is limited understanding of how individuals with varying socio-economic backgrounds reach a certain dietary intake. We investigated how quantitative and qualitative data on dietary guidelines adherence correspond and complement each other, to what extent determinants of guideline adherence in quantitative data reflect findings on determinants derived from qualitative data and which of these determinants emerged as interdependent in the qualitative data. DESIGN: This mixed-methods study used quantitative questionnaire data (n 1492) and qualitative data collected via semi-structured telephone interviews (n 24). Quantitative data on determinants and their association with total guideline adherence (scored 0-150) were assessed through linear regression. Directed content analysis was used for qualitative data. SETTING: Dutch urban areas. PARTICIPANTS: Adults aged 18-65 years. RESULTS: A range of determinants emerged from both data sources, for example higher levels of cognitive restraint (ß 5·6, 95 % CI 4·2, 7·1), habit strength of vegetables (ß 4·0, 95 % CI 3·3, 4·7) and cooking skills (ß 4·7, 95 % CI 3·5, 5·9), were associated with higher adherence. Qualitative data additionally suggested the influence of food prices, strong dietary habits and the social aspect of eating, and for the determinants cognitive restraint, habit strength related to vegetables, food prices and home cooking, some variation between interviewees with varying socio-economic backgrounds emerged in how these determinants affected guideline adherence. CONCLUSIONS: This mixed-methods exploration provides a richer understanding of why adults with varying socio-economic backgrounds do or do not adhere to dietary guidelines. Results can guide future interventions promoting healthy diets across populations.