Dietary amino acids and risk of stroke subtypes: a prospective analysis of 356,000 participants in seven European countries

PURPOSE

Previously reported associations of protein-rich foods with stroke subtypes have prompted interest in the assessment of individual amino acids. We examined the associations of dietary amino acids with risks of ischaemic and haemorrhagic stroke in the EPIC study.

METHODS

We analysed data from 356,142 participants from seven European countries. Dietary intakes of 19 individual amino acids were assessed using validated country-specific dietary questionnaires, calibrated using additional 24-h dietary recalls. Multivariable-adjusted Cox regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of ischaemic and haemorrhagic stroke in relation to the intake of each amino acid. The role of blood pressure as a potential mechanism was assessed in 267,642 (75%) participants.

RESULTS

After a median follow-up of 12.9 years, 4295 participants had an ischaemic stroke and 1375 participants had a haemorrhagic stroke. After correction for multiple testing, a higher intake of proline (as a percent of total protein) was associated with a 12% lower risk of ischaemic stroke (HR per 1 SD higher intake 0.88; 95% CI 0.82, 0.94). The association persisted after mutual adjustment for all other amino acids, systolic and diastolic blood pressure. The inverse associations of isoleucine, leucine, valine, phenylalanine, threonine, tryptophan, glutamic acid, serine and tyrosine with ischaemic stroke were each attenuated with adjustment for proline intake. For haemorrhagic stroke, no statistically significant associations were observed in the continuous analyses after correcting for multiple testing.

CONCLUSION

Higher proline intake may be associated with a lower risk of ischaemic stroke, independent of other dietary amino acids and blood pressure.

The cost-effectiveness of a uniform versus age-based threshold for one-off screening for prevention of cardiovascular disease

The objective of this article was to assess the cost-effectiveness of screening strategies for cardiovascular diseases (CVD). A decision analytic model was constructed to estimate the costs and benefits of one-off screening strategies differentiated by screening age, sex and the threshold for initiating statin therapy ("uniform" or "age-adjusted") from the Spanish NHS perspective. The age-adjusted thresholds were configured so that the same number of people at high risk would be treated as under the uniform threshold. Health benefit was measured in quality-adjusted life years (QALY). Transition rates were estimated from the European Prospective Investigation into Cancer and Nutrition (EPIC-CVD), a large multicentre nested case-cohort study with 12 years of follow-up. Unit costs of primary care, hospitalizations and CVD care were taken from the Spanish health system. Univariate and probabilistic sensitivity analyses were employed. The comparator was no systematic screening program. The base case model showed that the most efficient one-off strategy is to screen both men and women at 40 years old using a uniform risk threshold for initiating statin treatment (Incremental Cost-Effectiveness Ratio of €3,274/QALY and €6,085/QALY for men and women, respectively). Re-allocating statin treatment towards younger individuals at high risk for their age and sex would not offset the benefit obtained using those same resources to treat older individuals. Results are sensitive to assumptions about CVD incidence rates. To conclude, one-off screening for CVD using a uniform risk threshold appears cost-effective compared with no systematic screening. These results should be evaluated in clinical studies.

Precision prognostics for the development of complications in diabetes

Individuals with diabetes face higher risks for macro- and microvascular complications than their non-diabetic counterparts. The concept of precision medicine in diabetes aims to optimise treatment decisions for individual patients to reduce the risk of major diabetic complications, including cardiovascular outcomes, retinopathy, nephropathy, neuropathy and overall mortality. In this context, prognostic models can be used to estimate an individual's risk for relevant complications based on individual risk profiles. This review aims to place the concept of prediction modelling into the context of precision prognostics. As opposed to identification of diabetes subsets, the development of prediction models, including the selection of predictors based on their longitudinal association with the outcome of interest and their discriminatory ability, allows estimation of an individual's absolute risk of complications. As a consequence, such models provide information about potential patient subgroups and their treatment needs. This review provides insight into the methodological issues specifically related to the development and validation of prediction models for diabetes complications. We summarise existing prediction models for macro- and microvascular complications, commonly included predictors, and examples of available validation studies. The review also discusses the potential of non-classical risk markers and omics-based predictors. Finally, it gives insight into the requirements and challenges related to the clinical applications and implementation of developed predictions models to optimise medical decision making.

Immunoglobulin G N-Glycosylation Signatures in Incident Type 2 Diabetes and Cardiovascular Disease

OBJECTIVE

N-glycosylation is a functional posttranslational modification of immunoglobulins (Igs). We hypothesized that specific IgG N-glycans are associated with incident type 2 diabetes and cardiovascular disease (CVD).

RESEARCH DESIGN AND METHODS

We performed case-cohort studies within the population-based European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort (2,127 in the type 2 diabetes subcohort [741 incident cases]; 2,175 in the CVD subcohort [417 myocardial infarction and stroke cases]). Relative abundances of 24 IgG N-glycan peaks (IgG-GPs) were measured by ultraperformance liquid chromatography, and eight glycosylation traits were derived based on structural similarity. End point-associated IgG-GPs were preselected with fractional polynomials, and prospective associations were estimated in confounder-adjusted Cox models. Diabetes risk associations were validated in three independent studies.

RESULTS

After adjustment for confounders and multiple testing correction, IgG-GP7, IgG-GP8, IgG-GP9, IgG-GP11, and IgG-GP19 were associated with type 2 diabetes risk. A score based on these IgG-GPs was associated with a higher diabetes risk in EPIC-Potsdam and independent validation studies (843 total cases, 3,149 total non-cases, pooled estimate per SD increase 1.50 [95% CI 1.37-1.64]). Associations of IgG-GPs with CVD risk differed between men and women. In women, IgG-GP9 was inversely associated with CVD risk (hazard ratio [HR] per SD 0.80 [95% CI 0.65-0.98]). In men, a weighted score based on IgG-GP19 and IgG-GP23 was associated with higher CVD risk (HR per SD 1.47 [95% CI 1.20-1.80]). In addition, several derived traits were associated with cardiometabolic disease incidence.

CONCLUSIONS

Selected IgG N-glycans are associated with cardiometabolic risk beyond classic risk factors, including clinical biomarkers.

Retinol and Retinol Binding Protein 4 Levels and Cardiometabolic Disease Risk

BACKGROUND

Despite mechanistic studies linking retinol and RBP4 (retinol binding protein 4) to the pathogenesis of cardiovascular diseases (CVD) and type 2 diabetes (T2D), epidemiological evidence is still conflicting. We investigated whether conflicting results of previous studies may be explained by differences in the association of retinol and RBP4 with cardiometabolic risk across subgroups with distinct sex, hypertension state, liver, or kidney function.

METHODS

We used case-cohorts nested in the EPIC (European Prospective Investigation Into Cancer and Nutrition)-Potsdam cohort (N=27 548) comprising a random sample of participants (n=2500) and all physician-verified cases of incident CVD (n=508, median follow-up time 8.2 years) and T2D (n=820, median follow-up time 6.3 years). We estimated nonlinear and linear multivariable-adjusted associations between the biomarkers and cardiometabolic diseases by restricted cubic splines and Cox regression, respectively, testing potential interactions with hypertension, liver, and kidney function. Additionally, we performed 2-sample Mendelian Randomization analyses in publicly available data.

RESULTS

The association of retinol with cardiometabolic risk was modified by hypertension state (P interaction CVD<0.001; P interaction T2D<0.001). Retinol was associated with lower cardiometabolic risk in participants with treated hypertension (hazard ratio_{per SD} [95% CI]: CVD, 0.71 [0.56-0.90]; T2D, 0.81 [0.70-0.94]) but with higher cardiometabolic risk in normotensive participants (CVD, 1.32 [1.06-1.64]; T2D, 1.15 [0.98-1.36]). Our analyses also indicated a significant interaction between RBP4 and hypertension on CVD risk (P interaction=0.04). Regarding T2D risk, we observed a u-shaped association with RBP4 in women (P nonlinearity=0.01, P effect=0.02) and no statistically significant association in men. The biomarkers' interactions with liver or kidney function were not statistically significant. Hypertension state-specific associations for retinol concentrations with cardiovascular mortality risk were replicated in National Health and Nutrition Examination Survey III.

CONCLUSIONS

Our findings suggest a hypertension-dependent relationship between plasma retinol and cardiometabolic risk and complex interactions of RBP4 with sex and hypertension on cardiometabolic risk.

German Diabetes Risk Score for the Determination of the Individual Type 2 Diabetes Risk

BACKGROUND

The German Diabetes Risk Score (GDRS) currently enables prediction of the individual risk of developing type 2 diabetes (T2D) within five years. The aim of this study is to extend the prediction period of the GDRS, including its non-clinical version and its HbA1c extension, to 10 years, and to perform external validation.

METHODS

In data from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study (n = 25 393), Cox proportional hazards regression was used to reweight the points that were used to calculate the five-year risk. Two population-based prospective cohorts (EPIC-Heidelberg n = 23 624, GNHIES98 cohort n = 3717) were used for external validation. Discrimination was represented by C-indices, and calibration by calibration plots and the expected-to-observed (E/O) ratio.

RESULTS

Prediction performance in EPIC-Potsdam was very good (C-index for the non-clinical model: 0.834) and was confirmed in EPIC-Heidelberg (0.843) and in the GNHIES98 cohort (0.851). Among persons in the GNHIES98 cohort with a greater than 10% predicted probability of disease, 14.9% developed T2D within 10 years (positive predictive value). The models were very well calibrated in EPIC-Potsdam (E/O ratio for the non-clinical model: 1.08), slightly overestimated the risk in EPIC-Heidelberg (1.34), and predicted T2D very well in the GNHIES98 cohort after recalibration (1.06).

CONCLUSION

The extended GDRS prediction period of 10 years, with a non-clinical version and an HbA1c extension that will soon be available in both German and English, enables the even longer-range, evidence-based identification of high-risk individuals with many different applications, including medical screening.

Prospective evaluation of 92 serum protein biomarkers for early detection of ovarian cancer

BACKGROUND

CA125 is the best available yet insufficiently sensitive biomarker for early detection of ovarian cancer. There is a need to identify novel biomarkers, which individually or in combination with CA125 can achieve adequate sensitivity and specificity for the detection of earlier-stage ovarian cancer.

METHODS

In the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we measured serum levels of 92 preselected proteins for 91 women who had blood sampled ≤18 months prior to ovarian cancer diagnosis, and 182 matched controls. We evaluated the discriminatory performance of the proteins as potential early diagnostic biomarkers of ovarian cancer.

RESULTS

Nine of the 92 markers; CA125, HE4, FOLR1, KLK11, WISP1, MDK, CXCL13, MSLN and ADAM8 showed an area under the ROC curve (AUC) of ≥0.70 for discriminating between women diagnosed with ovarian cancer and women who remained cancer-free. All, except ADAM8, had shown at least equal discrimination in previous case-control comparisons. The discrimination of the biomarkers, however, was low for the lag-time of >9-18 months and paired combinations of CA125 with any of the 8 markers did not improve discrimination compared to CA125 alone.

CONCLUSION

Using pre-diagnostic serum samples, this study identified markers with good discrimination for the lag-time of 0-9 months. However, the discrimination was low in blood samples collected more than 9 months prior to diagnosis, and none of the markers showed major improvement in discrimination when added to CA125.

SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe

AIMS

The aim of this study was to develop, validate, and illustrate an updated prediction model (SCORE2) to estimate 10-year fatal and non-fatal cardiovascular disease (CVD) risk in individuals without previous CVD or diabetes aged 40-69 years in Europe.

METHODS AND RESULTS

We derived risk prediction models using individual-participant data from 45 cohorts in 13 countries (677 684 individuals, 30 121 CVD events). We used sex-specific and competing risk-adjusted models, including age, smoking status, systolic blood pressure, and total- and HDL-cholesterol. We defined four risk regions in Europe according to country-specific CVD mortality, recalibrating models to each region using expected incidences and risk factor distributions. Region-specific incidence was estimated using CVD mortality and incidence data on 10 776 466 individuals. For external validation, we analysed data from 25 additional cohorts in 15 European countries (1 133 181 individuals, 43 492 CVD events). After applying the derived risk prediction models to external validation cohorts, C-indices ranged from 0.67 (0.65-0.68) to 0.81 (0.76-0.86). Predicted CVD risk varied several-fold across European regions. For example, the estimated 10-year CVD risk for a 50-year-old smoker, with a systolic blood pressure of 140 mmHg, total cholesterol of 5.5 mmol/L, and HDL-cholesterol of 1.3 mmol/L, ranged from 5.9% for men in low-risk countries to 14.0% for men in very high-risk countries, and from 4.2% for women in low-risk countries to 13.7% for women in very high-risk countries.

CONCLUSION

SCORE2-a new algorithm derived, calibrated, and validated to predict 10-year risk of first-onset CVD in European populations-enhances the identification of individuals at higher risk of developing CVD across Europe.

Plasma N-Glycans as Emerging Biomarkers of Cardiometabolic Risk: A Prospective Investigation in the EPIC-Potsdam Cohort Study

OBJECTIVE

Plasma protein N-glycan profiling integrates information on enzymatic protein glycosylation, which is a highly controlled ubiquitous posttranslational modification. Here we investigate the ability of the plasma N-glycome to predict incidence of type 2 diabetes and cardiovascular diseases (CVDs; i.e., myocardial infarction and stroke).

RESEARCH DESIGN AND METHODS

Based on the prospective European Prospective Investigation of Cancer (EPIC)-Potsdam cohort (n = 27,548), we constructed case-cohorts including a random subsample of 2,500 participants and all physician-verified incident cases of type 2 diabetes (n = 820; median follow-up time 6.5 years) and CVD (n = 508; median follow-up time 8.2 years). Information on the relative abundance of 39 N-glycan groups in baseline plasma samples was generated by chromatographic profiling. We selected predictive N-glycans for type 2 diabetes and CVD separately, based on cross-validated machine learning, nonlinear model building, and construction of weighted prediction scores. This workflow for CVD was applied separately in men and women.

RESULTS

The N-glycan-based type 2 diabetes score was strongly predictive for diabetes risk in an internal validation cohort (weighted C-index 0.83, 95% CI 0.78-0.88), and this finding was externally validated in the Finland Cardiovascular Risk Study (FINRISK) cohort. N-glycans were moderately predictive for CVD incidence (weighted C-indices 0.66, 95% CI 0.60-0.72, for men; 0.64, 95% CI 0.55-0.73, for women). Information on the selected N-glycans improved the accuracy of established and clinically applied risk prediction scores for type 2 diabetes and CVD.

CONCLUSIONS

Selected N-glycans improve type 2 diabetes and CVD prediction beyond established risk markers. Plasma protein N-glycan profiling may thus be useful for risk stratification in the context of precisely targeted primary prevention of cardiometabolic diseases.

Perceived diabetes risk and related determinants in individuals with high actual diabetes risk: results from a nationwide population-based survey

Objective

The purpose of this study was first, to examine perceived diabetes risk compared with actual diabetes risk in the general population and second, to investigate which factors determine whether persons at increased actual risk also perceive themselves at elevated risk.

Research design and methods

The study comprised adults (aged 18-97 years) without known diabetes from a nationwide survey on diabetes-related knowledge and information needs in Germany in 2017. Actual diabetes risk was calculated by an established risk score estimating the 5-year probability of developing type 2 diabetes and was compared with perceived risk of getting diabetes over the next 5 years (response options: 'almost no risk', 'slight risk', 'moderate risk', 'high risk'; n = 2327). Among adults with an increased actual diabetes risk (n=639), determinants of perceived risk were investigated using multivariable logistic regression analysis.

Results

Across groups with a 'low' (<2%), 'still low' (2% to<5%), 'elevated' (5% to <10%), and 'high' (≥10%) actual diabetes risk, a proportion of 89.0%, 84.5%, 79.3%, and 78.9%, respectively, perceived their diabetes risk as almost absent or slight. Among those with an increased (elevated/high) actual risk, independent determinants of an increased (moderate/high) perceived risk included younger age (OR 0.92 (95% CI 0.88 to 0.96) per year), family history of diabetes (2.10 (1.06-4.16)), and being informed about an increased diabetes risk by a physician (3.27 (1.51-7.07)), but none of further diabetes risk factors, healthcare behaviors or beliefs about diabetes.

Conclusions

Across categories of actual diabetes risk, perceived diabetes risk was low, even if actual diabetes risk was high. For effective strategies of primary diabetes prevention, attention should be directed to risk communication at the population level as well as in primary care practice.