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Background: Sub-Saharan Africa is facing a double burden of malnutrition: vitamin A deficiency (VAD) prevails, whereas the nutrition-related chronic conditions type 2 diabetes (T2D) and hypertension are emerging. Serum retinol a VAD marker increases in kidney disease and decreases in inflammation, which can partly be attributed to alterations in the vitamin A transport proteins retinol-binding protein 4 (RBP4) and prealbumin. Kidney dysfunction and inflammation commonly accompany T2D and hypertension.
Objective: Among urban Ghanaians, we investigated the associations of T2D and hypertension with serum retinol as well as the importance of kidney function and inflammation in this regard.
Design: A hospital-based, case-control study in individuals for risk factors of T2D, hypertension, or both was conducted in Kumasi, Ghana (328 controls, 197 with T2D, 354 with hypertension, and 340 with T2D plus hypertension). In 1219 blood samples, serum retinol, RBP4, and prealbumin were measured. Urinary albumin and estimated glomerular filtration rate (eGFR) defined kidney function. C-reactive protein (CRP) >5 mg/L indicated inflammation. We identified associations of T2D and hypertension with retinol by linear regression and calculated the contribution of RBP4, prealbumin, urinary albumin, eGFR, and CRP to these associations as the percentages of the explained variance of retinol.
Results: VAD (retinol <1.05 mu mol/L) was present in 10% of this predominantly female, middle-aged, overweight, and deprived population. Hypertension, but not T2D, was positively associated with retinol (beta: 0.12; 95% CI: 0.08, 0,17), adjusted for age, sex, socioeconomic factors, anthropometric measurements, and lifestyle. In addition to RBP4 (72%) and prealbumin (22%), the effect of increased retinol on individuals with hypertension was mainly attributed to impaired kidney function (eGFR: 30%; urinary albumin: 5%) but not to inflammation.
Conclusions: In patients with hypertension, VAD might be underestimated because of increased serum retinol in the context of kidney dysfunction. Thus, the interpretation of serum retinol in sub-Saharan Africa should account for hypertension status.
Abdominal and general adiposity are independently associated with mortality, but there is no consensus on how best to assess abdominal adiposity. We compared the ability of alternative waist indices to complement body mass index (BMI) when assessing all-cause mortality. We used data from 352,985 participants in the European Prospective Investigation into Cancer and Nutrition (EPIC) and Cox proportional hazards models adjusted for other risk factors. During a mean follow-up of 16.1 years, 38,178 participants died. Combining in one model BMI and a strongly correlated waist index altered the association patterns with mortality, to a predominantly negative association for BMI and a stronger positive association for the waist index, while combining BMI with the uncorrelated A Body Shape Index (ABSI) preserved the association patterns. Sex-specific cohort-wide quartiles of waist indices correlated with BMI could not separate high-risk from low-risk individuals within underweight (BMI<18.5 kg/m(2)) or obese (BMI30 kg/m(2)) categories, while the highest quartile of ABSI separated 18-39% of the individuals within each BMI category, which had 22-55% higher risk of death. In conclusion, only a waist index independent of BMI by design, such as ABSI, complements BMI and enables efficient risk stratification, which could facilitate personalisation of screening, treatment and monitoring.
The Net Reclassification Improvement (NRI) has become a popular metric for evaluating improvement in disease prediction models through the past years. The concept is relatively straightforward but usage and interpretation has been different across studies. While no thresholds exist for evaluating the degree of improvement, many studies have relied solely on the significance of the NRI estimate. However, recent studies recommend that statistical testing with the NRI should be avoided. We propose using confidence ellipses around the estimated values of event and non-event NRIs which might provide the best measure of variability around the point estimates. Our developments are illustrated using practical examples from EPIC-Potsdam study.
Over the last few years, the vegan diet has become increasingly popular in Germany. It has been proposed that this diet is generally lower in fat, but less is known about the impact on fatty acid (FA) profiles. Therefore, the cross-sectional "Risks and Benefits of a Vegan Diet" (RBVD) study (n = 72) was used to investigate dietary FA intake as well as plasma phospholipid FA in vegans (n = 36) compared to omnivores (n = 36). Vegans had a significantly lower dietary intake of total fat (median 86 g/day, IQR 64-111) in comparison to omnivores (median 104 g/day, IQR 88-143, p = 0.004). Further, vegans had a lower intake of saturated fatty acids (SFA) (p < 0.0001) and monounsaturated fatty acids (MUFA) (p = 0.001) compared to omnivores. Vegans had a higher intake in total polyunsaturated fatty acids (PUFA), omega-3 and omega-6 PUFA compared to omnivores, but without statistical significance after Bonferroni correction. According to plasma phospholipid profiles, relatively lower proportions of SFA (p < 0.0001), total trans fatty acids (TFA) (p = 0.0004) and omega-3-FA (p < 0.0001), but higher proportions of omega-6-FA (p < 0.0001) were observed in vegans. With the exception of omega-3 PUFA, a vegan diet is associated with a more favorable dietary fat intake and more favorable plasma FA profiles and therefore may reduce cardiovascular risk.
Background:
Epidemiological evidence indicates that diets rich in plant foods are associated with a lower risk of ischaemic heart disease (IHD), but there is sparse information on fruit and vegetable subtypes and sources of dietary fibre. This study examined the associations of major plant foods, their subtypes and dietary fibre with risk of IHD in the European Prospective Investigation into Cancer and Nutrition (EPIC).
Methods:
We conducted a prospective analysis of 490 311 men and women without a history of myocardial infarction or stroke at recruitment (12.6 years of follow-up, n cases = 8504), in 10 European countries. Dietary intake was assessed using validated questionnaires, calibrated with 24-h recalls. Multivariable Cox regressions were used to estimate hazard ratios (HR) of IHD.
Results:
There was a lower risk of IHD with a higher intake of fruit and vegetables combined [HR per 200 g/day higher intake 0.94, 95% confidence interval (CI): 0.90-0.99, P-trend = 0.009], and with total fruits (per 100 g/day 0.97, 0.95-1.00, P-trend = 0.021). There was no evidence for a reduced risk for fruit subtypes, except for bananas. Risk was lower with higher intakes of nuts and seeds (per 10 g/day 0.90, 0.82-0.98, Ptrend = 0.020), total fibre (per 10 g/day 0.91, 0.85-0.98, P-trend = 0.015), fruit and vegetable fibre (per 4 g/day 0.95, 0.91-0.99, P-trend = 0.022) and fruit fibre (per 2 g/day 0.97, 0.95-1.00, P-trend = 0.045). No associations were observed between vegetables, vegetables subtypes, legumes, cereals and IHD risk.
Conclusions:
In this large prospective study, we found some small inverse associations between plant foods and IHD risk, with fruit and vegetables combined being the most strongly inversely associated with risk. Whether these small associations are causal remains unclear.
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.
Cardiovascular complications are commonly associated with obesity. However, a subgroup of obese individuals may not be at an increased risk for cardiovascular complications; these individuals are said to have metabolically healthy obesity (MHO). In contrast, metabolically unhealthy individuals are at high risk of cardiovascular disease (CVD), irrespective of BMI; thus, this group can include individuals within the normal weight category (BMI 18.5-24.9kg/m(2)). This review provides a summary of prospective studies on MHO and metabolically unhealthy normal-weight (MUHNW) phenotypes. Notably, there is ongoing dispute surrounding the concept of MHO, including the lack of a uniform definition and the potentially transient nature of metabolic health status. This review highlights the relevance of alternative measures of body fatness, specifically measures of fat distribution, for determining MHO and MUHNW. It also highlights alternative approaches of risk stratification, which account for the continuum of risk in relation to CVD, which is observable for most risk factors. Moreover, studies evaluating the transition from metabolically healthy to unhealthy phenotypes and potential determinants for such conversions are discussed. Finally, the review proposes several strategies for the use of epidemiological research to further inform the current debate on metabolic health and its determination across different stages of body fatness.
Aims/hypothesis This study aimed to evaluate associations of height as well as components of height (sitting height and leg length) with risk of type 2 diabetes and to explore to what extent associations are explainable by liver fat and cardiometabolic risk markers. Methods A case-cohort study within the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study comprising 26,437 participants who provided blood samples was designed. We randomly selected a subcohort of 2500 individuals (2029 diabetes-free at baseline and with anamnestic, anthropometrical and metabolic data for analysis). Of the 820 incident diabetes cases identified in the full cohort during 7 years of follow-up, 698 remained for analyses after similar exclusions. Results After adjustment for age, potential lifestyle confounders, education and waist circumference, greater height was related to lower diabetes risk (HR per 10 cm, men 0.59 [95% CI 0.47, 0.75] and women 0.67 [0.51, 0.88], respectively). Leg length was related to lower risk among men and women, but only among men if adjusted for total height. Adjustment for liver fat and triacylglycerols, adiponectin and C-reactive protein substantially attenuated associations between height and diabetes risk, particularly among women. Conclusions/interpretation We observed inverse associations between height and risk of type 2 diabetes, which was largely related to leg length among men. The inverse associations may be partly driven by lower liver fat content and a more favourable cardiometabolic profile.
The pathophysiological influence of gene-lifestyle interactions on the risk to develop type 2 diabetes (T2D) is currently under intensive research. This systematic review summarizes the evidence for gene-lifestyle interactions regarding T2D incidence. MEDLINE, EMBASE, and Web of Science were systematically searched until 31 January 2019 to identify publication with (a) prospective study design; (b) T2D incidence; (c) gene-diet, gene-physical activity, and gene-weight loss intervention interaction; and (d) population who are healthy or prediabetic. Of 66 eligible publications, 28 reported significant interactions. A variety of different genetic variants and dietary factors were studied. Variants at TCF7L2 were most frequently investigated and showed interactions with fiber and whole grain on T2D incidence. Further gene-diet interactions were reported for, eg, a western dietary pattern with a T2D-GRS, fat and carbohydrate with IRS1 rs2943641, and heme iron with variants of HFE. Physical activity showed interaction with HNF1B, IRS1, PPAR gamma, ADRA2B, SLC2A2, and ABCC8 variants and weight loss interventions with ENPP1, PPAR gamma, ADIPOR2, ADRA2B, TNF alpha, and LIPC variants. However, most findings represent single study findings obtained in European ethnicities. Although some interactions have been reported, their conclusiveness is still low, as most findings were not yet replicated across multiple study populations.
Background Cardiovascular disease risk among individuals across different categories of BMI might depend on their metabolic health. It remains unclear to what extent metabolic health status changes over time and whether this affects cardiovascular disease risk. In this study, we aimed to examine the association between metabolic health and its change over time and cardiovascular disease risk across BMI categories. Findings During 2 127 391 person-years of follow-up with a median follow-up of 24 years, we documented 6306 cases of cardiovascular disease including 3304 myocardial infarction cases and 3080 strokes. Cardiovascular disease risk of women with metabolically healthy obesity was increased compared with women with metabolically healthy normal weight (HR 1.39, 95% CI 1.15-1.68), but risk was considerably higher in women with metabolically unhealthy normal weight (2.43, 2.19-2.68), overweight (2.61, 2.36-2.89) and obesity (3.15, 2.83-3.50). The majority of metabolically healthy women converted to unhealthy phenotypes (2555 [84%] of 3027 women with obesity, 22 215 [68%] of 32 882 women with normal-weight after 20 years). Women who maintained metabolically healthy obesity during follow-up were still at a higher cardiovascular disease risk compared with women with stable healthy normal weight (HR 1.57, 1.03-2.38), yet this risk was lower than for initially metabolically healthy women who converted to an unhealthy phenotype (normal-weight 1.90, 1.66-2.17 vs obesity 2.74, 2.30-3.27). Particularly incident diabetes and hypertension increased the risk among women with initial metabolic health. Interpretation Even when metabolic health is maintained during long periods of time, obesity remains a risk factor for cardiovascular disease. However, risks are highest for metabolically unhealthy women across all BMI categories. A large proportion of metabolically healthy women converted to an unhealthy phenotype over time across all BMI categories, which is associated with an increased cardiovascular disease risk. Copyright (C) 2018 Elsevier Ltd. All rights reserved.