Adherence to a food group-based dietary guideline and incidence of prediabetes and type 2 diabetes

Development of an objectively measured walkability index for the Netherlands

Background

Walkability indices have been developed and linked to behavioural and health outcomes elsewhere in the world, but not comprehensively for Europe. We aimed to 1) develop a theory-based and evidence-informed Dutch walkability index, 2) examine its cross-sectional associations with total and purpose-specific walking behaviours of adults across socioeconomic (SES) and urbanisation strata, 3) explore which walkability components drive these associations.

Methods

Components of the index included: population density, retail and service density, land use mix, street connectivity, green space, sidewalk density and public transport density. Each of the seven components was calculated for three Euclidean buffers: 150 m, 500 m and 1000 m around every 6-digit postal code location and for every administrative neighbourhood in GIS. Componential z-scores were averaged, and final indices normalized between 0 and 100. Data on self-reported demographic characteristics and walking behaviours of 16,055 adult respondents (aged 18–65) were extracted from the Dutch National Travel Survey 2017. Using Tobit regression modelling adjusted for individual- and household-level confounders, we assessed the associations between walkability and minutes walking in total, for non-discretionary and discretionary purposes. By assessing the attenuation in associations between partial indices and walking outcomes, we identified which of the seven components drive these associations. We also tested for effect modification by urbanization degree, SES, age and sex.

Results

In fully adjusted models, a 10% increase in walkability was associated with a maximum increase of 8.5 min of total walking per day (95%CI: 7.1–9.9). This association was consistent across buffer sizes and purposes of walking. Public transport density was driving the index’s association with walking outcomes. Stratified results showed that associations with minutes of non-discretionary walking were stronger in rural compared to very urban areas, in neighbourhoods with low SES compared to high SES, and in middle-aged (36–49 years) compared to young (18–35 years old) and older adults (50–65 years old).

Conclusions

The walkability index was cross-sectionally associated with Dutch adult’s walking behaviours, indicating its validity for further use in research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12966-022-01270-8.

Adherence to the Dutch healthy diet index and change in glycemic control and cardiometabolic markers in people with type 2 diabetes

Purpose

To investigate whether adherence to the Dutch Healthy Diet index 2015 (DHD15-index) is associated with change in glycemic control and cardio-metabolic markers over two-year follow-up in people with type 2 diabetes (T2D).

Methods

This prospective cohort study included 1202 individuals with T2D (mean age 68.7 ± 9.0 years; 62.5% male; mean HbA1c 53.8 ± 11.7 mmol/mol) from the Diabetes Care System cohort. Baseline dietary intake was assessed using a validated food frequency questionnaire, and adherence to the DHD15-index was estimated (range 0–130). HbA1c, fasting glucose, blood lipids (HDL and LDL cholesterol, cholesterol ratio), blood pressure, estimated glomerular filtration rate (eGFR), and BMI were measured at baseline, and after one- and two-year follow-up. Linear mixed model analyses were conducted to examine the associations between adherence to the DHD15-index and glycemic control and the cardio-metabolic outcomes, adjusting for energy intake, sociodemographic and lifestyle characteristics, and medication.

Results

Highest adherence (T3) to the DHD15-index was not associated with change in HbA1c, compared to lowest adherence (T1) [β_T3vsT1: 0.62 mmol/mol (− 0.94; 2.19), P_trend = 0.44]. There was a non-linear association with fasting glucose, where moderate adherence (T2) was associated with a decrease in fasting glucose [β_T2vsT1: − 0.29 mmol/L (− 0.55; − 0.03), P_trend = 0.30]. Higher adherence to the DHD15-index was associated with a decrease in BMI [β_10point: − 0.41 kg/m² (− 0.60; − 0.21), P_trend < 0.001], but not with blood lipids, blood pressure or kidney function.

Conclusion

In this well-controlled population of people with T2D, adherence to the DHD15-index was associated with a decrease in BMI, but not with change in glycemic control or other cardio-metabolic parameters.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00394-022-02847-6.

Mediterranean Diet and Healthy Eating in Subjects with Prediabetes from the Mollerussa Prospective Observational Cohort Study

We aimed to assess differences in dietary patterns (i.e., Mediterranean diet and healthy eating indexes) between participants with prediabetes and those with normal glucose tolerance. Secondarily, we analyzed factors related to prediabetes and dietary patterns. This was a cross-sectional study design. From a sample of 594 participants recruited in the Mollerussa study cohort, a total of 535 participants (216 with prediabetes and 319 with normal glucose tolerance) were included. The alternate Mediterranean Diet score (aMED) and the alternate Healthy Eating Index (aHEI) were calculated. Bivariable and multivariable analyses were performed. There was no difference in the mean aMED and aHEI scores between groups (3.2 (1.8) in the normoglycemic group and 3.4 (1.8) in the prediabetes group, p = 0.164 for the aMED and 38.6 (7.3) in the normoglycemic group and 38.7 (6.7) in the prediabetes group, p = 0.877 for the aHEI, respectively). Nevertheless, women had a higher mean of aMED and aHEI scores in the prediabetes group (3.7 (1.9), p = 0.001 and 40.5 (6.9), p < 0.001, respectively); moreover, they had a higher mean of aHEI in the group with normoglycemia (39.8 (6.6); p = 0.001). No differences were observed in daily food intake between both study groups; consistent with this finding, we did not find major differences in nutrient intake between groups. In the multivariable analyses, the aMED and aHEI were not associated with prediabetes (odds ratio (OR): 1.19, 95% confidence interval (CI): 0.75-1.87; p = 0.460 and OR: 1.32, 95% CI: 0.83-2.10; p = 0.246, respectively); however, age (OR: 1.04, 95% CI: 1.02-1.05; p < 0.001), dyslipidemia (OR: 2.02, 95% CI: 1.27-3.22; p = 0.003) and body mass index (BMI) (OR: 1.09, 95% CI: 1.05-1.14; p < 0.001) were positively associated with prediabetes. Physical activity was associated with a lower frequency of prediabetes (OR: 0.48, 95% CI: 0.31-0.72; p = 0.001). In conclusion, subjects with prediabetes did not show a different dietary pattern compared with a normal glucose tolerance group. However, further research is needed on this issue.

Deep phenotyping meets big data: the Geoscience and hEalth Cohort COnsortium (GECCO) data to enable exposome studies in The Netherlands

Environmental exposures are increasingly investigated as possible drivers of health behaviours and disease outcomes. So-called exposome studies that aim to identify and better understand the effects of exposures on behaviours and disease risk across the life course require high-quality environmental exposure data. The Netherlands has a great variety of environmental data available, including high spatial and often temporal resolution information on urban infrastructure, physico-chemical exposures, presence and availability of community services, and others. Until recently, these environmental data were scattered and measured at varying spatial scales, impeding linkage to individual-level (cohort) data as they were not operationalised as personal exposures, that is, the exposure to a certain environmental characteristic specific for a person. Within the Geoscience and hEalth Cohort COnsortium (GECCO) and with support of the Global Geo Health Data Center (GGHDC), a platform has been set up in The Netherlands where environmental variables are centralised, operationalised as personal exposures, and used to enrich 23 cohort studies and provided to researchers upon request. We here present and detail a series of personal exposure data sets that are available within GECCO to date, covering personal exposures of all residents of The Netherlands (currently about 17 M) over the full land surface of the country, and discuss challenges and opportunities for its use now and in the near future.

Supplementation with alpha-linolenic acid and inflammation: a feasibility trial

Consumption of omega-3 fatty acids, including the precursor α-linolenic acid (ALA) is often sub-optimal and not in line with international guidelines. Supplementation is debatable, but some individuals, e.g., pre-diabetic, low-grade inflammation, cardiometabolic yet otherwise healthy subjects, might benefit from supra-physiological omega-3 intake, particularly to lessen inflammation. We explored the feasibility of a large clinical trial by performing a pilot study to evaluate adherence, palatability, and self-reported side effects of ALA administration in a group of volunteers. We enrolled 12 individuals with borderline dyslipidemia or overweight, treated with dietary advice according to international guidelines and who had insufficient intakes of essential fatty acids. Subjects were followed for nutritional counselling and were matched with appropriate controls. Patients were administered 6 g/day of ALA, for two months. We report the absence of side effects. such as fishy aftertaste and gastrointestinal distress, in addition to a slight decrease of C-reactive protein concentrations (Identifier: ISRCTN13118704).

Association of Dietary Patterns and Type-2 Diabetes Mellitus in Metabolically Homogeneous Subgroups in the KORA FF4 Study

There is evidence that a change in lifestyle, especially physical activity and diet, can reduce the risk of developing type-2 diabetes mellitus (T2DM). However, the response to dietary changes varies among individuals due to differences in metabolic characteristics. Therefore, we investigated the association between dietary patterns and T2DM while taking into account these differences. For 1287 participants of the population-based KORA FF4 study (Cooperative Health Research in the Region of Augsburg), we identified three metabolically-homogenous subgroups (metabotypes) using 16 clinical markers. Based on usual dietary intake data, two diet quality scores, the Mediterranean Diet Score (MDS) and the Alternate Healthy Eating Index (AHEI), were calculated. We explored the associations between T2DM and diet quality scores. Multi-variable adjusted models, including metabotype subgroup, were fitted. In addition, analyses stratified by metabotype were carried out. We found significant interaction effects between metabotype and both diet quality scores (p < 0.05). In the analysis stratified by metabotype, significant negative associations between T2DM and both diet quality scores were detected only in the metabolically-unfavorable homogenous subgroup (Odds Ratio (OR) = 0.62, 95% confidence interval (CI) = 0.39-0.90 for AHEI and OR = 0.60, 95% CI = 0.40-0.96 for MDS). Prospective studies taking metabotype into account are needed to confirm our results, which allow for the tailoring of dietary recommendations in the prevention of T2DM.