A Model of Individual BMI Trajectories.

A risk factor model of body mass index (BMI) is an important building block of health simulations aimed at estimating government policy effects with regard to overweight and obesity. We created a model that generates representative population level distributions and that also mimics realistic BMI trajectories at an individual level so that policies aimed at individuals can be simulated. The model is constructed by combining several datasets. First, the population level distribution is extracted from a large, cross-sectional dataset. The trend in this distribution is estimated from historical data. In addition, longitudinal data are used to model how individuals move along typical trajectories over time. The model faithfully describes the population level distribution of BMI, stratified by sex, level of education and age. It is able to generate life course trajectories for individuals which seem plausible, but it does not capture extreme fluctuations, such as rapid weight loss.

Relative validity of habitual sugar and low/no-calorie sweetener consumption assessed by food frequency questionnaire, multiple 24-h dietary recalls and urinary biomarkers: an observational study within the SWEET project.

BACKGROUND: Studies investigating associations between sweeteners and health yield inconsistent results, possibly due to subjective self-report dietary assessment methods. OBJECTIVES: We compared the performance of a food frequency questionnaire (FFQ), multiple 24-h dietary recalls (24hRs), and urinary biomarkers to estimate intake of sugars and low/no-calorie sweeteners (LNCSs). METHODS: Participants (n = 848, age 54 ± 12 y) from a 2-y observational study completed 1 semiquantitative FFQ and ≥ 3 nonconsecutive 24hRs. Both methods assessed intake of sugars (mono- and disaccharides, sucrose, fructose, free and added sugars) and sweetened foods and beverages (sugary foods, fruit juice, and sugar or LNCS-containing beverages [sugar-sweetened beverages and low/no-calorie sweetened beverages (LNCSBs)]); 24hRs also included LNCS-containing foods and tabletop sweeteners (low/no-calorie sweetened foods [LNCSFs]). Urinary excretion of sugars (fructose+sucrose) and LNCSs (acesulfame K+sucralose+steviol glucuronide+cyclamate+saccharin) were simultaneously assessed using ultrapressure liquid chromatography coupled to tandem mass spectrometry in 288 participants with 3 annual 24-h urine samples. Methods were compared using, amongst others, validity coefficients (correlations corrected for measurement error). RESULTS: Median (interquartile range) FFQ intakes ranged from 0 (0-7) g/d for LNCSBs to 94 (73-117) g/d for mono- and disaccharides. LNCSB use was reported by 32% of participants. Median LNCSB+LNCSF intake using 24hRs was 1 (0-50) g/d and reported by 58%. Total sugar excretions were detected in 100% of samples [56 (37-85) mg/d] and LNCSs in 99% of urine samples [3 (1-10) mg/d]. Comparing FFQ against 24hRs showed VCs ranging from 0.38 (fruit juice) to 0.74 (LNCSB). VCs for comparing FFQ with urinary excretions were 0.25 to 0.29 for sugars and 0.39 for LNCSBs; for 24hR they amounted to 0.31-0.38 for sugars, 0.37 for LNCSBs, and 0.45 for LNCSFs. CONCLUSIONS: The validity of the FFQ against 24hRs for the assessment of sugars and LNCSBs ranged from moderate to good. Comparing self-reports and urine excretions showed moderate agreement but highlighted an important underestimation of LNCS exposure using self-reports.

Constructing synthetic populations in the age of big data.

BACKGROUND: To develop public health intervention models using micro-simulations, extensive personal information about inhabitants is needed, such as socio-demographic, economic and health figures. Confidentiality is an essential characteristic of such data, while the data should reflect realistic scenarios. Collection of such data is possible only in secured environments and not directly available for open-source micro-simulation models. The aim of this paper is to illustrate a method of construction of synthetic data by predicting individual features through models based on confidential data on health and socio-economic determinants of the entire Dutch population. METHODS: Administrative records and health registry data were linked to socio-economic characteristics and self-reported lifestyle factors. For the entire Dutch population (n = 16,778,708), all socio-demographic information except lifestyle factors was available. Lifestyle factors were available from the 2012 Dutch Health Monitor (n = 370,835). Regression model was used to sequentially predict individual features. RESULTS: The synthetic population resembles the original confidential population. Features predicted in the first stages of the sequential procedure are virtually similar to those in the original population, while those predicted in later stages of the sequential procedure carry the accumulation of limitations furthered by data quality and previously modelled features. CONCLUSIONS: By combining socio-demographic, economic, health and lifestyle related data at individual level on a large scale, our method provides us with a powerful tool to construct a synthetic population of good quality and with no confidentiality issues.

Adopting a child perspective for exposome research on mental health and cognitive development - Conceptualisation and opportunities.

Mental disorders among children and adolescents pose a significant global challenge. The exposome framework covering the totality of internal, social and physical exposures over a lifetime provides opportunities to better understand the causes of and processes related to mental health, and cognitive functioning. The paper presents a conceptual framework on exposome, mental health, and cognitive development in children and adolescents, with potential mediating pathways, providing a possibility for interventions along the life course. The paper underscores the significance of adopting a child perspective to the exposome, acknowledging children's specific vulnerability, including differential exposures, susceptibility of effects and capacity to respond; their susceptibility during development and growth, highlighting neurodevelopmental processes from conception to young adulthood that are highly sensitive to external exposures. Further, critical periods when exposures may have significant effects on a child's development and future health are addressed. The paper stresses that children's behaviour, physiology, activity pattern and place for activities make them differently vulnerable to environmental pollutants, and calls for child-specific assessment methods, currently lacking within today's health frameworks. The importance of understanding the interplay between structure and agency is emphasized, where agency is guided by social structures and practices and vice-versa. An intersectional approach that acknowledges the interplay of social and physical exposures as well as a global and rural perspective on exposome is further pointed out. To advance the exposome field, interdisciplinary efforts that involve multiple scientific disciplines are crucial. By adopting a child perspective and incorporating an exposome approach, we can gain a comprehensive understanding of how exposures impact children's mental health and cognitive development leading to better outcomes.

Predicting self-perceived general health status using machine learning: an external exposome study.

BACKGROUND: Self-perceived general health (SPGH) is a general health indicator commonly used in epidemiological research and is associated with a wide range of exposures from different domains. However, most studies on SPGH only investigated a limited set of exposures and did not take the entire external exposome into account. We aimed to develop predictive models for SPGH based on exposome datasets using machine learning techniques and identify the most important predictors of poor SPGH status. METHODS: Random forest (RF) was used on two datasets based on personal characteristics from the 2012 and 2016 editions of the Dutch national health survey, enriched with environmental and neighborhood characteristics. Model performance was determined using the area under the curve (AUC) score. The most important predictors were identified using a variable importance procedure and individual effects of exposures using partial dependence and accumulated local effect plots. The final 2012 dataset contained information on 199,840 individuals and 81 variables, whereas the final 2016 dataset had 244,557 individuals with 91 variables. RESULTS: Our RF models had overall good predictive performance (2012: AUC = 0.864 (CI: 0.852-0.876); 2016: AUC = 0.890 (CI: 0.883-0.896)) and the most important predictors were "Control of own life", "Physical activity", "Loneliness" and "Making ends meet". Subjects who felt insufficiently in control of their own life, scored high on the De Jong-Gierveld loneliness scale or had difficulty in making ends meet were more likely to have poor SPGH status, whereas increased physical activity per week reduced the probability of poor SPGH. We observed associations between some neighborhood and environmental characteristics, but these variables did not contribute to the overall predictive strength of the models. CONCLUSIONS: This study identified that within an external exposome dataset, the most important predictors for SPGH status are related to mental wellbeing, physical exercise, loneliness, and financial status.

Validation of the smartphone-based dietary assessment tool "Traqq" for assessing actual dietary intake by repeated 2-h recalls in adults: comparison with 24-h recalls and urinary biomarkers.

BACKGROUND: Conventional dietary assessment methods are affected by measurement errors. We developed a smartphone-based 2-h recall (2hR) methodology to reduce participant burden and memory-related bias. OBJECTIVE: Assessing the validity of the 2hR method against traditional 24-h recalls (24hRs) and objective biomarkers. METHODS: Dietary intake was assessed in 215 Dutch adults on 6 randomly selected nonconsecutive days (i.e., 3 2hR-days and 3 24hRs) during a 4-wk period. Sixty-three participants provided 4 24-h urine samples, to assess urinary nitrogen and potassium concentrations. RESULTS: Intake estimates of energy (2052±503 kcal vs. 1976±483 kcal) and nutrients (e.g., protein: 78±23 g vs. 71±19 g; fat: 84±30 g vs. 79±26 g; carbohydrates: 220±60 g vs. 216±60 g) were slightly higher with 2hR-days than with 24hRs. Comparing self-reported protein and potassium intake to urinary nitrogen and potassium concentrations indicated a slightly higher accuracy of 2hR-days than 24hRs (protein: -14% vs. -18%; potassium: -11% vs. -16%). Correlation coefficients between methods ranged from 0.41 to 0.75 for energy and macronutrients and from 0.41 to 0.62 for micronutrients. Generally, regularly consumed food groups showed small differences in intake (<10%) and good correlations (>0.60). Intake of energy, nutrients, and food groups showed similar reproducibility (intraclass correlation coefficient) for 2hR-days and 24hRs. CONCLUSIONS: Comparing 2hR-days with 24hRs showed a relatively similar group-level bias for energy, most nutrients, and food groups. Differences were mostly due to higher intake estimates by 2hR-days. Biomarker comparisons showed less underestimation by 2hR-days as compared with 24hRs, suggesting that 2hR-days are a valid approach to assess the intake of energy, nutrients, and food groups. This trial was registered at the Dutch Central Committee on Research Involving Human Subjects (CCMO) registry as ABR. No. NL69065.081.19.

Pitfalls in the statistical analysis of microbiome amplicon sequencing data.

Microbiome data are characterized by several aspects that make them challenging to analyse statistically: they are compositional, high dimensional and rich in zeros. A large array of statistical methods exist to analyse these data. Some are borrowed from other fields, such as ecology or RNA-sequencing, while others are custom-made for microbiome data. The large range of available methods, and which is continuously expanding, means that researchers have to invest considerable effort in choosing what method(s) to apply. In this paper we list 14 statistical methods or approaches that we think should be generally avoided. In several cases this is because we believe the assumptions behind the method are unlikely to be met for microbiome data. In other cases we see methods that are used in ways they are not intended to be used. We believe researchers would be helped by more critical evaluations of existing methods, as not all methods in use are suitable or have been sufficiently reviewed. We hope this paper contributes to a critical discussion on what methods are appropriate to use in the analysis of microbiome data.

llperm: a permutation of regressor residuals test for microbiome data.

BACKGROUND: Differential abundance testing is an important aspect of microbiome data analysis, where each taxa is fitted with a statistical test or a regression model. However, many models do not provide a good fit to real microbiome data. This has been shown to result in high false positive rates. Permutation tests are a good alternative, but a regression approach is desired for small data sets with many covariates, where stratification is not an option. RESULTS: We implement an R package 'llperm' where the The Permutation of Regressor Residuals (PRR) test can be applied to any likelihood based model, not only generalized linear models. This enables distributions with zero-inflation and overdispersion, making the test suitable for count regression models popular in microbiome data analysis. Simulations based on a real data set show that the PRR-test approach is able to maintain the correct nominal false positive rate expected from the null hypothesis, while having equal or greater power to detect the true positives as models based on likelihood at a given false positive rate. CONCLUSIONS: Standard count regression models can have a shockingly high false positive rate in microbiome data sets. As they may lead to false conclusions, the guaranteed nominal false positive rate gained from the PRR-test can be viewed as a major benefit.

Early environmental quality and life-course mental health effects: The Equal-Life project.

BACKGROUND: There is increasing evidence that a complex interplay of factors within environments in which children grows up, contributes to children's suboptimal mental health and cognitive development. The concept of the life-course exposome helps to study the impact of the physical and social environment, including social inequities, on cognitive development and mental health over time. METHODS: Equal-Life develops and tests combined exposures and their effects on children's mental health and cognitive development. Data from eight birth-cohorts and three school studies (N = 240.000) linked to exposure data, will provide insights and policy guidance into aspects of physical and social exposures hitherto untapped, at different scale levels and timeframes, while accounting for social inequities. Reasoning from the outcome point of view, relevant stakeholders participate in the formulation and validation of research questions, and in the formulation of environmental hazards. Exposure assessment combines GIS-based environmental indicators with omics approaches and new data sources, forming the early-life exposome. Statistical tools integrate data at different spatial and temporal granularity and combine exploratory machine learning models with hypothesis-driven causal modeling. CONCLUSIONS: Equal-Life contributes to the development and utilization of the exposome concept by (1) integrating the internal, physical and social exposomes, (2) studying a distinct set of life-course effects on a child's development and mental health (3) characterizing the child's environment at different developmental stages and in different activity spaces, (4) looking at supportive environments for child development, rather than merely pollutants, and (5) combining physical, social indicators with novel effect markers and using new data sources describing child activity patterns and environments.

Diverging metabolic effects of 2 energy-restricted diets differing in nutrient quality: a 12-week randomized controlled trial in subjects with abdominal obesity.

BACKGROUND: Despite the established relation between energy restriction (ER) and metabolic health, the most beneficial nutrient composition of a weight-loss diet is still a subject of debate. OBJECTIVES: The aim of the study was to examine the additional effects of nutrient quality on top of ER. METHODS: A parallel-designed, 12-week 25% ER dietary intervention study was conducted (clinicaltrials.gov: NCT02194504). Participants aged 40-70 years with abdominal obesity were randomized over 3 groups: a 25% ER high-nutrient-quality diet (n = 40); a 25% ER low-nutrient-quality diet (n = 40); or a habitual diet (n = 30). Both ER diets were nutritionally adequate, and the high-nutrient-quality ER diet was enriched in MUFAs, n-3 PUFAs, fiber, and plant protein and reduced in fructose. Before and after the intervention, intrahepatic lipids, body fat distribution, fasting and postprandial responses to a mixed-meal shake challenge test of cardiometabolic risk factors, lipoproteins, vascular measurements, and adipose tissue transcriptome were assessed. RESULTS: The high-nutrient-quality ER diet (-8.4 ± 3.2) induced 2.1 kg more weight loss (P = 0.007) than the low-nutrient-quality ER diet (-6.3 ± 3.9), reduced fasting serum total cholesterol (P = 0.014) and plasma triglycerides (P < 0.001), promoted an antiatherogenic lipoprotein profile, and induced a more pronounced decrease in adipose tissue gene expression of energy metabolism pathways than the low-quality ER diet. Explorative analyses showed that the difference in weight loss between the two ER diets was specifically present in insulin-sensitive subjects (HOMA-IR ≤ 2.5), in whom the high-nutrient-quality diet induced 3.9 kg more weight loss than the low-nutrient-quality diet. CONCLUSIONS: A high-nutrient-quality 25% ER diet is more beneficial for cardiometabolic health than a low-nutrient-quality 25% ER diet. Overweight, insulin-sensitive subjects may benefit more from a high- than a low-nutrient-quality ER diet with respect to weight loss, due to potential attenuation of glucose-induced lipid synthesis in adipose tissue.

The mediating role of unhealthy behavior in the relationship between shift work and perceived health.

BACKGROUND: Little is known about the relationship between shift work and perceived health, including potential underlying mechanisms such as unhealthy behaviors. The aim of this study was to investigate whether unhealthy behaviors mediate the relationship between shift work and perceived mental and physical health, taking into account potential differences by level of education. METHODS: Data from 1633 workers participating in the Doetinchem Cohort Study during 1995-2016 were used. Being engaged in shift work was determined at 1 year preceding the assessment of health behaviors. Mental and physical health were assessed after 5 years of follow-up by the 5-item Mental Health Inventory and the physical functioning scale of the 36-item Short Form Health Survey. Smoking, physical inactivity, alcohol consumption, and overweight were considered as potential mediators and education was treated as moderator. Moderated mediation analyses using generalized estimated equations were performed. RESULTS: Shift work was not statistically significantly related to either mental or physical health. Despite this, statistically significant mediation effects of smoking (Beta - 0.09; 95% Confidence Interval - 0.20 - -0.01, respectively B -0.09; 95%CI -0.21 - -0.01) and physical inactivity (B 0.11; 95%CI 0.03-0.23, respectively B 0.08; 95%CI 0.01-0.18) were found in the relationship between shift work and mental or physical health. Direct and indirect effects outweighed each other in the relationship between shift work and mental health, since the direction of these effects was opposite. The relationship between shift work, unhealthy behavior, and health was not different by educational level. CONCLUSION: Shift workers did not report lower mental or physical health than non-shift workers. Though mediation effects of unhealthy behavior were observed in the relationship between shift work and perceived health, these small effects had minor public health relevance.

Mapping chronic disease prevalence based on medication use and socio-demographic variables: an application of LASSO on administrative data sources in healthcare in the Netherlands.

BACKGROUND: Policymakers generally lack sufficiently detailed health information to develop localized health policy plans. Chronic disease prevalence mapping is difficult as accurate direct sources are often lacking. Improvement is possible by adding extra information such as medication use and demographic information to identify disease. The aim of the current study was to obtain small geographic area prevalence estimates for four common chronic diseases by modelling based on medication use and socio-economic variables and next to investigate regional patterns of disease. METHODS: Administrative hospital records and general practitioner registry data were linked to medication use and socio-economic characteristics. The training set (n = 707,021) contained GP diagnosis and/or hospital admission diagnosis as the standard for disease prevalence. For the entire Dutch population (n = 16,777,888), all information except GP diagnosis and hospital admission was available. LASSO regression models for binary outcomes were used to select variables strongly associated with disease. Dutch municipality (non-)standardized prevalence estimates for stroke, CHD, COPD and diabetes were then based on averages of predicted probabilities for each individual inhabitant. RESULTS: Adding medication use data as a predictor substantially improved model performance. Estimates at the municipality level performed best for diabetes with a weighted percentage error (WPE) of 6.8%, and worst for COPD (WPE 14.5%)Disease prevalence showed clear regional patterns, also after standardization for age. CONCLUSION: Adding medication use as an indicator of disease prevalence next to socio-economic variables substantially improved estimates at the municipality level. The resulting individual disease probabilities could be aggregated into any desired regional level and provide a useful tool to identify regional patterns and inform local policy.

Identification of Lifestyle Behaviors Associated with Recurrence and Survival in Colorectal Cancer Patients Using Random Survival Forests.

Current lifestyle recommendations for cancer survivors are the same as those for the general public to decrease their risk of cancer. However, it is unclear which lifestyle behaviors are most important for prognosis. We aimed to identify which lifestyle behaviors were most important regarding colorectal cancer (CRC) recurrence and all-cause mortality with a data-driven method. The study consisted of 1180 newly diagnosed stage I-III CRC patients from a prospective cohort study. Lifestyle behaviors included in the current recommendations, as well as additional lifestyle behaviors related to diet, physical activity, adiposity, alcohol use, and smoking were assessed six months after diagnosis. These behaviors were simultaneously analyzed as potential predictors of recurrence or all-cause mortality with Random Survival Forests (RSFs). We observed 148 recurrences during 2.6-year median follow-up and 152 deaths during 4.8-year median follow-up. Higher intakes of sugary drinks were associated with increased recurrence risk. For all-cause mortality, fruit and vegetable, liquid fat and oil, and animal protein intake were identified as the most important lifestyle behaviors. These behaviors showed non-linear associations with all-cause mortality. Our exploratory RSF findings give new ideas on potential associations between certain lifestyle behaviors and CRC prognosis that still need to be confirmed in other cohorts of CRC survivors.

A Systematic Review and Meta-Analysis of Validation Studies Performed on Dietary Record Apps.

Mobile dietary record apps have been increasingly validated by studies with various study designs. This review aims to evaluate the overall accuracy of dietary record apps in measuring the intake of energy, macro- and micronutrients, and food groups in real-life settings and the designs of validation studies. We systematically searched mobile dietary record validation studies published during the period from 2013 to 2019. We identified 14 studies for the systematic review, of which 11 studies were suitable for meta-analyses on energy intake and 8 studies on macronutrient intake. Mean differences and SDs of nutrient estimations between the app and the reference method from studies were pooled using a random-effects model. All apps underestimated energy intake when compared with their reference methods, with a pooled effect of -202 kcal/d (95% CI: -319, -85 kcal/d); the heterogeneity of studies was 72%. After stratification, studies that used the same food-composition table for both the app and the reference method had a lower level of heterogeneity (0%) and a pooled effect of -57 kcal/d (95% CI: -116, 2 kcal/d). The heterogeneity of studies in the differences in carbohydrate, fat, and protein intake was 54%, 73%, and 80%, with the pooled effect of -18.8 g/d, -12.7 g/d, and -12.2 g/d, respectively, after excluding outliers. The intakes of micronutrients and food groups were statistically nonsignificantly underestimated by the apps in most cases. In conclusion, dietary record apps underestimated food consumption compared with traditional dietary assessment methods. Moreover, varying study designs have been found across studies. Recommended practices for conducting validation studies were formulated including considering biomarkers as the reference, testing in a larger and more representative study population for a longer period, avoiding the learning effect of each method, and comparing food group or food item consumption in addition to comparing energy and nutrient intakes.

Associations Between Measures of Social Distancing and Severe Acute Respiratory Syndrome Coronavirus 2 Seropositivity: A Nationwide Population-based Study in the Netherlands.

This large, nationwide, population-based, seroepidemiological study provides evidence of the effectiveness of physical distancing (>1.5 m) and indoor group size reductions in reducing severe acute respiratory syndrome coronavirus 2 infection. Additionally, young adults may play an important role in viral spread, contrary to children up until age 12 years with whom close contact is permitted. CLINICAL TRIALS REGISTRATION: NTR8473.

Lifestyle after colorectal cancer diagnosis in relation to recurrence and all-cause mortality.

BACKGROUND: An unhealthy lifestyle is associated with the risk of colorectal cancer (CRC), but it is unclear whether overall lifestyle after a CRC diagnosis is associated with risks of recurrence and mortality. OBJECTIVES: To examine associations between postdiagnosis lifestyle and changes in lifestyle after a CRC diagnosis with risks of CRC recurrence and all-cause mortality. METHODS: The study population included 1425 newly diagnosed, stage I-III CRC patients from 2 prospective cohort studies enrolled between 2010 and 2016. Lifestyle, including BMI, physical activity, diet, and alcohol intake, was assessed at diagnosis and at 6 months postdiagnosis. We assigned lifestyle scores based on concordance with 2 sets of cancer prevention guidelines-from the World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) and the American Cancer Society (ACS)-and national disease prevention guidelines. Higher scores indicate healthier lifestyles. We computed adjusted HRs and 95% CIs using Cox regression. RESULTS: We observed 164 recurrences during a 2.8-year median follow-up and 171 deaths during a 4.4-year median follow-up. No associations were observed for CRC recurrence. A lifestyle more consistent with the ACS recommendations was associated with a lower all-cause mortality risk (HR per +1 SD, 0.85; 95% CI: 0.73-0.995). The same tendency was observed for higher WCRF/AICR (HR, 0.92; 95% CI: 0.78-1.08) and national (HR, 0.90; 95% CI: 0.77-1.05) lifestyle scores, although these associations were statistically nonsignificant. Generally, no statistically significant associations were observed for BMI, physical activity, diet, or alcohol. Improving one's lifestyle after diagnosis (+1 SD) was associated with a lower all-cause mortality risk for the ACS (HR, 0.80; 95% CI: 0.67-0.96) and national (HR, 0.84; 95% CI: 0.70-0.999) scores, yet was statistically nonsignificant for the WCRF/AICR score (HR, 0.94; 95% CI: 0.78-1.13). CONCLUSIONS: A healthy lifestyle after CRC diagnosis and improvements therein were not associated with the risk of CRC recurrence, but were associated with a decreased all-cause mortality risk.

Low Meat Consumption in the Netherlands Is Associated With Higher Intake of Fish, Nuts, Seeds, Cheese, Sweets, and Snacks: Results From a Two-Part Model.

Studies on sustainable diets show a need for replacement of animal-based foods by plant-based foods, which is also called "the protein transition." To gain insight into the acceptability of such diet shifts, this study evaluated which current food sources people consume at varying amounts of meat consumption. The study population consisted of 4,313 participants aged 1-79 years of the Dutch National Food Consumption Survey 2012-2016, which assessed diet using two nonconsecutive 24-h dietary recalls. A two-part statistical model was used that accounts for both repeated measures and the correlation between probability and amount of consumption. Results are presented for quartiles of low to high meat consumption, by age and sex. Depending on age and sex, a higher consumption of fish (>100%), nuts and seeds (73-156%), cheese (34-111%), and sweets and snacks (28-81%) is observed in the lowest quartile of meat consumption compared to the highest. For fish, nuts, seeds, and cheese, this increase is mainly due to probability of consumption (>100%, 61-93%, and 16-64%, respectively). For sweets and snacks, the increase is mainly due to the amount of consumption (26-72%). Probability of potato consumption is 29-51% lower at low meat consumption. Vegetable consumption is lower mainly due to amount of consumption (6-29%). The results from the two-part model suggest that shifting away from a traditional Dutch high meat-vegetable-potatoes pattern is associated with higher probability of consuming fish, nuts and seeds, and cheese, but also increased amounts of sweets and snacks. This illustrates that analyzing the probability and amount part separately in relation to behavioral or physiological determinants extends our understanding of the diet according to meat consumption. These insights are important when developing realistic and acceptable food-based dietary guidelines for meat reduction.

Validity Coefficient of Repeated Measurements of Urinary Marker of Sugar Intake Is Comparable to Urinary Nitrogen as Marker of Protein Intake in Free-living Subjects.

BACKGROUND: Studies do not show consistent relationships between self-reported intake of sugar and outcome of disease. To overcome the drawbacks of self-reported intake methods, we investigated whether there is an agreement in ranking of individuals between their self-reported sugar intake and urinary sucrose and fructose. METHODS: We used data of 198 Dutch adults (106 women) from the DUPLO study. Sugar intake of all foods and drinks consumed over 24-hour period was estimated by collecting duplicate portions (DP) and 24-hour recalls (24hR), telephone (24hRT) and Web-based (24hRW), while sugar excretion was based on 24-hour urine samples. Sugar content of 24hR was calculated using a newly developed sugar database and sugar content of DPs and urine samples was calculated using high-performance liquid chromatography-atomic emission spectrometry and LC/MS-MS, respectively. Measurement error models assessed validity coefficients (VC) and attenuation factors (AF). Coefficients were compared with those of protein biomarker. RESULTS: The VC for the marker, using DP as reference, showed comparability with substantially better ranking of participants (0.72 for women and 0.93 for men), than 24hRT (0.57 and 0.78) or 24hRW (0.70 and 0.78) as reference in the sucrose models. The VC of the sucrose models was within 10% of the protein models, except for the model with 24hRT as reference, among women. The AF started at higher values and increased by a greater factor compared with the VC. CONCLUSIONS: Repeated measurements of urinary sucrose and fructose as a marker of daily sucrose intake had a ranking performance comparable to urinary nitrogen as marker of protein intake in free-living Dutch adults. IMPACT: The validation of the sugar biomarker in a free-living population with three different dietary assessment methods and its comparable ranking ability with a good recovery biomarker (i.e., protein biomarker) have important research applications. The biomarker may be used for validating dietary assessment methods, for monitoring compliance in human feeding studies, for monitoring the effect of public health interventions, and as a surrogate for ranking subjects according to sucrose intake when information on sucrose in food composition databases is lacking.

Health expenditure of employees versus self-employed individuals; a 5 year study.

It is unclear to what extent self-employed choose to become self-employed. This study aimed to compare the health care expenditures-as a proxy for health-of self-employed individuals in the year before they started their business, to that of employees. Differences by sex, age, and industry were studied. In total, 5,741,457 individuals aged 25-65 years who were listed in the tax data between 2010 and 2015 with data on their health insurance claims were included. Self-employed and employees were stratified according to sex, age, household position, personal income, region, and industry for each of the years covered. Weighted linear regression was used to compare health care expenditures in the preceding (year x-1) between self-employed and employees (in year x). Compared with employees, expenditures for hospital care, pharmaceutical care and mental health care were lower among self-employed in the year before they started their business. Differences were most pronounced for men, individuals ≥40 years and those working in the industry and energy sector, construction, financial institutions, and government and care. We conclude that healthy individuals are overrepresented among the self-employed, which is more pronounced in certain subgroups. Further qualitative research is needed to investigate the reasons why these subgroups are more likely to choose to become self-employed.

Associations of Abdominal Skeletal Muscle Mass, Fat Mass, and Mortality among Men and Women with Stage I-III Colorectal Cancer.

BACKGROUND: The associations of abdominal skeletal muscle mass index (SMI), visceral and subcutaneous adipose tissue (VAT and SAT, respectively), and mortality among patients with stage I-III colorectal cancer may differ for men and women, but only few studies stratified their data into men and women. We investigated associations of abdominal SMI, VAT, and SAT with overall mortality among men and among women with stage I-III colorectal cancer. METHODS: SMI, VAT, and SAT were assessed from abdominal CT images for 1,998 patients with stage I-III colorectal cancer diagnosed between 2006 and 2015. Restricted cubic splines (RCS) were used to investigate associations of SMI, VAT, and SAT with overall mortality. RESULTS: Average age of the participants was 67.9 ± 10.6 years and 58% were men. During a median follow-up of 4.3 years, 546 (27%) patients died. Among men, the association of SMI and mortality was statistically significant in a nonlinear way in the RCS analyses, with lower SMI levels associated with higher mortality. SMI was not associated with mortality among women. SAT was associated with mortality in a nonlinear way for men and for women, with lower SAT levels being associated with higher mortality. VAT was not significantly associated with mortality in men or women. CONCLUSION: Associations of abdominal skeletal muscle mass with mortality among patients with colorectal cancer were not the same for men and for women. IMPACT: This study stresses the importance for more attention on sex-related differences in body composition and cancer outcomes.