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Background and PurposeIt used to be a common practice in the field of nutritional epidemiology to analyze separate nutrients, foods, or food groups. However, in reality, nutrients and foods are consumed in combination. The introduction of dietary patterns (DP) and their analysis has revolutionized this field, making it possible to take into account the synergistic effects of foods and to account for the complex interaction among nutrients and foods. Three approaches of DP analysis exist: (1) the hypothesis-based approach (based on prior knowledge regarding the current understanding of dietary components and their health relation), (2) the exploratory approach (solely relying on dietary intake data), and (3) the hybrid approach (a combination of both approaches). During the recent past, complementary approaches for DP analysis have emerged both conceptually and methodologically.MethodWe have summarized the recent developments that include incorporating the Treelet transformation method as a complementary exploratory approach in a narrative review.ResultsUses, peculiarities, strengths, limitations, and scope of recent developments in DP analysis are outlined. Next, the narrative review gives an overview of the literature that takes into account potential relevant dietary-related factors, specifically the metabolome and the gut microbiome in DP analysis. Then the review deals with the aspect of data processing that is needed prior to DP analysis, particularly when dietary data arise from assessment methods other than the long-established food frequency questionnaire. Lastly, potential opportunities for upcoming DP analysis are summarized in the outlook. ConclusionBiological factors like the metabolome and the microbiome are crucial to understand diet-disease relationships. Therefore, the inclusion of these factors in DP analysis might provide deeper insights.

Background/objectivesAdolescence is a critical period for both the development of overweight and the transition toward a later chronotype, often accompanied by an increase in social jetlag. This study assessed whether changes in chronotype and social jetlag, are linked to changes in body composition during adolescence.Subjects/methodsWe used data from the DONALD open cohort study, collected between 2014 and 2019, from 213 adolescents (9–17 years at baseline, 45% females) having at least two measures of chronotype and anthropometry (N = 572). Chronotype was assessed with the Munich Chronotype Questionnaire and defined as: midpoint of sleep corrected for sleep-debt (MSFsc) accumulated over the week (later MSFsc represents later chronotype). Social jetlag (SJL) defines the difference between midpoint of sleep during week and weekend. Calculations for Fat Free Mass Index (FFMI [kg/m2)]) and Fat Mass Index (FMI) [kg/m2)]) were based on body fat percentage, weight, and height. To analyze the associations, we used linear mixed-effect regression models. Finally, the total cohort was split into three biologically relevant age groups (cut-off set at <12 years, ≥12 to ≤15 years and >15 years).ResultsMedian follow-up was 2.1 years. Overall, change toward a later chronotype was significantly related with an increase in FMI (ß: 0.05, 95% CI: 0.01–0.08). A 1 h increase in social jetlag predicted an increase in BMI-SDS of 0.08 SDS units (95% CI: 0.01–0.14) and in FMI of 0.04 kg/m2 (95% CI: 0.003–0.08). Associations were stronger for the age group ≥12 to ≤15 years (p for interaction: <0.001). No relationship was found with FFMI.ConclusionsChanges in MSFsc and SJL during adolescence were associated with concurrent changes in BMI-SDS and FMI. The age ≥12 to ≤15 years appears to be a sensitive period in which chronobiological changes were clearly associated with increasing body fatness.

To apply FFQ, knowledge about portion sizes is relevant. According to increased energy and nutrient requirements, average portion sizes of foods are supposed to increase during growth. We provide empirically derived portion sizes for 4- to 18-year-olds in different age groups to facilitate analyses of FFQ data in children and adolescents. Using data from the dynamic DOrtmund Nutritional and Anthropometric Longitudinally Designed cohort study, quantile regression for smoothing percentiles was used to derive portion sizes as a function of age from which age- and food group-specific portion sizes were calculated as median food group intake (g). Dortmund, Germany. Data from 3-day weighed dietary records (WDR) of 1,325 participants (♀: 653) were analysed. Participants provided in total 9,828 WDR (on average 7·5 per participant) between 1985 and 2022. WDR were grouped into five age groups, whereby each age group covered 3 years of age. In total, 11 955 food items were reported and categorised into sixteen major food groups with seventy-one sub-groups. Portion sizes tended to increase with age, except for - and . Comparing 4- to 6-year-olds to 16- to 18-year-olds, portion size increased between 22·2 % ( : 18 g . 22 g) and 173·3 % ( : 15 g . 41 g). We provide empirically derived portion sizes for children and adolescents. These data are useful to establish dietary assessment methods based on estimates of portion sizes, such as FFQ, for children and adolescents.

Andre Franke and colleagues perform a genome-wide association study for the gut microbiome, examining the influence of host genetics on overall microbial variation and individual taxa. They find significant associations at the VDR (vitamin D receptor) locus and observe correlations between microbiota and metabolites of VDR, including bile acids. Human gut microbiota is an important determinant for health and disease, and recent studies emphasize the numerous factors shaping its diversity. Here we performed a genome-wide association study (GWAS) of the gut microbiota using two cohorts from northern Germany totaling 1,812 individuals. Comprehensively controlling for diet and non-genetic parameters, we identify genome-wide significant associations for overall microbial variation and individual taxa at multiple genetic loci, including the VDR gene (encoding vitamin D receptor). We observe significant shifts in the microbiota of Vdr −/− mice relative to control mice and correlations between the microbiota and serum measurements of selected bile and fatty acids in humans, including known ligands and downstream metabolites of VDR. Genome-wide significant ( P < 5 × 10 −8 ) associations at multiple additional loci identify other important points of host–microbe intersection, notably several disease susceptibility genes and sterol metabolism pathway components. Non-genetic and genetic factors each account for approximately 10% of the variation in gut microbiota, whereby individual effects are relatively small.

Recently, epidemiological studies have suggested that fluoride is a human developmental neurotoxicant that reduces measures of intelligence in children, placing it into the same category as toxic metals (lead, methylmercury, arsenic) and polychlorinated biphenyls. If true, this assessment would be highly relevant considering the widespread fluoridation of drinking water and the worldwide use of fluoride in oral hygiene products such as toothpaste. To gain a deeper understanding of these assertions, we reviewed the levels of human exposure, as well as results from animal experiments, particularly focusing on developmental toxicity, and the molecular mechanisms by which fluoride can cause adverse effects. Moreover, in vitro studies investigating fluoride in neuronal cells and precursor/stem cells were analyzed, and 23 epidemiological studies published since 2012 were considered. The results show that the margin of exposure (MoE) between no observed adverse effect levels (NOAELs) in animal studies and the current adequate intake (AI) of fluoride (50 µg/kg b.w./day) in humans ranges between 50 and 210, depending on the specific animal experiment used as reference. Even for unusually high fluoride exposure levels, an MoE of at least ten was obtained. Furthermore, concentrations of fluoride in human plasma are much lower than fluoride concentrations, causing effects in cell cultures. In contrast, 21 of 23 recent epidemiological studies report an association between high fluoride exposure and reduced intelligence. The discrepancy between experimental and epidemiological evidence may be reconciled with deficiencies inherent in most of these epidemiological studies on a putative association between fluoride and intelligence, especially with respect to adequate consideration of potential confounding factors, e.g., socioeconomic status, residence, breast feeding, low birth weight, maternal intelligence, and exposure to other neurotoxic chemicals. In conclusion, based on the totality of currently available scientific evidence, the present review does not support the presumption that fluoride should be assessed as a human developmental neurotoxicant at the current exposure levels in Europe.

Purpose: Aim of this study was to investigate the association between postdiagnosis body mass index (BMI) and all-cause mortality in colorectal cancer (CRC) survivors in a prospective study and meta-analysis. Methods: We conducted a prospective cohort study on 2,143 CRC survivors in Germany. Participants were recruited to the study on average 4 years after diagnosis, and postdiagnosis BMI was assessed at recruitment using a self-administered questionnaire. CRC survivors were followed up for a mean time of 3.5 years. The association between BMI and all-cause mortality was investigated using multivariable Cox proportional hazards models. Additionally, we performed a meta-analysis of studies on postdiagnosis BMI and all-cause mortality (n = 5, including this study) by applying random-effects models. Results: In the prospective analysis, 349 participants died. BMI was not statistically significantly associated with all-cause mortality. Compared to normal weight survivors, the hazard ratios (HRs) [95 % confidence interval (CI)] for all-cause mortality in underweight, overweight and obese survivors were 1.65 (0.79–3.45), 0.80 (0.62–1.03) and 0.84 (0.62–1.14), respectively. In the meta-analysis, individuals with underweight were at increased risk for all-cause mortality [HR (95 % CI) 1.72 (1.18–2.49)], whereas individuals with overweight had a lower risk [HR (95 % CI) 0.79 (0.71–0.88)], compared to normal weight subjects. For obesity, the risk of mortality was also reduced with only borderline significance [HR (95 % CI) 0.88 (0.77–1.00)]. Conclusions: While the present study as well as single previously published studies showed that overweight was associated with a non-significant reduced risk for all-cause mortality, our meta-analysis indicated a decreased mortality risk among overweight CRC survivors.

Metabolomics-derived metabolites (henceforth metabolites) may mediate the relationship between modifiable risk factors and clinical biomarkers of metabolic health (henceforth clinical biomarkers). We set out to study the associations of metabolites with clinical biomarkers and a potential mediation effect in a population of young adults. First, we conducted a systematic literature review searching for metabolites associated with 11 clinical biomarkers (inflammation markers, glucose, blood pressure or blood lipids). Second, we replicated the identified associations in a study population of n = 218 (88 males and 130 females, average age of 18 years) participants of the DONALD Study. Sex-stratified linear regression models adjusted for age and BMI and corrected for multiple testing were calculated. Third, we investigated our previously reported metabolites associated with anthropometric and dietary factors mediators in sex-stratified causal mediation analysis. For all steps, both urine and blood metabolites were considered. We found 41 metabolites in the literature associated with clinical biomarkers meeting our inclusion criteria. We were able to replicate an inverse association of betaine with CRP in women, between body mass index and C-reactive protein (CRP) and between body fat and leptin. There was no evidence of mediation by lifestyle-related metabolites after correction for multiple testing. We were only able to partially replicate previous findings in our age group and did not find evidence of mediation. The complex interactions between lifestyle factors, the metabolome, and clinical biomarkers warrant further investigation.

Background The group of colorectal cancer (CRC) survivors continues to grow worldwide. Understanding health-related quality of life (HRQOL) determinants and consequences of HRQOL impairments in long-term CRC survivors may help to individualize survivorship care plans. We aimed to i) examine the HRQOL status of CRC long-term survivors, ii) identify cross-sectional sociodemographic and clinical correlates of HRQOL, and iii) investigate the prospective association of HRQOL after CRC diagnosis with all-cause mortality. Methods We assessed HRQOL within a Northern German cohort of 1294 CRC survivors at a median of 6 years after CRC diagnosis using the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30). Cross-sectional correlates of different HRQOL dimensions were analyzed using multivariable-adjusted logistic regression models with HRQOL as a binary variable. With multivariable-adjusted Cox proportional hazards regression models, hazard ratios (HR) of all-cause mortality were estimated per 10-point-increments of an HRQOL summary score, a global quality of life scale, and HRQOL functioning and symptom domains. Results The median HRQOL summary score was 87 (interquartile range: 75–94). Sex, age, education, tumor location, metastases, other cancers, type of therapy, and current stoma were identified as correlates of different HRQOL scales. After a median follow-up time of 7 years after HRQOL assessment, 175 participants had died. Nearly all HRQOL domains, except for cognitive functioning and diarrhea, were significantly associated with all-cause mortality. A 10-point-increment in the summary score decreased the risk of death by 24% (HR: 0.76; 95% CI: 0.70–0.82). Conclusions HRQOL in CRC survivors appeared to be relatively high in the long term. Various clinical and sociodemographic factors were cross-sectionally associated with HRQOL in long-term CRC survivors. Lower HRQOL was associated with increased all-cause mortality. Individualized healthcare programs for CRC survivors (including psychosocial screening and interventions) are needed to detect decreased HRQOL and to further improve long-term HRQOL and survival.

The prevalence of obesity, defined as a BMI of ≥30·0 kg/m2, has increased substantially over previous decades to about 20% in industrialized countries, and a further increase is expected in the future. Epidemiological studies have shown that obesity is a risk factor for: post-menopausal breast cancer; cancers of the endometrium, colon and kidney; malignant adenomas of the oesophagus. Obese subjects have an approximately 1·5–3·5-fold increased risk of developing these cancers compared with normal-weight subjects, and it has been estimated that between 15 and 45% of these cancers can be attributed to overweight (BMI 25·0–29·9 kg/m2) and obesity in Europe. More recent studies suggest that obesity may also increase the risk of other types of cancer, including pancreatic, hepatic and gallbladder cancer. The underlying mechanisms for the increased cancer risk as a result of obesity are unclear and may vary by cancer site and also depend on the distribution of body fat. Thus, abdominal obesity as defined by waist circumference or waist:hip ratio has been shown to be more strongly related to certain cancer types than obesity as defined by BMI. Possible mechanisms that relate obesity to cancer risk include insulin resistance and resultant chronic hyperinsulinaemia, increased production of insulin-like growth factors or increased bioavailability of steroid hormones. Recent research also suggests that adipose tissue-derived hormones and cytokines (adipokines), such as leptin, adiponectin and inflammatory markers, may reflect mechanisms linked to tumourigenesis.

Background Reports on body mass index (BMI) trajectories from childhood into late adolescence, their determinants, and subsequent cardiometabolic risk markers, particularly among European populations have been few. Moreover, sex-specific investigation is necessary considering the sex difference in BMI, and the sex-specific association between BMI and some cardiometabolic risk markers. Methods Using a sample from the DOrtmund Nutritional and Anthropometric Longitudinally Designed study, we explored sex-specific trajectories of the BMI standard deviation score (SDS) from 4 to 18 years of age in 354 males and 335 females by latent (class) growth models. The determinants of trajectory were assessed by logistic regression. We identified cardiometabolic risk markers that were highly associated with BMI SDS trajectory by random forest regression, and finally we used generalized linear models to investigate differences in the identified cardiometabolic risk markers between pairs of trajectories. Results We observed four: ‘low-normal weight’, ‘mid-normal weight’, ‘high-normal weight’, and ‘overweight’, and three: ‘‘low-normal weight’, ‘mid-normal weight’, and ‘high-normal weight’ trajectories in males and females, respectively. Higher maternal prepregnancy BMI was associated with the ‘overweight’ trajectory, and with ‘high-normal weight’ trajectory in both sexes. In addition, employed mothers and first-born status were associated with ‘high-normal weight’ trajectory in females. BMI SDS trajectory was associated with high-density lipoprotein-cholesterol and interleukin-18 (IL-18) in males, and diastolic blood pressure and interleukin-6 (IL-6) in females. However, only males following the ‘overweight’ trajectory had significantly higher IL-18 when compared to their ‘low-normal weight’ counterpart. Conclusions We identified sex-specific distinct trajectories of BMI SDS from childhood into late adolescence, higher maternal prepregnancy BMI as a common determinant of the ‘high-normal weight’ and ‘overweight’ trajectories, and ‘overweight’ trajectory being associated with elevated IL-18 in late adolescence–young adulthood. This study emphasizes the role of maternal prepregnancy BMI in overweight, and highlights IL-18 as a cardiometabolic signature of overweight across life.