Abstract
Purpose
To provide an update on the cohort profile of the DOrtmund Nutritional and Anthropometric Longitudinally Designed (DONALD) study, including objectives, study design, methods and description of the comprehensive data pool, as well as to summarize the most important research findings of recent years.
Methods
In 1985, the open (dynamic) cohort started to collect information on diet, growth, development, and metabolism of healthy children and adolescents in Dortmund, Germany. Detailed data are collected annually during infancy, childhood, and adolescence of the participants, including a 3-day weighed dietary record, a 24-h urine sample, anthropometric and medical examinations as well as interviews on lifestyle.
Results
Even if the basic examination modules have not changed since the start over 35 years ago, the DONALD study has been continuously further developed by introducing new modules. As such, participants are also invited for follow-up examinations during adulthood since 2005, including an additional fasting blood withdrawal. Overall, 2375 (♂: 1177; ♀: 1198) participants were recruited in the DONALD study between 1985 and 2022. Data from ~ 30,700 anthropometric measurements, ~ 19,200 dietary records, ~ 10,600 24-h urine and ~ 1300 blood samples are available from an observation period of over 35 years.
Conclusion
The DONALD study provides a large data pool for longitudinal studies on nutrition, growth, and health in childhood and adolescence, its impact on the development of diseases in early adult life as well as dietary intake trends over more than three decades.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Introduction
It has long been recognized that adequate nutrition is essential for physical and mental development in childhood and adolescence [1]. In addition, childhood and adolescence represent important periods in life for shaping dietary and lifestyle patterns, which may track into adulthood [2]. Nutrition across the life course of childhood, adolescence and early adulthood also affects health outcomes, i.e., the development of non-communicable diseases later in life [3,4,5]. However, many research questions about the specific role of nutritional, metabolic, and lifestyle factors during childhood on healthy growth as well as their impact on the development of diseases in later life are still unresolved. This may be due to the fact, that in researching the impact of nutrition on the growing organism, continuous and heterogeneous changes of the body and its functions in the course of development from infancy to adulthood pose a particular difficulty for data collection and analyses [6]. Therefore, comprehensive and close-meshed long-term studies with prospective and repeated detailed data collection on nutrition, metabolism, development, and growth among children and adolescents are essential to gain scientifically proven knowledge about nutrition and health associations. Findings from these studies may furthermore help to prevent chronic diseases in populations by providing research-based information for the development of evidence-based food-based dietary guidelines and further public health measures.
Against this background, the DOrtmund Nutritional and Anthropometric Longitudinally Designed (DONALD) study was designed more than 35 years ago as an open (dynamic) cohort study, in Dortmund, Germany. The study was first described in an international journal in 2004 [6]. A second description followed in 2012, albeit in German only [7]. Due to the dynamic design of the DONALD study, methods are continuously expanded. Hence, the aim of the current paper is to provide a detailed study update, including the description of study design, comprehensive methodology as well as available data and recent study results, to outline its great potential for addressing current research topics in nutritional epidemiology.
Study objectives
The DONALD study was initiated in 1985 at the former German Research Institute of Child Nutrition Dortmund (Forschungsinstitut für Kinderernährung Dortmund, FKE). Since 2012, the study has been affiliated to the Faculty of Agriculture at Bonn University and is coordinated at the Institute of Nutrition and Food Sciences.
The longitudinal study started to collect information on diet, growth, development, and metabolism of healthy children and adolescents in August 1985. Initially, the following main research objectives were defined:
-
Analyses of interactions between nutrition, metabolism, development, and growth in healthy children
-
Description of intra- and inter-individual as well as age and time trends in dietary intake and patterns
-
Determination of nutritional needs in children and adolescents
-
Development of metabolic reference data based on 24-h urinary excretion levels in healthy children and adolescents
-
Provision of dietary intake data for specific exposure assessments [6]
Over the past 35 years, however, the focus has continuously been widened to enable the investigation of further complex and societally relevant epidemiological research questions. For example, since data collection has continued into participants’ adulthood, a further aim of the study is to investigate the impact of diet and lifestyle in childhood and adolescence on health outcomes in adulthood. In addition, with advances in omics-technologies, phenotypic and genotypic data have been expanded.
Study design and methods
Study recruitment
The DONALD study started with a cross-sectional sample of children and adolescents (~ 640 participants, > 2 years old) recruited in the first decade since 1985. In addition, since study start, 35–40 infants are newly recruited every year. The recruitment of participants is carried out unsystematically through maternity wards, pediatric practices, other pediatric institutions, personal contacts or recommendations from study participants, in the city of Dortmund and the surrounding communities. Eligible infants are healthy (i.e., no prevalent diseases affecting growth and/or diet), with parents willing to participate in a long-term study and with at least one parent having sufficient knowledge of the German language. To date, these inclusion criteria have not been changed.
Study design
According to the design of the DONALD study (Fig. 1), participants are examined for the first time at the age of 3 months. The participants return to the study center for three more visits in the first year of life, two in the second year and annually thereafter until young adulthood. Yearly examinations include 3-day weighed dietary records, anthropometric measurements, the collection of one 24-h urine sample (starting at age 3–4 years), medical examinations as well as interviews on lifestyle (e.g., physical activity, chronobiology, socioeconomic factors). Between 1986 and 2009, ~ 580 children and adolescents participated in a reduced program of the DONALD study without collecting dietary records and 24-h urines (Fig. S1). Participants were allocated to this reduced program, if they were not compliant to complete dietary records and/or 24-h urines. However, the frequency of study center visits for anthropometric and medical examination followed the study protocol.
The study is non-invasive until the age of 18 years. Since 2005, participants older than 18 years are also invited to provide a fasting blood sample. Parental examinations (anthropometric measurements, measurement of blood pressure, lifestyle interviews) take place every 4-years. The design of the DONALD study was approved by the Ethics Committee of the University of Bonn (project identification of the most recent version: 185/20), according to the guidelines of the Declaration of Helsinki. All examinations are performed with parental as well as from adolescents (16 years) onwards, with participants’ written consent.
Data assessment
Anthropometrics
During the study center visit, trained nurses carry out detailed anthropometric measurements according to standardized procedures, using regularly calibrated instruments [8, 9]. Quality controls of the measurements are regularly conducted.
For the measurements, participants are wearing underwear only as well as no shoes. Since 1985, height or length (to 0.1 cm), weight (to 100 g), upper arm circumference (to 0.1 cm), as well as four skinfolds (biceps, triceps, subscapular and iliac skinfolds, to 0.1 mm) are measured at each examination. Among children < 2 years of age, recumbent length is measured. From the age of 2 years onwards, standing height is measured. Both are assessed using a digital stadiometer (Harpenden, Crymych, UK). Body weight is measured using an infant weighing scale (until 08/2018: Mettler PS 15; Mettler Toledo, Columbus, OH; from 08/2018 onwards: Seca 757; Seca Weighing and Measuring System, Germany) as well as an electronic scale (Seca 920; from 02/2006 onwards Seca 701; Seca Weighing and Measuring System, Germany) among older participants. To estimate body composition, [10,11,12], four skinfolds (biceps, triceps, iliacal, and supradings) are measured on the right side of the body using a skinfold calliper (Holtain Ltd, Croswell, Dyfed, UK), duplicate measurements are taken since 2006. Head circumference (accurate to 0.1 cm) has been assessed at every examination since 1985, from the first visit to age 5 as well as at ages 18 and 21, using diameter measuring tapes (until 09/2012: Circumference Diameter Tape, Chasmors, London, UK; from 09/2012 onwards: Diameter Measuring Tape, Richter, Germany). In addition, since 1985, sitting height (to 0.1 cm) is recorded among participants ≥ 3 years at each visit using a sitting height measuring device (custom-made for the DONALD study).
Since 2005, from the age of 3 years onwards, hip (around the widest portion of the buttocks) and waist (at the midpoint between lower rib and iliac crest) circumference (to 0.1 cm) are assessed. Abdominal circumference (to 0.1 cm) is been measured since 1985 at every visit to the study center, from study entry until 2 years of age. Measurement of upper and lower leg circumference was performed at each visit/every age of the participants between 1985 and 2006. Since 2006, these measurements are taken only at the first visit as well as at the visits at age 5, 10, 15, and 18–25.
Until 2004, anthropometric measurements were taken every 6 months during puberty (girls: 8–14 years, boys: 10–16 years) to reflect rapid anthropometric changes during this period.
Medical examination
At each study center visit, trained physicians conduct a medical examination. Furthermore, information about the current and past (related to the time period since the last visit) health status of the participant as well as the age-appropriate physical development are collected. A detailed anamnesis interview inquired among others chronic diseases that are documented as ICD codes. Other examinations include measurements of blood pressure, intima-media thickness of the carotid artery and thyroid volume.
The measurement of systolic and diastolic blood pressure is usually performed at regular time intervals from the age of 4 (until 2004) or 6 (since October 2004) years onwards by trained nurses, according to standard procedures (participant in seated position with legs uncrossed, feet at the floor, using the right arm, arm supported at heart level) [13, 14]. Until 1994, a random zero sphygmomanometer (Random Zero Sphygmanometer 77,075, Hawksley & Sons, UK) is used for the measurements, whereas a standard mercury sphygmomanometer (Sphygmomanometer Mercury 300, Speidel & Keller, Germany) was used between 1994 and 2020. Different cuff sizes are used according to arm circumferences. At each measurement occasion, two consecutive blood pressure readings are taken after a rest of ~ 5 min. Since 2020, a digital sphygmomanometer (Mindray VS-600, Mindray, Germany) is used.
Since 2008, intima-media thickness is measured every 2 years in schoolchildren (11–17 years) as well as during adulthood. For this, vascular condition of the left and right common carotid artery is determined by high-resolution ultrasonography (Until 2016: Mindray DP3300, Mindray Bio‐Medical Electronics Co., Ltd., Shenzhen, China; since 2016: Sonoscape S11, Sonoscape Co. Ltd., Shenzhen, China). Measurements are taken after a 10-min resting period in participants in a supine position, head slightly to the right or left according to a standard procedure [15,16,17].
Since 2012, the thyroid volume has also been determined by ultrasonography under the same conditions as the intima-media examination. Data are collected from children, every 2 years between 7 and 17 years, as well as at each visit during adulthood. Thyroid-lobe height, width, and depth were measured. From these data, thyroid volume is calculated by the formula of Brunn et al. [18].
Dietary assessment
Paper-based 3-day weighed dietary records are used to collect information on food and nutrient intake from all participants from infancy to adulthood. To estimate breast milk quantities by test-weighing [19] during infancy, mothers are provided a baby scale (Soehnle multina 8300, Soehnle, Germany) to weigh the child before and after each feeding to the nearest 10 g [20].
From the start of complementary feeding onwards, all foods and beverages consumed by the participant, as well as leftovers, are weighed and recorded over 3 consecutive days to the nearest 1 g by the parents or, later on, by participants themselves. If there is no digital scale in the household, families are provided with a digital scale (currently WEDO Digi 2000, WEDO, Germany) and are instructed by a dietician on how to use it for weighing. If exact weighing is not possible, e.g., when eating out-of-home meals or snacks, household measures (e.g., spoons, cups) are allowed for semiquantitative recording.
The beginning of the 3-day dietary recording is chosen by the participant themselves usually within 8 weeks after the study center visit. Requested information includes name of the food or drink, date, time and location of eating, as well as recipes (ingredients and preparation). The participants are asked to describe the food as detailed as possible, e.g., to also record the fat content of dairy products or bread with iodized salt. For commercial food items, the packages or the food labels are collected by the participants and information on the packaging (name, manufacturer, brand, labeled nutrients, list of ingredients) are added to the dietary record data. Medication and dietary supplement use are also recorded. After the 3 days of recording, a trained dietician picks up all materials at the family’s home or picture of packaging are collected via an online messenger. The dietician crosschecks the dietary record using standard questions (e.g., on brand, manufacturer, homemade recipes, preparation method, type of table salt) in a short interview/phone call with the participant. Missing information can, thus, be added. In addition, it is inquired whether unusual events during the recording days might have changed the habitual dietary behavior.
All recorded food items are coded and linked to the continuously updated in-house nutrient database „LEBensmittelTABelle“ (food table, LEBTAB [21]). LEBTAB was developed during the course of the DONALD study and contains detailed data (energy and 72 nutrients; version III) on all basic (e.g., apple or milk) and commercial composite foods (i.e., infant formula, commercial complementary food as well as ready-to-eat-meals/-products, fortified food, food for dietetic purpose, and nutrient supplements), which were recorded by DONALD participants. Data entries for basic foods are based mainly on the German standard food table “Bundeslebensmittelschlüssel“ (BLS; version 3.02; https://www.blsdb.de/). Missing food items are complemented by data from other national food tables, e.g., by the U.S. Department of Agriculture (USDA) [22]. Energy and nutrient contents of composite foods are estimated by recipe simulation using labeled ingredients and declared nutrient contents taking into account nutrient fortification. In addition, food additives and flavorings have been recorded qualitatively in the estimated recipes since 2004.
Energy and nutrient data in LEBTAB refer to the raw food item. To take the loss of vitamins during food preparation into account, corresponding subtractions are made based on average nutrient losses during preparation as published by the German Nutrition Society [23]. The 3-day weighed records have been validated against the biomarker of 24-h urinary total nitrogen excretion [24], which showed that protein intake in children and adolescents can be estimated with acceptable validity by weighed dietary records.
Due to the longitudinal character of the DONALD study, recipe modifications or reformulations of commercial food products by the manufacturers are updated continuously. Therefore, each modified product receives a new entry in the nutrient database, while the entry for the product with the old recipe is marked and retained in the database. Until the end of 2022, LEBTAB contains more than 17,000 entries, i.e., ~ 1500 basic foods, ~ 15,700 composite foods (including ~ 4900 fortified products), and ~ 470 medication (e.g., antibiotics, analgesics) and supplements (e.g., nutrients, botanicals). In addition, as part of specific research projects, LEBTAB or at least parts of the included food codes, is regularly expanded to additional nutritional information (e.g., whole grain [25], glycemic index [25], isoflavones [26] or anthocyanin [27], or free sugar [28]).
Interviews
Early life factors
At the first visit, each child’s birth characteristics are retrieved from the German standardized pregnancy document called “Mutterpass”. This includes information on pregnancy duration, type of birth (vaginal or cesarean), birth weight, birth size, head circumference of the child as well as the APGAR score at 5 and 10 min after birth [29]. In addition, a detailed interview on pregnancy is conducted with the mother. To be able to calculate weight gain during pregnancy, maternal body weight at the beginning and the end of pregnancy is inquired about. Furthermore, information on pregnancy symptoms and illnesses is collected, e.g., occurrence and duration of nausea as well as feeling of fullness/reflux and its consequences for eating behavior, iron deficiency, high blood pressure, diabetes or kidney diseases. Questions on maternal lifestyle during pregnancy concern smoking behavior before and during pregnancy, including number of cigarettes/day; alcohol consumption and whether, how long and until which month of pregnancy the mother did exercise. In addition, mothers are asked about their intake of dietary supplements during pregnancy. A detailed questionnaire on breastfeeding behavior is completed at all visits during infancy. Thus, data on the duration of full and partial breastfeeding are collected. In addition, mothers are asked whether they had planned to breastfeed before the birth of their child and the reason for stopping breastfeeding. Formula feeding as well as the start of complementary feeding are also documented.
Physical activity
In DONALD, a standardized questionnaire based on the Adolescent Physical Activity Recall Questionnaires [30] and questions from the German Health Interview and Examination Survey for Children and Adolescents (KiGGS) [31] is used to assess physical activity as well as sedentary behavior since 2004. Questions on physical activity include the duration and frequency of organized (e.g., club sport, gym) and unorganized sports (e.g., playing football with friends, cycling) as well as how the participant usually gets to school (e.g., by car, bus, bike or walking). Furthermore, duration and frequency of sedentary activities, i.e., watching television or reading a book, are asked.
Since June 2004, muscle function/physical performance tests have been used every 2 years from the age of 6 and on every occasion in adulthood with the help of a Ground Reaction Force Platform (GRFP; NOVOTEC Medical GmbH). In addition, since 2004 on the same occasions, muscle strength/grip strength is determined using a dynamometer (Jamar Jackson, MI, 49203 U.S.A.) on the non-dominant hand (participant in an upright-seated position and feet at the floor).
Chronobiology
Since 2014, chronotype of DONALD participants during adolescence has been assessed with the Munich Chronotype Questionnaire (MCTQ). The MCTQ includes information on sleep duration and bed times, separately for free and for school/working days, and therefore allows to calculate chronotypes and social jetlag according to Roenneberg [32, 33].
Further interviews
At each visit, further personal interviews with participants’ parents and/or older participants themselves are conducted by the study pediatricians, including inquiring about data on childcare, use of vitamin D and fluoride supplements and preventive medical services. From the age of 12 onwards, participants are also regularly asked to complete questionnaires on smoking habits, intake of alcoholic beverages, and restrictive dietary behavior in absence of their parents.
Urine sampling and analysis
From 3 or 4 years onwards, 24-h urine is collected once annually preferably on the third day of dietary recording. Parents as well as older children receive in-person and written instructions on how to collect complete 24-h urine samples according to a standardized procedure. The collection starts after voiding of the bladder upon getting up in the morning. This micturition is completely discarded. The time of this micturition is recorded and defines the start of the 24-h collection, which ends with the first micturition on the following day. During the collection period, the participants store micturitions immediately in preservative-free, Extran-cleaned (Extran, MA03; Merck, Darmstadt, Germany) 1-L plastic containers at less than − 12 °C before transfer to the study center. The exact times of micturition as well as possible comments on the collection are recorded on a predefined protocol sheet. The dietician picks up the 24-h urine samples together with the dietary record, checks plausibility of the collection protocol, inquires parents and/or participants about the completeness of the urine samples, and adds this information to the protocol sheet if necessary. After the transfer to the study center, the containers are stored at − 22 °C until analysis and subsequent biobanking. For routine laboratory measures and aliquotation, the samples are thawed and stirred. Urine volume, creatinine, urea, sodium, potassium, osmolality, and pH value are analyzed in the DONALD study laboratory. Subsequently, five aliquots of 20 ml each from each 24-h urine are stored at − 22 °C in the DONALD biobank for further research. Since the beginning of the DONALD study in 1985, over 11,000 24-h urine samples were collected and added to the biobank (Table 2).
Until 2019, for younger participants (0 − < 3 years), a spot urine is collected at the study center visit using a urine bag (Braun AG, Germany). After creatinine concentration measurements, samples were stored preservative free at − 22 °C in the urine biobank of the DONALD study (n = 1134).
Blood withdrawal and analyses
Since 2005, participants over the age of 18 have been asked to provide a venous fasting blood sample. Blood samples are centrifuged directly at 4 °C within 15 min (3100 U/ min). The blood samples are routinely analyzed to determine among others blood cell levels (e.g., leucocytes, thrombocytes, erythrocytes), hemoglobin, hematocrit, liver enzymes (GOT, GPT, GGT), cholesterol and blood lipid levels (e.g., total cholesterol, LDL, HDL), triglycerides, blood sugar, iron levels and thyroid parameters (TSH, fT3, fT4). Aliquots of serum, plasma (citrate, EDTA) and buffy coat (citrate, EDTA) samples (each 500 µl) are stored at − 80 °C in the DONALD biobank for further research.
Parental data
The study nurses collect maternal and/or paternal anthropometric (body weight and height, skinfolds, hip and waist circumference) as well as medical data (blood pressure) every 4 years using the same methodology and equipment as for the participants. In addition, parents are asked about previous chronic diseases (including hypertension, hyperlipidemia, diabetes, fatty liver disease), the intake of medication as well as diagnosed allergies. Furthermore, data on parental lifestyle (e.g., smoking in household, physical activity, specific dietary practices) and socioeconomic status (e.g., parental education and employment) are collected from both parents.
During adulthood (from the age of 21 onwards), the participants themselves are asked about their parents’ data, e.g., chronic diseases as well as if necessary, year of mortality and the cause of death.
Additional data collection
Additional modules
In addition to the basic protocol of the DONALD study, further modules among subgroups are established within the framework of specific projects (Table S1). Methods are described in short as follows.
Stool samples were collected between 2017 and 2018 in the HEALTHMARK (metabolic HEALTH through nutrition, microbiota and tryptophan bioMARKers) project from adult participants (≥ 18 years) to be able to explore gut microbiome-related research questions. Finally, microbiome data based on 16S ribosomal RNA sequencing of n = 128 individuals (77 double and 51 single stool samples) are available [34]. Currently, i.e., since 2023, further stool samples have been collected from the adult participants as part of the PerMiCCion (PERsonalized MIcrobiome-Based Approaches to Early Onset Colorectal Cancer PreventION, Diagnosis and Management) project (http://www.permiccion.de/).
A variety of additional modules were made possible within the framework of the nutrition research competence cluster Diet Body Brain (DietBB; www.diet-body-brain.de), including the collection of genetics, data on fluid intelligence and personality-related characteristics accelerometer. Between 2015 and 2022, saliva samples were used for DNA extraction and one time collected among children and adolescents between 6 and 17 years (n = 170). Among adult participants, blood samples (buffy coats (n = 440) and PaxGene samples (n = 287)) are used to analyze DNA and RNA. These data allow the investigation of associations between nutrition, genetics, and health.
To investigate associations between personality-related characteristics and nutrition or anthropometry as part of DietBB (2016–2020), individual personality data were collected. For this, game-based experiments were conducted. Among 5–6 year olds (n = 126; until June 2016 also 4 year olds), delay of gratification was assessed, via Stanford marshmallow experiment [35]. To assess economic preferences, experiments on time, social, and risk preferences [36] were performed among 7–9 year olds (n = 168). In addition, validated questionnaires on personality by Decker et al. [36] were used, which include personality questions on the consumption of sweets (n = 125; age 5–6 years, until June 2016 also 4 year olds) or pocket money (n = 167; age 7–9 years).
Fluid intelligence was assessed using the Culture Fair Intelligence (CFT) 1-R (n = 89) [37] or the CFT 20-R test (8.5–18 years n = 139; > 18 years n = 99) [38] among a DONALD subgroup in 2017–2018. The cognition test was carried out once per participant. CFT 1-R was used among participants between 5 and < 8.5 years, CFT 20-R was used among participants from 8.5 years onwards. Both tests were developed language-free and figure-based to minimize cultural or social disadvantages or advantages [37, 38].
Accelerometer (CentrePoint Insight Watch from the company ActiGraph) data have been collected from 2020 until 2022 to determine physical activity and sleep characteristics (quality and duration) among participants between 6–18 years (n = 127) and > 18 years (n = 36).
In a further additional module in 1989–1999, a single peripheral quantitative computed tomography (pQCT) analysis of the forearm was undertaken in 371 participants aged 6–18 years. Details of these data collection and measurements can be found in numerous publications (among others: [39,40,41]).
Additional biosample analyses
Additional analyses of biosamples (urine and blood) are carried out within the framework of specific research projects, partly in cooperating laboratories. According to the respective research question, project-specific metabolic parameters were measured in urine samples among subgroups of the study. These include among others metabolomics (e.g., described in [42]), hippuric acid as biomarkers for flavonoid intake (e.g., described in [43]), fructose/sucrose excretion as biomarkers for total sugar intake (described in [44, 45]) and nutritionally and metabolically relevant hormone metabolites, e.g., adrenal androgens, glucocorticoids (described in [46, 47]). Similar to these urinary analyses, additional blood analyses were conducted within specific research projects, e.g., untargeted metabolomics or several serum (e.g., hormones (IGF-1, leptin), inflammatory markers (IL-18, E-selectin, IL1-ra, hs-CRP) and EDTA plasma parameters (e.g., hormones (insulin, adiponectin, fT3, fT4, TSH), inflammatory markers (IL-6, IL-18, hs-CRP, sICAM-1, omentin, fetuin-A), triglycerides, liver enzymes (GOT, GGT, GPT, ALT, AST)). Data from blood sample analyses are used primarily to determine metabolic health during adulthood, e.g., type II diabetes [3, 48], non-alcoholic fatty liver [4, 49] or cardiometabolic diseases [5, 50].
Data availability
The DONALD study aims to make research data available in accordance to the FAIR principles [51], and within the framework of data protection regulations. Research data can be made available upon request to epi@uni-bonn.de.
The study has been registered at the German Clinical Trials Register (DRKS00029092) and metadata of the study is published in the metadata portal for observational studies in Nutritional Epidemiology, which participated in the INTIMIC project (https://mica.mdc-berlin.de/study/donald) as well as in the NFDI4Health metadata portal (https://csh.nfdi4health.de/resource/45).
Results
DONALD cohort
Overall, 2375 (♂: 1177; ♀: 1198) participants have been recruited between 1985 and 2021. Study sample characteristics are shown in Table 1. Compared to the general population in Germany, participating families in DONALD are characterized by a rather high socioeconomic status (Table 1, [6]). The high socioeconomic status is also reflected in the prevalence of overweight and obesity in DONALD (Fig. 2).
Prevalence for overweight and obesity among male (3–6: N = 2785, n = 802; 7–10: N = 2771, n = 762; 11–13: N = 2501, n = 630; 14–17: N = 2512, n = 593) and female (3–6: N = 2676, n = 801; 7–10: N = 3182, n = 725, 11–13: N = 2483, n = 645; 14–17: N = 1876, n = 527) participants of the DONALD study in different age groups. Prevalence for overweight (BMI-SDS > 90th percentile and < 97th percentile) and obesity (BMI-SDS > 97th percentile) was calculated based on the German reference percentiles for children and adolescents by Kromeyer–Hauschild [52]
The data pool of the DONALD study, i.e., numbers of 3-day weighed dietary records, anthropometric measurements, 24-h urine and blood samples in the DONALD study (1985–2021) are presented in Table 2, stratified by age and sex groups. In total, data from ~ 30,700 anthropometric measurements, ~ 19,200 dietary records, ~ 10,600 24-h urine, and ~ 1300 blood samples are available. Although the study protocol requires data to be collected from infants, children, and adolescents at least once a year (Fig. 1), the assessments are based on a voluntary basis. Therefore, and due to the very time-consuming data collection, data are not available from each participants for all assessments at all ages and the total number of assessments differ. In addition, the reduced program (Fig. S1) of the study as well as additional measurements every 6 months during puberty between 1985 and 2004 in the cohort, led to the relatively high number of anthropometric examinations. Even though the number of participants is continuously increasing, the number of collected records and urine samples per year is decreasing over the last two decades (data not shown). Socioeconomic parameters of families also changed over the study years. The proportion of parents with a high level of education (≥ 12 years of schooling) increased, as did the proportion of mothers who were employed (data not shown).
Exemplary results of recent years
Since 1985, the DONALD study has led to numerous nutritional, growth, and metabolism-related publications in national and international journals. Exemplary results of the DONALD study in recent years are briefly described in Table 3. Results of the DONALD studies are incorporated into the work of various national and international professional societies. For example, results of the project on complementary feeding [53, 54] were taken up by the Nutrition Commission of the European Society for Paediatric Gastroenterology, Hepatology, and Nutrition (ESPGHAN) [55] as well as by the British Nutrition Foundation [56], in their position papers on complementary feeding. As the DONALD study was the first study, which provided data on free sugar intake among children and adolescents in Germany [28], these were included in the consensus paper by the German Obesity Society, German Diabetes Society and German Nutrition Society on quantitative recommendations for sugar intake in Germany [57]. In addition, the European Food Safety Authority also refers repeatedly to different data from the DONALD study in its scientific opinions and recommendations (e.g., [58,59,60]).
Discussion
Due to its comprehensive and longitudinal design, the DONALD study offers the possibility to investigate prospectively a wide range of complex research questions on interaction of nutrition, growth, metabolism and long-term health. The study is furthermore characterized by the use of precise and close-meshed data collection methods, e.g., 3-day weighed dietary records. Furthermore, the 24-h urine allows metabolic insights and determination of biomarker for dietary intake. As the DONALD study is also collecting detailed data of participants during adulthood since 2005, it is further possible to investigate the impact of nutrition and lifestyle in phases, which are discussed as potentially critical for later disease risk, such as infancy/early childhood, primary school age or adolescence [77, 78]. This unique data collection enabled among others, the examination of long-term age and time trends on nutrition among children and adolescents in Germany over more than 3 decades from 1985 until today. Thus, results of the DONALD study contribute to the monitoring of habitual nutrient and food intake of children and adolescents, and therefore contribute to the basis for public health measures, including implementation in national and international nutrition recommendations or position papers [55,56,57].
To our knowledge, there is no comparable study in Germany or Europe with such a large number of repeated measurements among participants from infancy to adulthood and has such an extensive pool of nutritional, health and lifestyle data. There are some cohorts, such as IDEFICS (Identification and prevention of dietary and lifestyle-induced health effects in children and infants)/I.Family [79], KiGGs/Motorik-Modul Longitudinal Study [80], ALSPAC (Avon Longitudinal Study of Pregnancy and Childhood) [81], the Generation R [82] or Bogalusa Heart Study [83], which are larger and more representative. However, their study design includes fewer repeated measurements and/or does not cover detailed nutritional data or the entire observation in infancy, childhood, and adolescence, as well as continuing data collection in adulthood. To investigate research questions on the impact of nutrition and lifestyle in childhood and adolescence on the development of diseases in later life, prospective data on nutrition and its changes from infancy to adulthood are essential. The DONALD cohort can help to fill this research gap.
Although the design and methods of the DONALD study remained the same, some changes in the study population can be observed. The mean age of the study sample increases, as there are more adults invited to be examined. However, in evaluations of pediatric samples, an adjustment for age is mandatory. Second, the number of collected records and urine samples per year is decreasing over the last 2 decades. A decline in compliance with the study protocol was observed. At present, we can only speculate about the causes. One reason could be the more frequent occupation of both parents during the last decades in Germany, which leads to a greater time burden on families.
Nevertheless, the design of the study also leads to limitations. The detailed methodology and the elaborate design of the DONALD study result in a selected, non-representative study sample. Therefore, compared to general population in Germany, DONALD participants have a rather high socioeconomic status [6]. However, even in representative studies, it is difficult to recruit participants with a lower socioeconomic status for longitudinal observations. In addition, representative studies in Germany such as the nationwide SuSe study (“Stillen und Säuglingsernährung”) [84] or EsKiMo (“Ernährungsstudie als KiGGS-Modul”; substudy of a national survey named KiGGS (Kinder- und Jugendgesundheitssurveys)) [85, 86] show similar results, e.g., similar energy and nutrient intakes. The prevalence of overweight and obesity in DONALD is comparatively low, but similar to participants with a rather high socioeconomic status in KiGGS [87]. Furthermore, the study design leads to a rather small study sample. The statistical power for complex analyses requiring large numbers of cases is, therefore, rather low.
In summary, the DONALD study provides a large data pool for epidemiological research questions, which is continuously growing due to the dynamic, longitudinal design. In addition to descriptive evaluations, cross-sectional analyses and age trends, it is also possible to investigate long-term time trend analyses (> 35 years) as well as prospective exposure assessments. The study is, thus, essential for past and future monitoring of diet, growth, development, and health of children and adolescents in Germany and offers the potential to contribute to filling several research gaps.
Data availability
Data of the DONALD study are available upon request to epi@uni-bonn.de.
References
Martin HP (1973) Nutrition: its relationship to children’s physical, mental, and emotional development. Am J Clin Nutr 26:766–775. https://doi.org/10.1093/ajcn/26.7.766
Mikkilä V, Räsänen L, Raitakari OT et al (2005) Consistent dietary patterns identified from childhood to adulthood: the cardiovascular risk in Young Finns Study. Br J Nutr 93:923–931. https://doi.org/10.1079/bjn20051418
Penczynski KJ, Herder C, Krupp D et al (2019) Flavonoid intake from fruit and vegetables during adolescence is prospectively associated with a favourable risk factor profile for type 2 diabetes in early adulthood. Eur J Nutr 58:1159–1172. https://doi.org/10.1007/s00394-018-1631-3
Schnermann ME, Schulz C-A, Perrar I et al (2022) A healthy lifestyle during adolescence was inversely associated with fatty liver indices in early adulthood—findings from the DONALD cohort study. Br J Nutr. https://doi.org/10.1017/S0007114522001313
Schnermann ME, Schulz C-A, Herder C et al (2021) A lifestyle pattern during adolescence is associated with cardiovascular risk markers in young adults: results from the DONALD cohort study. J Nutr Sci 10:e92. https://doi.org/10.1017/jns.2021.84
Kroke A, Manz F, Kersting M et al (2004) The DONALD Study. History, current status and future perspectives. Eur J Nutr 43:45–54. https://doi.org/10.1007/s00394-004-0445-7
Buyken AE, Alexy U, Kersting M et al (2012) Die DONALD Kohorte. Ein aktueller Überblick zu 25 Jahren Forschung im Rahmen der Dortmund Nutritional and Anthropometric Longitudinally Designed Study (The DONALD cohort. An updated overview on 25 years of research based on the Dortmund Nutritional and Anthropometric Longitudinally Designed study). Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 55:875–884. https://doi.org/10.1007/s00103-012-1503-6
Flügel B, Greil H, Sommer K (1986) Anthropologischer Atlas: Grundlagen und Daten; Deutsche Demokratische Republik, 1. Aufl. Verl. Tribüne, Berlin
Lohman TG, Roche AF, Martorell R (eds) (1991) Anthropometric standardization reference manual, Abridged. Human Kinetics Books, Champaign
Durnin JV, Womersley J (1974) Body fat assessed from total body density and its estimation from skinfold thickness: measurements on 481 men and women aged from 16 to 72 years. Br J Nutr 32:77–97. https://doi.org/10.1079/bjn19740060
Slaughter MH, Lohman TG, Boileau RA et al (1988) Skinfold equations for estimation of body fatness in children and youth. Hum Biol 60:709–723
Deurenberg P, Weststrate JA, Seidell JC (2007) Body mass index as a measure of body fatness: age- and sex-specific prediction formulas. Br J Nutr 65:105
Krupp D, Shi L, Remer T (2014) Longitudinal relationships between diet-dependent renal acid load and blood pressure development in healthy children. Kidney Int 85:204–210. https://doi.org/10.1038/ki.2013.331
Krupp D, Westhoff TH, Esche J et al (2018) Prospective relation of adolescent citrate excretion and net acid excretion capacity with blood pressure in young adulthood. Am J Physiol Renal Physiol 315:F1228–F1235. https://doi.org/10.1152/ajprenal.00144.2018
Nyasordzi J, Penczynski K, Remer T et al (2020) Early life factors and their relevance to intima-media thickness of the common carotid artery in early adulthood. PLoS One 15:e0233227. https://doi.org/10.1371/journal.pone.0233227
Kassenärztliche Bundesvereinigung (2008) Vereinbarung von Qualitätssicherungsmaßnahmen nach § 135 Abs. 2 SGB V zur Ultraschalldiagnostik (Ultraschall-Vereinbarung). http://www.kbv.de/media/sp/Ultraschallvereinbarung.pdf. Accessed 29 Jun 2022
(1996) Ultraschall-Kursbuch: Nach den Richtlinien der DEGUM und der KBV, 2., überarbeitete und erweiterte Auflage. Georg Thieme Verlag, Stuttgart
Brunn J, Block U, Ruf G et al (1981) Volumetrie der Schilddrüsenlappen mittels Real-time-Sonographie (Volumetric analysis of thyroid lobes by real-time ultrasound (author’s transl)). Dtsch Med Wochenschr 106:1338–1340. https://doi.org/10.1055/s-2008-1070506
Butte NF, Garza C, Smith EO et al (1983) Evaluation of the deuterium dilution technique against the test-weighing procedure for the determination of breast milk intake. Am J Clin Nutr 37:996–1003. https://doi.org/10.1093/ajcn/37.6.996
Schoen S, Sichert-Hellert W, Kersting M (2009) Validation of energy requirement equations for estimation of breast milk consumption in infants. Public Health Nutr 12:2309–2316. https://doi.org/10.1017/S1368980009005503
Sichert-Hellert W, Kersting M, Chahda C et al (2007) German food composition database for dietary evaluations in children and adolescents. J Food Compos Anal 20:63–70. https://doi.org/10.1016/j.jfca.2006.05.004
U.S. Department of Agriculture Food Composition. https://nal.usda.gov/legacy/fnic/food-composition. Accessed 07 Feb 2022
Deutsche Gesellschaft für Ernährung (2015) D-A-CH Referenzwerte für die Nährstoffzufuhr, 2nd edn. Neuer Umschau Buchverlag GmbH, Neustadt
Bokhof B, Günther ALB, Berg-Beckhoff G et al (2010) Validation of protein intake assessed from weighed dietary records against protein estimated from 24 h urine samples in children, adolescents and young adults participating in the Dortmund Nutritional and Longitudinally Designed (DONALD) Study. Public Health Nutr 13:826–834. https://doi.org/10.1017/S136898000999317X
Goletzke J, Buyken AE, Joslowski G et al (2014) Increased intake of carbohydrates from sources with a higher glycemic index and lower consumption of whole grains during puberty are prospectively associated with higher IL-6 concentrations in younger adulthood among healthy individuals. J Nutr 144:1586–1593. https://doi.org/10.3945/jn.114.193391
Cheng G, Remer T, Prinz-Langenohl R et al (2010) Relation of isoflavones and fiber intake in childhood to the timing of puberty. Am J Clin Nutr 92:556–564. https://doi.org/10.3945/ajcn.2010.29394
Drossard C, Bolzenius K, Kunz C et al (2013) Anthocyanins in the diet of children and adolescents: intake, sources and trends. Eur J Nutr 52:667–676. https://doi.org/10.1007/s00394-012-0371-z
Perrar I, Schmitting S, Della Corte KW et al (2020) Age and time trends in sugar intake among children and adolescents: results from the DONALD study. Eur J Nutr 59:1043–1054. https://doi.org/10.1007/s00394-019-01965-y
American Academy of Pediatrics Committee on Fetus and Newborn, American College of Obstetricians and Gynecologists Committee on Obstetric Practice (2015) The Apgar score. Pediatrics 136:819–822. https://doi.org/10.1542/peds.2015-2651
Booth ML, Okely AD, Chey TN et al (2002) The reliability and validity of the Adolescent Physical Activity Recall Questionnaire. Med Sci Sports Exerc 34:1986–1995. https://doi.org/10.1097/00005768-200212000-00019
Finger JD, Mensink GBM, Banzer W et al (2014) Physical activity, aerobic fitness and parental socio-economic position among adolescents: the German Health Interview and Examination Survey for Children and Adolescents 2003–2006 (KiGGS). Int J Behav Nutr Phys Act 11:43. https://doi.org/10.1186/1479-5868-11-43
Roenneberg T, Kuehnle T, Juda M et al (2007) Epidemiology of the human circadian clock. Sleep Med Rev 11:429–438. https://doi.org/10.1016/j.smrv.2007.07.005
Roenneberg T, Allebrandt KV, Merrow M et al (2012) Social jetlag and obesity. Curr Biol 22:939–943. https://doi.org/10.1016/j.cub.2012.03.038
Oluwagbemigun K, O’Donovan AN, Berding K et al (2021) Long-term dietary intake from infancy to late adolescence is associated with gut microbiota composition in young adulthood. Am J Clin Nutr 113:647–656. https://doi.org/10.1093/ajcn/nqaa340
Mischel W, Ebbesen EB (1970) Attention in delay of gratification. J Pers Soc Psychol 16:329–337. https://doi.org/10.1037/h0029815
Deckers T, Falk A, Kosse F et al (2021) Socio-economic status and inequalities in children’s IQ and economic preferences. J Polit Econ 129(9):2504–2545
Weiß RH, Osterland J (2012) CFT 1-R: Grundintelligenztest Skala 1, 1st edn. Hogrefe, Göttingen
Weiß RH (2006) CFT 20-R: Grundintelligenztest Skala 2, 1st edn. Hogrefe, Göttingen
Alexy U, Remer T, Manz F et al (2005) Long-term protein intake and dietary potential renal acid load are associated with bone modeling and remodeling at the proximal radius in healthy children. Am J Clin Nutr 82:1107–1114. https://doi.org/10.1093/ajcn/82.5.1107
Remer T, Manz F, Alexy U et al (2011) Long-term high urinary potential renal acid load and low nitrogen excretion predict reduced diaphyseal bone mass and bone size in children. J Clin Endocrinol Metab 96:2861–2868. https://doi.org/10.1210/jc.2011-1005
Esche J, Johner S, Shi L et al (2016) Urinary citrate, an index of acid-base status, predicts bone strength in youths and fracture risk in adult females. J Clin Endocrinol Metab 101:4914–4921. https://doi.org/10.1210/jc.2016-2677
Oluwagbemigun K, Anesi A, Ulaszewska M et al (2020) Longitudinal relationship of amino acids and indole metabolites with long-term body mass index and cardiometabolic risk markers in young individuals. Sci Rep 10:6399. https://doi.org/10.1038/s41598-020-63313-z
Penczynski KJ, Krupp D, Bring A et al (2017) Relative validation of 24-h urinary hippuric acid excretion as a biomarker for dietary flavonoid intake from fruit and vegetables in healthy adolescents. Eur J Nutr 56:757–766. https://doi.org/10.1007/s00394-015-1121-9
Johner SA, Libuda L, Shi L et al (2010) Urinary fructose: a potential biomarker for dietary fructose intake in children. Eur J Clin Nutr 64:1365–1370. https://doi.org/10.1038/ejcn.2010.160
Perrar I, Gray N, Kuhnle GG et al (2020) Sugar intake among German adolescents: trends from 1990 to 2016 based on biomarker excretion in 24-h urine samples. Br J Nutr. https://doi.org/10.1017/S0007114520000665
Remer T, Shi L, Buyken AE et al (2010) Prepubertal adrenarchal androgens and animal protein intake independently and differentially influence pubertal timing. J Clin Endocrinol Metab 95:3002–3009. https://doi.org/10.1210/jc.2009-2583
Shi L, Wudy SA, Buyken AE et al (2011) Prepubertal glucocorticoid status and pubertal timing. J Clin Endocrinol Metab 96:E891–E898. https://doi.org/10.1210/jc.2010-2935
Diederichs T, Herder C, Roßbach S et al (2017) Carbohydrates from sources with a higher Glycemic Index during adolescence: is evening rather than morning intake relevant for risk markers of type 2 diabetes in young adulthood? Nutrients 9:591. https://doi.org/10.3390/nu9060591
Perrar I, Buyken AE, Penczynski KJ et al (2021) Relevance of fructose intake in adolescence for fatty liver indices in young adulthood. Eur J Nutr 60:3029–3041. https://doi.org/10.1007/s00394-020-02463-2
Oluwagbemigun K, Buyken AE, Alexy U et al (2019) Developmental trajectories of body mass index from childhood into late adolescence and subsequent late adolescence-young adulthood cardiometabolic risk markers. Cardiovasc Diabetol 18:9. https://doi.org/10.1186/s12933-019-0813-5
Wilkinson MD, Dumontier M, Aalbersberg IJJ et al (2016) The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3:160018. https://doi.org/10.1038/sdata.2016.18
Kromeyer-Hauschild K, Wabitsch M, Kunze D et al (2001) Perzentile für den Body-mass-Index für das Kindes- und Jugendalter unter Heranziehung verschiedener deutscher Stichproben. Monatsschr Kinderheilkd 149:807–818. https://doi.org/10.1007/s001120170107
Foterek K, Buyken AE, Bolzenius K et al (2016) Commercial complementary food consumption is prospectively associated with added sugar intake in childhood. Br J Nutr 115:2067–2074. https://doi.org/10.1017/S0007114516001367
Foterek K, Hilbig A, Alexy U (2015) Associations between commercial complementary food consumption and fruit and vegetable intake in children. Results of the DONALD study. Appetite 85:84–90. https://doi.org/10.1016/j.appet.2014.11.015
Fewtrell M, Bronsky J, Campoy C et al (2017) Complementary feeding: a position paper by the European Society for Paediatric Gastroenterology, Hepatology, and Nutrition (ESPGHAN) Committee on Nutrition. J Pediatr Gastroenterol Nutr 64:119–132. https://doi.org/10.1097/MPG.0000000000001454
Chambers L, Hetherington M, Cooke L et al (2016) Reaching consensus on a ‘vegetables first’ approach to complementary feeding. Nutr Bull 41:270–276. https://doi.org/10.1111/nbu.12220
Ernst JB, Arens-Azevêdo U, Bitzer B, Bosy-Westphal A, de Zwaan M, Egert S, Fritsche A, Gerlach S, Hauner H, Heseker H, Koletzko B, Müller-Wieland D, Schulze M, Virmani K, Watzl B, Buyken AE for the German Obesity Society (DAG), German Diabetes Society (DDG) and German Nutrition Society (DGE) (2019) Quantitative recommendation on sugar intake in Germany. Short version of the consensus paper by the German Obesity Society (DAG), German Diabetes Society (DDG) and German Nutrition Society (DGE). Ernahrungs Umschau 66(2):26–34. https://doi.org/10.4455/eu.2019.006
Turck D, Bresson J-L, Burlingame B et al (2017) Scientific Opinion on the safety and suitability for use by infants of follow-on formulae with a protein content of at least 1.6 g/100 kcal. EFSA J 15:e04781. https://doi.org/10.2903/j.efsa.2017.4781
European Food Safety Authority (2012) Scientific opinion on dietary reference values for protein. EFSA J 10:2557. https://doi.org/10.2903/j.efsa.2012.2557
Turck D, Bohn T, Castenmiller J et al (2022) Tolerable upper intake level for dietary sugars. EFSA J 20:e07074. https://doi.org/10.2903/j.efsa.2022.7074
Montejano Vallejo R, Schulz C-A, van de Locht K et al (2022) Associations of Adherence to a Dietary Index Based on the EAT-lancet reference diet with nutritional, anthropometric, and ecological sustainability parameters: results from the German DONALD Cohort Study. J Nutr 152:1763–1772. https://doi.org/10.1093/jn/nxac094
Oluwagbemigun K, Schnermann ME, Schmid M et al (2022) A prospective investigation into the association between the gut microbiome composition and cognitive performance among healthy young adults. Gut Pathog 14:15. https://doi.org/10.1186/s13099-022-00487-z
Brachem C, Langenau J, Weinhold L et al (2020) Associations of BMI and body fat with urine metabolome in adolescents are sex-specific: a cross-sectional study. Metabolites. https://doi.org/10.3390/metabo10080330
Brachem C, Oluwagbemigun K, Langenau J et al (2022) Exploring the association between habitual food intake and the urine and blood metabolome in adolescents and young adults: a cohort study. Mol Nutr Food Res 66:e2200023. https://doi.org/10.1002/mnfr.202200023
Brachem C, Weinhold L, Alexy U et al (2023) Replication and mediation of the association between the metabolome and clinical markers of metabolic health in an adolescent cohort study. Sci Rep 13:3296. https://doi.org/10.1038/s41598-023-30231-9
Muli S, Brachem C, Alexy U et al (2023) Exploring the association of physical activity with the plasma and urine metabolome in adolescents and young adults. Nutr Metab (Lond) 20:23. https://doi.org/10.1186/s12986-023-00742-3
Schnermann ME, Schulz C-A, Ludwig C et al (2022) A lifestyle score in childhood and adolescence was positively associated with subsequently measured fluid intelligence in the DONALD cohort study. Eur J Nutr. https://doi.org/10.1007/s00394-022-02921-z
Remer T, Johner SA et al (2016) DONALD – ein Sensor für die Jodversorgung in Deutschland. Ernährungs Umschau. https://doi.org/10.4455/eu.2016.036
Remer T, Hua Y, Esche J et al (2022) The DONALD study as a longitudinal sensor of nutritional developments: iodine and salt intake over more than 30 years in German children. Eur J Nutr 61:2143–2151. https://doi.org/10.1007/s00394-022-02801-6
Johner SA, Thamm M, Stehle P et al (2014) Interrelations between thyrotropin levels and iodine status in thyroid-healthy children. Thyroid 24:1071–1079. https://doi.org/10.1089/thy.2013.0480
Roßbach S, Diederichs T, Nöthlings U et al (2018) Relevance of chronotype for eating patterns in adolescents. Chronobiol Int 35:336–347. https://doi.org/10.1080/07420528.2017.1406493
Diederichs T, Roßbach S, Herder C et al (2016) Relevance of morning and evening energy and macronutrient intake during childhood for body composition in early adolescence. Nutrients 8:716. https://doi.org/10.3390/nu8110716
Wittmann M, Dinich J, Merrow M et al (2006) Social Jetlag: misalignment of biological and social time. Chronobiol Int 23:497–509. https://doi.org/10.1080/07420520500545979
Perrar I, Schadow AM, Schmitting S et al (2020) Time and age trends in free sugar intake from food groups among children and adolescents between 1985 and 2016. Nutrients 12:20. https://doi.org/10.3390/nu12010020
WHO (2015) Guideline: sugars intake for adults and children. Nonserial Publications. World Health Organization, Geneva
Hilbig A, Foterek K, Kersting M et al (2015) Home-made and commercial complementary meals in German infants: results of the DONALD study. J Hum Nutr Diet 28:613–622. https://doi.org/10.1111/jhn.12325
Dietz WH (1994) Critical periods in childhood for the development of obesity. Am J Clin Nutr 59:955–959. https://doi.org/10.1093/ajcn/59.5.955
Lanigan J, Singhal A (2009) Early nutrition and long-term health: a practical approach. Proc Nutr Soc 68:422–429. https://doi.org/10.1017/S002966510999019X
Ahrens W, Siani A, Adan R et al (2017) Cohort Profile: the transition from childhood to adolescence in European children-how I.Family extends the IDEFICS cohort. Int J Epidemiol 46:1394–1395j. https://doi.org/10.1093/ije/dyw317
Wagner MO, Bös K, Jekauc D et al (2014) Cohort profile: the Motorik-Modul Longitudinal Study: physical fitness and physical activity as determinants of health development in German children and adolescents. Int J Epidemiol 43:1410–1416. https://doi.org/10.1093/ije/dyt098
Fraser A, Macdonald-Wallis C, Tilling K et al (2013) Cohort Profile: the avon longitudinal study of parents and children: ALSPAC mothers cohort. Int J Epidemiol 42:97–110. https://doi.org/10.1093/ije/dys066
Kooijman MN, Kruithof CJ, van Duijn CM et al (2016) The Generation R Study: design and cohort update 2017. Eur J Epidemiol 31:1243–1264. https://doi.org/10.1007/s10654-016-0224-9
Pickoff AS, Berenson GS, Schlant RC (1995) Introduction to the symposium celebrating the Bogalusa Heart Study. Am J Med Sci 310(Suppl 1):S1-2. https://doi.org/10.1097/00000441-199512000-00001
Holtermann B, Dulon M (2001) Stillen in der Geburtsklinik und im 1. Lebensjahr: Ergebnisse der bundesweiten SuSe-Studie (Breast feeding in the maternity clinic and in the 1st year of life: results of the comprehensive country-wide SuSe Study). Kinderkrankenschwester 20:519–521
Mensink GB, Haftenberger M, Thamm M (2001) Original Communications-Validity of DISHES 98, a computerised dietary history interview: energy and macronutrient intake. Eur J Clin Nutr 55(6):409–417
Mensink GBM (2007) Die aktuelle Nährstoffversorgung von Kindern und Jugendlichen in Deutschland. Ernährungsumschau 636–646
Robert Koch-Institut (2018) Übergewicht und Adipositas im Kindes- und Jugendalter in Deutschland – Querschnittergebnisse aus KiGGS Welle 2 und Trends. RKI-Bib1 (Robert Koch-Institut)
Acknowledgements
We would like to thank Professor Friedrich Manz († 2015) for initiating the DONALD study. The participation of all children and their families in the DONALD study is gratefully acknowledged. We also thank the DONALD staff for carrying out the anthropometric measurements, administering the questionnaires, collecting and coding the dietary records as well as conducting the laboratory analyses.
Funding
Open Access funding enabled and organized by Projekt DEAL. The DONALD study is financially supported by the Ministry of Science and Research of North Rhine-Westphalia, Germany. The German Federal Ministry of Education and Research (Grant Number: 01EA1705A) funded the collection of microbiome and metabolomic data for the HEALTHMARK project. The collection of further stool samples in the PerMiCCion project is funded by the Federal Ministry of Education and Research (BMBF, Grant 100554612). Data collection of genetics, data on fluid intelligence, personality-related characteristics, accelerometer, eye-tracking and metabolomics was supported by Diet–Body–Brain (DietBB), the Competence Cluster in Nutrition Research, funded by the Federal Ministry of Education and Research (Grant Number: 01EA1410A). Further analyses of metabolomic data were funded by The German Research Foundation (DFG) within the FOODMETCH project (Grant Number: 406710821). The analysis of urinary hippuric acid excretion was funded by the Wereld Kanker Onderzoek Fonds (WCRF NL), as part of the WCRF international grant programme (Grant Number: 2013/975). The analyses of sugar excretion in 24-h urine samples was funded by the German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE), Grant Number: 2816HS024. Analyses of additional blood parameters were funded by World Cancer Research Fund International (WCRF, Grant Number: 2010/248), WCRF NL, as part of the WCRF international grant programme (Grant Number: 2013/975), the German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE; Grant Number 2810HS035 and 2810HS035) and by the German Research Foundation (DFG; Grant Number: Bu1807/3–1 and AL 1794/1–2).
Author information
Authors and Affiliations
Contributions
The authors’ responsibilities are as follows: IP, UA, UN conceptualized the project. IP wrote the manuscript. IP and UA conducted the statistical analysis. UN supervised the project and had primary responsibility for the final content. All authors made substantial contributions, critically read and revised the manuscript as well as approved the final version.
Corresponding author
Ethics declarations
Conflict of interest
The authors have no relevant financial or non-financial interests to disclose.
Ethical approval
The DONALD study was approved by the Ethics Committee of the University of Bonn, Germany.
Informed consent
All assessments in the DONALD study were performed with parental and later on participants’ written informed consent.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Perrar, I., Alexy, U. & Nöthlings, U. Cohort profile update–overview of over 35 years of research in the Dortmund Nutritional and Anthropometric Longitudinally Designed (DONALD) study. Eur J Nutr 63, 727–740 (2024). https://doi.org/10.1007/s00394-023-03290-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00394-023-03290-x