Different osteocalcin forms, markers of metabolic syndrome and anthropometric measures in children within the IDEFICS cohort.

OBJECTIVE: Osteocalcin (OC), an aboundant non-collagenous bone protein, is inversely associated with parameters of glucose metabolism. Interactions between bone tissue and energy metabolism have not been thoroughly investigated during childhood. This study investigated OC, metabolic parameters and anthropometric characteristics in normal weight and overweight/obese children. METHODS: This study comprised 108 (46 normal weight/62 overweight/obese) Swedish 2-9year old children. Anthropometric data, insulin, glucose, glycosylated haemoglobin (HbA1c), HOMA index, vitamin D, adiponectin, total OC, carboxylated OC (cOC) and undercarboxylated OC (ucOC) were analysed. RESULTS: No difference was found for total OC between the normal and overweight/obese groups, with a mean (±SD) value of 82.6 (±2.8) ng/mL and 77.0 (±2.4) ng/mL, (P=0.11), respectively. Overweight children had lower cOC levels, mean 69.1 (±2.2) ng/mL, vs. normal weight children, mean 75.6 (±2.5) ng/mL (P=0.03). The mean ucOC levels of 7.9 (±0.4) ng/mL in overweight children did not differ vs. normal weight children, mean level 7.0 (±0.4) ng/mL, (P=0.067). None of the three OC forms correlated with any of the measured parameters. CONCLUSIONS: The cOC levels were lower in overweight children. There was no correlation between the three OC forms and any of the measured anthropometric or metabolic parameters. OC has been suggested to have a possible metabolic role, but in general the current study in prepubertal children does not support the hypothesis of an association between OC and a positive metabolic profile.

Evaluating the predictive ability of childhood body mass index classification systems for overweight and obesity at 18 years.

AIM: To evaluate the performance of three childhood body mass index classification systems defining weight status at age 10, for predicting overweight and obesity at 18 years, according to the World Health Organization adult body mass index classification. METHODS: Weight and height of 4235 Swedish girls and boys were measured both at around ages 10 and 18 years. Predictive ability of the extended International Obesity Task Force body mass index cut-offs (2012), the World Health Organization body mass index-for-age (2007) and a Swedish body mass index reference (2001) were assessed for sensitivity and specificity. RESULTS: For predicting overweight including obesity at 18 years, the World Health Organization 2007 and the Swedish body mass index reference 2001 had similar sensitivity, 68% and 71%. The International Obesity Task Force 2012 had a significantly lower sensitivity, 53%. Specificity was 82-91% and highest for International Obesity Task Force 2012. For predicting obesity, the sensitivity for International Obesity Task Force 2012 was 29%, significantly lower than for the other two, 63% and 70%. Specificity was 94-100%, and highest for International Obesity Task Force 2012. CONCLUSIONS: In situations when optimal screening sensitivity is required for identifying as many high-risk children as possible, the World Health Organization 2007 and the Swedish body mass index reference 2001 performed better than the International Obesity Task Force 2012. However, it is important to keep in mind that the International Obesity Task Force 2012 will identify the fewest false positives.

Vitamin D: epidemiology of cardiovascular risks and events.

Vitamin D may influence blood pressure through the renin-angiotensin system, parathyroid hormone levels, myocardial function, inflammation, and vascular calcification. In the past several years, a number of high-quality prospective studies have examined 25(OH)vitamin D (25(OH)D) levels in relation to risk of cardiovascular disease (CVD). Studies consistently show that levels of 25(OH)D below 20-25 ng/mL are associated with an increased risk of CVD incidence or mortality. Risk appears especially elevated at 25(OH)D levels below 10 or 15 ng/mL. It is unclear if levels higher that 25 ng/mL provide further benefits for CVD disease. Currently, results from randomized clinical trials are sparse and do not allow a definitive conclusion. Given other potential benefits of vitamin D, and low potential for toxicity, deficient levels below 25-30 ng/mL should be avoided and treated when identified. Further observational and randomized clinical trial data are important to better characterize the optimal range for 25(OH)D.

The genetic structure of the Swedish population.

Patterns of genetic diversity have previously been shown to mirror geography on a global scale and within continents and individual countries. Using genome-wide SNP data on 5174 Swedes with extensive geographical coverage, we analyzed the genetic structure of the Swedish population. We observed strong differences between the far northern counties and the remaining counties. The population of Dalarna county, in north middle Sweden, which borders southern Norway, also appears to differ markedly from other counties, possibly due to this county having more individuals with remote Finnish or Norwegian ancestry than other counties. An analysis of genetic differentiation (based on pairwise F(st)) indicated that the population of Sweden's southernmost counties are genetically closer to the HapMap CEU samples of Northern European ancestry than to the populations of Sweden's northernmost counties. In a comparison of extended homozygous segments, we detected a clear divide between southern and northern Sweden with small differences between the southern counties and considerably more segments in northern Sweden. Both the increased degree of homozygosity in the north and the large genetic differences between the south and the north may have arisen due to a small population in the north and the vast geographical distances between towns and villages in the north, in contrast to the more densely settled southern parts of Sweden. Our findings have implications for future genome-wide association studies (GWAS) with respect to the matching of cases and controls and the need for within-county matching. We have shown that genetic differences within a single country may be substantial, even when viewed on a European scale. Thus, population stratification needs to be accounted for, even within a country like Sweden, which is often perceived to be relatively homogenous and a favourable resource for genetic mapping, otherwise inferences based on genetic data may lead to false conclusions.

NordicDB: a Nordic pool and portal for genome-wide control data.

A cost-efficient way to increase power in a genetic association study is to pool controls from different sources. The genotyping effort can then be directed to large case series. The Nordic Control database, NordicDB, has been set up as a unique resource in the Nordic area and the data are available for authorized users through the web portal (http://www.nordicdb.org). The current version of NordicDB pools together high-density genome-wide SNP information from ∼5000 controls originating from Finnish, Swedish and Danish studies and shows country-specific allele frequencies for SNP markers. The genetic homogeneity of the samples was investigated using multidimensional scaling (MDS) analysis and pairwise allele frequency differences between the studies. The plot of the first two MDS components showed excellent resemblance to the geographical placement of the samples, with a clear NW-SE gradient. We advise researchers to assess the impact of population structure when incorporating NordicDB controls in association studies. This harmonized Nordic database presents a unique genome-wide resource for future genetic association studies in the Nordic countries.

Bias correction of estimates of familial risk from population-based cohort studies.

BACKGROUND: In addition to guiding molecular epidemiology investigations, estimates of the increased risk of disease in relatives of affected persons are also important for screening and counselling decisions. Since precise estimation of such familial risks (FRs) requires large sample sizes, many of the estimates in common use have been obtained from historical electronic records of disease in entire populations, where the relatives of affected and unaffected persons are compared. These estimates may be biased due to failure to identify relatives as affected if they are diagnosed before the start-up date of disease registration. METHODS: This article presents a method for correcting the bias in FR estimates from such misclassification of family history, using a simple formula that depends on the prevalence and sensitivity of the observed family history. The sensitivity is estimated by using the R package poplab to create realistic populations of related individuals and then imposing the start-up effect of disease registration. RESULTS: For a range of FRs, the truncation of family history is demonstrated to result in non-differential misclassification, and sensitivity that has little or no dependence on the FR. The bias is most pronounced for high FRs and for registers with a short life span, and increases with the age of the study cohort. In all the situations studied, the bias-corrected estimates are in excellent agreement with the true values. CONCLUSIONS: In summary, our method can correct the inevitable bias in FRs induced by using electronic population data, and is a feasible alternative to the use of validation samples.

Evaluation of bias in familial risk estimates: a study of common cancers using Swedish population-based registers.

BACKGROUND: Bias in estimates of familial cancer may result if population-based registers fail to identify relatives as affected when disease occurs before the start-up of registration (ie, "left-truncation" of family history). METHODS: Apparent familial relative risks (among offspring of parents with cancer) of colorectal, lung, breast, and prostate cancers and melanoma in a Swedish cohort were compared with relative risks in a simulated population. The study cohort (approximately 7 million individuals) was based on the Swedish MultiGenerational Register linked to the Swedish Cancer Register for the period 1961-2002. A similar population of related individuals (approximately 7 million) with complete family information was simulated by using the R-package PopLab and used to estimate the sensitivity of the observed family history. This sensitivity was then used to calculate corrected age group-specific and overall risks, which were compared with the apparent familial risks of cancer in the cohort. RESULT: The apparent familial risks for colorectal, lung, breast, and prostate cancers and melanoma were 1.99 (95% confidence interval [CI] = 1.85 to 2.14), 2.05 (95% CI = 1.86 to 2.26), 1.84 (95% CI = 1.76 to 1.92), 2.33 (95% CI = 2.19 to 2.48), and 2.68 (95% CI = 2.35 to 3.07), with corresponding absolute rates of 3.69, 2.59, 16.05, 10.38, and 2.96 per 10 000 person-years, among offspring of parents diagnosed with the same cancer. Corrected age group-specific and overall estimates of the familial risks were close to these apparent risks for all studied cancers (all approximately 2.0), except for melanoma. For melanoma, the corrected estimate of 3.18 (95% CI = 2.73 to 3.64) was somewhat larger than the apparent estimate and was not included in the confidence interval for the apparent estimate. When the exposure of interest was a parent affected at a younger age, this bias was more pronounced; the apparent estimate for melanoma changed from 4.07 (95% CI = 3.21 to 5.16) to 5.67 (95% CI = 4.51 to 6.83) after correction. CONCLUSIONS: For common cancers, risk estimates from the Swedish MultiGenerational cohort do not generally appear to be biased by left-truncation.

The impact of truncation and missing family links in population-based registers on familial risk estimates.

Family history information is often incomplete in population-based disease registers because of truncation and/or missing family links. In this study, the authors simulated complete populations of related individuals with realistic age, family structure, and incidence rates. After mimicking the realities of register-based data, such as left truncation of family history and missing family links due to death, the authors explored recovery of familial association parameters from standard epidemiologic models. Truncation of family history produced almost no bias for a familial risk of 2 and 50 years of follow-up, but it had a dramatic impact when the familial risk was 10. The age distribution of disease and the magnitude of background incidence rates also affected family history loss and thus the magnitude of bias. One can safeguard against bias by starting follow-up later, with the number of registration years to be ignored in the analysis depending on the value of familial risk. The missing familial links due to death had no effect, except when there was differential mortality for cases with and without a family history of disease. In summary, truncation, and to a lesser extent missing family links, induces bias in familial risk estimates from population-based registers.

"Population lab": the creation of virtual populations for genetic epidemiology research.

BACKGROUND: Studies of familial aggregation of disease routinely use linked population registers to construct retrospective cohorts. Although such resources have provided numerous estimates of familial risk, little is known regarding the sensitivity of the estimates to assumed disease models, changing demographics and incidence, and incompleteness of the data. Furthermore, there are no standard tools for testing the validity of estimates from standard epidemiologic designs and from new analytic strategies using register data. METHODS: We present a method and a software package for simulating realistic populations of related individuals, using easily available vital statistics (population counts and fertility and mortality rates). The virtual population is stored in a pedigree file, allowing for easy retrieval of relatives and family structures. We simulate breast cancer in our population using age-specific incidence rates. RESULTS: The Swedish population is simulated as dynamically evolving over the calendar period 1955-2002. The simulated and real population agree well on important features such as age profile, sibship size distribution, and average age at first birth. Using breast cancer as an example, we present several models of familial disease aggregation and show that the parameters used in the simulations are faithfully estimated. In addition, we illustrate how our simulated population provides insight into how incomplete family history in real register data can affect estimates of familial risk. CONCLUSIONS: This simulation method can be used to investigate various underlying models of disease aggregation in families and enhance the development of optimal approaches for family studies. The software package, Population Lab, is available for free download (http://www.meb.ki.se/ approximately marrei/software/poplab/ and http://cran.at.r-project.org/).