Effect of genome and environment on metabolic and inflammatory profiles

Abstract

Twin and family studies have established the contribution of genetic factors to variation in metabolic, hematologic and immunological parameters. The majority of these studies analyzed single or combined traits into pre-defined syndromes. In the present study, we explore an alternative multivariate approach in which a broad range of metabolic, hematologic, and immunological traits are analyzed simultaneously to determine the resemblance of monozygotic (MZ) twin pairs, twin-spouse pairs and unrelated, non-cohabiting individuals. A total of 517 participants from the Netherlands Twin Register, including 210 MZ twin pairs and 64 twin-spouse pairs, took part in the study. Data were collected on body composition, blood pressure, heart rate, and multiple biomarkers assessed in fasting blood samples, including lipid levels, glucose, insulin, liver enzymes, hematological measurements and cytokine levels. For all 51 measured traits, pair-wise Pearson correlations, correcting for family relatedness, were calculated across all the individuals in the cohort. Hierarchical clustering techniques were applied to group the measured traits into sub-clusters based on similarity. Sub-clusters were observed among metabolic traits and among inflammatory markers. We defined a phenotypic profile as the collection of all the traits measured for a given individual. Average within-pair similarity of phenotypic profiles was determined for the groups of MZ twin pairs, spouse pairs and pairs of unrelated individuals. The average similarity across the full phenotypic profile was higher for MZ twin pairs than for spouse pairs, and lowest for pairs of unrelated individuals. Cohabiting MZ twins were more similar in their phenotypic profile compared to MZ twins who no longer lived together. The correspondence in the phenotypic profile is therefore determined to a large degree by familial, mostly genetic, factors, while household factors contribute to a lesser degree to profile similarity.

Fig 1. Study overview and participant characterization.

A) Study Design. Pairs of twins were recruited for this study. A proportion of the cohort consists of twins who are co-habiting. Spouses were also recruited to index resemblance through shared household factors; B) Age distribution in twin study participants stratified by co-habitation status; C) Age distribution in study participants stratified by sex.

Fig 2. Hierarchical clustering of phenotypic measurements.

This figure shows the clustering of the phenotypic measurements based on their correlations with each other. Correlations between all pairs of phenotypes are computed, while partialling out the effect related to family membership. Positive correlations are shown in red, negative correlations in purple. The clusters are shown in green.

Fig 3. Trait similarity across twins, spouses and unrelated individuals.

This figure shows the boxplot with distribution across the 51 measured traits showing correlation in twins (cohabiting and not co-habiting), twin-spouse pairs and unrelated individuals. Average within pair correlation for the MZ twins living together is 0.56 (living together), for MZ twins living apart 0.48 (living apart), for twin-spouse pairs 0.08 and for non-related individuals 0.

Fig 4. Examples of traits and their concordance in twins.

MZ twin pair concordance for A) height, B) telomere length, C) IL6 receptor, D) IL6. In black the correlations for twins living apart, in red the correlations for twins living together. Values shown are sex and age corrected residuals (see Methods).

Fig 5. Correlation of phenotypic profiles across twins, spouses and unrelated individuals.

This boxplot shows the pairwise correlations of phenotypic profiles between individuals: cohabiting MZ twins (57 pairs), MZ twins living apart (153 pairs), twin-spouse pairs (64 pairs) and non-related individuals (132596 pairs).