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Early foundations of the metabolic syndrome may be laid as a consequence of changes in dietary supply to the rapidly growing fetus and/or postnatal offspring. This review highlights fetal developmental plasticity in cellular homeostasis that may manifest in adult life as the metabolic syndrome particularly if followed by a period of accelerated postnatal growth. The primary markers of the metabolic syndrome are central obesity, insulin resistance and hypertension. In this review, we consider the effect of changes in maternal nutrition during critical windows in fetal development on an individual's subsequent predisposition to the metabolic syndrome. The fetal origins of obesity, cardiovascular disease and insulin resistance have been investigated in a wide range of epidemiological and animal studies; these investigations highlight adaptations made by the nutritionally manipulated fetus that aim to maintain energy homeostasis to ensure survival. One consequence of such developmental plasticity may be a long term re-setting of cellular energy homeostasis, most probably via epigenetic modification of genes involved in a number of key regulatory pathways. For example, reduced maternal–fetal nutrition during early gestation to midgestation affects adipose tissue development and adiposity of the fetus by setting an increased number of adipocyte precursor cells. Importantly, clinically relevant adaptations to nutritional challenges in utero may only manifest as primary components of the metabolic syndrome if followed by a period of accelerated growth early in the postnatal period and/or if offspring become obese. Key Points Foundations for the metabolic syndrome may be laid down in very early life as a consequence of changes in dietary supply to the rapidly growing conceptus and/or postnatal offspring Critical windows of development, during which the fetus is susceptible to nutritional programming, must be considered in the etiology of the metabolic syndrome A common epigenetic mechanism is fundamental to developmental programming of endocrine balance and cellular energy metabolism; the latter determines the partition of energy between catabolism and energy storage

Hearing loss and hearing disorders represent possible mediating pathways in the associations between noise exposures and non-auditory health outcomes. In this context, we assessed whether the noise-obesity associations should consider hearing functions as possible mediators and applied Mendelian randomisation (MR) to investigate causal relationships between body constitution and hearing impairments. We obtained genetic associations from publicly available summary statistics from genome-wide association studies in European ancestry adult populations (N= from 210,088 to 360,564) for (i) body constitution: body mass index (BMI), waist circumference (WC) and body fat percentage (BFP), and (ii) hearing loss: sensorineural hearing loss, noise-induced hearing loss, and age-related hearing impairment (ARHI). We employed colocalisation analysis to investigate the genetic associations for BMI and ARHI liability within an FTO locus. We conducted bi-directional MR for the ‘forward’ (from body constitution to hearing) and ‘reverse’ directions. We applied the random effects inverse variance-weighted method as the main MR method, with additional sensitivity analyses. Colocalisation analysis suggested that BMI and ARHI shared a causal variant at the FTO gene. We did not find robust evidence for causal associations from body constitution to hearing loss and suggested that some associations may be driven by FTO variants. In the reverse analyses, ARHI was negatively associated with BMI [effect size – 0.22 (95% CI – 0.44 to – 0.01)] and BFP [effect size – 0.23 (95% CI – 0.45 to 0.00)], supporting the notion that ARHI may diminish body constitution. Finally, our data suggest that there is no strong evidence that hearing explains the association between noise exposure and body constitution.

Background/objectiveChildren BMI is a longitudinal phenotype, developing through interplays between genetic and environmental factors. Whilst childhood obesity is escalating, we require a better understanding of its early origins and variation across generations to prevent it.Subjects/methodsWe designed a cross-cohort study including 12,040 Finnish children from the Northern Finland Birth Cohorts 1966 and 1986 (NFBC1966 and NFBC1986) born before or at the start of the obesity epidemic. We used group-based trajectory modelling to identify BMI trajectories from 2 to 20 years. We subsequently tested their associations with early determinants (mother and child) and the possible difference between generations, adjusted for relevant biological and socioeconomic confounders.ResultsWe identified four BMI trajectories, ‘stable-low’ (34.8%), ‘normal’ (44.0%), ‘stable-high’ (17.5%) and ‘early-increase’ (3.7%). The ‘early-increase’ trajectory represented the highest risk for obesity. We analysed a dose-response association of maternal pre-pregnancy BMI and smoking with BMI trajectories. The directions of effect were consistent across generations and the effect sizes tended to increase from earlier generation to later. Respectively for NFBC1966 and NFBC1986, the adjusted risk ratios of being in the early-increase group were 1.08 (1.06–1.10) and 1.12 (1.09–1.15) per unit of pre-pregnancy BMI and 1.44 (1.05–1.96) and 1.48 (1.17–1.87) in offspring of smoking mothers compared to non-smokers. We observed similar relations with infant factors including birthweight for gestational age and peak weight velocity. In contrast, the age at adiposity peak in infancy was associated with the BMI trajectories in NFBC1966 but did not replicate in NFBC1986.ConclusionsExposures to adverse maternal predictors were associated with a higher risk obesity trajectory and were consistent across generations. However, we found a discordant association for the timing of adiposity peak over a 20-year period. This suggests the role of residual environmental factors, such as nutrition, and warrants additional research to understand the underlying gene–environment interplay.

Background Associations of low lung function with features of poor cardio-metabolic health have been reported. It is, however, unclear whether these co-morbidities reflect causal associations, shared genetic heritability or are confounded by environmental factors. Methods We performed three analyses: (1) cardio-metabolic health to lung function association tests in Northern Finland Birth cohort 1966, (2) cross-trait linkage disequilibrium score regression (LDSC) to compare genetic backgrounds and (3) Mendelian randomisation (MR) analysis to assess the causal effect of cardio-metabolic traits and disease on lung function, and vice versa (bidirectional MR). Genetic associations were obtained from the UK Biobank data or published large-scale genome-wide association studies ( N > 82,000). Results We observed a negative genetic correlation between lung function and cardio-metabolic traits and diseases. In Mendelian Randomisation analysis (MR), we found associations between type 2 diabetes (T2D) instruments and forced vital capacity (FVC) as well as FEV1/FVC. Body mass index (BMI) instruments were associated to all lung function traits and C-reactive protein (CRP) instruments to FVC. These genetic associations provide evidence for a causal effect of cardio-metabolic traits on lung function. Multivariable MR suggested independence of these causal effects from other tested cardio-metabolic traits and diseases. Analysis of lung function specific SNPs revealed a potential causal effect of FEV1/FVC on blood pressure. Conclusions The present study overcomes many limitations of observational studies by using Mendelian Randomisation. We provide evidence for an independent causal effect of T2D, CRP and BMI on lung function with some of the T2D effect on lung function being attributed to inflammatory mechanisms. Furthermore, this analysis suggests a potential causal effect of FEV1/FVC on blood pressure. Our detailed analysis of the interplay between cardio-metabolic traits and impaired lung function provides the opportunity to improve the quality of existing intervention strategies.

Coronary artery disease (CAD), type 2 diabetes (T2D) and depression are among the leading causes of chronic morbidity and mortality worldwide. Epidemiological studies indicate a substantial degree of multimorbidity, which may be explained by shared genetic influences. However, research exploring the presence of pleiotropic variants and genes common to CAD, T2D and depression is lacking. The present study aimed to identify genetic variants with effects on cross-trait liability to psycho-cardiometabolic diseases. We used genomic structural equation modelling to perform a multivariate genome-wide association study of multimorbidity ( N effective = 562,507), using summary statistics from univariate genome-wide association studies for CAD, T2D and major depression. CAD was moderately genetically correlated with T2D ( r g = 0.39, P = 2e-34) and weakly correlated with depression ( r g = 0.13, P = 3e-6). Depression was weakly correlated with T2D ( r g = 0.15, P = 4e-15). The latent multimorbidity factor explained the largest proportion of variance in T2D (45%), followed by CAD (35%) and depression (5%). We identified 11 independent SNPs associated with multimorbidity and 18 putative multimorbidity-associated genes. We observed enrichment in immune and inflammatory pathways. A greater polygenic risk score for multimorbidity in the UK Biobank ( N = 306,734) was associated with the co-occurrence of CAD, T2D and depression (OR per standard deviation = 1.91, 95% CI = 1.74–2.10, relative to the healthy group), validating this latent multimorbidity factor. Mendelian randomization analyses suggested potentially causal effects of BMI, body fat percentage, LDL cholesterol, total cholesterol, fasting insulin, income, insomnia, and childhood maltreatment. These findings advance our understanding of multimorbidity suggesting common genetic pathways.

Worldwide, we are observing a rising prevalence of dementia and mild cognitive impairments that often co-occur with the heightened incidence of non-communicable diseases in the elderly. It is suggested that type 2 diabetes and defects in glucose metabolism might predispose to poorer cognitive performances and more rapid decline in old age. to address existing knowledge gaps in this area, we systematically reviewed the literature to identify whether patients with type 2 diabetes (T2DM) and pre-diabetes are at a higher risk of poorer cognitive performance, and whether the risk (if any) might affect specific cognitive abilities. We concentrated the review on elderly individuals (65 years or older) at intake. In total, 3251 original articles were retrieved, of which 17 met our inclusion and quality control criteria, which comprised 12 structured questions used to define the articles. 11 of 17 studies found a statistically significant decline in cognition among individuals who had T2DM or pre-diabetes compared to their non-diabetic counterparts. The association between diabetes and cognitive decline was not always clear, and the extent of the cognitive tests used seemed to have the greatest effect on the results. Focusing on a population age 65 years and over, we found insufficient evidence to support an association between pre-diabetes stages and mild cognitive impairment. However, there is consistent evidence to support diabetes as an independent risk factor for low cognitive ability in the elderly. Finally, we found insufficient evidence to support effect of T2DM on distinct cognitive ability due to the scarcity of comparable findings.

Genetic effects on changes in human traits over time are understudied and may have important pathophysiological impact. We propose a framework that enables data quality control, implements mixed models to evaluate trajectories of change in traits, and estimates phenotypes to identify age-varying genetic effects in GWAS. Using childhood BMI as an example trait, we included 71,336 participants from six cohorts and estimated the slope and area under the BMI curve within four time periods (infancy, early childhood, late childhood and adolescence) for each participant, in addition to the age and BMI at the adiposity peak and the adiposity rebound. GWAS of the 12 estimated phenotypes identified 28 genome-wide significant variants at 13 loci, one of which (in DAOA) has not been previously associated with childhood or adult BMI. Genetic studies of changes in human traits over time could uncover unique biological mechanisms influencing quantitative traits. This article presents a framework to conduct GWAS of longitudinal data where the trait of interest follows a non-linear change over time. The framework is applied to childhood BMI, identifying 13 loci with age-varying genetic effects.

Babies born clinically Small- or Large-for-Gestational-Age (SGA or LGA; sex- and gestational age-adjusted birth weight (BW) <10 th or >90 th percentile, respectively), are at higher risks of complications. SGA and LGA include babies who have experienced environment-related growth-restriction or overgrowth, respectively, and babies who are heritably small or large. However, the relative proportions within each group are unclear. We assessed the extent to which common genetic variants underlying variation in birth weight influence the probability of being SGA or LGA. We calculated independent fetal and maternal genetic scores (GS) for BW in 11,951 babies and 5,182 mothers. These scores capture the direct fetal and indirect maternal (via intrauterine environment) genetic contributions to BW, respectively. We also calculated maternal fasting glucose (FG) and systolic blood pressure (SBP) GS. We tested associations between each GS and probability of SGA or LGA. For the BW GS, we used simulations to assess evidence of deviation from an expected polygenic model. Higher BW GS were strongly associated with lower odds of SGA and higher odds of LGA (OR fetal = 0.75 (0.71,0.80) and 1.32 (1.26,1.39); OR maternal = 0.81 (0.75,0.88) and 1.17 (1.09,1.25), respectively per 1 decile higher GS). We found evidence that the smallest 3% of babies had a higher BW GS, on average, than expected from their observed birth weight (assuming an additive polygenic model: P fetal = 0.014, P maternal = 0.062). Higher maternal SBP GS was associated with higher odds of SGA P = 0.005. We conclude that common genetic variants contribute to risk of SGA and LGA, but that additional factors become more important for risk of SGA in the smallest 3% of babies.

Background and aimsPrepregnancy maternal obesity is a global health problem and has been associated with offspring metabolic and mental ill-health. However, there is a knowledge gap in understanding potential neurobiological factors related to these associations. This study explored the relation between maternal prepregnancy body mass index (BMI) and offspring brain white matter microstructure at the age of 6, 10, and 26 years in three independent cohorts.Subjects and methodsThe study used data from three European birth cohorts (n = 116 children aged 6 years, n = 2466 children aged 10 years, and n = 437 young adults aged 26 years). Information on maternal prepregnancy BMI was obtained before or during pregnancy and offspring brain white matter microstructure was measured at age 6, 10, or 26 years. We used magnetic resonance imaging-derived fractional anisotropy (FA) and mean diffusivity (MD) as measures of white matter microstructure in the brainstem, callosal, limbic, association, and projection tracts. Linear regressions were fitted to examine the association of maternal BMI and offspring white matter microstructure, adjusting for several socioeconomic and lifestyle-related confounders, including education, smoking, and alcohol use.ResultsMaternal BMI was associated with higher FA and lower MD in multiple brain tracts, for example, association and projection fibers, in offspring aged 10 and 26 years, but not at 6 years. In each cohort maternal BMI was related to different white matter tract and thus no common associations across the cohorts were found.ConclusionsMaternal BMI was associated with higher FA and lower MD in multiple brain tracts in offspring aged 10 and 26 years, but not at 6 years of age. Future studies should examine whether our observations can be replicated and explore the potential causal nature of the findings.