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Narrative reviews of paediatric NAFLD quote prevalences in the general population that range from 9% to 37%; however, no systematic review of the prevalence of NAFLD in children/adolescents has been conducted. We aimed to estimate prevalence of non-alcoholic fatty liver disease (NAFLD) in young people and to determine whether this varies by BMI category, gender, age, diagnostic method, geographical region and study sample size. We conducted a systematic review and meta-analysis of all studies reporting a prevalence of NAFLD based on any diagnostic method in participants 1-19 years old, regardless of whether assessing NAFLD prevalence was the main aim of the study. The pooled mean prevalence of NAFLD in children from general population studies was 7.6% (95%CI: 5.5% to 10.3%) and 34.2% (95% CI: 27.8% to 41.2%) in studies based on child obesity clinics. In both populations there was marked heterogeneity between studies (I2 = 98%). There was evidence that prevalence was generally higher in males compared with females and increased incrementally with greater BMI. There was evidence for differences between regions in clinical population studies, with estimated prevalence being highest in Asia. There was no evidence that prevalence changed over time. Prevalence estimates in studies of children/adolescents attending obesity clinics and in obese children/adolescents from the general population were substantially lower when elevated alanine aminotransferase (ALT) was used to assess NAFLD compared with biopsies, ultrasound scan (USS) or magnetic resonance imaging (MRI). Our review suggests the prevalence of NAFLD in young people is high, particularly in those who are obese and in males.

Epidemiological studies have shown that weaker grip strength in later life is associated with disability, morbidity, and mortality. Grip strength is a key component of the sarcopenia and frailty phenotypes and yet it is unclear how individual measurements should be interpreted. Our objective was to produce cross-sectional centile values for grip strength across the life course. A secondary objective was to examine the impact of different aspects of measurement protocol. We combined 60,803 observations from 49,964 participants (26,687 female) of 12 general population studies in Great Britain. We produced centile curves for ages 4 to 90 and investigated the prevalence of weak grip, defined as strength at least 2.5 SDs below the gender-specific peak mean. We carried out a series of sensitivity analyses to assess the impact of dynamometer type and measurement position (seated or standing). Our results suggested three overall periods: an increase to peak in early adult life, maintenance through to midlife, and decline from midlife onwards. Males were on average stronger than females from adolescence onwards: males' peak median grip was 51 kg between ages 29 and 39, compared to 31 kg in females between ages 26 and 42. Weak grip strength, defined as strength at least 2.5 SDs below the gender-specific peak mean, increased sharply with age, reaching a prevalence of 23% in males and 27% in females by age 80. Sensitivity analyses suggested our findings were robust to differences in dynamometer type and measurement position. This is the first study to provide normative data for grip strength across the life course. These centile values have the potential to inform the clinical assessment of grip strength which is recognised as an important part of the identification of people with sarcopenia and frailty.

Easily identifiable risk factors including: obesity and ethnicity at high risk of diabetes are commonly used to indicate which women should be offered the oral glucose tolerance test (OGTT) to diagnose gestational diabetes (GDM). Evidence regarding these risk factors is limited however. We conducted a systematic review (SR) and meta-analysis and individual participant data (IPD) analysis to evaluate the performance of risk factors in identifying women with GDM. We searched MEDLINE, Medline in Process, Embase, Maternity and Infant Care and the Cochrane Central Register of Controlled Trials (CENTRAL) up to August 2016 and conducted additional reference checking. We included observational, cohort, case-control and cross-sectional studies reporting the performance characteristics of risk factors used to identify women at high risk of GDM. We had access to IPD from the Born in Bradford and Atlantic Diabetes in Pregnancy cohorts, all pregnant women in the two cohorts with data on risk factors and OGTT results were included. Twenty nine published studies with 211,698 women for the SR and a further 14,103 women from two birth cohorts (Born in Bradford and the Atlantic Diabetes in Pregnancy study) for the IPD analysis were included. Six studies assessed the screening performance of guidelines; six examined combinations of risk factors; eight evaluated the number of risk factors and nine examined prediction models or scores. Meta-analysis using data from published studies suggests that irrespective of the method used, risk factors do not identify women with GDM well. Using IPD and combining risk factors to produce the highest sensitivities, results in low specificities (and so higher false positives). Strategies that use the risk factors of age (>25 or >30) and BMI (>25 or 30) perform as well as other strategies with additional risk factors included. Risk factor screening methods are poor predictors of which pregnant women will be diagnosed with GDM. A simple approach of offering an OGTT to women 25 years or older and/or with a BMI of 25kg/m2 or more is as good as more complex risk prediction models. Research to identify more accurate (bio)markers is needed. Systematic Review Registration: PROSPERO CRD42013004608.

The extent to which baseline couple characteristics affect the probability of live birth and adverse perinatal outcomes after assisted conception is unknown. We utilised the Human Fertilisation and Embryology Authority database to examine the predictors of live birth in all in vitro fertilisation (IVF) cycles undertaken in the UK between 2003 and 2007 (n = 144,018). We examined the potential clinical utility of a validated model that pre-dated the introduction of intracytoplasmic sperm injection (ICSI) as compared to a novel model. For those treatment cycles that resulted in a live singleton birth (n = 24,226), we determined the associates of potential risk factors with preterm birth, low birth weight, and macrosomia. The overall rate of at least one live birth was 23.4 per 100 cycles (95% confidence interval [CI] 23.2-23.7). In multivariable models the odds of at least one live birth decreased with increasing maternal age, increasing duration of infertility, a greater number of previously unsuccessful IVF treatments, use of own oocytes, necessity for a second or third treatment cycle, or if it was not unexplained infertility. The association of own versus donor oocyte with reduced odds of live birth strengthened with increasing age of the mother. A previous IVF live birth increased the odds of future success (OR 1.58, 95% CI 1.46-1.71) more than that of a previous spontaneous live birth (OR 1.19, 95% CI 0.99-1.24); p-value for difference in estimate <0.001. Use of ICSI increased the odds of live birth, and male causes of infertility were associated with reduced odds of live birth only in couples who had not received ICSI. Prediction of live birth was feasible with moderate discrimination and excellent calibration; calibration was markedly improved in the novel compared to the established model. Preterm birth and low birth weight were increased if oocyte donation was required and ICSI was not used. Risk of macrosomia increased with advancing maternal age and a history of previous live births. Infertility due to cervical problems was associated with increased odds of all three outcomes-preterm birth, low birth weight, and macrosomia. Pending external validation, our results show that couple- and treatment-specific factors can be used to provide infertile couples with an accurate assessment of whether they have low or high risk of a successful outcome following IVF.

There is limited evidence to support the use of customised centile charts to identify those at risk of stillbirth and infant death at term. We sought to determine birth weight thresholds at which mortality and morbidity increased and the predictive ability of noncustomised (accounting for gestational age and sex) and partially customised centiles (additionally accounting for maternal height and parity) to identify fetuses at risk. This is a population-based linkage study of 979,912 term singleton pregnancies in Scotland, United Kingdom, between 1992 and 2010. The main exposures were noncustomised and partially customised birth weight centiles. The primary outcomes were infant death, stillbirth, overall mortality (infant and stillbirth), Apgar score <7 at 5 min, and admission to the neonatal unit. Optimal thresholds that predicted outcomes for both non- and partially customised birth weight centiles were calculated. Prediction of mortality between non- and partially customised birth weight centiles was compared using area under the receiver operator characteristic curve (AUROC) and net reclassification index (NRI). Birth weight ≤25th centile was associated with higher risk for all mortality and morbidity outcomes. For stillbirth, low Apgar score, and neonatal unit admission, risk also increased from the 85th centile. Similar patterns and magnitude of associations were observed for both non- and partially customised birth weight centiles. Partially customised birth weight centiles did not improve the discrimination of mortality (AUROC 0.61 [95%CI 0.60, 0.62]) compared with noncustomised birth weight centiles (AUROC 0.62 [95%CI 0.60, 0.63]) and slightly underperformed in reclassifying pregnancies to different risk categories for both fatal and non-fatal adverse outcomes (NRI -0.027 [95% CI -0.039, -0.016], p < 0.001). We were unable to fully customise centile charts because we lacked data on maternal weight and ethnicity. Additional analyses in an independent UK cohort (n = 10,515) suggested that lack of data on ethnicity in this population (in which national statistics show 98% are white British) and maternal weight would have misclassified ~15% of the large-for-gestation fetuses. At term, birth weight remains strongly associated with the risk of stillbirth and infant death and neonatal morbidity. Partial customisation does not improve prediction performance. Consideration of early term delivery or closer surveillance for those with a predicted birth weight ≤25th or ≥85th centile may reduce adverse outcomes. Replication of the analysis with fully customised centiles accounting for ethnicity is warranted.

Both positive and negative associations between higher body mass index (BMI) and Parkinson disease (PD) have been reported in observational studies, but it has been difficult to establish causality because of the possibility of residual confounding or reverse causation. To our knowledge, Mendelian randomisation (MR)-the use of genetic instrumental variables (IVs) to explore causal effects-has not previously been used to test the effect of BMI on PD. Two-sample MR was undertaken using genome-wide association (GWA) study data. The associations between the genetic instruments and BMI were obtained from the GIANT consortium and consisted of the per-allele difference in mean BMI for 77 independent variants that reached genome-wide significance. The per-allele difference in log-odds of PD for each of these variants was estimated from a recent meta-analysis, which included 13,708 cases of PD and 95,282 controls. The inverse-variance weighted method was used to estimate a pooled odds ratio (OR) for the effect of a 5-kg/m2 higher BMI on PD. Evidence of directional pleiotropy averaged across all variants was sought using MR-Egger regression. Frailty simulations were used to assess whether causal associations were affected by mortality selection. A combined genetic IV expected to confer a lifetime exposure of 5-kg/m2 higher BMI was associated with a lower risk of PD (OR 0.82, 95% CI 0.69-0.98). MR-Egger regression gave similar results, suggesting that directional pleiotropy was unlikely to be biasing the result (intercept 0.002; p = 0.654). However, the apparent protective influence of higher BMI could be at least partially induced by survival bias in the PD GWA study, as demonstrated by frailty simulations. Other important limitations of this application of MR include the inability to analyse non-linear associations, to undertake subgroup analyses, and to gain mechanistic insights. In this large study using two-sample MR, we found that variants known to influence BMI had effects on PD in a manner consistent with higher BMI leading to lower risk of PD. The mechanism underlying this apparent protective effect warrants further study.

In conventional epidemiology confounding of the exposure of interest with lifestyle or socioeconomic factors, and reverse causation whereby disease status influences exposure rather than vice versa, may invalidate causal interpretations of observed associations. Conversely, genetic variants should not be related to the confounding factors that distort associations in conventional observational epidemiological studies. Furthermore, disease onset will not influence genotype. Therefore, it has been suggested that genetic variants that are known to be associated with a modifiable (nongenetic) risk factor can be used to help determine the causal effect of this modifiable risk factor on disease outcomes. This approach, mendelian randomization, is increasingly being applied within epidemiological studies. However, there is debate about the underlying premise that associations between genotypes and disease outcomes are not confounded by other risk factors. We examined the extent to which genetic variants, on the one hand, and nongenetic environmental exposures or phenotypic characteristics on the other, tend to be associated with each other, to assess the degree of confounding that would exist in conventional epidemiological studies compared with mendelian randomization studies. We estimated pairwise correlations between nongenetic baseline variables and genetic variables in a cross-sectional study comparing the number of correlations that were statistically significant at the 5%, 1%, and 0.01% level (alpha = 0.05, 0.01, and 0.0001, respectively) with the number expected by chance if all variables were in fact uncorrelated, using a two-sided binomial exact test. We demonstrate that behavioural, socioeconomic, and physiological factors are strongly interrelated, with 45% of all possible pairwise associations between 96 nongenetic characteristics (n = 4,560 correlations) being significant at the p < 0.01 level (the ratio of observed to expected significant associations was 45; p-value for difference between observed and expected < 0.000001). Similar findings were observed for other levels of significance. In contrast, genetic variants showed no greater association with each other, or with the 96 behavioural, socioeconomic, and physiological factors, than would be expected by chance. These data illustrate why observational studies have produced misleading claims regarding potentially causal factors for disease. The findings demonstrate the potential power of a methodology that utilizes genetic variants as indicators of exposure level when studying environmentally modifiable risk factors.

Much of our understanding of the age-related progression of systolic blood pressure (SBP) comes from cross-sectional data, which do not directly capture within-individual change. We estimated life course trajectories of SBP using longitudinal data from seven population-based cohorts and one predominantly white collar occupational cohort, each from the United Kingdom and with data covering different but overlapping age periods. Data are from 30,372 individuals and comprise 102,583 SBP observations spanning from age 7 to 80+y. Multilevel models were fitted to each cohort. Four life course phases were evident in both sexes: a rapid increase in SBP coinciding with peak adolescent growth, a more gentle increase in early adulthood, a midlife acceleration beginning in the fourth decade, and a period of deceleration in late adulthood where increases in SBP slowed and SBP eventually declined. These phases were still present, although at lower levels, after adjusting for increases in body mass index though adulthood. The deceleration and decline in old age was less evident after excluding individuals who had taken antihypertensive medication. Compared to the population-based cohorts, the occupational cohort had a lower mean SBP, a shallower annual increase in midlife, and a later midlife acceleration. The maximum sex difference was found at age 26 (+8.2 mm Hg higher in men, 95% CI: 6.7, 9.8); women then experienced steeper rises and caught up by the seventh decade. Our investigation shows a general pattern of SBP progression from childhood in the UK, and suggests possible differences in this pattern during adulthood between a general population and an occupational population. Please see later in the article for the Editors' Summary.

Background Women who experience menopause are at higher cardiometabolic risk and often display adverse changes in metabolic biomarkers compared with pre-menopausal women. It remains elusive whether the changes in cardiometabolic biomarkers during the menopausal transition are due to ovarian aging or chronological aging. Well-conducted longitudinal studies are required to determine this. The aim of this study was to explore the cross-sectional and longitudinal associations of reproductive status, defined according to the 2012 Stages of Reproductive Aging Workshop criteria, with 74 metabolic biomarkers, and establish whether any associations are independent of age-related changes. Methods We determined cross-sectional associations of reproductive status with metabolic profiling in 3,312 UK midlife women. In a subgroup of 1,492 women who had repeat assessments after 2.5 years, we assessed how the change in reproductive status was associated with the changes in metabolic biomarkers. Metabolic profiles were measured by high-throughput quantitative nuclear magnetic resonance metabolomics. In longitudinal analyses, we compared the change in metabolic biomarkers for each reproductive-status category change to that of the reference of being pre-menopausal at both time points. As all women aged by a similar amount during follow-up, these analyses contribute to distinguishing age-related changes from those related to change in reproductive status. Results Consistent cross-sectional and longitudinal associations of menopause with a wide range of metabolic biomarkers were observed, suggesting the transition to menopause induces multiple metabolic changes independent of chronological aging. The metabolic changes included increased concentrations of very small very low-density lipoproteins, intermediate-density lipoproteins, low-density lipoproteins (LDLs), remnant, and LDL cholesterol, and reduced LDL particle size, all toward an atherogenic lipoprotein profile. Increased inflammation was suggested via an inflammatory biomarker, glycoprotein acetyls, but not via C-reactive protein. Also, levels of glutamine and albumin increased during the transition. Most of these metabolic changes seen at the time of becoming post-menopausal remained or became slightly stronger during the post-menopausal years. Conclusions The transition to post-menopause has effects on multiple circulating metabolic biomarkers, over and above the underlying age trajectory. The adverse changes in multiple apolipoprotein-B-containing lipoprotein subclasses and increased inflammation may underlie women’s increased cardiometabolic risk in their post-menopausal years.

Mendelian randomisation analyses use genetic variants as instrumental variables (IVs) to estimate causal effects of modifiable risk factors on disease outcomes. Genetic variants typically explain a small proportion of the variability in risk factors; hence Mendelian randomisation analyses can require large sample sizes. However, an increasing number of genetic variants have been found to be robustly associated with disease-related outcomes in genome-wide association studies. Use of multiple instruments can improve the precision of IV estimates, and also permit examination of underlying IV assumptions. We discuss the use of multiple genetic variants in Mendelian randomisation analyses with continuous outcome variables where all relationships are assumed to be linear. We describe possible violations of IV assumptions, and how multiple instrument analyses can be used to identify them. We present an example using four adiposity-associated genetic variants as IVs for the causal effect of fat mass on bone density, using data on 5509 children enrolled in the ALSPAC birth cohort study. We also use simulation studies to examine the effect of different sets of IVs on precision and bias. When each instrument independently explains variability in the risk factor, use of multiple instruments increases the precision of IV estimates. However, inclusion of weak instruments could increase finite sample bias. Missing data on multiple genetic variants can diminish the available sample size, compared with single instrument analyses. In simulations with additive genotype-risk factor effects, IV estimates using a weighted allele score had similar properties to estimates using multiple instruments. Under the correct conditions, multiple instrument analyses are a promising approach for Mendelian randomisation studies. Further research is required into multiple imputation methods to address missing data issues in IV estimation.