Universidade Federal de Pelotas

The importance of breastfeeding in low-income and middle-income countries is well recognised, but less consensus exists about its importance in high-income countries. In low-income and middle-income countries, only 37% of children younger than 6 months of age are exclusively breastfed. With few exceptions, breastfeeding duration is shorter in high-income countries than in those that are resource-poor. Our meta-analyses indicate protection against child infections and malocclusion, increases in intelligence, and probable reductions in overweight and diabetes. We did not find associations with allergic disorders such as asthma or with blood pressure or cholesterol, and we noted an increase in tooth decay with longer periods of breastfeeding. For nursing women, breastfeeding gave protection against breast cancer and it improved birth spacing, and it might also protect against ovarian cancer and type 2 diabetes. The scaling up of breastfeeding to a near universal level could prevent 823,000 annual deaths in children younger than 5 years and 20,000 annual deaths from breast cancer. Recent epidemiological and biological findings from during the past decade expand on the known benefits of breastfeeding for women and children, whether they are rich or poor.

BACKGROUND: Cross-sectional studies with binary outcomes analyzed by logistic regression are frequent in the epidemiological literature. However, the odds ratio can importantly overestimate the prevalence ratio, the measure of choice in these studies. Also, controlling for confounding is not equivalent for the two measures. In this paper we explore alternatives for modeling data of such studies with techniques that directly estimate the prevalence ratio. METHODS: We compared Cox regression with constant time at risk, Poisson regression and log-binomial regression against the standard Mantel-Haenszel estimators. Models with robust variance estimators in Cox and Poisson regressions and variance corrected by the scale parameter in Poisson regression were also evaluated. RESULTS: Three outcomes, from a cross-sectional study carried out in Pelotas, Brazil, with different levels of prevalence were explored: weight-for-age deficit (4%), asthma (31%) and mother in a paid job (52%). Unadjusted Cox/Poisson regression and Poisson regression with scale parameter adjusted by deviance performed worst in terms of interval estimates. Poisson regression with scale parameter adjusted by chi2 showed variable performance depending on the outcome prevalence. Cox/Poisson regression with robust variance, and log-binomial regression performed equally well when the model was correctly specified. CONCLUSIONS: Cox or Poisson regression with robust variance and log-binomial regression provide correct estimates and are a better alternative for the analysis of cross-sectional studies with binary outcomes than logistic regression, since the prevalence ratio is more interpretable and easier to communicate to non-specialists than the odds ratio. However, precautions are needed to avoid estimation problems in specific situations.

Background: Mendelian randomization (MR) is being increasingly used to strengthen causal inference in observational studies. Availability of summary data of genetic associations for a variety of phenotypes from large genome-wide association studies (GWAS) allows straightforward application of MR using summary data methods, typically in a two-sample design. In addition to the conventional inverse variance weighting (IVW) method, recently developed summary data MR methods, such as the MR-Egger and weighted median approaches, allow a relaxation of the instrumental variable assumptions. Methods: Here, a new method - the mode-based estimate (MBE) - is proposed to obtain a single causal effect estimate from multiple genetic instruments. The MBE is consistent when the largest number of similar (identical in infinite samples) individual-instrument causal effect estimates comes from valid instruments, even if the majority of instruments are invalid. We evaluate the performance of the method in simulations designed to mimic the two-sample summary data setting, and demonstrate its use by investigating the causal effect of plasma lipid fractions and urate levels on coronary heart disease risk. Results: The MBE presented less bias and lower type-I error rates than other methods under the null in many situations. Its power to detect a causal effect was smaller compared with the IVW and weighted median methods, but was larger than that of MR-Egger regression, with sample size requirements typically smaller than those available from GWAS consortia. Conclusions: The MBE relaxes the instrumental variable assumptions, and should be used in combination with other approaches in sensitivity analyses.

BACKGROUND: This paper discusses appropriate strategies for multivariate data analysis in epidemiological studies. METHODS: In studies where determinants of disease are sought, it is suggested that the complex hierarchical inter-relationships between these determinants are best managed through the use of conceptual frameworks. Failure to take these aspects into consideration is common in the epidemiological literature and leads to underestimation of the effects of distal determinants. RESULTS: An example of this analytical approach, which is not based purely on statistical associations, is given for assessing determinants of mortality due to diarrhoea in children. CONCLUSIONS: Conceptual frameworks provide guidance for the use of multivariate techniques and aid the interpretation of their results in the light of social and biological knowledge.

<ns4:p>This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into nine sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust methods and one on other approaches), data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 18 months.</ns4:p>

OBJECTIVE: Our goal was to describe worldwide growth-faltering patterns by using the new World Health Organization (WHO) standards. METHODS: We analyzed information available from the WHO Global Database on Child Growth and Malnutrition, comprising data from national anthropometric surveys from 54 countries. Anthropometric data comprise weight-for-age, length/height-for-age, and weight-for-length/height z scores. The WHO regions were used to aggregate countries: Europe and Central Asia; Latin America and the Caribbean; North Africa and Middle East; South Asia; and sub-Saharan Africa. RESULTS: Sample sizes ranged from 1000 to 47 000 children. Weight for length/height starts slightly above the standard in children aged 1 to 2 months and falters slightly until 9 months of age, picking up after that age and remaining close to the standard thereafter. Weight for age starts close to the standard and falters moderately until reaching approximately -1 z at 24 months and remaining reasonably stable after that. Length/height for age also starts close to the standard and falters dramatically until 24 months, showing noticeable bumps just after 24, 36, and 48 months but otherwise increasing slightly after 24 months. CONCLUSIONS: Comparison of child growth patterns in 54 countries with WHO standards shows that growth faltering in early childhood is even more pronounced than suggested by previous analyses based on the National Center for Health Statistics reference. These findings confirm the need to scale up interventions during the window of opportunity defined by pregnancy and the first 2 years of life, including prevention of low birth weight and appropriate infant feeding practices.

This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into ten sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust statistical methods and one on other approaches), extensions and additional analyses, data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 24 months.

BACKGROUND: It is presumed that physical activity (PA) level declines during the lifespan, particularly in adolescence. However, currently, there is no study that quantifies these changes and pools results for a common interpretation. Therefore, the purpose was to systematically review the international literature regarding PA change during adolescence, and to quantify that change according to a series of study variables, exploring gender-and-age differences. METHODS: An electronic search was conducted in the Medline/PubMed and Web of Science databases. Longitudinal studies with, at least, two PA measures throughout adolescence (10-19 years old) or the first PA measure during childhood and the second one during adolescence were selected. From each article, study project name, country, year of the first data collection, sample size, baseline age, follow-up duration, characteristics of the instrument (type, recall time, PA intensity and PA domain), unit of PA measure and report of statistical significance were collected. RESULTS: Overall, 26 studies matched the inclusion criteria. Most were carried out in the USA, assessed PA by questionnaire, and found a decline in PA levels during the adolescence. On average, the mean percentage PA change per year, across all studies, was -7.0 (95% confidence interval: -8.8 to -5.2), ranging from -18.8 to 7.8. The decline was significant according to most sub-groups of variables analysed. Although earlier studies revealed a higher PA decline in boys, the decline has been greater in girls in more recent studies (commenced after 1997). Moreover, although the decline among girls was higher in younger ages at baseline (9-12 years), it was higher in older ages (13-16 years) among boys. CONCLUSIONS: The decline of PA during adolescence is a consistent finding in the literature. Differences between boys and girls were observed and should be explored in future studies. Interventions that attempt to attenuate the PA decline, even without an increase in PA levels, could be considered as effective.

BACKGROUND: The magnitude of risk conferred by the interaction between tobacco and alcohol use on the risk of head and neck cancers is not clear because studies have used various methods to quantify the excess head and neck cancer burden. METHODS: We analyzed individual-level pooled data from 17 European and American case-control studies (11,221 cases and 16,168 controls) participating in the International Head and Neck Cancer Epidemiology consortium. We estimated the multiplicative interaction parameter (psi) and population attributable risks (PAR). RESULTS: A greater than multiplicative joint effect between ever tobacco and alcohol use was observed for head and neck cancer risk (psi = 2.15; 95% confidence interval, 1.53-3.04). The PAR for tobacco or alcohol was 72% (95% confidence interval, 61-79%) for head and neck cancer, of which 4% was due to alcohol alone, 33% was due to tobacco alone, and 35% was due to tobacco and alcohol combined. The total PAR differed by subsite (64% for oral cavity cancer, 72% for pharyngeal cancer, 89% for laryngeal cancer), by sex (74% for men, 57% for women), by age (33% for cases <45 years, 73% for cases >60 years), and by region (84% in Europe, 51% in North America, 83% in Latin America). CONCLUSIONS: Our results confirm that the joint effect between tobacco and alcohol use is greater than multiplicative on head and neck cancer risk. However, a substantial proportion of head and neck cancers cannot be attributed to tobacco or alcohol use, particularly for oral cavity cancer and for head and neck cancer among women and among young-onset cases.

AIM: To systematically review the evidence on the associations between breastfeeding and overweight/obesity, blood pressure, total cholesterol and type 2 diabetes. METHODS: Two independent literature searches were carried out using the MEDLINE, LILACS, SCIELO and Web of Science databases. Studies restricted to infants and those without an internal comparison group were excluded. Fixed- and random-effects models were used to pool the estimates. RESULTS: Breastfed subjects were less likely to be considered obese/overweight [pooled odds ratio: 0.74 (95% confidence interval (CI): 0.70; 0.78)] (n = 113). Among the 11 high-quality studies, the association was smaller [pooled odds ratio: 0.87 (95%CI: 0.76; 0.99)]. Total cholesterol (n = 46) was independent of breastfeeding [pooled mean difference: -0.01 mmol/L (95%CI: -0.05; 0.02)]. Systolic blood pressure (n = 43) was lower among breastfed subjects [mean difference: -0.80 (95%CI: -1.17; -0.43)], but no association was observed among larger studies, and for diastolic blood pressure (n = 38) [mean difference: -0.24 (95%CI: -0.50; 0.02)]. For type 2 diabetes (n = 11), the odds ratio was lower among those subjects who had been breastfed [pooled odds ratio: 0.65 (95%CI: 0.49; 0.86)]. CONCLUSION: Breastfeeding decreased the odds of type 2 diabetes and based on high-quality studies, decreased by 13% the odds of overweight/obesity. No associations were found for total cholesterol or blood pressure.

Heavy alcohol consumption is an established risk factor for hypertension; the mechanism by which alcohol consumption impact blood pressure (BP) regulation remains unknown. We hypothesized that a genome-wide association study accounting for gene-alcohol consumption interaction for BP might identify additional BP loci and contribute to the understanding of alcohol-related BP regulation. We conducted a large two-stage investigation incorporating joint testing of main genetic effects and single nucleotide variant (SNV)-alcohol consumption interactions. In Stage 1, genome-wide discovery meta-analyses in ≈131K individuals across several ancestry groups yielded 3,514 SNVs (245 loci) with suggestive evidence of association (P < 1.0 x 10-5). In Stage 2, these SNVs were tested for independent external replication in ≈440K individuals across multiple ancestries. We identified and replicated (at Bonferroni correction threshold) five novel BP loci (380 SNVs in 21 genes) and 49 previously reported BP loci (2,159 SNVs in 109 genes) in European ancestry, and in multi-ancestry meta-analyses (P < 5.0 x 10-8). For African ancestry samples, we detected 18 potentially novel BP loci (P < 5.0 x 10-8) in Stage 1 that warrant further replication. Additionally, correlated meta-analysis identified eight novel BP loci (11 genes). Several genes in these loci (e.g., PINX1, GATA4, BLK, FTO and GABBR2) have been previously reported to be associated with alcohol consumption. These findings provide insights into the role of alcohol consumption in the genetic architecture of hypertension.

Understanding the drivers and dynamics of global ultra-processed food (UPF) consumption is essential, given the evidence linking these foods with adverse health outcomes. In this synthesis review, we take two steps. First, we quantify per capita volumes and trends in UPF sales, and ingredients (sweeteners, fats, sodium and cosmetic additives) supplied by these foods, in countries classified by income and region. Second, we review the literature on food systems and political economy factors that likely explain the observed changes. We find evidence for a substantial expansion in the types and quantities of UPFs sold worldwide, representing a transition towards a more processed global diet but with wide variations between regions and countries. As countries grow richer, higher volumes and a wider variety of UPFs are sold. Sales are highest in Australasia, North America, Europe and Latin America but growing rapidly in Asia, the Middle East and Africa. These developments are closely linked with the industrialization of food systems, technological change and globalization, including growth in the market and political activities of transnational food corporations and inadequate policies to protect nutrition in these new contexts. The scale of dietary change underway, especially in highly populated middle-income countries, raises serious concern for global health.

BACKGROUND: At least 75% of head and neck cancers are attributable to a combination of cigarette smoking and alcohol drinking. A precise understanding of the independent association of each of these factors in the absence of the other with the risk of head and neck cancer is needed to elucidate mechanisms of head and neck carcinogenesis and to assess the efficacy of interventions aimed at controlling either risk factor. METHODS: We examined the extent to which head and neck cancer is associated with cigarette smoking among never drinkers and with alcohol drinking among never users of tobacco. We pooled individual-level data from 15 case-control studies that included 10,244 head and neck cancer case subjects and 15,227 control subjects, of whom 1072 case subjects and 5775 control subjects were never users of tobacco and 1598 case subjects and 4051 control subjects were never drinkers of alcohol. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using unconditional logistic regression models. All statistical tests were two-sided. RESULTS: Among never drinkers, cigarette smoking was associated with an increased risk of head and neck cancer (OR for ever versus never smoking = 2.13, 95% CI = 1.52 to 2.98), and there were clear dose-response relationships for the frequency, duration, and number of pack-years of cigarette smoking. Approximately 24% (95% CI = 16% to 31%) of head and neck cancer cases among nondrinkers in this study would have been prevented if these individuals had not smoked cigarettes. Among never users of tobacco, alcohol consumption was associated with an increased risk of head and neck cancer only when alcohol was consumed at high frequency (OR for three or more drinks per day versus never drinking = 2.04, 95% CI = 1.29 to 3.21). The association with high-frequency alcohol intake was limited to cancers of the oropharynx/hypopharynx and larynx. CONCLUSIONS: Our results represent the most precise estimates available of the independent association of each of the two main risk factors of head and neck cancer, and they exemplify the strengths of large-scale consortia in cancer epidemiology.

Randomized controlled trials (RCTs) are essential for evaluating the efficacy of clinical interventions, where the causal chain between the agent and the outcome is relatively short and simple and where results may be safely extrapolated to other settings. However, causal chains in public health interventions are complex, making RCT results subject to effect modification in different populations. Both the internal and external validity of RCT findings can be greatly enhanced by observational studies using adequacy or plausibility designs. For evaluating large-scale interventions, studies with plausibility designs are often the only feasible option and may provide valid evidence of impact. There is an urgent need to develop evaluation standards and protocols for use in circumstances where RCTs are not appropriate.

OBJECTIVE: To estimate worldwide prevalence of chronic low back pain according to age and sex. METHODS: We consulted Medline (PubMed), LILACS and EMBASE electronic databases. The search strategy used the following descriptors and combinations: back pain, prevalence, musculoskeletal diseases, chronic musculoskeletal pain, rheumatic, low back pain, musculoskeletal disorders and chronic low back pain. We selected cross-sectional population-based or cohort studies that assessed chronic low back pain as an outcome. We also assessed the quality of the selected studies as well as the chronic low back pain prevalence according to age and sex. RESULTS: The review included 28 studies. Based on our qualitative evaluation, around one third of the studies had low scores, mainly due to high non-response rates. Chronic low back pain prevalence was 4.2% in individuals aged between 24 and 39 years old and 19.6% in those aged between 20 and 59. Of nine studies with individuals aged 18 and above, six reported chronic low back pain between 3.9% and 10.2% and three, prevalence between 13.1% and 20.3%. In the Brazilian older population, chronic low back pain prevalence was 25.4%. CONCLUSIONS: Chronic low back pain prevalence increases linearly from the third decade of life on, until the 60 years of age, being more prevalent in women. Methodological approaches aiming to reduce high heterogeneity in case definitions of chronic low back pain are essential to consistency and comparative analysis between studies. A standard chronic low back pain definition should include the precise description of the anatomical area, pain duration and limitation level.

The World Health Organization (WHO) Multicentre Growth Reference Study (MGRS) is a community-based, multicountry project to develop new growth references for infants and young children. The design combines a longitudinal study from birth to 24 months with a cross-sectional study of children aged 18 to 71 months. The pooled sample from the six participating countries (Brazil, Ghana, India, Norway, Oman, and the United States) consists of about 8,500 children. The study subpopulations had socioeconomic conditions favorable to growth, and low mobility, with at least 20% of mothers following feeding recommendations and having access to breastfeeding support. The individual inclusion criteria were absence of health or environmental constraints on growth, adherence to MGRS feeding recommendations, absence of maternal smoking, single term birth, and absence of significant morbidity. In the longitudinal study, mothers and newborns were screened and enrolled at birth and visited at home 21 times: at weeks 1, 2, 4, and 6; monthly from 2 to 12 months; and every 2 months in their second year. In addition to the data collected on anthropometry and motor development, information was gathered on socioeconomic, demographic, and environmental characteristics, perinatal factors, morbidity, and feeding practices. The prescriptive approach taken is expected to provide a single international reference that represents the best description of physiological growth for all children under five years of age and to establish the breastfed infant as the normative model for growth and development.

Mendelian randomization studies are often performed in an instrumental variables framework, using germline genetic variants as instruments for modifiable disease risk factors or exposures.1–3 Mendelian randomization analysis depends on assuming that the genetic variants: (i) are associated with the exposure (the relevance assumption); (ii) have no common cause with the outcome (the independence assumption); and (iii) have effects on the outcome that are solely mediated by the exposure (the exclusion restriction assumption).1–3 Summary data Mendelian randomization refers to methods which use summary-level instrument-exposure and instrument-outcome association results (typically, per-allele regression coefficients and standard errors) to obtain causal effect estimates. Two-sample Mendelian randomization refers to the application of Mendelian randomization methods to summary association results estimated in non-overlapping sets of individuals. These data can be obtained from the published literature, typically from summary results provided by consortia of genome-wide association studies (GWAS), or estimated directly from individual-level participant data.4 Recent examples include studies evaluating the causal effects of adiposity-related traits on risk of breast, ovarian, prostate, lung and colorectal cancers,5 of body mass index on type 2 diabetes6 and of telomere length on several health outcomes.7 As with Mendelian randomization in general, two-sample Mendelian randomization is analogous to methods originally developed in econometrics.8,9 Recent developments in two-sample Mendelian randomization are based on methods originally developed for meta-analysis.10,11 Scatter plots of all empirical Mendelian randomization studies in PubMed from 1 January 2011 to 24 October 2016. Left panel: absolute number of one-sample (dotted line) and subsample and/or two-sample Mendelian randomization studies (solid line). Right panel: proportion of subsample and/or two-sample Mendelian randomization studies (among all one-sample and subsample and/or two-sample studies). The dotted line indicates the 50% value. Even though the most natural application of summary data Mendelian randomization methods is in the two-sample setting (i.e. when instrument-exposure and instrument-outcome associations were estimated in non-overlapping sets of individuals), it is possible in principle to use summary data methods in the one-sample context – i.e. when instrument-exposure and instrument-outcome associations are estimated in the same sample. However, summary data Mendelian randomization analyses using instrument-exposure and instrument-outcome associations in the same sample or in partially overlapping samples may be prone to weak instrument bias towards the exposure-outcome estimate that would obtained using conventional methods (typically a regression of the outcome on the exposure). Therefore, using instrument-exposure and instrument-outcome associations estimated in non-overlapping samples is preferable.14 However, given that many two-sample Mendelian randomization applications use summary data from large GWAS consortia, it is possible that in many cases the instrument-exposure and instrument-outcome datasets partially overlap due to studies participating in both consortia; and detecting if this occurs and to what degree may depend on careful assessment of the description of the studies included in each consortium. The aims of this paper are to highlight the importance of harmonizing the genetic instrumental variables used for estimating the instrument-exposure and instrument-outcome associations, and to propose steps for doing this and checking the quality of the harmonization process in two-sample Mendelian randomization. In a paper recently published in the IJE, the general issue of data harmonization is discussed.15 Appropriate data harmonization is clearly essential when combining two or more independently generated datasets. This is particularly true for two-sample Mendelian randomization, because GWAS results rarely have harmonized effect (or coded) alleles. In genetic association studies, it is often assumed that genetic variants have additive (or per-allele) effects, which corresponds to coding the genotypes numerically according to the number of copies of one of the alleles. So, if a given variant was coded as AA = 0, AC = 1 and CC = 2 (i.e. according to the number of copies of the C allele), then C is termed the effect allele, and A the other (or non-coded, or baseline) allele. Table 1 provides an overview of the steps typically required to harmonize datasets of summary results of genetic associations for two-sample Mendelian randomization, based on the guidelines provided by Fortier and colleagues.15. Below, we will focus on two-sample Mendelian randomization using summary results from GWAS consortia. Overview of the data harmonization process for two-sample Mendelian randomization applications, based on the guidelines provided by Fortier and colleagues15 At minimum, the effect allele must be available in all datasets to be harmonized. Additional variables, such as the other allelea and effect allele frequency, improve the harmonization potential Missing exposure-associated variants in the variant-outcome dataset may be replaced by proxies available in the latterb but this reduces the quality of the harmonization process iii. Consider whether the populations used to generate the datasets are sufficiently similar to harmonize them At minimum, the effect allele must be available in all datasets to be harmonized. Additional variables, such as the other allelea and effect allele frequency, improve the harmonization potential Missing exposure-associated variants in the variant-outcome dataset may be replaced by proxies available in the latterb but this reduces the quality of the harmonization process iii. Consider whether the populations used to generate the datasets are sufficiently similar to harmonize them Knowing the other allele is particularly useful for harmonization of palindromic variants. Variants in high linkage disequilibrium with the index variant in the relevant ancestry group. Not having the same allele pair could be a consequence of strand orientation differences between datasets. In this case, harmonizing strand orientation will result in shared allele pairs. Alternatively, if effect allele frequencies are available, they can be used to identify if the effect allele is the major or minor allele, and such classification can be used to check allele matching. Importantly, this strategy would only be reliable if the minor allele frequency is substantially below 50%. Multiply by -1 in the case of additive effect estimates (e.g. linear regression coefficients, log(odds ratio), risk differences) or elevate to the power of -1 in the case of multiplicative effect estimates (e.g. odds ratios). 1 (or 100%) minus the effect allele frequency in the raw dataset. Overview of the data harmonization process for two-sample Mendelian randomization applications, based on the guidelines provided by Fortier and colleagues15 At minimum, the effect allele must be available in all datasets to be harmonized. Additional variables, such as the other allelea and effect allele frequency, improve the harmonization potential Missing exposure-associated variants in the variant-outcome dataset may be replaced by proxies available in the latterb but this reduces the quality of the harmonization process iii. Consider whether the populations used to generate the datasets are sufficiently similar to harmonize them At minimum, the effect allele must be available in all datasets to be harmonized. Additional variables, such as the other allelea and effect allele frequency, improve the harmonization potential Missing exposure-associated variants in the variant-outcome dataset may be replaced by proxies available in the latterb but this reduces the quality of the harmonization process iii. Consider whether the populations used to generate the datasets are sufficiently similar to harmonize them Knowing the other allele is particularly useful for harmonization of palindromic variants. Variants in high linkage disequilibrium with the index variant in the relevant ancestry group. Not having the same allele pair could be a consequence of strand orientation differences between datasets. In this case, harmonizing strand orientation will result in shared allele pairs. Alternatively, if effect allele frequencies are available, they can be used to identify if the effect allele is the major or minor allele, and such classification can be used to check allele matching. Importantly, this strategy would only be reliable if the minor allele frequency is substantially below 50%. Multiply by -1 in the case of additive effect estimates (e.g. linear regression coefficients, log(odds ratio), risk differences) or elevate to the power of -1 in the case of multiplicative effect estimates (e.g. odds ratios). 1 (or 100%) minus the effect allele frequency in the raw dataset. Based on the research question, researchers will identify (often multiple) genetic variants associated with an adequate exposure phenotype. For example, if interest lies in studying the causal effects of adiposity measures on a given outcome, then one could use as instruments genetic variants identified in GWAS of anthropometric traits such as body mass index16 or waist circumference.17 The summary-level association results for the variants that reached genome-wide significance (i.e. P < 5.0 × 10-8) – hereafter referred to as dataset 1 – are normally extracted from published papers or a web repository. Although the focus of this paper is on genetic instruments selected based on a GWAS of the exposure phenotype, it is possible that instruments are selected using other criteria. For example, genetic instruments may be selected based on results of functional studies of gene expression regulation or limited to variants located within the gene of interest. For example, the C Reactive Protein (CRP) Coronary Heart Disease (CHD) Genetics Collaboration selected four genetic variants in the CRP gene region to evaluate the causal effect of CRP on CHD risk using Mendelian randomization.18 These four variants explain 98% of the genetic variation in this locus in populations of European ancestry, and have been shown to regulate circulating CRP levels without changing the protein sequence.19 Selecting instruments this way will likely yield results that are less prone to bias due to horizontal pleiotropy compared with selecting genome-wide significant genetic instruments scattered throughout the genome.20 In this case, data from the exposure GWAS (e.g. the large CRP GWAS21) can be used to obtain precise instrument-exposure (e.g. instrument-CRP) summary association results for the previously chosen instruments (e.g. the four genetic variants in the CRP gene region). After obtaining dataset 1, the steps listed below are commonly followed: Schematic representation of chromosomes, DNA and genetic variants in a diploid cell. Ensure that all variants in dataset 1 are associated with the exposure in the same direction, typically positive (i.e. the exposure-increasing allele is the effect allele). Such standardization is important for two main reasons: (i) it is required when applying a recently-developed summary data Mendelian randomization method called MR-Egger11; and (ii) it facilitates interpretation of plots and other forms of presenting results. When a variant is not coded in the desired way, it is necessary to ‘flip’ the variant, which implies that: the effect allele becomes the other allele, and vice versa; the regression coefficient (e.g. ln(odds ratio), mean differences, etc.) must be multiplied by -1; and the effect allele frequency must be subtracted from 1. Ensuring that datasets 1 and 2 are identically coded regarding effect (e.g. exposure increasing) and other alleles. For example, if a given instrument has A as the effect allele and C as the other allele in dataset 1, then one must ensure that this same instrument is coded as having A and C as the effect and other alleles, respectively, in dataset 2. Ensuring that both datasets are coded from the same strand is very important to reduce issues with palindromic variants (as discussed in more detail below; see Figure 2). The steps described above are illustrated in Table 2. Notice that, in Table 2, genetic variants are represented by ‘rs’ followed with a number. These are called rs numbers, which uniquely identify a genetic variant and contain information such as location (i.e. chromosome and position on the chromosome), its alleles, and other useful data. Also notice that one of the genetic instruments (rs3) was missing from the outcome GWAS, so it was replaced by an LD proxy (rs5) which was available in both exposure and outcome GWAS. When it is possible to find a suitable LD proxy that is available in both exposure and outcome GWAS, it is preferable to replace the target SNP by its LD proxy in both datasets to avoid problems with phasing (discussed above). Illustration of the process of data harmonization in two-sample Mendelian randomization using fictional data. Dataset 1 corresponds to instrument-exposure associations, and dataset 2 corresponds to instrument-outcome associations. It is assumed that both datasets are coded in the forward (5’→3’) strand The genetic instrument rs3 was not available in the outcome GWAS. Therefore it was replaced by rs5 which was available in both exposure and outcome GWAS. To this end, rs5 must be in high LD with rs3 in the relevant ancestry group. LD, linkage disequilibrium; SNP, single nucleotide polymorphism; EA, effect allele; OA, other allele; EAF, effect allele frequency; NA, not available. Illustration of the process of data harmonization in two-sample Mendelian randomization using fictional data. Dataset 1 corresponds to instrument-exposure associations, and dataset 2 corresponds to instrument-outcome associations. It is assumed that both datasets are coded in the forward (5’→3’) strand The genetic instrument rs3 was not available in the outcome GWAS. Therefore it was replaced by rs5 which was available in both exposure and outcome GWAS. To this end, rs5 must be in high LD with rs3 in the relevant ancestry group. LD, linkage disequilibrium; SNP, single nucleotide polymorphism; EA, effect allele; OA, other allele; EAF, effect allele frequency; NA, not available. Depending on the situation, additional steps may be required. For example, if instruments have been identified from elsewhere (as illustrated above with the CRP example) and an exposure GWAS is only being used to obtain precise estimates of the instrument-exposure association, it is possible that the genetic instruments are missing from the exposure GWAS, and it may be necessary to replace them with LD proxies. Another step, related to but not actually part of the data harmonization process per se, is to adjust the scale of the causal effect estimate. For example, Ference and colleagues wanted to evaluate the causal effect of a 10-mmHg reduction in systolic blood pressure (exposure) on CHD (outcome).23 If, for example, instrument-exposure associations (dataset are in systolic blood pressure per of the effect allele, then it would be necessary to both the regression coefficients and standard in dataset 1 not in dataset by However, it is important to that causal effect estimates from Mendelian randomization may not the estimates from a scale because the genetic variants to differences in in the is for a genetic variants and may the exposure The would be to quality useful check is to or such as the or datasets 1 and 2, and regarding effect allele data were used to generate the it is preferable to use effect allele frequencies estimated in In Table 2, the coefficient was harmonization LD and A similar check may be to standard linear regression may be a coefficient because may be a in standard if the sample used to generate datasets 1 and 2 are quality steps include that instrument-exposure associations are all coded in the desired and that the effect and other are the same between datasets. It is possible to identify by and datasets regarding standard absolute of the regression coefficients, and minor allele (i.e. the common allele of a given genetic this may or may not be the effect be problems in each of the data harmonization These of standardization of the of the instrument-exposure of LD proxies for missing using as between the target SNP and the LD proxy when the target SNP is used in one of the datasets and the proxy is used in the other as discussed of allele harmonization between genetic variants the case of selected LD between datasets can for example, if variants are as – – and is more one variant to this and the process if harmonization is Another potential in data harmonization is strand issues – i.e. when the effect and other in dataset 1 were based on the forward (5’→3’) DNA but on the strand in dataset 2, or vice 2). Therefore, studies effects of the same SNP using for example, an SNP with in dataset 1 may be as in dataset 2. In most cases can be identified but palindromic (i.e. to that pair with each other in a DNA see Figure are to harmonize because the are the same on both These that the effect allele frequency is and that the minor allele frequency is substantially below 50% in to identify are to with palindromic that have minor allele frequencies to them by LD analyses to evaluate on Mendelian randomization or It is common in GWAS to (i.e. of genotypes of genetic variants in the using genetic datasets (e.g. and from more samples as a Therefore, in GWAS the summary results available are in to the forward strand as a consequence of to a common However, this is not a that all datasets are for analyses because studies may be to or of the same which may differences regarding strand orientation or allele To the of harmonization that we are in a single genetic instrument with A and so an for this variant can be AC or Consider that the data are coded as AA = 0, AC = 1 and CC = 2 (i.e. C is the effect allele, and A the other allele). the effect allele is often an it is possible that effect allele coding between instrument-exposure and instrument-outcome datasets. that this to genetic variant of so that the instrument-exposure association was estimated with C as the effect allele, but A was the effect allele in the instrument-outcome the allele effect estimate of the genetic instrument on the and the allele effect estimate on In this case, the allele estimates would be and (i.e. the allele estimates in the allele is not the causal effect estimated using the would be However, if the are the causal effect estimate would be This that allele in a given instrument the of its causal effect the importance of allele harmonization in summary data Mendelian randomization. recently published two-sample Mendelian randomization studies that the causal effect of CRP levels (exposure) on risk can be used to the relevance of data harmonization for two-sample Mendelian randomization. In both studies, variants were used as genetic These variants were associated with CRP < 5.0 × 10-8) in a GWAS on more from which associations were associations were obtained from the GWAS by the a method that the outcome on an additive allele in detail and colleagues an odds of per in they result as to a in CRP However, and colleagues an odds of per in when combining instruments using effects the same datasets were it was possible that were due to differences in data To we extracted regression coefficients, standard and effect of all single nucleotide identified in the CRP GWAS from the paper (i.e. dataset only the were provided in the the other were obtained using the of variants were Summary results for all genetic variants in the GWAS were from the This dataset was to only the variants associated with CRP < 5.0 × 10-8) (dataset 2). datasets were according to rs variants were identified as having the same allele between datasets. we whether the effect this case, in datasets 1 and 2. of variants effect alleles. harmonized the effect in the two the regression using the as the the to harmonized datasets an odds of per in a result that was with but not with To the for differences, we compared harmonized datasets with the datasets provided in and In analyses using the same two datasets as both and colleagues and and we that all of the that were genome-wide associated with CRP (dataset were available in the GWAS (dataset with no for so we used all and colleagues of from analyses they were genome-wide significant in the and sample they were not in the and the a that they wanted genome-wide significant variants in both and and colleagues that of the CRP genome-wide significant were not in the GWAS dataset we find all they proxies with two of but not include the other This that are used in the analyses and see available as data However, as can be in of the are all analysis and the two proxies that use are to the same associations the that they proxy both of which were included in and colleagues and and colleagues are using overlapping sets of genetic instrumental variables for analysis to the used in the and studies that such differences in variants no on the results. The coefficients and standard in and dataset 1 were This indicates that effect and other provided in were the However, when we compared dataset 2 harmonization with the between the ln(odds were and the between the standard were then compared dataset 2 with the raw dataset from the In this the between the ln(odds was that many variants in dataset 2 were coded as in the raw dataset. This was of of harmonization between datasets 1 and 2, because (as discussed it was necessary to of the variants in the dataset so that the effect allele was the allele. the between standard of a in dataset 2, the between standard be 1 if are issues with allele After we that the coefficients and standard between and the raw datasets were from the single variant This variant odds standard of and per allele in the raw and in datasets in analysis this was assumed to to the allele), not this it be due to a because the standard were all other variants in dataset 2 were coded as in the raw we to the variant the same effect and other as in the raw dataset. Therefore, were two in dataset (i) of allele harmonization between datasets 1 and 2, which in of the effect in dataset 2 not to and (ii) a regarding variant the method to as provided in an odds of per in After the variant, the odds was After harmonization exclusion of the variant with the the odds was analysis using and the odds was These results that the differences between and results were due to of allele harmonization between datasets 1 and 2 to differences in methods to the causal effect estimate or the of the issues above were in dataset 2. When applying the to the odds was When using the odds was to the result they results are shown in Table In this example, data harmonization the of the causal effect estimate from (as by and colleagues and to of per in based on Mendelian randomization analyses using the NA, not using effects using a method that the outcome on an additive allele were provided in available as data of per in based on Mendelian randomization analyses using the NA, not using effects using a method that the outcome on an additive allele were provided in available as data allele can to bias in the causal effect estimate in the Although this would not be a when the true causal effect is the estimates can be if the true causal effect is The of this bias will depend on the proportion of effect allele and in the causal effect estimate. For example, the bias may the causal effect estimate when only a genetic instruments effect allele However, when most of the instruments or the variants are the bias may be to the causal effect estimate (as in and harmonization has been only discussed in Mendelian randomization guidelines published to In this process may not be because often have to be or have to be that are To bias due to data harmonization we that researchers the harmonized datasets (as as the used in two-sample Mendelian randomization This would of the harmonization process by and having to that harmonization has been that harmonization is performed using and that the are available with the to a in that can be used to harmonize summary-level datasets of genetic associations as as data and on are available that with harmonization and other steps of two-sample Mendelian randomization such as in the which is used in the recently developed web and analysis called both the and harmonized datasets and the harmonization process with information for will likely the that in data harmonization are two-sample Mendelian randomization The process of data harmonization be included when or the analysis This would for and check harmonized in whether or not the harmonization has been harmonization is clearly essential for two-sample Mendelian randomization analyses and be performed and clearly Two-sample Mendelian randomization studies are analogous to in that they can be performed using available data and can be used to papers – in the of data or in the In the of the of the that can in this has been It is important that the of two-sample Mendelian randomization studies shown in Figure 1 not the of and and recently by The of this is by the of a paper that that combining the two could be more Mendelian randomization to a a It is of that the in the of two-sample Mendelian randomization studies could result in studies using this more common in the literature, an that be by and data are available The and the of the is by the and a for on an within the the of

In response to the 2013 Update of the European Strategy for Particle Physics, the Future Circular Collider (FCC) study was launched, as an international collaboration hosted by CERN. This study covers a highest-luminosity high-energy lepton collider (FCC-ee) and an energy-frontier hadron collider (FCC-hh), which could, successively, be installed in the same 100 km tunnel. The scientific capabilities of the integrated FCC programme would serve the worldwide community throughout the 21st century. The FCC study also investigates an LHC energy upgrade, using FCC-hh technology. This document constitutes the second volume of the FCC Conceptual Design Report, devoted to the electron-positron collider FCC-ee. After summarizing the physics discovery opportunities, it presents the accelerator design, performance reach, a staged operation scenario, the underlying technologies, civil engineering, technical infrastructure, and an implementation plan. FCC-ee can be built with today’s technology. Most of the FCC-ee infrastructure could be reused for FCC-hh. Combining concepts from past and present lepton colliders and adding a few novel elements, the FCC-ee design promises outstandingly high luminosity. This will make the FCC-ee a unique precision instrument to study the heaviest known particles (Z, W and H bosons and the top quark), offering great direct and indirect sensitivity to new physics.

The association between obesity and morbidity resulting from chronic diseases is well known. This systematic review addresses studies of the role of rapid growth in infancy and childhood as possible determinants of overweight and obesity later in the life course. We reviewed MEDLINE for studies reporting on growth in infancy and childhood, as well as measures of weight or adiposity in later childhood, adolescence or adulthood. The methodological quality of the papers was assessed using the criteria suggested by Downs and Black. Sixteen articles that fulfilled review criteria were located. There was wide variability in the indicators used for defining rapid growth as well as overweight or obesity. The age range in which weight or adiposity was measured ranged from 3 to 70 years. In spite of differences in definitions used, 13 articles that reported on early rapid growth found significant associations with later overweight or adiposity. Efforts should be made to standardize the definition of rapid growth, as well as that of overweight and obesity in children and adolescents. The most frequent definition for rapid growth in this review was a Z-score change greater than 0.67 in weight for age between two different ages in childhood. Regarding obesity, the definition proposed by the International Obesity Task Force also appears to be most appropriate. The present results indicate that early growth is indeed associated with the prevalence of obesity later in the life course.

The Pierre Auger Observatory, located on a vast, high plain in western Argentina, is the world's largest cosmic ray observatory. The objectives of the Observatory are to probe the origin and characteristics of cosmic rays above 10 17 eV and to study the interactions of these, the most energetic particles observed in nature. The Auger design features an array of 1660 water Cherenkov particle detector stations spread over 3000 km 2 overlooked by 24 air fluorescence telescopes. In addition, three high elevation fluorescence telescopes overlook a 23.5 km 2 , 61-detector infilled array with 750 m spacing. The Observatory has been in successful operation since completion in 2008 and has recorded data from an exposure exceeding 40,000 km 2 sr yr. This paper describes the design and performance of the detectors, related subsystems and infrastructure that make up the Observatory.