dataesr/scientific-paragraphs-categorization huggingface dataset:
) is recorded by clinicians using a drop-down menu in the electronic health records. To improve the accuracy of this variable for research purposes, an algorithm which combines information from the clinician-recorded diabetes type variable and prescription data is used  [16, 17]. Thus, in the current dataset the date of diabetes diagnoses in the offspring ranged from 1956 to 2011, with diagnoses available from birth for all those born after 2000. Ethics approval for the creation and analysis of the linked dataset containing no personal identifying information was obtained from the London-Dulwich Research Ethics Committee (reference 16/LO/1410). Approvals were also obtained from the steering committee of the AMND, the Scottish Diabetes Research Network epidemiology subgroup committee and the privacy advisory committee of Information Services Division (ISD) Scotland.
The pseudonymised dataset was stored and analysed in the Grampian Data Safe Haven. Statistical analysis Data were analysed using IBM SPSS Statistics version 24 (IBM, Armonk, NY, USA). Differences in maternal and offspring characteristics according to BMI category were tested using ANOVA for continuous variables and χ 2 tests for categorical variables. The effect of maternal obesity on offspring incidence of any diabetes, type 1 diabetes or type 2 diabetes was tested using Cox proportional hazards models, stratifying for year of birth and adjusting for sex of the offspring, comparing outcomes in the offspring of mothers whose pregnancy BMI was underweight, overweight or obese, with those of women with normal BMI. Analyses were adjusted for potential confounding factors, including maternal history of diabetes before pregnancy, maternal history of hypertension, maternal age at delivery, gestation when weight was measured, deprivation category and parity.
For the two variables with missing data, on deprivation category and gestation when weight was measured, the missing data were coded to their own categories. In sensitivity analyses we first repeated the analyses after exclusion of women with a history of diabetes prior to pregnancy, to study associations between maternal obesity and offspring outcomes in women without known diabetes. We then repeated the analyses limiting them to those born between 1950 and 1976, i. e. with similar duration of follow-up to those in whom we observed a link between mothers being overweight or obese and offspring cardiovascular events and mortality rates  [2]. In addition, we repeated the analyses after excluding women with diabetes prior to pregnancy and with diabetes diagnosed during or after pregnancy. Finally, we examined interactions by sex and repeated the analyses separately for male and female offspring.
Discussion In this large cohort study including over 60 years of pregnancy data linked to the national diabetes dataset we found that, for pregnant women, being overweight or obese was associated with an increased incidence of type 2 diabetes in the offspring. This association with type 2 diabetes may lie on the causal pathway to explain the link between being overweight or obese during pregnancy and offspring cardiovascular disease and mortality rates  [2, 3] , and it highlights a potential target for intervention. Our finding of a link between being or obese and type 2 diabetes in the offspring is in accord with previous observations of associations of pregnant women being overweight and higher maternal weight with increased incidence of type 2 diabetes in the offspring  [11, 12] , and of maternal obesity with markers in the offspring of increased insulin resistance in childhood  [18]  and young adulthood  [5, 11, 19] ; it is also consistent with the findings of increased diabetes risk in offspring born to overweight mothers in the Helsinki Birth Cohort Study  [3].
Although the latter study could not distinguish between types of diabetes, the association between being overweight and risk of diabetes was stronger in women than in men, as observed in our dataset. The reasons for a sex difference in the findings are unknown but are possibly related to the smaller pancreatic volume  [20] , and hence beta cell reserve, in women compared with men, potentially programmed in utero  [21] , and the findings are consistent with those of other studies suggesting women may be more vulnerable to adverse metabolic programming influences  [22, 23]. Evidence suggests increasing incidence of type 1 diabetes in children born between 1989 and 2003  [24] , a time when rates of maternal obesity were dramatically rising, as also observed in our dataset. Associations between maternal obesity and childhoodonset type 1 diabetes have been reported in some studies  [6] [7] [8]  but not others  [9, 10].
Notably, two studies from Sweden observed an increased incidence of childhood , kg/m 2  22. Data are mean (SD), except where indicated as n (%) a Complete data available for all variables other than deprivation category and gestation weight measured where data were available for 100,397 and 112,340 women, respectively b Deprivation category according to area socioeconomic status: 1-2, most deprived; 3-4, middle; 5-6, least deprived type 1 diabetes in obese mothers without diabetes  [6, 8]. were unable to replicate this finding in our dataset. Reasons for the differences between the Swedish and Aberdeen cohorts are unknown and we only found an association of maternal obesity with offspring type 1 diabetes when we restricted the cohort to individuals who had at least 35 years of follow-up. The underlying mechanisms linking mothers being overweight/obese to type 2 diabetes in the offspring are not known.
In addition, we used national population-based health data for diagnosed diabetes (SCI-Diabetes), which allowed us to examine outcomes of both type 2 and type 1 diabetes in the same dataset. A further major strength of our study is that we included outcome data for offspring from birth to age 62 years, which   The main limitation is that as only maternal history of type 1 diabetes is recorded in the AMND, we were not able to use 'gestational diabetes' or 'type 2 diabetes' as covariates to determine whether there was a difference in offspring outcomes of mothers who were obese but did not have diabetes during pregnancy or who were both obese and had gestational or type 2 diabetes. However, we believe the number of women with pre-existing type 2 diabetes in this dataset would be negligible, particularly for mothers who were pregnant in the 1950s and 1960s.
In addition, the definition of gestational diabetes has changed over the study period and more detailed measurements of glucose levels taken during pregnancy would be required to generate a comparable definition. As hypertension is part of the metabolic syndrome, we included history of hypertension in our models as a proxy for insulin resistance. Data on maternal gestational weight gain were not available, although a recent study found no evidence that gestational weight gain influenced risk of childhood type 1 diabetes  [8]. A further limitation of our study is that we did not have data for all offspring on BMI or lifestyle factors (e. g. diet, exercise) that are known to increase the risk of type 2 diabetes. Indeed, it is plausible that our observations were due to increased BMI in the child, either through prenatal programming of BMI or through shared lifestyle of mother and child.
Adjusted HR, analyses were adjusted for maternal history of diabetes before pregnancy, maternal history of hypertension, maternal age at delivery, gestation when weight was measured, deprivation category, parity and sex of offspring allowed us to explore outcomes across the lifespan. Scottish Diabetes Survey data indicate that type diabetes prevalence increases with age to about 11% for 55-to 59-year-olds (i. e. the oldest age group in this study)[13]. To our knowledge, there are no other databases worldwide that would allow such linkage between maternal obesity records in pregnancy and confirmed diagnosis of diabetes in the offspring.
Viroids are minute unencapsidated non-coding circular RNAs known to be present and to cause diseases only in plants. Infections were associated with the occurrence of specific single-stranded RNAs similar in size to miRNAs and endogenous small interfering RNAs, and viroid pathogenicity is suspected to occur through RNA interference. We looked for sequence similarities between viroids and the seed region of human microRNAs (hsa-miRNAs). Viroid genomes were retrieved from GenBank and mature hsa-mi-RNAs were retrieved from miRBase. Two hundred 300-nucleotide-long sequences were randomly generated as controls. BLAST searches were performed using viroids as queries and hsa-miRNAs as subjects with relaxed parameters, and matches involving hsa-miRNA seed regions were considered. A total of 81,021 matches were found, and 1,501 that showed 100% identity with whole hsa-miRNA seed regions were selected.
Materials and Methods We first retrieved all viroid genomes available from the NCBI GenBank nucleotide sequence database (https://www. ncbi. nlm. nih. gov) and all available mature miRNA sequences from the miRBase database (www. mirbase. org) [15]. In addition, through the RAND-NA tool  [16] , we generated 200 random sequences with a size of 300 nt, similar to that of viroids, to use as controls. They consisted of 5 groups of 40 sequences with a G+C content of 40, 45, 50, 55, and 60%, chosen to cover the range of G+C-contents found in viroids. We performed BLAST searches using viroid sequences as queries and all mature miRNA sequences as subjects, with relaxed parameters including 4 as word size, 1,000 as e-value threshold, and no filter. The ten best BLAST results were thereafter filtered by keeping only viroids matching with mature hsa-miRNAs.
The same protocol was used by handling the random sequences. BLAST results were sorted based primarily on a match involving the seed region of hsa-miRNAs (2-7 nt), as well as thresholds for the BLAST scores, the length of the sequence alignments, the percentage of identity between viroids and mature hsa-miRNAs, and the e-value (Fig. 1 ). Finally, information on hsa-miRNAs whose seed region matched with viroid RNAs according to our criteria were searched for through the miRBase database and the NCBI PubMed database (https://www.
Results A total of 15,294 viroid sequences were used as queries for BLAST searches against a database of 35,828 mature miRNAs. This generated 1,048,575 hits (Fig. 1 ). Of them, 81,021 (7. 7%) corresponded to matches between viroids and hsa-miRNA seed regions. After considering cases where viroid fragments were 100% similar to the whole hsa-miRNA seed region from its start (position 1 or 2), 1,501 hits (1. 9%; 0. 14% of the total number of hits) remained (online suppl. Table  S1 ; for all online suppl. material, see www. karger. com/doi/10. 1159/000509212). BLAST results were found to have an e value < 1 in 170 (11. 3%) cases. Among these 1,501 hits, 644, 725, 130, 1, and 1 had a nucleotide alignment length of 11, 12, 13, 14, and 17 nt, respectively. The longest match (17 nt) involved Potato spindle tuber viroid strain DI285387_1_KR_ 1020130054489-A/33666 and hsa-miRNA-6774-5p, with a BLAST bit score of 32.
5. The second longest match (14 nt) was between Potato spindle tuber viroid strain A/2147 and hsa-miRNA-3145-5p (BLAST bit score of 27). After removing duplicates, 25 different viroid species were involved in matches with the seed region of 212 different hsa-miRNAs (Table  1 ). A same procedure was applied to the 200 random sequences of 300 nt used as controls for specificity assessment. BLAST searches using these sequences against the miRNA database generated 2,097 hits. Out of them, 101 (4. 8%) involved the whole seed region of several hsa-miRNAs with an identity of 100%. Among these 101 hits, 5, 3, and 1 had a nucleotide alignment length of 12, 13, and 17 nt, respectively. Hence, the proportion of these hits with an alignment length ≥12 nt was higher for viroids than for controls (57 vs.
3 ). c, f ). These sequence logos were built using the WebLogo online tool version 2. 8. 2  [17] , with 91 Chrysanthemum stunt viroid sequences and 341 Potato spindle tuber viroid sequences that were downloaded from the NCBI GenBank nucleotide sequence database using viroid names and "complete" and "genome" as search terms, and that were directly alignable on their full length (see also online suppl.
Results Participants. We used data from 8825 neonates from 24 studies in the Pregnancy And Childhood Epigenetics (PACE) Consortium, representing mainly European, but also African and Hispanic ethnicities with similar proportions of males and females. Details of participants used in all analyses are presented in Table  1 , Supplementary Data 1 and study-specific Supplementary Methods. Meta-analysis. Primary, secondary and follow-up analyses are outlined in the study design in Fig. 2. Methylation at 8170 CpGs, measured in neonatal blood using the Illumina Infinium® Human-Methylation450 BeadChip assay and adjusted for cell-type heterogeneity  [23] [24] [25]  , was associated with birthweight (false discovery rate (FDR) <0. 05), of which 1029 located in or near 807 genes survived the more stringent Bonferroni correction (p < 1.
In a look-up of the fetal and maternal SNPs from GWAS of birthweight in an online cord blood methylation quantitative trait loci (mQTL) database (mqtldb. org  39 ), 35 fetal and four maternal SNPs affected methylation at some CpG(s), but none at the 914 birthweight-associated CpGs specifically. Functional analyses. We compared the 914 birthweightrelated CpGs with a recently published list of 18,881 expression quantitative trait methylation sites (cis-eQTMs, +/-250 kb around ), [3, red] Results from analysis without exclusion for preterm births, pre-eclampsia and maternal diabetes, DNA methylation and birthweight as a continuous measure n = 5414), [4, purple] Results from logistic regression analysis without exclusion for preterm births, pre-eclampsia and maternal diabetes, for low (n = 178) vs normal (n = 4197) birthweight, [5, yellow] Results from logistic regression analysis for associations between DNA methylation and high (n = 1590) vs normal (n = 6114) birthweight, [6, green] Results from look-up analysis in methylation samples taken during childhood and its association with birthweight as a continuous measure (n = 2756).
05, Supplementary Data 13). Of these 23 CpGs, 13 were also found in the publicly available cis-eQTM list  40. In 84 Gambian children (age 2 years)  42  , we found two CpGs that were inversely associated with whole blood mRNA gene expression, but neither were found in the Spanish results or the publicly available cis-eQTM list. The 914 birthweight-associated CpGs showed no functional enrichment of Gene Ontology (GO) terms or Kyoto Encyclopedia of Genes and Genomes (KEGG) terms (FDR<0. 05). Mendelian randomization. We aimed to explore causality using MR analysis, in which genetic variants associated with methylation levels (methylation quantitative trait loci (mQTLs)) are used as instrumental variables to appraise causality. For 788 (86%) of the 914 birthweight-associated CpGs, no mQTLs were identified in a publicly available mQTL database  39.
Persistence of birthweight-related differential DNA methylation may not necessarily be a prerequisite for long-term effects, as transient differential methylation in early life may cause lasting functional alterations in organ structure and function that predispose to later adverse health effects. Methylation is known to be associated with gene expression  48. However, we found no consistent associations between birthweightrelated methylation and gene expression in two childhood studies. This could be due to the relatively small sample sizes, differences in ethnicities, age, or platforms to measure gene expression. The use of blood, which is likely only a possible surrogate tissue for fetal growth phenotypes, for gene expression analysis might also explain the lack of findings. We did find multiple cis-eQTMs among the birthweightrelated CpGs at which methylation was related to gene expression in blood when using a publicly available database from a larger adult sample  40  , providing some evidence that birthweight-related differentially methylated CpGs may be associated with gene expression.
DNA methylation patterns in neonatal blood, whilst easily accessible in large numbers, may not reflect the key tissue of importance in relation to birthweight. DNA methylation and gene expression in placental tissue may be important targets for future studies. DNA methylation varies between leucocyte subtypes  49  and we used an adult whole blood reference to correct for this in the main analyses  23, 24  , as the study-specific analyses were completed before the widespread availability of specific cord blood reference datasets  50, 51. However, we observed very similar findings in two studies (Generation R and GECKO) when we compared the results with those using one of the currently available cord blood references  50. Although we adjusted for potential major confounders that may affect both methylation and fetal growth, we acknowledge that the main results cannot ascertain causality.
Methods Participants. In the main EWAS meta-analysis we explored associations of neonatal blood DNA methylation with birthweight using data from 8825 neonates from 24 studies in the PACE Consortium  53  (Table  1 ). We removed multiple births from all analyses and excluded preterm births (<37 weeks) and offspring of mothers with pre-eclampsia or diabetes (three major pathological causes of differences in fetal growth). In follow-up analyses, we explored whether any sites found in the main analysis were discernible in relation to birthweight when examined in DNA from blood drawn during childhood (2-13 years; 2756 children from 10 studies), adolescence (16-18 years; 2906 adolescents from six studies) or adulthood (30-45 years; 1616 adults from three studies), see Supplementary Data 1B. Informed consent was obtained from all participants, and all studies received approval from local ethics committees.
Study-specific methods and ethical approval statements are provided in Supplementary Methods. Birthweight, DNA methylation and covariates. Our primary outcome was birthweight on a continuous scale (grams), adjusted for gestational age, and measured immediately after birth or retrospectively reported by mothers in questionnaires. In secondary analyses, we categorised and compared associations with high (>4000 g, n = 1593) versus normal (2500-4000g, n = 6377) birthweight. We also explored all associations with (continuous and categorical) birthweight in analyses that did not exclude women with pre-eclampsia, diabetes or preterm delivery, which also resulted in enough cases to explore low (<2500 g, n = 178) versus normal (2500-4000 g, n = 4197) birthweight (Supplementary Data 1C shows the characteristics of participants).
Characteristics are shown in study-specific Supplementary Methods and Supplementary Data 1B. Intrauterine factors. We used a hypergeometric test to explore the extent to which any of the birthweight-related CpGs overlapped with those previously associated with intrauterine exposure to smoking  14  (n = 568 CpGs), BMI  15  (n = 104 CpGs) and plasma folate  31  (n = 48 CpGs), using the same (Bonferroni-corrected) cut-off for statistical significance. No CpGs reached the Bonferroni-corrected cut-off for famine  32. We additionally appraised this overlap using the FDR<0. 05 cut-off for all traits (n = 8170 birthweight-related CpGs, n = 6703 smoking-related CpGs, n = 16,067 BMI-related CpGs, n = 443 folate-related CpGs, n = 7 famine-related CpGs). These FDR results were available from the publications for smoking, folate and famine, and we obtained them from the corresponding author for BMI.
We further explored methylation of birthweightassociated CpGs in relation to whole blood mRNA gene expression (transcript levels) within a 500 kb region of the CpGs (+/-250 kb, FDR<0. 05) in 112 Spanish 4-year-olds  41  and 84 Gambian 2-year-olds  42  (Supplementary Methods). To better understand the potential mechanisms linking DNA methylation and birthweight, we explored the potential functions of the birthweight-associated CpGs using GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. We used the missMethyl R package  63  , which enabled us to correct for the number of probes per gene on the 450k array, based on the November 2018 version of the GO and KEGG source databases. To filter out the large, general pathways we set the number of genes for each gene set between 15 and 1000, respectively.
We calculated FDR at 5% corrected P-values for enrichment. Mendelian randomization. MR uses genetic variants as instrumental variables to study the causal effect of exposures on outcomes  64, 65. We aimed to use two-sample MR  22, 66  to explore (a) evidence of a causal association of methylation levels at the identified CpGs with birthweight and (b) evidence of a causal association of these CpGs with later-life health outcomes (i. e. to explore our hypothesised causal mechanisms shown in Fig. 1 ). We did this by first searching a publicly available mQTL database  39  to identify cis-mQTLs within 1 Mb of each of the Bonferronicorrected, with I 2 ≤ 50%, birthweight-related differentially methylated CpGs. These mQTLs could then be used as genetic instrumental variables for methylation levels of the birthweight-related CpGs.
Results A summary of sample sizes and biological parameters monitored annually is presented in Table  1. Despite a 12-year long dataset, we were limited by gaps in some of our biological measures and environmental variables (Table  1 ). This prevented the use of a global approach with causal inference, such as path analysis  44  , as well as the use of principal component analysis to reduce the number of environmental covariables  45. For this reason, we decided to apply a hypothesis-based approach to target specific questions. We therefore built independent regression models for each biological parameter studied, to test the effects of one or two environmental covariates at a time, to avoid overfitting  45. A summary of the results is presented in Table  2.
Discussion Using a unique dataset of biological parameters from a 12-year long-term monitoring program in East Greenland, we found that little auks are impacted by current environmental changes occurring in the Arctic. (1) As expected, the proportion of ice-associated species in chick diet was related to SIC, but the proportion of C. finmarchicus (copepod of Atlantic origin) did not decrease with increasing SIC. (2) Despite substantial plasticity in foraging behaviour and diet, adult body condition and chick growth rates decreased when SIC increased. (3) Adult body  condition and chick growth rate were negatively related to summer levels of mercury. (4) Despite these changes, adult survival was not linked to environmental variables.
Methodological caveats. Overall, we detected strong environmental impacts on little auks, but our work entails methodological limitations. Indeed, organism answer to overall forcing is the integration of all environmental parameters, and disentangling the relative importance of each factor as well as their interactions requires advanced statistical methods that we could not apply due to temporal gaps in our datasets (Table  1 ). Consequently, as we used only one or two environmental independent variables at a time to explain one biological parameter with a hypothesis-based approach, the percentage of covariance explained by our significant relationships was low. It is therefore crucial to continue long-term monitoring programs to increase sample sizes. In addition, we possibly missed important additional environmental factors.