{"paper_id":"62cb3f83-3912-44e8-b739-12480f2f777f","body_text":"1 \nAdults prenatally exposed to the Dutch Famine exhibit a metabolic 1 \nsignature associated with a broad spectrum of common diseases 2 \n 3 \nM. Jazmin Taeubert1, Thomas B. Kuipers1, Jiayi Zhou2, Chihua Li3, Shuang Wang4, Tian 4 \nWang4, Elmar W. Tobi1, BBMRI-NL Metabolomics consortium^, Daniel W. Belsky2,3, L. H. 5 \nLumey1,3, Bastiaan T. Heijmans1*. 6 \n 7 \n1. Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The 8 \nNetherlands 9 \n2. Butler Columbia Aging Center, Columbia University, New York, United States 10 \n3. Department of Epidemiology, Mailman School of Public Health, Columbia University, 11 \nNew York, United States  12 \n4. Department of Biostatistics, Mailman School of Public Health, Columbia University, New 13 \nYork, United States  14 \n 15 \n* Corresponding author 16 \nE-mail: b.t.heijmans@lumc.nl (BTH) 17 \n 18 \n^Membership of the BBMRI-NL Metabolomics consortium is provided in the Supplemental 19 \nfile. 20 \n 21 \nKey words: cardiovascular disease risk, metabolomics, type-2 diabetes, prenatal adversity, 22 \nfamine, disease association. 23 \n 24 \n 25 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 5, 2024. ; https://doi.org/10.1101/2024.04.04.24305284doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\n 2 \nAbstract 26 \nBackground: Exposure to famine in the prenatal period is associated with an increased risk of 27 \nmetabolic disease, including obesity and type -2 diabetes. We employ ed nuclear magnetic 28 \nresonance (NMR) metabolomic profiling to provide a deeper insight into the metabolic changes 29 \nassociated with survival of prenatal famine exposure during the Dutch Famine at the end of 30 \nWorld War II and explore their link to disease. 31 \nMethods: NMR metabolomics data were generated from serum in 480 individuals prenatally 32 \nexposed to famine (mean 58.8 years, 0.5 SD) and 464 controls (mean 57.9 years, 5.4 SD). We 33 \ntested associations of prenatal famine exposure with levels of 168 individual metabolic 34 \nbiomarkers and compared the metabolic biomarker signature of famine exposure with those of 35 \n154 common diseases.  36 \nResults: Prenatal famine exposure was associated with higher concentrations of branched -37 \nchain amino acids ((iso)-leucine), aromatic amino acid (tyrosine), and glucose in later life (0.2-38 \n0.3 SD, p  < 3x10-3). The metabolic  biomarker signature of prenatal famine exposure was 39 \npositively correlated to that of incident type -2 diabetes (r = 0.77, p = 3x10 -27), also when re-40 \nestimating the signature of prenatal famine exposure among individuals without diabetes ( r = 41 \n0.67, p = 1x10-18). Remarkably, this association extended to 115 common diseases for which 42 \nsignatures were available (0.3 £ r £ 0.9, p < 3.2x10-4). Correlations among metabolic signatures 43 \nof famine exposure and disease outcomes were attenuated when the famine signature was 44 \nadjusted for body mass index. 45 \nConclusions: Prenatal famine exposure is associated with a metabolic biomarker signature that 46 \nstrongly resembles signatures of a diverse set of diseases , an observation that can in part be 47 \nattributed to a shared involvement of obesity. 48 \n 49 \n 50 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 5, 2024. ; https://doi.org/10.1101/2024.04.04.24305284doi: medRxiv preprint \n\n 3 \nBackground 51 \nMetabolomics is a powerful tool for illuminating molecular phenotypes underpinning disease 52 \n(1). With nuclear magnetic resonance (NMR) approaches, metabolomics is now possible within 53 \nlarge-scale epidemiologic studies and biobanks. Within these settings, NMR metabolomics is 54 \nrevealing a range of common and unique features to a broad spectrum of diseases  (2–5). Less 55 \nis known  about how the metabol ome may reflect or mediate effects of  prenatal exposure 56 \nhistories on disease pathogenesis. Here, we investigate the long-term metabolomic sequelae of 57 \ngestational exposure to famine, an established risk factor for the development of metabolic 58 \ndisease (6,7).  59 \nThe Dutch Hunger Winter of 1944 -1945, a 6-month famine at the end of World War II,  60 \nprovides a unique setting to study the long-term effects of an adverse prenatal environment 61 \n(6,8,9). Previous studies revealed that prenatal famine exposure is associated with an increased 62 \nrisk in unfavourable metabolic phenotypes in adultho od including increased fasting glucose 63 \nand triglyceride levels, obesity, and type-2 diabetes (10–17). These associations have also been 64 \nobserved for other historical famines (6). To date, a comprehensive view of metabolic changes 65 \nlinked to prenatal famine exposure is lacking. In this study, we seek to provide a deeper insight 66 \ninto the metabolomic profile associated with prenatal famine exposure.  67 \nWe profiled samples for 944 participants from the Dutch Hunger Winter Families Study 68 \nusing nuclear magnetic resonance (NMR) metabolomic s. We compared  prenatal famine-69 \nexposed individuals  to unexposed control participants on 168 different serum metaboli c 70 \nbiomarkers and also compared the metabolome-wide signature of prenatal famine exposure to 71 \nan atlas of signatures marking risk of a range of common diseases in order to characterize the 72 \nphenotype of prenatal famine exposure . Our study reveals specific metaboli c biomarker 73 \nalterations and broader connections with a range of chronic diseases , providing new insights 74 \ninto how prenatal famine exposure shapes health across the life course.  75 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 5, 2024. ; https://doi.org/10.1101/2024.04.04.24305284doi: medRxiv preprint \n\n 4 \n 76 \nMethods 77 \n 78 \nStudy population 79 \nThe Dutch Hunger Winter Families study (DHWFS) is described in detail elsewhere  (18). In 80 \nshort, historical birth records were retrieved from three institutions in famine-exposed cities of 81 \nall singleton births between 1 February 1945 and 31 March 1946  and a systematic sample of 82 \nbirths born in 1943 or 1947 . From these records we  identified infants whose mothers were 83 \nexposed to the famine during or immediately preceding that pregnancy and  unexposed time-84 \ncontrols born before or after the famine . These individuals  were invited to participate in a 85 \ntelephone interview and in a clinical examination, together with a same-sex sibling not exposed 86 \nto the famine (family-control).  87 \nThe Dutch Hunger Winter Families study was approved by the Medical Ethic s Committee of 88 \nLeiden University Medical Center (P02.082) and the participants provided verbal consent at 89 \nthe start of the telephone interview and written informed consent at the start of the clinical 90 \nexamination. 91 \nWe conducted 1,075 interviews and 971 clinical examinations between 2003 and 2005.  92 \nOne non-biological sibling identified with genetic analyses was excluded from the cohort.  93 \nNMR metabolomics profiling was performed on serum samples of 962 individuals. Our sample 94 \nfor this study included 944 individuals after excluding non-fasted samples (n = 17) and an 95 \noutlier in the metabolomics dataset as identified with principal component analysis  (n = 1)  96 \n(Supplemental Fig. 1).  97 \n 98 \nFamine exposure definitions 99 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 5, 2024. ; https://doi.org/10.1101/2024.04.04.24305284doi: medRxiv preprint \n\n 5 \nFood rations were distributed centrally and below 900 kcal/day between November 26, 1944 100 \nand May 15, 1945 (8). We defined famine exposure by the number of weeks during which the 101 \nmother was exposed to < 900  kcal/day after the last menstrual period (LMP)  recorded on the 102 \nbirth record (18). For analysis of timing of gestational exposure, we subdivide the gestational 103 \nperiod into units of 10 weeks. We considered the mother exposed in gestational weeks 1 -10, 104 \n11-20, 21-30, or 31 to delivery if these gestational time windows were entirely contained within 105 \nthis period and had an average exposure of < 900kcal/day during an entire gestation period of 106 \n10 weeks. As the famine lasted 6-months some participants were exposed to famine during two 107 \nadjacent 10-week periods. In chronological order , pregnancies with LMP  between 30 April 108 \n1944 and 24 August 1944 were considered exposed in weeks 31 to delivery; between 9 July 109 \n1944 and 15 October 1944 in pregnancy weeks 21-30; between 17 September 1944 and 24 110 \nDecember 1944 in pregnancy weeks 11-20, between 26 November 1944 and 4 March 1945 in 111 \npregnancy weeks 1-10. Individuals with a LMP between 4 February and 12 May  1945 were 112 \nexposed to an average of < 900kcal/day for less than 10 weeks before conception and up to 8 113 \nweeks post-conception, are denoted as the weeks 9-0 weeks group.  We defined individuals 114 \nexposed to one or at most two of these definitions exposed to ‘any’ gestational exposure.  115 \n 116 \nCharacteristics 117 \nInformation on health history, including information on the use of cholesterol-lowering drugs, 118 \nwas collected through telephone interviews. Measurement of height  was carried out to the 119 \nnearest millimeter using a portable stadiometer (Seca), and body weight was  measured to the 120 \nnearest 100 g by a portable scale (Seca). BMI was calculated from these measures (weight (kg) 121 \n/ [height (m)]2). Cholesterol measures were reported previously (14) and were assessed using 122 \nstandard enzymatic assays. LDL cholesterol was calculated for individuals with a triglyceride 123 \nconcentration lower than 400 mg/dl using the Friedewald formula. A blood draw was 124 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 5, 2024. ; https://doi.org/10.1101/2024.04.04.24305284doi: medRxiv preprint \n\n 6 \nperformed at the start of a 75 -g oral glucose test, and fasted glucose was quickly assessed in 125 \nserum by hexokinase reaction on a Modular P800 (Roche). The presence of type -2 diabetes 126 \nwas either determined through previous health history  or defined as fasting glucose ≥ 127 \n7.0mmol/l or 2hr glucose tolerance test ≥ 11.1mmol/l (19). 128 \n 129 \nMetabolic biomarker quantification 130 \nMetabolic biomarkers were measured from serum  samples using a high-throughput 1H-NMR 131 \nmetabolomics platform developed by Nightingale Health Ltd. (Helsinki, Finland; 132 \nnightingalehealth.com; biomarker quantification version 20 21). Details of the procedure and 133 \napplication of the NMR metabolomics platform have been described elsewhere  (20,21). This 134 \nmethod provides simultaneous quantification of 168 directly measured and 81 derived 135 \nmetabolic biomarkers, including 37 clinically validated metabolic biomarkers certified for 136 \ndiagnostics use . The metabolic biomarkers  measured include amino acids, ketone bodies, 137 \nlipids, fatty acids, and lipoprotein subclass distribution, particle size and composition. A subset 138 \nof the biomarkers was selected for inclusion in the presented analysis, focusing on the 168 139 \ndirectly measured metabolic biomarkers.  140 \nMissing values were set to the minimum value for each metabolic measure. A value of one 141 \nwas added to all metabolic biomarkers containing zeroes (i.e. x + 1), which indicated that they 142 \nwere below the limit of quantification. All metabolic biomarkers were then natural logarithmic 143 \ntransformed to obtain an approximately normal distribution. The metabolic biomarkers were 144 \nsubsequently scaled to standard deviation (SD) units (mean 0, SD 1) for use in the analysis. 145 \n 146 \nGenotype data generation and polygenic scores 147 \nFrom our metabolomics sample population, 931 individuals also had genotype data available. 148 \nGenotype data were measured using the Illumina Infinium TM Global Screening Array (GSA) 149 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 5, 2024. ; https://doi.org/10.1101/2024.04.04.24305284doi: medRxiv preprint \n\n 7 \ngenotyping platform (version 24 v3.0. Illumina Inc., San Diego, USA) by the Human Genomics 150 \nFacility in the Genetic Laboratory Rotterdam (Rotterdam, the Netherlands) . Imputation was 151 \nperformed using the 1000G P3v5 reference panel (22). Polygenic scores were calculated  for 152 \ntyrosine, leucine and glucose levels with the PRSice-2 software using the independent hits of 153 \npublicly available genome-wide association study (GWAS) summary statistics (Supplemental 154 \nTable 3) (23). The base GWAS study utilized the same NMR platform and had participants 155 \nwith the same ancestry  (European) as  those in  our study  (24). The polygenic scores were 156 \nresidualized on the first ten genetic principal components and subsequently scaled to standard 157 \ndeviation (SD) units (mean 0, SD 1) for analysis. 158 \n 159 \nStatistical analysis 160 \nAll analyses were performed in the R programming environment (R version 4.2.2). 161 \nFor all linear regression analyses, we used linear regression within a generalized estimating 162 \nequations framework to account for the correlation between sibships (R geepack package, 163 \nversion 1.3.9) (25) and adjusted for age, sex and cholesterol-lowering medication.  164 \nWe first validated the NMR measurements by testing the consistency between glucose, 165 \ntriglycerides, total cholesterol, LDL, and HDL cholesterol measured by routine clinical 166 \nchemistry and Nightingale Health NMR (Supplemental Fig. 2 ). Consistent with previous 167 \nstudies, c orrelations were high (r ³ 0.9) for all metabolic biomarkers  tested (26,27). We 168 \nsubsequently tested whether previously observed associations between prenatal famine 169 \nexposure and these five metabolic biomarkers as measured by routine clinical chemistry were 170 \nconsistently found when the same biomarkers were measured using Nightingale Health NMR.  171 \nNext, we performed a metabolome-wide association study of prenatal famine exposure by 172 \nassessing the relationship between famine exposure and 168 metabolic biomarkers. Due to the 173 \ncorrelated nature of the metabolic biomarkers, 95% of the variation in the 168 metabolic 174 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 5, 2024. ; https://doi.org/10.1101/2024.04.04.24305284doi: medRxiv preprint \n\n 8 \nbiomarkers was explained by 14 principal components. Therefore, as previously described (28–175 \n30), we corrected for 14 independent tests using Bonferroni multiple testing correction (p value 176 \n= 0.05/14 = 3.57×10−3). Sensitivity analyses were performed to assess the robustness of the 177 \nresults of the metabolome-wide association study of prenatal famine. First, to assess the effect 178 \nof famine exposure independent of BMI or type -2 diabetes, the main model was additionally 179 \nadjusted for BMI and type -2 diabetes. Second, to determine the extent to which the observed 180 \nassociations between prenatal famine exposure and metabolic biomarkers levels could be 181 \nattributed to genetics, the main model was additionally adjusted for the polygenic scores of the 182 \nmetabolic biomarkers. Third, to check for potential differences between sexes, sex -stratified 183 \nanalyses were performed adjusting for the same covariates as the main model and an interaction 184 \nterm for sex and metabolic marker was included in the model  to test whether potential 185 \ndifferences were statistically significant . Fourth, potential gestation timing specific effects of 186 \nfamine exposure were examined by subdividing famine exposure into  5 gestational time 187 \nwindows. In the regression analysis, the single indicator of famine exposure was replaced with 188 \nindicator variables identifying exposure within each of the gestational time windows.  189 \nTo expand our analysis  from focusing on individual metabolic biomarkers to broader 190 \nmetabolic biomarker signatures associated with disease, we compared the metabolic biomarker 191 \nsignature associated with prenatal famine exposure to the metabolic biomarker signature 192 \npredicting the future risk of type -2 diabetes. For this we utilized p ublished results from a 193 \nmetabolome-wide study on incident type-2 diabetes using UK Biobank data (2). Specifically, 194 \nwe correlated the effect sizes of famine exposure in DHWFS with the effect sizes in the UK 195 \nBiobank for incident type -2 diabetes across the 135 shared metabolic biomarkers in both 196 \ndatasets. 197 \nWe then extended our analysis and correlated the metabolic profile of prenatal famine to 198 \nthe publicly availably metabolic signatures of a large set of diseases as estimated with UK 199 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 5, 2024. ; https://doi.org/10.1101/2024.04.04.24305284doi: medRxiv preprint \n\n 9 \nBiobank data (4). Out of 674 incident diseases available in the metabolomics atlas, we selected 200 \nthose with at least 1000 cases (out of a total population ranging from ~103,300 to ~118,000) to 201 \nrepresent common diseases (n = 162), and those with at least one significant association with 202 \na metabolic biomarker in the UK Biobank analysis  (p < 5x10 -4) (n = 15 4), resulting in 154 203 \ndiseases. The effect sizes of the overlapping 168 metabolic biomarkers were correlated between 204 \nprenatal famine and each disease. We corrected for multiple testing using Bonferroni correction 205 \n(p value = 0.05/15 4 = 3.2x10-4). Finally, we re-estimated the effect sizes  of the association 206 \nbetween prenatal famine exposure and all 168 metabolic biomarkers, while additionally 207 \nadjusting for BMI. We then  repeated the correlation analysis comparing this BMI -adjusted 208 \nprenatal famine metabolic biomarker signature with the 154 metabolic biomarker disease 209 \nsignatures from the UK Biobank. 210 \n 211 \nResults  212 \n 213 \nPopulation characteristics 214 \nWithin the Dutch Hunger Winter Families Study, fasting NMR metabolomics data were 215 \navailable for 944 study participants . Among these participants, 480 ( 51%) were prenatally 216 \nexposed to famine, and 464  (49%) were controls (including unexposed time controls born at 217 \nthe same institution as the exposed individuals and unexposed same-sex sibling controls both 218 \nborn either before or conceived after the famine ). As previously reported, f amine-exposed 219 \nparticipants had an increased BMI (11) and a higher prevalence of type -2 diabetes (16) and 220 \ncontrols were on average 0.9 years younger than famine -exposed. No differences were 221 \nobserved in sex or the use of cholesterol lowering medication (Table 1). 222 \n 223 \nValidation of NMR metabolomics measures 224 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 5, 2024. ; https://doi.org/10.1101/2024.04.04.24305284doi: medRxiv preprint \n\n 10 \nWe first sought to validate the newly measured metabolomics measures in our study by 225 \ncorrelating them with previously measured clinical chemistry data available for five metabolic 226 \nbiomarkers, namely fasted glucose, triglycerides, total cholesterol, LDL, and HDL cholesterol 227 \n(14,17). The correlations were all high (r ³ 0.9) in line with previous studies (Supplemental 228 \nFig. 2) (4,31). Next, we examined the associations between prenatal famine exposure and these 229 \nfive metabolic biomarkers, as measured by clinical chemistry or NMR , and found the  effect 230 \nsizes to be consistent between the two measurements (Supplemental Table 1).  231 \n 232 \nMetabolome-wide association study on prenatal famine exposure 233 \nNext, we examined the association of any prenatal famine exposure with all 168 metabolic 234 \nbiomarkers individually. Prenatal famine exposure was associated with higher tyrosine (effect 235 \nsize 0.28 SD), leucine (0.21 SD), glucose (0.23 SD), and isoleucine (0.18 SD) concentrations  236 \n(p<3x10-3; all analyses adjusted for age, sex, and use of cholesterol-lowering drugs) (Fig. 1A, 237 \nSupplemental Table 2).  238 \nWe performed three sets of follow-up analyses for tyrosine, leucine, and glucose to gain 239 \nfurther insight into these associations. Isoleucine was excluded because it was highly correlated 240 \nwith leucine (r = 0.9), both are branched-chain amino acids, while leucine showed the stronger 241 \nassociation with famine exposure ( Fig. 1B, Supplemental Fig. 3). First, the associations 242 \nbetween prenatal famine exposure and tyrosine, leucine, and glucose remained after including 243 \nBMI or type-2 diabetes as a covariate in the model  (Fig. 1C). Second, we also considered 244 \ngenetics as a potential explanatory factor for these associations . Since the polygenic scores of 245 \neach metabolic biomarker explained only approximately 1%-5% of the ir variance 246 \n(Supplemental Table 3), the associations were not affected by including the polygenic scores 247 \nas covariates (Fig. 1C). Third, we explored whether associations between famine exposure and 248 \nthe three metabolic biomarkers were dependent on sex or the timing of exposure during 249 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 5, 2024. ; https://doi.org/10.1101/2024.04.04.24305284doi: medRxiv preprint \n\n 11 \ngestation (Supplemental Fig. 4). Effect sizes across different exposure timing subgroups were 250 \nsimilar to our estimates of the main analysis. In the sex stratified analysis, t he effect sizes for 251 \nglucose and tyrosine were lower in females than males, but we found no statistical evidence 252 \nfor effect modification (interaction p values > 0.39). 253 \n 254 \nComparison to metabolic biomarker signatures of diseases 255 \nThe individual metabolic biomarkers associated with prenatal famine exposure were previously 256 \nlinked to  type-2 diabetes (32). To further investigate whether these associations reflect an 257 \nincreased risk of type-2 diabetes among individuals prenatally exposed to famine , we utilized 258 \na previously reported metabolic biomarker signature of incident type -2 diabetes from UK 259 \nBiobank (2) and compared it to the complete set of biomarker associations in our study. 260 \nSpecifically, we took the effect sizes of 135 metabolic biomarkers for the risk of type-2 diabetes 261 \nand compared them to the effect sizes we observed for prenatal famine. The metabolic 262 \nbiomarker signature of p renatal famine exposure was highly correlated with that of incident  263 \ntype-2 diabetes (r = 0.77, p = 3x10-27; Fig. 2A). This similarity persisted when we re-estimated 264 \nthe effect sizes for prenatal famine exposure after excluding participants with type -2 diabetes 265 \n(r = 0.67, p = 1x10-18) (Fig. 2B). 266 \nTo explore whether the metabolic biomarker profile of prenatal famine exposure may 267 \nreflect a risk of diseases beyond type-2 diabetes, we extended the analysis to recently published 268 \natlas of signatures of a wide range of diseases in the UK Biobank (4). We focused on metabolic 269 \nbiomarker signatures for the future onset of disease obtained from individuals not affected by 270 \nthe disease of interest at baseline. For the analysis, we utilized a subset of 154 common incident 271 \ndiseases that had at least one metabolic biomarker association in the UK Biobank (p < 5x10-4). 272 \nRemarkably, the metabolic biomarker signature of prenatal famine exposure was positively 273 \ncorrelated with the metabolic biomarker signature of 115 diseases (75%; 0.3 £ r £ 0.9, p <  274 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 5, 2024. ; https://doi.org/10.1101/2024.04.04.24305284doi: medRxiv preprint \n\n 12 \n3.2x10-4), and negatively correlated with 13 diseases (8%; -0.9 £ r £ -0.4, p < 3.2x10 -4) 275 \n(Supplemental Table 4). The metabolic biomarker signature of prenatal famine exposure 276 \nexhibited the strongest correlation with the signature of the future risk of myocardial infarction 277 \n(r = 0.9, p = 1.8x10-47). Other diseases with a strong correlation (r ³ 0.7) included those related 278 \nto the digestive system, diseases with an endocrine, nutritional and metabolic component, as 279 \nwell as diseases of the nervous system (Fig. 3). The diseases displaying a negative correlation 280 \nwere primarily associated with  injury and other consequences of external causes , such as 281 \nfractures and open wounds (Supplemental Table 4). 282 \nWe hypothesized that a  potential common factor among the diseases  with a similar 283 \nmetabolic biomarker signature is obesity. To test this  hypothesis, we re-estimated the effect 284 \nsizes for prenatal famine exposure while additionally adjusting for BMI and then re-calculated 285 \nthe correlation between the resulting BMI -adjusted metabolic biomarker signature with the 286 \nsignatures of the 154 common incident diseases (Supplemental Table 4). The strength of the 287 \ncorrelations was consistently attenuated across all diseases (mean = -58%; SD = 19%). Among 288 \nthe 30 diseases whose metabolic biomarker signature was most similar to that of prenatal 289 \nfamine, the attenuation ranged between 27-48% and the correlations remained moderate (0.4 £ 290 \nr £ 0.6). The degree of attenuation was not linked to whether the disease had  an obvious 291 \nmetabolic component (Fig. 4). 292 \n 293 \nDiscussion 294 \nWe further defined the metabolic phenotype associated with prenatal famine exposure  using 295 \nnuclear magnetic resonance (NMR) metabolomic profiling. We show that prenatal exposure to 296 \nundernutrition is associated with specific metabolic differences later in life , including higher 297 \nlevels of branched -chain amino acids (BCAA), an aromatic amino acid, and glucose.  In 298 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 5, 2024. ; https://doi.org/10.1101/2024.04.04.24305284doi: medRxiv preprint \n\n 13 \naddition, we report that the metabolic biomarker signature of prenatal famine has marked 299 \nsimilarities to the signature of a wide range of common diseases. 300 \nOur study indicated  specific differences in the metabolic profiles o f famine -exposed 301 \nindividuals compared to controls. We observed that those exposed to famine prenatally have 302 \nhigher levels of the aromatic amino acid tyrosine, the branched-chain amino acids leucine and 303 \nisoleucine, and glucose six decades after exposure . All four metabolic biomarkers have been 304 \nlinked to type -2 diabetes and thus support the known association between prenatal famine 305 \nexposure and type -2 diabetes risk in adulthood  (6,33). In addition, h igher levels of o ther 306 \nbranched-chain and aromatic amino acids such as valine and phenylalanine that have also been 307 \nassociated with type-2 diabetes  showed a nominally significant association with prenatal 308 \nfamine exposure, further supporting the link between famine and type-2 diabetes risk  (33). 309 \nInterestingly, the metabolic biomarker associations with prenatal famine were independent of 310 \nBMI and type-2 diabetes status, indicating that they may not be fully driven by the higher BMI 311 \nand increased prevalence of type-2 diabetes among famine-exposed individuals.  312 \nThe link with a higher type -2 diabetes risk among individuals  exposed to famine  in the 313 \nprenatal period was reinforced by investigating the complete range of metabolic biomarkers. 314 \nWe observed a strong resemblance in  the metabolic biomarker signature of prenatal famine 315 \nwith that of the future onset of type -2 diabetes (33). Moreover, the strong correlation of the 316 \nfamine signature with the incident type -2 diabetes signature persisted after excluding 317 \nparticipants who were already diagnosed with type -2 diabetes at the time of assessment . This 318 \nresult reinforces that type-2 diabetes is a main health outcome of prenatal famine exposure (6) 319 \nand indicates that even exposed individuals not diagnosed with type -2 diabetes have an 320 \nincreased risk of developing this condition in the future.  321 \nUpon extending our analysis beyond type-2 diabetes, we observed a striking similarity 322 \nbetween the metabo lic biomarker signature of famine exposure and a wide range of other 323 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 5, 2024. ; https://doi.org/10.1101/2024.04.04.24305284doi: medRxiv preprint \n\n 14 \nincident disease signatures. This included conditions like disorders of lipoprotein metabolism, 324 \nobesity and type-2 diabetes, but also a priori less expected diseases like osteoarthritis, kidney 325 \nstones, and depressive disorders . Interestingly, these high correlations  were substantially 326 \nattenuated for all incident diseases when we repeated the analysis using a metabolic biomarker 327 \nprofile of prenatal famine that was adjusted for BMI. Our findings suggest that BMI is a shared 328 \nrisk factor for or consequence of the diseases and that the metabolic biomarkers measured by 329 \nthe NMR platform used  may have a particularly strong association with BMI.  Of note, after 330 \naccounting for BMI, moderate correlations between the metabolic biomarker profiles of 331 \nincident disease and prenatal famine remained. Our findings and previous studies highlight that 332 \nthe NMR platform applied is especially useful for disease risk prediction, but of limited value 333 \nto gain new  mechanistic insights.  Further studies  with more comprehensive metabolomics 334 \nplatforms are needed to fully understand why the metabolic biomarker signature of prenatal 335 \nfamine exposure links to a broad range of diseases, including effects independent of obesity.  336 \n 337 \nConclusions 338 \nPrenatal exposure  to famine  is associated with marked metabolic alterations later in life. 339 \nDifferences in individual metabolic biomarkers include higher levels of branched-chain amino 340 \nacids, aromatic amino acids, and glucose. Moreover, the metabol ic biomarker signature 341 \ncharacteristic of prenatal famine strongly resembles that of a diverse set of diseases. Overall, 342 \nour findings  underscore the broad impact of prenatal famine on  adult health and highlight 343 \nobesity as a plausible contributing factor. 344 \n 345 \n 346 \n 347 \n 348 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 5, 2024. ; https://doi.org/10.1101/2024.04.04.24305284doi: medRxiv preprint \n\n 15 \nAbbreviations 349 \nBCAA: branched-chain amino acids 350 \nBMI: body mass index 351 \nDHWFS: Dutch Hunger Winter Families study 352 \nGWAS: genome-wide association study 353 \nLMP: last menstrual period 354 \nNMR: nuclear magnetic resonance 355 \nSD: standard deviation 356 \n 357 \nDeclarations 358 \nEthics approval and consent to participate 359 \nThe Dutch Hunger Winter Families study was approved by the Medical Ethics Committee of 360 \nLeiden University Medical Center (P02.082) and the participants provided verbal consent and 361 \nwritten informed consent. 362 \nConsent for publication 363 \nNot applicable. 364 \nAvailability of data and materials 365 \nThe datasets supporting the conclusions of this article are included within the article and its 366 \nadditional files. The DHWFS data is available for replication purposes upon request to B. T. 367 \nHeijmans (b.t.heijmans@lumc.nl) and if replication is conducted within the secure Leiden 368 \nUniversity Medical Center network environment.  369 \nCompeting interests 370 \nThe authors declare that they have no competing interests. 371 \nFunding 372 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 5, 2024. ; https://doi.org/10.1101/2024.04.04.24305284doi: medRxiv preprint \n\n 16 \nThis research was supported by National Institute on Aging grants (R01AG066887). The NMR 373 \nmetabolomics measurement of the DHWFS samples was funded by the BBMRI -NL 374 \nMetabolomics consortium (a research infrastructure financed by the Dutch government, NWO 375 \n184.021.007 and 184.033.111). DWB is a fellow of the CIFAR CBD Network.  The funders 376 \nhad no role in study design, data collection and analysis, decision to publish, or preparation of 377 \nthe manuscript. 378 \nAuthors’ contributions 379 \nM.J.T., D.W.B., L.H.L. and B.T.H. were involved in the conception, design, and conduct of 380 \nthe study and interpretation of the results. M.J.T. performed the analyses and wrote the first 381 \ndraft of the manuscript, and all authors edited, reviewed, and approved the final version of the 382 \nmanuscript. B.T.H. is the guarantor of this work and, as such, had full access to all the data in 383 \nthe study and takes responsibility for the integrity of the data and the accuracy of the data 384 \nanalysis. 385 \nAcknowledgements  386 \nNot applicable. 387 \n 388 \nReferences 389 \n1. Wishart DS. Metabolomics for Investigating Physiological and Pathophysiological 390 \nProcesses. Physiol Rev. 2019 Oct 1;99(4):1819–75.  391 \n2. 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Bragg F, Trichia E, Aguilar-Ramirez D, Bešević J, Lewington S, Emberson J. 485 \nPredictive value of circulating NMR metabolic biomarkers for type 2 diabetes risk in 486 \nthe UK Biobank study. BMC Med. 2022 Dec 1;20(1).  487 \n  488 \n 489 \n 490 \n 491 \n 492 \n 493 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 5, 2024. ; https://doi.org/10.1101/2024.04.04.24305284doi: medRxiv preprint \n\n 21 \nTable 1. Population characteristics 494 \n \nControls \n(n = 464) \nFamine-exposed \n(n = 480) \np value \nAge, years (SD) 57.9 (5.4) 58.8 (0.5) 1.1x10-3  \nSex, males, n (%) 200 (43.1) 225 (46.9) 0.24 \nUse of cholesterol-lowering medication, n (%) 55 (11.9) 61 (12.7) 0.69 \nBody mass index, kg/m2 (SD) 27.0 (4.2) 28.2 (4.8) 1.6x10-4 \nType-2 diabetes, n (%) 38 (8.2) 61 (12.8) 0.02 \nValues are means (standard deviation) or numbers of subjects (valid %) shown for famine-exposed and 495 \ncontrols of the study population. Comparing the two categories by a two -sample t-test or chi -square 496 \ntest, as appropriate. 497 \n 498 \n 499 \n 500 \n 501 \n 502 \n 503 \n 504 \n 505 \n 506 \n 507 \n 508 \n 509 \n 510 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 5, 2024. ; https://doi.org/10.1101/2024.04.04.24305284doi: medRxiv preprint \n\n 22 \n 511 \nFig 1. Metabolome-wide association study on prenatal famine exposure . A. Association of prenatal famine 512 \nexposure with 168 metabolic biomarkers. Regression models were adjusted for age, sex and cholesterol-lowering 513 \nmedication and correlation within sibships were controlled for (main model). Scattered points represent metabolic 514 \nbiomarkers: the x-axis shows the effect size for the association of famine with the respective metabolic biomarker, 515 \nwhile the y-axis is negative log of the p value. The grey line represents the significance threshold for this analysis 516 \n(p value = 3.57x10−3). B. Heatmap showing the correlation of famine-associated metabolic biomarkers. Pearson’s 517 \ncorrelation was calculated for each metabolic biomarker pair. C. Sensitivity analyses on famine -associated 518 \nmetabolic biomarkers. Main: main model; BMI-adjusted: main model additionally adjusting for BMI; Diabetes-519 \nadjusted: main model additionally adjusted for type -2 diabetes; polygenic score (PGS) -adjusted: main model 520 \nadditionally adjusted for the polygenic score of the metabolic biomarkers. Effect estimates and 95% confidence 521 \nintervals are depicted for each model and are reported in standard -deviation (SD) units of the log -transformed 522 \nmetabolic biomarkers.  523 \n 524 \n 525 \n 526 \n 527 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 5, 2024. ; https://doi.org/10.1101/2024.04.04.24305284doi: medRxiv preprint \n\n 23 \n 528 \nFig 2. Correlation analysis of the metabolic biomarker signature associated with famine and the metabolic 529 \nbiomarker signature associated with incident type -2 diabetes. A. Overall metabolic biomarker signature 530 \ncomparison of 135 metabolic biomarkers for prenatal famine exposure and incident type-2 diabetes (r = 0.77, p = 531 \n3x10-27) as established in the UK Biobank Study in a 12-year follow-up. B. Overall metabolic biomarker signature 532 \ncomparison of 135 metabolic biomarkers for prenatal famine exposure (excluding 101 individuals with type -2 533 \ndiabetes from the analysis) and incident type -2 diabetes as established in the UK Biobank Study in a 12 -year 534 \nfollow-up (r = 0.67, p = 1x10 -18). The effect size estimates for each metabolic biomarker are shown as points. 535 \nFamine-associated metabolic biomarkers are indicated in blue.  536 \n 537 \n 538 \n 539 \n 540 \n 541 \n 542 \n 543 \n 544 \n 545 \n 546 \n 547 \n 548 \n 549 \n 550 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 5, 2024. ; https://doi.org/10.1101/2024.04.04.24305284doi: medRxiv preprint \n\n 24 \n 551 \nFig 3. Correlation analysis of the metabolic biomarker signature associated with famine and the metabolic 552 \nbiomarker signature associated with various common diseases. Heatmap showing the effect size estimates of 553 \nthe 30 most correlated diseases to prenatal famine exposure. The columns are clustered according to the metabolic 554 \nbiomarker effect sizes and the rows are ordered according to the correlation of the metabolic biomarker signature 555 \nof the disease to prenatal famine exposure (Pearson r for IK21 Acute myocardial infarction = 0.85, Pearson r for 556 \nE11 Type-2 diabetes mellitus = 0.69). Only metabolic biomarkers that are nominally associated with prenatal 557 \nfamine exposure are shown (p < 0.05).  The diseases are shown with their ICD-10 (International Classification of 558 \nDiseases 10th Revision) classification. The full names of the metabolic biomarkers can be found in Supplemental 559 \nTable 2. 560 \n 561 \n 562 \n 563 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 5, 2024. ; https://doi.org/10.1101/2024.04.04.24305284doi: medRxiv preprint \n\n 25 \n 564 \nFig 4. Effect of additional adjustment of BMI in the correlation analysis of the metabolic biomarker 565 \nsignature associated with famine and the metabolic biomarker signature associated with various common 566 \ndiseases. The main model within the DHWFS cohort was adjusted for age, sex, and cholesterol -lowering 567 \nmedication. The BMI-adjusted model within the DHWFS cohort was adjusted for age, sex, cholesterol-lowering 568 \nmedication, and BMI.  The effect sizes estimated for these two models of prenatal famine exposure were each 569 \ncorrelated to the effect sizes estimated for the risk of common diseases. The 30 diseases most correlated to the 570 \nmetabolic biomarker signature of prenatal famine exposure are shown.     571 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted April 5, 2024. ; https://doi.org/10.1101/2024.04.04.24305284doi: medRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}