Long-term health implications of early life malnutrition: an umbrella review and anticipation of latent attributable disease burden

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This study comprehensively summarised the latent health outcomes after early life malnutrition and anticipated the attributable latent disease burden of the current humanitarian catastrophe in Gaza over the next five decades. Methods This umbrella review searched MEDLINE, EMBASE, and Web of Science for quantitative systematic reviews assessing the adulthood health risks attributable to early-life malnutrition exposure. Using re-analysed relative effects and the 2021 Global Burden of Disease (GBD) regional estimates, we projected population attributable fraction (PAF) and further anticipated the adulthood disease burden attributable to early life malnutrition among population under 20 years old in Gaza over the next five decades. Results The umbrella review, including 16 meta-analyses and 758,417 individuals, suggested that people experienced malnutrition in early life may be facing 34% elevated risk of cancers (95% confidence interval [CI] 8–66%), 25% of hyperglycaemia (95% CI 10–42%), 33% of type 2 diabetes (95% CI 8–64%), 38% of hypertension (95% CI 21–57%) and 21% of coronary heart disease (95% CI 9–35%) in their later years. Assuming 50% of the population younger than 20 exposed to malnutrition, the anticipated disease burden would be 29,587 disability-adjusted life years (DALYs) (95% uncertainty interval [UI] 17,401 to 40,769) including 25,344 years of life lost (YLL) (95% UI 14,754 to 34,941) and 4,247 years lived with disability (YLD) (95% UI 1,197 to 7,519) attributable to early life malnutrition in 50 years. The PAF for type 2 diabetes would range from 11.4–23.8%, contributing to up to 13,669 DALYs (95% UI 2,549 to 22,989); the PAF for ischaemic heart disease would range from 7.7–17.1% contributing to up to 14,917 DALYs (95% UI 6,420 to 22,805); the PAF for cancers would range from 11.7–24.4% contributing to up to 18,575 DALYs (95% UI 3,560 to 30,367). Conclusions Early life exposure to malnutrition is likely associated with increased risks of type 2 diabetes, hypertension, ischaemic heart disease, and cancers. The current malnutrition in Gazan young people will probably be associated with major disease burden in 50 years. DOHaD malnutrition non-communicable diseases DALY global health Figures Figure 1 Figure 2 Introduction Malnutrition occurs when a region lacks sufficient food, resulting in widespread malnutrition and deaths from starvation. The primary causes of malnutrition stem from poverty, climate change, and regional conflicts 1 . The World Health Organization's 2023 report reveals that approximately 735 million people are currently struggling with hunger - an alarming increase of 122 million compared to 2019. Millions of children under the age of five are suffering from malnutrition 2 , with 45 million children under five suffer from wasting, a lethal form of malnutrition that increases their risk of death by up to twelve times 3 . Recent regional conflicts intensify the impact of malnutrition on humanity. For example, people in the northern governorates of Gaza received merely 10–15% of their necessary food calorie intake, resulting in a widespread malnutrition that affected the entire region between January and February 2024 2 . The severe malnutrition among children under five raised from a pre-conflict prevalence of 0.8% to an estimated 12.4–16.5% 5 , reflecting that malnutrition in early life remains stubborn in modern societies. Considering the critical nutritional requirements for the physical and developmental growth of children, malnutrition presents a considerable threat of starvation while also inflicting irreversible consequences on their long-term intellectual development and physical growth. The Developmental Origins of Health and Disease (DOHaD) 6 demonstrates that severe malnutrition during the early years of life leads to enduring physical and psychological health issues. Survivors, especially those in a conflict zones, face compounded stress and trauma, which elevates their risk for developing metabolic disorders later in adulthood, such as type 2 diabetes 7 8 , coronary heart disease 9 10 11 , and cancers 12 , 13 . Nowadays, the world is facing humanitarian crises more frequently, with complex and lasting impacts, affecting more children than ever 14 . Despite some meta-analyses exploring the link between childhood malnutrition exposure and later health problems, there's a lack of a comprehensive review that broadly summarizes and evaluates the existing meta-analytic evidence. This umbrella review synthesised the effect sizes of latent health outcomes associated with such early life adversities to inform both immediate public health responses and long-term health strategy planning. Methods Systematic review and study selection This umbrella review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines 15 . A comprehensive literature search in MEDLINE, EMBASE, and Web of Science was conducted from inception to 1st April 2024. The search strategy included terms and medical subject headings such as 'malnutrition', 'childhood', 'meta-analysis', and related synonyms (details in the Supplementary Table 1). Two independent researchers (J.Y. and Shen Li) screened titles and abstracts, retrieved full texts for eligibility, and extracted data on study characteristics and outcomes of interests. A third researcher (X.M.) resolved any discrepancies. We included quantitative systematic reviews and meta-analyses of observational studies that estimated the risk of long-term health-related outcomes in adults who exposed to malnutrition or long-term malnutrition during childhood. For outcomes reported in multiple meta-analyses, only the most recent one or the one with the largest number of included studies was included. Quality assessment Two researchers (J.Y. and Shen Li) assessed the methodological quality of each included meta-analysis using the AMSTAR2 (A Measurement Tool to Assess Systematic Reviews 2) 16 . The tool contains seven critical items and nine non-critical items. Each item was assessed as 'yes', 'partial yes', or 'no' according to the degree of compliance with the evaluation criteria. The overall methodological quality of the meta-analyses was rated as 'high', 'moderate', 'low', or 'critically low'. Zero or one non-critical item receiving 'no' indicates a 'high' rating. More than one non-critical item categorised as 'no' results in a 'moderate' rating. A single critical item being considered 'no' was rated 'low', while more than one critical item being considered 'no' escalates to 'critically low'. A third senior researcher (X.M.) solved the disagreements were solved by. Meta-analysis We re-checked extracted data of the included meta-analyses and re-estimated the effect sizes with DerSimonian-Laird random effects model. Study heterogeneity was evaluated using Q tests and the I 2 statistics. For estimates pooled by 10 cohorts or more, we assessed the publication using Egger’s regression, Begg's test, and trim-and-fill approach. We considered the presence of significant publication bias if the P value from Egger’s regression or Begg's test was lower than 0.05, or if substantial changes were observed in the estimates before and after applying the trim-and-fill method. Evidence grading criteria We graded the credibility of evidence using a five-tier evidence system: convincing (class I), highly suggestive (class II), suggestive (class III), weak (class IV), or non-significant (NS) 17 18 . The criteria for grading included sample sizes, P values, 95% confidence interval [CI], heterogeneity, risk of bias. The certainty of evidence was assessed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach 19 , with outcomes categorised as high, moderate, low, or very low quality. Details of the grading system are available in the Supplementary Table 2. Disease burden estimation in Gaza To implement the estimates for real-world events, we took the recent conflict in Gaza as an example, showing the latent disease burden of young survivors under 20 years exposed to a population-level shortage of food. We calculated population attributable fraction (PAF) with the proportion of the Gazan youth population under 20 years exposed to malnutrition, ranging from 40–100% according to the latest opinion 20 . The formula used was: $$\:\varvec{P}\varvec{A}\varvec{F}=\frac{\varvec{p}\cdot\:\left(\varvec{R}\varvec{R}-1\right)}{\varvec{p}\cdot\:\left(\varvec{R}\varvec{R}-1\right)+1}$$ Where \(\:\varvec{p}\) is the total prevalence of exposure in population , and \(\:\varvec{R}\varvec{R}\) is the total relative risk with exposure. The simulation projected the burden of disease attributable to malnutrition exposure using excess disability-adjusted life years (DALYs), applying PAF to the DALY rates from the Global Burden of Disease (GBD) 2021 study, adjusted by age and sex. For outcomes identified as positive in umbrella reviews and confirmed with corresponding DALY rates data in the GBD, binary outcomes were selected. We focused on the population aged under 20 in Palestine to represent those exposed to malnutrition during early life stages, and the disease burden for major non-communicable diseases (NCDs) over the following 50 years. The Gaza accounts for 40.7% of the Palestinian population 21 . We anticipated the burden from each disease by multiplying the DALY rates for major NCDs by the PAF for each age and sex group. We counted only the most burdensome patient-important outcomes among overlapping ones. The total burden of malnutrition exposure on NCDs was then determined by summing the excess DALYs for each specific disease. A one-way sensitivity analysis was conducted to assess the impact of variations in RRs and DALY estimates on the projected burden among survivors. All statistical analyses were carried out using the statistical software R V.4.3.0. Results Characteristics of the included meta-analysis Among 3,226 records (Figure 1), seven meta-analyses proved eligibility 11,12 22 23 24 25 26 , encompassing 16 comparisons with sample sizes ranging from 5,485 to 164,169 participants. Three meta-analyses only focused on cohort studies, whereas the remaining 13 included both cohort and cross-sectional studies. Figure 2 summarised the latent adverse health risks of early life malnutrition (details in the Supplementary Table 3). Supplementary Table 4 showed the details of AMSTAR2 assessment and Supplementary Table 5 showed the details of the GRADE profiles. Four meta-analyses 11,12 22 26 were rated as “low” quality and three 23 24 25 as “critically low” primarily due to the absence of a pre-determined protocol (AMSTAR2 critical item 2) and a lack of a list of excluded literature (AMSTAR2 critical item 7). A nthropometric parameters, glucose profiles and type 2 diabetes Early life exposure to malnutrition might be associated with a 0.84 cm increased waist circumference (mean difference [MD] 95% CI 0.10 to 1.58; very low certainty, Evidence Level IV) and 0.95 cm decreased adult height (MD 95% CI -1.46 to -0.45; very low certainty, Evidence Level IV). However, our umbrella review revealed no significant evidence on the association between early life malnutrition exposure and body mass index (very low certainty, NS), adulthood overweight (very low certainty, NS), or obesity (very low certainty, NS). In addition, our umbrella review demonstrated that early life exposure to malnutrition may be associated with a 25% higher risk of hyperglycaemia (95% CI 10% to 42%; very low certainty, Evidence Level III), 33% higher risk of type 2 diabetes (95% CI 8% to 64%; very low certainty, Evidence Level IV), 29% increased risk of metabolic syndrome (95% CI 16% to 44%; very low certainty, Evidence Level III) (Figure 2). Lipid profiles and c ardiovascular outcomes Our study found no significant association between early life malnutrition exposure and triacylglycerol (very low certainty, NS). However, early life exposure to malnutrition may be associated with a 0.15 mmol/L increased total cholesterol (MD 95% CI 0.00 to 0.31; very low certainty, Evidence Level IV), 0.15 mmol/L increased low-density lipoprotein cholesterol (MD 95% CI 0.05 to 0.25; very low certainty, Evidence Level IV), 27% higher risk of dyslipidaemia (95% CI 12% to 45%; low certainty, Evidence Level III), 38% increased risk of adult hypertension (95% CI 21% to 57%; very low certainty, Evidence Level III), 21% increased risk of coronary heart disease (95% CI 9% to 35%; very low certainty, Evidence Level III) in adult. However, we did not observe a significant association between early life malnutrition and stroke in adulthood (very low certainty, NS) (Figure 2). Cancer s Early life malnutrition may increase 34% risk of all cancers (95% CI 8% to 66%; low certainty, Evidence Level IV) in adulthood. Latent health outcomes and burden of malnutrition survivors in Gaza To avoid duplicate counting, we estimated the PAF for type 2 diabetes due to malnutrition exposure at 11.4% to 23.8% (Supplementary Table 6), depending on the prevalence of malnutrition exposure (40% to 100%). Among Gazan youth, the combined burden of type 2 diabetes and its complications could reach 13,669 DALYs. The PAF for hypertension would range from 13.1% to 27.2%, contributing a maximum of approximately 4,603 DALYs. For ischaemic heart disease, the PAF would be between 7.7% and 17.1%, resulting in up to 14,917 DALYs. Finally, cancers associated with malnutrition would have a PAF of 11.7% to 24.4%, impacting as much as 18,575 DALYs. With the exposure rate ranging from 40% to 100%, the excess DALYs in 50 years would range from 24,379 (95% uncertainty interval [UI] 14,228 to 33,877) to 51,764 (95% UI 31,461 to 69,182) among malnutrition survivors. Assuming under 50% malnutrition exposure, these DALYs would be derived from years of life lost (YLLs)-85.7%, in contrast to years lived with disability (YLDs) which would make up only 14.3%, as depicted in Supplementary Figure 1. For total excess DALYs, cancer would account for 37.6% (95% CI 10.3% to 74.2%) ranking the top contribution, followed by cardiovascular disease (CVD) at 36 3% (95% CI 17.8% to 63.5%), type 2 diabetes at 25.3% (95% CI 6.2% to 50.8%), and chronic kidney disease (CKD) at 5.5% (95% CI 2.5% to 9.7%), as shown in Table 2. The Tornado Diagram (Supplementary Figure 2) displayed one-way sensitivity analyses of the total DALY of survivors under 50% malnutrition exposure impacts from various RR and relative risks of NCDs. The analyses revealed that the total DALYs for survivors were significantly shaped by the RR and associated DALYs of cancers, type 2 diabetes, and ischaemic heart disease. Within the range of the values, the results showed robustness and the trend remained consistent. Discussion This study is the first umbrella review that comprehensively summarises the associations between early life malnutrition and adulthood health outcomes. Taking the example in the Gazan conflict in 2024, our projections anticipated an additional burden of 51,764 DALYs over the next 50 years for survivors. This public health crisis calls for both immediate actions to prevent further harm to the population, particularly children, as well as long-term strategies to provide the foundations for their recovery, and mitigate future health crises resulting from the increased burden of disease. The DOHaD hypothesis facilitates the understanding how early life malnutrition permanently influence the later life metabolism and associated health outcomes 27 , 28 . For instance, early life malnutrition is linked to epigenetic trail in pancreatic islets for reduced capacity of insulin secretion, which raises the risk of type 2 diabetes. 29 . Early life malnutrition seeded a "memory" of malnutritional environments from early life experience to adapt possible similar condition in adulthood, which might increase the vulnerability to chronic diseases in adulthood by preserving, a process referred to as "metabolic imprinting" 30 . The disruption of gut microbiota during critical developmental windows may further exacerbate the risk of NCDs by impairing immune function and metabolic processes, underscoring the intricate interplay between nutrition, gut microbiota, and long-term health. Early life is a critical period for the establishment and development of the gut microbiota 31 . Recent studies have suggested that the gut microbiome co-evolves with its host, beginning to stabilise later in childhood 32 , 33 . Further evidence from faecal microbiota transplantation studies indicates that undernutrition can causally disrupt normal gut microbiota development 34 . There exists a significant interaction between early life gut microbiota and the immune system, gastrointestinal integrity, and other bodily systems 35 , 36 . The gut microbiota and its related metabolites are thought to mediate the impact of environmental stressors on health and disease later in life 37 . With increasing regional conflicts, the childhood malnutrition are perceived as historical anomalies in the 21st century. The recognition of the lifelong health consequences facilitate clinicians and public health practitioners acknowledging this impact and considering individuals at higher risk for cardiovascular and metabolic diseases due to early life malnutrition. Intensive lifestyle interventions and health promotion strategies, which demonstrated their effectiveness in preventing non-communicable chronic diseases 38 , are necessary later in regions affected by malnutrition. This study also has some limitations. First, the estimates considered only quantitative synthesised evidence and might miss individual cohorts, which, nevertheless, are likely rare events or with small effects. This study covered the most burdensome conditions with robustness in the sensitivity analyses. Second, the anticipation for DALY and death are based on the secondary analyses of the GBD studies and low to very low certainty evidence. The uncertainty of the anticipation should be acknowledged and further validation may better facilitate the policy decision-making. Third, we did not consider the duration of exposure to malnutrition, which requests individual data for the dose-dependent analysis. Conclusion In summary, early life exposure to malnutrition is likely associated with an important increased risk in of later life type 2 diabetes, hypertension, ischaemic heart disease, cancer, and worsened metabolic parameters. The prevention of early life malnutrition may alleviate the disease burdens of chronic diseases and warrants attention by stakeholders. Declarations Declaration of interests All authors declare no competing interests. Ethics statements Patient consent for publication: Not applicable. Funding 1.3.5 Project for Disciplines of Excellence, West China Hospital, Sichuan University. Author Contribution S.L., X.M., and P.S. designed study. H.Z., J.Y., and Shen Li extracted all the data. J.Y. and Shen Li assessed the quality of included data. H.Z., J.Y., and Shen Li conducted data analyses. H.Z. and J.Y. wrote first draft with contributions from T.A., X.Y., J.L., C.Y. P.S., X.M., and S.L.. H.Z. and S.L. reviewed the final draft and checked for important intellectual content. All authors had full access to all the data in the study and the final responsibility for the decision to submit for publication. Acknowledgments S.L., X.M., and P.S. designed study. H.Z., J.Y., and Shen Li extracted all the data. J.Y. and Shen Li assessed the quality of included data. H.Z., J.Y., and Shen Li conducted data analyses. H.Z. and J.Y. wrote first draft with contributions from T.A., X.Y., J.L., C.Y. P.S., X.M., and S.L.. H.Z. and S.L. reviewed the final draft and checked for important intellectual content. All authors had full access to all the data in the study and the final responsibility for the decision to submit for publication. Data availability statement Data are available in a public, open access repository. Not applicable. References Hunger. famine are not accidents - they are created by the actions of people. Nature. 2023;619(7968):8. The State of. 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Excess DALYs attributable to NCDs of survivors under the age of 20 in Gaza 50 years post-exposure. Malnutrition Prevalence at 4 0% Malnutrition Prevalence at 5 0% Malnutrition Prevalence at 60% Malnutrition Prevalence at 70% Malnutrition Prevalence at 80% Malnutrition Prevalence at 90% Malnutrition Prevalence at 100% CVD Male 5548(2685, 8419) 6781(3305, 10230) 7960(3915, 11946) 9090(4512, 13561) 10174(5087, 15098) 11215(5635, 16551) 12216(6183, 17927) Female 2653(1379, 3961) 3239(1694, 4811) 3798(2003, 5611) 4332(2298, 6373) 4844(2591, 7094) 5334(2866, 7770) 5804(3139, 8418) Both 8181(4090, 12249) 9994(5026, 14851) 11728(5943, 17327) 13389(6827, 19669) 14980(7705, 21891) 16508(8565, 23992) 17975(9404, 25980) CKD Male 760(365, 1211) 919(444, 1456) 1068(520, 1678) 1208(592, 1891) 1340(662, 2083) 1465(729, 2270) 1583(788, 2443) Female 539(256, 865) 652(311, 1038) 758(364, 1199) 857(414, 1350) 951(463, 1490) 1039(510, 1619) 1123(553, 1741) Both 1297(649, 2035) 1568(792, 2442) 1823(927, 2826) 2062(1054, 3178) 2288(1172, 3505) 2500(1285, 3813) 2702(1396, 4097) Type 2 diabetes Male 3304(534, 6067) 3999(664, 7255) 4651(794, 8335) 5266(921, 9326) 5845(1048, 10234) 6394(1173, 11087) 6913(1298, 11888) Female 2681(433, 4893) 3245(540, 5843) 3774(645, 6720) 4273(749, 7519) 4743(852, 8260) 5188(954, 8941) 5609(1055, 9569) Both 5981(965, 10846) 7238(1200, 12952) 8419(1433, 14876) 9531(1663, 16648) 10581(1891, 18290) 11573(2116, 19811) 12513(2339, 21206) Cancers Male 5103(840, 9037) 6170(1045, 10769) 7171(1248, 12361) 8111(1449, 13821) 8998(1649, 15174) 9834(1846, 16407) 10626(2042, 17546) Female 3834(631, 6776) 4636(785, 8085) 5388(937, 9272) 6095(1089, 10368) 6760(1238, 11381) 7389(1387, 12308) 7984(1534, 13164) Both 8921(1464, 15650) 10786(1822, 18670) 12535(2176, 21410) 14180(2526, 23917) 15729(2874, 26229) 17192(3218, 28380) 18575(3560, 30367) Total Male 14715(8730, 20437) 17868(10673, 24657) 20850(12533, 28566) 23676(14336, 32246) 26358(16050, 35662) 28908(17734, 38896) 31337(19335, 41930) Female 9707(5486, 13688) 11771(6690, 16463) 13717(7874, 19038) 15556(8987, 21428) 17298(10056, 23677) 18950(11073, 25797) 20520(12049, 27788) Both 24379(14228, 33877) 29587(17401, 40769) 34506(20459, 47220) 39162(23379, 53260) 43578(26203, 58869) 47773(28909, 64183) 51764(31461, 69182) For the Gaza population under 20 years old, the additional DALYs attributable to NCDs after 50 years of exposure to malnutrition are displayed. DALYs: disability-adjusted life years; NCDs: non-communicable diseases; CKD: chronic kidney disease, including chronic kidney disease due to type 2 diabetes and hypertension; CVD: cardiovascular diseases, including ischemic heart disease and hypertensive heart disease. Additional Declarations No competing interests reported. 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University","correspondingAuthor":false,"prefix":"","firstName":"Jiaqing","middleName":"","lastName":"Yang","suffix":""},{"id":452779993,"identity":"8bba12a1-3042-44c6-ad32-a19611986099","order_by":2,"name":"Shen Li","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Shen","middleName":"","lastName":"Li","suffix":""},{"id":452779995,"identity":"71e2850f-cd5e-4d90-bdec-c52ce9a19b68","order_by":3,"name":"Thomas Agoritsas","email":"","orcid":"","institution":"University Hospital of Geneva","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Agoritsas","suffix":""},{"id":452779997,"identity":"defa3fdf-c917-4d51-b1ca-5f07cfe80c27","order_by":4,"name":"Xinggang Yang","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Xinggang","middleName":"","lastName":"Yang","suffix":""},{"id":452780006,"identity":"17723885-e899-497e-9419-b99ab5ce96bb","order_by":5,"name":"Jing Li","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Li","suffix":""},{"id":452780007,"identity":"518d82e4-201e-433c-a90b-95f95301c589","order_by":6,"name":"Chi Yuan","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Chi","middleName":"","lastName":"Yuan","suffix":""},{"id":452780008,"identity":"d2f89fc9-0478-48f2-bf12-b6643da6cc42","order_by":7,"name":"Peige Song","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Peige","middleName":"","lastName":"Song","suffix":""},{"id":452780009,"identity":"60a8235d-a3a0-4bc8-9c40-31d3affef92c","order_by":8,"name":"Xuelei Ma","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Xuelei","middleName":"","lastName":"Ma","suffix":""},{"id":452780010,"identity":"cfcf8faa-156b-4f14-a779-e44018418eeb","order_by":9,"name":"Sheyu Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYBACPmYGNhAtByIOPCBGCxtUizFYSwJRWhggWhIbQCRxWtjZnz342HY4fX7Y4YdAW+zkdBsIOozH3HBm2+HcjbfTDIBako3NDhDWwibNC9IyOwGk5UDiNsJa2J9J/wU6zHB2+gditTCYSTO2HU6Ql84h2hYeM8mec+mGG6RzCg4kGBDhF37+488kfpRZy8vPTt/84UOFnRxBLWDACIwaA7BKA2KUg8EfBgb5BqJVj4JRMApGwUgDAMTzP9X2UYtTAAAAAElFTkSuQmCC","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Sheyu","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-03-15 07:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6231236/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6231236/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82381368,"identity":"8c3f4f0d-dc8b-45dd-8378-98e788487a00","added_by":"auto","created_at":"2025-05-09 15:33:06","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":425185,"visible":true,"origin":"","legend":"\u003cp\u003ePreferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) study flowchart for study selection process\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6231236/v1/c5a7f43379e0ef3f7067cd68.jpeg"},{"id":82380801,"identity":"96514467-3684-474a-9f4f-4b465ab13602","added_by":"auto","created_at":"2025-05-09 15:25:10","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":177690,"visible":true,"origin":"","legend":"\u003cp\u003eSummary estimates of the association between early life exposure to malnutrition and adulthood health outcomes\u003c/p\u003e\n\u003cp\u003eCI, confidence interval; OR, odds ratio; RR, risk ratio; T, total number of studies; C, cohort studies; P, population-based case-control or cross-sectional studies; SSE, small study effect; ESB, excess significant bias; AMSTAR, a measurement tool to assess systematic reviews; GRADE, Grading of Recommendations, Assessment, Development, and Evaluation; NA, not available; NS, non-significant.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6231236/v1/07e9c7e3c273eec8cf5311de.jpeg"},{"id":82383277,"identity":"d545175c-5b4e-47b9-8f66-c14b2797cf16","added_by":"auto","created_at":"2025-05-09 15:57:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1442188,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6231236/v1/ef30b384-7a5f-4478-8dfe-34cc648ffaac.pdf"},{"id":82380776,"identity":"cf3d2d5a-6c27-44b1-8e7e-785a89210ee4","added_by":"auto","created_at":"2025-05-09 15:25:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20819,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.Adoptedsearchstrings.docx","url":"https://assets-eu.researchsquare.com/files/rs-6231236/v1/bd569cf073a09fcd21623ae7.docx"},{"id":82380777,"identity":"ea88135c-37ae-4112-a6be-dda2003d4c76","added_by":"auto","created_at":"2025-05-09 15:25:06","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18157,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable2..docx","url":"https://assets-eu.researchsquare.com/files/rs-6231236/v1/02dcca45c4527cf459143f0e.docx"},{"id":82380778,"identity":"edf9ab5f-7897-465d-8421-efcded2dbd50","added_by":"auto","created_at":"2025-05-09 15:25:06","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":16201,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable3..xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6231236/v1/112e94e8591ddd71f07e7d4c.xlsx"},{"id":82380785,"identity":"dc5b35d0-7335-408d-874f-bc1d867ca414","added_by":"auto","created_at":"2025-05-09 15:25:06","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":19803,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable4..docx","url":"https://assets-eu.researchsquare.com/files/rs-6231236/v1/dffa91b6c9876313c2d55037.docx"},{"id":82380782,"identity":"9a254354-e31a-44eb-9a76-693acc8a2975","added_by":"auto","created_at":"2025-05-09 15:25:06","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":22303,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable5..docx","url":"https://assets-eu.researchsquare.com/files/rs-6231236/v1/3af880cd00e844bb3605c440.docx"},{"id":82380789,"identity":"327671be-3d99-4dc9-a46d-edbcad4a7f0d","added_by":"auto","created_at":"2025-05-09 15:25:06","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":14873,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable6.docx","url":"https://assets-eu.researchsquare.com/files/rs-6231236/v1/a247bc9ace84ed08e92c9d37.docx"},{"id":82380792,"identity":"c1278b36-99ea-493d-8284-d76874062724","added_by":"auto","created_at":"2025-05-09 15:25:06","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":90409,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6231236/v1/af38fde3a0dc4a04796fb563.docx"},{"id":82380796,"identity":"d7327b9b-6423-4f25-9a1e-c99dffac8515","added_by":"auto","created_at":"2025-05-09 15:25:06","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":236993,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6231236/v1/0c51bbbae3ea26d259b2116d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Long-term health implications of early life malnutrition: an umbrella review and anticipation of latent attributable disease burden","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMalnutrition occurs when a region lacks sufficient food, resulting in widespread malnutrition and deaths from starvation. The primary causes of malnutrition stem from poverty, climate change, and regional conflicts \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The World Health Organization's 2023 report reveals that approximately 735\u0026nbsp;million people are currently struggling with hunger - an alarming increase of 122\u0026nbsp;million compared to 2019. Millions of children under the age of five are suffering from malnutrition \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, with 45\u0026nbsp;million children under five suffer from wasting, a lethal form of malnutrition that increases their risk of death by up to twelve times \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Recent regional conflicts intensify the impact of malnutrition on humanity. For example, people in the northern governorates of Gaza received merely 10\u0026ndash;15% of their necessary food calorie intake, resulting in a widespread malnutrition that affected the entire region between January and February 2024\u003csup\u003e2\u003c/sup\u003e. The severe malnutrition among children under five raised from a pre-conflict prevalence of 0.8% to an estimated 12.4\u0026ndash;16.5%\u003csup\u003e5\u003c/sup\u003e, reflecting that malnutrition in early life remains stubborn in modern societies.\u003c/p\u003e \u003cp\u003eConsidering the critical nutritional requirements for the physical and developmental growth of children, malnutrition presents a considerable threat of starvation while also inflicting irreversible consequences on their long-term intellectual development and physical growth. The Developmental Origins of Health and Disease (DOHaD) \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e demonstrates that severe malnutrition during the early years of life leads to enduring physical and psychological health issues. Survivors, especially those in a conflict zones, face compounded stress and trauma, which elevates their risk for developing metabolic disorders later in adulthood, such as type 2 diabetes \u003csup\u003e7 8\u003c/sup\u003e, coronary heart disease \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, and cancers \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNowadays, the world is facing humanitarian crises more frequently, with complex and lasting impacts, affecting more children than ever\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Despite some meta-analyses exploring the link between childhood malnutrition exposure and later health problems, there's a lack of a comprehensive review that broadly summarizes and evaluates the existing meta-analytic evidence. This umbrella review synthesised the effect sizes of latent health outcomes associated with such early life adversities to inform both immediate public health responses and long-term health strategy planning.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSystematic review and study selection\u003c/h2\u003e \u003cp\u003eThis umbrella review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. A comprehensive literature search in MEDLINE, EMBASE, and Web of Science was conducted from inception to 1st April 2024. The search strategy included terms and medical subject headings such as 'malnutrition', 'childhood', 'meta-analysis', and related synonyms (details in the Supplementary Table\u0026nbsp;1). Two independent researchers (J.Y. and Shen Li) screened titles and abstracts, retrieved full texts for eligibility, and extracted data on study characteristics and outcomes of interests. A third researcher (X.M.) resolved any discrepancies.\u003c/p\u003e \u003cp\u003eWe included quantitative systematic reviews and meta-analyses of observational studies that estimated the risk of long-term health-related outcomes in adults who exposed to malnutrition or long-term malnutrition during childhood. For outcomes reported in multiple meta-analyses, only the most recent one or the one with the largest number of included studies was included.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eQuality assessment\u003c/h3\u003e\n\u003cp\u003eTwo researchers (J.Y. and Shen Li) assessed the methodological quality of each included meta-analysis using the AMSTAR2 (A Measurement Tool to Assess Systematic Reviews 2) \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The tool contains seven critical items and nine non-critical items. Each item was assessed as 'yes', 'partial yes', or 'no' according to the degree of compliance with the evaluation criteria. The overall methodological quality of the meta-analyses was rated as 'high', 'moderate', 'low', or 'critically low'. Zero or one non-critical item receiving 'no' indicates a 'high' rating. More than one non-critical item categorised as 'no' results in a 'moderate' rating. A single critical item being considered 'no' was rated 'low', while more than one critical item being considered 'no' escalates to 'critically low'. A third senior researcher (X.M.) solved the disagreements were solved by.\u003c/p\u003e\n\u003ch3\u003eMeta-analysis\u003c/h3\u003e\n\u003cp\u003eWe re-checked extracted data of the included meta-analyses and re-estimated the effect sizes with DerSimonian-Laird random effects model. Study heterogeneity was evaluated using Q tests and the I\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e statistics. For estimates pooled by 10 cohorts or more, we assessed the publication using Egger\u0026rsquo;s regression, Begg's test, and trim-and-fill approach. We considered the presence of significant publication bias if the P value from Egger\u0026rsquo;s regression or Begg's test was lower than 0.05, or if substantial changes were observed in the estimates before and after applying the trim-and-fill method.\u003c/p\u003e\n\u003ch3\u003eEvidence grading criteria\u003c/h3\u003e\n\u003cp\u003eWe graded the credibility of evidence using a five-tier evidence system: convincing (class I), highly suggestive (class II), suggestive (class III), weak (class IV), or non-significant (NS) \u003csup\u003e17 18\u003c/sup\u003e. The criteria for grading included sample sizes, P values, 95% confidence interval [CI], heterogeneity, risk of bias. The certainty of evidence was assessed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, with outcomes categorised as high, moderate, low, or very low quality. Details of the grading system are available in the Supplementary Table\u0026nbsp;2.\u003c/p\u003e\n\u003ch3\u003eDisease burden estimation in Gaza\u003c/h3\u003e\n\u003cp\u003eTo implement the estimates for real-world events, we took the recent conflict in Gaza as an example, showing the latent disease burden of young survivors under 20 years exposed to a population-level shortage of food. We calculated population attributable fraction (PAF) with the proportion of the Gazan youth population under 20 years exposed to malnutrition, ranging from 40\u0026ndash;100% according to the latest opinion \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The formula used was:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{P}\\varvec{A}\\varvec{F}=\\frac{\\varvec{p}\\cdot\\:\\left(\\varvec{R}\\varvec{R}-1\\right)}{\\varvec{p}\\cdot\\:\\left(\\varvec{R}\\varvec{R}-1\\right)+1}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{p}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eis the total prevalence of exposure in population\u003c/em\u003e, \u003cem\u003eand\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{R}\\varvec{R}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eis the total relative risk with exposure.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe simulation projected the burden of disease attributable to malnutrition exposure using excess disability-adjusted life years (DALYs), applying PAF to the DALY rates from the Global Burden of Disease (GBD) 2021 study, adjusted by age and sex. For outcomes identified as positive in umbrella reviews and confirmed with corresponding DALY rates data in the GBD, binary outcomes were selected. We focused on the population aged under 20 in Palestine to represent those exposed to malnutrition during early life stages, and the disease burden for major non-communicable diseases (NCDs) over the following 50 years. The Gaza accounts for 40.7% of the Palestinian population \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. We anticipated the burden from each disease by multiplying the DALY rates for major NCDs by the PAF for each age and sex group. We counted only the most burdensome patient-important outcomes among overlapping ones. The total burden of malnutrition exposure on NCDs was then determined by summing the excess DALYs for each specific disease. A one-way sensitivity analysis was conducted to assess the impact of variations in RRs and DALY estimates on the projected burden among survivors.\u003c/p\u003e \u003cp\u003eAll statistical analyses were carried out using the statistical software R V.4.3.0.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;of the included meta-analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 3,226 records (Figure 1), seven meta-analyses proved eligibility \u003csup\u003e11,12\u003c/sup\u003e \u003csup\u003e22\u003c/sup\u003e \u003csup\u003e23\u003c/sup\u003e \u003csup\u003e24\u003c/sup\u003e \u003csup\u003e25\u003c/sup\u003e \u003csup\u003e26\u003c/sup\u003e, encompassing 16 comparisons with sample sizes ranging from 5,485 to 164,169 participants. Three meta-analyses only focused on cohort studies, whereas the remaining 13 included both cohort and cross-sectional studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 2 summarised the latent adverse health risks of early life malnutrition (details in the Supplementary Table 3). Supplementary Table 4 showed the details of AMSTAR2 assessment and Supplementary Table 5 showed the details of the GRADE profiles. Four meta-analyses \u003csup\u003e11,12\u003c/sup\u003e \u003csup\u003e22\u003c/sup\u003e \u003csup\u003e26\u003c/sup\u003e were rated as \u0026ldquo;low\u0026rdquo; quality and three \u003csup\u003e23\u003c/sup\u003e \u003csup\u003e24\u003c/sup\u003e \u003csup\u003e25\u003c/sup\u003e as \u0026ldquo;critically low\u0026rdquo; primarily due to the absence of a pre-determined protocol (AMSTAR2 critical item 2) and a lack of a list of excluded literature (AMSTAR2 critical item 7).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003enthropometric\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eparameters, glucose profiles and type 2 diabetes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEarly life exposure to malnutrition might be associated with a 0.84 cm increased waist circumference (mean difference [MD] 95% CI 0.10 to 1.58; very low certainty, Evidence Level IV) and 0.95 cm decreased adult height (MD 95% CI -1.46 to -0.45; very low certainty, Evidence Level IV). However, our umbrella review revealed no significant evidence on the association between early life malnutrition exposure and body mass index (very low certainty, NS), adulthood overweight (very low certainty, NS), or obesity (very low certainty, NS). In addition, our umbrella review demonstrated that early life exposure to malnutrition may be associated with a 25% higher risk of hyperglycaemia (95% CI 10% to 42%; very low certainty, Evidence Level III), 33% higher risk of type 2 diabetes (95% CI 8% to 64%; very low certainty, Evidence Level IV), 29% increased risk of metabolic syndrome (95% CI 16% to 44%; very low certainty, Evidence Level III) (Figure 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLipid profiles and c\u003c/strong\u003e\u003cstrong\u003eardiovascular outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study found no significant association between early life malnutrition exposure and triacylglycerol (very low certainty, NS). However, early life exposure to malnutrition may be associated with a 0.15 mmol/L increased total cholesterol (MD 95% CI 0.00 to 0.31; very low certainty, Evidence Level IV), 0.15 mmol/L increased low-density lipoprotein cholesterol (MD 95% CI 0.05 to 0.25; very low certainty, Evidence Level IV), 27% higher risk of dyslipidaemia (95% CI 12% to 45%; low certainty, Evidence Level III), 38% increased risk of adult hypertension (95% CI 21% to 57%; very low certainty, Evidence Level III), 21% increased risk of coronary heart disease (95% CI 9% to 35%; very low certainty, Evidence Level III) in adult. However, we did not observe a significant association between early life malnutrition and stroke in adulthood (very low certainty, NS) (Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCancer\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEarly life malnutrition may increase 34% risk of all cancers (95% CI 8% to 66%; low certainty, Evidence Level IV) in adulthood.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLatent health outcomes and burden of malnutrition survivors in Gaza\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo avoid duplicate counting, we estimated the PAF for type 2 diabetes due to malnutrition exposure at 11.4% to 23.8% (Supplementary Table 6), depending on\u0026nbsp;the prevalence of malnutrition exposure (40% to 100%). Among Gazan youth, the combined burden of type 2 diabetes and its complications could reach 13,669 DALYs. The PAF for hypertension would range from 13.1% to 27.2%, contributing a maximum of approximately 4,603 DALYs. For ischaemic heart disease, the PAF would be between 7.7% and 17.1%, resulting in up to 14,917 DALYs. Finally, cancers associated with malnutrition would have a PAF of 11.7% to 24.4%, impacting as much as 18,575 DALYs.\u003c/p\u003e\n\u003cp\u003eWith the exposure rate ranging from 40% to 100%, the excess DALYs in 50 years would range from 24,379 (95% uncertainty interval [UI] 14,228 to 33,877) to 51,764 (95% UI 31,461 to 69,182) among malnutrition survivors.\u0026nbsp;Assuming under 50% malnutrition exposure,\u0026nbsp;these DALYs\u0026nbsp;would be\u0026nbsp;derived from years of life lost\u0026nbsp;(YLLs)-85.7%, in contrast to years lived with disability\u0026nbsp;(YLDs)\u0026nbsp;which\u0026nbsp;would\u0026nbsp;make up only 14.3%, as depicted in\u0026nbsp;Supplementary\u0026nbsp;Figure 1.\u0026nbsp;For\u0026nbsp;total excess DALYs, cancer\u0026nbsp;would\u0026nbsp;account for 37.6%\u0026nbsp;(95%\u0026nbsp;CI\u0026nbsp;10.3%\u0026nbsp;to\u0026nbsp;74.2%) ranking the top contribution, followed by cardiovascular disease (CVD) at 36\u0026nbsp;3%\u0026nbsp;(95%\u0026nbsp;CI\u0026nbsp;17.8%\u0026nbsp;to\u0026nbsp;63.5%),\u0026nbsp;type 2 diabetes\u0026nbsp;at 25.3%\u0026nbsp;(95%\u0026nbsp;CI\u0026nbsp;6.2%\u0026nbsp;to\u0026nbsp;50.8%), and chronic kidney disease (CKD) at\u0026nbsp;5.5%\u0026nbsp;(95%\u0026nbsp;CI\u0026nbsp;2.5%\u0026nbsp;to\u0026nbsp;9.7%), as shown in Table\u0026nbsp;2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Tornado Diagram (Supplementary Figure 2) displayed one-way sensitivity analyses of the total DALY of survivors under 50% malnutrition exposure impacts from various RR and relative risks of NCDs. The analyses revealed that the total DALYs for survivors were significantly shaped by the RR and associated DALYs of cancers, type 2 diabetes, and ischaemic heart disease. Within the range of the values, the results showed robustness and the trend remained consistent.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is the first umbrella review that comprehensively summarises the associations between early life malnutrition and adulthood health outcomes. Taking the example in the Gazan conflict in 2024, our projections anticipated an additional burden of 51,764 DALYs over the next 50 years for survivors. This public health crisis calls for both immediate actions to prevent further harm to the population, particularly children, as well as long-term strategies to provide the foundations for their recovery, and mitigate future health crises resulting from the increased burden of disease.\u003c/p\u003e \u003cp\u003eThe DOHaD hypothesis facilitates the understanding how early life malnutrition permanently influence the later life metabolism and associated health outcomes \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. For instance, early life malnutrition is linked to epigenetic trail in pancreatic islets for reduced capacity of insulin secretion, which raises the risk of type 2 diabetes.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Early life malnutrition seeded a \"memory\" of malnutritional environments from early life experience to adapt possible similar condition in adulthood, which might increase the vulnerability to chronic diseases in adulthood by preserving, a process referred to as \"metabolic imprinting\" \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. The disruption of gut microbiota during critical developmental windows may further exacerbate the risk of NCDs by impairing immune function and metabolic processes, underscoring the intricate interplay between nutrition, gut microbiota, and long-term health. Early life is a critical period for the establishment and development of the gut microbiota \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Recent studies have suggested that the gut microbiome co-evolves with its host, beginning to stabilise later in childhood \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Further evidence from faecal microbiota transplantation studies indicates that undernutrition can causally disrupt normal gut microbiota development \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. There exists a significant interaction between early life gut microbiota and the immune system, gastrointestinal integrity, and other bodily systems \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The gut microbiota and its related metabolites are thought to mediate the impact of environmental stressors on health and disease later in life\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWith increasing regional conflicts, the childhood malnutrition are perceived as historical anomalies in the 21st century. The recognition of the lifelong health consequences facilitate clinicians and public health practitioners acknowledging this impact and considering individuals at higher risk for cardiovascular and metabolic diseases due to early life malnutrition. Intensive lifestyle interventions and health promotion strategies, which demonstrated their effectiveness in preventing non-communicable chronic diseases\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, are necessary later in regions affected by malnutrition.\u003c/p\u003e \u003cp\u003eThis study also has some limitations. First, the estimates considered only quantitative synthesised evidence and might miss individual cohorts, which, nevertheless, are likely rare events or with small effects. This study covered the most burdensome conditions with robustness in the sensitivity analyses. Second, the anticipation for DALY and death are based on the secondary analyses of the GBD studies and low to very low certainty evidence. The uncertainty of the anticipation should be acknowledged and further validation may better facilitate the policy decision-making. Third, we did not consider the duration of exposure to malnutrition, which requests individual data for the dose-dependent analysis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, early life exposure to malnutrition is likely associated with an important increased risk in of later life type 2 diabetes, hypertension, ischaemic heart disease, cancer, and worsened metabolic parameters. The prevention of early life malnutrition may alleviate the disease burdens of chronic diseases and warrants attention by stakeholders.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclaration of interests\u003c/h2\u003e\n\u003cp\u003eAll authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eEthics statements\u003c/h2\u003e\n\u003cp\u003ePatient consent for publication: Not applicable.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003e1.3.5 Project for Disciplines of Excellence, West China Hospital, Sichuan University.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eS.L., X.M., and P.S. designed study. H.Z., J.Y., and Shen Li extracted all the data. J.Y. and Shen Li assessed the quality of included data. H.Z., J.Y., and Shen Li conducted data analyses. H.Z. and J.Y. wrote first draft with contributions from T.A., X.Y., J.L., C.Y. P.S., X.M., and S.L.. H.Z. and S.L. reviewed the final draft and checked for important intellectual content. All authors had full access to all the data in the study and the final responsibility for the decision to submit for publication.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eS.L., X.M., and P.S. designed study. H.Z., J.Y., and Shen Li extracted all the data. J.Y. and Shen Li assessed the quality of included data. H.Z., J.Y., and Shen Li conducted data analyses. H.Z. and J.Y. wrote first draft with contributions from T.A., X.Y., J.L., C.Y. P.S., X.M., and S.L.. H.Z. and S.L. reviewed the final draft and checked for important intellectual content. All authors had full access to all the data in the study and the final responsibility for the decision to submit for publication.\u003c/p\u003e\n\u003ch2\u003eData availability statement\u003c/h2\u003e\n\u003cp\u003eData are available in a public, open access repository. Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHunger. famine are not accidents - they are created by the actions of people. Nature. 2023;619(7968):8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe State of. Food Security and Nutrition in the World 2023; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe State of. Food Security and Nutrition in the World 2022; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIPC, GAZA STRIP:. FAMINE REVIEW OF THE IPC ANALYSIS. December 21 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIPC. FAMINE REVIEW COMMITTEE. GAZA STRIP, MARCH 2024. March 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoffman DJ, Powell TL, Barrett ES, Hardy DB. Developmental origins of metabolic diseases. 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Cell Host Microbe. 2015;17(5):690\u0026ndash;703.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlanton LV, Charbonneau MR, Salih T et al. Gut bacteria that prevent growth impairments transmitted by microbiota from malnourished children. Science 2016; 351(6275).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarkar A, Yoo JY, Valeria Ozorio Dutra S, Morgan KH, Groer M. The Association between Early-Life Gut Microbiota and Long-Term Health and Diseases. J Clin Med 2021; 10(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGensollen T, Iyer SS, Kasper DL, Blumberg RS. How colonization by microbiota in early life shapes the immune system. Science. 2016;352(6285):539\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan Y, Pedersen O. Gut microbiota in human metabolic health and disease. 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Nutrients 2020; 12(12).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1.\u003c/strong\u003e Excess DALYs attributable to NCDs of survivors under the age of 20 in Gaza 50 years post-exposure.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"1030\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalnutrition Prevalence at\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalnutrition Prevalence at\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalnutrition Prevalence at 60%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalnutrition Prevalence at 70%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalnutrition Prevalence at 80%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalnutrition Prevalence at 90%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalnutrition Prevalence at 100%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCVD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e5548(2685, 8419)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e6781(3305, 10230)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e7960(3915, 11946)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e9090(4512, 13561)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e10174(5087, 15098)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e11215(5635, 16551)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e12216(6183, 17927)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e2653(1379, 3961)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e3239(1694, 4811)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e3798(2003, 5611)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e4332(2298, 6373)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e4844(2591, 7094)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e5334(2866, 7770)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e5804(3139, 8418)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBoth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e8181(4090, 12249)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e9994(5026, 14851)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e11728(5943, 17327)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e13389(6827, 19669)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e14980(7705, 21891)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e16508(8565, 23992)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e17975(9404, 25980)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCKD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e760(365, 1211)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e919(444, 1456)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1068(520, 1678)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1208(592, 1891)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1340(662, 2083)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1465(729, 2270)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1583(788, 2443)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e539(256, 865)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e652(311, 1038)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e758(364, 1199)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e857(414, 1350)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e951(463, 1490)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1039(510, 1619)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1123(553, 1741)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBoth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1297(649, 2035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1568(792, 2442)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1823(927, 2826)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e2062(1054, 3178)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e2288(1172, 3505)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e2500(1285, 3813)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e2702(1396, 4097)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType 2 diabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e3304(534, 6067)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e3999(664, 7255)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e4651(794, 8335)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e5266(921, 9326)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e5845(1048, 10234)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e6394(1173, 11087)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e6913(1298, 11888)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e2681(433, 4893)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e3245(540, 5843)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e3774(645, 6720)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e4273(749, 7519)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e4743(852, 8260)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e5188(954, 8941)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e5609(1055, 9569)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBoth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e5981(965, 10846)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e7238(1200, 12952)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e8419(1433, 14876)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e9531(1663, 16648)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e10581(1891, 18290)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e11573(2116, 19811)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e12513(2339, 21206)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCancers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e5103(840, 9037)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e6170(1045, 10769)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e7171(1248, 12361)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e8111(1449, 13821)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e8998(1649, 15174)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e9834(1846, 16407)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e10626(2042, 17546)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e3834(631, 6776)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e4636(785, 8085)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e5388(937, 9272)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e6095(1089, 10368)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e6760(1238, 11381)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e7389(1387, 12308)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e7984(1534, 13164)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBoth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e8921(1464, 15650)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e10786(1822, 18670)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e12535(2176, 21410)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e14180(2526, 23917)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e15729(2874, 26229)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e17192(3218, 28380)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e18575(3560, 30367)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e14715(8730, 20437)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e17868(10673, 24657)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e20850(12533, 28566)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e23676(14336, 32246)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e26358(16050, 35662)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e28908(17734, 38896)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e31337(19335, 41930)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e9707(5486, 13688)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e11771(6690, 16463)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e13717(7874, 19038)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e15556(8987, 21428)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e17298(10056, 23677)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e18950(11073, 25797)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e20520(12049, 27788)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBoth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e24379(14228, 33877)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e29587(17401, 40769)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e34506(20459, 47220)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e39162(23379, 53260)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e43578(26203, 58869)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e47773(28909, 64183)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e51764(31461, 69182)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFor the Gaza population under 20 years old, the additional DALYs attributable to NCDs after 50 years of exposure to malnutrition are displayed. DALYs: disability-adjusted life years; NCDs: non-communicable diseases; CKD: chronic kidney disease, including chronic kidney disease due to type 2 diabetes and hypertension; CVD: cardiovascular diseases, including ischemic heart disease and hypertensive heart disease.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"DOHaD, malnutrition, non-communicable diseases, DALY, global health","lastPublishedDoi":"10.21203/rs.3.rs-6231236/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6231236/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eEarly life malnutrition is posing substantial health risks to the adulthood. This study comprehensively summarised the latent health outcomes after early life malnutrition and anticipated the attributable latent disease burden of the current humanitarian catastrophe in Gaza over the next five decades.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis umbrella review searched MEDLINE, EMBASE, and Web of Science for quantitative systematic reviews assessing the adulthood health risks attributable to early-life malnutrition exposure. Using re-analysed relative effects and the 2021 Global Burden of Disease (GBD) regional estimates, we projected population attributable fraction (PAF) and further anticipated the adulthood disease burden attributable to early life malnutrition among population under 20 years old in Gaza over the next five decades.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe umbrella review, including 16 meta-analyses and 758,417 individuals, suggested that people experienced malnutrition in early life may be facing 34% elevated risk of cancers (95% confidence interval [CI] 8\u0026ndash;66%), 25% of hyperglycaemia (95% CI 10\u0026ndash;42%), 33% of type 2 diabetes (95% CI 8\u0026ndash;64%), 38% of hypertension (95% CI 21\u0026ndash;57%) and 21% of coronary heart disease (95% CI 9\u0026ndash;35%) in their later years. Assuming 50% of the population younger than 20 exposed to malnutrition, the anticipated disease burden would be 29,587 disability-adjusted life years (DALYs) (95% uncertainty interval [UI] 17,401 to 40,769) including 25,344 years of life lost (YLL) (95% UI 14,754 to 34,941) and 4,247 years lived with disability (YLD) (95% UI 1,197 to 7,519) attributable to early life malnutrition in 50 years. The PAF for type 2 diabetes would range from 11.4\u0026ndash;23.8%, contributing to up to 13,669 DALYs (95% UI 2,549 to 22,989); the PAF for ischaemic heart disease would range from 7.7\u0026ndash;17.1% contributing to up to 14,917 DALYs (95% UI 6,420 to 22,805); the PAF for cancers would range from 11.7\u0026ndash;24.4% contributing to up to 18,575 DALYs (95% UI 3,560 to 30,367).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eEarly life exposure to malnutrition is likely associated with increased risks of type 2 diabetes, hypertension, ischaemic heart disease, and cancers. The current malnutrition in Gazan young people will probably be associated with major disease burden in 50 years.\u003c/p\u003e","manuscriptTitle":"Long-term health implications of early life malnutrition: an umbrella review and anticipation of latent attributable disease burden","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 15:25:01","doi":"10.21203/rs.3.rs-6231236/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"312613664577902371049013984428031837648","date":"2025-05-21T21:17:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309655641133200954203118963536295478211","date":"2025-05-06T15:16:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-06T09:47:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-23T10:16:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-17T14:14:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-17T14:08:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-03-15T07:30:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6ab5dae4-c254-4447-9051-63dcbb959b24","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-05-09T15:25:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 15:25:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6231236","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6231236","identity":"rs-6231236","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}