Birth Weight Is Associated With Clonal Hematopoiesis of Indeterminate Potential and Cardiovascular Outcomes in Adulthood

Art Schuermans; Tetsushi Nakao; Yunfeng Ruan; Satoshi Koyama; Zhi Yu; Md Mesbah Uddin; Sara Haidermota; Whitney Hornsby; Adam J Lewandowski; Alexander G Bick; Abhishek Niroula; Siddhartha Jaiswal; Benjamin L Ebert; Pradeep Natarajan; Michael C Honigberg

doi:10.1161/JAHA.123.030220

. 2023 Jun 22;12(13):e030220. doi: 10.1161/JAHA.123.030220

Birth Weight Is Associated With Clonal Hematopoiesis of Indeterminate Potential and Cardiovascular Outcomes in Adulthood

Art Schuermans ^1,^2,³, Tetsushi Nakao ^1,^2,^4,⁵, Yunfeng Ruan ^1,², Satoshi Koyama ^1,², Zhi Yu ^1,², Md Mesbah Uddin ^1,², Sara Haidermota ^1,², Whitney Hornsby ^1,², Adam J Lewandowski ⁶, Alexander G Bick ⁷, Abhishek Niroula ^1,^4,⁸, Siddhartha Jaiswal ⁹, Benjamin L Ebert ^4,¹⁰, Pradeep Natarajan ^1,^2,¹¹, Michael C Honigberg ^1,^2,^11,^✉

PMCID: PMC10356089 PMID: 37345823

Abstract

Background

High and low birth weight are independently associated with increased cardiovascular disease risk in adulthood. Clonal hematopoiesis of indeterminate potential (CHIP), the age‐related clonal expansion of hematopoietic cells with preleukemic somatic mutations, predicts incident cardiovascular disease independent of traditional cardiovascular risk factors. Whether birth weight predicts development of CHIP later in life is unknown.

Methods and Results

A total of 221 047 adults enrolled in the UK Biobank with whole exome sequences and self‐reported birth weight were analyzed. Of those, 22 030 (11.5%) had low (<2.5 kg) and 29 292 (14.7%) high birth weight (>4.0 kg). CHIP prevalence was higher among participants with low (6.0%, P=0.049) and high (6.3%, P<0.001) versus normal birth weight (5.7%, ref.). Multivariable‐adjusted logistic regression analyses demonstrated that each 1‐kg increase in birth weight was associated with a 3% increased risk of CHIP (odds ratio, 1.03 [95% CI, 1.00–1.06]; P=0.04), driven by a stronger association observed between birth weight and DNMT3A CHIP (odds ratio, 1.04 per 1‐kg increase [95% CI, 1.01–1.08]; P=0.02). Mendelian randomization analyses supported a causal relationship of longer gestational age at delivery with DNMT3A CHIP. Multivariable Cox regression demonstrated that CHIP was independently and additively associated with incident cardiovascular disease or death across birth weight groups, with highest absolute risks in those with CHIP plus high or low birth weight.

Conclusions

Higher birth weight is associated with increased risk of developing CHIP in midlife, especially DNMT3A CHIP. These findings identify a novel risk factor for CHIP and provide insights into the relationships among early‐life environment, CHIP, cancer, and cardiovascular disease.

Keywords: birth weight, cardiovascular disease, clonal hematopoiesis, early life, genetics

Subject Categories: Genetics, Precision Medicine, Cardiovascular Disease

Nonstandard Abbreviations and Acronyms

CHIP: clonal hematopoiesis of indeterminate potential
MR: Mendelian randomization
VAF: variant allele frequency

Clinical Perspective.

What Is New?

Higher birth weight is independently associated with increased risk of developing clonal hematopoiesis of indeterminate potential (CHIP), especially DNMT3A CHIP.
Mendelian randomization suggests that longer gestational duration rather than intrauterine overnutrition is causally associated with DNMT3A CHIP.
CHIP stratifies risk of incident cardiovascular disease and death across birth weight categories.

What Are the Clinical Implications?

This study identifies birth weight as a novel risk factor for CHIP and highlights the role of nontraditional cardiovascular risk factors.

Cardiovascular disease (CVD) constitutes the leading global cause of death and disability, affecting over 500 million individuals worldwide. ¹ , ² Mounting data indicate that CVD processes begin early in life and are influenced by multiple genetic and environmental factors. ³ Identifying early‐life exposures linked to increased cardiovascular risk—and the mechanisms through which this risk is conferred—may enable targeted prevention and treatment strategies to reduce the burden of CVD in adulthood.

The notion that adverse intrauterine exposures can increase susceptibility to adult‐onset disease is known as the “fetal programming” hypothesis. ⁴ Low (<2.5 kg) and high (>4.0 kg) birth weight often reflect unfavorable intrauterine conditions (eg, nutritional imbalances or toxic exposures), ⁵ , ⁶ and both have been linked to CVD through mechanisms that appear partially independent of traditional cardiovascular risk factors. ⁷ , ⁸ , ⁹ Animal and human studies suggest that fetal undernutrition has long‐lasting effects on the expression of genes involved in glucose homeostasis, vascular health, and inflammation. ¹⁰ , ¹¹ Likewise, fetal overnutrition has been linked with epigenetic alterations affecting similar pathways, ¹² , ¹³ as well as increased adiposity that persists from birth to adulthood. ¹⁴ , ¹⁵ However, mechanisms linking high and low birth weight to later‐life CVD remain incompletely understood.

Clonal hematopoiesis of indeterminate potential (CHIP) is the clonal expansion of hematopoietic cells with preleukemic mutations in the absence of overt hematologic malignancy. ¹⁶ CHIP prevalence increases with age, affecting >10% of individuals aged >70 years. ¹⁷ , ¹⁸ , ¹⁹ Somatic mutations in genes involved in epigenetic regulation, such as DNMT3A, TET2, and ASXL1, are the most commonly detected drivers of CHIP. ²⁰ CHIP associates with increased risk of incident CVD, including coronary artery disease, ¹⁹ , ²¹ , ²² stroke, ²³ and heart failure. ²⁴ Phylogenetic analyses suggest that many of the mutations driving CHIP can occur early in life, including during the intrauterine period. ²⁵ , ²⁶ Whether early‐life exposures affect the risk of CHIP acquisition and whether CHIP contributes to the increased cardiovascular risk observed in adults born with low or high birth weight is currently unknown.

Therefore, in the present study, we tested the association of birth weight with CHIP in midlife using the UK Biobank and leveraged Mendelian randomization (MR) to test causality of any observed associations. Additionally, we tested the association of birth weight with incident CVD events and death and whether CHIP status modified these associations.

Methods

Study Population

The UK Biobank is a population‐based cohort of ≈500 000 UK residents aged 40 to 70 years at enrollment. ²⁷ Participants were recruited between 2006 and 2010 and followed prospectively via linkage to national health records. At the baseline study visit, participants underwent blood sampling and provided comprehensive demographic, lifestyle, and medical information. Whole exome sequences of blood DNA, obtained at study baseline, were available for ≈455 000 participants. Among this group, participants were excluded from the current study if they did not provide information on birth weight or history of maternal smoking during the perinatal period or if they had a history of hematologic malignancy at baseline. In addition, study participants were excluded if they had a self‐reported birth weight of <0.5 kg or >6.0 kg ²⁸ , ²⁹ , ³⁰ or if they had missing data on self‐reported race and ethnicity, genetic ancestry, or Townsend deprivation index (Figure 1). Follow‐up for outcomes occurred through March 2020.

UKB indicates UK Biobank; and WES, whole exome sequencing.

The current study was conducted under UK Biobank application number 7089. All participants provided informed consent for the use of their data at enrollment in the UK Biobank. The Massachusetts General Hospital Institutional Review Board approved the secondary use of these data for the present analysis. UK Biobank data are available to approved investigators by application (https://www.ukbiobank.ac.uk/).

Exposures and Covariates

The primary exposure was birth weight as a continuous variable, ascertained by participant self‐report at the baseline study visit. In addition, given nonlinear associations between birth weight and CVD, birth weight was further categorized as low (<2.5 kg), normal (2.5–4.0 kg), or high (>4.0 kg) in accordance with World Health Organization definitions (International Classification of Diseases, Eleventh Revision [ICD‐11], codes KA21–22). ³¹ Self‐reported maternal smoking during the perinatal period (yes versus no) constituted an additional secondary exposure. Early‐life history, medical information, medication use, and other health‐related habits were ascertained at study enrollment. Type 2 diabetes status was ascertained by qualifying ICD codes (Table S1) or self‐report. Anthropometric data, physical measures, and biological samples were collected by trained study staff. ²⁷

Sequencing and CHIP Detection

Whole exome sequencing of whole blood–derived DNA was performed on the Illumina NovaSeq 6000 platform at the Regeneron Genetics Center (Tarrytown, NY). ³² , ³³ Exome sequences were analyzed using GATK MuTect2 software ³⁴ to detect somatic mutations. Common germline variants and sequencing artifacts were excluded as reported previously. ³⁵ CHIP mutations were defined according to a prespecified list of pathogenic variants in 74 genes known to be drivers of clonal hematopoiesis and myeloid malignancies, as detailed in Table S2. ³⁶ , ³⁷ To minimize false‐positive CHIP calls, variants were kept for further curation only if the total read depth was ≥10, ³⁵ , ³⁸ the read depth for the alternate allele was ≥3, and there was ≥1 read in both forward and reverse directions supporting the alternate allele. ³⁵ CHIP was defined as variant allele frequency (VAF) ≥2%. ³⁷

Outcomes

The primary study outcome was the presence of any CHIP. Secondary outcomes included the 3 most commonly mutated CHIP drivers (DNMT3A, TET2, and ASXL1) ²⁰ separately, as well as non‐DNMT3A/TET2/ASXL1 CHIP and CHIP with VAF >10%, as CHIP clones above this threshold may be more strongly associated with clinical outcomes. ¹⁹ , ²¹

Subsequent models tested the association between birth weight and a composite outcome of incident CVD or death and whether CHIP status modified this association. The composite outcome of CVD or death included coronary artery disease, myocardial infarction, stroke, and death, and was defined by a combination of inpatient hospital billing ICD codes and UK death registries, as used previously. ²¹ Coronary artery disease was defined by billing codes for ischemic heart diseases and revascularization procedures. ICD codes used for defining incident outcomes are listed in Table S3. Secondary analyses tested associations of birth weight and CHIP with individual components of the composite outcome as well as hematologic malignancy, acute myeloid leukemia, and any cancer.

Statistical Analysis

The Shapiro–Wilk test was used to verify normality of continuous variables. Continuous participant characteristics were compared between birth weight groups using ANOVA or the Kruskal–Wallis test, as appropriate, while categorical characteristics were compared using the Pearson χ² test.

Primary analyses tested the association between birth weight and CHIP using multivariable logistic regression adjusted for study baseline variables, including sex, age, race and ethnicity, smoking status (ever versus never), the first 10 principal components of ancestry, prevalent coronary artery disease, prevalent type 2 diabetes, body mass index (BMI), Townsend deprivation index, and a history of maternal smoking around birth. Since 96.9% of the entire cohort was White, race was collapsed into a binary variable (White versus non‐White) for analysis. Secondary models used to evaluate the association between maternal smoking and CHIP were additionally adjusted for birth weight as a continuous variable. Imputation was performed for missing systolic blood pressure, BMI, total cholesterol, and high‐density lipoprotein cholesterol values. To explore possible nonrandomness of missing data, multivariable models for missingness were constructed using sex, age, race and ethnicity, smoking status, the first 10 principal components of ancestry, prevalent diabetes type 2, prevalent coronary artery disease, and medication use as covariates. Age, sex, race and ethnicity, and any covariates significantly associated with missingness were then incorporated in predictive models to impute missing data using the predict() function in R (R Foundation for Statistical Computing, Vienna, Austria). Multiple sensitivity analyses were performed, including those that (1) excluded participants with imputed covariate data; (2) excluded participants with cytopenias (defined by the presence of anemia [hemoglobin <13 g/dL for men or <12 g/dL for women], neutropenia [absolute neutrophil count <1.8×10⁹/L], or thrombocytopenia [platelet count <100×10⁹/L] at baseline); (3) included participants with a birth weight of <0.5 kg and >6.0 kg ²⁸ , ²⁹ , ³⁰ ; (4) additionally adjusted for prevalent cancer; (5) additionally adjusted for total and high‐density lipoprotein cholesterol; and (6) stratified by age <60 years versus ≥60 years.

Two‐sample MR was performed to infer causal relationships between birth weight and CHIP. MR tests the hypothesis that a given exposure–outcome relationship is causal by treating a random assortment of genetic polymorphisms at conception as instrumental variables. Additional information on the assumptions of MR can be found in Data S1. As birth weight is determined by both gestational age and intrauterine growth (birth weight Z score adjusted for gestational age, ie, small‐ or large‐for‐gestational‐age status), both variables ³⁹ , ⁴⁰ were tested as separate exposures with CHIP, DNMT3A CHIP, and TET2 CHIP as outcomes (Data S2). ³⁵ Since only 1 single‐nucleotide variant (SNV) associated with gestational age met the conventional significance threshold of P<5×10⁻⁸, ³⁹ our primary analyses instead used the robust adjusted profile score method, which is designed to accommodate subgenome‐wide significant variants as instruments while accounting for potential horizontal pleiotropy (ie, potential effects on the outcome via pathways other than the exposure of interest), ⁴¹ using a more lenient significance threshold of P<5×10⁻⁵. SNVs were clumped into independent loci using a window size of 10 Mb and a linkage disequilibrium R ² threshold <0.0001. The SNVs comprising each genetic instrument for our main MR analyses are listed in Tables S4 and S5. A variety of sensitivity analyses were performed to verify the robustness of our findings, including using the inverse variance weighted MR method, applying genome‐wide significance thresholds of P<5×10⁻⁸ and P<5×10⁻³, respectively, and clumping at a more lenient linkage disequilibrium R ² threshold <0.2. The MR‐Egger intercept and MR–pleiotropy residual sum and outlier tests were used to evaluate whether pleiotropy influenced any observed results. The MR‐Egger intercept test evaluates whether the intercept from the MR‐Egger analysis differs from 0. ⁴² , ⁴³ The MR–pleiotropy residual sum and outlier global test evaluates overall horizontal pleiotropy in a single MR analysis by comparing the observed distance of all SNVs to the regression line (ie, residual sum of squares) to the expected distance under the null hypothesis of no horizontal pleiotropy. ⁴⁴ Statistically significant MR‐Egger intercept and MR–pleiotropy residual sum and outlier global tests suggest that pleiotropy is likely to bias the MR estimate. As an additional sensitivity analysis, we tested the associations of birth weight Z score and gestational age with CHIP, DNMT3A CHIP, and TET2 CHIP using a 1‐sample MR approach. Genetic risk scores were constructed for all UK Biobank participants with available whole exome sequencing data and no missing covariates for 1‐sample MR analyses (N=450 897). Genetic risk scores were constructed using the same instrument selection parameters as those used for the primary 2‐sample MR analyses. Associations of genetic risk scores with CHIP phenotypes were tested using logistic regression models adjusted for sex, age, age², genotype array, race and ethnicity, and the first 10 principal components of ancestry. MR estimates were reported as odds ratio (OR) (95% CI) and β (95% CI). All MR analyses were performed using the TwoSampleMR (version 0.5.6) and mr.raps (version 0.2) packages in R. ⁴¹ , ⁴⁵ , ⁴⁶

We then assessed the associations of birth weight with incident CVD or death overall and stratified by CHIP status using Kaplan–Meier estimates and Cox proportional hazards models adjusted for sex, age, race and ethnicity, current or former smoking, the first 10 principal components of ancestry, prevalent type 2 diabetes, BMI, systolic blood pressure, blood pressure medication use, total cholesterol, high‐density lipoprotein cholesterol, cholesterol‐lowering medication use, and Townsend deprivation index. Secondary analyses exploring the associations of birth weight and CHIP with incident hematologic malignancy, acute myeloid leukemia, and cancer were adjusted for sex, age, smoking status, the first 10 principal components of ancestry, BMI, and Townsend deprivation index. We additionally tested for any interaction effects between continuous birth weight or birth weight category and CHIP status by incorporating interaction terms into the multivariable‐adjusted Cox proportional hazard models. Individuals with a diagnosis of CVD or cancer at baseline were excluded from the corresponding incident analyses. The proportional hazards assumption was confirmed using Schoenfeld residuals. All subjects who did not experience the indicated event were censored at the end of follow‐up.

A 2‐sided P value <0.05 was considered statistically significant for the primary outcome and supportive and hypothesis‐generating for secondary outcomes. All analyses were performed using R 4.1.0 (R Foundation for Statistical Computing).

Results

Description of the Study Cohort

All UK Biobank participants who underwent whole exome sequencing from blood DNA were considered for inclusion in the present study. After exclusions (Figure 1), 221 047 participants were included in the analytic cohort. The median age was 56 (interquartile range, 48–62) years at baseline, and 135 344 participants (61.2%) were women. The median birth weight was 3.32 (interquartile range, 2.95–3.66) kg, and the cohort included 22 030 (11.5%) participants with low and 29 292 (14.7%) with high birth weight (Figure S1). More participants with low birth weight reported maternal smoking in the perinatal period (8269 [37.5%] low‐birth‐weight participants versus 48 069 [28.3%] normal‐ and 7675 [26.2%] high‐birth‐weight participants). Baseline participant characteristics are listed in Table 1.

Table 1.

Baseline Characteristics of Participants Across Birth Weight Groups

Characteristic	Low birth weight (<2.5 kg)	Normal birth weight (2.5–4 kg)	High birth weight (>4 kg)	P value
Participants	22 030	169 725	29 292	…
Age at blood draw, y	57 (50 to 63)	55 (48 to 62)	57 (49 to 63)	<0.001
Sex, female	15 779 (71.6)	105 590 (62.2)	13 975 (47.7)	<0.001
Postmenopausal	9568 (73.5)^*	57 784 (64.8)^*	8217 (70.5)^*	<0.001
Age at menopause, y	50 (47 to 53)	50 (48 to 53)	50 (48 to 53)	<0.001
Race and ethnicity	…	…	…	<0.001
White	21 144 (96.0)	164 443 (96.9)	28 611 (97.7)	…
Asian	422 (1.9)	2044 (1.2)	210 (0.7)	…
Black	191 (0.9)	1388 (0.8)	219 (0.8)	…
Mixed	147 (0.7)	902 (0.5)	109 (0.4)	…
Other^†	126 (0.6)	948 (0.6)	143 (0.5)	…
Birth weight, kg	2.27 (1.81 to 2.35)	3.29 (3.06 to 3.63)	4.31 (4.08 to 4.54)	<0.001
Maternal smoking around birth	8269 (37.5)	48 069 (28.3)	7675 (26.2)	<0.001
Smoking status	…	…	…	<0.001
Never	13 366 (60.9)	98 074 (57.9)	15 027 (51.5)	…
Previous	6459 (29.5)	55 042 (32.5)	11 004 (37.7)	…
Current	2106 (9.6)	16 188 (9.6)	3157 (10.8)	…
BMI, kg/m²	26.7 (24.0 to 30.2)	26.4 (23.8 to 29.6)	27.3 (24.6 to 30.6)	<0.001
Systolic blood pressure, mm Hg	139 (126 to 154)	136 (124 to 150)	137 (125 to 150)	<0.001
Cholesterol, mg/dL	…	…	…	…
Total cholesterol	220.8 (191.9 to 251.7)	219.3 (191.9 to 248.6)	217.0 (188.8 to 246.3)	<0.001
HDL cholesterol	55.6 (46.3 to 66.4)	55.2 (46.3 to 66.0)	53.3 (44.8 to 63.7)	<0.001
LDL cholesterol	136.7 (114.3 to 160.6)	136.3 (114.7 to 159.1)	135.5 (113.9 to 158.3)	<0.001
Prevalent morbidities	…	…	…	…
Type 2 diabetes	660 (3.0)	2713 (1.6)	510 (1.7)	<0.001
Coronary artery disease	853 (3.9)	5060 (3.0)	1170 (4.0)	<0.001
Myocardial infarction	464 (2.1)	2832 (1.7)	656 (2.2)	<0.001
Stroke	362 (1.6)	2034 (1.2)	415 (1.4)	<0.001
Any cancer	1990 (9.0)	14 212 (8.4)	2521 (8.6)	0.003
Medication use	…	…	…	…
Cholesterol‐lowering medication	4145 (18.8)	23 094 (13.6)	4557 (15.6)	<0.001
Antihypertensive medication	1747 (7.9)	13 420 (7.9)	3127 (10.7)	<0.001
Townsend deprivation index	−2.08 (−3.61 to 0.61)	−2.30 (−3.73 to 0.16)	−2.29 (−3.71 to 0.20)	<0.001
CHIP	1326 (6.0)	9659 (5.7)	1839 (6.3)	<0.001

Open in a new tab

Group characteristics are presented as mean (SD) or median (interquartile range) for continuous variables, as appropriate, and as number (%) for categorical variables. Continuous participant characteristics were compared between birth weight groups using ANOVA or the Kruskal–Wallis test, as appropriate, while categorical characteristics were compared using Pearson χ² tests. BMI indicates body mass index; CHIP, clonal hematopoiesis of indeterminate potential; HDL, high‐density lipoprotein; and LDL, low‐density lipoprotein.

Percentages indicate the proportion of all women with data available on history of menopause (n=113 899/135344 women in total).

^{^†}

UK Biobank participants were asked to answer the question “What is your ethnic group?”, and could select “White,” “Mixed,” “Asian or Asian British,” “Black or Black British,” “Chinese,” “Other ethnic group,” or “Do not know”/”Prefer not to answer.” Participants who selected “Other ethnic group” are listed as “Other”.

CHIP Prevalence

The overall prevalence of CHIP among individuals included in the current study was 5.8% (n=12 824/221 047). The median VAF among CHIP carriers was 9% (interquartile range, 6%–16%), and CHIP clones with VAF >10% were observed in 2.6% (n=5706/221 047) of all participants. Most driver mutations were detected in DNMT3A, TET2, and ASXL1, affecting 3.3% (n=7217/221 047), 1.2% (n=2551/221 047), and 0.6% (n=1252/221 047) of all participants, respectively, and collectively accounting for 82.4% (n=10 563/12 824) of all CHIP cases. Multiple CHIP clones were detected in 7.1% of all CHIP carriers (n=907/12 824).

Birth Weight is Independently Associated With CHIP in Observational Analyses

Compared with CHIP prevalence in participants with normal birth weight (5.7%, n=9659/169 725), CHIP was significantly more common in individuals with low (6.0%, n=1326/22030; P=0.049) or high (6.3%, n=1839/29 292; P<0.001) birth weight in unadjusted analyses. Correspondingly, unadjusted CHIP prevalence increased progressively as birth weight decreased below ≈2.5 kg or increased above ≈4 kg (Figure S2).

In multivariable‐adjusted analysis (Figure 2, Table S6), birth weight was associated with any CHIP, yielding an OR of 1.03 (95% CI, 1.00–1.06; P=0.04) per 1‐kg increase in birth weight. This association appeared to be primarily driven by an increased odds of DNMT3A CHIP (OR, 1.04 [95% CI, 1.01–1.08] per 1‐kg increase in birth weight; P=0.02), rather than TET2, ASXL1, or non‐DNMT3A/TET2/ASXL1 CHIP. Furthermore, birth weight was associated with CHIP with VAF >10% (OR, 1.04 [95% CI, 1.00–1.08] per 1‐kg increase in birth weight; P=0.049) with consistent direction of association across driver mutations other than ASXL1 (Figure S3). In addition, among those with CHIP, high birth weight was associated with presence of multiple CHIP clones (OR, 1.23 [95% CI, 1.02–1.48]; P=0.03) but not with VAF (β, 0.003 [95% CI, −0.002 to 0.009]; P=0.19).

Odds ratios for any CHIP (A), *DNMT3A* CHIP (B), *TET2* CHIP (C), *ASXL1* CHIP (D), non‐DTA CHIP (E), and CHIP with VAF >10% (F) were calculated using logistic regression models that were adjusted for sex, age, race and ethnicity, smoking status, the first 10 principal components of ancestry, prevalent coronary artery disease, prevalent diabetes type 2, Townsend deprivation index, and body mass index. Models with birth weight as independent variable were further adjusted for maternal smoking, and models with maternal smoking as independent variable were further adjusted for birth weight. CHIP indicates clonal hematopoiesis of indeterminate potential; DTA, *DNMT3A/TET2/ASXL1*; and VAF, variant allele frequency.

We also evaluated the associations of low and high birth weight as categorical variables with CHIP (Figure 2). The directions of the associations were consistent with those observed in the analyses of birth weight as a continuous exposure, although not significantly different, versus those with normal birth weight for overall CHIP. Analyses restricted to variants in the DNMT3A hot spot R882, which is frequently mutated in patients with acute myeloid leukemia, ⁴⁷ revealed no significant difference in the proportion of participants with these mutations among DNMT3A carriers with low (12.0%, n=87/726), high (13.0%, n=133/1022), or normal birth weight (11.9%, n=651/5469; P=0.60). Moreover, continuous birth weight did not significantly associate with DNMT3A CHIP driven by R882 mutations (OR, 1.08 [95% CI, 0.98–1.20]; P=0.14). There were no associations between exposure to maternal smoking and overall CHIP (OR, 1.01 [95% CI, 0.97–1.05]; P=0.60) or CHIP with VAF >10% (OR, 1.01 [95% CI, 0.95–1.07]; P=0.76). Maternal smoking around birth demonstrated a suggestive but nonsignificant association with ASXL1 CHIP, the subtype of CHIP most strongly associated with adult smoking ⁴⁸ , ⁴⁹ , with an adjusted OR of 1.12 (95% CI, 0.99–1.28; P=0.08).

Sensitivity analyses showed consistent results throughout (Figures S4 through S8). Results from analyses stratified by sex showed directionally similar results between men (Figure S9) and women (Figure S10), including after further adjustment for age at menopause (Figure S11). There was a significant association of maternal smoking with ASXL1 CHIP in women (OR, 1.34 [95% CI, 1.11–1.61]; P=0.002) but not men (OR, 0.97 [95% CI, 0.81–1.16]; P=0.76; P _interaction=0.05). This significant association among women persisted in multivariable‐adjusted logistic regression models further adjusted for household smoking status (≥1 versus no smoking household members), childhood smoking (age when started smoking ≤18 years versus >18 years or never smoked), and smoking status at study enrollment (smoking versus nonsmoking) (OR, 1.29 [95% CI, 1.07–1.56]; P=0.007). Additional analyses stratified by age at blood draw <60 versus ≥60 years also yielded comparable results for the primary analysis (Figures S12 and S13). However, the associations between birth weight and DNMT3A CHIP were stronger among individuals <60 years old (OR, 1.06 [95% CI, 1.01–1.12] per 1‐kg increase in birth weight; P=0.03), whereas they attenuated in individuals ≥60 years old (OR, 1.03 [95% CI, 0.98–1.08] per 1‐kg increase in birth weight; P=0.43; P _interaction=0.54).

Mendelian Randomization Supports a Causal Effect of Gestational Age on CHIP

We next performed 2‐sample MR to infer the causal effects of gestational age and birth weight Z score standardized for gestational age on CHIP acquisition. A total of 96 and 57 genome‐wide significant (P<5×10⁻⁵), uncorrelated (linkage disequilibrium R ²<0.0001) SNVs were included in the primary genetic instruments for gestational age and birth weight Z score, respectively (Tables S4 and S5). MR demonstrated an effect of greater gestational age at delivery on DNMT3A CHIP with an OR of 1.11 (95% CI, 1.01–1.23) per 1‐SD increase in genetically predicted gestational age (β, 0.11 [95% CI, 0.01–0.21]; P=0.03; Figure 3, Table S7). However, birth weight Z score was not associated with DNMT3A CHIP. There were no significant effects of gestational age or birth weight Z score on overall CHIP or TET2 CHIP.

MR analyses were performed to infer causal associations between the main drivers of birth weight (ie, gestational age and birth weight Z score, respectively) and CHIP. The SNVs comprising each genetic instrument were determined by identifying genome‐wide significant (P <5×10⁻⁵), uncorrelated (linkage disequilibrium R ² <0.0001) SNVs for gestational age and birth weight Z score in genome‐wide association study summary statistics for both traits. ⁴⁰ , ⁴¹ Sensitivity analyses included using the inverse variance weighted MR method, applying significance thresholds of P <5×10⁻⁸ and P <5×10⁻³, clumping at linkage disequilibrium R ² <0.2, and performing 1‐sample MR analyses. For 1‐sample MR analyses, 450 897 UK Biobank participants with genetic data, CHIP calls, and no missing covariates were included. Associations of genetic risk scores with CHIP phenotypes (ie, 1‐sample MR analyses) were tested using logistic regression models adjusted for sex, age, age², genotype array, race and ethnicity, and the first 10 principal components of ancestry. All associations are expressed per SD increase in genetically predicted gestational age or birth weight Z score. *The significance thresholds for selecting instrumental variables were P <5×10⁻⁵ and P <5×10⁻⁸ for the inverse variance weighted MR analyses of gestational age and birth weight Z score, respectively. CHIP indicates clonal hematopoiesis of indeterminate potential; IVW, inverse variance weighted; LD, linkage disequilibrium; MR, Mendelian randomization; RAPS, robust adjusted profile score; and SNVs, single‐nucleotide variants.

The effect of gestational age on DNMT3A CHIP was consistent in all sensitivity analyses except 1 restricted to a single SNV with genome‐wide significance (Figure 3, Table S7). The MR‐Egger intercept and MR‐pleiotropy residual sum and outlier global test suggested that the genetic association of gestational age with CHIP was not driven by horizontal pleiotropy, with effect estimates that were directionally consistent with those from the primary analysis (Table S8). Consistent with the 2‐sample approach, 1‐sample MR analysis revealed a significant association of genetically predicted gestational age with DNMT3A CHIP (β, 0.11 [95% CI, 0.01–0.21]; P=0.03; Figure 3).

CHIP Is Associated With Additive Risk for Incident CVD Across Birth Weight Categories

Follow‐up for the composite outcome of incident CVD or death occurred over a median 11.0 (interquartile range, 10.2–11.7) years among participants without prevalent CVD at baseline. Incident CVD or death affected 18 837 (8.8%) participants, with coronary artery disease in 10 325 (4.8%) participants, myocardial infarction in 3062 (1.4%), stroke in 1946 (0.9%), and death in 10 434 (4.7%). Crude cumulative incidence of the composite outcome during follow‐up was greater among those born at low (10.3%, n=21 82/21 089; χ², P<0.001) or high (10.0%, n=2808/28 006; P<0.001) than those with normal birth weight (8.4%, n=13 848/164 079; Ref.; Table S9). Cumulative incidence of CVD or death was more common in participants with CHIP (12.9%, n=1570/12 193) than those without (8.6%, n=17 267/200 981; P<0.001).

Low birth weight was associated with a 12% increased risk of the composite outcome (adjusted hazard ratio [HR], 1.12 [95% CI, 1.07–1.17]; P<0.001), which was mainly driven by an increased risk of CVD (HR, 1.17 [95% CI, 1.10–1.24]; P<0.001). The association of low birth weight with higher CVD risk was significantly stronger in women versus men (HR, 1.10 [95% CI, 1.01–1.20]; P=0.04) and women (HR, 1.21 [95% CI, 1.12–1.31]; P<0.001), although significantly stronger in the latter group (P _interaction=0.02). This association in women persisted after adjusting for history of menopause (HR, 1.16 [95% CI, 1.06–1.26]; P<0.001) and age at menopause (HR, 1.14 [95% CI, 1.04–1.25]; P=0.006). By contrast, although unadjusted incidence of CVD or death was higher in those with high birth weight, this association attenuated after multivariable adjustment (HR, 0.98 [95% CI, 0.94–1.02]; P=0.36), suggesting this relationship was explained by conventional CVD risk factors (Table S9). In analyses evaluating birth weight as a continuous variable, each kg increase in birth weight was associated with an HR of 0.94 (95% CI, 0.92–0.96; P<0.001; Table S10). CHIP was associated with a 15% higher risk of CVD or death (HR, 1.15 [95% CI, 1.10–1.22]; P<0.001; Table S11), with greater risk observed among those who had CHIP with VAF >10% (HR, 1.23 [95% CI, 1.14–1.32]; P<0.001). Analyses of gene‐specific CHIP subtypes showed that DNMT3A, TET2, and ASXL1 CHIP yielded HRs for the composite outcome of CVD or death of 1.09 (95% CI, 1.01–1.17; P=0.02), 1.13 (95% CI, 1.02–1.27; P=0.02), and 1.29 (95% CI, 1.12–1.50; P<0.001) versus those without CHIP, respectively.

Next, we evaluated the joint association of birth weight and CHIP with the risk of CVD or death and whether CHIP status modified the association of birth weight with outcomes (Figure 4). Individuals with no CHIP and normal birth weight had the lowest crude cumulative incidence of the composite outcome (8.2%, n=12 715/154 863; Table 2). In presence of CHIP, both those with low and high birth weight had similar crude cumulative incidence and significantly increased adjusted hazards for the composite outcome of CVD or death (low birth weight and CHIP: HR, 1.33 [95% CI, 1.15–1.54]; P<0.001; high birth weight and CHIP: HR, 1.16 [95% CI, 1.02–1.31]; P=0.02). Comparable patterns were observed for the joint association of birth weight and DNMT3A CHIP, although associations of DNMT3A CHIP and normal and high birth weight with the composite outcome attenuated slightly (Table S12; Figure S14). CHIP carriers with low birth weight demonstrated the highest incidence rates for CVD throughout (Figure S15), and CHIP was associated with additive risk of CVD or death across birth weight categories. There were no statistically significant interactions between birth weight and any CHIP (Table S13) or DNMT3A CHIP (Table S14) for incident CVD or death.

CHIP indicates clonal hematopoiesis of indeterminate potential; and CVD, cardiovascular disease.

Table 2.

Incidence of Cardiovascular Disease or Death by Birth Weight Category and CHIP Status

CHIP		Low birth weight (<2.5 kg)	Normal birth weight (2.5–4 kg)	High birth weight (>4 kg)
Associations of CHIP vs no CHIP stratified by birth weight category
No	Adjusted HR (95% CI)	Ref.	Ref.	Ref.
	Crude cumulative incidence	10.1%	8.2%	9.7%
		(2000/19843)	(12 715/154863)	(2552/26275)
Yes	Adjusted HR (95% CI)	1.20 (1.03–1.40)^*	1.14 (1.07–1.21)^†	1.17 (1.04–1.34)^*
	Crude cumulative incidence	14.5%	12.3%	14.8%
		(181/1246)	(1133/9216)	(256/1731)
Joint associations of birth weight and CHIP categories compared with those with normal birth weight and no CHIP
No	Adjusted HR (95% CI)	1.12 (1.06–1.17)^†	Ref.	0.98 (0.94–1.02)
	Crude cumulative incidence	10.1%	8.2%	9.7%
		(2000/19843)	(12 715/154863)	(2552/26275)
Yes	Adjusted HR (95% CI)	1.33 (1.15–1.54)^†	1.14 (1.08–1.21)^†	1.16 (1.02–1.31)^*
	Crude cumulative incidence	14.5%	12.3%	14.8%
		(181/1246)	(1133/9216)	(256/1731)

Open in a new tab

Cumulative incidences are represented as percentage (number of cases with incident events/total number of participants). Hazard ratios for incident cardiovascular disease or death were calculated using Cox proportional hazard models that were adjusted for sex, age, race and ethnicity, smoking status, the first 10 principal components of ancestry, prevalent diabetes type 2, BMI, systolic blood pressure, blood pressure medication use, total cholesterol, HDL cholesterol, cholesterol‐lowering medication use, and Townsend deprivation index. In the upper part of the table, associations are shown for participants with vs without CHIP for all birth weight categories separately. In the lower part of the table, all participants were allocated to 1 of 6 categories on the basis of birth weight category (low vs high vs normal) and presence of CHIP (yes vs no); this variable was included as a categorical, nonordered variable, with normal birth weight without CHIP serving as the reference group. BMI indicates body mass index; CHIP, clonal hematopoiesis of indeterminate potential; HDL, high‐density lipoprotein; HR, hazard ratio; and LDL, low‐density lipoprotein.

P<0.05.

^{^†}

P<0.001.

In multivariable‐adjusted models including CHIP status as a covariate, higher continuous birth weight was nominally associated with increased risk of cancer (HR, 1.02 [95% CI, 1.00–1.05] per 1‐kg increase in birth weight; P=0.04) but not hematologic malignancy or acute myeloid leukemia (Table S10). None of these associations were statistically significant when testing birth weight as a categorical variable (Table S11). There were statistically significant interactions between birth weight as a continuous variable and CHIP (Table S13) and DNMT3A CHIP (Table S14) in models for incident acute myeloid leukemia. CHIP carriers with high birth weight had the highest incidence of cancer, hematologic malignancy, and acute myeloid leukemia (Figure S16).

Discussion

In this large population‐based cohort of adults with next‐generation sequencing, higher birth weight was independently associated with increased odds of having CHIP. Birth weight was most strongly associated with DNMT3A CHIP, and MR suggested that this relationship was causally driven by gestational age rather than large‐for‐gestational‐age status. Additionally, CHIP associated with an increased risk of CVD or death across birth weight categories, with the highest rates of these outcomes observed in participants with high or low birth weight and CHIP. These findings implicate birth weight as a novel risk factor for CHIP and provide insights into the complex relationships among early‐life environment, CHIP, cancer, and CVD.

First, early‐life exposures influence risk of acquiring CHIP later in life. Recent studies have yielded new insights into the longitudinal dynamics of clonal hematopoiesis and CHIP. ²⁶ , ⁵⁰ , ⁵¹ , ⁵² , ⁵³ , ⁵⁴ A large proportion of adults have latent CHIP mutations, which often originate at low VAF decades before the CHIP‐defining VAF threshold of 2% is reached. ²⁵ , ²⁶ , ⁵⁰ , ⁵¹ Emerging evidence indicates that these mutations may arise as early as during fetal development. ²⁵ Furthermore, animal and human studies have demonstrated that the hematopoietic stem cell compartment expands rapidly in utero but abruptly becomes quiescent during the early postnatal period. ²⁶ , ⁵⁵ Prolonged fetal growth may therefore lead to a higher number of hematopoietic stem cells, increasing the amount of replicating cells at risk for converting into clones carrying CHIP mutations. Furthermore, recent studies that modeled the clonal expansion of hematopoietic stem cells across the human life span have shown that the rate at which somatic mutations are acquired is substantially higher during prenatal than postnatal life. ²⁶ , ⁵⁰ , ⁵⁶ This pattern is particularly pronounced for DNMT3A CHIP, with previous research indicating that DNMT3A‐driven clones exhibit rapid growth early in life, followed by significant deceleration over time. ⁵⁰ This contrasts with other CHIP forms such as those driven by spliceosome genes (eg, SRSF2 and U2AF1), which predominantly emerge and grow later in life. Together with the putative causal relationship between gestational age and CHIP shown in the present study, these findings support the idea that fetal development constitutes a critical period in the process of clonal hematopoiesis.

Second, both high and low birth weight portend heightened risk of later‐life CVD, likely through distinct mechanisms. Our findings imply that low birth weight is linked to late‐onset CVD through mechanisms other than CHIP. Previous work has implicated changes in cardiac structure and function, ⁵⁷ , ⁵⁸ , ⁵⁹ , ⁶⁰ increased oxidative stress levels, ⁶¹ renin–angiotensin–aldosterone system imbalance, ⁶² and augmented systemic inflammation ⁶³ , ⁶⁴ as possible factors underlying the independent associations of low birth weight with incident CVD in adulthood. In contrast with low birth weight, the associations between high birth weight and late‐onset CVD appear to be predominantly mediated by conventional risk factors. ⁶⁵ Higher rates of overweight and obesity have been proposed as key contributors to the higher CVD rates in adults with high versus those with normal birth weight. ¹⁵ , ⁶⁶ While previous MR analysis has suggested that BMI may play a causal role in the development of larger CHIP clones, ⁶⁷ the present study showed that the association of birth weight with CHIP was independent of adult BMI, suggesting a distinct pathway from higher birth weight to CHIP acquisition. Importantly, this study found birth weight increases CHIP risk later in life mainly through mutations in DNMT3A. Prior studies have shown that DNMT3A CHIP exhibits a relatively weaker association with cardiovascular outcomes in comparison with other CHIP subtypes. ³⁹ , ⁶⁸ Nonetheless, this study suggests that DNMT3A CHIP is associated with the composite outcome of CVD and death. This aligns with previous research in smaller cohorts demonstrating that DNMT3A mutations portend adverse prognosis in populations at heightened cardiovascular risk, ⁶⁹ , ⁷⁰ as well as with experimental data demonstrating that DNMT3A CHIP may cause increased expression of proinflammatory genes leading to myocardial fibrosis. ⁷¹ , ⁷² While future research is needed to elucidate pathways to mitigate DNMT3A CHIP‐related cardiovascular risk, the present study corroborates the notion that DNMT3A CHIP may portend worse outcomes.

Third, this study expands a relatively short list of clinical risk factors for CHIP identified to date. The current study demonstrates a modest but significant association between birth weight and CHIP and may therefore help identify a subset of the population at increased risk for developing CHIP. In doing so, this study extends the findings of previous studies establishing chronologic age, ⁷³ , ⁷⁴ smoking, ⁴⁸ , ⁴⁹ diet quality, ⁷⁵ premature menopause, ²² HIV infection, ⁷⁶ , ⁷⁷ chemotherapy, ⁷⁸ and radiation exposure ⁷⁹ as risk factors for CHIP. ⁸⁰ CHIP detection via targeted screening could inform intensity of preventive CVD management and hematologic surveillance, but indiscriminate population‐wide screening for CHIP is impractical and not currently recommended. ⁸¹ Screening in populations at heightened risk, however, would be more cost effective. CHIP risk prediction will gain increasing clinical actionability as next‐generation sequencing costs continue to fall over time and as CHIP‐focused therapeutics emerge that call for a precision medicine approach.

Strengths of this study include detailed phenotyping, next‐generation sequencing, and longitudinal follow‐up in a uniquely large number of individuals. Several limitations must also be considered when interpreting these findings. First, birth weight was ascertained by participant self‐report, which may result in misclassification, although previous studies demonstrate that self‐reported birth weight correlates well with birth weight ascertained through medical records. ⁸² , ⁸³ Second, as most included participants were White, the generalizability of our findings to other races and ethnicities is limited. Third, because CHIP was only ascertained at baseline in the UK Biobank, we were unable to test whether longitudinal trajectories of clonal hematopoiesis differed between birth weight groups and whether CHIP carriers with high birth weight acquired driver mutations early in life. Fourth, previous studies using deep targeted sequencing methods have shown that smaller clones (ie, VAF <0.02) are present in a considerable proportion of elderly individuals. ²⁶ , ⁸⁴ While prior work suggests that some of these smaller clones may associate with cardiovascular outcomes in selected populations, ⁶⁹ it is unclear if low‐VAF clonal hematopoiesis predicts adverse outcomes in the general population. ⁸⁵ However, as the present study used large‐scale whole exome sequencing data rather than deep targeted sequencing, reliable assessment of smaller clones was not possible. ³⁷ Fifth, we did not have information on gestational age or birth weight Z score, precluding observational assessment of distinct contributions to the association between birth weight and CHIP, although MR analyses suggested that gestational age was the primary driver of observed associations.

In summary, we observed that birth weight is an independent predictor of CHIP acquisition in adulthood and that gestational age has a likely causal role in this relationship, yielding novel insights into the development of CHIP. In addition, CHIP stratified risk of incident CVD and death across birth weight categories. Further research is necessary to clarify mechanisms underlying these observed associations and determine implications for risk prediction and precision therapeutics.

Sources of Funding

A. Schuermans is supported by a grant from the Belgian American Educational Foundation. Dr Yu is supported by the National Heart, Lung, and Blood Institute (5T32HL007604‐37). Dr Lewandowski is supported by a British Heart Foundation Intermediate Research Fellowship (FS/18/3/33292). Dr Bick is supported by a Burroughs Wellcome Foundation Career Award for Medical Scientists and the National Institute of Health Director's Early Independence Award (DP5‐OD029586). Dr Niroula is supported by funding from the Knut and Alice Wallenberg Foundation (KAW 2017.0436). Dr Jaiswal is supported by the Burroughs Wellcome Fund Career Award for Medical Scientists, Fondation Leducq (TNE‐18CVD04), the Ludwig Center for Cancer Stem Cell Research at Stanford University, and the National Institutes of Health Director's New Innovator Award (DP2‐HL157540). Dr Ebert is supported by Fondation Leducq. Dr Natarajan is supported by a Hassenfeld Scholar Award from the Massachusetts General Hospital and grants from the National Heart, Lung, and Blood Institute (R01HL1427, R01HL148565, and R01HL148050) and from Fondation Leducq (TNE‐18CVD04). Dr Honigberg is supported by the National Heart, Lung, and Blood Institute (K08HL166687) and the American Heart Association (940166, 979465).

Disclosures

Dr Natarajan reports grant support from Amgen, Apple, AstraZeneca, Boston Scientific, and Novartis; spousal employment and equity at Vertex; consulting income from Apple, AstraZeneca, Novartis, Genentech/Roche, Blackstone Life Sciences, Foresite Labs, and TenSixteen Bio; and is a scientific advisory board member and shareholder of TenSixteen Bio and geneXwell, all unrelated to this work. Dr Ebert has received research funding from Celgene, Deerfield, Novartis, and Calico and consulting fees from GRAIL. He is a member of the scientific advisory board and shareholder for Neomorph Inc., TenSixteen Bio, Skyhawk Therapeutics, and Exo Therapeutics. Dr Bick and Dr Jaiswal are cofounders and equity holders in TenSixteen Bio. Dr Honigberg reports consulting fees from CRISPR Therapeutics, advisory board service for Miga Health, and grant support from Genentech. The remaining authors have no disclosures to report.

Supporting information

Data S1–S2

Tables S1–S14

Figures S1–S16

References 83–88

Click here for additional data file.^{(2.9MB, pdf)}

Acknowledgments

This research was conducted under UK Biobank application number 7089. The authors thank all UK Biobank participants.

This manuscript was sent to Jacquelyn Y. Taylor, PhD, PNP‐BC, RN, FAHA, FAAN, Guest Editor, for review by expert referees, editorial decision, and final disposition.

Supplemental Material is available at https://www.ahajournals.org/doi/suppl/10.1161/JAHA.123.030220

For Sources of Funding and Disclosures, see page 12.

REFERENCES

1. Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, Barengo NC, Beaton A, Benjamin EJ, Benziger CP, et al. Global burden of cardiovascular diseases and risk factors, 1990–2019: update from the GBD 2019 study. J Am Coll Cardiol. 2020;76:2982–3021. doi: 10.1016/J.JACC.2020.11.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
2. Abbafati C, Abbas KM, Abbasi‐Kangevari M, Abd‐Allah F, Abdelalim A, Abdollahi M, Abdollahpour I, Abegaz KH, Abolhassani H, Aboyans V, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019. Lancet. 2020;396:1204–1222. doi: 10.1016/S0140-6736(20)30925-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
3. Hayman LL, Meininger JC, Daniels SR, McCrindle BW, Helden L, Ross J, Dennison BA, Steinberger J, Williams CL. Primary prevention of cardiovascular disease in nursing practice: focus on children and youth. Circulation. 2007;116:344–357. doi: 10.1161/CIRCULATIONAHA.107.184595 [DOI] [PubMed] [Google Scholar]
4. Godfrey KM, Barker DJ. Fetal programming and adult health. Public Health Nutr. 2001;4:611–624. doi: 10.1079/PHN2001145 [DOI] [PubMed] [Google Scholar]
5. Ramakrishnan U. Nutrition and low birth weight: from research to practice. Am J Clin Nutr. 2004;79:17–21. doi: 10.1093/AJCN/79.1.17 [DOI] [PubMed] [Google Scholar]
6. Gillman MW, Rifas‐Shiman S, Berkey CS, Field AE, Colditz GA. Maternal gestational diabetes, birth weight, and adolescent obesity. Pediatrics. 2003;111:e221–e226. doi: 10.1542/PEDS.111.3.E221 [DOI] [PubMed] [Google Scholar]
7. Frankel S, Elwood P, Sweetnam P, Yarnell J, Davey SG. Birthweight, body‐mass index in middle age, and incident coronary heart disease. Lancet. 1996;348:1478–1480. doi: 10.1016/S0140-6736(96)03482-4 [DOI] [PubMed] [Google Scholar]
8. Zanetti D, Tikkanen E, Gustafsson S, Priest JR, Burgess S, Ingelsson E. Birthweight, type 2 diabetes mellitus, and cardiovascular disease: addressing the Barker hypothesis with Mendelian randomization. Circ Genom Precis Med. 2018;11:e002054. doi: 10.1161/CIRCGEN.117.002054 [DOI] [PMC free article] [PubMed] [Google Scholar]
9. Rashid A, Agarwala A, Novak E, Brown DL. Association of high birth weight with incident heart failure in the ARIC study. J Am Heart Assoc. 2019;8:e011524. doi: 10.1161/JAHA.118.011524 [DOI] [PMC free article] [PubMed] [Google Scholar]
10. Sandovici I, Smith NH, Nitert MD, Ackers‐Johnson M, Uribe‐Lewis S, Ito Y, Jones RH, Marquez VE, Cairns W, Tadayyon M, et al. Maternal diet and aging alter the epigenetic control of a promoter‐enhancer interaction at the Hnf4a gene in rat pancreatic islets. Proc Natl Acad Sci USA. 2011;108:5449–5454. doi: 10.1073/PNAS.1019007108 [DOI] [PMC free article] [PubMed] [Google Scholar]
11. Heijmans BT, Tobi EW, Stein AD, Putter H, Blauw GJ, Susser ES, Slagboom PE, Lumey LH. Persistent epigenetic differences associated with prenatal exposure to famine in humans. Proc Natl Acad Sci USA. 2008;105:17046–17049. doi: 10.1073/PNAS.0806560105 [DOI] [PMC free article] [PubMed] [Google Scholar]
12. Agha G, Hajj H, Rifas‐Shiman SL, Just AC, Hivert MF, Burris HH, Lin X, Litonjua AA, Oken E, DeMeo DL, et al. Birth weight‐for‐gestational age is associated with DNA methylation at birth and in childhood. Clin Epigenetics. 2016;8:1–12. doi: 10.1186/S13148-016-0285-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
13. Guénard F, Deshaies Y, Cianflone K, Kral JG, Marceau P, Vohl MC. Differential methylation in glucoregulatory genes of offspring born before vs. after maternal gastrointestinal bypass surgery. Proc Natl Acad Sci USA. 2013;110:11439–11444. doi: 10.1073/PNAS.1216959110 [DOI] [PMC free article] [PubMed] [Google Scholar]
14. Skilton MR, Siitonen N, Würtz P, Viikari JSA, Juonala M, Seppälä I, Laitinen T, Lehtimäki T, Taittonen L, Kähönen M, et al. High birth weight is associated with obesity and increased carotid wall thickness in young adults: the cardiovascular risk in young Finns study. Arterioscler Thromb Vasc Biol. 2014;34:1064–1068. doi: 10.1161/ATVBAHA.113.302934 [DOI] [PubMed] [Google Scholar]
15. Schellong K, Schulz S, Harder T, Plagemann A. Birth weight and long‐term overweight risk: systematic review and a meta‐analysis including 643,902 persons from 66 studies and 26 countries globally. PLoS One. 2012;7:e47776. doi: 10.1371/JOURNAL.PONE.0047776 [DOI] [PMC free article] [PubMed] [Google Scholar]
16. Steensma DP, Bejar R, Jaiswal S, Lindsley RC, Sekeres MA, Hasserjian RP, Ebert BL. Clonal hematopoiesis of indeterminate potential and its distinction from myelodysplastic syndromes. Blood. 2015;126:9. doi: 10.1182/BLOOD-2015-03-631747 [DOI] [PMC free article] [PubMed] [Google Scholar]
17. Xie M, Lu C, Wang J, McLellan MD, Johnson KJ, Wendl MC, McMichael JF, Schmidt HK, Yellapantula V, Miller CA, et al. Age‐related mutations associated with clonal hematopoietic expansion and malignancies. Nat Med. 2014;20:1472–1478. doi: 10.1038/nm.3733 [DOI] [PMC free article] [PubMed] [Google Scholar]
18. Genovese G, Kähler AK, Handsaker RE, Lindberg J, Rose SA, Bakhoum SF, Chambert K, Mick E, Neale BM, Fromer M, et al. Clonal hematopoiesis and blood‐cancer risk inferred from blood DNA sequence. N Engl J Med. 2014;371:2477–2487. doi: 10.1056/NEJMOA1409405 [DOI] [PMC free article] [PubMed] [Google Scholar]
19. Jaiswal S, Natarajan P, Silver AJ, Gibson CJ, Bick AG, Shvartz E, McConkey M, Gupta N, Gabriel S, Ardissino D, et al. Clonal hematopoiesis and risk of atherosclerotic cardiovascular disease. N Engl J Med. 2017;377:111–121. doi: 10.1056/NEJMOA1701719 [DOI] [PMC free article] [PubMed] [Google Scholar]
20. Sperling AS, Gibson CJ, Ebert BL. The genetics of myelodysplastic syndrome: from clonal hematopoiesis to secondary leukemia. Nat Rev Cancer. 2017;17:5. doi: 10.1038/NRC.2016.112 [DOI] [PMC free article] [PubMed] [Google Scholar]
21. Bick AG, Pirruccello JP, Griffin GK, Gupta N, Gabriel S, Saleheen D, Libby P, Kathiresan S, Natarajan P. Genetic interleukin 6 signaling deficiency attenuates cardiovascular risk in clonal hematopoiesis. Circulation. 2020;141:124–131. doi: 10.1161/CIRCULATIONAHA.119.044362 [DOI] [PMC free article] [PubMed] [Google Scholar]
22. Honigberg MC, Zekavat SM, Niroula A, Griffin GK, Bick AG, Pirruccello JP, Nakao T, Whitsel EA, Farland LV, Laurie C, et al. Premature menopause, clonal hematopoiesis, and coronary artery disease in postmenopausal women. Circulation. 2021;143:410–423. doi: 10.1161/CIRCULATIONAHA.120.051775 [DOI] [PMC free article] [PubMed] [Google Scholar]
23. Bhattacharya R, Zekavat SM, Haessler J, Fornage M, Raffield L, Uddin MM, Bick AG, Niroula A, Yu B, Gibson C, et al. Clonal hematopoiesis is associated with higher risk of stroke. Stroke. 2022;29:788–797. doi: 10.1161/STROKEAHA.121.037388 [DOI] [PMC free article] [PubMed] [Google Scholar]
24. Yu B, Roberts MB, Raffield LM, Zekavat SM, Nguyen NQH, Biggs ML, Brown MR, Griffin G, Desai P, Correa A, et al. Association of clonal hematopoiesis with incident heart failure. J Am Coll Cardiol. 2021;78:42–52. doi: 10.1016/J.JACC.2021.04.085 [DOI] [PMC free article] [PubMed] [Google Scholar]
25. Williams N, Lee J, Mitchell E, Moore L, Baxter EJ, Hewinson J, Dawson KJ, Menzies A, Godfrey AL, Green AR, et al. Life histories of myeloproliferative neoplasms inferred from phylogenies. Nature. 2022;602:162–168. doi: 10.1038/s41586-021-04312-6 [DOI] [PubMed] [Google Scholar]
26. Mitchell E, Spencer Chapman M, Williams N, Dawson KJ, Mende N, Calderbank EF, Jung H, Mitchell T, Coorens THH, Spencer DH, et al. Clonal dynamics of haematopoiesis across the human lifespan. Nature. 2022;606:343–350. doi: 10.1038/s41586-022-04786-y [DOI] [PMC free article] [PubMed] [Google Scholar]
27. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, Motyer A, Vukcevic D, Delaneau O, O'Connell J, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562:203–209. doi: 10.1038/s41586-018-0579-z [DOI] [PMC free article] [PubMed] [Google Scholar]
28. Huang C, Wei K, Ming P, Lee Y, Qin G, Yu Y, Li J. Maternal hypertensive disorder of pregnancy and mortality in offspring from birth to young adulthood: national population based cohort study. BMJ. 2022;379:e072157. doi: 10.1136/BMJ-2022-072157 [DOI] [PMC free article] [PubMed] [Google Scholar]
29. O'Neill KA, Murphy MFG, Bunch KJ, Puumala SE, Carozza SE, Chow EJ, Mueller BA, McLaughlin CC, Reynolds P, Vincent TJ, et al. Infant birthweight and risk of childhood cancer: international population‐based case control studies of 40 000 cases. Int J Epidemiol. 2015;44:153–168. doi: 10.1093/IJE/DYU265 [DOI] [PubMed] [Google Scholar]
30. Gissler M, Mohangoo AD, Blondel B, Chalmers J, MacFarlane A, Gaizauskiene A, Gatt M, Lack N, Sakkeus L, Zeitlin J. Perinatal health monitoring in Europe: results from the EURO‐PERISTAT project. Inform Health Soc Care. 2010;35:64–79. doi: 10.3109/17538157.2010.492923 [DOI] [PubMed] [Google Scholar]
31. International Classification of Diseases, Eleventh Revision (ICD‐11), World Health Organization (WHO) . Published 2019. Accessed October 26, 2022. https://icd.who.int/browse11/l‐m/en
32. van Hout CV, Tachmazidou I, Backman JD, Hoffman JD, Liu D, Pandey AK, Gonzaga‐Jauregui C, Khalid S, Ye B, Banerjee N, et al. Exome sequencing and characterization of 49,960 individuals in the UK Biobank. Nature. 2020;586:749–756. doi: 10.1038/s41586-020-2853-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
33. Backman JD, Li AH, Marcketta A, Sun D, Mbatchou J, Kessler MD, Benner C, Liu D, Locke AE, Balasubramanian S, et al. Exome sequencing and analysis of 454,787 UK Biobank participants. Nature. 2021;599:628–634. doi: 10.1038/s41586-021-04103-z [DOI] [PMC free article] [PubMed] [Google Scholar]
34. Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez C, Gabriel S, Meyerson M, Lander ES, Getz G. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol. 2013;31:213–219. doi: 10.1038/nbt.2514 [DOI] [PMC free article] [PubMed] [Google Scholar]
35. Uddin MM, Yu Z, Weinstock JS, Nakao T, Niroula A, Urbut SM, Koyama S, Zekavat SM, Paruchuri K, Silver AJ, et al. Germline genomic and phenomic landscape of clonal hematopoiesis in 323,112 individuals. medRxiv. Preprint posted online July 31, 2022. doi: 10.1101/2022.07.29.22278015 [DOI] [Google Scholar]
36. Niroula A, Sekar A, Murakami MA, Trinder M, Agrawal M, Wong WJ, Bick AG, Uddin MM, Gibson CJ, Griffin GK, et al. Distinction of lymphoid and myeloid clonal hematopoiesis. Nat Med. 2021;27:1921–1927. doi: 10.1038/s41591-021-01521-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
37. Bick AG, Weinstock JS, Nandakumar SK, Fulco CP, Bao EL, Zekavat SM, Szeto MD, Liao X, Leventhal MJ, Nasser J, et al. Inherited causes of clonal haematopoiesis in 97,691 whole genomes. Nature. 2020;586:763–768. doi: 10.1038/s41586-020-2819-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
38. Vlasschaert C, Mack T, Heimlich JB, Niroula A, Uddin MM, Weinstock JS, Sharber B, Silver AJ, Xu Y, Savona MR, et al. A practical approach to curate clonal hematopoiesis of indeterminate potential in human genetic datasets. Blood. 2023;141:2214–2223. doi: 10.1182/BLOOD.2022018825 [DOI] [PMC free article] [PubMed] [Google Scholar]
39. Liu X, Helenius D, Skotte L, Beaumont RN, Wielscher M, Geller F, Juodakis J, Mahajan A, Bradfield JP, Lin FTJ, et al. Variants in the fetal genome near pro‐inflammatory cytokine genes on 2q13 associate with gestational duration. Nat Commun. 2019;10:1–13. doi: 10.1038/s41467-019-11881-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
40. Horikoshi M, Yaghootkar H, Mook‐Kanamori DO, Sovio U, Taal HR, Hennig BJ, Bradfield JP, St Pourcain B, Evans DM, Charoen P, et al. New loci associated with birth weight identify genetic links between intrauterine growth and adult height and metabolism. Nat Genet. 2012;45:76–82. doi: 10.1038/ng.2477 [DOI] [PMC free article] [PubMed] [Google Scholar]
41. Zhao Q, Wang J, Hemani G, Bowden J, Small DS. Statistical inference in two‐sample summary‐data Mendelian randomization using robust adjusted profile score. Ann Stat. 2018;48:1742–1769. doi: 10.48550/arxiv.1801.09652 [DOI] [Google Scholar]
42. Bowden J, Smith GD, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512–525. doi: 10.1093/IJE/DYV080 [DOI] [PMC free article] [PubMed] [Google Scholar]
43. Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR‐Egger method. Eur J Epidemiol. 2017;32:377. doi: 10.1007/S10654-017-0255-X [DOI] [PMC free article] [PubMed] [Google Scholar]
44. Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693. doi: 10.1038/S41588-018-0099-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
45. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, Laurin C, Burgess S, Bowden J, Langdon R, et al. The MR‐base platform supports systematic causal inference across the human phenome. Elife. 2018;7:e34408. doi: 10.7554/ELIFE.34408 [DOI] [PMC free article] [PubMed] [Google Scholar]
46. Hemani G, Tilling K, Davey SG. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet. 2017;13:e1007081. doi: 10.1371/JOURNAL.PGEN.1007081 [DOI] [PMC free article] [PubMed] [Google Scholar]
47. Koya J, Kataoka K, Sato T, Bando M, Kato Y, Tsuruta‐Kishino T, Kobayashi H, Narukawa K, Miyoshi H, Shirahige K, et al. DNMT3A R882 mutants interact with polycomb proteins to block haematopoietic stem and leukaemic cell differentiation. Nat Commun. 2016;7:1–14. doi: 10.1038/ncomms10924 [DOI] [PMC free article] [PubMed] [Google Scholar]
48. Levin MG, Nakao T, Zekavat SM, Koyama S, Bick AG, Niroula A, Ebert B, Damrauer SM, Natarajan P. Genetics of smoking and risk of clonal hematopoiesis. Sci Rep. 2022;12:1–5. doi: 10.1038/s41598-022-09604-z [DOI] [PMC free article] [PubMed] [Google Scholar]
49. Dawoud AAZ, Tapper WJ, Cross NCP. Clonal myelopoiesis in the UK Biobank cohort: ASXL1 mutations are strongly associated with smoking. Leukemia. 2020;34:2660–2672. doi: 10.1038/s41375-020-0896-8 [DOI] [PubMed] [Google Scholar]
50. Fabre MA, de Almeida JG, Fiorillo E, Mitchell E, Damaskou A, Rak J, Orrù V, Marongiu M, Chapman MS, Vijayabaskar MS, et al. The longitudinal dynamics and natural history of clonal haematopoiesis. Nature. 2022;606:335–342. doi: 10.1038/s41586-022-04785-z [DOI] [PMC free article] [PubMed] [Google Scholar]
51. Robertson NA, Latorre‐Crespo E, Terradas‐Terradas M, Lemos‐Portela J, Purcell AC, Livesey BJ, Hillary RF, Murphy L, Fawkes A, MacGillivray L, et al. Longitudinal dynamics of clonal hematopoiesis identifies gene‐specific fitness effects. Nat Med. 2022;28:1439–1446. doi: 10.1038/s41591-022-01883-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
52. Uddin MM, Zhou Y, Bick AG, Burugula BB, Jaiswal S, Desai P, Honigberg MC, Love SA, Barac A, Hayden KM, et al. Longitudinal profiling of clonal hematopoiesis provides insight into clonal dynamics. Immun Ageing. 2022;19:1–10. doi: 10.1186/S12979-022-00278-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
53. Watson CJ, Papula AL, Poon GYP, Wong WH, Young AL, Druley TE, Fisher DS, Blundell JR. The evolutionary dynamics and fitness landscape of clonal hematopoiesis. Science. 2020;367:1449–1454. doi: 10.1126/SCIENCE.AAY9333 [DOI] [PubMed] [Google Scholar]
54. Lee‐Six H, Øbro NF, Shepherd MS, Grossmann S, Dawson K, Belmonte M, Osborne RJ, Huntly BJP, Martincorena I, Anderson E, et al. Population dynamics of normal human blood inferred from somatic mutations. Nature. 2018;561:473–478. doi: 10.1038/s41586-018-0497-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
55. Bowie MB, McKnight KD, Kent DG, McCaffrey L, Hoodless PA, Eaves CJ. Hematopoietic stem cells proliferate until after birth and show a reversible phase‐specific engraftment defect. J Clin Investig. 2006;116:2808. doi: 10.1172/JCI28310 [DOI] [PMC free article] [PubMed] [Google Scholar]
56. Spencer Chapman M, Ranzoni AM, Myers B, Williams N, Coorens THH, Mitchell E, Butler T, Dawson KJ, Hooks Y, Moore L, et al. Lineage tracing of human development through somatic mutations. Nature. 2021;595:85–90. doi: 10.1038/s41586-021-03548-6 [DOI] [PubMed] [Google Scholar]
57. Schuermans A, Lewandowski AJ. Understanding the preterm human heart: what do we know so far? Anat Rec (Hoboken). 2022;305:2099–2112. doi: 10.1002/ar.24875 [DOI] [PMC free article] [PubMed] [Google Scholar]
58. Lewandowski AJ, Augustine D, Lamata P, Davis EF, Lazdam M, Francis J, McCormick K, Wilkinson AR, Singhal A, Lucas A, et al. Preterm heart in adult life: cardiovascular magnetic resonance reveals distinct differences in left ventricular mass, geometry, and function. Circulation. 2013;127:197–206. doi: 10.1161/CIRCULATIONAHA.112.126920 [DOI] [PubMed] [Google Scholar]
59. Lewandowski AJ, Raman B, Bertagnolli M, Mohamed A, Williamson W, Pelado JL, McCance A, Lapidaire W, Neubauer S, Leeson P. Association of preterm birth with myocardial fibrosis and diastolic dysfunction in young adulthood. J Am Coll Cardiol. 2021;78:683–692. doi: 10.1016/j.jacc.2021.05.053 [DOI] [PMC free article] [PubMed] [Google Scholar]
60. Schuermans A, den Harink T, Raman B, Smillie RW, Alsharqi M, Mohamed A, Lapidaire W, van Deutekom AW, Leeson P, et al. Differing impact of preterm birth on the right and left atria in adulthood. J Am Heart Assoc. 2022;11:27305. doi: 10.1161/JAHA.122.027305 [DOI] [PMC free article] [PubMed] [Google Scholar]
61. Gupta P, Narang M, Banerjee BD, Basu S. Oxidative stress in term small for gestational age neonates born to undernourished mothers: a case control study. BMC Pediatr. 2004;4:1–7. doi: 10.1186/1471-2431-4-14/TABLES/3 [DOI] [PMC free article] [PubMed] [Google Scholar]
62. Paquette K, Fernandes RO, Xie LF, Cloutier A, Fallaha C, Girard‐Bock C, Mian MOR, Lukaszewski MA, Mâsse B, El‐Jalbout R, et al. Kidney size, renal function, Ang (angiotensin) peptides, and blood pressure in young adults born preterm. Hypertension. 2018;72:918–928. doi: 10.1161/HYPERTENSIONAHA.118.11397 [DOI] [PubMed] [Google Scholar]
63. Nordman H, Voutilainen R, Antikainen L, Jääskeläinen J. Plasma IL‐1 receptor antagonist concentration has an inverse association with birth weight in prepubertal children. J Endocr Soc. 2018;2:232–239. doi: 10.1210/JS.2017-00437 [DOI] [PMC free article] [PubMed] [Google Scholar]
64. Sattar N, McConnachie A, O'Reilly D, Upton MN, Greer IA, Davey Smith G, Watt G. Inverse association between birth weight and C‐reactive protein concentrations in the MIDSPAN Family Study. Arterioscler Thromb Vasc Biol. 2004;24:583–587. doi: 10.1161/01.ATV.0000118277.41584.63 [DOI] [PubMed] [Google Scholar]
65. Nordman H, Jääskeläinen J, Voutilainen R. Birth size as a determinant of cardiometabolic risk factors in children. Horm Res Paediatr. 2020;93:144–153. doi: 10.1159/000509932 [DOI] [PubMed] [Google Scholar]
66. Geserick M, Vogel M, Gausche R, Lipek T, Spielau U, Keller E, Pfäffle R, Kiess W, Körner A. Acceleration of BMI in early childhood and risk of sustained obesity. N Engl J Med. 2018;379:1303–1312. doi: 10.1056/NEJMOA1803527 [DOI] [PubMed] [Google Scholar]
67. Kar SP, Quiros PM, Gu M, Jiang T, Mitchell J, Langdon R, Iyer V, Barcena C, Vijayabaskar MS, Fabre MA, et al. Genome‐wide analyses of 200,453 individuals yield new insights into the causes and consequences of clonal hematopoiesis. Nat Genet. 2022;54:1155–1166. doi: 10.1038/s41588-022-01121-z [DOI] [PMC free article] [PubMed] [Google Scholar]
68. Arends CM, Liman TG, Strzelecka PM, Kufner A, Löwe P, Huo S, Stein CM, Piper SK, Tilgner M, Sperber PS, et al. Associations of clonal haematopoiesis with recurrent vascular events and death in patients with incident ischemic stroke. Blood. 2023;141:787–799. doi: 10.1182/BLOOD.2022017661 [DOI] [PubMed] [Google Scholar]
69. Assmus B, Cremer S, Kirschbaum K, Culmann D, Kiefer K, Dorsheimer L, Rasper T, Abou‐El‐Ardat K, Herrmann E, Berkowitsch A, et al. Clonal haematopoiesis in chronic ischaemic heart failure: prognostic role of clone size for DNMT3A‐ and TET2‐driver gene mutations. Eur Heart J. 2021;42:257–265. doi: 10.1093/EURHEARTJ/EHAA845 [DOI] [PubMed] [Google Scholar]
70. Wang S, Hu S, Luo X, Bao X, Li J, Liu M, Lv Y, Zhao C, Zeng M, Chen X, et al. Prevalence and prognostic significance of DNMT3A‐ and TET2‐ clonal haematopoiesis‐driver mutations in patients presenting with ST‐segment elevation myocardial infarction. EBioMedicine. 2022;78:103964. doi: 10.1016/J.EBIOM.2022.103964 [DOI] [PMC free article] [PubMed] [Google Scholar]
71. Shumliakivska M, Luxán G, Hemmerling I, Abplanalp WT, Li X, Scheller M, Müller‐Tidow C, Leuschner F, Schuhmacher B, Debes A, et al. DNMT3A clonal hematopoiesis‐driver mutations induce cardiac fibrosis by paracrine activation of fibroblasts. bioRxiv. Published online January 9, 2023. doi: 10.1101/2023.01.07.521766 [DOI] [PMC free article] [PubMed] [Google Scholar]
72. Abplanalp WT, Cremer S, John D, Hoffmann J, Schuhmacher B, Merten M, Rieger MA, Vasa‐Nicotera M, Zeiher AM, Dimmeler S. Clonal hematopoiesis‐driver DNMT3A mutations alter immune cells in heart failure. Circ Res. 2021;128:216–228. doi: 10.1161/CIRCRESAHA.120.317104 [DOI] [PubMed] [Google Scholar]
73. Zink F, Stacey SN, Norddahl GL, Frigge ML, Magnusson OT, Jonsdottir I, Thorgeirsson TE, Sigurdsson A, Gudjonsson SA, Gudmundsson J, et al. Clonal hematopoiesis, with and without candidate driver mutations, is common in the elderly. Blood. 2017;130:742–752. doi: 10.1182/BLOOD-2017-02-769869 [DOI] [PMC free article] [PubMed] [Google Scholar]
74. Jaiswal S, Fontanillas P, Flannick J, Manning A, Grauman PV, Mar BG, Lindsley RC, Mermel CH, Burtt N, Chavez A, et al. Age‐related clonal hematopoiesis associated with adverse outcomes. N Engl J Med. 2014;371:2488–2498. doi: 10.1056/NEJMOA1408617 [DOI] [PMC free article] [PubMed] [Google Scholar]
75. Bhattacharya R, Zekavat SM, Uddin MM, Pirruccello J, Niroula A, Gibson C, Griffin GK, Libby P, Ebert BL, Bick A, et al. Association of diet quality with prevalence of clonal hematopoiesis and adverse cardiovascular events. JAMA Cardiol. 2021;6:1069–1077. doi: 10.1001/jamacardio.2021.1678 [DOI] [PMC free article] [PubMed] [Google Scholar]
76. Dharan NJ, Yeh P, Bloch M, Yeung MM, Baker D, Guinto J, Roth N, Ftouni S, Ognenovska K, Smith D, et al. HIV is associated with an increased risk of age‐related clonal hematopoiesis among older adults. Nat Med. 2021;27:1006–1011. doi: 10.1038/s41591-021-01357-y [DOI] [PubMed] [Google Scholar]
77. Bick AG, Popadin K, Thorball CW, Uddin MM, Zanni MV, Yu B, Cavassini M, Rauch A, Tarr P, Schmid P, et al. Increased prevalence of clonal hematopoiesis of indeterminate potential amongst people living with HIV. Sci Rep. 2022;12:1–7. doi: 10.1038/s41598-021-04308-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
78. Hsu JI, Dayaram T, Tovy A, De Braekeleer E, Jeong M, Wang F, Zhang J, Heffernan TP, Gera S, Kovacs JJ, et al. PPM1D mutations drive clonal hematopoiesis in response to cytotoxic chemotherapy. Cell Stem Cell. 2018;23:700–713.e6. doi: 10.1016/J.STEM.2018.10.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
79. Coombs CC, Zehir A, Devlin SM, Kishtagari A, Syed A, Jonsson P, Hyman DM, Solit DB, Robson ME, Baselga J, et al. Therapy‐related clonal hematopoiesis in patients with non‐hematologic cancers is common and impacts clinical outcome. Cell Stem Cell. 2017;21:374. doi: 10.1016/J.STEM.2017.07.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
80. Jakubek YA, Reiner AP, Honigberg MC. Risk factors for clonal hematopoiesis of indeterminate potential and mosaic chromosomal alterations. Transl Res. 2023;255:171–180. doi: 10.1016/J.TRSL.2022.11.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
81. Libby P, Sidlow R, Lin AE, Gupta D, Jones LW, Moslehi J, Zeiher A, Jaiswal S, Schulz C, Blankstein R, et al. Clonal hematopoiesis: crossroads of aging, cardiovascular disease, and cancer: JACC review topic of the week. J Am Coll Cardiol. 2019;74:567–577. doi: 10.1016/J.JACC.2019.06.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
82. Sanderson M, Williams MA, White E, Daling JR, Holt VL, Malone KE, Self SG, Moore DE. Validity and reliability of subject and mother reporting of perinatal factors. Am J Epidemiol. 1998;147:136–140. doi: 10.1093/OXFORDJOURNALS.AJE.A009425 [DOI] [PubMed] [Google Scholar]
83. Wodskou PM, Hundrup YA, Obel EB, Jørgensen T. Validity of self‐reported birthweight among middle‐aged and elderly women in the Danish Nurse Cohort Study. Acta Obstet Gynecol Scand. 2010;89:1134–1139. doi: 10.3109/00016349.2010.500370 [DOI] [PubMed] [Google Scholar]
84. Hecker JS, Hartmann L, Rivière J, Buck MC, van der Garde M, Rothenberg‐Thurley M, Fischer L, Winter S, Ksienzyk B, Ziemann F, et al. CHIP and hips: clonal hematopoiesis is common in patients undergoing hip arthroplasty and is associated with autoimmune disease. Blood. 2021;138:1727–1732. doi: 10.1182/BLOOD.2020010163 [DOI] [PubMed] [Google Scholar]
85. Young AL, Challen GA, Birmann BM, Druley TE. Clonal haematopoiesis harbouring AML‐associated mutations is ubiquitous in healthy adults. Nat Commun. 2016;7:1–7. doi: 10.1038/ncomms12484 [DOI] [PMC free article] [PubMed] [Google Scholar]
86. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:601. doi: 10.1136/BMJ.K601 [DOI] [PMC free article] [PubMed] [Google Scholar]
87. Emdin CA, Khera AV, Natarajan P, Klarin D, Zekavat SM, Hsiao AJ, Kathiresan S. Genetic association of waist‐to‐hip ratio with cardiometabolic traits, type 2 diabetes and coronary heart disease. JAMA. 2017;317:626. doi: 10.1001/JAMA.2016.21042 [DOI] [PMC free article] [PubMed] [Google Scholar]
88. Smith GD, Ebrahim S. “Mendelian randomization”: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32:1–22. doi: 10.1093/IJE/DYG070 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1–S2

Tables S1–S14

Figures S1–S16

References 83–88

Click here for additional data file.^{(2.9MB, pdf)}

[jah38544-bib-0001] 1. Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, Barengo NC, Beaton A, Benjamin EJ, Benziger CP, et al. Global burden of cardiovascular diseases and risk factors, 1990–2019: update from the GBD 2019 study. J Am Coll Cardiol. 2020;76:2982–3021. doi: 10.1016/J.JACC.2020.11.010 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0002] 2. Abbafati C, Abbas KM, Abbasi‐Kangevari M, Abd‐Allah F, Abdelalim A, Abdollahi M, Abdollahpour I, Abegaz KH, Abolhassani H, Aboyans V, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019. Lancet. 2020;396:1204–1222. doi: 10.1016/S0140-6736(20)30925-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0003] 3. Hayman LL, Meininger JC, Daniels SR, McCrindle BW, Helden L, Ross J, Dennison BA, Steinberger J, Williams CL. Primary prevention of cardiovascular disease in nursing practice: focus on children and youth. Circulation. 2007;116:344–357. doi: 10.1161/CIRCULATIONAHA.107.184595 [DOI] [PubMed] [Google Scholar]

[jah38544-bib-0004] 4. Godfrey KM, Barker DJ. Fetal programming and adult health. Public Health Nutr. 2001;4:611–624. doi: 10.1079/PHN2001145 [DOI] [PubMed] [Google Scholar]

[jah38544-bib-0005] 5. Ramakrishnan U. Nutrition and low birth weight: from research to practice. Am J Clin Nutr. 2004;79:17–21. doi: 10.1093/AJCN/79.1.17 [DOI] [PubMed] [Google Scholar]

[jah38544-bib-0006] 6. Gillman MW, Rifas‐Shiman S, Berkey CS, Field AE, Colditz GA. Maternal gestational diabetes, birth weight, and adolescent obesity. Pediatrics. 2003;111:e221–e226. doi: 10.1542/PEDS.111.3.E221 [DOI] [PubMed] [Google Scholar]

[jah38544-bib-0007] 7. Frankel S, Elwood P, Sweetnam P, Yarnell J, Davey SG. Birthweight, body‐mass index in middle age, and incident coronary heart disease. Lancet. 1996;348:1478–1480. doi: 10.1016/S0140-6736(96)03482-4 [DOI] [PubMed] [Google Scholar]

[jah38544-bib-0008] 8. Zanetti D, Tikkanen E, Gustafsson S, Priest JR, Burgess S, Ingelsson E. Birthweight, type 2 diabetes mellitus, and cardiovascular disease: addressing the Barker hypothesis with Mendelian randomization. Circ Genom Precis Med. 2018;11:e002054. doi: 10.1161/CIRCGEN.117.002054 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0009] 9. Rashid A, Agarwala A, Novak E, Brown DL. Association of high birth weight with incident heart failure in the ARIC study. J Am Heart Assoc. 2019;8:e011524. doi: 10.1161/JAHA.118.011524 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0010] 10. Sandovici I, Smith NH, Nitert MD, Ackers‐Johnson M, Uribe‐Lewis S, Ito Y, Jones RH, Marquez VE, Cairns W, Tadayyon M, et al. Maternal diet and aging alter the epigenetic control of a promoter‐enhancer interaction at the Hnf4a gene in rat pancreatic islets. Proc Natl Acad Sci USA. 2011;108:5449–5454. doi: 10.1073/PNAS.1019007108 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0011] 11. Heijmans BT, Tobi EW, Stein AD, Putter H, Blauw GJ, Susser ES, Slagboom PE, Lumey LH. Persistent epigenetic differences associated with prenatal exposure to famine in humans. Proc Natl Acad Sci USA. 2008;105:17046–17049. doi: 10.1073/PNAS.0806560105 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0012] 12. Agha G, Hajj H, Rifas‐Shiman SL, Just AC, Hivert MF, Burris HH, Lin X, Litonjua AA, Oken E, DeMeo DL, et al. Birth weight‐for‐gestational age is associated with DNA methylation at birth and in childhood. Clin Epigenetics. 2016;8:1–12. doi: 10.1186/S13148-016-0285-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0013] 13. Guénard F, Deshaies Y, Cianflone K, Kral JG, Marceau P, Vohl MC. Differential methylation in glucoregulatory genes of offspring born before vs. after maternal gastrointestinal bypass surgery. Proc Natl Acad Sci USA. 2013;110:11439–11444. doi: 10.1073/PNAS.1216959110 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0014] 14. Skilton MR, Siitonen N, Würtz P, Viikari JSA, Juonala M, Seppälä I, Laitinen T, Lehtimäki T, Taittonen L, Kähönen M, et al. High birth weight is associated with obesity and increased carotid wall thickness in young adults: the cardiovascular risk in young Finns study. Arterioscler Thromb Vasc Biol. 2014;34:1064–1068. doi: 10.1161/ATVBAHA.113.302934 [DOI] [PubMed] [Google Scholar]

[jah38544-bib-0015] 15. Schellong K, Schulz S, Harder T, Plagemann A. Birth weight and long‐term overweight risk: systematic review and a meta‐analysis including 643,902 persons from 66 studies and 26 countries globally. PLoS One. 2012;7:e47776. doi: 10.1371/JOURNAL.PONE.0047776 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0016] 16. Steensma DP, Bejar R, Jaiswal S, Lindsley RC, Sekeres MA, Hasserjian RP, Ebert BL. Clonal hematopoiesis of indeterminate potential and its distinction from myelodysplastic syndromes. Blood. 2015;126:9. doi: 10.1182/BLOOD-2015-03-631747 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0017] 17. Xie M, Lu C, Wang J, McLellan MD, Johnson KJ, Wendl MC, McMichael JF, Schmidt HK, Yellapantula V, Miller CA, et al. Age‐related mutations associated with clonal hematopoietic expansion and malignancies. Nat Med. 2014;20:1472–1478. doi: 10.1038/nm.3733 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0018] 18. Genovese G, Kähler AK, Handsaker RE, Lindberg J, Rose SA, Bakhoum SF, Chambert K, Mick E, Neale BM, Fromer M, et al. Clonal hematopoiesis and blood‐cancer risk inferred from blood DNA sequence. N Engl J Med. 2014;371:2477–2487. doi: 10.1056/NEJMOA1409405 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0019] 19. Jaiswal S, Natarajan P, Silver AJ, Gibson CJ, Bick AG, Shvartz E, McConkey M, Gupta N, Gabriel S, Ardissino D, et al. Clonal hematopoiesis and risk of atherosclerotic cardiovascular disease. N Engl J Med. 2017;377:111–121. doi: 10.1056/NEJMOA1701719 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0020] 20. Sperling AS, Gibson CJ, Ebert BL. The genetics of myelodysplastic syndrome: from clonal hematopoiesis to secondary leukemia. Nat Rev Cancer. 2017;17:5. doi: 10.1038/NRC.2016.112 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0021] 21. Bick AG, Pirruccello JP, Griffin GK, Gupta N, Gabriel S, Saleheen D, Libby P, Kathiresan S, Natarajan P. Genetic interleukin 6 signaling deficiency attenuates cardiovascular risk in clonal hematopoiesis. Circulation. 2020;141:124–131. doi: 10.1161/CIRCULATIONAHA.119.044362 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0022] 22. Honigberg MC, Zekavat SM, Niroula A, Griffin GK, Bick AG, Pirruccello JP, Nakao T, Whitsel EA, Farland LV, Laurie C, et al. Premature menopause, clonal hematopoiesis, and coronary artery disease in postmenopausal women. Circulation. 2021;143:410–423. doi: 10.1161/CIRCULATIONAHA.120.051775 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0023] 23. Bhattacharya R, Zekavat SM, Haessler J, Fornage M, Raffield L, Uddin MM, Bick AG, Niroula A, Yu B, Gibson C, et al. Clonal hematopoiesis is associated with higher risk of stroke. Stroke. 2022;29:788–797. doi: 10.1161/STROKEAHA.121.037388 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0024] 24. Yu B, Roberts MB, Raffield LM, Zekavat SM, Nguyen NQH, Biggs ML, Brown MR, Griffin G, Desai P, Correa A, et al. Association of clonal hematopoiesis with incident heart failure. J Am Coll Cardiol. 2021;78:42–52. doi: 10.1016/J.JACC.2021.04.085 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0025] 25. Williams N, Lee J, Mitchell E, Moore L, Baxter EJ, Hewinson J, Dawson KJ, Menzies A, Godfrey AL, Green AR, et al. Life histories of myeloproliferative neoplasms inferred from phylogenies. Nature. 2022;602:162–168. doi: 10.1038/s41586-021-04312-6 [DOI] [PubMed] [Google Scholar]

[jah38544-bib-0026] 26. Mitchell E, Spencer Chapman M, Williams N, Dawson KJ, Mende N, Calderbank EF, Jung H, Mitchell T, Coorens THH, Spencer DH, et al. Clonal dynamics of haematopoiesis across the human lifespan. Nature. 2022;606:343–350. doi: 10.1038/s41586-022-04786-y [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0027] 27. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, Motyer A, Vukcevic D, Delaneau O, O'Connell J, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562:203–209. doi: 10.1038/s41586-018-0579-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0028] 28. Huang C, Wei K, Ming P, Lee Y, Qin G, Yu Y, Li J. Maternal hypertensive disorder of pregnancy and mortality in offspring from birth to young adulthood: national population based cohort study. BMJ. 2022;379:e072157. doi: 10.1136/BMJ-2022-072157 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0029] 29. O'Neill KA, Murphy MFG, Bunch KJ, Puumala SE, Carozza SE, Chow EJ, Mueller BA, McLaughlin CC, Reynolds P, Vincent TJ, et al. Infant birthweight and risk of childhood cancer: international population‐based case control studies of 40 000 cases. Int J Epidemiol. 2015;44:153–168. doi: 10.1093/IJE/DYU265 [DOI] [PubMed] [Google Scholar]

[jah38544-bib-0030] 30. Gissler M, Mohangoo AD, Blondel B, Chalmers J, MacFarlane A, Gaizauskiene A, Gatt M, Lack N, Sakkeus L, Zeitlin J. Perinatal health monitoring in Europe: results from the EURO‐PERISTAT project. Inform Health Soc Care. 2010;35:64–79. doi: 10.3109/17538157.2010.492923 [DOI] [PubMed] [Google Scholar]

[jah38544-bib-0031] 31. International Classification of Diseases, Eleventh Revision (ICD‐11), World Health Organization (WHO) . Published 2019. Accessed October 26, 2022. https://icd.who.int/browse11/l‐m/en

[jah38544-bib-0032] 32. van Hout CV, Tachmazidou I, Backman JD, Hoffman JD, Liu D, Pandey AK, Gonzaga‐Jauregui C, Khalid S, Ye B, Banerjee N, et al. Exome sequencing and characterization of 49,960 individuals in the UK Biobank. Nature. 2020;586:749–756. doi: 10.1038/s41586-020-2853-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0033] 33. Backman JD, Li AH, Marcketta A, Sun D, Mbatchou J, Kessler MD, Benner C, Liu D, Locke AE, Balasubramanian S, et al. Exome sequencing and analysis of 454,787 UK Biobank participants. Nature. 2021;599:628–634. doi: 10.1038/s41586-021-04103-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0034] 34. Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez C, Gabriel S, Meyerson M, Lander ES, Getz G. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol. 2013;31:213–219. doi: 10.1038/nbt.2514 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0035] 35. Uddin MM, Yu Z, Weinstock JS, Nakao T, Niroula A, Urbut SM, Koyama S, Zekavat SM, Paruchuri K, Silver AJ, et al. Germline genomic and phenomic landscape of clonal hematopoiesis in 323,112 individuals. medRxiv. Preprint posted online July 31, 2022. doi: 10.1101/2022.07.29.22278015 [DOI] [Google Scholar]

[jah38544-bib-0036] 36. Niroula A, Sekar A, Murakami MA, Trinder M, Agrawal M, Wong WJ, Bick AG, Uddin MM, Gibson CJ, Griffin GK, et al. Distinction of lymphoid and myeloid clonal hematopoiesis. Nat Med. 2021;27:1921–1927. doi: 10.1038/s41591-021-01521-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0037] 37. Bick AG, Weinstock JS, Nandakumar SK, Fulco CP, Bao EL, Zekavat SM, Szeto MD, Liao X, Leventhal MJ, Nasser J, et al. Inherited causes of clonal haematopoiesis in 97,691 whole genomes. Nature. 2020;586:763–768. doi: 10.1038/s41586-020-2819-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0038] 38. Vlasschaert C, Mack T, Heimlich JB, Niroula A, Uddin MM, Weinstock JS, Sharber B, Silver AJ, Xu Y, Savona MR, et al. A practical approach to curate clonal hematopoiesis of indeterminate potential in human genetic datasets. Blood. 2023;141:2214–2223. doi: 10.1182/BLOOD.2022018825 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0039] 39. Liu X, Helenius D, Skotte L, Beaumont RN, Wielscher M, Geller F, Juodakis J, Mahajan A, Bradfield JP, Lin FTJ, et al. Variants in the fetal genome near pro‐inflammatory cytokine genes on 2q13 associate with gestational duration. Nat Commun. 2019;10:1–13. doi: 10.1038/s41467-019-11881-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0040] 40. Horikoshi M, Yaghootkar H, Mook‐Kanamori DO, Sovio U, Taal HR, Hennig BJ, Bradfield JP, St Pourcain B, Evans DM, Charoen P, et al. New loci associated with birth weight identify genetic links between intrauterine growth and adult height and metabolism. Nat Genet. 2012;45:76–82. doi: 10.1038/ng.2477 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0041] 41. Zhao Q, Wang J, Hemani G, Bowden J, Small DS. Statistical inference in two‐sample summary‐data Mendelian randomization using robust adjusted profile score. Ann Stat. 2018;48:1742–1769. doi: 10.48550/arxiv.1801.09652 [DOI] [Google Scholar]

[jah38544-bib-0042] 42. Bowden J, Smith GD, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512–525. doi: 10.1093/IJE/DYV080 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0043] 43. Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR‐Egger method. Eur J Epidemiol. 2017;32:377. doi: 10.1007/S10654-017-0255-X [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0044] 44. Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693. doi: 10.1038/S41588-018-0099-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0045] 45. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, Laurin C, Burgess S, Bowden J, Langdon R, et al. The MR‐base platform supports systematic causal inference across the human phenome. Elife. 2018;7:e34408. doi: 10.7554/ELIFE.34408 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0046] 46. Hemani G, Tilling K, Davey SG. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet. 2017;13:e1007081. doi: 10.1371/JOURNAL.PGEN.1007081 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0047] 47. Koya J, Kataoka K, Sato T, Bando M, Kato Y, Tsuruta‐Kishino T, Kobayashi H, Narukawa K, Miyoshi H, Shirahige K, et al. DNMT3A R882 mutants interact with polycomb proteins to block haematopoietic stem and leukaemic cell differentiation. Nat Commun. 2016;7:1–14. doi: 10.1038/ncomms10924 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0048] 48. Levin MG, Nakao T, Zekavat SM, Koyama S, Bick AG, Niroula A, Ebert B, Damrauer SM, Natarajan P. Genetics of smoking and risk of clonal hematopoiesis. Sci Rep. 2022;12:1–5. doi: 10.1038/s41598-022-09604-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0049] 49. Dawoud AAZ, Tapper WJ, Cross NCP. Clonal myelopoiesis in the UK Biobank cohort: ASXL1 mutations are strongly associated with smoking. Leukemia. 2020;34:2660–2672. doi: 10.1038/s41375-020-0896-8 [DOI] [PubMed] [Google Scholar]

[jah38544-bib-0050] 50. Fabre MA, de Almeida JG, Fiorillo E, Mitchell E, Damaskou A, Rak J, Orrù V, Marongiu M, Chapman MS, Vijayabaskar MS, et al. The longitudinal dynamics and natural history of clonal haematopoiesis. Nature. 2022;606:335–342. doi: 10.1038/s41586-022-04785-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0051] 51. Robertson NA, Latorre‐Crespo E, Terradas‐Terradas M, Lemos‐Portela J, Purcell AC, Livesey BJ, Hillary RF, Murphy L, Fawkes A, MacGillivray L, et al. Longitudinal dynamics of clonal hematopoiesis identifies gene‐specific fitness effects. Nat Med. 2022;28:1439–1446. doi: 10.1038/s41591-022-01883-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0052] 52. Uddin MM, Zhou Y, Bick AG, Burugula BB, Jaiswal S, Desai P, Honigberg MC, Love SA, Barac A, Hayden KM, et al. Longitudinal profiling of clonal hematopoiesis provides insight into clonal dynamics. Immun Ageing. 2022;19:1–10. doi: 10.1186/S12979-022-00278-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0053] 53. Watson CJ, Papula AL, Poon GYP, Wong WH, Young AL, Druley TE, Fisher DS, Blundell JR. The evolutionary dynamics and fitness landscape of clonal hematopoiesis. Science. 2020;367:1449–1454. doi: 10.1126/SCIENCE.AAY9333 [DOI] [PubMed] [Google Scholar]

[jah38544-bib-0054] 54. Lee‐Six H, Øbro NF, Shepherd MS, Grossmann S, Dawson K, Belmonte M, Osborne RJ, Huntly BJP, Martincorena I, Anderson E, et al. Population dynamics of normal human blood inferred from somatic mutations. Nature. 2018;561:473–478. doi: 10.1038/s41586-018-0497-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0055] 55. Bowie MB, McKnight KD, Kent DG, McCaffrey L, Hoodless PA, Eaves CJ. Hematopoietic stem cells proliferate until after birth and show a reversible phase‐specific engraftment defect. J Clin Investig. 2006;116:2808. doi: 10.1172/JCI28310 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0056] 56. Spencer Chapman M, Ranzoni AM, Myers B, Williams N, Coorens THH, Mitchell E, Butler T, Dawson KJ, Hooks Y, Moore L, et al. Lineage tracing of human development through somatic mutations. Nature. 2021;595:85–90. doi: 10.1038/s41586-021-03548-6 [DOI] [PubMed] [Google Scholar]

[jah38544-bib-0057] 57. Schuermans A, Lewandowski AJ. Understanding the preterm human heart: what do we know so far? Anat Rec (Hoboken). 2022;305:2099–2112. doi: 10.1002/ar.24875 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0058] 58. Lewandowski AJ, Augustine D, Lamata P, Davis EF, Lazdam M, Francis J, McCormick K, Wilkinson AR, Singhal A, Lucas A, et al. Preterm heart in adult life: cardiovascular magnetic resonance reveals distinct differences in left ventricular mass, geometry, and function. Circulation. 2013;127:197–206. doi: 10.1161/CIRCULATIONAHA.112.126920 [DOI] [PubMed] [Google Scholar]

[jah38544-bib-0059] 59. Lewandowski AJ, Raman B, Bertagnolli M, Mohamed A, Williamson W, Pelado JL, McCance A, Lapidaire W, Neubauer S, Leeson P. Association of preterm birth with myocardial fibrosis and diastolic dysfunction in young adulthood. J Am Coll Cardiol. 2021;78:683–692. doi: 10.1016/j.jacc.2021.05.053 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0060] 60. Schuermans A, den Harink T, Raman B, Smillie RW, Alsharqi M, Mohamed A, Lapidaire W, van Deutekom AW, Leeson P, et al. Differing impact of preterm birth on the right and left atria in adulthood. J Am Heart Assoc. 2022;11:27305. doi: 10.1161/JAHA.122.027305 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0061] 61. Gupta P, Narang M, Banerjee BD, Basu S. Oxidative stress in term small for gestational age neonates born to undernourished mothers: a case control study. BMC Pediatr. 2004;4:1–7. doi: 10.1186/1471-2431-4-14/TABLES/3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0062] 62. Paquette K, Fernandes RO, Xie LF, Cloutier A, Fallaha C, Girard‐Bock C, Mian MOR, Lukaszewski MA, Mâsse B, El‐Jalbout R, et al. Kidney size, renal function, Ang (angiotensin) peptides, and blood pressure in young adults born preterm. Hypertension. 2018;72:918–928. doi: 10.1161/HYPERTENSIONAHA.118.11397 [DOI] [PubMed] [Google Scholar]

[jah38544-bib-0063] 63. Nordman H, Voutilainen R, Antikainen L, Jääskeläinen J. Plasma IL‐1 receptor antagonist concentration has an inverse association with birth weight in prepubertal children. J Endocr Soc. 2018;2:232–239. doi: 10.1210/JS.2017-00437 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0064] 64. Sattar N, McConnachie A, O'Reilly D, Upton MN, Greer IA, Davey Smith G, Watt G. Inverse association between birth weight and C‐reactive protein concentrations in the MIDSPAN Family Study. Arterioscler Thromb Vasc Biol. 2004;24:583–587. doi: 10.1161/01.ATV.0000118277.41584.63 [DOI] [PubMed] [Google Scholar]

[jah38544-bib-0065] 65. Nordman H, Jääskeläinen J, Voutilainen R. Birth size as a determinant of cardiometabolic risk factors in children. Horm Res Paediatr. 2020;93:144–153. doi: 10.1159/000509932 [DOI] [PubMed] [Google Scholar]

[jah38544-bib-0066] 66. Geserick M, Vogel M, Gausche R, Lipek T, Spielau U, Keller E, Pfäffle R, Kiess W, Körner A. Acceleration of BMI in early childhood and risk of sustained obesity. N Engl J Med. 2018;379:1303–1312. doi: 10.1056/NEJMOA1803527 [DOI] [PubMed] [Google Scholar]

[jah38544-bib-0067] 67. Kar SP, Quiros PM, Gu M, Jiang T, Mitchell J, Langdon R, Iyer V, Barcena C, Vijayabaskar MS, Fabre MA, et al. Genome‐wide analyses of 200,453 individuals yield new insights into the causes and consequences of clonal hematopoiesis. Nat Genet. 2022;54:1155–1166. doi: 10.1038/s41588-022-01121-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0068] 68. Arends CM, Liman TG, Strzelecka PM, Kufner A, Löwe P, Huo S, Stein CM, Piper SK, Tilgner M, Sperber PS, et al. Associations of clonal haematopoiesis with recurrent vascular events and death in patients with incident ischemic stroke. Blood. 2023;141:787–799. doi: 10.1182/BLOOD.2022017661 [DOI] [PubMed] [Google Scholar]

[jah38544-bib-0069] 69. Assmus B, Cremer S, Kirschbaum K, Culmann D, Kiefer K, Dorsheimer L, Rasper T, Abou‐El‐Ardat K, Herrmann E, Berkowitsch A, et al. Clonal haematopoiesis in chronic ischaemic heart failure: prognostic role of clone size for DNMT3A‐ and TET2‐driver gene mutations. Eur Heart J. 2021;42:257–265. doi: 10.1093/EURHEARTJ/EHAA845 [DOI] [PubMed] [Google Scholar]

[jah38544-bib-0070] 70. Wang S, Hu S, Luo X, Bao X, Li J, Liu M, Lv Y, Zhao C, Zeng M, Chen X, et al. Prevalence and prognostic significance of DNMT3A‐ and TET2‐ clonal haematopoiesis‐driver mutations in patients presenting with ST‐segment elevation myocardial infarction. EBioMedicine. 2022;78:103964. doi: 10.1016/J.EBIOM.2022.103964 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0071] 71. Shumliakivska M, Luxán G, Hemmerling I, Abplanalp WT, Li X, Scheller M, Müller‐Tidow C, Leuschner F, Schuhmacher B, Debes A, et al. DNMT3A clonal hematopoiesis‐driver mutations induce cardiac fibrosis by paracrine activation of fibroblasts. bioRxiv. Published online January 9, 2023. doi: 10.1101/2023.01.07.521766 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0072] 72. Abplanalp WT, Cremer S, John D, Hoffmann J, Schuhmacher B, Merten M, Rieger MA, Vasa‐Nicotera M, Zeiher AM, Dimmeler S. Clonal hematopoiesis‐driver DNMT3A mutations alter immune cells in heart failure. Circ Res. 2021;128:216–228. doi: 10.1161/CIRCRESAHA.120.317104 [DOI] [PubMed] [Google Scholar]

[jah38544-bib-0073] 73. Zink F, Stacey SN, Norddahl GL, Frigge ML, Magnusson OT, Jonsdottir I, Thorgeirsson TE, Sigurdsson A, Gudjonsson SA, Gudmundsson J, et al. Clonal hematopoiesis, with and without candidate driver mutations, is common in the elderly. Blood. 2017;130:742–752. doi: 10.1182/BLOOD-2017-02-769869 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0074] 74. Jaiswal S, Fontanillas P, Flannick J, Manning A, Grauman PV, Mar BG, Lindsley RC, Mermel CH, Burtt N, Chavez A, et al. Age‐related clonal hematopoiesis associated with adverse outcomes. N Engl J Med. 2014;371:2488–2498. doi: 10.1056/NEJMOA1408617 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0075] 75. Bhattacharya R, Zekavat SM, Uddin MM, Pirruccello J, Niroula A, Gibson C, Griffin GK, Libby P, Ebert BL, Bick A, et al. Association of diet quality with prevalence of clonal hematopoiesis and adverse cardiovascular events. JAMA Cardiol. 2021;6:1069–1077. doi: 10.1001/jamacardio.2021.1678 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0076] 76. Dharan NJ, Yeh P, Bloch M, Yeung MM, Baker D, Guinto J, Roth N, Ftouni S, Ognenovska K, Smith D, et al. HIV is associated with an increased risk of age‐related clonal hematopoiesis among older adults. Nat Med. 2021;27:1006–1011. doi: 10.1038/s41591-021-01357-y [DOI] [PubMed] [Google Scholar]

[jah38544-bib-0077] 77. Bick AG, Popadin K, Thorball CW, Uddin MM, Zanni MV, Yu B, Cavassini M, Rauch A, Tarr P, Schmid P, et al. Increased prevalence of clonal hematopoiesis of indeterminate potential amongst people living with HIV. Sci Rep. 2022;12:1–7. doi: 10.1038/s41598-021-04308-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0078] 78. Hsu JI, Dayaram T, Tovy A, De Braekeleer E, Jeong M, Wang F, Zhang J, Heffernan TP, Gera S, Kovacs JJ, et al. PPM1D mutations drive clonal hematopoiesis in response to cytotoxic chemotherapy. Cell Stem Cell. 2018;23:700–713.e6. doi: 10.1016/J.STEM.2018.10.004 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0079] 79. Coombs CC, Zehir A, Devlin SM, Kishtagari A, Syed A, Jonsson P, Hyman DM, Solit DB, Robson ME, Baselga J, et al. Therapy‐related clonal hematopoiesis in patients with non‐hematologic cancers is common and impacts clinical outcome. Cell Stem Cell. 2017;21:374. doi: 10.1016/J.STEM.2017.07.010 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0080] 80. Jakubek YA, Reiner AP, Honigberg MC. Risk factors for clonal hematopoiesis of indeterminate potential and mosaic chromosomal alterations. Transl Res. 2023;255:171–180. doi: 10.1016/J.TRSL.2022.11.009 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0081] 81. Libby P, Sidlow R, Lin AE, Gupta D, Jones LW, Moslehi J, Zeiher A, Jaiswal S, Schulz C, Blankstein R, et al. Clonal hematopoiesis: crossroads of aging, cardiovascular disease, and cancer: JACC review topic of the week. J Am Coll Cardiol. 2019;74:567–577. doi: 10.1016/J.JACC.2019.06.007 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0082] 82. Sanderson M, Williams MA, White E, Daling JR, Holt VL, Malone KE, Self SG, Moore DE. Validity and reliability of subject and mother reporting of perinatal factors. Am J Epidemiol. 1998;147:136–140. doi: 10.1093/OXFORDJOURNALS.AJE.A009425 [DOI] [PubMed] [Google Scholar]

[jah38544-bib-0083] 83. Wodskou PM, Hundrup YA, Obel EB, Jørgensen T. Validity of self‐reported birthweight among middle‐aged and elderly women in the Danish Nurse Cohort Study. Acta Obstet Gynecol Scand. 2010;89:1134–1139. doi: 10.3109/00016349.2010.500370 [DOI] [PubMed] [Google Scholar]

[jah38544-bib-0084] 84. Hecker JS, Hartmann L, Rivière J, Buck MC, van der Garde M, Rothenberg‐Thurley M, Fischer L, Winter S, Ksienzyk B, Ziemann F, et al. CHIP and hips: clonal hematopoiesis is common in patients undergoing hip arthroplasty and is associated with autoimmune disease. Blood. 2021;138:1727–1732. doi: 10.1182/BLOOD.2020010163 [DOI] [PubMed] [Google Scholar]

[jah38544-bib-0085] 85. Young AL, Challen GA, Birmann BM, Druley TE. Clonal haematopoiesis harbouring AML‐associated mutations is ubiquitous in healthy adults. Nat Commun. 2016;7:1–7. doi: 10.1038/ncomms12484 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0086] 86. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:601. doi: 10.1136/BMJ.K601 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0087] 87. Emdin CA, Khera AV, Natarajan P, Klarin D, Zekavat SM, Hsiao AJ, Kathiresan S. Genetic association of waist‐to‐hip ratio with cardiometabolic traits, type 2 diabetes and coronary heart disease. JAMA. 2017;317:626. doi: 10.1001/JAMA.2016.21042 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38544-bib-0088] 88. Smith GD, Ebrahim S. “Mendelian randomization”: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32:1–22. doi: 10.1093/IJE/DYG070 [DOI] [PubMed] [Google Scholar]

PERMALINK

Birth Weight Is Associated With Clonal Hematopoiesis of Indeterminate Potential and Cardiovascular Outcomes in Adulthood

Art Schuermans, BSc

Tetsushi Nakao, MD, PhD

Yunfeng Ruan, PhD

Satoshi Koyama, MD, PhD

Zhi Yu, MB, PhD

Md Mesbah Uddin, PhD

Sara Haidermota, BS

Whitney Hornsby, PhD

Adam J Lewandowski, DPhil

Alexander G Bick, MD, PhD

Abhishek Niroula, PhD

Siddhartha Jaiswal, MD, PhD

Benjamin L Ebert, MD, PhD

Pradeep Natarajan, MD, MMSc

Michael C Honigberg, MD, MPP

Abstract

Background

Methods and Results

Conclusions

Nonstandard Abbreviations and Acronyms

Clinical Perspective.

What Is New?

What Are the Clinical Implications?

Methods

Study Population

Figure 1. Study flow chart.

Exposures and Covariates

Sequencing and CHIP Detection

Outcomes

Statistical Analysis

Results

Description of the Study Cohort

Table 1.

CHIP Prevalence

Birth Weight is Independently Associated With CHIP in Observational Analyses

Figure 2. Observational associations between early‐life factors and CHIP.

Mendelian Randomization Supports a Causal Effect of Gestational Age on CHIP

Figure 3. Genetic associations of gestational age and birth weight Z score with CHIP using MR.

CHIP Is Associated With Additive Risk for Incident CVD Across Birth Weight Categories

Figure 4. Kaplan–Meier estimates of freedom from CVD or death by birth weight group and CHIP status.

Table 2.

Discussion

Sources of Funding

Disclosures

Supporting information

Acknowledgments

REFERENCES

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases