Abstract
Background
The associations between birth weight (BW), cardiovascular health (CVH) as assessed by Life’s Essential 8 (LE8) and cardiovascular diseases (CVDs) remain unclear. We aimed to assess the independent and joint associations of BW and CVH with risks of CVDs.
Methods
This study included 143,910 UK Biobank participants without CVD at baseline (2006–2010). BW was self-reported, and CVH was evaluated using the LE8 score. Cox models were employed to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for incident total CVD and nine cardiovascular conditions.
Results
During a median 15.1-year follow-up, 24,071 CVD cases occurred. Compared with normal BW (2.5–4.0 kg), low BW was associated with increased risks of total CVD (adjusted HR [aHR]: 1.13, 95% CI [1.08 to 1.17], P < 0.001), CVD death (aHR: 1.25 [1.11 to 1.40]), and six other CVDs (aHR ranging from 1.09 to 1.36). High BW was associated with higher risks of aortic aneurysom (aHR: 1.20 [1.03 to 1.39], P = 0.02) and dysrhythmias (aHR: 1.08 [1.03 to 1.14], P = 0.003), but lower risks of peripheral artery disease (aHR: 0.78 [0.67 to 0.91], P = 0.002) and ischemic heart disease (aHR: 0.94 [0.89 to 1.00, P = 0.05). Compared with low LE8 score, high LE8 score was associated with 55%-60% lower risk of total CVD in participants with various BW, and no interactions were found.
Conclusions
Low BW was associated with an increased risk of CVD, whereas high BW showed a neutral association. High CVH, as evaluated by LE8, was associated with a reduced incidence of CVD irrespective of BW.
Keywords: Birth weight, Cardiovascular health, Cardiovascular diseases
Graphical abstract
1. Background
Cardiovascular diseases (CVD) remain the leading cause of global mortality and the major contributor to poor quality of life and excessive health expenditure [1]. The “Developmental Origins of Health and Disease” hypothesis postulates that fetal intrauterine growth restriction might play a pivotal role in programming metabolic development, leading to enduring physiological changes that predispose the body to metabolic diseases [2,3]. Birth weight, as a surrogate for fetal intrauterine growth restriction, has been well-established in its association with cardiometabolic risk factors like obesity, hypertension and type 2 diabetes [[4], [5], [6], [7]]. A few studies have examined the association between birth weight and incident CVD, with limited scope focusing on coronary heart disease (CHD) and stroke[[5], [6], [7]]. However, CVD outcomes encompass a wide range of circulatory system conditions in addition to these cardiometabolic diseases. Expanding our understanding of birth weight in relation to a broad spectrum of CVD outcomes and exploring potential preventive measures hold substantial public health significance.
A high level of cardiovascular health (CVH) has consistently demonstrated a strong association with a reduced risk of incident CVD [8,9]. To objectively assess and enhance CVH at both the population and individual levels, the American Heart Association introduced a construct called Life’s Simple 7 (LS7) in 2010 [10]. The components of LS7 CVH metrics included diet, physical activity, cigarette smoking, body mass index (BMI), blood glucose, total cholesterol, and blood pressure. Recently, this construct has been updated to Life's Essential 8 (LE8), which enhanced the definitions and incorporated a new scoring algorithm for quantification of CVH metrics. Furthermore, sleep health was included as an additional component [11,12]. CVH levels evaluated by LE8 has been reported to be associated with decreased risk of atherosclerotic CVD and increased life expectancy [[13], [14], [15], [16], [17]]. Studies investigating the joint associations of birth weight and LE8 CVH score in adulthood with incident CVD are currently lacking.
In this study, we aimed to use the data from the large United Kingdom Biobank (UKB) prospective cohort to examine the associations of birth weight with risks of a broad spectrum of CVD outcomes and the interaction between birth weight and CVH evaluated by LE8 in adulthood on the risk of CVD on multiplicative and additive scales.
2. Methods
2.1. Study design and population
The UKB cohort study recruited over 500,000 participants aged 37–73 years from England, Scotland, and Wales throughout the United Kingdom between 2006 and 2010. The study design and data collection procedures has been described previously [18]. Our study included participants who reported their birth weight. We excluded those who had diagnosed CVD at baseline, which was identified through hospital inpatient records, death registrations, and self-reported diagnosis. We also excluded those with any missing data on LE8 components. Finally, a total of 143,910 participants were included in the final analysis (Supplementary Figure S1).
2.2. Assessment of birth weight and LE8 CVH
Participants were instructed to report their birth weight in either kilogram (kg) or imperial pounds and ounces, with the latter subsequently converted into kilograms. The validity of self-reported birth weight in the UKB has been evaluated in a previous study [19]. According to WHO criteria, birth weight was categorized into low (<2.5 kg), normal (2.5–4.0 kg), and high (>4.0 kg) [20].
The LE8 CVH comprises four behavioral (diet, physical activity, nicotine exposure, sleep) and four biological (BMI, blood lipids, blood glucose, and blood pressure) components [12]. We used a previously defined eating pattern as a proxy measure of the Dietary Approaches to Stop Hypertension (DASH) score to measure the dietary quality[21,22]. This pattern was measured based on the consumption frequency of ten foods including fruits, vegetables, whole grains, fish, dairy, vegetable oils, refined grains, processed meats, unprocessed meats, and sugar-sweetened beverages (Supplementary Table S1). The information on physical activity (metabolic equivalent of task minutes per week), nicotine exposure, sleep duration, and medication use for cholesterol, blood pressure, or diabetes was collected by using a touchscreen questionnaire. BMI was calculated as weight in kilograms divided by the square of standing height in meters. Blood samples were collected for the assessment of blood lipids and blood glucose. Serum cholesterol (mmol/L) and blood glucose level (mmol/L) was measured by enzymatic method. Non-HDL cholesterol was calculated as total serum cholesterol minus HDL cholesterol. History of diabetes was identified through hospital inpatient records, death registrations, and self-reported diagnosis. Diastolic and systolic blood pressure were recorded as the averages of two measurements. Each metric was scored according to the LE8 algorithm, ranging from 0 to 100 points. The overall LE8 score was calculated as the unweighted average score across all eight metrics, also ranging from 0 to 100 points. The detailed assessment and quantifications for each CVH metric are provided in Supplementary Table S2.
Table 1.
Baseline characteristics of 143,910 participants in the UKB according to birth weight categories.
| Overall | Birth weight (kg) |
|||
|---|---|---|---|---|
| <2.5 | 2.5–4.0 | >4.0 | ||
| n | 143,910 | 13,652 | 111,341 | 18,917 |
| Men, n ( %) | 58,874 (40.9) | 4091 (30.0) | 44,399 (39.9) | 10,384 (54.9) |
| Age (year), mean ± SD | 54.4 ± 8.0 | 55.4 ± 8.0 | 54.1 ± 8.0 | 55.2 ± 8.1 |
| Race/ethnicity, White, n ( %) | 139,886 (97.2) | 13,133 (96.2) | 108,241 (97.2) | 18,512 (97.9) |
| Education, n ( %) | ||||
| College or university degree | 54,913 (38.2) | 4467 (32.7) | 43,416 (39.0) | 7030 (37.2) |
| A levels/AS levels or equivalent | 18,998 (13.2) | 1653 (12.1) | 14,990 (13.5) | 2355 (12.4) |
| O levels/GCSE levels or equivalent | 32,252 (22.4) | 3139 (23.0) | 24,999 (22.5) | 4114 (21.7) |
| CSEs or equivalent | 8085 (5.6) | 909 (6.7) | 6224 (5.6) | 952 (5.0) |
| NVQ or HND or HNC or equivalent | 8259 (5.7) | 864 (6.3) | 6073 (5.5) | 1322 (7.0) |
| Other professional qualifications | 6797 (4.7) | 731 (5.4) | 5176 (4.6) | 890 (4.7) |
| None of above | 14,606 (10.1) | 1889 (13.8) | 10,463 (9.4) | 2254 (11.9) |
| Household income (£), n ( %) | ||||
| <18,000 | 21,631 (15.0) | 2563 (18.8) | 16,095 (14.5) | 2973 (15.7) |
| 18,000–30,999 | 30,095 (20.9) | 3259 (23.9) | 22,833 (20.5) | 4003 (21.2) |
| 31,000–51,999 | 50,759 (35.3) | 4711 (34.5) | 39,482 (35.5) | 6566 (34.7) |
| 52,000–100,000 | 32,407 (22.5) | 2517 (18.4) | 25,718 (23.1) | 4172 (22.1) |
| >100,000 | 9018 (6.3) | 602 (4.4) | 7213 (6.5) | 1203 (6.4) |
| Index of multiple deprivation, median (IQR) | 12.0 (6.8, 20.8) | 12.4 (7.3, 22.8) | 11.9 (6.7, 20.4) | 12.1 (6.9, 21.2) |
| Family history of CVD, n ( %) | 75,554 (52.5) | 7797 (57.1) | 57,777 (51.9) | 9980 (52.8) |
| Postmenopause (for women only), n ( %) | 58,911 (69.3) | 7172 (75.0) | 45,533 (68.0) | 6206 (72.7) |
| Ideal diet components, median (IQR) | 3.0 (2.0, 4.0) | 3.0 (2.0, 4.0) | 3.0 (2.0, 4.0) | 3.0 (2.0, 4.0) |
| Physical activities, MET minutes per week, median (IQR) | ||||
| Moderate activities | 480.0 (120.0, 1200.0) | 480.0 (120.0, 1200.0) | 480.0 (120.0, 1200.0) | 480.0 (160.0, 1200.0) |
| Vigorous activities | 240.0 (0.0, 960.0) | 240.0 (0.0, 960.0) | 240.0 (0.0, 960.0) | 240.0 (0.0, 960.0) |
| Smoking status, n ( %) | ||||
| Never | 94,127 (65.4) | 9377 (68.7) | 73,514 (66.0) | 11,236 (59.4) |
| Previous | 34,197 (23.8) | 2836 (20.8) | 25,981 (23.3) | 5380 (28.4) |
| Current | 15,586 (10.8) | 1439 (10.5) | 11,846 (10.6) | 2301 (12.2) |
| Sleep duration (hours), mean ± SD | 7.2 ± 1.0 | 7.2 ± 1.1 | 7.2 ± 1.0 | 7.1 ± 1.0 |
| BMI (kg/m2), mean ± SD | 27.1 ± 4.7 | 27.2 ± 5.0 | 26.9 ± 4.7 | 27.8 ± 4.7 |
| Non-HDL cholesterol (mg/dl), mean ± SD | 166.0 ± 40.5 | 167.5 ± 41.3 | 165.9 ± 40.5 | 165.8 ± 40.0 |
| HbA1c (mmol/L), mean ± SD | 35.3 ± 5.9 | 36.0 ± 6.4 | 35.2 ± 5.8 | 35.1 ± 5.6 |
| Blood pressure (mm Hg), mean ± SD | ||||
| Systolic blood pressure | 136.2 ± 18.3 | 138.4 ± 18.8 | 135.9 ± 18.3 | 136.3 ± 17.8 |
| Diastolic blood pressure | 82.2 ± 10.1 | 82.8 ± 10.1 | 82.1 ± 10.1 | 82.3 ± 10.0 |
| Follow-up time, year, median (IQR) | 15.1 (14.2, 15.9) | 15.1 (14.1, 15.9) | 15.1 (14.2, 15.9) | 15.1 (14.1, 15.9) |
| Incidence of total CVD, n (/1000 PY) | 24,071 (12.0) | 2570 (13.7) | 17,837 (11.4) | 3664 (14.1) |
Continuous variables with normal distribution were described as mean ± SD. Non-normal distributed continuous variables were described as median (IQR). Categorical variables were described as n ( %).
Abbreviations: BMI, body mass index; CVD, cardiovascular disease; CSE, Certificate of Secondary Education; GCSE, General Certificate of Secondary Education; HNC, Higher National Certificate; HND, Higher National Diploma; NVQ, National Vocational Qualification; Hb1Ac, glycated hemoglobin A1c; HDL, high-density lipoprotein; IQR, interquartile range; MET, metabolic equivalent of task; PY, person-year; SD, standard deviation; UKB, United Kingdom Biobank; SD, standard deviation.
2.3. Covariables
Baseline characteristics including age, sex, race and ethnicity (White or non-White), household income (<£18,000; £18,000–30,999; £31,000–51,999; £52,000–100,000; or >£100,000), education (College or university degree; A levels/AS levels or equivalent; O levels/GCSE levels or equivalent; CSEs or equivalent; NVQ or HND or HNC or equivalent; Other professional qualifications; or none of above), family history of CVD (yes or no), and menopause status (premenopausal or postmenopausal, for women only) were collected through a touchscreen questionnaire. Index of multiple deprivation (IMD) is an area-based measure for deprivation composed of data on income, employment, health, education, housing, living environment, access to services, and community safety. The proportion of missing data of covariables ranged from 0.30 % for race/ethnicity to 13.0 % for household income. These missing values were imputed with median for continuous variables and mode for categorical variables.
2.4. Ascertainment of outcomes
The outcomes of this study were the incidence of total CVD and nine specific cardiovascular conditions including CVD death, atherosclerotic diseases (ischemic heart disease, stroke, peripheral artery disease, and aortic aneurysm), and others (heart failure, non-rheumatic valvular disease, dysrhythmias, and venous thromboembolism). These diseases were diagnosed according to International Classification of Diseases codes (versions 9 and 10) based on data derived from hospital inpatient records and death registrations (Supplementary Table S3). The follow-up duration for each participant was calculated from the date of enrollment in the cohort until the occurrence of CVD, loss to follow-up, death, or the end of follow-up (July 8, 2024 in England, July 3, 2024 in Wales, and May 14, 2024 in Scotland), whichever occurred first.
2.5. Statistical analysis
Cox proportional hazard regression models were employed to estimate hazard ratios (HR) with 95 % confidence intervals (CIs) for the associations between birth weight and the incident CVD, with normal birth weight as reference. Absolute risk differences (ARDs) with 95 % CIs were estimated from cumulative risks at the end of the study using the g-formula. Model 1 adjusted for age, sex, and race/ethnicity. Model 2 additionally adjusted for family history of CVD, and indicators of adult socioeconomic status (household income, education, and IMD), and results from this model served as the main basis for analyses, as the primary exposures of interest were birth weight and LE8. Model 3 (full model) further adjusted for behavioral factors (diet scores, smoking status, physical activities, sleep duration), as well as BMI, all included as covariates associated with CVD risk. This model was primarily used as a sensitivity analysis. The sex difference for the birth weight-CVD associations was examined by stratifying participants by sex. For the analyses in women, menopause status was additionally adjusted in model 3 as a covariate associated with CVD risk.
To assess the potential non-linear relationship between birth weight and incident CVD, we treated birth weight as continuous variable and included restricted cubic spline term for birth weight with three knots located at the 5th, 50th, and 95th percentiles in model 3. The P value for non-linearity was calculated using the likelihood ratio test. Model 3 was used for the non-linear analysis to account for established CVD covariates, including behavioral factors and BMI, thereby minimizing residual confounding.
To examine the joint associations of birth weight and LE8 score with incident CVD, we cross-classified participants according to birth weight (<2.5 kg, 2.5–4.0 kg, or >4.0 kg) and LE8 score (<50, 50–79, or ≥80) categories, with the group of low birth weight and low LE8 score as the reference. We adjusted for the covariates in model 2 in the joint association analyses.
To examine the interaction between birth weight and LE8 CVH on the risk of CVD on multiplicative and additive scales, we classified birth weight (<2.5 kg or ≥2.5 kg) and the CVH (low or moderate/high) as two binary variables. In model 2, we included the cross-product term of birth weight and CVH. The P values for multiplicative interactions were calculated by comparing log-likelihood ratios of the models with and without a cross-product term of birth weight and CVH [23]. We assessed the relative excess risk due to interaction (RERI), attributable proportion due to interaction, and synergy index as indices of additive interaction [[24], [25], [26]].
We calculated population-attributable fractions (PAF) to estimate the hypothetical reduction in population-level risks for the CVD attributed to a combination of enhancing CVH and preventing instances of low birth weight. PAF represents the proportion of CVD cases that could be theoretically reduced if the exposures were eliminated from the population, assuming a causal relation between the risk factors and the CVD [27,28]. We estimated the PAF by comparing individuals in six distinct low-risk categories to the rest of the population. The category 1 included individuals with birthweight ≥2.5 kg and four moderate or high behavior metrics. The categories 2 to 5 included individuals with birthweight ≥2.5 kg, four moderate or high behavior metrics, and each of the four moderate or high biological metrics (BMI, blood lipids, blood glucose, and blood pressure). The category 6 included individuals with birthweight ≥2.5 kg, four moderate or high behavior metrics, and four moderate or high biological metrics. The PAFs with the 95 % CIs were estimated using the multivariable-adjusted logistic models with the averisk R package [28].
All analyses were conducted using R software (version 4.2.1), with 95 % confidence intervals (CI) and two-sided p values calculated for statistical inference.
3. Results
3.1. Baseline characteristics
Among the 143,910 participants, 97.2 % were White, 40.9 % were men, and the mean (SD) age at analysis baseline was 54.4 (8.0) years. Of all the participants, 9.5 % had low birth weight, 77.4 % had normal birth weight, and 13.1 % had high birth weight. Compared with the individuals with normal birth weight, those with low birth weight tended to be women, have lower educational level and household income, and postmenopausal (for women only); while those with high birth weight were more likely to be men, and postmenopausal (for women only).
3.2. Association of birth weight with incident CVD
During a median follow-up of 15.1 (IQR: 14.2 to 15.9) years, a total of 24,071 (12.0/1000 person-years [PY]) incident total CVD were documented. Compared with the participants with normal birth weight (11.4/1000 PY), those with low (13.7/1000 PY) and high birth weight (14.1/1000 PY) had higher incident rate of total CVD. After multivariable adjustment, low birth weight was associated with a higher risk of total CVD (adjusted hazard ratio [aHR] 1.13 [95 % CI: 1.08 to 1.17], P < 0.001) and CVD death (aHR 1.25 [1.11 to 1.40], P < 0.001) compared with normal birth weight. The corresponding ARDs were 18.15 events per 1000 PY (95 % CI, 11.74–24.56) for total CVD and 6.19 events per 1000 PY (95 % CI, 2.71–9.67) for CVD mortality. Analyses for the specific cardiovascular conditions indicated that the associations between low birth weight and atherosclerotic diseases were evident, with the HRs ranging from 1.36 (95 % CI 1.18 to 1.58) for peripheral artery disease to 1.20 (95 % CI 1.13 to 1.29) for ischemic heart disease, except for aortic aneurysm (aHR 1.04 [0.84 to 1.28], P = 0.73). Moreover, we found low birth weight was also significantly associated with 19 %, 22 % and 9 % higher risks for heart failure (aHR 1.19 [1.05 to 1.35], P = 0.005), non-rheumatic valvular disease (aHR 1.22 [1.08 to 1.37], P = 0.001), and dysrhythmias (aHR 1.09 [1.03 to 1.17], P = 0.006), while no evidence of increased risk for venous thromboembolism (Fig. 1, Supplementary Table S4). In stratification analysis by sex, the association between low birth weight and risks of CVD death (aHR: 1.44 [1.23 to 1.68] in women vs 1.07 [0.90 to 1.28] in men, P for interaction=0.01) were stronger in women than in men (Supplementary Table S5).
Fig. 1.
Hazard ratios of incident CVD according to birth weight among 143,910 participants in the UKB. Cox proportional hazards models were used to examine the association between birth weight and the risk of CVD with normal birth weight as the reference. Models were adjusted for age, sex, ethnicity (White, yes or no), family history of CVD (yes or no), household income (£, <18,000; 18,000–30,999; 31,000–51,999; 52,000–100,000; or >100,000), education (College or university degree; A levels/AS levels or equivalent; O levels/GCSE levels or equivalent; CSEs or equivalent; NVQ or HND or HNC or equivalent; Other professional qualifications; or None of above), index of multiple deprivation, diet score, smoking status (never, previous, or current), moderate or vigorous physical activities per week (metabolic equivalent of task minutes), sleep duration per day (hours), and body mass index. Abbreviations: CVD, cardiovascular disease; CI, confidence interval; HR, hazard ratio; PY, person-year.
We did not find significant association between high birth weight and risks of total CVD and CVD death. However, compared with the participants with normal birth weight, those with high birth weight had a decreased risk of peripheral artery disease (aHR 0.78 [0.67 to 0.91], P = 0.002) and ischemic heart disease (aHR 0.94 [0.89, 1.00], P = 0.05), corresponding to ADRs of −2.51 per 1000 PY (95 % CI: −4.00 to −1.03) and −3.59 (95 % CI: −7.07 to −0.11) per 1000 PY, respectively. Incontrast, high birth weight was associated with increased risks of aortic aneurysm (aHR 1.20 [1.03 to 1.39], P = 0.02) and dysrhythmias (aHR 1.08 [1.03 to 1.14], P = 0.003), with corresponding ARDs of 1.55 events per 1000 PY (95 % CI: 0.14 to 2.96), and 5.76 events per 1000PY (95 % CI: 1.96 to 9.56), respectively (Fig. 1, Supplementary Table S4).
We used restricted cubic spline regression analyses to examine the non-linear associations between birth weight and incident CVD. The results showed significant non-linear associations of birth weight with risks of total CVD and six specific cardiovascular conditions including ischemic heart disease, stroke, aortic aneurysm, heart failure, non-rheumatic valvular disease, and dysrhythmias, displaying U- or l- shaped relationship (P for non-linear <0.05) (Supplementary Figure S2).
3.3. Joint association of birth weight and CVH with incident CVD
Table 2 showed the associations between CVH and incident total CVD stratified by birth weight. Across three birth weight groups, high CVH consistently demonstrated risk reductions in incident total CVD compared with low CVH, with the aHRs (95 % CIs) for total CVD of 0.44 (0.37 to 0.52), 0.40 (0.37 to 0.42), and 0.45 (0.39 to 0.52) in low, normal, and high birth weight groups, respectively. The joint association analyses with low birth weight and low CVH as the reference demonstrated a strong gradient reduction in risk of incident total CVD along with the increment of CVH level and birth weight (Fig. 2). Specifically, participants with high CVH and normal birth weight had the lowest risk of incident total CVD, resulting in a 63 % reduction in risk for total CVD (aHR 0.37 [0.33 to 0.43]) and 50 % to 80 % reduction in risks for other cardiovascular conditions (Supplementary Table S6). The interaction analyses indicate a multicative interaction between BW and CVH on the risks of CVD death (P for multiplicative interaction=0.01) and stroke (P for multiplicative interaction=0.04), with individuals who had both LBW and poor CVH exhibiting markedly elevated risks (Supplementary Table S7).
Table 2.
Incidence and hazard ratios of CVD according to CVH levels assessed by LE8 across birth weight categories among 143,910 participants in the UKB.
| Outcome | CVH | Birth weight (kg) |
|||||
|---|---|---|---|---|---|---|---|
| <2.5 |
2.5–4.0 |
>4.0 |
|||||
| Cases/PY | HR (95 % CI) | Cases/PY | HR (95 % CI) | Cases/PY | HR (95 % CI) | ||
| Total CVD | |||||||
| Low | 223/8846 | Ref. | 1352/58,297 | Ref. | 307/11,838 | Ref. | |
| Moderate | 1968/133,900 | 0.60 (0.52, 0.69) | 13,505/1053,546 | 0.56 (0.53, 0.59) | 2787/182,472 | 0.59 (0.53, 0.67) | |
| High | 379/45,506 | 0.44 (0.37, 0.52) | 2980/447,693 | 0.40 (0.37, 0.42) | 570/65,490 | 0.45 (0.39, 0.52) | |
| CVD death | |||||||
| Low | 34/10,229 | Ref. | 278/66,673 | Ref. | 50/13,726 | Ref. | |
| Moderate | 262/146,318 | 0.56 (0.39, 0.80) | 1518/1137,970 | 0.34 (0.30, 0.39) | 343/199,712 | 0.50 (0.37, 0.68) | |
| High | 41/48,041 | 0.38 (0.24, 0.61) | 207/466,436 | 0.18 (0.15, 0.21) | 38/69,104 | 0.24 (0.16, 0.37) | |
| Peripheral artery disease | |||||||
| Low | 32/10,074 | Ref. | 130/65,978 | Ref. | 18/13,622 | Ref. | |
| Moderate | 148/145,469 | 0.36 (0.24, 0.53) | 810/1133,329 | 0.41 (0.34, 0.49) | 122/199,014 | 0.50 (0.30, 0.83) | |
| High | 35/47,822 | 0.35 (0.22, 0.58) | 225/465,033 | 0.38 (0.30, 0.47) | 41/68,823 | 0.63 (0.35, 1.12) | |
| Ischemic heart disease | |||||||
| Low | 116/9460 | Ref. | 679/62,398 | Ref. | 152/12,828 | Ref. | |
| Moderate | 804/140,936 | 0.49 (0.41, 0.60) | 5403/1102,015 | 0.49 (0.45, 0.53) | 1107/192,679 | 0.53 (0.44, 0.62) | |
| High | 120/47,154 | 0.30 (0.23, 0.38) | 806/461,270 | 0.25 (0.22, 0.28) | 160/68,138 | 0.30 (0.24, 0.38) | |
| Stroke | |||||||
| Low | 28/10,088 | Ref. | 210/65,582 | Ref. | 48/13,443 | Ref. | |
| Moderate | 363/144,234 | 0.97 (0.65, 1.42) | 2039/1126,916 | 0.56 (0.49, 0.65) | 412/197,572 | 0.59 (0.44, 0.81) | |
| High | 54/47,746 | 0.57 (0.36, 0.90) | 448/463,872 | 0.41 (0.34, 0.48) | 83/68,592 | 0.47 (0.33, 0.68) | |
| Aortic aneurysm | |||||||
| Low | 9/10,189 | Ref. | 88/66,183 | Ref. | 25/13,622 | Ref. | |
| Moderate | 78/145,963 | 0.63 (0.32, 1.28) | 569/1135,086 | 0.40 (0.32, 0.50) | 157/198,974 | 0.42 (0.27, 0.64) | |
| High | 10/47,991 | 0.35 (0.14, 0.88) | 110/465,850 | 0.29 (0.22, 0.39) | 30/68,955 | 0.34 (0.20, 0.59) | |
| Heart failure | |||||||
| Low | 35/10,085 | Ref. | 207/65,774 | Ref. | 36/13,519 | Ref. | |
| Moderate | 232/145,094 | 0.47 (0.32, 0.67) | 1449/1130,925 | 0.42 (0.37, 0.49) | 317/198,297 | 0.65 (0.46, 0.92) | |
| High | 28/47,880 | 0.23 (0.14, 0.38) | 198/465,486 | 0.21 (0.17, 0.25) | 46/68,888 | 0.41 (0.26, 0.64) | |
| Non-rheumatic valvular disease | |||||||
| Low | 24/10,120 | Ref. | 157/65,918 | Ref. | 21/13,604 | Ref. | |
| Moderate | 246/144,939 | 0.73 (0.48, 1.12) | 1527/1129,678 | 0.56 (0.48, 0.66) | 343/198,034 | 1.10 (0.71, 1.72) | |
| High | 54/47,732 | 0.68 (0.41, 1.10) | 319/464,586 | 0.40 (0.33, 0.49) | 72/68,705 | 0.93 (0.57, 1.52) | |
| Dysrhythmias | |||||||
| Low | 85/9738 | Ref. | 535/63,645 | Ref. | 116/13,091 | Ref. | |
| Moderate | 828/141,524 | 0.66 (0.53, 0.83) | 5801/1103,048 | 0.62 (0.57, 0.68) | 1379/191,543 | 0.81 (0.67, 0.98) | |
| High | 175/46,860 | 0.57 (0.44, 0.75) | 1373/457,659 | 0.50 (0.46, 0.56) | 282/67,364 | 0.64 (0.51, 0.80) | |
| Venous thromboembolism | |||||||
| Low | 20/9932 | Ref. | 126/64,808 | Ref. | 33/13,294 | Ref. | |
| Moderate | 182/143,222 | 0.65 (0.41, 1.03) | 1307/1116,641 | 0.61 (0.51, 0.74) | 266/195,578 | 0.57 (0.39, 0.82) | |
| High | 32/47,412 | 0.42 (0.24, 0.74) | 277/460,449 | 0.40 (0.32, 0.49) | 45/68,163 | 0.35 (0.22, 0.55) | |
Cox proportional hazard ratio was used to calculate the hazard ratio with 95 % CI with adjustment for age, sex, ethnicity (white, yes/no), family history of CVD (yes/no), household income (£, <18,000; 18,000–30,999; 31,000–51,999; 52,000–100,000; or >100,000), education (College or university degree; A levels/AS levels or equivalent; O levels/GCSE levels or equivalent; CSEs or equivalent; NVQ or HND or HNC or equivalent; Other professional qualifications; or None of above), and index of multiple deprivation.
Abbreviations: CI, confidence interval; CVD, cardiovascular disease; CVH, cardiovascular health; HR, hazard ratio; LE8, life’s essential 8; PY, person years.
Fig. 2.
Hazard ratios of total CVD according to joint categories of birth weight and LE8 CVH among 143,910 participants in the UKB. Cox proportional hazards models were used to examine the joint association of birth weight and CVH with CVD, with adjustment for age, sex, ethnicity (white, yes or no), family history of CVD (yes or no), household income (£, <18,000; 18,000–30,999; 31,000–51,999; 52,000–100,000; or >100,000), education (College or university degree; A levels/AS levels or equivalent; O levels/GCSE levels or equivalent; CSEs or equivalent; NVQ or HND or HNC or equivalent; Other professional qualifications; None of above), index of multiple deprivation. In the multiplicative or additive interaction analyses, birth weight (<2.5 kg or ≥2.5 kg) and the CVH (low or moderate/high) was considered as two binary variables. Abbreviations: CI, Confidence interval; CVD, cardiovascular disease; CVH, cardiovascular health; LE8, Life’s Essential 8.
3.4. Population-attributable fractions of birth weight and CVH for CVD
The PAF of a combination of low birth weight and four low behavioral CVH metrics for total CVD was 5.78 % (5.17 % to 6.38 %), suggesting 5.78 % of the total CVD could have been prevented if all the participants had birth weight ≥2.5 kg and achieved four moderate/high score in behavior metrics (diet, physical activity, nicotine exposure and sleep) according to the definition of LE8. When we additionally included each of the four biological metrics (BMI, blood lipids, blood glucose, and blood pressure) into the risk factor combination, the estimated PAFs for total CVD increased to 11.16 % (10.20 % to 12.12 %), 8.22 % (6.98 % to 9.46 %), 7.35 % (6.78 % to 7.92 %), and 13.47 % (13.23 to 13.70 %), respectively. Notably, when we took birth weight and all eight LE8 components into consideration, the PAF for total CVD increased to 19.38 % (17.91 to 20.85), which means that 19 % of total CVD could have been prevented if the population had birth weight ≥2.5 kg and moderate/high scores in all the eight CVH metrics (Table 3). The PAFs for the specific CVD subtypes ranged from 9.87 % for peripheral artery disease to 33.46 % for CVD death (Supplementary Table S8).
Table 3.
Population attributable risk of total CVD associated with birth weight and LE8 CVH among 143,910 participants in the UKB.
| Low risk categoriesb | Percentage of population | No. of CVD cases | Relative risk (95 % CI)a | Population attributable fraction ( %)c |
|---|---|---|---|---|
| Birth weight ≥2.5 kg plus: | ||||
| four moderate/high behavior metrics | 47.9 | 10,624 | 0.83 (0.81, 0.85) | 5.78 (5.17, 6.38) |
| four moderate/high behavior metrics + BMI | 38.9 | 7787 | 0.73 (0.71, 0.75) | 11.16 (10.20, 12.12) |
| four moderate/high behavior metrics + blood lipids | 21.8 | 4266 | 0.83 (0.81, 0.86) | 8.22 (6.98, 9.46) |
| four moderate/high behavior metrics + blood glucose | 46.3 | 9896 | 0.79 (0.77, 0.81) | 7.35 (6.78, 7.92) |
| four moderate/high behavior metrics + blood pressure | 33.1 | 5821 | 0.70 (0.68, 0.73) | 13.47 (13.23, 13.70) |
| eight moderate/high LE8 CVH metrics | 14.2 | 1955 | 0.65 (0.62, 0.68) | 19.38 (17.91, 20.85) |
Relative risks with 95 % CIs were calculated using logistic models comparing individuals in the low-risk categories with the rest of the population. Models were adjusted for age, sex, ethnicity (white, yes/no), family history of CVD (yes/no), household income (<18,000; 18,000–30,999; 31,000–51,999; 52,000–100,000; or >100,000), education (College or university degree; A levels/AS levels or equivalent; O levels/GCSE levels or equivalent; CSEs or equivalent; NVQ or HND or HNC or equivalent; Other professional qualifications; or None of above), and index of multiple deprivation.
Four moderate/high behavior metrics included diet, physical activity, nicotine exposure, sleep.
The population attributable fraction ( %) estimated the proportion of CVD events in a population that could potentially be prevented if all individuals were categorized into relevant low-risk categories.
Abbreviations: CI, confidence interval; CVD, cardiovascular disease; LE8, Life’s Essential 8; CVH, cardiovascular health; BMI, body mass index.
4. Discussion
4.1. Main findings
In this prospective cohort study, we observed that low birth weight was linked to an elevated risk of various CVD outcomes. Conversely, high birth weight showed a neutral association with the overall risk of CVD, despite its inverse relationship with the risk of peripheral artery disease and a positive association with the risk of dysrhythmias. Enhanced CVH was associated with significant reduction in risk of CVD death and stroke with multiplicative interactions. Furthermore, our results indicated that approximately 20 % of total CVD cases and 33 % of CVD-related deaths could have been prevented if the participants had a birth weight of ≥2.5 kg and demonstrated moderate to high levels of all eight CVH metrics, as assessed by LE8.
4.2. Interpretation
Birth weight serves as a key indicator of the intrauterine environment. Low birth weight contributes to disruptions in hypothalamic-pituitary-adrenal axis activity, altered glucocorticoid activity, endothelial dysfunction, and directs energy allocation toward the development of vital organs [2,29,30]. Despite the established link between low birth weight and conditions such as obesity and insulin resistance,[[31], [32], [33], [34]] the associations between birth weight and the risk of CVD have not been extensively explored. Early evidence from a cohort study involving 10,803 Aberdeen participants born in the 1950s demonstrated the association between low birth weight and the increased risk of CHD and stroke. Subsequent study, pooling data from Health Professionals Follow-up Study (HPFS) (n = 20,169), Nurses’ Health Study (NHS) (n = 52,380), and NHS II (n = 85,350), also revealed a heightened risk of CHD among individuals with low birth weight. However, the association with incident stroke persisted in women (the NHS and the NHS II) but not in men (the HPFS) [7,35]. Several studies reported nonsignificant associations between low birth weight and risks of heart failure and atrial fibrillation [[36], [37], [38], [39]]. However, there has been limited assessment of the relationship between low birth weight and other cardiovascular conditions, despite the diverse spectrum of CVDs.
Our present study, based on a large prospective cohort, expanded the scope of outcomes to encompass a broad range of cardiovascular conditions. We identified significant inverse associations between low birth weight and the risks of total CVD and CVD-related death. Moreover, the association with CVD death was more pronounced in women than in men. Specifically, our findings demonstrated that low birth weight was significantly associated with an increased risk of peripheral artery disease, ischemic heart disease, stroke, heart failure, non-rheumatic valvular disease, and dysrhythmias. However, there was no significant association with the risks of aortic aneurysm and venous thromboembolic events. Notably, a significant sex difference was observed in the association between low birth weight and the risk of ischemic heart disease, with a stronger association in women than in men. In general, our findings, along with those of previous studies, consistently indicate a detrimental association between low birth weight and CVD risk, although disparities exist in terms of associations with specific cardiovascular conditions and the observed sex differences. The variations in population characteristics, incident rates of cardiovascular conditions, and sample sizes may contribute to some inconsistencies among studies.
It is noteworthy that evidence suggests a sex difference in the association between birth weight and the risks of CVD, particularly in the case of CHD and stroke. Among 10,803 participants born in Scotland in the 1950s, the association of low birth weight with the risk of CHD was more pronounced in women than in men [35]. The HPFS, NHS, and NHS II studies indicated that lower birth weight was associated with an increased risk of stroke only among women [40]. In our study, while no significant sex difference was detected for total CVD, the associations were more prominent in women than in men, with significance observed for ischemic heart disease and CVD death. Although not entirely clear, the higher prevalence of low birth weight, faster-growing fetuses, and greater vulnerability among women may contribute to the mechanisms underlying such a sex difference [41,42].
Despite the detrimental association between low birth weight and CVD risk, the association between high birth weight and the risk of CVD remains uncertain. Evidence from the NHS including 66,111 women and Women's Health Initiative involving 63,815 women did not support significant associations between high birth weight and risks of CHD or stroke [6,43]. Among 4594 individuals in the Swedish Twin Registry cohort, high birth weight was associated with an increased risk of angina pectoris, but the association became insignificant after adjustment for potential confounders [44]. In contrast to these studies regarding atherosclerotic diseases, evidence from cohort studies proposed links between high birth weight and increased risks of atrial fibrillation and heart failure [37,45]. In our study, we did not find evident associations between high birth weight and risks of total CVD and CVD-related death. Importantly, high birth weight was associated with a decreased risk of peripheral artery disease and ischemic heart disease, potentially reflecting more favorable early-life growth and vascular development. Conversely, high birth weight has also been associated with increased risks of aortic aneurysm and arrhythmias. The mechanisms underlying the heterogeneous associations of high birth weight with cardiovascular outcomes remain unclear. Proposed pathways include long-term alternations in glucose-insulin metabolism, lipid regulation, and inflammatory responses, which may differentially influence vascular and cardia remodeling [46]. Evidence from Mendelian randomization studies provides additional clues. A large MR analysis suggested that genetically predicted higher birth weight was not robustly associated with ischemic heart disease [47], indicating that some protective effects in observational studies may reflect residual confounding. In contrast, a recent multivariable MR study reported that higher birth weight was linked to reduced risk of cerebral aneurysm [48], independent of obesity-related traits and smoking, and emerging evidence suggests it may also influence the risk of atrial fibrillation [46]. These findings imply that the impact of high birth weight on cardiovascular health is more intricate than that of low birth weight. This complexity underscores the need for additional investigations through extensive transracial prospective cohort studies and wet-lab research to elucidate the mechanisms that underlie the distinct associations between high birth weight and cardiovascular conditions.
Compelling evidence has established a connection between lifestyle factors and the development of cardiometabolic disorders [[49], [50], [51], [52]]. However, only a limited number of studies have delved into the interplay between birth weight and behavioral factors concerning the subsequent risk of cardiometabolic diseases. For instance, among 52,114 women in the NHS II, a significant additive interaction was discovered between birth weight and lifestyle factors (BMI, physical activity, alcohol consumption, diet, and medication) in relation to the risk of hypertension [51]. Pooling data from the HPFS, NHS and NHS II suggested an additive interaction between birth weight and lifestyle factors (BMI, smoking, physical activity, alcohol consumption, and diet) on the risk of diabetes, though not for stroke or CHD [40,52]. Similarly, the Stockholm Pubic Health Cohort study found no evidence of interactions for the risk of a more comprehensive spectrum of CVD outcomes [53]. However, these studies did not adequately consider biological factors, which may exert a more substantial influence on CVD than behaviors did [54]. LE8 was recently introduced as a new CVH metric, including four lifestyle factors and four biological factors. It has been shown to protect against CVD and mortality. Our study investigated the interaction between birth weight and LE8 score in relation to the risk of CVD and shown that a high LE8 score was associated with a decreased risk of CVD, irrespective of birth weight. Overall, our findings underscored the importance of maintaining ideal cardiovascular health in reducing the risk of developing CVD, even in the presence of a non-optimal intrauterine developmental environment.
The strengths of our study included the prospective cohort design, large sample size, outcomes of a broad spectrum of cardiovascular conditions, and the examination of interactions between birth weight and CVH evaluated by the novel LE8 algorithm. Our study has limitations. First, despite similar baseline characteristics between participants who reported birth weight and those who did not, the exclusion of participants without birth weight information may introduce potential selection bias. Second, birth weight was self-reported, which may introduce recall bias. Previous validation studies suggest moderate overall validity but greater misclarssification among low birth weight individuals, typically leading to attenuated associations with later health outcomes [55]. Our estimates are therefore likely conservative, and the true associations may be stronger. Third, we cannot make causal inference for our findings due to the residual confounding. Finally, the majority of participants were White, which limits the generalizability of our findings to other ethnic populations.
5. Conclusion
Low birth weight was linked to increased risks of total CVD, CVD-related mortality, and a diverse array of cardiovascular conditions. Conversely, while high birth weight did not show associations with the risks of total CVD and CVD-related death, it was correlated with a decreased risk of peripheral artery disease and an increased risk of dysrhythmias. The promotion of ideal cardiovascular health, as assessed by the LE8 score, demonstrated a beneficial association with CVD risk independent of birth weight. Our findings underscore the significance of enhancing both prenatal and postnatal environmental factors for the prevention of cardiovascular disease.
Declarations
Ethics approval and consent to participate
The UKB study was approved by the Research Ethics Committee and written informed consent was obtained from all participants.
Availability of data and materials
All data used in analysis are available from UKB upon request (https://www.ukbiobank.ac.uk).
Funding
Guangzhou Foundation for Basic and Applied Basic Research (2023A04J0539) and Medical Scientific Research Foundation of Guangdong Province (B2025312) to XZ. The funding sources were not involved in the design, conduct or reporting of the study.
CRediT authorship contribution statement
Jinping Long: Writing – original draft, Conceptualization. Anping Feng: Writing – review & editing, Writing – original draft, Formal analysis. Qingmei Cui: Writing – original draft. Zhilin Liu: Writing – original draft. Shiqun Chen: Writing – original draft. Siqi Chen: Writing – original draft. Miaomiao Zhang: Writing – original draft. Danlin Zheng: Writing – original draft. Hailan Huang: Writing – original draft. Ran An: Writing – original draft. Xia Zou: Writing – review & editing, Writing – original draft, Formal analysis, Conceptualization.
Declaration of competing interest
The authors have no conflicts of interest to declare.
Acknowledgements
This research was conducted using the UK Biobank Resource under Application Number 98973. The authors would like to express their sincere gratitude to all the participants and staff of the UK Biobank for their dedication and contribution to the research. Participants were not involved in the design, conduct, reporting, or dissemination plans of this study.
Footnotes
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.ajpc.2025.101273.
Contributor Information
Ran An, Email: anran@gdph.org.cn.
Xia Zou, Email: zouxia@gdph.org.cn.
Appendix. Supplementary materials
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
All data used in analysis are available from UKB upon request (https://www.ukbiobank.ac.uk).