Abstract
Background
Cardiovascular disease originates in early life. We aimed to investigate the association between breastfeeding in infancy and cardiovascular disease in adult life.
Methods
We followed 364,240 participants from UK Biobank aged 40–73 years from 2006 – 2010 to 2021. Information on breastfeeding in infancy was self-reported by questionnaire. Cox proportional hazard regression models were used to estimate the hazard ratios (HR) and 95% confidence intervals (CI) for the association between breastfeeding and cardiovascular disease in middle-aged and older adulthood. The multivariable Cox models were used by adjusting for the age (used as the time scale), sex, ethnicity, assessment centre, birth weight, multiple birth status, maternal smoking during pregnancy, Townsend deprivation index, smoking status, alcohol drinker status, physical activity, and menopausal status for women. Binary and multinomial multivariable logistic regression models were used to explore the associations of breastfeeding in infancy with cardiovascular disease risk factors including obesity, body composition, metabolic and inflammatory disorders.
Results
During a median of 12.6 years of follow-up, we documented 29,796 new cases of cardiovascular disease, including 24,797 coronary heart disease and 6229 stroke. The multivariable adjusted HRs for breastfed versus non-breastfed were 0.94 (95% CI: 0.91, 0.96) for cardiovascular disease, 0.94 (95% CI: 0.91, 0.96) for coronary heart disease, and 0.95 (95% CI: 0.89, 1.01) for stroke. Furthermore, the strength of observed association between breastfeeding and cardiovascular disease seems to decrease with age (P for interaction <0.001), and increase with polygenic risk for cardiovascular disease (P for interaction <0.001). Consistently, breastfeeding in infancy was associated with cardiovascular disease risk factors including lower body mass index 0.92 (95% CI: 0.89, 0.95), body fat percentage 0.85 (95% CI: 0.83, 0.87), android to gynoid fat ratio 0.89 (95% CI: 0.83, 0.96), visceral adipose tissue 0.92 (95% CI: 0.84, 1.01), as well as lower C-reactive protein level 0.95 (95% CI: 0.94, 0.97) and a lower risk of metabolic syndrome 0.89 (95% CI: 0.85, 0.92).
Conclusions
Breastfeeding in infancy was associated with a lower risk of cardiovascular disease in middle-aged and older adulthood. Promoting breastfeeding is vital not only for promoting child health, but also for halting the increasing trend of cardiovascular disease in adults.
Keywords: Breastfeeding, Cardiovascular disease, Genetic predisposition, Obesity, Fat distribution, Metabolic syndrome
1. Introduction
Cardiovascular disease accounted for 32% of all deaths worldwide [1]. Although cardiovascular disease has been traditionally considered as a disease of adulthood, increasing evidence has shown that it may have fetal and early life origin [[2], [3], [4]]. The fetal origin hypothesis can be supported by the established association observed between low birth weight and increased risk of cardiovascular disease [[5], [6], [7], [8]]. However, limited studies have investigated the association between early life nutrition and cardiovascular disease in adulthood.
Recognized as the optimal nutritional source for infants, the impact of breastfeeding on adult cardiovascular health is still unclear. A previous meta-analysis of four observational studies showed no association between breastfeeding in infancy and cardiovascular disease in adults (pooled odds ratio: 1.06, 95% CI: 0.94,1.20) [9]. Subsequently, a study based on the Nurses' Health Study and reported a slight but non-significant in cardiovascular disease risk in adults breastfed as infants as well. The hazard ratio (HR) for incident coronary heart disease and stroke was 0.92 (95% CI: 0.80,1.05) and 0.91 (95% CI: 0.79,1.06), respectively. When combining coronary heart disease and stroke, the risk of developing cardiovascular disease was slightly lower in women who breastfed (0.91, 95% CI: 0.83,1.01) [10]. Limited sample size may restrict the statistical power. UK Biobank recruited over 500,000 participants with 76% provided early life information providing the potential to refine the research question. Recently, a significant correlation (0.97, 95% CI: 0.94,1.00, P = 0.041) was found in an UK Biobank study [11]. However, these studies did not explore whether this association varied with genetic predisposition and age.
This study aims to fill this gap by leveraging data from 0.36 million adults aged 40–73 years in the UK Biobank study. Our objective is to investigate the association between breastfeeding in infancy and cardiovascular disease in middle-aged and older adulthood, and in particular how this association may be influenced by age and genetic susceptibility to cardiovascular disease. We also extended our investigation to cardiovascular disease risk factors including obesity, fat distribution, metabolic and inflammatory disorders to reinforce the robustness of our findings.
2. Methods
2.1. Study population
UK Biobank is a large prospective cohort study recruiting half a million participants aged 37–73 years from 22 assessment centres across England, Scotland, and Wales, between 2006 and 2010. The UK Biobank study was approved by the National Health Service’s National Research Ethics Service. All participants provided written informed consent. Details of the UK Biobank’s rationale, design and survey methods have been described elsewhere [12]. Among the 502,408 participants, we excluded those with missing information on breastfed history (n = 118,774), aged less than 40 years (n = 6), underweight (n = 2049), and patients who had self-reported or a history of cardiovascular disease according to hospital inpatient data before baseline (n = 17,339), leaving a total of 364,240 participants for the final analysis. The exclusion of underweight participants was based on the consideration that this group may have underlying health conditions or different lifestyle factors, which could potentially confound the results. The targeted population of the parent study consists of community-dwelling individuals from the UK Biobank.
2.2. Breastfeeding in infancy
Breastfeeding in infancy was obtained from baseline touchscreen questionnaire. Participants were asked to recall the following question: “Were you breastfed when you were a baby?”. Breasted as a baby was defined as answer of yes to the question.
2.3. Cardiovascular disease
Participants were followed from the date of baseline assessment until death, loss of follow-up, or end of study period (England: 30 September 2021; Scotland: 31 July 2021; Wales: 28 February 2018), whichever came first, to define cardiovascular disease. Information on cardiovascular disease was identified by linking to hospital inpatient data of the Health Episode Statistics (England and Wales) and to the Scottish Morbidity Records. The occurrence of cardiovascular disease was defined according to the International Classification of Diseases 10 revision [ICD-10] codes, including coronary heart disease (I20–I25), and stroke (I60–I64).
2.4. Cardiovascular disease risk factors
Body mass index (BMI) was calculated as weight (kg)/(height (m))2, with both weight and height being objectively measured. Waist to hip ratio was calculated as waist circumference divided by hip circumference. Whole body and site-specific (trunk, leg, arm) fat percentage were derived from bioelectrical impedance analysis (BIA; 2006–2010; n = 357,914). Android and gynoid tissue fat percentage were derived from dual-energy x-ray absorptiometry (DXA; 2014+; n = 31,545). Visceral and subcutaneous adipose tissue volume were derived from abdominal magnetic resonance imaging (MRI; 2014+; n = 19,319). Notably, all the above indices of fat distribution were expressed as sex-specific quartiles (Supplement Table 1). Metabolic syndrome was defined according to the most current harmonized criteria, which diagnosed the condition when three or more of the following abnormalities are present: (1) elevated waist circumference (waist circumference ≥ 102 cm in men or ≥88 cm in women; (2) elevated fasting glucose (fasting glucose ≥ 5.6 mmol/L, taking antidiabetic medications (or insulin), or previously diagnosed as type 2 diabetes mellitus patients); (3) elevated blood pressure (systolic blood pressure ≥ 130 mm Hg and/or diastolic blood pressure ≥ 85 mm Hg or current use of antihypertensive agents); (4) elevated triglycerides (triglyceride ≥ 1.7 mmol/L); (5) reduced high-density lipoprotein cholesterol (HDL-C; HDL-C ≤ 1.03 mmol/L in men or ≤1.30 mmol/L in women) [13]. C-reactive protein levels were categorized as low (<1.0), moderate (1.0–2.9), high (3.0–9.9), or very high (≥10.0) [14].
2.5. Ascertainment of covariates
Covariates were obtained from baseline touchscreen questionnaires and consisted of: demographic characteristics including age (years), sex (women or men), ethnicity (white or non-white), and assessment centre (England, Scotland or Wales), early life factors including maternal smoking during pregnancy (yes or no), birth weight (low, normal or high), and multiple birth status (yes or no), and later life factors including Townsend deprivation index (quintiles, higher score implies lower socioeconomic status) [15], smoking status (never, previous or current), alcohol drinker status (never, previous or current), physical activity (low, moderate or high), and menopause status for women (premenopausal or postmenopausal). Missing data were treated as its own category for above-mentioned covariates.
2.6. Statistical analyses
Chi-square tests were used to test for differences in baseline characteristics by breastfeeding in infancy. Cox proportional hazard regression models were used to estimate the hazard ratio (HR) and 95% confidence interval (CI) for the association between breastfeeding in infancy and cardiovascular disease incidence. Age was used as the time scale in all time-to-event analyses. Schoenfeld residuals tests were used to check the proportional hazard assumption. In cases where the assumption was violated, particularly in the analyses involving the interaction with age, a flexible parametric survival model was used, which allow the HRs to change over time scale. Stratified analyses by polygenic risk score for cardiovascular disease were further conducted to estimate potential modification effects. Three sets of models were used. The basic model (model l) was adjusted for attained age (as time scale), sex, ethnicity, and assessment centre. The model 2 was adjusted for additional early life factors including birth weight, multiple birth status, and maternal smoking during pregnancy. On the basis of model 2, we additionally adjusted for adult life factors including Townsend deprivation index, smoking status, alcohol drinker status, physical activity, and menopausal status for women in model 3.
To explore whether the relationship between breastfeeding in infancy and cardiovascular disease is influenced by genetic susceptibility to cardiovascular disease, this study further conducted stratified analyses by polygenic risk score for cardiovascular disease to assess potential modification effects. The polygenic risk score is a standard used to evaluate an individual's risk of cardiovascular disease, calculated using genotype effect sizes from genome-wide association study (GWAS) summary statistics [16]. The UK Biobank research team employed machine learning models to evaluate the associations between all studied genes and disease risks, creating a genetic risk scoring system that predicts the risk levels for each UK Biobank participant for various diseases, including cardiovascular disease, breast cancer, and diabetes [17]. In this study, the polygenic risk score for cardiovascular disease was defined and grouped based on the calculations results by the UK Biobank research team. Participants with polygenic risk scores in the lowest decile (<10%) were categorized as the low-risk group, those in the middle range (10%–90%) as the medium-risk group, and those in the highest decile (>90%) as the high-risk group.
Logistic regression analyses were used for additional analyses to explore the associations of breastfeeding in infancy with cardiovascular disease risk factors including obesity, fat distribution, metabolic and inflammatory disorders. The basic model (model l) was adjusted for attained age (used as the time scale), sex, ethnicity, and assessment centre. Model 2 was further adjusted for additional early life factors including birth weight, multiple birth status, and maternal smoking during pregnancy. Model 3 additionally adjusted for later life factors including Townsend deprivation index, smoking status, alcohol drinker status, physical activity, and menopausal status for women.
All statistical analyses were performed with R version 4.1.0 and Stata version 16.0. P values < 0.05 (two sided) were considered statistically significant.
2.7. Patient and public involvement
No patients were involved in setting the research question or the outcome measures, nor were they involved in the design or implementation of the study. There are no plans to involve patients in dissemination.
3. Results
3.1. Participants characteristic
Table 1 shows the baseline characteristics of the study participants. Among 364,240 participants, 262,905 (72.2%) were breastfed in infancy. Compared with non-breastfed, participants who were breastfed in infancy were older and were more likely to be women.
Table 1.
Participants characteristics of the study population by breastfeeding in infancy.
| Overall | Breastfeeding in infancy |
||
|---|---|---|---|
| No (n = 101,335) | Yes (n = 262,905) | ||
| Age (mean ± SD) | 55.6 (8.10) | 53.2 (8.24) | 56.5 (7.85) |
| Age, years | |||
| 40−49 | 98,046 (26.9%) | 39,444 (38.9%) | 58,602 (22.3%) |
| 50−59 | 127,554 (35.0%) | 33,762 (33.3%) | 93,792 (35.7%) |
| 60−69 | 137,244 (37.7%) | 27,899 (27.5%) | 109,345 (41.6%) |
| ≥70 | 1396 (0.38%) | 230 (0.23%) | 1166 (0.44%) |
| Sex | |||
| Women | 213,072 (58.5%) | 63,873 (63.0%) | 149,199 (56.8%) |
| Men | 151,168 (41.5%) | 37,462 (37.0%) | 113,706 (43.2%) |
| Ethnicity | |||
| White | 341,593 (93.8%) | 98,800 (97.5%) | 242,793 (92.4%) |
| Non-white | 21,548 (5.92%) | 2297 (2.27%) | 19,251 (7.32%) |
| Missing | 1099 (0.30%) | 238(0.23%) | 861(0.33%) |
| Assessment centre | |||
| England | 322,883 (88.6%) | 86,774 (85.6%) | 236,109 (89.8%) |
| Scotland | 26,081 (7.16%) | 9480 (9.36%) | 16,601 (6.31%) |
| Wales | 15,276 (4.19%) | 5081 (5.01%) | 10,195 (3.88%) |
| Early life factors | |||
| Multiple birth status | |||
| No | 352,363 (96.7%) | 95,312 (94.1%) | 257,051 (97.8%) |
| Yes | 8350 (2.29%) | 3599 (3.55%) | 4751 (1.81%) |
| Missing | 3527 (0.97%) | 2424 (2.39%) | 1103 (0.42%) |
| Maternal smoking during pregnancy | |||
| No | 234,258 (64.3%) | 57,582 (56.8%) | 176,676 (67.2%) |
| Yes | 91,259 (25.1%) | 31,652 (31.2%) | 59,607 (22.7%) |
| Missing | 38,723 (10.6%) | 12,101 (11.9%) | 26,622 (10.1%) |
| Birth weight | |||
| Low (<2.5 kg) | 22,350 (6.14%) | 9562 (9.44%) | 12,788 (4.86%) |
| Normal (2.5−4 kg) | 183,725 (50.4%) | 54,084 (53.4%) | 129,641 (49.3%) |
| High (>4 kg) | 30,574 (8.39%) | 8087 (7.98%) | 22,487 (8.55%) |
| Missing | 127,591 (35.0%) | 29,602 (29.2%) | 97,989 (37.3%) |
| Later life factors | |||
| Townsend deprivation index | |||
| 1st quintile (least deprived) | 74,480 (20.4%) | 19,986 (19.7%) | 54,494 (20.7%) |
| 2nd quintile | 72,987 (20.0%) | 20,026 (19.8%) | 52,961 (20.1%) |
| 3rd quintile | 73,094 (20.1%) | 20,198 (19.9%) | 52,896 (20.1%) |
| 4th quintile | 72,925 (20.0%) | 20,548 (20.3%) | 52,377 (19.9%) |
| 5th quintile (most deprived) | 70,304 (19.3%) | 20,435 (20.2%) | 49,869 (19.0%) |
| Missing | 450 (0.12%) | 142 (0.14%) | 308 (0.12%) |
| Smoking status | |||
| Never | 205,138 (56.3%) | 58,524 (57.8%) | 146,614 (55.8%) |
| Previous | 121,045 (33.2%) | 31,225 (30.8%) | 89,820 (34.2%) |
| Current | 36,928 (10.1%) | 11,292 (11.1%) | 25,636 (9.75%) |
| Missing | 1129 (0.31%) | 294 (0.29%) | 835 (0.32%) |
| Alcohol drinker status | |||
| Never | 16,169 (4.44%) | 3687 (3.64%) | 12,482 (4.75%) |
| Previous | 12,199 (3.35%) | 3590 (3.54%) | 8609 (3.27%) |
| Current | 335,495 (92.1%) | 93,967 (92.7%) | 241,528 (91.9%) |
| Missing | 377 (0.10%) | 91 (0.09%) | 286 (0.11%) |
| Physical activity | |||
| Low | 54,840 (15.1%) | 15,636 (15.4%) | 39,204 (14.9%) |
| Moderate | 121,244 (33.3%) | 33,080 (32.6%) | 88,164 (33.5%) |
| High | 121,143 (33.3%) | 33,219 (32.8%) | 87,924 (33.4%) |
| Missing | 67,013 (18.4%) | 19,400 (19.1%) | 47,613 (18.1%) |
| Menopause statusa | |||
| Premenopausal | 151,168 (41.5%) | 37,462 (37.0%) | 113,706 (43.2%) |
| Postmenopausal | 55,795 (15.3%) | 22,382 (22.1%) | 33,413 (12.7%) |
| Missing | 124,324 (34.1%) | 31,246 (30.8%) | 93,078 (35.4%) |
P value less than 0.05 breastfed groups for all variables listed. Values are numbers (percentages).
Restricted only in women.
3.2. Breastfeeding in infancy and cardiovascular disease incidence in middle-aged and older adulthood
Fig. 1 shows the association between breastfeeding in infancy and cardiovascular disease incidence in middle-aged and older adulthood. During a median of 12.6 years of follow-up, we identified 29,796 cardiovascular disease events, 24,797 coronary heart disease events, and 6229 stroke events. Breastfeeding in infancy was associated with a lower risk of cardiovascular disease, with a hazard ratio (95% CI) of 0.91 (0.89, 0.94) for cardiovascular disease, 0.91 (0.88, 0.94) for coronary heart disease, and 0.93 (0.87, 0.98) for stroke, after adjusting for sex, ethnicity, and assessment centre. Further adjusting for early and later life factors found consistent results.
Fig. 1.
Breastfeeding in infancy and cardiovascular disease incidence in middle-aged and older adulthood.
a Adjusted for sex, ethnicity, and assessment centre.
b Further adjusted for early life factors including birth weight, multiple birth status, and maternal smoking during pregnancy.
c Additionally adjusted for adult life factors including Townsend deprivation index, smoking status, alcohol drinker status, physical activity, and menopausal status for women.
Subgroup analyses shows that the association between breastfeeding in infancy and cardiovascular disease incidence in middle-aged and older adulthood were modified by genetic susceptibility to cardiovascular disease (P for interaction <0.001), with an adjusted hazard ratio of 0.92 (0.88, 0.95) for high, 0.93 (0.92, 0.94) for medium and 0.97 (0.92, 1.01) for low cardiovascular disease polygenic risk score, respectively (Fig. 2). Furthermore, the strength of observed association between breastfeeding and cardiovascular disease seems to decrease with age (P for interaction <0.001). Specifically, the hazard ratio is 0.88 (0.86, 0.91) at age 50, 0.92 (0.91, 0.93) at age 60, and 0.94 (0.93, 0.96) at age 70, respectively (Fig. 3).
Fig. 2.
Breastfeeding in infancy and cardiovascular disease incidence in middle-aged and older adulthood, stratified by polygenic risk score for cardiovascular disease. The polygenic risk score was defined by the UK Biobank research group and categorized as low (<10th percentile, n = 35,288), medium (10-90th percentile, n = 282,301), and high (>90th percentile, n = 35,286) genetic risk. Age-standardized incidence rates per 1000-person years were calculated for each sex group for cardiovascular disease incidence. Cox proportional hazard regression models were adjusted for age, sex, ethnicity, assessment centre, early life factors (birth weight, multiple birth status, and maternal smoking during pregnancy), and adult life factors (Townsend deprivation index, smoking status, alcohol drinker status, physical activity, and menopausal status for women).
Fig. 3.
Breastfeeding in infancy and cardiovascular disease incidence in middle-aged and older adulthood, by attained age. Hazard ratios and 95% confidence intervals were estimated from flexible parametric survival models, allowing the hazard ratios to change vary over age. Models were adjusted for age, sex, ethnicity, assessment centre, early life factors (birth weight, multiple birth status, and maternal smoking during pregnancy), and adult life factors (Townsend deprivation index, smoking status, alcohol drinker status, physical activity, and menopausal status for women).
3.3. Breastfeeding in infancy and cardiovascular disease risk factors in middle-aged and older adulthood
To further reinforced the robustness of our findings, we also investigated other cardiovascular disease risk factors in middle-aged and older adulthood (Fig. 4). Breastfeeding in infancy was associated with lower body mass index, waist circumference, waist to hip ratio, waist to height ratio, lower body fat percentage derived from BIA, android to gynoid fat ratio derived from DXA, and visceral adipose tissue volume derived from MRI. Moreover, breastfeeding in infancy was also associated with lower C-reactive protein level, and a lower risk of metabolic syndrome.
Fig. 4.
Breastfeeding in infancy and cardiovascular disease risk factors in middle-aged and older adulthood.
a Adjusted for age, sex, ethnicity, and assessment centre.
b Further adjusted for early life factors including birth weight, multiple birth status, and maternal smoking during pregnancy.
c Additionally adjusted for later life factors including Townsend deprivation index, smoking status, alcohol drinker status, physical activity, and menopausal status for women.
d Calculated using multinomial logistic regression models.
e Calculated using binomial logistic regression models.
f Restricted to participants (n = 357,914) having information on body fat percentage derived from bioelectrical impedance analysis.
g Restricted to participants (n = 31,545) having information on android and gynoid tissue fat percentage derived from dual-energy x-ray absorptiometry.
h Restricted to participants (n = 19,319) having information on visceral adipose tissue volume derived from abdominal magnetic resonance imaging.
4. Discussion
In our investigation involving 0.36 million UK Biobank participants aged between 40–73 years, we discovered a noteworthy association between breastfeeding in infancy and a 6% reduced risk of cardiovascular disease in adulthood. Interestingly, the association between breastfeeding and lower cardiovascular disease risk appears to diminish with age. Importantly, individuals carrying a higher genetic risk for cardiovascular disease appeared to derive the most benefit from early-life breastfeeding. Despite the modest 6% reduction in cardiovascular disease risk observed with breastfeeding in infancy, our study presents a significant message. Currently, only 40% of infants worldwide are exclusively breastfed for the recommended six months, a rate that has remained stagnant for the past two decades [18]. Given these statistics, the impact of not breastfeeding on cardiovascular disease can not be overlooked; it potentially corresponds to hundreds of thousands of preventable cardiovascular-related deaths globally.
Research investigating the association between breastfeeding in infancy and cardiovascular disease risk requires substantial sample sizes and event numbers, inherently limiting the quantity of such studies. Notably, the US Nurses' Health Study found a non-significant association between breastfeeding in infancy and cardiovascular event risk (HR: 0.91, 95% CI: 0.83, 1.01) [10]. A similar trend emerged from the recent UK Biobank study [11]. Our study adds valuable insight to this limited body of evidence, demonstrating a statistically significant lifelong benefit of breastfeeding on cardiovascular health (HR: 0.94, 95% CI: 0.91, 0.96). Moreover, we uniquely considered the modifying effects of age and genetic predisposition in our analyses.
We observed that the association between breastfeeding and reduced cardiovascular disease risk, while diminishing, persists up to the age of 85 years. This study is, to our knowledge, the first to reveal the lifelong impact of breastfeeding on cardiovascular health. Cardiovascular disease is a growing public health threats in both developing and developed countries, with limited success in prevention at the population level. Our findings suggest that breastfeeding may serve as a potential target for early prevention of cardiovascular disease.
In addition, we found that the association between breastfeeding in infancy and cardiovascular disease was stronger in individuals with genetic predisposition to cardiovascular disease. As a high polygenic score is a strong risk factor for severe obesity and associated diseases [19], some studies have evaluated the associations between genetics, early life nutrition, and adult cardiovascular risk factors. For instance, Wang et al. found that longer breastfeeding duration was particularly beneficial for individuals genetically predisposed to high birth weight [20]. Similarly, a study based on Avon Longitudinal Study of Parents and Children (ALSPAC) cohort suggested that breastfeeding should be prioritized as an intervention when using polygenic scores to identify target populations to reduce the incidence of obesity and related non-communicable diseases [21]. Our findings thus provide evidence suggesting that the adverse effect of genes on subsequent cardiovascular disease may be offset by breastfeeding in infancy. Given that genetic risk is unmodifiable, our findings raise the possibility of an early prevention strategy of breastfeeding in infancy targeting individuals at high genetic risk.
The consistent results between breastfeeding in infancy and cardiovascular disease risk factors in adults further reinforced the robustness of our findings. We found that breastfeeding in infancy was associated with a lower risk of adult obesity, regardless of whether obesity was defined by body mass index or body fat percentage. In addition to overall obesity, we further found that breastfeeding in infancy had an effect on fat distribution in later life. Notably, we used three methods of measuring fat distribution, including bioelectrical impedance analysis, dual-energy x-ray absorptiometry and magnetic resonance imaging, which were more accurate than anthropometric methods used in previous studies [22,23]. Furthermore, the results remained unchanged after additional adjustment for body mass index, suggesting that the effect of breastfeeding in infancy on adult fat distribution was independent of body mass index.
Breastfeeding in infancy was also associated with a lower risk of adult metabolic syndrome in our study. In adulthood, only one prospective study has investigated and reported a nonsignificant association between breastfeeding in infancy and metabolic syndrome in adulthood (odds ratio [OR]: 0.95, 95% CI: 0.80, 1.10) [24]. In our study, we found almost the same effect size (OR: 0.95, 95% CI: 0.94, 0.97), but with much better power (364,240 versus 158), thus providing the first evidence in middle-aged and older adulthood. We also observed that breastfeeding in infancy was associated with lower C-reactive protein level, suggesting that chronic systemic inflammation may at least partially mediate the association between breastfeeding and cardiovascular disease.
The biological mechanisms underlying the benefits of breastfeeding on cardiovascular health in adults are not well documented. These benefits may stem from the micronutrients and bioactive components present in breast milk. Compared to infant formula, breast milk contains lower levels of fat and protein. Studies have shown that high fat and protein content can lead to increased secretion of insulin-like growth factor 1 (IGF-1), which subsequently stimulates adipocytes and contributes to overweight conditions [25]. Early-life nutritional differences can have long-term effects on the metabolic system, possibly mediated through changes in appetite and metabolism [26]. Additionally, bioactive components in breast milk have long-term programming effects on functions, such as leptin and growth hormone-releasing peptides, which influence energy balance regulation by altering glucose-insulin metabolism and hypothalamic development, thereby affecting subsequent cardiovascular development [27].
Our study has several limitations. Firstly, as our analyses are observational, we can not infer causality from these results. Future research is needed to validate these findings and explore causal relationships. Secondly, the information on breastfeeding in infancy was self-reported, which can introduce recall bias and inaccuracies. Additionally, the data lacked detailed information on both the duration and intensity of breastfeeding, including whether it was exclusively breastfed or mixed with other types of feeding. To minimize these issues, the UK Biobank provided participants with standardized questionnaires and conducted follow-up checks to ensure data consistency. Thirdly, although we have adjusted for early and later life factors in our analysis, the effect of unmeasured confounding such as maternal BMI, gestational age and parental education level, can not totally be excluded. Further research with more comprehensive data is needed to better understand the health effects of breastfeeding, considering residual confounding and unmeasured social determinants of health. Fourthly, participants’ CVD risk factors were collected at baseline with only a small subset of participants having repeated measures data, which limits our ability to conduct longitudinal analyses. Fifthly, there is a potential for survivor bias, as our participants are survivors who have reached the age of study enrollment. If breastfeeding influenced survival and cardiovascular disease risk, our sample might not fully represent the broader population, particularly those who may have died earlier due to higher cardiovascular disease risks. This could lead to an underestimation of the true effect of breastfeeding, as the surviving participants may have had additional protective factors. Sixthly, we lack specific data on subclinical cardiovascular disease, which limits our analysis of the impact of breastfeeding on subclinical conditions. Lastly, the generalizability of our findings is limited by the characteristics of the UK Biobank cohort. Most participants are white, predominantly urban, and living in large UK cities. The cohort also has a higher educational attainment compared to the general population, and other demographic and socioeconomic factors that may not represent the broader population. These selection biases should be considered when interpreting the results.
5. Conclusions
In conclusion, our study provides the evidence showing that breastfeeding may have life-long benefits for cardiovascular health. Specially, we found that breastfeeding in infancy was associated with a lower risk of cardiovascular disease in middle-aged and older adulthood. Therefore, promoting breastfeeding in infancy is vital not only for promoting child health, but also for halting the increasing trend of cardiovascular disease in adults, especially in those with genetic predisposition to cardiovascular disease.
Funding
This study was funded by the National Key R&D Program of China (grant 2022YFC2705300) and “Leading goose” R&D Program of Zhejiang (grant 2024C03180). WH is supported by the Hundred Talents Program of Zhejiang University. SZ is supported by the Cyrus Tang Foundation (grant 419600-11102), the China Medical Board (grant 12-108 and 15-216), and the Hsun K. Chou Fund of Zhejiang University Education Foundation (grant 419600-11107). The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Conflict of interest
The authors declare that they have no conflict of interest.
Data availability
The UK Biobank data are available for application to the UK Biobank (www.ukbiobank.ac.uk/).
CRediT authorship contribution statement
Shanshan Li: Conceptualization, Methodology, Formal analysis, Writing - original draft. Xiaoyan Wang: Conceptualization, Methodology, Formal analysis, Writing - original draft. Xinmei Li: Writing - review & editing. Weiwei Zhang: Writing - review & editing. Yingying Guo: Writing - review & editing. Nuo Xu: Writing - review & editing. Junkai Luo: Writing - review & editing. Shankuan Zhu: Conceptualization, Funding acquisition, Project administration, Supervision. Wei He: Conceptualization, Funding acquisition, Project administration, Supervision.
Acknowledgment
This research was conducted using the UK Biobank Resource (application No. 69972).
Footnotes
Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.jnha.2024.100347.
Contributor Information
Shankuan Zhu, Email: zsk@zju.edu.cn.
Wei He, Email: zjuhewei@zju.edu.cn.
Appendix A. Supplementary data
The following is Supplementary data to this article:
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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
The UK Biobank data are available for application to the UK Biobank (www.ukbiobank.ac.uk/).