Birth Weight and Risk of Type 2 Diabetes Mellitus, Cardiovascular Disease, and Hypertension in Adults: A Meta‐Analysis of 7 646 267 Participants From 135 Studies
Abstract
Background
Low birth weight has been associated with increased risk of type 2 diabetes mellitus, cardiovascular disease, and hypertension, but the risk at high birth weight levels remains uncertain. This systematic review and meta‐analysis aimed to clarify the shape of associations between birth weight and aforementioned diseases in adults and assessed sex‐specific risks.
Methods and Results
We systematically searched PubMed, EMBASE, and Web of Science for studies published between 1980 and October 2016. Studies of birth weight and type 2 diabetes mellitus (T2DM), cardiovascular disease (CVD), and hypertension were included. Random‐effects models were used to derive the summary relative risks and corresponding 95% confidence intervals.We identified 49 studies with 4 053 367 participants assessing the association between birth weight and T2DM, 33 studies with 5 949 477 participants for CVD, and 53 studies with 4 335 149 participants for hypertension and high blood pressure. Sex‐specific binary analyses showed that only females had an increased risk of T2DM and CVD at the upper tail of the birth weight distribution. While categorical analyses of 6 birth weight groups and dose‐response analyses showed J‐shaped associations of birth weight with T2DM and CVD, the association was inverse with hypertension. The lowest risks for T2DM, CVD, and hypertension were observed at 3.5 to 4.0, 4.0 to 4.5, and 4.0 to 4.5 kg, respectively.
Conclusions
These findings indicate that birth weight is associated with risk of T2DM and CVD in a J‐shaped manner and that this is more pronounced among females.
Clinical Perspective
What Is New?
•
This meta‐analysis shows that birth weight is associated with risk of type 2 diabetes mellitus and cardiovascular disease in a J‐shaped manner; however birth weight is inversely associated with risk of hypertension.
•
Birth weight associates more strongly with type 2 diabetes mellitus and cardiovascular disease in females than males at the higher end of the birth weight distribution.
What Are the Clinical Implications?
•
In keeping with current recommendations, our study highlights the importance of supporting lifestyle and behavioral changes among pregnant women to control their modifiable risk factors during pregnancy to reduce the number of babies being born with low or high birth weight.
The rising prevalence of chronic diseases such as type 2 diabetes mellitus (T2DM), cardiovascular disease (CVD), and hypertension has been recognized as a public health problem affecting both developed and developing countries.1 Genes and their interactions with the environment are thought to drive cardiometabolic disease risk. Growing evidence from observational studies has suggested that low birth weight, an indicator of intrauterine environment, increases the risk for T2DM, CVD, and hypertension in adulthood.2, 3, 4, 5, 6 We are less certain of the effect of high birth weight, a consequence of maternal overweight/obesity and gestational diabetes mellitus,7 on chronic disease risk later in life.
Numerous studies investigating the effect of birth weight on T2DM, CVD, and hypertension risk later in life have reported estimates that varied substantially across studies.2, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17
Studies have consistently reported a U‐shaped association between birth weight and T2DM,15, 18, 19 while studies examining birth weight and CVD have been inconsistent, reporting either an inverse12, 13, 20, 21 or a U‐shaped2, 22 association. The inconsistency of these findings might be explained by differences in the definition of high birth weight.23, 24 The association between birth weight and hypertension is reported as inverse in the majority of studies.3, 6, 10 Specifically in the case of hypertension, however, the absence of a nadir in the association at the higher end of the birth weight distribution might be attributed to sample size.
A great number of studies have been published in recent years, which allow us to perform a more specific and detailed analysis of the strength and shape of the dose‐response relationship between birth weight and T2DM, CVD, and hypertension in adulthood. Furthermore, it has been suggested that the shape of association might differ between females19 and males.25 This brings into question whether sex‐stratified analyses should replace the conventional approach of treating sex as a confounding variable. We undertook to systematically review the literature and conduct meta‐analyses, which would examine the shape of the association between birth weight and risks of T2DM, CVD, and hypertension in adulthood. Additional stratified analyses would also shed light on sex‐specific risk profiles.
Methods
Data are available on request from Dr Knop. Alternatively, the reader may consider extracting results from references cited in our meta‐analyses.
Literature Search Strategy
In keeping with the Cochrane methodology, we systematically queried PubMed, EMBASE, and Web of Science for studies published between January 1966 and October 2016. We combined search terms describing the exposure with each outcome of interest. Keywords for birth weight included: “Birth weight,” “low birth weight,” “high birth weight,” “intrauterine growth restriction,” “fetal macrosomia,” “large for gestational age,” “small for gestational age,” and “ponderal index.” Keywords describing T2DM included: “Diabetes Mellitus type 2,” “glucose levels,” and “insulin levels.” We examined CVD using the keywords: “Cardiovascular disease,” “cardiovascular mortality,” “coronary heart disease,” and “stroke.” Finally, we investigated hypertension with the keywords: “Hypertension” and “blood pressure.” A search strategy combining MESH terms and full‐text options was used. All synonyms were included. The search was limited to studies with human participants that were published in the English language. Two authors (MRK and TG) independently screened titles and abstracts of all articles retrieved, evaluated the eligibility of articles based on a full‐text review, and extracted data. Where there were differences in opinion on the eligibility of an article the authors sought to achieve a consensus by means of discussion. Our senior author (TH) was consulted when there were disagreements regarding article eligibility. We adhered to the Meta‐analysis of Observational Studies in Epidemiology group's recommendations when reporting our meta‐analyses.26
Selection of Articles
Inclusion was restricted to studies that assessed the association between birth weight and T2DM, CVD, or hypertension and studies reporting on systolic blood pressure (SBP) and diastolic blood pressure (DBP) in relationship to birth weight. The reference lists of original articles and reviews were scanned to identify other relevant studies. A study could be excluded on any of the following grounds: (1) Being designed as a review, meta‐analysis, or twin‐study; (2) having a small sample size (<250); (3) low age (<18 years), unless the mean age of the study population was >18 years; (4) insufficient measures of exposure, such as overlapping and/or unobtainable birth weight ranges or mean and standard deviation for studies reporting birth weight as categorical data; (5) insufficient reporting of outcomes such as graphically illustrated odds ratio (OR) or hazards ratio (HR) presented without risk estimates in writing; (6) describing risk estimates without accompanying standard deviation, standard error, or 95% confidence interval (CI); (7) identical outcomes originating from the same cohort reported in multiple studies, whereby the study with the longest follow‐up period or largest sample size was included; (8) the full article was inaccessible. Refer to Figure 1 for the summary of articles collected in our selection process.
Data Extraction
A standard data extraction form was used by 2 authors (M.K. and T.G.) independently to collect the following information: Article metadata including the name of the first author; study metadata including sample size, case number, and risk estimates for T2DM, CVD, and hypertension for defined birth weight groups; ORs or HRs per 1 kg increase in birth weight for T2DM, CVD, and hypertension; β‐coefficients for SBP and DBP per 1 kg increase in birth weight; and measures of SBP and DBP or differences in SBP and DBP for defined birth weight groups. Results reported in previous meta‐analyses14, 15, 27 that were not extractable from the original article were also included. Study characteristics such as location (country and region), age, sex, birth weight ascertainment method, assessment and definition of outcome, and confounding factors were also ascertained. Our senior author (TH) was consulted when there were disagreements regarding data extraction.
Definition of Outcomes
Definitions of T2DM, CVD, and hypertension varied across studies. The definition of T2DM followed WHO criteria in most studies while a smaller number identified the condition based on International Classification of Diseases (ICD)‐codes (Table S1). A subset designated the prescription of anti‐hyperglycemic drugs as a proxy indicator. The definition of CVD followed ICD‐codes as shown in Table S2. This meant that coronary heart disease, stroke, and myocardial infarction met the definition of CVD regardless of whether the immediate outcome was fatal. Studies which did not use ICD‐codes to identify CVD applied a combination of different criteria such as ROSE/WHO chest pain questionnaire, ECG findings, and blood test results. Hypertension was defined as SBP >140 mm Hg and/or DBP >90 mm Hg. A minority of studies, either adopted population‐specific definitions of hypertension, used ICD‐codes or designated the prescription of antihypertensive medication as a proxy indicator (Table S3). A number of studies examined self‐reported outcomes.
Statistical Analyses
We recorded the multivariable model that adjusted for the most covariates whenever >1 model was reported within the same study. Unadjusted estimates were calculated based on the numbers of cases and controls within defined birth weight categories whenever studies did not report risk estimates. Risk estimates were pooled within defined birth weight categories whenever studies failed to provide case or control numbers. If an individual study gave rise to >1 estimate, the pool‐first approach28 was applied to obtain a single study‐specific risk estimate. We did not pool estimates across birth weight groups that did not fit into the chosen categories. Consequently, several published categorical risk estimates were excluded from our analyses. Birth weights reported in pounds were converted to kilograms using a conversion rate of 0.454 kg/lb. Risk estimates reported per unit decrease were converted to estimates per unit increase. All step‐wise changes were expressed as change per 1 kg interval. Odds ratio and HR were pooled to estimate risk difference between selected subgroups. The random effects model was employed throughout our analyses using the method described by DerSimonian and Laird.29
Sex‐neutral and sex‐specific dichotomous comparisons were performed using 3 different cut‐offs for birth weight: 2.5, 4.0, and 4.5 kg. The majority of studies under review set the threshold for low birth weight at 2.5 kg which was in keeping with the literature.30 The definition of high birth weight or macrosomic infants varied between studies. Therefore we conducted separate analyses for both the 4.0 and 4.5 kg thresholds for the high birth weight. Equidistant birth weight categories were used to visually inspect the nature of the relationship between birth weight and T2DM, CVD, and hypertension. Based on the Akaike information criterion,31 a restricted cubic spline regression model with 3 knots was applied to elicit any potential non‐linear dose‐response relation. Spline variable estimates were subsequently used to derive the generalized least squares trend estimation of pooled dose‐response data.28
Subsequently, we assessed effect coefficients that predicted the continuous outcomes of SBP and DBP per 1 kg increase in birth weight. Differences in absolute SBP and DBP levels were assessed in repeated binary analyses using 2.5 and 4.0 kg cut‐offs for birth weight. We also used binary analyses to examine the differences in BP (blood pressure) comparing either low birth weight (<2.5 kg) or high birth weight (>4.0 kg) with normal birth weight (≥2.5 to ≤4.0 kg).
Cochrane Q‐test and associated I2‐statistic were calculated to assess the impact of between‐study heterogeneity and total variability in the effect estimate.32 The 95% confidence intervals (95% CI) for the I2–statistic were calculated using the test based method proposed by Higgins et al33 Initially we estimated the comparative risks for developing T2DM, CVD, and hypertension for each kilogram increase in birth weight. Influence analyses were performed to examine the robustness of this pooled risk estimate and to assess heterogeneity. Subgroup analyses and meta‐regression analyses were performed to examine whether study characteristics such as study design, sample size, and region influenced the associations seen or explained heterogeneity across studies.
To assess potential publication bias, we inspected funnel plots. We tested for funnel plot asymmetry in meta‐analyses comprising >10 studies using Egger's linear regression test.34 Trim and fill analysis following the methods outlined by Duval and Tweedie35 was performed for meta‐analyses where funnel plots revealed asymmetry. We set P<0.05 as the threshold for statistical significance. All statistical analyses were conducted in STATA, version 11.
Results
Of the aforementioned 135 publications in total, 49 reported the relationship between birth weight and T2DM. Thirty‐three articles described birth weight in relationship to CVD, and a further 53 discussed hypertension and/or BP. Study characteristics are presented in Tables S1 through S3.
Type 2 Diabetes Mellitus
Thirty‐two studies assessed the risk of T2DM per 1 kg increase in birth weight. Of 26 studies5, 19, 25, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58 showing an inverse association, 125, 19, 25, 37, 41, 43, 46, 51, 52, 53, 54, 58 reported statistically significant associations. Of the 6 studies17, 18, 59, 60, 61, 62 showing a positive association, only 1 study59 reported statistically significant risks. According to our meta‐analysis, each kilogram increment in birth weight was associated with a 22% (OR ratio: 0.78, 95% CI: 0.70–0.87) reduction in risk of later developing T2DM.
There was evidence of moderate to highly significant heterogeneity among these 32 studies (I2=72.6%, 95% CI: 62.5–81.1). Influence analysis identified 2 study populations, Saskatchewan Indians and the general population of Saskatchewan59 that accounted for ≈20% of the overall heterogeneity. When these populations were omitted from our analysis the overall reduction in T2DM risk was amplified to 28% (OR ratio: 0.72, 95% CI: 0.66–0.78). Two other study populations, Pima Indians18 and nurses,19 accounted for an additional 15% of overall heterogeneity. Their omission from analyses did not influence the overall risk estimate.
Table shows the subgroup analyses. An asterisk denotes estimates, which exclude the 2 populations from Saskatchewan. When the Saskatchewan studies were omitted, we observed a balancing of I2‐statistics and changes in the risk estimates. A meta‐regression analysis also revealed that study region strongly influenced the association between birth weight and risk of T2DM (P<0.0001).
| Type 2 Diabetes Mellitus | Cardiovascular Disease | Hypertension | Systolic Blood Pressure | Diastolic Blood Pressure | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| HR (95% CI) | I2 (%) | HR (95% CI) | I2 (%) | HR (95% CI) | I2 (%) | Coeff. (95% CI) | I2 (%) | Coeff. (95% CI) | I2 (%) | ||
| Unstratified risk | 0.78 (0.70; 0.87) | 72.6 | 0.84 (0.81; 0.86) | 0.0 | 0.77 (0.68; 0.88) | 31.9 | −1.36 (−1.62; −1.09) | 91.3 | −0.33 (−0.54; −0.13) | 66.2 | |
| Age, y | |||||||||||
| ≥40 | 0.70 (0.64; 0.77) | 50.7 | 0.84 (0.81; 0.87) | 13.7 | 0.77 (0.61; 0.97) | 40.7 | −1.38 (−1.91; −0.84) | 60.5 | −0.23 (−0.57; 0.11) | 73.5 | |
| <40 | 1.09 (0.85; 1.40) | 77.7 | 0.83 (0.74; 0.92) | 0.00 | 0.74 (0.56; 0.98) | 59.2 | −1.37 (−1.69; −1.05) | 94.5 | −0.51 (−0.93; −0.10) | 43.9 | |
| Sex | |||||||||||
| Female | 1.04 (0.81; 1.33) | 87.5 | 0.81 (0.76; 0.87) | 0.00 | 0.80 (0.70; 0.92) | 13.7 | −1.12 (−1.66; −0.59) | 89.9 | −0.35 (−0.60; −0.10) | 83.2 | |
| Femalea | 0.83 (0.77; 0.88) | 0.00 | |||||||||
| Male | 0.79 (0.62; 1.01) | 72.5 | 0.86 (0.80; 0.92) | 39.6 | 0.81 (0.59; 1.07) | 57.9 | −1.05 (−1.35; −0.75) | 91.1 | 0.14 (−0.79; 1.06) | 81.1 | |
| Malea | 0.70 (0.60; 0.82) | 18.0 | |||||||||
| Combined | 0.78 (0.70; 0.87) | 72.6 | 0.84 (0.81; 0.86) | 0.00 | 0.77 (0.68; 0.88) | 31.9 | −2.26 (−2.87; −1.65) | 41.2 | −0.66 (−1.25; −0.07) | 37.0 | |
| Region | |||||||||||
| N. America | 1.03 (0.85; 1.24) | 83.2 | 0.80 (0.74; 0.86) | 0.82 (0.74; 0.91) | −0.61 (−1.13; −0.08) | 95.8 | −0.31 (−0.49; −0.14) | 82.1 | |||
| N. Americaa | 0.84 (0.75; 0.94) | 39.9 | |||||||||
| Europe | 0.67 (0.60; 0.74) | 41.5 | 0.85 (0.82; 0.88) | 0.00 | 0.77 (0.61; 0.97) | 40.7 | −1.26 (−1.54; −0.98) | 83.0 | −0.22 (−0.85; 0.40) | 61.0 | |
| Asia | 0.79 (0.56; 1.11) | 35.3 | 0.67 (0.47; 0.96) | 0.00 | −2.90 (−5.45; −0.35) | −1.70 (−3.48; 0.08) | |||||
| Australia | 0.93 (0.78–1.12) | −2.33 (−3.68; −0.98) | 73.5 | 0.72 (−0.93; 2.37) | |||||||
| S. America | 0.60 (0.42; 0.87) | −3.64 (−5.20; −2.07) | −1.65 (−2.84; −0.45) | ||||||||
| Study design | |||||||||||
| CH | 0.74 (0.67; 0.82) | 56.0 | 0.84 (0.81; 0.87) | 12.0 | 0.77 (0.68; 0.88) | 31.9 | −1.35 (−1.66; −1.03) | 92.5 | −0.33 (−0.54; −0.13) | 66.2 | |
| CC | 0.92 (0.67; 1.26) | 86.5 | |||||||||
| CCa | 0.62 (0.52; 0.73) | 0.00 | |||||||||
| CS | 0.62 (0.47; 0.81) | 0.00 | 0.80 (0.66; 0.96) | 0.00 | −1.56 (−2.20; −0.92) | 86.5 | |||||
| BW | |||||||||||
| Self‐report | 0.76 (0.70; 0.84) | 32.3 | 0.81 (0.75; 0.86) | 0.00 | 0.82 (0.74; 0.91) | −0.72 (−1.08; −0.36) | 87.1 | −0.33 (−0.50; −0.16) | 75.9 | ||
| NR | 0.81 (0.68; 0.95) | 78.5 | 0.85 (0.81; 0.88) | 9.7 | 0.73 (0.60; 0.90) | 39.4 | −1.66 (−1.95; −1.36) | 83.7 | −0.33 (−0.99; 0.33) | 65.2 | |
| OC | |||||||||||
| Self‐report | 0.74 (0.66; 0.84) | 44.3 | 0.80 (0.74; 0.85) | 0.00 | 0.82 (0.74; 0.91) | −0.61 (−1.13; −0.08) | 95.8 | −0.31 (−0.49; −0.14) | 82.1 | ||
| NR | 0.91 (0.71; 1.17) | 87.8 | 0.85 (0.81; 0.88) | 6.1 | 0.73 (0.60; 0.90) | 39.4 | |||||
| PE | 0.72 (0.60; 0.87) | 56.2 | 0.89 (0.74; 1.08) | −1.47 (−1.74; −1.21) | 83.8 | −0.39 (−0.98; 0.20) | 62.6 | ||||
| Risk statistics | |||||||||||
| Odds ratio | 0.79 (0.71; 0.88) | 73.4 | 0.84 (0.78; 0.89) | 0.00 | 0.73 (0.60; 0.90) | 39.4 | |||||
| Hazard ratio | 0.62 (0.37; 1.05) | 66.2 | 0.84 (0.80; 0.88) | 24.3 | 0.82 (0.74; 0.91) | ||||||
| Sample size | |||||||||||
| <500 | 0.68 (0.45,1.03) | 53.3 | 0.67 (0.53; 0.85) | 0.00 | −1.89 (−2.61; −1.18) | 85.7 | 0.06 (−1.17; 1.29) | 52.3 | |||
| ≥500 to 1000 | 0.86 (0.65; 1.14) | 78.8 | 0.54 (0.31; 0.92) | 0.00 | 0.77 (0.50; 1.17) | 72.3 | −2.08 (−2.92; −1.23) | 36.4 | −0.65 (−2.13; 0.84) | 84.3 | |
| ≥1000 | 0.76 (0.68; 0.84) | 69.6 | 0.84 (0.81; 0.86) | 0.00 | 0.82 (0.74; 0.91) | −1.08 (−1.41; −0.74) | 95.8 | −0.33 (−0.48; −0.18) | 58.9 | ||
| Diag. | |||||||||||
| CVD | 0.83 (0.77; 0.88) | 38.1 | |||||||||
| CHD | 0.85 (0.81; 0.88) | 0.00 | |||||||||
John Wiley & Sons, Ltd
BW indicates birth weight ascertainment method; CC, case control; CH, cohort; CHD, coronary heart disease; CI, confidence interval; CS, cross sectional; CVD, cardiovascular disease; Diag., diagnosis; HR, hazard ratio; NR, National Register; OC, outcome assessment method; PE, physical examination.
a
Subgroup analysis excludes Saskatchewan populations (T2DM).
Binary analyses examining low birth weight indicated that participants with birth weight <2.5 kg experienced a 45% (OR: 1.45, 95% CI: 1.33–1.59) higher risk of T2DM than those with birth weight ≥2.5 kg. The relationship was stronger for females (OR: 1.45, 95% CI: 1.34–1.57) than males (OR: 1.34, 95% CI: 1.05–1.62). Binary analyses examining high birth weights >4.5 kg showed no significant differences in T2DM risk when compared against the ≤4.5 kg category (OR: 1.08, 95% CI: 0.95–1.23) (Figure 2). However, through sex‐stratified analyses, we uncovered a 19% (OR: 1.19, 95% CI: 1.01–1.40) higher risk of T2DM for females with birth weight >4.5 kg.
Cardiovascular Diseases
Nineteen studies2, 6, 8, 11, 21, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76 assessed the risk of CVD per 1 kg increase in birth weight. Among them, 1011, 21, 65, 68, 70, 71, 72, 73, 75, 76 reported a statistically significant inverse association. None of the studies reported a positive association. Overall, each kilogram increment in birth weight was associated with a 16.5% (OR: 0.84, 95% CI: 0.81–0.86) reduction in risk of later developing CVD.
There was little evidence of heterogeneity (I2=0.00, 95% CI: 0–46.1). When the study with the most considerable influence was omitted, the overall risk estimate increased marginally to 17.6% (OR: 0.82, 95% CI: 0.80–0.85) highlighting the robustness of the risk estimate. Meta‐regression analysis and stratification by study characteristics did not reveal essential alteration in risk estimates (Table). When studies were stratified by type of diagnosis we observed in similar risk estimates for CVD and coronary heart disease (CHD), 17% (OR: 0.83, 95% CI: 0.77–0.88) and 15% (OR: 0.85, 95% CI: 0.81–0.88), respectively. Binary analyses for CVD illustrated a pattern that was similar to T2DM (Figure 2). Participants with birth weight <2.5 kg experienced 30% (OR: 1.30, 95% CI: 1.01–1.67) higher risk of CVD compared with those with birth weight ≥2.5 kg. Compared with the birth weight ≤4.5 kg category, high birth weight (>4.5 kg) was not associated with any differences in risk. However, when we stratified for sex, we detected an increased risk of CVD in high birth weight females (Figure 2).
Hypertension
Only 5 studies examining the risks of hypertension per 1 kg increase in birth weight were retrieved.41, 77, 78, 79, 80 Of these, 441, 77, 78, 80 were statistically significant. Overall, each kilogram increment in birth weight was associated with a 23% (OR: 0.77, 95% CI: 0.68–0.88) reduction in risk. There was some evidence of heterogeneity (I2=32%, 95% CI: 0–74.1) with 1 study78 accounting for half of the overall point estimate. When this study was omitted, the overall risk estimate declined to 20% (OR: 0.80, 95% CI: 0.72–0.90). Binary analyses did not show differences in sex‐specific risk estimates at either tail of the birth weight distribution (Figure 2). Birth weight <2.5 kg was associated with 30% (OR: 1.30, 95% CI: 1.16–1.46) increased risk of hypertension compared with birth weight ≥2.5 kg. Unlike T2DM and CVD, there was no increase in the risk of hypertension seen in the birth weight >4.5 kg category.
Systolic and Diastolic Blood Pressure
Each kilogram increase in birth weight reduced SBP by 1.36 mm Hg (95% CI: −1.62 to −1.09 mm Hg). Similarly, each kilogram increase in birth weight reduced DBP by 0.33 mm Hg (95% CI: −0.54 to −0.13 mm Hg) (Table and Figure S1). We evaluated SBP and DBP for 5 birth weight categories. In support of the inverse association identified for hypertension, an inverse relationship between birth weight and BP was seen in Figure S2, when younger individuals (aged <30 years) were excluded.
Shape of Association Between Birth Weight and T2DM, CVD, and Hypertension
Assuming a non‐linear association, as supported by our binary analyses for T2DM and CVD, we performed categorical analyses using 6 birth weight groups to investigate the nature of the associations further. Sixteen, twelve, and thirteen studies provided 76, 71, and 59 estimates for T2DM, CVD, and hypertension, respectively. The birth weight category 4.0 to 4.5 kg was chosen as the reference group since this combination allowed the largest number of studies to be included. J‐shaped associations between birth weight and T2DM (Figures 3A and 4A) and CVD (Figures 3B and 4B) were observed. Participants with birth weight >4.5 kg had a 19% (OR: 1.19, 95% CI: 1.04–1.36) higher risk of T2DM and a 22% (OR: 1.22, 95% CI: 1.08 to1.37) higher risk of CVD compared with those from the reference birth weight group (4.0–4.5 kg). Similar shapes of association were observed in categorical analyses using 3 birth weight groups (Figure S3). Figures 3C and 4C display an inverse association between birth weight and hypertension with the dose‐response analysis indicating a non‐linear relationship, which may imply a negative exponential association rather than a negative linear. However, binary analyses (Figure 2C) and categorical analyses using 3 groups (Figure S3) provided results which did not support a negative exponential association consistently. The lowest risks for T2DM, CVD, and hypertension were observed at 3.5 to 4.0, 4.0 to 4.5, and 4.0 to 4.5 kg birth weights, respectively.
Publication Bias
Visual inspection of funnel plot revealed symmetry and Egger's test was not statistically significant (P>0.1) for all meta‐analyses displayed in Figure S4. Trim and fill analyses had no significant impact on the risk estimates, which overall suggested that there was no publication bias.
Discussion
In the present comprehensive meta‐analyses, we systematically examine the shape of the association between birth weight and risks of T2DM, CVD, and hypertension and further assess sex‐specific risks.
We found a 22% reduction in the risk of T2DM per 1 kg increase in birth weight; a 16% reduction in the risk of CVD; and a 23% reduced risk in hypertension per 1 kg increase in birth weight. We also observed that macrosomic infants (>4.5 kg) had 19% higher risk of T2DM in adult life compared with those with a birth weight ranging from 4.0 to 4.5 kg; a 22% increased risk of CVD compared with the reference group in sex‐neutral analyses. Our plot of sex‐neutral risk estimates in all 6 categories showed a J‐shaped association with an increased risk of T2DM and CVD at the upper tail driven by birth weight >4.5 kg. Our results provide robust evidence that birth weight is associated with the risk of T2DM and CVD in a J‐shaped manner that is more pronounced among females. Our results were consistent with previous meta‐analysis studies examining birth weight and T2DM,9 CVD,10, 14 and hypertension12, 27, 81, 82 but includes a much larger number of studies, more detailed dose‐response, subgroup and sex‐stratified analyses. Harder et al9 described a U‐shaped association where high birth weight (>4.0 kg) and low birth weight (<2.5 kg) categories showed a greater risk of T2DM that was statistically significant when compared with normal birth weight. A subgroup analysis within the same study suggested that the increased risk seen in higher birth weight (>4.0 kg) was mainly driven by participants with birth weight >4.5 kg.9 Our analyses established that the contradictions over the shapes of association were likely attributable to the authors’ preference in the treatment of the exposure variable. To improve the sensitivity of our analyses, we divided birth weight into 6 equidistant categories. Dose‐response analysis helped identify which relationships were non‐linear. Furthermore, recently published findings from large studies have allowed us to examine sex‐neutral T2DM risk in not 1 but 2 high birth weight subcategories.
Two previous meta‐analyses found no evidence of sex differences in the inverse association between birth weight and CHD.10, 14 The results from our linear model examining CVD risks suggest likewise. Interestingly our sex‐stratified binary analysis demonstrated a higher risk of CVD for females at the upper tail of the birth weight distribution. This difference, however, was not statistically significant. Nevertheless, our sex‐neutral dose‐response model showed that the highest birth weight category experienced a 22% greater risk of CVD than the reference group. We suspect that the increase in risk in this group might still have been attributable to females from the >4.5 kg birth weight category. In keeping with established literature, the same dose‐response model demonstrated even greater vulnerability at the lower tail of the birth weight distribution. In addition, we found significant inverse associations between birth weight and BP, particularly SBP, which is consistent with the previous evidence.12, 27, 81, 82 The plots displayed in Figure S2 showing lower BP estimates in the low birth weight category could be explained by differences in the age of study participants.83 Two previous meta‐analyses offer conflicting findings on the sex‐specific patterns of association between birth weight and BP.81, 84 The present study did not identify significant sex‐specific relationships.
Potential Mechanisms
The biological mechanisms underlying our findings are still a matter of debate. The observed associations may have originated in utero where metabolic stress may have led to epigenetic changes, decreased leptin levels, reduced nephron counts, and altered intracellular insulin signaling pathways.85, 86 In keeping with the fetal programming hypothesis, these small yet significant changes would have been amplified in critical periods of fetal growth resulting in delayed or disordered organ maturation which in turn could have resulted in crucial disruptions to endocrine and cardiovascular systems later in life.86
Other studies have suggested that malnutrition in the perinatal period might explain the associations between low birth weight and T2DM and long‐term CVD risks.37 In settings where neonatal care is available, it has been postulated that low birth weight babies are highly likely to be overfed, leading to “malprogramming” of neuroendocrine circuits. This is, in turn, thought to lead to excess weight and diabetogenic disturbances throughout life.87, 88 On the opposite side of the perinatal “malprogramming” spectrum, it has been documented that macrosomic offspring of mothers who suffered diabetes mellitus during pregnancy experienced an increased risk of developing T2DM themselves.89
Furthermore, the gene‐environment interaction may only account for a part of our findings.80, 90, 91 For example, previous evidence from cohort studies suggest that low birth weight and genetic susceptibility to obesity may synergistically affect the risk of T2DM in later life.92 Combined with unhealthy lifestyles the overall effects of low birth weight on T2DM91 and hypertension80 were greater than the sum of risks contributed by individual factors. Naturally, more comprehensive investigations are required to explore the precise mechanism.
It has been shown previously that females were intrinsically less sensitive to insulin than males throughout life. This placed them at particular risk of developing insulin resistance and therefore more susceptible to the development of T2DM.93 Considering the pivotal role of insulin in lipid metabolism, BP regulation and atherosclerotic disease progression differences in insulin resistance might offer an elegant explanation for the increased risk of CVD seen in high birth weight females.94 Furthermore, birth weight charts indicate that females tend to be lighter and smaller than males at birth. When sex‐neutral cut‐offs are applied, we would expect low birth weight to be more prevalent in females and high birth weight more common in males. Therefore, we could speculate that macrosomic female infants, in fact, represent a more extreme manifestation than their male counterparts. In addition to insulin resistance, sexual dimorphism of body composition at birth may also correspond with a more extreme phenotype in girls compared with boys. At birth, males and females may have similar fat mass; however, males are both longer and have greater lean mass.95 This may imply that sex‐identical definitions of high birth weight among females and males may not be appropriate if the intention is to evaluate risk according to birth weight. For epidemiological analyses, this would imply that sex should be treated as an effect modifier in the relationship between high birth weight and chronic disease risk.
Public Health Implications
Given that the prevalence of both low and high birth weight is increasing,96 these findings are highly relevant to population health and chronic disease risk estimates. Prenatal events do not fully explain the association between birth weight and chronic disease. Instead, the relationship is influenced by multiple genetic effects and postnatal exposures.97 For example, rapid catch‐up growth and early childhood growth trajectories have been shown to independently influence risk factor development and chronic disease incidence.98, 99 Later in life unhealthy diets, physical inactivity, and unfavorable body composition, play an important part towards chronic disease risk. Birth weight as a risk factor is not modifiable, but when viewed at the population level it offers insight into potentially vulnerable subgroups.100 Our findings help justify investment in robust maternal and child health services.
Strengths and Limitations
We have undertaken a comprehensive meta‐analysis to clarify the strength and shape between birth weight and risks of T2DM, CVD, and hypertension in adulthood. A major strength of this article is the scope of our systematic review and treatment of the main exposure of interest. Furthermore, we have taken a comprehensive statistical approach that has allowed us to better understand past contradictions over the shape and nature of risk associations between birth weight and chronic disease outcomes. Finally, a large number of studies and larger sample size guaranteed sufficient statistical power for our analyses.
However, several limitations need to be considered. First, our findings might have been affected by reporting bias when we limited our search terms to studies published in English. Citation bias may have also been introduced when we identified additional studies from reference lists of original studies and past systematic reviews. Nevertheless, when we checked the funnel plots, we noted weak evidence of publication bias. Second, the sex‐specific risk estimates for binary analyses were drawn from a limited pool of available studies and should be treated with caution. Third, this systematic review and meta‐analysis was based on observational studies. This meant that even though we focused on adjusted effect measures the findings were susceptible to residual or unmeasured confounders, such as gestational age, mode of delivery, and gestational diabetes mellitus. The J‐shaped association might be considered a recent phenomenon arising in recent birth cohorts where high birth weight because of maternal obesity and gestational diabetes mellitus have become more prevalent. Moreover, potential confounders of chronic disease risk such as physical inactivity, unhealthy diets, and unfavorable body composition were not considered in our analyses. Fourth, we pooled OR and HR to estimate risk difference between selected subgroups. It can be argued that this was inappropriate because of mathematical differences, yet similar meta‐analyses did not differentiate between OR and HR.10, 14 When estimates were stratified by comparative risk statistics (OR and HR) in linear and categorical models we found no marked difference in the overall effect size or direction. Fifth, we did not perform quality assessment of included studies owing to previous observations how subjectivity allowed the same study to be categorized as both low and high quality. Sixth, our meta‐analysis included both prospective and retrospective studies which might have introduced recall bias; however, most studies provided recorded birth weights and outcomes.
Lastly, analyses undertaken by other authors that used binary or continuous exposure measures of birth weight might have been inappropriate since we suspected a J‐shaped risk of T2DM and CVD across the whole birth weight range. The monotonically decreasing risk was likely to underestimate the true risk in the low (<2.5 kg) and ideal birth weight (≥2.5–4.5 kg) ranges. A cut‐off of 2.5 kg in a binary analysis would have underestimated the risk conferred by high birth weights. A cut‐off at 4.5 kg in binary analysis consequently neglected the elevated risk in the lowest birth weight groups.
Conclusion
These findings suggest that birth weight is associated with the risk of T2DM and CVD in a J‐shaped manner, and is inversely associated with risk of hypertension in adulthood. Furthermore, birth weight associates more strongly with T2DM and CVD in females than males at the higher end of the birth weight distribution. Future studies assessing the association between birth weight and chronic disease later in life should explicitly investigate potential sex differences. Sex might in fact act as an effect modifier rather than a confounder at the upper tail of the birth weight distribution.
More investigations are required to uncover the true causal pre‐ and postnatal exposures for the development of strategies for primary prevention.
Author Contributions
M.R.K. and T.H. designed the study, drafted the study protocol, and planned analyses. M.R.K. wrote the first draft of the article. M.R.K. and T.G. conducted the data collection and combined statistical analysis. A.W.G. contributed to the article structure and language polishing. All of the authors contributed to the design, analysis, interpretation, and drafting of this article. All authors had reviewed and approved the drafts of the article.
Sources of Funding
This study was supported by the National University of Singapore start‐up grant: R‐608‐000‐139‐133; Singapore Ministry of Education Tier 1 grant: R‐608‐000‐161‐114; Peking University start‐up grant: 71013Y0026; Beijing Technology and Business University Grant: 88442Y0033.
Disclosures
None.
Supplemental Material
Table S1. Characteristics of Studies Included in the Meta‐Analysis for Birth Weight and Type 2 Diabetes Mellitus
Table S2. Characteristics of Studies Included in the Meta‐Analysis for Birth Weight and Cardiovascular Disease
Table S3. Characteristics of Studies Included in the Meta‐Analysis for Birth Weight and Hypertension and Blood Pressure
Figure S1. Sex‐neutral (N) and sex‐specific (F/M) association between birth weight (BW) and SDB and DBP. Birth weight (BW) is represented as a continuous variable to access the change in systolic and diastolic blood pressure per 1 kg increase in birth weight and as a binary variable with 2 differences cutoffs for low and high birth weight (2.5 and 4.0 kg). Low and high birth weight are also compared with normal birth weight (2.5–4.0 kg). Circles represent the pooled β‐coefficient or mean differences from a random‐effects model, horizontal lines their confidence interval (CI), and I2 the heterogeneity detected in each meta‐analysis. “n” is the number of studies contributing to the pooled estimate. Several studies only provide sex‐neutral estimates. Therefore, the number of studies contributing in the sex‐specific meta‐analysis does not add up to the number of studies.
Figure S2. Mean systolic and diastolic blood pressure for 5 birth weight groups. Circles represent the pooled mean for each birth weight group and the vertical lines their confidence interval (CI). A and C, Includes all studies. B and D, Includes studies where blood pressure was measured in subjects aged >30 years. Mean (95% CI) in (A) <2.5 kg (125.45 [122.27; 128.63]), 2.5 to 3.0 kg (130.38 [126.37; 134.39]), 3.0 to 3.5 kg (129.81 [126.17; 133.44]), 3.5 to 4.0 kg (126.70 [122.31; 131.10]), >4.0 kg (126.40 ([122.37; 130.43]). Mean (95% CI) in (B) <2.5 kg (131.47 [127.99; 134.95]), 2.5 to 3.0 kg (130.38 [126.37; 134.39]), 3.0 to 3.5 kg (129.81 [126.17; 133.44]), 3.5 to 4.0 kg (128.70 [123.80; 133.19]), >4.0 kg (128.73 [124.19; 133.26]). Mean (95% CI) in (C) <2.5 kg (76.22 [74.29; 78.29]), 2.5 to 3.0 kg (79.46 [76.46; 82.45]), 3.0 to 3.5 kg (78.93 [76.40; 81.46]), 3.5 to 4.0 kg (76.88 [73.69; 80.08]), >4.0 kg (75.96 [73.39; 78.53]). Mean (95% CI) in (D) <2.5 kg (79.0 [76.79; 82.61]), 2.5 to 3.0 kg (79.46 [76.46; 82.45]), 3.0 to 3.5 kg (78.93 [76.40; 81.46]), 3.5 to 4.0 kg (78.52 [75.32; 81.72]), >4.0 kg (78.07 [74.89; 81.25]).
Figure S3. Shape of association between birth weight and type 2 diabetes mellitus, cardiovascular disease, and hypertension. The circle represents the exponentiated log‐transformed pooled risk estimate within 2 birth weights groups compared with the reference group (hollow circle). Birth weight is categorized as <2.5, 2.5 to 4.0, >4.0 kg to construct A, B, and C, and <2.5, 2.5 to 4.5, >4.5 kg to construct D, E, and F. “n” is the number of studies contributing to the pooled risk estimate. Risk estimate (95% confidence interval [CI]) in (A) <2.5 kg (1.348 [1.241; 1.457]), >4.0 kg (0.898 [0.829; 0.967]). Risk estimate (95% CI) in (B) <2.5 kg (1.274 [0.991; 1.556]), >4.0 kg (0.969 [0.894; 1.044]). Risk estimate (95% CI) in (C) <2.5 kg (1.276 [1.152; 1.401]), >4.0 kg (1.271 [0.994; 1.441]). Risk estimate (95% CI) in (D) <2.5kg (1.407 [1.278; 1.536]), >4.5 kg (1.094 [0.953; 1.235]). Risk estimate (95% CI) in (E) <2.5 kg (1.136 [1.009; 1.262]), >4.5 kg (1.126 [0.991; 1.262]). Risk estimate (95% CI) in (F) <2.5 kg (1.248 [1.169; 1.326]), >4.5 kg (1.000 [0.841; 1.159]).
Figure S4. Funnel plots. Funnel plot and Egger's test for all meta‐analysis containing >10 studies. Fill and trim estimates are provided for plots that appears asymmetrical regardless of results from Egger's test.
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© 2018 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
History
Received: 16 February 2018
Accepted: 26 September 2018
Published online: 29 November 2018
Published in print: 4 December 2018
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(J Am Heart Assoc. 2018;7:e008870. https://doi.org/10.1161/JAHA.118.008870)
Authors
†
Dr Knop and Dr Geng contributed equally to this work.
Funding Information
National University of Singapore: R‐608‐000‐139‐133
Singapore Ministry of Education: R‐608‐000‐161‐114
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