Abstract
Aims/hypothesis
The association between lower birthweight and metabolic syndrome may result from fetal undernutrition (‘fetal programming hypothesis’) and/or genes causing both low birthweight and insulin resistance (‘fetal insulin hypothesis’). We studied associations between the birthweight of parents and metabolic syndrome in the offspring.
Methods
We identified men and women (aged 35-68 years) born in Holdsworth Memorial Hospital (HMH), Mysore, India and their offspring (20-46 years); 283 offspring of 193 mothers and 223 offspring of 144 fathers were studied. Investigations included anthropometry, oral glucose tolerance, plasma insulin and lipid concentrations and blood pressure. The metabolic syndrome was defined using WHO criteria.
Results
Among the offspring, lower birthweight was associated with an increased risk of glucose intolerance (impaired glucose intolerance, impaired fasting glucose or type 2 diabetes) and higher cholesterol and triglyceride concentrations (p=<0.05 for all adjusted for sex and age). Most outcomes in the offspring, including most individual components of the metabolic syndrome, were unrelated to parental birthweight. However, both maternal and paternal birthweight were inversely related to offspring metabolic syndrome [OR 0.36 (95% CI: 0.13-1.01) per kg, p=0.053 for mother-offspring pairs; OR 0.26 (0.07-0.93), p=0.04 for father-offspring pairs, adjusted for offspring age, sex, BMI and socio-economic status]. Maternal birthweight was inversely related to offspring systolic blood pressure [β=-2.5 mmHg (-5.00 to 0.03) per kg maternal birthweight; p=0.052].
Conclusions/interpretations
Factors in both parents may influence the risk of metabolic syndrome in their offspring. There are several possible explanations, but the findings are consistent with the ‘fetal insulin’ (genetic) hypothesis.
Keywords: Birthweight, Impaired glucose tolerance, India, Intergenerational effects, Insulin resistance, Maternal effects, Metabolic syndrome, Paternal effects
Introduction
Lower birthweight is associated with an increased risk of adult Coronary Heart Disease (CHD) and the related disorders insulin resistance, type 2 diabetes and the metabolic syndrome [1-3]. The ‘fetal origins’ hypothesis proposes that this reflects undernutrition during critical periods of intrauterine development, which permanently alters (or ‘programmes’) the body’s metabolism [1]. An alternative hypothesis is that genes that either increase insulin resistance or reduce insulin secretion result in both impaired insulin-mediated fetal growth and later CHD and diabetes (the ’fetal insulin hypothesis’) [4].
Several recent studies have tested these hypotheses using intergenerational data. These have generally examined disease outcomes in men and/or women in relation to the birthweight of their children. Diabetes in fathers, for example, is associated with lower offspring birthweight [5-9]. The opposite is true for diabetes in mothers, because gestational diabetes causes fetal macrosomia [10-11]. However, lower offspring birthweight is associated with cardiovascular disease and insulin resistance in both parents [8,12-18]. Associations between these outcomes in the mother and lower birthweight in her offspring are consistent with the ‘fetal origins’ hypothesis; a mother who experienced a poor environment during her early development might be programmed both to develop disease and to produce smaller babies (Figure 1). This hypothesis could not, however, explain the similar associations in fathers. One explanation could be adverse adult behaviours or environmental factors that are shared between fathers and mothers (eg smoking or poor diet). These could increase the risk of disease in both parents, and the mothers would be at increased risk of having lower birthweight babies. This is an unlikely explanation in the above studies, since adjusting for socio-economic status did not diminish the associations [12,14-16]. An alternative explanation is genetic transmission, and the findings from these studies are compatible with the genetic ‘fetal insulin’ hypothesis. To study this in an Indian population, we identified families living in Mysore, South India, in which two generations were born in one hospital. This enabled us to study the relationship between maternal and paternal birthweight, and insulin resistance, The metabolic syndrome and other CHD risk factors in the young adult offspring. Associations of these outcomes with lower birthweight in both parents, or in fathers, would support the ‘fetal insulin’ hypothesis.
Figure 1. Intergenerational pathways of disease.
The 3 scenarios are not mutually exclusive. In all cases, associations in mother-offspring pairs may be obscured by maternal gestational diabetes which causes macrosomia (high birthweight) and an increased risk of glucose intolerance in the next generation.
METHODS
The Holdsworth Memorial Hospital (HMH), Mysore, South India has preserved obstetric records containing the date of birth and birthweight of all babies born in the hospital since 1934. The birth records also contain the names, religion and caste of both parents, the area of the city in which the family lived at the time of birth, and the mother’s detailed obstetric history. We used this information to match men and women living in Mysore to their birth records, using strict criteria [19]. Since the baby’s name is not recorded in the birth records (Indian babies are named several weeks after birth) and many adults do not know their date of birth or age, the process relies on a match to both parents’ names and an exact match of the number, sex and age of a subject’s older siblings with their mother’s recorded obstetric history.
First generation
In 1993, people born in the hospital were identified by a house-to-house census of a 2-square-mile area around HMH. Of 7,800 households visited, 1,311 individuals said they were born as singletons in the hospital during 1934-1954. Of these, 536 were matched to their birth records and 517 participated in a study to measure the prevalence of CHD [19] and abnormal glucose tolerance [20]. Between 1995 and 2001 the original 2-square-mile area was resurveyed and the census extended to an 8-square-mile area A further 4,748 men and women said to be born in HMH during 1934-1966 were identified; 2,892 were matched to their records and 552 agreed to undergo the same investigations, which took place between 1997 and 2003 (Figure 2).
Figure 2.
Second generation
Of 1,069 first generation men and women studied, 221 women and 166 men had adult sons and daughters also born at HMH. From the 221 mothers we identified 206 sons and 209 daughters aged ≥20 years living in or near Mysore, making a total of 415 mother-offspring pairs. From the 166 fathers we identified 152 sons and 144 daughters aged ≥20 years making a total of 296 father-offspring pairs. Two hundred and eighty three offspring of 193 mothers and 223 offspring of 144 fathers participated in the study (of whom only 19 had data for both parents), and are included in this analysis. The mother-offspring study was carried out during 1997-1999; the father-offspring study, during which more mother-offspring pairs were identified and studied, took place during 2001-2003.
Participants attended hospital for investigations after a 12-hour overnight fast. Fasting blood samples were collected for measurement of plasma glucose and insulin, and serum total cholesterol and triglyceride concentrations. Excluding those who were known to have type 2 diabetes (20 men and 23 women in the first generation and 1 man and 3 women in the second generation) subjects were given a 75 g oral glucose load and further blood samples for plasma glucose and insulin were collected 120 minutes later. Fasting insulin values were not available for 8 fathers, 5 mothers and 39 offspring because of difficult venesection, sample haemolysis or technical problems in the laboratory. Blood pressure was measured with an automated recorder (Dinamap Model 8100, Critikon Inc., Tampa, Florida, USA) after the subjects had been lying down for 5 minutes.
In order to define CHD, a 12-lead electrocardiogram (ECG) was recorded, the Rose chest pain questionnaire was administered [21], and subjects were asked if they had a history of coronary artery bypass surgery. Weight, height, skinfold thicknesses (triceps and subscapular), hip and waist circumferences were measured by one of three observers. Information on smoking, alcohol consumption and socio-economic status were obtained by questionnaire. Socio-economic status was assessed using the Kuppuswamy score [22], a standard questionnaire method for urban Indian populations, based on family size, housing (type of locality, number of people per room, basic amenities), education, occupation and income. The HMH Ethics Committee granted permission for the study and informed verbal consent was obtained from participants.
Plasma glucose concentrations were analysed using a standard glucose oxidase method and insulin concentrations were measured using an in-house time resolved, fluoroimmunoassay (DELFIA) method [23]. Assays for the mother-offspring study and father-offspring study were carried out in the Department of Biochemistry, Southampton General Hospital, Southampton, UK and Diabetes Research Centre, KEM Hospital, Pune, India, respectively. Serum total and HDL-cholesterol and triglyceride concentrations were measured by standard enzymatic methods.
Type 2 diabetes was defined as either a history of diabetes diagnosed by a doctor and requiring medication, or a fasting glucose concentration of ≥7.0 mmol/l, or a 120-minute glucose concentration of ≥11.1 mmol/l [24]. Impaired glucose tolerance (IGT) was defined as a fasting plasma glucose concentration of <7.0 mmol/l and a 120-minute glucose concentration of ≥7.8mmol/l and <11.1 mmol/l. Impaired fasting glycaemia (IFG) was defined as a fasting glucose concentration of ≥6.1 mmol/l and <7.0mmol/l and a 120-minute glucose concentration of <7.8 mmol/l. The presence of IFG, IGT or diabetes was defined as ‘glucose intolerance’. Insulin resistance was estimated using the homeostasis model assessment equation (HOMA) [25]. The metabolic syndrome was defined based on WHO criteria [24] as glucose intolerance, and insulin resistance values ≥ the 75th percentile value for our population, together with any two of the following: raised arterial pressure (systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg), raised serum triglyceride concentration (≥1.7 mmol/l), low serum HDL cholesterol concentration (< 0.9 mmol/l for men; <1.0 mmol/l for women) and central obesity (waist/hip ratio (WHR) >0.90 for men; > 0.85 for women) or body mass index (BMI) >30 kg/m2).
ECGs were Minnesota coded [26] by two independent coders, who had no access to the birth data. CHD was defined as the presence of typical angina according to Rose/WHO questionnaire [21], or ECG Minnesota codes 1-1 or 1-2 (Q and Q-S codes) [26] or a history of coronary artery angioplasty or bypass graft surgery.
Statistical Methods
Statistical analysis was carried out using ‘STATA’ version 6 (Stata Corporation, College Station, Texas, USA). Birth measurements were often recorded in imperial units and were converted into metric values. Parent and offspring subscapular/triceps ratio, triglyceride and insulin concentrations, and insulin resistance, and offspring BMI and 120-minute plasma glucose concentrations had skewed distributions and were log transformed to satisfy assumptions of normality. The father’s fasting and 120-minute plasma glucose concentrations and offspring age and fasting glucose concentrations did not transform well using logarithms and were ‘normalised’ by ranking each variable and assigning a new value to create normal scores.
Associations between the parent’s birth size and outcomes in the offspring were examined by multiple linear and logistic regression, using variables as continuous where appropriate. To overcome the difficulty of mothers and fathers having more than one child in the study, multi-level models were used, allowing us to include the maximum number of parent-offspring pairs. Random effects (including random slopes for continuous outcomes) were estimated using the GLLAMM (generalized linear latent and mixed models) command [27]. Model sensitivity was investigated by comparing the coefficients with those obtained when the number of quadrature points was increased or decreased by 4. Sizes of regression effects between offspring of mothers and offspring of fathers were compared by first calculating the standardised beta coefficients, which can also be interpreted as partial correlations; these were converted to z-scores using Fisher’s transformation, and compared using z-tests.
RESULTS
Among mother-offspring pairs 43.8% were Hindus, 47.7% were Muslims and 8.5% were Christians. Corresponding figures for father-offspring pairs were 33.6%, 55.2% and 11.2%. The characteristics of the parents and offspring are shown in Table 1. The parents’ birthweights were similar to those of all other births in the hospital during the relevant years (1934 to 1966; studied n=337 v not studied n=24,487; 2730 (SD 413) g v 2720 (461) g, p for difference=0.7). The offspring were heavier at birth than the other babies born in the relevant years (1953 to 1982; studied n=487 v not studied n=38,463; 2897 (455) g v 2836 (479) g, p for difference=0.01).
Table 1. Characteristics of the mothers, fathers and offspring; mean (SD) birthweight, age when studied, adult anthropometry and components of the metabolic syndrome.
| Mothers | Daughters | Sons | ||||
|---|---|---|---|---|---|---|
| n | Mean (SD) | n | Mean (SD) | n | Mean (SD) | |
| Birthweight (g) | 193 | 2707 (416) | 135 | 2829 (400) | 148 | 2930 (518) |
| Age (years) a | 193 | 47.2 (43.8,51.7) | 135 | 27.7 (23.5,33.4) | 148 | 26.2 (22.3,32.7) |
| Adult height (cm) | 193 | 151.4 (5.5) | 135 | 154.1 (5.2) | 148 | 167.9 (6.4) |
| BMI (kg/m2) a | 193 | 25.5 (22.5,28.7) | 135 | 21.6 (19.2,24.8) | 148 | 22.0 (18.8,24.6) |
| 120-min glucose (mmol/l) a | 168 | 7.2 (6, 9.2) | 131 | 5.8 (5.1,6.9) | 147 | 5.6 (4.9, 6.3) |
| IFG No (%) | 189 | 2 (1.1 %) | 133 | 1 ( 0.8 %) | 147 | 2 (1.4 %) |
| IGT No (%) | 189 | 44 (23.3 %) | 133 | 13 (9.8 %) | 147 | 6 (4.1 %) |
| Diabetes No (%) | 189 | 47 (24.9 %) | 133 | 9 (6.8 %) | 147 | 6 (4.1 %) |
| IFG / IGT or DM No (%) | 189 | 93 (49.2 %) | 133 | 23 (17.3 %) | 147 | 14 (9.5 %) |
| Insulin resistance (HOMA) a | 188 | 1.9 (1.0,3.7) | 117 | 1.2 (0.8,1.9) | 127 | 1.1 (0.6,2.0) |
| Metabolic syndrome No (%) | 186 | 71 (38.2 %) | 114 | 15 (13.2 %) | 126 | 18 (14.3 %) |
| Cholesterol (mmol/l) | 193 | 4.9 (1.3) | 135 | 4.2 (1.1) | 148 | 4.2 (1.1) |
| Triglyceride (mmol/l) a | 193 | 1.6 (1.1,1.9) | 135 | 0.9 (0.7, 1.4) | 148 | 1.3 (0.9, 1.9) |
| Systolic blood pressure (mmHg) | 193 | 131 (17) | 135 | 115 (9) | 148 | 122 (9) |
| Waist/hip ratio | 193 | 0.85 (0.06) | 134 | 0.81 (0.06) | 148 | 0.86 (0.07) |
| Subscapular/triceps ratio a | 193 | 1.4 (1.2,1.6) | 135 | 1.4 (1.2,1.6) | 148 | 1.9 (1.5,2.2) |
| Coronary heart disease No (%) | 193 | 17 (8.8 %) | 134 | 5 (3.7 %) | 148 | 5 (3.4%) |
| Fathers | Sons | Daughters | ||||
|---|---|---|---|---|---|---|
| n | Mean (SD) | n | Mean (SD) | n | Mean (SD) | |
| Birthweight (g) | 144 | 2766 (410) | 117 | 3026 (472) | 106 | 2834 (401) |
| Age (years) a | 144 | 48.7 (45.4,53.5) | 117 | 23.5 (21.3,26.9) | 106 | 23.5 (21.5,28.6) |
| Adult height (cm) | 144 | 165.4 (5.8) | 117 | 169.3 (6.1) | 106 | 155.2 (4.9) |
| BMI (kg/m2) a | 144 | 23.5 (20.7,25.9) | 117 | 20.8 (18.1,24.1) | 106 | 21.9 (18.8,25.2) |
| 120-min glucose (mmol/l) a | 123 | 6.7 (5.8,8.7) | 116 | 5.6 (4.7,6.4) | 103 | 5.9 (4.8,6.9) |
| IFG No (%) | 141 | 2 (1.4 %) | 117 | 3 (2.6 %) | 104 | 5 (4.8 %) |
| IGT No (%) | 141 | 20 (14.2 %) | 117 | 8 (6.8 %) | 104 | 11 (10.6 %) |
| Diabetes No (%) | 141 | 47 (33.3 %) | 117 | 4 (3.4 %) | 104 | 1 (0.9 %) |
| IFG / IGT or DM No (%) | 141 | 69 (48.9 %) | 117 | 15 (12.8 %) | 104 | 17 (16.4 %) |
| Insulin resistance (HOMA) a | 136 | 1.6 (0.9,3.6) | 117 | 1.3 (0.8,2.4) | 106 | 1.9 (1.2,2.9) |
| Metabolic syndrome No (%) | 135 | 53 (39.3 %) | 116 | 9 (7.8 %) | 104 | 9 (8.7 %) |
| Cholesterol (mmol/l) | 143 | 5.2 (0.9) | 117 | 4.3 (1.3) | 106 | 4.6 (1.2) |
| Triglycerides (mmol/l) a | 143 | 1.8 (1.3,2.4) | 117 | 1.2 (0.9, 1.6) | 106 | 0.9 (0.7, 1.4) |
| Systolic blood pressure (mmHg) | 144 | 133 (18) | 117 | 120 (8) | 106 | 112 (8) |
| Waist/hip ratio | 144 | 0.93 (0.07) | 116 | 0.86 (0.06) | 106 | 0.83 (0.06) |
| Subscapular/triceps ratio a | 142 | 1.9 (1.5,2.3) | 117 | 1.9 (1.7,2.4) | 101 | 1.4 (1.3,1.7) |
| Coronary heart disease No (%) | 141 | 17 (12.1 %) | 117 | 9 (7.7 %) | 105 | 5 (4.8 %) |
Median (Inter Quartile Range (IQR)) 19 offspring had data from both parents, and appear in both the mother-offspring and father-offspring sections of the table.
Relationships of offspring birthweight to their own adult outcomes
Among all the offspring combined (n=487), 120-minute glucose, cholesterol and triglyceride concentrations, systolic blood pressure, WHR, subscapular/triceps ratio (SSTR), and the prevalence of IGT, diabetes, glucose intolerance and the metabolic syndrome increased with age (p<0.05 for all). All except SSTR rose with increasing BMI (p≤0.01 for all). Women were more insulin resistant than men (p=0.009), while men had higher triglyceride concentrations, WHR, SSTR and systolic blood pressure (p<0.001 for all). Twenty-four offspring were defined as having CHD; 1 had a history of bypass surgery, 23 had Rose questionnaire angina, and none had Q-wave ECG changes.
Table 2 shows outcomes in the offspring according to their own birthweight. In the offspring combined, glucose intolerance, total cholesterol and triglyceride concentrations, and SSTR were inversely related to birthweight. These associations strengthened after adjusting for BMI, when 120-minute glucose, IGT and insulin resistance also became significantly inversely related to birthweight. The trends were similar in offspring of mothers and offspring of fathers. Apparently stronger inverse associations of insulin resistance and cholesterol with birthweight in offspring of fathers than in offspring of mothers (Table 2) were not statistically significantly different. The associations did not change after adjusting for socio-economic status, and were similar after excluding offspring where the mother was found to be diabetic in our study (we did not have data on diabetes during pregnancy).
Table 2. Offspring outcomes (median (IQR)) according to their own birthweight.
| All | Offspring birthweight (kg) | ||||||
|---|---|---|---|---|---|---|---|
| ≤2.5 | -3.0 | >3.0 | |||||
| Max n=118 | Max n=200 | Max n=169 | Effect size (95% CI) per kg offspring birthweight adjusted for age and sex | p | Effect size (95% CI) per kg offspring birthweight adjusted for age, sex and BMI | p | |
| 120-min glucose (mmol/l) c | 5.9 (5.3, 7.2) | 5.8 (5.0, 6.9) | 5.4 (4.7, 6.2) | 0.95 (0.90 - 1.00) | 0.056 | 0.92 (0.88 - 0.97) | 0.003 |
| IFG No (%) a | 5 (4.3 %) | 3 (1.5 %) | 2 (1.2 %) | 0.45 (0.08 - 2.52) | 0.4 | 0.47 (0.08 - 2.72) | 0.4 |
| IGT No (%) a | 11 (9.5 %) | 19 (9.6 %) | 7 (4.1 %) | 0.39 (0.15 - 1.00) | 0.051 | 0.34 (0.14 - 0.81) | 0.02 |
| Diabetes No (%) a | 9 (7.8 %) | 8 (4.1 %) | 3 (1.8 %) | 0.49 (0.13 - 1.82) | 0.3 | 0.27 (0.06 - 1.14) | 0.08 |
| IFG / IGT or DM No (%) a | 25 (21.6 %) | 30 (15.2 %) | 12 (7.1 %) | 0.29 (0.11 - 0.75) | 0.01 | 0.19 (0.06 - 0.56) | 0.003 |
| Insulin resistance (HOMA) c | 1.4 (0.9, 2.5) | 1.3 (0.8, 2.4) | 1.3 (0.8, 2.2) | 0.91 (0.77 - 1.08) | 0.3 | 0.83 (0.71 - 0.96) | 0.01 |
| Metabolic syndrome No %) a | 14 (13.2 %) | 23 (12.6 %) | 14 (9.2 %) | 1.13 (0.47 - 2.71) | 0.8 | 0.67 (0.28 - 1.57) | 0.4 |
| Cholesterol (mmol/l) b | 4.4 (3.8, 5.2) | 4.2 (3.5, 5.1) | 4.2 (3.4, 4.8) | -0.27 (-0.51to -0.04) | 0.02 | -0.33 (-0.57 to -0.09) | 0.008 |
| Triglycerides (mmol/l) c | 1.4 (0.8, 1.8) | 1.0 (0.8, 1.5) | 1.0 (0.7, 1.5) | 0.89 (0.79 - 0.99) | 0.03 | 0.86 (0.78 - 0.95) | 0.003 |
| Systolic blood pressure (mmHg) b | 118 (112, 124) | 115 (109, 121) | 119 (111, 125) | -0.02 (-2.01 to 1.98) | 0.9 | -0.73 (-2.60 to 1.14) | 0.4 |
| Waist/hip ratio b | 0.85 (0.80, 0.89) | 0.84 (0.79, 0.89) | 0.83 (0.79, 0.88) | -0.005 (-0.02 to 0.007) d | 0.4 | - - | - |
| Subscapular/triceps ratio c | 1.7 (1.4, 2.1) | 1.6 (1.3, 1.9) | 1.6 (1.4, 1.9) | 0.95 (0.90 - 0.99) | 0.048 | 0.94 (0.89 - 0.99) | 0.03 |
| Coronary heart disease No (%) | 9 (7.7 %) | 12 (6.0 %) | 3 (1.8 %) | 0.44 (0.16 - 1.22) | 0.1 | 0.44 (0.16 - 1.21) | 0.1 |
| OFFSPRING OF MOTHERS | n=76 | n=113 | n=94 | ||||
| 120-min glucose (mmol/l) c | 5.9 (5.3, 7.1) | 5.7 (5.0, 6.6) | 5.4 (4.8, 6.2) | 0.94 (0.87 - 1.02) | 0.1 | 0.91 (0.85 - 0.97) | 0.006 |
| IFG No (%) a | 2 (2.6 %) | 1 (0.9 %) | 0 (0 %) | 0.35 (0.02 - 6.77) | 0.5 | 0.38 (0.02 - 7.51) | 0.5 |
| IGT No (%) a | 7 (9.2 %) | 9 (8.2 %) | 3 (3.2 %) | 0.40 (0.12 - 1.36) | 0.1 | 0.28 (0.07 - 1.07) | 0.06 |
| Diabetes No (%) a | 6 (7.9 %) | 7 (6.4 %) | 2 (2.1 %) | 0.58 (0.13 - 2.58) | 0.5 | 0.33 (0.07 - 1.46) | 0.1 |
| IFG / IGT or DM No (%) a | 15 (19.7 %) | 17 (15.5 %) | 5 (5.3 %) | 0.33 (0.10 - 1.09) | 0.07 | 0.21 (0.06 - 0.76) | 0.02 |
| Insulin resistance (HOMA) c | 1.2 (0.7, 1.9) | 1.2 (0.7, 2.2) | 1.2 (0.6, 1.9) | 1.03 (0.82 - 1.29) | 0.8 | 0.91 (0.75 - 1.11) | 0.4 |
| Metabolic syndrome No (%) a | 8 (12.1 %) | 14 (14.6 %) | 11 (14.1 %) | 2.13 (0.76 - 5.97) | 0.2 | 1.14 (0.40 - 3.26) | 0.8 |
| Cholesterol (mmol/l) b | 4.4 (3.6, 5.1) | 4.1 (3.4, 4.9) | 4.1 (3.4, 4.7) | -0.06 (-0.35 to 0.23) | 0.7 | -0.13 (-0.43 to 0.16) | 0.4 |
| Triglycerides (mmol/l) c | 1.4 (0.8, 1.9) | 1.0 (0.8, 1.5) | 0.9 (0.7, 1.5) | 0.89 (0.77 - 1.02) | 0.09 | 0.84 (0.74 - 0.97) | 0.02 |
| Systolic blood pressure (mmHg) b | 118 (114, 125) | 117 (110, 122) | 121 (112, 127) | 0.51 (-2.10 to 3.13) | 0.7 | -0.26 (-2.74 to 2.22) | 0.8 |
| Waist/hip ratio b | 0.84 (0.79, 0.90) | 0.83 (0.78, 0.88) | 0.83 (0.79, 0.88) | 0.005 (-0.01 to 0.02) d | 0.5 | - - | - |
| Subscapular/triceps ratio c | 1.7 (1.4, 2.1) | 1.5 (1.3, 1.9) | 1.5 (1.3, 1.9) | 0.98 (0.91 - 1.05) | 0.5 | 0.97 (0.90 - 1.04) | 0.4 |
| Coronary heart disease No (%) | 5 (6.6 %) | 4 (3.6 %) | 1 (1.1 %) | 0.36 (0.07 - 1.96) | 0.2 | 0.35 (0.06 - 1.92) | 0.2 |
| OFFSPRING OF FATHERS | n=47 | n=92 | n=84 | ||||
| 120-min glucose (mmol/l) c | 5.9 (4.9, 7.2) | 5.9 (4.9, 7.0) | 5.4 (4.7, 6.2) | 0.94 (0.87 - 1.02) | 0.1 | 0.93 (0.87 - 1.00) | 0.051 |
| IFG No (%) a | 4 (8.9 %) | 2 (2.2 %) | 2 (2.4 %) | 0.42 (0.07 - 2.54) | 0.3 | 0.46 (0.08 - 2.75) | 0.4 |
| IGT No (%) a | 4 (8.9 %) | 10 (10.9 %) | 5 (6 %) | 0.48 (0.13 - 1.77) | 0.3 | 0.45 (0.14 - 1.46) | 0.2 |
| Diabetes No (%) a | 3 (6.7 %) | 1 (1.1 %) | 1 (1.2 %) | 0.26 (0.03 - 2.49) d | 0.2 | - - | - |
| IFG / IGT or DM No (%) a | 11 (24.4 %) | 13 (14.1%) | 8 (9.5 %) | 0.22 (0.04 - 1.22) | 0.08 | 0.19 (0.04 - 1.01) | 0.05 |
| Insulin resistance (HOMA) c | 2.0 (1.3, 3.6) | 1.4 (1.0, 2.7) | 1.4 0.8, 2.3) | 0.78 (0.63 - 0.97) | 0.03 | 0.71 (0.58 - 0.87) | 0.001 |
| Metabolic syndrome No (%) a | 6 (13.3 %) | 9 (9.9 %) | 3 (3.6 %) | 0.35 (0.07 - 1.88) | 0.2 | 0.24 (0.06 - 1.01) | 0.05 |
| Cholesterol (mmol/l) b | 4.4 (3.8, 5.4) | 4.3 (3.5, 5.2) | 4.2 (3.5, 4.8) | -0.42 (-0.79 to -0.04) | 0.03 | -0.44 (-0.81 to -0.07) | 0.02 |
| Triglycerides (mmol/l) c | 1.3 ((0.8, 1.8) | 1.1 (0.7, 1.5) | 1.0 (0.8, 1.5) | 0.87 (0.75 - 1.01) | 0.06 | 0.86 (0.75 - 0.99) | 0.04 |
| Systolic blood pressure (mmHg) b | 117 (109, 124) | 115 (109, 120) | 119 (111, 126) | -0.38 (-2.99 to 2.25) | 0.8 | -0.32 (-3.26 to 2.63) | 0.8 |
| Waist/hip ratio b | 0.83 (0.80, 0.89) | 0.84 (0.80, 0.89) | 0.83 (0.79, 0.88) | -0.01 (-0.03 to 0.003) d | 0.1 | - - | - |
| Subscapular/triceps ratio c | 1.7 (1.5, 2.1) | 1.7 (1.3, 2.0) | 1.7 (1.5, 2.1) | 0.93 (0.86 - 1.00) | 0.06 | 0.93 (0.86 - 1.00) | 0.06 |
| Coronary heart disease No (%) | 4 (8.7 %) | 8 (8.7 %) | 2 (2.4 %) | 0.50 (0.14 - 1.83) | 0.3 | 0.50 (0.14 - 1.85) | 0.3 |
Binary Variable
Continuous normally distributed variable
Continuous logged variable.
p values are calculated using birthweight and outcome variables as continuous where appropriate. Estimates of effect size are given as odds ratios + 95% confidence intervals for binary outcome variables, as β-coefficients for continuous normally distributed variables or as exponentiated b-coefficients (eβ) for continuous logged variables; Exponentiated β values indicate the percentage change in the outcome per unit change in the predictor. For example an eβ of 0.77 (95% CI: 0.62 to 0.97) indicates that the outcome decreases by 23% (95% CI: 38% to 3%) per unit change in the predictor. BMI-adjusted values are not provided for waist/hip ratio, due to a high positive correlation between BMI and waist/hip ratio.
Indicates that the random slope effect could not be modelled.
Relationships of the parent’s birthsize to outcomes in the offspring
Among the mother-offspring pairs, maternal birthweight was inversely related to the risk of CHD in the offspring (Table 3). After adjusting for offspring BMI and socio-economic status, there were inverse associations of borderline statistical significance between maternal birthweight and offspring systolic blood pressure and the metabolic syndrome. These associations remained similar after further adjustment for offspring birthweight (offspring CHD, metabolic syndrome and systolic blood pressure p<0.05 for all) and maternal systolic blood pressure (offspring systolic blood pressure p=0.01).
Table 3. Offspring outcomes (median (IQR)) according to the birthweight of the parents.
| MOTHER-OFFSPRING PAIRS | Mother’s birthweight (kg) | ||||||
|---|---|---|---|---|---|---|---|
| ≤2.5 | -3.0 | >3.5 | Effect size (95% CI) per kg parent’s bwt adjusted for age and sex | p | Effect size (95% CI) per kg parent’s bwt adjusted for age, sex, BMI and SES | p | |
| Max n=108 | Max n=112 | Max n=63 | |||||
| 120-min glucose (mmol/l) c | 5.7 (4.9, 6.8) | 5.6 (5.0, 6.5) | 5.8 (5.0, 6.4) | 0.99 (0.92 - 1.07) | 0.8 | 0.97 (0.91 - 1.04) | 0.4 |
| IFG No (%) a | 1 (0.9 %) | 1 (0.9 %) | 1 (1.6 %) | 0.77 (0.07 - 8.78) | 0.8 | 0.66 (0.05 - 8.74) | 0.8 |
| IGT No (%) a | 5 (4.8%) | 8 (7.1%) | 6 (9.5 %) | 1.66 (0.57 - 4.89) | 0.4 | 1.52 (0.48 - 4.81) | 0.5 |
| Diabetes No (%) a | 6 (5.7%) | 8 (7.1%) | 1 (1.2 %) | 0.35 (0.10 - 1.17) | 0.09 | 0.35 (0.10 - 1.17) | 0.09 |
| IFG / IGT or DM No (%) a | 12 (11.4%) | 17 (15.2%) | 8 (12.7 %) | 0.88 (0.29 - 2.67) | 0.8 | 0.82 (0.29 - 2.35) | 0.7 |
| Insulin resistance (HOMA) c | 1.3 (0.7, 1.9) | 1.1 (0.6, 2.2) | 1.2 (0.7, 2.2) | 0.90 (0.71 - 1.14) | 0.4 | 0.91 (0.76 - 1.09) | 0.3 |
| Metabolic syndrome No (%) a | 15 (16.0%) | 11 (11.6%) | 7 (13.7 %) | 0.39 (0.14 - 1.16) | 0.069 | 0.36 (0.13 - 1.01) | 0.053 |
| Cholesterol (mmol/l) b | 4.2 (3.5, 4.8) | 4.4 (3.4, 5.2) | 4.0 (3.4, 4.5) | -0.15 (-0.44 to 0.14) | 0.3 | -0.20 (-0.50 to 0.09) | 0.2 |
| Triglycerides (mmol/l) c | 1.1 (0.8, 1.8) | 1.0 (0.8, 1.6) | 1.0 (0.7, 1.6) | 0.93 (0.80 - 1.09) | 0.4 | 0.91 (0.78 - 1.06) | 0.2 |
| Systolic blood pressure (mmHg) b | 121 (114, 126) | 115 (111, 122) | 120 (111, 124) | -2.33 (-5.07 to 0.42) | 0.09 | -2.49 (-5.00 to 0.03) | 0.052 |
| Waist/hip ratio b | 0.84 (0.79, 0.89) | 0.82 (0.77, 0.87) | 0.84 (0.79, 0.88) | -0.007 (-0.02 to 0.009) d | 0.4 | - - | - |
| Subscapular/triceps ratio c | 1.6 (1.3, 2.0) | 1.6 (1.3, 1.9) | 1.5 (1.3, 1.8) | 1.01 (0.93 - 1.09) | 0.9 | 1.00 (0.93 - 1.09) | 0.9 |
| Coronary heart disease No (%) | 7 (6.5 %) | 1 (0.9 %) | 2 (3.2 %) | 0.18 (0.04 - 0.79) | 0.02 | 0.18 (0.04 - 0.79) | 0.02 |
| Father’s birthweight (kg) | |||||||
|---|---|---|---|---|---|---|---|
| ≤2.5 | -3.0 | >3.5 | |||||
| FATHER-OFFSPRING PAIRS | Max n=57 | Max n=111 | Max n=55 | OR (95% CI) | p | OR (95% CI) | p |
| 120-min glucose (mmol/l) c | 5.9 (4.8, 7.2) | 5.8 (4.9, 6.6) | 5.4 (4.7, 6.3) | 0.96 (0.88 - 1.04) | 0.3 | 0.96 (0.88 - 1.04) | 0.3 |
| IFG No (%) a | 2 (3.5 %) | 4 (3.7 %) | 2 (3.6 %) | 1.28 (0.25 - 6.43) | 0.8 | 1.29 (0.25 - 6.82) | 0.8 |
| IGT No (%) a | 8 (14.0 %) | 8 (7.3 %) | 3 (5.5 %) | 0.47 (0.12 - 1.93) | 0.3 | 0.54 (0.18 - 1.62) | 0.3 |
| Diabetes No (%) a | 3 (5.3 %) | 0 (0 %) | 2 (3.6 %) | 1.07 (0.10 - 11.2) e | 0.9 | 0.91 (0.09 - 8.90) | 0.9 |
| IFG / IGT or DM No (%) a | 13 (22.8 %) | 12 (11.0 %) | 7 (12.7 %) | 0.25 (0.03 - 1.87) | 0.2 | 0.19 (0.02 - 1.50) | 0.1 |
| Insulin resistance (HOMA) c | 2.0 (1.4, 3.4) | 1.3 (0.9, 2.3) | 1.6 (0.9,3.0) | 0.83 (0.63 - 1.09) | 0.2 | 0.82 (0.65 - 1.02) | 0.077 |
| Metabolic syndrome No (%) a | 9 (15.8 %) | 6 (5.6 %) | 3 (5.5 %) | 0.29 (0.08 - 1.05) | 0.060 | 0.26 (0.07 - 0.93) | 0.04 |
| Cholesterol (mmol/l) b | 4.5 (3.7, 5.7) | 4.2 (3.5, 5.0) | 4.3 (3.7, 5.2) | -0.16 (-0.57 to 0.26) | 0.5 | -0.16 (-0.57 to 0.25) | 0.5 |
| Triglycerides (mmol/l) c | 1.3 (0.9, 1.9) | 1.0 (0.7, 1.4) | 1.2 (0.8, 1.5) | 0.88 (0.73 - 1.05) | 0.2 | 0.88 (0.74 - 1.03) | 0.1 |
| Systolic blood pressure (mmHg) b | 116 (109, 123) | 116 (110, 122) | 115 (111, 123) | -0.42 (-3.27 to 2.42) | 0.8 | -0.02 (-2.82 to 2.77) | 0.9 |
| Waist/hip ratio b | 0.85 (0.79, 0.88) | 0.82 (0.79, 0.88) | 0.86 (0.82, 0.89) | -0.006 (-0.03 to 0.02) d | 0.6 | - - | - |
| Subscapular/triceps ratio c | 1.7 (1.4, 2.0) | 1.7 (1.4, 2.2) | 1.7 (1.5, 1.9) | 0.97 (0.89 - 1.06) | 0.5 | 0.97 (0.89 - 1.05) | 0.5 |
| Coronary heart disease No (%) | 3 (5.4 %) | 10 (9.0 %) | 1 (1.8 %) | 0.75 (0.20 - 2.82) | 0.7 | 0.74 (0.17 - 3.22) | 0.7 |
Binary Variable
Continuous normally distributed variable
Continuous logged variable.
p values are calculated using birthweight and outcome variables. Estimates of effect size are given as odds ratios + 95% confidence intervals for binary outcome variables, as β-coefficients for continuous normally distributed variables or as exponentiated β-coefficients (eβ) for continuous logged variable; Exponentiated β values indicate the percentage change in the outcome per unit change in the predictor. BMI-adjusted values are not provided for waist/hip ratio, due to a high positive correlation between BMI and waist/hip ratio;
indicates that the random slope effect could not be modelled;
indicates that a population-average model was used due to difficulties with modelling the random effect.
Among the father-offspring pairs, there were no direct associations between paternal birthweight and offspring outcomes (Table 3). After adjusting for offspring BMI and socio-economic status, there was an inverse association between paternal birthweight and offspring metabolic syndrome. This association was similar after further adjusting for offspring birthweight (p=0.056). There were no significant differences in the strength of associations, between mother-offspring and father-offspring groups.
Outcomes in the parents
We examined risk factors in the parents according to their own birthweight (see table 4 in the electronic supplementary material on website). There was an inverse association between birthweight and adult subscapular/triceps skinfold ratio. After adjusting for adult BMI, there was an inverse association between birthweight and adult triglyceride concentrations.
Table 4. Outcomes in the parents (median (IQR)) according to their own birthweight.
| Outcomes in the Parents | Parents Birthweight (kg) | ||||||
|---|---|---|---|---|---|---|---|
| ≤2.5 | -3.0 | >3 | |||||
| Max n=116 | Max n=153 | Max n=68 | Effect size (95% CI) per kg bwt adjusted for age and sex | p | Effect size (95% CI) per kg bwt adjusted for age, sex and BMI | p | |
| 120-min glucose (mmol/l) d | 7.0 (5.6, 8.7) | 6.9 (5.8, 8.5) | 7.1 (6.0, 10.2) | -0.035 (-0.16 to 0.09) | 0.6 | -0.10 (-0.22 to 0.02) | 0.1 |
| IFG No (%)a | 2 (1.8 %) | 2 (1.3 %) | 0 (0 %) | 0.13 (0.01 - 2.10) | 0.2 | 0.13 (0.01 - 1.94) | 0.1 |
| IGT No (%)a | 20 (17.7 %) | 31 (20.7 %) | 13 (19.4 %) | 1.16 (0.54 - 2.47) | 0.7 | 0.91 (0.42 - 1.96) | 0.8 |
| Diabetes No (%)a | 34 (30.1 %) | 38 (25.3 %) | 21 (31.3 %) | 0.98 (0.55 - 1.76) | 0.9 | 0.87 (0.47 - 1.62) | 0.7 |
| IFG / IGT or DM No (%)a | 56 (49.6 %) | 71 (47.3 %) | 34 (50.8 %) | 0.96 (0.54 - 1.70) | 0.9 | 0.74 (0.40 - 1.36) | 0.3 |
| Insulin resistance (HOMA) c | 1.7 (0.8, 2.9) | 1.7 (1.0, 3.7) | 2.2 (1.1, 4.5) | 1.24 (0.96 - 1.61) | 0.1 | 0.95 (0.75 - 1.19) | 0.6 |
| Metabolic syndrome No (%)a | 41 (36.3 %) | 52 (36.4 %) | 30 (45.5 %) | 1.30 (0.73 - 2.33) | 0.4 | 0.79 (0.41 - 1.50) | 0.5 |
| Cholesterol (mmol/l) b | 4.9 (4.2, 5.9) | 5.1 (4.4, 5.7) | 4.9 (4.4, 5.6) | -0.13 (-0.42 to 0.16) | 0.4 | -0.20 (-0.50 to 0.09) | 0.2 |
| Triglycerides (mmol/l) c | 1.6 (1.3, 2.1) | 1.6 (1.1, 2.2) | 1.7 (1.1, 2.1) | 0.90 (0.80 - 1.02) | 0.09 | 0.84 (0.75 - 0.95) | 0.004 |
| Systolic blood pressure (mmHg) b | 125 (119, 134) | 130 (120, 141) | 133 (123, 144) | 2.66 (-1.78 to 7.10) | 0.2 | 0.98 (-3.43 to 5.38) | 0.7 |
| Waist/hip ratio b | 0.87 (0.82, 0.92) | 0.88 (0.83, 0.93) | 0.91 (0.87, 0.93) | 0.01 (-0.005 to 0.03) | 0.2 | ||
| Subscapular/triceps ratio c | 1.6 (1.3, 1.9) | 1.6 (1.3, 1.9) | 1.5 (1.3, 2.0) | 0.92 (0.86 - 0.99) | 0.024 | 0.91 (0.85 - 0.98) | 0.011 |
| Coronary heart disease No (%) a | 13 (11.4 %) | 16 (10.5 %) | 5 (7.4 %) | 0.77 (0.31 - 1.93) | 0.6 | 0.78 (0.30 - 2.0) | 0.6 |
Binary Variable
Continuous normally distributed variable
Continuous logged variable
Continuous normalised variable.
p values are calculated using birthweight and outcome variables as continuous where appropriate. Estimates of effect size are given as odds ratios + 95% confidence intervals for binary outcome variables, as β-coefficients for continuous normally distributed / normalised variables or as exponentiated β-coefficients (eβ) for continuous logged variable; Exponentiated β values indicate the percentage change in the outcome per unit change in the predictor. BMI-adjusted values are not provided for waist/hip ratio, due to a high positive correlation between BMI and waist/hip ratio.
We also examined outcomes in the parents according to offspring birthweight (see Table 5 in the electronic supplementary material on website). The prevalence of diabetes among mothers, and of glucose intolerance among fathers, showed positive associations with offspring birthweight, both of borderline statistical significance. Of 34 cases of CHD among the parents, 1 had a history of bypass surgery, 12 had Q-wave changes on ECG and 21 had Rose questionnaire angina. The risk of CHD among mothers was inversely related to offspring birthweight (borderline statistical significance). There were no associations between offspring birth size and the metabolic syndrome or insulin resistance in the parents.
Table 5. Outcomes in the mothers and fathers (median (IQR)) according to offspring birthweight.
| MOTHER-OFFSPRING | Offspring Birthweight (kg) | ||||||
|---|---|---|---|---|---|---|---|
| ≤2.5 | -3.0 | >3 | Effect size (95% CI) per kg offspring bwt adjusted for parents’ age | p | Effect size (95% CI) per kg offspring bwt adjusted for parents’ age and BMI | p | |
| Outcomes in the Mothers | Max n=62 | Max n=76 | Max n=55 | ||||
| 120-min glucose (mmol/l) b | 7.2 (5.7, 8.4) | 6.8 (5.9, 9.4) | 7.8 (6.8, 10.3) | 1.02 (-0.30 to 2.33) | 0.1 | 0.58 (-0.78 to 1.93) | 0.4 |
| IFG No (%)a | 0 (0 %) | 2 (2.7 %) | 0 (0 %) | 0.28 (0.01 - 5.89) | 0.4 | 0.17 (0.01 - 4.71) | 0.3 |
| IGT No (%)a | 16 (26.2 %) | 14 (18.9 %) | 14 (25.9 %) | 0.88 (0.42 - 1.81) | 0.7 | 0.72 (0.34 - 1.54) | 0.4 |
| Diabetes No (%)a | 12 (19.7 %) | 19 (25.7 %) | 16 (29.6 %) | 1.89 (0.92 - 3.86) | 0.08 | 1.66 (0.79 - 3.47) | 0.2 |
| IFG / IGT or DM No (%)a | 28 (45.9 %) | 35 (47.3 %) | 30 (55.6 %) | 1.38 (0.75 - 2.56) | 0.3 | 1.07 (0.56 - 2.06) | 0.8 |
| Insulin resistance (HOMA) c | 1.6 (0.9, 3.3) | 2.2 (0.9, 4.6) | 2.2 (1.2, 3.6) | 1.14 (0.86 - 1.53) | 0.4 | 0.84 (0.65 - 1.07) | 0.2 |
| Metabolic syndrome No (%)a | 24 (39.3 %) | 27 (37.5 %) | 20 (37.7 %) | 1.25 (0.65 - 2.41) | 0.5 | 0.73 (0.35 - 1.52) | 0.4 |
| Cholesterol (mmol/l) b | 4.9 (4.1, 5.8) | 4.8 (4.2, 5.6) | 4.7 (4.1, 5.6) | 0.26 (-0.11 to 0.63) | 0.2 | 0.18 (-0.19 to 0.57) | 0.3 |
| Triglycerides (mmol/l) c | 1.5 (1.2, 1.9) | 1.6 (1.1, 2.0) | 1.5 (0.9, 2.0) | 0.97 (0.84 - 1.12) | 0.7 | 0.89 (0.77 - 1.02) | 0.1 |
| Systolic blood pressure (mmHg) b | 129 (119, 139) | 130 (119, 139) | 130 (121, 137) | 2.91 (-1.92 to 7.75) | 0.2 | 0.66 (-4.22 to 5.55) | 0.8 |
| Waist/hip ratio b | 0.85 (0.81, 0.89) | 0.85 (0.81, 0.89) | 0.84 (0.81, 0.88) | 0.01 (-0.01 to 0.03) | 0.3 | ||
| Subscapular/triceps ratio c | 1.5 (1.2, 1.6) | 1.3 (1.1, 1.5) | 1.5 (1.2, 1.6) | 1.03 (0.95 - 1.11) | 0.5 | 1.01 (0.93 - 1.09) | 0.9 |
| Coronary heart disease No (%)a | 9 (14.5 %) | 5 (6.6 %) | 3 (5.5 %) | 0.34 (0.11 - 1.07) | 0.065 | 0.32 (0.10 - 1.06) | 0.063 |
| FATHER-OFFSPRING | |||||||
|---|---|---|---|---|---|---|---|
| Outcomes in the Fathers | Max n=32 | Max n=65 | Max n=47 | Effect size (95% CI) | p | Effect size (95% CI) | p |
| 120-min glucose (mmol/l) d | 6.6 (5.8, 7.7) | 6.6 (5.3, 8.5) | 7.8 (5.8, 10.7) | 0.31 (-0.09 to 0.71) | 0.1 | 0.27 (-0.11 to 0.65) | 0.2 |
| IFG No (%)a | 1 (3.2) | 1 (1.6 %) | 0 (0 %) | 0.15 (0.003 - 8.02) | 0.4 | 0.15 (0.003 - 8.61) | 0.4 |
| IGT No (%)a | 3 (9.7 %) | 10 (15.6 %) | 7 (15.2 %) | 1.63 (0.53 - 4.99) | 0.4 | 1.44 (0.46 - 4.58) | 0.5 |
| Diabetes No (%)a | 6 (19.4 %) | 19 (29.7 %) | 22 (47.8 %) | 1.94 (0.82 - 4.56) | 0.1 | 1.92 (0.82 - 4.54) | 0.1 |
| IFG / IGT or DM No (%)a | 10 (32.3 %) | 30 (46.9 %) | 29 (63.0 %) | 2.19 (0.94 - 5.08) | 0.068 | 2.13 (0.90 - 5.02) | 0.08 |
| Insulin resistance (HOMA) c | 1.6 (0.8, 3.1) | 1.6 (1.0, 4.1) | 1.8 (0.9, 4.4) | 0.90 (0.62 - 1.29) | 0.6 | 0.80 (0.59 - 1.09) | 0.2 |
| Metabolic syndrome No (%)a | 7 (24.1 %) | 27 (43.6 %) | 19 (42.2 %) | 1.59 (0.67 - 3.81) | 0.3 | 1.35 (0.50 - 3.65) | 0.6 |
| Cholesterol (mmol/l) b | 5.3 (4.9, 5.9) | 5.2 (4.4, 5.9) | 5.1 (4.7, 5.6) | -0.18 (-0.55 to 0.20) | 0.4 | -0.19 (-0.57 to 0.19) | 0.3 |
| Triglycerides (mmol/l) c | 1.9 (1.4, 2.5) | 1.9 (1.5, 2.4) | 1.6 (1.2, 2.3) | 0.89 (0.75 - 1.06) | 0.2 | 0.87 (0.74 - 1.04) | 0.1 |
| Systolic blood pressure (mmHg) b | 131 (122, 141) | 129 (121, 140) | 133 (125, 148) | 4.75 (-1.94 to 11.44) | 0.2 | 4.44 (-2.27 to 11.14) | 0.2 |
| Waist/hip ratio b | 0.92 (0.89, 0.99) | 0.93 (0.90, 0.98) | 0.94 (0.89, 0.98) | 0.003 (-0.02 to 0.03) | 0.8 | ||
| Subscapular/triceps ratio c | 1.9 (1.7, 2.5) | 1.7 (1.5, 2.2) | 2.0 (1.6, 2.5) | 1.04 (0.93 - 1.16) | 0.5 | 1.05 (0.94 - 1.17) | 0.4 |
| Coronary heart disease No (%)a | 1 (3.2 %) | 10 (15.9 %) | 6 (12.8 %) | 1.96 (0.61 - 6.28) | 0.3 | 1.98 (0.61 - 6.40) | 0.3 |
Binary Variable
Continuous normally distributed variable
Continuous logged variable
Continuous normalised variable.
All p values are calculated using birthweight and outcome variables. Estimates of effect size are given as odds ratios + 95% confidence intervals for binary outcome variables, as β-coefficients for continuous normally distributed/normalised variables or as exponentiated β-coefficients (eβ) for continuous logged variable; Exponentiated β values indicate the percentage change in the outcome per unit change in the predictor. BMI-adjusted values are not provided for waist/hip ratio, due to a high positive correlation between BMI and waist/hip ratio.
Discussion
The main aim of this intergenerational study was to examine cardiovascular risk factors in Indian adults in relation to their parents’ birthweight. There were inverse associations, of borderline statistical significance, and of similar strength, between the birthweight of both mothers and fathers and the risk of metabolic syndrome in their offspring. We also observed inverse associations between maternal birthweight and offspring CHD risk and systolic blood pressure. None of the other outcomes, including most of the individual components of the metabolic syndrome, were related to the birthweight of the parents.
There are several mechanisms that could produce inverse relationships between parental birthweight and offspring outcomes (Figure 1). Firstly, exposure of a female fetus to an adverse intrauterine environment could permanently alter the structure of her reproductive tract, the quality of her ova (her lifetime’s supply of which develop in utero), her growth potential and/or her metabolism, resulting in impaired fetal development when she becomes pregnant, which in turn programmes disease in her offspring. Theoretically, exposure of a male fetus could have long-term effects on sperm quality, leading to abnormalities of fetal development and later disease but we know of no examples of this. Materno-fetal programming could explain the inverse associations between maternal birthweight and offspring CHD and blood pressure. Only one other study has examined offspring outcomes according to parental birthweight. This showed, in the UK as in Mysore, an inverse association between maternal birthweight and offspring blood pressure [28]. Secondly, an adverse adult environment (eg. socio-economic deprivation) or behaviours (eg. smoking or poor diet), which cause both low birthweight and adult disease, could be shared by both parents, persist in the same family and result in both mother-offspring and father-offspring associations. This explanation seems unlikely in India, where CHD and the metabolic syndrome are predominantly diseases of higher socio-economic status, although effects of socio-economic status in childhood may differ from effects in adult life [29]. Smoking is common among Indian men, but very few Indian women smoke. Thirdly, there may be common genes, or epigenetic changes, that result in both low birthweight and adult disease, and could account for both mother-offspring and father-offspring associations. Our study cannot distinguish between these three possibilities, but does suggest both paternal and maternal effects.
Studies in western populations [5-8, 12-18] and in India [30,31] have examined parental outcomes in relation to offspring birthweight. The western studies showed inverse relationships between offspring birthweight and paternal and maternal insulin resistance measured in middle-to-old age [8,13]. The Indian studies had shorter follow-up times than ours, the parents were younger and the offspring were children. Both showed the expected positive associations of maternal insulin resistance and offspring birthweight [30,31]. Paternal insulin resistance was unrelated to offspring birthweight in one study [30] and positively related in the other [31]. In Mysore parent and offspring birthweights were correlated [32] and if the inverse associations between parental birthweight and offspring metabolic syndrome in our study were genetic in origin, we would expect to see similar inverse associations between offspring birthweight and parental outcomes. The absence of such associations in our study could be explained by the small sample size, effects of medications and disease complications in this older age group, and, in mother-offspring pairs, macrosomic effects of maternal gestational diabetes.
As in other populations [1,2,3,10,11,33,34], we found inverse associations between the birthweight of the offspring and their own glucose intolerance and lipid concentrations, adding to evidence that lower birthweight is linked to a higher risk of adult disease. As in most such studies, associations between risk factors and birthweight strengthened after adjustment for adult BMI. BMI adjustment and its interpretation have been controversial [35]. Our findings suggest either that at any given adult BMI, the risk factor profile is more adverse in men and women of lower birthweight, or that an increase in BMI between birth and adult life is important for the development of increased CHD risk.
Among the parents, birthweight was also inversely related to subscapular/triceps skin fold ratio, and (after BMI adjustment) triglyceride concentrations, but not to risk of glucose intolerance or the metabolic syndrome. The lack of an association between birthweight and adult glucose intolerance, were reported in an earlier study of the Mysore cohort, of which our parents’ sample was a sub-set [20]. The risk of glucose intolerance in the earlier study was increased in men and women of higher ponderal index at birth [20] and we suggested that this could reflect the effects of gestational diabetes. A possible explanation for the difference in trends with birthweight in the two generations is that effects of gestational diabetes may manifest only in old age. Unlike other populations [36-39], we did not find associations, in either generation, between birthweight and blood pressure. We suggested in the older cohort [40] that this may have been because a large number were on anti-hypertensive medication. The absence of an association in the younger cohort, however, suggests that birthweight is simply not related to blood pressure in this population.
A strength of our study was that birth data were extracted from hospital birth records. However, it was limited to people born in one hospital, still living in Mysore, able to provide enough information to match them to their birth records and willing to take part in the study. They comprised a very small proportion of the original births in the hospital (4.3% for the first generation and <1.0% for the second). Lack of statistical power was therefore a major weakness of the study. Losses to follow-up would have included death (infant mortality rate ranged between 106 and 200 per 1000 live births, and childhood mortality between 162 and 300 in the period 1934 and 1982), migration out of Mysore, and failure to match subjects to their birth records. By using strict criteria we ensured accurate matching, but with the disadvantage that many likely matches were excluded. Data for the second generation were only available if they were born in HMH. There is a large choice of public and private maternity hospitals in contemporary Mysore. Almost all the first generation subjects had children, but most were born elsewhere; 15% of Mysore births take place at HMH, 3% at home and the rest at other hospitals. Few of the offspring in our study had birth data from both parents; the ideal study would have compared mother-offspring and father-offspring effects in the same subjects. Another weakness of the study was that CHD was defined using the Rose chest pain questionnaire and Q-wave ECG changes, both of which may be non-specific.
In conclusion, a small sample size was a major weakness of our study. This highlights the difficulty of obtaining good intergenerational data using routine record systems in a developing country. Our study raises more questions than it answers. Most of the outcomes we studied, including the individual components of the metabolic syndrome were unrelated to parental birthweight. The inverse associations between both maternal and paternal birthweight and metabolic syndrome in the offspring were of borderline significance. These may be chance findings but are consistent with inter-generational studies (of parental outcomes in relation to offspring birthweight) in western populations. It suggests that factors in both parents may contribute to the risk of metabolic syndrome in the offspring. There are several possible explanations for this, but the findings are most consistent with the fetal insulin (genetic) hypothesis. Further studies in developing countries, using data from cohorts with better follow-up rates are required.
Acknowledgements
We are grateful to the men and women who participated and B.D.R. Paul and S.C. Karat, Medical Directors of HMH. We thank N.Venkatachala and the Medical Records staff for access to birth records. M.N. Jayakumar, T. Lawrence, K.J. Chachyamma, M.N. Swarnagowri, M. Surekha, A. Saroja, G. Gopal Singh, T.K. Srinivas and S. Shobha contributed to the study. We thank P. Wood, Endocrine Laboratory, Southampton General Hospital, UK, and C.S. Yajnik and D.S. Bhat, Diabetes Centre, KEM Hospital, Pune, India, for laboratory assays. ECGs were coded by N. Keen and C. Rose. We acknowledge the support of SNEHA-India. The study was funded by the Department For International Development, UK and the Wellcome Trust.
Duality of interest: none.
Abbreviations
- CHD
Coronary Heart Disease
- HMH
Holdsworth Memorial Hospital
- IQR
Inter Quartile Range
- RIR-HOMA
Relative Insulin Resistance-Homeostasis Model Assessment
- SSTR
Subscapular-Triceps Ratio
References
- 1.Barker DJP. Mothers, babies and health in later life. London: Churchill Livingstone; 1998. [Google Scholar]
- 2.Hales CN, Barker DJP, Clark PMS, Cox LJ, Fall CHD. Fetal and infant growth and impaired glucose tolerance at age 64 years. BMJ. 1991;303:1019–22. doi: 10.1136/bmj.303.6809.1019. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Barker DJP, Hales CN, Fall CHD, Osmond C, Phipps K, Clark PMS. Type II (non-insulin-dependent) diabetes mellitus, hypertension and hyperlipidaemia (syndrome X): relation to fetal growth. Diabetologia. 1993;36:62–67. doi: 10.1007/BF00399095. [DOI] [PubMed] [Google Scholar]
- 4.Hattersley AT, Tooke JE. The fetal insulin hypothesis: an alternative explanation of the association of low birthweight with diabetes and vascular disease. Lancet. 1999;353:1789–92. doi: 10.1016/S0140-6736(98)07546-1. [DOI] [PubMed] [Google Scholar]
- 5.Lindsay RS, Dabelea D, Roumain J, Hanson RL, Bennett PH, Knowler WC. Type 2 diabetes and low birthweight. The role of paternal inheritance in the association of low birthweight and diabetes. Diabetes. 2000;49:445–449. doi: 10.2337/diabetes.49.3.445. [DOI] [PubMed] [Google Scholar]
- 6.Davey Smith G, Sterne JAC, Tynelius P, Rasmussen F. Birth characteristics of offspring and parental diabetes: Evidence for the fetal insulin hypothesis. Journal of Epidemiology and community health. 2001;58:126–128. doi: 10.1136/jech.58.2.126. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Hypponen E, Davey Smith G, Power C. Parental diabetes and birthweight of offspring: Intergenerational cohort study. BMJ. 2003;326:19–20. doi: 10.1136/bmj.326.7379.19. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Wannamethee SG, Lawlor DA, Whincup PH, Walker M, Ebrahim S, Davey Smith G. Birthweight of offspring and paternal insulin resistance and paternal diabetes in late adulthood: cross sectional survey. Diabetologia. 2004;47:12–18. doi: 10.1007/s00125-003-1270-x. [DOI] [PubMed] [Google Scholar]
- 9.Krishnaveni GV, Hill JC, Leary S, et al. Anthropometry, glucose tolerance and insulin concentrations in Indian children: relationships to maternal glucose and insulin concentrations during pregnancy. Diabetes Care. 2005;28:2919–2925. doi: 10.2337/diacare.28.12.2919. [DOI] [PubMed] [Google Scholar]
- 10.McCance DR, Petit DJ, Hanson RL, Jacobsson LT, Knowlder WC, Bennett PH. Birthweight and non insulin dependant diabetes: thrifty genotype, thrifty phenotype, or surviving small baby genotype? BMJ. 1994;308:942–945. doi: 10.1136/bmj.308.6934.942. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Rich-Edwards JW, Colditz GA, Stampfer MJ, et al. Birthweight and the risk for type 2 diabetes mellitus in adult women. Ann Intern Med. 1999;130:278–284. doi: 10.7326/0003-4819-130-4_part_1-199902160-00005. [DOI] [PubMed] [Google Scholar]
- 12.Davey Smith G, Hart C, Ferrell C, et al. Birthweight of offspring and mortality in the Renfrew and Paisley study: prospective observational study. BMJ. 1997;315:1189–1193. doi: 10.1136/bmj.315.7117.1189. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Lawlor DA, Davey Smith G, Ebrahim S. Birthweight of offspring and insulin resistance in late adulthood: cross sectional survey. BMJ. 2002;325:359–362. doi: 10.1136/bmj.325.7360.359. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Lawlor DA, Davey Smith G, Whincup P, et al. Association between offspring birthweight and atherosclerosis in middle aged men and women: British Regional Heart study. J Epidemiol Community Health. 2003;57:462–463. doi: 10.1136/jech.57.6.462. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Davey Smith G, Sterne J, Tynelius P, Lawlor DA, Rasmussen F. Birthweight of offspring and subsequent cardiovascular mortality of the parents. Epidemiology. 2005;16:563–569. doi: 10.1097/01.ede.0000164790.96316.c0. [DOI] [PubMed] [Google Scholar]
- 16.Davey Smith G, Harding S, Rosato M. Relation between infant’s birthweight and mothers’ mortality: prospective observational study. BMJ. 2000;320:839–840. doi: 10.1136/bmj.320.7238.839. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Davey Smith G, Whitley E, Gissler M, Hemminki E. Birth dimensions of offspring, premature birth, and the mortality of mothers. Lancet. 2000;356:2066–2067. doi: 10.1016/S0140-6736(00)03406-1. [DOI] [PubMed] [Google Scholar]
- 18.Smith GCS, Pell JP, Walsh D. Pregnancy complications and maternal risk of ischaemic heart disease: a retrospective cohort study of 129 290 births. Lancet. 2001;357:2002–2006. doi: 10.1016/S0140-6736(00)05112-6. [DOI] [PubMed] [Google Scholar]
- 19.Stein C, Fall CHD, Kumaran K, Osmond C, Cox V, Barker DJP. Fetal growth and coronary heart disease in South India. Lancet. 1996;348:1269–1273. doi: 10.1016/s0140-6736(96)04547-3. [DOI] [PubMed] [Google Scholar]
- 20.Fall CHD, Stein CE, Kumaran K, et al. Size at birth, maternal weight, and type2 diabetes in South India. Diabet Med. 1998;15:220–227. doi: 10.1002/(SICI)1096-9136(199803)15:3<220::AID-DIA544>3.0.CO;2-O. [DOI] [PubMed] [Google Scholar]
- 21.Rose GA, Blackburn H, Gillum RF, Prineas RJ. Cardiovascular Survey Methods. Second Edition Geneva: World Health Organisation; 1982. [Google Scholar]
- 22.Kuppuswamy B. Manual of socio-economic status scale. Delhi: Manasayan Publication; 1962. [Google Scholar]
- 23.Toivonen E, Hemmila I, Marniemi J, Jorgensen PN, Zeuthen J, Lovgren T. Two-site time-resolved immunofluorometric assay of human insulin. Clin Chem. 1986;32:637–640. [PubMed] [Google Scholar]
- 24.Department of Noncommunicable Disease Surveillance. World Health Organization, Geneva Definition, Diagnosis and Classification of Diabetes Mellitus and its complications. Report of a WHO consultation-part 1. 1999. [accessed 28 January 2005]. Available from http://www.who.int/diabetes/currentpublications/en/
- 25.Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting glucose and insulin concentrations in man. Diabetologia. 1985;28:412–419. doi: 10.1007/BF00280883. [DOI] [PubMed] [Google Scholar]
- 26.Prineas RJ, Crow RS, Blackburn H. The Minnesota code manual of electrocardiographic findings: standards and procedures for measurement and classification. Boston: John Wright; 1982. [Google Scholar]
- 27.Rabe-Hesketh S, Skrondal A. Multilevel and longitudinal modelling using stata. College Station, Texas, USA: Stata Press; 2005. [Google Scholar]
- 28.Barker DJP. Growth in utero and blood pressure levels in the next generation. J Hypertens. 2000;18:843–846. doi: 10.1097/00004872-200018070-00004. [DOI] [PubMed] [Google Scholar]
- 29.Lawlor DA, Harro M, Weddekop N, et al. Association of socioeconomic position with insulin resistance among children from Denmark, Estonia, and Portugal: cross sectional study. BMJ. 2005;331:186–7. doi: 10.1136/bmj.331.7510.183. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Yajnik CS, Coyaji KA, Joglekar CV, Kellingray S, Fall C. Paternal insulin resistance and fetal growth: problem for the ‘fetal insulin’ and the ‘fetal origins’ hypotheses. Diabetologia. 2001;44:1197–1201. doi: 10.1007/s001250100622. [DOI] [PubMed] [Google Scholar]
- 31.Yajnik CS, Joglekar CV, Pandit AN, et al. Higher offspring birth weight predicts the metabolic syndrome in mothers but not fathers 8 years after delivery: The Pune children’s study. Diabetes. 2003;52:2090–2096. doi: 10.2337/diabetes.52.8.2090. [DOI] [PubMed] [Google Scholar]
- 32.Veena SR, Kumaran K, Swarnagowri, et al. Intergenerational effects on size at birth in South India. Paediatric and Perinatal Epidemiology. 2004;18:361–370. doi: 10.1111/j.1365-3016.2004.00579.x. [DOI] [PubMed] [Google Scholar]
- 33.Law CM, Gordon GS, Sheill AW, Barker DJ, Hales CN. Thinness at birth and glucose tolerance in seven-year-old children. Diabet Med. 1995;12:24–29. doi: 10.1111/j.1464-5491.1995.tb02057.x. [DOI] [PubMed] [Google Scholar]
- 34.Bavdekar A, Yajnik CS, Fall CH, et al. Insulin resistance syndrome in 8-year old children: small at birth, big at 8 years, or both? Diabetes. 1999;48:2422–29. doi: 10.2337/diabetes.48.12.2422. [DOI] [PubMed] [Google Scholar]
- 35.Lucas A, Fewtrell MS, Cole TJ. Fetal origins of adult disease - the hypothesis revisited. BMJ. 2001;323:572–3. doi: 10.1136/bmj.319.7204.245. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Barker DJP, Bull AR, Osmond C, Simmonds SJ. Fetal and placental size and risk of hypertension in adult life. BMJ. 1990;301:259–62. doi: 10.1136/bmj.301.6746.259. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Martyn CN, Barker DJP, Jespersen S, et al. Growth in utero, adult blood pressure and arterial compliance. British Heart Journal. 1995;73:116–21. doi: 10.1136/hrt.73.2.116. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Law CM, Sheill AW. Is blood pressure inversely related to birthweight? The strength of evidence from a systemic review of the literature. J Hypertens. 1996;14:935–41. [PubMed] [Google Scholar]
- 39.Huxley RR, Sheill AW, Law CM. The role of size at birth and postnatal catch-up growth in determining systolic blood pressure: a systematic review of the literature. J Hypertens. 2000;18:815–31. doi: 10.1097/00004872-200018070-00002. [DOI] [PubMed] [Google Scholar]
- 40.Kumaran K, Fall CHD, Martyn CN, Vijayakumar M, Stein C, Shier R. Blood pressure, arterial compliance, and left ventricular mass: no relation to small size at birth in South Indian adults. Heart. 2000;83:272–277. doi: 10.1136/heart.83.3.272. [DOI] [PMC free article] [PubMed] [Google Scholar]