Indian Journal of Public Health

: 2022  |  Volume : 66  |  Issue : 1  |  Page : 9--14

Association of birth weight with risk factors of cardiovascular diseases: A birth cohort analysis from a rural area of Northern India

Divya Valecha1, P. V. M. Lakshmi2, Naresh Sachdeva3, Rajesh Kumar4,  
1 Former Junior Resident, Department of Community Medicine and School of Public Health, Post Graduate Institute of Medical Education and Research, Chandigarh, India
2 Professor, Department of Community Medicine and School of Public Health, Post Graduate Institute of Medical Education and Research, Chandigarh, India
3 Additional Professor, Department of Endocrinology, Post Graduate Institute of Medical Education and Research, Chandigarh, India
4 Former Professor, Department of Community Medicine and School of Public Health, Post Graduate Institute of Medical Education and Research, Chandigarh, India

Correspondence Address:
Rajesh Kumar
Department of Community Medicine and School of Public Health, Post Graduate Institute of Medical Education and Research, Chandigarh


Background: Fetal origin of cardiovascular diseases (CVD) hypothesis has been explored mostly in retrospective studies. Objectives: A prospective study was conducted to find the association of birth weight with CVD risk factors. Methods: A cohort of 243 babies born in 1992–1993 in ten villages of Raipur Rani Block in India, were followed-up in 2016–2017. WHO STEPS methods were used to assess the risk factors of CVDs. A total of 213 (87.8%) participants were examined; blood samples were collected from 207. Multivariable regression analysis was done to adjust for the confounding variables. Results: Study participants were 22–24 year old, 27.7% were exposed to tobacco and 24.8% consumed alcohol, 3.3% were taking >5 servings of fruits and vegetables per day, 35.7% were physically inactive, 28.6% were overweight (body mass index [BMI] ≥23 kg/m2), 12.2% had hypertension, 16% had high cholesterol (≥200 mg/dl), 16.4% had insulin resistance (IR) (Homeostatic Model Assessment-IR >3), and 20.7% were born with low birth weight (<2.5 kg). Multivariable regression analysis revealed inverse relationship between birth weight and systolic blood pressure (regression coefficient ‒3.72 mmHg, 95% confidence interval ‒7.249; ‒0.183, P < 0.05). Conclusion: Birth weight has inverse relationship with blood pressure. Effect of birth weight on CVDs should also be studied in future follow-ups.

How to cite this article:
Valecha D, Lakshmi P, Sachdeva N, Kumar R. Association of birth weight with risk factors of cardiovascular diseases: A birth cohort analysis from a rural area of Northern India.Indian J Public Health 2022;66:9-14

How to cite this URL:
Valecha D, Lakshmi P, Sachdeva N, Kumar R. Association of birth weight with risk factors of cardiovascular diseases: A birth cohort analysis from a rural area of Northern India. Indian J Public Health [serial online] 2022 [cited 2022 May 18 ];66:9-14
Available from:

Full Text


Noncommunicable diseases (NCDs) are now a major contributor to global disease burden. Of 56.4 million deaths in 2015, 70% were due to NCDs.[1] Cardiovascular diseases (CVDs) account for most of the NCD deaths. Primarily CVDs have been associated with excess dietary intake, reduced physical activity, and tobacco use. However, according to “fetal origins hypothesis” the disease process starts in childhood but manifests as heart attacks and strokes in later years. In 1995, Professor David Barker had proposed that “fetal under-nutrition in middle to late gestation, leads to disproportionate foetal growth, programs later coronary heart disease.”[2] Several studies have shown that low birth weight (LBW) is associated with CVDs.[3],[4],[5]

However, studies on fetal origin hypothesis have several limitations. Majority of the studies are from developed countries. Inappropriate adjustment for current weight and other confounders has been done in many studies and selection bias may also operate in some retrospective studies. Therefore, prospective cohort studies are required in developing countries, where LBW prevalence is very high. We followed-up a cohort of children born in a rural community of Haryana to find out association between birth weight and risk factors of CVDs.

 Materials and Methods

This birth cohort study was conducted in ten villages of Raipur Rani Community Development Block located in Panchkula district of Haryana State in India. A cohort of 243 babies born from September 1992 to November 1993 was followed-up during May 2016 to March 2017. This birth cohort size was considered sufficient comparing CVD risk factors between LBW (<2500 g) and normal birth weight (≥2500 g) using mean and standard error from a previous study.[6]

After obtaining ethical approval from Institutional Ethics Committee, written informed consent was obtained from each participant. Information on age, sex, education, work status, and behavioral risk factors was inquired, and anthropometric measurements were taken using WHO STEPS Instrument.[7] The modified Kuppuswamy's socioeconomic status (SES) scale was used for the assessment of SES.[8]

Height was measured in the Frankfurt plane to the nearest mm. Weight was measured to nearest 0.1 kg using the digital weighing balance. Waist circumference was measured at the midpoint between the lower margin of the last palpable rib and the top of the iliac crest, while hip circumference was measured at the maximum circumference over the buttocks.[9] Triceps skin fold thickness was measured using Lange skinfold callipers. Body composition was measured using the Body Composition Monitor (Tanita Ironman, Model- BC554).[10]

Blood pressure was measured on the left arm at the level of the heart in the sitting position using digital sphygmomanometer (Omron HEM-7172, Omron Healthcare Co. Pvt. Ltd., Kyoto, Japan).[11] An average of three readings was taken with first reading measured after a period of rest for at least 15 min and subsequent 2 readings were taken at an interval of 3 min each from the previous reading. Fasting blood samples (7 ml) were collected. Blood sugar and lipid profile tests were done using standard methods using Semi Auto Analyzer (Transasia). Fasting plasma insulin was tested by Electro-chemiluminescence Immunoassay using Auto Analyser (Roche Diagnostics).

Data was analysed using Statistical Package for the Social Sciences (SPSS) version 16 (SPSS Inc., Chicago, IL, USA) and Epi Info version (The Centre for Disease Control and Prevention, Atlanta, USA). Birth weight <2500 g was categorized as LBW. Body mass index (BMI), waist-hip ratio, triceps skin fold thickness, and systolic and diastolic blood pressure, fasting plasma glucose (FPG), lipid profile (total cholesterol, low-density lipoprotein [LDL], high-density lipoprotein [HDL], triglycerides, very low density lipoprotein [VLDL]) and insulin resistance (IR) were categorized using standard cutoffs.

IR was calculated using Homeostatic Model Assessment-IR (HOMA-IR) that uses FPG and insulin levels in a formula (FPG * FPI/405, values in mg/dl and IU/l, respectively).[12] LDL cholesterol was calculated using Fried Wald equation (TC-HDL-TG/5 all values in mg/dl) for those participants who had total cholesterol and triglycerides within the normal range.[13] For those who had high total cholesterol or triglyceride levels, LDL cholesterol was directly tested. International Diabetes Federation definition was used to diagnose metabolic syndrome.[14]

Metabolic Equivalents (MET) scores were calculated adopting existing guidelines.[15] individuals were classified as active if they achieved a score of at least 600 METs per week. To assess diet, servings of fruits and vegetables consumed in a typical week were combined and then divided by seven to estimate fruits and vegetables consumption per day.

To compare the distribution of CVD risk factors in the two exposure groups (LBW and NBW), mean and 95% confidence intervals were computed, and hypothesis was tested using independent t-test. Nonparametric tests were used for variables which were not found to be normally distributed. The relationship between birth weight, BMI, and systolic blood pressure (SBP) was explored in stratified analysis. Multivariable regression analysis was done to control for potential confounding factors.


Out of the 243 eligible cohort members, 213 could be interviewed. Nine participants could not be traced due to incomplete addresses, 19 could not be interviewed because they were residing in far off places, and two participants had gone abroad for higher studies.

Out of the 213 study participants, blood samples could not be collected from six. One of them had gone for military training; one had gone for higher studies in another far-off district. One participant did not consent for blood sample, while three could not come to sample collection site due to long distance from their residence place.

Socio-demographic characteristics

Out of 213 study participants, 44 (20.7%) were born with LBW (<2500 g). Most (64.3%) of the participants were aged 23 years, 16% were 22 years old and 19.7% were 24 years old. Males were 58.2%. Most (90.2%) had completed secondary or higher education. Only 8% of them had per capita income of more than INR 10,000/month (USD 135).

Prevalence of cardiovascular diseases risk factors

Exposure to tobacco was 27.7% (59/213); 21.8% (27/124) of males were currently smoking whereas none of the females smoked. None of the females had ever consumed alcohol, whereas 42.7% (53/124) of the males had consumed an alcoholic drink; most of them (42/124) were occasional drinkers. Only 3.3% participants were consuming on an average >5 servings of fruits and/or vegetables per day. Overall, 35.7% of participants were physically inactive (26.6% males and 48.3% females).

Overall 10.8% participants had high body fat percentage (8.9% among males, 13.5% among females). The prevalence of overweight (BMI ≥25 kg/m2 and <30 kg/m2) and obesity (BMI ≥30 kg/m2) was 13.1% and 3.8%, respectively. According to the WHO guidelines for Asian population,[16] overall 28.6% participants were overweight (BMI ≥23 kg/m2) (33.8% among males and 21.4% among females). The prevalence of abdominal obesity (waist-hip-ratio >0.90 for males and >0.80 for females) was 24.2% among males and 61.8% among females. According to waist circumference cutoff (≥90 cm in males, ≥80 cm in females) abdominal obesity was 20.2% among males and 25.8% among females.

The prevalence of hypertension (SBP ≥140 mmHg and/or DBP ≥90 mmHg or on anti-hypertension treatment) was 19.4% among males and 2.3% among females. The prevalence of prehypertension was also high (44.6%). None of the participants had impaired fasting glucose (IFG) or diabetes mellitus as per the WHO cutoff points. However, when American Diabetes Association cutoff level of 100 mg/dl was used, 7 (3.4%) had IFG. Elevated fasting plasma insulin level (≥25 mIU/L) was observed in 9 (4.4%). The prevalence of IR (HOMA-IR >3) was 16.4% (18.2% among males and 14% among females) and 12.2% had metabolic syndrome.

Mean total cholesterol level was 163.2 mg/dl (standard deviation 38.5 mg/dl). The prevalence of high total cholesterol level (>240 mg/dl) was 3.4%; hypertriglyceridemia (≥150 mg/dl) was 13.5%. Only one participant had high LDL cholesterol (>160 mg/dl), but 16.4% had borderline high levels (>130 mg/dl) of LDL. The prevalence of low HDL cholesterol level (<40 mg/dl) was 45.9%. The prevalence of high VLDL cholesterol (≥30 mg/dl) was 13%.

Association between birth weight and risk factors of cardiovascular diseases

The distribution of risk factors of CVD in low and normal birth weight groups is presented in [Table 1]. LBW group had lower values of all the risk factors except systolic and diastolic blood pressure. BMI and FPG was significantly lower in LBW group (P = 0.001 and 0.02, respectively). The LBW group had significantly lower waist hip ratio and triceps skin fold thickness (P = 0.044 and 0.042, respectively).{Table 1}

Current BMI was found to be associated with both exposure (birth weight) and outcome variables (CVD risk factors). There was evidence of confounding by current BMI. In stratified analysis, mean SBP was found to be higher in LBW group compared to NBW group in both BMI categories (less than median and more than or equal to median) [Table 2].{Table 2}

Multivariable linear regression analyses for normally distributed outcome variables are shown in [Table 3]. Separate models were created for each outcome variable. Age, sex, current BMI, SES score, tobacco exposure (pack years), alcohol (total standard alcoholic drinks consumed in past 30 days), diet (servings of fruits and vegetables consumed per day), physical activity (MET-minutes/week), and family history (HTN/DM/stroke/heart attack) were included in all models. Significant inverse linear association of SBP was revealed with LBW after adjusting for confounding variables (P = 0.039).{Table 3}


An inverse linear relationship between birth weight and SBP was revealed after adjusting for the known confounding variables in rural birth cohort of young Indians. Their profile of CVD risk factors was similar to rural youths in other parts of India.

Cardiovascular diseases risk factors

Around 22% of males were smoking tobacco in the present study which is comparable to 25.9% smoking rates among males (15 years and above) in rural India.[17] The prevalence of drinking alcohol was 24.9%, which is comparable to 4th round of National Family Health Survey (NFHS-4) estimate of Rural India (29.5%).[18] The prevalence of physical inactivity was much lower in this study (26.6% in males, 48.3% in females) as compared to the recently conducted Indian Council of Medical Research-India Diabetes (ICMR-INDIAB) study[19] which has reported an overall prevalence of physical inactivity in 40.3% of the males and in 59.6% of the females in rural area of three Indian States and one Union Territory.

Mean BMI was higher in males as compared to females (21.9 vs. 20.7; P < 0.05). However, females had higher prevalence of abdominal obesity (25.8% in females vs. 20.2% in males). This is in contrast to other studies which report a higher prevalence of overweight and obesity in females including both abdominal and generalized obesity.[20]

The prevalence of hypertension was significantly higher in males (19.4%) as compared to females (2.3%). According to NFHS-4, the prevalence of hypertension in age group of 15–49 years in rural India is estimated to be 12.6% in males and 8.5% in females. None of the participants had IFG or overt diabetes mellitus. However, 16.4% participants had high IR. These individuals are at increased risk of prediabetes and Type 2 diabetes. The prevalence of hypercholesterolemia, high LDL cholesterol, hypertriglyceridemia, low HDL cholesterol was comparable to the recent ICMR-INDIAB phase-I study, except that hypertriglyceridemia was much lower in this cohort.[21]

Association of birth weight with cardiovascular diseases risk factors

Several studies in past have reported inverse linear relationship between birth weight and SBP which is supported by this cohort study also.[3],[5] Reviews of several such studies estimate that a one kg decrease in birth weight is associated with a 2–4 mmHg increase in SBP.[3]

There are several potential confounding factors in estimating the effect of birth weight on SBP.[22] Another potential confounder is current body size. Though majority of the studies that report the inverse relationship of birth weight with SBP had made adjustment for current body size, fewer studies had made adjustment for other confounding factors.[3]

Adjustment for current body size (BMI) was done in the regression model as BMI was associated with birth weight as well as with SBP. Even though birth weight and SBP did not have a statistically significant association in univariate analysis, but in the multivariable analysis, inverse linear relationship between birth weight and SBP was found to be statistically significant after adjusting for the effect of current BMI, suggesting a strong confounding effect of current BMI. In this study, all risk factors were strongly related to current BMI; hence, adjustment was done for all the risk factors.

Further, it has also been found in some previous studies that the effect of birth weight on SBP amplifies with age. In one observational study, it was found that inverse association between birth weight and SBP was amplified with age, with significant interaction of age and birth weight on SBP (P < 0.001).[22] This was also found in another cohort study (age range: 4–50 years), that birth weight and SBP relation was amplified with advancing age substantially, irrespective of current BMI adjustment.[23] It is suggested that fetal programming and the increasing burden of unhealthy lifestyle behaviors may affect the development of adult hypertension synergistically. Previous study on Raipur Rani Birth Cohort done in 2000–2001 at 7–8 years of age did not find any significant association of birth weight with SBP or DBP.[24] In 2016-2017, at 22–24 year age, an inverse association between birth weight and SBP has been observed in this cohort after adjusting for the effect of other confounders, suggesting amplification by age.

In multivariable linear regression model of present study, relation between birth weight and FPG was not found to be statistically significant (P = 0.09) after adjusting for confounding variables including current BMI. Similarly, association between birth weight and IR (HOMA-IR) was not found to be statistically significant (P = 0.6). Several studies in the past have reported an inverse linear relation between birth weight and risk of Type 2 diabetes.[25],[26] While others including a recent meta-analysis have reported a U-shaped relation with both high birth weight and LBW associated with increased risk of Type 2 diabetes.[27] Follow-up of the current cohort is needed to explore this association, when they enter into high risk age group (>40 years) for diabetes.

Previous studies on fetal growth and adult lipid levels have shown inconsistent association. A systematic review of 28 studies estimated a decrease of only 0.05 m mol/L per one Kg increase in birth weight.[28] Such a small effect is likely to have limited public health impact. Some studies particularly the larger ones have reported sex differences in the association between birth weight and TC levels. These have consistently found a stronger association between lower birth weight and higher TC in men but weak or no association in women while others have found no sex differences.[29] The size of our study is too small to detect sex difference in this association.

Compared to other birth cohort studies from India which have evaluated fetal origins hypothesis, our study had the highest follow-up (88%). In a hospital-based cohort study from King Edward Memorial Hospital, Pune, 477 children were studied at 8 years. Follow-up of 75% of this cohort at 21 years age found that higher glucose, insulin, and HOMA-IR at 8 years were related to higher glucose, insulin, HOMA-IR and BP, and lipids at 21 years independent of 8-year adiposity.[30]

Strengths and limitations

The prospective birth cohort study from the rural area of Haryana with participants now in the age group 22–24 years is one of its kinds in India. 20.7% cohort members were LBW and can be considered a representative sample. Most of the other studies on association of birth weight and CVD risk factors were conducted retrospectively and birth weight data were not available for many study participants. Hence, in this study, there was no chance of recall bias or selection bias at enrolment in the follow-up study. Moreover, this study is based in rural area. Very few previous studies are from rural areas; most of the studies have been conducted in urban areas where epidemiologic transitions have occurred and CVD risk factors, especially overweight and obesity are at higher level.

In this study, the birth weight was recorded by a single community health worker within 24 h of birth. No differential loss to follow-up was noted as 7 out of 30 participants that were not followed up were in LBW group. The exposure status (birth weight) was not known to the interviewer or the laboratory technician. Hence, there was no bias due to prior knowledge of exposure status.


An inverse linear relationship between birth weight and SBP was observed after adjusting for known confounding variables including adjustment for current BMI. An interaction between effects of birth weight and current size is also possible which needs to be explored further. It would be interesting to note any amplification with age on the development of CVDs in the future follow-up of this cohort.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.


1World Health Organization. World health statistics 2017: monitoring health for the SDGs, sustainable development goals. Geneva: World Health Organization, 2017. Available from [Last accessed on 2022 Feb 19].
2Barker DJ. Fetal origins of coronary heart disease. BMJ 1995;311:171-4.
3Huxley R, Neil A, Collins R. Unravelling the fetal origins hypothesis: Is there really an inverse association between birthweight and subsequent blood pressure? Lancet 2002;360:659-65.
4Risnes KR, Vatten LJ, Baker JL, Jameson K, Sovio U, Kajantie E, et al. Birthweight and mortality in adulthood: A systematic review and meta-analysis. Int J Epidemiol 2011;40:647-61.
5Mu M, Wang SF, Sheng J, Zhao Y, Li HZ, Hu CL, et al. Birth weight and subsequent blood pressure: A meta-analysis. Arch Cardiovasc Dis 2012;105:99-113.
6Krishna M, Kalyanaraman K, Veena SR, Krishanveni GV, Karat SC, Cox V, et al. Cohort profile: The 1934-66 Mysore birth records cohort in South India. Int J Epidemiol 2015;44:1833-41.
7WHO STEPS Instrument (Core and Expanded): World Health Organization. Available from [Last accessed on 2022 Feb 19].
8Aggarwal OP, Bhasin SK, Sharma AK, Chhabra P, Aggarwal A, Rajoura OP, et al. A new instrument (Scale) for measuring the socioeconomic status of a family: Preliminary study. Indian J Community Med 2005;30:111-4.
9National Health and Nutrition Examination Survey. Anthropometry Procedures Manual. USA, Atlanta: Center for Disease Control and Prevention, 2017. Available from [Last accessed on 2022 Feb 19].
10Tanita BC-554 IRONMAN® Body Composition Monitor. Available from: [Last accessed on 2022 Feb 19].
11Omron Automatic Blood Pressure Monitor HEM-7120 Basic. Available from [Last accessed on 2022 Feb 19].
12Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC, et al. Homeostasis model assessment: Insulin resistance and β-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28:412-9.
13Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem 1972;18:499-502.
14International Diabetes Federation. The IDF Consensus Worldwide Definition of the Metabolic Syndrome. Brussels: International Diabetes Federation; 2006.
15World Health Organization. Global Physical Activity Questionnaire (GPAQ) Analysis Guide. Geneva: World Health Organization; 2002. Available from: com/search?q=16.+Global+Physical+Activity+Questionnaire+ (GPAQ)+Analysis+Guide.+Geneva% 3A+World+Health+Organization. [Last accessed on 2022 Feb 19].
16WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004;363:157-63.
17International Institute for Population Sciences. Global Adult Tobacco Survey (GATS) India: 2009-2010. India: Ministry of Health and Family Welfare; 2010.
18International Institute for Population Sciences. National Family Health Survey (NFHS-4), 2015-16. India: Ministry of Health and Family Welfare; 2017.
19Anjana RM, Pradeepa R, Das AK, Deepa M, Bhansali A, Joshi SR, et al. Physical activity and inactivity patterns in India – Results from the ICMR-INDIAB study (Phase-1) [ICMR-INDIAB-5]. Int J Behav Nutr Phys Act 2014;11:26.
20Pradeepa R, Anjana RM, Joshi SR, Bhansali A, Deepa M, Joshi PP, et al. Prevalence of generalized & abdominal obesity in urban & rural India – The ICMR-INDIAB study (Phase-I) [ICMR- NDIAB-3]. Indian J Med Res 2015;142:139-50.
21Joshi SR, Anjana RM, Deepa M, Pradeepa R, Bhansali A, Dhandania VK, et al. Prevalence of dyslipidemia in urban and rural India: The ICMR-INDIAB study. PLoS One 2014;9:e96808.
22Davies AA, Smith GD, May MT, Ben-Shlomo Y. Association between birth weight and blood pressure is robust, amplifies with age, and may be underestimated. Hypertension 2006;48:431-6.
23Chen W, Srinivasan SR, Berenson GS. Amplification of the association between birthweight and blood pressure with age: The Bogalusa heart study. J Hypertens 2010;28:2046-52.
24Kumar R, Bandyopadhyay S, Aggarwal AK, Khullar M. Relation between birthweight and blood pressure among 7-8 year old rural children in India. Int J Epidemiol 2004;33:87-91.
25Lithell HO, McKeigue PM, Berglund L, Mohsen R, Lithell UB, Leon DA. Relation of size at birth to non-insulin dependent diabetes and insulin concentrations in men aged 50-60 years. BMJ 1996;312:406-10.
26Hales CN. Non-insulin-dependent diabetes mellitus. Br Med Bull 1997;53:109-22.
27Harder T, Rodekamp E, Schellong K, Dudenhausen JW, Plagemann A. Birth weight and subsequent risk of type 2 diabetes: A meta-analysis. Am J Epidemiol 2007;165:849-57.
28Owen CG, Whincup PH, Odoki K, Gilg JA, Cook DG. Birth weight and blood cholesterol level: A study in adolescents and systematic review. Pediatrics 2003;111:1081-9.
29Skidmore PM, Hardy RJ, Kuh DJ, Langenberg C, Wadsworth ME. Birth weight and lipids in a national birth cohort study. Arterioscler Thromb Vasc Biol 2004;24:588-94.
30Yajnik CS, Katre PA, Joshi SM, Kumaran K, Bhat DS, Lubree HG, et al. Higher glucose, insulin and insulin resistance (HOMA-IR) in childhood predict adverse cardiovascular risk in early adulthood: The Pune children's study. Diabetologia 2015;58:1626-36.