Portal ENSP - Escola Nacional de Saúde Pública Sergio Arouca Portal FIOCRUZ - Fundação Oswaldo Cruz

Cadernos de Saúde Pública

ISSN 1678-4464

37 nº.7

Rio de Janeiro, Julho 2021


Subnutrição e crescimento baixo durante o primeiro ano de vida estão associados à glicemia, mas não à resistência insulínica na vida adulta

Isabel Pereyra, Sandra López-Arana, Bernardo L. Horta


  • Artigo
  • Autores
  • Comentários (0)
  • Informações Suplementares

O estudo teve como objetivo avaliar se o peso, estatura e crescimento condicional durante o primeiro ano de vida estão associados à glicemia e à resistência insulínica entre adultos jovens. O estudo usou um desenho longitudinal não concorrente. O estudo de coorte de base populacional analisou pessoas de idade entre 22 e 28 anos. Estimamos os escores-z desde o nascimento até o primeiro ano, e os lactentes foram classificados como: baixa estatura para idade, sobrepeso, obesidade, subnutrição e risco de subnutrição, usando os pontos de corte propostos pela Organização Mundial da Saúde (Child Growth Standards, 2006). Foram estimadas as variáveis de peso condicional e ganho de estatura. Foram avaliadas na vida adulta a glicemia, insulina e avaliação do modelo de homeostase da resistência à insulina (HOMA-IR, em inglês) e estimador de sensibilidade à insulina de ponto único (SPISE, em inglês). Utilizamos regressão linear multivariada, incluindo as variáveis com associação significativa com a glicemia e a resistência insulínica. Foram avaliados 1.070 indivíduos, e a glicemia na idade adulta foi maior naqueles com subnutrição ou riso de subnutrição aos 12 meses de idade (coeficiente β = 2,77; IC95%: 0,37; 5,21). Em relação ao peso normal, indivíduos com sobrepeso aos 12 meses mostraram a menor glicemia (coeficiente β = -2,39; IC95%: -4,32; -0,36). O ganho ponderal condicional no primeiro ano de vida mostrou associação negativa com glicemia na vida adulta (coeficiente β = -0,65; IC95%: -1,23; -0,08). O SPISE foi mais alto entre indivíduos subnutridos e mostrou associação negativa com o ganho ponderal condicional e o crescimento linear condicional no primeiro ano. Como conclusão, o estudo mostrou que a subnutrição e o crescimento baixo estiveram associados a glicemia mais elevada.

Diabetes Mellitus Tipo 2; Resistência à Insulina; Adulto Jovem; Crescimento



The prevalence of noncommunicable diseases (NCDs), such as type 2 diabetes mellitus, reached epidemic proportions worldwide. In the natural history of this disease, there are three developmental stages: compensatory hyperinsulinemia, prediabetes, and diabetes. In the first, the insulin resistance is present, but the β-cell mass retains the ability to synthesize insulin in adequate amounts for metabolic requirements. In the second stage, fasting glycemia values can be higher than 100mg/dL, but never exceeds 126mg/dL. And in the third stage, there are manifest disorders of carbohydrate metabolism, and fasting glycemia at any time of the day is higher than 126mg/dL 1,2,3,4,5.

The recognition of insulin resistance in persons at risk is significant to implement preventive measures, since type 2 diabetes mellitus causes major health care costs and burden for the patient. The standard technique for assessment of insulin sensitivity is the hyperinsulinemic-euglycemic clamp, however, although this technology has been used to study the insulin secretion and the insulin sensitivity, it is time-consuming and difficult to apply method in epidemiological studies. The homeostasis model assessment of insulin resistance (HOMA-IR) approach, introduced by Matthews et al. has been widely used in clinical research to assess insulin sensitivity 6, however, it is not clear which cut-off value is the best to define insulin resistance and it has been described significant differences in insulin levels in different countries 7. Moreover, single point insulin sensitivity estimator (SPISE) is another formula to calculate IR and smaller scores are associated with insulin resistance 8.

Concerning the life cycle determinants of type 2 diabetes mellitus, it has been reported that birth weight, breastfeeding, nutritional status, and early growth would be associated with later risk of type 2 diabetes mellitus 9,10,11,12,13,14. The faster growth in childhood would yield adjustment of hepatic metabolism, that regulates the production of endogenous glucose, mostly through glycogenolysis and gluconeogenesis 3,5. On the other hand, it was also reported that poor nutrition in early childhood results in a high risk of chronic diseases associated with an alteration of body composition 15,16,17,18,19.

Low birth weight subjects that also presented undernutrition in postnatal life have higher mortality by cardiovascular disease in adulthood 18. Indeed, in the Helsinki Birth Cohort Study, and Hertfordshire cohort, type 2 diabetes mellitus was associated with low weight in the first year 20,21. Being born small for gestational age, or being stunted at age 2 years were both inversely associated with lean mass in adulthood, possible explanations are related to the preferential restriction of skeletal muscle growth that occurs during growth restriction 19. It seems clear that undernutrition in critical periods affects the development of pancreatic β-cells, which would lead to β-cell failure and the development of type 2 diabetes mellitus 22.

Conditional variables express how a child deviates from its expected height or weight, based on its previous measures and the growth of the studied population 23. Evidence about the long-term consequences of weight gain in childhood is not as clear 24. It has been reported that excessive weight gain in every age range from birth to mid-childhood increases the risk of NCDs 25, on the other hand, it has been reported that this association may be modified by the timing of weight gain 26. A study in low- and middle- income countries observed an association of conditional relative weight gain at 2 years old with the risk of adult overweight and a slightly increase in the risk of elevated blood pressure, but it was unrelated to dysglycemia 27. However, faster mid-childhood relative weight gain was strongly associated with fat mass than fat-free mass, and increased the risk of elevated blood pressure and dysglycemia 27.

Adair et al. 27 observed that faster linear growth at 2 years was associated with a reduced risk of high fat-free mass (β coefficient [β] = 0.40), (95% confidence interval [95%CI]: 0.37; 0.42) than a reduced risk of high fat mass (β = 0.27; 95%CI: 0.25; 0.29), and increased the likelihood of adult overweight (β = 1.24; 95%CI: 1.17; 1.31) (mostly related to lean mass). Although, in the Birth to Twenty Plus cohort, relative linear growth at all-time points except for between years 5-8 was associated with increased fat mass at 22 years 28.

In light of the evidence, it is important to assess age-speciï¬Âc conditional measures, considering the strong correlation between repeated measurements over time and avoiding the “reversal paradox” 29. Our research group thought that suboptimal growth at age 0-1 year tend to determine higher risk of dysglycemia and insulin resistance among young adults. Such diseases need to be studied based on a life-course perspective, and prenatal and childhood growth need to be considered in order to understand the pathogenesis of several NCDs, as indicated by Eriksson 30. This study used data from the cohort of newborns in Limache and Olmué in Chile in the years 1974-1978 and 1988-1992 (1,070 participants, aged between 22 and 28 years), and aimed to investigate whether weight, length, and conditional growth during the first year are associated with glycemia and insulin resistance that are determinants of type 2 diabetes mellitus among young adults.

Materials and methods

Study design

A non-concurrent longitudinal design was used in the study, which was located in the region of Valparaiso, Chile, in the municipalities of Limache and Olmué. These municipalities are relatively near to the country's capital city of Santiago. Currently, the total population of both municipalities combined is 63,637 inhabitants and the Human Development Index (HDI) is 0.726 in Limache and 0.712 in Olmué (Instituto Nacional de Estadísticas Chile. Censos de población y vivienda 2002 y 2017. https://reportescomunales.bcn.cl/2017/index.php/Limache/Población, accessed on 16/Jun/2019) (Programa de las Naciones Unidas para el Desarrollo. http://desarrollohumano.cl/idh/category/serie-dh-s/, accessed on 13/Jun/2019), though, at the time this study started, in 2001, the numbers were similar, 0.722 and 0.701 respectively 31. The participants were sons and daughters of women residing in these municipalities. Data from two cohorts of people born in 1974-1978 (Cohort 1) and 1988-1992 (Cohort 2) were evaluated.

The survey in Cohort 1 involved 1,232 participants randomly selected from 3,096 children born between 1974 and 1978. The Cohort 2 included 1,000 participants randomly selected from 2,650 children born between 1988 and 1992. Exclusion of people with missing data for birth weight, fasting plasma glucose (FPG), and fasting insulin resulted in an analytical sample of 1,941 study participants. The Cohort 1 was initially evaluated between 2001 and 2003 when the participants were aged from 22 to 28 years. The subjects were interviewed about socioeconomic and behavioral variables, they were examined (anthropometry, blood pressure) and donated a blood sample. Furthermore, trained personnel collected data from the birth records, pediatric clinical notes which provided data on birth anthropometry and the first year of life, and diseases diagnosed in the primary care center. The data collection in Cohort 2 was carried out from 2015 to 2018 and followed the same procedures. Further details on the study methodology have been published elsewhere 32,33.

Information on birthweight, gestational age, and weight and length in the first year were collected from health records. The birth records at Chilean health services contain data about the mothers as well as their newborn babies. Length in the first year was recorded to the nearest 0.5cm and weight to the nearest 0.5kg. Birth weight was recorded for 1,941 babies, and anthropometric measurements in the first year for 1,070, on which most of our analyses are based.

Socioeconomic information, behavioral measures, feeding practices, and physical activity were evaluated using a structured questionnaire applied by interviewers when the participants were aged between 22 and 28 years. In adulthood, anthropometric measures were assessed, by trained nutritionists. Subjects were weighed and measured with light clothing, according to standardized techniques. Blood sample was obtained following a 12-h fast. HDL cholesterol was measured by precipitation according to the technique of Seigler and Wu and triglycerides were measured using enzymatic methods (HUMAN factor, Gesellschaft für Biochemica und Diagnostica, Germany). An enzymatic colorimetric method (GOD/PAP, Human Diagnostic, Germany) was used to record blood glucose levels. Additionally, insulin was determined by radioimmunoassay to calculate insulin resistance using the HOMA-IR.


Glycemia (mg/dL), fasting insulin (µU/mL), HOMA-IR, and SPISE were evaluated in adulthood, at a mean age of 24.7 years.

The following formulas were used for HOMA-IR and SPISE:

Eq.: 1


Eq.: 2


Insulin resistance was defined by an HOMA-IR score higher than 2.6 and SPISE lower than 6.61 8,31. HOMA and SPISE were analyzed as continuous outcome too.

Nutritional status

Using the WHO Child Growth Standards, 200634, we estimated length-for-age (LAZ), weight-for-length (WLZ), weight-for-age and birthweight (WAZ) z-scores. WHO Anthro version 3.2.2 (https://www.who.int/tools/child-growth-standards/software) was used in processing anthropometric data; 0.25% of measurements were excluded from the analyses, after being flagged as outliers according to the valid ranges accepted by WHO (WAZ < -6 or > 5; LAZ < -6 or > 6; WLZ < -5 or > 5 z-scores).

Infants with a LAZ 2 standard deviation (SD) or more below the World Health Organization (WHO) reference were classified as stunted and < -3 SD as severely stunted. Infants with a WAZ 2 SD or more below the WHO reference were classified as underweight, and severe underweight as a z score < -3 SD. Those with WLZ > 2 SD above WHO reference were classified as overweight and > 3 SD as obese. A child whose WLZ was less than -2 SD was deemed wasted and at risk of wasting with a WLZ < -1 SD 35,36.

Conditional growth

Conditional variables were obtained by regressing weight or length on birthweight and earlier measures of weight and length, and standardized residuals were derived. At each time point, the conditional variable represents growth during a time interval, and a positive value represents a weight gain or linear growth faster than predicted in that period. To estimate the conditional, length at a given age, length was regressed on previous weight and length. Therefore, conditional length at 12 months of age was estimated by regressing LAZ z-scores in this same period on birthweight. On the other hand, conditional relative weight gain was estimated from length at that age and previous measures of length and weight. Therefore, conditional relative weight was derived by regressing weight and length at 12 months of age on birthweight.

Confounding variables

Socioeconomic level was defined on schooling and occupation of the main home breadwinner, generating 6 socioeconomic groups. This classification was based on social stratification matrix method proposed by the European Society for Opinion and Marketing Research (ESOMAR), validated in Chile 37. In this study, due to the lack of people in the two upper categories, we established four categories: (i) very low; (ii) low; (iii) middle and (iv) high.

Information on birthweight in grams and gestational age in weeks were retrieved from health records. Birth cohort was defined as based on the year of birth.

Data analyses

Descriptive analyses included calculation of means and standard deviations for quantitative variables and proportions and 95%CI for categorical outcomes.

Multiple linear regression was used to adjust the estimates for the following confounders: sex, socioeconomic level, birthweight, and gestational age. We tested whether birth cohort had an association with nutritional status and conditional growth during the first year and at the same time with adult glycemia and insulin resistance. The associations with conditional length, LAZ, and WAZ were significant (p = 0.045, 0.004, and 0.012 respectively). Preliminary analysis showed that the additional associations tested, including WLZ, conditional relative weight, Glycemia, fasting insulin, HOMA-IR, and SPISE were not significant (data not shown). Therefore, we considered birth cohort as a confounder variable in the adjusted analyses.

Furthermore, for conditional length gain from birth to 6 months of age, estimates were also adjusted for birthweight z-score and length at birth z-score. On the other hand, both conditional relative weight gain and conditional length gain were controlled from birth to 6 months of age. For conditional length gain from 6 to 12 months of age, estimates were also adjusted for LAZ and WAZ at 6 months, length at birth and birthweight z-score and for relative weight gain also from 6 to 12 months of age.

Statistical significance level was defined by a p-value (α) of < 0.05. Data analysis was performed using Stata version 13.0 (https://www.stata.com/).

Ethical aspects

Young adults were invited to participate in the study and asked to sign the informed consent form prior to data collection. The study protocol was approved by the Ethics Research Committee of Faculty of Medicine of University of Chile and carried out by the Department of Nutrition, with the support of Wellcome Trust and the National Research Fund of Chile (Fondecyt). All surveys used a similar methodology 33.


In the original cohorts, 2,232 subjects were interviewed. Information on blood sample at 22-28 years of age was available for 1,941 subjects (87% of the original sample). We were able to get information on weight and length from birth to 12 months from 1,070 individuals (47.9% of the original cohort). Supplementary Material (http://cadernos.ensp.fiocruz.br/static//arquivo/suppl-e00120320_4316.pdf) shows that those subjects who were included in the current analysis were similar to those who were excluded, according to anthropometry at birth, anthropometric measurements in adulthood, and biochemical parameters.

The mean age at the time of assessment in adulthood was 24.7 years. Table 1 shows characteristics of the studied subjects according to sex. For males, mean birth weight was 3,230g, whereas for women was 3,170g (p < 0.0001), mean length at birth was 49.6cm and 49.3cm for men and women (p < 0.0001), respectively. Prevalence of insulin resistance according to HOMA-IR was 36.6% (95%CI: 34.4; 38.9) and SPISE was 54.8% (95%CI: 51.8; 57.9). Mean SPISE was 6.46 (SD = 1.83), fasting glycemia 85.7mg/dL (SD = 9.50), and fasting insulin 11.49µU/mL (SD = 4.65), with differences between men and women (p < 0.0001). Prevalence of prediabetes (glycemia fasting level ≥ 100mg/dL and < 126mg/dL) was 4.5% and 0.3% of diabetes (glycemia fasting level ≥ 126mg/dL), higher in males (p = 0.008).



Tab.: 1
Table 1 Characteristics of the study subjects.


Table 2 shows that those subjects who were stunted at 12 months were more likely to have higher glycemia, but this relation was no longer statistically significant (p = 0.11) after the adjustment for sex, cohort of birth, gestational age, birthweight and socioeconomic level. SPISE was higher among underweight subjects (β = 0.82; 95%CI: 0.27; 1.37), wasting and at risk of wasting was positively associated with glycemia (β = 2.67; 95%CI: 0.19; 5.14). Independent of birth weight and the other covariates (sex, socio-economic level, cohort, and gestational age), higher WLZ at 12 months was associated with lower glycemia (β = -2.39; 95%CI: -4.32; -0.36).



Tab.: 2
Table 2 Coefficients from multiple linear regression relating glucidic metabolism and insulin resistance in young adults according to nutritional status at 12 months (n = 1,070).


Table 3 shows that conditional relative weight gain in the first year was negatively associated with glycemia in adulthood (β = -0.65; 95%CI: -1.23; -0.08), and this was mainly due to weight gain in the first months of life. For instance, higher weight accretion from 0 to 6 months was associated with a lower glycemia (β = -0.83; 95%CI: -1.44; -0.22), meanwhile from 6 to 12 months weight accretion was not statistically associated with glycemia (β = -0.07; 95%CI: -0.68; 0.55). Negative associations were also observed for conditional relative weight gain at 6 and 12 months of life with SPISE. Conditional linear growth at 6 months was negatively related to SPISE. There was no association of relative conditional weight gain and conditional linear growth during the first year with other results related to the metabolism of carbohydrate and IR, such as the level of fasting insulin, HOMA-IR index, prediabetes, and diabetes.



Tab.: 3
Table 3 Coefficients from multiple linear regression, relating glucidic metabolism and insulin resistance in young adults according to conditional linear growth and conditional relative weight gain (z-score) during first year of life (n = 1,070).



Wasting individuals and at risk of wasting at their first year was positively associated with glycemia, on the other hand, overweight and obesity was only negatively associated with glycemia. Independent of birth weight, relative weight gain from 0 to 12 months was negatively associated with glycemia and SPISE.

We studied more than 1,000 adults who were born at a time of rapid nutritional transition. This study confirms the low prevalence of impaired fasting glucose (IFG) and type 2 diabetes mellitus among young adults. On the other hand, the prevalence of insulin resistance was 36.6% and mean HOMA-IR was 2.5. The study highlighted the high prevalence of insulin resistance measured by SPISE. This suggests that the components of each index influence the insulin resistance prediction. We believe that SPISE has partial low values because of blood lipids profile and body mass index (BMI) effects, consistent with prior analysis in these cohort 32,38. If more studies were replicated with these two indices, it would greatly increase our knowledge about the best mechanism to diagnose insulin resistance in the population. Also, it would provide further knowledge for directing interventions to prevent type 2 diabetes mellitus, with an emphasis on those who are in the first stage of the disease.

This study results demonstrated that early life adversity has a consistent and significant effect on later development of risk factors associated with type 2 diabetes mellitus. Individuals stunted in the first year showed higher glycemia, but after controlling for confounders, the magnitude of the association decreased and the confidence interval slightly included the reference. Therefore, we cannot discard that this association was due to the random. Our findings are remarkably similar to those of Reid et al. 39, who observed that stunted individuals at early years have higher risk of metabolic disorders in adolescence (total cholesterol, low-density lipoprotein cholesterol, triglycerides, insulin, and HOMA-IR).

Young individuals exposed to wasting and at risk of wasting at one year, demonstrated higher glycemia. Notably, this association was independent of birth weight, sex, gestational age, and current socioeconomic level. These results suggest that early life malnutrition may increase the risk of carbohydrate metabolism-related disorders.

On other hand, underweight at first year of life was associated with higher mean of SPISE levels, regardless of sex, socioeconomic level, birth cohort, gestational age, and birthweight. Other studies suggested that individuals who are small in the first years of life, present greater risk for several metabolic conditions as impaired glucose tolerance 40,41. This aspect highlights the need for more studies focusing on SPISE and other tests for insulin resistance which are based on lipoprotein metabolism and nutritional status. For example, SPISE was applied only in studies of European countries, and it may be inappropriate for other populations 8. We cannot rule out the possibility of unfitness of SPISE to evaluate insulin resistance in this population.

Overweight children at one year presented lower glycemia in the adulthood, but they did not present lower insulin, HOMA-IR nor higher SPISE. Previous studies on the association of overweight at childhood with glycemia, insulin, and insulin resistance have shown mixed results 1,42,43. There have been few studies of adults with a history of overweight at first year that report disorders of carbohydrate metabolism, and the results of these studies have shown either the same relationship or no relationship, when compared with our study 40,44.

In this cohort study, changes in WAZ were strongly associated with glycemia at the age of 22-28 years, suggesting that the risk for high glycemia may be associated with poor relative weight gain in the first year. Furthermore, this study showed that weight gain during the first, but not the second 6 months of life was related with lower glycemia, a condition usually associated with lower risk of type 2 diabetes mellitus.

Notably, we were unable to find in the published literature the association of weight gain in the first 6 months of life or between 0 and 12 months with lower SPISE at adult age. Suggesting that early childhood relative weight gain is associated with increased insulin resistance measured by SPISE. Based on these contradictory results, it is unlikely that relative weight gain fully explains the association, which, in fact, SPISE values are influenced by currently nutritional status and blood lipids. One may speculate that the rapid weight gain in the first year may somehow contribute to adverse lipid profile and BMI in adulthood altering the expected association with insulin resistance when we use SPISE.

Studies on the epidemiology of nutritional status have focused much more attention on weight than on height, however, when it is alone, the height has been associated with several outcomes 45. The results of our study support the hypothesis that growth in childhood would produce hepatic metabolism adjustment, demonstrated by the negative association of conditional linear growth with SPISE, which may be a marker linking early growth with adult determinant of type 2 diabetes mellitus.

The main strength of our study is the availability of data from two population-based birth cohorts, in the same location, born in different decades and periods of epidemiological transition. Thus far, there are few studies in countries with accelerating nutritional transition that have examined the relative significance of birth weight, nutritional status, and successive periods of weight and length gain in early life on markers of type 2 diabetes mellitus in young adults.

Nonetheless, some limitations should be considered; (1) the small sample size may have reduced statistical power to detect the minimum mean and regression coefficients differences of outcome variables; (2) we cannot rule out the possibility of selection bias due to missing data; (3) our main results are restricted to subjects with complete information on growth measurements at birth, at first year and at 22-28 years of age, further measurements between these ages were not available in our study, and therefore we had limited ability to determine associations with specific age periods of growth, such as the BMI rebound and weight gain during adolescence; (4) we assumed that health records were the gold standard for birthweight and weight and length in the first year, but these record might have some errors. However, in a Colombian study, which aimed to examine the validity of child birthweight recall by mothers, only 33% of mothers recalled their children's birthweights exactly as they appeared in hospital records 46. Although, apparently, health records are a better option than recall, and it should be used with caution in epidemiological studies because there is a significant gap between the data that are captured during routine clinical care and the structured data needed for secondary analyses 47.

To sum up, we observed normal distributions of glycemia and prevalence of IFG and type 2 diabetes mellitus, but high insulin resistance, a consistent association with underweight, overweight, and conditional relative weight gain. In conclusion, interventions to improve the growth in children with stunting, at risk of wasting or with slow weight gain during the first year can prevent the onset of high level of glycemia in a large proportion of young adults. Based on the perspective of public health, it is important not only to consider nutritional status at one time but also to apply complementary methods to analyze growth trajectories, considering the strong correlation between repeated measurements over time and determinants of type 2 diabetes mellitus. The findings about SPISE suggest that more research is needed to understand the role of early nutritional status and growth on levels of this indicator, this possibility would be tested by analyses of cohorts addressing outcomes related to body composition, glucose concentrations, and lipid profiles.


1.   Bacha F, Saad R, Gungor N, Janosky J, Arslanian SA. Obesity, regional fat distribution, and syndrome X in obese black versus white adolescents: race differential in diabetogenic and atherogenic risk factors. J Clin Endocrinol Metab 2003; 88:2534-40.
2.   Gungor N, Bacha F, Saad R, Janosky J, Arslanian S. Youth type 2 diabetes. Diabetes Care 2005; 28:638-44.
3.   Nolan CJ, Prentki M. Insulin resistance and insulin hypersecretion in the metabolic syndrome and type 2 diabetes: time for a conceptual framework shift. Diabetes Vasc Dis Res 2019; 16:118-27.
4.   Arslanian S, Bacha F, Saad R, Gungor N. Family history of type 2 diabetes is sensitivity and an impaired balance between insulin sensitivity and insulin secretion in white youth. Diabetes Care 2005; 28:127-30.
5.   Nolan CJ, Damm P, Prentki M. Type 2 diabetes across generations: from pathophysiology to prevention and management. Lancet 2011; 378:169-81.
6.   Van Der A MP, Knibbe CAJ, Boer A, Van Der Vorst MMJ. Definition of insulin resistance affects prevalence rate in pediatric patients: a systematic review and call for consensus. J Pediatr Endocrinol Metab 2017; 30:123-31.
7.   Timoteo AT, Miranda F, Carmo MM, Ferreira RC. Optimal cut-off value for homeostasis model assessment (HOMA) index of insulin-resistance in a population of patients admitted electively in a Portuguese cardiology ward. Acta Med Port 2014; 27:473-9.
8.   Paulmichl K, Hatunic M, Højlund K, Jotic A, Krebs M, Mitrakou A, et al. Modification and validation of the triglyceride-to-HDL cholesterol ratio as a surrogate of insulin sensitivity in white juveniles and adults without diabetes mellitus: the single point insulin sensitivity estimator (SPISE). Clin Chem 2016; 62:1211-9.
9.   Langley-Evans SC. Developmental programming of health and disease. Proc Nutr Soc 2006; 65:97-105.
10.   Gillman MW, Davey-Smith G, Whincup PH, Owen CG, Cook DG, Martin RM. The effect of breastfeeding on mean body mass index throughout life: a quantitative review of published and unpublished observational evidence. Am J Clin Nutr 2018; 82:1298-307.
11.   Horta PM, Verly Junior E, Santos LC. Usual diet quality among 8- to 12-year-old Brazilian children. Cad Saúde Pública 2019; 35:e00044418.
12.   Horta BL, Victora CG. Long-term health effects of breastfeeding: a systematic review. Geneva: Wold Health Organization; 2013.
13.   Carolan-Olah M, Duarte-Gardea M, Lechuga J. A critical review: early life nutrition and prenatal programming for adult disease. J Clin Nurs 2015; 24:3716-29.
14.   Berends LM, Fernandez-Twinn DS, Martin-Gronert MS, Cripps RL, Ozanne SE. Catch-up growth following intra-uterine growth-restriction programmes an insulin-resistant phenotype in adipose tissue. Int J Obes 2013; 37:1051-7.
15.   Henriksen T, Clausen T. The fetal origins hypothesis: placental insufficiency and inheritance versus maternal malnutrition in well-nourished populations. Acta Obstet Gynecol Scand 2002; 81:112-4.
16.   Karlberg K, Luo ZC. Foetal size to final height. Acta Paediatr 2000; 89:632-6.
17.   Hochberg Z. Evolutionary perspective in child growth. Rambam Maimonides Med J 2011; 2:e0057.
18.   Hanson MA, Gluckman PD. Early developmental conditioning of later health and disease: physiology or pathophysiology? Physiol Rev 2014; 94:1027-76.
19.   Brown LD. Future metabolic health. J Endocrinol 2014; 221:R13-29.
20.   Kajantie E, Barker DJP, Osmond C, Forsén T, Eriksson JG. Growth before 2 years of age and serum lipids 60 years later: the Helsinki Birth Cohort Study. Int J Epidemiol 2008; 37:280-9.
21.   Eriksson JG, Forsén T, Tuomilehto J, Osmond C, Barker DJP. Early adiposity rebound in childhood and risk of type 2 diabetes in adult life. Diabetologia 2003; 46:190-4.
22.   Langley-Evans SC, McMullen S. Developmental origins of adult disease. Med Princ Pract 2010; 19:87-98.
23.   Martorell R, Horta BL, Adair LS, Stein AD, Richter L, Fall CHD, et al. Weight gain in the first two years of life is an important predictor of schooling outcomes in pooled analyses from five birth cohorts from low- and middle-income countries. J Nutr 2010; 140:348-54.
24.   Stettler N, Iotova V. Early growth patterns and long-term obesity risk. Curr Opin Clin Nutr Metab Care 2010; 13:294-9.
25.   França GVA, Rolfe EL, Horta BL, Gigante DP, Yudkin JS, Ong KK, et al. Associations of birth weight, linear growth and relative weight gain throughout life with abdominal fat depots in adulthood: the 1982 Pelotas (Brazil) birth cohort study. Int J Obes (Lond) 2016; 40:14-21.
26.   Norris SA, Osmond C, Gigante D, Kuzawa CW, Ramakrishnan L, Lee NR, et al. Size at birth, weight gain in infancy and childhood, and adult diabetes risk in five low- or middle-income country birth cohorts. Diabetes Care 2012; 35:72-9.
27.   Adair LS, Fall CHD, Osmond C, Stein AD, Martorell R, Ramirez-Zea M, et al. Associations of linear growth and relative weight gain during early life with adult health and human capital in countries of low and middle income: findings from five birth cohort studies. Lancet 2013; 382:525-34.
28.   Prioreschi A, Munthali RJ, Kagura J, Said-Mohamed R, Rolfe EDL, Micklesfield LK, et al. The associations between adult body composition and abdominal adiposity outcomes, and relative weight gain and linear growth from birth to age 22 in the Birth to Twenty Plus cohort, South Africa. PLoS One 2018; 13:e0190483.
29.   Tu YK, Ellison GTH, Gilthorpe MS. Growth, current size and the role of the "reversal paradox" in the foetal origins of adult disease: an illustration using vector geometry. Epidemiol Perspect Innov 2006; 3:9.
30.   Eriksson JG. Early growth and coronary heart disease and type 2 diabetes : findings from the Helsinki Birth Cohort Study (HBCS). Am J Clin Nutr 2011; 94(6 Suppl):1799S-802S.
31.   Programa de las Naciones Unidas para el Desarrollo. Desarrollo humano en Chile. Santiago de Chile: Programa de las Naciones Unidas para el Desarrollo; 2006.
32.   Bustos P, Silva AAM, Amigo H, Bettiol H, Barbieri MA. Metabolic syndrome in young adults from two socioeconomic Latin American settings. Nutr Metab Cardiovasc Dis 2007; 17:581-9.
33.   Amigo H, Bustos P, Zumelzú E, Rona RJ. Cohort Profile: The Limache, Chile, birth cohort study. Int J Epidemiol 2014; 43:1031-9.
34.   WHO Multicentre Growth Reference Study Group. WHO Child Growth Standards: length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age. Methods and development. Geneva: World Health Organization; 2006.
35.   WHO Multicentre Growth Reference Study Group. WHO Child Growth Standards based on length/height, weight and age. Acta Paediatr Suppl 2006; 450:76-85
36.   Caulfield LE, Richard SA, Rivera JA, Musgrove P, Black RE. Stunting, wasting, and micronutrient deficiency disorders. disease control priorities in developing countries. Washington DC: World Bank; 2006.
37.   Adimark S. El nivel socioeconómico ESOMAR: manual de aplicación. http://www.microweb.cl/idm/documentos/ESOMAR.pdf (accessed on 13/Jun/2019).
38.   Amigo H, Bustos P, Alvarado ME, Barbieri M, Bettiol H, Silva AAM, et al. Size at birth and lipoprotein concentrations in adulthood: two prospective studies in Latin American cities. J Epidemiol Community Health 2010; 64:855-9.
39.   Reid B, Harbin M, Arend J, Kelly A, Dengel D, Gunnar M. Early life adversity with height stunting is associated with cardiometabolic risk in adolescents independent of body mass index. J Pediatr 2018; 202:143-9.
40.   Buffarini R, Restrepo-Méndez MC, Silveira VM, Miranda JJ, Gonçalves HD, Oliveira IO, et al. Distribution of glycated haemoglobin according to early-life and contemporary characteristics in adolescents and adults without diabetes: the 1982 and 1993 Pelotas birth cohorts. PLoS One 2016; 11:e0162614.
41.   Kopec G, Shekhawat PS, Mhanna MJ. Prevalence of diabetes and obesity in association with prematurity and growth restriction. Diabetes Metab Syndr Obes 2017; 10:285-95.
42.   Koskinen J, Magnussen CG, Sinaiko A, Woo J, Urbina E, Jacobs DR, et al. Childhood age and associations between childhood metabolic syndrome and adult risk for metabolic syndrome, type 2 diabetes mellitus and carotid intima media thickness: the International Childhood Cardiovascular Cohort Consortium. J Am Heart Assoc 2017; 6:e005632.
43.   Cho E-H, Min JW, Choi SS, Choi HS, Kim S-W. Identification of maturity-onset diabetes of the young caused by glucokinase mutations detected using whole-exome sequencing. Endocrinol Metab (Seoul) 2017; 32:296-301.
44.   Bjerregaard L, Jensen B, Angquist L, Osler M, Sorensen T, Baker J. Change in overweight from childhood to early adulthood and risk of type 2 diabetes. N Engl J Med 2018; 378:1302-12.
45.   Araújo CLP, Hallal PC, Nader GA, Menezes AMB, Victora CG. Size at birth and height in early adolescence: a prospective birth cohort study. Cad Saúde Pública 2008; 24:871-8.
46.   Boeke CE, Marín C, Oliveros H, Mora-Plazas M, Agudelo-Cañas S, Villamor E. Validity of maternal birthweight recall among colombian children. Matern Child Health J 2012; 16:753-9.
47.   Soto M, Capurro D, Catalán S. Evaluating the data completeness in the electronic health record after the implementation of an outpatient electronic health record. Stud Health Technol Inform 2015; 216:885.

This is an open-access article distributed under the terms of the Creative Commons Attribution License


Cadernos de Saúde Pública | Reports in Public Health

Rua Leopoldo Bulhões 1480 - Rio de Janeiro RJ 21041-210 Brasil

Secretaria Editorial +55 21 2598-2511.

  • APOIO:

©2015 | Cadernos de Saúde Pública - Escola Nacional de Saúde Pública Sergio Arouca | Fundação Oswaldo Cruz. - Ministério da Saúde Governo Federal | Desenvolvido por Riocom Design