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BMI trajectories from birth to young adulthood associate with distinct cardiometabolic profiles

BMC Medicine volume 22, Article number: 510 (2024) Cite this article

Abstract

Background

Numerous studies have investigated links between body mass index (BMI) trajectories and cardiovascular risk, yet discrepancies in BMI measurement duration and timing of the cardiovascular-related outcome evaluation have led to inconsistent findings.

Methods

We included participants from the Swedish birth cohort (BAMSE) and applied latent class mixture modeling to identify BMI trajectories using data of multiple BMI measures (≥ 4 times) from birth until 24-year follow-up (n = 3204). Subsequently, we analyzed the associations of BMI trajectories with lipids (n = 1974), blood pressure (n = 2022), HbA1c (n = 941), and blood leukocytes (n = 1973) using linear regression. We also investigated the circulating levels of 92 inflammation-related proteins (n = 1866) across BMI trajectories.

Results

Six distinct BMI groups were identified, denoted as increasing—persistent high (n = 74; 2.3%), high—accelerated increasing (n = 209; 6.5%), increasing—accelerated resolving (n = 142; 4.4%), normal—above normal (n = 721; 22.5%), stable normal (n = 1608; 50.2%), and decreasing—persistent low (n = 450; 14.1%) BMI groups. The increasing—persistent high and high—accelerated increasing BMI groups had higher levels of total cholesterol [mean difference (95% confidence intervals): 0.30 (0.04–0.56) and 0.16 (0.02–0.31) mmol/L], triglyceride, low-density lipoprotein, hemoglobin A1C [3.61 (2.17–5.54) and 1.18 (0.40–1.98) mmol/mol], and low-density lipoprotein/high-density lipoprotein ratios, but a lower level of high-density lipoprotein than the stable normal BMI group. These two groups also had higher leukocyte cell counts and higher circulating levels of 28 inflammation-related proteins. No increased cardiometabolic markers were observed in the increasing—accelerated resolving BMI group.

Conclusions

Participants with persistently high or accelerated increasing BMI trajectories from birth to young adulthood have elevated levels of cardiometabolic risk markers at young adulthood than those with stable normal BMI. However, a raised BMI in childhood may not be inherently harmful to cardiometabolic health, provided it does not persist into adulthood.

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Background

Obesity has become more prevalent in the past decades [1] and is a strong risk factor for cardiovascular diseases (CVD) [2]. Obesity is considered a state of low-grade chronic inflammation [3] and is associated with insulin resistance [2]. Indeed, obesity may accelerate the atherosclerotic process, that is generally initiated in early life, through these mechanisms [2]. Moreover, excess adiposity can activate the renin–angiotensin–aldosterone and sympathetic nervous systems, contributing to high blood pressure and dyslipidemia [2, 4] Obesity can also result in myocardial fat accumulation and fibrosis, in turn leading to left ventricular remodeling and subsequent left ventricular dysfunction, atrial fibrillation, and heart failure [2].

Body mass index (BMI) is a commonly used measurement to assess adiposity in routine clinical practice and research. Longitudinal information on BMI can capture dynamic shifts over time, such as the age of obesity onset and its progression. Numerous studies have found that certain BMI trajectories are associated with CVD and cardiovascular risk factors [5,6,7,8]. Particularly, some cohort studies have focused on BMI trajectories starting from childhood and the association with cardiometabolic risk in later life [9,10,11,12,13,14,15,16].

However, birth weight and BMI development in early life are associated with subsequent CVD [15]. Thus, BMI trajectories from birth are presumably sensitive to identifying individuals with a high risk of CVD in later life. Although several previous studies have attempted to assess BMI trajectories from birth [9,10,11,12,13], outcomes were typically assessed in early adolescence when cardiometabolic profiles often have not yet exhibited significant differences. In addition, most prior studies had limited sample sizes, which were not powered enough to identify specific BMI trajectories and their associations with subsequent outcomes. Therefore, there is a clear need for studying BMI trajectories from birth throughout childhood and their association with cardiovascular risk in a large longitudinal cohort with available information throughout childhood, starting from birth.

To this end, we conducted a longitudinal cohort with multiple BMI measurements from birth and a range of cardiometabolic profiles assessed at early adulthood to investigate BMI trajectories from birth and the association with cardiometabolic risk. In addition, we performed a comprehensive literature review on published studies to date that applied latent class group modeling or equivalents to identify BMI trajectories and assessed associations with cardiovascular risk in early or middle adulthood.

Methods

Study population

The Swedish population-based birth BAMSE (Swedish abbreviation for Child [Barn], Allergy, Milieu, Stockholm, Epidemiological) cohort recruited 4089 infants from inner-city, urban, and suburban districts of Stockholm, Sweden, between February 1994 and November 1996 and followed them from birth until 26 years (with questionnaires and/or clinical visit follow-up at 1, 2, 4, 8, 12, 16, 24, and 26 years) [17]. The study was approved by the Regional Ethical Review Board in Stockholm (Ref 2016/1380–31/2). The parents and participants signed their informed consent, under the Helsinki Declaration.

Assessment of body mass index

We retrieved information on birth weight and length from the Swedish Medical Birth Register and collected weight and height from school and healthcare records at ages around 6 (± 2 weeks), 12, and 18 months (± 4 weeks), as well as 2, 3, 4, 5 (± 6 months), 7, 10, and 12 years (− 6 to + 11 months) [18]. Moreover, clinical assessments, including BMI, were carried out at around age 4, 8, 16, and 24 years following standardized protocols at each visit by trained nurses during the follow-up. Furthermore, self-reported weight and height data were also collected at recruitment and ages 12, 16, and 24 years. The hierarchy for utilizing height and weight information prioritized data from clinical investigations and the Swedish Medical Birth Register, followed by school and healthcare records, with self-reported data being considered last (Additional file 1: Table S1).

BMI was calculated as weight in kilograms divided by the square of height in meters (kg/m2). Subsequently, it was standardized into z-scores employing the World Health Organization child growth standards for ages 0–5 years [19] and growth reference data for those aged 5–19 years [20]. At the age of 24 years, BMI was transformed into sex-specific BMI z-scores based on the observed values within our cohort. We only included participants with at least four BMI measurements in this analysis, leading to a total of 3204 participants (Additional file 2: Fig. S1) with a mean of 10.5 BMI measurements (interquartile range: 8–13). Besides, we conducted a sensitivity analysis to compare BMI differences between participants with and without BMI data at 24 years of age. Additionally, bioimpedance measurements were conducted using the Tanita MC 780 body composition monitor (Tanita Corp., Tokyo, Japan) at the clinical follow-up of age 24 and 26 years, following the manufacturer’s guidelines (n = 1958 and n = 931, respectively). We derived fat mass index (FMI) and fat-free mass index (FFMI), calculated as masses in kilograms divided by the square of height in meters (kg/m2).

Assessment of cardiometabolic profiles

Resting systolic and diastolic blood pressure was measured at 24 years of age (n = 2022) by trained nurses using standardized protocols and an automatic blood pressure meter (Omron HBP–1300, Omron Electronics, Kyoto, Japan). Blood pressure was assessed three times for each participant, with a 1-min pause between measurements. The analysis incorporated the mean blood pressure derived from all three measurements for each participant.

Blood samples collected at the clinical follow-up at age 24 years were used to quantify blood lipid levels by Karolinska University Laboratory (Stockholm, Sweden), including triglyceride (TG), total cholesterol, low-density lipoprotein (LDL), and high-density lipoprotein (HDL) (n = 1974). The levels of 92 inflammation-related proteins (Additional file 1: Table S2) were analyzed in ethylenediaminetetraacetic acid (EDTA) plasma samples from 1866 participants included in the current study (Inflammation Panel version 95,302, Olink Proteomics, Uppsala, Sweden). Details of the protein measurement have been described previously [17]. Additionally, we also assessed hemoglobin A1C (HbA1c) levels in blood among 941 participants at the clinical follow-up of age 26 years at Karolinska University Laboratory (Stockholm, Sweden). In addition, clinical-related thresholds were utilized to delineate heightened cardiometabolic risks (details in Additional file 3) [21,22,23].

Statistical analyses

Latent BMI trajectories identification

Latent class mixture modeling (LCMM) was employed to investigate the longitudinal progression of BMI z-scores from birth to age 24 years. Several criteria, such as the mean absolute error loss, the Bayesian Information Criterion (BIC), log-likelihood, and the values of mean posterior class membership probabilities, and clinical plausibility were used to select the BMI trajectories. Additional file 3 offers full details regarding the methodologies employed for model construction and the determination of the optimal number of trajectories [24, 25].

Associations of BMI z-scores trajectories with cardiometabolic profiles

Covariates were compared between different BMI z-scores trajectories using t-test/ANOVA, Kruskal–Wallis rank sum test, chi-squared tests, and Fisher’s exact test as appropriate. We examined the associations of BMI trajectories with blood pressure, blood lipid, leukocytes, and HbA1c using multivariable linear regression models (details in Additional file 3) [26]. Besides, logistic regression was used to examine the association of BMI trajectories with any cardiometabolic risk. In addition, we performed a stratified analysis by sex to assess the differences in the associations between males and females. Sensitivity analyses were conducted for cardiometabolic profiles in early adulthood, incorporating BMI z-scores at birth, BMI z-scores at the 24 years, or the fat mass index into the regression models, respectively.

Protein concentrations were normalized based on inverse normal transformation [27]. We first compared plasma inflammation-related protein levels across BMI trajectories using ANOVA. We then employed multivariable linear regression to analyze the associations of BMI trajectories with proteins with significant associations with BMI trajectories (p < 0.05) (Additional file 1: Table S3). Multiple comparisons were corrected by applying Benjamini-Hochberg’s method [28]. We examined the protein associated with cardiometabolic profiles for their expression patterns in tissues and cells [29] by using the Human Protein Atlas (https://www.proteinatlas.org).

All the analyses were performed using R 4.2.2.

Results

BMI trajectories from birth to young adulthood

Participants included in the BMI trajectory analysis showed, compared to those excluded, higher parental education levels, less maternal smoking during pregnancy, older maternal delivery age, lower parity before the index person was born, and more exclusive breast feeding (Additional file 1: Table S4). To explore potential BMI trajectories in BAMSE, we initially compared models with various terms using mean absolute error loss from cross-validation. We found that LCMM models with quadratic terms showed greater predictive accuracy compared to linear ones, functioning similarly to those with cubic terms (Additional file 1: Table S5). Subsequently, we identified distinct BMI trajectories based on criteria such as BIC, log-likelihood, mean posterior class membership probabilities > 0.7, and clinical plausibility (Additional file 1: Table S6). Consequently, our analysis revealed six trajectories, which included (1) the increasing—persistent high BMI group (n = 74 [2.3%])—BMI z-score was normal in early life, increased during childhood, and kept high in adolescence; (2) the high—accelerated increasing BMI group (n = 209 [6.5%])—BMI z-score was high at early life, kept high during childhood, and increased during adolescence; (3) the increasing—accelerated resolving BMI group (n = 142 [4.4%])—BMI z-score was low at early life, increased sharply during childhood, but decreased in adolescence; (4) the normal—above normal BMI group (n = 721 [22.5%])—BMI was normal at the early life and increased above the average level but within the normal range throughout childhood and adolescence; (5) the stable normal BMI group (n = 1608 [50.2%])—BMI z-score was consistently normal from birth until young adulthood; and (6) the decreasing—persistent low BMI group (n = 450 [14.1%])—BMI z-score was normal at the early life and decreasing to lower than average and keep low in adolescence (Fig. 1 for BMI z-scores, Additional file 2: Fig. S2 for raw BMI values, and Additional file 1: Table S7 for statistics). In the sensitivity analysis, participants with BMI data at 24 years showed lower BMI z-scores at specific time points compared to those without BMI data at 24 years. These differences were observed at 12 and 16 years in the increasing—persistent high BMI group; at 6 months, 1 year, 12 years, and 16 years in the high—accelerated increasing BMI group; and at 6 months, 1 year, and 16 years in the normal—above normal BMI group (Additional file 2: Fig. S3). Similar patterns of such differences were observed among BMI values (Additional file 2: Fig. S4).

Fig. 1
figure 1

Body mass index trajectories from birth to young adulthood in the BAMSE cohort. Body mass index trajectories were identified through latent class mixture models using z-score of body mass index. The dots show the mean values for body mass index z-scores around each follow-up point. The lines show the loess-smoothed body mass index trajectories for the six identified trajectory groups. The statistics (mean and standard deviation of BMI in each group) of this figure are presented in Table S7. BMI, body mass index

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Associations of BMI trajectories with cardiometabolic profiles in young adulthood

Higher proportion of males was observed in the increasing—accelerated resolving BMI group and the normal—above normal BMI group than in the stable normal BMI group. Participants in the increasing—persistent high, increasing—accelerated resolving, and normal—above normal BMI groups were more likely to be born prematurely, by cesarean section, and to mothers with hypertension during pregnancy than those in the stable normal BMI group. Participants in the decreasing—persistent low BMI group were more likely to be born to mothers with low BMI at early pregnancy than those in the stable normal BMI group, whereas the other groups had mothers with higher BMI at early pregnancy. Additionally, participants in the high—accelerated increasing, increasing—accelerated resolving, and normal—above normal BMI groups were more likely to be born to mothers who smoked during early pregnancy (Table 1). Similar patterns of such differences between BMI subgroups were observed among males and females (Additional file 1: Tables S8 and S9). Participants in the increasing—persistent high, high—accelerated increasing, and increasing—accelerated resolving BMI group had higher FMI and FFMI in young adulthood than those in the stable normal group (Additional file 1: Table S8). In contrast, participants in the decreasing—persistent low BMI group had lower levels of these indicators than the stable normal BMI group. Similar patterns were observed when males and females were analyzed separately (Additional file 1: Table S10).

Table 1 Characteristics of study participants according to body mass index trajectories
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Participants in the increasing—persistent high or high—accelerated increasing BMI groups had higher levels of total cholesterol [mean difference (MD) (95% confidence intervals (CI)): 0.30 (0.04–0.56) and 0.16 (0.02–0.31) mmol/L], TG [0.48 (0.25–0.76) and 0.32 (0.21–0.45) mmol/L], LDL [0.29 (0.05–0.53) and 0.31 (0.18–0.45) mmol/L], LDL/HDL ratio [0.48 (0.23–0.74) and 0.61 (0.46–0.75)], TG/HDL ratio [0.51 (0.30–0.79) and 0.42 (0.31–0.56)], and HbA1c [3.61 (2.17–5.54) and 1.18 (0.40–1.98) mmol/mol] but a lower level of HDL [− 0.30 (− 0.39 to − 0.21) and − 0.31 (− 0.37 to − 0.26) mmol/L] than those in the stable normal BMI group (Fig. 2 and Additional file 1: Table S11). Besides, there is decreased TG [− 0.14 (− 0.22, − 0.03) mmol/L] in the participants with increasing—accelerated resolving BMI group. There was no significant difference in these clinical chemistry measurements comparing the normal—above normal and the decreasing—persistent low BMI groups with the stable normal BMI group. Compared to the stable normal BMI group, the increasing—persistent high BMI group had higher diastolic blood pressure [4.42 (1.80–6.98) mmHg] and the high—accelerated increasing BMI group had higher systolic blood pressure [2.51 (0.37–4.84) mmHg]. In contrast, the decreasing—persistent low BMI group had lower systolic blood pressure. The results remained similar in males and females (Additional file 1: Tables S12 and S13). There was no substantial change in the associations observed for HDL and HbA1c in the increasing—persistent high BMI group and TG in the increasing—accelerated resolving BMI group, as well as for total cholesterol, HDL, LDL/HDL ratio, and TG/HDL ratio in the high—accelerated increasing BMI group, after accounting for the FMI in young adulthood, while most other associations shifted towards the null (Additional file 2: Fig. S5). Besides, similar trend changes were observed in models that accounted for BMI z-scores at 24 years (Additional file 1: Table S14), while additionally adjusting for BMI z-scores at birth did not change the associations (Additional file 1: Table S15). Furthermore, the increasing—persistent high and high—accelerated increasing groups also exhibited heightened clinical-related cardiometabolic risks (odds ratio = 4.45 (2.17–9.14) and 3.42 (2.18–5.38), respectively, Additional file 2: Fig. S6).

Fig. 2
figure 2

Association of BMI trajectories with blood pressure, blood lipids, and HbA1c at young adulthood determined by linear regression. The stable normal BMI group was the reference group. The y-axis displays the β coefficients along with their corresponding 95% confidence intervals. The models were adjusted for age, sex, smoking status, parental education, maternal smoking during pregnancy, maternal body mass index at early pregnancy, maternal hypertension, parity before the index person was born, and cesarean section. The statistics of this figure are presented in Table S11. HDL, high-density lipoprotein; LDL, low-density lipoprotein; HbA1c, hemoglobin A1C; TG, triglycerides. *: Significant difference between stable normal and other groups (p < 0.05)

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Participants in the increasing—persistent high and high—accelerated increasing BMI groups had a higher leukocyte cell count in young adulthood than those in the stable normal BMI group (Additional file 1: Table S16). These two groups also had higher levels of 28 inflammation-related proteins than the stable normal BMI group (Fig. 3 and Additional file 1: Table S17). Besides, several of these proteins exhibited high or moderate expression levels in the heart muscle, smooth muscle, and adipose tissue (Additional file 2: Fig. S7).

Fig. 3
figure 3

Association of BMI trajectories with circulating inflammatory proteins determined by linear regression. The stable normal BMI group was the reference group. The x-axis displays the β coefficients along with their corresponding 95% confidence intervals. The models were adjusted for sex, smoking status, parental education, maternal smoking during pregnancy, maternal BMI at early pregnancy, maternal hypertension, parity before the index person was born, and cesarean section. The statistics of this figure are presented in Table S17. *: Significant difference between stable normal and other groups (p < 0.05)

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Discussion

In our longitudinal birth cohort study, we identified six distinct BMI trajectories from birth until young adulthood, namely the increasing—persistent high, high—accelerated increasing, increasing—accelerated resolving, normal—above normal, stable normal, and decreasing—persistent low BMI group. We found that the increasing—persistent high and the high—accelerated increasing BMI groups had markedly higher levels of several blood lipids (total cholesterol, TG, LDL, LDL/HDL ratio, TG/HDL ratio) and HbA1c but a lower level of HDL than the stable normal BMI group. The associations for HDL, TG/HDL ratio, and HbA1c were still observed after adjusting for FMI in young adulthood. Both the increasing—persistent high and high—accelerated increasing BMI groups had higher leukocyte cell count and higher levels of a range of inflammation-related proteins. There was no significant difference in the clinical cardiometabolic profiles in young adulthood between the increasing—accelerated resolving group and the stable normal BMI group.

Comparison with previous studies

Previous studies showed diverse BMI trajectories from birth or young age until later life. In our literature review (Table 2 and Additional file 1: Table S18), we included studies that (1) applied latent class group modeling or equivalents to identify BMI trajectories from childhood, (2) classified study individuals by BMI trajectories, and analyzed the associations with cardiovascular risk in later life, and (3) outcomes were measured no later than middle age (40–50 years), leading to a total of 31 eligible studies.

Table 2 Summary of previous studies on the association of BMI trajectories in childhood with cardiovascular risk in young adulthood
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Three trajectories, i.e., groups of stable BMI within the normal range, persistently high BMI, and persistently low BMI over time, are generally observed. It is suggested by some [13, 15, 16, 39], though not all [9, 14], of them that compared to the group of stable BMI within the normal range over time, the persistently high BMI group had higher risks of hypertension and diabetes, higher levels of systolic blood pressure (SBP), diastolic blood pressure (DBP), HbA1c, glucose, insulin, insulin resistance, total cholesterol, TG, LDL, and ApoB, as well as a lower level of HDL. In contrast, the persistently low BMI group had a lower risk of hypertension and lower levels of SBP, DBP, LDL, and insulin [11, 13]. These findings are predominantly observed in studies with follow-up from early adulthood up to middle age, while studies with follow-up limited to adolescence, particularly those birth cohort studies included in our literature review, struggle to replicate those findings. Cardiometabolic risks induced by overweight/obesity may be established at an early age, but they are generally unlikely to be detected before adulthood. The present study, with follow-up extending until young adulthood, yielded findings consistent with prior studies focusing on BMI trajectories from early to middle adulthood. Additionally, we found that the high—accelerated increasing BMI group had higher levels of total cholesterol, triglycerides, LDL, HDL/LDL ratio, HbA1c, and TG/HDL ratio, and a lower level of HDL than the stable normal BMI group.

The role of long-term high BMI may present differently in glucose and lipid mechanisms. We observed that differences in TG/HDL ratio, a surrogate marker of insulin resistance [52], comparing the increasing—persistent high BMI group and the high—accelerated increasing BMI group to the stable normal BMI group remained unchanged after adjusting for FMI measured at young adulthood. However, no increased cardiometabolic markers were observed in the increasing—accelerated resolving BMI group. Similarly, Buscot and colleagues reported that individuals who became overweight/obese during young adulthood but later resolved their BMI had a lower risk of diabetes than those who remained consistently obese/overweight [16]. This suggests that adverse changes in glucose metabolisms may commence with the onset of overweight or obesity and are likely to be mitigated by the normalization of high BMI at an earlier age. In contrast, lipids are likely to reflect current body fat status. We found that the elevated blood lipid levels in the increasing—persistent high and high—accelerated increasing BMI groups compared to the stable normal BMI group diminished after adjusting for the FMI in young adulthood. A previous study reported similar patterns in men specifically that the associations of BMI trajectories with TG and HDL approached the null after adjustment for body adiposity at the time of outcome evaluation [12]. However, current BMI is to a large extent dependent on earlier BMI. Thus, obesity prevention targeting children may have substantial significance in reducing cardiometabolic risk in later life.

Excess fat mass is likely to induce systemic inflammation [3, 53]. Our previous work suggested that a high percentage of body fat was associated with increased levels of inflammation-related plasma proteins [54]. The present study corroborates our earlier findings. Notably, similar findings regarding increased protein levels were reported in the UK Biobank [55]. Furthermore, a higher level of leukocytes was observed in the two groups in comparison to the stable normal BMI group. Although only a few studies of BMI trajectories have focused on inflammatory biomarkers, their findings are largely similar. Both Kim et al. and Oluwagbemigun et al. reported that individuals with high BMI had a higher level of IL-6 than those with low BMI [10, 48]. Similar associations were observed for CRP in two prior studies [10, 37].

This study had several strengths. First, our population-based longitudinal cohort study design and large sample size make our findings important from a public health point of view. Second, the analysis of cardiometabolic profiles concerning BMI trajectories is particularly advantageous in young adulthood when it is not too early to distinguish the risk across BMI trajectories and not too late to be able to reduce cardiometabolic risk induced by modifiable factors [56]. Several limitations, however, should be noted in this study. First, not all participants in BAMSE were included in the analysis due to the lack of information on BMI and outcomes. The participants included in the BMI trajectory analysis had higher socioeconomic status and lower smoking exposure compared to those excluded, which may introduce potential selection bias. Second, the generalizability of our findings may be limited to the context of a similar welfare system and lifestyle as in Sweden. Third, only a subset of participants had body fat mass index data at age 24. Therefore, our findings on the potential influence of current body fat status on the associations between BMI trajectories and cardiometabolic profiles require further replication.

Conclusions

Participants with normal birth weight, increasing rapidly during childhood and remaining high in adolescence, or with high birth weight and increasing over time have higher risks of adverse cardiometabolic profiles (i.e., lipids, glucose metabolism, and inflammation) than those with stable normal BMI. However, a raised BMI in childhood may not be inherently harmful to cardiometabolic health, provided it does not persist into adulthood. Obesity prevention targeting children may reduce the risk of adverse cardiometabolic profiles and subsequently lower the risk of cardiometabolic diseases in later life.

Data availability

The data that support the findings of this study are available on reasonable request from the principal investigators of the BAMSE cohort (I.K., A.B., and E.M.). The data are not publicly available due to the privacy and confidentiality of the research participants.

Abbreviations

BAMSE:

Child [Barn], Allergy, Milieu, Stockholm, Epidemiological

BMI:

Body mass index

BIC:

The Bayesian Information Criterion

CI:

Confidence intervals

CVD:

Cardiovascular diseases

DBP:

Diastolic blood pressure

EDTA:

Ethylenediaminetetraacetic acid

FFMI:

Fat-free mass index

FMI:

Fat mass index

HbA1c:

Hemoglobin A1C

HDL:

High-density lipoprotein

LCMM:

Latent class mixture modeling

LDL:

Low-density lipoprotein

MD:

Mean difference

SBP:

Systolic blood pressure

TG:

Triglyceride

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Acknowledgements

The authors thank all participants, study nurses, data managers, and researchers of the BAMSE cohort.

Funding

Open access funding provided by Karolinska Institute. This study was supported by grants from the European Research Council (TRIBAL, grant agreement 757919), the Swedish Research Council (2018–02524), the Swedish Research Council for Health, Working Life and Welfare (FORTE 2017–01146), Formas (2016–01646), the Swedish Heart–Lung Foundation, Strategic Research Area (SFO) Epidemiology, Karolinska Institutet, Region Stockholm (ALF project, and for cohort and database maintenance), and the Swedish Asthma and Allergy Association’s Research Foundation. Gang Wang was supported by the Office of China Postdoctoral Council (No. 56 Document of OCPC, 2022).

Author information

Author notes

  1. Gang Wang and Dang Wei are equal contributors of first authors.

  2. Anna Bergström and Erik Melén are equal contributors of last authors.

Authors and Affiliations

  1. Division of Internal Medicine, Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China

    Gang Wang

  2. Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, 118 83, Stockholm, Sweden

    Gang Wang, Simon Kebede Merid, Sandra Ekström, Susanna Klevebro, Natalia Hernandez-Pacheco, Sophia Björkander, Inger Kull & Erik Melén

  3. Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden

    Gang Wang, Dang Wei, Sandra Ekström, Petter Ljungman & Anna Bergström

  4. West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China

    Gang Wang

  5. Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden

    Sandra Ekström & Anna Bergström

  6. Sachs’ Children and Youth Hospital, Södersjukhuset, Stockholm, Sweden

    Susanna Klevebro, Inger Kull & Erik Melén

  7. CIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain

    Natalia Hernandez-Pacheco

  8. Department of Cardiology, Danderyd Hospital, Stockholm, Sweden

    Petter Ljungman

  9. Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden

    Jochen M. Schwenk

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Contributions

G.W., D.W., A.B., and E.M. designed the study and outlined the contents of the manuscript. G.W. and D.W. were responsible for the practical conduct of the study, including the planning, coordination, and analysis of the data, and the writing of the manuscript under the supervision of E.M. S.K.M. contributed to trajectories and the cross-validation analysis. S.E., S.K., N.H.P., S.B., P.L., J.M.S. and I.K. revised the work critically for the content. All authors contributed to the interpretation of the data, read and approved the final manuscript.

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Twitter handles: @ErikMelen (Erik Melén).

Corresponding author

Correspondence to Gang Wang.

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Ethics approval and consent to participate

The study was approved by the Regional Ethical Review Board in Stockholm (Ref 2016/1380–31/2). The parents and participants signed their informed consent, under the Helsinki Declaration.

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The authors declare no competing interests.

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Supplementary Information

12916_2024_3741_MOESM1_ESM.pdf

Additional file 1: Tables S1–S18. Table S1 The data source of body mass index used in the current study. Table S2 Full names of the 92 proteins included in the Proseek Multiplex Inflammation I panel. Table S3 Inflammation-related protein levels at 24 years according to body mass index trajectories. Table S4 Characteristics of the included and excluded participants. Table S5 The absolute error loss across latent class mixture models incorporating linear, quadratic, and cubic terms. Table S6 The results of latent class mixture models incorporating quadratic terms. Table S7 The mean and standard deviation of BMI and BMI z-scores for all the follow-up points for the BMI groups. Table S8 Characteristics of males in each body mass index trajectory. Table S9 Characteristics of females according to body mass index trajectories. Table S10 Bioimpedance at 24 years and 26 years according to body mass index trajectories. Table S11 Mean differences and 95% confidence intervals for the associations of body mass index trajectories with cardiometabolic profile at late adolescence and young adulthood by linear regression. Table S12 Mean differences and 95% confidence intervals for the associations of body mass index trajectories with cardiometabolic profile at late adolescence and young adulthood by linear regression in males. Table S13 Mean differences and 95% confidence intervals for the associations of body mass index trajectories with cardiometabolic profile at late adolescence and young adulthood by linear regression in females. Table S14 Mean differences and 95% confidence intervals of the sensitivity analysis which additionally included the BMI z-scores at 24 years. Table S15 Mean differences and 95% confidence intervals of the sensitivity analysis which additionally included the BMI z-scores at birth. Table S16 Blood cell counts at 24 years according to body mass index trajectories. Table S17 Mean differences and 95% confidence intervals for the associations of body mass index trajectories with inflammation-related proteins in young adulthood by linear regression. Table S18 The list of studies included in the literature review.

12916_2024_3741_MOESM2_ESM.pdf

Additional file 2: Figures S1–S7. Fig. S1 Flow chart of study participants in the BAMSE cohort. Fig. S2 Body mass index trajectories from birth to young adulthood in the BAMSE cohort. Fig. S3 Body mass index z-scores for participants with or without BMI data at 24 years of age in six BMI groups. Fig. S4 Body mass index values for participants with or without BMI data at 24 years of age in six BMI groups. Fig. S5 Association of body mass index trajectories with blood pressure, blood lipid, and HbA1c in young adulthood after additionally adjusting for fat mass index determined by linear regression. Fig. S6 Association of BMI trajectories with any heightened cardiometabolic risk at young adulthood determined by logistic regression. Fig. S7 Protein expression levels in the heart muscle, smooth muscle, and adipose tissue.

Additional file 3. Additional methods.

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Wang, G., Wei, D., Kebede Merid, S. et al. BMI trajectories from birth to young adulthood associate with distinct cardiometabolic profiles. BMC Med 22, 510 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12916-024-03741-0

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BMC Medicine

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